Attribution of Air Quality Benefits to Clean Winter Heating Policies in China: Combining Machine Learning with Causal Inference

Environ Sci Technol. 2023 Nov 21;57(46):17707-17717. doi: 10.1021/acs.est.2c06800. Epub 2023 Feb 1.

Abstract

Heating is a major source of air pollution. To improve air quality, a range of clean heating policies were implemented in China over the past decade. Here, we evaluated the impacts of winter heating and clean heating policies on air quality in China using a novel, observation-based causal inference approach. During 2015-2021, winter heating causally increased annual PM2.5, daily maximum 8-h average O3, and SO2 by 4.6, 2.5, and 2.3 μg m-3, respectively. From 2015 to 2021, the impacts of winter heating on PM2.5 in Beijing and surrounding cities (i.e., "2 + 26" cities) decreased by 5.9 μg m-3 (41.3%), whereas that in other northern cities only decreased by 1.2 μg m-3 (12.9%). This demonstrates the effectiveness of stricter clean heating policies on PM2.5 in "2 + 26" cities. Overall, clean heating policies caused the annual PM2.5 in mainland China to reduce by 1.9 μg m-3 from 2015 to 2021, potentially avoiding 23,556 premature deaths in 2021.

Keywords: air pollution; causal inference; clean heating; machine learning; weather normalization; winter heating.

MeSH terms

  • Air Pollutants* / analysis
  • Air Pollution* / analysis
  • Air Pollution* / prevention & control
  • China
  • Cities
  • Environmental Monitoring
  • Heating
  • Machine Learning
  • Particulate Matter / analysis
  • Policy
  • Seasons

Substances

  • Air Pollutants
  • Particulate Matter