from collections import defaultdict
import message_ix
import pandas as pd
from message_ix import make_df
from message_ix_models import ScenarioInfo
from message_ix_models.model.material.data_util import (
calculate_ini_new_cap,
read_sector_data,
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.util import (
broadcast,
nodes_ex_world,
package_data_path,
same_node,
)
def gen_mock_demand_cement(scenario: message_ix.Scenario) -> pd.DataFrame:
s_info = ScenarioInfo(scenario)
nodes = s_info.N
nodes.remove("World")
# 2019 production by country (USGS)
# p43 of https://pubs.usgs.gov/periodicals/mcs2020/mcs2020-cement.pdf
# For R12: China and CPA demand divided by 0.1 and 0.9.
# The order:
# r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\
# 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"]
if "R12_CHN" in nodes:
nodes.remove("R12_GLB")
sheet_n = "data_R12"
region_set = "R12_"
demand2020_top = [76, 229.5, 0, 57, 55, 60, 89, 54, 129, 320, 51, 2065.5]
# the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf
demand2020_rest = [
4100 * 0.051 - 76,
(4100 * 0.14 - 155) * 0.2 * 0.1,
4100 * 0.064 * 0.5,
4100 * 0.026 - 57,
4100 * 0.046 * 0.5 - 55,
(4100 * 0.14 - 155) * 0.2,
4100 * 0.046 * 0.5,
12,
4100 * 0.003,
(4100 * 0.14 - 155) * 0.6,
4100 * 0.064 * 0.5 - 51,
(4100 * 0.14 - 155) * 0.2 * 0.9,
]
else:
nodes.remove("R11_GLB")
sheet_n = "data_R11"
region_set = "R11_"
demand2020_top = [76, 2295, 0, 57, 55, 60, 89, 54, 129, 320, 51]
# the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf
demand2020_rest = [
4100 * 0.051 - 76,
(4100 * 0.14 - 155) * 0.2,
4100 * 0.064 * 0.5,
4100 * 0.026 - 57,
4100 * 0.046 * 0.5 - 55,
(4100 * 0.14 - 155) * 0.2,
4100 * 0.046 * 0.5,
12,
4100 * 0.003,
(4100 * 0.14 - 155) * 0.6,
4100 * 0.064 * 0.5 - 51,
]
# SSP2 R11 baseline GDP projection
gdp_growth = pd.read_excel(
package_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"),
sheet_name=sheet_n,
)
gdp_growth = gdp_growth.loc[
(gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World")
].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1)
d = [a + b for a, b in zip(demand2020_top, demand2020_rest)]
gdp_growth["Region"] = region_set + gdp_growth["Region"]
# # Regions setting for IMAGE
# region_cement = pd.read_excel(
# package_data_path("material", "CEMENT.BvR2010.xlsx"),
# sheet_name="Timer_Regions", skiprows=range(0,3))[['Region #', 'Name']]\
# .drop_duplicates().sort_values(by='Region #')
#
# region_cement = region_cement.loc[region_cement['Region #'] < 999]
# region_cement['node'] = \
# ['R11_NAM', 'R11_NAM',
# 'R11_LAM', 'R11_LAM',
# 'R11_LAM', 'R11_LAM',
# 'R11_AFR', 'R11_AFR',
# 'R11_AFR', 'R11_AFR',
# 'R11_WEU', 'R11_EEU',
# 'R11_EEU', 'R11_FSU',
# 'R11_FSU', 'R11_FSU',
# 'R11_MEA', 'R11_SAS',
# 'R11_PAS', 'R11_CPA',
# 'R11_PAS', 'R11_PAS',
# 'R11_PAO', 'R11_PAO',
# 'R11_SAS', 'R11_AFR']
#
# # Cement demand 2010 [Mt/year] (IMAGE)
# demand2010_cement = pd.read_excel(
# package_data_path("material", "CEMENT.BvR2010.xlsx"),
# sheet_name="Domestic Consumption", skiprows=range(0,3)).\
# groupby(by=["Region #"]).sum()[[2010]].\
# join(region_cement.set_index('Region #'), on='Region #').\
# rename(columns={2010:'value'})
#
# demand2010_cement = demand2010_cement.groupby(by=['node']).sum().reset_index()
# demand2010_cement['value'] = demand2010_cement['value'] / 1e9 # kg to Mt
# Directly assigned countries from the table on p43
demand2020_cement = (
pd.DataFrame({"Region": nodes, "value": d})
.join(gdp_growth.set_index("Region"), on="Region")
.rename(columns={"Region": "node"})
)
# demand2010_cement = demand2010_cement.\
# join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node')
demand2020_cement.iloc[:, 3:] = (
demand2020_cement.iloc[:, 3:]
.div(demand2020_cement[2020], axis=0)
.multiply(demand2020_cement["value"], axis=0)
)
# Do this if we have 2020 demand values for buildings
# sp = get_spec()
# if 'buildings' in sp['add'].set['technology']:
# val = get_scen_mat_demand("cement",scenario) # Mt in 2020
# print("Base year demand of {}:".format("cement"), val)
# # demand2020_cement['value'] = demand2020_cement['value'] - val['value']
# # Scale down all years' demand values by the 2020 ratio
# demand2020_cement.iloc[:,3:] = demand2020_cement.iloc[:,3:].\
# multiply(demand2020_cement[2020]- val['value'], axis=0).\
# div(demand2020_cement[2020], axis=0)
# print("UPDATE {} demand for 2020!".format("cement"))
#
demand2020_cement = pd.melt(
demand2020_cement.drop(["value", "Scenario"], axis=1),
id_vars=["node"],
var_name="year",
value_name="value",
)
return demand2020_cement
[docs]def gen_data_cement(
scenario: message_ix.Scenario, dry_run: bool = False
) -> dict[str, pd.DataFrame]:
"""Generate data for materials representation of cement industry."""
# Load configuration
context = read_config()
config = read_config()["material"]["cement"]
ssp = get_ssp_from_context(context)
# Information about scenario, e.g. node, year
s_info = ScenarioInfo(scenario)
context.datafile = "Global_steel_cement_MESSAGE.xlsx"
# Techno-economic assumptions
data_cement = read_sector_data(scenario, "cement", "Global_cement_MESSAGE.xlsx")
# Special treatment for time-dependent Parameters
data_cement_ts = read_timeseries(scenario, "cement", "Global_cement_MESSAGE.xlsx")
tec_ts = set(data_cement_ts.technology) # set of tecs with var_cost
# List of data frames, to be concatenated together at end
results = defaultdict(list)
# For each technology there are different input and output combinations
# Iterate over technologies
yv_ya = s_info.yv_ya
yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1980]
# Do not parametrize GLB region the same way
nodes = nodes_ex_world(s_info.N)
for t in config["technology"]["add"]:
t = t.id
params = data_cement.loc[(data_cement["technology"] == t), "parameter"].unique()
# Special treatment for time-varying params
if t in tec_ts:
common = dict(
time="year",
time_origin="year",
time_dest="year",
)
param_name = data_cement_ts.loc[
(data_cement_ts["technology"] == t), "parameter"
]
for p in set(param_name):
val = data_cement_ts.loc[
(data_cement_ts["technology"] == t)
& (data_cement_ts["parameter"] == p),
"value",
]
# units = data_cement_ts.loc[
# (data_cement_ts["technology"] == t)
# & (data_cement_ts["parameter"] == p),
# "units",
# ].values[0]
mod = data_cement_ts.loc[
(data_cement_ts["technology"] == t)
& (data_cement_ts["parameter"] == p),
"mode",
]
yr = data_cement_ts.loc[
(data_cement_ts["technology"] == t)
& (data_cement_ts["parameter"] == p),
"year",
]
if p == "var_cost":
df = make_df(
p,
technology=t,
value=val,
unit="t",
year_vtg=yr,
year_act=yr,
mode=mod,
**common,
).pipe(broadcast, node_loc=nodes)
else:
rg = data_cement_ts.loc[
(data_cement_ts["technology"] == t)
& (data_cement_ts["parameter"] == p),
"region",
]
df = make_df(
p,
technology=t,
value=val,
unit="t",
year_vtg=yr,
year_act=yr,
mode=mod,
node_loc=rg,
**common,
)
results[p].append(df)
# Iterate over parameters
for par in params:
# Obtain the parameter names, commodity,level,emission
split = par.split("|")
param_name = split[0]
# Obtain the scalar value for the parameter
val = data_cement.loc[
((data_cement["technology"] == t) & (data_cement["parameter"] == par)),
"value",
] # .values
regions = data_cement.loc[
((data_cement["technology"] == t) & (data_cement["parameter"] == par)),
"region",
] # .values
common = dict(
year_vtg=yv_ya.year_vtg,
year_act=yv_ya.year_act,
# mode="M1",
time="year",
time_origin="year",
time_dest="year",
)
for rg in regions:
# For the parameters which inlcudes index names
if len(split) > 1:
if (param_name == "input") | (param_name == "output"):
# Assign commodity and level names
com = split[1]
lev = split[2]
mod = split[3]
df = make_df(
param_name,
technology=t,
commodity=com,
level=lev,
value=val[regions[regions == rg].index[0]],
mode=mod,
unit="t",
node_loc=rg,
**common,
).pipe(same_node)
elif param_name == "emission_factor":
# Assign the emisson type
emi = split[1]
mod = split[2]
df = make_df(
param_name,
technology=t,
value=val[regions[regions == rg].index[0]],
emission=emi,
mode=mod,
unit="t",
node_loc=rg,
**common,
) # .pipe(broadcast, \
# node_loc=nodes))
else: # time-independent var_cost
mod = split[1]
df = make_df(
param_name,
technology=t,
value=val[regions[regions == rg].index[0]],
mode=mod,
unit="t",
node_loc=rg,
**common,
) # .pipe(broadcast, node_loc=nodes))
# Parameters with only parameter name
else:
df = make_df(
param_name,
technology=t,
value=val[regions[regions == rg].index[0]],
unit="t",
node_loc=rg,
**common,
) # .pipe(broadcast, node_loc=nodes))
if len(regions) == 1:
df["node_loc"] = None
df = df.pipe(broadcast, node_loc=nodes).pipe(same_node)
results[param_name].append(df)
# Create external demand param
parname = "demand"
df_demand = material_demand_calc.derive_demand("cement", scenario, ssp=ssp)
results[parname].append(df_demand)
# Add CCS as addon
parname = "addon_conversion"
technology_1 = ["clinker_dry_cement"]
df_1 = make_df(
parname, mode="M1", type_addon="dry_ccs_cement", value=1, unit="-", **common
).pipe(broadcast, node=nodes, technology=technology_1)
technology_2 = ["clinker_wet_cement"]
df_2 = make_df(
parname, mode="M1", type_addon="wet_ccs_cement", value=1, unit="-", **common
).pipe(broadcast, node=nodes, technology=technology_2)
results[parname].append(df_1)
results[parname].append(df_2)
# Concatenate to one data frame per parameter
results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()}
results["initial_new_capacity_up"] = pd.concat(
[
calculate_ini_new_cap(
df_demand=df_demand.copy(deep=True),
technology="clinker_dry_ccs_cement",
material="cement",
),
calculate_ini_new_cap(
df_demand=df_demand.copy(deep=True),
technology="clinker_wet_ccs_cement",
material="cement",
),
]
)
return results