Automatic modeling of socioeconomic drivers of energy consumption and pollution using Bayesian symbolic regression

Predicting countries’ energy consumption and pollution levels precisely from socioeconomic drivers will be essential to support sustainable policy-making in an effective manner. Current predictive models, like the widely used STIRPAT equation, are based on rigid mathematical expressions that assume constant elasticities. Using a Bayesian approach symbolic regression, here we explore vast amount of suitable model link between energy-related impacts drivers. We find closed-form analytical outperform well-established equation whose structure challenges assumption elasticities adopted literature. Our work unfolds new avenues apply machine learning algorithms derive data environmental studies, which could help better models solutions problems.