The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM(10), PM(2.5,) and NO(2) concentrations in New York state using city-level daily data through two Machine Learning experiments. PM(2.5) and NO(2) are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis. In line with the causal findings, a unidirectional causal effect is found from PM(2.5) to Deaths, NO(2) to Deaths, and economic growth to both PM(2.5) and NO(2). Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM(2.5) to Deaths, NO(2) to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM(2.5) and NO(2)) in New York state.