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<p>Under the background of the continuous acceleration of urbanization, the convergence of smart city and low-carbon city initiatives offers novel approaches to enhancing urban governance efficiency and advancing sustainable urban development. As an important representative of emerging technologies, artificial intelligence-empowered new quality productivity (AI-NQP) has become a key driving force to promote the high-quality development of the smart city<b>–</b>urbanization<b>–</b>low-carbon city (SUL) system. This paper is based on panel data from 289 Chinese cities from 2013 to 2023, using the entropy method to construct the AI-NQP and SUL system development index, and empirical analysis is conducted using system generalized method of moments (SYS-GMM), and panel vector autoregression model (PVAR). The results show: (1)&#xa0;both the SUL system and the AI-NQP development index have exhibited a continuous upward trend over time, but there is an obvious “east strong, west weak” spatial pattern; (2) AI-NQP exerts a markedly positive impact on the evolution of the SUL system, with a 1% increase in AI-NQP leading to an average 0.56% improvement in the SUL system level; (3) the mediating effect test indicates that innovation output level (IOL) and industrial intelligence level (IIL) are key transmission mechanisms through which AI-NQP promotes the SUL system; (4) Further evidence from the PVAR model indicates that AI-NQP has a notably positive effect on SUL system development over the short term, but this impact gradually weakens as system adaptability increases. In the long term, the SUL system promotes continuous innovation and diffusion of AI-NQP through reverse effects of urban demand and institutional environment. This article not only provides a empirical support for understanding how AI-NQP promotes the integrated evolution of urban multiple systems, but also offers policy recommendations for building high-quality, sustainable urban governance models.</p>

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The mechanism of new quality productivity empowered by AI on the multi-level development of the smart city–urbanization–low-carbon city system

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Yuxin Zhang,
  • Xingchen Lai,
  • Yao Zhang,
  • Panpan Liu,
  • Hiroatsu Fukuda

摘要

Under the background of the continuous acceleration of urbanization, the convergence of smart city and low-carbon city initiatives offers novel approaches to enhancing urban governance efficiency and advancing sustainable urban development. As an important representative of emerging technologies, artificial intelligence-empowered new quality productivity (AI-NQP) has become a key driving force to promote the high-quality development of the smart city–urbanization–low-carbon city (SUL) system. This paper is based on panel data from 289 Chinese cities from 2013 to 2023, using the entropy method to construct the AI-NQP and SUL system development index, and empirical analysis is conducted using system generalized method of moments (SYS-GMM), and panel vector autoregression model (PVAR). The results show: (1) both the SUL system and the AI-NQP development index have exhibited a continuous upward trend over time, but there is an obvious “east strong, west weak” spatial pattern; (2) AI-NQP exerts a markedly positive impact on the evolution of the SUL system, with a 1% increase in AI-NQP leading to an average 0.56% improvement in the SUL system level; (3) the mediating effect test indicates that innovation output level (IOL) and industrial intelligence level (IIL) are key transmission mechanisms through which AI-NQP promotes the SUL system; (4) Further evidence from the PVAR model indicates that AI-NQP has a notably positive effect on SUL system development over the short term, but this impact gradually weakens as system adaptability increases. In the long term, the SUL system promotes continuous innovation and diffusion of AI-NQP through reverse effects of urban demand and institutional environment. This article not only provides a empirical support for understanding how AI-NQP promotes the integrated evolution of urban multiple systems, but also offers policy recommendations for building high-quality, sustainable urban governance models.