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空气污染与 COVID-19 之间的非对称关系:来自非线性面板自回归分布滞后模型的证据。

The asymmetric nexus between air pollution and COVID-19: Evidence from a non-linear panel autoregressive distributed lag model.

机构信息

Olin Business School, Washington University in St. Louis, St. Louis, USA.

Business School, Guilin University of Electronic Technology, China.

出版信息

Environ Res. 2022 Jun;209:112848. doi: 10.1016/j.envres.2022.112848. Epub 2022 Jan 29.

Abstract

The emergence of a new coronavirus (COVID-19) has become a major global concern that has damaged human health and disturbing environmental quality. Some researchers have identified a positive relationship between air pollution (fine particulate matter PM) and COVID-19. Nonetheless, no inclusive investigation has comprehensively examined this relationship for a tropical climate such as India. This study aims to address this knowledge gap by investigating the nexus between air pollution and COVID-19 in the ten most affected Indian states using daily observations from 9th March to September 20, 2020. The study has used the newly developed Hidden Panel Cointegration test and Nonlinear Panel Autoregressive Distributed Lag (NPARDL) model for asymmetric analysis. Empirical results illustrate an asymmetric relationship between PM and COVID-19 cases. More precisely, a 1% change in the positive shocks of PM increases the COVID-19 cases by 0.439%. Besides, the estimates of individual states expose the heterogeneous effects of PM on COVID-19. The asymmetric causality test of Hatemi-J's (2011) also suggests that the positive shocks on PM Granger-cause positive shocks on COVID19 cases. Research findings indicate that air pollution is the root cause of this outbreak; thus, the government should recognize this channel and implement robust policy guidelines to control the spread of environmental pollution.

摘要

一种新型冠状病毒(COVID-19)的出现已成为一个重大的全球关注焦点,它不仅损害了人类健康,还破坏了环境质量。一些研究人员已经发现,空气污染(细颗粒物 PM)与 COVID-19 之间存在正相关关系。然而,针对印度等热带气候,还没有全面的包容性研究来全面考察这种关系。本研究旨在通过使用 2020 年 3 月 9 日至 9 月 20 日的每日观测数据,调查印度十个受影响最严重的邦的空气污染与 COVID-19 之间的关系,以填补这一知识空白。该研究使用了新开发的隐藏面板协整检验和非线性面板自回归分布滞后(NPARDL)模型进行非对称分析。实证结果说明了 PM 和 COVID-19 病例之间存在非对称关系。更确切地说,PM 的正冲击变化 1%会使 COVID-19 病例增加 0.439%。此外,各州的估计值揭示了 PM 对 COVID-19 的异质影响。Hatemi-J(2011)的不对称因果检验也表明,PM 的正冲击会引起 COVID19 病例的正冲击。研究结果表明,空气污染是此次疫情爆发的根源;因此,政府应该认识到这一渠道,并实施强有力的政策准则来控制环境污染的蔓延。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c906/8800540/acc7ced5296d/gr1_lrg.jpg

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