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aeromaps.models.impacts.emissions.co2_emissions

co2_emissions

This module contains models for calculating CO2 emissions and related factors.

KayaFactors 

KayaFactors(name='kaya_factors', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute Kaya factors for CO2 emissions calculation.

Parameters:

Name Type Description Default
name str

Name of the model instance ('kaya_factors' by default).

 'kaya_factors'
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def __init__(self, name="kaya_factors", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(ask, rtk, energy_consumption_passenger_dropin_fuel_without_operations, energy_consumption_passenger_hydrogen_without_operations, energy_consumption_passenger_electric_without_operations, energy_consumption_passenger_dropin_fuel, energy_consumption_passenger_hydrogen, energy_consumption_passenger_electric, energy_consumption_freight_dropin_fuel_without_operations, energy_consumption_freight_hydrogen_without_operations, energy_consumption_freight_electric_without_operations, energy_consumption_freight_dropin_fuel, energy_consumption_freight_hydrogen, energy_consumption_freight_electric, energy_consumption_dropin_fuel, energy_consumption_hydrogen, energy_consumption_electric, energy_consumption, dropin_fuel_mean_co2_emission_factor, hydrogen_mean_co2_emission_factor, electric_mean_co2_emission_factor)

Execute the computation of Kaya factors for CO2 emissions calculation.

Parameters:

Name Type Description Default
ask Series

Available seat kilometers (ASK) [ASK].

 required
rtk Series

Revenue ton kilometers (RTK) [RTK].

 required
energy_consumption_passenger_dropin_fuel_without_operations Series

Energy consumption for passenger transport using drop-in fuels without operational improvements [MJ].

 required
energy_consumption_passenger_hydrogen_without_operations Series

Energy consumption for passenger transport using hydrogen without operational improvements [MJ].

 required
energy_consumption_passenger_electric_without_operations Series

Energy consumption for passenger transport using electricity without operational improvements [MJ].

 required
energy_consumption_passenger_dropin_fuel Series

Energy consumption for passenger transport using drop-in fuels [MJ].

 required
energy_consumption_passenger_hydrogen Series

Energy consumption for passenger transport using hydrogen [MJ].

 required
energy_consumption_passenger_electric Series

Energy consumption for passenger transport using electricity [MJ].

 required
energy_consumption_freight_dropin_fuel_without_operations Series

Energy consumption for freight transport using drop-in fuels without operational improvements [MJ].

 required
energy_consumption_freight_hydrogen_without_operations Series

Energy consumption for freight transport using hydrogen without operational improvements [MJ].

 required
energy_consumption_freight_electric_without_operations Series

Energy consumption for freight transport using electricity without operational improvements [MJ].

 required
energy_consumption_freight_dropin_fuel Series

Energy consumption for freight transport using drop-in fuels [MJ].

 required
energy_consumption_freight_hydrogen Series

Energy consumption for freight transport using hydrogen [MJ].

 required
energy_consumption_freight_electric Series

Energy consumption for freight transport using electricity [MJ].

 required
energy_consumption_dropin_fuel Series

Total energy consumption using drop-in fuels [MJ].

 required
energy_consumption_hydrogen Series

Total energy consumption using hydrogen [MJ].

 required
energy_consumption_electric Series

Total energy consumption using electricity [MJ].

 required
energy_consumption Series

Total energy consumption [MJ].

 required
dropin_fuel_mean_co2_emission_factor Series

Mean CO2 emission factor for drop-in fuels [gCO2/MJ].

 required
hydrogen_mean_co2_emission_factor Series

Mean CO2 emission factor for hydrogen [gCO2/MJ].

 required
electric_mean_co2_emission_factor Series

Mean CO2 emission factor for electricity [gCO2/MJ].

 required

Returns:

Type Description
energy_per_ask_mean_without_operations

Energy consumption per ASK without operational improvements [MJ/ASK].
energy_per_ask_mean

Energy consumption per ASK [MJ/ASK].
energy_per_rtk_mean_without_operations

Energy consumption per RTK without operational improvements [MJ/RTK].
energy_per_rtk_mean

Energy consumption per RTK [MJ/RTK].
co2_per_energy_mean

CO2 emissions per unit of energy consumed [gCO2/MJ].
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def compute(
    self,
    ask: pd.Series,
    rtk: pd.Series,
    energy_consumption_passenger_dropin_fuel_without_operations: pd.Series,
    energy_consumption_passenger_hydrogen_without_operations: pd.Series,
    energy_consumption_passenger_electric_without_operations: pd.Series,
    energy_consumption_passenger_dropin_fuel: pd.Series,
    energy_consumption_passenger_hydrogen: pd.Series,
    energy_consumption_passenger_electric: pd.Series,
    energy_consumption_freight_dropin_fuel_without_operations: pd.Series,
    energy_consumption_freight_hydrogen_without_operations: pd.Series,
    energy_consumption_freight_electric_without_operations: pd.Series,
    energy_consumption_freight_dropin_fuel: pd.Series,
    energy_consumption_freight_hydrogen: pd.Series,
    energy_consumption_freight_electric: pd.Series,
    energy_consumption_dropin_fuel: pd.Series,
    energy_consumption_hydrogen: pd.Series,
    energy_consumption_electric: pd.Series,
    energy_consumption: pd.Series,
    dropin_fuel_mean_co2_emission_factor: pd.Series,
    hydrogen_mean_co2_emission_factor: pd.Series,
    electric_mean_co2_emission_factor: pd.Series,
) -> Tuple[pd.Series, pd.Series, pd.Series, pd.Series, pd.Series]:
    """
    Execute the computation of Kaya factors for CO2 emissions calculation.

    Parameters
    ----------
    ask
        Available seat kilometers (ASK) [ASK].
    rtk
        Revenue ton kilometers (RTK) [RTK].
    energy_consumption_passenger_dropin_fuel_without_operations
        Energy consumption for passenger transport using drop-in fuels without operational improvements [MJ].
    energy_consumption_passenger_hydrogen_without_operations
        Energy consumption for passenger transport using hydrogen without operational improvements [MJ].
    energy_consumption_passenger_electric_without_operations
        Energy consumption for passenger transport using electricity without operational improvements [MJ].
    energy_consumption_passenger_dropin_fuel
        Energy consumption for passenger transport using drop-in fuels [MJ].
    energy_consumption_passenger_hydrogen
        Energy consumption for passenger transport using hydrogen [MJ].
    energy_consumption_passenger_electric
        Energy consumption for passenger transport using electricity [MJ].
    energy_consumption_freight_dropin_fuel_without_operations
        Energy consumption for freight transport using drop-in fuels without operational improvements [MJ].
    energy_consumption_freight_hydrogen_without_operations
        Energy consumption for freight transport using hydrogen without operational improvements [MJ].
    energy_consumption_freight_electric_without_operations
        Energy consumption for freight transport using electricity without operational improvements [MJ].
    energy_consumption_freight_dropin_fuel
        Energy consumption for freight transport using drop-in fuels [MJ].
    energy_consumption_freight_hydrogen
        Energy consumption for freight transport using hydrogen [MJ].
    energy_consumption_freight_electric
        Energy consumption for freight transport using electricity [MJ].
    energy_consumption_dropin_fuel
        Total energy consumption using drop-in fuels [MJ].
    energy_consumption_hydrogen
        Total energy consumption using hydrogen [MJ].
    energy_consumption_electric
        Total energy consumption using electricity [MJ].
    energy_consumption
        Total energy consumption [MJ].
    dropin_fuel_mean_co2_emission_factor
        Mean CO2 emission factor for drop-in fuels [gCO2/MJ].
    hydrogen_mean_co2_emission_factor
        Mean CO2 emission factor for hydrogen [gCO2/MJ].
    electric_mean_co2_emission_factor
        Mean CO2 emission factor for electricity [gCO2/MJ].

    Returns
    -------
    energy_per_ask_mean_without_operations
        Energy consumption per ASK without operational improvements [MJ/ASK].
    energy_per_ask_mean
        Energy consumption per ASK [MJ/ASK].
    energy_per_rtk_mean_without_operations
        Energy consumption per RTK without operational improvements [MJ/RTK].
    energy_per_rtk_mean
        Energy consumption per RTK [MJ/RTK].
    co2_per_energy_mean
        CO2 emissions per unit of energy consumed [gCO2/MJ].
    """
    energy_per_ask_mean_without_operations = (
        +energy_consumption_passenger_dropin_fuel_without_operations
        + energy_consumption_passenger_hydrogen_without_operations
        + energy_consumption_passenger_electric_without_operations
    ) / ask

    energy_per_ask_mean = (
        +energy_consumption_passenger_dropin_fuel
        + energy_consumption_passenger_hydrogen
        + energy_consumption_passenger_electric
    ) / ask

    energy_per_rtk_mean_without_operations = (
        +energy_consumption_freight_dropin_fuel_without_operations
        + energy_consumption_freight_hydrogen_without_operations
        + energy_consumption_freight_electric_without_operations
    ) / rtk

    energy_per_rtk_mean = (
        +energy_consumption_freight_dropin_fuel
        + energy_consumption_freight_hydrogen
        + energy_consumption_freight_electric
    ) / rtk

    # TODO
    #  --> Better way than fillna to handle years where no energy is produced?

    co2_per_energy_mean = (
        +dropin_fuel_mean_co2_emission_factor.fillna(0) * energy_consumption_dropin_fuel
        + hydrogen_mean_co2_emission_factor.fillna(0) * energy_consumption_hydrogen
        + electric_mean_co2_emission_factor.fillna(0) * energy_consumption_electric
    ) / energy_consumption

    self.df.loc[:, "energy_per_ask_mean_without_operations"] = (
        energy_per_ask_mean_without_operations
    )
    self.df.loc[:, "energy_per_rtk_mean_without_operations"] = (
        energy_per_rtk_mean_without_operations
    )
    self.df.loc[:, "energy_per_ask_mean"] = energy_per_ask_mean
    self.df.loc[:, "energy_per_rtk_mean"] = energy_per_rtk_mean
    self.df.loc[:, "co2_per_energy_mean"] = co2_per_energy_mean

    return (
        energy_per_ask_mean_without_operations,
        energy_per_ask_mean,
        energy_per_rtk_mean_without_operations,
        energy_per_rtk_mean,
        co2_per_energy_mean,
    )

CO2Emissions 

CO2Emissions(name='co2_emissions', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute CO2 emissions.

Parameters:

Name Type Description Default
name str

Name of the model instance ('co2_emissions' by default).

 'co2_emissions'
Documentation

Inputs - load_factor: Load factor [%]. - rpk_: Passenger RPK [RPK]. - rtk_: Freight RTK [RTK]. - energy_per_ask_: Passenger MJ/ASK. - ask_share: Passenger energy shares [%]. - energy_per_rtk: Freight MJ/RTK. - rtk_share: Freight energy shares [%]. - _mean_co2_emission_factor: Mean CO2 factor [gCO2/MJ]. Outputs - co2_emissions: Per-market CO2 [MtCO2]. - co2_emissions_passenger: Passenger total [MtCO2]. - co2_emissions_freight: Freight total [MtCO2]. - co2_emissions: Passenger + freight [MtCO2]. Notes - is the MarketManager id (passenger and freight markets). - is one of: dropin_fuel, hydrogen, electric. - I/O names are generated from configuration and passed to GEMSEO via self.input_names and self.output_names grammars.

Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def __init__(self, name="co2_emissions", *args, **kwargs):
    super().__init__(name=name, model_type="custom", *args, **kwargs)
    self.climate_historical_data = None
    self.markets = None

custom_setup 

custom_setup()

Dynamically build input_names and output_names based on the markets manager. Specific function for custom AeroMAPSModel instances.

Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def custom_setup(self):
    """
    Dynamically build input_names and output_names based on the markets manager.
    Specific function for custom AeroMAPSModel instances.
    """
    energy_types = ["dropin_fuel", "hydrogen", "electric"]
    self.input_names = {
        "load_factor": pd.Series([0.0]),
    }
    self.output_names = {}

    # Per-passenger-market inputs and per-market output.
    for market in self.markets.get(traffic_type="passenger"):
        mid = market.id
        self.input_names[f"rpk_{mid}"] = pd.Series([0.0])
        for et in energy_types:
            self.input_names[f"energy_per_ask_{mid}_{et}"] = pd.Series([0.0])
            self.input_names[f"ask_{mid}_{et}_share"] = pd.Series([0.0])
        self.output_names[f"co2_emissions_{mid}"] = pd.Series([0.0])

    # Per-freight-market inputs and per-market output.
    for market in self.markets.get(traffic_type="freight"):
        mid = market.id
        self.input_names[f"rtk_{mid}"] = pd.Series([0.0])
        for et in energy_types:
            self.input_names[f"energy_per_rtk_{mid}_{et}"] = pd.Series([0.0])
            self.input_names[f"rtk_{mid}_{et}_share"] = pd.Series([0.0])
        self.output_names[f"co2_emissions_{mid}"] = pd.Series([0.0])

    # Mean CO2 emission factors (per energy type, global).
    for et in energy_types:
        self.input_names[f"{et}_mean_co2_emission_factor"] = pd.Series([0.0])

    # Aggregate outputs.
    self.output_names["co2_emissions_passenger"] = pd.Series([0.0])
    self.output_names["co2_emissions_freight"] = pd.Series([0.0])
    self.output_names["co2_emissions"] = pd.Series([0.0])

compute 

compute(input_data)

CO2 emissions per market and aggregates.

Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def compute(self, input_data) -> dict:
    """
    CO2 emissions per market and aggregates.
    """
    energy_types = ["dropin_fuel", "hydrogen", "electric"]

    # Locally fill incomplete emission factors with zeros so that sums are not nan.
    co2_emission_factor_by_energy_type = {}
    for energy_type in energy_types:
        co2_emission_factor = input_data[f"{energy_type}_mean_co2_emission_factor"]
        co2_emission_factor.fillna(0, inplace=True)
        co2_emission_factor_by_energy_type[energy_type] = co2_emission_factor

    load_factor = input_data["load_factor"]
    output_data = {}

    # Per-passenger-market CO2 emissions.
    co2_emissions_passenger = None
    for market in self.markets.get(traffic_type="passenger"):
        mid = market.id
        rpk_market = input_data[f"rpk_{mid}"]
        co2_weighted_energy_intensity_sum = None
        for energy_type in energy_types:
            energy_per_ask = input_data[f"energy_per_ask_{mid}_{energy_type}"].fillna(0)
            ask_share = input_data[f"ask_{mid}_{energy_type}_share"]
            co2_weighted_energy_intensity = (
                ask_share
                / 100
                * (energy_per_ask * co2_emission_factor_by_energy_type[energy_type])
            )
            co2_weighted_energy_intensity_sum = (
                co2_weighted_energy_intensity
                if co2_weighted_energy_intensity_sum is None
                else co2_weighted_energy_intensity_sum + co2_weighted_energy_intensity
            )
        co2_emissions_market = (
            rpk_market / (load_factor / 100) * co2_weighted_energy_intensity_sum * 10 ** (-12)
        )
        output_data[f"co2_emissions_{mid}"] = co2_emissions_market
        co2_emissions_passenger = (
            co2_emissions_market
            if co2_emissions_passenger is None
            else co2_emissions_passenger + co2_emissions_market
        )

    # Per-freight-market CO2 emissions.
    co2_emissions_freight = None
    for market in self.markets.get(traffic_type="freight"):
        mid = market.id
        rtk_market = input_data[f"rtk_{mid}"]
        co2_weighted_energy_intensity_sum = None
        for energy_type in energy_types:
            energy_per_rtk = input_data[f"energy_per_rtk_{mid}_{energy_type}"].fillna(0)
            rtk_share = input_data[f"rtk_{mid}_{energy_type}_share"]
            co2_weighted_energy_intensity = (
                rtk_share
                / 100
                * (energy_per_rtk * co2_emission_factor_by_energy_type[energy_type])
            )
            co2_weighted_energy_intensity_sum = (
                co2_weighted_energy_intensity
                if co2_weighted_energy_intensity_sum is None
                else co2_weighted_energy_intensity_sum + co2_weighted_energy_intensity
            )
        co2_emissions_market = rtk_market * co2_weighted_energy_intensity_sum * 10 ** (-12)
        output_data[f"co2_emissions_{mid}"] = co2_emissions_market
        co2_emissions_freight = (
            co2_emissions_market
            if co2_emissions_freight is None
            else co2_emissions_freight + co2_emissions_market
        )

    # Defensive defaults if no markets.
    if co2_emissions_passenger is None:
        co2_emissions_passenger = pd.Series(0.0, index=self.df.index)
    if co2_emissions_freight is None:
        co2_emissions_freight = pd.Series(0.0, index=self.df.index)

    # Update climate DataFrame side-effect (matches legacy behaviour).
    historical_co2_emissions_for_temperature = self.climate_historical_data[:, 1]
    self.df_climate.loc[
        self.climate_historic_start_year : self.historic_start_year - 1, "co2_emissions"
    ] = historical_co2_emissions_for_temperature[
        : self.historic_start_year - self.climate_historic_start_year
    ]
    self.df_climate.loc[self.historic_start_year : self.end_year, "co2_emissions"] = (
        co2_emissions_passenger + co2_emissions_freight
    )

    co2_emissions_total = self.df_climate["co2_emissions"]

    output_data["co2_emissions_passenger"] = co2_emissions_passenger
    output_data["co2_emissions_freight"] = co2_emissions_freight
    output_data["co2_emissions"] = co2_emissions_total

    self._store_outputs(output_data, climate_outputs_keys=["co2_emissions"])
    return output_data

CumulativeCO2Emissions 

CumulativeCO2Emissions(name='cumulative_co2_emissions', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute cumulative CO2 emissions.

Parameters:

Name Type Description Default
name str

Name of the model instance ('cumulative_co2_emissions' by default).

 'cumulative_co2_emissions'
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def __init__(self, name="cumulative_co2_emissions", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(co2_emissions, carbon_budget_reference_year)

Execute the computation of cumulative CO2 emissions.

Parameters:

Name Type Description Default
co2_emissions Series

Annual CO2 emissions [MtCO2].

 required
carbon_budget_reference_year int

Fixed reference year from which the budget-comparable cumulative is summed (default 2019), matching the aviation carbon budget framing.

 required

Returns:

Type Description
cumulative_co2_emissions

Cumulative CO2 emissions over the prospective window (prospection_start_year -> end_year) [GtCO2].
cumulative_co2_emissions_from_carbon_budget_reference_year

Cumulative CO2 emissions summed from carbon_budget_reference_year -> end_year [GtCO2]. Includes observed historic emissions from the reference year onward, so it is directly comparable to the aviation carbon budget regardless of prospection_start_year.
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def compute(
    self,
    co2_emissions: pd.Series,
    carbon_budget_reference_year: int,
) -> Tuple[pd.Series, pd.Series]:
    """
    Execute the computation of cumulative CO2 emissions.

    Parameters
    ----------
    co2_emissions
        Annual CO2 emissions [MtCO2].
    carbon_budget_reference_year
        Fixed reference year from which the budget-comparable cumulative is
        summed (default 2019), matching the aviation carbon budget framing.

    Returns
    -------
    cumulative_co2_emissions
        Cumulative CO2 emissions over the prospective window
        (prospection_start_year -> end_year) [GtCO2].
    cumulative_co2_emissions_from_carbon_budget_reference_year
        Cumulative CO2 emissions summed from carbon_budget_reference_year ->
        end_year [GtCO2]. Includes observed historic emissions from the
        reference year onward, so it is directly comparable to the aviation
        carbon budget regardless of prospection_start_year.

    """
    cumulative_co2_emissions = (
        co2_emissions.loc[self.prospection_start_year : self.end_year] / 1000
    ).cumsum()

    cumulative_co2_emissions_from_carbon_budget_reference_year = (
        co2_emissions.loc[carbon_budget_reference_year : self.end_year] / 1000
    ).cumsum()

    self.df["cumulative_co2_emissions"] = cumulative_co2_emissions
    self.df["cumulative_co2_emissions_from_carbon_budget_reference_year"] = (
        cumulative_co2_emissions_from_carbon_budget_reference_year
    )

    return (
        cumulative_co2_emissions,
        cumulative_co2_emissions_from_carbon_budget_reference_year,
    )

DetailedCo2Emissions 

DetailedCo2Emissions(name='detailed_co2_emissions', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute detailed CO2 emissions breakdown.

Parameters:

Name Type Description Default
name str

Name of the model instance ('detailed_co2_emissions' by default).

 'detailed_co2_emissions'
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def __init__(self, name="detailed_co2_emissions", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(rpk_reference, rtk_reference, rpk, rtk, load_factor, energy_per_ask_mean, energy_per_rtk_mean, energy_per_ask_mean_without_operations, energy_per_rtk_mean_without_operations, co2_per_energy_mean)

Execute the computation of detailed CO2 emissions breakdown.

Parameters:

Name Type Description Default
rpk_reference Series

Number of Revenue Passenger Kilometer (RPK) for all passenger air transport with a baseline air traffic growth [RPK].

 required
rtk_reference Series

Number of Revenue Tonne Kilometer (RTK) for freight air transport with a baseline air traffic growth [RTK].

 required
rpk Series

Revenue passenger kilometers (RPK) [RPK].

 required
rtk Series

Revenue ton kilometers (RTK) [RTK].

 required
load_factor Series

Load factor [%].

 required
energy_per_ask_mean Series

Mean energy consumption per ASK for passenger market [MJ/ASK].

 required
energy_per_rtk_mean Series

Mean energy consumption per RTK for freight market [MJ/RTK].

 required
energy_per_ask_mean_without_operations Series

Mean energy consumption per ASK for passenger market without considering operation improvements [MJ/ASK].

 required
energy_per_rtk_mean_without_operations Series

Mean energy consumption per RTK for freight market without considering operation improvements [MJ/RTK].

 required
co2_per_energy_mean Series

Mean emission factor of aircraft energy [gCO2/MJ].

 required

Returns:

Type Description
co2_emissions_last_historical_year_technology_baseline3

CO2 emissions from all commercial air transport based on last-historical-year technological level with a baseline air traffic growth [MtCO2].
co2_emissions_last_historical_year_technology

CO2 emissions from all commercial air transport based on last-historical-year technological level [MtCO2].
co2_emissions_including_aircraft_efficiency

CO2 emissions from all commercial air transport including aircraft efficiency improvements [MtCO2].
co2_emissions_including_operations

CO2 emissions from all commercial air transport including aircraft efficiency and operation improvements [MtCO2].
co2_emissions_including_load_factor

CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements [MtCO2].
co2_emissions_including_energy

CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements and energy decarbonization [MtCO2].
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def compute(
    self,
    rpk_reference: pd.Series,
    rtk_reference: pd.Series,
    rpk: pd.Series,
    rtk: pd.Series,
    load_factor: pd.Series,
    energy_per_ask_mean: pd.Series,
    energy_per_rtk_mean: pd.Series,
    energy_per_ask_mean_without_operations: pd.Series,
    energy_per_rtk_mean_without_operations: pd.Series,
    co2_per_energy_mean: pd.Series,
) -> Tuple[pd.Series, pd.Series, pd.Series, pd.Series, pd.Series, pd.Series]:
    """
    Execute the computation of detailed CO2 emissions breakdown.

    Parameters
    ----------
    rpk_reference
        Number of Revenue Passenger Kilometer (RPK) for all passenger air transport with a baseline air traffic growth [RPK].
    rtk_reference
        Number of Revenue Tonne Kilometer (RTK) for freight air transport with a baseline air traffic growth [RTK].
    rpk
        Revenue passenger kilometers (RPK) [RPK].
    rtk
        Revenue ton kilometers (RTK) [RTK].
    load_factor
        Load factor [%].
    energy_per_ask_mean
        Mean energy consumption per ASK for passenger market [MJ/ASK].
    energy_per_rtk_mean
        Mean energy consumption per RTK for freight market [MJ/RTK].
    energy_per_ask_mean_without_operations
        Mean energy consumption per ASK for passenger market without considering operation improvements [MJ/ASK].
    energy_per_rtk_mean_without_operations
        Mean energy consumption per RTK for freight market without considering operation improvements [MJ/RTK].
    co2_per_energy_mean
        Mean emission factor of aircraft energy [gCO2/MJ].


    Returns
    -------
    co2_emissions_last_historical_year_technology_baseline3
        CO2 emissions from all commercial air transport based on last-historical-year technological level with a baseline air traffic growth [MtCO2].
    co2_emissions_last_historical_year_technology
        CO2 emissions from all commercial air transport based on last-historical-year technological level [MtCO2].
    co2_emissions_including_aircraft_efficiency
        CO2 emissions from all commercial air transport including aircraft efficiency improvements [MtCO2].
    co2_emissions_including_operations
        CO2 emissions from all commercial air transport including aircraft efficiency and operation improvements [MtCO2].
    co2_emissions_including_load_factor
        CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements [MtCO2].
    co2_emissions_including_energy
        CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements and energy decarbonization [MtCO2].

    """
    years = range(self.prospection_start_year - 1, self.end_year + 1)

    # Speedup operations: access right portions of vectors
    rpk_reference_local = rpk_reference.loc[years]
    rtk_reference_local = rtk_reference.loc[years]
    rpk_local = rpk.loc[years]
    rtk_local = rtk.loc[years]
    load_factor_local = load_factor.loc[years]
    energy_per_ask_mean_local = energy_per_ask_mean.loc[years]
    energy_per_rtk_mean_local = energy_per_rtk_mean.loc[years]
    energy_per_ask_mean_without_operations_local = energy_per_ask_mean_without_operations.loc[
        years
    ]
    energy_per_rtk_mean_without_operations_local = energy_per_rtk_mean_without_operations.loc[
        years
    ]
    co2_per_energy_mean_local = co2_per_energy_mean.loc[years]

    # Start year values
    load_factor_start_year_local = load_factor.loc[self.prospection_start_year - 1]
    energy_per_ask_mean_start_year_local = energy_per_ask_mean.loc[
        self.prospection_start_year - 1
    ]
    energy_per_rtk_mean_start_year_local = energy_per_rtk_mean.loc[
        self.prospection_start_year - 1
    ]
    energy_per_ask_mean_without_operations_start_year_local = (
        energy_per_ask_mean_without_operations.loc[self.prospection_start_year - 1]
    )
    energy_per_rtk_mean_without_operations_start_year_local = (
        energy_per_rtk_mean_without_operations.loc[self.prospection_start_year - 1]
    )
    co2_per_energy_mean_start_year_local = co2_per_energy_mean.loc[
        self.prospection_start_year - 1
    ]

    co2_emissions_last_historical_year_technology_baseline3 = (
        rpk_reference_local
        * energy_per_ask_mean_without_operations_start_year_local
        * energy_per_ask_mean_start_year_local
        / energy_per_ask_mean_without_operations_start_year_local
        / (load_factor_start_year_local / 100)
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    ) + (
        rtk_reference_local
        * energy_per_rtk_mean_without_operations_start_year_local
        * energy_per_rtk_mean_start_year_local
        / energy_per_rtk_mean_without_operations_start_year_local
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    )

    co2_emissions_last_historical_year_technology = (
        rpk_local
        * energy_per_ask_mean_without_operations_start_year_local
        * energy_per_ask_mean_start_year_local
        / energy_per_ask_mean_without_operations_start_year_local
        / (load_factor_start_year_local / 100)
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    ) + (
        rtk_local
        * energy_per_rtk_mean_without_operations_start_year_local
        * energy_per_rtk_mean_start_year_local
        / energy_per_rtk_mean_without_operations_start_year_local
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    )

    co2_emissions_including_aircraft_efficiency = (
        rpk_local
        * energy_per_ask_mean_without_operations_local
        * energy_per_ask_mean_start_year_local
        / energy_per_ask_mean_without_operations_start_year_local
        / (load_factor_start_year_local / 100)
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    ) + (
        rtk_local
        * energy_per_rtk_mean_without_operations_local
        * energy_per_rtk_mean_start_year_local
        / energy_per_rtk_mean_without_operations_start_year_local
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    )

    co2_emissions_including_operations = (
        rpk_local
        * energy_per_ask_mean_without_operations_local
        * energy_per_ask_mean_local
        / energy_per_ask_mean_without_operations_local
        / (load_factor_start_year_local / 100)
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    ) + (
        rtk_local
        * energy_per_rtk_mean_without_operations_local
        * energy_per_rtk_mean_local
        / energy_per_rtk_mean_without_operations_local
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    )

    co2_emissions_including_load_factor = (
        rpk_local
        * energy_per_ask_mean_without_operations_local
        * energy_per_ask_mean_local
        / energy_per_ask_mean_without_operations_local
        / (load_factor_local / 100)
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    ) + (
        rtk_local
        * energy_per_rtk_mean_without_operations_local
        * energy_per_rtk_mean_local
        / energy_per_rtk_mean_without_operations_local
        * co2_per_energy_mean_start_year_local
        * 10 ** (-12)
    )

    co2_emissions_including_energy = (
        rpk_local
        * energy_per_ask_mean_without_operations_local
        * energy_per_ask_mean_local
        / energy_per_ask_mean_without_operations_local
        / (load_factor_local / 100)
        * co2_per_energy_mean_local
        * 10 ** (-12)
    ) + (
        rtk_local
        * energy_per_rtk_mean_without_operations_local
        * energy_per_rtk_mean_local
        / energy_per_rtk_mean_without_operations_local
        * co2_per_energy_mean_local
        * 10 ** (-12)
    )

    self.df.loc[years, "co2_emissions_last_historical_year_technology_baseline3"] = (
        co2_emissions_last_historical_year_technology_baseline3
    )
    self.df.loc[years, "co2_emissions_last_historical_year_technology"] = (
        co2_emissions_last_historical_year_technology
    )
    self.df.loc[years, "co2_emissions_including_aircraft_efficiency"] = (
        co2_emissions_including_aircraft_efficiency
    )
    self.df.loc[years, "co2_emissions_including_operations"] = (
        co2_emissions_including_operations
    )
    self.df.loc[years, "co2_emissions_including_load_factor"] = (
        co2_emissions_including_load_factor
    )
    self.df.loc[years, "co2_emissions_including_energy"] = co2_emissions_including_energy

    return (
        co2_emissions_last_historical_year_technology_baseline3,
        co2_emissions_last_historical_year_technology,
        co2_emissions_including_aircraft_efficiency,
        co2_emissions_including_operations,
        co2_emissions_including_load_factor,
        co2_emissions_including_energy,
    )

DetailedCumulativeCO2Emissions 

DetailedCumulativeCO2Emissions(name='detailed_cumulative_co2_emissions', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute detailed cumulative CO2 emissions breakdown.

Parameters:

Name Type Description Default
name str

Name of the model instance ('detailed_cumulative_co2_emissions' by default).

 'detailed_cumulative_co2_emissions'
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def __init__(self, name="detailed_cumulative_co2_emissions", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(co2_emissions_last_historical_year_technology_baseline3, co2_emissions_last_historical_year_technology, co2_emissions_including_aircraft_efficiency, co2_emissions_including_operations, co2_emissions_including_load_factor, co2_emissions_including_energy)

Execute the computation of detailed cumulative CO2 emissions breakdown.

Parameters:

Name Type Description Default
co2_emissions_last_historical_year_technology_baseline3 Series

CO2 emissions from all commercial air transport based on last-historical-year technological level with a baseline air traffic growth [MtCO2].

 required
co2_emissions_last_historical_year_technology Series

CO2 emissions from all commercial air transport based on last-historical-year technological level [MtCO2].

 required
co2_emissions_including_aircraft_efficiency Series

CO2 emissions from all commercial air transport including aircraft efficiency improvements [MtCO2].

 required
co2_emissions_including_operations Series

CO2 emissions from all commercial air transport including aircraft efficiency and operations improvements [MtCO2].

 required
co2_emissions_including_load_factor Series

CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements [MtCO2].

 required
co2_emissions_including_energy Series

CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements and energy decarbonization [MtCO2].

 required

Returns:

Type Description
cumulative_co2_emissions_last_historical_year_technology_baseline3

Cumulative CO2 emissions from all commercial air transport based on last-historical-year technological level with a baseline air traffic growth [GtCO2].
cumulative_co2_emissions_last_historical_year_technology

Cumulative CO2 emissions from all commercial air transport based on last-historical-year technological level [GtCO2].
cumulative_co2_emissions_including_aircraft_efficiency

Cumulative CO2 emissions from all commercial air transport including aircraft efficiency improvements [GtCO2].
cumulative_co2_emissions_including_operations

Cumulative CO2 emissions from all commercial air transport including aircraft efficiency and operations improvements [GtCO2].
cumulative_co2_emissions_including_load_factor

Cumulative CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements [GtCO2].
cumulative_co2_emissions_including_energy

Cumulative CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements and energy decarbonization [GtCO2].
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def compute(
    self,
    co2_emissions_last_historical_year_technology_baseline3: pd.Series,
    co2_emissions_last_historical_year_technology: pd.Series,
    co2_emissions_including_aircraft_efficiency: pd.Series,
    co2_emissions_including_operations: pd.Series,
    co2_emissions_including_load_factor: pd.Series,
    co2_emissions_including_energy: pd.Series,
) -> Tuple[pd.Series, pd.Series, pd.Series, pd.Series, pd.Series, pd.Series]:
    """
    Execute the computation of detailed cumulative CO2 emissions breakdown.
    Parameters
    ----------
    co2_emissions_last_historical_year_technology_baseline3
        CO2 emissions from all commercial air transport based on last-historical-year technological level with a baseline air traffic growth [MtCO2].
    co2_emissions_last_historical_year_technology
        CO2 emissions from all commercial air transport based on last-historical-year technological level [MtCO2].
    co2_emissions_including_aircraft_efficiency
        CO2 emissions from all commercial air transport including aircraft efficiency improvements [MtCO2].
    co2_emissions_including_operations
        CO2 emissions from all commercial air transport including aircraft efficiency and operations improvements [MtCO2].
    co2_emissions_including_load_factor
        CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements [MtCO2].
    co2_emissions_including_energy
        CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor improvements and energy decarbonization [MtCO2].

    Returns
    -------
    cumulative_co2_emissions_last_historical_year_technology_baseline3
        Cumulative CO2 emissions from all commercial air transport based on last-historical-year technological level with a baseline air traffic growth [GtCO2].
    cumulative_co2_emissions_last_historical_year_technology
        Cumulative CO2 emissions from all commercial air transport based on last-historical-year technological level [GtCO2].
    cumulative_co2_emissions_including_aircraft_efficiency
        Cumulative CO2 emissions from all commercial air transport including aircraft efficiency improvements [GtCO2].
    cumulative_co2_emissions_including_operations
        Cumulative CO2 emissions from all commercial air transport including aircraft efficiency and operations improvements [GtCO2].
    cumulative_co2_emissions_including_load_factor
        Cumulative CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor
        improvements [GtCO2].
    cumulative_co2_emissions_including_energy
        Cumulative CO2 emissions from all commercial air transport including aircraft efficiency, operation and load factor
        improvements and energy decarbonization [GtCO2].

    """
    cumulative_co2_emissions_last_historical_year_technology_baseline3 = (
        co2_emissions_last_historical_year_technology_baseline3.loc[
            self.prospection_start_year : self.end_year
        ]
        / 1000
    ).cumsum()

    cumulative_co2_emissions_last_historical_year_technology = (
        co2_emissions_last_historical_year_technology.loc[
            self.prospection_start_year : self.end_year
        ]
        / 1000
    ).cumsum()

    cumulative_co2_emissions_including_aircraft_efficiency = (
        co2_emissions_including_aircraft_efficiency.loc[
            self.prospection_start_year : self.end_year
        ]
        / 1000
    ).cumsum()

    cumulative_co2_emissions_including_operations = (
        co2_emissions_including_operations.loc[self.prospection_start_year : self.end_year]
        / 1000
    ).cumsum()

    cumulative_co2_emissions_including_load_factor = (
        co2_emissions_including_load_factor.loc[self.prospection_start_year : self.end_year]
        / 1000
    ).cumsum()

    cumulative_co2_emissions_including_energy = (
        co2_emissions_including_energy.loc[self.prospection_start_year : self.end_year] / 1000
    ).cumsum()

    self.df["cumulative_co2_emissions_last_historical_year_technology_baseline3"] = (
        cumulative_co2_emissions_last_historical_year_technology_baseline3
    )
    self.df["cumulative_co2_emissions_last_historical_year_technology"] = (
        cumulative_co2_emissions_last_historical_year_technology
    )
    self.df["cumulative_co2_emissions_including_aircraft_efficiency"] = (
        cumulative_co2_emissions_including_aircraft_efficiency
    )
    self.df["cumulative_co2_emissions_including_operations"] = (
        cumulative_co2_emissions_including_operations
    )
    self.df["cumulative_co2_emissions_including_load_factor"] = (
        cumulative_co2_emissions_including_load_factor
    )
    self.df["cumulative_co2_emissions_including_energy"] = (
        cumulative_co2_emissions_including_energy
    )

    return (
        cumulative_co2_emissions_last_historical_year_technology_baseline3,
        cumulative_co2_emissions_last_historical_year_technology,
        cumulative_co2_emissions_including_aircraft_efficiency,
        cumulative_co2_emissions_including_operations,
        cumulative_co2_emissions_including_load_factor,
        cumulative_co2_emissions_including_energy,
    )

SimpleCO2Emissions 

SimpleCO2Emissions(name='simple_co2_emissions', *args, **kwargs)

Bases: AeroMAPSModel

Class to compute simple CO2 emissions.

Parameters:

Name Type Description Default
name str

Name of the model instance ('simple_co2_emissions' by default).

 'simple_co2_emissions'
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def __init__(self, name="simple_co2_emissions", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)
    self.climate_historical_data = None

compute 

compute(energy_consumption_init, dropin_fuel_mean_co2_emission_factor, hydrogen_mean_co2_emission_factor, electric_mean_co2_emission_factor, energy_consumption_dropin_fuel, energy_consumption_hydrogen, energy_consumption_electricity)

Simple CO2 emissions calculation

Parameters:

Name Type Description Default
energy_consumption_init Series

Historical energy consumption of aviation over 2000-2019 [MJ].

 required
dropin_fuel_mean_co2_emission_factor Series

Mean CO2 emission factor for drop-in fuels [gCO2/MJ].

 required
hydrogen_mean_co2_emission_factor Series

Mean CO2 emission factor for hydrogen [gCO2/MJ].

 required
electric_mean_co2_emission_factor Series

Mean CO2 emission factor for electric aviation [gCO2/MJ].

 required
energy_consumption_dropin_fuel Series

Energy consumption in the form of drop-in fuels from all commercial air transport [MJ].

 required
energy_consumption_hydrogen Series

Energy consumption in the form of hydrogen from all commercial air transport [MJ].

 required
energy_consumption_electricity Series

Energy consumption in the form of electricity from all commercial air transport [MJ].

 required

Returns:

Type Description
co2_emissions

CO2 emissions from all commercial air transport [MtCO2].
Source code in aeromaps/models/impacts/emissions/co2_emissions.py 
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def compute(
    self,
    energy_consumption_init: pd.Series,
    dropin_fuel_mean_co2_emission_factor: pd.Series,
    hydrogen_mean_co2_emission_factor: pd.Series,
    electric_mean_co2_emission_factor: pd.Series,
    energy_consumption_dropin_fuel: pd.Series,
    energy_consumption_hydrogen: pd.Series,
    energy_consumption_electricity: pd.Series,
) -> pd.Series:
    """
    Simple CO2 emissions calculation

    Parameters
    ----------
    energy_consumption_init
        Historical energy consumption of aviation over 2000-2019 [MJ].
    dropin_fuel_mean_co2_emission_factor
        Mean CO2 emission factor for drop-in fuels [gCO2/MJ].
    hydrogen_mean_co2_emission_factor
        Mean CO2 emission factor for hydrogen [gCO2/MJ].
    electric_mean_co2_emission_factor
        Mean CO2 emission factor for electric aviation [gCO2/MJ].
    energy_consumption_dropin_fuel
        Energy consumption in the form of drop-in fuels from all commercial air transport [MJ].
    energy_consumption_hydrogen
        Energy consumption in the form of hydrogen from all commercial air transport [MJ].
    energy_consumption_electricity
        Energy consumption in the form of electricity from all commercial air transport [MJ].

    Returns
    -------
    co2_emissions
        CO2 emissions from all commercial air transport [MtCO2].
    """

    ## Initialization
    historical_co2_emissions_for_temperature = self.climate_historical_data[:, 1]

    # Calculation
    for k in range(self.climate_historic_start_year, self.historic_start_year):
        self.df_climate.loc[k, "co2_emissions"] = historical_co2_emissions_for_temperature[
            k - self.climate_historic_start_year
        ]

    for k in range(self.historic_start_year, self.prospection_start_year):
        self.df_climate.loc[k, "co2_emissions"] = (
            dropin_fuel_mean_co2_emission_factor.loc[k]
            / 10**12
            * energy_consumption_init.loc[k]
        )

    for k in range(self.prospection_start_year, self.end_year + 1):
        self.df_climate.loc[k, "co2_emissions"] = (
            dropin_fuel_mean_co2_emission_factor.loc[k]
            / 10**12
            * energy_consumption_dropin_fuel.loc[k]
            + electric_mean_co2_emission_factor.loc[k]
            / 10**12
            * energy_consumption_electricity.loc[k]
            + hydrogen_mean_co2_emission_factor.loc[k]
            / 10**12
            * energy_consumption_hydrogen.loc[k]
        )

    co2_emissions = self.df_climate.loc[:, "co2_emissions"]

    return co2_emissions