Modeling the low-carbon behaviors' development paths of freight enterprises based on a survey in Zhejiang, China

被引:12
作者
Wu, Chengcheng [1 ]
Xiao, Le [1 ]
Hu, Zheng [1 ]
Zhou, Yijun [1 ]
机构
[1] Xihua Univ, Sch automobile & transportat, 9999 Hongguang Ave, Chengdu 610039, Peoples R China
关键词
Freight enterprise; Low-carbon behavior; Survey; Extreme value theory; EMISSIONS; TRANSPORTATION; POLICIES; IMPACT; CO2;
D O I
10.1016/j.scs.2022.103894
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Improving freight enterprises' low-carbon behaviors is a significant measure in reducing carbon emissions and thus promoting cities' sustainable development. This paper aimed at modeling how various freight enterprises' low-carbon behaviors vary with their development. A province-wide investigation of 980 freight enterprises was conducted, then the K-means clustering method and Extreme value theory (EVT) mode were adopted to identify the low-carbon development paths of freight enterprises. The results show that: a) freight enterprises with short distance freight transportation in low-GDP areas have the lowest low-carbon degree (LCD), but they have the highest probability to adopt low-carbon behaviors when LCD>30.37; b) freight enterprises in high-GDP regions have the lowest willingness to adopt low-carbon behaviors when the development degree is higher than 201.5. The findings help to understand the low-carbon development path of freight enterprises at different stages, and it's useful for government to accurately adopt low-carbon development measures in road freight transportation.
引用
收藏
页数:9
相关论文
共 47 条
[1]   Does environmental awareness fuel the electric vehicle market? A Twitter keyword analysis [J].
Austmann, Leonhard M. ;
Vigne, Samuel A. .
ENERGY ECONOMICS, 2021, 101
[2]   The impact of path selection on GHG emissions in city logistics [J].
Behnke, Martin ;
Kirschstein, Thomas .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2017, 106 :320-336
[3]   How to reduce the greenhouse gas emissions and air pollution caused by light and heavy duty vehicles with battery-electric, fuel cell-electric and catenary trucks [J].
Breuer, Janos Lucian ;
Samsun, Remzi Can ;
Stolten, Detlef ;
Peters, Ralf .
ENVIRONMENT INTERNATIONAL, 2021, 152
[4]  
European Alternative Fuels Observatory, 2017, CER VAR 2017 INS COM
[5]   Limiting forms of the frequency distribution of the largest or smallest member of a sample [J].
Fisher, RA ;
Tippett, LHC .
PROCEEDINGS OF THE CAMBRIDGE PHILOSOPHICAL SOCIETY, 1928, 24 :180-190
[6]   Multi-type Bayesian hierarchical modeling of traffic conflict extremes for crash estimation [J].
Fu, Chuanyun ;
Sayed, Tarek ;
Zheng, Lai .
ACCIDENT ANALYSIS AND PREVENTION, 2021, 160
[7]   Measuring systemic risk via GAS models and extreme value theory: Revisiting the 2007 financial crisis [J].
Gavronski, Pedro Gerhardt ;
Ziegelmann, Flavio A. .
FINANCE RESEARCH LETTERS, 2021, 38
[8]   The limited distribution of the maximum term of a random series [J].
Gnedenko, B .
ANNALS OF MATHEMATICS, 1943, 44 :423-453
[9]   The battery charging station location problem: Impact of users' range anxiety and distance convenience [J].
Guo, Fang ;
Yang, Jun ;
Lu, Jianyi .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2018, 114 :1-18
[10]  
Han J, 2012, MOR KAUF D, P1