Chinese Journal of Urban and Environmental Studies

The rampant spread of coronavirus disease 2019 (COVID-19) is worth examining from different angles. From the angle of urban spatial patterns and urban morphology, urban spatial agglomeration, polarization of spatial patterns, and the concept of centralization adopted in planning urban space are objective conditions that have aggravated the pandemic. This paper examines the mechanisms and effects that may have helped the spread of the pandemic. Considering the rapid spread and severe outcome of the coronavirus disease due to the spatial agglomeration of urban population, it is required to make a trade-off among economic, ecological and safe rationalities, and put safe rationality first.

Transportation is the most basic element in the development of tourism, with expressways being the main channel for tourism transportation in the era of driving tours. The relationship between transportation and tourism development has always been one of the hot topics in tourism geography study. Since the expressway service area is the main node of tourist service facilities in the expressway transportation network, we conducted a field research on 125 service areas and 177 tourist attractions in Guizhou Province, and obtained relevant data. By using the centrality measures for social network analysis, and combining the kernel density analysis with bivariate spatial autocorrelation analysis, this paper explores the centrality characteristics of expressway transportation network, the spatial distribution regularity of tourist attractions, and the spatial coupling between them. The main study conclusions are as follows: (1) The expressway service areas in Guizhou Province gradually decrease from a single core to the periphery. (2) The spatial distribution of tourist attractions presents a weak circular structure with multiple cores in a scattered manner, and the regional differences are apparent. (3) There is positive spatial correlation between the expressway networks and tourist attractions. The spatial coupling between the expressway transportation network and tourist attractions can be divided into four types of clusters: high–high cluster, high–low cluster, low–high cluster, and low–low cluster, with high–high and low–low clusters as the main types. The high–high cluster is the area with the best coupling effect and the most mature tourism. The high–low and low–high clusters have a mismatch between transportation and tourism resources. For such clusters, the spatial coupling can be realized by changing the functions of the service area or improving the quality of the tourist attraction. They have great development potential. The low–low cluster is far away from tourist attractions and markets, thus it has a small value of tourism development, and it is the critical area for future improvement. The conclusion of this study can provide a theoretical basis and scientific suggestions for promoting integrated and coordinated sustainable development of transportation and tourism.

The issue of urbanization has gained much importance over the last few decades due to its significant influence on economic growth and environmental quality, especially in developing countries. The non-linearity puzzle has been a long-debated issue, and prior studies provide mixed evidence. This study addresses the issue of urbanization using the measure of urban agglomeration and investigates the non-linear relation between urbanization and CO₂ emissions at the regional level. The South Asian region represents approximately one-fourth of the world population and its urbanization needs to be addressed properly. This paper uses the annual data over the period of 1974–2014 for four South Asian countries, namely, Pakistan, India, Bangladesh, and Nepal. The panel cointegration tests establish the long-run relation between urbanization, urban agglomeration, economic growth, trade openness, energy consumption, financial development, and CO₂ emissions. The fully modified ordinary least squares (FMOLS) model further confirms the existence of Environmental Kuznets Curve (EKC) in South Asia. Moreover, urbanization has an inverted U-shaped relation with CO₂ emissions, while urban agglomeration has a U-shaped nexus with CO₂ emissions for overall sample. The bidirectional causal relationship has also been confirmed between urbanization and CO₂ emissions, between urban agglomeration and CO₂ emissions, between financial development and CO₂ emissions both in the long-run and short-run. On the other hand, unidirectional causality runs from economic growth, trade openness, and energy consumption to CO₂ emissions in the long-run. The rising trend of urban agglomeration in metropolitan cities in South Asia is adversely affecting the environment. The current study has implications for policymakers and respective governments to adhere to more stringent urban planning.

Accurate estimation of CO₂ emissions is a prerequisite for scientific low-carbon emission policymaking. Based on 20 types of energy consumption data at the prefecture level in China, this paper re-estimates the CO₂ emissions of 198 prefecture-level cities in 2016 by using the method of carbon emission coefficient. The spatial pattern and scale characteristics are analyzed, and the conclusions are as follows: (1) Overall, China’s urban CO₂ emissions show a certain degree of spatial separation in terms of the total amount, per capita emissions, and emission intensity. Cities with the highest CO₂ emissions in China are mainly concentrated in North China, East China and Chongqing, while cities with the highest per capita CO₂ emissions and emission intensity are mainly concentrated in Northwest and North China. (2) Different types of cities have different CO₂ emission characteristics. Resource-based cities have a higher total amount and emission intensity; tourism and underdeveloped cities both have lower values; while super-large-sized cities and many very-large-sized cities have higher CO₂ emissions, but their emission intensities are usually lower; and no obvious rules are found in other cities. (3) Spatial analysis shows that cities with higher CO₂ emissions are clustered. The Beijing–Tianjin–Hebei region, the Yangtze River Delta region, Shandong Province, and Shanxi–Henan–Anhui resource-producing areas are the agglomeration areas of high-emission cities. (4) Scale analysis shows that the characteristics of CO₂ emissions at different scales are different. Provincial-level research can help to identify the environmental impact and total effect of carbon emissions, while urban-scale research is helpful to explore the diversity and phases of cities. Finally, based on the main conclusions of this study, the corresponding urban low-carbon policy implications are drawn.

In 2011, China entered into an era of urban society with a majority of urban population. It is a miracle in human history and a momentous change in Chinese society. Under this context, this paper establishes an index system to measure the quality of urbanization, including the rate of household registered population and deviation of urbanization based on the three aspects of urban development, residents’ life and urban–rural integration and the five dimensions of population, economy, space, society and urban–rural coordination. The entropy method and inter-provincial panel data in 2011–2017 are used to conduct empirical analysis on the quality of urbanization of China and its evolution in this period. It is found that although urbanization quality has been improved significantly and regional disparities have been alleviated slightly after entering the urban society, the overall pattern has not changed. Beijing, Tianjin and Shanghai as well as the eastern provinces including Jiangsu, Zhejiang and Guangdong are among the top, while the northeastern region continues to decline and the western inland provinces are still left behind. The most pressing task is to promote high-quality urbanization. With people as the core and “market” as the support, efforts should be made to improve social construction and urban–rural coordination, narrow the gap between the rate of household registered population and the rate of permanent urban population, promote the new urbanization focusing on quality and finally, build a high-quality modern urban society.

Based on the panel data of 202 prefecture-level cities within 14 national-level city clusters in China from 2007 to 2016, we established a dynamic panel model to measure the economic growth effects of city clusters and analyzed the main influencing factors. The results show that: (i) Technology has a significant impact on the economic growth of city clusters; the narrowing development gap between regions can help city clusters produce good economic growth effects; the city clusters, if more agglomerated, can help better utilize factors, and thus promote coordinated regional development. (ii) City clusters with multiple central cities boast a stronger engine of economic growth, and the impacts of factors such as technology and clustering degree on their economic growth are more noticeable. (iii) Geographical factors will also affect city clusters’ economic growth. The economic growth of city clusters in Southern China has been more strongly powered by the factors such as technology, clustering degree and human capital than those in Northern China. From the spatial perspective and by using the threshold panel method, we further explored the mechanisms with which the central cities within a city cluster can influence economic growth depending on their accessibility. The results manifest that the more accessible the central cities within a city cluster are, the stronger role they can play in leading and driving the economic growth of surrounding areas. In the future, it is important to promote the transformation of single-core and dual-core city clusters into multi-core city clusters, and give full play to the role of central cities in leading the development of surrounding areas. It is also necessary to vigorously develop technology and transportation to further facilitate the high-quality growth of city clusters.