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<p>To address the increasingly fierce inter-governmental competition brought about by achieving the carbon neutrality goal, local governments urgently need to explore effective response tools. Drawing on a balanced panel of 30 Chinese provinces spanning the period 2012–2022, this paper applies both fixed-effects estimation and threshold regression techniques to investigate how artificial intelligence (AI) influence government low-carbon competitive pressure (GLCP). The empirical results indicate a clear mitigating effect of AI development on GLCP. Specifically, an increase of one standard deviation in the AI is associated with an average reduction of about 1.529% in GLCP. This mitigation effect is more pronounced in regions with abundant green space resources, relatively scarce human capital and a weak innovation foundation. Threshold analysis further indicates that green innovation and educational quality are the key moderating factors: when green innovation is below 0.0539 and educational quality is below 15.35, the positive effect of AI is the strongest. Therefore, it is suggested that AI be regarded as an important tool to support the government’s decarbonization policies. By promoting AI technology, regional innovation ecosystems and human capital investment in a coordinated manner, differentiated low-carbon development paths should be formulated based on local conditions.</p>

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Artificial intelligence and governmental low-carbon competition pressure: evidence from a 30-province panel using fixed effects and threshold regression

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Shiyu Zhang,
  • Cunlin Li,
  • Baojun Yang

摘要

To address the increasingly fierce inter-governmental competition brought about by achieving the carbon neutrality goal, local governments urgently need to explore effective response tools. Drawing on a balanced panel of 30 Chinese provinces spanning the period 2012–2022, this paper applies both fixed-effects estimation and threshold regression techniques to investigate how artificial intelligence (AI) influence government low-carbon competitive pressure (GLCP). The empirical results indicate a clear mitigating effect of AI development on GLCP. Specifically, an increase of one standard deviation in the AI is associated with an average reduction of about 1.529% in GLCP. This mitigation effect is more pronounced in regions with abundant green space resources, relatively scarce human capital and a weak innovation foundation. Threshold analysis further indicates that green innovation and educational quality are the key moderating factors: when green innovation is below 0.0539 and educational quality is below 15.35, the positive effect of AI is the strongest. Therefore, it is suggested that AI be regarded as an important tool to support the government’s decarbonization policies. By promoting AI technology, regional innovation ecosystems and human capital investment in a coordinated manner, differentiated low-carbon development paths should be formulated based on local conditions.