This paper quantitatively analyzes the economic resilience of resource-based cities (RBCs) in Northeast China in terms of resistance and recoverability during two economic crises: the Asian financial crisis and the global financial crisis. Moreover, it analyzes the main factors that affected regional resilience. There are three main findings. First, the RBCs in general demonstrated poor resistance during both recessions, but there were variations among the different types of RBCs. Petroleum and metal cities demonstrated the most resistance, whereas coal cities performed the worst. Second, the influential factors affecting economic resilience varied across the two economic cycles, but location advantage, research and development (R and D) intensity, foreign trade dependence ratio, and supporting policies had positive effects on resilience during both economic cycles, while the proportion of employed persons in resource industries had a negative effect. Industrial diversity had a weak and ambiguous effect on resilience. Third, the secondary industry was more resilient during the Asian financial crisis, but the tertiary industry was more resilient during the global financial crisis. This shift may be attributed to both the nature of the crises and the strength of the sectors at the time of the crises.
Resource‐based cities (RBCs) whose economies depend primarily on exploiting and processing natural resources usually have rigid, singular, and low‐end industrial structures, which often cripples their ability to cope with external disturbances such as international resource price fluctuations and economic downturns. This paper quantitatively analyzes the economic resilience of RBCs in China in terms of resistance and recoverability during the Asian financial crisis and the global financial crisis. Furthermore, it identifies the main factors affecting resilience. There are four main findings: First, RBCs were quickly and negatively impacted by the Asian financial crisis, which suggests that economic resistance was generally low during this period. In the recovery period, while the rate of recovery was slow at the beginning, economic recoverability improved after 2002. Economic resistance and recoverability were found to have a strong negative correlation. Second, at the beginning of the global financial crisis, the economic resistance of RBCs was generally high. However, after 2012, the number of cities that were severely affected by the economic crisis increased rapidly. Third, economic resistance varied across different types of RBCs. Coal‐based and forestry‐based cities had lower economic resistance, while oil & gas‐based cities were more resistant. RBCs in the Eastern region generally had low economic resistance, while the economic resilience of recessionary cities was also low. Finally, while factors affecting the economic resilience varied across the two economic cycles, we found that economic development, labor conditions and, most of all, the industrial structure had a statistically significant negative effect on economic resilience.
The relationship between economic development and energy consumption is revealed by employing cointegration theory, the index decomposition method, and a log-linear regression approach based on a case study of Jilin Province, China. The results suggest: 1) the economic development and energy consumption are interdetermined, whose relationship is positive and long-term. The economic development is highly depending on the energy in Jilin Province. 2) Under the condition of other unchanged factors, the change of industrial energy efficiency contributes to the energy saving, while that of industrial structure increases the energy consumption. 3) The industrial structure change enhances the energy intensity, but the energy utility efficiency change lowers it. From the view of contribution to the energy consumption, the contribution of industrial structure was more than that of the energy utility efficiency in 2000-2011. 4) In 2000-2011, the comprehensive energy intensity change and hydroelectricity energy intensity change were related to all industrial structures' change, and the influencing factors about structure of oil energy intensity change were more than those of coal energy intensity change; from the impact degree, agricultural proportion decreased exerted an positive and greater effect on lowering the energy intensity of comprehensive energy and hydroelectricity, and industrial one did on coal and natural gas. Some conclusions can be drawn as follows: the major way to promote the coordinated development of the industrial economy and energy consumption is to optimize the industrial structure by increasing the proportion of the tertiary industry and low energy consumption industrial sectors and to enhance the energy utility efficiency.
This study analyzed the spatial-temporal heterogeneity of green development efficiency and its influencing factors in the growing Xuzhou Metropolitan Area for the period 2000-2015. The slacks-based measure (SBM) model, spatial autocorrelation, and the geographically weighted regression (GWR) model were used to conduct the analysis. The conclusions were as follows: first, the overall efficiency of green development of the Xuzhou Metropolitan Area decreased, the regional differences and spatial agglomeration shrunk and differences within the region were the main contributors to the regional differences of green development efficiency. Second, the counties with high-efficiency green development were distributed along the coast, and along the routes of the Beijing-Shanghai and the Eastern Longhai railways. A developing axis of the high-efficiency counties was the main feature of the spatial pattern for green development efficiency. Third, regarding spatial correlation and green development efficiency, the High-High type counties in the Xuzhou Metropolitan Area formed a centralized distribution corridor along the inter-provincial border areas of Henan and Jiangsu, whereas the Low-Low type counties were concentrated in the external, marginal parts of the metropolitan area. Fourth, the major factors (ranked in decreasing order of impact) influencing green development efficiency were innovation, government regulations, the economic development level, energy consumption, and industrial structure. These factors exerted their influence to varying extents; the influence of the same factor had different effects in different regions and obvious spatial differences were observed for the different regions.
Material dematerialization is a basic approach to reduce the pressure on the resources and environment and to realize the sustainable development. The material flow analysis and decomposition method are used to calculate the direct material input (DMI) of 14 typical mining cities in Northeast China in 1995-2004 and to analyze the dematerialization and its driving factors in the different types of mining cities oriented by coal, petroleum, metallurgy and multi-resources. The results are as follows: 1) from 1995 to 2006, the increase rates of the DMI and the material input intensity of mining cities declined following the order of multi-resources, metallurgy, coal, and petroleum cities, and the material utilizing efficiency did following the order of petroleum, coal, metallurgy, and multi-resources cities; 2) during the research period, all the kinds of mining cities were in the situation of weak sustainable development in most years; 3) the pressure on resources and environment in the multi-resources cities was the most serious; 4) the petroleum cities showed the strong trend of sustainable development; and 5) in recent years, the driving function of economic development for material consuming has continuously strengthened and the controlling function of material utilizing efficiency for it has weakened. The key approaches to promote the development of circular economy of mining cities in Northeast China are put forward in the following aspects: 1) to strengthen the research and development of the technique of resources' cycling utilization, 2) to improve the utilizing efficiency of resources, and 3) to carry out the auditing system of resources utilization.
Based on the adaptive analysis paradigm, this paper constructs an evaluation index system and an evaluation model of the level of industrial ecology of a restricted development zone from the perspective of the industrial system and of the environmental system, and studies the spatial-temporal differentiation characteristics and the driving factors of the level of industrial ecology of the restricted development zone of the Shandong Province, China, by using a variety of measurement methods. The results show that: 1) In the temporal dimension, the level of industrial ecology of the research area increased from 2005 to 2017, while in the regional dimension, it was higher in the eastern coastal areas, followed by the northwestern area and the southwestern area; 2) In the spatial dimension, from 2005 to 2017 the level of industrial ecology of the research area had a clear spatial dependence, and the regional spatial agglomeration of the restricted development zones with similar industrial ecology levels become increasingly evident; 3) On the whole, the industrial ecology level in the study area had a clear spatial differentiation pattern, as it was higher in the north and in the east and lower in the south and in the west. Moreover, its evolution model changed from a 'three-core driven model' to a 'spatial scattered mosaic distribution model', and then to a 'single-core driven model'; 4) Industrial ecology was positively correlated with economic development, foreign investment, science and technology, and negatively correlated with the government role, while industrial structure and environmental regulation failed to pass the statistical significance test.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.