Is artificial intelligence an impediment or an impetus to renewable energy investment? Evidence from China

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics Pub Date : 2025-05-21 DOI:10.1016/j.eneco.2025.108550

Wen Li , Jing-Ping Li , Yun-Feng Wang , Sebastian-Emanuel Stan

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引用次数: 0

Abstract

This study investigates the bidirectional relationship between artificial intelligence (AI) and renewable energy investment, emphasizing their strategic importance in achieving global low-carbon objectives. Using a high-frequency dataset from 2010 to 2024, which includes monthly observations on the artificial intelligence robotics index (AIW) and the renewable energy index (ENI) in China, this research employs a bootstrap subsample rolling window Granger causality test to examine dynamic causal linkages. The findings reveal that AI accelerates renewable energy investment by enhancing energy forecasting, grid optimization, and intelligent energy management. However, its long-term impact is constrained by high capital costs, resource limitations, and regulatory uncertainty. Moreover, renewable energy development reciprocally promotes AI advancements, particularly in energy storage and autonomous energy systems, although this synergy is vulnerable to policy instability and economic downturns. This study makes significant contributions by providing empirical evidence on the evolving role of AI in renewable energy investments and offering practical policy insights. The results inform policy-makers, investors, and energy firms about optimizing AI applications in renewable energy, improving regulatory frameworks, and fostering economic conditions that accelerate the shift towards a sustainable, carbon-neutral economy. These insights have broad implications for countries aiming to leverage AI-driven solutions for sustainable energy innovation.

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人工智能是可再生能源投资的阻碍还是推动力？来自中国的证据

本研究探讨了人工智能（AI）与可再生能源投资之间的双向关系，强调了它们对实现全球低碳目标的战略重要性。本研究利用2010 - 2024年中国人工智能机器人指数（AIW）和可再生能源指数（ENI）的月度观测数据，采用自举子样本滚动窗Granger因果检验来检验动态因果关系。研究结果表明，人工智能通过增强能源预测、电网优化和智能能源管理来加速可再生能源投资。然而，其长期影响受到高资本成本、资源限制和监管不确定性的制约。此外，可再生能源的发展相互促进了人工智能的进步，特别是在储能和自主能源系统方面，尽管这种协同作用容易受到政策不稳定和经济衰退的影响。本研究通过提供人工智能在可再生能源投资中不断演变的作用的经验证据，并提供实用的政策见解，做出了重大贡献。研究结果为政策制定者、投资者和能源公司提供了优化人工智能在可再生能源中的应用、改善监管框架和培育加速向可持续、碳中和经济转变的经济条件的信息。这些见解对旨在利用人工智能驱动的解决方案进行可持续能源创新的国家具有广泛的影响。

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.