Why Measuring AI Environmental Impact of Organisations is Non-Trivial?

Loïc Guibert, David Bekri, Louise Aubet, Steve Berberat, Sébastien Rumley
Proceedings of the Fourth Swiss AI Days, PMLR 309:67-81, 2026.

Abstract

This paper presents the results and conclusions from the EIEIAE project, which defined a methodology to measure the environmental impact of AI services within companies and organisations. Several lessons can be learned from this project, as it highlighted several challenges that emerged when trying to produce reliable estimates of such impacts: (a) the industrial lack of transparency of AI providers, (b) the absence of accurate and exhaustive modelling of ICT environmental impacts, (c) the inherent complexity of gathering the necessary data for organisations, and (d) the fact that organisations are more focused on cost savings than on reducing their environmental impact.

Cite this Paper

BibTeX
@InProceedings{pmlr-v309-guibert26b, title = {Why Measuring AI Environmental Impact of Organisations is Non-Trivial?}, author = {Guibert, Lo{\"i}c and Bekri, David and Aubet, Louise and Berberat, Steve and Rumley, S{\'e}bastien}, booktitle = {Proceedings of the Fourth Swiss AI Days}, pages = {67--81}, year = {2026}, editor = {Kucharavy, Andrei and Delgado, Pamela and Schürch Todeschini, Valérie and Rumley, Sébastien}, volume = {309}, series = {Proceedings of Machine Learning Research}, month = {23--25 Mar}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v309/main/assets/guibert26b/guibert26b.pdf}, url = {https://proceedings.mlr.press/v309/guibert26b.html}, abstract = {This paper presents the results and conclusions from the EIEIAE project, which defined a methodology to measure the environmental impact of AI services within companies and organisations. Several lessons can be learned from this project, as it highlighted several challenges that emerged when trying to produce reliable estimates of such impacts: (a) the industrial lack of transparency of AI providers, (b) the absence of accurate and exhaustive modelling of ICT environmental impacts, (c) the inherent complexity of gathering the necessary data for organisations, and (d) the fact that organisations are more focused on cost savings than on reducing their environmental impact.} }
Endnote
%0 Conference Paper %T Why Measuring AI Environmental Impact of Organisations is Non-Trivial? %A Loïc Guibert %A David Bekri %A Louise Aubet %A Steve Berberat %A Sébastien Rumley %B Proceedings of the Fourth Swiss AI Days %C Proceedings of Machine Learning Research %D 2026 %E Andrei Kucharavy %E Pamela Delgado %E Valérie Schürch Todeschini %E Sébastien Rumley %F pmlr-v309-guibert26b %I PMLR %P 67--81 %U https://proceedings.mlr.press/v309/guibert26b.html %V 309 %X This paper presents the results and conclusions from the EIEIAE project, which defined a methodology to measure the environmental impact of AI services within companies and organisations. Several lessons can be learned from this project, as it highlighted several challenges that emerged when trying to produce reliable estimates of such impacts: (a) the industrial lack of transparency of AI providers, (b) the absence of accurate and exhaustive modelling of ICT environmental impacts, (c) the inherent complexity of gathering the necessary data for organisations, and (d) the fact that organisations are more focused on cost savings than on reducing their environmental impact.
APA
Guibert, L., Bekri, D., Aubet, L., Berberat, S. & Rumley, S.. (2026). Why Measuring AI Environmental Impact of Organisations is Non-Trivial?. Proceedings of the Fourth Swiss AI Days, in Proceedings of Machine Learning Research 309:67-81 Available from https://proceedings.mlr.press/v309/guibert26b.html.

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