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, PM₁₀, PM_2.5, and NO₂ concentrations in New York state using city-level daily data through two Machine Learning experiments. PM_2.5 and NO₂ 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, NO₂ to Deaths, and economic growth to both PM_2.5 and NO₂. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM_2.5 to Deaths, NO₂ 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 NO₂) in New York state.