Driving Green Technology Innovation via National Innovative City Policy—Evidence from a Combined DID, LSTM, and GRU Counterfactual Framework

In the context of global climate governance, green technology innovation is essential for urban sustainable development. To address the limitations of traditional linear econometric models, this study investigates the impact of the National Innovative City Pilot Policy on green innovation using a novel framework combining a Multi-period Difference-in-Differences model and Deep Learning Counterfactual Prediction. Analyzing panel data from 100 eastern Chinese cities between 2004 and 2023, the research reveals that the policy significantly and robustly enhances innovation levels in pilot cities. Furthermore, the policy operates through a dual-track synergistic governance mechanism, successfully combining government scientific and technological support with environmental regulation. Additionally, heterogeneity analysis reveals that the policy exerts a more pronounced driving effect on green innovation in small-to-medium-sized cities and regions with lower industrial upgrading levels. Finally, deep learning counterfactual trajectories demonstrate that the policy dividend exhibits a non-linear, long-term cumulative effect that expands over time—a dynamic that traditional linear models often underestimate. Ultimately, this study provides solid empirical evidence that a combined governance system of incentives and constraints effectively promotes innovation-driven, sustainable urban transitions.