AI and green credit: A new catalyst for green innovation in Chinese enterprises

Abstract

Research background: China has invested heavily in special credit funds to promote green transformation in enterprises. While green loans have financial characteristics, their pricing is not fully market-driven. This unique environmental regulation has a significant impact on the behavior of enterprises in green innovation, and the rapid integration of artificial intelligence (AI) adds complexity to the process.

Purpose of the article: This study aims to empirically investigate whether China's green credit policy, as a unique environmental regulatory instrument, has led to the "Porter Effect". The study examines the impact of the green credit policy on firms' green innovation in two different periods (2007–2012 and 2012–2020), while also assessing the heterogeneous impact on different types of firms. Particular attention is paid to how the integration of artificial intelligence (AI) and fintech has influenced the impact of the policy on corporate green innovation, especially by changing the transmission mechanisms related to operational and agency costs.

Methods: The Causal Forest method is applied to observational data from 1,510 listed companies in China between 2007 and 2020. This approach integrates the Neyman-Rubin framework with classical econometric techniques and machine learning to capture complex causal relationships and analyze the long-term effects of policy interventions over time, overcoming the limitations of dealing with nonexperimental data.

Findings & value added: The role of green credit policy in stimulating green innovation in enterprises is quite limited. However, the application of AI technology appears to play a significant role in amplifying the effects of green credit. The study suggests that while the classic "Porter hypothesis" may not be fully applicable in terms of corporate operating costs and innovation outcomes, the interplay of green credit policy and AI technology does indeed help reduce agency costs.

Keywords

Green Finance, Machine Learning, AI, Policy Evaluation

References

1. Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353–7360.
   View in Google Scholar
2. Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. Annals of Statistics, 47(2), 1148–1178.
   View in Google Scholar
3. Bahoo, S., Cucculelli, M., & Qamar, D. (2023). Artificial intelligence and corporate innovation: A review and research agenda. Technological Forecasting and Social Change, 188, 122264.
   View in Google Scholar
4. Carrión-Flores, C. E., Innes, R., & Sam, A. G. (2013). Do voluntary pollution reduction programs (VPRs) spur or deter environmental innovation? Evidence from 33/50. Journal of Environmental Economics and Management, 66(3), 444–459.
   View in Google Scholar
5. Conrad, K., & Wastl, D. (1995). The impact of environmental regulation on productivity in German industries. Empirical Economics, 20(4), 615–633.
   View in Google Scholar
6. Diamond, D. W. (1984). Financial intermediation and delegated monitoring. Review of Economic Studies, 51(3), 393.
   View in Google Scholar
7. Flammer, C. (2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499–516.
   View in Google Scholar
8. Gulen, H., Jens, C., & Page, B. (2020). An application of causal forest in corporate finance: How does financing affect investment? SSRN Electronic Journal.
   View in Google Scholar
9. Guo, J. (2019). The effects of environmental regulation on green technology innovation—Evidence of the porter effect in China. Finance & Trade Economics, 03, 147–160.
   View in Google Scholar
10. Hu, Z., Deng, L., Mao, J., & Xie, J. (2023). Heterogeneity in the effect of environmental protection expenditure in China: Causal inference from machine learning. Emerging Markets Finance and Trade, 59(3), 623–640.
    View in Google Scholar
11. Jaffe, A. B., Peterson, S. R., Portney, P. R., & Stavins, R. N. (1995). Environmental regulation and the competitiveness of U.S. manufacturing: What does the evidence tell us? Journal of Economic Literature, 33(1), 132–163.
    View in Google Scholar
12. Johnstone, N., Haščič, I., Poirier, J., Hemar, M., & Michel, C. (2012). Environmental policy stringency and technological innovation: Evidence from survey data and patent counts. Applied Economics, 44(17), 2157–2170.
    View in Google Scholar
13. Knittel, C., & Stolper, S. (2019). Using machine learning to target treatment: The case of household energy use. NBER Working Papers Series, w26531.
    View in Google Scholar
14. Kock, C. J., Santaló, J., & Diestre, L. (2012). Corporate governance and the environment: what type of governance creates greener companies? Corporate governance and the environment. Journal of Management Studies, 49(3), 492–514.
    View in Google Scholar
15. Krueger, P., Sautner, Z., & Starks, L. T. (2020). The importance of climate risks for institutional investors. Review of Financial Studies, 33(3), 1067–1111.
    View in Google Scholar
16. Ley, M., Stucki, T., & Woerter, M. (2016). The impact of energy prices on green innovation. Energy Journal, 37(1), 41–76.
    View in Google Scholar
17. Li, Z., Liao, G., Wang, Z., & Huang, Z. (2018). Green loan and subsidy for promoting clean production innovation. Journal of Cleaner Production, 187, 421–431.
    View in Google Scholar
18. Lu, J., Yan, Y., & Wang, T. X. (2021). The microeconomic effects of green credit policy—From the perspective of technological innovation and resource reallocation. China Industrial Economics, 01, 174–192.
    View in Google Scholar
19. Lundvall, B. Å., & Rikap, C. (2022). China's catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems. Research Policy, 51(1), 104395.
    View in Google Scholar
20. Luo, G., Guo, J., Yang, F., & Wang, C. (2023). Environmental regulation, green innovation and high-quality development of enterprise: Evidence from China. Journal of Cleaner Production, 418, 138112.
    View in Google Scholar
21. Ma, Y., Feng, G.-F., Yin, Z., & Chang, C.-P. (2024). ESG disclosures, green innovation, and greenwashing: All for sustainable development? Sustainable Development.
    View in Google Scholar
22. Ma, Y., Feng, G. F., & Chang, C. P. (2024). The impact of energy security on energy innovation: A non-linear analysis. Applied Economics.
    View in Google Scholar
23. Montmartin, B., & Herrera, M. (2015). Internal and external effects of R&D subsidies and fiscal incentives: Empirical evidence using spatial dynamic panel models. Research Policy, 44(5), 1065–1079.
    View in Google Scholar
24. Nie, X., & Wager, S. (2020). Quasi-oracle estimation of heterogeneous treatment effects. arXiv. http://arxiv.org/abs/1712.04912.
    View in Google Scholar
25. Palmer, K., Oates, W. E., & Portney, P. R. (1995). Tightening environmental standards: The benefit-cost or the no-cost paradigm? Journal of Economic Perspectives, 9(4), 119–132.
    View in Google Scholar
26. Peng, X. Y., Zou, X. Y., Zhao, X. X., & Chang, C. P. (2024). Understanding the behavior of ESG in both OPEC and non‐OPEC countries? The implications for sustainable development reaching. Sustainable Development, 32(3), 1940–1953.
    View in Google Scholar
27. Petroni, G., Bigliardi, B., & Galati, F. (2019). Rethinking the porter hypothesis: The underappreciated importance of value appropriation and pollution intensity. Review of Policy Research, 36(1), 121–140.
    View in Google Scholar
28. Porter, M. E., & Linde, C. van der. (1995). Toward a new conception of the environment-competitiveness relationship. Journal of Economic Perspectives, 9(4), 97–118.
    View in Google Scholar
29. Roth, J., Sant’Anna, P. H., Bilinski, A., & Poe, J. (2023). What’s trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218–2244.
    View in Google Scholar
30. Shang, X., & Niu, H. (2023). Does the digital transformation of banks affect green credit?. Finance Research Letters, 58, 104394.
    View in Google Scholar
31. Shive, S. A., & Forster, M. M. (2020). Corporate governance and pollution externalities of public and private firms. Review of Financial Studies, 33(3), 1296–1330.
    View in Google Scholar
32. Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242.
    View in Google Scholar
33. Wang, J. Z., Feng, G. F., Yin, Z. J., & Chang, C. P. (2024) Does women's political participation promote green innovation? Global evidence. Corporate Social Responsibility and Environmental Management.
    View in Google Scholar
34. Wang, X., & Wang, Y. (2021). Research on the green innovation promoted by green credit Policies. Journal of Management World, 06, 173–188.
    View in Google Scholar
35. Weber, T. A., & Neuhoff, K. (2010). Carbon markets and technological innovation. SSRN Electronic Journal, 60(2), 115–132.
    View in Google Scholar
36. Wen, H., Lee, C. C., & Zhou, F. (2021). Green credit policy, credit allocation efficiency and upgrade of energy-intensive enterprises. Energy Economics, 94, 105099.
    View in Google Scholar
37. Xie, R., Yuan, Y., & Huang, J. (2017). Different types of environmental regulations and heterogeneous influence on “green” productivity: Evidence from China. Ecological Economics, 132, 104–112.
    View in Google Scholar
38. Xu, J., & Cui, J. B. (2020). Low-carbon cities and firms’ green technological innovation. China Industrial Economics, 12, 178–196.
    View in Google Scholar
39. Yin, H. T., Wen, J., & Chang, C. P. (2023). Going green with artificial intelligence: The path of technological change towards the renewable energy transition. Oeconomia Copernicana, 14(4), 1059–1095.
    View in Google Scholar
40. Zerbib, O. D. (2019). The effect of pro-environmental preferences on bond prices: Evidence from green bonds. Journal of Banking & Finance, 98, 39–60.
    View in Google Scholar
41. Zhang, S., Wu, Z., He, Y., & Hao, Y. (2022). How does the green credit policy affect the technological innovation of enterprises? Evidence from China. Energy Economics, 113, 106236.
    View in Google Scholar
42. Zheng, M., Feng, G.-F., & Chang, C.-P.(2023). Is green finance capable of promoting renewable energy technology? Empirical investigation for 64 economies worldwide. Oeconomia Copernicana, 14(2), 483–510.
    View in Google Scholar
43. Zou, X.-Y., Peng, X.-Y., Zhao, X.-X., Ma, J., & Chang, C.-P. (2023). Does income inequality affect green innovation? A non-linear evidence. Technological and Economic Development of Economy.
    View in Google Scholar