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<p>Circular economy transitions require closing material loops and narrowing resource flows through improved energy efficiency, a foundational yet relatively underexplored aspect of systemic change. This paper examines whether and how exposure to artificial intelligence (AI) improves regional energy efficiency by reshaping labor market composition toward environmentally oriented employment. We analyze panel data from 274 Chinese cities from 2007 to 2021, constructing an AI exposure index that captures both industrial automation and AI enabled service sector transformation. To address endogeneity, we use instrumental variables based on U.S. robot adoption patterns and geographic proximity to external AI clusters, and find that a one standard deviation increase in AI exposure raises total factor energy efficiency by about 3.2 percent. The effect operates through automation of routine tasks alongside the preservation and upgrading of occupations requiring complex environmental judgment and energy optimization skills. Granular occupation, task data from online job postings reveal substantial increases in both green employment levels and green occupational shares in regions with high AI exposure. The efficiency gains concentrate where environmental regulation is stringent and digital infrastructure advanced, with the largest effects emerging in energy intensive sectors such as power generation and transportation. Workforce transformation thus constitutes an important pathway linking technological change to resource efficiency gains in circular economy transitions. Harnessing AI for circular economy objectives requires coordinated policy interventions across environmental regulation, digital infrastructure development, and workforce skill formation.</p>

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Artificial intelligence, greening of occupational structure and total factor energy efficiency

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

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  • Tianren Li,
  • Yao Hu,
  • Jiachao Peng,
  • Fushun Zhang

摘要

Circular economy transitions require closing material loops and narrowing resource flows through improved energy efficiency, a foundational yet relatively underexplored aspect of systemic change. This paper examines whether and how exposure to artificial intelligence (AI) improves regional energy efficiency by reshaping labor market composition toward environmentally oriented employment. We analyze panel data from 274 Chinese cities from 2007 to 2021, constructing an AI exposure index that captures both industrial automation and AI enabled service sector transformation. To address endogeneity, we use instrumental variables based on U.S. robot adoption patterns and geographic proximity to external AI clusters, and find that a one standard deviation increase in AI exposure raises total factor energy efficiency by about 3.2 percent. The effect operates through automation of routine tasks alongside the preservation and upgrading of occupations requiring complex environmental judgment and energy optimization skills. Granular occupation, task data from online job postings reveal substantial increases in both green employment levels and green occupational shares in regions with high AI exposure. The efficiency gains concentrate where environmental regulation is stringent and digital infrastructure advanced, with the largest effects emerging in energy intensive sectors such as power generation and transportation. Workforce transformation thus constitutes an important pathway linking technological change to resource efficiency gains in circular economy transitions. Harnessing AI for circular economy objectives requires coordinated policy interventions across environmental regulation, digital infrastructure development, and workforce skill formation.