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.

In this paper, we present a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP). The 2E-GUCRP arises in the field of Unmanned Aerial Vehicle (UAV) Routing Problem such as those encountered in the context of city logistics. In a typical cooperated system, the UAV is launched from the Ground Vehicle (GV) and automatically flies to the designated target. Meanwhile, acting as a mobile base station, the GV can charge or change the UAV’s battery on the designated landing points to enable the UAV to continue its mission. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) model, which could be solved by commercial software, is constructed to describe this problem. In order to quickly solve the medium-scale problems, two existing heuristics to solve 2E-VRP are improved. The computational experiments are set up to compare our model with the 2E-VRP. The results indicate that the 2E-GUCRP obtains a better efficiency. Further discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.

Recommendation system plays a crucial role in demand prediction, arousing attention from industry, business, government and academia. Widely employed in recommendation system, matrix factorization can well capture the potential relationships between users, items and latent variables. In this paper, we focus on a specific recommendation task on the large scale opinion-sharing online dataset called Epinions. We carried out recommendation experiments with the Bayesian probabilistic matrix factorization algorithm and the final results showed the superior performance in comparison to six representative recommendation algorithms. Meanwhile, the Bayesian probabilistic matrix factorization was investigated in depth and the potential advantage was explained from the model flexibility in parameters’ adjustment. The findings would guide further research on applications of Bayesian probabilistic matrix factorization and inspire more researchers to contribute in this domain.