Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

Marco Mele; Cosimo Magazzino; Nicolas Schneider; Floriana Nicolai

doi:10.1007/s11356-021-14264-z

Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

Environ Sci Pollut Res Int. 2021 Oct;28(37):52188-52201. doi: 10.1007/s11356-021-14264-z. Epub 2021 May 18.

Authors

Marco Mele¹, Cosimo Magazzino², Nicolas Schneider³, Floriana Nicolai⁴

Affiliations

¹ Department of Political Sciences, Roma Tre University, Rome, Italy. marco.mele@uniroma3.it.
² Department of Political Sciences, Roma Tre University, Rome, Italy.
³ Department of Economics, Paris-1 Pantheon-Sorbonne University, Paris, France.
⁴ , Rome, Italy.

Abstract

Although the literature on the relationship between economic growth and CO₂ emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960-2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO₂ emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO₂ increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

Keywords: CO2 emissions; Economic growth; Environmental policy; Italy; Machine learning.

MeSH terms

Algorithms
Carbon Dioxide* / analysis
Economic Development*
Environmental Pollution
Italy

Substances

Carbon Dioxide