Journal of Geographical Sciences ›› 2018, Vol. 28 ›› Issue (12): 1781-1792.doi: 10.1007/s11442-018-1565-y

• Research Articles • Previous Articles     Next Articles

Are Chinese resource-exhausted cities in remote locations?

Wei SUN1,2(), Lingxiao MAO1,2   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-08-30 Accepted:2017-10-28 Online:2018-12-20 Published:2018-12-27
  • About author:

    Author: Sun Wei (1975-), PhD and Associate Professor, specialized in regional sustainable development and spatial planning. E-mail: sunw@igsnrr.ac.cn

  • Supported by:
    National Natural Science Foundation of China, No.40701044

Abstract:

Numerous domestic scholars have argued that a remote location is the major factor preventing the transformation and sustainable development of resource-exhausted cities. Research to date, however, has not presented relevant evidence to support this hypothesis or explained how to identify the concept of ‘remoteness’. Resource-exhausted cities designated by the State Council of China were examined in this study alongside the provincial capital cities that contain such entities and three regional central cities that are closely connected to this phenomenon: Beijing, Shanghai, and Guangzhou. Spatial and temporal distances are used to calculate and evaluate the location remoteness degrees (LRDs) of resource-exhausted cities, in terms of both resource types and regions. The results indicate that resource-exhausted cities are indeed remote from the overall samples. Based on spatial distances, the LRDs are α1 = 1.36 (i.e., distance to provincial capital city) and β1 = 1.14 (i.e., distance to regional central city), but when based on temporal distances, α2 = 2.02 (i.e., distance to provincial capital city) and β2 = 1.44 (i.e., distance to regional central city). Clear differences are found in the LRDs between different regions and resource types, with those in western China and forest industrial cities the most obviously remote. Finally, the numbers of very remote resource-exhausted cities based on spatial and temporal distances (i.e., α > 1.5 ∩ β > 1.5) are 14 and 19, respectively, encompassing 17.9% and 24.4% of the total sampled. Similarly, 25 and 30 not remote resource-exhausted cities based on spatial and temporal distances (i.e., α ≤1.0 ∩ β ≤ 1.0) encompass 32.1% and 38.5% of the total, respectively. This study provided supporting information for the future development and policy making for resource-exhausted cities given different LRDs.

Key words: resource-exhausted cities, location remoteness degree, method of recognition, China