1# RECORD 54da27518406c538ad01d44349b80f8a974bfe462# TITLE: Economic effects of air quality on housing prices: evidence from Beijing, China3# YEAR: 2024.0 VENUE: Journal of Housing and the Built Environment DOI: 10.1007/s10901-023-10108-z4# AUTHORS: Yuanyuan Cai; Martijn J. Smit; M. Helbich5# PAGES: 24 CANDIDATE TABLE PAGES (1-based): [14, 15, 16, 18, 19, 20, 21]6# ABSTRACT: Air pollution is a major environmental urban issue, particularly in fast-growing cities in developing countries. Reducing air pollution is thus a challenge while evaluating the economic value of air quality is crucial for environmental policies made. However, few studies accurately estimate this value as they neglect the possible endogeneity issues, as well as the dynamic and heterogeneous effects of air pollution. Under the hedonic framework, we therefore assess the economic effect of fine particulate matter (PM2.5) on housing prices in Beijing, China. We construct a panel based on resale apartment transactions matched with average quarterly PM2.5 data between 2013 and 2019. To reduce the risk of an estimation bias, we apply an instrumental variable (IV) approach. Our results show that PM2.5 is negatively associated with housing prices. Households were willing to pay an extra 0.0852% per housing unit price for an average quarterly reduction in PM2.5 of 1 µg/m3. Furthermore, we argue that high-income dwellers tend to pay more for clean air. The negative effects of PM2.5 across regions are significant and different. Compared with that in the basic year 2013, the negative effect increases in the first 3 years and then decreases in the last 3 years. Our findings enhance our comprehension of the economic impact of air quality and make a valuable contribution to the nuanced understanding of willingness to pay for air quality, which is beneficial in assessing and optimizing environmental regulations.789===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) =====1011Vol.:(0123456789)12Journal of Housing and the Built Environment (2024) 39:885–90813https://doi.org/10.1007/s10901-023-10108-z141 315ARTICLE16Economic effects of air quality on housing prices: evidence 17from Beijing, China18Yuanyuan Cai1,2 · Martijn J. Smit2 · Marco Helbich2 19Received: 3 October 2022 / Accepted: 22 December 2023 / Published online: 29 January 2024 20© The Author(s) 202421Abstract22Air pollution is a major environmental urban issue, particularly in fast-growing cities in 23developing countries. Reducing air pollution is thus a challenge while evaluating the eco-24nomic value of air quality is crucial for environmental policies made. However, few studies 25accurately estimate this value as they neglect the possible endogeneity issues, as well as 26the dynamic and heterogeneous effects of air pollution. Under the hedonic framework, we 27therefore assess the economic effect of fine particulate matter (PM2.5) on housing prices in 28Beijing, China. We construct a panel based on resale apartment transactions matched with 29average quarterly PM2.5 data between 2013 and 2019. To reduce the risk of an estimation 30bias, we apply an instrumental variable (IV) approach. Our results show that PM2.5 is nega-31tively associated with housing prices. Households were willing to pay an extra 0.0852% 32per housing unit price for an average quarterly reduction in PM2.5 of 1 µg/m3. Furthermore, 33we argue that high-income dwellers tend to pay more for clean air. The negative effects 34of PM2.5 across regions are significant and different. Compared with that in the basic year 352013, the negative effect increases in the first 3 years and then decreases in the last 3 years. 36Our findings enhance our comprehension of the economic impact of air quality and make 37a valuable contribution to the nuanced understanding of willingness to pay for air quality, 38which is beneficial in assessing and optimizing environmental regulations.39Keywords Air pollution · Hedonic price · Instrumental variables · Heterogeneous effects · 40Dynamic impact · Willingness to pay41 * Martijn J. Smit 42 43m.j.smit@uu.nl44 45Yuanyuan Cai 46 47yuancai@hku.hk48 49Marco Helbich 50 51m.helbich@uu.nl521 53Department of Real Estate and Construction, Faculty of Architecture, The University of Hong 54Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong552 56Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht 57University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands585960===== PAGE 14 (score 79, numeric-paren 50, table-words 6, var-words 9) =====616289863 64Y. Cai et al.651 366difference showed that the coefficients of PM2.5 are significantly different over time, 67excluding between 2018 and 2019 (Table 11 in the Appendix). The coefficient for 2013 68was − 0.00232 and statistically significant at the 1% level. Compared with 2013, the esti-69mated coefficient decreased by − 0.00329 in 2014, by − 0.00341 in 2015, and by − 0.00217 70in 2016, indicating that the negative effect of PM2.5 intensified over time. However, the 71estimated coefficient increased by 0.00078 in 2016, 0.000134 in 2017, and 0.00014 in 2019 72(insignificant), relative to 2013, implying the PM2.5 negative effects reduced slightly over 73the last 3 years.74Table 3 Regression results75Heteroskedasticity-robust standard errors clustered at the community 76level. Standard errors in parentheses. Significance levels in all tables: 77*p < 0.1, **p < 0.05, ***p < 0.0178(1)79(2)80(4)81OLS82FE83FE2sls84PM2.585− 0.000740***86− 0.000104***87− 0.000852***88(0.0000564)89(0.0000183)90(0.0000489)91School920.0010993− 0.00050094− 0.00061995(0.00152)96(0.000621)97(0.000639)98Hospital990.002591000.00000956101− 0.0000938102(0.00193)103(0.00149)104(0.00153)105Park106− 0.00214107− 0.000844108− 0.000768109(0.00207)110(0.00128)111(0.00132)112Shopping mall1130.00255114− 0.000897115− 0.000798116(0.00236)117(0.00117)118(0.00120)119Farmer’s market1200.0003101210.0003661220.000478123(0.00185)124(0.000826)125(0.000850)126Leisure facilities1270.0113***128− 0.00154*129− 0.00136130(0.00195)131(0.000812)132(0.000834)133Basic services1340.00384**1350.0008591360.000895137(0.00185)138(0.000733)139(0.000753)140Population1410.190***142− 0.00397143− 0.00410144(0.00186)145(0.00439)146(0.00452)147D_Bus stop1480.00146149− 0.00187*150− 0.00200*151(0.00195)152(0.00108)153(0.00111)154D_Metro station1550.00111156− 0.000214157− 0.000120158(0.00239)159(0.00229)160(0.00235)161D_CBD162− 0.0162***16301640165(0.00241)166(.)167(.)168Constant169− 1.873***1700.300***1710.364***172(0.0410)173(0.0491)174(0.0506)175Community effect176#177Yes178Yes179Year-fixed effect180Yes181Yes182Yes183N18433,53818533,53818633,538187Adjusted R21880.4581890.8391900.830191192193===== PAGE 15 (score 31, numeric-paren 18, table-words 2, var-words 8) =====194195899196Economic effects of air quality on housing prices: evidence…1971 3198Our results suggest that PM2.5 had a stronger influence on housing prices after the 199severe smog in 2013 (Mei et al., 2020), but that recently the effect has weakened to a minor 200extent. This may be due to air quality improvements across Beijing over time as a result 201of various environmental policies, such as the Clean Air Action Plan 2013–2017 (Zhang 202et al., 2016). In 2017, the concentration was < 60 µg/m3 and met the targets outlined in Bei-203jing’s Clean Air Action Plan 2013–2017.2045 Conclusions205Considering the endogeneity issues and potential heterogeneity effect across space–time, 206this study provides more precise and comprehensive evidence of the value of air quality 207on housing prices by taking thermal inversions as IVs. By using resale transaction prices 208of residential apartments in Beijing from 2013 to 2019, we evaluate the economic effect of 209air quality (PM2.5) as a whole and how its effects vary across regions, income groups, and 210over time.211In keeping with most previous studies in both developed and developing countries 212(Chasco & Le Gallo, 2015; Hitaj et al., 2018; Tian et al., 2017), our results of Beijing 213confirm that PM2.5 is negatively associated with housing prices. We found that households 214are willing to pay 0.0852% of the housing unit price for a reduction of 1 µg/m3 in PM2.5 215concentrations. This result indicates that the MWTP in Beijing is relatively moderate com-216pared to that in developed countries (Bajari et al., 2012), and in other major Chinese cities 217such as Shanghai and Qingdao (Chen et al., 2018; Chen & Chen, 2012).218Quantile regression with IVs showed the heterogenous effects of PM2.5 across income 219levels, high-income households are willing to pay more for clean air than low-income 220groups, echoing the results of prior studies (Chen et al., 2018). It seems that the rich care 221more about the health risks attributed to air pollution and thus tend to pay extra for good 222air quality, while monetary costs exclude the poor from this amenity since they have to 223spend their money on fixed costs and living essentials (Dong & Zeng, 2018). Based on 224our findings, it seems reasonable to tax high-income households to a larger degree when 225air pollution funds are set up. Policymakers should be aware that supporting households 226with low incomes might be a way to compensate for their lost welfare as a result of the 227spatial inequality of air quality in the city. Besides, spatially stratified models showed that 228Table 4 Heterogeneous effects: 229results of quantile regression 230with IVs231Q25232Q50233Q75234Q90235PM2.5236− 0.0141***237− 0.0151***238− 0.0168***239− 0.0189***240(0.00039)241(0.00033)242(0.00028)243(0.00031)244Table 5 Heterogeneous effects: estimators based on the ring roads (FE2sls)245Ring2246Ring3247Ring4248Ring5249Ring6250PM2.5251− 0.00111***252− 0.000867***253− 0.00104***254− 0.000774***255− 0.000622***256(0.000176)257(0.0000932)258(0.000106)259(0.000124)260(0.0000809)261262263===== PAGE 16 (score 27, numeric-paren 14, table-words 1, var-words 10) =====264265900266 267Y. Cai et al.2681 3269the significantly negative influence of PM2.5 is different across ring roads. In this case, envi-270ronmental regulation should account for these regional differences rather than a ‘one size 271in all’ approach.272Finally, we examined whether the PM2.5 effect varies temporally relative to the year 2732013. We found that the negative impact gradually increased in 2014 and 2015; in 2016 it 274also increased but to a minor extent compared to the first 2 years. This could mean that the 275public became increasingly aware of health-threatening impacts owing to exposure to air 276pollutants in 2013. However, relative to 2013, the effect in 2017, 2018 and 2019 decreases. 277However, the difference did not reach statistical significance between 2018 and 2019. On 278the whole, the negative effect mainly declined in the last 3 years. This decline may be asso-279ciated with pollution control actions like the Clean Air Action Plan 2013–2017. In fact, 280the measured concentration levels of PM2.5 in 2017 and 2019 met the targets of that action 281plan (Zhang et al., 2016). Hence, the dynamic effects also implicitly provide evidence for 282the effectiveness of Beijing’s environmental regulation actions. Further research is needed 283to determine whether it is the current reduction in pollution or future government commit-284ments to improve air quality that impact the current willingness to pay.285Our study had several limitations that must be considered when interpreting our find-286ings. First, we incorporated only average quarterly PM2.5 concentrations as a proxy for air 287pollution rather than daily measurements. Though indirectly considered in our sensitiv-288ity tests with the AQI, we cannot exclude those other pollutants (e.g., SO2, NO2, PM10) 289that may affect housing prices differently. More pollutant indicators could be considered 290in future work. Second, air pollutant data stemmed from a restricted number of official 291monitoring stations that are unevenly distributed across space. While our air pollution data 292facilitated the incorporation of spatiotemporal trends in PM2.5, small-scale variations are 293likely to have been unrecognized. Whether and, if so, to what extent this limitation affected 294our estimates needs to be addressed in the future using high-resolution air pollution data. 295Finally, because we used a hedonic model specification, we only indirectly evaluated the 296willingness to pay for clean air based on the housing market conditions, without obtaining 297people’s perceptions directly. Future research is advised to combine subjective and objec-298tive measures to comprehensively explore the effect of air pollution.299Appendix300See Fig. 4 and Tables 7, 8, 9, 10 and 11.301Table 6 Changes in the PM2.5 estimates compared with 2013302Year3032013 (Base-304line)305201430620153072016308201730920183102019311PM2.5312− 0.00232***313− 0.00329***314− 0.00341***315− 0.00217***3160.000780***3170.000134**3180.000140**319(0.000041)320(0.000039)321(0.000041)322(0.00005)323(0.000048)324(0.00006)325(0.000071)326327328===== PAGE 17 (score 8, numeric-paren 0, table-words 1, var-words 6) =====329330901331Economic effects of air quality on housing prices: evidence…3321 3333Fig. 4 Comparation between the regression coefficients of PM2.5 across ring roads334Table 7 Overview of areas within each of Beijing’s ring roads335Ring road Construction 336period337Areas connected338Functions of the area 339inside each ring340Ring #13411920s–1950s342Boundary does not exist anymore; Tianan-343men, Forbidden City, and Di’an men344Historical areas345Ring #23461980s–1990s347Dongcheng Qu (Eastern Urban Precinct), 348Xicheng Qu (Western Urban Precinct), 349Xuanwu Precinct and Chongwen Precinct350Old city351Ring #33521980s–1990s353Beijing’s CBD (Guandongdian) and diplo-354matic communities (Dongzhimenwai/Liang-355maqiao, Jianguomenwai)356Central business district 357(CBD), as well as an 358important residential 359area for the local 360population361Ring #4362Completed in 2001363Connects the Zhongguancun technology hub, 364western Beijing, the Fengtai District, and 365eastern Beijing366Economic development 367zones, but also an 368important residential 369area for the local 370population371Ring #5372Completed in 2003373Located approximately 10 km from central 374Beijing, and links the suburban areas of 375Huantie, Shigezhuang, Dingfuzhuang, etc. 376Also passes through the Beijing Develop-377ment Area378Residential area for 379immigrant population380Ring #63812001–09382Shunyi District, Tongzhou District, Chang-383ping District, and Daxing District384Suburban districts385386387===== PAGE 18 (score 43, numeric-paren 29, table-words 2, var-words 7) =====388389902390 391Y. Cai et al.3921 3393Table 8 Regression results of 394housing prices in the first stage395Heteroskedasticity-robust standard errors clustered at community 396level, and standard errors in parentheses. Significance levels: *p < 0.1, 397**p < 0.05, ***p < 0.01398Variables399Coefficients400Variables401Coefficients402Floor area403− 0.00179***404Floor1405− 0.0324***406(0.0000294)407(0.00853)408Age1409#410Floor24110.00618412#413(0.00849)414Age24150.0431***416Floor34170.0110418(0.00245)419(0.00849)420Age34210.0492***422Floor44230.00149424(0.00269)425(0.00852)426Age44270.0631***428Floor54290.0131430(0.00306)431(0.00861)432Age24330.0336***434Bedroom number4350.0207***436(0.00445)437(0.000884)438Elevator439− 0.0210***440Orientation4410.0220***442(0.00132)443(0.000616)444Dec14450.0214***446Constant44710.86***448(0.00112)449(0.00897)450Dec2451− 0.00185452(0.00113)453Dec3454− 0.0157***455(0.00185)456Community effect457Yes458Quarter-fixed effect459Yes460N461333,269462Adjusted R24630.925464465466===== PAGE 19 (score 71, numeric-paren 51, table-words 2, var-words 9) =====467468903469Economic effects of air quality on housing prices: evidence…4701 3471Table 9 Robustness check for 472air quality: Regression estimates 473for AQI474Heteroskedasticity-robust standard errors clustered at community 475level, and standard errors in parentheses. Significance levels: *p < 0.1, 476**p < 0.05, ***p < 0.01477(1)478(2)479(3)480OLS481FE482FE2sls483AQI484− 0.000415***485− 0.000184***486− 0.00117***487(0.0000661)488(0.0000221)489(0.0000779)490School4910.00111492− 0.000475493− 0.000552494(0.00152)495(0.000621)496(0.000659)497Hospital4980.002624990.0000264500− 0.000113501(0.00193)502(0.00149)503(0.00158)504Park505− 0.00224506− 0.000873507− 0.000770508(0.00208)509(0.00128)510(0.00136)511Shopping mall5120.00244513− 0.000918514− 0.000873515(0.00237)516(0.00117)517(0.00124)518Farmer’s market5190.0002725200.0003345210.000444522(0.00185)523(0.000825)524(0.000877)525Leisure facilities5260.0112***527− 0.00157*528− 0.00145*529(0.00196)530(0.000811)531(0.000860)532Basic services5330.00372**5340.0008225350.000985536(0.00185)537(0.000732)538(0.000776)539Population5400.191***541− 0.00392542− 0.00387543(0.00187)544(0.00439)545(0.00466)546D_Bus stop5470.00143548− 0.00182*549− 0.00196*550(0.00196)551(0.00108)552(0.00114)553D_Metro station5540.00106555− 0.000220556− 0.000283557(0.00240)558(0.00229)559(0.00242)560D_CBD561− 0.0162***56205630564(0.00241)565(.)566(.)567Constant568− 1.956***5690.268***5700.435***571(0.0410)572(0.0491)573(0.0528)574Community effect575Yes576Yes577Yes578Year-Fixed effect579Yes580Yes581Yes582N58333,53858433,53858533,538586R25870.4555880.8395890.819590591592===== PAGE 20 (score 90, numeric-paren 66, table-words 2, var-words 10) =====593594904595 596Y. Cai et al.5971 3598Table 10 Robustness check for air quality: Regression estimates for subsample within 3 km distance from 599any air monitoring station600Standard errors in parentheses Significance levels: *p < 0.1, **p < 0.05, ***p < 0.01601(1)602(2)603(3)604(4)605OLS606FE607FE2sls608PM2.5609− 0.000717***610− 0.0000975***611− 0.000832***612(0.0000853)613(0.0000257)614(0.0000729)615AQI616− 0.00117***617(0.000114)618School619− 0.00165620− 0.00180**621− 0.00175*622− 0.00172*623(0.00230)624(0.000885)625(0.000911)626(0.000939)627Hospital6280.00256629− 0.000151630− 0.00007016310.0000255632(0.00286)633(0.00229)634(0.00236)635(0.00243)636Park637− 0.00305638− 0.000518639− 0.000300640− 0.000374641(0.00289)642(0.00170)643(0.00175)644(0.00180)645Shopping mall646− 0.00260647− 0.00185648− 0.00171649− 0.00181650(0.00355)651(0.00163)652(0.00168)653(0.00173)654Farmer’s market655− 0.00509*656− 0.000803657− 0.000657658− 0.000750659(0.00267)660(0.00115)661(0.00119)662(0.00123)663Leisure facilities6640.00914***665− 0.00170666− 0.00172667− 0.00174668(0.00299)669(0.00119)670(0.00123)671(0.00127)672Basic services6730.0141***6740.001566750.001346760.00149677(0.00280)678(0.00107)679(0.00110)680(0.00113)681Population6820.233***6830.001116840.00002206850.00131686(0.00326)687(0.00624)688(0.00642)689(0.00662)690D_Bus stop691− 0.00194692− 0.00205693− 0.00243694− 0.00232695(0.00301)696(0.00154)697(0.00158)698(0.00163)699D_Metro station7000.0333***701− 0.000692702− 0.000271703− 0.000280704(0.00361)705(0.00311)706(0.00319)707(0.00329)708D_CBD709− 0.0287***710071107120713(0.00362)714(.)715(.)716(.)717Constant718− 2.276***7190.370***7200.439***7210.504***722(0.0633)723(0.0706)724(0.0729)725(0.0761)726Community effect727Yes728Yes729Yes730Yes731Year-fixed effect732Yes733Yes734Yes735Yes736N73715,65673815,65673915,65674015,656741R27420.4507430.8527440.8437450.833746747748===== PAGE 21 (score 32, numeric-paren 20, table-words 1, var-words 8) =====749750905751Economic effects of air quality on housing prices: evidence…7521 3753Acknowledgements We would like to thank Dr. Chong Liu for offering the raw data on air pollutants and 754helping calculate the indicators as instructive variables in our paper. Also, Prof. Carolina Castaldi, Prof. 755Pieter Hooimeijer, Dr. Jinlong Gao and Dr. Zhiqiang Hu are thanked for their helpful comments on earlier 756versions of this paper. Yuanyuan Cai would like to thank Prof. Carolina Castaldi again for her encourage-757ment and coordination in this collaboration. The majority of her work on this article was conducted at UU.758Author contributions YC: Conceptualization, methodology, software, data curation, visualization, inves-759tigation, writing—original draft preparation, review and editing. MS: Software, visualization, validation, 760review and editing. MH: Writing—review and editing.761Declarations 762Conflict of interest The authors declare that they have no conflict of interest.763Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which 764permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give 765appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, 766and indicate if changes were made. The images or other third party material in this article are included in the 767article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is 768not included in the article’s Creative Commons licence and your intended use is not permitted by statutory 769regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright 770holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.771References772Adamowicz, W., Louviere, J., & Williams, M. (1994). Combining revealed and stated preference methods 773for valuing environmental amenities. Journal of Environmental Economics and Management, 26, 774271–292.775Adamowicz, W., Swait, J., Boxall, P., Louviere, J., & Williams, M. (1997). Perceptions versus objective 776measures of environmental quality in combined revealed and stated preference models of environmen-777tal valuation. Journal of Environmental Economics and Management, 32, 65–84.778Anas, A., Arnott, R., & Small, K. A. (1998). Urban spatial structure. Journal of Economic Literature, 36, 7791426–1464.780Anderson, R. J., Jr., & Crocker, T. D. (1971). Air pollution and residential property values. Urban Studies, 7818(3), 171–180.782Anselin, L., & Le Gallo, J. (2006). Interpolation of air quality measures in hedonic house price models: Spa-783tial aspects. Spatial Economic Analysis, 1(1), 31–52.784Aunan, K., & Wang, S. (2014). Internal migration and urbanization in China: Impacts on population expo-785sure to household air pollution (2000–2010). Science of the Total Environment, 481, 186–195.786Table 11 Chow test for the joint difference in the results of dynamic effect of PM2.5787Significance levels: *p < 0.1, **p < 0.05, ***p < 0.01788Chow-test7892013PM2.57902014PM2.57912015PM2.57922016PM2.57932017PM2.57942018PM2.57952019PM2.57962013PM2.57972014PM2.5798968.49***7992015PM2.58001258.95***80131.96***8022016PM2.580316.51***8041802.54***8053459.31***8062017PM2.58077371.72***80821,697.31***80936,889.20***81018,740.29***8112018PM2.58123010.20***8138652.71***81413,240.83***8157152.10***816568.12***8172019PM2.58182290.72***8196245.21***8208576.02***8214934.82***822352.83***8230.04824–825826827===== PAGE 22 (score 14, numeric-paren 0, table-words 0, var-words 14) =====828829906830 831Y. Cai et al.8321 3833Bajari, P., Fruehwirth, J. C., Kim, K. I., & Timmins, C. (2012). A rational expectations approach to hedonic 834price regressions with time-varying unobserved product attributes: The price of pollution. American 835Economic Review, 102(5), 1898–1926.836Baranzini, A., & Ramirez, J. V. (2006). Paying for quietness: The impact of noise on Geneva rents. Urban 837Studies, 42(4), 633–646.838Baranzini, A., Schaerer, C., & Thalmann, P. (2010). Using measured instead of perceived noise in hedonic 839models. Transportation Research Part D Transport and Environment, 15(8), 473–482.840Bayer, P., Keohane, N., & Timmins, C. (2009). Migration and hedonic valuation: The case of air quality. 841Journal of Environmental Economics and Management, 58, 1–14.842Bednarz, R. (1977). Pollution, property value, and air quality control strategies. Environment and Plan-843ning A, 9(11), 1307–1314.844Beijing Statistical Publishing. (2019). Beijing statistical yearbook. Beijing statistical yearbook.845Black, C., Ntani, G., Kenny, R., Tinati, T., Jarman, M., Lawrence, W., Barker, M., Inskip, H., Cooper, 846C., Moon, G., & Baird, J. (2012). Variety and quality of healthy foods differ according to neigh-847bourhood deprivation. Health and Place, 18, 1292–1299.848Brasington, D. M., & Hite, D. (2005). Demand for environmental quality: A spatial hedonic analysis. 849Regional Science and Urban Economics, 35, 57–82.850Cai, Y., & Gao, J. (2022). Unearthing the value of wet markets from urban housing prices: Evidence from 851Beijing, China. Habitat International, 122, 102532.852Chang, T. Y., Graff Zivin, J., Gross, T., & Neidell, M. (2019). The effect of pollution on worker produc-853tivity: Evidence from call center workers in China. American Economic Journal: Applied Econom-854ics, 11(1), 151–172.855Chasco, C., & Gallo, J. L. (2013). The impact of objective and subjective measures of air quality and 856noise on house prices: A multilevel approach for downtown Madrid. Economic Geography, 89, 857127–148.858Chasco, C., & Le Gallo, J. (2015). Heterogeneity in perceptions of noise and air pollution: A spatial quantile 859approach on the city of Madrid. Spatial Economic Analysis, 10(3), 317–343.860Chay, K. Y., & Greenstone, M. (2005). Does air quality matter? Evidence from the housing market. Journal 861of Political Economy, 113(2), 376–424.862Chen, Y., & Chen, L. (2012). Pricing for clean air: empirical evidence from Qingdao in China. The journal 863of world economy, 4, 140–160.864Chen, J., Hao, Q., & Yoon, C. (2018). Measuring the welfare cost of air pollution in Shanghai: Evidence 865from the housing market. Journal of Environmental Planning and Management, 61(10), 1744–1757.866Chen, S., & Jin, H. (2019). Pricing for the clean air: Evidence from Chinese housing market. Journal of 867Cleaner Production, 206, 297–306.868Chen, S., Oliva, P., & Zhang, P. (2022). The effect of air pollution on migration: Evidence from China. 869Journal of Development Economics, 156, 102833.870Dominici, F., Greenstone, M., & Sunstein, C. R. (2014). Particulate matter matters. Science, 344(6181), 871257–259.872Dong, K., & Zeng, X. (2018). Public willingness to pay for urban smog mitigation and its determinants: A 873case study of Beijing, China. Atmospheric Environment, 173, 355–363.874Ebenstein, A., Fan, M., Greenstone, M., He, G., Yin, P., & Zhou, M. (2015). Growth, pollution, and life 875expectancy: China from 1991–2012. American Economic Review, 105(5), 226–231.876Freeman, A. M. (1981). Hedonic prices, property values and measuring environmental benefits: A survey 877of the issues. In S. Strøm (Ed.), Measurement in public choice (pp. 13–32). Palgrave Macmillan.878Freeman, A. M., III. (1974). On estimating air pollution control benefits from land value studies. Journal 879of Environmental Economics and Management, 1(1), 74–83.880Freeman, R., Liang, W., Song, R., & Timmins, C. (2019). Willingness to pay for clean air in China. 881Journal of Environmental Economics and Management, 94, 188–216.882Gao, Y., Guo, X., Ji, H., Li, C., Ding, H., Briki, M., et al. (2016). Potential threat of heavy metals and PAHs 883in PM2.5 in different urban functional areas of Beijing. Atmospheric Research, 178–179, 6–16. https://884doi.org/10.1016/j.atmosres.2016.03.015.885Greenstone, M., & Hanna, R. (2014). Environmental regulations, air and water pollution, and infant 886mortality in India. American Economic Review, 104(10), 3038–3072.887He, G., Fan, M., & Zhou, M. (2016). The effect of air pollution on mortality in China: Evidence from the 8882008 Beijing Olympic Games. Journal of Environmental Economics and Management, 79, 18–39.889He, Y., & Collins, A. R. (2020). Does environmental pollution affect metropolitan housing prices? Evidence 890from Guangzhou, China (1987–2014). Applied Economics Letters, 27(3), 213–220.891