Create iosnac.py

pymrio/tools/iosnac.py

@@ -0,0 +1,222 @@
+# -*- coding: utf-8 -*-
+# %% [markdown]
+#  # Hybrid SNAC model 
+#
+
+# %% [markdown]
+# Copyright (C) 2023  XIO Sustainability Analytics, Inc
+#
+# Written by
+#
+# - Richard Wood
+# - Konstantin Stadler
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+# %% [markdown]
+# ## Usage Notes
+#
+# This file requires the extract of import/final demand multipliers from EXIOBASE.
+# These are included in the repository (./data/exio_no_bp_raw.xlsx), but can 
+# also reproduced using the ./exio_prepper.py script.
+
+# %% [markdown]
+# Import of required external packages. These are all available through pip/conda/mamba install.
+
+# %%
+import numpy as np
+import openpyxl as opx
+
+import pandas as pd
+import pymrio
+
+# %% [markdown]
+# Python internal packages (this don't need to be installed, part of standard python)
+
+# %%
+from pathlib import Path
+
+# %% [markdown]
+# ## Settings
+
+# %% [markdown]
+# ### Parameters
+# Run the model for the following years:
+
+
+def snac_coupling(Aimp, Ldom, Qimp_mrio):
+    """Coupling of MRIO based multipliers with domestic IO tables
+
+    Parameters
+    ----------
+
+    Aimp: pd.DataFrame
+        Import A matrix
+    Ldom: pd.DataFrame
+        Domestic L matrix
+    Qimp_mrio: pd.DataFrame
+        Import multipliers from MRIO
+
+    Returns
+    -------
+    dict with
+        QAimp: pd.DataFrame
+            QAimp matrix
+        QAimpLdom: pd.DataFrame
+            QAimpLdom matrix
+    """
+    QAimp = Qimp_mrio @ Aimp
+    QAimpLdom = QAimp @ Ldom
+
+    # Return the values as a tuple
+    return QAimp, QAimpLdom
+
+
+# %%
+def snac_findem_breakdown(
+    SLdom_diag, QAimp_Ldom_diag, Q_imp_exio_diag, Ydom_br, Yimp_br
+):
+    """Breakdown of the total fp into final demand categories
+
+    Components: domestic_footprint, import_footprint, total
+
+    Parameters
+    ----------
+    SLdom_diag: pd.DataFrame
+        Diagonalized SLdom matrix for one stressor/gas/impact
+    QAimp_Ldom_diag: pd.DataFrame
+        Diagonalized QAimpLdom matrix for one stressor/gas/impact
+    Q_imp_exio_diag: pd.DataFrame
+        Diagonalized Q_imp_exio matrix for one stressor/gas/impact
+    Ydom_br: pd.DataFrame
+        domestic_footprint final demand matrix without aggregates
+    Yimp_br: pd.DataFrame
+        Import final demand matrix without aggregates
+
+    Returns
+    --------
+    pd.Series
+
+        index: pd.MultiIndex
+
+            level 0: component
+                domestic_footprint, import_footprint, total_footprint
+            level 1: sector
+                sector names
+            level 2: category
+                final demand category
+
+
+    """
+
+    dom_breakdown = SLdom_diag @ Ydom_br
+    imp_breakdown = QAimp_Ldom_diag @ Ydom_br + Q_imp_exio_diag @ Yimp_br
+    tot_footprint_breakdown = dom_breakdown + imp_breakdown
+
+    index_names = ["component"] + list(dom_breakdown.index.names)
+
+    ds = pd.concat(
+        [dom_breakdown, imp_breakdown, tot_footprint_breakdown],
+        keys=["domestic_footprint", "import_footprint", "total_footprint"],
+        names=index_names,
+        axis=0,
+    ).stack()
+
+    return ds
+
+
+# %%
+def calc_snac_sector_accounts(
+    SLdom,
+    QAimp_Ldom,
+    Ydom_diag,
+    Qimp_mrio,
+    Yimp_diag,
+    Ydom_exp_diag,
+    Yimp_exp_diag,
+    total_imports,
+    pba,
+):
+    """Calculate and merge sector accounts
+
+    Parameters
+    ----------
+    SLdom: pd.DataFrame
+
+    QAimp_Ldom: pd.DataFrame
+
+    Qimp_mrio: pd.DataFrame
+
+    Ydom_diag: pd.DataFrame
+
+    Yimp_diag: pd.DataFrame
+
+    Ydom_exp_diag: pd.DataFrame
+
+    Yimp_exp_diag: pd.DataFrame
+
+    total_imports: pd.DataFrame
+
+    pba: pd.DataFrame
+
+    Returns
+    --------
+    pd.Series
+
+        index: pd.MultiIndex
+
+            level 0: component
+                domestic_footprint, import_footprint, total_footprint
+            level 1: emission
+                sector names
+            level 2: sector
+                sector names
+    """
+
+    total_footprint = SLdom @ Ydom_diag + QAimp_Ldom @ Ydom_diag + Qimp_mrio @ Yimp_diag
+
+    domestic_footprint = SLdom * Ydom_diag.sum(axis=1)
+    import_footprint = QAimp_Ldom @ Ydom_diag + Qimp_mrio @ Yimp_diag
+    export_footprint = (
+        SLdom @ Ydom_exp_diag + QAimp_Ldom @ Ydom_exp_diag + Qimp_mrio @ Yimp_exp_diag
+    )
+
+    footprint_gross_imports = Qimp_mrio * total_imports
+    footprint_gross_imports.columns.name = "sector"
+
+    index_names = ["component", "emission"]
+
+    ds = pd.concat(
+        [
+            total_footprint,
+            domestic_footprint,
+            import_footprint,
+            export_footprint,
+            footprint_gross_imports,
+            pba,
+        ],
+        keys=[
+            "total_footprint",
+            "domestic_footprint",
+            "import_footprint",
+            "footprint_gross_exports",
+            "footprint_gross_imports",
+            "production_account",
+        ],
+        names=index_names,
+        axis=0,
+    ).stack()
+
+    return ds
+