dynamic_characterization

This is a package for the dynamic characterization of Life Cycle Inventories with temporal information. It includes a collection of dynamic characterization functions for various environmental flows. We also provide a simple interface to apply these functions to an existing dynamic LCI (coming from, e.g., bw_temporalis or bw_timex).

The following dynamic characterization functions are currently included:

module	impact category	metric	covered emissions	source
ipcc_ar6	climate change	radiative forcing	247 GHGs	radiative efficiencies & lifetimes from IPCC AR6 Ch.7
original_temporalis_functions	climate change	radiative forcing	CO2, CH4	bw_temporalis

What do dynamic characterization functions do?

The functions are meant to work with a common input format of the dynamic inventory, collected in a pandas DataFrame that looks like this:

date	amount	flow	activity
101	33	1	2
312	21	4	2

Each function takes one row of this dynamic inventory dataframe (i.e. one emission at one point in time) and transform it according to some metric. The output generated by applying a very simple function to both rows of the input dataframe could look like:

date	amount	flow	activity
101	33	1	2
102	31	1	2
103	31	1	2
312	21	4	2
313	20	4	2
314	19	4	2

What do dynamic characterization functions look like?

Here’s an example of what such a function could look like:

def example_characterization_function(series: namedtuple, period: int = 2) -> namedtuple:
    date_beginning: np.datetime64 = series.date.to_numpy()
    dates_characterized: np.ndarray = date_beginning + np.arange(
        start=0, stop=period, dtype="timedelta64[D]"
    ).astype("timedelta64[s]")

    amount_beginning: float = series.amount

    # in reality, this would probably something more complex like an exponential decay function
    amount_characterized: np.ndarray = amount_beginning - np.arange(
        start=0, stop=period, dtype="int"
    )

    return namedtuple("CharacterizedRow", ["date", "amount", "flow", "activity"])(
        date=np.array(dates_characterized, dtype="datetime64[s]"),
        amount=amount_characterized,
        flow=series.flow,
        activity=series.activity,
    )

Support

If you have any questions or need help, do not hesitate to contact Timo Diepers (timo.diepers@ltt.rwth-aachen.de)