中国沙漠 ›› 2020, Vol. 40 ›› Issue (5): 25-31.DOI: 10.7522/j.issn.1000-694X.2020.00038

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董莹1,2(), 华中3, 陆志翔1, 许宝荣4, 邹松兵1,4()   

  1. 1.中国科学院西北生态环境资源研究院 内陆河流域生态水文重点实验室,甘肃 兰州 730000
    2.中国科学院大学,北京 100049
    3.中国科学院地理科学与资源研究所,北京 100101
    4.兰州大学 资源环境学院,甘肃 兰州 730000
  • 收稿日期:2020-03-05 修回日期:2020-04-25 出版日期:2020-09-28 发布日期:2020-09-28
  • 通讯作者: 邹松兵
  • 作者简介:邹松兵(E-mail:
  • 基金资助:

Cluster analysis of prefecture-level cities in Gansu Province for low carbon transformation

Ying Dong1,2(), Zhong Hua3, Zhixiang Lu1, Baorong Xu4, Songbing Zou1,4()   

  1. 1.Key Laboratory of Eco-Hydrology of Inland River Basin,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China
    2.University of Chinese Academy of Sciences,Beijing 100049,China
    3.Institute of Geographic Sciencse and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
    4.College of Earth and Environmental Sciences,Lanzhou University,Lanzhou 730000,China
  • Received:2020-03-05 Revised:2020-04-25 Online:2020-09-28 Published:2020-09-28
  • Contact: Songbing Zou



关键词: 碳减排, 聚类分析, 甘肃省


In the face of increasingly serious climate change, the action of the reduction of carbon emissions as one of the main measures to slow climate warming is imperative. There are both differences and similarities among the carbon emission characteristics of different regions, and the effective regional cluster analysis is the key to guide the research of regional low-carbon development by classification. Based on the problem of carbon emission and the possibility of emission reduction, this paper comprehensively evaluated the carbon emission reduction capacity of 14 prefecture-level cities in Gansu Province using the hierarchical clustering method. The results show that the 14 cities in Gansu Province can be divided into four groups, which have their own characteristics and whose emission reduction potential varies significantly. The classification results are comprehensive and do not fully reflect the regional continuity, thus, the classification guidance for carbon emission reduction cannot be based on geographical location. The effective low-carbon development paths with different characteristics should be implemented for different types of regions.

Key words: carbon emission reduction, clustering analysis, Gansu Province