A Neural Network Approach to the Environmental Kuznets Curve

Mikkel Bennedsen (), Eric Hillebrand () and Sebastian Jensen ()
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Mikkel Bennedsen: Aarhus University and CREATES, Postal: Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Eric Hillebrand: Aarhus University and CREATES, Postal: Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Sebastian Jensen: Aarhus University and CREATES, Postal: Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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). 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. JEL classifcation: C14, C23, C45, C51, C52, C53 Key words: Territorial carbon dioxide emissions, Consumption-based carbon dioxide emissions, Environmental Kuznets curve, Climate econometrics, Panel data, Machine learning, Neural networks

Pages: 65
Date: 2022-05-24
New Economics Papers: this item is included in nep-agr, nep-big, nep-cmp, nep-ecm, nep-eff, nep-ene, nep-env and nep-sea
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