自然资源学报

自然资源学报 ›› 2016, Vol. 31 ›› Issue (8): 1364-1377.doi: 10.11849/zrzyxb.20150905

• 资源评价 • 上一篇    下一篇

中国居民生活碳排放的区域差异及影响因素分析

刘莉娜1, 曲建升1, 2*, *, 黄雨生1, 王莉1, 曾静静1, 2, 边悦1   

  1. 1. 兰州大学资源环境学院西部环境教育部重点实验室,兰州 730000;
    2. 中国科学院兰州文献情报中心/全球变化研究信息中心,兰州 730000
  • 收稿日期:2015-08-20 修回日期:2016-01-29 出版日期:2016-08-20 发布日期:2016-08-20
  • 作者简介:刘莉娜（1987- ）,女,河北承德人,博士研究生,主要研究方向为二氧化碳减排与全球气候变化的政策分析。E-mail:liuln2015@163.com *通信作者简介：曲建升（1973- ）,男,山东莱阳人,研究员,博士,主要研究方向为气候政策分析与温室气体排放评估。E-mail:jsqu@lzb.ac.cn
  • 基金资助:

    国家重点基础研究发展规划项目计划（2016YFA0602800）; 国家自然科学基金项目（41371537）

Analyze on the Spatial-temporal Pattern and Influence Factors of China’s per Capita Household Carbon Emissions

LIU Li-na1, QU Jian-sheng1, 2, HUANG Yu-sheng1, WANG Li1, ZENG Jing-jing1, 2, BIAN Yue1   

  1. 1. Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;
    2. Information Center for Global Change Studies, Lanzhou Information Center, CAS, Lanzhou 730000, China
  • Received:2015-08-20 Revised:2016-01-29 Online:2016-08-20 Published:2016-08-20
  • Supported by:

    National Key Basic Research Program, No.2016YFA0602800; National Natural Science Foundation of China, No.41371537

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被引次数

15 | 11

摘要：

论文在总结划分居民生活碳排放（HCEs）类别的基础上,以中国HCEs量为测度指标,从时间和空间两个角度对1997—2012年人均HCEs进行分析,同时对2012年人均HCEs的空间格局分布及影响因素进行分析。基本结论如下：根据划分方法,HCEs按照碳排放源可分为直接和间接碳排放;按照基本生活需求可分为基本和发展碳排放;按照人类消费行为可分为交通、居住、食品、商品及服务碳排放五大类。从时间尺度来看,中国各类别人均HCEs均呈现逐年上升趋势。从空间差异来看,1997—2012年,我国各省人均HCEs表现出共同的空间分布特征。从空间格局分布来看,2012年中国人均居民生活碳排放呈现从东到西递减的趋势。从空间计量模型角度看,2012年,人均居民生活基本碳排放比重是影响人均HCEs空间差异的主要驱动因子。此外,各地区人均收入和人均GDP的差异也对人均HCEs的空间差异起到正相关作用。基于对中国人均HCEs的时空格局及影响因素分析,为我国制定碳减排举措提供科学依据并提出有效建议。

关键词: Moran’, s I, 居民生活碳排放, 空间分析模型, 中国

Abstract:

This paper calculates per capita household carbon emissions (HCEs) based on IPCC’s reference approach and Input-output analysis (IOA) of different categories of carbon emissions in China from 1997 to 2012. Its driving factors are also analyzed with the Spatial Error Model (SEM) and the Spatial Lag Model (SLM). The main purpose of this work is to emphasize the characteristics of household carbon emissions based on temporal scale and spatial scale. The results show that: 1) Based on different carbon sources, HCEs can be divided into direct and indirect emissions; based on different human needs, HCEs can be classified as basic and development emissions; based on different consumers’ behaviors, HCEs can be divided into transportation, housing, food, goods and service emissions. 2) At the time scale, both direct and indirect per capita HCEs, basic and development per capita HCEs, and each item of per capita HCEs based on behaviors exhibit the increasing tendency. 3) From the spatial perspective, there is a common pattern in spatial distributions of per capita HCEs. The cluster effect of per capita HCEs is stable. 4) From the space point of view, the per capita HCEs in China shows a decreasing tendency from east to west in 2012. 5) Based on the spatial analysis model, the proportion of basic HCEs per capita in the whole is the main driving factor. Meanwhile, per capita income and per capita GDP are also affecting per capita HCEs. On the basis of analyzing the spatial-temporal patterns and driving factors of per capita household carbon emissions, we provide scientific evidences and put forward effective suggestions for carbon emissions reduction measures and policies.

Key words: China, household carbon emissions, Moran’, s I, spatial analysis model

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