Spatial Distribution Characteristics and Influencing Factors of Urban Residents' Travel Carbon Emissions in Guangzhou

Spatial Distribution Characteristics and Influencing Factors of Urban Residents' Travel Carbon Emissions in Guangzhou

Jianfeng Lu (Southwest Jiaotong University, Chengdu, China), Jiahong Zhao (College of Civil and Traffic Engineering, Guangdong University of Technology, Guangzhou, China) and Haiyan Jiang (College of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou, China)
Copyright: © 2017 |Pages: 11
DOI: 10.4018/IJAL.2017070103
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Abstract

Transportation is an important source of carbon emissions. Since the level of urban traffic motorization enhanced quickly, the problem of carbon emissions derived from transportation has become a significant concern in recent years. It has become a key issue to study how to effectively reduce the carbon emissions of urban residents and to develop low-carbon urban traffic. The authors study 33 communities in Guangzhou city and analyze the characteristics of carbon emissions in each community, where the influence factors of carbon emissions are analyzed by establishing a multiple regression model. Finally, some policy suggestions are accordingly proposed to reduce the carbon emissions of residents.
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1. Introduction

Transportation is an important source of carbon emissions. Since the level of urban traffic motorization enhanced quickly, the problem of carbon emissions derived from transportation has become a significant concern in recent years. Residents’ travel behavior usually plays a key role in the urban transportation. For the development of the urban low carbon transport, there is a need to effectively control the carbon emission produced by residents. Accordingly, it has become a key issue to study how to effectively reduce the carbon emissions of urban residents and develop low-carbon urban traffic.

There has been much research on the characteristics and influencing factors of traffic carbon emissions. The residents’ travel carbon emissions are in accordance with the “60/20 rule”, that is, 20% of people contribute 60% of the total carbon emissions (Brand & Preston, 2010; Ko, Park, Lim, & Hwang, 2011). The studies on the factors of transportation emissions paid more attention to the social and economic attributes and travel preferences of families and individuals. Also, it was found that residents’ carbon emissions are affected by residents’ family characteristics, such as the rate of motor vehicle ownership and utilization, income condition, population size and composition, age structure, education background and occupational differences. Owned car group is not the car population produce more travel times and longer travel distance. Moreover, the relationsihp between family income and household travel way is different, the British 1 / 5 high-income families compared to 1 / 5 of low-income families have 1.3 times and 3 times the number of travel distance (Department for Transport, 2006). The impact of gender on household travel carbon emissions was second only to the level of the household economy (Barla, Miranda-Moreno, & Lee-Gosselin, 2011).

Household car ownership rate has the greatest impact on the residents’ trip carbon emissions (Aamaas, Borken-Kleefeld, & Peters, 2013). The travel distance and the probability of motorized travel have a significant positive effect on the traffic carbon emissions, while the impact of travel frequency is not significant. Meanwhile, the impact of travel structure is much greater than the total amount of travel (Ma et al., 2011). The investigation and research on the Nanjing city as a typical city in China, Ningbo and Changzhou, showed the main effect of the city residents’ daily commuter traffic carbon emission factor for the mode of transportation, travel distance, family income, age, and gender (Xu et al., 2014). Compared with the individual social and economic attributes and the residents’ attitude preferences, the residential space environment variables had more significant impact on the residents' commuting carbon emissions (Huang et al., 2014).

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