A most simple implementation of Kitzes (2013) in Python.

The file kitzes-2013.py contains a most simple implementation of Kitzes (2013) in Python with the packages NumPy and Pandas. It supplements Bunsen and Finkbeiner (2023) and is also available via Zenodo (Bunsen 2022). When working with this script, an acknowledgement is appreciated but by no means mandatory.

Note: Pymrio is a comprehensive package for Multi-Regional Input-Output Analysis (MRIO) in Python.

References

Bunsen, Jonas. 2022. ‘A Most Simple Implementation of Kitzes (2013) in Python’. Zenodo. https://doi.org/10.5281/zenodo.7431089.

Bunsen, Jonas, and Matthias Finkbeiner. 2023. ‘An Introductory Review of Input-Output Analysis in Sustainability Sciences Including Potential Implications of Aggregation’. Sustainability 15 (1): 46. https://doi.org/10.3390/su15010046.

Kitzes, Justin. 2013. ‘An Introduction to Environmentally-Extended Input-Output Analysis’. Resources 2 (4): 489–503. https://doi.org/10.3390/resources2040489.

Results overview

Inventory	Sector	Leontief inverse-based results	Series expansion-based results1
Production-based	Agriculture	8.00000	7.826207 (97.82%)
Production-based	Manufacturing	4.00000	3.945339 (98.63%)
Consumption-based	Agriculture	4.80000	4.711197 (98.15%)
Consumption-based	Manufacturing	7.20000	7.060348 (98.06%)

Output of kitzes-2013.py

>>> Leontief inverse-based results:

Production-based-inventory:
Agriculture      8.0
Manufacturing    4.0
dtype: float64

Consumption-based-inventory:
Agriculture      4.8
Manufacturing    7.2
dtype: float64

>>> Series expansion-based results:

Production-based-inventory:
Agriculture      7.826207
Manufacturing    3.945339
dtype: float64

Consumption-based-inventory:
Agriculture      4.711197
Manufacturing    7.060348
dtype: float64

Footnotes

1. Calculated for the first eleven production layers (see kitzes-2013.py). ↩