open access publication

Article, 2023

A neural network approach to the environmental Kuznets curve

ENERGY ECONOMICS, ISSN 0140-9883, 0140-9883, Volume 126, 10.1016/j.eneco.2023.106985

Contributors

Bennedsen, Mikkel 0000-0001-8040-1442 [1] Hillebrand, Eric 0000-0002-8461-1671 [1] Jensen, Sebastian (Corresponding author) [1]

Affiliations

  1. [1] Aarhus Univ, Dept Econ & Business Econ, Fuglesangs 4, DK-8210 Aarhus V, Denmark
  2. [NORA names: AU Aarhus University; University; Denmark; Europe, EU; Nordic; OECD]

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

Climate econometrics, Consumption-based carbon dioxide emissions, Environmental Kuznets curve, Machine learning Neural networks, Neural networks, Panel data, Production-based carbon dioxide emissions

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