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

Congbo Song, Bowen Liu, Kai Cheng, Matthew A. Cole, Qili Dai, Robert J.R. Elliott, Zongbo Shi

Research output: Contribution to journalArticlepeer-review

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 PM 2.5, daily maximum 8-h average O 3, and SO 2 by 4.6, 2.5, and 2.3 μg m -3, respectively. From 2015 to 2021, the impacts of winter heating on PM 2.5 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 PM 2.5 in "2 + 26" cities. Overall, clean heating policies caused the annual PM 2.5 in mainland China to reduce by 1.9 μg m -3 from 2015 to 2021, potentially avoiding 23,556 premature deaths in 2021.

Original languageEnglish
JournalEnvironmental Science and Technology
Early online date1 Feb 2023
DOIs
Publication statusPublished - 1 Feb 2023

Keywords

  • air pollution
  • causal inference
  • clean heating
  • machine learning
  • weather normalization
  • winter heating

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