Artificial intelligence and green development: Evidence from China on efficiency and equity

IF 13.5 2区经济学 Q1 ECONOMICS

Energy Economics Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI:10.1016/j.eneco.2026.109201

Tielong Wu

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Abstract

This study investigates the impact of Artificial Intelligence (AI) policy on urban green development, focusing on both efficiency and equity perspectives. We first apply a green growth model to analyze the green development. Then, using panel data from 286 cities in China between 2000 and 2023, we assess the Green Development Efficiency (GDE) of cities through Data Envelopment Analysis and analyze the impact of AI policy on efficiency with a Difference-in-Differences model. Finally, we do convergence analysis to explore the equity of green development across cities. The results show that AI policies lead to a “dividend” in the form of enhanced overall GDE, driven by improvements in technical and scale efficiency, increased patent outputs, and urban agglomeration. However, the study also reveals a “divide”, as the benefits are not equally distributed across cities. Convergence analysis reveals no global convergence across cities but identifies six convergent clubs and one divergent club. Ordered probit analysis shows that AI policies do not significantly affect a city's likelihood of transitioning between clubs. In the meantime, regional heterogeneity is observed, with the positive impact of AI policy being more pronounced in eastern regions, while smaller improvements are observed in central and western regions. Negative spillover effects are also found in cities within a 50 km radius of AI policy, where GDE decreases due to resource competition. While AI policies improve efficiency, they do not promote equitable green development. Future AI policies should focus on a more equitable green transition.

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人工智能与绿色发展：来自中国的效率与公平证据

本文从效率和公平两个角度探讨了人工智能（AI）政策对城市绿色发展的影响。首先运用绿色增长模型对绿色发展进行分析。然后，利用2000 - 2023年中国286个城市的面板数据，通过数据包络分析对城市绿色发展效率（GDE）进行评估，并利用差中差模型分析人工智能政策对效率的影响。最后，通过收敛性分析来探讨城市间绿色发展的公平性。研究结果表明，人工智能政策在技术效率和规模效率提高、专利产出增加和城市群的驱动下，以提高总体gdp的形式带来了“红利”。然而，该研究也揭示了一个“鸿沟”，因为城市之间的利益分配并不平均。趋同分析显示，各个城市之间没有全球趋同，但确定了六个趋同俱乐部和一个发散俱乐部。有序概率分析表明，人工智能政策不会显著影响城市在俱乐部之间过渡的可能性。与此同时，人工智能政策的积极影响在东部地区更为明显，而在中西部地区的改善幅度较小。在人工智能政策半径50公里以内的城市，由于资源竞争，GDE下降，也出现了负外溢效应。虽然人工智能政策提高了效率，但它们并没有促进公平的绿色发展。未来的人工智能政策应侧重于更公平的绿色转型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Energy Economics ECONOMICS-

CiteScore

18.60

自引率

12.50%

发文量

524

期刊介绍： Energy Economics is a field journal that focuses on energy economics and energy finance. It covers various themes including the exploitation, conversion, and use of energy, markets for energy commodities and derivatives, regulation and taxation, forecasting, environment and climate, international trade, development, and monetary policy. The journal welcomes contributions that utilize diverse methods such as experiments, surveys, econometrics, decomposition, simulation models, equilibrium models, optimization models, and analytical models. It publishes a combination of papers employing different methods to explore a wide range of topics. The journal's replication policy encourages the submission of replication studies, wherein researchers reproduce and extend the key results of original studies while explaining any differences. Energy Economics is indexed and abstracted in several databases including Environmental Abstracts, Fuel and Energy Abstracts, Social Sciences Citation Index, GEOBASE, Social & Behavioral Sciences, Journal of Economic Literature, INSPEC, and more.