Innovative policy synergies driving inclusive green growth: causal inference based on double machine learning

Abstract

Inclusive green growth aims to achieve multi-dimensional coordinated development of the economy, society and the environment. It is worth delving into how integrated innovation policies can systematically drive this goal. Utilizing a double machine learning model and panel data from 266 prefecture-level cities spanning 2006–2022, this study investigates the indirect transmission methods and impact effects of the “dual pilot” program—combining innovative city and national independent innovation demonstration zone initiatives—on inclusive green growth. The analysis is grounded in the Technology-Organization-Environment framework and investigates the policy’s heterogeneous effects across urban dimensions of location, characteristics, and scale, consistent with TOE theory. The results show that: 1. The “dual pilot” policy greatly encourages inclusive green growth and has a synergistic effect larger than the sum of its parts; this conclusion is supported by a number of reliability tests; 2. The mechanism tests indicate that the “dual pilot” policy can indirectly promote inclusive green growth by increasing R&D investment and facilitating the diffusion of technological innovation at the technological level, advancing the structure of human capital and upgrading the industrial structure at the organizational level, and strengthening both formal and informal environmental regulations at the environmental level; 3. Heterogeneity analysis shows that the policy’s positive impact on inclusive green growth is more pronounced in eastern cities, non-resource-based cities, and major-sized cities. This research broadens the perspective on how innovative city and national independent innovation demonstration zone development contributes to inclusive green growth, providing empirical evidence and practical insights for promoting such growth.