Automatic modeling of socio-economic drivers of energy consumption and pollution using Bayesian symbolic regression
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Date
2022-03
Publication Type
Journal Article
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yes
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Abstract
Predicting countries’ energy consumption and pollution levels precisely from socio-economic 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 socio-economic 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, which could help find better models and solutions in energy-related problems.
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published
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Journal / series
Volume
30
Pages / Article No.
596 - 607
Publisher
Elsevier
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Edition / version
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Software
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Date collected
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Subject
Surrogate model; symbolic regression; stochastic impacts by regression on population; affluence and technology (STIRPAT); greenhouse gas (GHG) emissions; Eora environmentally extended multi-region input-output database
Organisational unit
09655 - Guillén Gosálbez, Gonzalo / Guillén Gosálbez, Gonzalo