open access publication

Article, 2023

A neural network approach to the environmental Kuznets curve

Energy Economics, ISSN 0140-9883, Volume 126, 10.1016/j.eneco.2023.106985

Contributors

Bennedsen M. 0000-0001-8040-1442 [1] Hillebrand E. 0000-0002-8461-1671 [1] Jensen S. 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

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

Funders

  • Economics and Business Economics Seminar
  • Danmarks Frie Forskningsfond
  • Center for Research in Econometric Analysis of Time Series
  • Aarhus Universitet
  • Joint Econometrics-Finance seminar

Data Provider: Elsevier