Identifying the Group Differences in the Impact of Haze on Residents' Low-Carbon Travel

IF 4.5 3区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Journal of Global Information Management Pub Date : 2021-09-01 DOI:10.4018/jgim.309980

Bin Zhang, Zhen Xu, Liran Sun

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引用次数: 1

Abstract

This paper matches the large-scale survey data and the corresponding historical weather data to explore how air pollution impacts on low-carbon travel choices. The K-means algorithm is employed to cluster the personal characteristics of residents into five groups according to their travel behavior. The authors take ordered Logit models to identify the group differences in the impact of haze on the five types of low-carbon travel choices, combining with the theory of responsibility attribution and protection motivation theory. The results show that haze has a significant impact on the two groups, namely young office workers and students. The other three groups will not consider the influence of haze when choosing travel vehicles, travel distance, and travel time. The quantity of personally owned automobiles also has a significant impact on the group differences in low carbon travel choices. It is indicated that low carbon travel policies should be considered in the group differences in the future, and efforts should be made from supply and demand sides to guide residents to choose low-carbon travel.

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识别雾霾对居民低碳出行影响的群体差异

本文将大规模调查数据与相应的历史天气数据相匹配，探讨空气污染对低碳出行选择的影响。根据居民的出行行为，采用K-means算法将居民的个人特征分为五组。作者采用有序Logit模型，结合责任归属理论和保护动机理论，识别雾霾对五种低碳出行选择影响的群体差异。结果表明，雾霾对年轻上班族和学生这两个群体有显著影响。其他三组在选择出行车辆、出行距离和出行时间时不会考虑雾霾的影响。个人拥有的汽车数量对低碳出行选择的群体差异也有显著影响。指出未来应在群体差异中考虑低碳出行政策，从供需两端努力引导居民选择低碳出行。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Global Information Management INFORMATION SCIENCE & LIBRARY SCIENCE-

CiteScore

5.80

自引率

14.90%

发文量

118

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