Dandan Liu , Hecheng Man , Minghui Xie , Xueying Li , Qi Qiao
{"title":"Willingness to pay and health benefits of reducing PM2.5 and O3 in China's Jing-Jin-Ji region","authors":"Dandan Liu , Hecheng Man , Minghui Xie , Xueying Li , Qi Qiao","doi":"10.1016/j.scs.2025.106251","DOIUrl":null,"url":null,"abstract":"<div><div>Quantifying the health benefits of air quality improvement is critical to increase residents' attention to and participation in air pollution control. A health benefit evaluation model for reducing PM2.5 and O<sub>3</sub> by the contingent valuation method (CVM) based on the multiple bounded discrete choice (MBDC) elicitation technique is proposed in this study. This study focuses on the Jing-Jin-Ji region, the willingness to pay (WTP) for reducing PM2.5 and O<sub>3</sub> is obtained via the CVM based on MBDC elicitation technology under two scenarios. A logistic regression model is used to explore influence factor of WTP. Then, the health benefit for reducing PM2.5 and O<sub>3</sub> is estimated by statistical life values and disability-adjusted life years. The WTP was 2916.12-3426.00 yuan/person-year, which was mainly affected by influence degree of air pollution, pollution status, knowledge of the impact on air pollution. The health benefit of reducing PM2.5 and O<sub>3</sub> was 8.46 × 10<sup>5</sup>–4.18 × 10<sup>7</sup> yuan/year. This study provides a new approach into quantifying health benefits for improving air quality and provides a reference for the formulation of market-oriented incentive mechanisms.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106251"},"PeriodicalIF":10.5000,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670725001283","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Quantifying the health benefits of air quality improvement is critical to increase residents' attention to and participation in air pollution control. A health benefit evaluation model for reducing PM2.5 and O3 by the contingent valuation method (CVM) based on the multiple bounded discrete choice (MBDC) elicitation technique is proposed in this study. This study focuses on the Jing-Jin-Ji region, the willingness to pay (WTP) for reducing PM2.5 and O3 is obtained via the CVM based on MBDC elicitation technology under two scenarios. A logistic regression model is used to explore influence factor of WTP. Then, the health benefit for reducing PM2.5 and O3 is estimated by statistical life values and disability-adjusted life years. The WTP was 2916.12-3426.00 yuan/person-year, which was mainly affected by influence degree of air pollution, pollution status, knowledge of the impact on air pollution. The health benefit of reducing PM2.5 and O3 was 8.46 × 105–4.18 × 107 yuan/year. This study provides a new approach into quantifying health benefits for improving air quality and provides a reference for the formulation of market-oriented incentive mechanisms.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;