地理研究 ›› 2015, Vol. 34 ›› Issue (4): 751-761.doi: 10.11821/dlyj201504013

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广州市三个圈层社区居民通勤碳排放特征——以都府小区、南雅苑小区和丽江花园为例

黄晓燕1(), 刘夏琼2, 曹小曙2,3()   

  1. 1. 陕西师范大学西北国土资源研究中心 交通地理与空间规划研究所, 西安 710119
    2. 中山大学地理科学与规划学院, 广州 510275
    3. 陕西师范大学旅游与环境学院, 西安 710119
  • 收稿日期:2014-09-02 修回日期:2014-12-24 出版日期:2015-04-10 发布日期:2015-06-11
  • 作者简介:

    作者简介:黄晓燕(1981- ),女,云南勐腊人,博士,副教授,主要研究方向为城市规划与交通地理。E-mail: hxiaoy@snnu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41401127,41171139);中央高校基本科研业务费专项资金(GK261002143,GK201303006)

Commuting carbon emission characteristics of community residents of three spheres: A case study of three communities in Guangzhou city

Xiaoyan HUANG1(), Xiaqiong LIU2, Xiaoshu CAO2,3()   

  1. 1. Center for Land Resources Research in Northwest China, Institute of Transport Geography and Spatial Planning, Shaanxi Normal University, Xi'an 710119, China
    2. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
    3. College of Tourism and Environment Science, Shaanxi Normal University, Xi'an 710119, China
  • Received:2014-09-02 Revised:2014-12-24 Online:2015-04-10 Published:2015-06-11

摘要:

低碳城市已成为应对全球气候变化和促进人与自然和谐相处的重要研究领域,但从微观层面探讨城市居民通勤碳排放特征和影响因素的研究较为缺乏。利用对广州市不同圈层社区的问卷调查数据,对其通勤碳排放进行测算,采用分等定级和洛伦兹曲线分析社区间、社区内、个体间的分异,并建立了基于多元回归的社区居民通勤碳排放的影响模型。结果显示:社区居民通勤碳排放较符合“60-20”规律(温室气体排放最多的20%的人排放了总量的60%)。中心区和边缘区的通勤碳排放不均等性更大;不同个人、家庭和通勤属性的通勤者的碳排放量存在较大差异;通勤距离、出行交通方式、每天的通勤往返次数、个人的工作月收入和所在街道的人口密度对家庭通勤碳排放有显著影响。

关键词: 碳排放, 通勤, 多元回归模型, 社区, 广州市

Abstract:

In recent years, as low-carbon city gradually has become a significant research field on studies on global climate change, the scholars at home and abroad have launched a series of research from various angles. However, there is little research discussing about urban commuting carbon emission characteristics and the influencing factors of carbon emissions at disaggregated level. The data used in this study came from a 2011-2012 survey of 291 residents from households in three typical communities from different parts of Guangzhou. The variables used in this study consist of four categories: demographics, auto ownership and use, built environment characteristics, and attitudes. In the survey, respondents were asked to report the number of automobiles in their households and their travel behavior. The survey also contained a list of demographic characteristics, including household size, household income, gender, age, education underground, having a driver's license, and occupation. Consistent with previous studies, we develop a coefficient method for calculating the amount of commuting carbon emissions. In this study, we adopt categorization and Lorenz curve to analyze the differentiation of commuting carbon emissions characteristics between different individuals, inner-community and trans-community. Specifically, we developed a multiple regression analysis method to analyze the impact of selected 16 variables on commuting carbon emissions. The results of commuting carbon emission characteristics analyses show that commuting carbon emission of residents in urban districts is lower than that of their counterparts in county-level cities. Dufu, Nanyayuan and Lijiang Garden communities are consistent with the rule of "60-20" proposed from the UK experience. Multiple regression analysis suggests that commuting distance, travel modes of residents, commuting frequency, income and population density of the sub-district have significant effects on CO2 emissions. Finally, on the basis of the above findings, the paper puts forward some policy suggestions for reducing commuting carbon emissions.

Key words: CO2 emissions, commute, multiple regression model, community, Guangzhou city