Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions

Nowadays, there is an increasing use of digital technologies and Artificial Intelligence (AI) such as Machine Learning (ML) models that leverage data to optimize the performances of systems in almost all activity sectors, including ML models for optimizing solutions related to CO2 capture from the atmosphere or CO2 emissions reduction from human activities. However, on the other hand, the use of AI models is leading to an increasing energy consumption that also raises environmental issues (in terms of CO2 emissions) which are less studied in the literature. This then raises the new question of a more realistic estimate of the carbon footprint (CO2 emissions in particular) of AI models in general, and particularly AI models aimed at reducing CO2 emissions. Thus, in this paper, for an AI model in this latter context, we propose a method to quantify both his negative impacts (quantity of CO2 emissions emitted by the training and use of the model) and his positive impacts (quantity of CO2 emissions saved when the model is used). The method is evaluated with three state-of-the-art AI models: (i) an artificial neural network model for managing the energy demand of Brazilian households, (ii) an adaptive neuro-fuzzy inference system for photovoltaic power forecast in Tunisia, (iii) and a Bayesian regression model for the electric vehicle routing problem in Sweden and Luxembourg. Results show that, if only the positive impacts are considered, the reduction of CO2 emitted due to the usage of the models is significant, but depends on each context (34%, 73%, and 9%, respectively). However, when both positive and negative impacts are considered, the negative impacts are sometimes higher than the positive impacts (the first and the third model) for a nominal use (1 user). Nevertheless, the balance becomes highly positive again, when these two projects are scaled up (realistic projections with many users). The second model cannot be scaled up, but the balance remains positive, even if the gains are much smaller. More generally, the CO2 emissions gain metrics provided by our method can be used as new metrics for comparing the efficiency of AI models (for reducing CO2 emissions) beyond predictive capacity-based traditional ML evaluation metrics. Based on the lessons learned from our study, we also provide seven global recommendations that can contribute to the reduction of the carbon footprint of ML models in general.