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Air pollution lowers Chinese urbanites’ expressed happiness on social media

Nature Human Behaviourvolume 3pages237243 (2019) | Download Citation

Abstract

High levels of air pollution in China may contribute to the urban population’s reported low level of happiness1,2,3. To test this claim, we have constructed a daily city-level expressed happiness metric based on the sentiment in the contents of 210 million geotagged tweets on the Chinese largest microblog platform Sina Weibo4,5,6, and studied its dynamics relative to daily local air quality index and PM2.5 concentrations (fine particulate matter with diameters equal or smaller than 2.5 μm, the most prominent air pollutant in Chinese cities). Using daily data for 144 Chinese cities in 2014, we document that, on average, a one standard deviation increase in the PM2.5 concentration (or Air Quality Index) is associated with a 0.043 (or 0.046) standard deviation decrease in the happiness index. People suffer more on weekends, holidays and days with extreme weather conditions. The expressed happiness of women and the residents of both the cleanest and dirtiest cities are more sensitive to air pollution. Social media data provides real-time feedback for China’s government about rising quality of life concerns.

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The code that support the findings of this study is available from the corresponding author upon reasonable request.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

J.W. acknowledges the research support from the National Key Research and Development Program of China (no. 2017YFB0503500), and the Strategic Priority Research Program of the CAS (no. XDA19040503). S.Z. acknowledges the research support from the National Natural Science Foundation of China (no. 71625004), and MIT China Future City Lab. C.S. acknowledges National Natural Science Foundation of China (no. 71603158), Ministry of Education in China Project of Humanities and Social Sciences (no. 16YJC790090), ‘Chen Guang’ project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (no. 16CG43). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Siqi Zheng, Jianghao Wang.

Affiliations

  1. China Future City Lab, Department of Urban Studies and Planning, and Center for Real Estate, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Siqi Zheng
  2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    • Jianghao Wang
  3. School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai, China

    • Cong Sun
  4. Hang Lung Center for Real Estate and Department of Construction Management, Tsinghua University, Beijing, China

    • Xiaonan Zhang
  5. Department of Economics, University of Southern California, Los Angeles, CA, USA

    • Matthew E. Kahn

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Contributions

S.Z., J.W., C.S. and M.E.K. did the research design and wrote the paper. S.Z., J.W. and C.S. estimated the empirical models. J.W., C.S. and X.Z. collected and analysed the data.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Siqi Zheng or Cong Sun.

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DOI

https://doi.org/10.1038/s41562-018-0521-2