基于生态敏感条件的中国资源型城市去产能空间格局优化

doi:10.18402/resci.2016.10.13

资源科学 ›› 2016, Vol. 38 ›› Issue (10): 1962-1974.doi: 10.18402/resci.2016.10.13

基于生态敏感条件的中国资源型城市去产能空间格局优化

沈明^{1, 2}, 沈镭^{1, 2}, 钟帅^{1, 3}, 张超^{1, 2}, 孔含笑^{1, 2}

1. 中国科学院地理科学与资源研究所,北京 100101;
2. 中国科学院大学,北京 100049;
3. 湖北师范大学资源枯竭城市转型与发展研究中心,黄石 435002

收稿日期:2016-01-10 修回日期:2016-08-30 出版日期:2016-10-25 发布日期:2016-10-25
通讯作者: 沈镭,E-mail：shenl@igsnrr.ac.cn
作者简介:沈明,女,湖北鄂州人,博士生,研究方向为资源经济与政策。E-mail：shenming0604@163.com
基金资助:
国家自然科学基金面上项目（41271547）; 国土资源部公益性行业科研基金（2014YQKYQ0901）; 湖北师范学院资源枯竭城市转型与发展研究中心2015年开放基金重点项目（Kf2015z01）

The spatial optimization of mineral industrial capacity reduction planning of resource-based cities in China based on ecological sensitive areas

SHEN Ming^{1, 2}, SHEN Lei^{1, 2}, ZHONG Shuai^{1, 3}, ZHANG Chao^{1, 2}, KONG Hanxiao^{1, 2}

1. Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;
2. University of Chinese Academy of Sciences,Beijing 100049,China;
3. Transformation and Development Research Center of Resource-Exhausted Cities,Hubei Normal University,Huangshi 435002,China

Received:2016-01-10 Revised:2016-08-30 Online:2016-10-25 Published:2016-10-25

摘要/Abstract

摘要： “十三五”规划及供给侧改革背景下,资源型城市面临极大的去产能和生态保护压力。为促进矿业生态协调发展,本文基于SPSS和GIS刻画了中国212个资源型城市的矿业产能格局、生态敏感区格局及其空间耦合关系,在此基础上构建多目标优化模型,提出去产能空间格局优化方案。研究表明：①中国矿业产能分布显著集中,47.29%的产能集中于22个资源型城市;②资源型城市整体生态敏感性高,综合生态敏感区面积占比高达50.91%;③76.49%的矿业产能分布于II级和III级生态敏感区,矿业产能与生态敏感区分布不存在根本冲突;④30%的去产能目标假设下,到2020年预计削减矿业产能19.83亿t,生态避让效应达43.15%,125个资源型城市亟需去产能。其中煤炭、综合型、黑色金属和有色金属资源型城市产能削减量分别为9.55亿t、3.42亿t、2.39亿和2.43亿t;⑤建议按先南后北、先县级市后地级市原则分阶段有序调整矿业产能空间格局,科学合理推进全国资源型城市转型发展。

关键词: GIS, 叠加分析, 空间格局优化, 去产能, 生态敏感区, 中国, 资源型城市

Abstract: Against the background of 13th Five-Year Plan and “supply-side structural reforms”,resource-based cities have to cope with the pressures of mineral industrial capacity （MIC）reduction and eco-environmental protection. For coordinating mining and ecological protection,this study analyzed the MIC and integrated ecological sensitive areas （IESAs）layout as well as the MIC-IESAs spatial coupling relationship in 212 mining cities with the technology of geographic information system （GIS）and statistical package for social sciences （SPSS）. On the basis,the MIC reduction optimization plan would be proposed. The research conclusion shows that（1）a significant concentration has been found in the MIC distribution,as 47.29% of MIC is mainly concentrated in 22 mining cities;（2）the overall eco-environmental foundation are fragile with the IESAs account for 50.91% in mining cities;（3）the spatial coupling relationship shows that there is no fundamental conflict between MIC and IESAs,for 76.49% of MIC are distributed in IESAs-II and IESAs-III mining cities;（4）according to the MIC reduction optimization plan,the MIC cuts and the IESAs avoiding areas are expected to reach 1.983 billion and 43.15% respectively by 2020,125 mining cities should reduce the MIC positively . Furthermore,coal,general,ferrous metal,nonferrous metal,nonmetal types cities is expected to cut 0.955 billion tons,0.342 billion tons,0.239 billion tons and 0.243 billion tons of MIC respectively;（5）as the result,we suggest to implement the MIC reduction plan preferentially in South China and county regions. Based on the orderly MIC spatial optimization,the sustainable development and transformations of national mining cities would be advanced smoothly.

Key words: China, ecological sensitive areas, GIS, mining industrial capacity reduction, overlay analysis, resource-based cities, spatial optimization

沈明, 沈镭, 钟帅, 张超, 孔含笑. 基于生态敏感条件的中国资源型城市去产能空间格局优化[J]. 资源科学, 2016, 38(10): 1962-1974.

SHEN Ming, SHEN Lei, ZHONG Shuai, ZHANG Chao, KONG Hanxiao. The spatial optimization of mineral industrial capacity reduction planning of resource-based cities in China based on ecological sensitive areas[J]. Resources Science, 2016, 38(10): 1962-1974.

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基于生态敏感条件的中国资源型城市去产能空间格局优化

The spatial optimization of mineral industrial capacity reduction planning of resource-based cities in China based on ecological sensitive areas

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