Research on the Spatiotemporal Correlation Characteristics Between Artificial Intelligence and Energy Transition in China
中国における人工知能とエネルギー転換の時空間相関特性に関する研究 (AI 翻訳)
Delin Xin, Sansan Zhang, Rui Zhang, Tuantuan Chen, Qiang Zhao, Chen Li, Lijuan Chen, Bo Zhao
🤖 gxceed AI 要約
日本語
本論文は、中国30省を対象に、AI発展とエネルギー転換の間の時空間相関を分析。AI発展は東部に偏り、エネルギー転換水準は上昇傾向にあるが、両者の相関は単純ではなく、中央・東部で正の相関が顕著である。GTWRモデルにより動的メカニズムを解明。
English
This paper analyzes the spatiotemporal correlation between AI development and energy transition across 30 Chinese provinces. Results show that AI growth is concentrated in eastern regions, while overall energy transition levels are rising. The correlation is not uniformly positive; significant positive associations are concentrated in central and eastern provinces. A geographically and temporally weighted regression model reveals dynamic mechanisms.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国のAIとエネルギー転換の関係を実証的に示し、日本のGX政策（特に地域間格差や技術実装の時空間的影響）への示唆を含む。地方別の分析手法は日本の都道府県レベル研究に応用可能。
In the global GX context
This paper provides empirical evidence on the link between AI and energy transition in China, offering insights for global discussions on regional disparities and technology deployment. The spatiotemporal methodology can be applied to other countries' decarbonization pathways.
👥 読者別の含意
🔬研究者:Methodological framework (GTWR, spatial autocorrelation) for studying technology-transition dynamics.
🏛政策担当者:Policy implications for aligning AI development with energy transition goals, especially regional targeting.
📄 Abstract（原文）
Artificial intelligence (AI), which is advancing rapidly, offers a novel and important tool for driving sustainable energy transition, although the spatiotemporal correlation between the two is complex. Taking China’s 30 provinces as the study subjects, this research constructs an evaluation index system from the perspective of energy transition outcomes to assess the level of China’s energy transition. It evaluates the level of AI development based on the foundation of AI development, AI technological innovation, and AI application, and analyzes its spatiotemporal evolution characteristics. Pearson correlation analysis and bivariate local spatial autocorrelation are employed to investigate the spatiotemporal associations between energy transition and AI. In addition, the dynamic mechanisms linking the two are further investigated using a geographically and temporally weighted regression (GTWR) model. The results indicate that, first, innovation and application in AI were on the rise, while regional disparities were widening and a polarization phenomenon was emerging; AI development was concentrated in the eastern regions, with a decreasing trend toward the northwestern inland areas. Second, the overall level of China’s energy transition continued to rise, with a box-shaped clustering pattern observed across regions; Beijing, Inner Mongolia, Jiangsu, and Shandong had achieved a relatively high level of energy transition. Third, the development of AI did not always correlate positively with the energy transition. There was a significant positive correlation between AI technological innovation and application and the energy transition. There were significant differences in the spatial patterns linking AI development and the energy transition. The positive correlation between the two was significant and widespread, concentrated in the central and eastern provinces.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18125858first seen 2026-06-11 05:35:57
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