Infrastructure construction related to the new generation of information technology and 5G technology is an important measure taken by the Chinese government to promote regional economic development. Large-scale infrastructure investment is being carried out simultaneously in China’s core and peripheral regions. The COVID-19 pandemic has dealt a severe blow to China’s tourism industry, and the application of new technologies seems to blur the spatial boundaries of the tourism industry. Therefore, it is debatable whether the zealous development of large investment projects can really improve the competitiveness of the regional tourism industry. This paper discusses this topical issue by empirically analyzing data from 31 Chinese provinces and cities from 2008–2019 and draws the following conclusions (1) The continuous expansion of new infrastructure investment in China indeed has a positive effect on improving China’s overall tourism competitiveness. However, the inverted U-shaped relationship between the two shows that China should not blindly expand the scale of infrastructure construction and make appropriate investment according to the regional industrial development level. (2) Although convergent infrastructure plays an important role in regional industrial competitiveness, the marginal effect has begun to weaken, so the problem of scale inefficiency needs to be addressed. In contrast, the input of innovation infrastructure is insufficient to enhance industrial competitiveness and can be moderately increased to achieve better results. (3) China’s core economic areas have a good driving effect on new infrastructure investment, but the original technological innovation and transformation-type facilities are still the key to limiting the improvement of industrial competitiveness. Peripheral areas are more passive recipients with strong demand. Therefore, investment in various types of infrastructure can drive regional development.

The nighttime economy (NTE) is one of the primary measures used by the Chinese government to promote urban consumption and capital flow. Especially after COVID-19, more regulations were introduced by both the central and local governments to accelerate this commercial activity. However, the relationship between the NTE and urban development is controversial. There has been controversy over the relationship between the nighttime economy and urban development. We believe that organizations/individuals embedded in different regional contexts have different behavioral patterns, which, in turn, can make cities develop nighttime commercial activities differently. We wonder whether the nighttime economy’s large-scale development fits the diverse regional development. There is a lack of discussions of the spatial distribution of nighttime commercial activities from an urban geographical perspective, especially the differences and mechanisms of urban systems based on the nighttime economy. Based on existing research arguments, this article collects points of interest (POI) and nighttime light (NTL) remote sensing data (RSD) to spatialize nighttime economic activities in order to provide a reference for reasonable regional and urban economic planning. The nighttime economy (NTE) is one of the primary channels used by the Chinese government to promote urban consumption and capital flow, and the relationship between the NTE and urban development is controversial. Based on existing research, we selected the Yangtze River Delta (YRD) region as an example. We found that there are core–peripheral spatial patterns in nighttime commercial urban systems. The core is Shanghai, and provincial-level core cities form the second category, largely overlapping with the administrative urban system. Although the NTE is primarily concentrated in economically developed coastal areas, it spreads in the northwest–southeast direction, indicating that opportunities will arise in the geo-periphery. Although regulations encourage the growth of the NTE, infrastructure cannot fully support large-scale centralized expansion. The interaction of critical factors, such as urban policies, residents’ consumption, industrial structure, and economic foundations, may affect nighttime activities.

Live streaming commerce (LSC) effectively combines the traditional real economy and e-commerce. Based on more than half a million unique GIS data values on LSC activities sourced via Taobao (Alibaba), we traced the spatial distribution of different players along the supply chain and further highlighted the intermediary role of streamers in developing the inter-regional industry. This study guides industrial planning in a diversified regional context, especially in economically peripheral regions. Our results show the following outcomes: (1) in contrast to dispersed suppliers, streamers and consumers are highly clustered. This trend proves that streamers are rooted in a specific urban context while playing the role of an intermediary in inter-regional supply chains, effectively extending geographic interactivity between suppliers and (potential) customers. (2) LSC primarily promotes regional light industry, especially in economically peripheral and rural areas, and provides opportunities for rapid development in cities with skilled handicraft providers. (3) China’s LSC streams have a pyramid structure, and the top group is highly clustered in metropolitan regions, such as the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). This clustering makes it easier for streamers to work with large, well-known brands. The bottom group is mainly in charge of expanding the supply chain within the region and relies more on the local industrial base. It is diversified due to the different types of businesses or products. Ultimately, we draw attention to adaptive spatial planning and resource allocation in the context of the economic and geographic reforms brought by this growing industry, and discuss the policy implications based on the relationships between the supply of and demand for live streamers from a broader regional perspective.

China’s high‐tech innovation and marketization efficiency still need to be optimized, which restricts the promotion of regional innovation and economic development. On such practical problem, this paper mainly focuses on improvement of high‐tech efficiency of China, with the hope that the research can help to find ways to improve efficiency in both regions and industry development. Moreover, the impact on the high‐tech innovation stage and the marketization stage are analyzed, in order to make clear the main problems in the complex process of high‐tech innovation. This paper proposed the super‐SBM model and the panel regression model. The conclusions are as follows. (1) The efficiency of high‐tech innovation in China is improving, but there are great differences within regions. Therefore, the heterogeneous regional innovation context should be taken into consideration in the institutional management policies. (2) There is a significant positive correlation between government subsidies and R&D intensity in improving the high‐tech innovation efficiency. Government needs to carry out appropriate policy guidance, increase financial support, and encourage high‐tech enterprises to increase R&D investment. (3) Openness and better innovation environment play a positive role in the technology marketization stage; thus, the establishment of inter‐regional cooperation or transnational relations is an effect way. Forming a better innovation environment can also help to enhance international high‐technology cooperation and improve marketization efficiency.

The formation mechanism of innovation networks is one of the core issues in the current research of innovation networks, and proximity plays an important role in the formation and development of innovation networks; however, which proximity is more important and how different proximities interact remain to be further researched. This study conducts a social network analysis and adopts a spatial interaction model to examine innovation networks among 290 Chinese cities. The results reveal that, first, the hierarchical characteristics of Chinese cities’ innovation networks reflect a core periphery structure and the spatial patterns of large dispersion and small agglomeration. Further, bound by the Hu line, the hierarchy is high in the east and low in the west. Second, geographical, institutional, and cognitive proximities positively affect Chinese cities’ innovation networking. Cognitive proximity, particularly, has the highest impact. Geographical proximity reinforces the effect of institutional proximity, and thus, their interactions are complementary.