Can artificial intelligence help accelerate the transition to renewable energy?

Qian Zhao, Lu Wang, Sebastian-Emanuel Stan and Nawazish Mirza

Energy Economics, 2024, vol. 134, issue C

Abstract: Artificial intelligence (AI) has enormous potential in improving the efficiency and reducing the cost of energy systems; however, it is unclear whether it can help accelerate the transition from traditional fossil energy to renewable energy (RE). Previous studies have primarily focused on the applications of AI in the energy sector from a technical perspective; in contrast, this paper identifies the process and mechanism of AI's impact on the energy transition, using the wavelet-based quantile-on-quantile approach to estimate the impact of various AI quantiles on energy structure quantiles in different periods. Using data from China, the paper finds that the upper quantiles of AI increase the share of RE in total energy in the long term, demonstrating that AI can, by unlocking its vast potential, accelerate the transition to RE. However, in the short and medium terms, AI negatively impacts RE share, primarily due to considerable challenges in integrating AI into the RE sector. Moreover, the non-RE sector may temporarily benefit more from AI than the RE sector. These findings provide crucial insights for policymakers in coordinating AI's short- and long-term effects on the energy transition to effectively harness RE's potential and achieve the goal of a low-carbon economy.

Keywords: Artificial intelligence; Renewable energy; Energy transition; Quantile-on-quantile; Wavelet (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324002925

DOI: 10.1016/j.eneco.2024.107584

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