New Wurst Database

class premise.ecoinvent_modification.NewDatabase(*args: Any, **kwargs: Any)

Database Cleaner

class premise.clean_datasets.DatabaseCleaner(source_db: str, source_type: str, source_file_path: Path, version: str)

Class that cleans the datasets contained in the inventory database for further processing.

Variables:

source_type (str) – type of the database source. Can be ´brightway´ or ‘ecospold’.
source_db (str) – name of the source database if source_type == ‘brightway’
source_file_path (str) – filepath of the database if source_type == ‘ecospold’.

add_location_field_to_exchanges() → None

Add the location key to the production and technosphere exchanges in database.

Raises:: IndexError – if no corresponding activity (and reference product) can be found.

add_product_field_to_exchanges() → None

Add the product key to the production and technosphere exchanges in database.

For production exchanges, use the value of the reference_product field. For technosphere exchanges, search the activities in database and use the reference product.

Raises:: IndexError – if no corresponding activity (and reference product) can be found.

correct_biogas_activities(): Some activities producing biogas are not given any biogenic CO2 or energy input, leading to imbalanced carbon and energy flows when combusted.

find_location_given_lookup_dict(lookup_dict: Dict[str, str]) → List[str]

Return a list of location names, given the filtering conditions given in lookup_dict. It is, for example, used to return a list of location names based on the name and the unit of a dataset.

Parameters:: lookup_dict – a dictionary with filtering conditions
Returns:: a list of location names

find_product_given_lookup_dict(lookup_dict: Dict[str, str]) → List[str]

Return a list of location names, given the filtering conditions given in lookup_dict. It is, for example, used to return a list of location names based on the name and the unit of a dataset.

Parameters:: lookup_dict – a dictionary with filtering conditions
Returns:: a list of location names

fix_biosphere_flow_categories() → None: Add a categories for biosphere flows if missing. This happens when importing directly from ecospold files

fix_unset_technosphere_and_production_exchange_locations(matching_fields: Tuple[str, str] = ('name', 'unit')) → None

Give all the production and technopshere exchanges with a missing location name the location of the dataset they belong to. Modifies in place (does not return anything).

Parameters:: matching_fields (tuple) – filter conditions

prepare_datasets(keep_uncertainty_data) → List[dict]: Clean datasets for all databases listed in scenarios: fix location names, remove empty exchanges, etc.

transform_parameter_field() → None: Transform the parameter field of the database to a dictionary.

Inventory Imports

class premise.inventory_imports.BaseInventoryImport(database: List[dict], version_in: str, version_out: str, path: str | Path, system_model: str, keep_uncertainty_data: bool = False)

Base class for inventories that are to be merged with the wurst database.

Variables:: database – the target database for the import (the ecoinvent database),

unpacked to a list of dicts :ivar version_in: the ecoinvent database version of the inventory to import :ivar version_out: the ecoinvent database version the imported inventories should comply with :ivar path: the filepath of the inventories to import

__init__(database: List[dict], version_in: str, version_out: str, path: str | Path, system_model: str, keep_uncertainty_data: bool = False) → None: Create a BaseInventoryImport instance.

add_biosphere_links() → None: Add links for biosphere exchanges to import_db Modifies the import_db attribute in place.

add_product_field_to_exchanges() → None: Add the product key to the production and technosphere exchanges in import_db. Also add code field if missing. For production exchanges, use the value of the reference_product field. For technosphere exchanges, search the activities in import_db and use the reference product. If none is found, search the Ecoinvent database. Modifies the import_db attribute in place. :raises IndexError: if no corresponding activity (and reference product) can be found.

check_for_already_existing_datasets() → None: Check whether the inventories to be imported are not already in the source database.

correct_product_field(exc: tuple) → [<class 'str'>, None]: Find the correct name for the product field of the exchange :param exc: a dataset exchange :return: name of the product field of the exchange

display_unlinked_exchanges(): Display the list of unlinked exchanges using prettytable

load_inventory() → None: Load an inventory from a specified path. Sets the import_db attribute. :returns: Nothing.

merge_inventory() → List[dict]: Prepare import_db and merge the inventory to the ecoinvent database. Calls prepare_inventory(). Changes the database attribute. :returns: Nothing

prepare_inventory() → None: Prepare the inventory for the merger with Ecoinvent. Modifies import_db in-place. :returns: Nothing

remove_ds_and_modifiy_exchanges(name: str, ex_data: dict) → None: Remove an activity dataset from import_db and replace the corresponding technosphere exchanges by what is given as second argument. :param str name: name of activity to be removed :param dict ex_data: data to replace the corresponding exchanges :returns: Nothing

search_missing_exchanges(label: str, value: str) → List[dict]: Return a list of activities for which a given exchange cannot be found :param label: the label of the field to look for :param value: the value of the field to look for :return:

search_missing_field(field: str, scope: str = 'activity') → List[dict]: Find exchanges and activities that do not contain a specific field in imort_db :param str field: label of the field to search for. :param scope: “activity” or “all”. whether to search in the activity or the activity and its exchanges :returns: a list of dictionaries, activities and exchanges :rtype: list

Remind Data Collection

class premise.data_collection.IAMDataCollection(model: str, pathway: str, year: int, filepath_iam_files: Path, key: bytes, external_scenarios: dict | None = None, system_model: str = 'cutoff', system_model_args: dict | None = None, gains_scenario: str = 'CLE', use_absolute_efficiency: bool = False)

Variables:

model – name of the IAM model (e.g., “remind”)
pathway – name of the IAM scenario (e.g., “SSP2-Base”)
year – year to produce the database for
system_model – “cutoff” or “consequential”.

fetch_external_data_coal_power_plants(): Fetch data on coal power plants from external sources. Source: Oberschelp, C., Pfister, S., Raptis, C.E. et al. Global emission hotspots of coal power generation. Nat Sustain 2, 113–121 (2019). https://doi.org/10.1038/s41893-019-0221-6

get_iam_efficiencies(data: DataArray, efficiency_labels: dict | None = None, production_labels: dict | None = None, energy_labels: dict | None = None, use_absolute_efficiency: bool = False) → [<class 'xarray.core.dataarray.DataArray'>, None]

This method retrieves efficiency values for the specified sector, for a specified year, for each region provided by the IAM.

Parameters:

data – The data to process.
efficiency_labels – The efficiency labels to use.
production_labels – The production labels to use.
energy_labels – The energy labels to use.

Returns:

a multidimensional array with sector’s technologies market

share for a given year, for all regions.

Remind to Ecoinvent mapping

class premise.activity_maps.InventorySet(database: List[dict], version: str | None = None, model: str | None = None)

Hosts different filter sets to find equivalencies between premise terms and ecoinvent activities and exchanges.

It stores: * material_filters: filters for activities related to materials. * powerplant_filters: filters for activities related to power generation technologies. * powerplant_fuel_filters: filters for fuel providers in power generation technologies. * fuel_filters: filters for fuel providers in general. * emissions_map: REMIND emission labels as keys, ecoinvent emission labels as values

The functions generate_material_map(), generate_powerplant_map() and generate_fuel_map() can be used to extract the actual activity objects as dictionaries. These functions return the result of applying act_fltr() to the filter dictionaries.

generate_carbon_storage_map() → dict

Filter ecoinvent processes related to carbon storage.

Returns:: dictionary with el. prod. techs as keys (see below) and sets of related ecoinvent activities as values.
Return type:: dict

generate_cement_fuels_map() → dict

Filter ecoinvent processes related to cement production.

Returns:: dictionary with el. prod. techs as keys (see below) and sets of related ecoinvent activities as values.
Return type:: dict

generate_daccs_map() → dict

Filter ecoinvent processes related to direct air capture.

Returns:: dictionary with el. prod. techs as keys (see below) and sets of related ecoinvent activities as values.
Return type:: dict

generate_fuel_map() → dict

Filter ecoinvent processes related to fuel supply.

Returns:: dictionary with fuel names as keys (see below) and sets of related ecoinvent activities as values.
Return type:: dict

generate_gains_mapping(): Generate a dictionary with GAINS variables as keys and ecoinvent datasets as values.

generate_heat_map() → dict

Filter ecoinvent processes related to heat production.

Returns:: dictionary with heat prod. techs as keys (see below) and sets of related ecoinvent activities as values.
Return type:: dict

generate_material_map() → dict: Filter ecoinvent processes related to materials. Rerurns a dictionary with material names as keys (see below) and a set of related ecoinvent activities’ names as values.

generate_powerplant_fuels_map() → dict

Filter ecoinvent processes related to electricity production.

Returns:: dictionary with el. prod. techs as keys (see below) and sets of related ecoinvent activities as values.
Return type:: dict

generate_powerplant_map() → dict

Filter ecoinvent processes related to electricity production.

Returns:: dictionary with el. prod. techs as keys (see below) and sets of related ecoinvent activities as values.
Return type:: dict

generate_sets_from_filters(filtr: dict, database=None) → dict

Generate a dictionary with sets of activity names for technologies from the filter specifications.

Parameters:: filtr

activity_maps.InventorySet.act_fltr(). :return: dictionary with the same keys as provided in filter

and a set of activity data set names as values.

Return type:: dict

Electricity markets modelling

class premise.electricity.Electricity(database: List[dict], iam_data: IAMDataCollection, model: str, pathway: str, year: int, version: str, system_model: str, use_absolute_efficiency: bool = False, cache: dict | None = None, index: dict | None = None)

Class that modifies electricity markets in the database based on IAM output data. Inherits from transformation.BaseTransformation.

Variables:

database (list) – wurst database, which is a list of dictionaries
iam_data (xarray.DataArray) – IAM data
model (str) – name of the IAM model (e.g., “remind”, “image”)
year (int) – year of the pathway (e.g., 2030)

adjust_coal_power_plant_emissions() → None

Based on: Fetch data on coal power plants from external sources. Source: Oberschelp, C., Pfister, S., Raptis, C.E. et al. Global emission hotspots of coal power generation. Nat Sustain 2, 113–121 (2019). https://doi.org/10.1038/s41893-019-0221-6

We adjust efficiency and emissions of coal power plants, including coal-fired CHPs.

create_missing_power_plant_datasets() → None: Create missing power plant datasets. We use proxy datasets, copy them and rename them.

create_new_markets_high_voltage() → None: Create high voltage market groups for electricity, based on electricity mixes given by the IAM scenario. Contribution from solar power is added in low voltage market groups. Does not return anything. Modifies the database in place.

create_new_markets_low_voltage() → None: Create low voltage market groups for electricity, by receiving medium voltage market groups as input and adding transformation and distribution losses. Transformation and distribution losses are taken from ei37. Contribution from solar power is added here as well, as most is delivered at low voltage, although CSP delivers at high voltage. Does not return anything. Modifies the database in place.

create_new_markets_medium_voltage() → None: Create medium voltage market groups for electricity, by receiving high voltage market groups as inputs and adding transformation and distribution losses. Contribution from solar power is added in low voltage market groups. Does not return anything. Modifies the database in place.

create_region_specific_power_plants(): Some power plant inventories are not native to ecoinvent but imported. However, they are defined for a specific location (mostly European), but are used in many electricity markets (non-European). Hence, we create region-specific versions of these datasets, to align inputs providers with the geographical scope of the region.

generate_world_market(dataset: dict, regions: List[str]) → dict

Generate the world market for a given dataset and product variables.

Parameters:

dataset – The dataset for which to generate the world market.
regions – A dictionary of activity datasets, keyed by region.

This function generates the world market exchanges for a given dataset and set of product variables. It first filters out non-production exchanges from the dataset, and then calculates the total production volume for the world using the given product variables. For each region, it calculates the share of the production volume and adds a technosphere exchange to the dataset with the appropriate share.

get_production_per_tech_dict() → Dict[Tuple[str, str], float]: Create a dictionary with tuples (technology, country) as keys and production volumes as values. :return: technology to production volume dictionary :rtype: dict

get_production_weighted_share(supplier: dict, suppliers: List[dict]) → float: Return the share of production of an electricity-producing dataset in a specific location, relative to the summed production of similar technologies in locations contained in the same IAM region. :param supplier: electricity-producing dataset :type supplier: wurst dataset :param suppliers: list of electricity-producing datasets :type suppliers: list of wurst datasets :return: share of production relative to the total population :rtype: float

update_efficiency_of_solar_pv() → None: Update the efficiency of solar PV modules. We look at how many square meters are needed per kilowatt of installed capacity to obtain the current efficiency. Then we update the surface needed according to the projected efficiency. :return:

update_electricity_efficiency() → None

This method modifies each ecoinvent coal, gas, oil and biomass dataset using data from the IAM scenario. Return a wurst database with modified datasets.

Returns:: a wurst database, with rescaled electricity-producing datasets.
Return type:: list

update_electricity_markets() → None

Delete electricity markets. Create high, medium and low voltage market groups for electricity. Link electricity-consuming datasets to newly created market groups for electricity. Return a wurst database with modified datasets.

Returns:: a wurst database with new market groups for electricity
Return type:: list

update_ng_production_ds() → None: Relink updated datasets for natural gas extraction from http://www.esu-services.ch/fileadmin/download/publicLCI/meili-2021-LCI%20for%20the%20oil%20and%20gas%20extraction.pdf to high pressure natural gas markets.

write_log(dataset, status='created'): Write log file.