Strengthening the resilience of urban energy systems amid renewable energy transition: A new method based on double machine learning

Renewable energy transition can fully leverage transformative potential and provide impetus for improving the resilience of urban energy systems. This study takes China's Plan on Clean Energy Accommodation as a quasi-natural experiment and employs a dual machine learning model to explore the impact of the renewable energy transition on the resilience of urban energy systems, as well as the underlying mechanisms. It also examines the regional coordination effects of this transition. The findings reveal that the renewable energy transition significantly strengthens the resilience of urban energy systems. A heterogeneity analysis further shows that the transition has a more pronounced positive influence on energy system resilience in economically developed regions, northern cities, and those with high carbon emissions. Mechanism analysis indicates that the renewable energy transition enhances resilience through four main channels: promoting low-carbon awareness, reducing climate risks, improving energy efficiency, and increasing marketization. Additionally, the transition diminishes traditional geographical advantages, helping to narrow disparities in energy system resilience at the national, regional, and provincial levels, thereby demonstrating notable regional coordination effects. This study offers important insights for a deeper comprehension of the value of the renewable energy transition and for exploring ways to enhance urban energy system resilience.