Assessing the dynamics of artificial intelligence, renewable energy investment, and policy uncertainty in promoting green growth in China

As the world grapples with the challenge of balancing economic growth with environmental sustainability, the need for green growth has never been more pressing. This study examines the roles of artificial intelligence (AI), renewable energy investments (REI), and economic policy uncertainty (EPU) in shaping green growth in China, a country that is both a global leader in economic development and a major player in the green transition. Using a blend of innovative methods, including Dynamic Autoregressive Distributed Lag (DARDL) modeling, Kernel Regularized Least Squares (KRLS) machine learning, and Breitung-Candelon Spectral Granger-Causality analysis, we examine how these factors influence China’s sustainable development in both the short and long term. Our findings show that while AI and REI are key drivers of green growth, their full potential is hindered by the uncertainty surrounding economic policies. The results highlight that, without clear and stable policy frameworks, investments in green technologies are unlikely to reach their full potential. This study offers valuable insights into how AI and REI can be leveraged to foster sustainability, providing practical recommendations for policymakers to create the conditions necessary for green growth. Ultimately, it emphasizes the importance of stable, forward-thinking policies in enabling technological innovations to contribute meaningfully to a sustainable future for China and beyond.