@techreport{cd40cc568874402fa95d11f55cfac259,
title = "A Neural Network Approach to the Environmental Kuznets Curve",
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.",
keywords = "Territorial carbon dioxide emissions, Consumption-based carbon dioxide emissions, Environmental Kuznets curve, Climate econometrics, Panel data, Machine learning, Neural networks",
author = "Mikkel Bennedsen and Eric Hillebrand and Jensen, {Sebastian Mathias}",
year = "2022",
month = may,
language = "English",
series = "CREATES Research Paper",
publisher = "{\AA}rhus Universitet",
number = "2022-09",
type = "WorkingPaper",
institution = "{\AA}rhus Universitet",
}