The digital economy has aroused widespread concern. This paper studies the impact of the digital economy on innovation using a panel threshold model. Taking 30 provinces, municipalities, and autonomous regions in China as the research object, the time span is from 2013 to 2019. The data are from the National Bureau of Statistics of China (NBSC), China National Intellectual Property Administration (CAIPA), the China Stock Market and Accounting Research (CSMAR), and the Ministry of Industry and Information Technology (MIIT)of China. Data analysis is performed with ArcGIS 10.2 and STATA 16 software. The influence mechanism of digital economy on innovation is innovatively analyzed from the aspects of innovation elements, innovation tools, innovation subjects, and innovation environment. A digital economy development level index system is constructed using the entropy method, and the development level of China’s digital economy in time and space is analyzed. On this basis, the nonlinear impact of digital economy on innovation, i.e., the threshold effect, is innovatively studied using the panel threshold model. It is found that China’s digital economy develops rapidly, but there is a serious spatial imbalance, and there are great differences in the different dimensions of the digital economy. At the same time, the impact of digital economy on innovation has a double threshold effect with industrial structure as the threshold variable and a single threshold effect with urbanization level as the threshold variable. Specifically, the promoting effect of digital economy on innovation increases with the optimization of industrial structure or the improvement of urbanization level. This study enriches the theoretical research on the impact of digital economy on innovation, and it has important support and reference value for China’s development of digital economy and improvement of innovation capacity.

Transportation is an important factor affecting the balance of regional economic pattern. The construction of high-speed railway enhances the mobility of population, capital, technology and information resources between urban and rural areas. Will it further affect the income gap between urban and rural areas? Based on the nonlinear time-varying factor model, this paper analyzes the convergence of urban-rural income gap with the angle of high-speed railway. After rejecting the assumption of overall convergence in the traditional four economic regions, three convergence clubs of urban-rural income gap were found. For these ordered logit regression model is used to explore the initial factors that may affect the formation of “convergence club”. Empirical results show that the construction of High-speed railway has effectively narrows the urban-rural income gap in China, but it is not the cause of the formation of the three convergence clubs. The convergence effect of High-speed railway on the urban-rural income gap in China is still relatively weak.

Nowadays, China dominates logistics volumes, and its container logistics is associated with the largest sea ports, such as Shanghai, Shenzhen, and Ningbo. However, China’s coastal line is long and contains numerous million-container-handling sea ports. Current leading sea ports are located mostly in the south or at the middle point of the coastal line. Volumes are rather concentrated in these few areas. Despite the fact that China’s vast population is well-spread throughout the coastal line, major cities are also located in the hinterlands. Apart from some regions (e.g., the Pearl and the Yangtze River Delta) where there are many cities that are very close to each other, distances between cities are rather long in general. Therefore, this research examines the CO₂ emission reduction potential of using a larger number of sea ports (such as distribution hubs), as well as the interaction of these with analytically chosen dry ports. Results of the hypothetical country level container transportation model, using linear integer programming concerning 51 cities (largest hinterland and container sea port cities), showed that better and more equal use of sea ports serving the major cities will result in considerable emission reductions. This is the case, even if hinterland transport is completely based on road transports. However, in a situation where the dry port structure with railways is further applied, the results showed that it should be concentrated on a few hinterland points first, but also assure that most remote, million-people city locations get priority for the railway.

The impact of population structure on carbon emission has always been a key area of research in modern society. In this paper, we propose a new expanded STIRPAT model and panel co-integration method to analyze the relationship between population aging and carbon emission, based on the provincial panel data in China from 1999 to 2014. Empirical results show that there exists a significant inverted U-shaped curve between the population aging and carbon emission. There also exist regional discrepancies, where the impact of the population aging on carbon emission in the eastern region is significantly positive. By contrast, a negative relationship arises in the central and western regions. Finally, several suggestions for low carbon development are provided.

For decades, trade between Europe and China has grown consistently, which has resulted in increased container transportation volumes. Such transportation has been dominated by sea-based options. However, over the years, an air-based mode of transport was developed, while it has lately become increasingly popular to use railways utilizing the Trans-Siberian land bridge. This latter approach boomed amid the COVID-19 crisis in 2020. However, the railway container boom in Eurasia has deeper roots than just the COVID-19 era. As is illustrated in this research work, international trade containers (trade between Russia and other countries, mostly China) and transit containers (e.g., serving the Chinese–EU route) were already showing some significance as early as 2003–2004. In 2020, their volume was already measured in the millions, regardless of the railway data source being used. This is well above the starting period in the 1980s and 1990s, when total annual volumes were around 0.1 million twenty-foot equivalent units (TEU). Container capacity has developed over the years, first being used for international trade and only lately for transit. As a preliminary comparison to air freight, the growth rate was roughly double that in the two-decade observation period.

In recent years, there has been a lot of attention paid to China’s Belt and Road Initiative (BRI), which aims to invest in better connecting China, South-East Asia, Central Asia, the Middle East and Europe. As countries that share the same continent, and are in many cases without proper sea connection (landlocked), the key mode of long-distance transportation is railways. However, numerous countries have different levels of past investments, labor productivity, transportation profile, and culture surrounding railways, and all of this leads to differences in overall efficiency. In this research, we apply well established and widely used data envelopment analysis (DEA) to evaluate the longitudinal efficiency of railway operations. This is the first time such an analysis is completed on the Belt and Road member countries. Efficiency itself hardly improved at all during the examination period, whether in passenger and freight or just freight transports. China itself represents an important benchmark for many countries, as its efficiency is all the time highest possible. In the network benchmarking analysis, it was shown that China, Estonia, Latvia, and Israel are often proposed benchmarks for the others to increase their efficiency in the future. From efficiency development perspective, Chinese railway sector is beneficial and more balanced to be benchmarked as compared to other significantly sized railway countries, like India or Russia.