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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. • Increase of the use of AI models for optimizing solutions to reduce CO2 emissions. • Training and using AI models also generates CO2 emissions. • Method to quantify positive and negative environmental impacts of AI models. • Evaluations based on three state-of-the-art AI models. • Seven global recommendations for reducing CO2 emissions of AI models. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms of pollutant removal in Wastewater Treatment Plants (WWTPs) is crucial for controlling effluent quality efficiently. However, the numerous treatment units, operational factors, and the underlying interactions between these units and factors usually obfuscate the comprehensive and precise understanding of the processes. We have previously proposed a machine learning (ML) framework to uncover complex cause-and-effect relationships in WWTPs. However, only one interpretable ML model, Random forest (RF), was studied and the interpretation method was not granular enough to reveal very detailed relationships between operational factors and effluent parameters. Thus, in this paper, we present an upgraded framework involving three interpretable tree-based models (RF, XGboost and LightGBM), three metrics (R2, Root mean squared error (RMSE), and Mean absolute error (MAE)) and a more advanced interpretation system SHapley Additive exPlanations (SHAP). Details of the framework are provided along with a demonstration of its practical applicability based on a case study of the Umeå WWTP in Sweden. Results show that, for both labels TSS e (Total suspended solids in effluent) and PO4 e (Phosphate in effluent), the XGBoost models are optimal whereas the RF models are the least optimal, due to overfitting and polarized fitting. This study has yielded multiple new and significant findings with respect to the control of TSS e and PO4 e in the Umeå WWTP and other similarly configured WWTPs. Additionally, this study has produced two important generic findings relating to ML applications for WWTPs (or even other process industries) in terms of cause-and-effect investigations. First, the model comparison should be carried out from multiple perspectives to ensure that underlying details are fully revealed and examined. Second, using a precise, robust, and granular (feature attribution available for individual instances) explanation method can bring extra insight into both model comparison and model interpretation. SHAP is recommended as we found it to be of great value in this study. • Machine learning is applied to WWTP process to uncover detailed cause-and-effect information. • Multiple tree-based models are examined to select the optimal one for interpretation. • Multiple metrics are used to evaluate models' performances comprehensively. • For the first time SHapley Additive exPlanations is used for WWTP process analytics. • Results can help to develop advanced process control strategies. [ABSTRACT FROM AUTHOR]