A neural network approach to the environmental Kuznets curve

Mikkel Bennedsen, Eric Hillebrand and Sebastian Jensen

Energy Economics, 2023, vol. 126, issue C

Abstract: We investigate the relationship between per capita gross domestic product and per capita carbon dioxide emissions using national-level panel data for the period 1960–2018. We propose a novel semiparametric panel data methodology that combines country and time fixed effects with a nonparametric neural network regression component. Globally and for the regions OECD and Asia, we find evidence of an inverse U-shaped relationship, often referred to as an environmental Kuznets curve (EKC), in production-based emissions. For OECD, the EKC-shape disappears when using consumption-based emissions data, suggesting the EKC-shape observed for OECD is driven by emissions exports. For Asia, the EKC-shape becomes even more pronounced when using consumption-based emissions data and exhibits an earlier turning point.

Keywords: Production-based carbon dioxide emissions; Consumption-based carbon dioxide emissions; Environmental Kuznets curve; Climate econometrics; Panel data; Machine learning; Neural networks (search for similar items in EconPapers)
JEL-codes: C14 C23 C45 C51 C52 C53 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:126:y:2023:i:c:s0140988323004838

DOI: 10.1016/j.eneco.2023.106985

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