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The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning
Summary
Researchers used machine learning to analyze daily city-level data from New York state and found that fine particulate matter (PM2.5) and nitrogen dioxide (NO2) air pollution were the strongest predictors of COVID-19 death rates. The study also found that unsustainable economic growth drives higher emissions of these pollutants, linking economic activity to pandemic mortality through air quality.
The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM10, PM2.5, and NO2 concentrations in New York state using city-level daily data through two Machine Learning experiments. PM2.5 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 PM2.5 to Deaths, NO2 to Deaths, and economic growth to both PM2.5 and NO2. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM2.5 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 (PM2.5 and NO2) in New York state.