Articles producció científica> Enginyeria Química

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

  • Datos identificativos

    Identificador: imarina:9243292
    Autores:
    Vazquez, DanielGuimera, RogerSales-Pardo, MartaGuillen-Gosalbez, Gonzalo
    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.
  • Otros:

    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
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S2352550921003729?via%3Dihub
    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
    DOI del artículo: 10.1016/j.spc.2021.12.025
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Environmental Chemistry,Environmental Engineering,Environmental Studies,Green & Sustainable Science & Technology,Industrial and Manufacturing Engineering,Renewable Energy, Sustainability and the Environment
    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)
    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
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