Artificial Intelligence and Inclusive Green Growth: Causal Identification, Nonlinear Characteristics and Time-Varying Spatial Spillovers
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Abstract
Against the dual goals of carbon neutrality and common prosperity, inclusive green growth (IGG) has become the core path of urban sustainable development in China. Based on balanced panel data of 282 Chinese prefecture-level cities from 2016 to 2025, this paper constructs the IGG index via fixed-base range entropy weight method, and calculates the city-level AI development index through government work report text mining. We adopt IV-DML for causal identification, PSTR for nonlinear threshold estimation, and time-varying spatial Durbin model to capture dynamic cross-city spillovers, systematically exploring AI’s multi-dimensional impacts on IGG. The results reveal that AI significantly facilitates coordinated economic, social and ecological progress by expanding digital financial coverage, optimizing factor allocation and stimulating green technological innovation. Restricted by government science and technology fiscal input, AI’s enabling effect features smooth increasing marginal returns with two distinct transition thresholds. Spatially, AI initially triggers inter-city factor siphoning, then shifts to positive green technology radiation as regional integration deepens. Heterogeneity tests confirm resource-based cities and regions with mature digital infrastructure gain larger sustainability dividends from AI. Policy simulation indicates targeted allocation of AI-related S&T resources can improve urban low-carbon inclusive governance efficiency by 160%. This study supplies quantitative empirical support for integrated digital- green urban transformation and differentiated regional sustainable policy design.
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