Skip to main content

Does haze pollution damage urban innovation? Empirical evidence from China

Environmental Science and Pollution Research volume 28, pages 16334–16349 (2021)Cite this article

Abstract

The continuous outbreak of haze pollution attracted full attention and became one of the most severe environmental problems in China. Based on the panel data of 266 prefecture-level cities from 2000 to 2016, this paper investigates the effects of haze pollution on China’s urban innovation. Results show that (1) haze pollution does not damage urban innovation but forms a crisis-driven effect to stimulate it. (2) Haze pollution enhances the public’s environmental awareness, which induces the government to invest more in science and technology, and finally forces the improvement of urban innovation. (3) Haze pollution causes the loss of human capital and leading to a decrease in the number of people who engaged in scientific research, which weakens the city’s technological innovation ability. (4) The crisis-driven effect caused by haze pollution boosts the improvement of technological innovation in eastern cities, large cities, and northern cities. This study enriches the evidence on the relationship between haze pollution and urban innovation, which is significant for local governments to formulate green development and innovation-driven strategies.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. The North China Plain mainly covers Beijing, Tianjin, Hebei, Shandong, and parts of Henan, Anhui, and Jiangsu.

  2. Some studies use the average years of education per capita to measure the human capital of a certain region or province (e.g., Chen and Chen 2018; Chamarbagwala and Hilcías 2011; Földvári and Leeuwen 2009; Lan et al. 2012), but this indicator is not available at the city level.

  3. The city in our samples is distributed in 28 provinces or autonomous regions except Tibet. Specifically, we include all prefecture-level cities in 21 provinces or autonomous regions, like Jiangsu, but there are incomplete in seven provinces or autonomous regions, including Xiangyang in Hubei, Yingkou in Liaoning, Yuncheng in Shanxi, and Guyuan and Zhongwei in Ningxia. In addition, among the cities in Xinjiang, only Urumqi and Karamay are involved, and Qinghai only covers Xining and Haidong.

  4. The eastern region includes eight provinces, namely Hebei, Shandong, Liaoning, Jiangsu, Zhejiang, Fujian, Guangdong, and Hainan. There are eight provinces in the central region, and they are Heilongjiang, Jilin, Shanxi, Jiangxi, Anhui, Henan, Hubei, and Hunan. The remaining 11 provinces or autonomous regions belong to the western region, namely Xinjiang, Inner Mongolia, Ningxia, Gansu, Qinghai, Tibet, Yunnan, Guizhou, Sichuan, Guangxi, and Shaanxi.

  5. The division standard comes from the “Notice on Adjusting the Dividing Standards of Urban Size” issued by the China’s state council in 2014.

  6. Following conventional practices, we employ the Qinling Mountains and Huai River as the dividing line between China’s north and south. However, in the southern provinces, due to the central heating in Xuzhou, we regard it as a northern city. In contrast, in the northern provinces, Xinyang, Luohe, Zhoukou, Nanyang, and Hanzhong do not have central heating, so they are considered as southern cities in the samples.

References

  • Almond D, Chen Y, Greenstone M, Li H (2009) Winter heating or clean air? unintended impacts of China’s Huai river policy. Am Econ Rev 99:184–190

    Google Scholar 

  • Ambec S, Cohen MA, Elgie S, Lanoie P (2013) The Porter hypothesis at 20: can environmental regulation enhance innovation and competitiveness? Rev Environ Econ Policy 7:2–22

    Google Scholar 

  • Andersson R, Quigley JM, Wilhelmsson M (2005) Agglomeration and the spatial distribution of creativity. Pap Reg Sci 84:445–464

    Google Scholar 

  • Angrist JD, Pischke J (2008) Mostly harmless econometrics: an empiricist’s companion. Princeton University Press, Princeton

    Google Scholar 

  • Aragón FM, Miranda JJ, Oliva P (2017) Particulate matter and labor supply: the role of caregiving and non-linearities. J Environ Econ Manag 86:295–309

    Google Scholar 

  • Arceo E, Hanna R, Oliva P (2016) Does the effect of pollution on infant mortality differ between developing and developed countries? Evidence from Mexico city. Econ J 591:257–280

    Google Scholar 

  • Archibugi D, Planta M (1996) Measuring technological change through patents and innovation surveys. Technovation 16:451–468

    Google Scholar 

  • Audretsch DB, Feldman MP (1996) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640

    Google Scholar 

  • Berliant M, Fujita M (2009) Dynamics of knowledge creation and transfer: the two person case. Int J Econ Theory 5:155–179

    Google Scholar 

  • Bhattacharya U, Hsu P, Tian X, Xu Y (2017) What affects innovation more: policy or policy uncertainty? J Financ Quant Anal 52:1869–1901

    Google Scholar 

  • Brunekreef B, Holgate ST (2002) Air pollution and health. Lancet 360:1233–1242

    CAS  Google Scholar 

  • Brunel C (2019) Green innovation and green imports: links between environmental policies, innovation, and production. J Environ Manag 248:109290

    Google Scholar 

  • Brunnermeier SB, Cohen MA (2003) Determinants of environmental innovation in US manufacturing industries. J Environ Manag 45:278–293

    Google Scholar 

  • Caragliu A, Del Bo CF, Kourtit K, Nijkamp P (2016) The winner takes it all: forward-looking cities and urban innovation. Ann Reg Sci 56:617–645

    Google Scholar 

  • Chamarbagwala R, Hilcías EM (2011) The human capital consequences of civil war: evidence from guatemala. J Dev Econ 94:41–61

    Google Scholar 

  • Chang YC, Wang N (2010) Environmental regulations and emissions trading in China. Energy Policy 38:3356–3364

    Google Scholar 

  • Chang T, Graff Zivin J, Gross T, Neidell M (2016) Particulate pollution and the productivity of pear packers. Am Econ J Econ Pol 8:141–169

    Google Scholar 

  • Chen SY, Chen DK (2018) Air pollution, government regulations and high-quality economic development. Econ Res J 53:20–34

    CAS  Google Scholar 

  • Chen X, Shao S, Tian Z, Zhen X, Peng Y (2016) Impacts of air pollution and its spatial spillover effect on public health based on China’s big data sample. J Clean Prod 142:915–925

  • Chen S, Oliva P, Zhang P (2018) Air pollution and mental health: evidence from China. NBER Working Paper No. 24686

  • Cheng J, Yi J, Dai S, Xiong Y (2019) Can low-carbon city construction facilitate green growth? Evidence from China’s pilot low-carbon city initiative. J Clean Prod 231:1158–1170

    Google Scholar 

  • Cheung K, Ping L (2004) Spillover effects of FDI on innovation in China: evidence from the provincial data. China Econ Rev 15:25–44

    Google Scholar 

  • Chintrakarn P (2008) Environmental regulation and US states’ technical inefficiency. Econ Lett 100:363–365

    Google Scholar 

  • Chung Y, Dominici F, Wang Y, Coull BA, Bell ML (2015) Associations between long-term exposure to chemical constituents of fine particulate matter (PM2.5) and mortality in Medicare enrollees in the eastern United States. Environ Health Perspect 123:467–474

    Google Scholar 

  • Deschenes O, Wang H, Wang S, Zhang P (2020) The effect of air pollution on body weight and obesity: evidence from China. J Dev Econ 145:102461

    Google Scholar 

  • Faggian A, McCann P (2008) Human capital, graduate migration and innovation in British regions. Camb J Econ 33:317–333

    Google Scholar 

  • Fan Z, Zhang R, Liu X, Pan L (2016) China’s outward FDI efficiency along the belt and road. China Agric Econ Rev 8:455–479

    Google Scholar 

  • Fan F, Cao D, Ma N (2020) Is improvement of innovation efficiency conducive to haze governance? Empirical Evidence from 283 Chinese cities. Int J Environ Res Public Health 17:6095

    Google Scholar 

  • Fang D, Wang Q, Li H, Lu Y, Qian X (2016) Mortality effects assessment of ambient PM2.5 pollution in the 74 leading cities of China. Sci Total Environ 569:1545–1552

    Google Scholar 

  • Földvári P, Leeuwen B (2009) An alternative interpretation of ‘average years of education’ in growth regressions. Appl Econ Lett 16:945–949

    Google Scholar 

  • Gao Y, Guo X, Li C, Ding H, Tang L, Ji H (2015) Characteristics of PM2.5 in Miyun, the northeastern suburb of Beijing: chemical composition and evaluation of health risk. Environ Sci Pollut Res 22:16688–16699

    CAS  Google Scholar 

  • Greenstone M, Hanna R (2014) Environmental regulations, air and water pollution, and infant mortality in India. Am Econ Rev 104:3038–3072

    Google Scholar 

  • Guan J, Chen K (2012) Modeling the relative efficiency of national innovation systems. Res Policy 41:102–115

    Google Scholar 

  • Hamamoto M (2006) Environmental regulation and the productivity of Japanese manufacturing industries. Resour Energy Econ 28:299–312

    Google Scholar 

  • Hanna R, Oliva P (2015) The effect of pollution on labor supply: evidence from a natural experiment in Mexico city. J Public Econ 122:68–97

    Google Scholar 

  • Hao J, Wang S, Liu B, He K (2000) Designation of acid rain and SO2 control zones and control policies in China. Environ Lett 35:1901–1914

    Google Scholar 

  • Hausmann R, Hwang J, Rodrik D (2007) What you export matters. J Econ Growth 12:1–25

    Google Scholar 

  • He G, Liu T, Zhou M (2020) Straw burning, PM2.5, and death: evidence from China. J Dev Econ 145:102468

    Google Scholar 

  • Jans J, Johansson P, Peter NJ (2018) Economic status, air quality, and child health: evidence from inversion episodes. J Health Econ 61:220–232

    Google Scholar 

  • Jiang SQ, Shi AN, Peng ZH, Li X (2017) Major factors affecting cross-city R&D collaborations in China: evidence from cross-sectional co-patent data between 224 cities. Scientometrics 111:1251–1266

    Google Scholar 

  • Johnstone N, Hascic I, Popp D (2010) Renewable energy policies and technological innovation: evidence based on patent counts. Environ Resour Econ 45:133–155

    Google Scholar 

  • Jones BF (2014) The human capital stock: a generalized approach. Am Econ Rev 104:3752–3777

    Google Scholar 

  • Kang KN, Park H (2012) Influence of government R&D support and inter-firm collaborations on innovation in Korean biotechnology SMEs. Technovation 32:68–78

    Google Scholar 

  • Kim T (2019) Financing technological innovation: evidence from patent-intensive firms. Glob Econ Rev 48:350–362

    Google Scholar 

  • Kim Y, Yun S, Lee J, Ko E (2016) How consumer knowledge shapes green consumption: an empirical study on voluntary carbon offsetting. Int J Advert 35:23–41

  • Kleer R (2010) Government R&D subsidies as a signal for private investors. Res Policy 39:1361–1374

    Google Scholar 

  • Klette TJ, Møen J (2012) R&D investment responses to R&D subsidies: a theoretical analysis and a microeconometric study. World Rev Sci Technol Sustain Dev 9:169–203

  • Lan J, Kakinaka M, Huang X (2012) Foreign direct investment, human capital and environmental pollution in China. Environ Resour Econ 51:255–275

  • Li Z, Xi T, Zhou Z (2017) The effects of environmental provisions in RTAs on PM2.5 air pollution. Appl Econ 49:2630–2641

    Google Scholar 

  • Li W, Gu Y, Liu F, Li C (2019) The effect of command-and-control regulation on environmental technological innovation in China: a spatial econometric approach. Environ Sci Pollut Res 26:34789–34800

    Google Scholar 

  • Li X, Chen H, Li Y (2020) The effect of air pollution on children’s migration with parents: evidence from China. Environ Sci Pollut Res 27:12499–12513

    Google Scholar 

  • Lichter A, Pestel N (2017) Sommer E. Productivity effects of air pollution: evidence from professional soccer. Labour Econ 48:54–66

    Google Scholar 

  • Lin JY (2003) Development strategy, viability, and economic convergence. Econ Dev Cult Chang 51:277–308

    Google Scholar 

  • Liu X (2018) Dynamic evolution, spatial spillover effect of technological innovation and haze pollution in China. Energy Environ 29:968–988

    CAS  Google Scholar 

  • Ma Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, Tong S, Bi J, Huang L, Liu Y (2015) Satellite-based spatiotemporal trends in PM2. 5 concentrations: China, 2004–2013. Environ Health Perspect 124:184–192

    Google Scholar 

  • Mackinnon DP, Krull JL, Lockwood CM (2000) Equivalence of the mediation, confounding and suppression effect. Prev Sci 1:173–181

    CAS  Google Scholar 

  • Mankwi GN, Romer D, Weil DN (1992) A contribution to the empirics of economic growth. Q J Econ 107:407–437

    Google Scholar 

  • Nazelle AD, Morton BJ, Jerrett M (2010) Short trips: an opportunity for reducing mobile-source emissions? Transp Res Part D: Transp Environ 15:451–457

    Google Scholar 

  • Neidell MJ (2004) Air pollution, health, and socio-economic status: the effect of outdoor air quality on childhood asthma. J Health Econ 23:1209–1236

    Google Scholar 

  • Ning L, Wang F, Li J (2016) Urban innovation, regional externalities of foreign direct investment and industrial agglomeration: evidence from Chinese cities. Res Policy 45:830–843

    Google Scholar 

  • Niosi J (2010) Rethinking science, technology and innovation (STI) institutions in developing countries. Innovation 12:250–268

    Google Scholar 

  • Noailly J, Ryfisch D (2015) Multinational firms and the internationalization of green R&D: a review of the evidence and policy implications. Energy Policy 83:218–228.

  • Ostro B, Malig B, Broadwin R, Basu R, Gold EB, Bromberger JT, Derby C, Feinstein S, Greendale GA, Jackson EA (2014) Chronic PM2.5 exposure and inflammation: determining sensitive subgroups in mid-life women. Environ Res 132:168–175

    CAS  Google Scholar 

  • Ottaviano GI, Peri G (2006) The economic value of cultural diversity: evidence from US cities. J Econ Geogr 6:9–44

    Google Scholar 

  • Pui DYH, Chen S, Zuo Z (2014) PM2.5 in China: measurements, sources, visibility and health effects,and Mitigation. Particuology 13:1–26

    CAS  Google Scholar 

  • Qin Y, Zhu H (2018) Run away? Air pollution and emigration interests in China. J Popul Econ 31:1–32

    Google Scholar 

  • Ryswyk KV, Anastasopolos AT, Evans G, Sun L, Sabaliauskas K, Kulka R, Wallace L, Weichenthal S (2017) Metro commuter exposures to particulate air pollution and PM2.5-associated elements in three Canadian cities: the urban transportation exposure study. Environ Sci Technol 51:5713–5720

  • Stock JH, Yogo M (2005) Testing for weak instruments in linear IV regression, in identification and inference for econometric models: essay in honor of Thomas Rothenberg. Cambridge University Press, Cambridge

    Google Scholar 

  • Sun C, Xiang Y, Xu M (2016) The public perceptions and willingness to pay: from the perspective of the smog crisis in China. J Clean Prod 112:1635–1644

    Google Scholar 

  • Van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R, Verduzco C, Villeneuve PJ (2010) Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ Health Perspect 118:847–855

    Google Scholar 

  • Wang G, Liu S (2020) Is technological innovation the effective way to achieve the “double dividend” of environmental protection and industrial upgrading? Environ Sci Pollut Res 27:18541–18556

    Google Scholar 

  • Wang X, Duan Y, Liu P, Han G (2020) The influence of housing investment on urban innovation: an empirical analysis based on city-level panel data in China. Sustainability 12:2968

  • Wei K, Yao S, Liu A (2009) Foreign direct investment and regional inequality in China. Rev Dev Econ 13(4):778–791

    Google Scholar 

  • Xu B, Lu JY (2009) Foreign direct investment, processing trade, and the sophistication of China’s exports. China Econ Rev 20:425–439

    Google Scholar 

  • Xuan T, Yue WT (2014) Tolerance for failure and corporate innovation. Rev Financ Stud 27:211–255

    Google Scholar 

  • Yang T, Gbaguidi A, Yan P, Zhang W, Zhu L, Yao X, Wang Z, Chen H (2017) Model elucidating the sources and formation mechanisms of severe haze pollution over northeast mega-city cluster in China. Environ Pollut 230:692–700

    CAS  Google Scholar 

  • Yao S, Zhang Z (2001) On regional inequality and diverging clubs: a case study of contemporary China. J Comp Econ 29:466–484

    Google Scholar 

  • Ye Q, Fu JF, Mao JH, Shang SQ (2016) Haze is a risk factor contributing to the rapid spread of respiratory syncytial virus in children. Environ Sci Pollut Res 23:1–8

    CAS  Google Scholar 

  • Yi M, Wang Y, Sheng M, Sharp B, Zhang Y (2020) Effects of heterogeneous technological progress on haze pollution: evidence from China. Ecol Econ 169:106533. https://doi.org/10.1016/j.ecolecon.2019.106533

    Article  Google Scholar 

  • Zhang J, Mu Q (2018) Air pollution and defensive expenditures: evidence from particulate-filtering facemasks. J Environ Econ Manag 92:517–536

    Google Scholar 

  • Zhang D, Liu J, Li B (2014) Tackling air pollution in China—what do we learn from the great smog of 1950s in London. Sustainability 6:5322–5338

    Google Scholar 

  • Zhang K, Jiang W, Zhang S, Xu Y, Liu W (2019) The impact of differentiated technological innovation efficiencies of industrial enterprises on the local emissions of environmental pollutants in Anhui province, China, from 2012 to 2016. Environ Sci Pollut Res 26:27953–27970

    Google Scholar 

  • Zheng S, Wang J, Sun C, Zhang X, Kahn ME (2019) Air pollution lowers Chinese urbanites’expressed happiness on social media. Nat Hum Behav 3:237–243

    Google Scholar 

  • Zhou M, He G, Fan M, Wang Z, Liu Y, Ma J, Ma Z, Liu J, Liu Y, Wang L (2015) Smog episodes, fine particulate pollution and mortality in China. Environ Res 136:396–404

    CAS  Google Scholar 

  • Zivin JG, Neidell M (2012) The impact of pollution on worker productivity. Am Econ Rev 102:3652–3673

    Google Scholar 

Download references

Funding

We would like to thank the financial support from the Fundamental Research Funds for the Central Universities (QCDC-2020-21), and the Shanghai University of Finance and Economics Foundation for Postgraduate Innovation (CXJJ-2019-434, CXJJ-2019-428).

Author information

Affiliations

  1. School of Urban and Regional Science, Institute of Finance and Economics Research, Shanghai University of Finance and Economics, No. 777, Guoding Road, Yangpu District, Shanghai, 200433, People’s Republic of China

    Chunkai Zhao, Min Deng & Xiguang Cao

Authors
  1. Chunkai Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiguang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Chunkai Zhao. The first draft of the manuscript was written by Min Deng and Xiguang Cao, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Min Deng.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Responsible Editor: Philippe Garrigues

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhao, C., Deng, M. & Cao, X. Does haze pollution damage urban innovation? Empirical evidence from China. Environ Sci Pollut Res 28, 16334–16349 (2021). https://doi.org/10.1007/s11356-020-11874-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-020-11874-x

Keywords

  • China
  • Crisis-driven effect
  • Haze pollution
  • Public awareness
  • Urban innovation