In a groundbreaking study published in the journal *Energy Science & Engineering*, researchers have unveiled how artificial intelligence (AI) and green finance (GF) can significantly boost energy efficiency (EE) across different regions. Led by Hongji Zhou of the Business School at Nanjing Institute of Technology in Nanjing, Jiangsu, China, the research offers critical insights for policymakers and energy sector stakeholders aiming to enhance EE and drive sustainable development.
The study employs a super-efficiency SBM model to measure EE and a Tobit two-stage model to assess the impact of AI and GF on EE. The findings reveal a stark disparity in EE levels across regions, with the eastern regions outperforming central and western areas. “The EE of each region and the country is widely spread, indicating substantial room for improvement,” Zhou notes, highlighting the urgent need for targeted interventions.
At the national level, AI emerges as a powerful catalyst for EE improvement. The study shows that advancements in AI can effectively enhance EE, with the central region benefiting more than the eastern region. However, the impact in the western region is positive but statistically insignificant. “AI has a significant positive effect on EE, implying that advances in AI can effectively improve EE,” Zhou explains, underscoring the transformative potential of AI in the energy sector.
Green finance also plays a pivotal role in promoting EE, although its impact varies across regions. While GF has a positive effect nationally, its influence is most pronounced in the eastern region. In contrast, the central and western regions experience weaker effects. “GF promotes EE but the elasticity coefficient is small,” Zhou observes, suggesting that while GF is beneficial, its impact could be enhanced with more targeted policies.
The study also examines the role of other factors, such as energy endowment, environmental regulation, industrial structure, and technology level. Energy endowment is found to inhibit EE, while environmental regulation promotes it, particularly in the eastern region. The industrial structure coefficient reduces EE in all regions, and technology level inhibits EE only in the central region.
These findings have profound implications for the energy sector. By leveraging AI and GF, policymakers and energy companies can develop more effective strategies to improve EE and drive sustainable development. “The thesis through the analysis of the relationship between the three and the reliability of the conclusions drawn from the analysis, to be able to better play the GF and AI in the energy sector of the policy implementation effect, effectively improve EE, improve the energy structure, for the comprehensive promotion of the energy transition is of great significance,” Zhou concludes.
As the energy sector continues to evolve, this research provides a roadmap for harnessing the power of AI and GF to achieve greater energy efficiency and sustainability. By implementing the insights from this study, stakeholders can pave the way for a more efficient and environmentally friendly energy future.