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A Neural Network Approach to the Environmental Kuznets Curve

Author

Listed:
  • Mikkel Bennedsen

    (Aarhus University and CREATES)

  • Eric Hillebrand

    (Aarhus University and CREATES)

  • Sebastian Jensen

    (Aarhus University and CREATES)

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

Suggested Citation

  • Mikkel Bennedsen & Eric Hillebrand & Sebastian Jensen, 2022. "A Neural Network Approach to the Environmental Kuznets Curve," CREATES Research Papers 2022-09, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2022-09
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    More about this item

    Keywords

    territorial carbon dioxide emissions; consumption-based carbon dioxide emissions; environmental kuznets curve; climate econometrics; panel data; machine learning; neural networks;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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