Advanced Tutorial

mescal is a Brightway-powered Python package that helps you to integrate Life-Cycle Assessment (LCA) in your energy system model

Set up

%load_ext autoreload
%autoreload 2

# Import the required libraries
from mescal import *
import pandas as pd
import bw2data as bd

ecoinvent_version = '3.10.1' # choose the ecoinvent version you wish to use
esm_location = 'CH' # choose the version of energyscope for which you want to generate metrics
esm_year = 2050 # choose the year of the energyscope snapshot model or transition pathway time-step
spatialized_database = True # set to True if you want to use your spatialized version of ecoinvent
regionalize_foregrounds = ['Operation', 'Resource'] # set to 'all' if you want to regionalize the foreground inventories of all types of LCI datasets
premise_iam = 'image' # choose the IAM to which the premise database is linked
premise_ssp_rcp = 'SSP2-RCP26' # choose the SSP/RCP scenario to which the premise database is linked

# Set the name of your main LCI database (e.g., ecoinvent or premise database) here:
name_premise_db = f"ecoinvent_cutoff_{ecoinvent_version}_{premise_iam}_{premise_ssp_rcp}_{esm_year}"
name_biosphere_db = 'biosphere3'
if spatialized_database:
    name_premise_db += ' regionalized'
    name_spatialized_biosphere_db = 'biosphere3_spatialized_flows'
else:
    name_spatialized_biosphere_db = None

# Set the name of the new database with the ESM results
esm_db_name = f'EnergyScope_{esm_location}_{esm_year}'

# Main version of ecoinvent (without .1 if any)
ecoinvent_main_version = '.'.join(ecoinvent_version.split('.')[:2])

# Set the list of LCIA methods for which you want indicators (they must be registered in your brightway project)
if spatialized_database:
    lcia_methods=[
        f'IMPACT World+ Midpoint 2.1_regionalized for ecoinvent v{ecoinvent_main_version}',
        f'IMPACT World+ Damage 2.1_regionalized for ecoinvent v{ecoinvent_main_version}',
    ]
else:
    lcia_methods=[
        f'IMPACT World+ Midpoint 2.1 for ecoinvent v{ecoinvent_main_version}',
        f'IMPACT World+ Damage 2.1 for ecoinvent v{ecoinvent_main_version}',
        f'IMPACT World+ Footprint 2.1 for ecoinvent v{ecoinvent_main_version}',
    ]

Note: you can download IMPACT World+ methods in your brightway project following this notebook. Regionalized versions of ecoinvent and IMPACT World+ methods can be downloaded from this repository.

# Set up your Brightway project
bd.projects.set_current(f'ecoinvent{ecoinvent_version}') # put the name of your brightway project here

Load databases

premise_db = Database(name_premise_db, create_pickle=True)

2025-12-26 12:17:08,346 - Database - INFO - Loaded ecoinvent_cutoff_3.10.1_image_SSP2-RCP26_2050 regionalized from pickle!

if regionalize_foregrounds is not None and spatialized_database:
    spatialized_biosphere_db = Database(name_spatialized_biosphere_db)
else:
    spatialized_biosphere_db = None

Getting activity data

100%|██████████| 110559/110559 [00:01<00:00, 85887.84it/s]

Adding exchange data to activities

0it [00:00, ?it/s]

Filling out exchange data

100%|██████████| 110559/110559 [00:00<00:00, 3465962.51it/s]
2025-12-26 12:17:14,936 - Database - INFO - Loaded biosphere3_spatialized_flows from brightway!

Your data

For mescal to understand the structure of your energy system model, you need to provide it with a set of dataframes.

Mandatory dataframes

mapping = pd.read_csv(f'../dev/energyscope_data/{esm_location}/mapping.csv')
unit_conversion = pd.read_csv(f'../dev/energyscope_data/{esm_location}/unit_conversion.csv')
mapping_esm_flows_to_CPC = pd.read_csv(f'../dev/energyscope_data/{esm_location}/mapping_esm_flows_to_CPC.csv')
model = pd.read_csv(f'../dev/energyscope_data/{esm_location}/model.csv')

A mapping between the energy technologies and resources of the energy system model, and Life-Cycle Inventories datasets (LCI) from an LCI database (e.g., ecoinvent). The mapping should be provided in a dataframe with the following columns:

• Name: the name of the energy technology or resource in the energy model

• Type: the type of the energy technology or resource (i.e., ‘Construction’, ‘Decommission’, ‘Operation’, ‘Resource’ or ‘Flow’)

• Product: the name of the product of the energy technology or resource in the LCI database

• Activity: the name of the activity of the energy technology or resource in the LCI database

• Location: the name of the location of the energy technology or resource in the LCI database

• Unit: (optional) the physical unit of the energy technology or resource in the LCI database

• Database: the name of the database in your brightway project

If you wish to change the version of ecoinvent used in your mapping file, you can follow this notebook.

mapping.head()

	Name	Type	Product	Activity	Location	Database
0	ALKALINE_ELECTROLYSIS	Operation	hydrogen, gaseous, 20 bar	hydrogen production, gaseous, 20 bar, from AEC...	CH	ecoinvent_cutoff_3.10.1_image_SSP2-RCP26_2050 ...
1	ALKALINE_ELECTROLYSIS_PLANT	Construction	electrolyzer, 1MWe, AEC, Balance of Plant	electrolyzer production, 1MWe, AEC, Balance of...	RER	ecoinvent_cutoff_3.10.1_image_SSP2-RCP26_2050 ...
2	ALKALINE_ELECTROLYSIS_PLANT	Decommission	used fuel cell balance of plant, 1MWe, AEC	treatment of fuel cell balance of plant, 1MWe,...	RER	ecoinvent_cutoff_3.10.1_image_SSP2-RCP26_2050 ...
3	ALKALINE_ELECTROLYSIS_STACK	Construction	electrolyzer, 1MWe, AEC, Stack	electrolyzer production, 1MWe, AEC, Stack	RER	ecoinvent_cutoff_3.10.1_image_SSP2-RCP26_2050 ...
4	ALKALINE_ELECTROLYSIS_STACK	Decommission	used fuel cell stack, 1MWe, AEC	treatment of fuel cell stack, 1MWe, AEC	RER	ecoinvent_cutoff_3.10.1_image_SSP2-RCP26_2050 ...

A set of unit conversion factors between the energy system model and the LCI database. The conversion factors should be provided in a dataframe with the following columns:

• Name: the name of the energy technology or resource in the energy system model

• Type: the type of the energy technology or resource (i.e., ‘Construction’, ‘Decommission’, ‘Operation’, ‘Resource’, or ‘Other’). Other types can be added to fit your needs. The ‘Other’ category is meant for the unit conversion that are not specific to a technology or resource, but rather a generic type of product, e.g., conversion from kilogram to kilowatt hour for natural gas.

• Value: the numerical value of the conversion factor that will multiply the impact scores. It actually denotes the conversion from Impact / LCA unit to Impact / ESM unit.

• ESM: the unit of the energy technology or resource in the ESM

• LCA: the target unit of the energy technology or resource in the LCA database

• Assumptions & Sources (optional): additional information on the conversion factor.

The LCA and ESM columns should respect the ecoinvent naming convention. You may use the ecoinvent_unit_convention function to convert the unit naming convention you’ve used to the one of ecoinvent.

unit_conversion.head()

	Name	Type	Value	LCA	ESM
0	ACETIC_ACID	Resource	0.247423	kilogram	kilowatt hour
1	ACETONE	Resource	0.121655	kilogram	kilowatt hour
2	ALKALINE_ELECTROLYSIS	Construction	0.001556	unit	kilowatt
3	ALKALINE_ELECTROLYSIS	Decommission	0.001556	unit	kilowatt
4	ALKALINE_ELECTROLYSIS	Operation	0.030030	kilogram	kilowatt hour

A mapping between the energy system model flows and CPC categories. The mapping should be provided in a dataframe with the following columns:

• Flow: the name of the flow in the ESM

• Description: (optional) the description of the product

• CPC: the list of names of corresponding CPC categories

mapping_esm_flows_to_CPC.head()

	Flow	Description	CPC
0	BENZENE	Benzene	['33100: Coke and semi-coke of coal, of lignit...
1	BIO_DIESEL	Bio-diesel	['35491: Biodiesel']
2	CO2_A	Carbon dioxide (concentrated emissions)	['34210b: Carbon dioxide and monoxide']
3	CO2_C	Carbon dioxide (captured)	['34210b: Carbon dioxide and monoxide']
4	CO2_CS	Carbon dioxide (captured & stored)	['34210b: Carbon dioxide and monoxide']

The input and output flows of energy technologies. For a given technology, the inputs and outputs should be given with the same unit. Also, the main output flow should have 1 as an amount, i.e., all other flows as scaled with respect to the main output. It should be provided in a dataframe with the following columns:

• Name: the name of the energy technology in the energy system model

• Flow: the name of the input or output flow

• Amount: the numerical value of the flow (negative if input, positive if output)

model.head()

	Name	Flow	Amount
0	ACETIC_ACID	ACETIC_ACID	1.00
1	ACETONE	ACETONE	1.00
2	ALKALINE_ELECTROLYSIS	ELECTRICITY_HV	-1.72
3	ALKALINE_ELECTROLYSIS	H2_MP	1.00
4	ALKALINE_ELECTROLYSIS	HEAT_LOW_T_DECEN	0.26

Optional dataframes

technology_compositions = pd.read_csv(f'../dev/energyscope_data/{esm_location}/technology_compositions.csv')
technology_specifics = pd.read_csv(f'../dev/energyscope_data/{esm_location}/technology_specifics.csv')
lifetime = pd.read_csv(f'../dev/energyscope_data/{esm_location}/lifetime.csv')
efficiency = pd.read_csv(f'../dev/energyscope_data/{esm_location}/efficiency.csv')
mapping_product_to_CPC = pd.read_csv('../mescal/data/mapping_new_products_to_CPC.csv')
impact_abbrev = pd.read_csv('../dev/lcia/impact_abbrev.csv')
technologies_to_remove_from_layers = pd.read_csv(f'../dev/energyscope_data/{esm_location}/technologies_to_remove_from_layers.csv')
new_end_use_types = pd.read_csv(f'../dev/energyscope_data/{esm_location}/new_end_use_types.csv')
results_from_esm = pd.read_csv(f'../dev/energyscope_data/{esm_location}/results_ES.csv')

A set of composition of technologies, i.e., if one technology or resource in the energy model should be represented by a combination of LCI datasets. The composition should be provided in a dataframe with the following columns:

• Name: the name of the main energy technology or resource in the energy model

• Components: the list of names of subcomponents

• Type (optional): to distinguish between ‘Construction’ and ‘Decommission’ components (if not provided, all components are considered as ‘Construction’ components)

technology_compositions.head()

	Name	Components	Type
0	AN_DIG_SI	['AN_DIG_SI_PLANT', 'AN_DIG_SI_COGEN']	Construction
1	DEC_COGEN_GAS	['DEC_COGEN_GAS_CHP', 'DEC_COGEN_GAS_HEAT', 'D...	Construction
2	DEC_COGEN_OIL	['DEC_COGEN_OIL_BOILER', 'DEC_COGEN_OIL_TURBIN...	Construction
3	DEC_COGEN_WOOD	['DEC_COGEN_WOOD_BOILER', 'DEC_COGEN_WOOD_TURB...	Construction
4	DHN_COGEN_GAS	['DHN_COGEN_GAS_CHP', 'DHN_COGEN_GAS_HEAT', 'D...	Construction

A set of technologies with specific requirements. For instance, this stands for energy technologies without a construction phase, mobility technologies (if mismatch fuel in the LCI dataset), bio-processes (if mismatch fuel in the LCI dataset), etc. The requirements should be provided in a dataframe with the following columns:

• Name: the name of the energy technology in the energy model

• Specifics: the type of requirement. Can be ‘Background search’ (i.e., double-counting removal is run n levels in the background, n being defined in Amount), ‘Mobility’ (i.e., EUD types for which associated technologies are characterized as a mobility mean, to further add a FUEL layer), ‘No background search’ (i.e., double-counting removal is not applied beyond the activity direct exchanges), ‘No double-counting removal’ (i.e., the double-counting removal step is skipped), ‘Process’ (i.e., the technology is characterized as an industrial bio-process, to further add a PROCESS_FUEL layer), ‘DAC’ (for premise DAC technologies, to change the biogenic carbon flow into a fossil one), ‘Biofuel’ (i.e., adapt direct emissions to the biofuel input: partially change fossil carbon emissions into biogenic ones), ‘Import/Export’ (these activities will keep their original locations and will not be regionalized), ‘Carbon flows’ (the direct carbon emissions of these activities will be multiplied by a factor), ‘Add CC’ (add a carbon capture process to a technology, and modifies its direct carbon dioxide emissions according to the capture efficiency), ‘Add CO2 (flow_type)’ (add a resource/emission flow of CO2 to an activity, flow_type can be ‘fossil’, ‘non-fossil’, ‘from soil or biomass stock’, ‘in air’, or ‘non-fossil, resource correction’).

• Amount: the numerical value of the requirement (if relevant)

technology_specifics.head()

	Name	Specifics	Amount	Comment
0	CO2_TO_JETFUELS	Background search	0.0	NaN
1	CO2-To-Diesel	Background search	2.0	NaN
2	CROPS_TO_JETFUELS	Background search	4.0	NaN
3	FT	Background search	3.0	NaN
4	GASIFICATION_SNG	Background search	3.0	NaN

Energy technologies lifetimes in the ESM and the LCI database. For composition of technologies (if any), the main technology should not have a LCA lifetime (no LCI dataset is associated to it), while the sub-components should not have an ESM lifetime (they do not have a proper technology in the ESM). The lifetimes should be provided in a dataframe with the following columns:

• Name: the name of the energy technology in the energy system model

• ESM: the numerical value of the lifetime in the energy system model (if relevant)

• LCA: the numerical value of the lifetime in the LCI database (if relevant)

• Comment: (optional) additional information the Amount computation (if relevant)

lifetime.head()

	Name	ESM	LCA
0	ALKALINE_ELECTROLYSIS	10.0	NaN
1	ALKALINE_ELECTROLYSIS_PLANT	NaN	20.0
2	ALKALINE_ELECTROLYSIS_STACK	NaN	7.5
3	AL_MAKING	25.0	50.0
4	AL_MAKING_HR	25.0	50.0

If you want to account for the possible efficiency differences between the ESM and the LCI datasets, you can provide a set of (technologies, flow) couples for which efficiency differences will be corrected by the scaling the elementary flows. Taking the example of the diesel car, the couple should be something like (‘CAR_DIESEL’, ‘DIESEL’), the flow being the fuel of the technology responsible for direct emissions. Relevant biosphere flows will be the ratio between the LCA and ESM efficiencies. The (technology, flow) couples should be provided in a dataframe with the following columns:

• Name: the name of the energy technology in the ESM

• Flow: the name of the flow in the ESM

• Comment: (optional) additional information on the set of Flows

efficiency.head()

	Name	Flow	Comment
0	AN_DIG	['WET_BIOMASS']	NaN
1	AN_DIG_SI	['WET_BIOMASS']	NaN
2	BUS_CNG_STOICH	['NG_HP']	NaN
3	BUS_DIESEL	['DIESEL']	NaN
4	BUS_FC_HYBRID_CH4	['NG_HP']	NaN

In case you have a LCI database (partially) without CPC categories (which are necessary for the double-counting check), you can provide a mapping between the products and activities in the LCI database and the CPC categories. The mapping should be provided in a dataframe with the following columns:

• Name: the full or partial name of the product or activity in the LCI database

• CPC: the number and name of the corresponding CPC category

• Search type: can be ‘equals’ if the Name entry is an exactly the name to look for, or ‘contains’ if it is contained in the full name

• Where: can be ‘Product’ or ‘Activity’, whether the Name entry is meant for products or activities

mapping_product_to_CPC.head()

	Name	CPC	Search type	Where
0	amine-based silica	35310: Organic surface active agents, except soap	equals	Product
1	biodiesel	35491: Biodiesel	contains	Product
2	biogas	17200: Coal gas, water gas, producer gas and s...	equals	Product
3	biomass, used as fuel	31230: Wood in chips or particles	equals	Product
4	biomethane, from biogas upgrading, using amine...	12020: Natural gas, liquefied or in the gaseou...	equals	Product

An abbreviation scheme for the impact categories you aim to work with, to ease the readability in the ESM. The one of IMPACT World+ is available in this csv file. The abbreviations should be provided in a dataframe with the following columns:

• Impact_category: the name of the impact category, expressed as a tuple following brightway convention

• Unit: (optional) the unit of the impact category

• Abbrev: the abbreviation of the impact category

• AoP: the area of protection of the impact category. The normalization of indicators will be performed based of AoPs. If you do not have AoPs (e.g., midpoint-level indicators), set as many AoPs as you have impact categories.

impact_abbrev.head()

	Impact_category	Unit	Abbrev	AoP
0	('IMPACT World+ Damage 2.0.1', 'Ecosystem qual...	PDF.m2.yr	CCEQL	EQ
1	('IMPACT World+ Damage 2.0.1', 'Ecosystem qual...	PDF.m2.yr	CCEQS	EQ
2	('IMPACT World+ Damage 2.0.1', 'Ecosystem qual...	PDF.m2.yr	CCEQLB	EQ
3	('IMPACT World+ Damage 2.0.1', 'Ecosystem qual...	PDF.m2.yr	CCEQSB	EQ
4	('IMPACT World+ Damage 2.0.1', 'Human health',...	DALY	CCHHL	HH

In case you want to remove some energy technologies from energy layers in the results LCI datasets to be added to the LCI database, you can provide a list of technologies to remove. The list should be provided in a dataframe with the following columns:

• Layers: name of the layer(s). A layer is basically an energy vector, which is an output for some energy technologies and an input for some others.

• Technologies: the name of the energy technology to remove from the layer(s)

• Comment: (optional) a comment on the removal

technologies_to_remove_from_layers.head()

	Layers	Technologies	Comment
0	['ELECTRICITY_EHV', 'ELECTRICITY_HV']	['TRAFO_HE','TRAFO_EH']	The high and extra high voltage electricity ar...
1	['ELECTRICITY_LV']	['STO_ELEC']	The storage technologies should be removed (pr...
2	['NG_HP', 'NG_EHP']	['NG_EXP_EH', 'NG_EXP_EH_COGEN', 'NG_COMP_HE',...	The high and extra high pressure natural gas a...
3	['H2_LP', 'H2_MP', 'H2_HP', 'H2_EHP']	['H2_COMP_HE', 'H2_COMP_MH', 'H2_COMP_LM', 'H2...	All pressure levels for hydrogen are merged in...
4	['HEAT_HIGH_T', 'HEAT_LOW_T_DHN']	['HT_LT']	The high and low heat production at the DHN le...

If you want to reformat your end-use types (e.g., output layer) in order to better fit the LCI database, you can provide a list of new end-use types. The list should be provided in a dataframe with the following columns:

• Name: name of technologies for which the end-use type should be changed

• Search type: whether the Name entry is an exactly the name to look for (‘equals’), is contained in the full name (‘contains’), or is the beginning of the full name (‘startswith’)

• Old: the current end-use type

• New: the new end-use type

new_end_use_types.head()

	Name	Search type	Old	New
0	BUS	startswith	MOB_PUBLIC	MOB_PUBLIC_BUS
1	SCHOOLBUS	startswith	MOB_PUBLIC	MOB_PUBLIC_SCHOOLBUS
2	COACH	startswith	MOB_PUBLIC	MOB_PUBLIC_COACH
3	TRAIN	startswith	MOB_PUBLIC	MOB_PUBLIC_TRAIN
4	CAR	startswith	MOB_PRIVATE	MOB_PRIVATE_CAR

If you want to inject the results of your ESM back in the LCI database, you should provide the results in a dataframe with the following columns:

• Name: the name of the energy technology in the ESM

• Production: the annual production value of the energy technology in the ESM. All values should be provided with the same unit.

• Capacity: the installed capacity of the energy technology in the ESM. All values should be provided with the same unit.

• Year: the year of the ESM snapshot or transition pathway time-step.

• Year_inst: the year of installation of the energy technology. This is only required for PathwayESM if operation_metrics_for_all_time_steps is True.

results_from_esm.head()

	Name	Production	Capacity	Year	Year_inst
0	CAR_BEV_MEDRANGE_LOCAL	44880.00000	5.123288	2020	2020
1	CAR_BEV_MEDRANGE_LONGD	29920.00000	3.415525	2020	2020
2	CCGT_CC	1622.23145	0.217866	2020	2020
3	COACH_EV	17952.00000	2.049315	2020	2020
4	COMMUTER_RAIL_ELEC	44880.00000	5.123288	2020	2020

Optional geographical data

# sufficient match within ecoinvent
if esm_location == 'CA-QC':
    accepted_locations = ['CA-QC', 'CA']
elif esm_location == 'CH':
    accepted_locations = ['CH']
elif esm_location == 'core':
    accepted_locations = ['RER', 'WEU', 'EUR']
else:
    accepted_locations = ['GLO', 'RoW']

# Define the user-defined ranking
if esm_location == 'CA-QC':
    my_ranking = [
        'CA-QC', # Quebec
        'CA', # Canada
        'CAN', # Canada in IMAGE
        'CAZ', # Canada - Australia - New Zealand in REMIND
        'RNA', # North America
        'US', # United States
        'USA', # United States in REMIND and IMAGE
        'GLO', # Global average
        'RoW', # Rest of the world
    ]
elif esm_location == 'CH':
    my_ranking = [
        'CH',
        'NEU',
        'EUR',
        'WEU',
        'RER',
        'IAI Area, EU27 & EFTA',
        'GLO',
        'RoW'
    ]
elif esm_location == 'core':
    my_ranking = [
        'RER',
        'WEU',
        'EUR',
        'IAI Area, EU27 & EFTA',
        'CH',
        'BE',
        'IT',
        'GLO',
        'RoW',
    ]
else:
    my_ranking = [
        'GLO',
        'RoW',
    ]

Create a new database with additional CPC categories (optional)

In case you are working with a LCI database without CPC categories, you can create a new database with the CPC categories. The function create_new_database_with_CPC_categories takes as input the database with missing CPC categories, the name of the new database, and a mapping between the products and activities in the LCI database and the CPC categories. It creates a new database with the CPC categories. This step can take a few minutes depending on the size of the database.

# If necessary, add missing CPC categories to the database
premise_db.add_CPC_categories(overwrite_existing_CPC=True)

Initialize the ESM database

mapping.Database = name_premise_db

esm = ESM(
    # Mandatory inputs
    mapping=mapping,
    unit_conversion=unit_conversion,
    model=model,
    mapping_esm_flows_to_CPC_cat=mapping_esm_flows_to_CPC,
    main_database=premise_db,
    esm_db_name=esm_db_name,
    biosphere_db_name=name_biosphere_db,
    esm_location=esm_location if esm_location != 'core' else 'RER',

    # Optional inputs
    technology_compositions=technology_compositions,
    tech_specifics=technology_specifics,
    lifetime=lifetime,
    efficiency=efficiency,
    regionalize_foregrounds=regionalize_foregrounds,
    accepted_locations=accepted_locations,
    locations_ranking=my_ranking,
    spatialized_biosphere_db=spatialized_biosphere_db,
    results_path_file=f'results/energyscope_{esm_location}/{esm_year}/',
    remove_double_counting_to=['Operation', 'Construction'],
    extract_eol_from_construction=True,
    stop_background_search_when_first_flow_found=True,
)

Add or replace the location column based on a user-defined ranking (optional)

Based on a user-defined ranking, the location column of the mapping dataframe can be updated. The function change_location_mapping_file takes as input the mapping dataframe, the user-defined ranking, and the base database. It returns the mapping dataframe with the location column updated.

# Update mapping dataframe with better locations
esm.change_location_mapping_file()

esm.main_database.test_mapping_file(esm.mapping)

2025-12-26 12:17:27,811 - Database - INFO - Mapping successfully linked to the database

[]

Perform basic tests on input data

esm.clean_inputs()

esm.check_inputs()

2025-12-26 12:17:28,178 - Mescal - WARNING - List of technologies or resources that are in the model file but not in the mapping file. Their impact scores will be set to the default value: ['CARBON_CAPTURE', 'CO2_CS', 'CO2_E', 'DEC_RENOVATION', 'DHN_RENOVATION', 'DIESEL_S', 'ELEC_S', 'GASOLINE_S', 'H2_S', 'HT_LT', 'LT_DEC_WH', 'LT_DHN_WH', 'NG_S', 'PLANE', 'RES_GEO', 'RES_HYDRO', 'RES_SOLAR', 'RES_WIND', 'STO_CO2', 'STO_DIE', 'STO_ELEC', 'STO_GASO', 'STO_H2', 'STO_NG']
2025-12-26 12:17:28,195 - Mescal - WARNING - List of technologies or resources that are in the mapping file but not in the model file (this will not be a problem in the workflow): ['BATTERY', 'CAR_BEV_LOWRANGE', 'CAR_DME_D10_LOCAL', 'CAR_DME_D10_LONGD', 'DEC_TH_STORAGE', 'DHN_TH_STORAGE', 'EHP_H2_GRID', 'EHP_NG_GRID', 'EHV_GRID', 'HP_H2_GRID', 'HP_NG_GRID', 'HV_GRID', 'LP_H2_GRID', 'LP_NG_GRID', 'LV_GRID', 'MP_H2_GRID', 'MP_NG_GRID', 'MV_GRID']
2025-12-26 12:17:28,320 - Mescal - WARNING - List of flows that are in the mapping file but not in the unit conversion file. It might be an issue if unit conversions are required during the efficiency correction step: ['ALUMINUM', 'FOOD', 'HEAT_LOW_T_DECEN', 'HEAT_LOW_T_DHN', 'MOB_FREIGHT_LCV', 'MOB_FREIGHT_SEMI', 'MOB_FREIGHT_TRAIN', 'MOB_FREIGHT_TRUCK', 'MOB_PRIVATE_CAR', 'MOB_PRIVATE_SUV', 'MOB_PUBLIC_BUS', 'MOB_PUBLIC_COACH', 'MOB_PUBLIC_TRAIN', 'PAPER', 'STEEL', 'XYLENE']
2025-12-26 12:17:28,345 - Mescal - WARNING - Some technologies have no lifetime value for LCA in the lifetime file. Therefore, lifetime harmonization with the ESM will not be performed during the LCIA phase and capacity factor harmonization during the feedback of ESM results will not be performed either for those technologies: ['BATTERY', 'DEC_DIRECT_ELEC', 'DEC_SOLAR', 'DEEP_SALINE', 'DHN_DEEP_GEO', 'DIRECT_USAGE', 'DOGR', 'EHP_H2_GRID', 'ELEC_STO', 'EOR', 'GEOTHERMAL', 'H2_EXP_EH', 'H2_EXP_EH_COGEN', 'H2_EXP_HM', 'H2_EXP_HM_COGEN', 'H2_EXP_ML', 'H2_EXP_ML_COGEN', 'HP_H2_GRID', 'IND_DIRECT_ELEC', 'LP_H2_GRID', 'MINES_STORAGE', 'MP_H2_GRID', 'NG_COMP_HE', 'NG_COMP_LM', 'NG_COMP_MH', 'NG_EXP_EH', 'NG_EXP_EH_COGEN', 'NG_EXP_HM', 'NG_EXP_HM_COGEN', 'NG_EXP_ML', 'NG_EXP_ML_COGEN', 'TRAFO_EH', 'TRAFO_HE', 'TRAFO_HM', 'TRAFO_LM', 'TRAFO_MH', 'TRAFO_ML', 'UNMINEABLE_COAL_SEAM']
2025-12-26 12:17:28,367 - Mescal - WARNING - List of technologies that are in the tech_specifics file but not in the mapping file (this will not be a problem in the workflow): ['MOB_AVIATION', 'MOB_FREIGHT_RAIL', 'MOB_FREIGHT_ROAD', 'MOB_PRIVATE_LOCAL', 'MOB_PRIVATE_LONGD', 'MOB_PUBLIC_LOCAL', 'MOB_PUBLIC_LONGD']

Create ESM database after double-counting removal, efficiency harmonization, and lifetime harmonization

esm.create_esm_database()

2025-12-23 15:26:47,629 - Mescal - INFO - Starting to remove double-counted flows
100%|██████████| 215/215 [00:00<00:00, 731.76it/s]
2025-12-23 15:26:53,584 - Mescal - WARNING - No location found in your ranking for (natural gas, liquefied, natural gas production, liquefied) in the database ecoinvent_cutoff_3.10.1_image_SSP2-RCP26_2050 regionalized. Have to keep the initial location: EG
100%|██████████| 181/181 [01:48<00:00,  1.68it/s]
2025-12-23 15:30:01,870 - Mescal - INFO - Double-counting removal done in 194.2 seconds
2025-12-23 15:30:02,582 - Mescal - INFO - Starting to correct efficiency differences
2025-12-23 15:30:03,021 - Mescal - WARNING - No flow found for type(s) ['NG_HP'] in BUS_FC_HYBRID_CH4. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:03,353 - Mescal - WARNING - No flow found for type(s) ['NG_HP'] in CAR_FC_CH4_LOCAL. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:03,433 - Mescal - WARNING - No flow found for type(s) ['NG_HP'] in CAR_FC_CH4_LONGD. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:04,240 - Mescal - WARNING - No flow found for type(s) ['NG_HP'] in COACH_FC_HYBRID_CH4. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:05,228 - Mescal - WARNING - No flow found for type(s) ['WASTE'] in DHN_COGEN_WASTE. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:05,701 - Mescal - WARNING - No flow found for type(s) ['WASTE'] in IND_BOILER_WASTE. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:05,844 - Mescal - WARNING - No flow found for type(s) ['WASTE'] in IND_COGEN_WASTE. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:06,097 - Mescal - WARNING - No flow found for type(s) ['WOOD'] in PYROLYSIS. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:06,297 - Mescal - WARNING - No flow found for type(s) ['NG_HP'] in TRAIN_FREIGHT_NG. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:06,361 - Mescal - WARNING - No flow found for type(s) ['NG_HP'] in TRAIN_NG. The efficiency of this technology cannot be adjusted.
2025-12-23 15:30:09,783 - Mescal - INFO - Efficiency differences corrected in 7.2 seconds
2025-12-23 15:30:16,960 - Mescal - WARNING - Several names possible for the same type of flow in POLYPROPYLENE_PP: {'propylene', 'ethylene'}. Kept the first one.
2025-12-23 15:30:18,367 - Mescal - INFO - Starting to write database
Writing activities to SQLite3 database:
0% [##############################] 100% | ETA: 00:00:00
Total time elapsed: 00:00:00

Title: Writing activities to SQLite3 database:
  Started: 12/23/2025 15:30:41
  Finished: 12/23/2025 15:30:42
  Total time elapsed: 00:00:00
  CPU %: 89.10
  Memory %: 7.13

2025-12-23 15:31:59,168 - Database - INFO - EnergyScope_CH_2050_Test written to Brightway!
2025-12-23 15:31:59,825 - Mescal - INFO - Database written in 101.5 seconds

Computing the LCA metrics

R_long_direct_emissions, _, _ = esm.compute_impact_scores(
    methods=lcia_methods,
    assessment_type='direct emissions',
    overwrite=True,
)

Getting activity data

100%|██████████| 880/880 [00:00<00:00, 87082.40it/s]

Adding exchange data to activities

100%|██████████| 14620/14620 [00:04<00:00, 3287.59it/s]

Filling out exchange data

100%|██████████| 880/880 [00:01<00:00, 477.34it/s]
2025-12-23 15:32:10,326 - Database - INFO - Loaded EnergyScope_CH_2050_Test from brightway!
Writing activities to SQLite3 database:
0% [##############################] 100% | ETA: 00:00:00
Total time elapsed: 00:00:00

Title: Writing activities to SQLite3 database:
  Started: 12/23/2025 15:32:33
  Finished: 12/23/2025 15:32:33
  Total time elapsed: 00:00:00
  CPU %: 78.70
  Memory %: 12.36

2025-12-23 15:33:33,577 - Database - INFO - EnergyScope_CH_2050_Test_direct_emissions written to Brightway!
181it [00:01, 116.03it/s]

R_long_direct_emissions.to_csv(f'results/energyscope_{esm_location}/{esm_year}/impact_scores_direct_emissions.csv', index=False)

R_long_direct_emissions.head()

	Impact_category	New_code	Value	Name	Type	Impact_category (level 0)	Impact_category (level 1)	Impact_category (level 2)	Impact_category_unit	Functional unit
0	(IMPACT World+ Midpoint 2.1_regionalized for e...	bhststsh7mmysi4fyiexddlpcxqvck5g	0.0	ALKALINE_ELECTROLYSIS	Operation	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Climate change, long term	kg CO2 eq (long)	kilowatt hour
1	(IMPACT World+ Midpoint 2.1_regionalized for e...	bhststsh7mmysi4fyiexddlpcxqvck5g	0.0	ALKALINE_ELECTROLYSIS	Operation	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Climate change, short term	kg CO2 eq (short)	kilowatt hour
2	(IMPACT World+ Midpoint 2.1_regionalized for e...	bhststsh7mmysi4fyiexddlpcxqvck5g	0.0	ALKALINE_ELECTROLYSIS	Operation	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Fossil and nuclear energy use	MJ deprived	kilowatt hour
3	(IMPACT World+ Midpoint 2.1_regionalized for e...	bhststsh7mmysi4fyiexddlpcxqvck5g	0.0	ALKALINE_ELECTROLYSIS	Operation	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Freshwater acidification	kg SO2 eq	kilowatt hour
4	(IMPACT World+ Midpoint 2.1_regionalized for e...	bhststsh7mmysi4fyiexddlpcxqvck5g	0.0	ALKALINE_ELECTROLYSIS	Operation	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Freshwater ecotoxicity	CTUe	kilowatt hour

R_long, contrib_analysis_res, _ = esm.compute_impact_scores(
    methods=lcia_methods,
    contribution_analysis='both',
)

Getting activity data

100%|██████████| 880/880 [00:00<00:00, 124271.49it/s]

Adding exchange data to activities

100%|██████████| 14620/14620 [00:00<00:00, 35325.79it/s]

Filling out exchange data

100%|██████████| 880/880 [00:01<00:00, 658.70it/s]
2025-12-23 15:33:39,074 - Database - INFO - Loaded EnergyScope_CH_2050_Test from brightway!
596it [05:54,  1.68it/s]

R_long.to_csv(f'results/energyscope_{esm_location}/{esm_year}/impact_scores.csv', index=False)
contrib_analysis_res.to_csv(f'results/energyscope_{esm_location}/{esm_year}/contribution_analysis.csv', index=False)

R_long.head()

	Impact_category	New_code	Value	Name	Type	Impact_category (level 0)	Impact_category (level 1)	Impact_category (level 2)	Impact_category_unit	Functional unit
0	(IMPACT World+ Midpoint 2.1_regionalized for e...	djsx4kf89i179omk6lwr1hqptygdo6g2	239.149972	AL_MAKING	Construction	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Climate change, long term	kg CO2 eq (long)	kilogram per hour
1	(IMPACT World+ Midpoint 2.1_regionalized for e...	djsx4kf89i179omk6lwr1hqptygdo6g2	260.880150	AL_MAKING	Construction	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Climate change, short term	kg CO2 eq (short)	kilogram per hour
2	(IMPACT World+ Midpoint 2.1_regionalized for e...	djsx4kf89i179omk6lwr1hqptygdo6g2	3472.254845	AL_MAKING	Construction	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Fossil and nuclear energy use	MJ deprived	kilogram per hour
3	(IMPACT World+ Midpoint 2.1_regionalized for e...	djsx4kf89i179omk6lwr1hqptygdo6g2	1.672163	AL_MAKING	Construction	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Freshwater acidification	kg SO2 eq	kilogram per hour
4	(IMPACT World+ Midpoint 2.1_regionalized for e...	djsx4kf89i179omk6lwr1hqptygdo6g2	143450.673137	AL_MAKING	Construction	IMPACT World+ Midpoint 2.1_regionalized for ec...	Midpoint	Freshwater ecotoxicity	CTUe	kilogram per hour

contrib_analysis_res.head()

	score	amount	code	database	impact_category	act_database	act_code	contribution_type	act_name	act_type
0	118.979814	118.979814	349b29d1-3e58-4c66-98b9-9d1a076efd2e	biosphere3	(IMPACT World+ Midpoint 2.1_regionalized for e...	EnergyScope_CH_2050_Test	djsx4kf89i179omk6lwr1hqptygdo6g2	emissions	AL_MAKING	Construction
1	61.124035	61.124035	f9749677-9c9f-4678-ab55-c607dfdc2cb9	biosphere3	(IMPACT World+ Midpoint 2.1_regionalized for e...	EnergyScope_CH_2050_Test	djsx4kf89i179omk6lwr1hqptygdo6g2	emissions	AL_MAKING	Construction
2	45.521741	45.521741	aa7cac3a-3625-41d4-bc54-33e2cf11ec46	biosphere3	(IMPACT World+ Midpoint 2.1_regionalized for e...	EnergyScope_CH_2050_Test	djsx4kf89i179omk6lwr1hqptygdo6g2	emissions	AL_MAKING	Construction
3	5.670375	5.670375	70ef743b-3ed5-4a6d-b192-fb6d62378555	biosphere3	(IMPACT World+ Midpoint 2.1_regionalized for e...	EnergyScope_CH_2050_Test	djsx4kf89i179omk6lwr1hqptygdo6g2	emissions	AL_MAKING	Construction
4	2.566230	2.566230	ba2f3f82-c93a-47a5-822a-37ec97495275	biosphere3	(IMPACT World+ Midpoint 2.1_regionalized for e...	EnergyScope_CH_2050_Test	djsx4kf89i179omk6lwr1hqptygdo6g2	emissions	AL_MAKING	Construction

_, contrib_analysis_processes_res, _ = esm.compute_impact_scores(
    methods=lcia_methods,
    contribution_analysis='processes',
    contribution_analysis_limit=100,
)

Getting activity data

100%|██████████| 880/880 [00:00<00:00, 196621.96it/s]

Adding exchange data to activities

100%|██████████| 14620/14620 [00:00<00:00, 18463.85it/s]

Filling out exchange data

100%|██████████| 880/880 [00:00<00:00, 960.51it/s]
2025-12-23 15:39:58,051 - Database - INFO - Loaded EnergyScope_CH_2050_Test from brightway!
596it [22:28,  2.26s/it]

contrib_analysis_processes_res.to_csv(f'results/energyscope_{esm_location}/{esm_year}/contribution_analysis_processes.csv', index=False)

Convert the results in an AMPL format

# To skip the previous steps
R_long = pd.read_csv(f'results/energyscope_{esm_location}/{esm_year}/impact_scores.csv')
R_long_direct_emissions = pd.read_csv(f'results/energyscope_{esm_location}/{esm_year}/impact_scores_direct_emissions.csv')
contrib_analysis_processes_res = pd.read_csv(f'results/energyscope_{esm_location}/{esm_year}/contribution_analysis_processes.csv')

C:\Users\matth\AppData\Local\Temp\ipykernel_35040\263815357.py:4: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.
  contrib_analysis_processes_res = pd.read_csv(f'results/energyscope_{esm_location}/{esm_year}/contribution_analysis_processes.csv')

# Tou can specify which specific LCIA methods or impact categories you wish to use using one of the three following options:
# specific_lcia_methods = [f'IMPACT World+ Footprint 2.1 for ecoinvent v{ecoinvent_main_version}'] # selects the specific methods via their names
# specific_lcia_categories = ['Total ecosystem quality', 'Total human health']  # selects the specific impact categories via their names
specific_lcia_abbrev = ['TTHH', 'TTEQ', 'm_CCS'] # selects the specific impact categories via their abbreviations

# Additional information that can be added at the beginning of the AMPL .mod and .dat files
metadata = {
    'ecoinvent_version': ecoinvent_version,
    'year': esm_year,
    'spatialized': spatialized_database,
    'regionalized': regionalize_foregrounds,
    'iam': premise_iam,
    'ssp_rcp': premise_ssp_rcp,
    'lcia_methods': lcia_methods,
}

Normalize LCA indicators and create the .dat file

# Life-cycle impacts
esm.normalize_lca_metrics(
    R=R_long,
    mip_gap=1e-6,
    lcia_methods=lcia_methods,
    specific_lcia_abbrev=specific_lcia_abbrev,
    impact_abbrev=impact_abbrev,
    path=f'results/energyscope_{esm_location}/{esm_year}/',
    metadata=metadata,
)

# Direct impacts
esm.normalize_lca_metrics(
    R=R_long,
    R_direct=R_long_direct_emissions,
    mip_gap=1e-6,
    lcia_methods=lcia_methods,
    specific_lcia_abbrev=specific_lcia_abbrev,
    assessment_type='direct emissions',
    impact_abbrev=impact_abbrev,
    path=f'results/energyscope_{esm_location}/{esm_year}/',
    metadata=metadata,
    file_name='techs_lcia_direct',
)

# Territorial impacts
esm.normalize_lca_metrics(
    R=R_long,
    contrib_processes=contrib_analysis_processes_res,
    mip_gap=1e-6,
    lcia_methods=lcia_methods,
    specific_lcia_abbrev=specific_lcia_abbrev,
    assessment_type='territorial emissions',
    impact_abbrev=impact_abbrev,
    path=f'results/energyscope_{esm_location}/{esm_year}/',
    metadata=metadata,
    file_name='techs_lcia_territorial',
)

Getting activity data

100%|██████████| 880/880 [00:00<00:00, 48886.60it/s]

Adding exchange data to activities

100%|██████████| 14620/14620 [00:01<00:00, 10273.36it/s]

Filling out exchange data

100%|██████████| 880/880 [00:01<00:00, 489.38it/s]
2025-12-26 12:41:32,926 - Database - INFO - Loaded EnergyScope_CH_2050_Test from brightway!

Create the .mod file

# Life-cycle impacts
esm.generate_mod_file_ampl(
    lcia_methods=lcia_methods,
    specific_lcia_abbrev=specific_lcia_abbrev,
    impact_abbrev=impact_abbrev,
    path=f'results/energyscope_{esm_location}/{esm_year}/',
    metadata=metadata,
    energyscope_version='core' if esm_location == 'core' else 'epfl',
)

# Direct impacts
esm.generate_mod_file_ampl(
    lcia_methods=lcia_methods,
    specific_lcia_abbrev=specific_lcia_abbrev,
    assessment_type='direct emissions',
    impact_abbrev=impact_abbrev,
    path=f'results/energyscope_{esm_location}/{esm_year}/',
    metadata=metadata,
    file_name='objectives_direct',
    energyscope_version='core' if esm_location == 'core' else 'epfl',
)

# Territorial impacts
esm.generate_mod_file_ampl(
    lcia_methods=lcia_methods,
    specific_lcia_abbrev=specific_lcia_abbrev,
    assessment_type='territorial emissions',
    impact_abbrev=impact_abbrev,
    path=f'results/energyscope_{esm_location}/{esm_year}/',
    metadata=metadata,
    file_name='objectives_territorial',
    energyscope_version='core' if esm_location == 'core' else 'epfl',
)

Integrate the ESM results back in the LCI database

esm.create_new_database_with_esm_results(
    esm_results=results_from_esm,
    new_end_use_types=new_end_use_types,
    tech_to_remove_layers=technologies_to_remove_from_layers,
    write_database=True,
    remove_background_construction_flows=True,
    harmonize_with_esm=False,
)

esm.connect_esm_results_to_database(create_new_db=True)