Repositori institucional URV
| Autor/s de la URV: | Guimera Manrique, Roger / Sales Pardo, Marta |
| Autor segons l'article: | Vazquez, Daniel; Guimera, Roger; Sales-Pardo, Marta; Guillen-Gosalbez, Gonzalo |
| Adreça de correu electrònic de l'autor: | roger.guimera@urv.cat marta.sales@urv.cat |
| Identificador de l'autor: | 0000-0002-3597-4310 0000-0002-8140-6525 |
| Any de publicació de la revista: | 2022 |
| Tipus de publicació: | Journal Publications |
| Referència 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 |
| Referència a l'article segons font original: | Sustainable Production And Consumption. 30 596-607 |
| Resum: | 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. |
| DOI de l'article: | 10.1016/j.spc.2021.12.025 |
| Enllaç font original: | https://www.sciencedirect.com/science/article/pii/S2352550921003729?via%3Dihub |
| Versió de l'article dipositat: | info:eu-repo/semantics/publishedVersion |
| Accès a la llicència d'ús: | https://creativecommons.org/licenses/by/3.0/es/ |
| Departament: | Enginyeria Química |
| URL Document de llicència: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Àrees temàtiques: | 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 |
| Paraules clau: | 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) |
| Entitat: | Universitat Rovira i Virgili |
| Data d'alta del registre: | 2024-10-19 |
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