The impact of digitalization and energy transition policies on urban energy rebound effects in China: A double machine learning-based causal identification.

The digital economy has injected fresh momentum into China's growth, yet the accompanying energy rebound effect (ERE) deserves attention. This study measures the urban-level ERE in China from the technological progress perspective and treats the dual-pilot policies of China's National Big Data Comprehensive Experimental Zones (NBDCEZs) and New Energy Demonstration Cities (NEDCs) as a quasi-natural experiment. Using a double machine learning approach, we evaluate the impact of dual-pilot policies on urban ERE. The results show that dual-pilot policies significantly mitigate urban ERE, especially in resource-based cities, old industrial cities, and cities with advanced green finance. Mechanism analysis indicates that dual-pilot energy policies can mitigate the ERE through industrial structure optimization, green technological innovation, and energy consumption transition. Moreover, compared with a single policy, the combined implementation of the dual-pilot policies has a stronger mitigating effect on the ERE. These findings offer empirical evidence and policy insights for addressing the Jevons Paradox and advancing sustainable development.