Can Public Data Openness Promote Inclusive Green Growth in Chinese Cities—Empirical Evidence Based on Double Machine Learning

Against the backdrop of the deepening digital economy and accelerated green and low-carbon transformation, data factors have emerged as a core strategic resource driving the formation and allocation of new quality productive forces. Whether data can transition from closure to openness, thereby converting data dividends into inclusive green growth momentum that balances efficiency and equity, constitutes a strategic imperative in the process of Chinese-style modernization. Based on city-level data in China from 2010 to 2024, this paper measures the inclusive green growth (IGG) levels of 295 prefecture-level and above cities, and employs the double/debiased machine learning (DDML) model to examine the impact and underlying mechanisms of public data open platforms on urban IGG, treating the launch of these platforms as a quasi-natural experiment. The findings reveal that the launch of public data open platforms exerts a significant positive effect on urban IGG and is associated with improvements across multiple dimensions, including economic development, social equity, green production and consumption, and environmental protection. In terms of mechanisms, public data open platforms promote IGG through three pathways: advancing digital inclusive finance development, mitigating factor market distortions, and improving urban information circulation. The impact effects exhibit significant heterogeneity across cities with different endowments: cities with larger economic scales, more abundant human capital, and higher initial IGG levels benefit more from public data openness. Furthermore, the effect of public data open platforms on urban IGG displays a regional gradient pattern of “strongest in the east, moderate in the west, and weakest in the central region,” indicating that policy dividends are fully realized in information-intensive economies but remain insufficiently realized in regions with relatively lagging digital infrastructure.