Characterization of stressors¶

The characterization of stressors is a standard procedure to calculate the environmental and social impacts of economic activity. This is usually accomplished by multiplying (matrix-multiplication) the stressor-matrix with a characterization-matrix. Doing that in the matrix forms requires a 1:1 correspondence of the columns of the characterization matrix to the rows of the stressor-matrix.

Pymrio uses a different approach with matching the strings of the characterization table (given in long-format) to the available stressors. By doing that, the order of the entries in the characterization-table becomes unimportant. This implementation also allows to use characterization tables which includes characterization for stressors not present in the given satellite account. All characterizations relying on not available stressor will be automatically removed.

Example¶

For this example we use the test MRIO included in Pymrio. We also need the Pandas library for loading the characterization table and pathlib for some folder manipulation.

[1]:

from pathlib import Path

[2]:

import pandas as pd

[3]:

import pymrio
from pymrio.core.constants import PYMRIO_PATH  # noqa

To load the test MRIO we use:

[4]:

io = pymrio.load_test()

and the characterization table with some foo factors can be loaded by

[5]:

charact_table = pd.read_csv(
    (PYMRIO_PATH["test_mrio"] / Path("concordance") / "emissions_charact.tsv"),
    sep="\t",
)
charact_table

[5]:

	stressor	compartment	impact	factor	impact_unit
0	emission_type1	air	air water impact	0.002	t
1	emission_type2	water	air water impact	0.001	t
2	emission_type1	air	total emissions	1.000	kg
3	emission_type2	water	total emissions	1.000	kg
4	emission_type3	land	total emissions	1.000	kg
5	emission_type1	air	total air emissions	0.001	t

This table contains the columns ‘stressor’ and ‘compartment’ which correspond to the index names of the test_mrio emission satellite accounts:

[6]:

io.emissions.F

[6]:

	region	reg1								reg2		...	reg5		reg6
	sector	food	mining	manufactoring	electricity	construction	trade	transport	other	food	mining	...	transport	other	food	mining	manufactoring	electricity	construction	trade	transport	other
stressor	compartment
emission_type1	air	1848064.80	986448.090	23613787.00	28139100.00	2584141.80	4132656.3	21766987.0	7842090.6	1697937.30	347378.150	...	42299319	10773826.0	15777996.0	6420955.5	113172450.0	56022534.0	4861838.5	18195621	47046542.0	21632868
emission_type2	water	139250.47	22343.295	763569.18	273981.55	317396.51	1254477.8	1012999.1	2449178.0	204835.44	29463.944	...	4199841	7191006.3	4826108.1	1865625.1	12700193.0	753213.7	2699288.3	13892313	8765784.3	16782553

2 rows × 48 columns

Theses index-names / columns-names need to match in order to match characterization factors to the stressors.

The other columns names can be passed to the characterization method. By default the method assumes the following column names:

impact: name of the characterization/impact
factor: the numerical (float) multiplication value for a specific stressor to derive the impact/characterized account
impact_unit: the unit of the calculated characterization/impact

Alternative names can be passed through the parameters characterized_name_column, characterization_factors_column and characterized_unit_column.

Note, that units of stressor are currently not checked - units as given in the satellite account to be characterized are assumed. These can be seen by:

[7]:

io.emissions.unit

[7]:

		unit
stressor	compartment
emission_type1	air	kg
emission_type2	water	kg

Also note, that the charact_table contains a characterization called ‘total emissions’, for which the calculation requires a stressor not present in the satellite account. This will be automatically omitted.

To calculate the characterization we use

[8]:

impacts = io.emissions.characterize(charact_table, name="impacts")

WARNING:root:Impact >total emissions< removed - calculation requires stressors not present in extension >Emissions<

The parameter name is optional, if omitted the name will be set to extension_name + _characterized

The method call above results in a pymrio.Extension which can be inspected with the usual methods, e.g.:

[9]:

impacts.F

[9]:

region	reg1								reg2		...	reg5		reg6
sector	food	mining	manufactoring	electricity	construction	trade	transport	other	food	mining	...	transport	other	food	mining	manufactoring	electricity	construction	trade	transport	other
impact
air water impact	3835.38007	1995.239475	47991.14318	56552.18155	5485.68011	9519.7904	44546.9731	18133.3592	3600.71004	724.220244	...	88798.479	28738.6583	36382.1001	14707.5361	239045.093	112798.2817	12422.9653	50283.555	102858.8683	60048.289
total air emissions	1848.06480	986.448090	23613.78700	28139.10000	2584.14180	4132.6563	21766.9870	7842.0906	1697.93730	347.378150	...	42299.319	10773.8260	15777.9960	6420.9555	113172.450	56022.5340	4861.8385	18195.621	47046.5420	21632.868

2 rows × 48 columns

[10]:

impacts.F_Y

[10]:

region	reg1							reg2			...	reg5			reg6
category	Final consumption expenditure by households	Final consumption expenditure by non-profit organisations serving households (NPISH)	Final consumption expenditure by government	Gross fixed capital formation	Changes in inventories	Changes in valuables	Export	Final consumption expenditure by households	Final consumption expenditure by non-profit organisations serving households (NPISH)	Final consumption expenditure by government	...	Changes in inventories	Changes in valuables	Export	Final consumption expenditure by households	Final consumption expenditure by non-profit organisations serving households (NPISH)	Final consumption expenditure by government	Gross fixed capital formation	Changes in inventories	Changes in valuables	Export
impact
air water impact	183877.047	0.0	0.0	0.0	0.0	0.0	0.0	117347.860	0.0	0.0	...	0.0	0.0	0.0	1305918.65	0.0	0.0	0.0	0.0	0.0	0.0
total air emissions	62335.321	0.0	0.0	0.0	0.0	0.0	0.0	38566.929	0.0	0.0	...	0.0	0.0	0.0	571278.30	0.0	0.0	0.0	0.0	0.0	0.0

2 rows × 42 columns

and the extension can be added to the MRIO

[11]:

io.impacts = impacts

and used for subsequent calculations:

[12]:

io.calc_all()
io.impacts.D_cba

[12]:

region	reg1								reg2		...	reg5		reg6
sector	food	mining	manufactoring	electricity	construction	trade	transport	other	food	mining	...	transport	other	food	mining	manufactoring	electricity	construction	trade	transport	other
impact
air water impact	4354.677050	384.125264	211698.404833	23912.313854	7032.640535	8548.387702	22000.497397	40123.553814	3800.328439	42.024811	...	88433.836812	30080.437653	34765.284482	3227.857425	153582.938963	74236.159458	4580.343177	139321.814038	104944.664681	118394.939263
total air emissions	2056.183383	179.423536	97493.003893	11887.591799	3342.905658	3885.883601	10750.267281	15821.524280	1793.337828	19.145605	...	42095.049614	11386.614919	15172.354999	1345.317901	71450.748974	36831.672907	1836.695857	42415.684852	48054.090102	36022.975074

2 rows × 48 columns

Characterizing calculated results¶

The characterize method can also be used to characterize already calculated results. This works in the same way:

[13]:

io_aly = pymrio.load_test().calc_all()

[14]:

io_aly.emissions.D_cba

[14]:

	region	reg1								reg2		...	reg5		reg6
	sector	food	mining	manufactoring	electricity	construction	trade	transport	other	food	mining	...	transport	other	food	mining	manufactoring	electricity	construction	trade	transport	other
stressor	compartment
emission_type1	air	2.056183e+06	179423.535893	9.749300e+07	1.188759e+07	3.342906e+06	3.885884e+06	1.075027e+07	1.582152e+07	1.793338e+06	19145.604911	...	4.209505e+07	1.138661e+07	1.517235e+07	1.345318e+06	7.145075e+07	3.683167e+07	1.836696e+06	4.241568e+07	4.805409e+07	3.602298e+07
emission_type2	water	2.423103e+05	25278.192086	1.671240e+07	1.371303e+05	3.468292e+05	7.766205e+05	4.999628e+05	8.480505e+06	2.136528e+05	3733.601474	...	4.243738e+06	7.307208e+06	4.420574e+06	5.372216e+05	1.068144e+07	5.728136e+05	9.069515e+05	5.449044e+07	8.836484e+06	4.634899e+07

2 rows × 48 columns

[15]:

io_aly.impacts = io_aly.emissions.characterize(charact_table, name="impacts_new")

WARNING:root:Impact >total emissions< removed - calculation requires stressors not present in extension >Emissions<

Note, that all results which can be characterized directly (all flow accounts like D_cba, D_pba, …) are automatically included:

[16]:

io_aly.impacts.D_cba

[16]:

region	reg1								reg2		...	reg5		reg6
sector	food	mining	manufactoring	electricity	construction	trade	transport	other	food	mining	...	transport	other	food	mining	manufactoring	electricity	construction	trade	transport	other
impact
air water impact	4354.677050	384.125264	211698.404833	23912.313854	7032.640535	8548.387702	22000.497397	40123.553814	3800.328439	42.024811	...	88433.836812	30080.437653	34765.284482	3227.857425	153582.938963	74236.159458	4580.343177	139321.814038	104944.664681	118394.939263
total air emissions	2056.183383	179.423536	97493.003893	11887.591799	3342.905658	3885.883601	10750.267281	15821.524280	1793.337828	19.145605	...	42095.049614	11386.614919	15172.354999	1345.317901	71450.748974	36831.672907	1836.695857	42415.684852	48054.090102	36022.975074

2 rows × 48 columns

Whereas coefficient accounts (M, S) are removed:

[17]:

io_aly.impacts.M

To calculated these use

[18]:

io_aly.calc_all()
io_aly.impacts.M

[18]:

region	reg1								reg2		...	reg5		reg6
sector	food	mining	manufactoring	electricity	construction	trade	transport	other	food	mining	...	transport	other	food	mining	manufactoring	electricity	construction	trade	transport	other
impact
air water impact	0.022428	0.052572	0.000259	0.225083	0.000295	0.000092	0.000952	0.000291	0.000092	0.016122	...	0.007839	0.000481	0.000976	0.018407	0.000949	0.447245	0.007421	0.003222	0.001553	0.000739
total air emissions	0.010865	0.025999	0.000127	0.111897	0.000140	0.000042	0.000465	0.000132	0.000044	0.007718	...	0.003733	0.000182	0.000426	0.008250	0.000450	0.221901	0.002975	0.001207	0.000711	0.000277

2 rows × 48 columns

which will calculate the missing accounts.

For these calculations, the characterized accounts can also be used outside the MRIO system. Thus:

[19]:

independent_extension = io_aly.emissions.characterize(charact_table, name="impacts_new")

WARNING:root:Impact >total emissions< removed - calculation requires stressors not present in extension >Emissions<

[20]:

type(independent_extension)

[20]:

pymrio.core.mriosystem.Extension

[21]:

independent_extension.M

[22]:

independent_extension_calc = independent_extension.calc_system(x=io_aly.x, Y=io_aly.Y)

[23]:

independent_extension.M

[23]:

region	reg1								reg2		...	reg5		reg6
sector	food	mining	manufactoring	electricity	construction	trade	transport	other	food	mining	...	transport	other	food	mining	manufactoring	electricity	construction	trade	transport	other
impact
air water impact	0.022428	0.052572	0.000259	0.225083	0.000295	0.000092	0.000952	0.000291	0.000092	0.016122	...	0.007839	0.000481	0.000976	0.018407	0.000949	0.447245	0.007421	0.003222	0.001553	0.000739
total air emissions	0.010865	0.025999	0.000127	0.111897	0.000140	0.000042	0.000465	0.000132	0.000044	0.007718	...	0.003733	0.000182	0.000426	0.008250	0.000450	0.221901	0.002975	0.001207	0.000711	0.000277

2 rows × 48 columns

Inspecting the used characterization table¶

Pymrio automatically adjust the characterization table by removing accounts which can not be calculated using a given extension. The removed accounts are reported through a warning message (e.g. “WARNING:root:Impact >total emissions< removed - calculation requires stressors not present in extension >Emissions<” in the examples above).

It is also possible, to obtain the cleaned characterization-table for inspection and further use. To do so:

[24]:

impacts = io.emissions.characterize(
    charact_table, name="impacts", return_char_matrix=True
)

WARNING:root:Impact >total emissions< removed - calculation requires stressors not present in extension >Emissions<

This changes the return type from a pymrio.Extension to a named tuple

[25]:

type(impacts)

[25]:

pymrio.core.mriosystem.characterization

with

[26]:

impacts.extension

[26]:

<pymrio.core.mriosystem.Extension at 0x7f37e35c02e0>

and

[27]:

impacts.factors

[27]:

	stressor	compartment	impact	factor	impact_unit
0	emission_type1	air	air water impact	0.002	t
1	emission_type2	water	air water impact	0.001	t
5	emission_type1	air	total air emissions	0.001	t

The latter is the characterization table used for the calculation.

For further information see the characterization docstring:

[28]:

print(io.emissions.characterize.__doc__)

Characterize stressors

        Characterizes the extension with the characterization factors given in factors.
        Factors can contain more characterization factors which depend on stressors not
        present in the Extension - these will be automatically removed.

        Note
        ----
        Accordance of units is not checked - you must ensure that the
        characterization factors correspond to the units of the extension to be
        characterized.

        Parameters
        -----------
        factors: pd.DataFrame
            A dataframe in long format with numerical index and columns named
            index.names of the extension to be characterized and
            'characterized_name_column', 'characterization_factors_column',
            'characterized_unit_column'

        characterized_name_column: str (optional)
            Name of the column with the names of the
            characterized account (default: "impact")

        characterization_factors_column: str (optional)
            Name of the column with the factors for the
            characterization (default: "factor")

        characterized_unit_column: str (optional)
            Name of the column with the units of the characterized accounts
            characterization (default: "impact_unit")

        name: string (optional)
            The new name for the extension,
            if None (default): name of the current extension with suffix
            '_characterized'

        return_char_matrix: boolean (optional)
            If False (default), returns just the characterized extension.
            If True, returns a namedtuple with extension and the actually used
            characterization matrix.

        _meta: MRIOMetaData, optional
            Metadata handler for logging, optional. Internal

        Returns
        --------
        pymrio.Extensions or
        namedtuple with (extension: pymrio.Extension, factors: pd.DataFrame)
        depending on return_char_matrix. Only the factors used for the calculation
        are returned.