Journal
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 28, Issue 37, Pages 52188-52201Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11356-021-14264-z
Keywords
CO2 emissions; Economic growth; Italy; Machine learning; Environmental policy
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Funding
- Universita degli Studi Roma Tre within the CRUI-CARE Agreement
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This study evaluates the relationship between economic growth and CO2 emissions in Italy using machine learning tools and develops three different models. The results show an increase in CO2 emissions in the predicting model, contradicting the main literature. Therefore, appropriate policy recommendations are provided based on these findings.
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.
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