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.
Highlights
Global warming has become an increasing threat worldwide
Regarding Italy, it is yet known that this economy will not reach its environmental target setting a 20% reduction of carbon emissions front
This paper presents the first empirical assessment of the relationship between economic growth and CO2 emissions using an innovative machine learning (ML) approach in Italy
Summary
Global warming has become an increasing threat worldwide. In the near future, sea level rises and weather shocks are expected to be more frequent, making agricultural farmers and coastal populations more vulnerable. It has been demonstrated that elevated levels of polluting particles may adversely affect productivity (Agrawal et al 2003), cognitive performance (Ebenstein et al 2016), crime (Herrnstadt et al 2020), and health (Ebenstein et al 2017) They share central channels with society, the costs associated with air pollution were long ignored by environmental regulators in the past. To the best of our knowledge, no study inspected the GDP-CO2 nexus in Italy using a machine learning (ML) approach This is surprising since techniques derived from artificial intelligence (AI) have already brought valuable evidence on neighboring topics (Magazzino et al, 2020a, 2020b, 2021a, 2021b, 2021c, 2021d). In “Concluding remarks and policy implications” section, concluding remarks and policy insights are delivered
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