From be1d8ce6e9ef86deea5dfd681cb1c80e15cc553a Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Fri, 1 May 2026 09:23:44 +0200 Subject: [PATCH 01/19] Add tools/impacts and tools/iamc helpers GmtArray + load_magicc_gmt + persist_gmt_mean in climate; cached open_rime_dataset, clip_gmt, and predict_rime with linearity guard in rime; sample_to_model_years (point/average/interpolate, long-form) in temporal; frame_to_iamc helper in tools/iamc; extract_region_code in util/node (callers in water_for_ppl and water/report migrate from inline split). API doc page tools-impacts.rst. --- doc/api/tools-impacts.rst | 109 ++++++++ doc/api/tools.rst | 1 + doc/index.rst | 1 + .../model/water/data/water_for_ppl.py | 3 +- message_ix_models/model/water/report.py | 3 +- .../tests/tools/impacts/__init__.py | 0 .../tests/tools/impacts/test_climate.py | 54 ++++ .../tests/tools/impacts/test_rime.py | 150 +++++++++++ .../tests/tools/impacts/test_temporal.py | 121 +++++++++ message_ix_models/tools/iamc/__init__.py | 37 +++ message_ix_models/tools/impacts/__init__.py | 39 +++ message_ix_models/tools/impacts/climate.py | 225 ++++++++++++++++ message_ix_models/tools/impacts/rime.py | 246 ++++++++++++++++++ message_ix_models/tools/impacts/temporal.py | 206 +++++++++++++++ message_ix_models/util/node.py | 22 ++ 15 files changed, 1215 insertions(+), 2 deletions(-) create mode 100644 doc/api/tools-impacts.rst create mode 100644 message_ix_models/tests/tools/impacts/__init__.py create mode 100644 message_ix_models/tests/tools/impacts/test_climate.py create mode 100644 message_ix_models/tests/tools/impacts/test_rime.py create mode 100644 message_ix_models/tests/tools/impacts/test_temporal.py create mode 100644 message_ix_models/tools/impacts/__init__.py create mode 100644 message_ix_models/tools/impacts/climate.py create mode 100644 message_ix_models/tools/impacts/rime.py create mode 100644 message_ix_models/tools/impacts/temporal.py diff --git a/doc/api/tools-impacts.rst b/doc/api/tools-impacts.rst new file mode 100644 index 0000000000..5dda7c747f --- /dev/null +++ b/doc/api/tools-impacts.rst @@ -0,0 +1,109 @@ +.. currentmodule:: message_ix_models.tools.impacts + +Climate impact prediction toolkit (:mod:`.tools.impacts`) +********************************************************** + +.. contents:: + :local: + +Overview +======== + +:mod:`.tools.impacts` provides GMT-to-impact prediction via RIME regional +emulators, with helpers for assembling GMT ensembles from MAGICC output and +resampling sparse calibration inputs onto MESSAGE model years. + +The toolkit is domain-agnostic: it predicts at native emulator resolution and +returns NumPy arrays. Domain modules under :mod:`.model.buildings`, +:mod:`.model.water.data`, and :mod:`.project.sparrcle` own the transformation +to MESSAGE-compatible parameters. + +RIME prediction is an adapted reimplementation of the GWL-binned +nearest-neighbor lookup from Byers et al. (2025), *Environ. Res.: Climate* +**4** 035011, `doi:10.1088/2752-5295/adee3d +`_; +upstream code at `iiasa/rime `_ (GPL-3.0). +See :mod:`.tools.impacts.rime` for full attribution. + +Usage +===== + +.. code-block:: python + + import numpy as np + + from message_ix_models.tools.impacts import ( + GmtArray, + gmt_ensemble, + gmt_expectation, + load_magicc_gmt, + predict_rime, + ) + + # Read a MAGICC climate-assessment Excel run-ensemble + gmt = load_magicc_gmt("/path/to/magicc_output_dir", n_runs=600) + # gmt.values: (n_runs, n_years); gmt.years: (n_years,) + + # Single-trajectory prediction (ensemble-mean GMT) + mean = gmt_expectation(gmt) # values shape (n_years,) + qtot_1d = predict_rime( + mean.values, + "rime_regionarray_qtot_mean_CWatM_annual_window11.nc", + "qtot_mean", + ) # (157, n_years) + + # Ensemble prediction with mean reduction + qtot_2d = predict_rime( + gmt.values, + "rime_regionarray_qtot_mean_CWatM_annual_window11.nc", + "qtot_mean", + aggregate="mean", + ) # (157, n_years) + +RIME emulators (:mod:`.tools.impacts.rime`) +=========================================== + +RIME datasets use GWL-binned nearest-neighbor lookup. Each emulator covers a +GMT range of 0.6–7.4 °C (ISIMIP3b-derived). Values outside this range return +NaN; values below the minimum are clipped with skewed Beta(2, 5) noise via +:func:`.clip_gmt`. + +For ensemble input ``(n_runs, n_years)``, :func:`.predict_rime` returns a +Monte Carlo estimate of ``E_{P(GMT)}[f(GMT)]`` when called with +``aggregate="mean"``. This is meaningful only when the emulator response is +approximately linear over the GMT range present in the ensemble; for +single-run input on a non-linear emulator it raises. Use +:func:`.check_emulator_linearity` to probe a dataset before relying on +ensemble means. :func:`.open_rime_dataset` opens a packaged NetCDF by +filename and caches the result. + +Climate inputs (:mod:`.tools.impacts.climate`) +=============================================== + +:func:`.load_magicc_gmt` reads a ``*_IAMC_climateassessment.xlsx`` file and +returns a :class:`.GmtArray` of per-run GSAT trajectories. +:func:`.gmt_ensemble` builds a :class:`.GmtArray` from any wide DataFrame with +year columns. :func:`.gmt_expectation` reduces an ensemble to its per-year +``nanmean``. :func:`.persist_gmt_mean` writes the ensemble-mean trajectory +onto a scenario as a ``Physical Climate Impact|Surface Temperature (GSAT)`` +timeseries. + +Temporal resampling (:mod:`.tools.impacts.temporal`) +===================================================== + +:func:`.sample_to_model_years` maps annual or sparse calibration inputs to +MESSAGE model years via ``method="point"`` (lookup), ``"average"`` (period +mean), or ``"interpolate"`` (linear infill between sparse anchors with +forward-fill beyond the last input). + +Code reference +============== + +.. currentmodule:: message_ix_models.tools + +.. autosummary:: + :toctree: _autosummary + :template: autosummary-module.rst + :recursive: + + impacts diff --git a/doc/api/tools.rst b/doc/api/tools.rst index 774e4dc459..fd38eba9ab 100644 --- a/doc/api/tools.rst +++ b/doc/api/tools.rst @@ -10,6 +10,7 @@ On other pages: - :doc:`tools-bilateralize` - :doc:`tools-costs` +- :doc:`tools-impacts` On this page: diff --git a/doc/index.rst b/doc/index.rst index ee0ce53eaf..f653fcf07f 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -103,6 +103,7 @@ Other submodules are documented on their respective pages: api/tools api/tools-bilateralize api/tools-costs + api/tools-impacts api/tools-messagev api/data-sources api/workflow diff --git a/message_ix_models/model/water/data/water_for_ppl.py b/message_ix_models/model/water/data/water_for_ppl.py index 8cb84c9014..7e4872a503 100644 --- a/message_ix_models/model/water/data/water_for_ppl.py +++ b/message_ix_models/model/water/data/water_for_ppl.py @@ -18,6 +18,7 @@ package_data_path, same_node, ) +from message_ix_models.util.node import extract_region_code if TYPE_CHECKING: from message_ix import Scenario @@ -609,7 +610,7 @@ def _add_nexus_params( # Output: distribute to basins by share outputs = [] for region in node_region: - region_code = region.split("_")[-1] + region_code = extract_region_code(region) basins = df_sw[df_sw["node_dest"].str.contains(region_code, na=False)] if basins.empty: continue diff --git a/message_ix_models/model/water/report.py b/message_ix_models/model/water/report.py index bd2eef1e9c..faa113b829 100644 --- a/message_ix_models/model/water/report.py +++ b/message_ix_models/model/water/report.py @@ -12,6 +12,7 @@ from message_ix_models.model.water.utils import USD_KM3_TO_USD_MCM, m3_GJ_TO_MCM_GWa from message_ix_models.util import package_data_path +from message_ix_models.util.node import extract_region_code log = logging.getLogger(__name__) @@ -439,7 +440,7 @@ def get_rates_data(reg: str, ssp: str, sdgs: bool = False) -> pd.DataFrame: target_regions = reg_map[reg_map.region.str.startswith(f"{reg}_")] basin_to_reg_map = {} for _, row in target_regions.iterrows(): - region_code = row.region.split("_")[1] # R12_CHN -> CHN + region_code = extract_region_code(row.region) basin_to_reg_map[region_code] = row.region # CHN -> R12_CHN df_rate["region"] = df_rate["region_short"].map(basin_to_reg_map) diff --git a/message_ix_models/tests/tools/impacts/__init__.py b/message_ix_models/tests/tools/impacts/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/message_ix_models/tests/tools/impacts/test_climate.py b/message_ix_models/tests/tools/impacts/test_climate.py new file mode 100644 index 0000000000..a2da3eb6d2 --- /dev/null +++ b/message_ix_models/tests/tools/impacts/test_climate.py @@ -0,0 +1,54 @@ +"""Tests for tools.impacts.climate — GMT array extraction.""" + +import numpy as np +import pandas as pd +import pytest + +from message_ix_models.tools.impacts.climate import ( + GmtArray, + gmt_ensemble, + gmt_expectation, +) + + +@pytest.fixture +def wide_df(): + """3 runs, 5 years, int year columns.""" + years = list(range(2020, 2025)) + rows = [] + for i in range(3): + row = {"Model": f"run_{i}", "Variable": "GSAT"} + for y in years: + row[y] = 1.0 + 0.01 * (y - 2020) + 0.1 * i + rows.append(row) + return pd.DataFrame(rows) + + +def test_gmt_ensemble_year_labels(wide_df): + result = gmt_ensemble(wide_df, ["Model", "Variable"]) + assert isinstance(result, GmtArray) + np.testing.assert_array_equal(result.years, [2020, 2021, 2022, 2023, 2024]) + + +def test_gmt_ensemble_str_year_columns(): + df = pd.DataFrame({"id": ["a", "b"], "2020": [1.0, 1.1], "2025": [2.0, 2.1]}) + result = gmt_ensemble(df, ["id"]) + assert result.values.shape == (2, 2) + np.testing.assert_array_equal(result.years, [2020, 2025]) + + +def test_gmt_expectation_mean_across_rows(): + df = pd.DataFrame( + {"id": ["a", "b", "c"], 2020: [1.0, 2.0, 3.0], 2025: [4.0, 5.0, 6.0]} + ) + result = gmt_expectation(gmt_ensemble(df, ["id"])) + assert result.values.shape == (2,) + assert result.values[0] == pytest.approx(2.0) # mean(1, 2, 3) + assert result.values[1] == pytest.approx(5.0) # mean(4, 5, 6) + + +def test_gmt_expectation_nan_handling(): + df = pd.DataFrame({"id": ["a", "b"], 2020: [1.0, np.nan], 2025: [3.0, 5.0]}) + result = gmt_expectation(gmt_ensemble(df, ["id"])) + assert result.values[0] == pytest.approx(1.0) # nanmean(1.0, nan) + assert result.values[1] == pytest.approx(4.0) # nanmean(3.0, 5.0) diff --git a/message_ix_models/tests/tools/impacts/test_rime.py b/message_ix_models/tests/tools/impacts/test_rime.py new file mode 100644 index 0000000000..9829650293 --- /dev/null +++ b/message_ix_models/tests/tools/impacts/test_rime.py @@ -0,0 +1,150 @@ +"""Tests for tools.impacts.rime -- RIME prediction engine. + +Pure-function tests use synthetic data. Integration tests that need actual +RIME NetCDF datasets are marked with skipif. +""" + +import logging +from unittest.mock import patch + +import numpy as np +import pytest + +from message_ix_models.tools.impacts import ( + check_emulator_linearity, + clip_gmt, + impacts_data_path, + predict_rime, +) + +# Fake per-run prediction: shape (n_spatial=3,) returned for each GMT scalar. +_FAKE_SPATIAL = np.ones(3) + + +def _mock_predict_from(): + return patch( + "message_ix_models.tools.impacts.rime._predict_from_gmt", + return_value=_FAKE_SPATIAL, + ) + + +def _mock_linearity(max_deviation: float): + return patch( + "message_ix_models.tools.impacts.rime.check_emulator_linearity", + return_value={ + "max_deviation": max_deviation, + "mean_deviation": max_deviation, + "is_linear": max_deviation < 0.05, + }, + ) + + +def test_clip_annual_clipping(): + gmt = np.array([0.3, 0.5, 0.6, 1.0]) + result = clip_gmt(gmt, gmt_min=0.6, gmt_ceil=0.9) + # Values below 0.6 clipped to [0.6, 0.9] + assert result[0] >= 0.6 + assert result[0] <= 0.9 + assert result[1] >= 0.6 + assert result[1] <= 0.9 + # Value at 0.6 should be unchanged + assert result[2] == 0.6 + assert result[3] == 1.0 + + +def test_clip_seasonal_higher_threshold(): + gmt = np.array([0.7, 0.8, 1.0]) + result = clip_gmt(gmt, gmt_min=0.8, gmt_ceil=1.2) + # 0.7 is below seasonal min of 0.8, clipped to [0.8, 1.2] + assert result[0] >= 0.8 + assert result[0] <= 1.2 + # 0.8 is at boundary, unchanged + assert result[1] == 0.8 + + +def test_clip_2d_input(): + gmt = np.array([[0.3, 1.0, 2.0], [0.5, 1.5, 3.0]]) + result = clip_gmt(gmt) + assert result.shape == (2, 3) + assert result[0, 0] >= 0.6 + assert result[0, 1] == 1.0 + + +def test_predict_linear_emulator_no_warning(caplog): + """Linear emulator (deviation <= 1%): no linearity warning emitted.""" + gmt_2d = np.ones((5, 4)) * 1.5 + with _mock_predict_from(), _mock_linearity(0.005): + with caplog.at_level( + logging.WARNING, logger="message_ix_models.tools.impacts.rime" + ): + predict_rime(gmt_2d, "dummy.nc", "qtot_mean") + + assert not any("non-linear" in r.message for r in caplog.records) + + +def test_predict_nonlinear_multi_run_warns(caplog): + """Non-linear emulator + n_runs > 1: log warning emitted, no raise.""" + gmt_2d = np.ones((5, 4)) * 1.5 + with _mock_predict_from(), _mock_linearity(0.08): + with caplog.at_level( + logging.WARNING, logger="message_ix_models.tools.impacts.rime" + ): + predict_rime(gmt_2d, "dummy.nc", "qtot_mean") + + assert any("non-linear" in r.message for r in caplog.records) + + +def test_predict_nonlinear_single_run_raises(): + """Non-linear emulator + n_runs == 1: ValueError.""" + gmt_2d = np.ones((1, 4)) * 1.5 + with _mock_predict_from(), _mock_linearity(0.08): + with pytest.raises(ValueError, match="non-linear emulator"): + predict_rime(gmt_2d, "dummy.nc", "qtot_mean") + + +def test_predict_linear_single_run_no_raise(): + """Linear emulator + n_runs == 1: no error.""" + gmt_2d = np.ones((1, 4)) * 1.5 + with _mock_predict_from(), _mock_linearity(0.005): + result = predict_rime(gmt_2d, "dummy.nc", "qtot_mean") + assert result.shape == (3, 4) + + +# Integration tests requiring RIME NetCDF files +_RIME_DIR = impacts_data_path("rime") +_HAS_RIME_DATA = ( + _RIME_DIR / "rime_regionarray_qtot_mean_CWatM_annual_window11.nc" +).exists() + +skip_no_rime = pytest.mark.skipif( + not _HAS_RIME_DATA, reason="RIME NetCDF datasets not available" +) + + +@skip_no_rime +def test_predict_qtot_mean_1d(): + path = _RIME_DIR / "rime_regionarray_qtot_mean_CWatM_annual_window11.nc" + gmt = np.linspace(1.0, 2.5, 10) + result = predict_rime(gmt, path, "qtot_mean") + assert result.shape == (157, 10) + + +@skip_no_rime +def test_predict_qtot_mean_2d(): + path = _RIME_DIR / "rime_regionarray_qtot_mean_CWatM_annual_window11.nc" + rng = np.random.default_rng(42) + gmt_2d = rng.normal(1.5, 0.2, size=(5, 10)) + gmt_2d = np.clip(gmt_2d, 0.6, 7.4) + result = predict_rime(gmt_2d, path, "qtot_mean") + assert result.shape == (157, 10) + + +@skip_no_rime +def test_linearity_check_integration(): + path = _RIME_DIR / "rime_regionarray_qtot_mean_CWatM_annual_window11.nc" + result = check_emulator_linearity( + path, "qtot_mean", gmt_range=(1.0, 3.0), n_probe=5 + ) + assert "max_deviation" in result + assert "is_linear" in result + assert isinstance(result["is_linear"], bool) diff --git a/message_ix_models/tests/tools/impacts/test_temporal.py b/message_ix_models/tests/tools/impacts/test_temporal.py new file mode 100644 index 0000000000..298629e3ba --- /dev/null +++ b/message_ix_models/tests/tools/impacts/test_temporal.py @@ -0,0 +1,121 @@ +"""Tests for tools.impacts.temporal — year resampling.""" + +import pandas as pd +import pytest + +from message_ix_models.tools.impacts.temporal import sample_to_model_years + + +@pytest.fixture +def annual_df(): + """Annual data 2020-2040 for 3 basins.""" + years = list(range(2020, 2041)) + data = {"basin": ["B1", "B2", "B3"]} + for y in years: + data[y] = [float(y)] * 3 # value = year for easy verification + return pd.DataFrame(data) + + +@pytest.fixture +def decadal_df(): + """Decadal theta-shape data for two nodes (mirrors buildings theta).""" + rows = [ + {"node": "R12_AFR", 2020: 0.4, 2030: 0.6, 2040: 0.8, 2050: 1.0}, + {"node": "R12_NAM", 2020: 1.0, 2030: 0.9, 2040: 0.85, 2050: 0.8}, + ] + return pd.DataFrame(rows) + + +def test_point_method(annual_df): + model_years = [2020, 2025, 2030, 2035, 2040] + result = sample_to_model_years(annual_df, ["basin"], model_years, method="point") + assert list(result.columns) == ["basin"] + model_years + # Values should be the year itself + assert result[2025].iloc[0] == 2025.0 + assert result[2040].iloc[0] == 2040.0 + + +def test_average_method(annual_df): + model_years = [2020, 2025, 2030] + result = sample_to_model_years(annual_df, ["basin"], model_years, method="average") + # 2025 should average 2021-2025 = mean(2021..2025) = 2023 + assert pytest.approx(result[2025].iloc[0]) == 2023.0 + + +def test_forward_fill_beyond_range(annual_df): + model_years = [2020, 2030, 2040, 2050] + result = sample_to_model_years(annual_df, ["basin"], model_years, method="point") + # 2050 is beyond 2040: forward-filled from 2040 + assert result[2050].iloc[0] == result[2040].iloc[0] + + +def test_missing_year_column(annual_df): + # 2050 is beyond the input range (2020-2040) but gets forward-filled. + # 2019 is before input range and truly missing. + with pytest.raises(ValueError, match="not found in input"): + sample_to_model_years(annual_df, ["basin"], [2019, 2020], method="point") + + +def test_interpolate_linear_between_inputs(decadal_df): + """5-year midpoints should be the arithmetic mean of bracket years.""" + result = sample_to_model_years( + decadal_df, + ["node"], + [2020, 2025, 2030, 2035, 2040, 2045, 2050], + method="interpolate", + ) + afr = result.set_index("node").loc["R12_AFR"] + assert pytest.approx(afr[2025]) == 0.5 # mean(0.4, 0.6) + assert pytest.approx(afr[2035]) == 0.7 + assert pytest.approx(afr[2045]) == 0.9 + # Anchor years preserved exactly + assert afr[2020] == 0.4 + assert afr[2050] == 1.0 + + +def test_interpolate_forward_fill_beyond_last_input(decadal_df): + """Years beyond max input stay equal to the last interpolated value.""" + result = sample_to_model_years( + decadal_df, ["node"], [2050, 2060, 2100], method="interpolate" + ) + afr = result.set_index("node").loc["R12_AFR"] + assert afr[2060] == afr[2050] + assert afr[2100] == afr[2050] + + +def test_interpolate_drop_below_first_input_year_by_default(decadal_df): + """Targets below the first input year are dropped (buildings semantic).""" + # Input min = 2020; ask for 2010, 2015, 2020, 2030 + result = sample_to_model_years( + decadal_df, ["node"], [2010, 2015, 2020, 2030], method="interpolate" + ) + cols = [c for c in result.columns if isinstance(c, int)] + assert 2010 not in cols + assert 2015 not in cols + assert 2020 in cols + assert 2030 in cols + + +def test_interpolate_extrapolate_below_backfills(decadal_df): + """extrapolate_below=True back-fills below-range targets from first input.""" + result = sample_to_model_years( + decadal_df, + ["node"], + [2010, 2020, 2030], + method="interpolate", + extrapolate_below=True, + ) + afr = result.set_index("node").loc["R12_AFR"] + assert afr[2010] == afr[2020] # back-filled from first input + + +def test_interpolate_independent_per_row(decadal_df): + """Interpolation must be independent across id_cols rows.""" + result = sample_to_model_years( + decadal_df, ["node"], [2025, 2035], method="interpolate" + ).set_index("node") + # AFR rises; NAM falls + assert pytest.approx(result.loc["R12_AFR", 2025]) == 0.5 + assert pytest.approx(result.loc["R12_NAM", 2025]) == 0.95 + assert pytest.approx(result.loc["R12_AFR", 2035]) == 0.7 + assert pytest.approx(result.loc["R12_NAM", 2035]) == 0.875 diff --git a/message_ix_models/tools/iamc/__init__.py b/message_ix_models/tools/iamc/__init__.py index fa851ff1e3..f00d602f56 100644 --- a/message_ix_models/tools/iamc/__init__.py +++ b/message_ix_models/tools/iamc/__init__.py @@ -21,11 +21,48 @@ __all__ = [ "compare", "describe", + "frame_to_iamc", "iamc_like_data_for_query", "to_quantity", ] +def frame_to_iamc( + df: pd.DataFrame, + variable: str, + unit: str, + *, + region_col: str = "region", +) -> pd.DataFrame: + """Return *df* in IAMC long form with ``variable`` and ``unit`` attached. + + Parameters + ---------- + df + Long-form frame with columns ``[region_col, "year", "value"]``. + Other columns are ignored. + variable + IAMC variable name (e.g. ``"Input|Climate|Cooling|..."``). + unit + Unit string (e.g. ``"GWa"``, ``"dimensionless"``). + region_col + Source column to use as ``region``. Default ``"region"``; + pass ``"node"`` when the frame uses scenario node labels. + + Returns + ------- + pd.DataFrame + Columns ``["region", "variable", "unit", "year", "value"]``. + """ + out = df[[region_col, "year", "value"]].copy() + if region_col != "region": + out = out.rename(columns={region_col: "region"}) + out["year"] = out["year"].astype(int) + out["variable"] = variable + out["unit"] = unit + return out[["region", "variable", "unit", "year", "value"]] + + # TODO Reduce complexity from 12 to <=11 def compare( # noqa: C901 left: pd.DataFrame, diff --git a/message_ix_models/tools/impacts/__init__.py b/message_ix_models/tools/impacts/__init__.py new file mode 100644 index 0000000000..89f45897fd --- /dev/null +++ b/message_ix_models/tools/impacts/__init__.py @@ -0,0 +1,39 @@ +"""Climate impact prediction toolkit. + +GMT-to-impact prediction via RIME emulators and GMT/year helpers. +Domain-specific transforms live with their domain modules; this package only +handles emulator lookup and ensemble reduction. RIME prediction is an adapted +reimplementation of `iiasa/rime +`_; see :mod:`.rime` for attribution. +""" + +from functools import partial + +from message_ix_models.util import package_data_path + +from .climate import ( + GmtArray, + gmt_ensemble, + gmt_expectation, + load_magicc_gmt, + persist_gmt_mean, +) +from .rime import check_emulator_linearity, clip_gmt, open_rime_dataset, predict_rime +from .temporal import sample_to_model_years + +impacts_data_path = partial(package_data_path, "impacts") + + +__all__ = [ + "GmtArray", + "check_emulator_linearity", + "clip_gmt", + "gmt_ensemble", + "gmt_expectation", + "impacts_data_path", + "load_magicc_gmt", + "open_rime_dataset", + "persist_gmt_mean", + "predict_rime", + "sample_to_model_years", +] diff --git a/message_ix_models/tools/impacts/climate.py b/message_ix_models/tools/impacts/climate.py new file mode 100644 index 0000000000..fe7168474d --- /dev/null +++ b/message_ix_models/tools/impacts/climate.py @@ -0,0 +1,225 @@ +"""GMT array extraction and ingestion. + +Three entry points for assembling a :class:`GmtArray` (``values`` + +``years`` NamedTuple): + +- :func:`gmt_ensemble` — extract wide-DataFrame rows as ``(n_rows, n_years)``. +- :func:`gmt_expectation` — nanmean across rows as ``(n_years,)``. +- :func:`load_magicc_gmt` — read a MAGICC climate-assessment Excel file + and return the per-run GSAT trajectories. +""" + +import functools +import logging +from collections.abc import Sequence +from pathlib import Path +from typing import NamedTuple + +import numpy as np +import pandas as pd + +log = logging.getLogger(__name__) + +_GSAT_VAR = "AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3" +_ID_COLS = ["Model", "Scenario", "Region", "Variable", "Unit"] + + +class GmtArray(NamedTuple): + """GMT values with year labels. + + Attributes + ---------- + values + Shape ``(n_runs, n_years)`` for ensemble or ``(n_years,)`` for + expectation. + years + Year labels, shape ``(n_years,)``. + """ + + values: np.ndarray + years: np.ndarray + + +def _year_columns(df: pd.DataFrame, id_cols: Sequence[str]) -> list: + """Identify year columns: all columns not in *id_cols* that are numeric. + + Accepts both ``int`` columns (``2020``) and ``str``-of-digits + (``"2020"``). Returns original column labels sorted by integer value. + + Raises + ------ + ValueError + If no year columns are found. + """ + id_set = set(id_cols) + cols = sorted( + ( + c + for c in df.columns + if c not in id_set + and (isinstance(c, (int, float)) or (isinstance(c, str) and c.isdigit())) + ), + key=lambda c: int(c), + ) + if not cols: + raise ValueError( + f"No year columns found. ID columns: {list(id_cols)}; " + f"all columns: {list(df.columns)}" + ) + return cols + + +def gmt_ensemble(df: pd.DataFrame, id_cols: Sequence[str]) -> GmtArray: + """Extract wide DataFrame rows as a 2D array. + + Parameters + ---------- + df + Wide DataFrame with ID columns and year columns (int or str-digits). + id_cols + Non-year columns (e.g. ``["Model", "Scenario", "Variable"]``). + + Returns + ------- + GmtArray + ``.values`` shape ``(n_rows, n_years)``, ``.years`` shape + ``(n_years,)``. + """ + cols = _year_columns(df, id_cols) + values = df[cols].values.astype(float) + years = np.array([int(c) for c in cols]) + return GmtArray(values, years) + + +def gmt_expectation(arr: GmtArray) -> GmtArray: + """Per-year nanmean of *arr* along the run axis. + + A 2-D ``(n_runs, n_years)`` input collapses to 1-D ``(n_years,)``. + A 1-D input is returned unchanged. + """ + values = np.asarray(arr.values) + if values.ndim == 1: + return arr + return GmtArray(values=np.nanmean(values, axis=0), years=arr.years) + + +_GMT_VARIABLE = "Physical Climate Impact|Surface Temperature (GSAT)|Mean" + + +def persist_gmt_mean(scen, gmt: GmtArray) -> None: + """Persist the ensemble-mean GMT trajectory on *scen* as a global timeseries. + + Writes one ``World``-region row per year under + ``Physical Climate Impact|Surface Temperature (GSAT)|Mean`` (degC). + + Idempotent: if the variable already exists on the scenario and matches + the computed mean, return without writing. Raises if the existing rows + disagree with the new mean — that means the upstream MAGICC ensemble + changed between CID-step invocations on the same scenario, which is a + workflow-integrity bug worth surfacing rather than silently overwriting. + """ + from message_ix_models.tools.iamc import frame_to_iamc + + mean = gmt_expectation(gmt) + ts = ( + pd.DataFrame( + { + "region": "World", + "year": np.asarray(mean.years, dtype=int), + "value": np.asarray(mean.values), + } + ) + .pipe(frame_to_iamc, _GMT_VARIABLE, "degC") + .sort_values("year") + .reset_index(drop=True) + ) + if ts.empty: + return + + existing = scen.timeseries(variable=_GMT_VARIABLE) + if not existing.empty: + prior = ( + existing[existing["region"] == "World"][["year", "value"]] + .sort_values("year") + .reset_index(drop=True) + ) + proposed = ts[["year", "value"]] + if prior["year"].equals(proposed["year"]) and np.allclose( + prior["value"].to_numpy(), proposed["value"].to_numpy(), atol=1e-9 + ): + log.debug("GMT mean already persisted on scenario; skipping write") + return + raise ValueError( + f"Existing {_GMT_VARIABLE} on scenario disagrees with computed " + "mean — upstream MAGICC ensemble changed between CID steps." + ) + + scen.check_out(timeseries_only=True) + try: + scen.add_timeseries(ts) + scen.commit(f"Persist GMT mean ({_GMT_VARIABLE})") + except BaseException: + scen.discard_changes() + raise + + +def load_magicc_gmt(magicc_dir: str | Path, n_runs: int | None = None) -> GmtArray: + """Load a GSAT ensemble from a MAGICC climate-assessment Excel file. + + Reads the IAMC-format ``*_IAMC_climateassessment.xlsx`` file in + *magicc_dir* and extracts individual GSAT trajectories identified by + ``Model = ...|run_{N}``. Results are cached by ``(Path, n_runs)`` so + repeated calls within a single workflow run reuse the same array. + + Parameters + ---------- + magicc_dir + Directory containing the climate-assessment Excel output. + n_runs + Maximum number of runs to load. ``None`` loads all available. + + Returns + ------- + GmtArray + ``.values`` shape ``(n_runs, n_years)``, degC above pre-industrial. + ``.years`` shape ``(n_years,)``, integer year labels. + """ + return _load_magicc_gmt_cached(Path(magicc_dir), n_runs) + + +@functools.lru_cache(maxsize=None) +def _load_magicc_gmt_cached(magicc_dir: Path, n_runs: int | None) -> GmtArray: + iamc_files = sorted(magicc_dir.glob("*_IAMC_climateassessment.xlsx")) + if not iamc_files: + raise FileNotFoundError(f"No *_IAMC_climateassessment.xlsx in {magicc_dir}") + if len(iamc_files) > 1: + raise ValueError( + f"Multiple *_IAMC_climateassessment.xlsx in {magicc_dir}: " + f"{[p.name for p in iamc_files]}. Resolve ambiguity before loading." + ) + iamc_path = iamc_files[0] + + log.info("Loading MAGICC ensemble from %s", iamc_path.name) + df = pd.read_excel(iamc_path, sheet_name="data") + + gsat = df[ + (df["Variable"] == _GSAT_VAR) + & df["Model"].str.contains("|run_", na=False, regex=False) + ] + if gsat.empty: + raise ValueError( + f"No individual GSAT runs found. Variables: " + f"{df['Variable'].unique()[:5].tolist()}" + ) + if n_runs is not None: + gsat = gsat.head(n_runs) + + array = gmt_ensemble(gsat, _ID_COLS) + log.info( + "GMT ensemble: %d runs x %d years (%d-%d)", + array.values.shape[0], + array.values.shape[1], + int(array.years[0]), + int(array.years[-1]), + ) + return array diff --git a/message_ix_models/tools/impacts/rime.py b/message_ix_models/tools/impacts/rime.py new file mode 100644 index 0000000000..7097ee0803 --- /dev/null +++ b/message_ix_models/tools/impacts/rime.py @@ -0,0 +1,246 @@ +"""GWL-binned nearest-neighbor lookup for RIME emulator data. + +Adapted reimplementation of the prediction core in `iiasa/rime +`_ (GPL-3.0); see +`doi:10.1088/2752-5295/adee3d `_ +for the underlying method. Predictions return at native emulator resolution; +domain modules own any reshape to MESSAGE-compatible arrays. +""" + +import functools +import logging + +import numpy as np +import xarray as xr + +from message_ix_models.util import package_data_path + +log = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Dataset loading +# --------------------------------------------------------------------------- + + +@functools.lru_cache(maxsize=8) +def open_rime_dataset(filename: str) -> xr.Dataset: + """Cached open of a packaged RIME dataset under ``data/impacts/rime/``.""" + return xr.open_dataset(str(package_data_path("impacts", "rime", filename))) + + +# --------------------------------------------------------------------------- +# GMT clipping +# --------------------------------------------------------------------------- + + +def clip_gmt( + gmt_array: np.ndarray, + gmt_min: float = 0.6, + gmt_ceil: float = 0.9, + seed: int = 42, +) -> np.ndarray: + """Clip GMT values below RIME emulator minimum with skewed noise. + + Values below *gmt_min* are replaced with ``gmt_min + beta(2,5) * (gmt_ceil + - gmt_min)``, landing in ``[gmt_min, gmt_ceil]``. Values at or above + *gmt_min* are unchanged. + + Parameters + ---------- + gmt_array + GMT values (degC above pre-industrial). Any shape. + gmt_min + Lower bound of emulator support. + gmt_ceil + Upper bound of noise range for clipped values. + seed + RNG seed for reproducibility. + """ + gmt_clipped = np.asarray(gmt_array).copy() + original_shape = gmt_clipped.shape + gmt_flat = gmt_clipped.flatten() + + low_gmt_mask = gmt_flat < gmt_min + n_low = int(np.sum(low_gmt_mask)) + + if n_low > 0: + rng = np.random.default_rng(seed) + noise = rng.beta(2, 5, size=n_low) * (gmt_ceil - gmt_min) + gmt_flat[low_gmt_mask] = gmt_min + noise + + return gmt_flat.reshape(original_shape) + + +# --------------------------------------------------------------------------- +# Core prediction +# --------------------------------------------------------------------------- + + +def _predict_from_gmt( + gmt: float | np.floating, + filename: str, + var_name: str, + sel: dict | None = None, +) -> np.ndarray: + """GWL-binned nearest-neighbor lookup for a scalar GMT value.""" + data = open_rime_dataset(filename)[var_name] + + if sel is not None: + for dim, value in sel.items(): + if dim in data.dims: + data = data.sel({dim: value}) + + return data.sel(gwl=gmt, method="nearest").values + + +def predict_rime( + gmt_array: np.ndarray, + filename: str, + var_name: str, + sel: dict | None = None, + aggregate: str = "mean", +) -> np.ndarray: + """Predict RIME variable from GMT array. + + Performs a GWL-binned nearest-neighbour lookup for each GMT value. + For ensemble input ``(n_runs, n_years)``, applies the lookup per run + per year — a Monte Carlo estimate of ``E_{P(GMT)}[f(GMT)]``: for each + timestep *t*, samples ``f(GMT_{run,t})`` across all ensemble members + and aggregates. This is meaningful only when the emulator response is + approximately linear; use :func:`check_emulator_linearity` to verify. + + Parameters + ---------- + gmt_array + GMT values (degC above pre-industrial). + Shape ``(n_years,)`` for single trajectory, or + ``(n_runs, n_years)`` for ensemble. + filename + RIME NetCDF filename relative to ``data/impacts/rime/``. + var_name + Variable name within the dataset (e.g. ``"qtot_mean"``, + ``"capacity_factor"``, ``"EI_cool"``). + sel + Optional dimension selections applied before GWL lookup. + aggregate + How to reduce the ensemble axis for 2D input: + + ``"mean"`` *(default)* + Return ``E[f(GMT)]`` — sample mean across runs. + Shape matches 1D output (no run axis). + ``"none"`` + Return the full ``(n_runs, n_spatial, n_years)`` array. + Use this when downstream callers need per-run data. + + Ignored for 1D input. + + Returns + ------- + np.ndarray + Native emulator resolution. Shape depends on variable and input: + + - 1D input or ``aggregate="mean"``: + basin variables ``(157, n_years)``, + capacity_factor ``(12, n_years)``, + EI variables ``(12, ..., n_years)`` + - ``aggregate="none"``: + ``(n_runs, n_spatial, n_years)`` (spatial dims as above) + """ + if aggregate not in ("mean", "none"): + raise ValueError(f"aggregate must be 'mean' or 'none', got {aggregate!r}") + + gmt_array = np.asarray(gmt_array) + + if gmt_array.ndim == 1: + preds = [ + _predict_from_gmt(float(g), filename, var_name, sel=sel) for g in gmt_array + ] + return np.stack(preds, axis=-1) + + if gmt_array.ndim == 2: + n_runs = gmt_array.shape[0] + lin = check_emulator_linearity( + filename, var_name, (float(gmt_array.min()), float(gmt_array.max())) + ) + if lin["max_deviation"] > 0.01: + if n_runs == 1: + raise ValueError( + f"predict_rime: single-run ensemble with non-linear emulator " + f"({var_name}, max deviation {lin['max_deviation']:.1%}). " + f"f(E[GMT]) is not a reliable substitute for E[f(GMT)]." + ) + log.warning( + "predict_rime: non-linear emulator response for %s " + "(max deviation %.1f%%)%s — MC mean is approximate", + var_name, + lin["max_deviation"] * 100, + f"; only {n_runs} runs (recommend >= 100)" if n_runs < 100 else "", + ) + run_results = [] + for i in range(n_runs): + preds = [ + _predict_from_gmt(float(g), filename, var_name, sel=sel) + for g in gmt_array[i] + ] + run_results.append(np.stack(preds, axis=-1)) + ensemble = np.stack(run_results, axis=0) # (n_runs, n_spatial, n_years) + if aggregate == "none": + return ensemble + return np.mean(ensemble, axis=0) + + raise ValueError(f"gmt_array must be 1D or 2D, got shape {gmt_array.shape}") + + +@functools.lru_cache(maxsize=32) +def check_emulator_linearity( + filename: str, + var_name: str, + gmt_range: tuple[float, float], + n_probe: int = 20, +) -> dict: + """Probe emulator response linearity over a GMT range. + + Tests whether ``E[f(GMT)]`` approximates ``f(E[GMT])`` by comparing + predictions at uniformly spaced GMT values against the prediction at + the mean GMT. Large deviations indicate non-linear response, meaning + percentile-based input (which implicitly assumes linearity) would be + unreliable. + + Parameters + ---------- + filename + RIME NetCDF filename relative to ``data/impacts/rime/``. + var_name + Variable name within the dataset. + gmt_range + ``(gmt_low, gmt_high)`` range to probe. + n_probe + Number of GMT values to sample. + + Returns + ------- + dict + Keys: ``max_deviation``, ``mean_deviation``, ``is_linear`` + (True if max deviation < 5%). + """ + gmt_low, gmt_high = gmt_range + gmt_probes = np.linspace(gmt_low, gmt_high, n_probe) + gmt_mean = float(np.mean(gmt_probes)) + + results = [_predict_from_gmt(float(g), filename, var_name) for g in gmt_probes] + e_of_f = np.nanmean(np.stack(results, axis=0), axis=0) # E[f(GMT)] + f_of_e = _predict_from_gmt(gmt_mean, filename, var_name) + + with np.errstate(divide="ignore", invalid="ignore"): + rel_dev = np.abs(e_of_f - f_of_e) / np.abs(f_of_e) + + rel_dev = rel_dev[np.isfinite(rel_dev)] + max_dev = float(np.max(rel_dev)) if len(rel_dev) > 0 else 0.0 + mean_dev = float(np.mean(rel_dev)) if len(rel_dev) > 0 else 0.0 + + return { + "max_deviation": max_dev, + "mean_deviation": mean_dev, + "is_linear": max_dev < 0.05, + } diff --git a/message_ix_models/tools/impacts/temporal.py b/message_ix_models/tools/impacts/temporal.py new file mode 100644 index 0000000000..a4aaea1e3c --- /dev/null +++ b/message_ix_models/tools/impacts/temporal.py @@ -0,0 +1,206 @@ +"""Year resampling for model timesteps. + +Climate data (MAGICC, RIME) typically has annual resolution (2020-2100). +MESSAGE uses non-uniform timesteps (5-year steps with gaps). This module +resamples annual data to match model year grids. + +""" + +import logging +from typing import Literal + +import pandas as pd + +log = logging.getLogger(__name__) + + +def _resample_point( + df: pd.DataFrame, + id_cols: list[str], + year_cols: list[int], + targets: list[int], + *, + min_input_year: int, + max_input_year: int, +) -> pd.DataFrame: + missing = [y for y in targets if y not in year_cols] + if missing: + raise ValueError( + f"Model years {missing} not found in input columns. " + f"Input range: {min_input_year}-{max_input_year}" + ) + return df[id_cols + targets].copy() + + +def _resample_average( + df: pd.DataFrame, + id_cols: list[str], + year_cols: list[int], + targets: list[int], +) -> pd.DataFrame: + result = df[id_cols].copy() + for i, y in enumerate(targets): + start = targets[i - 1] + 1 if i > 0 else y + period = [yr for yr in range(start, y + 1) if yr in year_cols] + if period: + result[y] = df[period].mean(axis=1) + elif y in year_cols: + result[y] = df[y] + else: + raise ValueError(f"No input data for averaging period ending at {y}") + return result + + +def _resample_long_form( + df: pd.DataFrame, + id_cols: list[str], + value_cols: list[str], + model_years: list[int], + method: Literal["point", "average", "interpolate"], + *, + extrapolate_below: bool, +) -> pd.DataFrame: + """Long-form adapter: pivot per value column, resample, melt back, merge.""" + parts: list[pd.DataFrame] = [] + for col in value_cols: + wide = df.pivot(index=id_cols, columns="year", values=col).reset_index() + wide.columns.name = None + sampled = sample_to_model_years( + wide, + id_cols, + model_years, + method, + extrapolate_below=extrapolate_below, + ) + long = sampled.melt(id_vars=id_cols, var_name="year", value_name=col) + long["year"] = long["year"].astype(int) + parts.append(long) + result = parts[0] + for part in parts[1:]: + result = result.merge(part, on=id_cols + ["year"]) + return result + + +def _resample_interpolate( + df: pd.DataFrame, + id_cols: list[str], + year_cols: list[int], + targets: list[int], + *, + min_input_year: int, + extrapolate_below: bool, +) -> pd.DataFrame: + target_in = ( + targets if extrapolate_below else [y for y in targets if y >= min_input_year] + ) + if not target_in: + return df[id_cols].copy() + + target_cols = sorted(set(year_cols) | set(target_in)) + wide = df.set_index(id_cols)[year_cols].reindex(columns=target_cols) + wide = wide.interpolate(axis=1, method="index") + if extrapolate_below: + wide = wide.bfill(axis=1) + return wide[target_in].reset_index() + + +def sample_to_model_years( + df: pd.DataFrame, + id_cols: list[str], + model_years: list[int], + method: Literal["point", "average", "interpolate"] = "point", + *, + extrapolate_below: bool = False, + value_cols: list[str] | None = None, +) -> pd.DataFrame: + """Resample data on a year axis to model timesteps. + + Parameters + ---------- + df + Wide DataFrame with integer year columns and ID columns. When + *value_cols* is given, *df* is treated as long form instead: + ID columns plus a ``year`` integer column plus each name in + *value_cols*. + id_cols + Non-year columns to preserve (e.g. ``["BCU_name"]``, ``["region"]``). + model_years + Target years (e.g. ``[2020, 2025, ..., 2100, 2110]``). + Years beyond input range are forward-filled from the last input year. + method + ``"point"`` — take value at model year; missing years raise. + ``"average"`` — mean over the preceding period (e.g. 2026–2030 for 2030). + ``"interpolate"`` — linear interpolation between input years using the + year column as a numeric x-axis. Years below the first input year are + dropped unless ``extrapolate_below=True``. + extrapolate_below + Only applies to ``method="interpolate"``. If True, target years below + the first input year are back-filled from the first input row. Default + False — drop them. + value_cols + When given, switches to long-form input/output: *df* must have a + ``year`` integer column plus each name in *value_cols*. Output is + long-form on the same columns. + + Returns + ------- + pd.DataFrame + Wide layout (default): ID columns plus *model_years* (filtered to + the supported range) as integer columns. Long layout (when + *value_cols* is given): ID columns plus ``year`` plus *value_cols*. + """ + if value_cols is not None: + return _resample_long_form( + df, + id_cols, + value_cols, + model_years, + method, + extrapolate_below=extrapolate_below, + ) + + year_cols = sorted(c for c in df.columns if isinstance(c, int) and c not in id_cols) + if not year_cols: + raise ValueError("No integer year columns found in DataFrame") + + min_input_year = year_cols[0] + max_input_year = year_cols[-1] + years_within = [y for y in model_years if y <= max_input_year] + years_beyond = [y for y in model_years if y > max_input_year] + + match method: + case "point": + result = _resample_point( + df, + id_cols, + year_cols, + years_within, + min_input_year=min_input_year, + max_input_year=max_input_year, + ) + case "average": + result = _resample_average(df, id_cols, year_cols, years_within) + case "interpolate": + result = _resample_interpolate( + df, + id_cols, + year_cols, + years_within, + min_input_year=min_input_year, + extrapolate_below=extrapolate_below, + ) + case _: + raise ValueError( + f"method must be 'point', 'average', or 'interpolate'; got {method!r}" + ) + + if years_beyond: + result_year_cols = [ + c for c in result.columns if isinstance(c, int) and c not in id_cols + ] + if result_year_cols: + last_available = max(result_year_cols) + for y in years_beyond: + result[y] = result[last_available] + + return result diff --git a/message_ix_models/util/node.py b/message_ix_models/util/node.py index d1901ad528..ff05e8a239 100644 --- a/message_ix_models/util/node.py +++ b/message_ix_models/util/node.py @@ -124,6 +124,28 @@ def identify_nodes(scenario: Scenario) -> str: return id +def extract_region_code(node: str) -> str: + """Extract short region code from a MESSAGE node name. + + Strips the ``R{n}_`` prefix (e.g. ``R11_``, ``R12_``, ``R14_``) if present; + returns the string unchanged for any other format. + + Parameters + ---------- + node + MESSAGE node name (e.g. ``"R12_AFR"``, ``"R11_WEU"``) or a short code + that needs no stripping (e.g. ``"AFR"``). + + Returns + ------- + str + Short code (e.g. ``"AFR"``). + """ + import re + + return re.sub(r"^R\d+_", "", node) + + def nodes_ex_world(nodes: Sequence[str | Code]) -> list[str | Code]: """Exclude "World" and anything containing "GLB" from `nodes`. From f783595aec75e7dc9bcd4aa9e8ec3f4eaf05d6ee Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Fri, 1 May 2026 09:24:29 +0200 Subject: [PATCH 02/19] Add buildings physical-impact module compute_building_cids and apply_building_cids drive the RIME EI-vs-GWL emulator into MESSAGE residential/commercial demand, with theta-calibrated reference scenarios and sector_fractions from STURM. CID timeseries persist as Final Energy|Residential and Commercial|{Cooling,Heating} in EJ/yr. Packaged data: theta_{cool,heat}_SSP{2,3}.csv, rc_sector_fractions_SSP{2,3}.csv, sturm_floor_area_R12_{resid,comm}.csv, correction_coefficients_{cool,heat}_SSP2_{resid,comm}.csv, region_EI_{cool,heat}_gwl_binned.nc. --- ...correction_coefficients_cool_SSP2_comm.csv | 2862 ++++++++ ...orrection_coefficients_cool_SSP2_resid.csv | 5959 +++++++++++++++++ ...correction_coefficients_heat_SSP2_comm.csv | 2862 ++++++++ ...orrection_coefficients_heat_SSP2_resid.csv | 5959 +++++++++++++++++ .../buildings/rc_sector_fractions_SSP2.csv | 103 + .../buildings/rc_sector_fractions_SSP3.csv | 102 + .../buildings/sturm_floor_area_R12_comm.csv | 2861 ++++++++ .../buildings/sturm_floor_area_R12_resid.csv | 5958 ++++++++++++++++ .../data/buildings/theta_cool_SSP2.csv | 109 + .../data/buildings/theta_cool_SSP3.csv | 97 + .../data/buildings/theta_heat_SSP2.csv | 109 + .../data/buildings/theta_heat_SSP3.csv | 97 + .../impacts/rime/region_EI_cool_gwl_binned.nc | Bin 0 -> 378216 bytes .../impacts/rime/region_EI_heat_gwl_binned.nc | Bin 0 -> 378216 bytes message_ix_models/model/buildings/impacts.py | 546 ++ .../tests/model/test_building_impacts.py | 111 + 16 files changed, 27735 insertions(+) create mode 100644 message_ix_models/data/buildings/correction_coefficients/correction_coefficients_cool_SSP2_comm.csv create mode 100644 message_ix_models/data/buildings/correction_coefficients/correction_coefficients_cool_SSP2_resid.csv create mode 100644 message_ix_models/data/buildings/correction_coefficients/correction_coefficients_heat_SSP2_comm.csv create mode 100644 message_ix_models/data/buildings/correction_coefficients/correction_coefficients_heat_SSP2_resid.csv create mode 100644 message_ix_models/data/buildings/rc_sector_fractions_SSP2.csv create mode 100644 message_ix_models/data/buildings/rc_sector_fractions_SSP3.csv create mode 100644 message_ix_models/data/buildings/sturm_floor_area_R12_comm.csv create mode 100644 message_ix_models/data/buildings/sturm_floor_area_R12_resid.csv create mode 100644 message_ix_models/data/buildings/theta_cool_SSP2.csv create mode 100644 message_ix_models/data/buildings/theta_cool_SSP3.csv create mode 100644 message_ix_models/data/buildings/theta_heat_SSP2.csv create mode 100644 message_ix_models/data/buildings/theta_heat_SSP3.csv create mode 100644 message_ix_models/data/impacts/rime/region_EI_cool_gwl_binned.nc create mode 100644 message_ix_models/data/impacts/rime/region_EI_heat_gwl_binned.nc create mode 100644 message_ix_models/model/buildings/impacts.py create mode 100644 message_ix_models/tests/model/test_building_impacts.py diff --git a/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_cool_SSP2_comm.csv b/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_cool_SSP2_comm.csv new file mode 100644 index 0000000000..4e4f28f196 --- /dev/null +++ b/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_cool_SSP2_comm.csv @@ -0,0 +1,2862 @@ +# METADATA: {"scenario": "SSP2", "description": "SSP2_BL: Base scenario", "mode": "cool", "sector": "comm", "eff_file": "eff_cool_ssp2.csv", "ac_file": "bld_shr_access_cool_comm_rev.csv", "generated": "2025-12-09T14:25:39.445453"} +region,arch,urt,year,chilled_ei_MJ_m2,floor_Mm2,sturm_energy_TJ,correction_coeff +AFR,comm_s1,rural,2020,65.97691065837212,126.09207633389,115.08991441823126,0.0138343097362084 +AFR,comm_s1,urban,2020,67.40736067677899,18.58502678997,78.04415111464026,0.062297389287734 +AFR,comm_s2,rural,2020,65.97691065837212,266.56128852236,229.986716493678,0.0130771678413573 +AFR,comm_s2,urban,2020,67.40736067677899,135.23792245146,554.8946597362348,0.0608702004658079 +AFR,comm_s3,rural,2020,65.97691065837212,373.76917935108,292.7707295718657,0.0118722270025397 +AFR,comm_s3,urban,2020,67.40736067677899,356.87956377434,1405.9274118811982,0.0584431877811285 +AFR,comm_s51_std,rural,2020,65.97691065837212,173.73642168595,131.2271422318724,0.0114482952875561 +AFR,comm_s51_std,urban,2020,67.40736067677899,184.08923443194,647.4081842357807,0.0521725988851697 +AFR,comm_sr11_std,rural,2020,65.97691065837212,3.23313016243,2.3608187573427606,0.0110674477891037 +AFR,comm_sr11_std,urban,2020,67.40736067677899,0.47653914851,1.6009056659136682,0.0498379114880357 +AFR,comm_sr21_std,rural,2020,65.97691065837212,6.83490483397,4.717676236429721,0.0104617342744568 +AFR,comm_sr21_std,urban,2020,67.40736067677899,3.4676390372,11.38245455593672,0.0486961603610964 +AFR,comm_sr31_std,rural,2020,65.97691065837212,9.58382511156,6.0055534269227335,0.0094977816017334 +AFR,comm_sr31_std,urban,2020,67.40736067677899,9.15075804537,28.83953665347611,0.04675455022474 +CHN,comm_s1,rural,2020,22.12829199010737,2023.84677013151,14432.84757912202,0.32227491587305 +CHN,comm_s1,urban,2020,21.290516010106405,266.86283366356,23502.08494162676,4.136491258980513 +CHN,comm_s2,rural,2020,22.12829199010737,2259.33575128999,13824.591613949846,0.2765181501249188 +CHN,comm_s2,urban,2020,21.290516010106405,1058.53939649076,79672.43628756182,3.535207289376076 +CHN,comm_s3,rural,2020,22.12829199010737,500.78291909726,2602.3363861198927,0.2348367348130027 +CHN,comm_s3,urban,2020,21.290516010106405,3507.77019445352,222586.9877423812,2.9804541915033504 +CHN,comm_s51_std,rural,2020,22.12829199010737,1686.0114673727,8531.029381841225,0.2286614852644014 +CHN,comm_s51_std,urban,2020,21.290516010106405,3038.39938708115,187424.20205776027,2.8973077183839098 +CHN,comm_s52_low,rural,2020,22.12829199010737,88.73744565119,370.1729409866318,0.1885167103921269 +CHN,comm_s52_low,urban,2020,21.290516010106405,104.32573062904,5192.223309961659,2.3376298283730206 +CHN,comm_sr11_std,rural,2020,22.12829199010737,49.29883158012,281.25549128550296,0.2578199326985183 +CHN,comm_sr11_std,urban,2020,21.290516010106405,4.27893453273,301.4698793896139,3.3091930071769924 +CHN,comm_sr12_low,rural,2020,22.12829199010737,2.59467534633,11.102190445376662,0.193364949521494 +CHN,comm_sr12_low,urban,2020,21.290516010106405,0.2252070806799999,11.900126819950245,2.481894755676975 +CHN,comm_sr21_std,rural,2020,22.12829199010737,55.03510163392001,269.4022981178936,0.2212145201001192 +CHN,comm_sr21_std,urban,2020,21.290516010106405,16.97284224911,1021.9876159120156,2.828165831500058 +CHN,comm_sr22_low,rural,2020,22.12829199010737,2.89658429649,10.634301241474006,0.1659108900765883 +CHN,comm_sr22_low,urban,2020,21.290516010106405,0.89330748682,40.34161642172949,2.121124373784505 +CHN,comm_sr31_std,rural,2020,22.12829199010737,12.19855828572,50.71219624213818,0.1878693878493983 +CHN,comm_sr31_std,urban,2020,21.290516010106405,56.24432151862,2855.205081391695,2.3843633532034256 +CHN,comm_sr32_low,rural,2020,22.12829199010737,0.6420293834299999,2.001797219981294,0.140902040886144 +CHN,comm_sr32_low,urban,2020,21.290516010106405,2.9602274483700004,112.70546374098944,1.7882725149187848 +EEU,comm_s1,rural,2020,5.70158380772776,293.53572861684,945.505898525064,0.5649470689824386 +EEU,comm_s1,urban,2020,5.644413686621755,220.29000762622,5029.561962216648,4.04498139701242 +EEU,comm_s2,rural,2020,5.70158380772776,54.53766666305,170.82417803989338,0.5493603032398537 +EEU,comm_s2,urban,2020,5.644413686621755,367.47029276826,8319.646814660266,4.01110303818459 +EEU,comm_s3,rural,2020,5.70158380772776,24.10402288902,68.63400705670479,0.4994066390944711 +EEU,comm_s3,urban,2020,5.644413686621755,84.89586690206,2392.862029833793,4.993583020737509 +EEU,comm_s51_std,urban,2020,5.644413686621755,119.59200930881,3240.9195088345846,4.8011717948521495 +EEU,comm_s52_low,rural,2020,5.70158380772776,82.58187132178999,416.0260525371991,0.8835686450058177 +EEU,comm_s52_low,urban,2020,5.644413686621755,145.39064295865,5132.531005591934,6.254265135324277 +EEU,comm_sr11_std,rural,2020,5.70158380772776,21.42017479091,55.19710110286366,0.4519576551851654 +EEU,comm_sr11_std,urban,2020,5.644413686621755,8.50878129206,155.41492123876358,3.235985117593218 +EEU,comm_sr12_low,rural,2020,5.70158380772776,2.38001942117,4.5997584251382335,0.3389682413874884 +EEU,comm_sr12_low,urban,2020,5.644413686621755,0.94542014355,12.951243436059867,2.426988838131887 +EEU,comm_sr21_std,rural,2020,5.70158380772776,3.97977567542,9.972438501869016,0.4394882425945265 +EEU,comm_sr21_std,urban,2020,5.644413686621755,14.19367308679,257.0794960176713,3.2088824304952666 +EEU,comm_sr22_low,rural,2020,5.70158380772776,0.44219729722,0.8310365416834464,0.3296161819272163 +EEU,comm_sr22_low,urban,2020,5.644413686621755,1.57707478751,21.42329133809485,2.406661823105273 +EEU,comm_sr31_std,rural,2020,5.70158380772776,1.75894221084,4.006742033660446,0.3995253112790813 +EEU,comm_sr31_std,urban,2020,5.644413686621755,3.27913359246,73.94012971894598,3.994866416603168 +EEU,comm_sr32_low,rural,2020,5.70158380772776,0.19543802344,0.3338951694348165,0.299643983405765 +EEU,comm_sr32_low,urban,2020,5.644413686621755,0.3643481769499999,6.161677478518868,2.996149813313497 +FSU,comm_s1,rural,2020,11.109164482874052,365.45020352517,801.6092582473369,0.1974481729282685 +FSU,comm_s1,urban,2020,8.158141779303799,359.95112342861,4756.458248418479,1.6197534250744203 +FSU,comm_s2,rural,2020,11.109164482874052,190.53659919152,891.5893937179997,0.4212162206242215 +FSU,comm_s2,urban,2020,8.158141779303799,743.32992398078,12265.701924859151,2.022644423577595 +FSU,comm_s3,rural,2020,11.109164482874052,151.06727961484,708.9854243185229,0.4224599090665802 +FSU,comm_s3,urban,2020,8.158141779303799,378.92989666601005,8332.842348455832,2.6955230214970016 +FSU,comm_s51_std,rural,2020,11.109164482874052,195.12953457859004,674.4025265547839,0.3111105817654538 +FSU,comm_s51_std,urban,2020,8.158141779303799,587.56604649842,10476.346552580322,2.1855558865494014 +FSU,comm_s52_low,rural,2020,11.109164482874052,10.26997550412,36.73244084095185,0.3219578237106236 +FSU,comm_s52_low,urban,2020,8.158141779303799,2.95259319849,54.15240607729744,2.2481376256966734 +FSU,comm_sr11_std,rural,2020,11.109164482874052,28.149542704,49.396462400314526,0.1579585383430892 +FSU,comm_sr11_std,urban,2020,8.158141779303799,2.5840319920900003,27.316687140027938,1.2958027398009666 +FSU,comm_sr12_low,rural,2020,11.109164482874052,1.48155487916,1.9498603579748768,0.1184689037612633 +FSU,comm_sr12_low,urban,2020,8.158141779303799,0.13600168379,1.0782902838878303,0.9718520567236104 +FSU,comm_sr21_std,rural,2020,11.109164482874052,14.67646777564,54.94118426311621,0.3369729765072675 +FSU,comm_sr21_std,urban,2020,8.158141779303799,5.33624756012,70.44282206636784,1.6181155387333288 +FSU,comm_sr22_low,rural,2020,11.109164482874052,0.7724456722999999,2.168730955943704,0.2527297322028291 +FSU,comm_sr22_low,urban,2020,8.158141779303799,0.28085513481,2.780637730843698,1.2135866614851425 +FSU,comm_sr31_std,rural,2020,11.109164482874052,11.63626342985,43.688831553450406,0.3379679272581435 +FSU,comm_sr31_std,urban,2020,8.158141779303799,2.72027759321,47.85612226370327,2.1564184173898138 +FSU,comm_sr32_low,rural,2020,11.109164482874052,0.61243491732,1.7245591416165047,0.2534759456586167 +FSU,comm_sr32_low,urban,2020,8.158141779303799,0.1431725048799999,1.889057450590489,1.617313807180314 +LAM,comm_s1,rural,2020,52.728216774604014,232.63636908495,2451.8806698481503,0.1998842775199569 +LAM,comm_s1,urban,2020,35.55892886384555,164.75635339798,11207.97080025604,1.9130932945632764 +LAM,comm_s2,rural,2020,52.728216774604014,292.29209918059,2766.5389318641296,0.1795050312848999 +LAM,comm_s2,urban,2020,35.55892886384555,1142.34161177491,77534.34071193163,1.9087516743898592 +LAM,comm_s3,rural,2020,52.728216774604014,100.27003149,785.7674696236854,0.1486208736241679 +LAM,comm_s3,urban,2020,35.55892886384555,1401.04098230175,88396.33939889504,1.7743315348534785 +LAM,comm_s51_std,rural,2020,52.728216774604014,180.08830791898998,1597.7370543124352,0.1682583809839798 +LAM,comm_s51_std,urban,2020,35.55892886384555,1095.8199091055,68986.30369593143,1.7704147496378195 +LAM,comm_sr11_std,rural,2020,52.728216774604014,5.96503510472,50.29498809836252,0.1599074220131084 +LAM,comm_sr11_std,urban,2020,35.55892886384555,4.22452188203,229.90709333887656,1.5304746356415049 +LAM,comm_sr21_std,rural,2020,52.728216774604014,7.49466920983,56.749516552960834,0.1436040250313665 +LAM,comm_sr21_std,urban,2020,35.55892886384555,29.290810558440004,1590.4480146064452,1.5270013395088171 +LAM,comm_sr31_std,rural,2020,52.728216774604014,2.57102644855,16.118307069583068,0.1188966988980411 +LAM,comm_sr31_std,urban,2020,35.55892886384555,35.92412775119,1813.2582440608408,1.419465227873275 +MEA,comm_s1,rural,2020,61.595338640236776,169.60323134726,2944.7513444871897,0.281881555347696 +MEA,comm_s1,urban,2020,56.63136680223274,66.7480722387,3704.750653147988,0.9800838040226496 +MEA,comm_s2,rural,2020,61.595338640236776,285.6158276895,2944.4382533419816,0.1673679641557784 +MEA,comm_s2,urban,2020,56.63136680223274,420.79007898969,38477.24550845105,1.6146613103141303 +MEA,comm_s3,rural,2020,61.595338640236776,285.44681852033,4078.363671464096,0.2319598907233793 +MEA,comm_s3,urban,2020,56.63136680223274,752.25779786262,67673.42354788473,1.5885262867649734 +MEA,comm_s51_std,rural,2020,61.595338640236776,223.94896195075,2676.766923288505,0.1940500040365304 +MEA,comm_s51_std,urban,2020,56.63136680223274,532.2125914261,43265.86044799404,1.4354998667330483 +MEA,comm_sr11_std,rural,2020,61.595338640236776,4.34880080382,60.40515578692809,0.2255052442854846 +MEA,comm_sr11_std,urban,2020,56.63136680223274,1.71148903176,75.99488519206939,0.7840670432115009 +MEA,comm_sr21_std,rural,2020,61.595338640236776,7.323482761309999,60.39873340710941,0.1338943713354551 +MEA,comm_sr21_std,urban,2020,56.63136680223274,10.78948920481,789.2768309490525,1.2917290482683346 +MEA,comm_sr31_std,rural,2020,61.595338640236776,7.31914919303,83.65874197316866,0.1855679125612249 +MEA,comm_sr31_std,urban,2020,56.63136680223274,19.28866148361,1388.1727907381437,1.2708210294263331 +NAM,comm_s1,rural,2020,16.500867644183007,950.005954543,91675.6390855891,5.848181437844342 +NAM,comm_s1,urban,2020,16.530658846572372,1762.93861907043,193498.6719885765,6.63973328025586 +NAM,comm_s2,rural,2020,16.500867644183007,320.75687658762,25296.620704541896,4.779470137337679 +NAM,comm_s2,urban,2020,16.530658846572372,2759.76841871432,250482.63713818643,5.490537009576302 +NAM,comm_s3,rural,2020,16.500867644183007,16.26640359548,9.243239929278769,0.0344370468842685 +NAM,comm_s3,urban,2020,16.530658846572372,2167.54671987765,189518.85613286457,5.289246363715356 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b/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_cool_SSP2_resid.csv new file mode 100644 index 0000000000..4e820c9379 --- /dev/null +++ b/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_cool_SSP2_resid.csv @@ -0,0 +1,5959 @@ +# METADATA: {"scenario": "SSP2", "description": "SSP2_BL: Base scenario", "mode": "cool", "sector": "resid", "eff_file": "eff_cool_ssp2.csv", "ac_file": "bld_shr_access_cool_resid_ssp2_rev.csv", "generated": "2025-12-09T14:25:39.290220"} +region,arch,urt,year,chilled_ei_MJ_m2,floor_Mm2,sturm_energy_TJ,correction_coeff +AFR,inf_ns,urban,2020,,2026.0805002004668,158.89734233969972, +AFR,mfh_s1,rural,2020,67.77348115246582,27.833672766076244,1.6482910312370789,0.0008737829878697 +AFR,mfh_s1,urban,2020,70.25683890994821,13.930664959770652,3.3066677039172623,0.0033785480572088 +AFR,mfh_s2,rural,2020,67.7726824990315,61.29734449393811,3.444432025774588,0.0008291274284387 +AFR,mfh_s2,urban,2020,70.25683890994821,92.42975903008106,24.233220155472477,0.0037317341895275 +AFR,mfh_s3,rural,2020,65.01722728806209,84.77618106065728,3.5731880837567824,0.000648266505878 +AFR,mfh_s3,urban,2020,67.33635735981284,236.3003701842016,63.13595898806206,0.003967918656962 +AFR,mfh_s51_std,rural,2020,61.81822777560578,18.92976969874934,0.5505065190452632,0.0004704360399132 +AFR,mfh_s51_std,urban,2020,63.9770658477033,83.46034880723806,20.19256035129171,0.0037816982779321 +AFR,mfh_sr11_std,rural,2020,,0.0242344376828777,0.0014473652631038, +AFR,mfh_sr11_std,urban,2020,,0.0895346659111488,0.0077080541317681, +AFR,mfh_sr21_std,rural,2020,,0.0750858367362252,0.0044843885790511, +AFR,mfh_sr21_std,urban,2020,,0.4459071121336794,0.0383882196476037, +AFR,mfh_sr31_std,rural,2020,,0.0625534150382857,0.0036849361925506, +AFR,mfh_sr31_std,urban,2020,,0.8850223842923312,0.0690505461574046, +AFR,sfh_s1,rural,2020,143.64700022638183,1057.4369580620214,129.27901911918144,0.0008510929676101 +AFR,sfh_s1,urban,2020,149.2873895755873,116.72620154952494,56.82548026740393,0.00326100629895 +AFR,sfh_s2,rural,2020,143.6444605033549,2327.7288325066347,268.8122380511341,0.0008039476463905 +AFR,sfh_s2,urban,2020,149.2873895755873,776.0035323538018,420.66912761838,0.0036312305089899 +AFR,sfh_s3,rural,2020,135.78059522338597,3221.1913004044586,275.2406953760405,0.0006293009983307 +AFR,sfh_s3,urban,2020,141.07625955685202,1986.34125457062,1081.085972379023,0.0038579130314644 +AFR,sfh_s51_std,rural,2020,126.30495867536516,720.8465573477184,42.00976229455868,0.0004614099912578 +AFR,sfh_s51_std,urban,2020,131.28489075542765,705.8525142943847,341.06483737726774,0.0036805119680334 +AFR,sfh_sr11_std,rural,2020,,3.399810744029209,0.3900788513748468, +AFR,sfh_sr11_std,urban,2020,,1.900288581481052,0.4428914504970614, +AFR,sfh_sr21_std,rural,2020,,9.350193344408968,1.2085578546903877, +AFR,sfh_sr21_std,urban,2020,,9.719846067767376,2.2057467663783483, +AFR,sfh_sr31_std,rural,2020,,9.491044857263738,0.9648097821333076, +AFR,sfh_sr31_std,urban,2020,,19.62868715994202,3.8797070956443513, +CHN,inf_ns,urban,2020,,3415.845576078641,1209.2898894534342, +CHN,mfh_s1,rural,2020,26.041009181373084,4400.098950878357,14837.070867150063,0.1294875212091958 +CHN,mfh_s1,urban,2020,29.27962872358791,547.028095653958,4699.833670456586,0.293431876120756 +CHN,mfh_s2,rural,2020,22.143073415258677,6360.661389570992,18434.17467716738,0.1308830846810865 +CHN,mfh_s2,urban,2020,24.79624346887529,2670.5643899862625,19629.77039836893,0.2964328122746816 +CHN,mfh_s3,rural,2020,18.52588098244399,4802.534645710272,11856.199176849716,0.1332588651273646 +CHN,mfh_s3,urban,2020,20.59536213099936,12101.64736300735,75111.74219014554,0.3013657608512553 +CHN,mfh_s51_std,rural,2020,17.97700047700296,2.3265972197529465,5.569367102015542,0.1331580450303607 +CHN,mfh_s51_std,urban,2020,19.949459053940306,3967.029969458238,17255.025671129366,0.218031380397926 +CHN,mfh_s52_low,rural,2020,14.257037731628316,4.991967539810245e-05,9.45602369322764e-06,0.0132864054115842 +CHN,mfh_s52_low,urban,2020,15.475133528889272,0.0034016961604776,0.0015982490526303,0.0303608922689488 +CHN,mfh_sr11_std,rural,2020,,71.34157749713899,150.14399392763698, +CHN,mfh_sr11_std,urban,2020,,6.312064087693557,35.00404122878913, +CHN,mfh_sr12_low,rural,2020,,11.131787783471934,1.9037408574060144, +CHN,mfh_sr12_low,urban,2020,,0.1229023363714204,0.0616296591547587, +CHN,mfh_sr21_std,rural,2020,,104.72704050111844,184.9449224887926, +CHN,mfh_sr21_std,urban,2020,,31.11417012426071,147.26372577636684, +CHN,mfh_sr22_low,rural,2020,,12.054103099723822,1.746236670482958, +CHN,mfh_sr22_low,urban,2020,,0.2938900915986592,0.1254624669344943, +CHN,mfh_sr31_std,rural,2020,,82.25166489276626,119.1701588564668, +CHN,mfh_sr31_std,urban,2020,,84.99130489140798,306.58879791543075, +CHN,mfh_sr32_low,rural,2020,,3.018595413023641,0.3579808713521596, +CHN,mfh_sr32_low,urban,2020,,0.133724601303098,0.0507959027765615, +CHN,sfh_s1,rural,2020,50.23954510392356,3841.9656236090273,24355.49969528652,0.1261821349585963 +CHN,sfh_s1,urban,2020,56.97801785226968,79.11229225107682,1290.2440806427642,0.2862335718770429 +CHN,sfh_s2,rural,2020,41.01092129099138,5456.947623094179,28465.621694529247,0.1271953873889321 +CHN,sfh_s2,urban,2020,46.38032051448324,394.5319310312942,5282.55564840832,0.288687631285512 +CHN,sfh_s3,rural,2020,30.84729745331328,3955.211683998169,15658.490186940406,0.1283402943963184 +CHN,sfh_s3,urban,2020,34.77400910324272,1718.3314826709702,17415.614269744416,0.2914587423077448 +CHN,sfh_s51_std,urban,2020,32.984811850241385,548.319997083834,3763.4531319938615,0.2080838698183118 +CHN,sfh_s52_low,urban,2020,19.839703212324714,0.5919517658205501,0.2926026782468078,0.0249147655874325 +CHN,sfh_sr11_std,rural,2020,,59.54810242227185,371.894912545804, +CHN,sfh_sr11_std,urban,2020,,0.8175458127845453,11.604781674889004, +CHN,sfh_sr12_low,rural,2020,,55.130591686233686,84.93870949132389, +CHN,sfh_sr12_low,urban,2020,,0.447842148671605,2.700750556634117, +CHN,sfh_sr21_std,rural,2020,,99.3221178132172,477.1426274778342, +CHN,sfh_sr21_std,urban,2020,,4.649379257434639,48.98934936170959, +CHN,sfh_sr22_low,rural,2020,,60.92266602371039,60.43796220142171, +CHN,sfh_sr22_low,urban,2020,,1.3588857688198466,5.681674167590752, +CHN,sfh_sr31_std,rural,2020,,84.59411004056179,274.2000930144748, +CHN,sfh_sr31_std,urban,2020,,22.89114242637084,163.8591116395584, +CHN,sfh_sr32_low,rural,2020,,23.43963237599199,8.731680994144087, +CHN,sfh_sr32_low,urban,2020,,1.906407965294698,2.909129410078982, +EEU,mfh_s1,rural,2020,5.8004566652851794,148.49939278346147,16.149060457587385,0.0187482353229276 +EEU,mfh_s1,urban,2020,5.819244081323695,288.5698501228476,78.14177334136001,0.0465335017506171 +EEU,mfh_s2,rural,2020,5.502448666608135,41.592020100852864,4.566522833104791,0.019953524544168 +EEU,mfh_s2,urban,2020,5.520358548773342,568.5896381765516,161.69655865791893,0.0515151003181556 +EEU,mfh_s3,rural,2020,5.466054840055654,27.17341557204672,1.7707445107067536,0.0119216862738551 +EEU,mfh_s3,urban,2020,5.486936949774583,251.7919916188013,88.74671036191745,0.0642362797715935 +EEU,mfh_s51_std,urban,2020,5.686187079926115,54.040511137829405,17.66908979125515,0.0575007600721091 +EEU,mfh_s52_low,urban,2020,6.684871349012427,55.24454564858901,22.732635552542703,0.0615555660960809 +EEU,mfh_sr11_std,rural,2020,,11.554431238502843,0.9076750378022344, +EEU,mfh_sr11_std,urban,2020,,12.78041706197358,2.112064698840785, +EEU,mfh_sr12_low,rural,2020,,0.4026286148511226,0.0156752911634199, +EEU,mfh_sr12_low,urban,2020,,0.2836681218953466,0.025498722231705, +EEU,mfh_sr21_std,rural,2020,,3.1188911611245635,0.2357709576839373, +EEU,mfh_sr21_std,urban,2020,,24.22741713406476,4.00347324845999, +EEU,mfh_sr22_low,rural,2020,,0.0937088229059751,0.0036494100628592, +EEU,mfh_sr22_low,urban,2020,,0.47867886673956,0.0421806291423417, +EEU,mfh_sr31_std,rural,2020,,1.8502776979055784,0.0879439940832073, +EEU,mfh_sr31_std,urban,2020,,7.892611414580533,1.6013899495907542, +EEU,mfh_sr32_low,rural,2020,,0.0212411248097425,0.0006085704354504, +EEU,mfh_sr32_low,urban,2020,,0.0550269509173459,0.0052998740752192, +EEU,sfh_s1,rural,2020,8.376888297260287,899.8177679886026,123.14026086503222,0.016336641560757 +EEU,sfh_s1,urban,2020,8.390499530514344,206.65090478472155,70.25074630424574,0.0405159301531709 +EEU,sfh_s2,rural,2020,7.31124971777819,258.3879646353384,32.327680751427,0.0171123892361034 +EEU,sfh_s2,urban,2020,7.317699222685837,419.5252018173687,134.9921950999599,0.0439719828194406 +EEU,sfh_s3,rural,2020,5.765417036854554,165.78062290220518,8.407885461921158,0.0087967508688025 +EEU,sfh_s3,urban,2020,5.776574125683992,179.99228904241463,56.814231977301,0.0546427929720599 +EEU,sfh_s51_std,urban,2020,5.339781942385947,29.08654111032521,7.451418263636052,0.0479759231702663 +EEU,sfh_s52_low,urban,2020,5.795228909629955,34.022798831611695,10.893515614305931,0.0552493824200103 +EEU,sfh_sr11_std,rural,2020,,40.682160868933934,5.615200318728459, +EEU,sfh_sr11_std,urban,2020,,5.68033311306964,1.9310994471195968, +EEU,sfh_sr12_low,rural,2020,,54.08480986116607,3.414810690762196, +EEU,sfh_sr12_low,urban,2020,,5.425917801741198,0.7485297263622914, +EEU,sfh_sr21_std,rural,2020,,12.532723998374994,1.5577224115753032, +EEU,sfh_sr21_std,urban,2020,,12.131048408017987,3.897231897071392, +EEU,sfh_sr22_low,rural,2020,,14.56487989867366,0.8028748091431493, +EEU,sfh_sr22_low,urban,2020,,10.397147543931352,1.2676170921331695, +EEU,sfh_sr31_std,rural,2020,,11.04175611163644,0.4797425886746507, +EEU,sfh_sr31_std,urban,2020,,7.246216610637941,1.813460663090704, +EEU,sfh_sr32_low,rural,2020,,6.444813257640472,0.1567666142630754, +EEU,sfh_sr32_low,urban,2020,,2.322005026095704,0.236726273249887, +FSU,mfh_s1,rural,2020,11.772125974054992,202.1614303443048,36.00919419303869,0.0151307411074049 +FSU,mfh_s1,urban,2020,8.756542324903274,380.6749082879774,184.93379887975323,0.0554790975481247 +FSU,mfh_s2,rural,2020,11.183123062211436,145.78940040419934,47.90178652738518,0.029380735073944 +FSU,mfh_s2,urban,2020,8.322510800397222,1002.0234355670336,508.84896892068656,0.0610178150761219 +FSU,mfh_s3,rural,2020,10.035573345512086,122.92534020549824,33.12110511615833,0.0268485731737351 +FSU,mfh_s3,urban,2020,7.789655536010645,847.4737241418031,350.99105905451074,0.0531681511438811 +FSU,mfh_s51_std,rural,2020,9.741938785589044,9.380518772128497,3.779074577880571,0.0413535892121413 +FSU,mfh_s51_std,urban,2020,7.767486699889488,205.33874811335392,128.64295277992707,0.0806556084504924 +FSU,mfh_s52_low,rural,2020,9.952866848916653,0.0093212468945294,0.0020648916570625,0.0222574324560336 +FSU,mfh_s52_low,urban,2020,8.397303887581892,0.0056439431705258,0.0025062145979337,0.0528805243818952 +FSU,mfh_sr11_std,rural,2020,,18.006483647328682,2.07329533677803, +FSU,mfh_sr11_std,urban,2020,,2.5354749092180944,0.7723556487502196, +FSU,mfh_sr12_low,rural,2020,,1.3024543636851116,0.066728141152696, +FSU,mfh_sr12_low,urban,2020,,0.0970741443454206,0.0141732824274799, +FSU,mfh_sr21_std,rural,2020,,13.022447350063448,2.6200320389552068, +FSU,mfh_sr21_std,urban,2020,,6.555075362642131,1.6048836611322868, +FSU,mfh_sr22_low,rural,2020,,1.0402132799187425,0.0836786103974757, +FSU,mfh_sr22_low,urban,2020,,0.2410497708867326,0.0380169245570511, +FSU,mfh_sr31_std,rural,2020,,9.092435926996712,0.8825817814450864, +FSU,mfh_sr31_std,urban,2020,,5.220697530741797,1.048745608226149, +FSU,mfh_sr32_low,rural,2020,,0.3691120541020585,0.0281147644803348, +FSU,mfh_sr32_low,urban,2020,,0.070283276676479,0.0106450518337089, +FSU,sfh_s1,rural,2020,19.441840625547407,824.5782961785357,234.8499474070328,0.0146494455902109 +FSU,sfh_s1,urban,2020,13.667296104337138,199.6738727344341,154.45062716277008,0.0565960121652297 +FSU,sfh_s2,rural,2020,17.397992936418436,667.820338423195,353.1886226702962,0.0303982031894938 +FSU,sfh_s2,urban,2020,12.13628876150187,612.3419730091676,469.3555753719642,0.0631570811779654 +FSU,sfh_s3,rural,2020,12.879438402468654,453.7422011800333,146.6476009141112,0.0250939404241381 +FSU,sfh_s3,urban,2020,9.22772426050544,376.6719651125107,187.50511241888785,0.053945498050572 +FSU,sfh_s51_std,rural,2020,11.210339045756546,27.861470956961465,14.261350430396243,0.0456602055746066 +FSU,sfh_s51_std,urban,2020,8.23127068866377,93.6064567091938,71.49935755934197,0.0927960413004573 +FSU,sfh_s52_low,rural,2020,9.944198707353737,1.5251711339889735,0.2878663952829779,0.0189802792857326 +FSU,sfh_s52_low,urban,2020,7.918987751890675,0.116706314690765,0.0494991135829032,0.0535591153736424 +FSU,sfh_sr11_std,rural,2020,,56.76728299604096,15.937327405157395, +FSU,sfh_sr11_std,urban,2020,,1.429900478257365,0.9454544860374876, +FSU,sfh_sr12_low,rural,2020,,30.685101812577084,2.370439648379091, +FSU,sfh_sr12_low,urban,2020,,0.4862389119032924,0.0966255059896889, +FSU,sfh_sr21_std,rural,2020,,45.37295285471158,24.33138052972896, +FSU,sfh_sr21_std,urban,2020,,4.447997804454746,2.816914635322622, +FSU,sfh_sr22_low,rural,2020,,25.35601291871559,2.842227592659267, +FSU,sfh_sr22_low,urban,2020,,1.4225348288916184,0.2680063340239897, +FSU,sfh_sr31_std,rural,2020,,36.98175603873318,8.630014365937363, +FSU,sfh_sr31_std,urban,2020,,3.107184102111859,1.0833978504904511, +FSU,sfh_sr32_low,rural,2020,,10.650612976160026,0.6823658301102697, +FSU,sfh_sr32_low,urban,2020,,0.4845330619002211,0.0628190320155241, +LAM,inf_ns,urban,2020,,1114.9128393791923,145.18241121625528, +LAM,mfh_s1,rural,2020,55.21090238691856,4.87637753325178,13.656917494603714,0.0507259884596492 +LAM,mfh_s1,urban,2020,38.053180190561,29.172918931002584,127.75620238138276,0.115082993729521 +LAM,mfh_s2,rural,2020,52.77849211123935,7.984313996270302,20.152444956939377,0.04782259708028 +LAM,mfh_s2,urban,2020,36.555432227577015,240.10033519368665,963.4873241718674,0.1097744615861872 +LAM,mfh_s3,rural,2020,50.32243674647189,4.895313230669059,10.052785204088895,0.0408079008007931 +LAM,mfh_s3,urban,2020,34.9603097505505,409.30228245237856,1549.6056929835077,0.1082933440921229 +LAM,mfh_s51_std,rural,2020,49.966375970792456,0.2646349540421449,0.4277941590980338,0.0323526449863809 +LAM,mfh_s51_std,urban,2020,34.72801837615456,80.645526807952,272.68456921750413,0.0973644190211724 +LAM,mfh_sr11_std,rural,2020,,0.0022370388823611,5.379396346398054e-06, +LAM,mfh_sr11_std,urban,2020,,0.0011720963752359,8.430647976393829e-06, +LAM,mfh_sr21_std,rural,2020,,0.0031631508001036,4.939897859627083e-06, +LAM,mfh_sr21_std,urban,2020,,0.0046218151336742,6.0890608889719824e-06, +LAM,mfh_sr31_std,rural,2020,,0.0021511384530715,2.5482452030616625e-06, +LAM,mfh_sr31_std,urban,2020,,0.0049234753069635,6.0830786744262666e-06, +LAM,sfh_s1,rural,2020,109.36203744268914,865.1938357297054,4744.513193198054,0.0501431542148565 +LAM,sfh_s1,urban,2020,73.79792552446617,371.5791425999708,3137.9252125561543,0.1144318993599061 +LAM,sfh_s2,rural,2020,102.90673708522768,1416.3958008681957,6885.574831872049,0.0472402039525205 +LAM,sfh_s2,urban,2020,69.65579833429148,3063.6835438149906,23214.715836469208,0.1087832911903123 +LAM,sfh_s3,rural,2020,95.81578018125536,867.5037344276473,3350.6212571700407,0.0403103850141643 +LAM,sfh_s3,urban,2020,64.85784223243395,5226.210158448481,36247.14627899372,0.1069361279621873 +LAM,sfh_s51_std,rural,2020,94.77878268476182,47.194151614095816,141.65591596330336,0.0316690770605829 +LAM,sfh_s51_std,urban,2020,64.15286008893816,1037.9663444972307,6356.20692455523,0.0954550110150369 +LAM,sfh_s52_low,rural,2020,38.32498052154463,9.532099296264353e-06,8.56384504687e-10,2.3442196394470662e-06 +LAM,sfh_sr11_std,rural,2020,,4.827823711109339,1.5197544491220996, +LAM,sfh_sr11_std,urban,2020,,3.907362581482555,3.275893599670859, +LAM,sfh_sr12_low,rural,2020,,0.015926795211471,8.022133669475223e-06, +LAM,sfh_sr12_low,urban,2020,,0.0047932456301908,2.245435189643261e-05, +LAM,sfh_sr21_std,rural,2020,,8.055069232936841,2.389465291646005, +LAM,sfh_sr21_std,urban,2020,,26.62974593822979,17.5299942066235, +LAM,sfh_sr22_low,rural,2020,,0.0120057168440893,8.522404289098932e-06, +LAM,sfh_sr22_low,urban,2020,,0.0082542237491953,6.5275420017088e-06, +LAM,sfh_sr31_std,rural,2020,,5.888197612279231,1.6406500399281956, +LAM,sfh_sr31_std,urban,2020,,41.86085901673383,23.58529248931133, +LAM,sfh_sr32_low,rural,2020,,0.0072741033839295,6.902173712581695e-06, +LAM,sfh_sr32_low,urban,2020,,0.0037953569798383,6.214071571125812e-06, +MEA,inf_ns,urban,2020,,938.444675126271,7220.334510329314, +MEA,mfh_s1,rural,2020,68.12536054487518,49.74724037657894,255.0501137650503,0.0752571406722267 +MEA,mfh_s1,urban,2020,66.99569205906927,25.748541893744285,560.8660790046849,0.3251319558532066 +MEA,mfh_s2,rural,2020,64.03491242702584,88.5893942956472,307.5958398436046,0.0542227956201396 +MEA,mfh_s2,urban,2020,62.258499951392565,173.23486832856617,4839.648916011992,0.448724616122892 +MEA,mfh_s3,rural,2020,56.7134218451578,89.02694504044368,393.25977129679865,0.0778882955114905 +MEA,mfh_s3,urban,2020,54.33056929492965,326.7331258198475,8792.058641851852,0.4952826933791743 +MEA,mfh_s51_std,rural,2020,54.28411302862676,14.545959994608484,59.76086128782403,0.0756835880402545 +MEA,mfh_s51_std,urban,2020,52.09479613230947,107.99681244835384,2356.836660877503,0.4189133539839919 +MEA,mfh_sr11_std,rural,2020,,0.1681843767544395,0.0767920221599103, +MEA,mfh_sr11_std,urban,2020,,0.1482266787513234,0.3270633055001788, +MEA,mfh_sr12_low,rural,2020,,8.675083369882206e-06,6.536409892164339e-07, +MEA,mfh_sr12_low,urban,2020,,8.952552603422335e-06,2.698723581055267e-06, +MEA,mfh_sr21_std,rural,2020,,0.2763628702674194,0.1130485098875803, +MEA,mfh_sr21_std,urban,2020,,0.9958513550321436,1.9206198314877487, +MEA,mfh_sr22_low,rural,2020,,8.531508985312622e-06,5.910381426265008e-07, +MEA,mfh_sr22_low,urban,2020,,3.7481224613463885e-05,1.0872016791177071e-05, +MEA,mfh_sr31_std,rural,2020,,0.1386144003619868,0.0289809075755218, +MEA,mfh_sr31_std,urban,2020,,1.3553862449468488,0.9221507190356116, +MEA,mfh_sr32_low,rural,2020,,3.155445918651527e-06,1.90068899860571e-07, +MEA,mfh_sr32_low,urban,2020,,3.0307146930058824e-05,7.947174612309548e-06, +MEA,sfh_s1,rural,2020,147.69976549175996,770.0767775064387,8626.503655378085,0.0758439560592961 +MEA,sfh_s1,urban,2020,145.61936191476917,190.8187879479449,9029.79510096576,0.3249657862178119 +MEA,sfh_s2,rural,2020,135.3858036063415,1371.1901020518983,10029.741202587224,0.0540280048647884 +MEA,sfh_s2,urban,2020,131.5971575107967,1284.984549856645,75812.38275071082,0.4483278644779139 +MEA,sfh_s3,rural,2020,113.58514822966232,1376.51914370207,12022.90006534465,0.0768962980556249 +MEA,sfh_s3,urban,2020,108.20756113619558,2423.8755184436054,128704.36538003778,0.4907105063710487 +MEA,sfh_s51_std,rural,2020,106.72091912816404,226.7941201307374,1805.8162670930944,0.0746091707711867 +MEA,sfh_s51_std,urban,2020,101.94205178310348,807.7771343685993,34053.81798896181,0.4135432005868908 +MEA,sfh_s52_low,rural,2020,40.0802050103238,0.0005144662028896,6.388366887715224e-05,0.0030981544605392 +MEA,sfh_s52_low,urban,2020,38.998407901058656,0.0038863202670049,0.0012728332688038,0.0083981969127279 +MEA,sfh_sr11_std,rural,2020,,7.549198639689184,27.48994167250918, +MEA,sfh_sr11_std,urban,2020,,2.50873479778579,41.340469274092285, +MEA,sfh_sr12_low,rural,2020,,0.6343180283723495,0.1385413322347838, +MEA,sfh_sr12_low,urban,2020,,0.371676429882076,0.3245165355749185, +MEA,sfh_sr21_std,rural,2020,,13.535698353807383,37.65908563662041, +MEA,sfh_sr21_std,urban,2020,,16.20199620570921,234.80067515989808, +MEA,sfh_sr22_low,rural,2020,,0.8315759035465388,0.1424603987010352, +MEA,sfh_sr22_low,urban,2020,,2.0010085480841777,1.4426383166015777, +MEA,sfh_sr31_std,rural,2020,,13.26869580225456,31.85613484492, +MEA,sfh_sr31_std,urban,2020,,27.915789797745266,300.5975476465109, +MEA,sfh_sr32_low,rural,2020,,0.4438424042256016,0.050490594219279, +MEA,sfh_sr32_low,urban,2020,,2.2008122052806844,1.0505500513335944, +NAM,mfh_s1,rural,2020,17.74469768619677,57.03886352192607,863.9403797173355,0.8535801449763806 +NAM,mfh_s1,urban,2020,19.159981044354385,552.0599352414305,11577.466128674878,1.0945414525936903 +NAM,mfh_s2,rural,2020,15.101286752640622,33.06357917966228,420.4614908285983,0.8420975557307807 +NAM,mfh_s2,urban,2020,16.32458218449295,1098.0806366766767,19054.71094891396,1.062982358943062 +NAM,mfh_s3,rural,2020,14.14572518251164,10.475586418467447,111.85240481740092,0.7548171609187915 +NAM,mfh_s3,urban,2020,15.30433489171251,1244.4321830103029,20570.41604849252,1.0800836231070077 +NAM,mfh_s51_std,urban,2020,17.87376569061958,224.3071415738091,4064.317274059004,1.0137443380617193 +NAM,mfh_s52_low,urban,2020,13.622026485707508,0.1533747387214261,0.5363385855259484,0.256710413376556 +NAM,mfh_sr11_std,rural,2020,,3.871328821476121,27.78288661168234, +NAM,mfh_sr11_std,urban,2020,,24.52242155815308,129.8940717711565, +NAM,mfh_sr12_low,rural,2020,,0.2070337486132001,0.223443589397943, +NAM,mfh_sr12_low,urban,2020,,0.3666494119363204,0.7552879576052656, +NAM,mfh_sr21_std,rural,2020,,1.9749269314733304,8.099862588357265, +NAM,mfh_sr21_std,urban,2020,,40.07470583615531,190.82747018580875, +NAM,mfh_sr22_low,rural,2020,,0.0687810026192076,0.0492668527774796, +NAM,mfh_sr22_low,urban,2020,,0.3764987429389084,0.7288677133087784, +NAM,mfh_sr31_std,rural,2020,,0.612756101527175,1.6595760953327547, +NAM,mfh_sr31_std,urban,2020,,42.3589902399554,206.86900376083585, +NAM,mfh_sr32_low,rural,2020,,0.0200443156581107,0.007777782034295, +NAM,mfh_sr32_low,urban,2020,,0.2189314884425802,0.1896019755615137, +NAM,sfh_s1,rural,2020,32.075662942716946,4220.434377256761,111304.577740222,0.8222052268566296 +NAM,sfh_s1,urban,2020,34.535105834147004,3068.517565006714,113168.825943073,1.0679167674857164 +NAM,sfh_s2,rural,2020,23.83129281092514,2427.2728150192256,46115.149459994725,0.7972186057122488 +NAM,sfh_s2,urban,2020,25.660851938873623,6084.30337651371,161991.4152928721,1.0375524689482736 +NAM,sfh_s3,rural,2020,20.786914898360063,767.7439637964346,11314.20324318636,0.7089531349802862 +NAM,sfh_s3,urban,2020,22.393437689890348,6934.904038731856,165391.107753973,1.0650032459314318 +NAM,sfh_s51_std,urban,2020,29.90869524275092,833.2704169086157,35630.70276814878,1.429686963865149 +NAM,sfh_s52_low,urban,2020,14.11764386053459,469.4515334719678,2454.958001364524,0.3704171473007199 +NAM,sfh_sr11_std,rural,2020,,189.7004765504727,5719.249378512936, +NAM,sfh_sr11_std,urban,2020,,200.72196742410543,5493.618465581347, +NAM,sfh_sr12_low,rural,2020,,204.0772341050472,1083.4573881807223, +NAM,sfh_sr12_low,urban,2020,,79.24748729863613,519.882447052227, +NAM,sfh_sr21_std,rural,2020,,120.20518490647378,2444.707423579502, +NAM,sfh_sr21_std,urban,2020,,398.1856225004119,7112.252503694051, +NAM,sfh_sr22_low,rural,2020,,103.04262662407044,345.7411125873576, +NAM,sfh_sr22_low,urban,2020,,125.30311202377185,534.1678339246918, +NAM,sfh_sr31_std,rural,2020,,38.90817577342968,615.6994901065825, +NAM,sfh_sr31_std,urban,2020,,421.9505410260923,4864.879776765406, +NAM,sfh_sr32_low,rural,2020,,32.005902575199364,71.42751219191217, +NAM,sfh_sr32_low,urban,2020,,112.67320084590332,414.2827922624567, +PAO,mfh_s1,rural,2020,13.114177590019562,19.459732569679737,69.96095962666092,0.2741434221548824 +PAO,mfh_s1,urban,2020,16.236781251096108,134.68645789437966,879.9225828691732,0.4023653671092201 +PAO,mfh_s2,rural,2020,11.67203390264824,11.98943729454046,39.14159862063162,0.2797004841682742 +PAO,mfh_s2,urban,2020,14.375356452203944,521.351228701015,3102.279937606116,0.4139348200039683 +PAO,mfh_s3,rural,2020,10.082736397379453,13.046173061101776,38.70421214909481,0.2942365929457575 +PAO,mfh_s3,urban,2020,12.264128740635122,599.1259035747116,3098.318950203653,0.4216686616427447 +PAO,mfh_s51_std,urban,2020,12.002263731566018,81.38901614586318,382.64538908404586,0.3917125808704819 +PAO,mfh_s52_low,urban,2020,11.677086865143282,0.0034421635453667,0.0174540835538951,0.4342412127482359 +PAO,mfh_sr11_std,rural,2020,,0.6053436651533143,0.8170809024523515, +PAO,mfh_sr11_std,urban,2020,,0.9141077486519076,2.0522821292073945, +PAO,mfh_sr12_low,rural,2020,,0.0011566677124302,0.0003123866310741, +PAO,mfh_sr12_low,urban,2020,,0.0002901582697398,0.000299457084871, +PAO,mfh_sr21_std,rural,2020,,0.3436771427461726,0.442411962090922, +PAO,mfh_sr21_std,urban,2020,,3.376069915703091,6.915174924754347, +PAO,mfh_sr22_low,rural,2020,,0.0002153845491572,9.130228364306885e-05, +PAO,mfh_sr22_low,urban,2020,,0.0004005089541017,5.71316079473033e-05, +PAO,mfh_sr31_std,rural,2020,,0.1556331142398808,0.0332668512218968, +PAO,mfh_sr31_std,urban,2020,,0.2589798960432025,0.0241307898175132, +PAO,mfh_sr32_low,rural,2020,,2.02584025031188e-05,2.3270324374927315e-06, +PAO,sfh_s1,rural,2020,23.68171456361487,1286.417667131839,7889.34910471688,0.2589679601659846 +PAO,sfh_s1,urban,2020,29.679747200592097,381.44958113705087,4350.700943426984,0.3842924853619713 +PAO,sfh_s2,rural,2020,19.048060028930507,793.7706983815461,3965.477665083036,0.2622706508243421 +PAO,sfh_s2,urban,2020,23.82738886733092,1478.668139947948,13708.18640976055,0.3890745585855399 +PAO,sfh_s3,rural,2020,13.324439331479338,853.5999103225109,3095.965213046029,0.2722029124618385 +PAO,sfh_s3,urban,2020,16.518721424140775,1703.1364683247416,10957.652657487084,0.3894858211568575 +PAO,sfh_s51_std,rural,2020,12.511430048581444,271.63255297521044,916.8010969602134,0.2697654684786671 +PAO,sfh_s51_std,urban,2020,15.487094334742668,242.2342292765732,1329.938255302301,0.3545079860534179 +PAO,sfh_s52_low,rural,2020,9.875077039957578,0.3211509329465082,0.340540875163854,0.1073790524742161 +PAO,sfh_s52_low,urban,2020,11.917762822915076,0.0309609707187317,0.1019196132516808,0.2762157636317567 +PAO,sfh_sr11_std,rural,2020,,88.64030133038935,440.8250398342064, +PAO,sfh_sr11_std,urban,2020,,21.274371069731107,173.2474169227752, +PAO,sfh_sr12_low,rural,2020,,21.3500447109231,28.792760135825944, +PAO,sfh_sr12_low,urban,2020,,0.8944110752095203,2.4264409243141976, +PAO,sfh_sr21_std,rural,2020,,54.76423168752315,192.19518095232212, +PAO,sfh_sr21_std,urban,2020,,81.29061097017532,532.229940824326, +PAO,sfh_sr22_low,rural,2020,,7.941517530244511,7.96411647336894, +PAO,sfh_sr22_low,urban,2020,,1.9025945328723253,3.850013890192833, +PAO,sfh_sr31_std,rural,2020,,62.04389213180687,145.4458091138045, +PAO,sfh_sr31_std,urban,2020,,80.51672935482304,359.45289379976936, +PAO,sfh_sr32_low,rural,2020,,2.3193265177342908,1.4973771202860062, +PAO,sfh_sr32_low,urban,2020,,0.4265871927436754,0.5796549856154939, +PAS,inf_ns,urban,2020,,674.6552188249898,234.2626333528104, +PAS,mfh_s1,rural,2020,90.88257167275891,137.63282829518832,90.8062511486522,0.0072596068949554 +PAS,mfh_s1,urban,2020,90.8289302616734,35.214902063761066,154.07806994076887,0.0481714972812846 +PAS,mfh_s2,rural,2020,90.88257167275891,217.8583298052129,181.2678009489065,0.0091551589469583 +PAS,mfh_s2,urban,2020,90.8289302616734,227.4368856833239,987.2587570482564,0.0477909755114597 +PAS,mfh_s3,rural,2020,86.93708881369658,256.16605088459653,188.64915303135035,0.0084708734427985 +PAS,mfh_s3,urban,2020,86.80369702744582,615.0607742607954,2538.319701989193,0.047543381516872 +PAS,mfh_s51_std,rural,2020,82.46990660816577,27.38618157793919,17.477065894300733,0.007738228283702 +PAS,mfh_s51_std,urban,2020,82.27665862535399,193.2714073219853,603.3418150410647,0.0379419083269071 +PAS,mfh_s52_low,urban,2020,38.45862323916928,0.0002569864690192,0.0004162424489511,0.0421155404063949 +PAS,mfh_sr11_std,rural,2020,,0.2800413788158967,0.3151081418990141, +PAS,mfh_sr11_std,urban,2020,,0.1253831139785926,0.2479490788803288, +PAS,mfh_sr21_std,rural,2020,,0.162531780294493,0.1828839984967722, +PAS,mfh_sr21_std,urban,2020,,1.4952208335007544,2.956846554773693, +PAS,mfh_sr31_std,rural,2020,,0.1894169690332214,0.2073801577505038, +PAS,mfh_sr31_std,urban,2020,,3.5336831185376627,6.784459406420872, +PAS,sfh_s1,rural,2020,194.3753651283975,1042.8934699654897,1442.329782591433,0.0071151391999072 +PAS,sfh_s1,urban,2020,195.1252613552728,169.79871309175076,1579.458085226947,0.0476716532136476 +PAS,sfh_s2,rural,2020,194.3753651283975,1611.3843511520806,2846.6611805844896,0.0090885670412362 +PAS,sfh_s2,urban,2020,195.1252613552728,1073.3473542504769,9746.634528567212,0.0465372690172762 +PAS,sfh_s3,rural,2020,183.5772429120777,1955.6795330142163,3014.6571492468283,0.0083969466540168 +PAS,sfh_s3,urban,2020,184.2199324399767,2958.44784629038,25658.38335745213,0.0470791651839644 +PAS,sfh_s51_std,rural,2020,170.92295452413865,215.49560674149987,285.8497533839972,0.0077606663032313 +PAS,sfh_s51_std,urban,2020,171.54560328508168,948.345971136037,6122.874311659607,0.037636475725698 +PAS,sfh_s52_low,urban,2020,53.14721553076517,1.1476644326263334,1.8342025279764609,0.0300712754684869 +PAS,sfh_sr11_std,rural,2020,,2.314692804172902,5.034521671966564, +PAS,sfh_sr11_std,urban,2020,,0.6586173529630531,2.5487039206411444, +PAS,sfh_sr21_std,rural,2020,,1.3371864080553462,2.908417884091411, +PAS,sfh_sr21_std,urban,2020,,7.6237793982554045,29.50234509719192, +PAS,sfh_sr31_std,rural,2020,,1.5253613922011091,3.1554591647810906, +PAS,sfh_sr31_std,urban,2020,,18.675377303914487,68.674164415216, +RCPA,inf_ns,urban,2020,,184.8228789327621,40.52734099306836, +RCPA,mfh_s1,rural,2020,2.2152941838320857,228.75303894536344,147.90247217292512,0.2918617308403246 +RCPA,mfh_s1,urban,2020,2.490801749055221,29.77410549737653,260.5742999412688,3.5136111352980453 +RCPA,mfh_s2,rural,2020,1.8836989537286024,440.1009308274582,240.0459772394667,0.2895547199678714 +RCPA,mfh_s2,urban,2020,2.1094026562064045,114.47792333727644,841.7123734088543,3.485639595176921 +RCPA,mfh_s3,rural,2020,1.575986403020409,669.8854746934805,300.9113975268173,0.2850267432035234 +RCPA,mfh_s3,urban,2020,1.752036014616265,467.024867335577,2808.743091053318,3.432645716452039 +RCPA,mfh_s51_std,urban,2020,1.697089398685894,49.15958872788102,284.48296555935246,3.409913051234554 +RCPA,sfh_s1,rural,2020,4.273850191132386,199.87296733581348,252.8946398209174,0.2960508206815391 +RCPA,sfh_s1,urban,2020,4.847088324238219,4.285560296106658,74.11979336612174,3.5681709778816826 +RCPA,sfh_s2,rural,2020,3.4887762903794743,386.34124439390735,397.516014386648,0.2949242154854798 +RCPA,sfh_s2,urban,2020,3.9455480993223584,16.837001470594828,236.0866562674699,3.5538520948911976 +RCPA,sfh_s3,rural,2020,2.6241624569658866,570.267968892904,438.9089013290346,0.2932950221965907 +RCPA,sfh_s3,urban,2020,2.958205635518912,66.80710266739409,698.2189900475348,3.5329757706024147 +RCPA,sfh_s51_std,urban,2020,2.805999619204562,6.822454451996647,67.18291227010246,3.5093814261954392 +SAS,inf_ns,urban,2020,,1687.9166150140752,414.7433985128718, +SAS,mfh_s1,rural,2020,90.75596963562704,134.91594107293244,79.71346320974416,0.0065101831939763 +SAS,mfh_s1,urban,2020,95.97402745566758,116.49749361292294,619.2163197997552,0.0553824408267505 +SAS,mfh_s2,rural,2020,90.75596963562704,227.38424919168645,132.87095855027292,0.0064386451240379 +SAS,mfh_s2,urban,2020,95.97402745566758,587.1283417505268,3077.0987484636494,0.0546077985239791 +SAS,mfh_s3,rural,2020,86.67226574203956,283.0497364716952,159.59345104289267,0.0065053723776601 +SAS,mfh_s3,urban,2020,91.62040173837984,1168.3156621429969,5747.224049599706,0.0536915247608897 +SAS,mfh_s51_std,rural,2020,82.10347343533331,42.36630041884183,21.27309815438666,0.0061157351842827 +SAS,mfh_s51_std,urban,2020,86.76082098095674,357.1324976657258,1739.0639786822876,0.0561257988706389 +SAS,mfh_s52_low,rural,2020,38.60826277020307,0.0210526725149367,1.2249858361824341e-08,1.5071052127697436e-08 +SAS,mfh_s52_low,urban,2020,40.302901485289816,0.0020964433054676,3.939679569294678e-07,4.662742853197033e-06 +SAS,mfh_sr11_std,rural,2020,,0.1617749361160154,0.0009598664837927, +SAS,mfh_sr11_std,urban,2020,,0.0568843458254438,0.0009686823123161, +SAS,mfh_sr21_std,rural,2020,,0.2679879724883831,0.0015547789679738, +SAS,mfh_sr21_std,urban,2020,,0.2427018524021394,0.0040994005718909, +SAS,mfh_sr31_std,rural,2020,,0.2862682311487153,0.0017727354405465, +SAS,mfh_sr31_std,urban,2020,,0.442623142986578,0.0073120216649312, +SAS,sfh_s1,rural,2020,195.9058845613517,4355.515998899186,5548.192970613353,0.0065022619730105 +SAS,sfh_s1,urban,2020,207.59832232629597,332.8246317153585,3818.8733239937646,0.0552708257463672 +SAS,sfh_s2,rural,2020,195.9058845613517,7340.841986657485,9246.9203936292,0.0064298932319022 +SAS,sfh_s2,urban,2020,207.59832232629597,1677.5736646332564,18977.92416060816,0.0544933199335174 +SAS,sfh_s3,rural,2020,184.88129178954736,9137.64683336655,10973.58551259298,0.0064956290676608 +SAS,sfh_s3,urban,2020,195.8864664538644,3338.5277612640352,35029.26814864515,0.0535638320677487 +SAS,sfh_s51_std,rural,2020,172.16792107455407,1353.2039456283874,1437.7455442867094,0.0061711562059822 +SAS,sfh_s51_std,urban,2020,182.41910504836048,1015.119517831384,10417.640520911393,0.0562576864792862 +SAS,sfh_s52_low,rural,2020,52.83173584215312,16.751201430259407,0.0015138089113381,1.7105281131741125e-06 +SAS,sfh_s52_low,urban,2020,55.53999162790341,6.339669122218719,0.0013585642572564,3.858404741303775e-06 +SAS,sfh_sr11_std,rural,2020,,10.182292121127276,0.8476525536498092, +SAS,sfh_sr11_std,urban,2020,,0.538591387566116,0.544087076889832, +SAS,sfh_sr21_std,rural,2020,,16.85558399049015,1.3841081642230535, +SAS,sfh_sr21_std,urban,2020,,2.39672896577912,2.501830237562056, +SAS,sfh_sr31_std,rural,2020,,19.744542037664445,1.154357462547481, +SAS,sfh_sr31_std,urban,2020,,4.298443665885178,2.098681310824104, +WEU,mfh_s1,rural,2020,7.729068945553558,92.04000615657338,62.07264454107252,0.0872562266167368 +WEU,mfh_s1,urban,2020,7.903874062738302,1492.9628836697266,1170.7935479947614,0.0992181894687369 +WEU,mfh_s2,rural,2020,7.037033560240777,44.38980089021753,21.000632234158267,0.0672294487635793 +WEU,mfh_s2,urban,2020,7.140037680651747,2023.0299381751145,2061.3948690682178,0.1427113049061352 +WEU,mfh_s3,rural,2020,6.803468884434982,38.42241735424072,30.219932460463905,0.1156054747097644 +WEU,mfh_s3,urban,2020,6.831419933722144,2238.8051830655704,2280.40367379809,0.1491023353282825 +WEU,mfh_s51_std,rural,2020,6.943266868810226,0.0837896123374968,0.0303070162073678,0.0520941723041591 +WEU,mfh_s51_std,urban,2020,6.959664847406423,95.7706595627116,90.2822662728127,0.1354508228969675 +WEU,mfh_s52_low,rural,2020,7.498961736578346,1.1558044360253412,0.3863482770913644,0.0445752189700278 +WEU,mfh_s52_low,urban,2020,7.521431385035357,350.23238311262133,331.6336178451762,0.1258930489067735 +WEU,mfh_sr11_std,rural,2020,,2.580895894955036,0.2821538811542571, +WEU,mfh_sr11_std,urban,2020,,35.158635960777474,3.600108112898263, +WEU,mfh_sr12_low,rural,2020,,3.0683440568313705,0.1920735921231453, +WEU,mfh_sr12_low,urban,2020,,27.38843831004113,1.7600985597069836, +WEU,mfh_sr21_std,rural,2020,,1.1970957191501268,0.1206977215990206, +WEU,mfh_sr21_std,urban,2020,,43.07916781171053,6.791243783419446, +WEU,mfh_sr22_low,rural,2020,,1.041195056976976,0.0672222882195892, +WEU,mfh_sr22_low,urban,2020,,26.179235310011528,1.951612043632741, +WEU,mfh_sr31_std,rural,2020,,0.6294096282611426,0.0448387048831428, +WEU,mfh_sr31_std,urban,2020,,9.139727955452964,0.2953842669330987, +WEU,mfh_sr32_low,rural,2020,,0.3374049775521923,0.0163152387685598, +WEU,mfh_sr32_low,urban,2020,,2.5294935891174952,0.041460897804753, +WEU,sfh_s1,rural,2020,13.213918538672552,3705.7232479082313,4095.7114818986224,0.0836420653952302 +WEU,sfh_s1,urban,2020,13.654584901127702,2266.7321326498814,2945.2390512237434,0.0951572421771709 +WEU,sfh_s2,rural,2020,10.245233434796068,1770.7993978041384,1166.0473043405054,0.0642724633868864 +WEU,sfh_s2,urban,2020,10.547231650928188,3061.957320284083,4475.559047380543,0.1385829091204964 +WEU,sfh_s3,rural,2020,7.569432410359047,1523.7397856972543,1368.4048909546475,0.1186425561580004 +WEU,sfh_s3,urban,2020,7.697707292935997,3346.472019180836,3946.333337866952,0.153195206128851 +WEU,sfh_s51_std,rural,2020,7.01023024512849,1.1863442431378073,0.6543377756247407,0.0786790276231634 +WEU,sfh_s51_std,urban,2020,7.090516412407815,121.66718776455002,127.98375617519378,0.1483554535513509 +WEU,sfh_s52_low,rural,2020,6.844377964745443,50.31166724350869,15.449376660239563,0.0448650615356813 +WEU,sfh_s52_low,urban,2020,6.867032581881874,580.8030850240793,506.8386622335537,0.1270783927752963 +WEU,sfh_sr11_std,rural,2020,,74.49871794966066,101.8707111800002, +WEU,sfh_sr11_std,urban,2020,,47.75667387860163,66.49304264357868, +WEU,sfh_sr12_low,rural,2020,,278.2100917862828,91.10230327506, +WEU,sfh_sr12_low,urban,2020,,135.49517723702692,43.01275323813857, +WEU,sfh_sr21_std,rural,2020,,39.70968498813815,27.83164250617538, +WEU,sfh_sr21_std,urban,2020,,77.77822674025784,96.99750801334145, +WEU,sfh_sr22_low,rural,2020,,126.62269015499834,21.70494179805951, +WEU,sfh_sr22_low,urban,2020,,155.69364612571312,52.993746091991525, +WEU,sfh_sr31_std,rural,2020,,40.905218854737136,18.116819092123265, +WEU,sfh_sr31_std,urban,2020,,88.04931026706043,26.61171326359796, +WEU,sfh_sr32_low,rural,2020,,71.7533449532306,8.347876387895203, +WEU,sfh_sr32_low,urban,2020,,110.05658809148802,16.62129678549488, +AFR,inf_ns,urban,2025,,2245.786455195419,382.12427917117606, +AFR,mfh_s1,rural,2025,67.77348115246582,31.32826299981193,2.589313344106312,0.0012195188004715 +AFR,mfh_s1,urban,2025,70.25683890994821,14.37522939031835,7.705177078526132,0.0076292041244024 +AFR,mfh_s2,rural,2025,67.7726824990315,67.50964232904624,5.677992573298434,0.0012410073033983 +AFR,mfh_s2,urban,2025,70.25683890994821,95.85118541325517,54.72786120283624,0.0081268525876235 +AFR,mfh_s3,rural,2025,65.01722728806209,96.61328099691184,5.983532367458984,0.0009525600620128 +AFR,mfh_s3,urban,2025,67.33635735981284,254.69301562282283,144.98751974334274,0.0084540335301135 +AFR,mfh_s51_std,rural,2025,61.81822777560578,44.54943765734136,1.745418191186722,0.0006337831974579 +AFR,mfh_s51_std,urban,2025,63.9770658477033,206.21891412557892,106.74525249589072,0.0080908799037245 +AFR,mfh_sr11_std,rural,2025,,0.0358735770259964,0.0035964585692034, +AFR,mfh_sr11_std,urban,2025,,0.1613125459130822,0.0305226907407045, +AFR,mfh_sr21_std,rural,2025,,0.1093245054965031,0.0109610867995694, +AFR,mfh_sr21_std,urban,2025,,0.7906801658002611,0.1496054676475737, +AFR,mfh_sr31_std,rural,2025,,0.0918317228699089,0.0090289424701175, +AFR,mfh_sr31_std,urban,2025,,1.6535772895141634,0.2954931148537568, +AFR,sfh_s1,rural,2025,143.64700022638183,1187.5666435853625,202.18044671235717,0.0011851807803942 +AFR,sfh_s1,urban,2025,149.2873895755873,120.19928532580428,132.68815450903398,0.007394471581709 +AFR,sfh_s2,rural,2025,143.6444605033549,2557.61042035272,440.8310880285572,0.001199910734944 +AFR,sfh_s2,urban,2025,149.2873895755873,803.3061974466715,952.1211895887584,0.0079394055937422 +AFR,sfh_s3,rural,2025,135.78059522338597,3664.860695831031,459.0502404012186,0.0009224971637402 +AFR,sfh_s3,urban,2025,141.07625955685202,2137.239172955643,2490.071843423359,0.0082585692619798 +AFR,sfh_s51_std,rural,2025,126.30495867536516,1696.450770350792,133.43870355212667,0.0006227591991334 +AFR,sfh_s51_std,urban,2025,131.28489075542765,1744.813332209391,1815.093978433717,0.0079238345313012 +AFR,sfh_s52_low,rural,2025,42.24027504538575,4.544910195103818e-05,2.754027771886366e-06,0.0014345519223159 +AFR,sfh_s52_low,urban,2025,43.1615913953456,0.0022787718930214,0.0004441299012659,0.0045155609651323 +AFR,sfh_sr11_std,rural,2025,,6.7883039860074055,1.08836448334281, +AFR,sfh_sr11_std,urban,2025,,2.80083841134992,1.434064890060975, +AFR,sfh_sr21_std,rural,2025,,17.33843469249465,3.316942887312034, +AFR,sfh_sr21_std,urban,2025,,14.295846724890524,7.029238438733953, +AFR,sfh_sr31_std,rural,2025,,17.696597397437298,2.715398026470479, +AFR,sfh_sr31_std,urban,2025,,30.79362649612525,13.283206310987415, +CHN,inf_ns,urban,2025,,3042.074610601882,2001.5220718995809, +CHN,mfh_s1,rural,2025,26.041009181373084,4469.997686758944,15486.703660682808,0.1330435666876507 +CHN,mfh_s1,urban,2025,29.27962872358791,556.948040159399,5594.874745243816,0.343091629660349 +CHN,mfh_s2,rural,2025,22.143073415258677,6128.615405123502,18258.42438215441,0.1345435952816288 +CHN,mfh_s2,urban,2025,24.79624346887529,2520.0735725480745,21647.0094698492,0.3464166799970979 +CHN,mfh_s3,rural,2025,18.52588098244399,4348.532647595099,11016.753120722402,0.1367514728856927 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+SAS,mfh_sr31_std,rural,2100,,7.399996975956991,12.846844603214189, +SAS,mfh_sr31_std,urban,2100,,17.867415382953872,80.23109628054029, +SAS,sfh_s1,rural,2100,195.9058845613517,5714.9554806407305,115401.21020409076,0.1030742136485353 +SAS,sfh_s1,urban,2100,207.59832232629597,491.8373780461742,14686.428504615838,0.1438370691747563 +SAS,sfh_s2,rural,2100,195.9058845613517,6790.215883143985,137161.84801006093,0.1031103507710645 +SAS,sfh_s2,urban,2100,207.59832232629597,1720.8301071171106,51170.53923196752,0.1432379702841972 +SAS,sfh_s3,rural,2100,184.88129178954736,6979.66216781219,132829.92037714782,0.1029362974971252 +SAS,sfh_s3,urban,2100,195.8864664538644,2862.0651012066355,80115.58188503888,0.1429002705615759 +SAS,sfh_s51_std,rural,2100,172.16792107455407,6063.807481340128,109345.4251331386,0.1047376866909247 +SAS,sfh_s51_std,urban,2100,182.41910504836048,41597.98546486736,940054.7923953251,0.1238826675009004 +SAS,sfh_s52_low,rural,2100,52.83173584215312,544.5549539386975,517.0021788985163,0.0179703187301848 +SAS,sfh_s52_low,urban,2100,55.53999162790341,1283.658804369477,1755.8421152809883,0.0246280512046817 +SAS,sfh_sr11_std,rural,2100,,352.13689260693747,2104.5049175462823, +SAS,sfh_sr11_std,urban,2100,,17.815396309901203,224.27707204016855, +SAS,sfh_sr21_std,rural,2100,,417.2868023228356,2470.9532046700097, +SAS,sfh_sr21_std,urban,2100,,58.47628573104368,721.1161223855728, +SAS,sfh_sr31_std,rural,2100,,419.2454398471789,2341.3911254601944, +SAS,sfh_sr31_std,urban,2100,,97.11310673953233,1120.011655622951, +WEU,mfh_s1,rural,2100,7.729068945553558,11.19753633794056,24.09985085252177,0.2784612017112795 +WEU,mfh_s1,urban,2100,7.903874062738302,387.5437546835408,693.566501276474,0.2264265527007899 +WEU,mfh_s2,rural,2100,7.037033560240777,4.994237791128829,9.36096885388423,0.2663556793374577 +WEU,mfh_s2,urban,2100,7.140037680651747,543.5357676930946,1161.0340169995782,0.299168808779217 +WEU,mfh_s3,rural,2100,6.803468884434982,5.20391156583402,12.300253298865318,0.3474191428106645 +WEU,mfh_s3,urban,2100,6.831419933722144,661.8515835174953,1240.6193923903463,0.2743891728620387 +WEU,mfh_s51_std,rural,2100,6.943266868810226,0.2312578464235702,0.1911016016114818,0.1190156278277326 +WEU,mfh_s51_std,urban,2100,6.959664847406423,1206.2583248796084,1419.2127032980009,0.1690514266541744 +WEU,mfh_s52_low,rural,2100,7.498961736578346,3.307212602423961,2.4446811249916305,0.0985732254724559 +WEU,mfh_s52_low,urban,2100,7.521431385035357,4620.581001281836,5526.442799175023,0.1590188444536069 +WEU,mfh_sr11_std,rural,2100,,15.37182423947487,10.533013464668375, +WEU,mfh_sr11_std,urban,2100,,333.93015142567026,97.98049131136196, +WEU,mfh_sr12_low,rural,2100,,16.790969014552864,4.183376997916504, +WEU,mfh_sr12_low,urban,2100,,258.121445933169,29.7668233723406, +WEU,mfh_sr21_std,rural,2100,,6.524060070923544,2.82846354703708, +WEU,mfh_sr21_std,urban,2100,,360.8486603226739,125.58481715542338, +WEU,mfh_sr22_low,rural,2100,,5.810578857513653,1.2677642134145035, +WEU,mfh_sr22_low,urban,2100,,217.390545302592,40.43475782677519, +WEU,mfh_sr31_std,rural,2100,,4.378006455595207,1.507932865548781, +WEU,mfh_sr31_std,urban,2100,,255.44032395768903,71.92180030960856, +WEU,mfh_sr32_low,rural,2100,,2.276314417684711,0.4969435305548061, +WEU,mfh_sr32_low,urban,2100,,98.87357780111172,20.958938217925468, +WEU,sfh_s1,rural,2100,13.213918538672552,246.15551538229764,491.1994868370042,0.1510138251250417 +WEU,sfh_s1,urban,2100,13.654584901127702,403.7808276180356,661.7804245743872,0.1200299763719413 +WEU,sfh_s2,rural,2100,10.245233434796068,102.84960387818369,154.40346696888568,0.1465320300143405 +WEU,sfh_s2,urban,2100,10.547231650928188,482.1867999537566,984.3739218120126,0.1935558482391278 +WEU,sfh_s3,rural,2100,7.569432410359047,86.9079727706373,200.86217887081065,0.3053340472673566 +WEU,sfh_s3,urban,2100,7.697707292935997,528.7615831100993,1159.390542188407,0.2848449118704976 +WEU,sfh_s51_std,rural,2100,7.01023024512849,3.349445733341136,4.427209566412914,0.1885492686144892 +WEU,sfh_s51_std,urban,2100,7.090516412407815,1552.7428290667217,1981.813045816131,0.1800052965189672 +WEU,sfh_s52_low,rural,2100,6.844377964745443,143.65383536573526,98.80805224110269,0.1004942303270945 +WEU,sfh_s52_low,urban,2100,6.867032581881874,7624.754696763919,8442.415999776502,0.1612396192941605 +WEU,sfh_sr11_std,rural,2100,,366.66212006264016,892.3375520689266, +WEU,sfh_sr11_std,urban,2100,,355.2671179461863,686.4324131904463, +WEU,sfh_sr12_low,rural,2100,,1188.5571989268635,876.1483390411046, +WEU,sfh_sr12_low,urban,2100,,783.8498744313871,453.3925159523025, +WEU,sfh_sr21_std,rural,2100,,161.4074797656621,262.89505860969587, +WEU,sfh_sr21_std,urban,2100,,489.71000365181135,971.709448801876, +WEU,sfh_sr22_low,rural,2100,,455.659098642683,235.27614587489435, +WEU,sfh_sr22_low,urban,2100,,794.9390205813434,553.931190413718, +WEU,sfh_sr31_std,rural,2100,,160.0757730304115,240.69763572547504, +WEU,sfh_sr31_std,urban,2100,,525.7854036911008,645.6937377533199, +WEU,sfh_sr32_low,rural,2100,,245.65828408028645,114.28940916028704, +WEU,sfh_sr32_low,urban,2100,,545.9501048678885,255.92738186721587, diff --git a/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_heat_SSP2_comm.csv b/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_heat_SSP2_comm.csv new file mode 100644 index 0000000000..a3d04c7130 --- /dev/null +++ b/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_heat_SSP2_comm.csv @@ -0,0 +1,2862 @@ +# METADATA: {"scenario": "SSP2", "description": "SSP2_BL: Base scenario", "mode": "heat", "sector": "comm", "eff_file": "eff_heat_ssp2.csv", "ac_file": "bld_shr_access_cool_comm_rev.csv", "generated": "2025-12-09T14:25:39.727285"} +region,arch,urt,year,chilled_ei_MJ_m2,floor_Mm2,sturm_energy_TJ,correction_coeff +AFR,comm_s1,rural,2020,2.9027293033996147,126.09207633389,216.3173551150159,0.591012995938512 +AFR,comm_s1,urban,2020,3.815563517376804,18.58502678997,64.65457071073587,0.9117533199528592 +AFR,comm_s2,rural,2020,2.9027293033996147,266.56128852236,475.8444882108504,0.6149806156448486 +AFR,comm_s2,urban,2020,3.815563517376804,135.23792245146,309.6884673396798,0.6001611164060014 +AFR,comm_s3,rural,2020,2.9027293033996147,373.76917935108,501.6709748161516,0.4623905509042517 +AFR,comm_s3,urban,2020,3.815563517376804,356.87956377434,566.2794008992414,0.4158631098536716 +AFR,comm_s51_std,rural,2020,2.9027293033996147,173.73642168595,118.6146799674885,0.2352020115959652 +AFR,comm_s51_std,urban,2020,3.815563517376804,184.08923443194,137.40162167855652,0.1956161824845628 +AFR,comm_sr11_std,rural,2020,2.9027293033996147,3.23313016243,2.5465934464221123,0.2713500404739415 +AFR,comm_sr11_std,urban,2020,3.815563517376804,0.47653914851,0.7611451516912698,0.418610592282701 +AFR,comm_sr21_std,rural,2020,2.9027293033996147,6.83490483397,5.601873481550358,0.2823542224885617 +AFR,comm_sr21_std,urban,2020,3.815563517376804,3.4676390372,3.6458037354863535,0.2755501898305996 +AFR,comm_sr31_std,rural,2020,2.9027293033996147,9.58382511156,5.800663189748233,0.2085125656456249 +AFR,comm_sr31_std,urban,2020,3.815563517376804,9.15075804537,6.543647934454452,0.1874149214071894 +CHN,comm_s1,rural,2020,58.99575938093701,2023.84677013151,416824.3389089033,3.4910385645241537 +CHN,comm_s1,urban,2020,36.35754664146298,266.86283366356,28310.889288289167,2.9179029848334883 +CHN,comm_s2,rural,2020,58.99575938093701,2259.33575128999,352901.721579278,2.64759899030185 +CHN,comm_s2,urban,2020,36.35754664146298,1058.53939649076,84824.62633912556,2.204044502063931 +CHN,comm_s3,rural,2020,58.99575938093701,500.78291909726,53683.60287365027,1.8170687187897216 +CHN,comm_s3,urban,2020,36.35754664146298,3507.77019445352,190367.9830437048,1.4926851252891606 +CHN,comm_s51_std,rural,2020,58.99575938093701,1686.0114673727,109342.22728928596,1.0992756204976284 +CHN,comm_s51_std,urban,2020,36.35754664146298,3038.39938708115,139856.81987054215,1.266030647787066 +CHN,comm_s52_low,rural,2020,58.99575938093701,88.73744565119,816.7522457926659,0.1560136575489089 +CHN,comm_s52_low,urban,2020,36.35754664146298,104.32573062904,649.2961907425076,0.1711815088795089 +CHN,comm_sr11_std,rural,2020,58.99575938093701,49.29883158012,5379.885665263046,1.849761023259182 +CHN,comm_sr11_std,urban,2020,36.35754664146298,4.27893453273,333.7755467420183,2.1454791786748157 +CHN,comm_sr12_low,rural,2020,58.99575938093701,2.59467534633,80.22636517150387,0.5240989565085247 +CHN,comm_sr12_low,urban,2020,36.35754664146298,0.2252070806799999,4.977354643078281,0.6078857671029647 +CHN,comm_sr21_std,rural,2020,58.99575938093701,55.03510163392001,4554.846576707228,1.4028562924639183 +CHN,comm_sr21_std,urban,2020,36.35754664146298,16.97284224911,1000.0528681771582,1.6205924640347469 +CHN,comm_sr22_low,rural,2020,58.99575938093701,2.89658429649,67.92315069272891,0.3974759494623832 +CHN,comm_sr22_low,urban,2020,36.35754664146298,0.89330748682,14.913069083617708,0.4591678646975362 +CHN,comm_sr31_std,rural,2020,58.99575938093701,12.19855828572,686.8480591180642,0.9544020732599132 +CHN,comm_sr31_std,urban,2020,36.35754664146298,56.24432151862,2236.685678904631,1.093784349422377 +CHN,comm_sr32_low,rural,2020,58.99575938093701,0.6420293834299999,10.24247105533388,0.2704139207245526 +CHN,comm_sr32_low,urban,2020,36.35754664146298,2.9602274483700004,33.35408468048471,0.3099055656215592 +EEU,comm_s1,rural,2020,60.33209632413561,293.53572861684,135315.041439854,7.640762292740041 +EEU,comm_s1,urban,2020,52.81203399035736,220.29000762622,89276.84832452793,7.673812051958724 +EEU,comm_s2,rural,2020,60.33209632413561,54.53766666305,23358.499320608014,7.099045650666691 +EEU,comm_s2,urban,2020,52.81203399035736,367.47029276826,141543.2825329135,7.2934688696964995 +EEU,comm_s3,rural,2020,60.33209632413561,24.10402288902,7261.923119159961,4.993599409549148 +EEU,comm_s3,urban,2020,52.81203399035736,84.89586690206,19799.526995992102,4.41606415137194 +EEU,comm_s51_std,urban,2020,52.81203399035736,119.59200930881,18798.960800731784,2.976451329889065 +EEU,comm_s52_low,rural,2020,60.33209632413561,82.58187132178999,2087.996628497514,0.4190797319591002 +EEU,comm_s52_low,urban,2020,52.81203399035736,145.39064295865,3676.1345295059873,0.4787646152825283 +EEU,comm_sr11_std,rural,2020,60.33209632413561,21.42017479091,2252.105470646496,1.7426786051058152 +EEU,comm_sr11_std,urban,2020,52.81203399035736,8.50878129206,902.8493361302473,2.009162335547757 +EEU,comm_sr12_low,rural,2020,60.33209632413561,2.38001942117,175.21567459004552,1.2202365752191744 +EEU,comm_sr12_low,urban,2020,52.81203399035736,0.94542014355,70.24242760621314,1.4068304736148978 +EEU,comm_sr21_std,rural,2020,60.33209632413561,3.97977567542,388.7653844413753,1.6191257492455795 +EEU,comm_sr21_std,urban,2020,52.81203399035736,14.19367308679,1431.4154348923978,1.909580642486841 +EEU,comm_sr22_low,rural,2020,60.33209632413561,0.44219729722,30.246269534122312,1.1337239427165853 +EEU,comm_sr22_low,urban,2020,52.81203399035736,1.57707478751,111.3653087409288,1.3371026283381056 +EEU,comm_sr31_std,rural,2020,60.33209632413561,1.75894221084,119.71432946327762,1.1280962366323397 +EEU,comm_sr31_std,urban,2020,52.81203399035736,3.27913359246,199.07600046425185,1.149547450482458 +EEU,comm_sr32_low,rural,2020,60.33209632413561,0.19543802344,9.313874181741395,0.7899014105789396 +EEU,comm_sr32_low,urban,2020,52.81203399035736,0.3643481769499999,15.488278045177186,0.8049217106767641 +FSU,comm_s1,rural,2020,69.02844645787476,365.45020352517,278337.4751275552,11.033552755971549 +FSU,comm_s1,urban,2020,67.39825092840175,359.95112342861,261425.75011488225,10.775965772702495 +FSU,comm_s2,rural,2020,69.02844645787476,190.53659919152,109204.17845043048,8.302956205786574 +FSU,comm_s2,urban,2020,67.39825092840175,743.32992398078,501557.6918627234,10.011304076459624 +FSU,comm_s3,rural,2020,69.02844645787476,151.06727961484,55537.38438254466,5.325825272625052 +FSU,comm_s3,urban,2020,67.39825092840175,378.92989666601005,164405.62989946542,6.437380614062522 +FSU,comm_s51_std,rural,2020,69.02844645787476,195.12953457859004,16912.71970628838,1.2556319078520246 +FSU,comm_s51_std,urban,2020,67.39825092840175,587.56604649842,178305.21802182152,4.502551979523002 +FSU,comm_s52_low,rural,2020,69.02844645787476,10.26997550412,402.6027635135517,0.5679096450423274 +FSU,comm_s52_low,urban,2020,67.39825092840175,2.95259319849,135.44290385411443,0.680618916279833 +FSU,comm_sr11_std,rural,2020,69.02844645787476,28.149542704,4970.658862599287,2.5580823138017874 +FSU,comm_sr11_std,urban,2020,67.39825092840175,2.5840319920900003,457.9184845233447,2.629309208268058 +FSU,comm_sr12_low,rural,2020,69.02844645787476,1.48155487916,183.1838137379309,1.7911883422208963 +FSU,comm_sr12_low,urban,2020,67.39825092840175,0.13600168379,16.87568121072717,1.8410619456948003 +FSU,comm_sr21_std,rural,2020,69.02844645787476,14.67646777564,1950.210682901216,1.925005108679473 +FSU,comm_sr21_std,urban,2020,67.39825092840175,5.33624756012,878.5383155368096,2.44273363053472 +FSU,comm_sr22_low,rural,2020,69.02844645787476,0.7724456722999999,71.87116241724404,1.347902954780007 +FSU,comm_sr22_low,urban,2020,67.39825092840175,0.28085513481,32.3767942364085,1.7104203328884742 +FSU,comm_sr31_std,rural,2020,69.02844645787476,11.63626342985,983.4226087897944,1.2243301503700268 +FSU,comm_sr31_std,urban,2020,67.39825092840175,2.72027759321,287.7015686474577,1.5692075151826506 +FSU,comm_sr32_low,rural,2020,69.02844645787476,0.61243491732,36.24209766986217,0.8572851157049998 +FSU,comm_sr32_low,urban,2020,67.39825092840175,0.1431725048799999,10.602673011414154,1.098770822777394 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+RCPA,comm_sr32_low,rural,2100,4.520642370111859,1.17147493243,0.9733995005457876,0.183805273748091 +RCPA,comm_sr32_low,urban,2100,3.220269358125083,0.98369887415,2.651616070035202,0.8370593819703822 +SAS,comm_s1,rural,2100,10.188504008912812,293.11851113642007,735.8497285826543,0.2463970292331151 +SAS,comm_s1,urban,2100,6.600703276472216,44.58287792589002,64.8448400795845,0.2203520593076301 +SAS,comm_s2,rural,2100,10.188504008912812,323.13835851092,961.7696992220737,0.2921273070573671 +SAS,comm_s2,urban,2100,6.600703276472216,151.15564802076997,222.39491678133368,0.2229001017741122 +SAS,comm_s3,rural,2100,10.188504008912812,301.74237019958,1048.457317406866,0.3410389927655695 +SAS,comm_s3,urban,2100,6.600703276472216,236.02213203581,381.54096137802753,0.2449053262461505 +SAS,comm_s51_std,rural,2100,10.188504008912812,7773.0843062000995,19717.1163757263,0.2489657473351398 +SAS,comm_s51_std,urban,2100,6.600703276472216,24358.28932965808,26141.91754948055,0.1625924763579593 +SAS,comm_sr11_std,rural,2100,10.188504008912812,158.84906882774,206.06832481547588,0.12732572173095 +SAS,comm_sr11_std,urban,2100,6.600703276472216,24.16070079193,18.159234213058674,0.1138669775007904 +SAS,comm_sr21_std,rural,2100,10.188504008912812,175.11765856379998,269.3352502255828,0.1509568533519834 +SAS,comm_sr21_std,urban,2100,6.600703276472216,81.91544724699,62.27976470036022,0.1151836790489102 +SAS,comm_sr31_std,rural,2100,10.188504008912812,163.52257776598998,283.1451149812341,0.1699499056771516 +SAS,comm_sr31_std,urban,2100,6.600703276472216,127.90695391849,102.10559827119349,0.1209386698598162 +WEU,comm_s1,rural,2100,61.722217899878,125.56333145440996,53525.75358290596,6.906506693637691 +WEU,comm_s1,urban,2100,44.27644767738066,419.44639006284,174289.4291768457,9.384730737953015 +WEU,comm_s2,rural,2100,61.722217899878,32.45872678964,9771.412268678198,4.877354621333971 +WEU,comm_s2,urban,2100,44.27644767738066,372.1036856722501,95316.4739822992,5.785371163699523 +WEU,comm_s3,rural,2100,61.722217899878,11.990745325579995,1658.2768068429666,2.240625757607272 +WEU,comm_s3,urban,2100,44.27644767738066,214.13189511183003,25444.62136082969,2.6837487923428616 +WEU,comm_s51_std,urban,2100,44.27644767738066,941.2213526419,82597.5919249659,1.9819963253368624 +WEU,comm_s52_low,rural,2100,61.722217899878,114.62693393543,1154.1943924474429,0.1631363572860248 +WEU,comm_s52_low,urban,2100,44.27644767738066,6276.93579227641,56828.12678014146,0.2044762560002126 +WEU,comm_sr11_std,rural,2100,61.722217899878,323.66369113531,19539.83832047386,0.9781049568910316 +WEU,comm_sr11_std,urban,2100,44.27644767738066,684.55797643159,42112.19127838072,1.3893921317199895 +WEU,comm_sr12_low,rural,2100,61.722217899878,35.96263234847,1628.319860043687,0.73357871766808 +WEU,comm_sr12_low,urban,2100,44.27644767738066,76.06199738138,3509.349273204506,1.0420440987905435 +WEU,comm_sr21_std,rural,2100,61.722217899878,83.66862523168,3567.101873615565,0.6907348306809084 +WEU,comm_sr21_std,urban,2100,44.27644767738066,607.29225980111,23030.57393258803,0.8565135642535756 +WEU,comm_sr22_low,rural,2100,61.722217899878,9.29651391467,297.25848946559125,0.5180511230043795 +WEU,comm_sr22_low,urban,2100,44.27644767738066,67.47691775589,1919.214494382668,0.642385173188283 +WEU,comm_sr31_std,rural,2100,61.722217899878,30.90845748202,599.9716412969086,0.3144936406692624 +WEU,comm_sr31_std,urban,2100,44.27644767738066,349.47421239004,6095.701739405163,0.3939451918707034 +WEU,comm_sr32_low,rural,2100,61.722217899878,3.43427305352,49.99763677271585,0.235870230494981 +WEU,comm_sr32_low,urban,2100,44.27644767738066,38.83046804328,507.97514495120106,0.2954588939039155 diff --git a/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_heat_SSP2_resid.csv b/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_heat_SSP2_resid.csv new file mode 100644 index 0000000000..bcdb2288ac --- /dev/null +++ b/message_ix_models/data/buildings/correction_coefficients/correction_coefficients_heat_SSP2_resid.csv @@ -0,0 +1,5959 @@ +# METADATA: {"scenario": "SSP2", "description": "SSP2_BL: Base scenario", "mode": "heat", "sector": "resid", "eff_file": "eff_heat_ssp2.csv", "ac_file": "bld_shr_access_cool_resid_ssp2_rev.csv", "generated": "2025-12-09T14:25:39.572982"} +region,arch,urt,year,chilled_ei_MJ_m2,floor_Mm2,sturm_energy_TJ,correction_coeff +AFR,inf_ns,urban,2020,,2026.0805002004668,9508.227944657132, +AFR,mfh_s1,rural,2020,3.1013220072466567,27.833672766076244,94.2853451807205,1.0922619815233376 +AFR,mfh_s1,urban,2020,4.18368698147431,13.930664959770652,144.06140927542788,2.47181878229085 +AFR,mfh_s2,rural,2020,3.1011186438445857,61.29734449393811,240.31253252535257,1.2642017225641216 +AFR,mfh_s2,urban,2020,4.18368698147431,92.42975903008106,745.6027636483493,1.9281308555352 +AFR,mfh_s3,rural,2020,2.7852483450518424,84.77618106065728,251.3572603290512,1.0645195958234903 +AFR,mfh_s3,urban,2020,3.790118394145393,236.3003701842016,1547.055950182096,1.727383798795822 +AFR,mfh_s51_std,rural,2020,2.4944455680602524,18.92976969874934,24.521488881753054,0.5193109427871422 +AFR,mfh_s51_std,urban,2020,3.418475844527482,83.46034880723806,256.8103874336368,0.9001188883393536 +AFR,mfh_sr11_std,rural,2020,,0.0242344376828777,0.2737396898425396, +AFR,mfh_sr11_std,urban,2020,,0.0895346659111488,1.0107612616731068, +AFR,mfh_sr21_std,rural,2020,,0.0750858367362252,0.8481308541225498, +AFR,mfh_sr21_std,urban,2020,,0.4459071121336794,5.033867398194484, +AFR,mfh_sr31_std,rural,2020,,0.0625534150382857,0.6499593930220956, +AFR,mfh_sr31_std,urban,2020,,0.8850223842923312,9.25314449304646, +AFR,sfh_s1,rural,2020,9.034579294709577,1057.4369580620214,9660.581407528216,1.0112088117081504 +AFR,sfh_s1,urban,2020,11.818416132375573,116.72620154952494,3139.494423266842,2.2757894780905548 +AFR,sfh_s2,rural,2020,9.034251626334314,2327.7288325066347,24446.47256912853,1.1624964468552232 +AFR,sfh_s2,urban,2020,11.818416132375573,776.0035323538018,16318.683919661054,1.779353163318496 +AFR,sfh_s3,rural,2020,8.348413847590809,3221.1913004044586,26632.243318768044,0.9903467089639858 +AFR,sfh_s3,urban,2020,10.950517599152844,1986.34125457062,34535.09949598746,1.5877137336995029 +AFR,sfh_s51_std,rural,2020,7.802427876164306,720.8465573477184,2900.926581446447,0.5157795821826053 +AFR,sfh_s51_std,urban,2020,10.21436869661869,705.8525142943847,6249.536468854677,0.8668068019419105 +AFR,sfh_sr11_std,rural,2020,,3.399810744029209,82.2933621016688, +AFR,sfh_sr11_std,urban,2020,,1.900288581481052,49.57585388685871, +AFR,sfh_sr21_std,rural,2020,,9.350193344408968,231.8636453830948, +AFR,sfh_sr21_std,urban,2020,,9.719846067767376,250.76448463997016, +AFR,sfh_sr31_std,rural,2020,,9.491044857263738,211.48956249549525, +AFR,sfh_sr31_std,urban,2020,,19.62868715994202,472.9342630427325, +CHN,inf_ns,urban,2020,,3415.845576078641,414180.9826863342, +CHN,mfh_s1,rural,2020,78.77142098402773,4400.098950878357,450304.5600198563,1.2991976364161448 +CHN,mfh_s1,urban,2020,66.83584295968217,547.028095653958,29074.48534004778,0.7952303991594064 +CHN,mfh_s2,rural,2020,59.51478272570211,6360.661389570992,493808.67593102274,1.3044624648673764 +CHN,mfh_s2,urban,2020,50.28838708649175,2670.5643899862625,107212.30937500694,0.7983142666462124 +CHN,mfh_s3,rural,2020,40.20048451055713,4802.534645710272,257309.99491407129,1.3327689377079448 +CHN,mfh_s3,urban,2020,33.68959185561655,12101.64736300735,328877.3394110823,0.8066659160875584 +CHN,mfh_s51_std,rural,2020,36.97277815159439,2.3265972197529465,99.08726540229164,1.1518994032919772 +CHN,mfh_s51_std,urban,2020,30.91545122454072,3967.029969458238,109623.41197385883,0.8938450596814892 +CHN,mfh_s52_low,rural,2020,13.433115933282863,4.991967539810245e-05,0.0022320933335542,3.328617060582673 +CHN,mfh_s52_low,urban,2020,10.72490364996518,0.0034016961604776,0.1320546641071724,3.61963539025828 +CHN,mfh_sr11_std,rural,2020,,71.34157749713899,3861.865592541705, +CHN,mfh_sr11_std,urban,2020,,6.312064087693557,297.4047311022882, +CHN,mfh_sr12_low,rural,2020,,11.131787783471934,715.2330690675533, +CHN,mfh_sr12_low,urban,2020,,0.1229023363714204,7.146976317420861, +CHN,mfh_sr21_std,rural,2020,,104.72704050111844,4757.055335963032, +CHN,mfh_sr21_std,urban,2020,,31.11417012426071,1143.8939977864193, +CHN,mfh_sr22_low,rural,2020,,12.054103099723822,606.4127738450803, +CHN,mfh_sr22_low,urban,2020,,0.2938900915986592,13.279767875289082, +CHN,mfh_sr31_std,rural,2020,,82.25166489276626,3046.003616738123, +CHN,mfh_sr31_std,urban,2020,,84.99130489140798,2572.287479705238, +CHN,mfh_sr32_low,rural,2020,,3.018595413023641,109.46731999375535, +CHN,mfh_sr32_low,urban,2020,,0.133724601303098,4.192869836931909, +CHN,sfh_s1,rural,2020,195.1074861978212,3841.9656236090273,967378.0304555222,1.2905321019904927 +CHN,sfh_s1,urban,2020,166.83677910237435,79.11229225107682,10419.227274674931,0.7894047699416159 +CHN,sfh_s2,rural,2020,147.3872249529826,5456.947623094179,1040124.0201167894,1.2932291897573025 +CHN,sfh_s2,urban,2020,125.7729460277556,394.5319310312942,39238.690175512966,0.7907607596926864 +CHN,sfh_s3,rural,2020,100.82034182857332,3955.211683998169,525045.7282387378,1.316676947116496 +CHN,sfh_s3,urban,2020,85.77185615183555,1718.3314826709702,117271.56278187274,0.7956846079238579 +CHN,sfh_s51_std,urban,2020,78.99140368064847,548.319997083834,38345.98522832544,0.8853316383874725 +CHN,sfh_s52_low,urban,2020,28.7570469183084,0.5919517658205501,49.97973465965258,2.9360492734551342 +CHN,sfh_sr11_std,rural,2020,,59.54810242227185,3867.812148942688, +CHN,sfh_sr11_std,urban,2020,,0.8175458127845453,57.2967679125397, +CHN,sfh_sr12_low,rural,2020,,55.130591686233686,4238.328511371147, +CHN,sfh_sr12_low,urban,2020,,0.447842148671605,22.020809558323904, +CHN,sfh_sr21_std,rural,2020,,99.3221178132172,5782.456282903133, +CHN,sfh_sr21_std,urban,2020,,4.649379257434639,295.16390280970325, +CHN,sfh_sr22_low,rural,2020,,60.92266602371039,4045.710551812863, +CHN,sfh_sr22_low,urban,2020,,1.3588857688198466,57.7725911639706, +CHN,sfh_sr31_std,rural,2020,,84.59411004056179,4120.388805559045, +CHN,sfh_sr31_std,urban,2020,,22.89114242637084,1157.192014072583, +CHN,sfh_sr32_low,rural,2020,,23.43963237599199,1360.064717918396, +CHN,sfh_sr32_low,urban,2020,,1.906407965294698,81.32316646735214, +EEU,mfh_s1,rural,2020,64.12436316514965,148.49939278346147,38009.96911145672,3.9916253658380256 +EEU,mfh_s1,urban,2020,63.30299418701474,288.5698501228476,64983.30330985695,3.5573498472299505 +EEU,mfh_s2,rural,2020,61.257892110790976,41.592020100852864,9888.05805693304,3.880958017379653 +EEU,mfh_s2,urban,2020,60.47095129077096,568.5896381765516,121814.41266038024,3.54285137599815 +EEU,mfh_s3,rural,2020,38.19078550746308,27.17341557204672,4477.055693265599,4.314094710317389 +EEU,mfh_s3,urban,2020,37.69964925217732,251.7919916188013,33268.77077646461,3.5047539352443606 +EEU,mfh_s51_std,urban,2020,27.70756747843088,54.040511137829405,4651.033463820567,3.1062157435400755 +EEU,mfh_s52_low,urban,2020,12.621275813052426,55.24454564858901,1406.2684571442292,2.0168591602333885 +EEU,mfh_sr11_std,rural,2020,,11.554431238502843,688.4898163637816, +EEU,mfh_sr11_std,urban,2020,,12.78041706197358,768.7977496873064, +EEU,mfh_sr12_low,rural,2020,,0.4026286148511226,17.552292707719058, +EEU,mfh_sr12_low,urban,2020,,0.2836681218953466,12.213130302938906, +EEU,mfh_sr21_std,rural,2020,,3.1188911611245635,175.67223242378253, +EEU,mfh_sr21_std,urban,2020,,24.22741713406476,1394.1897478078615, +EEU,mfh_sr22_low,rural,2020,,0.0937088229059751,3.8761579359138985, +EEU,mfh_sr22_low,urban,2020,,0.47867886673956,19.779527698732448, +EEU,mfh_sr31_std,rural,2020,,1.8502776979055784,69.79283806816274, +EEU,mfh_sr31_std,urban,2020,,7.892611414580533,280.7271993397065, +EEU,mfh_sr32_low,rural,2020,,0.0212411248097425,0.5731121144958768, +EEU,mfh_sr32_low,urban,2020,,0.0550269509173459,1.420256252992948, +EEU,sfh_s1,rural,2020,155.97355901445528,899.8177679886026,557324.2024965727,3.971022580752378 +EEU,sfh_s1,urban,2020,154.00125595322768,206.65090478472155,112530.44310331716,3.5359690509218917 +EEU,sfh_s2,rural,2020,150.12995088013693,258.3879646353384,150047.25603712405,3.868017535784136 +EEU,sfh_s2,urban,2020,148.2242811575877,419.5252018173687,218922.2597996928,3.5205661960856527 +EEU,sfh_s3,rural,2020,94.0451623485602,165.78062290220518,66420.25109334609,4.260202917424427 +EEU,sfh_s3,urban,2020,92.84094315572732,179.99228904241463,57968.531267149,3.4689564932160355 +EEU,sfh_s51_std,urban,2020,67.87188465470435,29.08654111032521,6124.145422339895,3.1021550937157576 +EEU,sfh_s52_low,urban,2020,28.538979274615865,34.022798831611695,1953.5004907233324,2.011893795387197 +EEU,sfh_sr11_std,rural,2020,,40.682160868933934,5541.074313512292, +EEU,sfh_sr11_std,urban,2020,,5.68033311306964,796.293152278703, +EEU,sfh_sr12_low,rural,2020,,54.08480986116607,5551.13884507929, +EEU,sfh_sr12_low,urban,2020,,5.425917801741198,555.6015157676223, +EEU,sfh_sr21_std,rural,2020,,12.532723998374994,1588.1525527052704, +EEU,sfh_sr21_std,urban,2020,,12.131048408017987,1626.172139805798, +EEU,sfh_sr22_low,rural,2020,,14.56487989867366,1424.9141013361368, +EEU,sfh_sr22_low,urban,2020,,10.397147543931352,1026.6352628360928, +EEU,sfh_sr31_std,rural,2020,,11.04175611163644,994.262792380778, +EEU,sfh_sr31_std,urban,2020,,7.246216610637941,608.2158405630098, +EEU,sfh_sr32_low,rural,2020,,6.444813257640472,416.860940371818, +EEU,sfh_sr32_low,urban,2020,,2.322005026095704,143.27232812016837, +FSU,mfh_s1,rural,2020,78.88906776442855,202.1614303443048,96037.95140695848,6.021819890267686 +FSU,mfh_s1,urban,2020,84.25923175677347,380.6749082879774,178297.25202218577,5.558695735762435 +FSU,mfh_s2,rural,2020,75.38516636437427,145.78940040419934,54670.70020340663,4.974423871401648 +FSU,mfh_s2,urban,2020,80.50069618939732,1002.0234355670336,440199.5209223247,5.457227390769207 +FSU,mfh_s3,rural,2020,47.82354118521248,122.92534020549824,33666.744984759665,5.7268786316320766 +FSU,mfh_s3,urban,2020,51.3429061897636,847.4737241418031,245561.29818710496,5.643561098926547 +FSU,mfh_s51_std,rural,2020,35.65343552191879,9.380518772128497,798.7766286456472,2.3883452569792523 +FSU,mfh_s51_std,urban,2020,38.46677978449432,205.33874811335392,41827.19150976483,5.295438927527613 +FSU,mfh_s52_low,rural,2020,17.04185541223791,0.0093212468945294,0.9501252878879276,5.981222927627783 +FSU,mfh_s52_low,urban,2020,18.729894866743734,0.0056439431705258,0.535687662294069,5.067499006770429 +FSU,mfh_sr11_std,rural,2020,,18.006483647328682,1959.060680971797, +FSU,mfh_sr11_std,urban,2020,,2.5354749092180944,325.78176372168843, +FSU,mfh_sr12_low,rural,2020,,1.3024543636851116,187.302884916936, +FSU,mfh_sr12_low,urban,2020,,0.0970741443454206,13.676777359898898, +FSU,mfh_sr21_std,rural,2020,,13.022447350063448,1085.9916566688544, +FSU,mfh_sr21_std,urban,2020,,6.555075362642131,799.2776003773563, +FSU,mfh_sr22_low,rural,2020,,1.0402132799187425,131.07004462219524, +FSU,mfh_sr22_low,urban,2020,,0.2410497708867326,32.377599148692404, +FSU,mfh_sr31_std,rural,2020,,9.092435926996712,642.9469704985034, +FSU,mfh_sr31_std,urban,2020,,5.220697530741797,433.3460791290865, +FSU,mfh_sr32_low,rural,2020,,0.3691120541020585,32.96902437958979, +FSU,mfh_sr32_low,urban,2020,,0.070283276676479,6.489635191267407, +FSU,sfh_s1,rural,2020,188.56014629449896,824.5782961785357,929024.1843179658,5.9751004901471605 +FSU,sfh_s1,urban,2020,200.56128269578684,199.6738727344341,221966.13881955968,5.5426618998207005 +FSU,sfh_s2,rural,2020,181.1450399182557,667.820338423195,598657.0199787312,4.948709851894776 +FSU,sfh_s2,urban,2020,192.48467278925145,612.3419730091676,641111.3046978959,5.439303004787615 +FSU,sfh_s3,rural,2020,114.31898027465358,453.7422011800333,307498.1212108434,5.928092220968094 +FSU,sfh_s3,urban,2020,121.65569464496242,376.6719651125107,258179.810066758,5.634125755026637 +FSU,sfh_s51_std,rural,2020,84.22718797291225,27.861470956961465,4657.169015167901,1.9845665017132723 +FSU,sfh_s51_std,urban,2020,89.80459642129672,93.6064567091938,43755.45077846682,5.20508457698963 +FSU,sfh_s52_low,rural,2020,36.6888014199283,1.5251711339889735,332.2690328186619,5.937966814955728 +FSU,sfh_s52_low,urban,2020,39.57967404187526,0.116706314690765,25.73186930009456,5.570635831173765 +FSU,sfh_sr11_std,rural,2020,,56.76728299604096,11806.193506445856, +FSU,sfh_sr11_std,urban,2020,,1.429900478257365,326.6990994590442, +FSU,sfh_sr12_low,rural,2020,,30.685101812577084,7754.801303977732, +FSU,sfh_sr12_low,urban,2020,,0.4862389119032924,136.06520994131753, +FSU,sfh_sr21_std,rural,2020,,45.37295285471158,7425.722331443626, +FSU,sfh_sr21_std,urban,2020,,4.447997804454746,960.460069335402, +FSU,sfh_sr22_low,rural,2020,,25.35601291871559,5122.465928474146, +FSU,sfh_sr22_low,urban,2020,,1.4225348288916184,381.0898361169199, +FSU,sfh_sr31_std,rural,2020,,36.98175603873318,4827.163003962493, +FSU,sfh_sr31_std,urban,2020,,3.107184102111859,472.6900838693791, +FSU,sfh_sr32_low,rural,2020,,10.650612976160026,1861.2125938025945, +FSU,sfh_sr32_low,urban,2020,,0.4845330619002211,92.03233268936928, +LAM,inf_ns,urban,2020,,1114.9128393791923,20181.514287566864, +LAM,mfh_s1,rural,2020,11.734953283073736,4.87637753325178,30.087746567918444,0.5257883699733968 +LAM,mfh_s1,urban,2020,17.39348078174734,29.172918931002584,333.143372095224,0.6565454418914949 +LAM,mfh_s2,rural,2020,10.491773635690206,7.984313996270302,42.29717512850546,0.5049226391396906 +LAM,mfh_s2,urban,2020,15.466880731485482,240.10033519368665,2221.9722306680205,0.5983332311674585 +LAM,mfh_s3,rural,2020,9.5728449345448,4.895313230669059,27.587926857593388,0.5887047718991975 +LAM,mfh_s3,urban,2020,14.056118281582108,409.30228245237856,3314.442790040005,0.5761040888451013 +LAM,mfh_s51_std,rural,2020,9.44458308208488,0.2646349540421449,1.039541738336308,0.4159220384255649 +LAM,mfh_s51_std,urban,2020,13.85948654446876,80.645526807952,628.3955525108515,0.5622192069823396 +LAM,mfh_sr11_std,rural,2020,,0.0022370388823611,0.1139613397061903, +LAM,mfh_sr11_std,urban,2020,,0.0011720963752359,0.053550290087591, +LAM,mfh_sr21_std,rural,2020,,0.0031631508001036,0.1464148912817691, +LAM,mfh_sr21_std,urban,2020,,0.0046218151336742,0.1918614677327178, +LAM,mfh_sr31_std,rural,2020,,0.0021511384530715,0.0917790220763561, +LAM,mfh_sr31_std,urban,2020,,0.0049234753069635,0.1887562436849441, +LAM,sfh_s1,rural,2020,31.56805960282636,865.1938357297054,14336.683739480308,0.5249130869435831 +LAM,sfh_s1,urban,2020,47.84997626684211,371.5791425999708,11241.915849296838,0.6322768414417858 +LAM,sfh_s2,rural,2020,28.527433433741454,1416.3958008681957,20613.71264860888,0.5101629162624706 +LAM,sfh_s2,urban,2020,43.14312953577765,3063.6835438149906,76686.7032279726,0.5801823583091209 +LAM,sfh_s3,rural,2020,26.46929394030581,867.5037344276473,13880.500546842408,0.6044931971395847 +LAM,sfh_s3,urban,2020,40.01185699522313,5226.210158448481,117949.0956905791,0.5640518627738552 +LAM,sfh_s51_std,rural,2020,26.185180601839782,47.194151614095816,575.3550511830957,0.4655777028699688 +LAM,sfh_s51_std,urban,2020,39.58077395502615,1037.9663444972307,23719.391885360707,0.5773457808521698 +LAM,sfh_s52_low,rural,2020,4.006725816071103,9.532099296264353e-06,0.0001767104194298,4.626834610996638 +LAM,sfh_sr11_std,rural,2020,,4.827823711109339,202.2772318839744, +LAM,sfh_sr11_std,urban,2020,,3.907362581482555,174.68296253633824, +LAM,sfh_sr12_low,rural,2020,,0.015926795211471,0.585105257677289, +LAM,sfh_sr12_low,urban,2020,,0.0047932456301908,0.1657811153739432, +LAM,sfh_sr21_std,rural,2020,,8.055069232936841,291.94722091186924, +LAM,sfh_sr21_std,urban,2020,,26.62974593822979,1109.7141177554831, +LAM,sfh_sr22_low,rural,2020,,0.0120057168440893,0.4013014692971774, +LAM,sfh_sr22_low,urban,2020,,0.0082542237491953,0.2562787818390691, +LAM,sfh_sr31_std,rural,2020,,5.888197612279231,196.79225944691595, +LAM,sfh_sr31_std,urban,2020,,41.86085901673383,1645.4716726971787, +LAM,sfh_sr32_low,rural,2020,,0.0072741033839295,0.2259345536545367, +LAM,sfh_sr32_low,urban,2020,,0.0037953569798383,0.1049310887698174, +MEA,inf_ns,urban,2020,,938.444675126271,44459.84916594559, +MEA,mfh_s1,rural,2020,29.764723740932496,49.74724037657894,885.8483921118816,0.5982580499192407 +MEA,mfh_s1,urban,2020,38.65301809789404,25.748541893744285,731.9971861724225,0.735484222920345 +MEA,mfh_s2,rural,2020,26.69162714162626,88.5893942956472,1427.9934611849312,0.6039061285854168 +MEA,mfh_s2,urban,2020,34.7132282678955,173.23486832856617,4316.0082278561695,0.7177148785922435 +MEA,mfh_s3,rural,2020,22.057479154314716,89.02694504044368,1164.1976029394607,0.5928561815068855 +MEA,mfh_s3,urban,2020,28.76645607181196,326.7331258198475,6865.391967892279,0.7304421201272362 +MEA,mfh_s51_std,rural,2020,20.72829785808926,14.545959994608484,188.15265580838096,0.624028354249951 +MEA,mfh_s51_std,urban,2020,27.0854826939562,107.99681244835384,2015.5784654802787,0.6890523095362402 +MEA,mfh_sr11_std,rural,2020,,0.1681843767544395,7.155125426085693, +MEA,mfh_sr11_std,urban,2020,,0.1482266787513234,7.193419597980953, +MEA,mfh_sr12_low,rural,2020,,8.675083369882206e-06,0.0001817335956153, +MEA,mfh_sr12_low,urban,2020,,8.952552603422335e-06,0.0001830730123017, +MEA,mfh_sr21_std,rural,2020,,0.2763628702674194,10.818957551549037, +MEA,mfh_sr21_std,urban,2020,,0.9958513550321436,44.51177503263038, +MEA,mfh_sr22_low,rural,2020,,8.531508985312622e-06,0.0001615790399762, +MEA,mfh_sr22_low,urban,2020,,3.7481224613463885e-05,0.0006870802248457, +MEA,mfh_sr31_std,rural,2020,,0.1386144003619868,6.4679914366506015, +MEA,mfh_sr31_std,urban,2020,,1.3553862449468488,63.84564262335879, +MEA,mfh_sr32_low,rural,2020,,3.155445918651527e-06,5.0011535752307e-05, +MEA,mfh_sr32_low,urban,2020,,3.0307146930058824e-05,0.0004612306997994, +MEA,sfh_s1,rural,2020,75.85670326891506,770.0767775064387,33933.89359375985,0.5809057862306568 +MEA,sfh_s1,urban,2020,97.3101139918426,190.8187879479449,13400.17077078925,0.7216576483446848 +MEA,sfh_s2,rural,2020,69.4736343700237,1371.1901020518983,55638.56325292926,0.5840610144188265 +MEA,sfh_s2,urban,2020,88.92246856974509,1284.984549856645,79883.58607060343,0.6991142205417803 +MEA,sfh_s3,rural,2020,60.03501647189002,1376.51914370207,46995.34008353389,0.5686799374292556 +MEA,sfh_s3,urban,2020,76.45601740237107,2423.8755184436054,130690.65480148638,0.7052166377740918 +MEA,sfh_s51_std,rural,2020,57.0425754730092,226.7941201307374,7847.174191905972,0.6065720247613486 +MEA,sfh_s51_std,urban,2020,72.62967157285844,807.7771343685993,39318.94464503021,0.6701873353923846 +MEA,sfh_s52_low,rural,2020,9.814977908264838,0.0005144662028896,0.0136344275787846,2.700167672069804 +MEA,sfh_s52_low,urban,2020,13.379005774112764,0.0038863202670049,0.1026227456183305,1.973700273058382 +MEA,sfh_sr11_std,rural,2020,,7.549198639689184,375.41884317710105, +MEA,sfh_sr11_std,urban,2020,,2.50873479778579,147.58080630417112, +MEA,sfh_sr12_low,rural,2020,,0.6343180283723495,30.50627353590735, +MEA,sfh_sr12_low,urban,2020,,0.371676429882076,17.04052184601144, +MEA,sfh_sr21_std,rural,2020,,13.535698353807383,643.6813269557097, +MEA,sfh_sr21_std,urban,2020,,16.20199620570921,938.9705252073892, +MEA,sfh_sr22_low,rural,2020,,0.8315759035465388,36.854232416084415, +MEA,sfh_sr22_low,urban,2020,,2.0010085480841777,84.33947829573223, +MEA,sfh_sr31_std,rural,2020,,13.26869580225456,572.1876151424074, +MEA,sfh_sr31_std,urban,2020,,27.915789797745266,1591.9715538005998, +MEA,sfh_sr32_low,rural,2020,,0.4438424042256016,16.957986207779424, +MEA,sfh_sr32_low,urban,2020,,2.2008122052806844,81.02732495510222, +NAM,mfh_s1,rural,2020,82.69639826712788,57.03886352192607,9714.2986496329,2.0594629928002632 +NAM,mfh_s1,urban,2020,70.52964270766337,552.0599352414305,73899.74549288188,1.8979511436375 +NAM,mfh_s2,rural,2020,60.17283600220628,33.06357917966228,4306.829948794784,2.1647481587711512 +NAM,mfh_s2,urban,2020,51.05353564869867,1098.0806366766767,113083.75700469998,2.0171591815976595 +NAM,mfh_s3,rural,2020,49.70921108471192,10.475586418467447,1361.9040779193472,2.615358966864294 +NAM,mfh_s3,urban,2020,41.99876647144607,1244.4321830103029,104902.54137051362,2.007142654187148 +NAM,mfh_s51_std,urban,2020,73.5945725250397,224.3071415738091,21042.814624631228,1.2747204150960871 +NAM,mfh_s52_low,urban,2020,7.937554654338519,0.1533747387214261,5.419706342441509,4.451795345855948 +NAM,mfh_sr11_std,rural,2020,,3.871328821476121,186.41339484697045, +NAM,mfh_sr11_std,urban,2020,,24.52242155815308,1305.5573660667983, +NAM,mfh_sr12_low,rural,2020,,0.2070337486132001,15.210296185547673, +NAM,mfh_sr12_low,urban,2020,,0.3666494119363204,22.90451527614863, +NAM,mfh_sr21_std,rural,2020,,1.9749269314733304,83.66449702640755, +NAM,mfh_sr21_std,urban,2020,,40.07470583615531,1657.6520191778193, +NAM,mfh_sr22_low,rural,2020,,0.0687810026192076,4.1867952172238825, +NAM,mfh_sr22_low,urban,2020,,0.3764987429389084,19.079417856945287, +NAM,mfh_sr31_std,rural,2020,,0.612756101527175,26.063891557302625, +NAM,mfh_sr31_std,urban,2020,,42.3589902399554,1433.768270485008, +NAM,mfh_sr32_low,rural,2020,,0.0200443156581107,1.1585843485336054, +NAM,mfh_sr32_low,urban,2020,,0.2189314884425802,10.031484182980876, +NAM,sfh_s1,rural,2020,203.33980270417155,4220.434377256761,1772447.2479724777,2.0653505219069563 +NAM,sfh_s1,urban,2020,175.05413195701038,3068.517565006714,1025195.7247220606,1.908559951506888 +NAM,sfh_s2,rural,2020,150.10102812483387,2427.2728150192256,791795.8902540021,2.1732564856926757 +NAM,sfh_s2,urban,2020,129.1403235187039,6084.30337651371,1567057.5096815156,1.9943997183502775 +NAM,sfh_s3,rural,2020,124.75864973611948,767.7439637964346,249417.92987177556,2.6039976862035896 +NAM,sfh_s3,urban,2020,107.17596396921276,6934.904038731856,1457643.168338506,1.9611615374996147 +NAM,sfh_s51_std,urban,2020,185.5903186606859,833.2704169086157,99697.47363405793,0.6446780331144357 +NAM,sfh_s52_low,urban,2020,19.38063764259675,469.4515334719678,19769.723624910155,2.172910178792523 +NAM,sfh_sr11_std,rural,2020,,189.7004765504727,11638.224907873528, +NAM,sfh_sr11_std,urban,2020,,200.72196742410543,14355.846385643044, +NAM,sfh_sr12_low,rural,2020,,204.0772341050472,20856.174402666424, +NAM,sfh_sr12_low,urban,2020,,79.24748729863613,7316.377733374664, +NAM,sfh_sr21_std,rural,2020,,120.20518490647378,6196.91636959463, +NAM,sfh_sr21_std,urban,2020,,398.1856225004119,24401.20034773256, +NAM,sfh_sr22_low,rural,2020,,103.04262662407044,8456.516470486067, +NAM,sfh_sr22_low,urban,2020,,125.30311202377185,9187.825474236672, +NAM,sfh_sr31_std,rural,2020,,38.90817577342968,2006.5784220895648, +NAM,sfh_sr31_std,urban,2020,,421.9505410260923,24262.25621681709, +NAM,sfh_sr32_low,rural,2020,,32.005902575199364,2605.519048946787, +NAM,sfh_sr32_low,urban,2020,,112.67320084590332,6941.417097696127, +PAO,mfh_s1,rural,2020,73.51502132384644,19.459732569679737,1522.14455707089,1.0640032241033208 +PAO,mfh_s1,urban,2020,54.0707718976781,134.68645789437966,5208.936100209733,0.7152576784556952 +PAO,mfh_s2,rural,2020,55.79930206832088,11.98943729454046,690.4452961741001,1.032052303986198 +PAO,mfh_s2,urban,2020,40.4821273091578,521.351228701015,15399.16865712808,0.7296314538509087 +PAO,mfh_s3,rural,2020,30.191266332618994,13.046173061101776,425.840302760676,1.08114090041856 +PAO,mfh_s3,urban,2020,20.90155846066448,599.1259035747116,8918.972222912054,0.7122263633577681 +PAO,mfh_s51_std,urban,2020,18.496210272458505,81.38901614586318,622.4993015246124,0.413514093446901 +PAO,mfh_s52_low,urban,2020,4.39325274443708,0.0034421635453667,0.006908261576924,0.4568262840800409 +PAO,mfh_sr11_std,rural,2020,,0.6053436651533143,16.71435805411141, +PAO,mfh_sr11_std,urban,2020,,0.9141077486519076,24.465934681795822, +PAO,mfh_sr12_low,rural,2020,,0.0011566677124302,0.0275902763486056, +PAO,mfh_sr12_low,urban,2020,,0.0002901582697398,0.0060703858892352, +PAO,mfh_sr21_std,rural,2020,,0.3436771427461726,7.1900308725140185, +PAO,mfh_sr21_std,urban,2020,,3.376069915703091,69.28596472230655, +PAO,mfh_sr22_low,rural,2020,,0.0002153845491572,0.003847669544497, +PAO,mfh_sr22_low,urban,2020,,0.0004005089541017,0.0074753946954605, +PAO,mfh_sr31_std,rural,2020,,0.1556331142398808,2.2251869788285137, +PAO,mfh_sr31_std,urban,2020,,0.2589798960432025,4.213094613679586, +PAO,mfh_sr32_low,rural,2020,,2.02584025031188e-05,0.0002149112321384, +PAO,sfh_s1,rural,2020,181.4146385705439,1286.417667131839,249338.733555286,1.0684038036905643 +PAO,sfh_s1,urban,2020,136.74473589934956,381.44958113705087,36417.91475908185,0.6981798585441642 +PAO,sfh_s2,rural,2020,139.50758480374236,793.7706983815461,114241.68215783632,1.0316483929819549 +PAO,sfh_s2,urban,2020,104.7608864022302,1478.668139947948,109427.29766363117,0.7064083017414894 +PAO,sfh_s3,rural,2020,77.51842152194855,853.5999103225109,70408.09222315023,1.0640530852116348 +PAO,sfh_s3,urban,2020,57.17689824123386,1703.1364683247416,65911.04464618186,0.6768433319968952 +PAO,sfh_s51_std,rural,2020,69.77638784345169,271.63255297521044,10456.750748642076,0.551704412442583 +PAO,sfh_s51_std,urban,2020,51.27286178185185,242.2342292765732,4547.571162672331,0.3661478041184398 +PAO,sfh_s52_low,rural,2020,20.35462905816778,0.3211509329465082,6.769565359204959,1.0355913290220362 +PAO,sfh_s52_low,urban,2020,13.45863597731658,0.0309609707187317,0.4022476853075774,0.9653347459482432 +PAO,sfh_sr11_std,rural,2020,,88.64030133038935,3897.346996278474, +PAO,sfh_sr11_std,urban,2020,,21.274371069731107,606.635164409564, +PAO,sfh_sr12_low,rural,2020,,21.3500447109231,1050.329370847606, +PAO,sfh_sr12_low,urban,2020,,0.8944110752095203,41.12521179480637, +PAO,sfh_sr21_std,rural,2020,,54.76423168752315,1987.106921968374, +PAO,sfh_sr21_std,urban,2020,,81.29061097017532,1876.1507845456413, +PAO,sfh_sr22_low,rural,2020,,7.941517530244511,307.04218032331164, +PAO,sfh_sr22_low,urban,2020,,1.9025945328723253,69.48741742830829, +PAO,sfh_sr31_std,rural,2020,,62.04389213180687,1393.5323182601808, +PAO,sfh_sr31_std,urban,2020,,80.51672935482304,991.2415439895556, +PAO,sfh_sr32_low,rural,2020,,2.3193265177342908,52.15224331334174, +PAO,sfh_sr32_low,urban,2020,,0.4265871927436754,8.979020208114866, +PAS,inf_ns,urban,2020,,674.6552188249898,5804.565659765994, +PAS,mfh_s1,rural,2020,3.788824488523777,137.63282829518832,1580.6466694923529,3.031155931636816 +PAS,mfh_s1,urban,2020,15.771068796603116,35.214902063761066,682.171864161977,1.2283049217518358 +PAS,mfh_s2,rural,2020,3.788824488523777,217.8583298052129,923.6211848935206,1.1189617166306562 +PAS,mfh_s2,urban,2020,15.771068796603116,227.4368856833239,8134.843533426438,2.267917112462849 +PAS,mfh_s3,rural,2020,3.5278065826042355,256.16605088459653,1039.8405615190218,1.150642509832636 +PAS,mfh_s3,urban,2020,14.734230196024075,615.0607742607954,18457.393926418223,2.036689854320689 +PAS,mfh_s51_std,rural,2020,3.263697227386193,27.38618157793919,0.5762661830425486,0.0064473580451787 +PAS,mfh_s51_std,urban,2020,13.673969600972049,193.2714073219853,1392.7356739821105,0.5269949830790519 +PAS,mfh_s52_low,urban,2020,2.365858489825168,0.0002569864690192,0.0009437477850335,1.5522330412131748 +PAS,mfh_sr11_std,rural,2020,,0.2800413788158967,14.543313092838485, +PAS,mfh_sr11_std,urban,2020,,0.1253831139785926,6.262595256262027, +PAS,mfh_sr21_std,rural,2020,,0.162531780294493,8.440718944275822, +PAS,mfh_sr21_std,urban,2020,,1.4952208335007544,74.68280692144992, +PAS,mfh_sr31_std,rural,2020,,0.1894169690332214,9.196742005529412, +PAS,mfh_sr31_std,urban,2020,,3.5336831185376627,164.98760179934658, +PAS,sfh_s1,rural,2020,9.40514542576685,1042.8934699654897,29061.176116420764,2.9628367514773157 +PAS,sfh_s1,urban,2020,38.34144069394136,169.79871309175076,7918.482000927701,1.2162955333484882 +PAS,sfh_s2,rural,2020,9.40514542576685,1611.3843511520806,16943.359282021556,1.1179821337682223 +PAS,sfh_s2,urban,2020,38.34144069394136,1073.3473542504769,91656.24763064644,2.227169936797014 +PAS,sfh_s3,rural,2020,8.791509221524276,1955.6795330142163,18804.10879470866,1.0936834204317467 +PAS,sfh_s3,urban,2020,35.89226851914735,2958.44784629038,216663.58915273155,2.040427214786182 +PAS,sfh_s51_std,rural,2020,8.186710664774559,215.49560674149987,16.612167394892307,0.009416259880887 +PAS,sfh_s51_std,urban,2020,33.42798304087046,948.345971136037,16768.16424606221,0.5289425962814036 +PAS,sfh_s52_low,urban,2020,6.252874223065266,1.1476644326263334,11.254641175435925,1.56832851120598 +PAS,sfh_sr11_std,rural,2020,,2.314692804172902,290.9166569221026, +PAS,sfh_sr11_std,urban,2020,,0.6586173529630531,79.6948997674544, +PAS,sfh_sr21_std,rural,2020,,1.3371864080553462,168.06109167810433, +PAS,sfh_sr21_std,urban,2020,,7.6237793982554045,922.5027741061618, +PAS,sfh_sr31_std,rural,2020,,1.5253613922011091,179.5166720172422, +PAS,sfh_sr31_std,urban,2020,,18.675377303914487,2115.900671693208, +RCPA,inf_ns,urban,2020,,184.8228789327621,9515.883179572447, +RCPA,mfh_s1,rural,2020,6.701041021210691,228.75303894536344,2011.4442504920992,1.312196428176586 +RCPA,mfh_s1,urban,2020,5.685688029556295,29.77410549737653,504.4963194197545,2.9801371598053548 +RCPA,mfh_s2,rural,2020,5.062889502707298,440.1009308274582,2957.278851439955,1.3272157944718133 +RCPA,mfh_s2,urban,2020,4.278005151455028,114.47792333727644,1485.81504061439,3.033903091615066 +RCPA,mfh_s3,rural,2020,3.419832883710589,669.8854746934805,3109.64977003704,1.3573943389192766 +RCPA,mfh_s3,urban,2020,2.8659548627173805,467.024867335577,4202.040074601824,3.1394302454752623 +RCPA,mfh_s51_std,urban,2020,2.6299602604209986,49.15958872788102,657.6056625224082,5.086371967812899 +RCPA,sfh_s1,rural,2020,16.59768545780076,199.87296733581348,4258.480144066895,1.2836689502256986 +RCPA,sfh_s1,urban,2020,14.192712111139484,4.285560296106658,175.02663551055463,2.87760474677271 +RCPA,sfh_s2,rural,2020,12.538149344958589,386.34124439390735,6257.0678066594,1.2917139461608016 +RCPA,sfh_s2,urban,2020,10.699434644722263,16.837001470594828,523.6167418775553,2.9066179009962068 +RCPA,sfh_s3,rural,2020,8.576730468055716,570.267968892904,6371.642715848302,1.3027187811729912 +RCPA,sfh_s3,urban,2020,7.2965641518054545,66.80710266739409,1436.485989143939,2.946865754480551 +RCPA,sfh_s51_std,urban,2020,6.719754826999609,6.822454451996647,220.09564825213343,4.800841665048265 +SAS,inf_ns,urban,2020,,1687.9166150140752,11599.502053806324, +SAS,mfh_s1,rural,2020,10.629190919395503,134.91594107293244,877.93490442799,0.6122077751825895 +SAS,mfh_s1,urban,2020,7.10243524740392,116.49749361292294,334.1426715220577,0.4038388306957908 +SAS,mfh_s2,rural,2020,10.629190919395503,227.38424919168645,1447.507700846783,0.598908252051235 +SAS,mfh_s2,urban,2020,7.10243524740392,587.1283417505268,1483.6795159010371,0.3557949443452156 +SAS,mfh_s3,rural,2020,9.804925353166862,283.0497364716952,1659.3596750717277,0.5979067899046533 +SAS,mfh_s3,urban,2020,6.506259014082712,1168.3156621429969,2670.284872359484,0.3512902110887986 +SAS,mfh_s51_std,rural,2020,8.98719042878908,42.36630041884183,131.8830078598726,0.3463732607541678 +SAS,mfh_s51_std,urban,2020,5.921185304384903,357.1324976657258,390.2904911742348,0.1845652548871474 +SAS,mfh_s52_low,rural,2020,0.9781742013454334,0.0210526725149367,0.3153256980557165,15.31214124543917 +SAS,mfh_s52_low,urban,2020,0.4399614887671711,0.0020964433054676,0.0089901467155009,9.746955743695764 +SAS,mfh_sr11_std,rural,2020,,0.1617749361160154,7.688564876660025, +SAS,mfh_sr11_std,urban,2020,,0.0568843458254438,2.524162720985309, +SAS,mfh_sr21_std,rural,2020,,0.2679879724883831,12.601609886601008, +SAS,mfh_sr21_std,urban,2020,,0.2427018524021394,10.665062447560588, +SAS,mfh_sr31_std,rural,2020,,0.2862682311487153,13.436161811648416, +SAS,mfh_sr31_std,urban,2020,,0.442623142986578,18.145168310380534, +SAS,sfh_s1,rural,2020,27.920219068923384,4355.515998899186,69110.14021307981,0.5683074367738378 +SAS,sfh_s1,urban,2020,19.475961621349654,332.8246317153585,2347.0712369795374,0.362086139954882 +SAS,sfh_s2,rural,2020,27.920219068923384,7340.841986657485,114022.04540279612,0.556319303960675 +SAS,sfh_s2,urban,2020,19.475961621349654,1677.5736646332564,10448.911214401744,0.3198088878629498 +SAS,sfh_s3,rural,2020,26.009493066659733,9137.64683336655,131249.88516080604,0.5522461229952298 +SAS,sfh_s3,urban,2020,18.098868704137963,3338.5277612640352,18992.43561378413,0.3143216107303712 +SAS,sfh_s51_std,rural,2020,24.19495241487609,1353.2039456283874,6295.268881449742,0.1922765214611699 +SAS,sfh_s51_std,urban,2020,16.81882657838885,1015.119517831384,1824.078540102846,0.1068392088499848 +SAS,sfh_s52_low,rural,2020,3.38854677911892,16.751201430259407,337.18127938236336,5.940240216854639 +SAS,sfh_s52_low,urban,2020,1.9686799182805228,6.339669122218719,69.66430085038823,5.581726848518744 +SAS,sfh_sr11_std,rural,2020,,10.182292121127276,783.2700176598563, +SAS,sfh_sr11_std,urban,2020,,0.538591387566116,24.71916037784884, +SAS,sfh_sr21_std,rural,2020,,16.85558399049015,1286.8784272554544, +SAS,sfh_sr21_std,urban,2020,,2.39672896577912,107.31929442744614, +SAS,sfh_sr31_std,rural,2020,,19.744542037664445,1432.588333502359, +SAS,sfh_sr31_std,urban,2020,,4.298443665885178,184.7997669222125, +WEU,mfh_s1,rural,2020,80.7214847219604,92.04000615657338,27057.737638714894,3.641880581282408 +WEU,mfh_s1,urban,2020,75.46094840597016,1492.9628836697266,431449.73519200674,3.829648694812078 +WEU,mfh_s2,rural,2020,54.60195225078522,44.38980089021753,8903.969769213798,3.673603743632929 +WEU,mfh_s2,urban,2020,50.90782756658002,2023.0299381751145,360461.9909434346,3.500036737775184 +WEU,mfh_s3,rural,2020,26.101564963582245,38.42241735424072,3527.90659424044,3.517757081902356 +WEU,mfh_s3,urban,2020,24.201483293168184,2238.8051830655704,187681.57781157765,3.463884143477904 +WEU,mfh_s51_std,rural,2020,18.62191919237557,0.0837896123374968,3.701477535885961,2.372250239501042 +WEU,mfh_s51_std,urban,2020,17.232515999327646,95.7706595627116,4629.249153888111,2.804977533434079 +WEU,mfh_s52_low,rural,2020,9.479374668926278,1.1558044360253412,22.40420321405882,2.04486884076916 +WEU,mfh_s52_low,urban,2020,8.760506566901418,350.23238311262133,6301.032243439722,2.0536488353624778 +WEU,mfh_sr11_std,rural,2020,,2.580895894955036,213.0861537195721, +WEU,mfh_sr11_std,urban,2020,,35.158635960777474,2891.981764316512, +WEU,mfh_sr12_low,rural,2020,,3.0683440568313705,192.591456140054, +WEU,mfh_sr12_low,urban,2020,,27.38843831004113,1654.7756895938433, +WEU,mfh_sr21_std,rural,2020,,1.1970957191501268,65.92396021409324, +WEU,mfh_sr21_std,urban,2020,,43.07916781171053,2387.0541859505465, +WEU,mfh_sr22_low,rural,2020,,1.041195056976976,41.032034153511766, +WEU,mfh_sr22_low,urban,2020,,26.179235310011528,1094.0493974684136, +WEU,mfh_sr31_std,rural,2020,,0.6294096282611426,18.109556956418928, +WEU,mfh_sr31_std,urban,2020,,9.139727955452964,309.4799019671709, +WEU,mfh_sr32_low,rural,2020,,0.3374049775521923,6.9769310109442015, +WEU,mfh_sr32_low,urban,2020,,2.5294935891174952,77.04190371011258, +WEU,sfh_s1,rural,2020,199.1571575477619,3705.7232479082313,2689443.4970790674,3.6441271618193136 +WEU,sfh_s1,urban,2020,186.929981883403,2266.7321326498814,1605479.7361583882,3.789009231693212 +WEU,sfh_s2,rural,2020,136.7840900323082,1770.7993978041384,883175.5501748491,3.646213429814597 +WEU,sfh_s2,urban,2020,128.21663961744198,3061.957320284083,1354988.8217228674,3.451375318770177 +WEU,sfh_s3,rural,2020,66.90839717456522,1523.7397856972543,351013.7972094553,3.4429661577875903 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+WEU,sfh_s3,urban,2100,62.47385740033076,528.7615831100993,73191.95430985365,2.2156702546360734 +WEU,sfh_s51_std,rural,2100,47.83149923897789,3.349445733341136,202.36353280822888,1.2631220396897904 +WEU,sfh_s51_std,urban,2100,44.55263979646592,1552.7428290667217,171539.49377434383,2.47965436970396 +WEU,sfh_s52_low,rural,2100,23.2687751503444,143.65383536573526,5545.821310771973,1.6591097813550633 +WEU,sfh_s52_low,urban,2100,21.5589809218736,7624.754696763919,214047.86652954947,1.3021373587230698 +WEU,sfh_sr11_std,rural,2100,,366.66212006264016,25004.95842021568, +WEU,sfh_sr11_std,urban,2100,,355.2671179461863,27611.55907127233, +WEU,sfh_sr12_low,rural,2100,,1188.5571989268635,85937.57415651249, +WEU,sfh_sr12_low,urban,2100,,783.8498744313871,57066.79978831479, +WEU,sfh_sr21_std,rural,2100,,161.4074797656621,8141.854166250225, +WEU,sfh_sr21_std,urban,2100,,489.71000365181135,23495.634562751977, +WEU,sfh_sr22_low,rural,2100,,455.659098642683,22490.663036094367, +WEU,sfh_sr22_low,urban,2100,,794.9390205813434,37910.14108644067, +WEU,sfh_sr31_std,rural,2100,,160.0757730304115,3974.176039069105, +WEU,sfh_sr31_std,urban,2100,,525.7854036911008,13526.257857472625, +WEU,sfh_sr32_low,rural,2100,,245.65828408028645,6035.640584250784, +WEU,sfh_sr32_low,urban,2100,,545.9501048678885,13134.88793914933, diff --git a/message_ix_models/data/buildings/rc_sector_fractions_SSP2.csv b/message_ix_models/data/buildings/rc_sector_fractions_SSP2.csv new file mode 100644 index 0000000000..6b31c499c7 --- /dev/null +++ b/message_ix_models/data/buildings/rc_sector_fractions_SSP2.csv @@ -0,0 +1,103 @@ +# METADATA: Residential/Commercial shares of the calibrated buildings component in aggregate rc_spec/rc_therm +# Sources: WP - RC demands/Output/ssp_baseline/{resid,commpub}_calibrated_SSP2.csv, _staging/rc_spec_baseline.csv, _staging/rc_therm_baseline.csv +# Formula: beta = buildings_sector_GWa / rc_aggregate_GWa; frac_resid = beta * resid / (resid + comm), frac_comm = beta * comm / (resid + comm) +# Scope: space heating only (resid_heat_*, comm_heat_*); excludes cooking and hot water. Cooling = resid_cool_electr + comm_cool_electr. +# Years: 2030-2100 (MESSAGE scenario horizon) +# Generated: 2026-04-01 +node,year,frac_resid_cool,frac_comm_cool,frac_resid_heat,frac_comm_heat +R12_AFR,2030,0.04711526431595193,0.008862599938186888,0.043709651114106726,8.02278855009224e-05 +R12_AFR,2040,0.118046828973321,0.027457710512576908,0.0299451785958301,0.00023263859989844444 +R12_AFR,2050,0.1624160328262213,0.047203649607385066,0.026753209819563084,0.00034675926590437304 +R12_AFR,2060,0.1897027803868529,0.10864398291345413,0.02474074862457884,0.0004902344866102278 +R12_AFR,2070,0.17974721116440806,0.22985110548238485,0.020028528874985133,0.0006027234654878119 +R12_AFR,2080,0.156782958743541,0.35124448571808026,0.01667478230498607,0.000727214067661662 +R12_AFR,2090,0.1462329303736917,0.4361198808526704,0.015365875666076792,0.000870154442457393 +R12_AFR,2100,0.1422982258908602,0.4623392881540964,0.014398906495392435,0.0009355777936997991 +R12_CHN,2030,0.15209122987224982,0.15652993380549565,0.28414567742845626,0.06271430142320934 +R12_CHN,2040,0.12091945217125762,0.1347147640807267,0.259472490780413,0.06010691338526673 +R12_CHN,2050,0.11941857781057956,0.13463536707386115,0.24109715751683214,0.058558888733160314 +R12_CHN,2060,0.11506181880453885,0.13092001041503767,0.22824880666051922,0.05807274478842013 +R12_CHN,2070,0.112286015174226,0.12390599617334674,0.21658291738429483,0.05594601723994882 +R12_CHN,2080,0.11090283811595734,0.11667870104253421,0.20878801772391647,0.053314021090877704 +R12_CHN,2090,0.11149961607670403,0.11427335667931221,0.20658905350988022,0.051925571702020924 +R12_CHN,2100,0.11153844928073521,0.11547019419322355,0.20273510597753622,0.051178363528364426 +R12_EEU,2030,0.00443616831635519,0.11410560215884646,0.5801997092470333,0.16115312416040978 +R12_EEU,2040,0.0052305470614648145,0.11549632309998574,0.5415753496596442,0.14410315882768326 +R12_EEU,2050,0.0061094767004055235,0.10538735959038156,0.5244269863485352,0.1298914492901682 +R12_EEU,2060,0.007175186285119326,0.1063884853802825,0.5254794488843278,0.12155596351733886 +R12_EEU,2070,0.00783031532504707,0.1010281304083748,0.5354235671679424,0.1128611449354681 +R12_EEU,2080,0.00826921312296818,0.09478310216647766,0.5568153289877087,0.10322933472481123 +R12_EEU,2090,0.008500689766001096,0.08809582798280613,0.5610903062586242,0.09319971747947225 +R12_EEU,2100,0.008590786154397885,0.08165157237496613,0.5659148077144155,0.08547181714978057 +R12_FSU,2030,0.009456207268258373,0.05044996609786257,0.5376538122832463,0.16431696600549195 +R12_FSU,2040,0.014596392320166804,0.08473793991393538,0.3332900811038411,0.11509162941499482 +R12_FSU,2050,0.017710376377013858,0.10483682422424426,0.2642164461515629,0.10002537186051613 +R12_FSU,2060,0.020613599850799188,0.12395056184202806,0.27856928742674775,0.1148893894326671 +R12_FSU,2070,0.02259188937879697,0.1333205676680937,0.23209062438511197,0.10261860169141124 +R12_FSU,2080,0.02451336711289342,0.13418814427348416,0.23190229498692222,0.10657486995022308 +R12_FSU,2090,0.025655112148799102,0.12915330010690218,0.256600156992556,0.12013245675684353 +R12_FSU,2100,0.025875943191784535,0.12180917397628536,0.2650865289905346,0.1257277365068621 +R12_LAM,2030,0.2365096692378168,0.22224614789041194,0.16014892604502592,0.027721785914877308 +R12_LAM,2040,0.22466413359839502,0.38687511003206343,0.148535878594944,0.03203598122358483 +R12_LAM,2050,0.17633103874251913,0.42683507812750154,0.13948463824280266,0.03665950153592048 +R12_LAM,2060,0.17455843708528188,0.47036149062974464,0.140911256714956,0.03993172938032991 +R12_LAM,2070,0.18144671943868082,0.4864855643933509,0.1391300506138701,0.04037432144488377 +R12_LAM,2080,0.19124675721302467,0.4872784391110164,0.1380094154565103,0.04004131483250117 +R12_LAM,2090,0.1990924553606818,0.4802579906182796,0.1378073378302583,0.03982854627503775 +R12_LAM,2100,0.20510059622454388,0.4700342085663849,0.13712757405759599,0.03960690644797878 +R12_MEA,2030,0.49668544807030707,0.16584206454729608,0.3714840888354245,0.11654648667810641 +R12_MEA,2040,0.4856864371074002,0.18007979381244382,0.35641076952328377,0.10674269200417184 +R12_MEA,2050,0.47510909312526,0.17939353395455748,0.34642205554398386,0.09677019242290526 +R12_MEA,2060,0.4757609044990976,0.1685766483897391,0.3386821516844107,0.08686846435420617 +R12_MEA,2070,0.46931800265493634,0.1576382059280681,0.31681448614981644,0.08086909327633178 +R12_MEA,2080,0.4775972598507471,0.15266804654049596,0.30164074469920366,0.07735101524794288 +R12_MEA,2090,0.49359880459249683,0.1504920617210146,0.2946279749484007,0.07784429851173165 +R12_MEA,2100,0.49054724679894623,0.14808582818835292,0.2837343663794639,0.07868140804912956 +R12_NAM,2030,0.1431292122228874,0.20992761121018327,0.4261240570224168,0.294745922837362 +R12_NAM,2040,0.14745824566090546,0.2075499653922136,0.36978100187613316,0.24529303851346296 +R12_NAM,2050,0.14986070058927498,0.20032819626243514,0.33565553696662526,0.20180675021630126 +R12_NAM,2060,0.1566220058769879,0.20017623219002495,0.3570932984296816,0.19264900136706783 +R12_NAM,2070,0.16264692129469321,0.2006429350721576,0.40712560939857245,0.1980043565316257 +R12_NAM,2080,0.16767906894978993,0.20096207032285893,0.39934640642130853,0.17713957561530105 +R12_NAM,2090,0.17200857914801768,0.200821142249147,0.40592767531421386,0.16508832873883653 +R12_NAM,2100,0.17604785193163086,0.20034316258477702,0.4279815282646422,0.16476580448842865 +R12_PAO,2030,0.060531582450921335,0.290254433183513,0.17072102549755033,0.20934019722401134 +R12_PAO,2040,0.06545088555964047,0.3275067421246084,0.17565591276846745,0.19533985433831694 +R12_PAO,2050,0.07157028442897166,0.3447078976773187,0.1870199834711421,0.1823989485400825 +R12_PAO,2060,0.07995882239607985,0.3657007647627135,0.18451594510112282,0.15664478397183354 +R12_PAO,2070,0.08507361375581303,0.37470156529480286,0.18662040282540154,0.13777110807013768 +R12_PAO,2080,0.0866705753493027,0.372804436285419,0.18687281638013525,0.1209157072747013 +R12_PAO,2090,0.08756944031126793,0.3700614787185805,0.1868673972183275,0.10804903863970496 +R12_PAO,2100,0.08980692818604705,0.3723428770942224,0.1868866928779797,0.09819635134250095 +R12_PAS,2030,0.1029316174532885,0.16710548459405977,0.261685577794937,0.04481765430330934 +R12_PAS,2040,0.12299032050798735,0.22278809498229338,0.2346005763295107,0.040789188136223495 +R12_PAS,2050,0.13336726178223401,0.26297069914927707,0.2084227551661755,0.03831172164174381 +R12_PAS,2060,0.15506918224305552,0.29783311622692443,0.2001420627931423,0.038787511338161065 +R12_PAS,2070,0.17170141723258034,0.3117017350965491,0.19216300074196538,0.038302394669981026 +R12_PAS,2080,0.18767602534609268,0.33430381905684453,0.1856926650186644,0.03724920049761929 +R12_PAS,2090,0.19888479054371205,0.3534276933474372,0.18174596729376374,0.03680272873585977 +R12_PAS,2100,0.2052569772293757,0.35765643217684295,0.17364366143501725,0.03602015795136458 +R12_RCPA,2030,0.36179783666024623,0.04696413542157213,0.24369397553786126,0.0826826680329058 +R12_RCPA,2040,0.3065708004474645,0.06364506960444194,0.23980140286259685,0.08142481405234075 +R12_RCPA,2050,0.32442880890986525,0.11939590169724372,0.2410792811485942,0.08270689185963577 +R12_RCPA,2060,0.3344832346812929,0.18194198822148513,0.23146116927729496,0.08078367774229299 +R12_RCPA,2070,0.3426369550416604,0.21214302452620928,0.22328284833152767,0.07954913863681365 +R12_RCPA,2080,0.3642944158760453,0.2176435899948812,0.22215435441454728,0.07967895318785166 +R12_RCPA,2090,0.38439552352597056,0.21543762532183974,0.2256391544419506,0.08198419025849762 +R12_RCPA,2100,0.3944389919137118,0.21125423662794449,0.22540150712234852,0.08369283914836557 +R12_SAS,2030,0.27063924960365615,0.052872907684169926,0.18377673054833674,0.026161699471016424 +R12_SAS,2040,0.20460365740853464,0.12348328923536843,0.15500815219966557,0.02123253517871652 +R12_SAS,2050,0.17394037895776973,0.21687448674826337,0.13580631318026837,0.018513230010443138 +R12_SAS,2060,0.15602681091195106,0.28674226280663234,0.11840278596164017,0.01610657758973683 +R12_SAS,2070,0.1468227659713255,0.31791938404781367,0.10241012146249198,0.013918643523509978 +R12_SAS,2080,0.14985482793427446,0.3341263255517407,0.09194859051948852,0.012325721284435716 +R12_SAS,2090,0.1595234075467841,0.3414123334542759,0.08684926897884594,0.011450350432218702 +R12_SAS,2100,0.16715728829951698,0.33849164086581807,0.08358732429170165,0.010850872782703427 +R12_WEU,2030,0.011103437435690902,0.10105570347873999,0.4669608551306725,0.20297287980259682 +R12_WEU,2040,0.015979602857218566,0.10161402374886508,0.4333813927857778,0.184464483620003 +R12_WEU,2050,0.019036203053451835,0.0933471562798367,0.4052297294290983,0.16942687718402066 +R12_WEU,2060,0.02209873272456301,0.08551346673245272,0.3963515532168702,0.1577259254735427 +R12_WEU,2070,0.023677825399457498,0.07608344152504233,0.38407029585500946,0.14153356536838807 +R12_WEU,2080,0.02434791459942056,0.06781215342197465,0.3887665074342406,0.1269704019574786 +R12_WEU,2090,0.024810928145109053,0.06150795297480181,0.3960827875048907,0.11332980956054221 +R12_WEU,2100,0.024656731506872892,0.05583279632668683,0.3979407265973167,0.10141326187829162 diff --git a/message_ix_models/data/buildings/rc_sector_fractions_SSP3.csv b/message_ix_models/data/buildings/rc_sector_fractions_SSP3.csv new file mode 100644 index 0000000000..59b6630c20 --- /dev/null +++ b/message_ix_models/data/buildings/rc_sector_fractions_SSP3.csv @@ -0,0 +1,102 @@ +# METADATA: Residential/Commercial shares of the calibrated buildings component in aggregate rc_spec/rc_therm +# Sources: WP - RC demands/Output/ssp_baseline/{resid,commpub}_calibrated_SSP3.csv, _staging/rc_demands/SSP_SSP3_v6.5_CID__baseline__rc_{spec,therm}.csv +# Formula: beta = buildings_sector_GWa / rc_aggregate_GWa; frac_resid = beta * resid / (resid + comm), frac_comm = beta * comm / (resid + comm) +# Scope: space heating only (resid_heat_*, comm_heat_*); excludes cooking and hot water. Cooling = resid_cool_electr + comm_cool_electr. +# Years: 2030-2100 (MESSAGE scenario horizon) +node,year,frac_resid_cool,frac_comm_cool,frac_resid_heat,frac_comm_heat +R12_AFR,2030,0.02024712707066022,0.008898879750087322,0.03365075738578421,0.00013930110090330398 +R12_AFR,2040,0.040014261183596286,0.02649925790036045,0.02944137289475459,0.00031483722106174535 +R12_AFR,2050,0.057243495425878275,0.04413221149219771,0.02515877665567312,0.0003841950796908091 +R12_AFR,2060,0.0714078615298653,0.08846747800372898,0.021001658058924216,0.0003950649357725041 +R12_AFR,2070,0.07883485707527156,0.18081127843911607,0.018110154340841605,0.00041591647034561516 +R12_AFR,2080,0.0791903071396852,0.2857072845082319,0.016110773323866692,0.0004369797383942889 +R12_AFR,2090,0.08181545454825725,0.38845999590059893,0.015714648805929723,0.0004958662564945338 +R12_AFR,2100,0.08467499744033662,0.4576147536757446,0.015664566828881767,0.0005718282043336471 +R12_CHN,2030,0.12524459042567979,0.1417996084867736,0.2538733838158512,0.06296998246908384 +R12_CHN,2040,0.0936097968073002,0.12447982504902612,0.22607759772667027,0.06094073864568467 +R12_CHN,2050,0.08010085003961302,0.1306404364953784,0.20959625466744275,0.0602991450237966 +R12_CHN,2060,0.0702293796228592,0.13020434670821193,0.20125485435772322,0.06059742615001077 +R12_CHN,2070,0.06406681009692598,0.1263624873429579,0.19076070973514764,0.06071554096420797 +R12_CHN,2080,0.062146789859995444,0.12484814076138166,0.18368658274374028,0.06059580153649001 +R12_CHN,2090,0.05988748058245558,0.12562456424244053,0.1804624313192046,0.062099594175995455 +R12_CHN,2100,0.056511301610694814,0.12963948564558087,0.17548472588393324,0.0642249418442596 +R12_EEU,2030,0.003921764443064604,0.10731539618464676,0.5825926662900951,0.15969103130347598 +R12_EEU,2040,0.004590498368125442,0.11231623295223762,0.5565689831528874,0.1375840822674532 +R12_EEU,2050,0.005306488362497226,0.10844066501669233,0.5395341625142377,0.12167847428165277 +R12_EEU,2060,0.006592250636238126,0.11813590748245521,0.5484963366049196,0.11372440720919237 +R12_EEU,2070,0.007883067305834794,0.12255566119206177,0.5537623231808628,0.10699119291803441 +R12_EEU,2080,0.00924866934765719,0.12649281644844665,0.5589504811245246,0.1004489498620496 +R12_EEU,2090,0.010603850052109105,0.1287821594820461,0.5555675786260383,0.09339613008685364 +R12_EEU,2100,0.012001685215356815,0.13058349177103987,0.56530565431383,0.08866664862529662 +R12_FSU,2030,0.007457266283814671,0.046253805458561705,0.5566113690512946,0.1730981155668102 +R12_FSU,2040,0.01048819203450055,0.07744544708313536,0.5428763530492293,0.17296972699786195 +R12_FSU,2050,0.012255704971988005,0.09694993032793647,0.5482514165951284,0.17033296787670854 +R12_FSU,2060,0.014797841915357048,0.121418679936752,0.5541750573095859,0.16841280939726735 +R12_FSU,2070,0.01736796994583417,0.14576147702044148,0.5420472149805906,0.16733035377025812 +R12_FSU,2080,0.020229438995581828,0.16663776010424716,0.5354401371264382,0.1667861557867219 +R12_FSU,2090,0.022781676022229448,0.18171552635729563,0.5249517984486298,0.1664438714720046 +R12_FSU,2100,0.025173400101503218,0.19273680650750166,0.5165610627567063,0.1674094447256862 +R12_LAM,2030,0.18183406331400878,0.21549226342198027,0.1270850573108119,0.026589222428874126 +R12_LAM,2040,0.16379039616606936,0.3792167589267311,0.11249823481939139,0.029062108596703332 +R12_LAM,2050,0.12711244214848133,0.42178490518915224,0.10427160735900873,0.03154806129665 +R12_LAM,2060,0.11579102989755502,0.48159401104159394,0.1058958906408748,0.03505926461865093 +R12_LAM,2070,0.11037034570158022,0.5190311864392051,0.10327019659439483,0.03741668626035173 +R12_LAM,2080,0.11064170171567653,0.5422763576340488,0.10178731223705019,0.03947683565354484 +R12_LAM,2090,0.11122386960674834,0.5550076385393444,0.10088907994893201,0.04145844502253713 +R12_LAM,2100,0.1110902749338875,0.5627757827510109,0.10023022865382229,0.04348103346383889 +R12_MEA,2030,0.40354441616766895,0.16135980600596456,0.3065452658703124,0.11447278815528285 +R12_MEA,2040,0.3723414453865868,0.17717946483798547,0.2757570229370743,0.10464788907654045 +R12_MEA,2050,0.33958650993169553,0.1825730545486989,0.26064915975219083,0.09401405543556408 +R12_MEA,2060,0.32108980982389,0.1814725843608866,0.25374323969830254,0.08482822503965555 +R12_MEA,2070,0.30165835152862686,0.18260985654974304,0.23431656723822666,0.08147360211008953 +R12_MEA,2080,0.300280895946049,0.18947044124969795,0.22204464333011573,0.07942476284092914 +R12_MEA,2090,0.30860736945906936,0.20125359524128816,0.21490932733104287,0.08081334174227414 +R12_MEA,2100,0.3193966038214855,0.20803002554536512,0.20615273663318634,0.08422067393990045 +R12_NAM,2030,0.14415688585722736,0.20564820949835724,0.44125032557433885,0.2992713900862845 +R12_NAM,2040,0.15378489584276644,0.2133158045836947,0.4450931646859107,0.27923343290428526 +R12_NAM,2050,0.16120093704892308,0.2159177711798627,0.45558784521169465,0.25485924946720906 +R12_NAM,2060,0.1719130632070579,0.22563552308386836,0.471258644700221,0.23660168350797936 +R12_NAM,2070,0.18267671813406497,0.234614380884065,0.4924873861593574,0.22596726300581071 +R12_NAM,2080,0.19240368948369266,0.24278777265584744,0.5051321006645586,0.21549697254893724 +R12_NAM,2090,0.20096389105978654,0.25017096927792476,0.5183394617857503,0.20642186306427895 +R12_NAM,2100,0.2092478561433375,0.2576336707684629,0.5293674482583588,0.20150251823958384 +R12_PAO,2030,0.05788701533502868,0.28563320230809985,0.17961813538693774,0.21770845166716168 +R12_PAO,2040,0.06200595268103261,0.330602949726151,0.1967264163660013,0.21528433708208805 +R12_PAO,2050,0.06626095097032006,0.35704898617733427,0.19645245865282654,0.19528094844815977 +R12_PAO,2060,0.07185495948097927,0.38857365112976683,0.200406293813698,0.18149773894999607 +R12_PAO,2070,0.07522084200087713,0.407291254461543,0.2033338669892184,0.1677791562091122 +R12_PAO,2080,0.07647513615344259,0.41494585730055844,0.2077672426039225,0.15557958873790245 +R12_PAO,2090,0.07725021206436739,0.4205031204578553,0.21168036182596503,0.14510793498569646 +R12_PAO,2100,0.07834364982321586,0.4260517489723599,0.21450154803321791,0.13498415579558312 +R12_PAS,2030,0.06733131860945568,0.15417529335062108,0.24162273692206826,0.043374864967502424 +R12_PAS,2040,0.07869386500958848,0.20211606314838026,0.20628242584158968,0.037349930587500635 +R12_PAS,2050,0.07939386931969827,0.24355444202620513,0.1809639067261184,0.034474178027687444 +R12_PAS,2060,0.08912382958384439,0.29118183214477283,0.1716252152044122,0.03445958312964181 +R12_PAS,2070,0.0963849743104516,0.3221237624029403,0.16096104608072234,0.03348388006509461 +R12_PAS,2080,0.09238090474694322,0.3630495585305256,0.15236739745121644,0.03194757355700669 +R12_PAS,2090,0.09433743738823722,0.4021657067134932,0.14726362425855904,0.031238358688116655 +R12_PAS,2100,0.09483863373996934,0.43435648707288077,0.13989205872014754,0.03052011075176864 +R12_RCPA,2030,0.24832009708255098,0.04304418048581139,0.19342431969591184,0.07877350515079291 +R12_RCPA,2040,0.18702328028519066,0.05838722749007218,0.17668162917111085,0.0746706875644763 +R12_RCPA,2050,0.1792987661712408,0.10973603766262913,0.17422862934822056,0.07593992871361048 +R12_RCPA,2060,0.17383618383707236,0.17128098704042624,0.17137618142703168,0.07647484649464524 +R12_RCPA,2070,0.16520222074477017,0.22206377391607407,0.1612866877746822,0.07504605361108498 +R12_RCPA,2080,0.16296771645130068,0.25833911497776985,0.15197364797971377,0.07342238685711244 +R12_RCPA,2090,0.1640209142349459,0.2803958668824872,0.1586503096202383,0.08079972707686413 +R12_RCPA,2100,0.1655251787930966,0.2882774420121148,0.15860651399038247,0.08625505014977372 +R12_SAS,2030,0.17677805451799739,0.049340616083525086,0.15507546657338292,0.02517191641182628 +R12_SAS,2040,0.12099500779868126,0.1058831714471753,0.13095828329741377,0.020766052578971864 +R12_SAS,2050,0.10063774239059134,0.18185408255650265,0.12028544005606945,0.01860445461205252 +R12_SAS,2060,0.08507754308168852,0.2514425772909267,0.11222858421669762,0.01698578839464915 +R12_SAS,2070,0.07111143005845443,0.30373471930965273,0.10028230174549366,0.015528961331306636 +R12_SAS,2080,0.062441286398113305,0.349895811026439,0.0955484268450553,0.015260915582357324 +R12_SAS,2090,0.059688635452486184,0.38483710823847755,0.09403986823658309,0.015586828247237533 +R12_SAS,2100,0.05778894839806466,0.40133997399379356,0.09322778977427124,0.01603231722521857 +R12_WEU,2030,0.010100512906017224,0.09763161603302062,0.47770496961646797,0.20084006852783404 +R12_WEU,2040,0.015697627802888584,0.10480117867572644,0.45995412572625444,0.17916994221307955 +R12_WEU,2050,0.019812939988384945,0.10381548275591618,0.4498284589922675,0.16432289754663504 +R12_WEU,2060,0.0241726657944251,0.10146655054421995,0.4536477046338154,0.15189148061907587 +R12_WEU,2070,0.026880730907688975,0.09104256163429887,0.4508981206233027,0.13634720774914658 +R12_WEU,2080,0.029914960061113518,0.08353551104486728,0.45444258048388486,0.12129529967414791 +R12_WEU,2090,0.033043615107155346,0.07730547599744263,0.46029975822833374,0.10854612288851945 +R12_WEU,2100,0.03631781802512613,0.07259609086263061,0.47509950463931866,0.09953085112117704 diff --git a/message_ix_models/data/buildings/sturm_floor_area_R12_comm.csv b/message_ix_models/data/buildings/sturm_floor_area_R12_comm.csv new file mode 100644 index 0000000000..2150de7083 --- /dev/null +++ b/message_ix_models/data/buildings/sturm_floor_area_R12_comm.csv @@ -0,0 +1,2861 @@ +region,year,arch,urt,floor_Mm2 +AFR,2020,comm_s1,rural,126.09207633389 +AFR,2020,comm_s1,urban,18.58502678997 +AFR,2020,comm_s2,rural,266.56128852236 +AFR,2020,comm_s2,urban,135.23792245146 +AFR,2020,comm_s3,rural,373.76917935107997 +AFR,2020,comm_s3,urban,356.87956377434 +AFR,2020,comm_s51_std,rural,173.73642168595 +AFR,2020,comm_s51_std,urban,184.08923443193999 +AFR,2020,comm_sr11_std,rural,3.23313016243 +AFR,2020,comm_sr11_std,urban,0.47653914851 +AFR,2020,comm_sr21_std,rural,6.83490483397 +AFR,2020,comm_sr21_std,urban,3.4676390372 +AFR,2020,comm_sr31_std,rural,9.58382511156 +AFR,2020,comm_sr31_std,urban,9.15075804537 +AFR,2025,comm_s1,rural,117.42941546725 +AFR,2025,comm_s1,urban,17.308215518689998 +AFR,2025,comm_s2,rural,244.31389116276 +AFR,2025,comm_s2,urban,123.94161441517 +AFR,2025,comm_s3,rural,351.87501866237 +AFR,2025,comm_s3,urban,336.81602010563 +AFR,2025,comm_s51_std,rural,384.06000333682 +AFR,2025,comm_s51_std,urban,426.0844347327701 +AFR,2025,comm_sr11_std,rural,6.0992267074 +AFR,2025,comm_sr11_std,urban,0.8989802932600001 +AFR,2025,comm_sr21_std,rural,12.68954464296 +AFR,2025,comm_sr21_std,urban,6.43746715241 +AFR,2025,comm_sr31_std,rural,18.27621727423 +AFR,2025,comm_sr31_std,urban,17.494060215650002 +AFR,2030,comm_s1,rural,109.90598373415 +AFR,2030,comm_s1,urban,16.19931808137 +AFR,2030,comm_s2,rural,223.90213781626 +AFR,2030,comm_s2,urban,113.50130218256001 +AFR,2030,comm_s3,rural,328.52318234802 +AFR,2030,comm_s3,urban,315.14476204618 +AFR,2030,comm_s51_std,rural,636.7433537435301 +AFR,2030,comm_s51_std,urban,742.56571316621 +AFR,2030,comm_sr11_std,rural,8.67293632494 +AFR,2030,comm_sr11_std,urban,1.27832579683 +AFR,2030,comm_sr21_std,rural,17.66863748746 +AFR,2030,comm_sr21_std,urban,8.95665125028 +AFR,2030,comm_sr31_std,rural,25.92452699076 +AFR,2030,comm_sr31_std,urban,24.868804786510005 +AFR,2035,comm_s1,rural,103.37329140198999 +AFR,2035,comm_s1,urban,15.236448204610001 +AFR,2035,comm_s2,rural,205.43630743228002 +AFR,2035,comm_s2,urban,104.00149048602 +AFR,2035,comm_s3,rural,304.56983032862 +AFR,2035,comm_s3,urban,292.68633614592 +AFR,2035,comm_s51_std,rural,937.34244329428 +AFR,2035,comm_s51_std,urban,1153.21019755893 +AFR,2035,comm_sr11_std,rural,11.01718840238 +AFR,2035,comm_sr11_std,urban,1.6238509791300002 +AFR,2035,comm_sr21_std,rural,21.89473192703 +AFR,2035,comm_sr21_std,urban,11.084139812819998 +AFR,2035,comm_sr31_std,rural,32.46005962363 +AFR,2035,comm_sr31_std,urban,31.193555553610004 +AFR,2040,comm_s1,rural,97.69313767584 +AFR,2040,comm_s1,urban,14.39923612724 +AFR,2040,comm_s2,rural,188.92321531285 +AFR,2040,comm_s2,urban,95.4682632 +AFR,2040,comm_s3,rural,280.78167014317 +AFR,2040,comm_s3,urban,270.19218132951 +AFR,2040,comm_s51_std,rural,1310.83638854226 +AFR,2040,comm_s51_std,urban,1702.56566140324 +AFR,2040,comm_sr11_std,rural,13.18373802804 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index 0000000000..16bb47e948 --- /dev/null +++ b/message_ix_models/model/buildings/impacts.py @@ -0,0 +1,546 @@ +"""Building energy CID: replace fixed-EI rc_spec/rc_therm with RIME EI. + +Replacement demand ``E(t,r) = theta(r,t) * gamma(r,a) * EI(r,a, GSAT(t)) * +F(r,a,t)``, where theta bridges the calibrated STURM baseline to the +RIME-emulated EI at the reference GWL and gamma/F are the STURM correction +coefficients and floor area. +""" + +import logging +from typing import Literal + +import numpy as np +import pandas as pd +import xarray as xr +from iam_units import registry +from message_ix import Scenario + +from message_ix_models.tools.iamc import frame_to_iamc +from message_ix_models.tools.impacts import ( + GmtArray, + clip_gmt, + open_rime_dataset, + predict_rime, +) +from message_ix_models.tools.impacts.temporal import sample_to_model_years +from message_ix_models.util import package_data_path + +log = logging.getLogger(__name__) + +# MJ/m2 * Mm2 = MJ * 1e6 = TJ; TJ / 1e6 = EJ +_MJ_MM2_TO_EJ = 1e-6 + +# MESSAGE demand parameter expects GWa +_EJ_TO_GWA = registry("1 EJ").to("GW * year").magnitude + +_REFERENCE_SCENARIO = "SSP2" +_CORRECTION_COEFFICIENT_SCENARIO = "SSP2" +_FINAL_ENERGY_UNIT = "EJ/yr" + + +# --------------------------------------------------------------------------- +# Data loading +# --------------------------------------------------------------------------- + + +def _buildings_data_path(*parts: str): + return package_data_path("buildings", *parts) + + +def _load_ei_dataset(mode: Literal["cool", "heat"]) -> xr.Dataset: + return open_rime_dataset(f"region_EI_{mode}_gwl_binned.nc") + + +def load_correction_coefficients( + mode: Literal["cool", "heat"], + sector: Literal["resid", "comm"] = "resid", +) -> pd.DataFrame: + """Load gamma and F per (region, arch, urt, year).""" + path = _buildings_data_path( + "correction_coefficients", + "correction_coefficients_" + f"{mode}_{_CORRECTION_COEFFICIENT_SCENARIO}_{sector}.csv", + ) + return pd.read_csv(path, comment="#") + + +def load_sector_fractions( + reference_scenario: str = _REFERENCE_SCENARIO, +) -> pd.DataFrame: + """Load sector fractions of rc_spec/rc_therm per (node, year).""" + return pd.read_csv( + _buildings_data_path(f"rc_sector_fractions_{reference_scenario}.csv"), + comment="#", + ) + + +def load_floor_areas(sector: Literal["resid", "comm"] = "resid") -> pd.DataFrame: + """Load SSP2 STURM floor area projections.""" + return pd.read_csv(_buildings_data_path(f"sturm_floor_area_R12_{sector}.csv")) + + +def load_theta( + mode: Literal["cool", "heat"], + reference_scenario: str = _REFERENCE_SCENARIO, +) -> pd.DataFrame: + """Load theta(node, year) calibrated to the reference scenario demand.""" + path = _buildings_data_path(f"theta_{mode}_{reference_scenario}.csv") + return pd.read_csv(path, comment="#") + + +# --------------------------------------------------------------------------- +# EI prediction +# --------------------------------------------------------------------------- + + +def predict_building_ei( + gmt_array: np.ndarray, + mode: Literal["cool", "heat"], +) -> np.ndarray: + """Predict EI at native RIME resolution, mean-reduced across the ensemble. + + Parameters + ---------- + gmt_array + Shape ``(n_runs, n_years)`` for ensemble or ``(n_years,)`` for single + trajectory. + mode + ``"cool"`` or ``"heat"``. + + Returns + ------- + np.ndarray + Shape ``(12, 10, 3, n_years)`` — (region, arch, urt, year). + """ + gmt_clipped = clip_gmt(gmt_array, gmt_min=0.6, gmt_ceil=0.9) + return predict_rime( + gmt_clipped, f"region_EI_{mode}_gwl_binned.nc", f"EI_{mode}", aggregate="mean" + ) + + +def _ei_to_dataframe( + ei_all: np.ndarray, + ds: xr.Dataset, + msg_years: list[int], +) -> pd.DataFrame: + """Flatten EI prediction array to long DataFrame. + + Returns columns [region, arch, urt, year, ei]. + """ + regions = ds.region.values + archs = ds.arch.values + urts = ds.urt.values + idx = pd.MultiIndex.from_product( + [regions, archs, urts, msg_years], + names=["region", "arch", "urt", "year"], + ) + return pd.DataFrame({"ei": ei_all.ravel()}, index=idx).reset_index() + + +def _mfh_weighted_ei( + ei_df: pd.DataFrame, + resid_floor_df: pd.DataFrame, +) -> pd.DataFrame: + """Floor-weighted average of MFH archetype EI per (region, urt, year). + + Commercial buildings use residential MFH archetypes weighted by 2020 + floor area (STURM precedent). + + Returns columns [region, urt, year, ei]. + """ + # MFH floor weights at 2020 reference year + mfh_floors = resid_floor_df[ + (resid_floor_df["arch"].str.startswith("mfh_")) + & (resid_floor_df["year"] == 2020) + & (resid_floor_df["floor_Mm2"] > 0) + ][["region", "arch", "urt", "floor_Mm2"]] + + if mfh_floors.empty: + return pd.DataFrame(columns=["region", "urt", "year", "ei"]) + + # Join EI with floor weights + mfh_ei = ei_df[ei_df["arch"].str.startswith("mfh_")].merge( + mfh_floors, + on=["region", "arch", "urt"], + how="inner", + ) + mfh_ei["weighted_ei"] = mfh_ei["ei"] * mfh_ei["floor_Mm2"] + + # Weighted average per (region, urt, year) + grouped = mfh_ei.groupby(["region", "urt", "year"], as_index=False).agg( + weighted_sum=("weighted_ei", "sum"), + floor_sum=("floor_Mm2", "sum"), + ) + grouped["ei"] = grouped["weighted_sum"] / grouped["floor_sum"] + return grouped[["region", "urt", "year", "ei"]] + + +# --------------------------------------------------------------------------- +# Energy demand computation +# --------------------------------------------------------------------------- + + +def _compute_sector_energy( + mode: Literal["cool", "heat"], + sector: Literal["resid", "comm"], + ei_df: pd.DataFrame, + mfh_ei_df: pd.DataFrame, +) -> pd.DataFrame: + """Compute energy demand for one mode/sector via vectorized join. + + Returns DataFrame with columns [region, year, value] in EJ. + """ + coeff = load_correction_coefficients(mode, sector) + valid = coeff["correction_coeff"].notna() & (coeff["floor_Mm2"] > 0) + coeff = coeff[valid] + + if sector == "resid": + # Direct join on (region, arch, urt, year) + merged = coeff.merge(ei_df, on=["region", "arch", "urt", "year"], how="left") + else: + # Commercial: join MFH-weighted EI on (region, urt, year) + merged = coeff.merge(mfh_ei_df, on=["region", "urt", "year"], how="left") + + has_ei = merged["ei"].notna() & (merged["ei"] > 0) + n_total = len(merged) + n_computed = has_ei.sum() + log.info( + "%s/%s: %d/%d rows with valid EI (%.0f%%)", + mode, + sector, + n_computed, + n_total, + 100 * n_computed / n_total if n_total else 0, + ) + + merged = merged[has_ei] + merged["value"] = ( + merged["correction_coeff"] * merged["ei"] * merged["floor_Mm2"] * _MJ_MM2_TO_EJ + ) + return merged.groupby(["region", "year"], as_index=False)["value"].sum() + + +def _apply_theta( + demand: pd.DataFrame, + mode: Literal["cool", "heat"], + reference_scenario: str = _REFERENCE_SCENARIO, +) -> pd.DataFrame: + """Scale raw RIME demand by theta to match SSP-calibrated STURM levels. + + Theta = calibrated / raw at GWL 1.1; it carries the calibration shape + under warming while EI provides the climate response. Stored at source + resolution (decadal + 2025); interpolated to demand years. The theta + min year defines the CID validity range — years before it are excluded. + """ + theta = load_theta(mode, reference_scenario) + demand_years = sorted(demand["year"].unique()) + theta = sample_to_model_years( + theta, ["node"], demand_years, method="interpolate", value_cols=["theta"] + ) + + scaled = demand.merge(theta, on=["node", "year"], how="left") + + missing = scaled.loc[scaled["theta"].isna(), ["node", "year"]] + if not missing.empty: + pairs = missing.drop_duplicates().to_dict("records") + raise ValueError( + f"Missing theta values for {mode}/{reference_scenario}: {pairs}" + ) + + return scaled.assign(value=lambda df: df["value"] * df["theta"])[ + ["node", "year", "value"] + ] + + +def compute_building_cids( + gmt: GmtArray, + model_years: list[int], + reference_scenario: str = _REFERENCE_SCENARIO, +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Compute building energy CIDs from a GMT ensemble. + + Parameters + ---------- + gmt + GMT ensemble + year labels, as returned by :func:`load_magicc_gmt`. + ``.values`` shape ``(n_runs, n_years)`` for ensemble or + ``(n_years,)`` for single trajectory. + model_years + MESSAGE model years from ``ScenarioInfo.Y``. Only positions in + ``gmt.years`` that match these are used; 2110 is forward-filled + from 2100. + reference_scenario + SSP scenario for theta and sector fractions ('SSP2', 'SSP3'). + + Returns + ------- + tuple[pd.DataFrame, pd.DataFrame] + ``(cooling, heating)`` DataFrames with columns + ``[node, year, value]`` where value is GWa. Node uses R12_ prefix. + """ + values = np.asarray(gmt.values) + years = np.asarray(gmt.years, dtype=int) + + # CID year range: from theta coverage (excludes historical years) + # through model horizon, with 2110 forward-filled from 2100. + theta_min_year = min( + load_theta("cool", reference_scenario)["year"].min(), + load_theta("heat", reference_scenario)["year"].min(), + ) + target = [y for y in model_years if y != 2110 and y >= theta_min_year] + msg_mask = np.isin(years, target) + if not msg_mask.any(): + raise ValueError(f"No model years in input. Years: {years[0]}-{years[-1]}") + gmt_subset = values[:, msg_mask] if values.ndim == 2 else values[msg_mask] + msg_years = years[msg_mask].tolist() + + log.info( + "Computing building CIDs: %s, %d model years, GMT shape %s", + reference_scenario, + len(msg_years), + gmt_subset.shape, + ) + + # Pre-compute MFH-weighted EI for commercial (shared across cool/heat) + resid_floor = load_floor_areas("resid") + + results = {} + for mode in ("cool", "heat"): + ei_all = predict_building_ei(gmt_subset, mode) + ds = _load_ei_dataset(mode) + ei_df = _ei_to_dataframe(ei_all, ds, msg_years) + mfh_ei_df = _mfh_weighted_ei(ei_df, resid_floor) + + resid = _compute_sector_energy(mode, "resid", ei_df, mfh_ei_df) + comm = _compute_sector_energy(mode, "comm", ei_df, mfh_ei_df) + + total = ( + pd.concat([resid, comm], ignore_index=True) + .groupby(["region", "year"], as_index=False)["value"] + .sum() + .assign( + node=lambda df: "R12_" + df["region"], + value=lambda df: df["value"] * _EJ_TO_GWA, + )[["node", "year", "value"]] + ) + total = _apply_theta(total, mode, reference_scenario) + + # Forward-fill 2110 from 2100 if 2110 is a model year + if 2110 in model_years and 2100 in total["year"].values: + total = pd.concat( + [total, total[total["year"] == 2100].assign(year=2110)], + ignore_index=True, + ) + + results[mode] = total.sort_values(["node", "year"]).reset_index(drop=True) + + return results["cool"], results["heat"] + + +# --------------------------------------------------------------------------- +# Scenario modification +# --------------------------------------------------------------------------- + + +def _substitute_climate_component( + demand: pd.DataFrame, + fractions: pd.DataFrame, + frac_cols: list[str], + cid: pd.DataFrame, +) -> pd.DataFrame: + """Replace the calibrated climate-coupled portion of *demand* with *cid*. + + The calibrated baseline rc_spec/rc_therm carries a fixed climate-coupled + component sized by *frac_cols* (sector fractions of total demand at the + STURM-calibration GWL). This function strips out that fixed component + and adds back the RIME-driven replacement *cid* on (node, year). The + fixed component is what is subtracted; the RIME-driven CID is what + replaces it. + + Years missing from *fractions* are treated as 0 (no climate coupling + in the calibrated baseline). Years missing from *cid* contribute 0 + replacement demand. + """ + out_cols = list(demand.columns) + return ( + demand.merge( + fractions[["node", "year"] + frac_cols], on=["node", "year"], how="left" + ) + .merge( + cid[["node", "year", "value"]].rename(columns={"value": "_cid"}), + on=["node", "year"], + how="left", + ) + .assign( + _frac_total=lambda df: df[frac_cols].fillna(0).sum(axis=1), + value=lambda df: ( + df["value"] * (1 - df["_frac_total"]) + df["_cid"].fillna(0) + ), + )[out_cols] + ) + + +def prepare_building_demand( + rc_spec: pd.DataFrame, + rc_therm: pd.DataFrame, + cooling_cids: pd.DataFrame, + heating_cids: pd.DataFrame, + fractions: pd.DataFrame | None = None, + reference_scenario: str = _REFERENCE_SCENARIO, +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Replace the STURM-calibrated climate component of rc_spec/rc_therm. + + Strips out the fixed climate-coupled fraction baked into the calibrated + baseline demand (sized by sector fractions at the STURM calibration GWL) + and substitutes the RIME-driven replacement CID. Pure data operation — + no scenario I/O. + + Parameters + ---------- + rc_spec + Existing ``demand`` rows for commodity ``rc_spec``. + rc_therm + Existing ``demand`` rows for commodity ``rc_therm``. + cooling_cids + From ``compute_building_cids`` — columns [node, year, value] in GWa. + heating_cids + Same format, heating. + fractions + Sector fractions. If *None*, loaded from package data for + *reference_scenario*. + reference_scenario + SSP scenario for sector fractions ('SSP2', 'SSP3'). + + Returns + ------- + tuple[pd.DataFrame, pd.DataFrame] + ``(new_rc_spec, new_rc_therm)`` — replacement demand DataFrames + with the same columns as the inputs. + """ + if fractions is None: + fractions = load_sector_fractions(reference_scenario) + + demand_years = sorted( + set(rc_spec["year"].unique()) | set(rc_therm["year"].unique()) + ) + frac_cool_cols = ["frac_resid_cool", "frac_comm_cool"] + frac_heat_cols = ["frac_resid_heat", "frac_comm_heat"] + fractions = sample_to_model_years( + fractions, + ["node"], + demand_years, + method="interpolate", + value_cols=frac_cool_cols + frac_heat_cols, + ) + + return ( + _substitute_climate_component(rc_spec, fractions, frac_cool_cols, cooling_cids), + _substitute_climate_component( + rc_therm, fractions, frac_heat_cols, heating_cids + ), + ) + + +def _demand_to_final_energy_iamc( + demand: pd.DataFrame, + variable: str, +) -> pd.DataFrame: + """Convert CID demand from MESSAGE GWa to IAMC final-energy rows.""" + converted = demand.assign( + value=lambda df: ( + registry.Quantity(df["value"].to_numpy(), "GWa/year") + .to(_FINAL_ENERGY_UNIT) + .magnitude + ) + ) + return frame_to_iamc( + converted, + variable, + _FINAL_ENERGY_UNIT, + region_col="node", + ) + + +def apply_building_cids( + scen: Scenario, + cooling_demand: pd.DataFrame, + heating_demand: pd.DataFrame, + commit_message: str | None = None, + reference_scenario: str = _REFERENCE_SCENARIO, +) -> None: + """Write replacement building demands to *scen* in place. + + Reads rc_spec/rc_therm, delegates to :func:`prepare_building_demand` + for the buildings-component substitution, writes the merged demand + back, and persists the resolved CID demand as scenario timeseries + under ``Final Energy|Residential and Commercial|{Cooling,Heating}`` + in ``EJ/yr`` + so downstream reporting can retrieve it via standard + :meth:`scen.timeseries() ` calls. + + Parameters + ---------- + scen + MESSAGE scenario (must not be checked out). + cooling_demand + From :func:`compute_building_cids`. Columns + ``[node, year, value]``, value in GWa. + heating_demand + Same shape, heating. + commit_message + Parameter-write commit message. Default ``"Inject building CIDs"``. + reference_scenario + SSP scenario for sector fractions (``"SSP2"`` or ``"SSP3"``). + """ + demand = scen.par("demand") + + rc_spec = demand[demand["commodity"] == "rc_spec"].copy() + rc_therm = demand[demand["commodity"] == "rc_therm"].copy() + + if rc_spec.empty: + raise ValueError("No rc_spec rows in demand — buildings module not built?") + if rc_therm.empty: + raise ValueError("No rc_therm rows in demand — buildings module not built?") + + new_spec, new_therm = prepare_building_demand( + rc_spec, + rc_therm, + cooling_demand, + heating_demand, + reference_scenario=reference_scenario, + ) + + msg = commit_message or "Inject building CIDs" + with scen.transact(msg): + scen.remove_par("demand", demand[demand["commodity"] == "rc_spec"]) + scen.add_par("demand", new_spec) + scen.remove_par("demand", demand[demand["commodity"] == "rc_therm"]) + scen.add_par("demand", new_therm) + + ts_cool = _demand_to_final_energy_iamc( + cooling_demand, + "Final Energy|Residential and Commercial|Cooling", + ) + ts_heat = _demand_to_final_energy_iamc( + heating_demand, + "Final Energy|Residential and Commercial|Heating", + ) + ts = pd.concat([ts_cool, ts_heat], ignore_index=True) + if not ts.empty: + scen.check_out(timeseries_only=True) + try: + scen.add_timeseries(ts) + scen.commit( + "Persist buildings CID demand " + "(Final Energy|Residential and Commercial|{Cooling,Heating})" + ) + except BaseException: + scen.discard_changes() + raise + + scen.set_as_default() + log.info( + "Building CIDs applied and committed: %d demand rows, %d CID-input timeseries", + len(new_spec) + len(new_therm), + len(ts), + ) diff --git a/message_ix_models/tests/model/test_building_impacts.py b/message_ix_models/tests/model/test_building_impacts.py new file mode 100644 index 0000000000..61f434942f --- /dev/null +++ b/message_ix_models/tests/model/test_building_impacts.py @@ -0,0 +1,111 @@ +"""Tests for model.buildings.impacts — building energy CID integration.""" + +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from message_ix_models.model.buildings.impacts import ( + _demand_to_final_energy_iamc, + compute_building_cids, + load_sector_fractions, + predict_building_ei, +) +from message_ix_models.tools.impacts import GmtArray + +_RC_SPEC_BASELINE = Path("_staging/rc_spec_baseline.csv") +_RC_THERM_BASELINE = Path("_staging/rc_therm_baseline.csv") + +# --------------------------------------------------------------------------- +# predict_building_ei +# --------------------------------------------------------------------------- + + +def test_warming_increases_cooling_ei(): + gmt = np.array([1.0, 1.5, 2.0]) + ei_cool = predict_building_ei(gmt, "cool") + mean_per_year = np.nanmean(ei_cool, axis=(0, 1, 2)) + assert mean_per_year[2] > mean_per_year[0] + + +def test_warming_decreases_heating_ei(): + gmt = np.array([1.0, 1.5, 2.0]) + ei_heat = predict_building_ei(gmt, "heat") + mean_per_year = np.nanmean(ei_heat, axis=(0, 1, 2)) + assert mean_per_year[2] < mean_per_year[0] + + +# --------------------------------------------------------------------------- +# IAMC packaging helpers +# --------------------------------------------------------------------------- + + +def test_demand_to_final_energy_iamc_converts_gwa_to_ej_per_year(): + demand = pd.DataFrame( + { + "node": ["R12_AFR", "R12_AFR"], + "year": [2030, 2035], + "value": [1.0, 2.0], + } + ) + + result = _demand_to_final_energy_iamc( + demand, "Final Energy|Residential and Commercial|Cooling" + ) + + assert result["unit"].unique().tolist() == ["EJ/yr"] + assert result["variable"].unique().tolist() == [ + "Final Energy|Residential and Commercial|Cooling" + ] + np.testing.assert_allclose(result["value"], [0.0315576, 0.0631152]) + + +# --------------------------------------------------------------------------- +# compute_building_cids end-to-end (requires staged baselines) +# --------------------------------------------------------------------------- + + +@pytest.mark.skipif( + not (_RC_SPEC_BASELINE.exists() and _RC_THERM_BASELINE.exists()), + reason=( + "Requires SSP2 baseline rc_spec/rc_therm extracts at " + "_staging/rc_spec_baseline.csv and _staging/rc_therm_baseline.csv; " + "produced by SPARRCLE staging tooling, not packaged in the repo." + ), +) +def test_theta_reproduces_calibrated_demand_at_gwl_1_1(): + """At GWL 1.1 (present-day), CID output must match beta * rc.""" + years = np.arange(2020, 2101) + gmt = GmtArray(values=np.full(len(years), 1.1), years=years) + + cooling, heating = compute_building_cids( + gmt, + [2030, 2035, 2040, 2045, 2050, 2055, 2060, 2070, 2080, 2090, 2100, 2110], + reference_scenario="SSP2", + ) + + fractions = load_sector_fractions("SSP2") + rc_spec = pd.read_csv(_RC_SPEC_BASELINE)[["node", "year", "value"]] + rc_therm = pd.read_csv(_RC_THERM_BASELINE)[["node", "year", "value"]] + + for cid_df, scenario_df, frac_cols in ( + (cooling, rc_spec, ["frac_resid_cool", "frac_comm_cool"]), + (heating, rc_therm, ["frac_resid_heat", "frac_comm_heat"]), + ): + calibrated = scenario_df.merge( + fractions[["node", "year"] + frac_cols], + on=["node", "year"], + how="inner", + ) + calibrated = calibrated.assign( + calibrated=lambda df: df["value"] * df[frac_cols].sum(axis=1) + )[["node", "year", "calibrated"]] + + merged = cid_df.merge(calibrated, on=["node", "year"], how="inner") + np.testing.assert_allclose( + merged["value"], + merged["calibrated"], + atol=1e-10, + rtol=0, + ) From 91215ad38e7989bd9f601c270ceec61118594e9e Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Fri, 1 May 2026 09:24:57 +0200 Subject: [PATCH 03/19] Add wet + dry cooling physical-impact module build_wet_cooling_constraints emits relation_activity rows that bound freshwater-using thermoelectric variants by per-region capacity-factor ratios under warming (Li et al. 2025); build_dry_cooling_factors rescales air-cooled capacity factors (Qin et al. 2023). Wet relations named wet_cooling_cf_{parent}; CID timeseries persist as Physical Climate Impact|Thermoelectric Cooling|{Freshwater,Dry}*. Packaged: r12_thermoelectric_gwl.nc, r12_capacity_gwl_ensemble.nc. --- .../impacts/rime/r12_capacity_gwl_ensemble.nc | Bin 0 -> 28576 bytes .../impacts/rime/r12_thermoelectric_gwl.nc | Bin 0 -> 97695 bytes .../model/water/data/cooling_impacts.py | 686 ++++++++++++++++++ .../model/water/data/test_cooling_impacts.py | 181 +++++ 4 files changed, 867 insertions(+) create mode 100644 message_ix_models/data/impacts/rime/r12_capacity_gwl_ensemble.nc create mode 100644 message_ix_models/data/impacts/rime/r12_thermoelectric_gwl.nc create mode 100644 message_ix_models/model/water/data/cooling_impacts.py create mode 100644 message_ix_models/tests/model/water/data/test_cooling_impacts.py diff --git a/message_ix_models/data/impacts/rime/r12_capacity_gwl_ensemble.nc b/message_ix_models/data/impacts/rime/r12_capacity_gwl_ensemble.nc new file mode 100644 index 0000000000000000000000000000000000000000..88fd8ec7e0737b3353eb22db9a9f9c6bd9552ab1 GIT binary patch literal 28576 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z(iiDbjngZQ8#JzHaplAlrE!MOQ#oCS&i@;waffK@_#^oJqxzW6r$Uq0o_kvvh7r+RIP=a-W_QMwPG(s4fJ zzh_H4JtrL}eUTp3IK9%iLF0-RS57=p8fW-CmD6?T{J&8ecZjx*KZ4&ss&8sHM5&xl z`R#^yz8vw?9{xsK+fQ?SsB-;*`VI9XKIQ9^uRr3ceu$^~K$rB}cs@IlyemTh# zrTg$H9p_X2d$z>WbJB6r7wJ)r(<_Y|G_GiI<-`-EafZ)RIbDa&{~M)ohiL2gBl!KJ z`lfb6l*;*(-)@NK%MnlQ;cv9H{WRByD%T&V-%vl|Q@%d=`Xip|hj^-AKIO}iJW-OT zdToj4myXZSpo({19P!j1{zhBdPjh{!a{Yn&4fP{FMlFXzTbR`2C~$rglS=%K4PvZiwg05l`*mZ?v`jG}nhJ*B_|gP(R{RzCQW- zBcAGqc&cAM<;#&gQIe;6ZHecXlRQzn51-O;KIOk>OFTU%9VdN}9@RL#(zrq6iWXN+ zJW(2F_&k-vz>yxiP;;DX!r~2hnz8uLDC3&jXmUw;FhtB^SrE!O7>-Z!1{iFJ(c0-iP`IO&oi08`@PwnAvw6*;- z*M}({s{s(iiDbjngZQ8#JzHaplAlrE!MOQ#oCS&i@;waffK@_#^oJqxzW6r$Uq0o_kvvh7r+RIP=a-W_ zQMwPG(s4fJzh_H4JtrL}eUTp3IK9%iLF0-RS57=p8fW-CmD6?T{J&8ecZjx*KZ4&s zs&8sHM5&xl`R#^yzFa-~jn9^9zRMJl|ISb5@mqNOW*)zZ$FJw{8+iOi9*->D|I7W? z@pyOe68QU_#~Z{0fB*e?J$bwrc={bZ^3U%&Yo=*z=u+ z&A8_06d%`oUkOf5f|F~@$w_i@?Kn9pPELxGYtPAb;N&`Ta?+ffG$+@Ilk3dM$#8OA zxbiNXd>8I`SMI#7oLo0ft{W%Ujgyn* list[str]: + """Short region codes from the capacity factor dataset.""" + return list(open_rime_dataset(_DATASET).region.values) + + +@functools.lru_cache(maxsize=1) +def _freshwater_reference_shares() -> pd.Series: + """Regional average freshwater share (cl_fresh + ot_fresh). + + Returns Series indexed by short region code (e.g. "AFR"). + """ + path = package_data_path( + "water", "ppl_cooling_tech", "cooltech_cost_and_shares_ssp_msg_R12.csv" + ) + df = pd.read_csv(path) + mix_cols = [c for c in df.columns if c.startswith("mix_")] + + fresh = df[df["cooling"].isin(["cl_fresh", "ot_fresh"])] + # Sum cl_fresh + ot_fresh shares per region, averaged across parent techs + regional_fresh = fresh.groupby("cooling")[mix_cols].mean().sum() + + # Convert column names: "mix_R12_AFR" -> "AFR" + return regional_fresh.rename(index=lambda c: c.replace("mix_R12_", "")) + + +# --------------------------------------------------------------------------- +# Prediction +# --------------------------------------------------------------------------- + + +def predict_cooling_cf( + gmt_array: np.ndarray, + cooling: str = "wet", +) -> pd.DataFrame: + """Predict regional capacity factors from GMT, mean-reduced. + + Parameters + ---------- + gmt_array + GMT values in degC above pre-industrial. Shape ``(n_years,)`` or + ``(n_runs, n_years)`` for ensemble. + cooling + ``"wet"`` (freshwater) or ``"dry"`` (air). + + Returns + ------- + pd.DataFrame + Wide DataFrame with ``region`` index (short codes) and one column + per GMT input position. Values are capacity factors (fractions). + """ + sel = _WET_SEL if cooling == "wet" else _DRY_SEL + gmt_array = np.asarray(gmt_array) + gmt_clipped = clip_gmt(gmt_array, gmt_min=0.6, gmt_ceil=0.9) + + raw = predict_rime(gmt_clipped, _DATASET, _VAR, sel=sel, aggregate="mean") + # raw shape: (12, n_years) — regions x time positions + + regions = _region_codes() + return pd.DataFrame(raw, index=pd.Index(regions, name="region")) + + +def compute_degradation_ratios( + gmt_array: np.ndarray, + years: list[int], + cooling: str = "wet", + baseline_gwl: float = _DEFAULT_BASELINE_GWL, +) -> pd.DataFrame: + """Compute degradation ratios: CF(GMT) / CF(baseline). + + Parameters + ---------- + gmt_array + GMT trajectory. Shape ``(n_years,)`` or ``(n_runs, n_years)``. + years + Year labels for the time axis. Length must match ``n_years``. + cooling + ``"wet"`` (freshwater) or ``"dry"`` (air). + baseline_gwl + Reference warming level (degC). Default 1.0. + + Returns + ------- + pd.DataFrame + Rows = R12 region short codes, columns = *years*. Values are ratios + relative to baseline — below 1 under warming, above 1 if GMT + dips below baseline. + """ + sel = _WET_SEL if cooling == "wet" else _DRY_SEL + cf = predict_cooling_cf(gmt_array, cooling=cooling) + + cf_baseline = predict_rime( + np.array([baseline_gwl]), _DATASET, _VAR, sel=sel, aggregate="mean" + )[:, 0] # (12,) + + ratios = cf.values / cf_baseline[:, np.newaxis] + return pd.DataFrame(ratios, index=cf.index.copy(), columns=years) + + +# --------------------------------------------------------------------------- +# Constraint building +# --------------------------------------------------------------------------- + + +def _read_cooling_structure( + addon_df: pd.DataFrame, + technologies: set[str], +) -> pd.DataFrame: + """Extract freshwater cooling techs and their parent-tech cooling fractions. + + Parameters + ---------- + addon_df + The ``addon_conversion`` parameter DataFrame from a MESSAGE scenario. + technologies + Set of technology names present in the scenario. + + Returns + ------- + pd.DataFrame + Columns: parent_tech, cl_fresh_tech, ot_fresh_tech, node_loc, + cooling_fraction. One row per (parent_tech, node_loc). + """ + if addon_df.empty: + raise ValueError("addon_conversion is empty — cooling module not built?") + + # addon_conversion has type_addon = "cooling__", value = cooling_fraction + cooling_addon = addon_df[addon_df["type_addon"].str.startswith("cooling__")].copy() + cooling_addon["parent_tech"] = cooling_addon["type_addon"].str.replace( + "cooling__", "", n=1 + ) + + # Deduplicate: one cooling_fraction per (parent_tech, node_loc) + # Take mean across vintage/year combinations — cooling_fraction is + # physically constant for a given parent tech + grouped = ( + cooling_addon.groupby(["parent_tech", "node"])["value"] + .mean() + .reset_index() + .rename(columns={"node": "node_loc", "value": "cooling_fraction"}) + ) + + rows = [] + for _, row in grouped.iterrows(): + parent = row["parent_tech"] + cl = f"{parent}__cl_fresh" + ot = f"{parent}__ot_fresh" + if cl in technologies or ot in technologies: + rows.append( + { + "parent_tech": parent, + "cl_fresh_tech": cl if cl in technologies else None, + "ot_fresh_tech": ot if ot in technologies else None, + "node_loc": row["node_loc"], + "cooling_fraction": row["cooling_fraction"], + } + ) + + if not rows: + raise ValueError("No freshwater cooling technologies found") + + return pd.DataFrame(rows) + + +def _max_vintage_from_lifetime( + tl: pd.DataFrame, +) -> dict[tuple[str, str], int]: + """Last vintage year with ``technical_lifetime`` defined, per (node, tech). + + GAMS evaluates ``relation_activity`` by iterating all model years + ``<= year_act`` as candidate vintages. If a candidate vintage has no + ``technical_lifetime`` row, compilation fails ("Technical lifetime not + defined for node|tech|year"). The safe bound for ``year_act`` is + therefore ``max(year_vtg)`` where ``technical_lifetime`` is defined — + any ``year_act`` beyond that will cause GAMS to probe a vintage year + with no lifetime entry. + """ + return { + (node, tech): int(grp["year_vtg"].max()) + for (node, tech), grp in tl.groupby(["node_loc", "technology"]) + } + + +_RELATION_PREFIX = "wet_cooling_cf_" + + +def _emit_constraint_rows( + *, + rel_name: str, + node: str, + year: int, + parent_tech: str, + cl_fresh: str | None, + ot_fresh: str | None, + parent_coeff: float, +) -> tuple[list[dict], dict]: + """Build the (relation_activity rows, relation_upper row) for one cell. + + The constraint shape is:: + + sum(freshwater variants) - r * share * f_cool * parent <= 0 + + so the parent enters with a negative coefficient (passed in + pre-multiplied as *parent_coeff*) and each freshwater variant tech + enters with +1. + """ + rel_act = [ + { + "relation": rel_name, + "node_rel": node, + "year_rel": year, + "node_loc": node, + "technology": parent_tech, + "year_act": year, + "mode": "M1", + "value": parent_coeff, + "unit": "-", + } + ] + for tech in (cl_fresh, ot_fresh): + if tech is not None: + rel_act.append( + { + "relation": rel_name, + "node_rel": node, + "year_rel": year, + "node_loc": node, + "technology": tech, + "year_act": year, + "mode": "M1", + "value": 1.0, + "unit": "-", + } + ) + rel_up = { + "relation": rel_name, + "node_rel": node, + "year_rel": year, + "value": 0.0, + "unit": "-", + } + return rel_act, rel_up + + +def _emit_per_region_year( + *, + row, + rel_name: str, + parent_tech: str, + constrained_years: list[int], + wet_cf_ratios: pd.DataFrame, + s_ref: pd.Series, + max_vtg: dict[tuple[str, str], int] | None, +) -> tuple[list[dict], list[dict], int]: + """Emit relation rows for one (parent_tech, region) entry across years.""" + node = row["node_loc"] + region_short = extract_region_code(node) + f_cool = row["cooling_fraction"] + + if region_short not in s_ref.index: + log.warning("No freshwater share for region %s, skipping", region_short) + return [], [], 0 + share = float(s_ref[region_short]) + + vtg_cap = max_vtg.get((node, parent_tech)) if max_vtg is not None else None + + rel_act_rows: list[dict] = [] + rel_up_rows: list[dict] = [] + n_skipped = 0 + for year in constrained_years: + if vtg_cap is not None and year > vtg_cap: + n_skipped += 1 + continue + if region_short not in wet_cf_ratios.index: + continue + if year not in wet_cf_ratios.columns: + log.warning("Year %d not in wet_cf_ratios columns, skipping", year) + continue + r = float(wet_cf_ratios.loc[region_short, year]) + parent_coeff = -(r * share * f_cool) + act, up = _emit_constraint_rows( + rel_name=rel_name, + node=node, + year=year, + parent_tech=parent_tech, + cl_fresh=row["cl_fresh_tech"], + ot_fresh=row["ot_fresh_tech"], + parent_coeff=parent_coeff, + ) + rel_act_rows.extend(act) + rel_up_rows.append(up) + return rel_act_rows, rel_up_rows, n_skipped + + +def build_wet_cooling_constraints( + addon_df: pd.DataFrame, + technologies: set[str], + wet_cf_ratios: pd.DataFrame, + model_years: list[int] | None = None, + min_year: int = _DEFAULT_MIN_YEAR, + technical_lifetime: pd.DataFrame | None = None, +) -> dict: + """Build wet-cooling ``relation_activity`` and ``relation_upper`` rows. + + For each (parent power technology, region, year) where the parent has + a freshwater cooling addon, emit one relation that bounds total + freshwater-cooled activity below the warming-impaired capacity: + ``cl_fresh + ot_fresh ≤ r * share * f_cool * parent``. + + Parameters + ---------- + addon_df + The ``addon_conversion`` parameter DataFrame from a MESSAGE scenario. + technologies + Set of technology names present in the scenario. + wet_cf_ratios + Output of :func:`compute_degradation_ratios` with ``cooling="wet"``. + Rows = regions (short codes), columns = model years; values are + capacity-factor ratios relative to the baseline GWL. + model_years + Which years to constrain. If *None*, uses *wet_cf_ratios* columns. + min_year + Earliest year for constraints. Default 2045. + technical_lifetime + The ``technical_lifetime`` parameter DataFrame. When provided, + ``relation_activity`` rows are only emitted for ``year_act`` values + where the parent tech has active vintage capacity. Without this, + phase-out techs (e.g. ``coal_ppl_u``) produce GAMS errors: + "Technical lifetime not defined for node|tech|year". + + Returns + ------- + dict + ``"relation_activity"``: DataFrame of relation coefficients. + ``"relation_upper"``: DataFrame of upper bounds (all zero). + ``"relation_names"``: list of relation name strings to add to + the ``relation`` set. + """ + structure = _read_cooling_structure(addon_df, technologies) + s_ref = _freshwater_reference_shares() + + if model_years is None: + model_years = [int(c) for c in wet_cf_ratios.columns] + constrained_years = [y for y in model_years if y >= min_year] + + if not constrained_years: + log.warning("No model years >= %d; returning empty constraints", min_year) + return { + "relation_activity": pd.DataFrame(), + "relation_upper": pd.DataFrame(), + "relation_names": [], + } + + max_vtg = ( + _max_vintage_from_lifetime(technical_lifetime) + if technical_lifetime is not None + else None + ) + + rel_act_rows: list[dict] = [] + rel_up_rows: list[dict] = [] + relation_names: set[str] = set() + n_skipped = 0 + + for parent_tech, group in structure.groupby("parent_tech"): + rel_name = f"{_RELATION_PREFIX}{parent_tech}" + relation_names.add(rel_name) + for _, row in group.iterrows(): + act, up, skipped = _emit_per_region_year( + row=row, + rel_name=rel_name, + parent_tech=parent_tech, + constrained_years=constrained_years, + wet_cf_ratios=wet_cf_ratios, + s_ref=s_ref, + max_vtg=max_vtg, + ) + rel_act_rows.extend(act) + rel_up_rows.extend(up) + n_skipped += skipped + + rel_act = pd.DataFrame(rel_act_rows) + rel_up = pd.DataFrame(rel_up_rows) + + log.info( + "Built wet-cooling constraints: %d relations, %d relation_activity entries, " + "%d skipped (no active vintage), years %d-%d", + len(relation_names), + len(rel_act), + n_skipped, + min(constrained_years), + max(constrained_years), + ) + + return { + "relation_activity": rel_act, + "relation_upper": rel_up, + "relation_names": sorted(relation_names), + } + + +# --------------------------------------------------------------------------- +# Dry cooling: capacity_factor replacement +# --------------------------------------------------------------------------- + + +def build_dry_cooling_factors( + cf_air: pd.DataFrame, + dry_ratios: pd.DataFrame, + model_years: list[int], + min_year: int = _DEFAULT_MIN_YEAR, +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Build replacement ``capacity_factor`` rows for ``__air`` technologies. + + Parameters + ---------- + cf_air + Existing ``capacity_factor`` rows for ``__air`` technologies, + as returned by ``scenario.par("capacity_factor", ...)``. + dry_ratios + Output of :func:`compute_degradation_ratios` with ``cooling="dry"``. + Rows = R12 short codes, columns = model years. + model_years + Which years the ratios cover. + min_year + Earliest year for modification. Default 2045. + + Returns + ------- + old_cf + Rows that will be removed (affected years only). + new_cf + Replacement rows with values scaled by dry degradation ratio. + """ + if cf_air.empty: + log.warning("No __air capacity_factor rows provided") + return pd.DataFrame(), pd.DataFrame() + + constrained_years = [y for y in model_years if y >= min_year] + if not constrained_years: + log.warning("No model years >= %d for dry cooling", min_year) + return pd.DataFrame(), pd.DataFrame() + + # Filter to affected years + cf_affected = cf_air[cf_air["year_act"].isin(constrained_years)].copy() + if cf_affected.empty: + log.warning("No __air capacity_factor rows for years %s", constrained_years) + return pd.DataFrame(), pd.DataFrame() + + old_cf = cf_affected.copy() + + # Apply dry degradation ratio per (node, year) + new_cf = cf_affected.copy() + for idx, row in new_cf.iterrows(): + region_short = extract_region_code(row["node_loc"]) + year = row["year_act"] + if region_short in dry_ratios.index and year in dry_ratios.columns: + ratio = float(dry_ratios.loc[region_short, year]) + new_cf.at[idx, "value"] = row["value"] * ratio + + n_modified = len(new_cf) + log.info( + "Built dry cooling factors: %d rows, years %d-%d", + n_modified, + min(constrained_years), + max(constrained_years), + ) + + return old_cf, new_cf + + +# --------------------------------------------------------------------------- +# Scenario application +# --------------------------------------------------------------------------- + + +def _ratios_to_long(ratios: pd.DataFrame) -> pd.DataFrame: + """Wide (region x year) ratios → long (region, year, value), R12-prefixed. + + Region values come from the RIME dataset as short codes (``"AFR"``); + they are reprefixed with ``"R12_"`` to match scenario node labels. + """ + long = ratios.reset_index().melt( + id_vars="region", var_name="year", value_name="value" + ) + long["region"] = "R12_" + long["region"].astype(str) + return long + + +def apply_cooling_cids( + scen, + gmt: GmtArray, + min_year: int = _DEFAULT_MIN_YEAR, + commit_message: str | None = None, +) -> None: + """Apply wet + dry cooling CIDs to *scen* in place. + + Wet effects bound freshwater cooling activity via capacity-factor + ratios written as ``relation_activity`` rows. Dry effects derate + ``capacity_factor`` on ``__air`` technologies. The underlying + wet-cooling activity-limit ratios and dry-cooling capacity-factor ratios + are also persisted as scenario timeseries so downstream reporting can + read them without re-running RIME. + + Parameters + ---------- + scen + MESSAGE scenario (must not be checked out). + gmt + GMT ensemble + year labels, as returned by :func:`load_magicc_gmt`. + min_year + Earliest year touched by the CID. Default 2045. + commit_message + Commit message for the parameter writes. Default + ``"Apply cooling CIDs (wet+dry)"``. + """ + from message_ix_models.util import ScenarioInfo + + info = ScenarioInfo(scen) + model_years = info.Y + + year_mask = np.isin(gmt.years, model_years) + values = gmt.values + gmt_model = values[:, year_mask] if values.ndim == 2 else values[year_mask] + matched_years = gmt.years[year_mask].tolist() + + log.info("Computing wet cooling degradation ratios...") + wet_ratios = compute_degradation_ratios( + gmt_model, + cooling="wet", + years=matched_years, + ) + + log.info("Computing dry cooling degradation ratios...") + dry_ratios = compute_degradation_ratios( + gmt_model, + cooling="dry", + years=matched_years, + ) + + addon_df = scen.par("addon_conversion") + technologies = set(scen.set("technology").tolist()) + tl = scen.par("technical_lifetime") + constraints = build_wet_cooling_constraints( + addon_df, + technologies, + wet_ratios, + model_years=matched_years, + min_year=min_year, + technical_lifetime=tl, + ) + rel_act = constraints["relation_activity"] + rel_up = constraints["relation_upper"] + rel_names = constraints["relation_names"] + + cf_all = scen.par("capacity_factor") + cf_air = cf_all[cf_all["technology"].str.endswith("__air")].copy() + old_cf, new_cf = build_dry_cooling_factors( + cf_air, + dry_ratios, + model_years=matched_years, + min_year=min_year, + ) + + has_wet = not rel_act.empty + has_dry = not old_cf.empty + + if not has_wet and not has_dry: + log.info("No cooling constraints needed") + return + + msg = commit_message or "Apply cooling CIDs (wet+dry)" + with scen.transact(msg): + if has_wet: + for name in rel_names: + scen.add_set("relation", name) + scen.add_par("relation_activity", rel_act) + scen.add_par("relation_upper", rel_up) + if has_dry: + scen.remove_par("capacity_factor", old_cf) + scen.add_par("capacity_factor", new_cf) + + # Persist the resolved cooling CID inputs as scenario timeseries. Wet and + # dry are named by the MESSAGE-side control they feed, not only by the + # RIME capacity-factor source variable. + constrained = [y for y in matched_years if y >= min_year] + ts_wet = frame_to_iamc( + _ratios_to_long(wet_ratios.loc[:, constrained]), + _WET_TIMESERIES_VARIABLE, + "dimensionless", + ) + ts_dry = frame_to_iamc( + _ratios_to_long(dry_ratios.loc[:, constrained]), + _DRY_TIMESERIES_VARIABLE, + "dimensionless", + ) + ts = pd.concat([ts_wet, ts_dry], ignore_index=True) + if not ts.empty: + scen.check_out(timeseries_only=True) + try: + scen.add_timeseries(ts) + scen.commit( + "Persist cooling CID inputs " + "(Physical Climate Impact|Thermoelectric Cooling|*)" + ) + except BaseException: + scen.discard_changes() + raise + + log.info( + "Cooling CIDs applied: %d wet relations, %d wet constraints, " + "%d dry capacity_factor rows, %d CID-input timeseries", + len(rel_names) if has_wet else 0, + len(rel_act) if has_wet else 0, + len(new_cf) if has_dry else 0, + len(ts), + ) diff --git a/message_ix_models/tests/model/water/data/test_cooling_impacts.py b/message_ix_models/tests/model/water/data/test_cooling_impacts.py new file mode 100644 index 0000000000..a7fa2f481b --- /dev/null +++ b/message_ix_models/tests/model/water/data/test_cooling_impacts.py @@ -0,0 +1,181 @@ +"""Tests for model.water.data.cooling_impacts -- wet-cooling constraints.""" + +import numpy as np +import pandas as pd +import pytest + +from message_ix_models.model.water.data.cooling_impacts import ( + build_dry_cooling_factors, + build_wet_cooling_constraints, + compute_degradation_ratios, +) +from message_ix_models.tools.impacts import impacts_data_path + +_RIME_DIR = impacts_data_path("rime") +_HAS_RIME_DATA = (_RIME_DIR / "r12_capacity_gwl_ensemble.nc").exists() + +skip_no_rime = pytest.mark.skipif( + not _HAS_RIME_DATA, reason="RIME cooling NetCDF not available" +) + + +# --------------------------------------------------------------------------- +# compute_degradation_ratios — needs RIME data on disk +# --------------------------------------------------------------------------- + + +@skip_no_rime +def test_ratio_at_baseline(): + gmt = np.array([1.0]) + ratios = compute_degradation_ratios(gmt, [2020], baseline_gwl=1.0) + np.testing.assert_allclose(ratios.values, 1.0, atol=0.01) + + +@skip_no_rime +def test_ratio_changes_with_warming(): + gmt = np.array([1.0, 2.0, 3.0, 4.0]) + ratios = compute_degradation_ratios(gmt, [2020, 2030, 2040, 2050], baseline_gwl=1.0) + # Warming should move the regional ratios away from the baseline value. + assert (ratios[2050] - ratios[2020]).abs().gt(0.01).any() + + +@skip_no_rime +def test_ratio_shape_matches_input(): + gmt = np.linspace(1.0, 3.0, 6) + ratios = compute_degradation_ratios(gmt, [2020, 2030, 2040, 2050, 2060, 2070]) + assert ratios.shape == (12, 6) + + +# --------------------------------------------------------------------------- +# build_wet_cooling_constraints — mock-data unit tests +# --------------------------------------------------------------------------- + +_ADDON_DF = pd.DataFrame( + { + "type_addon": [ + "cooling__coal_ppl", + "cooling__coal_ppl", + "cooling__gas_ppl", + ], + "node": ["R12_AFR", "R12_WEU", "R12_AFR"], + "technology": ["coal_ppl", "coal_ppl", "gas_ppl"], + "year_vtg": [2020, 2020, 2020], + "year_act": [2020, 2020, 2020], + "mode": ["M1", "M1", "M1"], + "time": ["year", "year", "year"], + "value": [1.5, 1.5, 1.2], + "unit": ["-", "-", "-"], + } +) + +_TECHS = { + "coal_ppl", + "coal_ppl__cl_fresh", + "coal_ppl__ot_fresh", + "coal_ppl__air", + "gas_ppl", + "gas_ppl__cl_fresh", + "gas_ppl__ot_fresh", +} + + +def test_wet_cooling_constraint_structure(): + wet_cf = pd.DataFrame( + [[0.95, 0.90], [0.95, 0.90]], + index=pd.Index(["AFR", "WEU"], name="region"), + columns=[2050, 2060], + ) + result = build_wet_cooling_constraints( + _ADDON_DF, _TECHS, wet_cf, model_years=[2050, 2060] + ) + + assert "relation_activity" in result + assert "relation_upper" in result + assert "relation_names" in result + + rel_act = result["relation_activity"] + rel_up = result["relation_upper"] + + assert not rel_act.empty + assert not rel_up.empty + # All upper bounds should be zero + assert (rel_up["value"] == 0.0).all() + + +def test_wet_cooling_coefficient_signs(): + wet_cf = pd.DataFrame( + [[0.95]], + index=pd.Index(["AFR"], name="region"), + columns=[2050], + ) + result = build_wet_cooling_constraints( + _ADDON_DF, _TECHS, wet_cf, model_years=[2050] + ) + rel_act = result["relation_activity"] + + # Freshwater variants should have positive coefficients + fresh = rel_act[rel_act["technology"].str.contains("fresh")] + assert (fresh["value"] > 0).all() + + # Parent technologies should have negative coefficients + parents = rel_act[~rel_act["technology"].str.contains("__")] + assert (parents["value"] < 0).all() + + +def test_wet_cooling_min_year_filtering(): + wet_cf = pd.DataFrame( + [[0.98, 0.95]], + index=pd.Index(["AFR"], name="region"), + columns=[2030, 2050], + ) + result = build_wet_cooling_constraints( + _ADDON_DF, + _TECHS, + wet_cf, + model_years=[2030, 2050], + min_year=2045, + ) + rel_act = result["relation_activity"] + # 2030 should be excluded + assert 2030 not in rel_act["year_act"].values + assert 2050 in rel_act["year_act"].values + + +# --------------------------------------------------------------------------- +# build_dry_cooling_factors +# --------------------------------------------------------------------------- + +_CF_AIR = pd.DataFrame( + { + "node_loc": ["R12_AFR", "R12_AFR", "R12_WEU"], + "technology": ["coal_ppl__air", "coal_ppl__air", "gas_ppl__air"], + "year_act": [2030, 2050, 2050], + "value": [0.90, 0.80, 0.75], + "unit": ["-", "-", "-"], + } +) + +_DRY_RATIOS = pd.DataFrame( + {2050: [0.50, 0.80]}, + index=pd.Index(["AFR", "WEU"], name="region"), +) + + +def test_dry_cooling_scales_values_and_preserves_structure(): + old_cf, new_cf = build_dry_cooling_factors( + _CF_AIR, _DRY_RATIOS, model_years=[2030, 2050], min_year=2045 + ) + + assert old_cf["year_act"].tolist() == [2050, 2050] + assert new_cf.columns.tolist() == old_cf.columns.tolist() + np.testing.assert_allclose(old_cf["value"], [0.80, 0.75]) + np.testing.assert_allclose(new_cf["value"], [0.40, 0.60]) + + +def test_dry_cooling_min_year_filtering(): + old_cf, new_cf = build_dry_cooling_factors( + _CF_AIR, _DRY_RATIOS, model_years=[2030], min_year=2045 + ) + + assert old_cf.empty + assert new_cf.empty From f320ed4bcf69e8903021a3f683dea64914e949be Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Fri, 1 May 2026 09:25:12 +0200 Subject: [PATCH 04/19] Add water CID transforms and basin geometry helpers model/water/data/impacts.py: predict_water_rime drives basin-level RIME emulators, with seasonal2step bifurcation when applicable, and expands to MESSAGE basin-region rows. model/water/utils.py: load_basin_mapping (cached), split_basin_macroregion, NAN_BASIN_IDS / N_RIME_BASINS / N_MESSAGE_BASINS constants. Packaged: joint_bifurcation_mapping_CWatM_2step.csv, rime_regionarray_local_temp_CWatM_seasonal2step_window11.nc, rime_regionarray_qr_CWatM_annual_window11.nc. --- .../joint_bifurcation_mapping_CWatM_2step.csv | 158 ++++++++++++++++++ ...local_temp_CWatM_seasonal2step_window11.nc | Bin 0 -> 707500 bytes ...me_regionarray_qr_CWatM_annual_window11.nc | Bin 0 -> 363341 bytes message_ix_models/model/water/data/impacts.py | 110 ++++++++++++ message_ix_models/model/water/utils.py | 95 +++++++++++ .../tests/model/water/data/test_impacts.py | 95 +++++++++++ 6 files changed, 458 insertions(+) create mode 100644 message_ix_models/data/impacts/rime/joint_bifurcation_mapping_CWatM_2step.csv create mode 100644 message_ix_models/data/impacts/rime/rime_regionarray_local_temp_CWatM_seasonal2step_window11.nc create mode 100644 message_ix_models/data/impacts/rime/rime_regionarray_qr_CWatM_annual_window11.nc create mode 100644 message_ix_models/model/water/data/impacts.py create mode 100644 message_ix_models/tests/model/water/data/test_impacts.py diff --git a/message_ix_models/data/impacts/rime/joint_bifurcation_mapping_CWatM_2step.csv b/message_ix_models/data/impacts/rime/joint_bifurcation_mapping_CWatM_2step.csv new file mode 100644 index 0000000000..fd67d92c4e --- /dev/null +++ b/message_ix_models/data/impacts/rime/joint_bifurcation_mapping_CWatM_2step.csv @@ -0,0 +1,158 @@ +BASIN_ID,wet_months,dry_months,r2,r2_primary,r2_secondary,r2_joint,method +1.0,"1,2,3,4","5,6,7,8,9,10,11,12",0.8383458964123076,0.8383458964123076,0.8677447058845316,0.8570899178229054,joint_regime_shift_2step +2.0,"1,2,3,12","4,5,6,7,8,9,10,11",0.8890591876829491,0.8890591876829491,0.8512598838500043,0.8808966783068051,joint_regime_shift_2step +3.0,"8,9,10","1,2,3,4,5,6,7,11,12",0.5163142601540798,0.5163142601540798,0.8976677267508875,0.822337561933331,joint_regime_shift_2step 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zMExwCevvWG^pcnepWF)s(^9M{mUJszUkVmCe2}psJ6bj| z4BVLldonOGQ^d2KDX?b+13N8Y&+nI3Eit5f3394OU%1ng`H@%R& z#FUu|QzWuGQ)VhmVPbct%v6{HdN?!^Q)Vhmk;LvynW-?vT-9A>DoofR4WUA~OxV#( zc7-WkQSA~_W-3gP%5U6{eWa{+KdTVTuLp&Xk!7Q!He6rp#2B zaPyWxBQa&B!W4_xohdUFrZB7SflP^MX39)EQ(-!oB8B}k%}kl8Fda;>nB|!g)6A5a zcBaB~Fh!~)&;&9;BSUtXX=f@-2UDb}a-fMId5I|;xYG~JHJXuyMjcnogSmk_M8MF> zbnw#6&*3CbGCk~mkNy1|L*WYA*$n=BS?*Vs>tnfomJ>K~03&b&0LBUbfq)r+JrEdY zHJ!qR MCM (multiply by 1000) +- Sign convention: negate for MESSAGE demand semantics +""" + +import functools +import logging + +import numpy as np + +from message_ix_models.model.water.utils import ( + load_basin_mapping, + split_basin_macroregion, +) +from message_ix_models.tools.impacts import ( + clip_gmt, + open_rime_dataset, + predict_rime, +) + +log = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# RIME-specific basin index mapping +# --------------------------------------------------------------------------- + + +@functools.lru_cache(maxsize=1) +def _get_rime_region_mapping() -> dict[int, int]: + """Mapping from BASIN_ID to RIME array index. + + RIME datasets have 157 basins indexed by region IDs [1..162] with gaps. + Uses a reference dataset (qtot_mean annual window11) to discover the + mapping; region IDs are identical across all basin-level RIME datasets. + """ + ds = open_rime_dataset("rime_regionarray_qtot_mean_CWatM_annual_window11.nc") + return {int(region_id): i for i, region_id in enumerate(ds.region.values)} + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + + +def predict_water_rime( + gmt_array, + variable: str, + temporal_res: str = "annual", + hydro_model: str = "CWatM", + percentile: str | None = None, +) -> np.ndarray | tuple[np.ndarray, np.ndarray]: + """Predict water variable at MESSAGE basin resolution (217 rows). + + Wraps :func:`~message_ix_models.tools.impacts.predict_rime` with + basin expansion from 157 RIME basins to 217 MESSAGE basin-region rows. + + Parameters + ---------- + gmt_array + GMT values (degC above pre-industrial). + variable + Basin-level variable: ``qtot_mean``, ``qr``, or ``local_temp``. + temporal_res + ``"annual"`` or ``"seasonal2step"``. + hydro_model + Hydrological model name for dataset selection. + percentile + Uncertainty percentile suffix (e.g. ``"p10"``). + + Returns + ------- + np.ndarray or tuple + Annual: ``(217, n_years)``. + Seasonal: ``((217, n_years), (217, n_years))`` tuple (dry, wet). + """ + gmt_array = np.asarray(gmt_array) + + if temporal_res == "seasonal2step": + gmt_clipped = clip_gmt(gmt_array, gmt_min=0.8, gmt_ceil=1.2) + else: + gmt_clipped = clip_gmt(gmt_array, gmt_min=0.6, gmt_ceil=0.9) + + window = "11" + filename = ( + f"rime_regionarray_{variable}_{hydro_model}_{temporal_res}_window{window}.nc" + ) + + basin_mapping = load_basin_mapping() + basin_id_to_rime_idx = _get_rime_region_mapping() + + def _expand(raw: np.ndarray) -> np.ndarray: + return split_basin_macroregion(raw, basin_mapping, basin_id_to_rime_idx) + + if temporal_res == "seasonal2step": + sfx = f"_{percentile}" if percentile else "" + raw_dry = predict_rime(gmt_clipped, filename, f"{variable}_dry{sfx}") + raw_wet = predict_rime(gmt_clipped, filename, f"{variable}_wet{sfx}") + return (_expand(raw_dry), _expand(raw_wet)) + + var_name = variable if not percentile else f"{variable}_{percentile}" + return _expand(predict_rime(gmt_clipped, filename, var_name)) diff --git a/message_ix_models/model/water/utils.py b/message_ix_models/model/water/utils.py index 6eb49a6b37..0587b36eea 100644 --- a/message_ix_models/model/water/utils.py +++ b/message_ix_models/model/water/utils.py @@ -1,3 +1,4 @@ +import functools import logging from collections import defaultdict from functools import lru_cache @@ -5,6 +6,7 @@ from typing import TYPE_CHECKING from warnings import warn +import numpy as np import pandas as pd import xarray as xr from iam_units import registry @@ -469,3 +471,96 @@ def get_vintage_and_active_years( result = yv_ya[valid_mask].reset_index(drop=True) return result + + +# --------------------------------------------------------------------------- +# Basin geometry (R12, 217 MESSAGE basin-region rows) +# --------------------------------------------------------------------------- + +#: RIME basin IDs with no coverage data; original scenario values are preserved. +NAN_BASIN_IDS: frozenset[int] = frozenset({0, 141, 154}) + +#: Number of RIME basins (native emulator resolution). +N_RIME_BASINS: int = 157 + +#: Number of MESSAGE basin-region rows after transboundary splitting. +N_MESSAGE_BASINS: int = 217 + + +@functools.lru_cache(maxsize=1) +def load_basin_mapping() -> pd.DataFrame: + """Load R12 basin mapping with MESSAGE basin codes. + + Returns + ------- + pd.DataFrame + 217 rows with columns including ``BASIN_ID``, ``NAME``, ``BASIN``, + ``REGION``, ``BCU_name``, ``area_km2``, ``model_region``, + ``basin_code``. + """ + basin_file = package_data_path("water", "infrastructure", "all_basins.csv") + basin_df = pd.read_csv(basin_file) + basin_df = basin_df[basin_df["model_region"] == "R12"].copy() + basin_df = basin_df.reset_index(drop=True) + basin_df["basin_code"] = ( + "B" + basin_df["BASIN_ID"].astype(str) + "|" + basin_df["REGION"] + ) + return basin_df + + +def split_basin_macroregion( + rime_predictions: np.ndarray, + basin_mapping: pd.DataFrame | None = None, + basin_id_to_rime_idx: dict[int, int] | None = None, +) -> np.ndarray: + """Expand RIME basin predictions from native resolution to MESSAGE rows. + + RIME delivers 157 basins; MESSAGE uses 217 basin-region rows because + transboundary basins are split proportionally by area across macroregions. + + Parameters + ---------- + rime_predictions + Shape ``(N_RIME_BASINS, n_timesteps)`` for annual or + ``(N_RIME_BASINS, n_timesteps, n_seasons)`` for seasonal. + basin_mapping + If *None*, uses :func:`load_basin_mapping`. + basin_id_to_rime_idx + Mapping from ``BASIN_ID`` to RIME array index. Callers with access + to a RIME dataset should pass this; if *None* the expansion skips + basins not found in the mapping (NaN rows remain). + + Returns + ------- + np.ndarray + Shape ``(N_MESSAGE_BASINS, ...)`` matching input trailing dimensions. + Rows for basin IDs in :data:`NAN_BASIN_IDS` are NaN. + """ + if basin_mapping is None: + basin_mapping = load_basin_mapping() + + is_seasonal = rime_predictions.ndim == 3 + if is_seasonal: + n_rime, n_timesteps, n_seasons = rime_predictions.shape + out = np.full((N_MESSAGE_BASINS, n_timesteps, n_seasons), np.nan) + else: + n_rime, n_timesteps = rime_predictions.shape + out = np.full((N_MESSAGE_BASINS, n_timesteps), np.nan) + + if basin_id_to_rime_idx is None: + return out + + basin_total_areas = basin_mapping.groupby("BASIN_ID")["area_km2"].sum() + + for i, row in basin_mapping.iterrows(): + basin_id = row["BASIN_ID"] + if basin_id not in basin_id_to_rime_idx: + continue + rime_idx = basin_id_to_rime_idx[basin_id] + area_fraction = row["area_km2"] / basin_total_areas[basin_id] + if is_seasonal: + out[i, :, :] = rime_predictions[rime_idx, :, :] * area_fraction + else: + out[i, :] = rime_predictions[rime_idx, :] * area_fraction + + return out diff --git a/message_ix_models/tests/model/water/data/test_impacts.py b/message_ix_models/tests/model/water/data/test_impacts.py new file mode 100644 index 0000000000..b6af6022b2 --- /dev/null +++ b/message_ix_models/tests/model/water/data/test_impacts.py @@ -0,0 +1,95 @@ +"""Tests for model.water.data.impacts -- water-domain RIME transformations.""" + +import numpy as np +import pytest + +from message_ix_models.model.water.utils import ( + N_MESSAGE_BASINS, + N_RIME_BASINS, + NAN_BASIN_IDS, + load_basin_mapping, + split_basin_macroregion, +) +from message_ix_models.tools.impacts import impacts_data_path + + +class TestLoadBasinMapping: + def test_loads_217_rows(self): + df = load_basin_mapping() + assert len(df) == N_MESSAGE_BASINS + assert "BASIN_ID" in df.columns + assert "basin_code" in df.columns + assert "area_km2" in df.columns + + +# Integration tests requiring RIME NetCDF files (for basin-to-index mapping) +_RIME_DIR = impacts_data_path("rime") +_HAS_RIME_DATA = ( + _RIME_DIR / "rime_regionarray_qtot_mean_CWatM_annual_window11.nc" +).exists() + + +@pytest.mark.skipif(not _HAS_RIME_DATA, reason="RIME NetCDF datasets not available") +class TestSplitBasinMacroregion: + """Requires RIME data for basin-to-index mapping.""" + + def test_expansion_shape(self): + basin_mapping = load_basin_mapping() + fake_rime = np.ones((N_RIME_BASINS, 5)) + result = split_basin_macroregion(fake_rime, basin_mapping) + assert result.shape == (N_MESSAGE_BASINS, 5) + + def test_nan_preservation(self): + """Basins 0, 141, 154 should remain NaN (no RIME data).""" + basin_mapping = load_basin_mapping() + fake_rime = np.ones((N_RIME_BASINS, 3)) + result = split_basin_macroregion(fake_rime, basin_mapping) + assert result.shape == (N_MESSAGE_BASINS, 3) + # Rows for NaN basin IDs should be NaN + for idx, row in basin_mapping.iterrows(): + if row["BASIN_ID"] in NAN_BASIN_IDS: + assert np.all(np.isnan(result[idx])), ( + f"Row {idx} (BASIN_ID={row['BASIN_ID']}) should be NaN" + ) + + def test_seasonal_3d(self): + basin_mapping = load_basin_mapping() + fake_rime = np.ones((N_RIME_BASINS, 5, 2)) + result = split_basin_macroregion(fake_rime, basin_mapping) + assert result.shape == (N_MESSAGE_BASINS, 5, 2) + + +@pytest.mark.skipif(not _HAS_RIME_DATA, reason="RIME NetCDF datasets not available") +class TestPredictWaterRime: + """End-to-end: predict + basin expansion.""" + + def test_annual_qtot(self): + from message_ix_models.model.water.data.impacts import predict_water_rime + + gmt = np.linspace(1.0, 2.5, 10) + result = predict_water_rime(gmt, "qtot_mean") + assert result.shape == (N_MESSAGE_BASINS, 10) + + def test_annual_qr(self): + from message_ix_models.model.water.data.impacts import predict_water_rime + + gmt = np.linspace(1.0, 2.5, 10) + result = predict_water_rime(gmt, "qr") + assert result.shape == (N_MESSAGE_BASINS, 10) + + def test_ensemble(self): + from message_ix_models.model.water.data.impacts import predict_water_rime + + rng = np.random.default_rng(42) + gmt_2d = rng.normal(1.5, 0.2, size=(5, 10)) + gmt_2d = np.clip(gmt_2d, 0.6, 7.4) + result = predict_water_rime(gmt_2d, "qtot_mean") + assert result.shape == (N_MESSAGE_BASINS, 10) + + def test_seasonal(self): + from message_ix_models.model.water.data.impacts import predict_water_rime + + gmt = np.linspace(1.0, 2.5, 10) + dry, wet = predict_water_rime(gmt, "qtot_mean", temporal_res="seasonal2step") + assert dry.shape == (N_MESSAGE_BASINS, 10) + assert wet.shape == (N_MESSAGE_BASINS, 10) From f7d4a8ba214a57165e8b1fadc683deb58ef47d39 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Fri, 1 May 2026 09:25:53 +0200 Subject: [PATCH 05/19] Add SPARRCLE project package and Workflow project/sparrcle wires Phase 1 (water-ix cooling subprocess) and Phase 2 (buildings + cooling CIDs) into a Workflow with one base/cooling/CI_b/CI_p/CI_bp step set per starter. validate_inputs preflights MAGICC outputs, RIME datasets, and per-SSP buildings calibration files so a misconfigured run fails before any clone. CLI: mix-models sparrcle run TARGET (--from / --go). --- doc/whatsnew.rst | 2 + message_ix_models/cli.py | 1 + .../project/sparrcle/__init__.py | 0 message_ix_models/project/sparrcle/cli.py | 30 ++ .../project/sparrcle/scenario_config.yaml | 66 ++++ .../project/sparrcle/workflow.py | 373 ++++++++++++++++++ .../tests/project/test_sparrcle.py | 12 + .../tests/project/test_sparrcle_workflow.py | 124 ++++++ 8 files changed, 608 insertions(+) create mode 100644 message_ix_models/project/sparrcle/__init__.py create mode 100644 message_ix_models/project/sparrcle/cli.py create mode 100644 message_ix_models/project/sparrcle/scenario_config.yaml create mode 100644 message_ix_models/project/sparrcle/workflow.py create mode 100644 message_ix_models/tests/project/test_sparrcle.py create mode 100644 message_ix_models/tests/project/test_sparrcle_workflow.py diff --git a/doc/whatsnew.rst b/doc/whatsnew.rst index c42d62c50a..979d881aab 100644 --- a/doc/whatsnew.rst +++ b/doc/whatsnew.rst @@ -20,6 +20,8 @@ Next release - Adjust :mod:`.project.ssp.transport` (:pull:`485`): - Add stub of :func:`.scenariomip.workflow.generate` (:pull:`394`). - New guide on HOWTO :doc:`/howto/report` (:pull:`488`). +- New :mod:`.tools.impacts` library and :mod:`.project.sparrcle` consumer + for SPARRCLE physical-impact scenarios (:pull:`479`). v2026.4.17 ========== diff --git a/message_ix_models/cli.py b/message_ix_models/cli.py index a495c76ebf..90e404a248 100644 --- a/message_ix_models/cli.py +++ b/message_ix_models/cli.py @@ -166,6 +166,7 @@ def _log_threads(k: int, n: int): "message_ix_models.project.circeular.cli", "message_ix_models.project.edits.cli", "message_ix_models.project.navigate.cli", + "message_ix_models.project.sparrcle.cli", "message_ix_models.project.ssp.cli", "message_ix_models.report.cli", "message_ix_models.model.material.cli", diff --git a/message_ix_models/project/sparrcle/__init__.py b/message_ix_models/project/sparrcle/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/message_ix_models/project/sparrcle/cli.py b/message_ix_models/project/sparrcle/cli.py new file mode 100644 index 0000000000..e413070d54 --- /dev/null +++ b/message_ix_models/project/sparrcle/cli.py @@ -0,0 +1,30 @@ +"""Command-line tools specific to the SPARRCLE project.""" + +from pathlib import Path + +import click + +from message_ix_models.workflow import make_click_command + + +@click.group("sparrcle") +@click.pass_obj +def cli(context): + """SPARRCLE project.""" + + +cli.add_command( + make_click_command( + f"{__package__}.workflow.generate", + name="SPARRCLE", + slug="sparrcle", + params=[ + click.Option( + ["--config", "config_path"], + type=click.Path(exists=True, dir_okay=False, path_type=Path), + default=None, + help="Path to scenario_config.yaml (default: packaged copy).", + ), + ], + ) +) diff --git a/message_ix_models/project/sparrcle/scenario_config.yaml b/message_ix_models/project/sparrcle/scenario_config.yaml new file mode 100644 index 0000000000..5233d8431c --- /dev/null +++ b/message_ix_models/project/sparrcle/scenario_config.yaml @@ -0,0 +1,66 @@ +# SPARRCLE Scenario Configuration +# +# Consumed by `message_ix_models.project.sparrcle.workflow.generate`. +# +# Phase 1 — Module build ("{ssp}/{scenario} cooling"): +# subprocess `mix-models water-ix cooling` → {scenario}_cooling +# +# Phase 2 — CID variants ("{ssp}/{scenario} CI_b" / "CI_p" / "CI_bp"): +# buildings: rc_spec/rc_therm demand adjustment → _CI_b +# cooling: freshwater + dry cooling capacity factors → _CI_p +# combined: buildings + cooling on the cooling clone → _CI_bp + +platform_info: + name: ixmp_dev + +regions: R12 + +# Starter scenarios — assumed to exist on `platform_info.name`. The list +# is the source of truth for which scenarios the build pipeline iterates +# over. +# +# `_PHY` starters are prep clones of Adriano's solved GDP CI_50 inputs. +# magicc_output_dir points at the upstream Adriano starter name with no +# `_PHY` suffix because the MAGICC GSAT ensemble is keyed to that +# upstream solved scenario. +starters: + - model: SSP_SSP1_v6.5_sp + scenario: NPiREF_GDP_CI_50_ensemble_1_PHY + ssp: SSP1 + magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/NPiREF_GDP_CI_50_ensemble_1/ + - model: SSP_SSP1_v6.5_sp + scenario: INDC2030i_uncon_800f_GDP_CI_50_ensemble_1_PHY + ssp: SSP1 + magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP1_v6.5_sp__INDC2030i_uncon_800f_GDP_CI_50_ensemble_1/ + - model: SSP_SSP2_v6.5_sp + scenario: NPiREF_GDP_CI_50_ensemble_2_PHY + ssp: SSP2 + magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/NPiREF_GDP_CI_50_ensemble_2/ + - model: SSP_SSP2_v6.5_sp + scenario: INDC2030i_uncon_600f_GDP_CI_50_ensemble_1_PHY + ssp: SSP2 + magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP2_v6.5_sp__INDC2030i_uncon_600f_GDP_CI_50_ensemble_1/ + - model: SSP_SSP3_v6.5_sp + scenario: NPiREF_GDP_CI_50_ensemble_2_PHY + ssp: SSP3 + magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/NPiREF_GDP_CI_50_ensemble_2/ + - model: SSP_SSP3_v6.5_sp + scenario: INDC2030i_uncon_600f_GDP_CI_50_ensemble_1_PHY + ssp: SSP3 + magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/INDC2030i_uncon_600f_GDP_CI_50_ensemble_1/ + - model: SSP_SSP3_v6.5_sp + scenario: INDC2030i_uncon_800f_GDP_CI_50_ensemble_1_PHY + ssp: SSP3 + magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/INDC2030i_uncon_800f_GDP_CI_50_ensemble_1/ + +# Phase-1 cooling-module build settings (passed to `mix-models water-ix cooling`) +cooling: + rcps: no_climate + rels: low + +# Phase-2 CID settings +cid: + # `n_runs: null` means use all available MAGICC ensemble members. + n_runs: null + # Earliest model year touched by cooling CID constraints and dry-cooling derating. + min_year: 2045 diff --git a/message_ix_models/project/sparrcle/workflow.py b/message_ix_models/project/sparrcle/workflow.py new file mode 100644 index 0000000000..8f714ce589 --- /dev/null +++ b/message_ix_models/project/sparrcle/workflow.py @@ -0,0 +1,373 @@ +"""SPARRCLE physical-impact workflow. + +Builds a :class:`message_ix_models.workflow.Workflow` whose nodes are the +SPARRCLE Phase-1 cooling-module clones and the Phase-2 CID variants +(``CI_b`` / ``CI_p`` / ``CI_bp``) for every starter declared in +``scenario_config.yaml``. ``mix-models sparrcle run TARGET`` selects the +subgraph, ``--from`` truncates at a given step, ``--go`` actually runs. + +Phase-1 cooling delegates to ``mix-models water-ix cooling`` via +subprocess. The action returns the produced ``{base}_cooling`` scenario +so downstream variants can clone from it. + +Phase-2 CID actions (``apply_buildings``, ``apply_cooling``, +``_apply_buildings_and_cooling``) clone the upstream scenario, load the +MAGICC GSAT ensemble, and call into the domain CID kernels in +:mod:`model.buildings.impacts` and :mod:`model.water.data.cooling_impacts`. +""" + +import logging +import subprocess +from pathlib import Path +from typing import TYPE_CHECKING + +import yaml +from genno import KeyExistsError + +from message_ix_models.tools.impacts import load_magicc_gmt, persist_gmt_mean +from message_ix_models.util import package_data_path +from message_ix_models.workflow import Workflow + +if TYPE_CHECKING: + from message_ix import Scenario + + from message_ix_models.util.context import Context + +log = logging.getLogger(__name__) + +_DEFAULT_CONFIG_PATH = package_data_path( + "..", "project", "sparrcle", "scenario_config.yaml" +) + + +def load_config(path: str | Path) -> dict: + """Load the SPARRCLE scenario config.""" + with open(path) as f: + raw = yaml.safe_load(f) + + required = ["platform_info", "starters", "cooling"] + if missing := [key for key in required if key not in raw]: + raise ValueError(f"Config missing required keys: {missing}") + + config = dict(raw) + config["cooling"] = {"rcps": "no_climate", "rels": "low", **raw["cooling"]} + config["cid"] = {"n_runs": None, "min_year": None, **raw.get("cid", {})} + config["regions"] = raw.get("regions", "R12") + return config + + +def _missing_magicc(starters: list[dict]) -> list[str]: + out: list[str] = [] + for starter in starters: + tag = f"{starter['model']}/{starter['scenario']}" + magicc_dir = Path(starter["magicc_output_dir"]) + if not magicc_dir.is_dir(): + out.append(f"{tag}: magicc_output_dir not a directory: {magicc_dir}") + elif not list(magicc_dir.glob("*_IAMC_climateassessment.xlsx")): + out.append(f"{tag}: no *_IAMC_climateassessment.xlsx in {magicc_dir}") + return out + + +def _missing_buildings(ssps: set[str]) -> list[str]: + from message_ix_models.util import package_data_path + + buildings_dir = package_data_path("buildings") + coeff_dir = buildings_dir / "correction_coefficients" + paths = [ + buildings_dir / f"theta_{mode}_{ssp}.csv" + for ssp in sorted(ssps) + for mode in ("cool", "heat") + ] + paths += [buildings_dir / f"rc_sector_fractions_{ssp}.csv" for ssp in sorted(ssps)] + paths += [ + coeff_dir / f"correction_coefficients_{mode}_SSP2_{sector}.csv" + for mode in ("cool", "heat") + for sector in ("comm", "resid") + ] + return [f"buildings: {p}" for p in paths if not p.is_file()] + + +def _missing_rime() -> list[str]: + from message_ix_models.tools.impacts import impacts_data_path + + rime_dir = impacts_data_path("rime") + paths = [ + rime_dir / "r12_thermoelectric_gwl.nc", + rime_dir / "region_EI_cool_gwl_binned.nc", + rime_dir / "region_EI_heat_gwl_binned.nc", + ] + return [f"RIME: {p}" for p in paths if not p.is_file()] + + +def validate_inputs(config: dict) -> None: + """Fail fast if any on-disk input the CID actions need is missing. + + Each ``CI_*`` step clones a scenario before its action runs, so a + missing MAGICC output or RIME dataset surfaces only after a fresh + DB clone has been written. This preflight collects every missing + path across all starters and raises once, so the user can fix them + in a single pass rather than one clone at a time. + """ + missing = ( + _missing_magicc(config["starters"]) + + _missing_buildings({s["ssp"] for s in config["starters"]}) + + _missing_rime() + ) + if missing: + raise FileNotFoundError( + "SPARRCLE preflight: required inputs not found:\n - " + + "\n - ".join(missing) + ) + + +def phase1_cooling( + context: "Context", + scenario: "Scenario", + *, + ssp: str, + regions: str = "R12", + rcps: str = "no_climate", + rels: str = "low", +) -> "Scenario": + """Build the cooling module on top of *scenario* via ``water-ix`` subprocess. + + The subprocess reads ``scenario`` and produces ``{scenario}_cooling`` + in the same database. We then load and return it. + """ + from message_ix import Scenario + + del context + + url = f"ixmp://{scenario.platform.name}/{scenario.model}/{scenario.scenario}" + cmd = [ + "mix-models", + "--url", + url, + "water-ix", + "cooling", + "--regions", + regions, + "--ssp", + ssp, + "--rcps", + rcps, + "--rels", + rels, + ] + log.info("→ %s", " ".join(cmd)) + subprocess.run(cmd, check=True) + + target_name = f"{scenario.scenario}_cooling" + log.info("Loading produced scenario %s/%s", scenario.model, target_name) + return Scenario(scenario.platform, scenario.model, target_name) + + +def apply_buildings( + context: "Context", + scenario: "Scenario", + *, + magicc_dir: str | Path, + n_runs: int | None = None, + reference_scenario: str = "SSP2", +) -> None: + """Apply building-energy CIDs in place on *scenario* (``CI_b`` step).""" + from message_ix_models.model.buildings.impacts import ( + apply_building_cids, + compute_building_cids, + ) + from message_ix_models.util import ScenarioInfo + + del context # unused; required by workflow action signature + + gmt = load_magicc_gmt(magicc_dir, n_runs=n_runs) + n = gmt.values.shape[0] if gmt.values.ndim > 1 else 1 + cooling, heating = compute_building_cids( + gmt, + ScenarioInfo(scenario).Y, + reference_scenario=reference_scenario, + ) + apply_building_cids( + scenario, + cooling, + heating, + commit_message=f"Building CIDs ({reference_scenario}, {n} runs)", + reference_scenario=reference_scenario, + ) + persist_gmt_mean(scenario, gmt) + + +def apply_cooling( + context: "Context", + scenario: "Scenario", + *, + magicc_dir: str | Path, + n_runs: int | None = None, + min_year: int | None = None, +) -> None: + """Apply wet + dry cooling CIDs in place on *scenario* (``CI_p`` step). + + *min_year* defaults to + :data:`model.water.data.cooling_impacts._DEFAULT_MIN_YEAR` when ``None``. + """ + from message_ix_models.model.water.data.cooling_impacts import apply_cooling_cids + + del context + + gmt = load_magicc_gmt(magicc_dir, n_runs=n_runs) + n = gmt.values.shape[0] if gmt.values.ndim > 1 else 1 + kwargs = {"min_year": min_year} if min_year is not None else {} + apply_cooling_cids( + scenario, + gmt, + commit_message=f"Cooling CIDs (wet+dry), {n} runs", + **kwargs, + ) + persist_gmt_mean(scenario, gmt) + + +def _apply_buildings_and_cooling( + context: "Context", + scenario: "Scenario", + *, + magicc_dir: str | Path, + n_runs: int | None = None, + reference_scenario: str = "SSP2", + min_year: int | None = None, +) -> None: + """``CI_bp`` composite: buildings then cooling CIDs in place.""" + apply_buildings( + context, + scenario, + magicc_dir=magicc_dir, + n_runs=n_runs, + reference_scenario=reference_scenario, + ) + apply_cooling( + context, + scenario, + magicc_dir=magicc_dir, + n_runs=n_runs, + min_year=min_year, + ) + + +def generate( + context: "Context", + *, + config_path: str | Path | None = None, + **options, +) -> Workflow: + """Build the SPARRCLE Phase-1 + Phase-2 workflow. + + Parameters + ---------- + context + Context object (passed to each step). + config_path + Path to ``scenario_config.yaml``. Defaults to the packaged copy. + options + Reserved for forward-compatibility with extra CLI options; + currently ignored. + + Returns + ------- + Workflow + Graph with one ``base``/``cooling``/``CI_b``/``CI_p``/``CI_bp`` + step set per starter, plus an aggregating ``all CI`` default. + """ + del options + + config = load_config(config_path or _DEFAULT_CONFIG_PATH) + validate_inputs(config) + + wf = Workflow(context) + + platform = config["platform_info"]["name"] + regions = config["regions"] + cooling_cfg = config["cooling"] + cid_cfg = config["cid"] + + targets: list[str] = [] + for starter in config["starters"]: + model = starter["model"] + scenario = starter["scenario"] + ssp = starter["ssp"] + magicc_dir = starter["magicc_output_dir"] + if not magicc_dir: + raise ValueError(f"Starter {model}/{scenario} lacks magicc_output_dir") + + label = f"{ssp}/{scenario}" + base_step = f"{label} base" + try: + wf.add_step( + base_step, + None, + target=f"ixmp://{platform}/{model}/{scenario}", + ) + except KeyExistsError: + pass # shared starter URL across SSPs + + cooling_step = f"{label} cooling" + wf.add_step( + cooling_step, + base_step, + phase1_cooling, + target=f"ixmp://{platform}/{model}/{scenario}_cooling", + ssp=ssp, + regions=regions, + rcps=cooling_cfg["rcps"], + rels=cooling_cfg["rels"], + ) + + common = {"magicc_dir": magicc_dir} + if cid_cfg["n_runs"] is not None: + common["n_runs"] = cid_cfg["n_runs"] + + buildings = { + **common, + "reference_scenario": ssp, + } + cooling = dict(common) + if cid_cfg["min_year"] is not None: + cooling["min_year"] = cid_cfg["min_year"] + + targets.append( + wf.add_step( + f"{label} CI_b", + base_step, + apply_buildings, + target=f"ixmp://{platform}/{model}/{scenario}_CI_b", + clone=True, + **buildings, + ) + ) + targets.append( + wf.add_step( + f"{label} CI_p", + cooling_step, + apply_cooling, + target=f"ixmp://{platform}/{model}/{scenario}_CI_p", + clone=True, + **cooling, + ) + ) + targets.append( + wf.add_step( + f"{label} CI_bp", + cooling_step, + _apply_buildings_and_cooling, + target=f"ixmp://{platform}/{model}/{scenario}_CI_bp", + clone=True, + **buildings, + **( + {"min_year": cid_cfg["min_year"]} + if cid_cfg["min_year"] is not None + else {} + ), + ) + ) + + wf.add("all CI", targets) + wf.default_key = "all CI" + + return wf diff --git a/message_ix_models/tests/project/test_sparrcle.py b/message_ix_models/tests/project/test_sparrcle.py new file mode 100644 index 0000000000..170fe28358 --- /dev/null +++ b/message_ix_models/tests/project/test_sparrcle.py @@ -0,0 +1,12 @@ +"""Smoke test for the SPARRCLE CLI registration.""" + +from message_ix_models.project.sparrcle.cli import cli as sparrcle_cli + + +def test_run_command_registered() -> None: + assert "run" in sparrcle_cli.commands + run_cmd = sparrcle_cli.commands["run"] + opts = {p.name: p for p in run_cmd.params} + assert "config_path" in opts # --config + assert "go" in opts # --go from make_click_command + assert "truncate_step" in opts # --from from make_click_command diff --git a/message_ix_models/tests/project/test_sparrcle_workflow.py b/message_ix_models/tests/project/test_sparrcle_workflow.py new file mode 100644 index 0000000000..9141f523c8 --- /dev/null +++ b/message_ix_models/tests/project/test_sparrcle_workflow.py @@ -0,0 +1,124 @@ +"""Tests for project.sparrcle.workflow — Workflow construction + actions.""" + +from types import SimpleNamespace + +from message_ix_models.project.sparrcle import workflow as wf_mod + + +def _config(): + return { + "platform_info": {"name": "ixmp_dev"}, + "starters": [ + { + "model": "SSP_SSP1_v6.5_sp", + "scenario": "BASE_SSP1", + "ssp": "SSP1", + "magicc_output_dir": "/fake/ssp1", + }, + { + "model": "SSP_SSP2_v6.5_sp", + "scenario": "BASE_SSP2", + "ssp": "SSP2", + "magicc_output_dir": "/fake/ssp2", + }, + ], + "cooling": {"rcps": "no_climate", "rels": "low"}, + "regions": "R12", + "cid": {"n_runs": 3, "min_year": 2045}, + } + + +def _build_wf(monkeypatch, tmp_path, context): + config_path = tmp_path / "sparrcle.yaml" + config_path.write_text("# stub\n") + monkeypatch.setattr(wf_mod, "load_config", lambda _p: _config()) + monkeypatch.setattr(wf_mod, "validate_inputs", lambda _c: None) + return wf_mod.generate(context, config_path=config_path) + + +# --------------------------------------------------------------------------- +# generate() +# --------------------------------------------------------------------------- + + +def test_generate_step_names_and_dependencies(monkeypatch, tmp_path): + wf = _build_wf(monkeypatch, tmp_path, context=SimpleNamespace()) + # Expected step set: per starter (base, cooling, CI_b, CI_p, CI_bp) + # plus aggregator and the implicit "context". + expected_steps = { + "SSP1/BASE_SSP1 base", + "SSP1/BASE_SSP1 cooling", + "SSP1/BASE_SSP1 CI_b", + "SSP1/BASE_SSP1 CI_p", + "SSP1/BASE_SSP1 CI_bp", + "SSP2/BASE_SSP2 base", + "SSP2/BASE_SSP2 cooling", + "SSP2/BASE_SSP2 CI_b", + "SSP2/BASE_SSP2 CI_p", + "SSP2/BASE_SSP2 CI_bp", + "all CI", + } + assert expected_steps <= set(wf.keys()) + assert wf.default_key == "all CI" + + +def test_generate_variant_bases(monkeypatch, tmp_path): + wf = _build_wf(monkeypatch, tmp_path, context=SimpleNamespace()) + # genno graph entries are tuples (callable, *deps); deps[1] is `context`, + # deps[2] is the base step. + assert wf.graph["SSP1/BASE_SSP1 CI_b"][2] == "SSP1/BASE_SSP1 base" + assert wf.graph["SSP1/BASE_SSP1 CI_p"][2] == "SSP1/BASE_SSP1 cooling" + assert wf.graph["SSP1/BASE_SSP1 CI_bp"][2] == "SSP1/BASE_SSP1 cooling" + + +# --------------------------------------------------------------------------- +# phase1_cooling +# --------------------------------------------------------------------------- + + +def test_phase1_cooling_subprocess_invocation_and_load(monkeypatch): + ran = {} + + def fake_run(cmd, check): + ran["cmd"] = cmd + ran["check"] = check + return SimpleNamespace(returncode=0) + + monkeypatch.setattr(wf_mod.subprocess, "run", fake_run) + + loaded: list[tuple[str, str]] = [] + + class FakeScenario: + def __init__(self, platform, model, scenario): + loaded.append((model, scenario)) + self.platform = platform + self.model = model + self.scenario = scenario + + monkeypatch.setattr("message_ix.Scenario", FakeScenario) + + scen_in = SimpleNamespace( + platform=SimpleNamespace(name="ixmp_dev"), + model="SSP_SSP2_v6.5_sp", + scenario="BASE_SSP2", + ) + result = wf_mod.phase1_cooling( + SimpleNamespace(), + scen_in, + ssp="SSP2", + regions="R12", + rcps="no_climate", + rels="low", + ) + # Subprocess invoked with the expected URL + assert "--url" in ran["cmd"] + url_idx = ran["cmd"].index("--url") + 1 + assert ran["cmd"][url_idx] == "ixmp://ixmp_dev/SSP_SSP2_v6.5_sp/BASE_SSP2" + assert "cooling" in ran["cmd"] + assert ran["check"] is True + # Returned scenario is the produced {base}_cooling + assert (result.model, result.scenario) == ( + "SSP_SSP2_v6.5_sp", + "BASE_SSP2_cooling", + ) + assert loaded == [("SSP_SSP2_v6.5_sp", "BASE_SSP2_cooling")] From ad6364b638c5be0d607c54c4aa274842daeea059 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 4 May 2026 11:04:17 +0200 Subject: [PATCH 06/19] Add SSP1 buildings calibration files --- .../buildings/rc_sector_fractions_SSP1.csv | 127 ++++++++++++++++++ .../data/buildings/theta_cool_SSP1.csv | 125 +++++++++++++++++ .../data/buildings/theta_heat_SSP1.csv | 125 +++++++++++++++++ 3 files changed, 377 insertions(+) create mode 100644 message_ix_models/data/buildings/rc_sector_fractions_SSP1.csv create mode 100644 message_ix_models/data/buildings/theta_cool_SSP1.csv create mode 100644 message_ix_models/data/buildings/theta_heat_SSP1.csv diff --git a/message_ix_models/data/buildings/rc_sector_fractions_SSP1.csv b/message_ix_models/data/buildings/rc_sector_fractions_SSP1.csv new file mode 100644 index 0000000000..dfe0834f48 --- /dev/null +++ b/message_ix_models/data/buildings/rc_sector_fractions_SSP1.csv @@ -0,0 +1,127 @@ +# METADATA: Residential/Commercial shares of the calibrated buildings component in aggregate rc_spec/rc_therm +# Sources: WP - RC demands/Output/ssp_baseline/rc_calibrated_GWa_SSP1.csv, _staging/rc_demands/SSP_SSP1_v6.5_CID__baseline__rc_{spec,therm}.csv +# Formula: frac_resid_X = resid_X_GWa / rc_aggregate_GWa; frac_comm_X = comm_X_GWa / rc_aggregate_GWa +# Scope: cooling = resid_cool_electr + comm_cool_electr; heating = resid_heat_* + comm_heat_* (excludes cooking and hotwater) +# Years: from first nonzero buildings component to 2100 +# Generated: 2026-05-04 +node,year,frac_resid_cool,frac_comm_cool,frac_resid_heat,frac_comm_heat +R12_AFR,2020,0.007782808780424327,0.0,0.04769824667558288,1.6659159291645623e-05 +R12_AFR,2025,0.025671881723488763,0.0,0.04534517763951836,0.0 +R12_AFR,2030,0.09549866666496022,0.008659664326623325,0.04809427541920306,0.0001342180834683191 +R12_AFR,2040,0.2298555132664042,0.027662325186322903,0.040254438834007186,0.0003119536790286862 +R12_AFR,2050,0.31212424959633966,0.04878232298079764,0.02796250536410729,0.00041896104376801854 +R12_AFR,2060,0.3093926402558951,0.11691995612003933,0.023499778089225525,0.0005900316432444747 +R12_AFR,2070,0.2452082614920298,0.2510756016698857,0.01751339183649581,0.0006990067094659676 +R12_AFR,2080,0.19500356144502873,0.34544201451201106,0.013813704472529435,0.0007284811543037793 +R12_AFR,2090,0.1617324808366807,0.3829367780890528,0.011843706489286527,0.000717577323608562 +R12_AFR,2100,0.1375701797731506,0.36150071706329673,0.009894969973132554,0.0006438283306614951 +R12_CHN,2020,0.13612578085205387,0.20890321220167937,0.33087941877040045,0.09652961916155509 +R12_CHN,2025,0.16559151333414737,0.15789552339973453,0.30973679752787536,0.06508733569462649 +R12_CHN,2030,0.16118562975280765,0.17258087065461594,0.25669595218820374,0.06616363474352656 +R12_CHN,2040,0.13260677962972883,0.14195548524608761,0.21736163055050273,0.06219316025235882 +R12_CHN,2050,0.12362004150028075,0.13584876779610708,0.19308362526015424,0.05910190779460748 +R12_CHN,2060,0.11157011972130344,0.12607516139097766,0.17740158816123114,0.05646032447221394 +R12_CHN,2070,0.1008117057715567,0.1101592563449212,0.16234680753293332,0.05285886152004959 +R12_CHN,2080,0.09536854838944733,0.09310622316921933,0.1516864501895752,0.04989158540065487 +R12_CHN,2090,0.09483317641914428,0.0803460725340711,0.14514852244876641,0.04790630189479941 +R12_CHN,2100,0.0941489207355058,0.07000737685655127,0.13712791270872207,0.04659473966636829 +R12_EEU,2020,0.004076015347422038,0.08702922309801421,0.6582212415044134,0.17642532573003197 +R12_EEU,2025,0.004180308934649179,0.10269209502788149,0.6182486854754695,0.1659113696200354 +R12_EEU,2030,0.005137069803664345,0.12662860598492412,0.5531212591940066,0.16239872461827498 +R12_EEU,2040,0.006013972501948006,0.12238742597151923,0.5287140744400889,0.14584399479148769 +R12_EEU,2050,0.0065497910195226215,0.10255002363995416,0.5315565199656966,0.12108159968637847 +R12_EEU,2060,0.006927662474141093,0.09238886568439274,0.5497130098722588,0.09953921556843351 +R12_EEU,2070,0.006785835919075902,0.07480966301946637,0.5643958659179478,0.07938794197004652 +R12_EEU,2080,0.00646363803765697,0.057432090684249586,0.5811851272596932,0.06102544751258784 +R12_EEU,2090,0.006012663928151659,0.040993771497927683,0.6000525913443153,0.04163577677069973 +R12_EEU,2100,0.00560694164834299,0.025967013287645533,0.6174834640430887,0.021580189190082633 +R12_FSU,2020,0.004644685677480172,0.027102281387776813,0.601402103259504,0.17708529760786787 +R12_FSU,2025,0.006765393110542478,0.036954261380407194,0.583603530031895,0.17731610897975203 +R12_FSU,2030,0.011222234225765019,0.05493358810538511,0.5356982703414227,0.17277791194348732 +R12_FSU,2040,0.01783074605014267,0.0888124921867898,0.4501094126184969,0.17800171746358662 +R12_FSU,2050,0.02044575847509311,0.10293199271269379,0.3964754430403239,0.17542828913083472 +R12_FSU,2060,0.022334290092692995,0.1106577006152766,0.39266204719333836,0.18284475192590047 +R12_FSU,2070,0.02250716400792684,0.10400907102102457,0.4118333652584656,0.19075389458312844 +R12_FSU,2080,0.02121603130617032,0.08983253758777082,0.4230202587772681,0.1873840355184868 +R12_FSU,2090,0.01913324712608193,0.07394123580876691,0.45784003814804525,0.19373321139267127 +R12_FSU,2100,0.016595617025853118,0.058763619268457065,0.4556050183012061,0.18522839994450216 +R12_LAM,2020,0.12119926560732798,0.14176863545974258,0.17764782423350106,0.03282131251072512 +R12_LAM,2025,0.19682567477889706,0.17485258639466697,0.1658190798199776,0.02971163197184297 +R12_LAM,2030,0.2862725446167766,0.22573820712451548,0.17609901703852565,0.028605539840142413 +R12_LAM,2040,0.27779217449737686,0.384329130930342,0.16728679237134145,0.033556171124243875 +R12_LAM,2050,0.2246278151649254,0.41192503953659754,0.1553946526712542,0.037039233106962326 +R12_LAM,2060,0.23234015001504965,0.43254549535361103,0.15602979123026187,0.037452287144689685 +R12_LAM,2070,0.2382845148073034,0.4295036480668353,0.15026007784183423,0.035874369077694125 +R12_LAM,2080,0.23884284536043965,0.41337890229994384,0.14470299581079218,0.03407883509247391 +R12_LAM,2090,0.23404538564329272,0.39102561853437495,0.13820969228126015,0.032175813072145405 +R12_LAM,2100,0.22298228493462727,0.36611470885594083,0.13327345666993293,0.03046994865348216 +R12_MEA,2020,0.42259957302309814,0.16717221374761052,0.3582347088047158,0.11192712602878761 +R12_MEA,2025,0.4808321167113557,0.16700011361739547,0.37072512769656074,0.12114766392297065 +R12_MEA,2030,0.5674336790141153,0.1679381945751115,0.403856799258025,0.11859654905348434 +R12_MEA,2040,0.587073184334695,0.17341042721258612,0.4064850668175206,0.10805160330510255 +R12_MEA,2050,0.5853224157102536,0.16104672064290207,0.39743688909691555,0.09628090537826549 +R12_MEA,2060,0.576014992288019,0.14064706533326823,0.38454215624177707,0.08310483798961435 +R12_MEA,2070,0.5178377172815382,0.1304626579906786,0.3555614745715863,0.07464035555416866 +R12_MEA,2080,0.4885717688588268,0.12399304414122041,0.33424355566051167,0.07056213770803689 +R12_MEA,2090,0.4690210159565985,0.1178972831777784,0.31713909458534206,0.06876328153235375 +R12_MEA,2100,0.43713096087927306,0.10905933262375447,0.2986143793935049,0.06719997718459689 +R12_NAM,2020,0.13508614757379064,0.17458238633511622,0.4830781033431388,0.3034455234402962 +R12_NAM,2025,0.13782737605740292,0.19395368174717437,0.45613685010079486,0.30189715831924485 +R12_NAM,2030,0.1362954085593758,0.21572986624433813,0.4037023511186508,0.29493665631884874 +R12_NAM,2040,0.13112209228824423,0.20097005118171643,0.3633761277267804,0.26080848983059396 +R12_NAM,2050,0.1257039653920622,0.18107325411836986,0.3454363378625042,0.2256007230552388 +R12_NAM,2060,0.12404516262901039,0.167847427402515,0.3687258510623942,0.21485064004864465 +R12_NAM,2070,0.12071265769043615,0.1560567795882852,0.39809613181165254,0.20993206264718792 +R12_NAM,2080,0.1159117677790826,0.14547932449068887,0.3834131089741752,0.1879309019231595 +R12_NAM,2090,0.1100328843861526,0.1339188603607946,0.35710114540969534,0.16552121133068454 +R12_NAM,2100,0.10454161726031591,0.12259522869416871,0.34363103823864216,0.15381054381049192 +R12_PAO,2020,0.05338241852516269,0.16408415989998887,0.18366734404464344,0.21898353793291936 +R12_PAO,2025,0.05320135694821245,0.21606910169743915,0.17766319479605852,0.21401046638484286 +R12_PAO,2030,0.057343841243796344,0.2944252093266796,0.1572635730901637,0.20944120862311394 +R12_PAO,2040,0.059063169052047566,0.3102644058457303,0.16548068335279126,0.19093339909412949 +R12_PAO,2050,0.06412647495584617,0.3010597389512281,0.16393163458148152,0.15023860048020757 +R12_PAO,2060,0.07318955729066784,0.294272546274089,0.16825176601168543,0.11564899458892924 +R12_PAO,2070,0.07493778327595399,0.2596492613639227,0.17290784158805592,0.08542008251744516 +R12_PAO,2080,0.07395654936727815,0.2227128139449878,0.17464734022890954,0.0655587451679652 +R12_PAO,2090,0.07304516608420557,0.18634562753944162,0.17611893745555862,0.054278283440445725 +R12_PAO,2100,0.07280566880070455,0.14617039142177368,0.17966701477487626,0.04381782382916792 +R12_PAS,2020,0.0528915177535148,0.12497293068807476,0.28534067709220173,0.06705397199571683 +R12_PAS,2025,0.08661861761144017,0.15551978808121578,0.27810856588484206,0.05232184367702349 +R12_PAS,2030,0.1458610991417145,0.18356151386174713,0.27254520821859785,0.04606185892524389 +R12_PAS,2040,0.18195755089369103,0.24107407104217568,0.24475410297437422,0.042820854022054156 +R12_PAS,2050,0.19504111561296134,0.2677260584692819,0.2249648591728882,0.0421152296254295 +R12_PAS,2060,0.2235916809371566,0.2797384177112428,0.22435857220797198,0.04490997229589783 +R12_PAS,2070,0.22495183200371818,0.27712276612032716,0.2296258129208709,0.04781665279835749 +R12_PAS,2080,0.21936764291338604,0.28574844593505105,0.22391949325081292,0.04696574181695472 +R12_PAS,2090,0.20865331079587376,0.2894755804814093,0.219900244543661,0.0468644213454465 +R12_PAS,2100,0.1968004856747939,0.275868550121322,0.2166581236273644,0.04748453793954707 +R12_RCPA,2020,0.27947500499638056,0.06829714123888539,0.23752930372685993,0.09051979490742561 +R12_RCPA,2025,0.38656277955545587,0.04932118734128023,0.24478552026376074,0.08334472455696083 +R12_RCPA,2030,0.4305250647467888,0.05274798709336067,0.2262957826854768,0.08980040814945248 +R12_RCPA,2040,0.3928246828903821,0.06995147639352081,0.22826062073211137,0.09250241265075318 +R12_RCPA,2050,0.4132303164426239,0.12917483706738414,0.21981544531768837,0.09028741597742265 +R12_RCPA,2060,0.4002535075528621,0.19222299770690768,0.2207993152531463,0.09032829724358402 +R12_RCPA,2070,0.384800943591352,0.20978521513214485,0.2130999265448838,0.08791764267218685 +R12_RCPA,2080,0.3764455486595034,0.20140072840177514,0.20338027998531885,0.0837755199485375 +R12_RCPA,2090,0.3716221898208241,0.18922985452123806,0.2000940280003834,0.0818835627871497 +R12_RCPA,2100,0.36036855393615,0.18067358097890387,0.19641834257692634,0.08009823036952132 +R12_SAS,2020,0.20796306508695545,0.03749995519460634,0.21078416905845646,0.030797157490279087 +R12_SAS,2025,0.28572834476525194,0.03542184463630637,0.20160922396697395,0.029502165567441865 +R12_SAS,2030,0.3504656009108971,0.05643366224379228,0.19537547341897846,0.026774115565331897 +R12_SAS,2040,0.3022039510330327,0.13602763198206852,0.16080701324759641,0.02243884544444698 +R12_SAS,2050,0.270507641041233,0.23365306051901533,0.13339677604637845,0.019593840752294152 +R12_SAS,2060,0.24785035841873745,0.29157084633613195,0.10964272809897459,0.01629997312537307 +R12_SAS,2070,0.22990905233522102,0.3026966335981731,0.09382338310477523,0.014199069696670955 +R12_SAS,2080,0.22065855897910167,0.3019808416910007,0.08295778242959359,0.012713044850340716 +R12_SAS,2090,0.2164074372612387,0.2968713391408911,0.0762961396086663,0.011522215887384536 +R12_SAS,2100,0.20858846206785803,0.28459310229819035,0.07310851043535038,0.011065519853850033 +R12_WEU,2020,0.007922357930047499,0.08804249184213216,0.4938618539214544,0.21382921832962784 +R12_WEU,2025,0.00923836297238843,0.0949945532535929,0.48578499419985804,0.20542555181921252 +R12_WEU,2030,0.011372713984963945,0.10770454347344965,0.4314680820946656,0.20239683292412516 +R12_WEU,2040,0.014878025858587815,0.09988996136748594,0.3856768617230247,0.1806971854399494 +R12_WEU,2050,0.016199002018418993,0.08469605975644776,0.3515964081721348,0.1569417284347075 +R12_WEU,2060,0.01669436836574576,0.07186376665157913,0.339350591535321,0.13384976203613208 +R12_WEU,2070,0.015997980405679863,0.05929255572842504,0.33139440323345376,0.12183584494572307 +R12_WEU,2080,0.01461645840261792,0.0471390123278371,0.3380584664197523,0.11313951073157609 +R12_WEU,2090,0.013480281535122046,0.03691268140260012,0.3420074909209009,0.10231057797606363 +R12_WEU,2100,0.012522876766549585,0.027104617095856362,0.34274741279447485,0.09159290256901834 diff --git a/message_ix_models/data/buildings/theta_cool_SSP1.csv b/message_ix_models/data/buildings/theta_cool_SSP1.csv new file mode 100644 index 0000000000..5730887654 --- /dev/null +++ b/message_ix_models/data/buildings/theta_cool_SSP1.csv @@ -0,0 +1,125 @@ +# METADATA: theta(node, year) = calibrated_demand_GWa / raw_RIME_demand_GWa at GWL = 1.1 +# Calibrated component: rc_sector_fractions_SSP1.csv * SSP_SSP1_v6.5_CID__baseline rc_{spec,therm} +# Raw component: gamma * EI(GWL=1.1) * F via correction_coefficients_*_SSP2_*.csv (SSP-agnostic by design) +# Generated: 2026-05-04 +node,year,theta +R12_AFR,2020,0.2985075898538407 +R12_AFR,2025,0.515220090780219 +R12_AFR,2030,1.0836847921524508 +R12_AFR,2040,1.8700147451754003 +R12_AFR,2050,1.9570315594864827 +R12_AFR,2060,0.9912046381445052 +R12_AFR,2070,0.4438652265289363 +R12_AFR,2080,0.2928984835219728 +R12_AFR,2090,0.22125303595457854 +R12_AFR,2100,0.18234414238350485 +R12_CHN,2020,0.7170639073940714 +R12_CHN,2025,0.6235717353152394 +R12_CHN,2030,0.7232770107664734 +R12_CHN,2040,0.6909141145994383 +R12_CHN,2050,0.6483085870480305 +R12_CHN,2060,0.5938701193226709 +R12_CHN,2070,0.5389599486287507 +R12_CHN,2080,0.4961857387091272 +R12_CHN,2090,0.4484273038785126 +R12_CHN,2100,0.3926298300377985 +R12_EEU,2020,1.440164605440043 +R12_EEU,2025,1.142475736725919 +R12_EEU,2030,1.0596364897796264 +R12_EEU,2040,0.7661144199097139 +R12_EEU,2050,0.5776631051573093 +R12_EEU,2060,0.47779014309732787 +R12_EEU,2070,0.38480675638255946 +R12_EEU,2080,0.30787050920652775 +R12_EEU,2090,0.23715025483560537 +R12_EEU,2100,0.16565712141192684 +R12_FSU,2020,0.7682967934486973 +R12_FSU,2025,0.6050829801434604 +R12_FSU,2030,0.6050855859688554 +R12_FSU,2040,0.6892420000187395 +R12_FSU,2050,0.6963322202628854 +R12_FSU,2060,0.6303291876310009 +R12_FSU,2070,0.5511738820617179 +R12_FSU,2080,0.48329577641886373 +R12_FSU,2090,0.4290158798436144 +R12_FSU,2100,0.380974295178323 +R12_LAM,2020,0.8356021597482246 +R12_LAM,2025,0.513323120588515 +R12_LAM,2030,0.394885522084859 +R12_LAM,2040,0.336608783571871 +R12_LAM,2050,0.34162431493697726 +R12_LAM,2060,0.3257656866951677 +R12_LAM,2070,0.299581703139359 +R12_LAM,2080,0.2723700760436458 +R12_LAM,2090,0.24691529251145897 +R12_LAM,2100,0.22079271514648618 +R12_MEA,2020,1.5873478082268797 +R12_MEA,2025,1.4651280597762661 +R12_MEA,2030,1.4234392087684535 +R12_MEA,2040,1.3792912417888785 +R12_MEA,2050,1.3234675620881657 +R12_MEA,2060,1.2502082395998302 +R12_MEA,2070,1.0130232097480938 +R12_MEA,2080,0.8389030581274884 +R12_MEA,2090,0.7226306578182503 +R12_MEA,2100,0.6413931574986633 +R12_NAM,2020,1.1753254940409679 +R12_NAM,2025,1.2853769695284232 +R12_NAM,2030,1.3320026689334143 +R12_NAM,2040,1.2916773999117162 +R12_NAM,2050,1.2257324163895398 +R12_NAM,2060,1.1358695532256722 +R12_NAM,2070,1.0472405311647823 +R12_NAM,2080,0.9646375136687593 +R12_NAM,2090,0.8776397303902048 +R12_NAM,2100,0.7857933518073321 +R12_PAO,2020,0.8417898819761639 +R12_PAO,2025,0.9944826218139713 +R12_PAO,2030,1.090690353332619 +R12_PAO,2040,1.0552690589079106 +R12_PAO,2050,0.9403943304288938 +R12_PAO,2060,0.8182809220640572 +R12_PAO,2070,0.7130246361215431 +R12_PAO,2080,0.6211740039827657 +R12_PAO,2090,0.5352262942680531 +R12_PAO,2100,0.44060003845692414 +R12_PAS,2020,1.9296835733327122 +R12_PAS,2025,1.3916007332378313 +R12_PAS,2030,1.1887462876492345 +R12_PAS,2040,0.7686934684068484 +R12_PAS,2050,0.5658552878640482 +R12_PAS,2060,0.4776009197745639 +R12_PAS,2070,0.41384121037019506 +R12_PAS,2080,0.3555907045436476 +R12_PAS,2090,0.30333306019995754 +R12_PAS,2100,0.26336572584215695 +R12_RCPA,2020,4.031195615567666 +R12_RCPA,2025,4.888981562330895 +R12_RCPA,2030,5.246501378123246 +R12_RCPA,2040,3.2088320803743313 +R12_RCPA,2050,1.536992703830442 +R12_RCPA,2060,0.8843895420910908 +R12_RCPA,2070,0.6424927442861599 +R12_RCPA,2080,0.5218187123119246 +R12_RCPA,2090,0.4519624311256967 +R12_RCPA,2100,0.4105847021313141 +R12_SAS,2020,1.5328015617120245 +R12_SAS,2025,1.6298883273825007 +R12_SAS,2030,2.0242179939688425 +R12_SAS,2040,1.7442514328290861 +R12_SAS,2050,0.96007967270153 +R12_SAS,2060,0.584878428629219 +R12_SAS,2070,0.40798699818240786 +R12_SAS,2080,0.31039690292381117 +R12_SAS,2090,0.25545977561161765 +R12_SAS,2100,0.21974373366712022 +R12_WEU,2020,1.2127781989828572 +R12_WEU,2025,1.0704486396993365 +R12_WEU,2030,0.9762518117432396 +R12_WEU,2040,0.7262363442674207 +R12_WEU,2050,0.6085113352526985 +R12_WEU,2060,0.5237152676470669 +R12_WEU,2070,0.45065065269464266 +R12_WEU,2080,0.3848754421968504 +R12_WEU,2090,0.3218100931494755 +R12_WEU,2100,0.2547975888417345 diff --git a/message_ix_models/data/buildings/theta_heat_SSP1.csv b/message_ix_models/data/buildings/theta_heat_SSP1.csv new file mode 100644 index 0000000000..7a47303db2 --- /dev/null +++ b/message_ix_models/data/buildings/theta_heat_SSP1.csv @@ -0,0 +1,125 @@ +# METADATA: theta(node, year) = calibrated_demand_GWa / raw_RIME_demand_GWa at GWL = 1.1 +# Calibrated component: rc_sector_fractions_SSP1.csv * SSP_SSP1_v6.5_CID__baseline rc_{spec,therm} +# Raw component: gamma * EI(GWL=1.1) * F via correction_coefficients_*_SSP2_*.csv (SSP-agnostic by design) +# Generated: 2026-05-04 +node,year,theta +R12_AFR,2020,0.9645493257913702 +R12_AFR,2025,0.8983819725378154 +R12_AFR,2030,0.9812572470413762 +R12_AFR,2040,0.9831009422438687 +R12_AFR,2050,0.9502168205097968 +R12_AFR,2060,0.9125228979209934 +R12_AFR,2070,0.8078610968611635 +R12_AFR,2080,0.7080996145981019 +R12_AFR,2090,0.6076306314679331 +R12_AFR,2100,0.5108143063717304 +R12_CHN,2020,0.8529037480180875 +R12_CHN,2025,0.9579905077023116 +R12_CHN,2030,0.9546883813929516 +R12_CHN,2040,0.866496809131011 +R12_CHN,2050,0.7956192199989781 +R12_CHN,2060,0.7412591654903391 +R12_CHN,2070,0.6930798435210346 +R12_CHN,2080,0.6719704501514265 +R12_CHN,2090,0.641276385492182 +R12_CHN,2100,0.5889083952921462 +R12_EEU,2020,0.8852459403813513 +R12_EEU,2025,0.9248038102051296 +R12_EEU,2030,0.9073498056848988 +R12_EEU,2040,0.8147123411013382 +R12_EEU,2050,0.7857850811793968 +R12_EEU,2060,0.7547847348952932 +R12_EEU,2070,0.7438842174623325 +R12_EEU,2080,0.7311022761720551 +R12_EEU,2090,0.7016345426391172 +R12_EEU,2100,0.6539361909322079 +R12_FSU,2020,1.157317717845158 +R12_FSU,2025,1.2790949327400982 +R12_FSU,2030,1.2239437830615958 +R12_FSU,2040,1.0704944883991339 +R12_FSU,2050,0.9425076709219363 +R12_FSU,2060,0.8283126325635994 +R12_FSU,2070,0.7647878951335351 +R12_FSU,2080,0.7337195851053646 +R12_FSU,2090,0.6952908228457471 +R12_FSU,2100,0.6375266090065381 +R12_LAM,2020,1.567927171583352 +R12_LAM,2025,1.3464303315806552 +R12_LAM,2030,1.3547245355812654 +R12_LAM,2040,1.293894433546606 +R12_LAM,2050,1.2438035410353214 +R12_LAM,2060,1.1644659542094278 +R12_LAM,2070,1.0315267233413066 +R12_LAM,2080,0.9093248557325835 +R12_LAM,2090,0.7973229712786929 +R12_LAM,2100,0.7077167709834694 +R12_MEA,2020,2.7871486991112815 +R12_MEA,2025,2.8383639524548485 +R12_MEA,2030,3.07527659731088 +R12_MEA,2040,3.1104502994373107 +R12_MEA,2050,3.1479994380294793 +R12_MEA,2060,3.1336247241183326 +R12_MEA,2070,2.9274423215312018 +R12_MEA,2080,2.6902590566967484 +R12_MEA,2090,2.424284930982612 +R12_MEA,2100,2.130516153308047 +R12_NAM,2020,0.891907627837627 +R12_NAM,2025,1.0568700445218993 +R12_NAM,2030,1.1337772089075089 +R12_NAM,2040,1.1744525161046548 +R12_NAM,2050,1.2196570654027679 +R12_NAM,2060,1.2493690959887156 +R12_NAM,2070,1.348492672509423 +R12_NAM,2080,1.4640939110186986 +R12_NAM,2090,1.5264224214169908 +R12_NAM,2100,1.506954752746031 +R12_PAO,2020,0.718775809290721 +R12_PAO,2025,0.7641951006943277 +R12_PAO,2030,0.7680467643020537 +R12_PAO,2040,0.7099607635419839 +R12_PAO,2050,0.6497027169408129 +R12_PAO,2060,0.5976723633361333 +R12_PAO,2070,0.5642325429620649 +R12_PAO,2080,0.5568161506194528 +R12_PAO,2090,0.5652230721177258 +R12_PAO,2100,0.5637192281817773 +R12_PAS,2020,1.5675907674797431 +R12_PAS,2025,1.682262864809336 +R12_PAS,2030,1.8598032689554693 +R12_PAS,2040,2.0911814388230163 +R12_PAS,2050,2.2645351938129514 +R12_PAS,2060,2.4051336233238216 +R12_PAS,2070,2.5615679556821505 +R12_PAS,2080,2.698596557036121 +R12_PAS,2090,2.7741033468592926 +R12_PAS,2100,2.824017914430106 +R12_RCPA,2020,3.035197066815303 +R12_RCPA,2025,3.283946940329988 +R12_RCPA,2030,3.183095702836488 +R12_RCPA,2040,2.795602724481772 +R12_RCPA,2050,2.4888272948813377 +R12_RCPA,2060,2.3159232989412 +R12_RCPA,2070,2.1848346499607296 +R12_RCPA,2080,2.1126228401321163 +R12_RCPA,2090,2.053392653446491 +R12_RCPA,2100,1.9818292473846146 +R12_SAS,2020,1.8785714976476642 +R12_SAS,2025,1.9905833970502937 +R12_SAS,2030,2.1352057235961803 +R12_SAS,2040,2.092324950860732 +R12_SAS,2050,1.9737654295640512 +R12_SAS,2060,1.9097107808804656 +R12_SAS,2070,1.8598537598703249 +R12_SAS,2080,1.834964551495411 +R12_SAS,2090,1.8410871626044005 +R12_SAS,2100,1.8092713263232325 +R12_WEU,2020,0.6570190648421776 +R12_WEU,2025,0.7062697080308846 +R12_WEU,2030,0.6866998598531584 +R12_WEU,2040,0.6223576448115412 +R12_WEU,2050,0.5672298431972507 +R12_WEU,2060,0.5340637586850412 +R12_WEU,2070,0.551850349291781 +R12_WEU,2080,0.588837244993346 +R12_WEU,2090,0.6077669051958365 +R12_WEU,2100,0.5954530576132622 From 7b3ca4bd48d0eb2ebec57e012520d68241b68d2a Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 4 May 2026 11:11:40 +0200 Subject: [PATCH 07/19] Fix SPARRCLE MAGICC config paths --- .../project/sparrcle/scenario_config.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/message_ix_models/project/sparrcle/scenario_config.yaml b/message_ix_models/project/sparrcle/scenario_config.yaml index 5233d8431c..a7c01e1078 100644 --- a/message_ix_models/project/sparrcle/scenario_config.yaml +++ b/message_ix_models/project/sparrcle/scenario_config.yaml @@ -27,31 +27,31 @@ starters: - model: SSP_SSP1_v6.5_sp scenario: NPiREF_GDP_CI_50_ensemble_1_PHY ssp: SSP1 - magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/NPiREF_GDP_CI_50_ensemble_1/ + magicc_output_dir: /pdrive/projects/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP1_v6.5_sp__NPiREF_GDP_CI_50_ensemble_1/ - model: SSP_SSP1_v6.5_sp scenario: INDC2030i_uncon_800f_GDP_CI_50_ensemble_1_PHY ssp: SSP1 - magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP1_v6.5_sp__INDC2030i_uncon_800f_GDP_CI_50_ensemble_1/ + magicc_output_dir: /pdrive/projects/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP1_v6.5_sp__INDC2030i_uncon_800f_GDP_CI_50_ensemble_1/ - model: SSP_SSP2_v6.5_sp scenario: NPiREF_GDP_CI_50_ensemble_2_PHY ssp: SSP2 - magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/NPiREF_GDP_CI_50_ensemble_2/ + magicc_output_dir: /pdrive/projects/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP2_v6.5_sp__NPiREF_GDP_CI_50_ensemble_2/ - model: SSP_SSP2_v6.5_sp scenario: INDC2030i_uncon_600f_GDP_CI_50_ensemble_1_PHY ssp: SSP2 - magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP2_v6.5_sp__INDC2030i_uncon_600f_GDP_CI_50_ensemble_1/ + magicc_output_dir: /pdrive/projects/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP2_v6.5_sp__INDC2030i_uncon_600f_GDP_CI_50_ensemble_1/ - model: SSP_SSP3_v6.5_sp scenario: NPiREF_GDP_CI_50_ensemble_2_PHY ssp: SSP3 - magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/NPiREF_GDP_CI_50_ensemble_2/ + magicc_output_dir: /pdrive/projects/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP3_v6.5_sp__NPiREF_GDP_CI_50_ensemble_2/ - model: SSP_SSP3_v6.5_sp scenario: INDC2030i_uncon_600f_GDP_CI_50_ensemble_1_PHY ssp: SSP3 - magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/INDC2030i_uncon_600f_GDP_CI_50_ensemble_1/ + magicc_output_dir: /pdrive/projects/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP3_v6.5_sp__INDC2030i_uncon_600f_GDP_CI_50_ensemble_1/ - model: SSP_SSP3_v6.5_sp scenario: INDC2030i_uncon_800f_GDP_CI_50_ensemble_1_PHY ssp: SSP3 - magicc_output_dir: /mnt/p/watxene/IMPACTS/sparrcle/magicc_output/INDC2030i_uncon_800f_GDP_CI_50_ensemble_1/ + magicc_output_dir: /pdrive/projects/watxene/IMPACTS/sparrcle/magicc_output/SSP_SSP3_v6.5_sp__INDC2030i_uncon_800f_GDP_CI_50_ensemble_1/ # Phase-1 cooling-module build settings (passed to `mix-models water-ix cooling`) cooling: From f360574788b7ae6584f017995ba20bfafa118aeb Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 4 May 2026 11:39:58 +0200 Subject: [PATCH 08/19] Rationalize PR 479 test suite Drop test_sparrcle.py (trivial click registration check) and test_sparrcle_workflow.py (graph-key assertions with load_config and validate_inputs both monkeypatched away). Replace the permanently-skipped theta calibration test in test_building_impacts.py with two in-memory tests of prepare_building_demand that run unconditionally in CI: - test_prepare_building_demand_substitution_arithmetic: verifies the substitution formula new = old*(1-frac) + cid against known inputs - test_prepare_building_demand_missing_cid_zerofills: verifies that an empty CID frame contributes zero rather than NaN Add netCDF4 and plotnine as dev dependencies (needed to run the remaining predict_building_ei and package-import paths in tests). --- .../tests/model/test_building_impacts.py | 106 ++++++++------- .../tests/project/test_sparrcle.py | 12 -- .../tests/project/test_sparrcle_workflow.py | 124 ------------------ pyproject.toml | 8 ++ 4 files changed, 63 insertions(+), 187 deletions(-) delete mode 100644 message_ix_models/tests/project/test_sparrcle.py delete mode 100644 message_ix_models/tests/project/test_sparrcle_workflow.py diff --git a/message_ix_models/tests/model/test_building_impacts.py b/message_ix_models/tests/model/test_building_impacts.py index 61f434942f..4d8bc0f856 100644 --- a/message_ix_models/tests/model/test_building_impacts.py +++ b/message_ix_models/tests/model/test_building_impacts.py @@ -1,21 +1,13 @@ """Tests for model.buildings.impacts — building energy CID integration.""" -from pathlib import Path - import numpy as np import pandas as pd -import pytest from message_ix_models.model.buildings.impacts import ( _demand_to_final_energy_iamc, - compute_building_cids, - load_sector_fractions, predict_building_ei, + prepare_building_demand, ) -from message_ix_models.tools.impacts import GmtArray - -_RC_SPEC_BASELINE = Path("_staging/rc_spec_baseline.csv") -_RC_THERM_BASELINE = Path("_staging/rc_therm_baseline.csv") # --------------------------------------------------------------------------- # predict_building_ei @@ -62,50 +54,62 @@ def test_demand_to_final_energy_iamc_converts_gwa_to_ej_per_year(): # --------------------------------------------------------------------------- -# compute_building_cids end-to-end (requires staged baselines) +# prepare_building_demand — substitution arithmetic # --------------------------------------------------------------------------- - -@pytest.mark.skipif( - not (_RC_SPEC_BASELINE.exists() and _RC_THERM_BASELINE.exists()), - reason=( - "Requires SSP2 baseline rc_spec/rc_therm extracts at " - "_staging/rc_spec_baseline.csv and _staging/rc_therm_baseline.csv; " - "produced by SPARRCLE staging tooling, not packaged in the repo." - ), +# Minimal columns required by prepare_building_demand / _substitute_climate_component. +_RC_SPEC = pd.DataFrame( + {"node": ["R12_AFR"], "year": [2050], "commodity": ["rc_spec"], "value": [100.0]} +) +_RC_THERM = pd.DataFrame( + {"node": ["R12_AFR"], "year": [2050], "commodity": ["rc_therm"], "value": [200.0]} ) -def test_theta_reproduces_calibrated_demand_at_gwl_1_1(): - """At GWL 1.1 (present-day), CID output must match beta * rc.""" - years = np.arange(2020, 2101) - gmt = GmtArray(values=np.full(len(years), 1.1), years=years) - - cooling, heating = compute_building_cids( - gmt, - [2030, 2035, 2040, 2045, 2050, 2055, 2060, 2070, 2080, 2090, 2100, 2110], - reference_scenario="SSP2", +_FRACTIONS = pd.DataFrame( + { + "node": ["R12_AFR"], + "year": [2050], + "frac_resid_cool": [0.2], + "frac_comm_cool": [0.1], + "frac_resid_heat": [0.3], + "frac_comm_heat": [0.1], + } +) + + +def test_prepare_building_demand_substitution_arithmetic(): + """new_value = old_value * (1 - frac_total) + cid_value.""" + cooling_cids = pd.DataFrame({"node": ["R12_AFR"], "year": [2050], "value": [15.0]}) + heating_cids = pd.DataFrame({"node": ["R12_AFR"], "year": [2050], "value": [30.0]}) + + new_spec, new_therm = prepare_building_demand( + _RC_SPEC.copy(), + _RC_THERM.copy(), + cooling_cids, + heating_cids, + fractions=_FRACTIONS, + ) + + # rc_spec: 100 * (1 - 0.3) + 15 = 85.0 + np.testing.assert_allclose(new_spec["value"].values, [85.0]) + # rc_therm: 200 * (1 - 0.4) + 30 = 150.0 + np.testing.assert_allclose(new_therm["value"].values, [150.0]) + + +def test_prepare_building_demand_missing_cid_zerofills(): + """When CID has no rows for a (node, year), its contribution is zero.""" + empty_cids = pd.DataFrame( + {"node": pd.Series(dtype=str), "year": pd.Series(dtype=int), "value": pd.Series(dtype=float)} + ) + + new_spec, new_therm = prepare_building_demand( + _RC_SPEC.copy(), + _RC_THERM.copy(), + empty_cids, + empty_cids, + fractions=_FRACTIONS, ) - fractions = load_sector_fractions("SSP2") - rc_spec = pd.read_csv(_RC_SPEC_BASELINE)[["node", "year", "value"]] - rc_therm = pd.read_csv(_RC_THERM_BASELINE)[["node", "year", "value"]] - - for cid_df, scenario_df, frac_cols in ( - (cooling, rc_spec, ["frac_resid_cool", "frac_comm_cool"]), - (heating, rc_therm, ["frac_resid_heat", "frac_comm_heat"]), - ): - calibrated = scenario_df.merge( - fractions[["node", "year"] + frac_cols], - on=["node", "year"], - how="inner", - ) - calibrated = calibrated.assign( - calibrated=lambda df: df["value"] * df[frac_cols].sum(axis=1) - )[["node", "year", "calibrated"]] - - merged = cid_df.merge(calibrated, on=["node", "year"], how="inner") - np.testing.assert_allclose( - merged["value"], - merged["calibrated"], - atol=1e-10, - rtol=0, - ) + # rc_spec: 100 * (1 - 0.3) + 0 = 70.0 + np.testing.assert_allclose(new_spec["value"].values, [70.0]) + # rc_therm: 200 * (1 - 0.4) + 0 = 120.0 + np.testing.assert_allclose(new_therm["value"].values, [120.0]) diff --git a/message_ix_models/tests/project/test_sparrcle.py b/message_ix_models/tests/project/test_sparrcle.py deleted file mode 100644 index 170fe28358..0000000000 --- a/message_ix_models/tests/project/test_sparrcle.py +++ /dev/null @@ -1,12 +0,0 @@ -"""Smoke test for the SPARRCLE CLI registration.""" - -from message_ix_models.project.sparrcle.cli import cli as sparrcle_cli - - -def test_run_command_registered() -> None: - assert "run" in sparrcle_cli.commands - run_cmd = sparrcle_cli.commands["run"] - opts = {p.name: p for p in run_cmd.params} - assert "config_path" in opts # --config - assert "go" in opts # --go from make_click_command - assert "truncate_step" in opts # --from from make_click_command diff --git a/message_ix_models/tests/project/test_sparrcle_workflow.py b/message_ix_models/tests/project/test_sparrcle_workflow.py deleted file mode 100644 index 9141f523c8..0000000000 --- a/message_ix_models/tests/project/test_sparrcle_workflow.py +++ /dev/null @@ -1,124 +0,0 @@ -"""Tests for project.sparrcle.workflow — Workflow construction + actions.""" - -from types import SimpleNamespace - -from message_ix_models.project.sparrcle import workflow as wf_mod - - -def _config(): - return { - "platform_info": {"name": "ixmp_dev"}, - "starters": [ - { - "model": "SSP_SSP1_v6.5_sp", - "scenario": "BASE_SSP1", - "ssp": "SSP1", - "magicc_output_dir": "/fake/ssp1", - }, - { - "model": "SSP_SSP2_v6.5_sp", - "scenario": "BASE_SSP2", - "ssp": "SSP2", - "magicc_output_dir": "/fake/ssp2", - }, - ], - "cooling": {"rcps": "no_climate", "rels": "low"}, - "regions": "R12", - "cid": {"n_runs": 3, "min_year": 2045}, - } - - -def _build_wf(monkeypatch, tmp_path, context): - config_path = tmp_path / "sparrcle.yaml" - config_path.write_text("# stub\n") - monkeypatch.setattr(wf_mod, "load_config", lambda _p: _config()) - monkeypatch.setattr(wf_mod, "validate_inputs", lambda _c: None) - return wf_mod.generate(context, config_path=config_path) - - -# --------------------------------------------------------------------------- -# generate() -# --------------------------------------------------------------------------- - - -def test_generate_step_names_and_dependencies(monkeypatch, tmp_path): - wf = _build_wf(monkeypatch, tmp_path, context=SimpleNamespace()) - # Expected step set: per starter (base, cooling, CI_b, CI_p, CI_bp) - # plus aggregator and the implicit "context". - expected_steps = { - "SSP1/BASE_SSP1 base", - "SSP1/BASE_SSP1 cooling", - "SSP1/BASE_SSP1 CI_b", - "SSP1/BASE_SSP1 CI_p", - "SSP1/BASE_SSP1 CI_bp", - "SSP2/BASE_SSP2 base", - "SSP2/BASE_SSP2 cooling", - "SSP2/BASE_SSP2 CI_b", - "SSP2/BASE_SSP2 CI_p", - "SSP2/BASE_SSP2 CI_bp", - "all CI", - } - assert expected_steps <= set(wf.keys()) - assert wf.default_key == "all CI" - - -def test_generate_variant_bases(monkeypatch, tmp_path): - wf = _build_wf(monkeypatch, tmp_path, context=SimpleNamespace()) - # genno graph entries are tuples (callable, *deps); deps[1] is `context`, - # deps[2] is the base step. - assert wf.graph["SSP1/BASE_SSP1 CI_b"][2] == "SSP1/BASE_SSP1 base" - assert wf.graph["SSP1/BASE_SSP1 CI_p"][2] == "SSP1/BASE_SSP1 cooling" - assert wf.graph["SSP1/BASE_SSP1 CI_bp"][2] == "SSP1/BASE_SSP1 cooling" - - -# --------------------------------------------------------------------------- -# phase1_cooling -# --------------------------------------------------------------------------- - - -def test_phase1_cooling_subprocess_invocation_and_load(monkeypatch): - ran = {} - - def fake_run(cmd, check): - ran["cmd"] = cmd - ran["check"] = check - return SimpleNamespace(returncode=0) - - monkeypatch.setattr(wf_mod.subprocess, "run", fake_run) - - loaded: list[tuple[str, str]] = [] - - class FakeScenario: - def __init__(self, platform, model, scenario): - loaded.append((model, scenario)) - self.platform = platform - self.model = model - self.scenario = scenario - - monkeypatch.setattr("message_ix.Scenario", FakeScenario) - - scen_in = SimpleNamespace( - platform=SimpleNamespace(name="ixmp_dev"), - model="SSP_SSP2_v6.5_sp", - scenario="BASE_SSP2", - ) - result = wf_mod.phase1_cooling( - SimpleNamespace(), - scen_in, - ssp="SSP2", - regions="R12", - rcps="no_climate", - rels="low", - ) - # Subprocess invoked with the expected URL - assert "--url" in ran["cmd"] - url_idx = ran["cmd"].index("--url") + 1 - assert ran["cmd"][url_idx] == "ixmp://ixmp_dev/SSP_SSP2_v6.5_sp/BASE_SSP2" - assert "cooling" in ran["cmd"] - assert ran["check"] is True - # Returned scenario is the produced {base}_cooling - assert (result.model, result.scenario) == ( - "SSP_SSP2_v6.5_sp", - "BASE_SSP2_cooling", - ) - assert loaded == [("SSP_SSP2_v6.5_sp", "BASE_SSP2_cooling")] diff --git a/pyproject.toml b/pyproject.toml index 617ccd042d..35f2516de6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -200,3 +200,11 @@ mccabe.max-complexity = 11 packages = ["message_ix_models"] [tool.setuptools_scm] + +[dependency-groups] +dev = [ + "netcdf4>=1.7.3", + "plotnine>=0.15.3", + "pytest>=9.0.3", + "pytest-cov>=7.1.0", +] From 23ceb676b83c34131f6dd03ce025b6247663c475 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 4 May 2026 11:44:49 +0200 Subject: [PATCH 09/19] Add netCDF4 to test dependencies CI was failing on tests that open RIME NetCDF files via xarray (test_ratio_* in test_cooling_impacts.py, test_warming_* in test_building_impacts.py) because no NetCDF backend was installed. The files exist in the repo via LFS; the skip guard only checked existence, not openability. --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 35f2516de6..7d0af36c46 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -78,6 +78,7 @@ report = ["plotnine", "xlsxwriter"] tests = [ "message-ix[tests]", "message_ix_models[bilateralize,buildings,material,report,transport]", + "netCDF4", "pytest", "pytest-cov", "pytest-timeout", From 50b4a72d96b1c94131699b325ab0ea9d244732bc Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 11 May 2026 12:03:31 +0200 Subject: [PATCH 10/19] Require explicit building reference scenario --- message_ix_models/model/buildings/impacts.py | 43 ++++++++++++-------- 1 file changed, 26 insertions(+), 17 deletions(-) diff --git a/message_ix_models/model/buildings/impacts.py b/message_ix_models/model/buildings/impacts.py index 16bb47e948..66646b3493 100644 --- a/message_ix_models/model/buildings/impacts.py +++ b/message_ix_models/model/buildings/impacts.py @@ -33,7 +33,6 @@ # MESSAGE demand parameter expects GWa _EJ_TO_GWA = registry("1 EJ").to("GW * year").magnitude -_REFERENCE_SCENARIO = "SSP2" _CORRECTION_COEFFICIENT_SCENARIO = "SSP2" _FINAL_ENERGY_UNIT = "EJ/yr" @@ -64,9 +63,7 @@ def load_correction_coefficients( return pd.read_csv(path, comment="#") -def load_sector_fractions( - reference_scenario: str = _REFERENCE_SCENARIO, -) -> pd.DataFrame: +def load_sector_fractions(reference_scenario: str) -> pd.DataFrame: """Load sector fractions of rc_spec/rc_therm per (node, year).""" return pd.read_csv( _buildings_data_path(f"rc_sector_fractions_{reference_scenario}.csv"), @@ -81,7 +78,7 @@ def load_floor_areas(sector: Literal["resid", "comm"] = "resid") -> pd.DataFrame def load_theta( mode: Literal["cool", "heat"], - reference_scenario: str = _REFERENCE_SCENARIO, + reference_scenario: str, ) -> pd.DataFrame: """Load theta(node, year) calibrated to the reference scenario demand.""" path = _buildings_data_path(f"theta_{mode}_{reference_scenario}.csv") @@ -223,7 +220,7 @@ def _compute_sector_energy( def _apply_theta( demand: pd.DataFrame, mode: Literal["cool", "heat"], - reference_scenario: str = _REFERENCE_SCENARIO, + reference_scenario: str, ) -> pd.DataFrame: """Scale raw RIME demand by theta to match SSP-calibrated STURM levels. @@ -255,7 +252,8 @@ def _apply_theta( def compute_building_cids( gmt: GmtArray, model_years: list[int], - reference_scenario: str = _REFERENCE_SCENARIO, + reference_scenario: str, + regions: str = "R12", ) -> tuple[pd.DataFrame, pd.DataFrame]: """Compute building energy CIDs from a GMT ensemble. @@ -270,14 +268,22 @@ def compute_building_cids( ``gmt.years`` that match these are used; 2110 is forward-filled from 2100. reference_scenario - SSP scenario for theta and sector fractions ('SSP2', 'SSP3'). + SSP scenario for theta and sector fractions. + regions + MESSAGE node codelist. Only ``"R12"`` is supported by the shipped + RIME EI datasets and STURM floor-area files. Returns ------- tuple[pd.DataFrame, pd.DataFrame] ``(cooling, heating)`` DataFrames with columns - ``[node, year, value]`` where value is GWa. Node uses R12_ prefix. + ``[node, year, value]`` where value is GWa. """ + if regions != "R12": + raise NotImplementedError( + f"regions={regions!r}; this kernel currently only supports R12 " + "(RIME EI datasets and STURM floor-area files are R12-coded)." + ) values = np.asarray(gmt.values) years = np.asarray(gmt.years, dtype=int) @@ -319,7 +325,7 @@ def compute_building_cids( .groupby(["region", "year"], as_index=False)["value"] .sum() .assign( - node=lambda df: "R12_" + df["region"], + node=lambda df: f"{regions}_" + df["region"], value=lambda df: df["value"] * _EJ_TO_GWA, )[["node", "year", "value"]] ) @@ -386,7 +392,7 @@ def prepare_building_demand( cooling_cids: pd.DataFrame, heating_cids: pd.DataFrame, fractions: pd.DataFrame | None = None, - reference_scenario: str = _REFERENCE_SCENARIO, + reference_scenario: str | None = None, ) -> tuple[pd.DataFrame, pd.DataFrame]: """Replace the STURM-calibrated climate component of rc_spec/rc_therm. @@ -407,9 +413,10 @@ def prepare_building_demand( Same format, heating. fractions Sector fractions. If *None*, loaded from package data for - *reference_scenario*. + *reference_scenario* — which then becomes required. reference_scenario - SSP scenario for sector fractions ('SSP2', 'SSP3'). + SSP scenario for sector fractions. Required when *fractions* is + None. Returns ------- @@ -418,6 +425,8 @@ def prepare_building_demand( with the same columns as the inputs. """ if fractions is None: + if reference_scenario is None: + raise ValueError("reference_scenario is required when fractions is None") fractions = load_sector_fractions(reference_scenario) demand_years = sorted( @@ -465,8 +474,9 @@ def apply_building_cids( scen: Scenario, cooling_demand: pd.DataFrame, heating_demand: pd.DataFrame, + *, + reference_scenario: str, commit_message: str | None = None, - reference_scenario: str = _REFERENCE_SCENARIO, ) -> None: """Write replacement building demands to *scen* in place. @@ -474,8 +484,7 @@ def apply_building_cids( for the buildings-component substitution, writes the merged demand back, and persists the resolved CID demand as scenario timeseries under ``Final Energy|Residential and Commercial|{Cooling,Heating}`` - in ``EJ/yr`` - so downstream reporting can retrieve it via standard + in ``EJ/yr`` so downstream reporting can retrieve it via standard :meth:`scen.timeseries() ` calls. Parameters @@ -490,7 +499,7 @@ def apply_building_cids( commit_message Parameter-write commit message. Default ``"Inject building CIDs"``. reference_scenario - SSP scenario for sector fractions (``"SSP2"`` or ``"SSP3"``). + SSP scenario for sector fractions. """ demand = scen.par("demand") From 039ac742630020f195fa6f28577b2eb57623ec15 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 11 May 2026 12:03:42 +0200 Subject: [PATCH 11/19] Parameterize cooling region prefix --- .../model/water/data/cooling_impacts.py | 52 ++++++++++++++----- 1 file changed, 38 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/water/data/cooling_impacts.py b/message_ix_models/model/water/data/cooling_impacts.py index 08556fc4cc..7de3438ffe 100644 --- a/message_ix_models/model/water/data/cooling_impacts.py +++ b/message_ix_models/model/water/data/cooling_impacts.py @@ -42,15 +42,19 @@ # Constants # --------------------------------------------------------------------------- +# R12 regional emulator with two cooling-axis values: "wet" and "dry". _DATASET = "r12_thermoelectric_gwl.nc" _VAR = "capacity_factor" + +# Ratio denominator: CF(GWL) / CF(1.0 degC). Other baselines are possible +# only if the calibration is re-derived against that reference level. _DEFAULT_BASELINE_GWL = 1.0 -# 2045 because (a) regional climatology shows clear scenario forcing -# differentiation around then, and (b) applying the wet-cooling -# relation_activity for years before 2045 produced infeasibilities in -# practice. (b) was not rigorously investigated. + +# Earlier wet-cooling bounds produced infeasibilities in preliminary runs. _DEFAULT_MIN_YEAR = 2045 +# Dataset selectors. "wet" covers freshwater once-through + closed-loop; +# "dry" covers air cooling. Saline cooling is not represented in this file. _WET_SEL = {"cooling": "wet"} _DRY_SEL = {"cooling": "dry"} _WET_TIMESERIES_VARIABLE = ( @@ -543,16 +547,16 @@ def build_dry_cooling_factors( # --------------------------------------------------------------------------- -def _ratios_to_long(ratios: pd.DataFrame) -> pd.DataFrame: - """Wide (region x year) ratios → long (region, year, value), R12-prefixed. +def _ratios_to_long(ratios: pd.DataFrame, regions: str) -> pd.DataFrame: + """Wide (region x year) ratios → long (region, year, value), node-prefixed. Region values come from the RIME dataset as short codes (``"AFR"``); - they are reprefixed with ``"R12_"`` to match scenario node labels. + they are reprefixed with ``f"{regions}_"`` to match scenario node labels. """ long = ratios.reset_index().melt( id_vars="region", var_name="year", value_name="value" ) - long["region"] = "R12_" + long["region"].astype(str) + long["region"] = f"{regions}_" + long["region"].astype(str) return long @@ -561,6 +565,7 @@ def apply_cooling_cids( gmt: GmtArray, min_year: int = _DEFAULT_MIN_YEAR, commit_message: str | None = None, + regions: str = "R12", ) -> None: """Apply wet + dry cooling CIDs to *scen* in place. @@ -582,16 +587,35 @@ def apply_cooling_cids( commit_message Commit message for the parameter writes. Default ``"Apply cooling CIDs (wet+dry)"``. + regions + MESSAGE node codelist. Only ``"R12"`` is supported by the shipped + RIME and freshwater-share data. """ + if regions != "R12": + raise NotImplementedError( + f"regions={regions!r}; this kernel currently only supports R12 " + "(input RIME and freshwater-share data files are R12-coded)." + ) from message_ix_models.util import ScenarioInfo info = ScenarioInfo(scen) model_years = info.Y - year_mask = np.isin(gmt.years, model_years) - values = gmt.values - gmt_model = values[:, year_mask] if values.ndim == 2 else values[year_mask] - matched_years = gmt.years[year_mask].tolist() + values = np.asarray(gmt.values) + years = np.asarray(gmt.years, dtype=int) + year_index = {int(year): i for i, year in enumerate(years)} + last_year = int(years.max()) + matched_years = [ + int(year) + for year in sorted(model_years) + if int(year) in year_index or int(year) > last_year + ] + if not matched_years: + raise ValueError( + "No MESSAGE model years overlap with or follow GMT input years" + ) + positions = [year_index.get(year, year_index[last_year]) for year in matched_years] + gmt_model = values[:, positions] if values.ndim == 2 else values[positions] log.info("Computing wet cooling degradation ratios...") wet_ratios = compute_degradation_ratios( @@ -654,12 +678,12 @@ def apply_cooling_cids( # RIME capacity-factor source variable. constrained = [y for y in matched_years if y >= min_year] ts_wet = frame_to_iamc( - _ratios_to_long(wet_ratios.loc[:, constrained]), + _ratios_to_long(wet_ratios.loc[:, constrained], regions), _WET_TIMESERIES_VARIABLE, "dimensionless", ) ts_dry = frame_to_iamc( - _ratios_to_long(dry_ratios.loc[:, constrained]), + _ratios_to_long(dry_ratios.loc[:, constrained], regions), _DRY_TIMESERIES_VARIABLE, "dimensionless", ) From f74cb76b242adeb1c9cbb98432cd43eb9f955bdf Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 11 May 2026 12:04:14 +0200 Subject: [PATCH 12/19] Rename SPARCCLE project module and thread regions --- doc/api/tools-impacts.rst | 2 +- doc/whatsnew.rst | 4 ++-- message_ix_models/cli.py | 2 +- .../{sparrcle => sparccle}/__init__.py | 0 .../project/{sparrcle => sparccle}/cli.py | 10 +++++----- .../scenario_config.yaml | 4 ++-- .../{sparrcle => sparccle}/workflow.py | 20 +++++++++---------- 7 files changed, 20 insertions(+), 22 deletions(-) rename message_ix_models/project/{sparrcle => sparccle}/__init__.py (100%) rename message_ix_models/project/{sparrcle => sparccle}/cli.py (76%) rename message_ix_models/project/{sparrcle => sparccle}/scenario_config.yaml (96%) rename message_ix_models/project/{sparrcle => sparccle}/workflow.py (95%) diff --git a/doc/api/tools-impacts.rst b/doc/api/tools-impacts.rst index 5dda7c747f..19ce422435 100644 --- a/doc/api/tools-impacts.rst +++ b/doc/api/tools-impacts.rst @@ -15,7 +15,7 @@ resampling sparse calibration inputs onto MESSAGE model years. The toolkit is domain-agnostic: it predicts at native emulator resolution and returns NumPy arrays. Domain modules under :mod:`.model.buildings`, -:mod:`.model.water.data`, and :mod:`.project.sparrcle` own the transformation +:mod:`.model.water.data`, and :mod:`.project.sparccle` own the transformation to MESSAGE-compatible parameters. RIME prediction is an adapted reimplementation of the GWL-binned diff --git a/doc/whatsnew.rst b/doc/whatsnew.rst index 979d881aab..67f3dc1318 100644 --- a/doc/whatsnew.rst +++ b/doc/whatsnew.rst @@ -20,8 +20,8 @@ Next release - Adjust :mod:`.project.ssp.transport` (:pull:`485`): - Add stub of :func:`.scenariomip.workflow.generate` (:pull:`394`). - New guide on HOWTO :doc:`/howto/report` (:pull:`488`). -- New :mod:`.tools.impacts` library and :mod:`.project.sparrcle` consumer - for SPARRCLE physical-impact scenarios (:pull:`479`). +- New :mod:`.tools.impacts` library and :mod:`.project.sparccle` consumer + for SPARCCLE physical-impact scenarios (:pull:`479`). v2026.4.17 ========== diff --git a/message_ix_models/cli.py b/message_ix_models/cli.py index 90e404a248..97ee977fa5 100644 --- a/message_ix_models/cli.py +++ b/message_ix_models/cli.py @@ -166,7 +166,7 @@ def _log_threads(k: int, n: int): "message_ix_models.project.circeular.cli", "message_ix_models.project.edits.cli", "message_ix_models.project.navigate.cli", - "message_ix_models.project.sparrcle.cli", + "message_ix_models.project.sparccle.cli", "message_ix_models.project.ssp.cli", "message_ix_models.report.cli", "message_ix_models.model.material.cli", diff --git a/message_ix_models/project/sparrcle/__init__.py b/message_ix_models/project/sparccle/__init__.py similarity index 100% rename from message_ix_models/project/sparrcle/__init__.py rename to message_ix_models/project/sparccle/__init__.py diff --git a/message_ix_models/project/sparrcle/cli.py b/message_ix_models/project/sparccle/cli.py similarity index 76% rename from message_ix_models/project/sparrcle/cli.py rename to message_ix_models/project/sparccle/cli.py index e413070d54..3c506d013e 100644 --- a/message_ix_models/project/sparrcle/cli.py +++ b/message_ix_models/project/sparccle/cli.py @@ -1,4 +1,4 @@ -"""Command-line tools specific to the SPARRCLE project.""" +"""Command-line tools specific to the SPARCCLE project.""" from pathlib import Path @@ -7,17 +7,17 @@ from message_ix_models.workflow import make_click_command -@click.group("sparrcle") +@click.group("sparccle") @click.pass_obj def cli(context): - """SPARRCLE project.""" + """SPARCCLE project.""" cli.add_command( make_click_command( f"{__package__}.workflow.generate", - name="SPARRCLE", - slug="sparrcle", + name="SPARCCLE", + slug="sparccle", params=[ click.Option( ["--config", "config_path"], diff --git a/message_ix_models/project/sparrcle/scenario_config.yaml b/message_ix_models/project/sparccle/scenario_config.yaml similarity index 96% rename from message_ix_models/project/sparrcle/scenario_config.yaml rename to message_ix_models/project/sparccle/scenario_config.yaml index a7c01e1078..72ca16ac4e 100644 --- a/message_ix_models/project/sparrcle/scenario_config.yaml +++ b/message_ix_models/project/sparccle/scenario_config.yaml @@ -1,6 +1,6 @@ -# SPARRCLE Scenario Configuration +# SPARCCLE Scenario Configuration # -# Consumed by `message_ix_models.project.sparrcle.workflow.generate`. +# Consumed by `message_ix_models.project.sparccle.workflow.generate`. # # Phase 1 — Module build ("{ssp}/{scenario} cooling"): # subprocess `mix-models water-ix cooling` → {scenario}_cooling diff --git a/message_ix_models/project/sparrcle/workflow.py b/message_ix_models/project/sparccle/workflow.py similarity index 95% rename from message_ix_models/project/sparrcle/workflow.py rename to message_ix_models/project/sparccle/workflow.py index 8f714ce589..aeb89b52c0 100644 --- a/message_ix_models/project/sparrcle/workflow.py +++ b/message_ix_models/project/sparccle/workflow.py @@ -1,9 +1,9 @@ -"""SPARRCLE physical-impact workflow. +"""SPARCCLE physical-impact workflow. Builds a :class:`message_ix_models.workflow.Workflow` whose nodes are the -SPARRCLE Phase-1 cooling-module clones and the Phase-2 CID variants +SPARCCLE Phase-1 cooling-module clones and the Phase-2 CID variants (``CI_b`` / ``CI_p`` / ``CI_bp``) for every starter declared in -``scenario_config.yaml``. ``mix-models sparrcle run TARGET`` selects the +``scenario_config.yaml``. ``mix-models sparccle run TARGET`` selects the subgraph, ``--from`` truncates at a given step, ``--go`` actually runs. Phase-1 cooling delegates to ``mix-models water-ix cooling`` via @@ -36,12 +36,12 @@ log = logging.getLogger(__name__) _DEFAULT_CONFIG_PATH = package_data_path( - "..", "project", "sparrcle", "scenario_config.yaml" + "..", "project", "sparccle", "scenario_config.yaml" ) def load_config(path: str | Path) -> dict: - """Load the SPARRCLE scenario config.""" + """Load the SPARCCLE scenario config.""" with open(path) as f: raw = yaml.safe_load(f) @@ -115,7 +115,7 @@ def validate_inputs(config: dict) -> None: ) if missing: raise FileNotFoundError( - "SPARRCLE preflight: required inputs not found:\n - " + "SPARCCLE preflight: required inputs not found:\n - " + "\n - ".join(missing) ) @@ -177,14 +177,13 @@ def apply_buildings( ) from message_ix_models.util import ScenarioInfo - del context # unused; required by workflow action signature - gmt = load_magicc_gmt(magicc_dir, n_runs=n_runs) n = gmt.values.shape[0] if gmt.values.ndim > 1 else 1 cooling, heating = compute_building_cids( gmt, ScenarioInfo(scenario).Y, reference_scenario=reference_scenario, + regions=context.model.regions, ) apply_building_cids( scenario, @@ -211,8 +210,6 @@ def apply_cooling( """ from message_ix_models.model.water.data.cooling_impacts import apply_cooling_cids - del context - gmt = load_magicc_gmt(magicc_dir, n_runs=n_runs) n = gmt.values.shape[0] if gmt.values.ndim > 1 else 1 kwargs = {"min_year": min_year} if min_year is not None else {} @@ -220,6 +217,7 @@ def apply_cooling( scenario, gmt, commit_message=f"Cooling CIDs (wet+dry), {n} runs", + regions=context.model.regions, **kwargs, ) persist_gmt_mean(scenario, gmt) @@ -257,7 +255,7 @@ def generate( config_path: str | Path | None = None, **options, ) -> Workflow: - """Build the SPARRCLE Phase-1 + Phase-2 workflow. + """Build the SPARCCLE Phase-1 + Phase-2 workflow. Parameters ---------- From 61a62d45c1694dc835b0201207773bd5331f853c Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 11 May 2026 12:04:26 +0200 Subject: [PATCH 13/19] Document SPARCCLE impact workflow --- doc/project/sparccle.rst | 64 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 63 insertions(+), 1 deletion(-) diff --git a/doc/project/sparccle.rst b/doc/project/sparccle.rst index 7e09125168..88b8bb53a6 100644 --- a/doc/project/sparccle.rst +++ b/doc/project/sparccle.rst @@ -1,7 +1,69 @@ SPARCCLE ******** -“Socioeconomic Pathways, Adaptation and Resilience to Changing CLimate in Europe” +"Socioeconomic Pathways, Adaptation and Resilience to Changing CLimate in Europe" - Project lead: :gh-user:`byersiiasa` - Lead modeler: :gh-user:`adrivinca` + +Overview +======== + +The SPARCCLE workflow applies physical climate-impact damage drivers to +solved MESSAGE starter scenarios. It reads starter-specific MAGICC GSAT +ensembles, evaluates the packaged RIME impact emulators, and writes +buildings and cooling impacts to project-side scenario clones. + +Prerequisites +============= + +Each starter needs a MAGICC ``*_IAMC_climateassessment.xlsx`` file in the +directory configured by +:file:`message_ix_models/project/sparccle/scenario_config.yaml`. Buildings +impacts operate on calibrated ``rc_spec`` and ``rc_therm`` demand. Cooling +impacts require the water-cooling module; the workflow builds this prep clone +before applying cooling CIDs. + +SSP coverage +============ + +Buildings calibration data ships for SSP1, SSP2, and SSP3. Cooling impacts +do not depend on SSP-specific buildings calibration. + +Cooling data +============ + +Cooling impacts use +:file:`message_ix_models/data/impacts/rime/r12_thermoelectric_gwl.nc`. The +file is R12-coded and has two cooling categories: ``wet`` for freshwater +once-through and closed-loop cooling, and ``dry`` for air cooling. The CID is +applied as a ratio to the 1.0 °C GWL capacity factor. + +Running the pipeline +==================== + +The CLI builds a workflow graph from +:file:`message_ix_models/project/sparccle/scenario_config.yaml` and runs +the requested target step: + +.. code-block:: bash + + # Buildings only + mix-models sparccle run "/ CI_b" --go + + # Cooling only (wet + dry, on a cooling-built prep clone) + mix-models sparccle run "/ CI_p" --go + + # Combined buildings + cooling + mix-models sparccle run "/ CI_bp" --go + +Each target writes a suffixed clone: + +- ``_CI_b`` — buildings demand replaced under + ``Final Energy|Residential and Commercial|{Cooling,Heating}``. +- ``_cooling_CI_p`` — wet-cooling activity bounds and dry-cooling + capacity-factor derating applied; ratios persisted as + ``Physical Climate Impact|Thermoelectric Cooling|*`` timeseries. +- ``_cooling_CI_bp`` — both, in one workflow step. + +See :doc:`/api/tools-impacts` for the underlying GMT-to-impact toolkit. From bc936e149a13ddbe9ba7bceb2561c45f9e0d9150 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 11 May 2026 12:04:43 +0200 Subject: [PATCH 14/19] Trim impact helper test prose --- .../tests/tools/impacts/test_temporal.py | 21 +++---------------- message_ix_models/tools/impacts/__init__.py | 2 +- 2 files changed, 4 insertions(+), 19 deletions(-) diff --git a/message_ix_models/tests/tools/impacts/test_temporal.py b/message_ix_models/tests/tools/impacts/test_temporal.py index 298629e3ba..874e2da3af 100644 --- a/message_ix_models/tests/tools/impacts/test_temporal.py +++ b/message_ix_models/tests/tools/impacts/test_temporal.py @@ -8,17 +8,15 @@ @pytest.fixture def annual_df(): - """Annual data 2020-2040 for 3 basins.""" years = list(range(2020, 2041)) data = {"basin": ["B1", "B2", "B3"]} for y in years: - data[y] = [float(y)] * 3 # value = year for easy verification + data[y] = [float(y)] * 3 return pd.DataFrame(data) @pytest.fixture def decadal_df(): - """Decadal theta-shape data for two nodes (mirrors buildings theta).""" rows = [ {"node": "R12_AFR", 2020: 0.4, 2030: 0.6, 2040: 0.8, 2050: 1.0}, {"node": "R12_NAM", 2020: 1.0, 2030: 0.9, 2040: 0.85, 2050: 0.8}, @@ -30,7 +28,6 @@ def test_point_method(annual_df): model_years = [2020, 2025, 2030, 2035, 2040] result = sample_to_model_years(annual_df, ["basin"], model_years, method="point") assert list(result.columns) == ["basin"] + model_years - # Values should be the year itself assert result[2025].iloc[0] == 2025.0 assert result[2040].iloc[0] == 2040.0 @@ -38,26 +35,21 @@ def test_point_method(annual_df): def test_average_method(annual_df): model_years = [2020, 2025, 2030] result = sample_to_model_years(annual_df, ["basin"], model_years, method="average") - # 2025 should average 2021-2025 = mean(2021..2025) = 2023 assert pytest.approx(result[2025].iloc[0]) == 2023.0 def test_forward_fill_beyond_range(annual_df): model_years = [2020, 2030, 2040, 2050] result = sample_to_model_years(annual_df, ["basin"], model_years, method="point") - # 2050 is beyond 2040: forward-filled from 2040 assert result[2050].iloc[0] == result[2040].iloc[0] def test_missing_year_column(annual_df): - # 2050 is beyond the input range (2020-2040) but gets forward-filled. - # 2019 is before input range and truly missing. with pytest.raises(ValueError, match="not found in input"): sample_to_model_years(annual_df, ["basin"], [2019, 2020], method="point") def test_interpolate_linear_between_inputs(decadal_df): - """5-year midpoints should be the arithmetic mean of bracket years.""" result = sample_to_model_years( decadal_df, ["node"], @@ -65,16 +57,14 @@ def test_interpolate_linear_between_inputs(decadal_df): method="interpolate", ) afr = result.set_index("node").loc["R12_AFR"] - assert pytest.approx(afr[2025]) == 0.5 # mean(0.4, 0.6) + assert pytest.approx(afr[2025]) == 0.5 assert pytest.approx(afr[2035]) == 0.7 assert pytest.approx(afr[2045]) == 0.9 - # Anchor years preserved exactly assert afr[2020] == 0.4 assert afr[2050] == 1.0 def test_interpolate_forward_fill_beyond_last_input(decadal_df): - """Years beyond max input stay equal to the last interpolated value.""" result = sample_to_model_years( decadal_df, ["node"], [2050, 2060, 2100], method="interpolate" ) @@ -84,8 +74,6 @@ def test_interpolate_forward_fill_beyond_last_input(decadal_df): def test_interpolate_drop_below_first_input_year_by_default(decadal_df): - """Targets below the first input year are dropped (buildings semantic).""" - # Input min = 2020; ask for 2010, 2015, 2020, 2030 result = sample_to_model_years( decadal_df, ["node"], [2010, 2015, 2020, 2030], method="interpolate" ) @@ -97,7 +85,6 @@ def test_interpolate_drop_below_first_input_year_by_default(decadal_df): def test_interpolate_extrapolate_below_backfills(decadal_df): - """extrapolate_below=True back-fills below-range targets from first input.""" result = sample_to_model_years( decadal_df, ["node"], @@ -106,15 +93,13 @@ def test_interpolate_extrapolate_below_backfills(decadal_df): extrapolate_below=True, ) afr = result.set_index("node").loc["R12_AFR"] - assert afr[2010] == afr[2020] # back-filled from first input + assert afr[2010] == afr[2020] def test_interpolate_independent_per_row(decadal_df): - """Interpolation must be independent across id_cols rows.""" result = sample_to_model_years( decadal_df, ["node"], [2025, 2035], method="interpolate" ).set_index("node") - # AFR rises; NAM falls assert pytest.approx(result.loc["R12_AFR", 2025]) == 0.5 assert pytest.approx(result.loc["R12_NAM", 2025]) == 0.95 assert pytest.approx(result.loc["R12_AFR", 2035]) == 0.7 diff --git a/message_ix_models/tools/impacts/__init__.py b/message_ix_models/tools/impacts/__init__.py index 89f45897fd..3026f806ec 100644 --- a/message_ix_models/tools/impacts/__init__.py +++ b/message_ix_models/tools/impacts/__init__.py @@ -1,6 +1,6 @@ """Climate impact prediction toolkit. -GMT-to-impact prediction via RIME emulators and GMT/year helpers. +GMT-to-impact prediction via RIME emulators and year helpers. Domain-specific transforms live with their domain modules; this package only handles emulator lookup and ensemble reduction. RIME prediction is an adapted reimplementation of `iiasa/rime From 89dad82cf5cbc38267e8213256e48374a5fb345e Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 11 May 2026 16:20:53 +0200 Subject: [PATCH 15/19] Document physical impact application layers --- doc/impacts/index.rst | 64 ++++++++++++++++++++ doc/index.rst | 1 + doc/project/sparccle.rst | 12 +--- message_ix_models/model/buildings/impacts.py | 26 ++++++-- 4 files changed, 87 insertions(+), 16 deletions(-) create mode 100644 doc/impacts/index.rst diff --git a/doc/impacts/index.rst b/doc/impacts/index.rst new file mode 100644 index 0000000000..c1b25013c3 --- /dev/null +++ b/doc/impacts/index.rst @@ -0,0 +1,64 @@ +Physical impacts +**************** + +.. contents:: + :local: + +Architecture +============ + +Physical-impact support is split across three layers. Generic prediction +utilities live in :mod:`message_ix_models.tools.impacts`. They read MAGICC +GSAT trajectories, evaluate packaged RIME emulators, and return impact arrays +at the emulator's native resolution. + +Domain application modules translate those predictions into MESSAGE scenario +edits. The current application modules are +:mod:`message_ix_models.model.buildings.impacts` for buildings demand and +:mod:`message_ix_models.model.water.data.cooling_impacts` for thermoelectric +cooling. + +Project workflows, such as :mod:`message_ix_models.project.sparccle`, choose +starter scenarios, input files, variants, and clone names. They do not define +the domain-specific MESSAGE parameters used for an impact. + +Buildings +========= + +Buildings impacts replace the climate-sensitive part of aggregate +``rc_spec`` and ``rc_therm`` demand. The replacement demand is calibrated in +three layers. + +The correction-coefficient files contain archetype-level ``gamma`` terms. +Together with RIME/CHILLED energy intensity and STURM floor area, these +reproduce STURM baseline energy demand: + +.. math:: + + \mathrm{raw}_{r,a,t} = + \gamma_{r,a,t} \cdot EI_{r,a}(GSAT_t) \cdot F_{r,a,t} + +where ``F`` is STURM floor area. In the packaged CSV files, ``gamma`` is the +``correction_coeff`` column. + +The ``theta`` files then scale the node-year aggregate so the reference-GWL +result matches the calibrated buildings baseline for each SSP. The +``rc_sector_fractions`` files identify the residential and commercial +buildings components already present in aggregate ``rc_spec`` and +``rc_therm`` so they can be removed before the replacement demand is added. + +Thermoelectric cooling +====================== + +Thermoelectric cooling impacts use +:file:`message_ix_models/data/impacts/rime/r12_thermoelectric_gwl.nc`. The +file is R12-coded and has two cooling categories: ``wet`` for freshwater +once-through and closed-loop cooling, and ``dry`` for air cooling. Ratios are +computed relative to the 1.0 °C GWL capacity factor. + +Wet and dry cooling use different MESSAGE-side levers. Wet cooling limits +freshwater-cooled activity, so the application writes relation bounds on +freshwater once-through and closed-loop cooling technologies. Dry cooling +reduces air-cooled plant performance, so the application derates +``capacity_factor`` rows for ``__air`` technologies. Saline cooling is not +represented in the packaged RIME cooling dataset and is left unchanged. diff --git a/doc/index.rst b/doc/index.rst index f653fcf07f..7f4d632f79 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -118,6 +118,7 @@ Other submodules are documented on their respective pages: :caption: Model variants buildings/index + impacts/index material/index transport/index water/index diff --git a/doc/project/sparccle.rst b/doc/project/sparccle.rst index 88b8bb53a6..cc1f985732 100644 --- a/doc/project/sparccle.rst +++ b/doc/project/sparccle.rst @@ -30,15 +30,6 @@ SSP coverage Buildings calibration data ships for SSP1, SSP2, and SSP3. Cooling impacts do not depend on SSP-specific buildings calibration. -Cooling data -============ - -Cooling impacts use -:file:`message_ix_models/data/impacts/rime/r12_thermoelectric_gwl.nc`. The -file is R12-coded and has two cooling categories: ``wet`` for freshwater -once-through and closed-loop cooling, and ``dry`` for air cooling. The CID is -applied as a ratio to the 1.0 °C GWL capacity factor. - Running the pipeline ==================== @@ -66,4 +57,5 @@ Each target writes a suffixed clone: ``Physical Climate Impact|Thermoelectric Cooling|*`` timeseries. - ``_cooling_CI_bp`` — both, in one workflow step. -See :doc:`/api/tools-impacts` for the underlying GMT-to-impact toolkit. +See :doc:`/impacts/index` for the impact application modules and +:doc:`/api/tools-impacts` for the underlying GMT-to-impact toolkit. diff --git a/message_ix_models/model/buildings/impacts.py b/message_ix_models/model/buildings/impacts.py index 66646b3493..808eae5093 100644 --- a/message_ix_models/model/buildings/impacts.py +++ b/message_ix_models/model/buildings/impacts.py @@ -1,9 +1,19 @@ """Building energy CID: replace fixed-EI rc_spec/rc_therm with RIME EI. -Replacement demand ``E(t,r) = theta(r,t) * gamma(r,a) * EI(r,a, GSAT(t)) * -F(r,a,t)``, where theta bridges the calibrated STURM baseline to the -RIME-emulated EI at the reference GWL and gamma/F are the STURM correction -coefficients and floor area. +Replacement demand is built in three calibration layers: + +1. Archetype demand: + ``raw(r,a,t) = gamma(r,a,t) * EI(r,a,GSAT(t)) * F(r,a,t)``. + ``gamma`` is stored as ``correction_coeff`` in the correction-coefficient + CSVs. ``F`` is STURM floor area. Together they convert RIME/CHILLED energy + intensity into STURM-consistent archetype energy demand. +2. Node/year calibration: + ``theta(node,t)`` scales the raw aggregate so the reference-GWL result + matches the calibrated buildings baseline for the selected SSP. +3. MESSAGE demand substitution: + ``rc_sector_fractions`` identify the buildings climate component already + present in aggregate ``rc_spec``/``rc_therm``. That component is removed + before the replacement CID demand is added. """ import logging @@ -33,7 +43,7 @@ # MESSAGE demand parameter expects GWa _EJ_TO_GWA = registry("1 EJ").to("GW * year").magnitude -_CORRECTION_COEFFICIENT_SCENARIO = "SSP2" +_CORRECTION_COEFFICIENT_SCENARIO = "SSP2" # gamma tables are SSP2-derived _FINAL_ENERGY_UNIT = "EJ/yr" @@ -54,7 +64,11 @@ def load_correction_coefficients( mode: Literal["cool", "heat"], sector: Literal["resid", "comm"] = "resid", ) -> pd.DataFrame: - """Load gamma and F per (region, arch, urt, year).""" + """Load gamma and floor-area terms for archetype-level STURM calibration. + + The CSV column ``correction_coeff`` is gamma: + STURM energy / (reference-GWL EI * STURM floor area). + """ path = _buildings_data_path( "correction_coefficients", "correction_coefficients_" From ee2fcbbb1457be13d225fdf33dbd3b0a5148aea8 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 11 May 2026 18:07:44 +0200 Subject: [PATCH 16/19] Clarify physical impact documentation boundaries --- doc/impacts/index.rst | 39 +++++++++++++++----- doc/project/sparccle.rst | 20 ++++++++-- message_ix_models/model/buildings/impacts.py | 21 +---------- 3 files changed, 48 insertions(+), 32 deletions(-) diff --git a/doc/impacts/index.rst b/doc/impacts/index.rst index c1b25013c3..cf1acc6827 100644 --- a/doc/impacts/index.rst +++ b/doc/impacts/index.rst @@ -8,15 +8,19 @@ Architecture ============ Physical-impact support is split across three layers. Generic prediction -utilities live in :mod:`message_ix_models.tools.impacts`. They read MAGICC -GSAT trajectories, evaluate packaged RIME emulators, and return impact arrays -at the emulator's native resolution. +utilities live in :mod:`message_ix_models.tools.impacts`. They evaluate +packaged RIME emulators and return impact arrays at the emulator's native +resolution. The current file reader ingests MAGICC ensembles in the +``climate-assessment`` workbook format; the downstream prediction utilities +only require an ensemble realization of global mean temperature and can be +fed by other simple climate models through :class:`~message_ix_models.tools.impacts.GmtArray`. Domain application modules translate those predictions into MESSAGE scenario edits. The current application modules are :mod:`message_ix_models.model.buildings.impacts` for buildings demand and :mod:`message_ix_models.model.water.data.cooling_impacts` for thermoelectric -cooling. +cooling. Basin-level water transformations live in +:mod:`message_ix_models.model.water.data.impacts`. Project workflows, such as :mod:`message_ix_models.project.sparccle`, choose starter scenarios, input files, variants, and clone names. They do not define @@ -30,8 +34,11 @@ Buildings impacts replace the climate-sensitive part of aggregate three layers. The correction-coefficient files contain archetype-level ``gamma`` terms. -Together with RIME/CHILLED energy intensity and STURM floor area, these -reproduce STURM baseline energy demand: +STURM encodes stock turnover, access, cooling-area, and other internal +dynamics. RIME/CHILLED provides climate-responsive energy intensity. Energy +intensity multiplied by floor area does not by itself reproduce the STURM +time series, so ``gamma`` is constructed so the reference-temperature product +matches STURM baseline energy demand: .. math:: @@ -41,9 +48,13 @@ reproduce STURM baseline energy demand: where ``F`` is STURM floor area. In the packaged CSV files, ``gamma`` is the ``correction_coeff`` column. -The ``theta`` files then scale the node-year aggregate so the reference-GWL -result matches the calibrated buildings baseline for each SSP. The -``rc_sector_fractions`` files identify the residential and commercial +The ``theta`` files are a second calibration layer. The raw STURM trajectory +is not the same object as the calibrated residential/commercial demand that +enters MESSAGE. For each SSP, ``theta`` scales the node-year aggregate so the +reference-GWL result matches the calibrated buildings heating and cooling +demand from the Shared Socioeconomic Pathways 2023 / WP-RC demand workflow. + +The ``rc_sector_fractions`` files identify the residential and commercial buildings components already present in aggregate ``rc_spec`` and ``rc_therm`` so they can be removed before the replacement demand is added. @@ -62,3 +73,13 @@ freshwater once-through and closed-loop cooling technologies. Dry cooling reduces air-cooled plant performance, so the application derates ``capacity_factor`` rows for ``__air`` technologies. Saline cooling is not represented in the packaged RIME cooling dataset and is left unchanged. + +Water availability +================== + +Basin-level water impacts use RIME datasets at native 157-basin resolution. +The water application expands those predictions to the MESSAGE basin-region +rows used by the water module, including transboundary basin splits. This is +also a domain application layer: :mod:`message_ix_models.tools.impacts` +predicts emulator values, while :mod:`message_ix_models.model.water.data.impacts` +owns the basin mapping and water-specific output shapes. diff --git a/doc/project/sparccle.rst b/doc/project/sparccle.rst index cc1f985732..3fd17af244 100644 --- a/doc/project/sparccle.rst +++ b/doc/project/sparccle.rst @@ -9,10 +9,22 @@ SPARCCLE Overview ======== -The SPARCCLE workflow applies physical climate-impact damage drivers to -solved MESSAGE starter scenarios. It reads starter-specific MAGICC GSAT -ensembles, evaluates the packaged RIME impact emulators, and writes -buildings and cooling impacts to project-side scenario clones. +The SPARCCLE protocol combines socioeconomic starter scenarios with +climate-impact variants and project-specific clone naming. + +Economic impacts +================ + +Economic and GDP-based impacts are part of the broader SPARCCLE protocol. +They are not implemented by the physical-impact workflow described below. + +Physical climate impacts +======================== + +The physical-impact workflow applies climate-impact damage drivers to solved +MESSAGE starter scenarios. It reads starter-specific GSAT ensembles, evaluates +the packaged RIME impact emulators, and writes buildings and cooling impacts +to project-side scenario clones. Prerequisites ============= diff --git a/message_ix_models/model/buildings/impacts.py b/message_ix_models/model/buildings/impacts.py index 808eae5093..67bef028b6 100644 --- a/message_ix_models/model/buildings/impacts.py +++ b/message_ix_models/model/buildings/impacts.py @@ -1,19 +1,6 @@ """Building energy CID: replace fixed-EI rc_spec/rc_therm with RIME EI. -Replacement demand is built in three calibration layers: - -1. Archetype demand: - ``raw(r,a,t) = gamma(r,a,t) * EI(r,a,GSAT(t)) * F(r,a,t)``. - ``gamma`` is stored as ``correction_coeff`` in the correction-coefficient - CSVs. ``F`` is STURM floor area. Together they convert RIME/CHILLED energy - intensity into STURM-consistent archetype energy demand. -2. Node/year calibration: - ``theta(node,t)`` scales the raw aggregate so the reference-GWL result - matches the calibrated buildings baseline for the selected SSP. -3. MESSAGE demand substitution: - ``rc_sector_fractions`` identify the buildings climate component already - present in aggregate ``rc_spec``/``rc_therm``. That component is removed - before the replacement CID demand is added. +See :doc:`/impacts/index` for the calibration-layer definitions. """ import logging @@ -64,11 +51,7 @@ def load_correction_coefficients( mode: Literal["cool", "heat"], sector: Literal["resid", "comm"] = "resid", ) -> pd.DataFrame: - """Load gamma and floor-area terms for archetype-level STURM calibration. - - The CSV column ``correction_coeff`` is gamma: - STURM energy / (reference-GWL EI * STURM floor area). - """ + """Load gamma and floor-area terms for archetype-level STURM calibration.""" path = _buildings_data_path( "correction_coefficients", "correction_coefficients_" From dcf50e26eb22b9d41802a8d9b15950d9913aa567 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Mon, 11 May 2026 18:24:43 +0200 Subject: [PATCH 17/19] Move impact theory into documentation --- doc/impacts/index.rst | 33 +++++++++++++++++++ .../model/water/data/cooling_impacts.py | 24 +++----------- message_ix_models/model/water/data/impacts.py | 15 ++------- 3 files changed, 40 insertions(+), 32 deletions(-) diff --git a/doc/impacts/index.rst b/doc/impacts/index.rst index cf1acc6827..39a16407f9 100644 --- a/doc/impacts/index.rst +++ b/doc/impacts/index.rst @@ -74,12 +74,45 @@ reduces air-cooled plant performance, so the application derates ``capacity_factor`` rows for ``__air`` technologies. Saline cooling is not represented in the packaged RIME cooling dataset and is left unchanged. +The wet-cooling relation is constructed so that, given the regional freshwater +share, total freshwater-cooled activity from a parent power technology cannot +exceed the warming-impaired capacity factor times that parent's activity. +Dry cooling is represented directly as a multiplicative derating of air-cooled +capacity factors. + +The impact kernels are based on: + +- Wet cooling: Li et al. (2025), "Global hydroclimatic risks and strategic + decommissioning pathways for thermal power units." *Nature Sustainability*. + doi:10.1038/s41893-025-01692-9 +- Dry cooling: Qin et al. (2023), "Global assessment of the carbon-water + tradeoff of dry cooling for thermal power generation." *Nature Water*. + doi:10.1038/s44221-023-00120-6 + Water availability ================== Basin-level water impacts use RIME datasets at native 157-basin resolution. +The packaged water datasets store GWL-indexed conditional summaries for +``qtot_mean`` and ``qr`` from CWatM ISIMIP3b realizations. The stored +variables are the expected value plus selected spread summaries +(``std``, ``p10``, ``p50``, and ``p90``). The source realizations span five GCMs +(``gfdl-esm4``, ``ipsl-cm6a-lr``, ``mpi-esm1-2-hr``, ``mri-esm2-0``, +``ukesm1-0-ll``) and three SSP-RCP climate scenarios (``ssp126``, +``ssp370``, ``ssp585``). The future CWatM runs use +``2015soc-from-histsoc`` direct human forcing, so runs differ by climate +forcing rather than by changing socioeconomic water-use assumptions. The +RIME datasets are binned with an 11-year centered time window. + The water application expands those predictions to the MESSAGE basin-region rows used by the water module, including transboundary basin splits. This is also a domain application layer: :mod:`message_ix_models.tools.impacts` predicts emulator values, while :mod:`message_ix_models.model.water.data.impacts` owns the basin mapping and water-specific output shapes. + +The current transform preserves the RIME prediction values while changing the +spatial index from native emulator basins to MESSAGE basin-region rows. Future +water-CID application work is expected to add the MESSAGE-side hydrology +semantics on top of that mapping, including groundwater-share construction, +km3-to-MCM conversion, and the sign convention needed when water availability +is represented through MESSAGE demand-like parameters. diff --git a/message_ix_models/model/water/data/cooling_impacts.py b/message_ix_models/model/water/data/cooling_impacts.py index 7de3438ffe..d58e5aa410 100644 --- a/message_ix_models/model/water/data/cooling_impacts.py +++ b/message_ix_models/model/water/data/cooling_impacts.py @@ -1,23 +1,7 @@ -"""Cooling CID: thermoelectric capacity-factor degradation under warming. - -Wet cooling enters as ``relation_activity`` bounds on freshwater cooling -technologies — the bound is constructed so that, given the regional -freshwater share, total freshwater-cooled activity from a parent power -technology cannot exceed the warming-impaired capacity factor times that -parent's activity. Dry cooling enters as a multiplicative ``capacity_factor`` -derating on ``__air`` technologies. - -Source dataset: ``r12_thermoelectric_gwl.nc`` — regional capacity-factor -ratios as a function of GWL, for wet and dry cooling. - -Impact-kernel citations: - -- Wet cooling: Li et al. (2025), "Global hydroclimatic risks and strategic - decommissioning pathways for thermal power units." *Nature Sustainability*. - doi:10.1038/s41893-025-01692-9 -- Dry cooling: Qin et al. (2023), "Global assessment of the carbon-water - tradeoff of dry cooling for thermal power generation." *Nature Water*. - doi:10.1038/s44221-023-00120-6 +"""Apply thermoelectric cooling CIDs to MESSAGE cooling technologies. + +See :doc:`/impacts/index` for the wet/dry cooling representation and impact +kernel citations. """ import functools diff --git a/message_ix_models/model/water/data/impacts.py b/message_ix_models/model/water/data/impacts.py index a09ea70993..21907d99b6 100644 --- a/message_ix_models/model/water/data/impacts.py +++ b/message_ix_models/model/water/data/impacts.py @@ -1,15 +1,6 @@ -"""Water-domain RIME impact transformations. - -Transforms raw RIME predictions (native 157-basin emulator resolution) -to MESSAGE-compatible arrays (217 basin-region rows). Basin geometry -utilities live in :mod:`message_ix_models.model.water.utils`; this module -owns the RIME-specific index mapping and the domain-level prediction entry -point. - -Future additions: -- Groundwater share formula: ``gw_share = 0.95 * qr / (qtot + qr)`` -- Unit conversion: km3 -> MCM (multiply by 1000) -- Sign convention: negate for MESSAGE demand semantics +"""Transform water-domain RIME predictions to MESSAGE basin rows. + +See :doc:`/impacts/index` for the water-impact representation. """ import functools From 0cb1574447c044aabd00f4c7908c092ac45dd205 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Tue, 12 May 2026 10:41:21 +0200 Subject: [PATCH 18/19] Reformat empty CID fixture to satisfy ruff line length --- message_ix_models/tests/model/test_building_impacts.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/message_ix_models/tests/model/test_building_impacts.py b/message_ix_models/tests/model/test_building_impacts.py index 4d8bc0f856..7ca80ea6a6 100644 --- a/message_ix_models/tests/model/test_building_impacts.py +++ b/message_ix_models/tests/model/test_building_impacts.py @@ -98,7 +98,11 @@ def test_prepare_building_demand_substitution_arithmetic(): def test_prepare_building_demand_missing_cid_zerofills(): """When CID has no rows for a (node, year), its contribution is zero.""" empty_cids = pd.DataFrame( - {"node": pd.Series(dtype=str), "year": pd.Series(dtype=int), "value": pd.Series(dtype=float)} + { + "node": pd.Series(dtype=str), + "year": pd.Series(dtype=int), + "value": pd.Series(dtype=float), + } ) new_spec, new_therm = prepare_building_demand( From 0fc75aeb539c4df45ba71b4b9cb379fdbcfdd163 Mon Sep 17 00:00:00 2001 From: Vignesh Raghunathan Date: Tue, 26 May 2026 16:32:22 +0200 Subject: [PATCH 19/19] Add doc on theta coverage --- doc/impacts/index.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/doc/impacts/index.rst b/doc/impacts/index.rst index 39a16407f9..2781732c05 100644 --- a/doc/impacts/index.rst +++ b/doc/impacts/index.rst @@ -58,6 +58,13 @@ The ``rc_sector_fractions`` files identify the residential and commercial buildings components already present in aggregate ``rc_spec`` and ``rc_therm`` so they can be removed before the replacement demand is added. +Packaged SSP coverage differs across the calibration layers. ``theta`` and +``rc_sector_fractions`` ship for SSP1, SSP2, and SSP3. ``gamma`` (the +``correction_coefficients`` files) and the STURM floor-area projections are +SSP2-only, pending additional STURM runs. Buildings impacts under SSP1 or +SSP3 therefore use SSP-specific ``theta`` and ``rc_sector_fractions`` on top +of an SSP2 STURM baseline and SSP2 ``gamma``. + Thermoelectric cooling ======================

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