Intelligent transformation and green development: a double debiased machine learning evaluation of china’s IIP initiatives

Abstract In promoting the transformation, upgrading, and green development of traditional industries the Chinese government has been introducing Integration of Informatization and Industrialization Pilot (IIP) initiatives based on intelligent manufacturing and the green economy. As such this study examines the impact of China’s IIP policy on the green development of publicly listed manufacturing firms by applying both Difference-in-Differences (DID) and double debiased machine learning (DDML) models to a dataset that spans the period 2007–2022. The evidence suggests that the IIP policy initiative significantly improves firms’ green development via the mediating effect of intelligent transformation. Robustness checks, including DDML model regression and Propensity Score Matching-DID (PSM-DID) with nearest neighbour matching, consistently demonstrate significant improvements in environmental efficiency and productivity due to the IIP. Moreover, these effects are notably pronounced in the Yangtze River Economic Belt, heavily polluting industries, and larger firms. This research addresses a gap in micro-level policy analysis, highlighting the potential of intelligent manufacturing to promote sustainable practices. By offering both theoretical and practical insights, the findings guide policymakers and businesses in leveraging informatization and industrialization for green development.