Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Dec 24:9:791575.
doi: 10.3389/fpubh.2021.791575. eCollection 2021.

Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai

Ziliang Lai  1   2 , Xinghua Liu  1   2 , Wenxiang Li  3 , Ye Li  1   2 , Guojian Zou  1   2 , Meiting Tu  1   2   4
Affiliations

Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai

Ziliang Lai et al. Front Public Health. .

Erratum in

Abstract

Previous studies have paid little attention to the spatial heterogeneity of residents' marginal willingness to pay (MWTP) for clean air at a city level. To fill this gap, this study adopts a geographically weighted regression (GWR) model to quantify the spatial heterogeneity of residents' MWTP for clean air in Shanghai. First, Shanghai was divided into 218 census tracts and each tract was the smallest research unit. Then, the impacts of air pollutants and other built environment variables on housing prices were chosen to reflect residents' MWTP and a GWR model was used to analyze the spatial heterogeneity of the MWTP. Finally, the total losses caused by air pollutants in Shanghai were estimated from the perspective of housing market value. Empirical results show that air pollutants have a negative impact on housing prices. Using the marginal rate of transformation between housing prices and air pollutants, the results show Shanghai residents, on average, are willing to pay 50 and 99 Yuan/m2 to reduce the mean concentration of PM2.5 and NO2 by 1 μg/m3, respectively. Moreover, residents' MWTP for clean air is higher in the suburbs and lower in the city center. This study can help city policymakers formulate regional air management policies and provide support for the green and sustainable development of the real estate market in China.

Keywords: air pollution; geographically weighted regression; housing prices; marginal willingness to pay; spatial heterogeneity.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Study area.
Figure 2
Figure 2
The annual mean concentrations of air pollutants in Shanghai, 2001–2019.
Figure 3
Figure 3
Distribution of SO2 (μg/m3) in Shanghai.
Figure 4
Figure 4
Distribution of NO2 (μg/m3) in Shanghai.
Figure 5
Figure 5
Distribution of PM2.5 (μg/m3) in Shanghai.
Figure 6
Figure 6
Distribution of PM10 (μg/m3) in Shanghai.
Figure 7
Figure 7
Diagram of the data processing.
Figure 8
Figure 8
Observed housing prices (Yuan/m2) in Shanghai.
Figure 9
Figure 9
Predicted housing prices (Yuan/m2) in Shanghai based on the GWR model.
Figure 10
Figure 10
The spatial heterogeneity of MWTP for PM2.5.
Figure 11
Figure 11
The spatial heterogeneity of MWTP for NO2.
Figure 12
Figure 12
The spatial heterogeneity of MWTP for metro station density.
Figure 13
Figure 13
The spatial heterogeneity of MWTP for road network density.
Figure 14
Figure 14
The spatial heterogeneity of MWTP for educational services.
Figure 15
Figure 15
The spatial heterogeneity of MWTP for medical institutions.
Figure 16
Figure 16
The spatial heterogeneity of MWTP for the distance from the city center.
See this image and copyright information in PMC

References

    1. Chen J, Hao Q, Yoon C. Measuring the welfare cost of air pollution in Shanghai: evidence from the housing market. J Environ Plan Manag. (2018) 61:1744–57. 10.1080/09640568.2017.1371581 - DOI
    1. Li WX, Pu ZY, Li YY, Tu MT. How does ridesplitting reduce emissions from ridesourcing? A spatiotemporal analysis in Chengdu, China. Transport Res Part D-Transport Environ. (2021) 95. 10.1016/j.trd.2021.102885 - DOI
    1. Li WX, Bao L, Wang LQ, Li Y, Mai XM. Comparative evaluation of global low-carbon urban transport. Technol Forecast Soc Chang. (2019) 143:14–26. English. 10.1016/j.techfore.2019.02.008 - DOI - PubMed
    1. Zhang BB, Wu BB, Liu J. PM2.5 pollution-related health effects and willingness to pay for improved air quality: evidence from China's prefecture-level cities. J Clean Prod. (2020) 273: 122876. English. 10.1016/j.jclepro.2020.122876 - DOI
    1. Dong J, Zeng X, Mou X, Li X. Pay for clean air or not? The impact of air quality on China's real estate price. Syst Eng Theor Pract. (2020) 40:1613–26. Chinese. 10.12011/1000-6788-2020-0436-14 - DOI

Publication types