Contingent valuation of health and mood impacts of PM2.5 in Beijing, China

Yin Hao, Massimo Pizzol, Jette Bredahl Jacobsen, Linyu Xu

Research output: Contribution to journalJournal articleResearchpeer-review

26 Citations (Scopus)


Air pollution from PM2.5 affects many cities worldwide, causing both health impacts and mood depression. One of the obstacles to implementing environmental regulations for PM2.5 reduction is that there are limited studies of PM2.5 welfare loss and few investigations of mood depression caused by PM2.5. This article describes a survey study conducted in Beijing, China to estimate the welfare loss due to PM2.5. In total, 1709 participants completed either a face-to-face or online survey. A contingent valuation method was applied to elicit people's willingness to pay to avoid PM2.5 pollution and willingness to accept a compensation for such pollution. The payment/compensation was evaluated for two outcome variables: perceived health impacts and mood depression caused by PM2.5 pollution. This is one of few papers that explicitly studies the effects of PM2.5 on subjective well-being, and to the authors' knowledge, the first to estimate welfare loss from PM2.5 using a random forest model. Compared to the standard Turnbull, probit, and two-part models, the random forest model gave the best fit to the data, suggesting that this may be a useful tool for future studies too. The welfare loss due to health impacts and mood depression is CNY 1388.4/person/year and CNY 897.7/person/year respectively, indicating that the public attaches great importance to mood, feelings and happiness. The study provides scientific support to the development of economic policy instruments for PM2.5 control in China.
Original languageEnglish
JournalScience of the Total Environment
Pages (from-to)1269-1282
Number of pages14
Publication statusPublished - 15 Jul 2018


  • PM2.5
  • welfare loss
  • Health impacts
  • Mood impacts
  • Random forest


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