Autor según el artículo: Vazquez, Daniel; Guimera, Roger; Sales-Pardo, Marta; Guillen-Gosalbez, Gonzalo
Departamento: Enginyeria Química
Autor/es de la URV: Guimera Manrique, Roger / Sales Pardo, Marta
Palabras clave: Symbolic regression Surrogate model Stochastic impacts by regression on population Stirpat Greenhouse gas (ghg) emissions Eora environmentally extended multi-region input-output database Affluence and technology (stirpat) symbolic regression stochastic impacts by regression on population input-output database impact greenhouse gas (ghg) emissions footprint eora environmentally extended multi-region china algorithm affluence and technology (stirpat)
Resumen: 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 to symbolic regression, here we explore a vast amount of suitable mathematical expressions to model the link between energy-related impacts and socioeconomic drivers. We find closed-form analytical expressions that outperform the well-established STIRPAT equation and whose mathematical structure challenges the assumption of constant elasticities adopted in the literature. Our work unfolds new avenues to apply machine learning algorithms to derive analytical expressions from data in environmental studies, which could help find better models and solutions in energy-related problems.
Áreas temáticas: Renewable energy, sustainability and the environment Materiais Interdisciplinar Industrial and manufacturing engineering Green & sustainable science & technology Environmental studies Environmental engineering Environmental chemistry Engenharias iii Ciências ambientais Administração pública e de empresas, ciências contábeis e turismo
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: roger.guimera@urv.cat marta.sales@urv.cat
Identificador del autor: 0000-0002-3597-4310 0000-0002-8140-6525
Fecha de alta del registro: 2024-10-19
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Sustainable Production And Consumption. 30 596-607
Referencia de l'ítem segons les normes APA: Vazquez, Daniel; Guimera, Roger; Sales-Pardo, Marta; Guillen-Gosalbez, Gonzalo (2022). Automatic modeling of socioeconomic drivers of energy consumption and pollution using Bayesian symbolic regression. Sustainable Production And Consumption, 30(), 596-607. DOI: 10.1016/j.spc.2021.12.025
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV