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

Abstract

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.

A Neural Network Evidence of the Nexus Among Air Pollution, Economic Growth, and COVID-19 Deaths in the Hubei Area

“…To sum up, the DL results are in line with empirical findings presented in studies by Coccia [5], Magazzino et al [9,28], and Mele et al [10] because a clear relationship emerged among air pollution, economic growth, and deaths due to COVID-19 pandemic.…”

“…Magazzino et al [8] investigated the relationship between renewable energy and GDP acceleration in Brazil that could offset the harmful effects of the COVID-19 global pandemic. Magazzino et al [9] assessed the relationship between COVID-19related deaths, economic growth, PM 10 , PM 2.5 , and NO 2 concentrations in the New York state. Mele et al [10] found that threshold values of NO 2 connected to COVID-19 ranged from 15.8 µg/m 3 for Lyon, 21.08 µg/m 3 for Marseille, and 22.9 µg/m 3 for Paris.…”

Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture

“…Even though each neuron is relatively slow, the parallelism partly explains the faster speed of the brain in performing tasks that require the simultaneous processing of a large number of data. In essence, it is a sophisticated statistical system with excellent noise immunity; if some units of the system were to malfunction, the network as a whole would have reduced performance but would hardly encounter a system crash (for more details, see [58][59][60][61][62][63][64][65][66][67][68]).…”

The 2020 Italian Spring Lockdown: A Multidisciplinary Analysis over the Milan Urban Area

“…This is especially clear when considering both the social and economic impacts of a lockdown, which is not sustainable, as it increases the risk of an economic recession, with an associated spike in job and GDP losses, and withers social and cultural life. To the contrary, environmental concerns, despite their "public" dimension, always seemed to be far less crucial [89], even when considering the first evidence provided regarding the correlation between the COVID-19-related death rate and pollution [90][91][92] or, more specifically, NO x [93]. Therefore, once the lockdown is released, its results should be used as convincing arguments for the population to accept urban car-free zones.…”