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

Although the literature on the relationship between economic growth and CO(2) 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 CO(2) emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO(2) increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-021-14264-z.