Details

Autor(en) / Beteiligte

• Mele, Marco
• Magazzino, Cosimo

Titel

Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence

Ist Teil von

• Environmental science and pollution research international, 2021-01, Vol.28 (3), p.2669-2677

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2021

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• This study uses two different approaches to explore the relationship between pollution emissions, economic growth, and COVID-19 deaths in India. Using a time series approach and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto causality tests were performed. The results highlight unidirectional causality between economic growth and pollution. Then, a D2C algorithm on proportion-based causality is applied, implementing the Oryx 2.0.8 protocol in Apache. The underlying hypothesis is that a predetermined pollution concentration, caused by economic growth, could foster COVID-19 by making the respiratory system more susceptible to infection. We use data (from January 29 to May 18, 2020) on confirmed deaths (total and daily) and air pollution concentration levels for 25 major Indian cities. We verify a ML causal link between PM 2.5 , CO 2 , NO 2 , and COVID-19 deaths. The implications require careful policy design.