Extend methanol fs downstream supply chain

* Add additional technology "meth_fs_ind" between meth_t_d and demand
* Move CO2_Emision from "steam_craker_petro" and "meth_t_d" to
"production_HVC" and "meth_fs_ind"

Author: macflo8

message_ix_models/data/material/methanol/methanol_techno_economic.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:61bee83deb171587bf1087a8689ae7401eac31692c200de7a8be78e9c56b3933
-size 619006
+oid sha256:c2a06bb96944e11c097ce01187fb4a69e0553d973aad35f256bd760b05be9c08
+size 611057

message_ix_models/data/material/set.yaml

@@ -640,6 +640,7 @@ methanol:
     - meth_trd
     - meth_exp
     - meth_imp
+    - meth_ind_fs
     remove:
     - sp_meth_I
     - meth_rc

message_ix_models/model/material/data_methanol_new.py

@@ -6,8 +6,10 @@
 from message_ix import make_df
 
 import message_ix_models.util
+from message_ix_models import ScenarioInfo
+from message_ix_models.model.material.data_util import gen_plastics_emission_factors
 from message_ix_models.model.material.material_demand import material_demand_calc
-from message_ix_models.model.material.util import read_config
+from message_ix_models.model.material.util import combine_df_dictionaries, read_config
 from message_ix_models.util import broadcast, same_node
 
 if TYPE_CHECKING:
@@ -88,6 +90,13 @@ def gen_data_methanol_new(scenario: "Scenario") -> Dict[str, pd.DataFrame]:
     )
     pars_dict["demand"] = df_final
 
+    s_info = ScenarioInfo(scenario)
+    downstream_tec_pars = gen_meth_fs_downstream(s_info)
+    meth_downstream_emi = gen_plastics_emission_factors(s_info, "methanol")
+    pars_dict = combine_df_dictionaries(
+        pars_dict, downstream_tec_pars, meth_downstream_emi
+    )
+
     scen_rel_set = scenario.set("relation")
     for par in ["activity", "upper", "lower"]:
         df_rel = pars_dict[f"relation_{par}"]
@@ -270,3 +279,47 @@ def unpivot_input_data(df: pd.DataFrame, par_name: str):
             ].index
         )
     return make_df(par_name, **df_final_full)
+
+
+def gen_meth_fs_downstream(s_info: "ScenarioInfo") -> Dict[str, pd.DataFrame]:
+    # input parameter
+    yv_ya = s_info.yv_ya
+    year_all = yv_ya["year_act"].unique()
+
+    tec_name = "meth_ind_fs"
+    cols = {
+        "technology": tec_name,
+        "commodity": "methanol",
+        "mode": "M1",
+        "level": "final_material",
+        "time": "year",
+        "time_origin": "year",
+        "value": 1,
+        "unit": "Mt",
+    }
+    df_in = (
+        make_df("input", **cols)
+        .pipe(broadcast, node_loc=s_info.N, year_act=year_all)
+        .pipe(same_node)
+    )
+    df_in["year_vtg"] = df_in["year_act"]
+
+    # output parameter
+    tec_name = "meth_ind_fs"
+    cols = {
+        "technology": tec_name,
+        "commodity": "methanol",
+        "mode": "M1",
+        "level": "demand",
+        "time": "year",
+        "time_dest": "year",
+        "value": 1,
+        "unit": "Mt",
+    }
+    df_out = (
+        make_df("output", **cols)
+        .pipe(broadcast, node_loc=s_info.N, year_act=year_all)
+        .pipe(same_node)
+    )
+    df_out["year_vtg"] = df_out["year_act"]
+    return dict(input=df_in, output=df_out)

message_ix_models/model/material/data_petro.py

@@ -6,9 +6,16 @@
 from message_ix import make_df
 
 from message_ix_models import ScenarioInfo
-from message_ix_models.model.material.data_util import read_timeseries
+from message_ix_models.model.material.data_util import (
+    gen_plastics_emission_factors,
+    read_timeseries,
+)
 from message_ix_models.model.material.material_demand import material_demand_calc
-from message_ix_models.model.material.util import get_ssp_from_context, read_config
+from message_ix_models.model.material.util import (
+    combine_df_dictionaries,
+    get_ssp_from_context,
+    read_config,
+)
 from message_ix_models.util import (
     broadcast,
     nodes_ex_world,
@@ -545,6 +552,9 @@ def gen_data_petro_chemicals(
     df["technology"] = "gas_processing_petro"
     results["relation_activity"] = df
 
+    meth_downstream_emi = gen_plastics_emission_factors(s_info, "HVCs")
+    results = combine_df_dictionaries(results, meth_downstream_emi)
+
     # TODO: move this to input xlsx file
     df_gro = results["growth_activity_up"]
     drop_idx = df_gro[

message_ix_models/model/material/data_util.py

@@ -424,9 +424,9 @@ def read_sector_data(
     list_ef = data_df[["Parameter", "Species", "Mode"]].apply(list, axis=1)
 
     data_df["parameter"] = list_series.str.join("|")
-    data_df.loc[
-        data_df["Parameter"] == "emission_factor", "parameter"
-    ] = list_ef.str.join("|")
+    data_df.loc[data_df["Parameter"] == "emission_factor", "parameter"] = (
+        list_ef.str.join("|")
+    )
 
     data_df = data_df.drop(["Parameter", "Level", "Commodity", "Mode"], axis=1)
     data_df = data_df.drop(data_df[data_df.Value == ""].index)
@@ -1866,7 +1866,7 @@ def calibrate_for_SSPs(scenario: "Scenario") -> None:
 
 def gen_plastics_emission_factors(
     info, species: Literal["methanol", "HVCs"]
-) -> pd.DataFrame:
+) -> dict[str, pd.DataFrame]:
     """Generate "CO2_Emission" relation parameter that
     represents stored carbon in produced plastics.
     The calculation considers:
@@ -1890,12 +1890,8 @@ def gen_plastics_emission_factors(
     pd.DataFrame
     """
 
-    if species != "HVCs":
-        raise NotImplementedError
+    tec_species_map = {"methanol": "meth_ind_fs", "HVCs": "production_HVC"}
 
-    tec_species_map = {"methanol": NotImplemented, "HVCs": "production_HVC"}
-
-    # TODO: do same calculation for methanol not used for MTO but other plastics
     carbon_pars = read_yaml_file(
         package_data_path(
             "material", "petrochemicals", "chemicals_carbon_parameters.yaml"
@@ -1943,14 +1939,4 @@ def apply_eol_factor(row, pars):
     co2_emi_rel["value"] = co2_emi_rel.apply(
         lambda x: apply_eol_factor(x, end_of_life_pars), axis=1
     ).mul(-1)
-    return co2_emi_rel
-
-
-if __name__ == "__main__":
-    from ixmp import Platform
-    from message_ix import Scenario
-
-    mp = Platform("ixmp_dev")
-    scen = Scenario(mp, "MESSAGEix-Materials", "test_c901_refactoring")
-    s_info = ScenarioInfo(scen)
-    gen_plastics_emission_factors(s_info, "methanol")
+    return {"relation_activity": co2_emi_rel}

message_ix_models/model/material/material_demand/material_demand_calc.py

@@ -520,7 +520,7 @@ def get_demand_t1_with_income_elasticity(
 
     df_melt = df_demand.melt(ignore_index=False).reset_index()
 
-    level = "demand" if chemical == "HVC" else "final_material"
+    level = "demand" if chemical in ["HVC", "methanol"] else "final_material"
 
     return make_df(
         "demand",

message_ix_models/tests/model/material/test_data_util.py

@@ -132,14 +132,14 @@ def test_map_iea_db_to_msg_regs() -> None:
     "species",
     [
         "HVCs",
-        pytest.param("methanol", marks=pytest.mark.xfail(raises=NotImplementedError)),
+        "methanol",
     ],
 )
 def test_gen_plastics_emission_factors(species):
     info = ScenarioInfo()
     info.set["node"] = ["node0", "node1"]
     info.set["year"] = [2020, 2025]
-    out = gen_plastics_emission_factors(info, "HVCs")
+    out = gen_plastics_emission_factors(info, species)["relation_activity"]
 
     assert not out.isna().any(axis=None)  # Completely full