1. Introduction
In recent years, incentivizing green travel modes has become a hot topic in the field of transportation [
1,
2,
3,
4]. Both designing appropriate incentivizing measures to guide travelers towards green travel mode choices and understanding the influencing factors leading to these choices are of importance for several reasons. Firstly, the global transportation industry has already become the fastest growing source of carbon emissions [
5]. Secondly, incentives to switch to green travel can reduce private car usage [
6], thereby alleviating traffic congestion and improving transportation efficiency [
7]. Besides these practical reasons, studying the process of how individuals choose green travel modes can enrich the travel behavior theory, which can further guide the implementation of relevant low-carbon policies [
8].
Environmental awareness campaigns and educational initiatives were often used to promote green travel. However, these approaches may not provide sufficient motivation for individuals to change their travel behavior due to insufficient incentives [
9]. Carbon credit systems, as a new mechanism of carbon emission consumption reduction regarding people’s daily lives, are an innovative approach and an extension from the existing carbon emission trading system among countries or companies to individual persons. It uses carbon credits as an incentive for rewarding and quantifying the carbon reduction contribution of individual residents. The rewards further encourage whole societies to participate in low-carbon lifestyles. Therefore, this paper focuses on studying the incentive effect of carbon credits on green travel and their various influencing factors.
Travelers’ acceptance of carbon credits, as well as green travel, is related to an individual’s preference and attitude. Although incorporating attitudinal variables into mode choice models are considered in the previous research [
2,
10,
11], measurement errors associated with latent variables are still not well handled in discrete choice models. To fill this gap, we employ an integrated choice and latent variable (ICLV) model focusing on the role of attitudinal variables, which are treated as the latent variables, to well handle this problem. The ICLV model provides a useful modeling framework to adequately analyze structural relationships between sociodemographic characteristics, attitudes, and mode choice behavior [
1,
12]. Through highlighting the roles of attitudes and acceptance, the present work provides deeper insights into the intricate mechanism of daily travel mode choice considering carbon credit incentives (CCIs), which has received limited attention in previous research. Moreover, it elucidates the direct and indirect impact of travelers’ demographic and socioeconomic attributes on their mode choice, primarily through the influence of their attitudes.
The rest of this manuscript is organized as follows.
Section 2 presents a literature review of carbon credits and illustrates the reason for applying the ICLV model in this study by comparing previous studies based on utilizing carbon credits to incentivize green mode choice behavior. The
Section 3 gives a detailed description of data used in this research, followed by an explanation and application of the ICLV model in
Section 4.
Section 5 presents the results and discussion, which show how latent variables and observable variables influence the process of travelers’ choice of different green travel modes with incentives. Finally, the conclusions are drawn in
Section 6.
2. Literature Review
The concept of carbon credit originated from the California Zero-emission-vehicle Act in 1990, promoting low-emission vehicles through mandatory market mechanisms. In 1996, individual carbon credits trading was proposed to encourage emission reduction [
13]. Tradable Energy Quotas (TEQs) were allowed for trading between low- and high-emission contributors. Personal Carbon Allowances, Household Carbon Trading, and Tradable Fuel Permits were proposed between 2005 and 2008 [
9,
14,
15]. Research in this field then shifted towards individual carbon trading and its impact on energy consumption choices. In the context of transportation, by providing individuals with incentives and rewards for choosing eco-friendly modes of travel, such as public transportation or cycling, the carbon credit system aims to promote sustainable behaviors and reduce carbon emissions.
Consequently, the carbon credit system’s influence on travel behavior has gained attention lately. In 2011, personal tradable carbon permits were introduced for road transportation [
16]. The emission reduction effects between the carbon credit system and carbon tax policies were also compared, highlighting the incentivizing power of carbon credit on low-carbon travel [
17]. Later on, more empirical studies and implementation cases of incentivizing green travel through carbon credits emerged, which can be found in Xiamen, Nanjing, and Wuxi in China. These research and applications of the development in the carbon credit system have demonstrated its impact on travel behavior choices.
However, previous studies have ignored the significance of individual attitudes and preferences in modeling the effect of CCIs on mode choice decisions. Although some early research on travel behavior either directly incorporated attitudinal scores from Likert scale surveys into discrete choice models [
2,
11] or utilized factor scores derived from observed indicators [
10], both of the above approaches have inherent methodological limitations in adequately capturing the complex relationship between attitudes and mode choice. The former treats Likert scores as error-free predictors of underlying attitudes. However, the underlying attitudes themselves, rather than the survey responses, may have a causal relationship with choice behavior [
12]. For the latter approach, simply extracting data from the scale results does not effectively handle the measurement errors in latent variables [
10]. Moreover, these approaches fail to adequately consider the proved potential association between an individual’s sociodemographic characteristics and attitudes discovered in related research [
18,
19]. As indicated in previous behavior-related studies, the significance of individual attitudes and preferences cannot be neglected as research on travel behavior [
20,
21,
22] increasingly uncovers the influence of attitudes and preferences, such as attitudes towards transportation modes, environmental concerns, and perceived safety, in shaping travel decisions.
To fill up this gap, the ICLV model is proposed to address the ignored associated attitudinal variables in travel behavior modeling [
1,
12,
22,
23,
24,
25]. Specifically, the ICLV model combines a latent variable model with a discrete choice model to explore the structural relationship between observed and unobserved variables, as well as the measurement relationship between latent variables and outcomes within a unified framework. Its advantage in understanding mode choice behavior by incorporating attitudes and preferences has already been demonstrated [
26,
27], resulting in a comprehensive understanding on the influence of attitudinal factors on travel decisions, which could also be impacted by unconventional green travel incentive policies, such as individualized marketing strategies and campaigns [
28]. Additionally, unobserved variables such as psychological factors and attitudes [
29] can be well incorporated in the ICLV model to investigate the motivating effects of carbon credits on green travel choice. This will fill the gap both theoretically by enriching the travel behavior theory in circumstances of green travel incentivization and practically by providing fundamental essentials for making transport policies.
4. Methodology
Different from sociodemographic and travel behavior, the six perceptions are not observable. As a result, they must be considered as latent variables. In order to include them in the mode choice model, one common approach is to add factor scores derived from attitude statements directly into the utility of the choices. However, this approach cannot avoid potential risks, including measurement errors and endogeneity biases, which have been highlighted in previous studies [
12,
23,
24]. To solve these risks, the ICLV model is proposed to combine a discrete choice model with one or more latent variables.
In order to specifically study the inductive effect of carbon credits on different green travel modes, we selected three categories of green travel modes: walking, cycling, and bus/metro, and established three corresponding travel choice models, respectively. In each discrete choice model, the dependent variable was binary: whether the person was induced by carbon credits to choose a green travel mode (walking, cycling, and bus/metro) or not. In order to facilitate the horizontal comparison of the induction effect of carbon credits on different green travel modes, we made an assumption that the default travel mode of all surveyed travelers was driving.
The entire framework of the ICLV model is shown in
Figure 3. Within the ICLV framework, exogenous variables exert an influence on latent variables (LVs). The structural equation model (SEM) captures the relationship between these LVs and incorporates another model, known as the measurement model. The measurement model utilizes the LVs as predictors to estimate the survey responses of individuals who have concerns in terms of attitude and perception statements [
34]. In our case, the measurement model connects LVs to statements regarding the perception of carbon credit in the daily travel of respondents. Three models corresponding to the three different incentivized green travel modes (i.e., walking, cycling, metro/bus) are proposed with a similar framework. The discrete choice model describes the utility of each alternative travel mode based on trip-specific, sociodemographic, and attitudinal characteristics.
For each individual
, there is a set of mutually exclusive alternatives
. Based on our previous assumptions, the ICLV model for the three different incentivized green travel modes can be defined as
, representing the choice of driving, and
representing one of the choices of walking, cycling, or metro/subway in each corresponding model. The utility maximization function is defined as in Equation (1).
where
represents the indicator variable for individual
choosing mode option
. If a traveler chooses travel mode
, the value of
is
, resulting in utility
as shown in Equation (2); otherwise,
.
where
is a systematic component (observable) of the utility function and
is a random disturbance (error term). In the ICLV model, which incorporates the effects of latent variables on mode utility,
is represented in Equation (3).
where
is the observed variable vector representing the socioeconomic attributes of individual travelers.
is the observed variable vector representing the attributes of each travel mode.
is the
dimensional latent variable vector. In our case,
; specifically, it includes subjective norms, degree of cognition, perceived usefulness, perceived ease of use, environmental awareness, and behavioral intentions.
are the unknown parameter vectors to be estimated.
In the structural equation of the latent variable model, it is assumed that the relationship between the observed variable vector and the latent variable vector is a linear function [
35]. In this study, the structural equation represents the relationship among the latent variables as shown in Equation (4).
where
represents the observed variable vector of
dimensional socioeconomic attributes that influence the latent variables. In our case,
; specifically, it includes the following socio-economic attributes: gender, age, education level, occupation, income, marital status, the number of children raised, driver’s license possession, driving experience, the frequency of driving, and the energy type of the car driven.
is the
dimensional unknown parameter matrix to be estimated.
is the error term.
For the measurement equation in the latent variable model, we also assume a linear function as the estimation function [
36]. Therefore, the mathematical expression of the measurement equation is as follows (Equation (5)):
where
is the
dimensional observed variable vector of the latent variables.
represents the number of indicators for each latent variable.
is the
dimensional unknown parameter matrix to be estimated.
is the error term. For the error terms
, in order to facilitate subsequent model parameter estimation, it is assumed that they satisfy the following relationship, as shown in Equations (6) and (7).
The structural equation models and corresponding discrete choice models for the three green travel modes form the basis of the ICLV model for daily travel mode choice considering CCIs. Specifically, Equations (4) and (5) form the structural equation model that describes the relationship among the latent variables. Equations (1) and (2) form the discrete choice model. In our case, the structural equation can be represented specifically as follows (Equations (8) and (9)):
To estimate unknown parameters of the ICLV model, the maximum likelihood method is used. We let
, Equations (2) and (3) can be rewritten as Equation (10):
where
for estimation, set
.
Here, it is assumed that vector
follows a multivariate normal distribution with covariance matrix
and mean vector
. Let
be the vector of unknown parameters under this condition,
represents the vector of indicator variables where the element in the row
is denoted as
, and
represents the probability density function of a
dimensional normal distribution. When the sample size is
, the likelihood function for the unknown parameter
can be calculated as shown in Equations (11) and (12).
To intuitively analyze the impact of travelers’ acceptance of carbon credits on their choice of green travel modes as in the aforementioned ICLV model, a new latent variable named
is introduced. This categorical variable is used to directly describe the acceptance level of carbon credit platforms for each surveyed individual. The specific definition of the variable is as shown in Equation (13).
For individual
n in the structural model, regression weights for predicting the unobserved variables from the observed variables can be calculated by Equation (14).
where
is the matrix of regression weights,
is the matrix of covariances among the observed variables, and
is the matrix of covariances between the unobserved and observed variables. The fitted results of all measurement variables from the six latent variables in the ICLV model are utilized. Using the estimated weight matrix from the structural equation, the weighted score for each individual’s acceptance level of carbon credits is calculated. As the acceptance level for each respondent is represented with a five-point Likert scale, the acceptance levels are discretized based on interval distribution to obtain the acceptance categories for the surveyed individuals.
The Biogeme Python programming package [
37] is used to estimate the ICLV model. Traditional multinomial logit (MNL) models are also run to compare the results with the ICLV model in order to check its superiority.
5. Results and Discussion
Table 4,
Table 5 and
Table 6 illustrate the estimated results of all ICLV models and MNL models that are used in the situation of daily travel mode choice behavior considering CCIs (our case uses a data set of a sample size n = 345). Each model includes only variables that are significantly associated with the utility. Model 1 represents the ICLV model, where all six latent variables related to awareness and attitudes regarding carbon credits are included. Model 2 represents the ICLV model with the acceptance level (i.e.,
ACCEPT) as a latent variable, rather than the latent variables that are used in Model 1. Model 3 is the MNL model as the baseline, which does not include any latent variables. The results are further explained from the perspective of trip purpose, demographic features, and perception related to attitude and preference as follows. Based on the model fitting results in
Table 4,
Table 5 and
Table 6 (AIC and BIC values), despite the ICLV model incorporating more variables, the AIC values for the ICLV model are consistently smaller than those of the corresponding MNL models. This indicates that the ICLV model not only explains more variables (i.e., the latent psychological variables in this study) but also outperforms the basic MNL model in terms of goodness of fit.
5.1. Carbon Credits’ Effect on Different Trip Purpose
For commuting trips, the utility term for the walking mode is not significant, while for cycling and the metro/bus, it shows a significant positive correlation (0.752 and 1.061). For other trip purposes like recreation or medical trips, there is a significant negative correlation with the induction effect from carbon credits. It means that when having such a trip purpose, travelers prioritize personal needs, convenience, comfort, and time costs over CCIs.
However, an exception exists for shopping trips as travelers for shopping are more inclined to accept CCIs. This is due to their higher concern for economic factors, such as the case that implementing carbon credits on app platforms of large shopping malls could specifically target these travelers. Additionally, shopping trips typically happen in cases of shorter distances, making walking or cycling a more feasible alternative of green travel mode.
5.2. Carbon Credits’ Effect on Various Demographics
Higher education levels show a positive correlation with the effect of CCIs across all green travel modes, with the most significant impact observed for cycling. This aligns with existing research on cycling preferences among educated commuters [
38].
Conversely, the effect of CCIs is limited among high-income individuals. While income positively correlates with the acceptance level of carbon credits in the latent variable model, the final choice of green travel modes reveals a negative correlation between income and the effect of CCIs. High-income individuals prioritize travel convenience and comfort, making them less sensitive to material incentives provided by carbon credits. These individuals would like to financially gain less to make their travel more convenient and comfortable, which also means that these people may be those would like to pay for their ideal or accustomed trips. To attract high-income people to choose green transportation options, expanding the range of incentive measures beyond material rewards, such as personal recognition and other non-material incentives, should be considered.
It is also found that carbon credits have a stronger incentive effect on older individuals. Regardless of the specific green travel mode, the results from the ICLV models consistently show that age is positively correlated with the effect of CCI. Considering the current implementation of carbon credit platforms, the material incentives provided to travelers are relatively limited and often only older travelers satisfy an accumulative travel frequency or mileage. Younger individuals, compared to older ones, may prefer more direct or substantial material incentives. Therefore, carbon credits can be improved with more customized and attractive styles targeting young individuals.
New energy vehicle owners are potential targets for carbon credit-induced green travel as they show a higher acceptance of carbon credits, according to the results from the latent variable model. The ICLV model also indicates a stronger effect of CCIs on new energy vehicle owners compared to drivers of traditional fuel-powered cars, though this positive correlation is not significant for all green travel modes. For walking, owners of new energy vehicles are more inclined to choose this green travel option under the inducement of carbon credits. However, the incentive effect is not as significant for the other two green travel options. Public transportation (bus/subway) is only one-tenth as effective as walking to new energy vehicle owners. As for cycling, the model results indicate that there is no significant relationship between the possession of new energy vehicles and the effect of CCIs.
Gender differences affect the effect of CCIs. For cycling and walking, carbon credits have a stronger effect on male individuals than on female individuals. This may be due to females prioritizing safety and comfort in their travels. However, for public transportation modes like buses and subways, the gender difference in the incentive effect is relatively small, possibly because public transportation is perceived as safe and convenient for both genders.
Travelers’ driving habits pose challenges to the effectiveness of CCIs in promoting green travel. Indeed, despite the original intention of carbon credits to encourage travelers to reduce driving cars, the results indicate that many travelers already have relatively fixed driving habits. This poses significant resistance to the effectiveness of carbon credits. For all green travel options, the model results show that the frequency of driving and the driving age of travelers are generally negatively correlated with the effect of CCIs, regardless of statistical significance. Although many variables related to driving habits were considered, they were ultimately excluded from the final ICLV models due to a lack of statistical significance. However, it can be inferred that commuters’ habitual driving behavior may resist the induction effect of carbon credits despite the lack of statistical significance in the result.
5.3. Carbon Credits’ Effect on Perceptions
Perceived usefulness has the most significant impact on the effect of CCIs for green travel. It positively correlates with this effect across all travel modes, indicating that individuals who perceive the benefits of using carbon credits are more motivated to adopt green travel modes. Their belief in the value and effectiveness of carbon credits drives their decision making and acceptance of this incentive.
Environmental awareness and perceived ease of use weakly positively correlate with the effect of CCIs. Higher environmental awareness makes individuals more receptive to using carbon credits as an incentive, aligning with their environmental values. Additionally, a user-friendly process for earning and redeeming carbon credits encourages engagement in green travel behavior.
Subjective norms, however, negatively correlate with the effect of CCIs. Individuals’ perception of social norms and others’ expectations influence their acceptance of carbon credits. Lower social support or expectations regarding the use of carbon credits for green travel may decrease individuals’ inclination to adopt the incentive. This negative correlation suggests the need for improved promotion and publicity of the carbon credit system to enhance its acceptance in society.
Unexpectedly, when the target green travel mode is walking, the degree of carbon credit cognition negatively correlates with the incentive effect, contrary to expectations. The possible reasons are that the current carbon credit system lacks systematic means of recording low-carbon travel behaviors, especially for walking, which involves short-duration and random travel. Improving the monitoring system for green travel behaviors when dynamically accumulating carbon credits for sustainable transportation choices is important.
The exclusion of the behavioral intention variable from all ICLV models due to a lack of statistical significance is noteworthy. It highlights the gap between public environmental intentions and actual behaviors, which is a limitation in many environmental policies. In practical terms, the findings emphasize the need for policymakers, organizations, and program designers to focus on the design and implementation of effective incentive systems to encourage green behavior. This could involve reevaluating and enhancing existing incentive structures or developing innovative approaches that align with individuals’ motivations and preferences.
Beyond our specific study, this observation has broader implications for the field of behavioral economics and environmental psychology. It underscores the complexity of human decision making in the context of sustainability and highlights the importance of considering external motivators and incentive mechanisms in designing effective interventions.
Although the acceptance level of carbon credits was quantified through latent variables, it does not directly equal to an incentive effect. The effective transformation of low-carbon intentions to behaviors requires reasonable incentive mechanisms, attractive reward schemes, educational campaigns, and other measures. Enhancing the actual effectiveness of carbon credits will encourage more people to choose green travel modes and achieve genuine environmental goals.
6. Conclusions and Future Research
This study explored daily travel mode choices with carbon credits as incentives using an ICLV model. Perceptions of carbon credits are included in the model as latent variables to examine its influence on travelers’ travel mode choices. The performance of the ICLV model is then compared with a traditional MNL model. Data from the questionnaire were used in the estimation and demonstrated the superiority of the ICLV model. The model results show that the ICLV model provides deeper insights into daily travel mode choices with carbon credits as incentives. Major conclusions obtained in the present work are summarized as follows. First, carbon credits were effective in promoting public transportation among commuters. Meanwhile, the impact of carbon credits varied across green travel modes, emphasizing the need to customize incentives according to individual trip features. Second, demographics also show a significant association with the incentive effect. Specifically, travelers with higher education levels show a positive correlation with the incentive effect, while higher incomes show a negative one. Older individuals were more responsive to the carbon credit system, and gender differences were also observed. Moreover, perceptions of carbon credits played a crucial role in travelers’ transport mode choices, where perceived usefulness has the most significant impact, followed by environmental awareness and ease of use. Subjective norms had a negative correlation, suggesting the need for improved societal attitudes through promotion. Behavioral intentions were not significant, highlighting the importance of effective incentive mechanisms. As can be concluded, customizing incentives and improving monitoring systems can enhance the role of carbon credits in promoting sustainable transportation choices.
Although online surveys may be less reliable than offline ones, our data meet validity requirements. A limitation of this study is the sample size, which could be improved by an additional survey. The samples may not fully cover all potential sociodemographics and travel attributes; yet, the model applied in this paper still provided valuable insights by analyzing carbon credits’ inducement effect on specific green travel modes with separate discrete choice models. Although the sociodemographics and travel attributes of the data may deviate slightly from the real world, this study proposed an approach that can be effectively used for a comparative analysis of carbon credits’ impact on diverse travel behaviors. For future research, exploring different incentive types of CCIs and employing longitudinal studies to track behavior changes over time may help us to better understand the incentive effect of CCIs on green travel. Establishing a comprehensive choice model that includes the overall mode choice distribution influenced by CCIs is also necessary in subsequent research.