地理研究 ›› 2015, Vol. 34 ›› Issue (8): 1471-1480.doi: 10.11821/dlyj201508006

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广州市社区出行低碳指数格局及其影响因素的空间异质性

杨文越1(), 李涛2, 曹小曙1,3   

  1. 1. 中山大学地理科学与规划学院,广州 510275
    2. 广东财经大学地理与旅游学院,广州 510320
    3. 陕西师范大学交通地理与空间规划研究所,西安 710119
  • 收稿日期:2014-12-20 修回日期:2015-05-07 出版日期:2015-08-25 发布日期:2015-08-20
  • 作者简介:

    作者简介:杨文越(1988- ),男,广东韶关人,博士研究生,主要研究方向为城市交通地理与土地利用。E-mail: yangwenyue900780@163.com

  • 基金资助:
    国家自然科学基金项目(41171139,41130747);中央高校基本科研业务费专项资金(GK201303006)

The spatial pattern of Community Travel Low Carbon Index(CTLCI) and spatial heterogeneity of the relationship between CTLCI and influencing factors in Guangzhou

Wenyue YANG1(), Tao LI2, Xiaoshu CAO1,3   

  1. 1. School of Geography Science and Planning, Sun Yat-sen University, Guangzhou 510275, China
    2. School of Geography & Tourism, Guangdong University of Finance & Economics, Guangzhou 510320
    3. Institute of Transport Geography and Spatial Planning, Shaanxi Normal University, Xi'an 710119, China
  • Received:2014-12-20 Revised:2015-05-07 Online:2015-08-25 Published:2015-08-20

摘要:

通过构建社区出行低碳指数(CTLCI)模型,对广州市社区出行低碳指数的空间格局及其差异特征进行了分析,并利用全局回归(OLS)模型和地理加权回归(GWR)模型对社区出行低碳指数的影响因素以及其间关系的空间异质性进行了研究。结果表明,广州市社区出行低碳指数由中心城区向外逐渐递增,呈明显的圈层结构。内圈层的社区出行低碳指数内部差异最小,中间过渡圈层的最大。社区人口密度对社区出行低碳指数的影响以正向作用为主,公共交通供给水平和路网密集程度对社区出行低碳指数的影响以负向作用为主,且它们的影响作用具有空间异质性。具体指出了在不同地域空间内社区人口密度、公共交通供给水平和路网密集程度对社区出行低碳指数在影响程度和作用方向上的差异,为减少广州城市交通碳排放、针对不同空间制定有效的低碳政策和构建低碳城市空间结构提供了科学依据。

关键词: 社区出行低碳指数, 空间异质性, 地理加权回归, 出行CO2排放, 广州市

Abstract:

This paper proposes the community travel low carbon index (CTLCI) model to demonstrate the spatial pattern and regional difference of CTLCI in Guangzhou. And then, it uses traditional ordinary least squares (OLS) model and geographically weighted regression (GWR) to examine the spatial heterogeneity of the relationship between CTLCI and influence factors. The results indicate that CTLCI is increasing from central city to the periphery in Guangzhou, being three-layer structure obviously. The internal difference of CTLCI is the least in the inner layer, and the biggest in the middle transition layer. From global statistical modeling, the community population density has positive effects on CTLCI, and public transport supply level and density of the road network have negative effects on CTLCI. The relationships between explanatory variables and CTLCI vary across the study area. The area where effects of the community population density on CTLCI are positive is mainly distributed in the outer layer and the middle transition layer. It is larger than that where effects are negative, which is mainly distributed in the inner layer. The community public transport supply level and the community density of the road network both have negative effects on CTLCI in most of the regional space. But the former covers more communities than the latter, because the effects of them have spatial difference. The paper points out the different influence of community population density, public transport supply level and density of the road network on CTLCI in different space specifically. This provides a scientific basis for reducing CO2 emissions from urban transport, constituting different policies to different districts and building urban spatial structure toward a low-carbon society.

Key words: community travel low carbon index, spatial heterogeneity, geographically weighted regression, CO2 emission, Guangzhou