| 居住自选择视角下的广州出行碳排放影响机理 |
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| 引用本文： | 杨文越,曹小曙.居住自选择视角下的广州出行碳排放影响机理[J].地理学报,2018,73(2):346-361. |
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| 作者姓名： | 杨文越 曹小曙 |
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| 作者单位： | 1. 华南农业大学林学与风景园林学院,广州 5106422. 中山大学地理科学与规划学院,广州 5102753. 陕西师范大学交通地理与空间规划研究所,西安 710119 |
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| 基金项目： | 国家自然科学基金项目(41701169, 41671160);广东省哲学社会科学规划项目(GD17YSH01) |
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| 摘 要： | 国内外已有不少研究从国家、城市和社区层面探讨了交通出行碳排放的影响因素,然而,很少研究考虑到居住自选择的影响。若忽略该影响,将很可能会错误地估计建成环境的作用,以至于相关规划与政策制定有所偏离。中国城市是否与西方国家一样也具有居住自选择效应?在考虑了居住自选择后,建成环境是否对出行碳排放具有显著的影响,如何产生影响?为了回答以上科学问题,基于2015年广州15个社区1239份问卷数据和出行O-D点智能查询系统（TIQS）的开发与应用,对居民出行碳排放进行了测度,并通过构建结构方程模型（SEM）探究了不同类型出行碳排放的影响机理。研究发现：中国城市同样存在居住自选择效应,转变居民出行方式选择偏好有利于减少出行碳排放。在控制居住自选择效应后,建成环境仍然对居民出行碳排放产生显著的影响。这些影响有的属于直接影响,有的则是通过影响其他中介变量,例如小汽车拥有或出行距离,进而再对出行碳排放造成间接影响。对于不同类型出行,其碳排放的影响机理并不一样。
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| 关 键 词： | 居住自选择 建成环境 出行碳排放 影响机理 广州 |
| 收稿时间： | 2017-01-25 |
The influence mechanism of travel-related CO2 emissions from the perspective of residential self-selection: A case study of Guangzhou |
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| YANG Wenyue,CAO Xiaoshu.The influence mechanism of travel-related CO2 emissions from the perspective of residential self-selection: A case study of Guangzhou[J].Acta Geographica Sinica,2018,73(2):346-361. |
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| Authors: | YANG Wenyue CAO Xiaoshu |
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| Institution: | 1. College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China2. School of Geography Science and Planning, Sun Yat-sen University, Guangzhou 510275, China3. Institute of Transport Geography and Spatial Planning, Shaanxi Normal University, Xi’an 710119, China; |
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| Abstract: | Numerous studies have examined the influencing factors of CO2 emissions from transportation at the national, city and community levels. However, fewer studies have considered the effect of residential self-selection. Ignoring this effect is likely to result in underestimating the role of the built environment, thus affecting relevant planning and policy development. Is the effect of residential self-selection in Chinese cities the same as in Western cities? How and to what extent does the built environment affect CO2 emissions from travel after controlling for the effect of residential self-selection? To address these questions, this paper first measures the CO2 emissions from travel on the basis of the Travel Intelligent Query System (TIQS) developed by us on the Baidu map LBS (Location Based Service) open platform, and 1239 questionnaires conducted in 15 communities in Guangzhou in 2015. It develops a structural equation model (SEM) to examine the effects of the influencing factors on CO2 emissions of trips with different purposes. The results show that the effect of residential self-selection also exists in Chinese cities. Changing residents' preference of travel mode will help reduce travel-related CO2 emissions. After controlling the effect of residential self-selection, the built environment still has significant effects on CO2 emissions from travel. Although some of them are direct effects, others are indirect effects that work through mediating variables, such as car ownership and travel distance. For different trip purposes, the mechanisms of CO2 emissions are not the same. Specifically, the distance to city public centers has a significant positive total effect on CO2 emissions from commuting trips, which is an indirect effect. Residential density significantly affects CO2 emissions from social, recreational and daily shopping trips, but it has no significant effect on CO2 emissions from commuting trips. Bus stop density is positively associated with CO2 emissions from commuting trips, and negatively associated with CO2 emissions from social and daily shopping trips. In addition, land-use mix has a negative effect on CO2 emissions from commuting, social and daily shopping trips, and metro station density and road network density have significant negative effects on CO2 emissions from all types of trip. These results suggest that it is necessary to comprehensively consider the effects of the built environment on CO2 emissions from different types of trip, and carry out targeted intervention on the built environment in related planning and policy development so as to guide the public to change their travel behavior and to promote low-carbon travel. |
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| Keywords: | residential self-selection built environment travel-related CO2 emissions influence mechanism Guangzhou |
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