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Air pollution and innovation-evidence from quasi-natural experiment of China’s Huai River policy

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Abstract

We examine the effect of air pollution on innovation activity by studying city-level patenting activities and air pollution index (API) in China. We use a novel quasi-natural experiment of China’s Huai River policy, which provides subsidy for winter heating to cities north of the Huai River but not to cities to the south, to estimate the impact of air pollution on innovation performance. Based on spatial regression discontinuity design, we find that API is 0.145 higher in the north and the number of patents per city is 25.5% lower in the north. Further analysis shows that increases in API are associated with significant reduction in the number of patents per city, suggesting that air pollution significantly impedes innovation activities.

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Data availability statement

Data used in this study are obtained from the following: Air Pollution Index (API) is from CSMAR, a public database with license fees. Names, city name, latitude and longitude data of individual inventors and patent data are sourced from Harvard Business School (HBS) patent and inventor database (a free public dataset) and United States Patent and Trademark Office (USPTO) data (a public dataset with license fees), and National Bureau of Economic Research (NBER) utility patent database (a free public dataset). Regional controls are sourced from China City Statistical Yearbook, a free public dataset. Temperature data is freely downloaded from the National Meteorological Information Center of China.

Notes

  1. https://www.who.int/china/news/detail/02-05-2018-who-issues-latest-global-air-quality-report-some-progress-but-more-attention-needed-to-avoid-dangerously-high-levels-of-air-pollution

  2. http://news.bbc.co.uk/2/hi/asia-pacific/6265098.stm.

  3. Other papers also suggest that ambient air pollution is harmful to human health, including Brunekreef and Holgate (2002), Pope et al. (2009), Pope and Dockery (2013), Tanaka (2015), Ebenstein et al. (2017), Freeman et al. (2019) and Ito and Zhang (2020).

  4. Following Ma et al. (2009) and Wu and Mathews (2012), we focus on patents filed to USPTO but not to Chinese State Intellectual Property Office because U.S. patents are usually regarded as having better technological capability and reliability (Kim et al. 2021; Chen et al. 2022).

  5. One possible explanation is the revised causality that firms and institutes innovate more to alleviate the damage from air pollution (Marinova and McAleer 2006; Popp 2006).

  6. See World Bank. (2005) China Heat Reform and Building Energy Efficiency Project. Tech. report, World Bank Group, Washington, DC. http://documents.worldbank.org/curated/en/458551468770148424/China-Heat-Reform-and-Building-Energy-Efficiency-Project.

  7. Although Wang et al. (2021a, b) and our paper both examine air pollution and innovation, our paper differs significantly from that of Wang et al. (2021a, b) in the following three ways: (1) source and quality of patent data; (2) identification strategy; and (3) scope of inventors. First, we use patent data from the Harvard Business School (HBS) patent and inventor database, which is based on all patents (not limited to the US firms) filed with the United States Patent and Trademark Office (USPTO). Patent data of Wang et al. (2021a, b) come from firm-level patents in the Chinese State Intellectual Property Office (CSIPO) covered in the China Stock Market & Accounting Research (CSMAR) database and supplemented by the WIND database. Following Ma et al. (2009) and Wu and Mathews (2012), we focus on patents filed with the USPTO but not to the CSIPO because U.S. patents are usually regarded as having better technological capability and reliability. Second, we differ from Wang et al. (2021a, b) in the identification strategy. We adopt a quasi-natural experiment using China’s Huai River Policy and perform spatial discontinuity regression. They use local thermal inversions as the exogenous instrumental variable and then perform two-stage least squares regression analysis. Based on our spatial RDD results, we interpret the economic effect of air pollution on innovation as about 25.5% lower in the north, which has more severe air pollution. Yet, the economic effect of air pollution is not clearly stated in Wang et al. (2021a, b). Finally, our paper differs from Wang et al. (2021a, b) in terms of the scope of research target. Wang et al. (2021a, b) include only listed firm innovation, but we include innovations from a much greater range of all institutes: listed firms, private firms, colleges, and research institutes. Our paper thus has broader implications for air pollution prevention because it shows that not only inventors at listed firms but many other inventors are suffering from the effects of air pollution.

  8. Available at: https://dataverse.harvard.edu/dataverse/patent. See Li et al. (2014) for a detailed description.

  9. The inventor data generated by Li et al. (2014) corrects inconsistent reports of individual inventors among different patent documents. They also distinguish different inventors who have the same names. The way they identify inventors is with a disambiguation algorithm, which considers all pairs of inventor-patent cases and then determines whether any two paired inventor’s names are subject to the same inventor career.

  10. We also try to examine design patent data from European Patent Office (EPO) Worldwide Patent Statistical Database (PATSTAT), which provides patent information from many patent and trademark offices including U.S., European, Japan, Korea and so on. We find that Chinese inventors apply very few design patents to USPTO. Yet, we find that less than 5% of US patents applied by Chinese inventors are design patents, the aggregate amount of design patents is fewer than 100 every year. In such background, we cannot have meaningful analysis on design patents.

  11. One should notice that we identify the location of an inventor rather than the location of a patent applicant, since they could be different when the inventor is hired by the firm that files the patent with the USPTO. For example, if inventor X (who lives in Hangzhou) is hired by company A (located in Shanghai), we identify the location of inventor X as Hangzhou.

  12. Data is available from here: http://data.cma.cn/en/?r=data/detail&dataCode=SURF_CLI_CHN_MUL_YER_CES.

  13. Similar to our previous section, we also perform diagnostic tests for the validity of the RDD design for our robustness checks and additional analyses. Our test results show that the conditions for applying the RDD are satisfied. To save space, we do not report these test results here, but they are available upon request.

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Acknowledgements

We thank Konan Chan, Ching-Hung Chang, Sheng-Syan Chen, Hung-Kun Chen, I-Ju Chen, Yan-Shing Chen, Chia-Wei Huang, Kenneth Huang, Po-Hsuan Hsu, Woan-lih Liang, Cheng-Few Lee (the editor), Carl Shen, and two anonymous referees for their valuable comments. This work was financially supported by the Center for Research in Econometric Theory and Applications (Grant no. 108L900202) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. Yanzhi Wang is also grateful for fundings from by National Science and Technology Council (formerly the Ministry of Science and Technology) in Taiwan (NSTC 107-3017-F-002-004 and NSTC 108-2410-H-002-085-MY2). Zhaohua Li recognizes financial support from Lincoln University. Zhuo Qiao gratefully acknowledges financial support from University of Macau (research Grant No.: MYRG2019-00070-FBA). The authors thanks Chutarat Noosuwan for research assistance and Michael Turton for editorial assistance.

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Authors and Affiliations

  1. Faculty of Business Administration, University of Macau, Taipa, Macau, China

    Zhuo Qiao

  2. Department of Financial and Business Systems, Lincoln University, Lincoln, New Zealand

    Zhaohua Li

  3. Department of Finance, College of Management, National Taiwan University, Taipei, Taiwan

    Yanzhi Wang

  4. Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei, Taiwan

    Yanzhi Wang

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Correspondence to Yanzhi Wang.

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Qiao, Z., Li, Z. & Wang, Y. Air pollution and innovation-evidence from quasi-natural experiment of China’s Huai River policy. Rev Quant Finan Acc 60, 425–443 (2023). https://doi.org/10.1007/s11156-022-01097-1

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