China's transport infrastructure distribution and its economic activities have largely the same pattern of spatial clusters. This paper aims to determine whether causal linkages exist between transport infrastructure investment and economic growth in China at national and regional levels. We examine causality in a panel cointegration and a Granger causality framework using time series data throughout the 1978–2008 period. The empirical findings show that in the long run, at the national level, there is unidirectional Granger causality from economic growth to transport infrastructure; at the regional level, there exists bidirectional causality in the affluent eastern region while the low-income central and western regions exhibit unidirectional Granger causality from economic growth to transport infrastructure. These results imply that an improvement in transport infrastructure alone is not sufficient for stimulating economic growth in the underdeveloped areas of China. To better realize the economic benefits brought by transport infrastructure, the Chinese government should pay serious attention to the development of complementary factors in the central and western provinces.

This study aims to explore the impact of transport capital stock on economic growth in Chinese regions. Using panel data for a sample of 28 provinces and municipalities over the period 1978–2008, the empirical findings show that there is significant spatial variation in the productivity effects of transport infrastructure in China. The highest output elasticity in the connection provinces of the central region means that transport investments there will yield the highest economic returns, which is in line with “the emergence of new economic center” theory. From a policy perspective, China had best give priority to the development of transport facilities in the backland region to realize the rise of Central China.

Road and transportation have a significant role in the prosperity, economic growth and development of the region. The main purpose of this study is to investigate the in-depth analysis of local resident’s attitude toward road and transport infrastructure (China-Pakistan economic corridor) and the wider economic, social, cultural and environmental impact on local people. Data were collected using questionnaire survey from the local people. Factor analysis and structural equation modeling approach were used to test the relation of the observed and latent variables. The result discovered road infrastructure has significant socio-economic and cultural impacts which significantly affect the local people support for CPEC development. It also revealed that more promotion and awareness regarding benefits of the project for the dwellers lead to more support of the local residents in the study area. This study investigated the attitude and support of the local people based on the road infrastructure social, economic, cultural and environmental impact which has never been examined in the existing literature.

Digital government construction is a complex system project, and data sharing is its governance niche. Cross-sectoral data sharing is the core issue of improving governance capacity in the construction of digital governments. Aimed at the dilemma of insufficient data sharing across departments, according to evolutionary game theory (EGT), we refined the game relationship between the data management department and the different government functional departments participating in cross-department data sharing. We used white Gaussian noise as a random perturbation, constructed a tripartite stochastic evolutionary game model, analyzed the stability of the stochastic game system and studied the influence of the main parameters on the evolution of the game system with the help of numerical simulation. The results show that there exists a positive stable point in the process of cross-department data sharing. The external effect of data sharing can be improved by enhancing the investment in data sharing by government functional departments. The accumulation of interagency trust relationships can gradually eliminate the differences in data sharing among different departments. The coordination mechanism of government data sharing and the construction of the “good and bad reviews” system can form an internal and external adjustment mechanism for functional departments and the data management department and can promote multiple departments to participate in cross-department data sharing more actively.