mescal.impact_assessment

Attributes

ca
calculation_setups

Classes

MultiLCA

Adaptation of the MultiLCA class from the bw2calc package in order to perform contribution analysis.

Functions

_add_virtual_technosphere_flow(→ dict)	Add a technosphere exchange to an activity
_aggregate_direct_emissions_activities(...)	Aggregate the activities subject to double counting for the same ESM technology
_is_empty(→ float)	Fill empty cells with the ESM value if the technology is not in the technology compositions file
compute_impact_scores(→ tuple[pandas.DataFrame, ...)	Compute the impact scores of the technologies and resources
compute_territorial_impact_scores(→ pandas.DataFrame)	Compute the territorial and abroad impact scores from the contribution analysis of processes.
validation_direct_carbon_emissions(...)	Returns a dataframe comparing the direct carbon emissions obtained from the LCIA phase (direct emissions module)

Module Contents

class mescal.impact_assessment.MultiLCA(cs_name: str, contribution_analysis: str, limit: int or float, limit_type: str, req_technosphere: bool, log_config=None)

Bases: object

Adaptation of the MultiLCA class from the bw2calc package in order to perform contribution analysis.

Wrapper class for performing LCA calculations with many functional units and LCIA methods. Needs to be passed a calculation_setup name. This class does not subclass the LCA class, and performs all calculations upon instantiation. Initialization creates self.results, which is a NumPy array of LCA scores, with rows of functional units and columns of LCIA methods. Ordering is the same as in the calculation_setup.

Initialize the MultiLCA_with_contribution_analysis class.

Parameters:

• cs_name (str) – name of the calculation setup to use

• contribution_analysis (str) – if ‘emissions’, the function will return the contribution analysis of top elementary flows. If ‘processes’, the function will return the contribution analysis of top processes. If ‘both’, it will return both.

• limit (int or float) – number of values to return (if limit_type is ‘number’), or percentage cutoff (if limit_type is ‘percent’)

• limit_type (str) – contribution analysis limit type, can be ‘percent’ or ‘number’

• req_technosphere (bool) – if True, the function will compute the requirements for technosphere flows

• log_config – log configuration for the LCA calculation

property all

Get all possible databases by merging all functional units

contribution_analysis

func_units

lca

limit

limit_type

method_matrices = []

methods

results

mescal.impact_assessment._add_virtual_technosphere_flow(act: dict, exc_act: dict, amount: float) → dict

Add a technosphere exchange to an activity

Parameters:

• act (dict) – activity to which the exchange is added

• exc_act (dict) – activity of the exchange

• amount (float) – amount of the exchange

Returns:

the activity with the new exchange

Return type:

dict

mescal.impact_assessment._aggregate_direct_emissions_activities(self, esm_db: mescal.database.Database, direct_emissions_db: list[dict], activities_subject_to_double_counting: pandas.DataFrame) → mescal.database.Database

Aggregate the activities subject to double counting for the same ESM technology

Parameters:

• esm_db (mescal.database.Database) – ESM database

• direct_emissions_db (list[dict]) – direct emissions ESM database before aggregation

• activities_subject_to_double_counting (pandas.DataFrame) – dataframe of activities subject to double counting

Returns:

aggregated direct emissions ESM database

Return type:

mescal.database.Database

mescal.impact_assessment._is_empty(self, row: pandas.Series) → float

Fill empty cells with the ESM value if the technology is not in the technology compositions file

Parameters:

row (pandas.Series) – row of the lifetime dataframe

Returns:

lifetime value

Return type:

float

mescal.impact_assessment.compute_impact_scores(self, methods: list[str], specific_lcia_categories: list[str] = None, specific_lcia_abbrev: list[str] = None, impact_abbrev: pandas.DataFrame = None, assessment_type: str = 'esm', overwrite: bool = True, contribution_analysis: str = None, contribution_analysis_limit_type: str = 'number', contribution_analysis_limit: float or int = 5, req_technosphere: bool = False) → tuple[pandas.DataFrame, pandas.DataFrame | None, pandas.DataFrame | None]

Compute the impact scores of the technologies and resources

Parameters:

• methods (list[str]) – list of life-cycle impact assessment methods for which LCA scores are computed

• specific_lcia_categories (list[str]) – restrict the impact assessment to specific LCIA categories

• specific_lcia_abbrev (list[str]) – restrict the impact assessment to specific LCIA categories identified by their abbreviation in the impact_abbrev file

• impact_abbrev (pandas.DataFrame) – dataframe containing the impact categories abbreviations

• assessment_type (str) – type of assessment, can be ‘esm’ for the computation of the energy system life-cycle impacts, or ‘direct emissions’ for the computation of direct emissions only

• overwrite (bool) – only relevant if assessment_type is ‘direct emissions’, if True, the direct emissions database will be overwritten if it exists

• contribution_analysis (str) – if ‘emissions’, the function will return the contribution analysis of top elementary flows. If ‘processes’, the function will return the contribution analysis of top processes. If ‘both’, it will return both.

• contribution_analysis_limit_type (str) – contribution analysis limit type, can be ‘percent’ or ‘number’. Default is ‘percent’.

• contribution_analysis_limit (float or int) – number of values to return (if limit_type is ‘number’), or percentage cutoff (if limit_type is ‘percent’). Default is 0.01.

• req_technosphere (bool) – if True, the function will compute the requirements for technosphere flows.

Returns:

impact scores dataframe of the technologies and resources for all selected impact categories, contribution analysis dataframe (None if contribution_analysis is None), and technosphere flows requirements dataframe (None if req_technosphere is False).

Return type:

tuple[pandas.DataFrame, pandas.DataFrame | None, pandas.DataFrame | None]

mescal.impact_assessment.compute_territorial_impact_scores(self, df_contrib_processes: pandas.DataFrame) → pandas.DataFrame

Compute the territorial and abroad impact scores from the contribution analysis of processes.

Parameters:

df_contrib_processes (pandas.DataFrame) – dataframe of the contribution analysis of processes

Returns:

territorial and abroad impact scores dataframe

Return type:

pandas.DataFrame

mescal.impact_assessment.validation_direct_carbon_emissions(self, R_direct: pandas.DataFrame, lcia_method_carbon_emissions: str, carbon_flow_in_esm: str or list[str], esm_results: pandas.DataFrame = None, return_df: bool = False, save_df: bool = True) → tuple[pd.DataFrame, pd.DataFrame | None] or None

Returns a dataframe comparing the direct carbon emissions obtained from the LCIA phase (direct emissions module) and direct carbon emissions from the ESM. Please make sure that carbon emissions are expressed with the same physical unit, e.g., kg CO2-eq., in the ESM and LCIA method (while functional units, e.g., kWh, are automatically converted to ESM units).

Parameters:

• R_direct (pandas.DataFrame) – dataframe containing the direct carbon emissions from the LCIA phase

• lcia_method_carbon_emissions (str) – name of the LCIA method for carbon emissions in brightway

• carbon_flow_in_esm (str or list[str]) – names(s) of the carbon flow(s) in the ESM

• esm_results (pandas.DataFrame) – dataframe containing the annual production of each technology in the ESM. It must contain the columns ‘Name’ and ‘Production’, and it can possibly contain the ‘Run’ and ‘Year’ columns too. If provided, the system’s direct emissions will be compared.

• return_df (bool) – if True, the function will return the dataframe

• save_df (bool) – if True, the function will save the dataframe to a csv file named direct_carbon_emissions_differences and a file named direct_carbon_emissions_differences_system (if esm_results is provided) in self.results_path_file

Returns:

dataframe comparing the direct carbon emissions from the LCIA phase and the ESM if return_df is True

Return type:

tuple[pd.DataFrame, pd.DataFrame | None] or None

mescal.impact_assessment.ca

mescal.impact_assessment.calculation_setups = None

static mescal.impact_assessment._get_impact_categories(methods: list[str]) → list[str]

Get all impact categories from a list of methods

Parameters:

methods (list[str]) – list of LCIA methods

Returns:

list of impact categories in the LCIA methods

Return type:

list[str]