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How does artificial intelligence development affect green technology innovation in China? Evidence from dynamic panel data analysis

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Abstract

As the global climate problem becomes increasingly serious, the green technology innovation to achieve “carbon peak and carbon neutral” has gradually become the global consensus of major countries, and how the rapid development of artificial intelligence (AI) technology affects green technology innovation (GTI) has received a great deal of attention in the field of economics. Therefore, based on China’s inter-provincial panel data from 2006 to 2019, the system GMM, dynamic panel threshold model, and quantile regression model were constructed to examine various influences of AI development on GTI under different environmental regulation intensity, research and development (R&D) investment, and institutional environmental threshold conditions. The findings presented that AI development significantly contributes to GTI and GTFP, with an impact coefficient of 0.0122 and 0.0084, and this influence is mainly reflected in the western region of China and is more obvious in the 2006–2012 period. AI development mainly enhances green technological efficiency, and it has dampening effects on green technological progress during the period 2013–2019. Additionally, there are non-linear threshold effects in the relationship between the level of AI development and GTI when environmental regulatory intensity, R&D investment, and institutional environment are in different level intervals. AI development will boost GTI only when the intensity of environmental regulation and institutional environment is above a certain threshold value. However, the AI development represented by industrial robot applications still has no obvious effect on GTI even when the R&D investment exceeds a certain threshold. Furthermore, the growth effect of AI development on GTI indicates a decreasing nonlinear pattern as the GTI’s quantile rises under the condition that R&D investment and institutional environment intensity cross the threshold, while this growth effect increases gradually with the rise of GTI’s quantile when the environmental regulation is above the threshold.

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Data availability

The data used during the current study are available from the corresponding author on reasonable request.

Notes

  1. Reference the World Bank Research Report: http://www.worldbank.org/research/2007/02/7503894/cost-pollution-china-economic-estimates-physical-damages

  2. Ministry of Environmental Protection: “Ministry of Environmental Protection reports air quality in some cities”, January 29, 2013.

  3. Reference the "Trusted Artificial Intelligence White Paper" issued by the China Academy of Information and Communications: http://www.caict.ac.cn/kxyj/qwfb/bps/202107/P020210709319866413974.pdf

  4. Economic information daily: http://www.xinhuanet.com/2017-10/19/c_1121823302.htm

  5. Reference to the website: “http://www.stats.gov.cn/tjsj./ndsj”, “https://data.cnki.net/Trade/yearbook/single/N2022020102?zcode=Z001” and “https://data.cnki.net/Trade/yearbook/single/N2021050066?zcode=Z023”, respectively.

  6. Reference to the website: “https://www.cnrds.com/Home/Index#/” and “https://ifr.org/#”, respectively.

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This work was supported by the National Social Science Found Major Projects of China (grant no. 14ZDB151).

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  1. School of Management Science and Engineering, Shandong University of Finance and Economics, No. 7366, East 2Nd Ring Road, Jinan, 250014, Shandong, China

    Kedong Yin, Fangfang Cai & Chong Huang

  2. Institute of Marine Economy and Management, Shandong University of Finance and Economics, No. 7366, East 2Nd Ring Road, Jinan, 250014, Shandong, China

    Kedong Yin & Chong Huang

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Conceptualization, C.H. and K.Y.; formal analysis, calculating and writing—original draft preparation, C.F; methodology and data curation C.F and C.H.; writing—review and editing, K.Y and C.H.; supervision, C.H. Both authors have read and agreed to the published version of the manuscript.

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Correspondence to Chong Huang.

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Yin, K., Cai, F. & Huang, C. How does artificial intelligence development affect green technology innovation in China? Evidence from dynamic panel data analysis. Environ Sci Pollut Res 30, 28066–28090 (2023). https://doi.org/10.1007/s11356-022-24088-0

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