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The Impact of Exogenous Pollution on Green Innovation

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Abstract

Does environmental quality affect firms’ activities that might improve that quality? In this paper, we use China's public heating policy as a quasi-experiment to investigate the impact of exogenous pollution differences on green innovation behavior. We use a regression discontinuity model, and carry out a suite of robustness tests. We consistently find that firms located in cities with an exogenous source of heavy pollution tend to adopt green innovation at a lower rate while we find no difference in the rate at which they adopt non-green innovation. We find a strong causal effect: being north of the boundary, where pollution levels are higher, leads firms to adopt less green innovation. Firms located in the heating areas report roughly 1 less green innovation per billion RMB of assets, a substantial difference given the average number of green innovations per billion RMB of assets of northern firms is 0.641.

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Notes

  1. Website link: https://www.baiten.cn.

  2. Website link: http://us.gtadata.com.

  3. In Appendix B, we describe what data are being used, and how we downloaded the data.

  4. In the appendix, we show a detailed summary statistics.

  5. The 10 industries include: mining, power generation, textile, steel, chemical, petrochemical, cement, metallurgical, pharmaceutical, paper.

  6. The variables used to calculate the propensity score and the Mahalanobis distance are firm’s total asset and the industry they belong to. In Appendix Table 11 we also show the regression result using dataset created using Mahalanobis distance matching.

  7. A comparison between the cost of heating in the south and north: the heating cost in the north is proximately 10 times of the cost in the south.

    Source: https://finance.sina.com.cn/china/20140102/162117824915.shtml?from=wap.

  8. Here is the company’s registration page:

    https://data.cyzone.cn/content/dbase/company?cat_id=637&content_id=1312510

  9. The user manual only has Chinese version, and can be downloaded here:

    https://www.baiten.cn/download?name=user_manual.pdf

  10. The English website of China National Intellectual Property Administration:

    http://english.cnipa.gov.cn/

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Funding

Woodward acknowledges support from Texas AgriLife Research with support from the USDA National Institute of Food and Agriculture, Hatch project 1011850.

Author information

Authors and Affiliations

  1. Department of Agricultural Economics, Texas A&M University, 2124 TAMU, College Station, TX, 77843-2124, US

    Ying Wang & Richard T. Woodward

  2. Business School, Hunan University, Hunan, Changsha, 410082, China

    Jing-Yue Liu

Authors
  1. Ying Wang
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  2. Richard T. Woodward
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  3. Jing-Yue Liu
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Corresponding author

Correspondence to Ying Wang.

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The authors have no relevant financial or non-financial interests to disclose.

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Appendices

Appendix A

See Tables 10 and 11.

Table 10 Detailed Summary Statistics with p-values for t-tests of difference in means between firms on either side of the boundary
Full size table
Table 11 The Effect of Public Heating from a Balanced Dataset from Mahalanobis Distance Matching
Full size table

Appendix B

Detailed data description on green innovation data

Our green innovation data are from the Baiten company, which is a company that provides patent service, searching, and consulting services.Footnote 8 On their website, https://www.baiten.cn/, one can search for the patent data of China and other countries. This paper uses only Chinese patent data. According to the user manual,Footnote 9 the patent data are from China National Intellectual Property Administration,Footnote 10 thus every patent filed in that bureau will be included as a patent count.

We first obtained the listed firms from China Stock Market & Accounting Research Database (CSMAR); there are 997 firms on the list. We also used the firm performance data from CSMAR. The location data used are the registration addresses from CSMAR. These are firm-level data, not plant-level data. The full list of firms is included in the dataset provided on Github.

For each listed firm, two searches were carried out, first to identify the firm’s patents that can be characterized as green innovations between 2013 and 2017, and then for all patents over this period. To gain access to the data, the Baiten company requires one to register using a Chinese phone number. Therefore, those who do not have a Chinese phone number cannot download the data through the website.

For example, for the Datang International Power Generation Co., Ltd., we carried out the following search from the search window at https://www.baiten.cn/:

cpa:(大唐国际发电股份有限公司) AND (ad:[2013 TO 2017]) AND (碳 or 环境 or 环保 or 节能 or 生态 or 废 or 清理 or 清洁 or 绿色 or 回收 or 能耗 or 循环 or 净化 or 脱硫 or节约资源 or 无污染)

There are different parts of the search code:

  • the name of the company searched: “大唐国际发电股份有限公司”;

  • the time range searched: “2013 TO 2017”; and

  • the types of patents to be reported

Our keywords to identify green innovation patents are primarily based on Li et al. (2018a, b) and Li et al. (2017), patents in the following categories are included in the list of green innovations: carbon (碳), environment (环境), environmental protection (环保), energy-saving (节能), ecology (生态), waste (废), clean (清理, 清洁), green (绿色), recycling (回收), energy consumption (能耗), natural cycling (循环), purification (净化), desulfurization (脱硫), resource-saving (节约资源), pollution-free (无污染).

The number of green innovations for each firm are then found by clicking on “专利趋势分析” (Patent trend analysis), revealing an image like the one below:

The central part of Fig. 

Fig. 5
figure 5

Representative figure showing the number of green innovations by a firm between 2013 and 2017

Full size image

5 shows the year and the number of green innovations for each year. These numbers were manually typed into our dataset.

To obtain the complete count of innovations, we use the same process, excluding the types of patents to be reported different searching codes. The search code for all innovations by a firm would be:

cpa:(大唐国际发电股份有限公司) AND (ad:[2013 TO 2017])

These steps are repeated for each company, until we have all the innovation data for all the listed companies.

All data and code in R used are available at https://github.com/faust1987/The-Impact-of-Exogenous-Pollution-on-Green-Innovation. Data are presented in both the original Chinese, and with key words translated into English.

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Cite this article

Wang, Y., Woodward, R.T. & Liu, JY. The Impact of Exogenous Pollution on Green Innovation. Environ Resource Econ 81, 1–24 (2022). https://doi.org/10.1007/s10640-021-00614-5

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