bw_temporalis.lcia.climate

Functions

characterize_co2(→ pandas.DataFrame)

Calculate the cumulative or marginal radiative forcing (CRF) from CO2 for each year in a given period.

characterize_methane(→ pandas.DataFrame)

Calculate the cumulative or marginal radiative forcing (CRF) from CH4 for each year in a given period.

Module Contents

bw_temporalis.lcia.climate.characterize_co2(series, period: int | None = 100, cumulative: bool | None = False) → pandas.DataFrame[source]

Calculate the cumulative or marginal radiative forcing (CRF) from CO2 for each year in a given period.

If cumulative is True, the cumulative CRF is calculated. If cumulative is False, the marginal CRF is calculated. Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (date/amount/flow/activity). For each year in the given period, the CRF is calculated. Units are watts/square meter/kilogram of CO2.

Returns:

  • A TimeSeries dataframe with the following columns

  • - date (datetime64[s])

  • - amount (float)

  • - flow (str)

  • - activity (str)

Notes

See also the relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013 See also the relevant scientific publication on the numerical calculation of CRF: http://pubs.acs.org/doi/abs/10.1021/acs.est.5b01118

See also

characterize_methane

The same function for CH4

bw_temporalis.lcia.climate.characterize_methane(series, period: int = 100, cumulative=False) → pandas.DataFrame[source]

Calculate the cumulative or marginal radiative forcing (CRF) from CH4 for each year in a given period.

If cumulative is True, the cumulative CRF is calculated. If cumulative is False, the marginal CRF is calculated. Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (date/amount/flow/activity). For earch year in the given period, the CRF is calculated. Units are watts/square meter/kilogram of CH4.

Parameters:

  • series (array-like) – A single row of the TimeSeries dataframe.

  • period (int, optional) – Time period for calculation (number of years), by default 100

  • cumulative (bool, optional) – Should the RF amounts be summed over time?

Returns:

  • A TimeSeries dataframe with the following columns

  • - date (datetime64[s])

  • - amount (float)

  • - flow (str)

  • - activity (str)

Notes

See also the relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013 See also the relevant scientific publication on the numerical calculation of CRF: http://pubs.acs.org/doi/abs/10.1021/acs.est.5b01118

See also

characterize_co2

The same function for CO2