Please wait a minute...
浙江大学学报(理学版)  2023, Vol. 50 Issue (3): 378-390    DOI: 10.3785/j.issn.1008-9497.2023.03.016
城市科学     
考虑群体差异的低碳出行意向与行为一致性研究
徐标1(),路庆昌1(),徐鹏程1,崔欣1,杜长皓2
1.长安大学 电子与控制工程学院,陕西 西安 710064
2.鲁西化工集团股份有限公司,山东 聊城 252000
A study on group difference between low-carbon transportation intention and consistent behavior
Biao XU1(),Qingchang LU1(),Pengcheng XU1,Xin CUI1,Changhao DU2
1.School of Electronics and Control Engineering,Chang'an University,Xi'an 710064,China
2.Luxi Chemical Group Co. ,Ltd. ,Liaocheng 252000,Shandong Province,China
 全文: PDF(3248 KB)   HTML( 3 )
摘要:

为探究城市居民低碳出行意向与行为差异的原因和异质性,基于大中小城市、老中青群体1 263名居民的低碳出行问卷调查数据,采用双变量Probit模型和组间平均边际效应(AME),分析了城市和群体间低碳出行意向与行为一致性的影响因素及群体差异。结果表明:有低碳出行意向与行为的居民比例仅为12.6%,有低碳出行意向而未向行为转变的居民占55.3%,意向与行为不一致的现象在一线城市和青年群体中尤为明显。从平均边际效应的结果看,有无私家车是导致一线、二线城市居民和中、青年群体低碳出行意向与行为不一致的关键因素。环境意识和主观规范对老年群体的低碳出行意向与行为一致性具有更显著的积极影响。研究所揭示的影响不同群体低碳出行意向与行为一致性的关键因素,可为低碳政策的异质化实施提供理论参考。

关键词: 低碳出行意向与行为一致性群体异质性双变量Probit模型平均边际效应    
Abstract:

As residents' low-carbon transportation plays a crucial role in alleviating transportation carbon emissions, many studies concentrate on the low-carbon transportation intention or behavior, but the consistency between the intention and behavior of different groups is neglected. Based on the low-carbon transportation questionnaire data of 1 263 residents in large, medium, and small cities that sorted into the old, middle-aged, and young groups, this study analyzed the factors influencing the consistency of low-carbon transportation intention and behavior, focusing on the region and generation difference, using bivariate Probit model and between-group average marginal effect (AME). The results show that the proportion of residents with both low-carbon transportation intention and relevant behavior is only 12.6%, while the proportion of residents who have low-carbon intention without consistent behavior account for 55.3%, and this inconsistency is particularly obvious for residents in large cities and the youth group. By analyzing the average marginal effect, the private car is a crucial factor contributing to inconsistentcy between intention and behavior among residents of large and medium-sized cities and middle-aged youth groups. Environmental awareness and subjective norm have more significant positive effects on middle-aged and elderly groups. The findings from this study provide an understanding of the key factors influencing the consistency of low-carbon transportation intention and behavior among different groups of people, and references for the heterogeneous implementation of low-carbon policies, thereby promoting residents' low-carbon travel guidance.

Key words: low-carbon transportation    intention-behavior consistency    group heterogeneity    bivariate Probit model    average marginal effect
收稿日期: 2022-03-24 出版日期: 2023-05-19
CLC:  U 491.1  
基金资助: 国家自然科学基金面上项目(71971029);陕西省自然科学基础研究计划项目(2021JC-28)
通讯作者: 路庆昌     E-mail: 2020132086@chd.edu.cn;qclu@chd.edu.cn
作者简介: 徐标(1997—),ORCID:https://orcid.org/0000-0001-6161-9382,男,硕士研究生,主要从事城市环境与交通行为建模研究,E-mail:2020132086@chd.edu.cn.
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
徐标
路庆昌
徐鹏程
崔欣
杜长皓

引用本文:

徐标,路庆昌,徐鹏程,崔欣,杜长皓. 考虑群体差异的低碳出行意向与行为一致性研究[J]. 浙江大学学报(理学版), 2023, 50(3): 378-390.

Biao XU,Qingchang LU,Pengcheng XU,Xin CUI,Changhao DU. A study on group difference between low-carbon transportation intention and consistent behavior. Journal of Zhejiang University (Science Edition), 2023, 50(3): 378-390.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2023.03.016        https://www.zjujournals.com/sci/CN/Y2023/V50/I3/378

图1  低碳出行意向与行为一致性概念框架
变量描述

总样本

N=1 263)

城市群体

上海

N=538)

南京

N=446)

中山

N=279)

青年

N=515)

中年

N=443)

老年

N=305)

性别55.459.250.755.658.853.252.8
44.640.849.344.441.246.847.2
年龄

青年[18,40)岁

中年[40,60)岁

老年60岁及以上

40.8

35.1

24.1

47.9

28.4

23.7

39.1

40.2

20.7

29.8

39.9

30.3

100
100
100
居住地上海市42.610048.134.844.6
南京市35.310036.237.530.6
中山市22.110015.727.724.8
学历

高中及以下

中专及大专

本科

硕士及以上

23.2

25.6

32.5

18.7

19.2

23.2

38.5

19.1

20.3

28.6

35.2

15.9

35.5

25.4

16.6

22.5

16.021.837.4
29.421.625.0
40.627.825.6
14.028.812.0
月收入

3 000元以下

[3 000,6 000)元

[6 000,9 000)元

[9 000,12 000)元

12 000元及以上

3.9

35.7

23.8

20.4

16.2

2.1

27.8

28.1

24.2

17.8

3.2

34.2

25.1

20.6

16.9

8.5

53.3

13.4

12.8

12.0

0.40.514.7
30.123.662.7
25.730.211.3
24.821.611.2
19.024.11.0
有无私家车

44.3

55.7

53.2

46.8

40.6

59.4

33.0

67.0

55.246.724.4
44.853.375.6
有无驾驶证

61.2

38.8

66.7

33.3

59.8

40.2

52.8

47.2

75.759.439.3
24.340.660.7
通勤距离

(0,5] km

(5,10] km

(10,15] km

(15,20] km

38.2

25.3

20.3

16.2

31.1

20.3

23.2

25.4

38.2

30.1

20.8

10.9

51.9

27.3

13.9

7.0

24.730.672.0
27.829.415.1
19.328.79.8
28.211.33.1

选择低碳

交通方式

32.2

67.8

31.2

68.8

51.1

48.9

56.7

43.3

44.045.266.1
56.054.833.9
表1  个人属性及出行特征描述统计 ( %)
SP变量问题描述题项
低碳态度低碳出行方式是舒适和享受的AT1
低碳出行方式是值得被鼓励的AT2
低碳出行方式可以满足日常的出行需求AT3
主观规范周围的人习惯使用低碳出行方式SE1
身边的人会促使你选择低碳出行SE2
社会的低碳氛围会促使你选择低碳出行SE3
环境意识碳排放引起的环境问题与人类生存有关EA1
低碳出行可以有效解决环境问题EA2
你有责任通过低碳出行来保护环境EA3
政策支持小汽车的限行限购会促使你选择低碳出行方式GS1
乘坐出租车的费用增加会促使你选择低碳出行方式GS2
政府对低碳出行的政策支持会鼓励你采用低碳出行方式GS3
低碳出行意向未来出行中你愿意采用低碳出行方式LCI
表2  偏好问题描述
图2  解释变量之间的相关性
共线性排序变量VIF共线性排序变量VIF
1有无私家车1.717通勤距离1.14
2月收入1.668主观规范1.13
3有无驾驶证1.529低碳态度1.11
4年龄1.3810环境意识1.06
5学历1.2811性别1.03
6政策支持1.16
表3  VIF检验结果
图3  居民低碳出行意向与行为占比
图4  低碳出行意向与行为的群体分布
解释变量上海(一线城市)南京(二线城市)中山(三线城市)
IBP11IBP11IBP11
性别(男)

0.084

(0.193)

0.217

(0.181)

0.050

(0.143)

0.096

(0.186)

-0.274

(0.178)

-0.040

(0.048)

-0.222

(0.201)

0.215

(0.201)

-0.013

(0.150)

年龄

-0.020

(0.088)

0.108

(0.093)

-0.001

(0.083)

0.006*

(0.151)

0.039

(0.098)

0.034

(0.065)

0.036

(0.194)

-0.008*

(0.124)

-0.018

(0.123)

学历

0.116**

(0.065)

0.025

(0.162)

0.019

(0.096)

0.013**

(0.148)

0.054**

(0.067)

0.012**

(0.038)

0.053***

(0.073)

0.115**

(0.071)

0.007**

(0.069)

月收入

-0.132

(0.095)

-0.284***

(0.083)

-0.068***

(0.022)

-0.245**

(0.139)

-0.015

(0.104)

-0.037

(0.057)

-0.060*

(0.121)

-0.199

(0.159)

-0.019

(0.132)

有无私家车

0.495*

(0.073)

-0.544**

(0.112)

-0.160***

(0.055)

-0.505**

(0.146)

-0.230

(0.235)

-0.116

(0.062)

-0.020

(0.232)

0.394

(0.238)

0.059

(0.163)

有无驾驶证

-0.008

(0.236)

-0.739***

(0.068)

-0.137***

(0.050)

0.018

(0.204)

-0.139

(0.200)

-0.025

(0.074)

-0.252**

(0.125)

-0.155

(0.222)

-0.079

(0.159)

通勤距离

0.095

(0.082)

0.041

(0.190)

0.0190

(0.121)

-0.053

(0.180)

-0.170*

(0.095)

0.041

(0.025)

0.050

(0.198)

-0.109**

(0.091)

-0.007**

(0.045)

低碳态度

0.725**

(0.062)

0.864***

(0.082)

0.247***

(0.041)

0.662**

(0.144)

0.198**

(0.086)

0.131**

(0.048)

0.820***

(0.091)

0.404**

(0.105)

0.240***

(0.035)

环境意识

0.101*

(0.071)

0.611

(0.214)

0.100

(0.071)

0.167*

(0.137)

-0.213

(0.248)

0.018

(0.090)

0.385*

(0.118)

0.262

(0.283)

0.124

(0.093)

政策支持

0.650**

(0.068)

0.422**

(0.092)

0.157**

(0.047)

0.994***

(0.126)

0.618***

(0.088)

0.218***

(0.046)

0.692**

(0.107)

0.369**

(0.106)

0.153**

(0.052)

主观规范

0.699**

(0.060)

0.397**

(0.091)

0.158**

(0.047)

0.785***

(0.121)

0.449***

(0.082)

0.198***

(0.047)

0.654**

(0.106)

0.341**

(0.111)

0.194**

(0.047)

常数项

-2.805**

(0.273)

-1.118**

(0.234)

-1.938*

(0.282)

-0.317

(0.312)

-2.136**

(0.318)

-1.629*

(0.346)

样本数538446279
athrho

0.397***

(0.124)

0.307***

(0.116)

-0.260**

(0.132)

Wald(χ2328.400***297.880***254.380***
表4  不同城市的估计结果
解释变量18-39岁(青年群体)40-59岁(中年群体)60岁及以上(老年群体)
IBP11IBP11IBP11
性别(男)

-0.043*

(0.068)

-0.016**

(0.065)

-0.010**

(0.019)

-0.241**

(0.115)

-0.127**

(0.104)

-0.058**

(0.026)

0.254*

(0.165)

0.010

(0.156)

0.052

(0.140)

年龄

-0.044

(0.079)

-0.087**

(0.075)

-0.010**

(0.022)

0.036

(0.210)

0.134

(0.200)

0.008

(0.026)

0.219

(0.183)

0.272**

(0.072)

0.102**

(0.047)

学历

0.019**

(0.026)

0.003

(0.126)

-0.004

(0.822)

0.023*

(0.126)

-0.039

(0.234)

-0.011

(0.009)

0.008*

(0.051)

0.005

(0.051)

0.000

(0.013)

月收入

-0.046

(0.089)

-0.091**

(0.068)

-0.026**

(0.011)

0.027

(0.058)

-0.097*

(0.116)

-0.014

(0.014)

0.003

(0.083)

-0.050

(0.080)

-0.010

(0.122)

有无私家车

0.033

(0.096)

-0.249***

(0.050)

-0.044***

(0.026)

0.068

(0.152)

-0.072**

(0.136)

-0.004*

(0.035)

0.039

(0.225)

0.802

(0.219)

0.173

(0.158)

有无驾驶证

-0.068

(0.078)

-0.281***

(0.044)

-0.067**

(0.021)

-0.230

(-0.147)

-0.146

(0.133)

-0.060

(0.034)

-0.207

(0.213)

-0.056

(0.207)

-0.055

(0.097)

通勤距离

0.003

(0.093)

-0.003

(0.133)

0.000

(0.009)

0.089*

(0.048)

0.040

(0.144)

0.020*

(0.012)

0.095

(0.065)

-0.247***

(0.061)

-0.031*

(0.046)

低碳态度

0.641**

(0.071)

0.422**

(0.070)

0.190**

(0.019)

0.973***

(0.068)

0.552***

(0.102)

0.242***

(0.024)

0.735***

(0.160)

0.455***

(0.059)

0.249***

(0.041)

环境意识

0.091**

(0.033)

0.382**

(0.067)

0.091**

(0.033)

0.253**

(0.074)

0.331***

(0.088)

0.098**

(0.037)

0.190

(0.196)

0.229

(0.202)

0.007

(0.112)

政策支持

0.720***

(0.021)

0.364

(0.170)

0.192

(0.019)

0.851**

(0.085)

0.501**

(0.101)

0.215**

(0.024)

0.901***

(0.154)

0.086***

(0.057)

0.209

(0.137)

主观规范

0.572**

(0.039)

0.356

(0.168)

0.166

(0.189)

0.680***

(0.062)

0.408***

(0.100)

0.173*

(0.024)

0.390**

(0.157)

0.125**

(0.076)

0.218***

(0.041)

常数项

-1.878**

(0.272)

-0.800

(0.264)

-2.529**

(0.588)

-1.485*

(0.537)

-2.627***

(0.630)

-1.631

(0.696)

样本数515443305
athrho

0.347***

(0.044)

0.223***

(0.067)

-0.362***

(0.105)

Wald(χ2374.460***341.950***287.620***
表5  不同代际的估计结果
图5  组间平均边际效应差
1 FIORI C, ARCIDIACONO V, FONTARAS G, et al. The effect of electrified mobility on the relationship between traffic conditions and energy consumption[J]. Transportation Research Part D (Transport and Environment), 2019, 67: 275-290. DOI:10.1016/j.trd.2018.11.018
doi: 10.1016/j.trd.2018.11.018
2 GENG J C, LONG R Y, CHEN H, et al. Urban residents' response to and evaluation of low-carbon travel policies: Evidence from a survey of five eastern cities in China[J]. Journal of Environmental Management, 2018, 217: 47-55. DOI:10.1016/j.jenvman.2018.03.091
doi: 10.1016/j.jenvman.2018.03.091
3 彭宏勤, 张国伍. 绿色交通和城市的可持续发展[J]. 交通运输系统工程与信息, 2018, 18(2): 1-6. DOI:10.16097/j.cnki.1009-6744.2018.02.001
PENG H Q, ZHANG G W. Green traffic and urban sustainable development[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(2): 1-6. DOI:10.16097/j.cnki.1009-6744. 2018.02.001
doi: 10.16097/j.cnki.1009-6744. 2018.02.001
4 马壮林, 崔姗姗, 胡大伟. 基于MIMIC模型的限行政策下城市居民低碳出行意向研究[J]. 吉林大学学报(工学版), 2021, 11(4): 1-10.
MA Z L, CUI S S, HU D W. Research on urban residents' low-carbon travel intention after the implementation of the driving restriction policy based on MIMIC model[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 11(4): 1-10.
5 WANG L L, XU J T, QIN P. Will a driving restriction policy reduce car trips?The case study of Beijing, China[J]. Transportation Research Part A (Policy and Practice), 2014, 67: 279-290. DOI:10.1016/j.tra.2014.07.014
doi: 10.1016/j.tra.2014.07.014
6 HU X J, WU N, CHEN N. Young people's behavioral intentions towards low-carbon travel: Extending the theory of planned behavior[J]. International Journal of Environmental Research and Public Health, 2021, 18(5): 2327. DOI:10. 3390/ijerph18052327
doi: 10. 3390/ijerph18052327
7 陈坚, 张弛, 庹永恒, 等. 考虑环境意识和出行习惯的公交出行选择行为模型[J]. 交通运输系统工程与信息, 2020, 20(4): 128-135. DOI:10.16097/j.cnki.1009-6744.2020.04.019
CHEN J, ZHANG C, TUO Y H, et al. Travel mode choice behavior model of public transit incorporating environmental concern and habit[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 128-135. DOI:10.16097/j.cnki.1009-6744.2020.04.019
doi: 10.16097/j.cnki.1009-6744.2020.04.019
8 BAI L, SZE N N, LIU P, et al. Effect of environmental awareness on electric bicycle users' mode choices[J]. Transportation Research Part D (Transport and Environment), 2020, 82: 102320. DOI:10.1016/j.trd.2020.102320
doi: 10.1016/j.trd.2020.102320
9 方晓平, 周倩然. 基于TPB的低碳交通出行方式研究[J]. 铁道科学与工程学报, 2019, 16(3): 804-811. DOI:10.19713/j.cnki.43-1423/u.2019.03.032
FANG X P, ZHOU Q R. The research of low-carbon transportation mode based on TPB[J]. Journal of Railway Science and Engineering, 2019, 16(3): 804-811. DOI:10.19713/j.cnki.43-1423/u.2019.03.032
doi: 10.19713/j.cnki.43-1423/u.2019.03.032
10 UNSWORTH K L, DMITRIEVA A, ADRIASOLA E. Changing behaviour: Increasing the effectiveness of workplace interventions in creating pro-environmental behaviour change[J]. Journal of Organizational Behavior, 2013, 34(2): 211-229. DOI:10.1002/job.1837
doi: 10.1002/job.1837
11 JIA N, LI L Y, LING S, et al. Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice: A cross-city study in China[J]. Transportation Research Part A (Policy and Practice), 2018, 111: 108-118. DOI:10. 1016/j.tra.2018.03.010
doi: 10. 1016/j.tra.2018.03.010
12 HAN S S, GREEN R, WANG M. Towards Low Carbon Cities in China[M]. London: Routledge, 2015: 21-36. doi:10.4324/9781315813721
doi: 10.4324/9781315813721
13 CHEN W D, LI J Q. Who are the low-carbon activists? Analysis of the influence mechanism and group characteristics of low-carbon behavior in Tianjin, China[J]. Science of the Total Environment, 2019, 683: 729-736. DOI:10.1016/j.scitotenv.2019.05.307
doi: 10.1016/j.scitotenv.2019.05.307
14 CLARK B, CHATTERJEE K, MELIA S. Changes to commute mode: The role of life events, spatial context and environmental attitude[J]. Transportation Research Part A(Policy and Practice), 2016, 89: 89-105. DOI:10.1016/j.tra.2016.05.005
doi: 10.1016/j.tra.2016.05.005
15 ZHANG J F, ZHANG L J, QIN Y C, et al. Influence of the built environment on urban residential low-carbon cognition in Zhengzhou, China[J]. Journal of Cleaner Production, 2020, 271: 122429. DOI:10.1016/j.jclepro.2020.122429
doi: 10.1016/j.jclepro.2020.122429
16 MA J, LIU Z L, CHAI Y W. The impact of urban form on CO2 emission from work and non-work trips: The case of Beijing, China[J]. Habitat International, 2015, 47: 1-10. DOI:10.1016/j.habitatint.2014.12.007
doi: 10.1016/j.habitatint.2014.12.007
17 LIU D Y, DU H B, SOUTHWORTH F, et al. The influence of social-psychological factors on the intention to choose low-carbon travel modes in Tianjin, China[J]. Transportation Research Part A(Policy and Practice), 2017, 105: 42-53. DOI:10. 1016/j.tra.2017.08.004
doi: 10. 1016/j.tra.2017.08.004
18 LI Q W, LONG R Y, CHEN H. Empirical study of the willingness of consumers to purchase low-carbon products by considering carbon labels: A case study[J]. Journal of Cleaner Production, 2017, 161: 1237-1250. DOI:10.1016/j.jclepro.2017.04.154
doi: 10.1016/j.jclepro.2017.04.154
19 LI J, SHEN J C, JIA B C. Exploring intention to use shared electric bicycles by the extended theory of planned behavior[J]. Sustainability, 2021, 13(8): 4137. DOI:10.3390/su13084137
doi: 10.3390/su13084137
20 ZHU M, HU X F, LIN Z Z, et al. Intention to adopt bicycle-sharing in China: Introducing environmental concern into the theory of planned behavior model[J]. Environmental Science and Pollution Research, 2020, 27(33): 41740-41750. DOI:10.1007/s11356-020-10135-1
doi: 10.1007/s11356-020-10135-1
21 FU X M. A novel perspective to enhance the role of TPB in predicting green travel: The moderation of affective-cognitive congruence of attitudes[J]. Transportation, 2021, 48(6): 3013-3035. DOI:10.1007/s11116-020-10153-5
doi: 10.1007/s11116-020-10153-5
22 WANG S Y, LI J, ZHAO D T. The impact of policy measures on consumer intention to adopt electric vehicles: Evidence from China[J]. Transportation Research Part A (Policy and Practic), 2017, 105: 14-26. DOI:10.1016/j.tra.2017.08.013
doi: 10.1016/j.tra.2017.08.013
23 LIU Y, LIU R, JIANG X. What drives low-carbon consumption behavior of Chinese college students? The regulation of situational factors[J]. Natural Hazards, 2019, 95(1): 173-191. DOI:10.1007/s11069-018-3497-3
doi: 10.1007/s11069-018-3497-3
24 RU X J, WANG S Y, CHEN Q, et al. Exploring the interaction effects of norms and attitudes on green travel intention: An empirical study in eastern China[J]. Journal of Cleaner Production, 2018, 197: 1317-1327. DOI:10.1016/j.jclepro.2018.06.293
doi: 10.1016/j.jclepro.2018.06.293
25 YANG Y, WANG C, LIU W L, et al. Microsimulation of low carbon urban transport policies in Beijing[J]. Energy Policy, 2017, 107: 561-572. DOI:10.1016/j.enpol.2017.05.021
doi: 10.1016/j.enpol.2017.05.021
26 国家统计局. 第七次全国人口普查公报(第四号)[R]. 北京: 国家统计局, 2021.
National Bureau of Statistics. The Seventh National Population Census bulletin (No 4)[R]. Beijing: National Bureau of Statistics, 2021.
27 王馨, 白凯, 李忠奇. 绿色出行视角下出行者对共享单车的认可度与实际使用行为研究:以西安市为例[J]. 浙江大学学报(理学版), 2021, 48(4): 488-498. DOI:10.3785/j.issn.1008-9497.2021. 04.012
WANG X, BAI K, LI Z Q. Approval degree and using behavior of travelers to shared bicycles from the perspective of green travel: A case study of Xi'an city[J]. Journal of Zhejiang University(Science Edition), 2021, 48(4): 488-498. DOI:10.3785/j.issn.1008-9497.2021.04.012
doi: 10.3785/j.issn.1008-9497.2021.04.012
28 刘建荣, 郝小妮. 考虑环保意识的低碳出行行为研究[J]. 交通运输系统工程与信息, 2019, 19(1): 26-32. DOI:10.16097/j.cnki.1009-6744.2019.01.005
LIU J R, HAO X N. Incorporating environmental consciousness into low-carbon traveling behavior[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(1): 26-32. DOI:10.16097/j.cnki.1009-6744.2019.01.005
doi: 10.16097/j.cnki.1009-6744.2019.01.005
29 何耀, 方晓平, 杨扬, 等. 公众低碳出行行为决策模型的研究[J]. 铁道科学与工程学报, 2017, 14(5): 1077-1085. DOI:10.3969/j.issn.1672-7029.2017. 05.027
HE Y, FANG X P, YANG Y, et al. The research of public low-carbon travel behavior decision model[J]. Journal of Railway Science and Engineering, 2017, 14(5): 1077-1085. DOI:10.3969/j.issn.1672-7029. 2017.05.027
doi: 10.3969/j.issn.1672-7029. 2017.05.027
No related articles found!