Avoidance behavior against air pollution: evidence from online search indices for anti-PM2.5 masks and air filters in Chinese cities

Abstract

This study investigates people’s behavioral responses to air pollution information in China. We find that elevated air pollution levels are positively associated with higher online searches for anti-PM2.5 masks and air filters. A 10 µg/m3 increase in PM2.5 is associated with a 3.6–8.4% increase of mobile queries for anti-PM2.5 masks, and 1.1–4.7% for air filters. Using a regression discontinuity design, we find that a haze alert issued when PM2.5 concentration exceeds 250 µg/m3 would double online queries for anti-PM2.5 masks and air filters. Online searches are also positively correlated with online sales. One day of severe pollution would induce a cost of 0.2 million USD on online expenditure on anti-PM2.5 masks. Some suggestive evidence shows that people in richer and polluted cities tend to search more for anti-PM2.5 masks and air filters than those in poorer and cleaner cities.

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Notes

  1. 1.

    35th Report of China Internet Development Statistics, China Internet Network Information Center, 2015.

  2. 2.

    http://report.iresearch.cn/html/20150212/246488.shtml.

  3. 3.

    There is no information on spatial differentiation of online searches within each city. Check http://index.baidu.com for more details.

  4. 4.

    If we assume that the government can forecast the air pollution levels in 1–3 days with high accuracy, we can compare the number of official haze alerts that were actually issued with the number of haze alerts that should be alerted. We find that the Beijing government might have missed nine blue alerts and one yellow alert during 2014, and has generally lowered down the levels of all the red and orange alerts based on actual data.

  5. 5.

    The significantly negative coefficients for air filters in Guangzhou and Chongqing indicate that people may tend to search less for air filters and search more for PM2.5 masks in southern cities. Considering the magnitude to be relatively small, this does not mask our major conclusion.

  6. 6.

    We are concerned that the leap at the threshold in Fig. 5 is mainly driven by a few points above the threshold, so we check the data and find that these points are from two severe episodes in February and October 2014 when orange alerts were issued (note that red alerts were supposed to be issued at this level of air pollution, but the first official red alert in history was not issued until December 8, 2015). We then extend the period to January 17, 2016 in the RDD. The results are robust, with the effect of alert on searches for anti-PM2.5 mask increasing while decreasing for air filter.

  7. 7.

    Ctrip is the largest online platform and agency for travel in China.

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Acknowledgements

This research was supported by the Hong Kong PhD Fellowship Scheme (PF13-10374) from the Research Grants Council of Hong Kong. The authors would like to thank Cong Sun for sharing the sales data. The authors also appreciate the very useful comments and suggestions of the four anonymous reviewers.

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Correspondence to Tong Liu.

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Appendix

Appendix

See Tables 8, 9, 10, 11, 12, 13, 14 and 15; Figs. 5 and 6.

Table 8 Panel estimation of PM2.5 and online search indices in eight cities (%)
Table 9 Moving average model on online search response to PM2.5 from mobiles (%)
Table 10 Distributed lag model on mobile search response to PM2.5 in eight cities (%)
Table 11 Dynamic online search response to PM2.5 from mobiles in eight cities (%)
Table 12 Online search response to PM2.5–U.S. Embassy from mobiles (%)
Table 13 Online search response to PM2.5 from PC in eight cities (%)
Table 14 Online search response to PM2.5 in mask in eight cities (%)
Table 15 Alert effect at PM2.5 = 250 µg/m3 using different polynomial orders

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Liu, T., He, G. & Lau, A. Avoidance behavior against air pollution: evidence from online search indices for anti-PM2.5 masks and air filters in Chinese cities. Environ Econ Policy Stud 20, 325–363 (2018). https://doi.org/10.1007/s10018-017-0196-3

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Keywords

  • Avoidance behavior
  • Air pollution
  • Haze alert
  • Online search
  • PM2.5

JEL Classification

  • Q53
  • I12
  • I18