Next Article in Journal
Human Exposure to Ferret Badger Rabies in Taiwan
Next Article in Special Issue
Novel Method for Perceiving Key Requirements of Customer Collaboration Low-Carbon Product Design
Previous Article in Journal
The Importance of External Contacts in Job Performance: A Study in Healthcare Organizations Using Social Network Analysis
Previous Article in Special Issue
A Dual-Channel Supply Chain Coordination under Carbon Cap-and-Trade Regulation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 15
Issue 7
10.3390/ijerph15071346
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China

by
Jing Li
1,*,
Kevin Lo
2 and
Meng Guo
3
1
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, Jilin, China
2
Department of Geography, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
3
School of Geographical Science, Northeast Normal University, Changchun 130024, Jilin, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(7), 1346; https://doi.org/10.3390/ijerph15071346
Submission received: 20 May 2018 / Revised: 18 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
(This article belongs to the Special Issue Green Environment, Green Operations and Sustainability)
Browse Figure
Versions Notes

Abstract

:
Choices regarding mode of travel have an evident effect on environment pollutants and public health. This paper makes a significant contribution by examining the differences between low-carbon and non-low-carbon travel mode choices during shopping trips, and how socio-economic characteristics impact individual travel behavior based on data gathered from a questionnaire conducted in Shenyang, China. The study found that, firstly, low-carbon travel modes were more common than non-low-carbon travel modes for shopping, and the average travel distance by non-low-carbon modes was a little longer than that of low-carbon modes. Secondly, suburban and wholesale specialized commercial centers attracted more residents travelling longer distances by non-low carbon modes, especially private car, compared to regional commercial centers in inner city areas. Thirdly, strong relationships between car ownership, gender, monthly income, and travel mode choices were identified in a binary logistic regression model. This study thus highlights the importance of sustainable transportation policies to advocate low-carbon travel modes and reduce carbon emissions.

1. Introduction

There is clear scientific agreement that carbon emissions are affecting the global climate with irreversible long-term consequences [1]. Mitigating energy-related emissions plays a key role in the future of sustainable urban development [2]. The transport sector, as the largest and fastest growing energy consuming sector and contributor to environmental externalities, is inseparably linked to the climate-change challenge; it is currently responsible for 23% of total energy-related GHG emissions [3,4,5]. Furthermore, it is predicted that China’s transport carbon emissions will continue to grow rapidly and will contribute about one-third of global CO2 emissions by 2030 [6,7]. In developed Western cities, it is widely believed that the automobile has provided the means for cities to spread, leading to extensive suburbanization, longer travel distances and low population densities. However, according to the latest data from the World Health Organization (WHO), nearly 7 million people die from air pollution each year, a large extent of which is caused by automobile exhaust emissions. With the affluent middle classes buying cars as soon as they can afford to, motor vehicle ownership and utilization are growing rapidly in China [8]. According to the China Statistical Yearbook 2017, the number of small private passenger cars nationwide rose from 10.8 million to 146.5 million from 2005 to 2016, with an average annual growth rate of 26.7%. With the recent surge in the numbers of private vehicles, reducing energy consumption in the transport sector and lowering the adverse effects of environmental pollutants on public health is regarded as an urgent priority in urban China presently [9].
The travel behavior of urban residents is of increasing interest to researchers and planners. Choices of travel mode for daily mobility have significant effects on the future development of urban regions due to urban sprawl and structural changes to growing metropolitan areas related to transportation development [10,11,12,13,14]. A particularly noteworthy trend in China is the increase in shopping trips, which exacerbate traffic congestion, environmental pollution, and public health issues [15,16]. The function of commercial centers is to supply goods and services to meet the needs and desires of the public [17]. In many advanced countries, one of the most marked changes in the spatial retail structure of cities since the beginning of the 20th century is decentralization [16,18]. On the one hand, China’s metropolises are following the same path, with suburban shopping malls, large supermarkets, and new formats to serve surrounding suburban residents [19,20]. On the other, the commercial and service sectors remain in city centers and maintain a strong accumulation effect, especially in municipal commercial districts. In the context of this situation, the layout of commercial centers further directly influences transport mode choices and environmental externalities during shopping trips [21,22,23,24,25].
Previous studies that have analyzed low-carbon development in the transport sector mainly focused on technological improvement strategies and policies to reduce carbon emissions [7,8,26,27]. Zheng et al. [28] proposed an integrated policy for decreasing the use of urban light-duty vehicles, improving fuel economy and promoting electric vehicles and biofuels to assure peak national vehicle GHG emissions are reached in China by approximately 2030. De Gennaro et al. [29], in a study endorsed by the European Council, stated that key areas for reducing greenhouse gas emissions in the transport sector are switching towards carbon-free or less carbon-intensive fuels and improving fuel efficiency. However, shifting toward less destructive modes of public transport and active travel has been proposed as another important strategy for achieving a significant reduction in carbon emissions from transportation, as technological innovation alone does not suffice [30,31,32]. Consequently, a large body of research on travel behavior has emerged which is directed toward developing more informed insights for policymakers [33,34,35,36]. The identification of factors influencing transport mode choice is particularly important for proposing effective climate change mitigation policies and strategies [37,38]. Extensive literature has discussed the links between the built environment and travel behavior [39,40,41,42,43]. In general, empirical studies have identified associations between the characteristics of the built environment such as density, street design, land use diversity, destination accessibility, distance to transit, and demographics, and travel behavior [37,44,45,46,47]. However, empirical studies on built environment influences cannot fully resolve the details of individual travel behavior, which can also be impacted by the socio-economic characteristics of individuals [48,49]. An important aspect of our study is to provide understanding through a modal shift towards low-carbon travel behavior alternatives capable of mitigating transport carbon emissions and negative environmental impacts of public health. Hence, a key question we focus on is: to what extent do personal socio-economic characteristics influence travel behavior by replacing the traditional built environment and travel behavior relationship approach? Previous studies on travel behavior mainly focused on daily commuting patterns [50,51,52,53]. However, there is still little evidence of non-working travel, especially for shopping trips, and travel behavior has become more complex due to the layout of commercial centers and rapid expansion of urban sprawl. Within this context, this paper aims to examine the differences between low-carbon and non-low-carbon travel mode choices for shopping trips, and how socio-economic characteristics impact individual travel behaviors within the city of Shenyang, one of the largest metropolitan areas in China.

2. Materials and Methods

2.1. Study Area

Shenyang city, located in the middle of Liaoning Province, is the provincial capital and the largest city in northeast China [54]. It contains nine urban districts: Heping, Shenhe, Dadong, Huanggu, Tiexi, Sujiatun, Hunnan, Yuhong, and Shenbei. There is a total area of 1232 square kilometers and 3.77 million people in the central urban area of Shenyang. Shenyang has been a city of heavy industry since the early 1900s, and is undergoing rapid economic redevelopment through new construction in the Shenyang metropolitan area [55,56]. Today, tertiary industries account for 42.5% of GDP. Commercial services have been greatly improved, with retail sales of consumer goods reaching 363.61 billion RMB in 2015. Eight typical commercial centers were selected as our field sites based on their location characteristics and types of retail present [57]. Middle Street and Taiyuan Street are two municipal commercial centers at the city center, with a large business circle covering the entire municipality of Shenyang, and even extending to surrounding cities. The commercial centers of Beihang, Xita-Beishi, and Tiexi are regional shopping centers located in the inner city, which mainly serve residents of the surrounding areas. Wuai and Nanta are specialized wholesale markets with retail activities. Hunnan commercial center, located in the suburbs, has gradually developed with the construction of the Hunnan New Area (Figure 1).

2.2. Data Collection

The data used in this study were collected through questionnaire surveys and interviews conducted with residents on shopping trips to eight typical commercial centers on weekends and weekdays. The survey consisted of three parts: one about travel behavior such as travel mode, travel time, frequency of trips and travel routes; another on personal socio-economic characteristics such as car ownership, gender, age, education, occupation, and monthly income; and a third on attitudes towards the development of public transport and their opinions on shopping by private car. A random sample of 1672 shoppers was invited to respond to the questionnaire, which was filled out by 1525 people, a 91.21% response rate.

2.3. Methodology

Logistic regressions have been widely used to identify the determinants of travel behavior in many contexts [58,59,60,61,62]. The transport mode data in our study were categorized as the binary response variables, ‘low-carbon transport mode’ (walking/cycling, electric bike, bus and metro) and ‘non-low-carbon transport mode’ (private car and taxi), and a binary logistic regression model was adopted to examine the major determinants of socio-economic characteristics of travel behavior during shopping trips. The binary logic model for this study allows for the prediction of binary outcomes (a value of 1 with a probability of p for the respondents’ travel decisions of non-low-carbon transport mode and a value of 0 with probability 1-p for choosing low-carbon transport mode), using one or more continuous or categorical variables of socio-economic characteristics as predictors [63,64]. The binary logistic regression model can be written as Equation (1) [65,66].
ln [ Y ^ 1 − Y ^ ] = β 0 + ∑ r = 1 n β r x r
where Y ^ is the probability of the outcome variable being equal to 1 (choosing non-low-carbon transport mode for shopping), β0 is the model constant, and xr is a continuous or categorical predictor variable. Parameter βr is the regression coefficient to be estimated.

3. Results and Discussions

3.1. Respondent Socio-Economic Characteristics

It is necessary to sum up the socio-economic characteristics of respondents surveyed in the eight typical commercial centers (Table 1). The results indicated that 36% of the 1525 respondents owned private cars. Regarding gender, females constituted 62.6% of the sample. Younger respondents were more likely to go shopping, especially those 35 years of age or younger, who made up 63.0%. The majority of respondents had received tertiary education (59.1%). A high proportion of the workforce in the survey sample (35.9%) was in business fields. Respondents earning income between 3000 and 5000 CNY per month accounted for the largest percentage of 30.8%.

3.2. Travel Behavior of Respondents during Shopping Trips

The mode choice behavior of respondents for shopping trips to the eight typical commercial centers was shown in Table 2. Overall, low-carbon travel modes (80.5%) were more frequent than non-low carbon modes for shopping. With regard to the specific mode of transport, nearly half the respondents preferred to go shopping by bus, 17.3% preferred to walk or cycle and 14.1% preferred to travel by private car. Average travel distance by non-low-carbon modes was slightly higher than that of low-carbon modes, which affected the total carbon emissions produced. Of all commercial centers, respondents travelling by non-low-carbon modes, especially private car, to Hunnan commercial center accounted for the largest proportion (27.5%). The average travel distance was 7.53 km. As Hunnan commercial center is located in the low-density suburbs with better road conditions and sufficient parking facilities, it attracts many residents driving private cars for shopping. From this, it could be suggested that large-scale suburban shopping centers increase city sprawl and induce more traffic [67]. The wholesale specialized commercial centers of Wuai and Nanta saw respondents travelling the longest distances, approximately 9 km by private car or taxi; this may be due to its relatively low wholesale prices. The municipal commercial centers of Middle Street and Taiyuan Street in the city center witnessed relatively long travel distances of 7.58 km and 7.03 km using non-low-carbon modes. An interesting finding was that there was no decline in the central business district, which had a strong contingent of both local and regional shoppers. Nevertheless, Carling et al. [68] indicated that the majority of shopping activity has moved from downtown to edge-of-town, such that shopping activity there is now 10 times higher than that downtown. Hence, in many developed countries, suburban shopping centers have come to dominate the retail structure in metropolitan areas, resulting in the deterioration of Central Business Districts [18]. As the inner city regional commercial centers of Xita-Beishi, Beihang, and Tiexi mainly serve residents in the closely-surrounding areas, most residents prefer to travel to these regional commercial centers by low-carbon transport modes, with the exception of Xita-Beishi, a specialty business street including flower markets, birds, fish, art, and antiques. Travel distances by low-carbon modes were comparatively short, approximately 4.20 km, 5.16 km, and 5.19 km on average to Xita-Beishi, Beihang and Tiexi, respectively. Interestingly, it is worth noting that there is a larger proportion of shoppers walking and cycling to these centers: 38.68%, 30.77%, and 23.65% in Xita-Beishi, Beihang, and Tiexi, respectively.

3.3. Impacts of Socio-Economic Factors on Transport Mode Choice

To test content validity, we developed a binary logistic regression model to explain how socio-economic factors affect transport mode choices during shopping trips. Socio-economic factors included gender, age, education level, occupation, and monthly income. The estimation results of the binary logistic regression model analysis for the choice between non-low-carbon and low-carbon transport mode are presented in Table 3. We found that the conclusions drawn regarding the effects of transport modes were based on models of modest statistical fit (Chi2 = 270.220; df = 15; p = 0.000), although their explanatory power was low (Nagelkerke R2 = 0.256). Using a significance test, we found the socio-economic variables of car ownership and gender to be significant at a 99.9% confidence level, and monthly income significant at a 99% confidence level. This may explain the increase in the use of non-low-carbon transport modes for shopping.
As with findings from previous studies, this study confirms car availability has the largest impact on the choice of non-low carbon transport modes for residents during shopping trips. The vehicle ownership results indicate that residents having their own vehicles were 5.629 times more likely to select non-low carbon modes for shopping trips than those who do not own a car. Plaut [69], Choi and Ahn [70], and Carse et al. [71] have also shown that car ownership is strongly associated with a reduced likelihood of choosing non-motorized modes of transportation, and the higher the number of cars available per adult in the household, the more likely that the trip would be made by non-low-carbon transport modes. Hence, reducing dependency on cars, including through the control of vehicle ownership and vehicle use intensity, improving public transport and non-motorized infrastructure and services, and developing new, cleaner fuel and fuel-efficient vehicles could be more effective strategies to reduce energy consumption, GHG emissions, and the adverse effects of environmental pollution in Chinese cities [7,72,73]. It was observed that males were 1.928 times more likely to use non-low-carbon modes for shopping than females. This finding further emphasized the role of gender differences, which was reflected in previous research. Cao [74] and Myers et al. [75] demonstrated a strong association between gender and travel mode choice. Women were significantly less likely than men to choose non-low-carbon modes, and female respondents interviewed in our study stated that they were more likely to use public transport, especially on days with bad weather or poor road conditions. Income is also an important driver of mode choice in travel behavior. From our data and analysis, we found that residents with monthly incomes of less than 2000 CNY, 2000–3000 CNY and 3000–5000 CNY were 0.421 times, 0.522 times and 0.588 times more likely to choose non-low-carbon modes during shopping trips, respectively, than residents with monthly income higher than 5000 CNY. Along with rising personal disposable incomes and an expanding middle class buying private cars as soon as they can afford to, private automobile ownership and usage are increasing, while public transport usage has almost universally declined in China in the past decades [76,77].

4. Conclusions

This study was performed by obtaining data from questionnaire surveys on weekends and weekdays in Shenyang, China, to explore low-carbon and non-low-carbon travel mode choice behavior during shopping trips to eight typical commercial centers, examining how socio-economic characteristics impact travel patterns and carbon emissions. Our study shows that low-carbon travel modes are more frequent than non-low-carbon modes for shopping. Furthermore, travel mode choice is associated with the location and retail type of the commercial centers. Suburban and specialized wholesale market-oriented commercial centers attract many residents travelling longer distances by non-low-carbon modes of transportation, especially private cars, for shopping. The municipal commercial centers of Middle Street and Taiyuan Street in the city center witness relatively long travel distances by non-low-carbon modes. The regional commercial centers of Xita-Beishi, Beihang, and Tiexi, in the inner city, attract shoppers who almost always choose low-carbon modes, and for whom the travel distances are shorter. The results of binary logistic regression indicate that car ownership, gender, and monthly income are significantly associated with travel mode choices.
The findings of the study have some implications for sustainable transport and low-carbon urban planning. First, they suggest that there should be a shift of emphasis from expanding transport networks to policies that encourage the use of low-carbon alternatives. According to previous research, the improvement of low-carbon transport infrastructure and the adoption of different strategies should depend on city size [3]. Notably, enhancing a better quality public transport system, especially bus services, should be prioritized in the metropolitan areas of China’s megacities to lower the use of non-low-carbon transport. It will be important for appropriate transport policies to be formulated and enhanced concerning accessibility, seat availability, reliability, affordability, comfort, and safety of public transport infrastructure and services. An equally important policy measure is addressing the spatial configuration of different types of retail centers, which is crucial in achieving the aims and goals of low-carbon urban planning, as supported by earlier findings [68]. Especially in the development of regional commercial centers surrounded by high-density housing, efficient public transport systems and good infrastructure for cycling and walking have effectively led to a reduction in travel distances, improved low-carbon travel modes, and reduced environmental pollution. More importantly, urban design policy should seek to encourage people with different socio-economic characteristics who have a strong preference for non-low-carbon transport modes to adopt sustainable travel behavior, and to raise environmental awareness. Finally, the implication is that, combined with technological investment into the development of new cleaner-fuel and fuel-efficient vehicles, sustainable low carbon transportation transport policies should be put in place as part of behavioral interventions, including car and fuel taxes, road pricing, and congestion charging. Parking constraints and parking fees are of particular priority to Hammadou and Papaix [78], who found that parking management policies are significant in shifting to low-carbon transport modes and reducing the adverse effects of air pollution on public health.

Author Contributions

Conceptualization, J.L. and K.L.; Methodology, J.L. and M.G.; Investigation, J.L.; Writing-Original Draft Preparation, J.L.; Writing-Review & Editing, K.L.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (41771179; 41571152; 41401478; 41201159), Strategic Planning Project from Institute of Northeast Geography and Agroecology (IGA), Chinese Academy of Sciences (Y6H2091001) and the Key Research Program of the Chinese Academy of Sciences (KSZD-EW-Z-021-03). The authors would like to acknowledge Mark Wang from the University of Melbourne for his advice on this study. Furthermore, we are truly grateful to the anonymous reviewers’ constructive comments and thoughtful suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Banister, D. Cities, mobility and climate change. J. Transp. Geogr. 2011, 19, 1538–1546. [Google Scholar] [CrossRef]
  2. Liu, B.; Evans, M.; Yu, S.; Roshchanka, V.; Dukkipati, S.; Sreenivas, A. Effective energy data management for low-carbon growth planning: An analytical framework for assessment. Energy Policy 2017, 107, 32–42. [Google Scholar] [CrossRef]
  3. Jain, D.; Tiwari, G. How the present would have looked like? Impact of non-motorized transport and public transport infrastructure on travel behavior, energy consumption and CO2 emissions—Delhi, Pune and Patna. Sustain. Cities Soc. 2016, 22, 1–10. [Google Scholar] [CrossRef]
  4. Alkhathlan, K.; Javid, M. Carbon emissions and oil consumption in Saudi Arabia. Renew. Sustain. Energy Rev. 2015, 48, 105–111. [Google Scholar] [CrossRef]
  5. Birago, D.; Opoku Mensah, S.; Sharma, S. Level of service delivery of public transport and mode choice in Accra, Ghana. Transp. Res. Pt. F Traffic Psychol. Behav. 2017, 46, 284–300. [Google Scholar] [CrossRef]
  6. Tian, Y.; Zhu, Q.; Lai, K.-H.; Venus Lun, Y.H. Analysis of greenhouse gas emissions of freight transport sector in China. J. Transp. Geogr. 2014, 40, 43–52. [Google Scholar] [CrossRef]
  7. Yang, Y.; Wang, C.; Liu, W.; Zhou, P. Microsimulation of low carbon urban transport policies in Beijing. Energy Policy 2017, 107, 561–572. [Google Scholar] [CrossRef]
  8. Bose, R.K. Urban transport scenarios in South Asia: Energy and environmental impact of enhanced public transport systems. Transp. Res. Rec. 2007, 2011, 116–126. [Google Scholar] [CrossRef]
  9. Xu, B.; Lin, B. Factors affecting carbon dioxide (CO2) emissions in China’s transport sector: A dynamic nonparametric additive regression model. J. Clean. Prod. 2015, 101, 311–322. [Google Scholar] [CrossRef]
  10. Salonen, M.; Broberg, A.; Kyttä, M.; Toivonen, T. Do suburban residents prefer the fastest or low-carbon travel modes? Combining public participation GIS and multimodal travel time analysis for daily mobility research. Appl. Geogr. 2014, 53, 438–448. [Google Scholar] [CrossRef]
  11. Tight, M.; Timms, P.; Banister, D.; Bowmaker, J.; Copas, J.; Day, A.; Drinkwater, D.; Givoni, M.; Gühnemann, A.; Lawler, M. Visions for a walking and cycling focussed urban transport system. J. Transp. Geogr. 2011, 19, 1580–1589. [Google Scholar] [CrossRef] [Green Version]
  12. Gutiérrez, J.; García-Palomares, J.C. New spatial patterns of mobility within the metropolitan area of Madrid: Towards more complex and dispersed flow networks. J. Transp. Geogr. 2007, 15, 18–30. [Google Scholar] [CrossRef]
  13. Knowles, R.D. Transport shaping space: Differential collapse in time–space. J. Transp. Geogr. 2006, 14, 407–425. [Google Scholar] [CrossRef]
  14. Määttä-Juntunen, H.; Antikainen, H.; Kotavaara, O.; Rusanen, J. Using GIS tools to estimate CO2 emissions related to the accessibility of large retail stores in the Oulu region, Finland. J. Transp. Geogr. 2011, 19, 346–354. [Google Scholar] [CrossRef]
  15. Zhang, M. Exploring the relationship between urban form and nonwork travel through time use analysis. Lands. Urban Plan. 2005, 73, 244–261. [Google Scholar] [CrossRef]
  16. Takahashi, T. Location competition in an Alonso–Mills–Muth city. Reg. Sci. Urban Econ. 2014, 48, 82–93. [Google Scholar] [CrossRef]
  17. Marjanen, H. Longitudinal study on consumer spatial shopping behaviour with special reference to out-of-town shopping: Experiences from Turku, Finland. J. Retail. Consum. Serv. 1995, 2, 163–174. [Google Scholar] [CrossRef]
  18. Yeates, M.; Montgomery, D. The changing commercial structure of non-metropolitan urban centres and vacancy rates. Can. Geogr. Géogr. Can. 1999, 43, 382–399. [Google Scholar] [CrossRef]
  19. Cao, L.; Chai, Y. Daily shopping activity space of the elderly in Shanghai city. Hum. Geogr. 2006, 21, 50–54. [Google Scholar]
  20. Jing, L.I.; Kevin, L.O.; Zhang, P.; Guo, M. Relationship between built environment, socio-economic factors and carbon emissions from shopping trip in Shenyang city, China. Chin. Geogr. Sci. 2017, 27, 722–734. [Google Scholar]
  21. Meng, M.; Koh, P.; Wong, Y.; Zhong, Y. Influences of urban characteristics on cycling: Experiences of four cities. Sustain. Cities Soc. 2014, 13, 78–88. [Google Scholar] [CrossRef]
  22. Guan, C.; Cui, G. Progress in research on foreign commercial geography since the 1990′s. World Reg. Stud. 2003, 12, 44–53. [Google Scholar]
  23. Shi, Y.; Wu, J.; Wang, S. Spatio-temporal features and the dynamic mechanism of shopping center expansion in Shanghai. Appl. Geogr. 2015, 65, 93–108. [Google Scholar] [CrossRef]
  24. Sun, G.; Chen, Z. The retrospect and prospect on the commercial spatial researches in China and the corresponding compaer with western countries since the 1920′s. Hum. Geogr. 2008, 23, 78–83. [Google Scholar]
  25. Jiangping, Z.; Chun, Z.; Xiaojian, C.; Wei, H.; Peng, Y. Has the legacy of danwei persisted in transformations? The jobs-housing balance and commuting efficiency in Xi’an. J. Transp. Geogr. 2014, 40, 64–76. [Google Scholar] [CrossRef]
  26. Fan, Y.; Khattak, A.J. Does urban form matter in solo and joint activity engagement? Lands. Urban Plan. 2009, 92, 199–209. [Google Scholar] [CrossRef]
  27. Huo, H.; Wang, M.; Zhang, X.; He, K.; Gong, H.; Jiang, K.; Jin, Y.; Shi, Y.; Yu, X. Projection of energy use and greenhouse gas emissions by motor vehicles in China: Policy options and impacts. Energy Policy 2012, 43, 37–48. [Google Scholar] [CrossRef]
  28. Zheng, B.; Zhang, Q.; Borken-Kleefeld, J.; Hong, H.; Guan, D.; Klimont, Z.; Peters, G.P.; He, K. How will greenhouse gas emissions from motor vehicles be constrained in China around 2030? Appl. Energy 2015, 156, 230–240. [Google Scholar] [CrossRef] [Green Version]
  29. De Gennaro, M.; Paffumi, E.; Martini, G. Big data for supporting low-carbon road transport policies in Europe: Applications, challenges and opportunities. Big Data Res. 2016, 6, 11–25. [Google Scholar] [CrossRef]
  30. Hensher, D.A. Sustainable public transport systems: Moving towards a value for money and network-based approach and away from blind commitment. Transp. Policy 2007, 14, 98–102. [Google Scholar] [CrossRef]
  31. Kwan, S.C.; Tainio, M.; Woodcock, J.; Sutan, R.; Hashim, J.H. The carbon savings and health co-benefits from the introduction of mass rapid transit system in Greater Kuala Lumpur, Malaysia. J. Transp. Health 2017, 6, 187–200. [Google Scholar] [CrossRef]
  32. El-Fadel, M.; Bou-Zeid, E. Transportation GHG emissions in developing countries.: The case of Lebanon. Transp. Res. Part D Transp. Environ. 1999, 4, 251–264. [Google Scholar] [CrossRef]
  33. Kunhikrishnan, P.; Srinivasan, K.K. Investigating behavioral differences in the choice of distinct Intermediate Public Transport (IPT) modes for work trips in Chennai city. Transp. Policy 2018, 61, 111–122. [Google Scholar] [CrossRef]
  34. Chaturvedi, V.; Kim, S.H. Long term energy and emission implications of a global shift to electricity-based public rail transportation system. Energy Policy 2015, 81, 176–185. [Google Scholar] [CrossRef]
  35. Rabl, A.; Nazelle, A.D. Benefits of shift from car to active transport. Transp. Policy 2012, 19, 121–131. [Google Scholar] [CrossRef]
  36. Bergstad, C.J.; Gamble, A.; Hagman, O.; Polk, M.; Gärling, T.; Olsson, L.E. Affective–symbolic and instrumental–independence psychological motives mediating effects of socio-demographic variables on daily car use. J. Transp. Geogr. 2011, 19, 33–38. [Google Scholar] [CrossRef]
  37. Kotval-K, Z.; Vojnovic, I. The socio-economics of travel behavior and environmental burdens: A Detroit, Michigan regional context. Transp. Res. Part D Transp. Environ. 2015, 41, 477–491. [Google Scholar] [CrossRef]
  38. Timilsina, G.R.; Shrestha, A. Transport sector CO2 emissions growth in Asia: Underlying factors and policy options. Energy Policy 2009, 37, 4523–4539. [Google Scholar] [CrossRef]
  39. Zahabi, S.A.H.; Miranda-Moreno, L.; Patterson, Z.; Barla, P. Spatio-temporal analysis of car distance, greenhouse gases and the effect of built environment: A latent class regression analysis. Transp. Res. Part A Policy Pract. 2015, 77, 1–13. [Google Scholar] [CrossRef] [Green Version]
  40. Waygood, E.; Sun, Y.; Susilo, Y.O. Transportation carbon dioxide emissions by built environment and family lifecycle: Case study of the Osaka metropolitan area. Transp. Res. Part D Transp. Environ. 2014, 31, 176–188. [Google Scholar] [CrossRef]
  41. Manoj, M.; Verma, A. Effect of built environment measures on trip distance and mode choice decision of non-workers from a city of a developing country, India. Transp. Res. Part D Transp. Environ. 2016, 46, 351–364. [Google Scholar] [CrossRef]
  42. Etminani-Ghasrodashti, R.; Ardeshiri, M. The impacts of built environment on home-based work and non-work trips: An empirical study from Iran. Transp. Res. Part A Policy Pract. 2016, 85, 196–207. [Google Scholar] [CrossRef]
  43. Cao, X.; Fan, Y. Exploring the influences of density on travel behavior using propensity score matching. Environ. Plan. B Plan. Des. 2012, 39, 459–470. [Google Scholar] [CrossRef]
  44. Newman, P.; Kenworthy, J.R. Gasoline consumption and cities: A comparison of U.S. cities with a global survey. J. Am. Plan. Assoc. 1987, 55, 24–37. [Google Scholar] [CrossRef]
  45. Ewing, R.; Cervero, R. Travel and the built environment. J. Am. Plan. Assoc. 2010, 76, 265–294. [Google Scholar] [CrossRef]
  46. Bhat, C.R.; Astroza, S.; Sidharthan, R.; Alam, M.J.B.; Khushefati, W.H. A joint count-continuous model of travel behavior with selection based on a multinomial probit residential density choice model. Transp. Res. Part B Methodol. 2014, 68, 31–51. [Google Scholar] [CrossRef] [Green Version]
  47. Hankey, S.; Marshall, J.D. Impacts of urban form on future us passenger-vehicle greenhouse gas emissions. Energy Policy 2010, 38, 4880–4887. [Google Scholar] [CrossRef]
  48. Bagley, M.N.; Mokhtarian, P.L. The impact of residential neighborhood type on travel behavior: A structural equations modeling approach. Ann. Reg. Sci. 2002, 36, 279–297. [Google Scholar] [CrossRef] [Green Version]
  49. Etminani-Ghasrodashti, R.; Ardeshiri, M. Modeling travel behavior by the structural relationships between lifestyle, built environment and non-working trips. Transp. Res. Part A Policy Pract. 2015, 78, 506–518. [Google Scholar] [CrossRef]
  50. Aguiléra, A.; Voisin, M.; Voisin, M. Urban form, commuting patterns and CO2 emissions: What differences between the municipality’s residents and its jobs? Transp. Res. Part A 2014, 69, 243–251. [Google Scholar] [CrossRef]
  51. Aguiléra, A.; Wenglenski, S.; Proulhac, L. Employment suburbanisation, reverse commuting and travel behaviour by residents of the central city in the Paris metropolitan area. Transp. Res. Part A Policy Pract. 2009, 43, 685–691. [Google Scholar] [CrossRef] [Green Version]
  52. Cirilli, A.; Veneri, P. Spatial structure and carbon dioxide (CO2) emissions due to commuting: An analysis of Italian urban areas. Reg. Stud. 2014, 48, 1993–2005. [Google Scholar] [CrossRef]
  53. Scott, D.M.; Kanaroglou, P.S.; Anderson, W.P. Impacts of commuting efficiency on congestion and emissions: Case of the Hamilton CMA, canada. Transp. Res. Part D Transp. Environ. 1997, 2, 245–257. [Google Scholar] [CrossRef]
  54. Jiang, G.; Zhang, R.; Ma, W.; Zhou, D.; Wang, X.; He, X. Cultivated land productivity potential improvement in land consolidation schemes in Shenyang, China: Assessment and policy implications. Land Use Policy 2017, 68, 80–88. [Google Scholar] [CrossRef]
  55. Sun, L.; Dong, H.; Geng, Y.; Li, Z.; Liu, Z.; Fujita, T.; Ohnishi, S.; Fujii, M. Uncovering driving forces on urban metabolism—A case of shenyang. J. Clean. Prod. 2016, 114, 171–179. [Google Scholar] [CrossRef]
  56. Zhao, Z.-Q.; He, B.-J.; Li, L.-G.; Wang, H.-B.; Darko, A. Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: Case study of Shenyang, China. Energ Build. 2017, 155, 282–295. [Google Scholar] [CrossRef]
  57. Li, J.; Lo, K.; Zhang, P.; Guo, M. Consumer travel behaviors and transport carbon emissions: A comparative study of commercial centers in Shenyang, China. Energies 2016, 9, 765. [Google Scholar] [CrossRef]
  58. Ng, C.P.; Law, T.H.; Wong, S.V.; Kulanthayan, S. Factors related to seatbelt-wearing among rear-seat passengers in Malaysia. Accid. Anal. Prev. 2013, 50, 351–360. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Nesheli, M.M.; Ceder, A.; Estines, S. Public transport user’s perception and decision assessment using tactic-based guidelines. Transp. Policy 2016, 49, 125–136. [Google Scholar] [CrossRef]
  60. Liu, B.S. Association of intersection approach speed with driver characteristics, vehicle type and traffic conditions comparing urban and suburban areas. Accid. Anal. Prev. 2007, 39, 216–223. [Google Scholar] [CrossRef] [PubMed]
  61. Szeto, W.Y.; Yang, L.; Wong, R.C.P.; Li, Y.C.; Wong, S.C. Spatio-temporal travel characteristics of the elderly in an ageing society. Travel Behav. Soc. 2017, 9, 10–20. [Google Scholar] [CrossRef]
  62. Omrani, H. Predicting travel mode of individuals by machine learning. Transp. Res. Procedia 2015, 10, 840–849. [Google Scholar] [CrossRef]
  63. Naznin, F.; Currie, G.; Logan, D. Exploring the impacts of factors contributing to tram-involved serious injury crashes on Melbourne tram routes. Accid. Anal. Prev. 2016, 94, 238–244. [Google Scholar] [CrossRef] [PubMed]
  64. Ramos, H.M.; Ollero, J.; Suárez-Llorens, A. A new explanatory index for evaluating the binary logistic regression based on the sensitivity of the estimated model. Stat. Probab. Lett. 2017, 120, 135–140. [Google Scholar] [CrossRef]
  65. Smallman-Raynor, M.R.; Rafferty, S.; Cliff, A.D. Variola minor in coalfield areas of England and Wales, 1921–1934: Geographical determinants of a national smallpox epidemic that spread out of effective control. Soc. Sci. Med. 2017, 180, 160–169. [Google Scholar] [CrossRef] [PubMed]
  66. Liu, Y.; Yue, W.; Fan, P.; Song, Y. Suburban residential development in the era of market-oriented land reform: The case of Hangzhou, China. Land Use Policy 2015, 42, 233–243. [Google Scholar] [CrossRef]
  67. Yu, B.; Yang, Z.; Cheng, C. Optimizing the distribution of shopping centers with parallel genetic algorithm. Eng. Appl. Artif. Intell. 2007, 20, 215–223. [Google Scholar] [CrossRef]
  68. Carling, K.; Håkansson, J.; Jia, T. Out-of-town shopping and its induced CO2-emissions. J. Retail. Consum. Serv. 2013, 20, 382–388. [Google Scholar] [CrossRef]
  69. Plaut, P.O. Non-motorized commuting in the US. Transp. Res. Part D Transp. Environ. 2005, 10, 347–356. [Google Scholar] [CrossRef]
  70. Choi, H.; Ahn, Y. A study on possibility of commuting trip using private motorized modes in cities around the world: Application of multilevel model. Transp. Res. Part D Transp. Environ. 2015, 41, 228–243. [Google Scholar] [CrossRef]
  71. Carse, A.; Goodman, A.; Mackett, R.L.; Panter, J.; Ogilvie, D. The factors influencing car use in a cycle-friendly city: The case of Cambridge. J. Transp. Geogr. 2013, 28, 67–74. [Google Scholar] [CrossRef] [PubMed]
  72. Hachem, C. Impact of neighborhood design on energy performance and GHG emissions. Appl. Energ 2016, 177, 422–434. [Google Scholar] [CrossRef]
  73. Andong, R.F.; Sajor, E. Urban sprawl, public transport, and increasing CO2 emissions: The case of Metro Manila, Philippines. Environ. Dev. Sustain. 2015, 19, 1–25. [Google Scholar] [CrossRef]
  74. Cao, X.J. Heterogeneous effects of neighborhood type on commute mode choice: An exploration of residential dissonance in the twin cities. J. Transp. Geogr. 2015, 48, 188–196. [Google Scholar] [CrossRef]
  75. Myers, A.M.; Trang, A.; Crizzle, A.M. Naturalistic study of winter driving practices by older men and women: Examination of weather, road conditions, trip purposes, and comfort. Can. J. Aging 2011, 30, 577–589. [Google Scholar] [CrossRef] [PubMed]
  76. Mu, R.; De Jong, M. Establishing the conditions for effective transit-oriented development in China: The case of Dalian. J. Transp. Geogr. 2012, 24, 234–249. [Google Scholar] [CrossRef]
  77. Li, J.; Zhang, P.; Lo, K.; Guo, M.; Wang, M. Reducing carbon emissions from shopping trips: Evidence from China. Energies 2015, 8, 10043–10057. [Google Scholar] [CrossRef]
  78. Hammadou, H.; Papaix, C. Policy packages for modal shift and CO2 reduction in Lille, France. Transp. Res. Part D 2015, 38, 105–116. [Google Scholar] [CrossRef]
Ijerph 15 01346 g001 550
Figure 1. Map of study area.
Figure 1. Map of study area.
Ijerph 15 01346 g001
Table
Table 1. Socio-economic characteristics of respondents (%).
Table 1. Socio-economic characteristics of respondents (%).
Car OwnershipGenderAge GroupEducationOccupationMonthly Income
Yes (36.2)
No (63.8)		Male (37.4)
Female (62.6)		≤18(1.97)
19–25(28.13)
26–35(34.82)
36–50(18.49)
≥51(16.59)		Below High school (26.16)
High school (14.75)
Undergraduate (54.75)
Above Master (4.33)		Public (15.34)
Business (35.93)
Self-employed (17.51)
Unemployed and retirement (31.21)		<2000 CNY (15.00)
2000–3000 (28.30)
3000–5000 (30.80)
>5000 (25.90)
Table
Table 2. Travel behavior data for shoppers at eight commercial centers.
Table 2. Travel behavior data for shoppers at eight commercial centers.
Commercial CenterLow-Carbon ModeNon-Low-Carbon Mode
Walking/CyclingElectric BikeBusMetroPrivate CarTaxi
Proportion (%)Distance (km)Proportion (%)Distance (km)Proportion (%)Distance (km)Proportion (%)Distance (km)Proportion (%)Distance (km)Proportion (%)Distance (km)
Wuai6.532.123.526.8166.338.172.5112.4013.0712.428.046.94
Nanta17.352.351.535.4362.768.291.028.2512.7611.244.596.78
Hunnan11.762.650.496.3030.889.4329.4113.9820.108.277.356.79
Middle Street15.382.080.455.6045.258.5419.9111.2015.388.523.626.64
Taiyuan Street6.772.310.524.3041.158.8228.138.1315.637.647.816.42
Xita-Beishi38.681.352.833.8332.087.110.944.5022.645.862.834.70
Beihang26.941.761.372.3355.256.061.8310.9510.506.224.115.47
Tiexi23.651.663.455.0647.487.0013.797.036.905.824.433.38
Table
Table 3. Logistic regression results of impacts on transport mode choice during shopping trips.
Table 3. Logistic regression results of impacts on transport mode choice during shopping trips.
Explanatory FactorsBS.E.WalsExp (B)95% C.I. for Exp (B)
LowerUpper
Car ownership (ref: no)1.728 ***0.158119.7455.6294.1317.671
Gender (ref: female)0.657 ***0.14520.4881.9281.4512.563
Monthly income(ref: >5000 CNY)
Monthly income (<2000)−0.866 **0.3038.1540.4210.2320.762
Monthly income (2000–3000)−0.650 **0.2079.8720.5220.3480.783
Monthly income (3000–5000)Age (ref: ≥51)−0.530 **0.1709.7770.5880.4220.820
Age (≤18)−0.3120.5360.3400.7320.2562.093
Age (19–25)0.0030.2800.0001.0030.5791.737
Age (26–35)0.3750.2671.9791.4550.8632.455
Age (36–50)Occupation (ref: retirement and unemployed)0.3420.2801.4921.4070.8132.435
Occupation (public)0.2040.2450.6931.2260.7591.981
Occupation (business)0.0920.2100.1921.0960.7261.655
Occupation (self-employed)Education (ref: above master)0.1060.2300.2141.1120.7091.745
Education (below high school)0.1420.4260.1111.1530.5002.655
Education (high school)0.6290.3822.7071.8760.8873.970
Education (undergraduate)0.3590.3511.0501.4320.7202.848
Constant−2.815 ***0.45538.2270.060
Pseudo R-Square (Nagelkerke)0.256
−2 Log Likelihood1251.411
Chi-Square270.220
*** p < 0.001; ** p < 0.01; * p < 0.05.

Share and Cite

MDPI and ACS Style

Li, J.; Lo, K.; Guo, M. Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China. Int. J. Environ. Res. Public Health 2018, 15, 1346. https://doi.org/10.3390/ijerph15071346

AMA Style

Li J, Lo K, Guo M. Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China. International Journal of Environmental Research and Public Health. 2018; 15(7):1346. https://doi.org/10.3390/ijerph15071346

Chicago/Turabian Style

Li, Jing, Kevin Lo, and Meng Guo. 2018. "Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China" International Journal of Environmental Research and Public Health 15, no. 7: 1346. https://doi.org/10.3390/ijerph15071346

APA Style

Li, J., Lo, K., & Guo, M. (2018). Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China. International Journal of Environmental Research and Public Health, 15(7), 1346. https://doi.org/10.3390/ijerph15071346

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop