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Title: Relationship between built environment, socio-economic factors and carbon emissions from shopping trip in Shenyang City, China
Author: J. Li, K. V. Lo, P. Y. Zhang and M. Guo
Corresponding Author: 李静
Source: Chinese Geographical Science
Issued Date: 2017
DOI: 10.1007/s11769-017-0904-8
Volume: 27, Issue:5, Pages:722-734
Abstract: Promoting active travel behavior and decreasing transport-related carbon dioxide (CO2) emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO2 emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO2 emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated CO2 emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was significant spatial autocorrelation with CO2 emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO2 emissions per trip were negatively correlated with several built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-economic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets.
WOS ID: WOS:000410024000005
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Content Type: 期刊论文
URI: http://ir.iga.ac.cn/handle/131322/7601
Appears in Collections:湿地与全球变化学科组_期刊论文

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