This study evaluates the impact of air pollution on economic activity in 309 regions across England between 2010 and 2021. Using concentration of particulate matter ( \(PM_{2.5}\) ) as a measure of air pollution and the gross value added (GVA) as a measure of economic activity, we apply Two-Stage Least Squares (2SLS) and double/debiased machine learning models with thermal inversion as an instrumental variable. Our findings reveal a significant negative causal relationship: A 1  \(\upmu \textrm{g}/\textrm{m}^3\) increase in \(PM_{2.5}\) concentrations leads to a 4.3% reduction in GVA per capita and a 1.6% reduction in GVA per job. Additionally, we find that air pollution impacts urban productivity more than rural areas, and it effects on high-skilled employment. These results emphasize the need for stricter air pollution policies. These findings have significant implications for the development of air pollution policies and regulations.

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A Double/Debiased Machine Learning-Based Method for the Analysis of Panel Data

  • Shimeng Yang,
  • Xinru Lin,
  • Kaiju Li,
  • Rui Ma,
  • Zhiguo Wang

摘要

This study evaluates the impact of air pollution on economic activity in 309 regions across England between 2010 and 2021. Using concentration of particulate matter ( \(PM_{2.5}\) ) as a measure of air pollution and the gross value added (GVA) as a measure of economic activity, we apply Two-Stage Least Squares (2SLS) and double/debiased machine learning models with thermal inversion as an instrumental variable. Our findings reveal a significant negative causal relationship: A 1  \(\upmu \textrm{g}/\textrm{m}^3\) increase in \(PM_{2.5}\) concentrations leads to a 4.3% reduction in GVA per capita and a 1.6% reduction in GVA per job. Additionally, we find that air pollution impacts urban productivity more than rural areas, and it effects on high-skilled employment. These results emphasize the need for stricter air pollution policies. These findings have significant implications for the development of air pollution policies and regulations.