Could AI and Sustainable Finance Drive Energy Sustainability? A Wavelet Quantile Correlation Analysis

Jiuhong Yu, Xiaohua Lai, Ting Sun and Cheng-To Lin

Emerging Markets Finance and Trade, 2025, vol. 61, issue 14, 4512-4526

Abstract: Investigating the nexus among sustainable finance (SF), artificial intelligence (AI), and energy-related uncertainty (ERU) is crucial for achieving energy sustainability. A new wavelet quantile correlation approach is utilized to analyze the connections between SF and ERU, AI and ERU, and SF and AI across different quantiles and frequencies. We observe that initially, ERU and SF show a weak positive correlation due to SF’s infancy and ambiguous market demands. This shifts to negative in the short-to-medium term as SF drives renewable energy, innovation, and resource optimization, reducing ERU. A strong positive link emerges from the medium to long term as the energy transition introduces uncertainties, necessitating SF for market stability. For ERU-AI, AI temporarily raises ERU due to high energy use and delayed efficiency. Over the medium-to-long term, AI reduces ERU by boosting efficiency, green transformations, timely regulations, and intelligent suggestions. Furthermore, SF and AI correlate positively, fueled by innovation, risk management, and green investment guidance, strengthened by AI maturity, market acceptance, and supportive policies in the long run. Amidst the backdrop of severe energy uncertainties, the article makes some policy suggestions to attain energy sustainability by developing sustainable finance and artificial intelligence.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:mes:emfitr:v:61:y:2025:i:14:p:4512-4526

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DOI: 10.1080/1540496X.2025.2519414

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