The Impact of Big Data Pilot Zones on Urban Ecological Resilience: Evidence from a Machine Learning Approach

Against the backdrop of the structural transition in China’s economic landscape, the implementation of digital economy policies—particularly through the Broadband China Demonstration Cities initiatives—has significantly enhanced urban ecological resilience. Based on panel data from 280 prefecture-level cities in China over the period 2013–2022, this study employs the national big data comprehensive pilot zone as a quasi-natural experiment and utilizes the dual machine learning method to examine how pilot zone construction influences urban ecological resilience. This analysis provides theoretical support for fostering green urban development. The results are summarized as follows. (1) The construction of national big data comprehensive pilot zones significantly enhances urban ecological resilience. The conclusion is robust to various tests, including the removal of outliers, changes in sample splitting ratios, and alterations in machine learning algorithms. (2) The construction of national big data comprehensive pilot zones indirectly improves urban ecological resilience through pathways of green innovation and energy efficiency. (3) This study assesses the heterogeneity of policy effects based on the generalized random forest (GRF) model to identify the sources of heterogeneity in policy effects, and conducts a comprehensive heterogeneity analysis from the three dimensions of resource endowments, geographical location characteristics, and the attributes of environmental protection zones. These findings enrich the analysis of the consequences of national big data comprehensive pilot zone policies and offer a theoretical basis and policy reference for how constructing big data pilot zones can better serve urban ecological development.