How can China's healthy city pilot policy improve energy efficiency? Insights from difference-in-differences and double/debiased machine learning approaches

Improving energy efficiency (EE) is a critical pathway toward resource conservation and urban environmental sustainability. While healthy city initiatives have been acknowledged for their positive environmental and public health outcomes, their implications for urban EE have received limited empirical attention. This study takes China's healthy city pilot (HCP) policy as a quasi-natural experiment to investigate how the HCP policy affects EE. Using panel data from 282 prefecture-level cities between 2006 and 2023, we employ a difference-in-differences (DID) approach and a double/debiased machine learning (DDML) method to ensure robust causal identification. The relevant results are threefold. (1) The HCP policy significantly improves EE in pilot cities, which is driven by increased clean energy adoption, enhanced green technology innovation capabilities, improved public transportation services, and raised public environmental awareness. (2) The result of moderating effect analysis reveals that government environmental regulation can amplify pilot cities' energy transition effect. (3) HCP policy implementation widens regional disparities in EE across pilot cities, exhibiting a Matthew effect, with greater benefits observed in cities with lower resource dependence, better healthcare, stronger digital economies, and more developed green foundations. We propose that alongside expanding healthy city initiatives informed by policy experience, governments should strengthen policy support for underdeveloped cities with weak environmental and economic foundations to advance the Healthy China strategy.