Unleashing the empowered effect of data resource on inclusive green growth: Based on double machine learning

Abstract

Inclusive green growth is considered an essential strategy for achieving economic high-quality development, requiring the empowered effect of data resource. We analyze whether data resources drive inclusive green growth using Double Machine Learning (DML) methods on a sample of 301 prefecture-level cities from 2000 to 2021. Our study examines the mediating roles of talent, technology, and capital and explores siphoning and spillover effects. We find that a 1 % increase in data resources correlates with a 2.4 % rise in inclusive green growth. Notably, effects vary by city size, location, and policy timing. Data resources mainly influence growth through talent rather than technology, with capital having a negative mediating effect. Overall, these empirical findings offer valuable policy insights for managers and policymakers, helping them to enhance the intermediary role of talent and capital and to incorporate the siphoning effect into governance strategies.