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Resource-dependent cities (RCs) are a vulnerable group of urban settlements that often face population shrinkage; however, population changes in RCs in China are not well understood. This study offers new insight into this matter through a robust analysis that features a longer time scale, a larger sample of RCs, and a finer unit of analysis. It finds new evidence that problems of population shrinkage in RCs are more serious than previous literature has suggested. Approximately 30% of the studied units have experienced either long-term or short-term population shrinkage, and many more are experiencing a slowing down of population growth. Problems are especially common among three types of RCs: the resource-depleted RCs, the forestry-based RCs, and RCs in Northeast China. These results underscore transition policy inadequacies in addressing population loss, and call for a more comprehensive and diversified population policy that tackles the multifaceted factors that contribute to population shrinkage, including lack of industrial support, maladjustment to market oriented reformation, poor urban environment and natural population decline. [ABSTRACT FROM AUTHOR]

The recession and revitalization of old industrial cities concerns urban industrial evolution and its characteristics. Based on the theory of evolutionary resilience, we developed an analytical framework for the industrial structure evolution of old industrial cities, and applied the framework to a case study in Shenyang. The following conclusions are drawn. First, since 1978, Shenyang’s industrial growth capacity has shown fluctuation between ‘contraction-expansion’. As the secondary industry has a much stronger expansionary and contractionary capacity for growth, this results in lacking stability leading to industrial structure transformation. Second, since 1999, the orientation towards a high-end manufacturing industry in Shenyang has weakened, and the evolution of the new and old growth path is characterized by low-end orientation. Third, since 2007, Shenyang’s industrial innovation output capacity has dropped sharply which has been significantly affected by scientific and technological personnel and enterprise-owed science and technology institutions and to a less extent by R&D expenditure. We applied the resilience theory to study the industrial evolution of an old industrial city, explored new study perspectives on industrial evolution and verified the applicability of the resilience theory. This paper provides a scientific reference for understanding the recent deceleration in economic growth in the Northeast old industrial base, and for exploring new paths toward revitalization. [ABSTRACT FROM AUTHOR]

Using data from the Economic Advisory Center of the State Information Center (SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions of car sales of different kinds of automakers (foreign automakers, Sino-foreign joint automakers, and Chinese automakers), and then identified spatial clusters using the local Moran's indexes. Location quotient analysis was applied to examine the relative advantage of each type of automaker in the local markets. To explain the variations of car sales across cities, we collected several socioeconomic variables and conducted regression analyses. Further, factor analysis was used to extract independent variables to avoid the problem of multicollinearity. By incorporating a spatial lag or spatial error in the models, we calibrated our spatial regression models to address the spatial dependence problem. The analytical results show that car sales varied significantly across cities in China, and most of the cities with higher car sales were the developed cities. Different automakers exhibit diverse spatial patterns in terms of car sales volume, spatial clusters, and location quotients. The scale and incomes factor were extracted and verified as the two most significant and positive factors that shape the spatial distributions of car sales, and together with the spatial effect, explained most of the variations of car sales across cities. [ABSTRACT FROM AUTHOR]

Promoting active travel behavior and decreasing transport-related carbon dioxide (CO) emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated CO emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was significant spatial autocorrelation with CO emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO emissions per trip were negatively correlated with several built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-economic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets. [ABSTRACT FROM AUTHOR]