add release note, simplify build_industry_sector_ratios_intermediate script

doc/configtables/industry.csv

@@ -17,6 +17,8 @@ HVC_primary_fraction,--,float,The fraction of high value chemicals (HVC) produce
 HVC_mechanical_recycling _fraction,--,float,The fraction of high value chemicals (HVC) produced using mechanical recycling
 HVC_chemical_recycling _fraction,--,float,The fraction of high value chemicals (HVC) produced using chemical recycling
 ,,,
+sector_ratios_fraction_future,--,Dictionary with planning horizons as keys.,The fraction of total progress in fuel and process switching achieved in the industry sector.
+basic_chemicals_without_NH3_production_today,Mt/a,float,"The amount of basic chemicals produced without ammonia (= 86 Mtethylene-equiv - 17 MtNH3)."
 HVC_production_today,MtHVC/a,float,"The amount of high value chemicals (HVC) produced. This includes ethylene, propylene and BTX. From `DECHEMA (2017) <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf>`_, Figure 16, page 107"
 Mwh_elec_per_tHVC _mechanical_recycling,MWh/tHVC,float,"The energy amount of electricity needed to produce a ton of high value chemical (HVC) using mechanical recycling. From SI of `Meys et al (2020) <https://doi.org/10.1016/j.resconrec.2020.105010>`_, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756."
 Mwh_elec_per_tHVC _chemical_recycling,MWh/tHVC,float,"The energy amount of electricity needed to produce a ton of high value chemical (HVC) using chemical recycling. The default value is based on pyrolysis and electric steam cracking. From `Material Economics (2019) <https://materialeconomics.com/latest-updates/industrial-transformation-2050>`_, page 125"

doc/release_notes.rst

@@ -10,6 +10,15 @@ Release Notes
 Upcoming Release
 ================
 
+* Improved representation of industry transition pathways. A new script was
+  added to interpolate industry sector ratios from today's status quo to future
+  systems (i.e. specific emissions and demands for energy and feedstocks). For
+  each country we gradually switch industry processes from today's specific
+  energy carrier usage per ton material output to the best-in-class energy
+  consumption of tomorrow. This is done on a per-country basis. The ratio of
+  today to tomorrow's energy consumption is set with the ``industry:
+  sector_ratios_fraction_future:`` parameter.
+
 * Bugfix: Correct units of subtracted chlorine and methanol demand in
   :mod:`build_industry_sector_ratios`.
 

scripts/build_industry_sector_ratios_intermediate.py

@@ -25,62 +25,52 @@ def build_industry_sector_ratios_intermediate():
     production = (
         pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
         / 1e3
-    )
-    production = production.unstack().swaplevel()
+    ).stack()
+    production.index.names = [None, None]
 
     # in MWh/t
     future_sector_ratios = pd.read_csv(
         snakemake.input.industry_sector_ratios, index_col=0
     )
 
-    production.index.names = [None, None]
-
     today_sector_ratios = demand.div(production, axis=1)
 
-    today_sector_ratios.drop(
-        columns=today_sector_ratios.columns[today_sector_ratios.isna().all()],
-        inplace=True,
-    )
+    today_sector_ratios.dropna(how="all", axis=1, inplace=True)
 
-    rename = pd.Series(today_sector_ratios.index, today_sector_ratios.index)
-    rename["waste"] = "biomass"
-    rename["electricity"] = "elec"
-    rename["solid"] = "coke"
-    rename["gas"] = "methane"
-    rename["other"] = "biomass"
-    rename["liquid"] = "naphtha"
-
-    today_sector_ratios.rename(rename, inplace=True)
+    rename = {
+        "waste": "biomass",
+        "electricity": "elec",
+        "solid": "coke",
+        "gas": "methane",
+        "other": "biomass",
+        "liquid": "naphtha",
+    }
+    today_sector_ratios = today_sector_ratios.rename(rename).groupby(level=0).sum()
 
     fraction_future = get(params["sector_ratios_fraction_future"], year)
 
     intermediate_sector_ratios = {}
-
-    for ct in today_sector_ratios.columns.unique(level=0):
-
-        intermediate_sector_ratio = future_sector_ratios.copy()
-
-        intermediate_sector_ratio.loc[
-            today_sector_ratios[ct].index, today_sector_ratios[ct].columns
-        ] = (
-            fraction_future
-            * intermediate_sector_ratio.loc[
-                today_sector_ratios[ct].index, today_sector_ratios[ct].columns
-            ]
-            + (1 - fraction_future) * today_sector_ratios[ct]
+    for ct, group in today_sector_ratios.T.groupby(level=0):
+        today_sector_ratios_ct = (
+            group.droplevel(0)
+            .T.reindex_like(future_sector_ratios)
+            .fillna(future_sector_ratios)
+        )
+        intermediate_sector_ratios[ct] = (
+            today_sector_ratios_ct * (1 - fraction_future)
+            + future_sector_ratios * fraction_future
         )
-        intermediate_sector_ratios[ct] = intermediate_sector_ratio
-
     intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1)
 
-    intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios)
-
 
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
 
-        snakemake = mock_snakemake("build_industry_sector_ratios_intermediate")
+        snakemake = mock_snakemake(
+            "build_industry_sector_ratios_intermediate",
+            planning_horizons="2030",
+        )
 
     year = int(snakemake.wildcards.planning_horizons[-4:])