Climate policy uncertainty and green total factor energy efficiency: Does the green finance matter?
研究了气候政策不确定性对城市绿色全要素能源效率的负面影响，发现绿色金融能缓解这种负面作用，且人工智能发展水平越高，绿色金融的促进效果越强。
This study investigates the impact of climate policy uncertainty (CPU) on green total factor energy efficiency (GTFEE) and examines the moderating role of green finance (GF). Using a panel data analysis framework combined with the super-efficient SBM-DEA model, the study finds that CPU has a significant negative effect on GTFEE, indicating that increased policy uncertainty hinders the improvement of urban energy efficiency. At the same time, GF plays an important moderating role in alleviating the negative impacts of CPU, particularly in environments with higher policy uncertainty, where GF can effectively promote energy efficiency. Additionally, the study discovers that the development of artificial intelligence (AI) industries significantly moderates the relationship between GF and GTFEE. In cities with more advanced AI technologies, AI helps boost energy efficiency. Overall, the findings offer important policy recommendations on how to improve energy efficiency through green finance in uncertain policy environments , with broad applicability, especially in advancing low-carbon economies.