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This chapter investigates productivity and cost patterns in the all-cargo US air transport sector. We empirically test the productivity growth influence of changes in unexplained technology, air operations movement characteristics, and factor input prices. Findings show productivity trends depicting negative growth for the 1993–2001 sample, then shifting measurably such that productivity trends depict positive growth for the 2002–2014 sample. The post 2001 growth was fueled by changes in unexplained technological advancements. We interpret this finding as an indication of the importance of technological innovation as a performance enhancer in this transport sector. Findings also reveal a lack of productivity change associated with changes in input prices and movement characteristics. We interpret input price findings as indicating increases in factor input prices such as wages and fuel prices are commensurate with enhanced labor and fuel productivity. The movement characteristic findings are attributable to a lack of sustained increases in load factors, stage length, network size and carrying more volume over the network (density).

The purpose of this paper is to explore the changes of the scale of urban tourists in mainland China under the impact of COVID-19 and, specifically, the following questions: how did the scale of domestic tourists change nationwide and in the seven geographic regions? What are the differences in the changes among the seven geographic regions? What are the changes in the hot spot areas and spatial clustering of domestic tourists across the country?

Using the data of domestic tourist arrivals in 337 cities in mainland China from 2018 to 2021, this research analyzes the absolute differences and relative differences in the scale of domestic tourists nationwide and in seven geographic divisions with the help of indicators such as range analysis, standard deviation, coefficient of variation and Herfindahl–Hirschman Index and explores the changes in the hot spot areas and spatial concentration degree of the spatial scale of domestic tourists nationwide under the influence of the epidemic using kernel density analysis and spatial auto-correlation analysis.

The absolute differences in all seven geographical divisions continue to increase during 2018–2021. The domestic tourism in southwest China is extremely uneven. Absolute differences in the northwest and northeast regions are relatively small, and the development in attracting domestic tourists is more balanced. Relative differences in southwest China are comparatively large, with the trend of uneven development being obvious. The northeast, northwest and eastern regions of China are small, and the development is more balanced. The popularity of domestic tourism in the Beijing–Tianjin–Hebei region, as well as the Yangtze River Delta region, continues to decline and then pick up in 2021. The inland southwest region became a new domestic tourism hot spot in 2021. The size of domestic tourists from 2018 to 2021 in mainland China cities shows a significant positive spatial correlation, and there is a spatial agglomeration phenomenon, but some regional agglomeration types change from 2018 to 2021.

The impact of the epidemic on the number and spatial scale of domestic tourism in China has been clarified, which makes up for the comparison of domestic tourism changes before and after the epidemic. A clear understanding of the changes in the number and spatial scale of domestic tourists in different regions after the epidemic is conducive to the development of domestic tourism revitalization strategies in accordance with the actual situation of each province and promotes the internal circulation of Chinese tourism.

This paper tries to clarify the quantitative scale of domestic tourism in different regions after the epidemic, which is conducive to the development of domestic tourism revitalization strategies in cities in different regions according to regional characteristics and the actual situation of each province and to promote the healthy operation of the internal circulation of tourism in China. This paper also tries to show the changes of domestic tourism market hot spots, agglomeration conditions changes before and after the outbreak and the clarity of tourists’ preference space changes.

Scale of domestic tourists; Absolute difference; Relative difference; Spatial hot spot distribution; Spatial agglomeration change

本文旨在探寻疫情影响下中国大陆城市游客规模演化规律, 具体而言, 疫情影响下, 全国及七大地理分区的国内游客量规模变化如何？七大地理地区的变化有何差异？以及疫情影响下, 全国国内游客空间规模的热点区域和空间集聚程度有何变化？

利用2018-2021年中国大陆337各城市的国内游客量数据, 借助极差、标准差、变异系数、赫芬达尔指等指标分析全国及七大地理分区国内游客规模的绝对差异和相对差异; 借助核密度分析、空间自相关分析等ArcGIS分析工具, 探寻疫情影响下全国国内游客空间规模的热点区域和空间集聚程度的变化情况。

①绝对差异方面, 七大地理分区的绝对差异均持续增大。西南地区的游客量的绝对差异巨大, 国内游发展极不均衡。西北地区、东北地区绝对差异相对较小, 在吸引国内游客方面发展较为均衡。②相对差异方面, 西南地区的国内游发展相对差异较大, 发展不均衡趋势明显; 东北地区、西北地区、华东地区的国内游发展相对差异较小, 发展较为均衡。③热点区域变化方面, 京津冀地区、长三角地区的国内旅游热度持续下降, 在2021年有所回升; 内陆西南地区在2021年成为新的国内游热点区域。④2018年至2021年城市国内游客量规模均呈现出显著的空间正相关的关系, 存在着空间集聚现象, 但部分区域集聚类型在2018到2021年间发生变化。

①理论意义：明晰了疫情对中国国内旅游人次的数量规模和空间规模的影响, 弥补了当前疫情前后国内旅游业变化对比的研究; 阐明了疫情前后中国城市国内游客空间格局的变化, 拓展了研究情景, 丰富了中国旅游业时空变化的相关研究。②实践意义：明晰了疫后不同地区国内旅游人次的数量规模和空间规模变化情况, 以及国内旅游市场热点变化和游客空间偏好变化, 有利于各地区城市对症下药, 制定符合各省份实际情况的国内旅游业振兴策略, 促进中国旅游业内循环。

The nature of immigration to the United States has varied tremendously over the course of the last 100 years. While the rate of immigrants in comparison to the total population was as high as 14% in the early 1900s, it steadily declined due to regulations passed at the beginning of the First World War reaching its lowest point in 1970 at less than 5% (Bernard, 1998). Yet, ever since the early 1970s, in response to the Immigration and Nationality Act Amendments that replaced national-origin quotas with a single annual worldwide ceiling for all other immigrants while eliminating any numerical limitations for immediate relatives of U.S. citizens, the number of immigrants has been continuously on the rise. In 1996, about 1 of every 10 residents in the United States was foreign born. This is exemplified by the fact that more than one fourth of the present foreign-born population of the United States arrived after 1990 (U.S. Department of Homeland Security, 2004).

This paper examines the relationship between transport connectivity and regional economic development in China. It develops measurements appropriate for transport connectivity based on a set of evaluation models. This model is used to analyze the logistic connectivity of China’s 31 provinces by focusing on 11 variables, including some new factors (Density of road network, Density of railway network, Number of Internet Users) not used in previous studies, over the 13-year period from 2002 to 2014. Using panel data regression analysis, the empirical results show a statistically significant and positive impact of transport connectivity (factors like Density of road network, Density of railway network and Number of Internet Users) on economic development in China. In particular, the Number of internet users is a key factor reflecting information connectivity in all the variables. Comparative analysis regarding economic development is conducted to benchmark between coastal provinces and interior provinces. Like most previous research, this study yields the same finding of higher impact of transport connectivity on economic development in eastern provinces than in western provinces. This study suggests that decentralized decision-making will be significantly more efficient for analyzing regional infrastructure development. It also shows that the influence of transport connectivity on economic development is dependent on a certain developmental stage. This suggests that an economic region should adopt different development strategies for transport connectivity during different stages of development.

With the development of regional economy, innovation network plays an increasingly prominent role in reducing regional innovation cost and enabling information, knowledge and capital flow and diffusion. Building an efficient innovation network has become a feasible way to improve regional innovation capabilities and performance. Thus, under the background of Chinese special triple helix structure, the specific mechanism of innovation network characteristics on regional innovation performance is still an issue that needs to be studied urgently.

This study conducts a multi-level regression analysis regional innovation panel data of China from the past four years to explore how the three dimensions of innovation network characteristics (i.e. network density, openness and strength) affect regional innovation performance.

The results show that there is an inverted-U relationship exists between network density and regional innovation performance. The network openness and regional innovation performance show a significantly positive relationship and a U-shaped relationship exists between network strength and regional innovation performance.

First, this study examines the relationship between network density, openness, strength and innovation ability in the network variable structure. However, this study does not analyze how absorptive capacity impacts the network structure and innovation performance of regional innovators. Second, innovation network intensity largely varies according to different types of enterprises or industries. Therefore, future studies can attempt to analyze the relationship between innovation network and innovation performance on the basis of the industry or the enterprise itself. Fourth, this study does not consider the change in the influence of innovation network structure on innovation ability.

The results of this study provide insights for the formulation of the regional innovation policy. First, enterprises must maintain good contact with research institutes, universities and technology intermediaries and promote resource, information and money flow between networks through formal and informal interactions. Enterprises can currently only interact with a limited number of innovative subjects due to their limited resources. Therefore, we should continue implementing the open policy of foreign capital utilization and absorb the technology, knowledge, management, ideas, talent and other resource advantages in the world. The investment environment in the central and western regions should be improved as soon as possible to guide foreign direct investments to the middle and the west part of China, thereby promoting the coordinated development of regional innovation ability and economy.

In 2015, the Chinese Government proposed “public entrepreneurship and innovation,” including technological, institutional, management and mode innovations. This study suggests that innovation subjects in the region should establish an interactive and dynamic innovation network among innovators.

The innovation of this study lies in the analysis of the characteristics of innovation network that significantly affect regional innovation performance, the exploration of different stages of innovation network construction path and provide theoretical guidance for the construction of innovation network.

The purpose of this paper is to investigate the relationships between structure characteristics of project network, types of conflicts and project success.

Network density and centrality were used to reflect the structure characteristics of project network. This study collected 254 valid responses from construction professionals (including project managers, department managers and project engineers) via a questionnaire survey and analyzed the data using structural equation modeling and bootstrapping techniques.

The results showed that network centrality of project stakeholders negatively affected project success, whereas the effect of network density on project success was non-significant. The network density was positively related to task conflict, whereas negatively related to process and relationship conflict. Network centrality was positively related to relationship conflict and had negative effects on task and process conflict. Project conflicts served as the mediator, weakening the relationship between network structure characteristics and project success.

This study provides direction for project managers and other stakeholders (e.g. owners, contractors and subcontractors) to appropriately establish social ties and manage conflicts to achieve project success. However, the potential influence of conflict transformation on project success, the dynamic nature of project networks and the network diagram were not addressed in the context of diverse culture. The future research should cover different stakeholders in order to get an integrative understanding of project networks and collect data from different cultural and industrial characteristics, extending and verifying the results.

The outcomes of the study provide evidence in regard to social network ties governance, which is comprised by the important or representative stakeholders, being a part of the effective strategy in improving project success. This study also contributes to the knowledge of conflict management in the project context, revealing the positive and negative of project conflicts and enriching the current understandings of the underlying mechanism of the project network characteristics on project success.

This study aims to examine the trend of industrial competition between the US and China, which is the most crucial determinant in the future development of the global economy. For decades, the global economy has strengthened the global production network based on the division of labor between countries. Thus, the ripple effect of competition between the two countries should be analyzed in terms of the global production network. Therefore, this study uses the product space model, which explains the development process of industries with comparative advantage by country. We constructed the model based on the products of HS 4-digit code for the 2010–2019 period. The analysis results on the trend of the industrial competitiveness of major countries are as follows. First, the current industrial competitiveness of China is concentrated on low-tech industries. In the case of high-tech items, China shows a tendency of lower export sophistication compared to major manufacturing powerhouses such as Germany, the US, Japan, and Korea. Second, with respect to the possibility of a future industrial structure upgrade evaluated by density, the trend of China overtaking other manufacturing powerhouses is observed. As implied by the product space model, the advancement of the industrial structure through active participation in international trade enhances the industrial competitiveness. Therefore, the outcome of US-China industrial competition depends on who ensures more openness and industrial complexity.

The current outbreak of COVID-19 is an unprecedented event in air transportation. In this study, we investigate the impact of COVID-19 on global air transportation through the lens of complex networks different at different scales, ranging from worldwide airport networks where airports are nodes and links between airports exist when direct flights exist, to international country networks where countries are contracted as nodes, and to domestic airport networks for representative countries/regions. We focus on the spatial-temporal evolutionary dynamics of COVID-19 in air transportation networks, discovering hidden patterns on flight frequency reduction. Our study provides a comprehensive empirical analysis on the impact of the COVID-19 pandemic on aviation from a complex system perspective.