Exploring the motivation-behavior gap in urban residents’ green travel behavior: A theoretical and empirical study

Resources, Conservation & Recycling 125 (2017) 282–292

Contents lists available at ScienceDirect

Resources, Conservation & Recycling journal homepage: www.elsevier.com/locate/resconrec

Full length article

Exploring the motivation-behavior gap in urban residents’ green travel behavior: A theoretical and empirical study ⁎

MARK

⁎

Jichao Geng, Ruyin Long , Hong Chen , Wenbo Li School of Management, China University of Mining and Technology, Xuzhou, Jiangsu 221116, PR China

A R T I C L E I N F O

A B S T R A C T

Keywords: Urban resident Travel behavior Motivation Motivation-behavior gap Exploratory factor analysis Multinomial logistic regression

The inconsistency between motivation and environmental behavior has been a critical issue troubling many researchers. From the two-dimensional perspectives of motivation and behavior, this study proposes a theoretical model with three colors (four types) of travel behavior: green, red, forced grey, and susceptible grey travel behavior. Based on 1244 questionnaires, the multinomial logistic regression was used to evaluate the effects of different types of travel behaviors in terms of motivations, government measures, and demographic characteristics. The results indicate that the green environmental motivation is a necessary but not sufficient condition for ensuring stable green travel behavior. These motives of economics, convenience, and comfort would adjust, disturb, or even change residents’ travel behaviors, resulting in the motivation-behavior gap. The theoretical model is reasonable and effective for distinguishing urban residents’ multiple motivations and explaining the motivation-behavior gap. In addition, the effects of gender, age, income, vehicle ownership, travel distance, and government measures show significant differences among different types of travel behavior. This study provides some countermeasure proposals targeted to specific colors of travel behavior to modify the motivation-behavior gap and to encourage urban residents to travel in a green way.

1. Introduction In the past two decades, sustainable transport and climate change have attracted wide attention globally. According to International Energy Agency forecasts, urban transport activities bear responsibility for about 40% of total transport energy use and greenhouse gas emissions in 2013 (IEA, 2016). For instance, if without certain controls, direct CO2 emissions from urban transport in Mexican will increase by 80% between 2013 and 2050 (IEA, 2016). China's carbon dioxide emissions in the transportation sector have increased dramatically–nearly tenfold during the 32 years from 1980 to 2012, which is growing at nearly 7.4% yearly. (Xu and Lin, 2015). Urban resident’s travel issue accounts for a large proportion of growth in global carbon emissions (Wang and Liu, 2015). For example, CO2 emissions from residents’ travel have increased by 328% in Beijing during 2000–2012, of which private cars accounting for 84% (Wang and Liu, 2015). Some researchers hold the view that progress in energy technology in recent years has reduced CO2 emissions in the transport sector, but this reduction has been offset by a surge in traffic demand (Allinson et al., 2016). An important measure to curb the traffic carbon emissions is to provide more energy-efficient options such as public transport (PT),

⁎

walking, and cycling instead of fossil-fueled vehicle to meet the same level of travel demand (IEA, 2016). Therefore, how to encourage consciously green travel behavior has become an essential issue in the area of transportation, energy, and ecological environment. Throughout the current research, many studies have focused on the factors influencing individual’s environmental behavior, such as value, norm, attitude, and intention (Anable, 2005; Chen et al., 2014; Botetzagias et al., 2015; Kandt et al., 2015; Clark et al., 2016; Fornara et al., 2016; Lizin et al., 2017). Like an iceberg, people’s environmental action is the part above sea level. The deeply buried part (i.e. motivation) below sea level will be the key to the sustainable environmental behavior (Yan, 2012). Many studies have shown that the pro-environmental attitude or motivation may not completely bring about the proenvironmental travel behavior, which leads to the motivation-behavior gap or attitude-behavior gap (Antimova et al., 2012; Geng et al., 2016; Geng et al., 2017). When residents are making travel decisions, they always consider many goals or preferences, such as comfort, convenience, safety, health, and economy in addition to environment protection (Geng et al., 2016). Hence, their motivations are different, and the moderating effects of external factors are also different, which finally lead to different travel behaviors (Geng et al., 2017). However, the current literature in the field of travel behavior

Corresponding authors. E-mail addresses: [email protected] (J. Geng), [email protected] (R. Long), [email protected] (H. Chen), [email protected] (W. Li).

http://dx.doi.org/10.1016/j.resconrec.2017.06.025 Received 12 April 2017; Received in revised form 25 June 2017; Accepted 25 June 2017 0921-3449/ © 2017 Elsevier B.V. All rights reserved.

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

usually discussed either people’ psychology states or their car use behaviors without considering a two-dimensional perspective that incorporates human’s minds and actions. Furthermore, the non-car use behavior would be an unstable choice without the internal motive powered by a pro-environmental will. This unstable green travel behavior remains a strong likelihood of changing into a non-green travel behavior if other motives, such as the economy, interfered (Geng et al., 2017). Therefore, the questions of what is green travel behavior, how to measure the latent motives behind travel behavior, and how to distinguish among multiple motivations and behaviors will define a new perspective for exploring the motivation-behavior gap and will also define the difficulty and the emphasis of the current research. Under these circumstances, this study is important in several ways. First, understanding the key drivers behind individuals’ travel behaviors is critical. These drivers provide an insight into what is truly green travel behavior and why people with pro-environmental motivation do not engage in it. Second, three colors of travel behavior each with a two-dimensional perspective of motivation and behavior are proposed and empirically discussed. The results can provide a reasonable explanation of the motivation-behavior gap. Finally, this study also aims to explore the effects of government measures on the three colors of travel behavior. The contribution of our study is to provide a new theoretical framework by the background of travel behavior. This framework may be applied to more extensive studies of other environment-friendly behaviors. It maybe provide new insights into people’s environmental psychology and extends current research on demandside management. The rest of this paper is structured as follows. Section 2 constructs a theoretical model of color-coded travel behavior. Section 3 introduces data descriptions and empirical methods. Sections 4 and 5 present the results and discussions. Section 6 concludes the paper and describes policy implications.

et al., 2017). In brief, the origin of motivation refers to the internal power of a specific motive. For a pro-environmental behavior, this power must be motivated by the drive from an individual’s values, knowledge, and cognition. This is obviously different from the mechanism of physiological motivations driven by biological needs. Second, the cognitive theory of motivation insists that GEM has the attribute of intensity that individuals have forces to overcome unfavorable conditions after assessing the expectation and valence of their environmental behavior (Bandura, 1993; Botetzagias et al., 2015). Bamberg and Schmidt (2003), Li et al. (2014), Jing et al. (2016) and Zailani et al. (2016) introduced Ajzen’s theory of planned behavior to find that intention and perceived behavioral control (PBC) are effective variables to explain the intensity and orientation of the sustainable travel behaviors. PBC includes both internal control beliefs similar to self-efficacy and external control beliefs that reflect people’s ability to perform a certain action. Intention is a direct factor that can predict behavior (Ajzen, 1991). Finally, the persistence of motivation can be measured by the individual’s persistence in a certain activity. Triandis’ theory of interpersonal behavior introduces past behavior or habit to describe the repeated action (Robinson, 2010). In the field of travel behavior, past behavior or habit are demonstrated to play a significant role in people’s routine behavior–travel mode choice (Klöckner and Matthies, 2004; Wood and Neal, 2007; Schwanen et al., 2012; Friedrichsmeier et al., 2013; Osman Idris et al., 2015). Although a common opinion of habit has not yet been given, some researchers proposed one widely recognized explanation that describes habit as a non-moral motive. This motive would be helpful for people to reduce the amount of information or cognitive resources they need without taking into account every detail of travel mode choice. (Verplanken et al., 1997; Wood and Neal, 2007; Friedrichsmeier et al., 2013; Geng et al., 2017).

2. Theoretical model of color-coded travel behavior

2.2. Goal-framing theory

2.1. Green environmental motivation

Motivations are rarely completely unified and homogeneous. Mostly they are diverse, mixed, and even interfering with each other. A certain action may be powered by different motives, and different actions may also be triggered by the same or similar motives. Hence, people’s proenvironmental behaviors are not all driven by GEM; some are powered by self-interested motives such as comfort, convenience, and economy. Lindenberg and Steg (2007) put forward the goal-framing theory and stated three goal frames (sometimes also called “multiple motives”) for environmental behaviors: hedonic, gain, and normative. People with hedonic goal frame aims to improve one’s emotional state to “feel better right now” (e.g., seeking comfort). Gain goal frame makes people consider the benefits and costs of a behavior in order to “protect one’s resources” (such as saving money and time). Normative goal frame, always be relevant to environmental behavior, makes people particularly sensitive to what ought to be done and how “to act appropriately”. In addition, the theory suggests that certain motives may conflict with or promote others. Although people’s action is commonly dominated by one main motive, the background motives seem to interfere with the main motive and thus affect behaviors (Geng et al., 2016; Geng et al., 2017). For example, an individual may have GEM, but also be drawn to other motives such as comfort and convenience, resulting in a conflict between motivations. Due to the positive affects brought about by car use, some people may well give up GEM and finally use the car (Van Acker et al., 2014; Geng et al., 2016; Geng et al., 2017). Thus, on the one hand, an individual with GEM may experience interference from other motivations. On the other hand, people’s green behaviors are sometimes forced by economic restrictions rather than GEM. For example, if individuals consider economic issues as their main motives, their green travel behaviors will be rapidly change into non-green travel behaviors when their economic conditions will afford the expenditures of car purchase and use. On the contrary, if people consider the GEM as the main motive, they may be more willing to conduct

Motivation is a mental construct that gives the reason for individual’s actions, desires, and needs. A motive is what drives an individual to perform in a certain way and even repeat it, or at least develop an intention for specific behavior (Geng et al., 2017). Although motivation is underlying and is always cloaked behind a certain behavior, it can be measured using its dimensions. Unlike other physiological motives, an environmental motive must be motivated by human’s cognitive system with the external stimuli of environmental pollutions. Individuals with green environmental motivation (GEM) can consciously reduce or resist other non-essential desires that are harmful to the environment (Geng et al., 2017). Hence, a person with GEM is more likely to implement environment-friendly behavior and to maintain the sustainability of this behavior (Carter and Ockwell, 2007; Clark et al., 2016). What is GEM? Due to the complexity of GEM, it does not yet have a complete and unified definition. However, it can be measured using four dimensions: origin, intensity, orientation, and persistence (Geng et al., 2017). First, the GEM must be originated by stable personal values, norms and cognitions; this view has already been confirmed by Schwartz’s norm-activation theory and Stern’s value-norm theory (Stern et al., 1999; Klöckner and Matthies, 2004; Fornara et al., 2016). Residents should not only have a values or a need to protect the environment, but also realized that their car use is an environmental issue more than a travel issue, and will bring about a negative influence on the environment (Klöckner and Friedrichsmeier, 2011; Paulssen et al., 2014; Geng et al., 2017). On the contrary, their non-car-use travel behavior can make contributions to environmental protection. Furthermore, people should be committed to conduct green travel behaviors, which is motivated by their self-expectations derived from values (Hunecke et al., 2001; Bamberg et al., 2007; Donald et al., 2014; Geng 283

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

Green travel behavior

Non-green travel behavior

Non-car use travel behavior

Car use travel behavior

Individuals of this type always have GEM, but are sensitive and susceptible to other motives. For example, possibly due to their preference for comfort and convenience, residents will not turn their GEM into green actions. ii Forced grey travel behavior (non-green environmental motivation and green travel mode choice, tagged Forced-Grey-TB). Although subjects of this type use green transportation for everyday trips, this is a reluctant, forced, and unstable behavior restrained by other conditions such as economics rather than being promoted by the proenvironmental motivation. If their economic condition permits, people of this type will probably transform to car-use behavior.

Fig. 1. Traditional concept of green travel behavior. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

stable green travel behaviors (Lindenberg and Steg, 2007; Geng et al., 2017). Therefore, previous studies that define green travel behavior only as “non-car use behavior” have serious limitations because they cannot fully explain and distinguish this unstable behavior accompanied by multiple motives.

2.4. Control variables Previous studies have found that the gender, age, income, car ownership, and other demographic variables have a significant impact on residents’ travel behaviors. For instance, Prillwitz and Barr (2011), López-Mosquera et al. (2015), and Clark et al. (2016) asserted that females, middle-aged people, and residents with high income and few private cars, are more likely to adopt environment-friendly travel modes. Yang et al. (2013) found that residents rely more on automobiles as their travel distances increase. Currently, researchers are more concerned about the effect of government measures on residents’ travel modes. For instance, a significant effect of road infrastructure construction has been found by Yang and Long (2016). With more bike paths and fast bus lanes, the usage of private automobiles will decrease. Based on an information intervention experiment, Geng et al. (2016) suggested that green information has a positive effect on the time spent walking, biking, and taking buses, but that residents with different travel motivations response differently to the information. Furthermore, some researchers found that PT subsidies and car restrictions significantly affect residents’ travel modes (Eliasson and Proost, 2015; Lu, 2016). In brief, researchers selected different control variables and therefore obtained different results (Makki et al., 2015; Geng et al., 2017). This study aims to propose a more reasonable theoretical model, which can explore the effect of different motivations and control variables on residents’ travel behaviors, as well as the motivation-behavior gap.

2.3. Color-coded travel behavior Under these circumstances, this paper proposes a theoretical model involving three colors of travel behavior each with two dimensions of motivation and behavior. Fig. 1 illustrates the traditional concept of green travel behavior, whereas Fig. 2 illustrates the proposed three colors of travel behavior and their meanings. Green travel behavior (both environmental motivation and travel mode choice are green, tagged Green-TB). As it is described in Fig. 2, this definition is different from the traditional concept of “non-car use behavior” (see Fig. 1). Subjects of this color not only have GEM, but also perform green actions. Hence, this is a more stringent concept of green travel behavior with two dimensions. Red travel behavior (neither environmental motivation nor travel mode choice are green, tagged Red-TB). Individuals of this color do not have the motivation to protect the environment for everyday trips and always use a non-environmentally-friendly way to travel. Hence, they are more dependent on car uses. Grey travel behavior (either environmental motivation or travel mode choice is green, tagged Grey-TB). Subjects of this type either lack GEM or fail to perform green travel actions, resulting in an inconsistency between motivation and behavior. This color of travel behavior is an unstable behavior ignored by traditional studies that discuss only car-use behavior. It may well explain why people with GEM do not perform green travel behavior. This color of travel behavior contains two specific types.

3. Methodology 3.1. Data collection

i Susceptible grey travel behavior (green environmental motivation and non-green travel mode choice, tagged Susceptible-Grey-TB).

Before the formal investigation, we did a pre-research in Xuzhou

Green travel behavior

Grey travel behavior

Red travel behavior

Green environemntal motivation

Non-green environmental motivation

Non-green environmental motivation

AND

EOR

AND

Green travel mode

Non-green travel mode

Non-green travel mode

Forced grey travel behavior

Susceptible grey travel behavior

Non-green environemntal motivation

Green environemntal motivation

AND

AND

Green travel mode

Non-green travel mode

“Motivation-behavior” gap 284

Fig. 2. Three colors of travel behavior. “AND” means “AND gate”, which is true when both conditions are true. “EOR” means “EOR gate”, which is true when only one condition is true. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

Table 1 The scales and related literature. Variables/ dimensions

Items

GEM origin

(1) Car use is one of the main environmental problems. (mean = 3.69, sd = 0.85) (2) It is urgent to do something about the environmental pollution caused by using the car. (mean = 3.52, sd = 0.98) (3) I am worried about the environmental pollution caused by cars. (mean = 3.83, sd = 0.84) (4) When I use the car, exhaust gases are emitted which have a negative effect on the global climate system. (mean = 3.75, sd = 0.92) (5) I realize that my car use will contribute to the environmental pollution, such as smog and haze. (mean = 3.45, sd = 0.81) (6) I am aware that my use of walking/bicycle/PT contributes to the environmental conservation. (mean = 3.95, sd = 0.85) (7) Because of my own values/principles, I feel a responsibility to use walking/bicycle/PT instead of the car for everyday trips. (mean = 3.74, sd = 0.84) (8) Regardless of what other people do, because of my own principles, I feel an obligation to use walking/bicycle/PT instead of the car for everyday trips. (mean = 3.66, sd = 0.91) (9) The aspect of environmental protection in travel mode choice is solidly anchored in my values. (mean = 3.35, sd = 0.85) (1) Once I choose environment-friendly mode like walking/bicycle/PT for everyday trips, it would be easy for me to stick to it even in challenging circumstances, such as bad weather. (mean = 3.08, sd = 1.07) (2) My autonomy to use environment-friendly mode like walking/bicycle/PT for everyday trips is large. (mean = 2.93, sd = 1.09) (3) I can overcome external conditions that are adverse to my use of environment-friendly modes like walking/bicycle/PT for everyday trips. (mean = 2.78, sd = 0.97) (1) My intention to protect the environment by using walking/bicycle/PT instead of the car for everyday trips is very strong. (mean = 3.86, sd = 1.04) (2) I plan to protect the environment by using walking/bicycle/PT instead of the car for everyday trips. (mean = 3.59, sd = 1.09) (3) My everyday use of walking/bicycle/PT instead of the car is manly for the purpose of protecting the environment. (mean = 3.45, sd = 1.14) Choosing environment-friendly mode like walking/bicycle/PT for everyday trips is something that… (1)…gives me a strange feeling when I don’t do it. (mean = 3.05, sd = 1.25) (2)...I do totally automatically. (mean = 4.05, sd = 1.01) (3)...I do without thinking about it. (mean = 3.35, sd = 0.94) (4)…is part of my routine. (mean = 3.85, sd = 0.97) (5)…does not require any active thought. (mean = 3.45, sd = 0.92) (1) The highways and mass transit systems in my city are perfect. (mean = 2.98, sd = 1.16) (2) The lines and site settings of public transport in my city are reasonable. (mean = 3.18, sd = 1.04) (3) The facilities and services of public transport are very good. (mean = 3.04, sd = 1.12) (4) In recent years, much advertising about green travel is promoted through mass media (television, newspaper, internet etc.). (mean = 3.86, sd = 0.89) (5) In recent years, the local government has carried out a number of travel awareness campaigns. (mean = 3.67, sd = 1.07) (6) It is far from enough for the local government to promote green travel by advertising. (mean = 3.53, sd = 0.99) (reversed) (7) The local government is still lacking effective restriction (e.g. tax) on the car use. (mean = 3.66, sd = 0.98) (reversed) (8) The local government is still doing far from enough to control the car use. (mean = 3.64, sd = 1.02) (reversed) (9) The local government has not yet introduced effective incentive or subsidy policy to encourage walking, cycling or PT use. (mean = 3.85, sd = 1.07) (reversed) What are your preferences in your daily travel mode choice? Please rank them in descending order: (1) Safety. (mean = 4.78, sd = 1.35) (2) Comfort. (mean = 3.35, sd = 1.85) (3) Convenience. (mean = 4.40, sd = 1.55) (4) Pro-environment. (mean = 2.79, sd = 1.47) (5) Economy. (mean = 3.07, sd = 1.39) (6) Health. (mean = 2.60, sd = 1.23)

GEM intensity

GEM orientation

GEM persistence

Attitudes to government measuresa

Multiple motivations

References

Hunecke et al. (2001); Bamberg and Schmidt (2003); Bamberg et al. (2007); Klöckner and Friedrichsmeier (2011);

Friedrichsmeier et al. (2013); Donald et al. (2014); Geng et al. (2017)

Klöckner and Matthies, (2004);

Bamberg et al. (2007); Donald et al. (2014); Geng et al. (2017) Bamberg et al. (2007); Friedrichsmeier et al. (2013)

Donald et al. (2014); Geng et al. (2017) Verplanken and Orbell (2003)

Friedrichsmeier et al. (2013) Donald et al. (2014) Geng et al. (2017)

Geng et al. (2017)

Geng et al. (2016) Geng et al. (2017)

PT: Public transport; sd: Standard deviation; reversed: The score has been reversed before the factor analysis and the multinomial logistic regression. a Items (1)-(3) measure the attitudes to infrastructure, items (4)-(6) measure the attitudes to advertising, items (7)-(9) measure the attitudes to subsidy and restriction policies.

(middle city ranked first) as typical cities in the field research (Geng et al., 2017). The interview of residents was referenced Geng et al. (2016). First, we randomly chose the communities or housing estates, and then randomly chose the buildings and households. Second, we visited each household and asked for only one family representative to fill out a questionnaire. Because sometimes our randomly chosen family member is not at home or too busy to fill in the questionnaire, the representatives of households are not all randomly chosen. Most of the households made their own choices to select a suitable member to fill in

city. A draft questionnaire was developed following some existing literature and improved according to the expert’s advices (see Table 1). The validity and reliability analysis of the draft questionnaire has also been modified through pre-research. One thousand and five hundred finalized questionnaires were distributed to community residents in Jiangsu Province using stratified random sampling. The data collection continued from August 2015 to December 12, 2015. We selected Xuzhou (northern city ranked sixth in private car ownership in Jiangsu Province), Suzhou city (southern city ranked second), and Nanjing

285

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

expressed as Eq. (2): πj + πi = 1(2) ∑

the questionnaire. Every respondent completed the questionnaire was able to receive a small gift. Finally, 1244 effective questionnaires were collected and used in data processing.

j ∈ (1,2, … i − 1.i + 1, … , n)

The likelihood function is defined as Eq. (3): n

L (β ) =

3.2. Measures

k=1

m

LL (β ) = Log (L (β )) =

n

∑ ∑ j=1

yjk ln [πj (xk )]. (4)

k=1 2

Correlation coefficient (R ) and the Likelihood Ratio Test (LRTS) are used as the goodness of fit test. The LRTS value is calculated using Eq. (5):

LRTSi (βj ) = −2[LLi (βj ) − LLi (βi )],

(5)

where LRTSi (βj ) reflects the LRTS that restricts the parameters estimated from variable xj; LLi (βj ) is the likelihood ratio when variable xj is restricted. The LRTS is ascertained by comparing the logarithmic likelihood values of the two cases of adding a variable and not adding a variable. 4. Results 4.1. Quantification of GEM Environmental motivation is latent, complex, and difficult to measure directly, but can be quantified using exploratory factor analysis. Table 3 and Table 4 show the results of the exploratory factor analysis and related tests. According to the determinant value of correlation matrix, the Kaiser-Meyer-Olkin (KMO) value, and the Bartlett spherical test (see Table 3), it is fit for factor analysis. As it is described in Table 4, principal components analysis using the varimax rotation revealed five factors with eigenvalues greater than 1.0. The five factors represented the 20 items of environmental motivation with a 72.734% contribution to the cumulative variance. These five factors were named environmental awareness, habit, personal norm, perceived behavioral control (PBC), and intention of environmental behavior. The comprehensive value for environmental motivation (F) was calculated using Eq. (6):

3.3. Multinomial logistic regression Because the dependent variables (different types of travel behavior) have many levels with no orders, multinomial logistic regression as the most popular discrete choice model wildly used in the field of transportation was introduced in this study. Following Geng et al. (2017), the procedure of this model can be briefly described as follows.

πj P (y = j/ x ) ⎤ ln ⎡ ⎤ = ln ⎡ = αj + βj1 x1 + βj2 x2 + ⋯ + βjn xk ⎢ ⎢ ⎥ P π ⎣ i⎦ ⎣ (y = i/ x ) ⎥ ⎦ n

∑

[πj (xk ) yjk ](3)where L is the likelihood assigned to

j=1

the available alternatives. If j is chosen, y = 1 otherwise y = 0. A logarithmic likelihood function is given in Eq. (4):

The first part of the questionnaire measured demographic variables such as gender, age, household income, travel distance, car ownership, and travel mode. Specifically, The travel mode measuring item referenced Klöckner and Friedrichsmeier (2011) and Geng et al. (2017), which asked, “What is the main travel mode (walking/bicycle (electric or non-powered)/public transport/car (private car or taxi)) that you use for everyday trips?”. The second part of the questionnaire measured environmental motivation using four dimensions with 20 items. Attitudes towards government measures, i.e., road infrastructure, advertising, and subsidy and restriction policies, were also measured. Likert’s five-step scale are provided, where 5 (strongly agree) represented the most favorable response and 1 (strongly disagree) represented the most unfavorable response. The last part of the questionnaire measured residents’ preferences for multiple motivations. In order to make a better distinction among people’s multiple travel motives, respondents were asked to rank six motivations (comfort, safety, convenience, economy, health, and environment protection) in descending order. These motivations were ranked from 6 to 1, where 6 means the most preferred motive and 1 the least (Geng et al., 2017). The items and their referenced literature were summarized in Table 1. Table 2 summarizes the sample distribution characteristics.

= αj +

m

∏ ∏

βjk xk ,

F = F1 × 0.247 + F2 × 0.231 + F3 × 0.186 + F4 × 0.176 + F5 × 0.16

k=1

j ∈ (1,2, …i − 1.i + 1, …, m)

(6)

The weights (0.247, 0.231, 0.186, 0.176, and 0.16 for F1–F5, respectively) were calculated and described in Table 4. The F1–F5 are the scores of the five factors.

(1)

where m is the choice set, αj is the estimated intercept, βjk is the estimated coefficient, and πj is the conditional probability that a person selects j instead of i.

4.2. Quantification of color-coded travel behavior

P (y = j / x )

ln ⎡ P (y = i / x ) ⎤ is the odds ratio (OR) of level j (select group) with ⎣ ⎦ respect to level i (reference group). Eq. (1) must meet the requirement

The methods of quantifying different types of travel behavior are

Table 2 Sample distribution of the questionnaire. Variables Gender Age

Travel distance(km)

Male Female 18–25 26–30 31–40 41–50 > 50 <1 1–3 3–5 5–10 10–15 15–20 > 20

Frequency

Percentage (%)

Variables

658 586 202 302 316 288 136 134 216 260 300 142 94 98

52.9 47.1 16.24 24.28 25.4 23.15 10.93 10.77 17.36 20.9 24.12 11.41 7.56 7.88

Monthly household income (RMB)

Car ownership Main travel mode

286

< 2000 2000–4000 4000–6000 6000–8000 8000–10,000 10,000−30000 > 30,000 Yes No Walking Bicycle PT Car/Taxi Total

Frequency

Percentage (%)

211 284 279 261 141 52 16 458 786 124 402 376 342 1244

16.96 22.83 22.43 20.98 11.33 4.18 1.29 36.82 63.18 9.97 32.32 30.23 27.48 100.00

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

Table 3 The rotated component matrix, determinant, KMO, Cronbach's Alpha, and Bartlett spherical test from exploratory factor analysis. Items of the GEM

Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

Cronbach's Alpha

GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM GEM

0.779 0.777 0.747 0.736 0.686 0.668 −0.041 −0.025 0.015 −0.077 0.033 0.197 0.025 0.061 0.244 0.182 0.045 0.271 0.256 0.256

−0.083 0.003 0.042 −0.046 −0.019 −0.008 0.886 0.873 0.786 0.776 0.741 0.137 −0.020 0.099 0.102 −0.057 −0.034 0.024 −0.009 −0.060

0.096 0.030 0.025 −0.034 0.096 −0.029 −0.017 0.058 0.010 −0.018 −0.067 0.969 0.876 0.814 0.035 0.154 0.039 0.077 0.077 −0.017

0.185 0.118 0.133 0.065 0.107 0.189 0.006 0.002 0.006 0.042 0.061 0.145 0.078 0.052 0.927 0.823 0.802 0.116 0.159 0.056

0.127 0.134 0.127 0.079 0.334 0.238 −0.038 −0.043 −0.026 0.041 0.010 0.033 0.058 0.134 0.122 0.086 0.072 0.850 0.812 0.797

0.808

origin (2) origin (3) origin (1) origin (4) origin (6) origin (5) persistence (4) persistence (5) persistence (2) persistence (3) persistence (1) orientation (3) orientation (2) orientation (1) intensity (2) intensity (1) intensity (3) origin (7) origin (8) origin (9)

0.766

0.843

0.829

0.737

Values in brackets represent the specific items (see Table 1). The KMO value was 0.814. The significance of Bartlett spherical test was 0.000. The determinant value of correlation matrix is 0.003.

described below. First, according to the calculated values of environmental motivation (F), the participants with F > 0 were defined as having a green environmental motivation, and those with F < 0 were defined as having a non-green environmental motivation (Eq. (8)). Eight samples had F = 0; these were removed from the set because the sample size was too small to influence the model.

EnvironmentalMotivation GreenenvironmentalmotivationifF ≥ 0 =⎧ − greenenvironmentalmotivationifF < 0 Non ⎨ ⎩

(8)

Second, walking, bicycling, and PT were classified as green travel modes (S = 1), whereas the car was regarded as a non-green travel mode (S = 0, see Eq. (9)).

Fig. 3. Percentages of four clusters of color-coded travel behavior among 1236 samples.

Color − coded travel behavior ⎧ Green travel behavior (assigned 1) if F≥0and S = 1 ⎪ Forced grey travel behavior (assigned 2) if F< 0and S=1 ⎪ = Susceptible grey travel behavior (assigned 3) if F ⎨ ⎪ ≥0and S = 0 ⎪ Red travel behavior (assigned 4) if F< 0and S = 0 ⎩

Greentravelmode (S = 1) ifusewalking , bicycleorPT Travelmode = ⎧ Non − greentravelmode (S = 1) ifusecar ⎨ ⎩ Finally, according to the theoretical model of color-coded travel behavior illustrated in Fig. 1, Green-TB, Forced-Grey-TB, SusceptibleGrey-TB, and Red-TB (the dependent variable) were assigned values of 1, 2, 3, or 4 respectively (Eq. (10)).

A pie chart (Fig. 3) is provided to illustrate the percentages of four clusters of color-coded travel behavior among 1236 samples (because 8 samples were removed from the total 1244 samples). We can clearly see in Fig. 3 that according to our classification, the Green-TB accounts for a largest proportion, followed by the Forced-Grey-TB, Susceptible-GreyTB, and finally Red-TB.

Table 4 Five factors revealed according to their cumulative contributions. Factor

Environmental awareness Habit Intention PBC Personal norm

Initial eigenvalue

(10)

Eigenvalue after Rotation

Weight

Total

Variance contribution (%)

Cumulative contribution (%)

Total

Variance contribution (%)

Cumulative contribution (%)

Variance/ Cumulative contribution

5.467 3.398 2.621 1.726 1.335

27.334 16.99 13.105 8.631 6.674

27.334 44.323 57.428 66.06 72.734

3.598 3.356 2.701 2.557 2.334

17.992 16.778 13.507 12.787 11.67

17.992 34.77 48.277 61.064 72.734

0.247 0.231 0.186 0.176 0.16

PBC: perceived behavioral control.

287

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

Table 5 Multinomial logistic regression results for multiple travel motivations. Variable

Constant

Comfort

Convenience

Safety

Pro-environment

Economy

-2LL(0) -2LL(β) Cox and Snell R2 Nagelkerke R2 McFadden R2

Color-coded behavior

Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey

Model_1(color-coded behavior)

Traditional behavior

βj

Wald

LRTS

5.48 2.67 1.01 −0.34 −0.08 −0.15 −0.23 −0.11 −0.15 −0.40 −0.16 −0.19 +0.35 −0.19 +0.03 +0.25 +0.43 −0.22 1794.70 1614.41 0.12 0.13 0.05

17.87(***) 3.89(**) 0.38 21.69(***) 1.19 2.77 8.38 (***) 1.61 3.03(*) 14.56(***) 2.16 2.10 9.63(***) 4.53(**) 0.16 6.74(***) 24.77(***) 4.77(**)

27.36(***)

Model_2 (traditional travel behavior)

βj

Wald

LRTS

Non-car use

3.74

16.45 (***)

17.30 (***)

34.17 (***)

Non-car use

–

–

–

10.76 (***)

Non-car use

–

–

–

20.64 (***)

Non-car use

−0.21

8.14 (***)

8.49 (***)

32.98 (***)

Non-car use

−0.22

4.37 (**)

4.64 (**)

36.36 (***)

Non-car use

–

–

–

732.87 698.28 0.02 0.02 0.01

Reference category: Red-TB in Model 1; car use behavior in Model 2. The multiple motivations were measured by the sorting item. Hence, to avoid multicollinearity and data redundancy, the health variable with minimum means and variances was treated as the reference category and was not included in the regression. −2LL(0) listed the whole information. −2LL(β) showed all fitting information. βj was the estimated coefficient. The significance of βj was tested by Wald tests. The LRTS was used Chi-square tests. *** P < 0.01, ** P < 0.05, * P < 0.1.

positive but non-significant impact on Susceptible-Gray-TB (βj = 0.03, p > 0.1). The influence of economics on Green-TB is significant and positive (βj = 0.25, p < 0.01), but the influence of comfort, convenience, and safety on Green-TB is significantly negative (βj < 0, p < 0.01). Economics (βj = −0.22, p < 0.05) and convenience (βj = −0.15, p < 0.1) have a significantly negative effect on Susceptible-Grey-TB. In summary, from the comparison between Model_1 and Model_2, color-coded travel behavior can better explain and differentiate the influence of multiple motivations on travel behaviors. The four types of travel behavior can also reveal a group’s degree of resolve against different non-green motivations. The Green-TB group with both the GEM and the green travel mode, which is strongly encouraged by pro-environmental motivation (βj > 0, p < 0.01), can significantly resist the interferences of comfort, convenience, and safety (βj < 0, p < 0.01). The Forced-Grey-TB group, though they use green travel modes for everyday trips, gives their most considerations to economic issues (βj > 0, p < 0.01) rather than pro-environmental views (βj < 0, p < 0.05). The Susceptible-Grey-TB group, however, does not always care about economics (βj < 0, p < 0.05). They have more vague motives and are more inclined to experience interference from various conditions (p > 0.1). The Red-TB group, by contrast, is more influenced by convenience and comfort (especially comfort).

4.3. Multinomial logistic regression of multiple motivations In order to verify the effectiveness and advantages of the theoretical model of color-coded travel behavior, a comparative analysis was also performed using multinomial logistic regression for traditional travel behavior. The assignment of traditional travel behavior was calculated using Eq. (11).

Traditionaltravelbehavior Greentravelbehavior (assigned0) ifS = 1 =⎧ Non − greentravelbehavior (assigned1) ifS = 0 ⎨ ⎩

(11)

Table 5 summarizes the significant effects of multiple motivations on the color-coded travel behavior and the traditional travel behavior. Specifically, Model_1 lists the regression results of the color-coded travel behavior, whereas Model_2 shows the results for traditional travel behavior. As for the traditional travel behavior (see Model 2 in Table 5), travel motives of comfort, convenience, and economy were not necessarily added as significant variables according to LRTS Chi-square tests (p > 0.1), and the effect of pro-environmental motivation on non-car use behavior appears as a negative correlation (βj = −0.22, p < 0.05) (Model_2 in Table 5). Hence, it is clear from Model 2 that the motivation-behavior gap indeed exists. Compared to car users, people using green travel modes are less sensitive to pro-environmental motivation. It seems that this “green travel behavior” is not necessarily accompanied by a preferential consideration of pro-environmental issues. Therefore, this model cannot provide an insight into the main causes of the motivation-behavior gap and how to modify or close it. As for the colored-coded travel behavior (see Model 1 in Table 5), each variable is validly introduced in the model according to the LRTS Chi-square test (p < 0.01), and the explanatory power of color-coded travel behavior on multiple travel motives is greatly enhanced. Compared to Red-TB, pro-environmental motivation has a significantly positive impact on Green-TB (βj = 0.35, p < 0.01) and a significantly negative impact on Forced-Grey-TB (βj = −0.19, p < 0.05) and a

4.4. Multinomial logistic regression of control variables Table 6 shows the significant effects of control variables on both color-coded and traditional travel behavior. The values of government measures were the means of related items. Model_3 lists the regression results of the color-coded travel behavior, whereas Model_4 shows the results for traditional travel behavior. As it is described in Model_4 in Table 6, compared to car users, residents using green travel modes have a lower proportion of car ownership (p < 0.01), a shorter travel distance (p < 0.01), and are more likely to be women (p < 0.1). However, beyond these, other 288

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

Table 6 Multinomial logistic regression results for control variables. Variable

Constant

Gendera

Age

Income

Car ownershipb

Distance

Infrastructure

Advertising

Policy

−2LL(0) −2LL(β) Cox and Snell R2 Nagelkerke R2 McFadden R2

Color-coded behavior

Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey Green Forced grey Susceptible grey

Model_3(color-coded behavior)

Traditional behavior

βj

Wald

LRTS

– – – −0.33 +0.13 +0.59 −0.08 −0.19 −0.23 −0.86 −1.21 −0.17 −3.16 −2.74 +0.35 −0.22 −0.23 −0.14 +0.15 +0.26 +0.25 +0.26 −0.21 −0.12 +0.48 −0.15 +0.52 3151.08 2553.31 0.38 0.42 0.29

– – – 1.70 0.25 2.67 1.43 10.17(***) 10.09(***) 18.51(***) 11.84(***) 0.91 118.14(***) 89.42(***) 0.59 11.83(***) 13.12(***) 3.59(**) 2.83(*) 5.36(**) 3.29(*) 4.73(**) 2.75(*) 0.62 15.98(***) 2.94(*) 12.38(***)

–

Model_4 (traditional behavior)

βj

Wald

LRTS

Non-car use

–

–

–

12.96 (***)

Non-car use

−0.37

2.98

3.01(*)

18.03 (***)

Non-car use

–

–

–

41.63 (***)

Non-car use

−0.14

5.34 (**)

5.28(**)

325.67 (***)

Non-car use

−3.14

186.78 (***)

322.61 (***)

14.48 (***)

Non-car use

−0.17

11.01 (***)

11.09 (***)

7.38 (**)

Non-car use

–

–

–

13.43 (***)

Non-car use

–

–

–

45.73(***)

Non-car use

–

–

–

1443.36 1023.32 0.28 0.31 0.21

Reference category: Red-TB in Model 1; car use behavior in Model 2. *** P < 0.01, ** P < 0.05, * P < 0.1. a Rreference category is “female”. b Rreference category is “with no vehicle”.

attitudes toward government policies, whereas the Forced-Grey-TB and Red-TB groups are less satisfied with these policies (βj < 0, p < 0.05).

control variables are not necessarily added as significant variables according to LRTS Chi-square tests (p > 0.1). In contrast, in Model_5, all control variables are validly introduced into the model according to the LRTS Chi-square test (p < 0.05), which show that the color-coded travel behavior can better explain the characteristics of different groups and their attitudes toward government measures. As for gender, the ratio of females in the Green-TB (βj = −0.33) group is higher than that in the Forced-Grey-TB (βj = 0.13) and Susceptible-Grey-TB groups (βj = 0.59), which clearly indicates women’s more stable performance in green travel (but p > 0.1). As for age, the Forced-Grey-TB (βj = −0.19, p < 0.01) and SusceptibleGrey-TB groups (βj = −0.23, p < 0.01) are younger, whereas the RedTB group is comparatively older. As for income, members of the GreenTB (βj = −0.86, p < 0.01) and Forced-Grey-TB groups (βj = −1.21, p < 0.01) tend to have lower household monthly income than the Susceptible-Grey-TB and Red-TB groups. In terms of distance, the differences among the four groups were significant (βj < 0, p < 0.05): the Red-TB group’s daily travel distances are significantly longer than those of the other groups. As for car ownership, although the Green-TB and Forced-Grey-TB groups have a lower proportion of car ownership (βj < 0, p < 0.01), the Susceptible-Grey-TB group (βj > 0, but p > 0.1) has a higher proportion of car ownership even than the RedTB group. As for infrastructure, the Red-TB group is less satisfied with infrastructure conditions than the other groups. Government advertising are more recognized by the Green-TB group (βj > 0, p < 0.05) than by other groups, especially the Grey-TB group (βj < 0, p < 0.1). In terms of policies, the Green-TB (βj = 0.48, p < 0.01) and Susceptible-Grey-TB groups (βj = 0.52, p < 0.01) are more inclined to have positive

5. Discussion Through empirical testing, the color-coded travel behavior model has been shown to be feasible and effective in distinguishing the effects of multiple motivations on travel behaviors and in further explaining the motivation-behavior gap. The three colors of travel behavior can reveal a group’s resolve for or against different travel motivations. The Green-TB group which strongly encouraged by the GEM can significantly resist the interference of comfort, convenience, and safety, but not of economics. The need to save money may well further promote their green travel behaviors. Affect and emotion might not have a significant influence on all environment-friendly behaviors, but did have been an essential indicator for people’s car use behavior (Hounsham, 2006; Geng et al., 2016). Morris and Guerra (2015) and Lancée et al. (2017) discovered that car passengers and car drivers experienced more positive emotions, followed by PT users who experienced the most negative emotions. It may be the reason why members of the Red-TB group are more depend on car uses. They usually make comfort and convenience as their dominate motivations and depend on car uses mainly because the car can not only save time and effort, but also induce pleasant emotions or show social status, identity, and a positive self-image (Steg, 2005; Hounsham, 2006; Zailani et al., 2016; Geng et al., 2016). The Grey-TB group often has many travel motives and is easily interfered with by other non-environmental factors. Specifically, the Forced-Grey-TB group that always has strong intentions for car use 289

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

in cooperation with the GEM (Geng et al., 2017). Therefore, future studies should explore the dynamic interactive process of multiple motivations to promote green transformation of people’s environmental behavior. In addition, more tailored policy or information interventions in a life-oriented perspective will be more clever strategies for encouraging residents’ sustainable environment-friendly travel and consumption behaviors (Geng et al., 2016; Wang et al., 2017).

gives their strongest consideration to economics followed by comfort, convenience, safety, and finally environment protection. Hence, restricted economic conditions have impelled them currently to take economics as their main goal and passively choose cheaper (green) travel modes. Once economic conditions permit, travel goals like comfort and convenience will rapidly become their main motives, and then the members of the Forced-Grey-TB group will certainly move into the Red-TB group. In contrast, the Susceptible-Grey-TB group with more vague motivations has the most unstable travel patterns. They do not care about economics, but are easily influenced by control variables or other motives such as comfort, which finally lead to their car-use behaviors. Jones and Ogilvie (2012) are in an effort to explore motivations regarding travel behavior and to interpret how active mobility might be improved more efficiently. They found that commuters were more likely promoted by concerns of convenience, speed, and cost performance of a specific travel mode. In Rokeach’s value theory, goals of comfort and convenience are deeper motives, i.e., ultimate values, compared to the GEM and economic motives that are instrumental values (Rokeach 1973; Ellaway et al., 2003; Geng et al., 2016). Hence, when these two motives conflict with each other, the GEM and economic motives will be weakened. Then the comfort and convenience motives will more likely determine behavior, resulting in susceptible group’s car-use behavior and the motivation-behavior gap. Hence, simply increasing public awareness of environment protection or restricting car use by means of policies does not go far enough. A question worthy of careful consideration is how to promote the motives of comfort, convenience, and environment protection together, which will probably lead to more effective measures for inducing green travel behaviors in this group. In addition, the health variable in our study has a minimum mean and variance value, which indicates that respondents generally show lowest preference for health. But Jones and Ogilvie (2012) insisted that although health concern was not a main motive, occasional increases in physical activity were often associated with the use of walking, cycling, and PT. The same conclusions are also found by Geng et al. (2016). Therefore, health motive might be an important catalyst or cofactor to promote people’s green travel behaviors from the side. On the whole, the control variables of the groups indicate a strong correspondence with multiple travel motives, which can provide more insight into the motivation-behavior gap. Overall, as the same findings in Liu et al. (2015), Clark et al. (2016), Shen et al. (2016), Geng et al. (2016), Geng et al.(2017), members with male sex, older age, higher income, longer travel distance, and high proportion of car ownership may be accustomed to enjoy a comfortable an convenience life, show a lower dissatisfaction to policies, not implement green travel behaviors, and more likely to be classified as the Red-TB group. Specifically, due to the Susceptible-Grey-TB group’s swing attitudes to policies and lower ability to resist the temptation of comfort, they may more easily change into the Red-TB group because their economic conditions can easily afford the spending in car use (Clark et al., 2016). In contrast, a lower satisfaction to policies, a younger age, a lower income, and a lower proportion of car ownership make the Forced-Grey-TB group reluctantly choose green modes for their everyday trips, even without impetus of the GEM. However, Geng et al. (2016) indicated that residents with gain goal frames, who are mostly members of the Grey-TB group, are more likely to be influenced by advertising information. The green information provided in the experiment can prolong their travel times by walking, cycling, and PT to the greatest degree. As for the Green-TB group with positive attitudes to government measures, their economic conditions and pro-environmental motives are consistent, resulting in sustainable green travel behaviors (Geng et al., 2017). In brief, a question worthy of careful consideration is how to highlight the GEM as a dominance motivation, or how to strengthen the GEM-promoted motivation (e.g., economy), or how to either reduce the GEM-obstructive motivation (e.g., comfort or convenience) or make it

6. Conclusions and policy implications 6.1. Conclusions (1) Based on the two-dimensional perspectives of motivation and behavior, this paper proposes a theoretical model with three colors (four types) of travel behavior, including Green-TB, Red-TB, Forced-Grey-TB, and Susceptible-Grey-TB. Through empirical testing, the model has proved to be a reasonable way to differentiate among motivations associated with different types of travel behaviors as well as providing a reasonable explanation of the motivation-behavior gap. (2) The Red-TB group prefers comfort and convenience, whereas the Green-TB group prefers environment protection. The Forced-GreyTB group gives more weight to economics, whereas the SusceptibleGrey-TB group with more vague motivations, is more easily influenced by other motivations, especially comfort. GEM is a necessary, but not sufficient condition for ensuring stable green travel behavior. These motives of economics, convenience, and comfort would adjust, disturb, or even change residents’ travel behaviors, resulting in the grey travel behavior and the motivation-behavior gap. A question worthy of careful consideration is how to promote the motives of comfort, convenience, and environment protection together, which will probably lead to more effective and clever strategies for inducing urban residents’ green travel behaviors. (3) Age, gender, household income, car ownership, travel distance, and attitudes toward government measures indicate a strong accordance with motivations and travel behaviors. Specifically, the Red-TB group is older, has higher household income, is less satisfied with government measures, and is more dependent on car use. The Green-TB group of medium age and income is more satisfied with government measures and always implements green travel behavior. The younger and low-income Forced-Grey-TB group often uses non-car travel modes, but this action is a forced and reluctant behavior induced by limited economic conditions. The SusceptibleGrey-TB group of medium age and high income hold swing attitudes to government measures. 6.2. Policy implications As shown in our study, due to people’s different characteristics and preferences, targeted policies must be designed. Hence, in combination with the theoretical model of color-coded travel behavior and related conclusions, certain suggestions as illustrated in Fig. 4 are provided here to close the motivation-behavior gap. (1) For the Red-TB group, the government should not only develop the infrastructure (e.g. bus rapid transit, rail transit and public bicycle system), but also improve their facilities, maintenance and services. A question worthy of careful consideration is how to provide positive experience about using walking, cycling, and PT so as to avoid emotional losses from car users switching to non-car uses (Geng et al., 2017). For example, for long distance trips, promoting alternative products such as rail transit, clean-energy vehicles and car sharing would possibly be a smart strategy (Shen et al., 2016). For short distance trips, bike-sharing program may be more attractive (Morris and Guerra, 2015; Geng et al., 2016). In addition, due to this group’s deep-rooted car-use habit, taxes, regulations, 290

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al.

Fig. 4. Countermeasure proposals targeted to color-coded travel behaviors. The dotted line indicated that the transformation from Forced-Grey-TB to Green-TB may contain a nonrequired or optional transition process from Forced-Grey-TB to Susceptible-Grey-TB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

and laws are needed to “push” this group’s gradual change from Red-TB to Susceptible-Grey-TB and finally to Green-TB. (2) The Susceptible-Grey-TB group which occasionally implements green travel behaviors is easily influenced by comfort, but not by economics. Residents of this type are more likely to be persuaded or encouraged by positive (pull) incentive measures in relevant to PT use rather than negative (push) control measures in restricting car use (Anable 2005; Bamberg 2006; Geng et al., 2016). Hence, except for related proposals for the Red-TB group, innovating advertising ways and providing information feedback about energy and emission in daily travel will be a useful strategy. The key problem is how to stimulate and highlight the absolute dominance of GEM to resist interferences from other motivations. Once this problem has been solved, the motivation-behavior gap will be reduced, and this group will gradually change into the Green-TB group. (3) For the Forced-Grey-TB group which gives more consideration to economics than to environment protection, education and economic incentives will be necessary guiding strategies. Education should not be limited to environmental information. Other customized and tailored information, including both social benefits and personal interest, are sometimes more effective (Geng et al., 2016). In addition, subsidies, rewards, compensation, incentive and lowcarbon trading will stimulate this group’s green transformation of travel behavior. It should be emphasized that this transformation may contain a transition process (the dotted line in Fig. 4) from Forced-Grey-TB to Susceptible-Grey-TB and then eventually to Green-TB. Hence, economic stimulus is an important strategy, but the solid construction of GEM is actually the foundation. In summary, for the Grey-TB group, the “push” and mandatory measures should be transformed to the “pull” with encouraging and catalytic measures. (4) For the Green-TB group, it is essential to determine how to spread their social influences. As demonstrated in López-Mosquera et al.

(2015), Fornara et al. (2016) and Delley and Brunner (2017), informational influence (e.g., trust in neighbors, relatives, and friends) emerged as the most powerful indicators in predicting environmentally responsible behaviors. Hence, on the one hand, the government need to carry out more travel awareness campaigns in communities (cities) and encourage residents’ participations. On the other hand, green information by a wide range of advertising and communication can be spread by environmentalists (Howell, 2014; Geng et al., 2016; Wei et al., 2016; Schmidt, 2016). These spread of idol effect, norm, or trust will be conducive to the formation of a low-carbon lifestyle, atmosphere and even fashion trends. In addition, subsidies, rewards, and low-carbon compensation by a carbon-emissions trading system will also be prospective and necessary. Acknowledgments I am very grateful for the constructive comments from the anonymous reviewers. This study was supported by the Fundamental Research Funds for the Central Universities (No. 2017XKZD12). References Ajzen, I., 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 14 (2), 137–144. Allinson, D., Irvine, K.N., Edmondson, J.L., Tiwary, T., Hill, G., Morris, J., Bell Margaret Davies, Z.G., Firth, S.K., Fisher, J., Gaston, K.J., 2016. Measurement and analysis of household carbon: the case of a UK city. Appl. Energy 164, 871–881. Anable, J., 2005. ‘Complacent car addicts’ or ‘aspiring environmentalists’? Identifying travel behaviour segments using attitude theory. Transp. Policy 12 (1), 65–78. Antimova, R., Nawijn, J., Peeters, P., 2012. The awareness/attitude-gap in sustainable tourism: a theoretical perspective. Tourism Rev. 67 (3), 7–16. Bamberg, S., Schmidt, P., 2003. Incentives, morality, or habit? Predicting students’ car use for university routes with the models of Ajzen, Schwartz, and Triandis. Environ. Behav. 35 (2), 264–285. Bamberg, S., Hunecke, M., Blöbaum, A., 2007. Social context, personal norms and the use

291

Resources, Conservation & Recycling 125 (2017) 282–292

J. Geng et al. of public transportation: two field studies. J. Environ. Psychol. 27 (3), 190–203. Bamberg, S., 2006. Is a residential relocation a good opportunity to change people’s travel behavior? Results from a theory-driven intervention study. Environ. Behav. 38 (6), 820–840. Bandura, A., 1993. Perceived self-efficacy in cognitive development and functioning. Educ. Psychol. 28 (2), 117–148. Botetzagias, I., Dima, A.F., Malesios, C., 2015. Extending the theory of planned behavior in the context of recycling: the role of moral norms and of demographic predictors. Resour. Conserv. Recycl. 95, 58–67. Carter, N., Ockwell, D., 2007. New labour, new environment? an analysis of the labour government's policy on climate change and biodiversity loss. Bull. Can. Pet. Geol. 34 (3), 364–378. Chen, H., Long, R.Y., Niu, W.J., Feng, Q., Yang, R.R., 2014. How does individual lowcarbon consumption behavior occur?–An analysis based on attitude process. Appl. Energy 116, 376–386. Clark, B., Chatterjee, K., Melia, S., 2016. Changes to commute mode: the role of life events: spatial context and environmental attitude. Transp. Res. Pt. A-Policy Pract. 89, 89–105. Delley, M., Brunner, T.A., 2017. Foodwaste within Swiss households: a segmentation of the population and suggestions for preventive measures. Resour. Conserv. Recycl. 122, 172–184. Donald, I.J., Cooper, S.R., Conchie, S.M., 2014. An extended theory of planned behaviour model of the psychological factors affecting commuters' transport mode use. J. Environ. Psychol. 40, 39–48. Eliasson, J., Proost, S., 2015. Is sustainable transport policy sustainable? Transp. Policy 37, 92–100. Ellaway, A., Macintyre, S., Hiscock, R., Kearns, A., 2003. In the driving seat: psychosocial benefits from private motor vehicle transport compared to public transport. Transp. Res. Pt. F-Traffic Psychol. Behav. 6 (3), 217–231. Fornara, F., Pattitoni, P., Mura, M., Strazzera, E., 2016. Predicting intention to improve household energy efficiency: the role of value-belief-norm theory, normative and informational influence: and specific attitude. J. Environ. Psychol. 45, 1–10. Friedrichsmeier, T., Matthies, E., Klöckner, C.A., 2013. Explaining stability in travel mode choice: an empirical comparison of two concepts of habit. Transp. Res. Pt. F-Traffic Psychol. Behav. 16, 1–13. Geng, J.C., Long, R.Y., Chen, H., 2016. Impact of information intervention on travel mode choice of urban residents with different goal frames: a controlled trial in Xuzhou, China. Transp. Res. Pt. A-Policy Pract. 91, 134–147. Geng, J.C., Long, R.Y., Chen, H., Yue, T., Li, W.B., Li, Q.W., 2017. Exploring multiple motivations on urban residents’ travel mode choices: an empirical study from Jiangsu province in China. Sustainability 9 (1), 136. Hounsham, S., 2006. Painting the town green. How to Persuade People to Be Environmentally Friendly. Green Engage Communications/Transport, London. Howell, R.A., 2014. Investigating the long-term impacts of climate change communications on individuals' attitudes and behavior. Environ. Behav. 46 (1), 70–101. Hunecke, M., Blöbaum, A., Matthies, E., Höger, R., 2001. Responsibility and environment ecological norm orientation and external factors in the domain of travel mode choice behavior. Environ. Behav. 33 (6), 830–852. IEA International Energy Agency, 2016. Energy Technology Perspectives: Towards Sustainable Urban Energy Systems. IEA, Paris. Jing, P., Wang, J., Zha, Q.F., He, M.L., Lu, Y., 2016. Understanding attitudes towards travel mode choice for environmentally sustainable transportation: extending the theory of panned behavior. Basic Clin. Pharmacol. Toxicol. 118, 65. Jones, C.H., Ogilvie, D., 2012. Motivations for active commuting: a qualitative investigation of the period of home or work relocation. Int. J. Behav. Nutr. Phys. Act. 9 (1), 109. Kandt, J., Rode, P., Hoffmann, C., Graff, A., Smith, D., 2015. Gauging interventions for sustainable travel: a comparative study of travel attitudes in Berlin and London. Transp. Res. Pt. A-Policy Pract. 80, 35–48. Klöckner, C.A., Friedrichsmeier, T., 2011. A multi-level approach to travel mode choice −how person characteristics and situation specific aspects determine car use in a student sample. Transp. Res. Pt. F-Traffic Psychol. Behav. 14 (4), 261–277. Klöckner, C.A., Matthies, E., 2004. How habits interfere with norm-directed behaviour: a normative decision-making model for travel mode choice. J. Environ. Psychol. 24 (3), 319–327. López-Mosquera, N., Lera-López, F., Sánchez, M., 2015. Key factors to explain recycling, car use and environmentally responsible purchase behaviors: a comparative perspective. Resour. Conserv. Recycl. 99, 29–39. Lancée, S., Veenhoven, R., Burger, M., 2017. Mood during commute in the Netherlands: what way of travel feels best for what kind of people? Res. Pt. A-Policy Pract.. http:// dx.doi.org/10.1016/j.tra.2017.04.025. (in press).

Li, L., Xiong, J., Chen, A., Zhao, S., Dong, Z., 2014. Key strategies for improving public transportation based on planned behavior theory: case study in Shanghai, China. J. Urban Plan. Dev. 141 (2), 04014019. Lindenberg, S., Steg, L., 2007. Normative, gain and hedonic goal frames guiding environmental behavior. J. Soc. Issues 63 (1), 117–137. Liu, L.C., Wu, G., Zhang, Y.J., 2015. Investigating the residential energy consumption behaviors in Beijing: a survey study. Nat. Hazards 75 (1), 243–263. Lizin, S., Van Dael, M., Van Passel, S., 2017. Battery pack recycling: behaviour change interventions derived from an integrative theory of planned behaviour study. Resour. Conserv. Recycl. 122, 66–82. Lu, X.Y., 2016. Effectiveness of government enforcement in driving restrictions: a case in Beijing, China. Environ. Econ. Policy Stud. 18, 1–30. Makki, A.A., Stewart, R.A., Beal, C.D., Panuwatwanich, K., 2015. Novel bottom-up urban water demand forecasting model: revealing the determinants, drivers and predictors of residential indoor end-use consumption. Resour. Conserv. Recycl. 95, 15–37. Morris, E.A., Guerra, E., 2015. Mood and mode: does how we travel affect how we feel? Transportation 42 (1), 25–43. Osman Idris, A., Habib, K.M.N., Tudela, A., Shalaby, A., 2015. Investigating the effects of psychological factors on commuting mode choice behaviour. Transp. Plan. Technol. 38 (3), 265–276. Paulssen, M., Temme, D., Vij, A., Walker, J.L., 2014. Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice. Transportation 41 (4), 1–16. Prillwitz, J., Barr, S., 2011. Moving towards sustainability? Mobility styles, attitudes and individual travel behavior. J. Transp. Geogr. 19, 1590–1600. Robinson, J., 2010. Triandis' Theory of Interpersonal Behaviour in Understanding Software Piracy Behaviour in the South African Context (Doctoral Dissertation). Rokeach, M., 1973. The Nature of Human Values, vol. 438 Free press, New York. Schmidt, K., 2016. Explaining and promoting household food waste-prevention by an environmental psychological based intervention study. Resour. Conserv. Recycl. 111, 53–66. Schwanen, T., Banister, D., Anable, J., 2012. Rethinking habits and their role in behaviour change: the case of low-carbon mobility. J. Transp. Geogr. 24 (4), 522–532. Shen, Q., Chen, P., Pan, H., 2016. Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city. Transp. Res. Pt. A-Policy Pract. 94, 31–44. Steg, L., 2005. Car use: lust and must. Instrumental, symbolic and affective motives for car use. Transp. Res. Pt. A-Policy Pract. 39 (2), 147–162. Stern, P.C., Dietz, T., Abel, T., Guagnano, G.A., Kalof, L., 1999. A value-belief-norm theory of support for social movements: the case of environmentalism. Hum. Ecol. Rev. 6 (2), 81–98. Van Acker, V., Mokhtarian, P.L., Witlox, F., 2014. Car availability explained by the structural relationships between lifestyles, residential location, and underlying residential and travel attitudes. Transp. Policy 35, 88–99. Verplanken, B., Orbell, S., 2003. Reflections on past behavior: a self-report index of habit strength. J. Appl. Soc. Psychol. 33 (6), 1313–1330. Verplanken, B., Aarts, H., Van Knippenberg, A., 1997. Habit, information acquisition, and the process of making travel mode choices. Eur. J. Soc. Psychol. 27 (5), 539–560. Wang, Z., Liu, W., 2015. Determinants of co2, emissions from household daily travel in beijing, china: individual travel characteristic perspectives. Appl. Energy 158, 292–299. Wang, B., Shao, C., Ji, X., 2017. Dynamic analysis of holiday travel behaviour with integrated multimodal travel information usage: a life-oriented approach. Res. Pt. APolicy Pract.. http://dx.doi.org/10.1016/j.tra.2017.03.013. (in press). Wei, J., Chen, H., Cui, X.T., Long, R.Y., 2016. Carbon capability of urban residents and its structure: evidence from a survey of Jiangsu province in China. Appl. Energy 173, 635–649. Wood, W., Neal, D.T., 2007. A new look at habits and the habit-goal interface. Psychol. Rev. 114 (4), 843. Xu, B., Lin, B., 2015. Carbon dioxide emissions reduction in China's transport sector: a dynamic VAR (vector autoregression) approach. Energy 83, 486–495. Yan, Y.J., 2012. Problems of quality of migrant workers and countermeasures from the perspective of iceberg model. Asian Agric. Res. 05, 48–50. Yang, R.R., Long, R.Y., 2016. Analysis of the influencing factors of the public willingness to participate in public bicycle projects and intervention strategies—A case study of Jiangsu province, China. Sustainability 8, 349. Yang, L., Zheng, G., Zhu, X., 2013. Cross-nested logit model for the joint choice of residential location, travel mode, and departure time. Habitat Int. 38, 157–166. Zailani, S., Iranmanesh, M., Masron, T.A., Chan, T.H., 2016. Is the intention to use public transport for different travel purposes determined by different factors? Transp. Res. Part D-Transp. Environ. 49, 18–24.

292