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

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

Energy Economics, 2025, vol. 147, issue C

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.

Keywords: Artificial intelligence; Renewable energy investment; Time varying; Granger causal relationship (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:147:y:2025:i:c:s0140988325003743

DOI: 10.1016/j.eneco.2025.108550

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