open access publication

Article, 2023

A neural network approach to the environmental Kuznets curve

Energy Economics, ISSN 0140-9883, 1873-6181, Volume 126, Page 106985, 10.1016/j.eneco.2023.106985

Contributors

Bennedsen, Mikkel 0000-0001-8040-1442 [1] Hillebrand, Eric T 0000-0002-8461-1671 [1] Jensen, Sebastian 0000-0003-2568-0148 (Corresponding author) [1]

Affiliations

  1. [1] Aarhus University
  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

Asia, Kuznets curve, OECD, U-shaped relationship, approach, carbon dioxide emissions, components, countries, curves, data, data methodology, dioxide emissions, domestic product, effect, emission, emission data, emission exports, environmental Kuznets curve, evidence, export, fixed effects, gross domestic product, inverse U-shaped relationship, methodology, network approach, neural network approach, panel data, panel data methodology, per capita carbon dioxide emissions, per capita gross domestic product, period, production, production-based emissions, region, regression component, relationship, time, time fixed effects

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