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Related Experiment Video

Updated: Aug 20, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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How does artificial intelligence development affect green technology innovation in China? Evidence from dynamic panel

Kedong Yin1,2, Fangfang Cai1, Chong Huang3,4

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

Environmental Science and Pollution Research International
|November 17, 2022
PubMed
Summary

Artificial intelligence (AI) significantly boosts green technology innovation (GTI) and efficiency, particularly in China

Keywords:
Artificial intelligence developmentDynamic panel threshold modelGreen technology innovationSpatial heterogeneitySystem GMMTemporal difference

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Area of Science:

  • Economics and Environmental Science
  • Focuses on the intersection of artificial intelligence, green technology, and economic policy.

Background:

  • Global climate change necessitates green technology innovation (GTI) for carbon neutrality goals.
  • Rapid advancements in artificial intelligence (AI) prompt investigation into its economic impacts on GTI.

Purpose of the Study:

  • To examine the influence of AI development on green technology innovation (GTI) and green technology innovation total factor productivity (GTFP).
  • To analyze these effects under varying conditions of environmental regulation, R&D investment, and institutional environment.
  • To identify non-linear and threshold effects of AI on GTI.

Main Methods:

  • Utilized China's inter-provincial panel data from 2006 to 2019.
  • Employed System Generalized Method of Moments (GMM), dynamic panel threshold, and quantile regression models.

Main Results:

  • AI development positively impacts GTI (coefficient 0.0122) and GTFP (coefficient 0.0084), with effects more pronounced in western China and during 2006-2012.
  • AI primarily enhances green technological efficiency, but showed dampening effects on green technological progress from 2013-2019.
  • Non-linear threshold effects exist: AI boosts GTI only when environmental regulation and institutional environment exceed certain levels.
  • Industrial robot applications (AI) showed no significant effect on GTI even with high R&D investment.
  • The growth effect of AI on GTI exhibits a decreasing non-linear pattern with rising GTI quantiles when R&D and institutional environment cross thresholds.
  • Conversely, this growth effect increases with GTI quantiles when environmental regulation is above its threshold.

Conclusions:

  • AI is a significant driver of green technology innovation and efficiency, but its impact is contingent on regulatory and investment thresholds.
  • Policy interventions must consider these thresholds to effectively leverage AI for achieving carbon neutrality goals.
  • The specific mechanisms and regional variations in AI's impact on GTI require further nuanced investigation.