The Impact of China’s Pilot Policy for Inefficient Land Redevelopment on Urban Green Innovation: Evidence from Machine Learning

Redevelopment of inefficient land constitutes an important pathway for urban renewal and facilitates high-quality urban development. This study combines the traditional difference-in-differences (DID) framework with the state-of-the-art Causal Random Forests (CRF) approach to systematically evaluate the impact of China’s pilot policy targeting inefficient land redevelopment on urban green innovation, using data spanning from 2009 to 2023. The DID results indicate that, on average, exposure to the pilot policy significantly increases the number of green patents at the city level, with supporting evidence for the parallel trends assumption. Further analysis using CRF reveals substantial heterogeneity in policy effects across cities. In particular, cities with larger populations and higher levels of economic development experience more pronounced increases in green innovation following policy implementation. The mechanism analysis further indicates that the pilot policy promotes the development of nationally certified high-tech enterprises, providing evidence consistent with the proposed high-tech enterprise development channel. This study contributes to the literature by introducing a land policy perspective to the analysis of urban green innovation and by uncovering the heterogeneous impacts of policy interventions. The findings provide empirical evidence for refining place-based land redevelopment policies and coordinating land governance with green innovation in urban renewal.