Zilong Wang’s research while affiliated with Nanjing University of Aeronautics and Astronautics and other places

Purpose This study aims to explore the structure and dynamics of China’s inter-provincial green technology transfer network, examining how different types of environmental regulation impact the network’s formation and persistence. The focus is on understanding how green technology transfer enhances provincial green innovation capabilities and how provinces contribute variably to the network. Design/methodology/approach Through channels such as green technology transfer, provinces can share their technological innovation resources and thus enhance their green innovation capabilities. This study constructs China’s inter-provincial green technology transfer network. Findings The results indicate that green technology transfer has taken place in most provinces. Over time, network edges, density and cluster coefficient have shown an increasing trend, indicating green technology transfer is on the rise. There are significant asymmetries and biases in the spatial distribution of the degree of inter-provincial green technology transfer networks. The network is structured as a core-edge structure and provinces play a heterogeneous role in green technology transfer. There are close technological ties between core provinces, while many provinces play a marginal role. Different types of environmental regulation have heterogeneous effects on network formation and persistence. Originality/value It offers valuable insights into the heterogeneous roles that provinces play within the network and highlights the importance of regulatory interactions in promoting the evolution of green technology transfer across regions.

This study uses the carbon emissions trading schemes (ETS) as a quasi-natural experiment to investigate how market-based environmental regulation influences technology cooperation. Analyzing a dataset from 2008 to 2021, our findings reveal that the carbon ETS policy significantly boosts the strength of intercity technology cooperation. We ensure the robustness of these results through placebo tests, PSM-DID, and by changing the dependent variable. Additionally, the carbon ETS encourages the formation of more technology cooperation partnerships between cities. Notably, when cooperation involves universities and research institutes (URI), the effects are particularly strong in both strength and partnerships. Furthermore, we find that the policy enhances technology cooperation by promoting environmental subsidies, environmental investment, and reducing institutional distance. The impact of carbon ETS is significant within a 250 km range, with URI cooperation strength remaining significant even beyond this distance. The effects of the carbon ETS on technology cooperation also vary depending on the type of city pair involved.

The authors examine how network externalities and various factors shape strategic interactions between manufacturers and technology suppliers in networked manufacturing contexts. Using an evolutionary game model, it explores manufacturers’ radical vs. incremental innovation choices alongside suppliers’ technology authorization vs. licensing strategies. The results show that both parties’ strategies often fluctuate without stabilizing under different initial conditions. While higher returns from radical innovation motivate manufacturers toward breakthrough efforts, suppliers consistently favor licensing regardless of profit-sharing adjustments and are highly sensitive to radical innovation costs. In contrast, changes in licensing fees predominantly influence manufacturers’ behaviors. Collaboration costs only lead to minor shifts in evolutionary trajectories.

With the dual constraints of resources and the environment, green growth has become the theme of industrial development in the new era. The transformation of economic growth patterns and supply-side structural reforms have introduced new requirements for industrial development. In this context, how a country’s environmental regulation policies affect its green growth is an empirical problem that requires resolution. Using an index based on the meta-frontier analysis framework, this study measures the regional industrial green index and its decomposition components under the constraints of energy and carbon emissions. We employed the panel vector autoregressive model and impulse response function tool to investigate the dynamic effects of different market-based environmental policies on industrial green total factor productivity (GTFP). Expenditure-style environmental regulation (lnEXP) generally exerts a limited effect on GTFP, while investment-style environmental regulation (lnINV) considerably promotes GTFP. The shock impacts of the two regulation policies on industrial GTFP exhibited considerable regional heterogeneity in terms of the response intensity, convergence path, and cumulative effect. The shock effect of lnINV was much higher in high-regulation regions than in low-regulation regions, while the opposite was observed for the impulse response effect of lnEXP. Moreover, a substitution relationship was determined between the two types of regulations in promoting GTFP. This study offers several policy recommendations in terms of encouraging “green investment,” deepening environmental tax reform, and coordinating the correlation between lnEXP and lnINV.

Accurate and timely fault diagnosis is of great significance for the stable operation of a distribution network. Traditional artificial intelligence-based localization methods rely heavily on large-scale labeled datasets, making them prone to being affected by distributed generators. To address this issue, this study proposes a fault location method for distribution systems using a cost-sensitive graph attention network model. In this approach, the physical structure of the distribution network is considered a crucial constraint for model training, thereby enhancing its information perception capabilities. Specifically, the electrical nodes and lines of the distribution network are mapped to the vertices and edges within the graph attention network, with the attention weights determined by the correlation of adjacent node fault characteristics, thus improving the sensitivity to grounding faults. Additionally, a cost-sensitive matrix is used to balance class distribution, enhancing the robustness and generalization ability of the model. Fault localization experiments were conducted on the IEEE-33 bus distribution system to validate the effectiveness of the proposed fault localization method. Factors such as data disturbance, varying fault grounding resistances, and distributed power supply access were employed to assess the model’s anti-interference performance. The experimental results demonstrate that the fault location method exhibits high positioning accuracy and excellent robustness.

Urban business circles are important locations for economic and social activities. Improving the vitality of urban business circles is conducive to stimulating the potential of the consumer market and promoting sustainable economic development. However, targeted research on the factors influencing business circle vitality is lacking. Therefore, in this study, we aimed to quantitatively examine the impact of the number and diversity of urban amenities on business circle vitality at the street block level using open-source geospatial big data from 32 Chinese metropolises. We found that the number of residential, transportation, educational, cultural, and recreational amenities and the diversity of catering and retail amenities had significant positive impacts on business circle vitality. Catering and retail diversity were the two most critical factors, followed by the number of transportation, cultural, and recreational amenities. However, the effect of urban amenities on business circle vitality varied considerably across different cities and business districts. The results of this study contribute to a holistic understanding of how to improve the vitality of business circles by optimizing urban amenities at the street block level.

Recent theoretical work shown the traditional measures of institutions to facilitate in Global Value Chains (GVCs) participation, however, little are known empirically about the importance of non-traditional institutional measures for the facilitation in Global Value Chains participation across countries. This paper makes a significant contribution to filling these gaps in the literature by using an instrumental approach for non-conventional institutional measures, supplemented by the economic freedom index and each of its individual dimensions among the regressors and standard controls, and a data set of sixty countries covering the years 2000 to 2018. To account for potential endogeneity of institutional measures and for other regressors, we apply GMM estimators and the findings reveal that participation in Global Value Chains is positively affected by economic freedom index and these findings are robust with both behind-the-border facilitation and at-border facilitation. Except access to sound money, all other institutional indicators are positively associated with participation in Global Value Chains. These findings imply that strengthening a country’s participation in global value chains can be accomplished by ensuring the integrity of its legal and economic institutions.

Technological innovation has emerged as a vital strategic resource in recent years in the context of global economic and technological competition. This study combines the concepts of ecosystem theory and evolutionary economics to model the functioning of three types of innovation ecosystems: independent, competitive, and cooperative. It then adopts an innovation systems approach to assess the efficiency of technological innovation in China from 2011 to 2021 by using the network DEA-based model. The results show that the overall average efficiency experienced a slight but noticeable increasing trajectory, particularly in Beijing, Heilongjiang, and Shaanxi, where the increases were particularly considerable. Regions exhibit greater technical innovation efficiency while operating inside independent innovation ecosystems, followed by cooperative innovation ecosystems, and the lowest efficiency is observed in competitive innovation ecosystems. From 2011 to 2021, the average efficiency of commercializing R&D in the 30 provinces of China varied between 0.68 and 0.81. The average efficiency of resource backflow had a W-shaped trend. Beijing was consistently efficient in resource backflow throughout the study period according to the DEA. On the contrary, the efficiency of resource backflow in Liaoning, Chongqing, and Xinjiang was relatively low. This study highlights the need for improved resource allocation to enhance innovation efficiency, providing valuable insights for policymakers in optimizing regional strategies and resource management. Graphical abstract

This paper considers the “main manufacturer-supplier” model in collaborative cooperation among firms which requires the leader to invest significant resources and bear huge risks. However, few scholars simultaneously consider innovation risks and incentive issues under the model. We construct a Stackelberg game incentive model with different cost-sharing ratios under the risk of technological innovation. We characterize the equilibrium of the model and highlight the key role played by the main manufacturer and supplier. The results are as follows: (a) Main manufacturer can implement the cost-sharing incentive strategy under certain conditions, which is, its profit coefficient is greater than 0.75 times that of supplier and the cost-sharing ratio’s expected coefficient is greater than the minimum threshold. (b) The optimal cost-sharing ratio is directly proportional to the profit coefficient of the main manufacturer, inversely proportional to that of the supplier, and shows an inverted U-shape function with the probability of successful technological innovation. (c)This strategy can motivate suppliers to invest more resources, reduce the investment of the main manufacturer, simultaneously increase the profits of the main manufacturer and supplier in the certain ranges of innovation success probability and profit coefficients, with significant incentive effects.