地理学报

地理学报 ›› 2013, Vol. 68 ›› Issue (2): 199-208.doi: 10.11821/xb201302005

• 城乡发展 • 上一篇    下一篇

中国资源枯竭城市的区位条件辨析

孙威1,2, 李洪省1,3   

  1. 1. 中国科学院地理科学与资源研究所, 北京 100101;
    2. 中国科学院区域可持续发展分析与模拟重点实验室, 北京 100101;
    3. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京 100101
  • 收稿日期:2012-05-03 修回日期:2012-11-01 出版日期:2013-02-20 发布日期:2013-02-20
  • 作者简介:孙威(1975-), 男, 河南开封人, 博士, 副研究员, 中国地理学会会员(S110008181M), 主要从事资源型城市可持续发展和区域规划研究.E-mail: sunw@igsnrr.ac.cn
  • 基金资助:

    国家科技支撑计划项目(2008BAH31B01); 国家自然科学基金项目(40701044)

Quantifying location condition of resources-exhausted cities in China

SUN Wei1,2, LI Hongsheng1,3   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China;
    3. State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing 100101, China
  • Received:2012-05-03 Revised:2012-11-01 Online:2013-02-20 Published:2013-02-20
  • Supported by:

    National Key Technology R&D Program, No.2008BAH31B01; National Natural Science Foundation of China, No.40701044

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摘要： 国内很多学者认为资源型城市区位偏远是资源型城市转型和可持续发展的主要障碍,但资源型城市是否真的区位偏远以及如何进行科学识别一直没有给予回答.本文以国务院分3批确定的78 个资源枯竭城市为对象,以资源枯竭城市所在省的省会城市和与资源枯竭城市联系紧密的北京、上海、广州3 个区域中心城市为参照对象,利用空间距离和时间距离相结合的计算方法,对资源枯竭城市的区位条件进行分类型和分地区的评价.研究发现：① 从总体样本看,资源枯竭城市的确存在区位偏远现象,基于空间距离测度的区位偏远度α1 = 1.36 (相对于省会城市) 和β1 = 1.14 (相对于区域中心城市),基于时间距离测度的区位偏远度α2 = 1.65 (相对于省会城市) 和β2 = 1.16 (相对于区域中心城市);② 从分类型看,不同省份和不同资源类型之间城市区位偏远度表现出明显差异,西部地区和森工型城市区位偏远度最大;③ 综合空间距离和时间距离的分析结果,资源枯竭城市区位很偏远的城市,即α > 1.5 ∩ β > 1.5,分别为18 个和23 个,分别占资源枯竭城市总数的23.1%和29.5%;区位不偏远的资源枯竭城市,即α ≤ 1.0 ∩ β ≤ 1.0,分别为30 个和35 个,分别占到资源枯竭城市总数的38.5%和44.9%.在此基础上,进一步提出不同区位条件的资源枯竭城市的发展方向和政策着力点.

关键词: 资源枯竭城市, 区位, 识别方法, 中国

Abstract: It has been widely acknowledged amongest domestic scholars that resources-based cities on a whole are located in remote and peripheral areas, which has become a major obstacle to successful transformation of these cities to sustainable development. Yet little attention has so far been given to quantitatively estimate whether these resources-based cities suffered from being remote and peripheral location, and how far. In this paper, we use 78 resources-exhausted cities identified by the State Council as the case cities to evaluate the location condition of resources-exhausted cities under different resources types and regions, based on the calculation method of spatial distance and time distance. The distance is how far these resources-exhausted cities are from the corresponding provincial capital cities and three regional central cities (Beijing, Shanghai, and Guangzhou) to which are closely linked resources-exhausted cities. The research findings include that: (1) From the perspective of overall sample cities, the location of resources-exhausted city is really remote. In the term of spatial distance, the location remoteness degree α1 is 1.36 (relative to the provincial capital city) and β1 is 1.14 (relative to regional center city), and in the term of of time distance, the location remoteness degree α2 = 1.65 (relative to provincial capital city) and β2 = 1.16 (relative to regional central city). (2) From the perspective of classification, location remoteness degrees of resources-exhausted cities show significant differences among different provinces and different resource types. The location remoteness degrees of resources-exhausted cities located in Western China and forestry-based cities are the largest. (3) From an integrated analysis of spatial distance and time distance, the number of resources-exhausted cities whose locations are very remote, namely α > 1.5 ∩ β > 1.5, respectively is 18 and 23, accounting for 23.1% and 29.5% of the total resouces-exhausted cities. The number of resources-exhausted cities whose location remoteness degrees are α ≤ 1.0 ∩ β ≤ 1.0, is 30 and 35 respectively, accounting for 38.5% and 44.9% of the total in China.

Key words: resources-exhausted city, location, identification method, China