摘要
面对日益严峻的气候变化问题,必须推进碳减排行动以减缓气候变暖。不同地区的碳排放特征不同,但又具有一定的相似性,有效的地区聚类分析是地区低碳发展分类指导研究的关键。基于碳排放与减排可能性问题,运用系统聚类法对甘肃省下辖14个地市州碳减排能力进行了综合评价。结果表明:甘肃省14个地市州可分为4类,不同类别地区各具特征,减排潜力差异明显;分类结果综合性强且不完全体现区域连续性,即不能以地理区位划分进行碳减排的分类指导。针对不同类型地区应实行各具特色的有效低碳发展路径。
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.
作者
董莹
华中
陆志翔
许宝荣
邹松兵
Dong Ying;Hua Zhong;Lu Zhixiang;Xu Baorong;Zou Songbing(Key Laboratory of Eco-Hydrology of Inland River Basin,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China;University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Geographic Sciencse and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;College of Earth and Environmental Sciences,Lanzhou University,Lanzhou 730000,China)
出处
《中国沙漠》
CSCD
北大核心
2020年第5期25-31,共7页
Journal of Desert Research
基金
甘肃省发展与改革委员会重大项目(2018HTBA00807)
能源基金会资助项目(G-1906-29643)。
关键词
碳减排
聚类分析
甘肃省
carbon emission reduction
clustering analysis
Gansu Province