Investigating the asymmetric impact of artificial intelligence on renewable energy under climate policy uncertainty

Lihui Tian, Xin Li, Cheng-Wen Lee and Cristi Spulbar ()

Energy Economics, 2024, vol. 137, issue C

Abstract: The focus on sustainable development and the transition to renewable energy sources has intensified due to the risks associated with climate change. This study provides new insights into the impact of artificial intelligence (AI) and climate policy uncertainty (CPU) on the development of renewable energy (RE) in China. Utilizing a nonlinear autoregressive distributed lag (NARDL) framework, the asymmetric relationship between these variables from January 2013 to April 2023 is revealed. The empirical results indicate a significant positive asymmetric effect of AI on RE development, with downturns in AI having a more pronounced influence compared to upswings. Additionally, CPU has a positive effect on RE development, also exhibiting an asymmetric pattern where declines in CPU have a more substantial impact than upturns. These findings highlight the critical roles of AI and CPU in renewable energy development and add new dimensions to existing research. Policymakers should consider these asymmetric dynamics when formulating strategies to facilitate the energy transition through climate policymaking and the advancement of AI-driven technologies.

Keywords: Artificial intelligence; Climate policy uncertainty; Energy transition (search for similar items in EconPapers)
JEL-codes: G18 O33 Q54 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988324005176
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:137:y:2024:i:c:s0140988324005176

DOI: 10.1016/j.eneco.2024.107809

Access Statistics for this article

Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().