Abstract
As artificial intelligence (AI) becomes increasingly integrated into economic and societal domains, it emerges as a pivotal force driving the shift toward low-carbon energy systems. This study examines how AI impacts the transformation of urban energy systems by utilizing a panel dataset comprising 278 Chinese spanning the years 2010–2019. The findings confirm that AI significantly enhances energy transition performance in urban settings. By precisely optimizing the integration and consumption of renewable energy, driving the energy efficiency revolution, and breaking the dependence on high-carbon energy development models, as well as enhancing grid resilience and ensuring energy supply security to overcome the vulnerabilities of the energy transition, AI also strengthens the innovation capacity of energy transition through accelerating technological breakthroughs and incubating new business models. Heterogeneity analysis reveals that AI better facilitates energy transition in those cities that are small and medium in size, cities with a solid industrial base, cities with a high level of economic clustering, and cities located in central and eastern China. Mechanism tests show that during AI-enabled transition processes, green technology innovation, human–machine compatibility, and energy efficiency play significant roles. Further analysis using a threshold model reveals that as electronic commerce, human capital, and business growth increase, AI's marginal effects on energy transition exhibit an incremental trend. This implies that improving digital infrastructure, raising human capital levels, and boosting economic growth are pathways to realizing the transformative effects of AI. This study assesses AI technology's effectiveness in promoting energy sustainability and high-quality development goals.
| Original language | English |
|---|---|
| Article number | e70050 |
| Journal | Growth and Change |
| Volume | 56 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Dec 2025 |
| Externally published | Yes |
Keywords
- artificial intelligence
- energy efficiency
- energy transition
- green technology innovation
- human–machine compatibility
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