Measuring policy diffusion intensity: A text-driven analysis of government documents

Abstract

Accurately measuring policy diffusion intensity is crucial for understanding innovation dissemination mechanisms, yet existing approaches face limitations in capturing its dynamic nature. By applying text-driven methods to government documents, this research constructs a two-dimensional quantitative indicator that integrates both hierarchical effectiveness and textual intensity to capture policy diffusion dynamics. Using the panel data analysis of 9091 government documents from 2007 to 2022, we systematically examine the diffusion mechanisms of China's low-carbon policies. Our findings reveal that learning, imitation, and coercion are the primary mechanisms driving the intensity of low-carbon policy diffusion, while economic competition plays an insignificant role. Furthermore, urban carbon emission levels and public environmental awareness promote policy diffusion, whereas energy consumption dependency inhibits it. By demonstrating an effective application of text mining techniques in measuring policy diffusion intensity, this study provides new methodological and empirical insights for understanding policy diffusion within multi-level governance systems.