地理科学进展 ›› 2019, Vol. 38 ›› Issue (11): 1814-1828.doi: 10.18306/dlkxjz.2019.11.016

• 研究综述 • 上一篇    下一篇

多尺度交通出行碳排放影响因素研究进展

杨文越1, 曹小曙2,*()   

  1. 1. 华南农业大学林学与风景园林学院,广州 510642
    2. 中山大学地理科学与规划学院,广州 510275
  • 收稿日期:2018-12-20 修回日期:2019-03-28 出版日期:2019-11-28 发布日期:2019-11-28
  • 通讯作者: 曹小曙 E-mail:caoxsh@mail.sysu.edu.cn
  • 作者简介:杨文越(1988— ),男,广东韶关人,博士,讲师,中国地理学会会员(S110012062M),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(41701169);国家自然科学基金项目(41671160);广东省哲学社会科学规划项目(GD17YSH01)

Progress of research on influencing factors of CO2 emissions from multi-scale transport

YANG Wenyue1, CAO Xiaoshu2,*()   

  1. 1. College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China
    2. School of Geography Science and Planning, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2018-12-20 Revised:2019-03-28 Online:2019-11-28 Published:2019-11-28
  • Contact: CAO Xiaoshu E-mail:caoxsh@mail.sysu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No(41701169);National Natural Science Foundation of China, No(41671160);Philosophy and Social Sciences Planning Project of Guangdong Province, No(GD17YSH01)

摘要:

减少交通出行碳排放是全球共同面对的重大议题之一,同时也是城市和交通可持续发展的重要目标。论文首先基于文献计量方法对近20年来的全球交通出行碳排放研究现状与趋势进行梳理与分析,在此基础上,分别从国家、城市和社区3个尺度对国家交通能源消耗及其碳排放的驱动力因素、城市形态对交通碳排放的影响以及社区建成环境对居民出行碳排放的影响研究进行了文献综述与归纳凝练。研究发现:① 国家尺度的研究早期大多基于时间序列数据,采用分解法探究交通能源消耗的主要驱动力;近年来,研究进一步根据能源消耗数据“自上而下”地测算交通碳排放,并通过构建面板数据模型探究社会经济、城市形态和交通发展因素对交通碳排放的影响。② 城市尺度的研究早期围绕紧凑城市是否一种低碳的城市形态而进行讨论,主要使用截面数据和相关分析方法;近年来,进一步拓展使用情景预测、GIS空间分析、空间回归、空间模拟等方法探究城市交通碳排放的空间差异及其与城市形态、城市中心分布形式之间的关系。③ 在社区尺度,研究多以截面、非集计的问卷调查数据为主,采用定量的数学模型探究居民社会经济属性和人口密度,土地利用混合度,与就业地、城市中心的距离,路网与交叉口密度、公共交通供给水平等建成环境要素对居民出行碳排放的影响。最后有针对性地提出了未来中国城市交通出行碳排放影响因素的研究趋势。

关键词: 交通, 出行, 碳排放, 影响因素, 多尺度分析

Abstract:

Reducing CO2 emissions from transport is a major issue worldwide. It is also an important goal for sustainable urban and transport development. Using bibliometric methods, this article reviews and summarizes the current research situation and trends of global CO2 emissions from transport over the past two decades. On this basis, the article reviews and analyzes the literature on the driving forces of energy consumption and its related CO2 emissions, and the impacts of urban form and neighborhood built environments on CO2 emissions from transport at the national, city, and neighborhood scales, respectively. Our study found that most of the early national-scale studies were based on time-series data, using decomposition methods to explore the main driving forces of transport energy consumption. In recent years, further studies calculated CO2 emissions from transport with a "top-down" approach based on energy consumption data, and explored the impact of socioeconomic, urban form, and transportation development factors on CO2 emissions from transport by constructing panel data models. Early city-scale studies focused on whether compact cities are a low-carbon urban form, mainly using cross-sectional data and correlation analysis methods. In recent years, scenario forecasting, GIS spatial analysis, spatial regression, spatial simulation, and other methods have been further developed to explore the spatial differences of urban transport carbon emissions and their relationships with urban morphology and urban center distribution. For the neighborhood-scale studies, mathematical models were used to examine the effects of residents’ demographics and built environments on CO2 emissions, mainly based on cross-sectional and disaggregated questionnaire survey data. The built environment factors include population density, land-use mix, distance to employment sites or distance to urban centers, road network and intersection density, and the supply level of public transport. At the end of the article, research trends of influencing factors of CO2 emissions from transport in urban China are analyzed with respect to the three aspects of study data, methodology, and research contents.

Key words: transport, travel, CO2 emissions, influencing factors, multi-scale analysis