Advertisement

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Discounting the future: The effect of collective motivation on investment decisions and acceptance of policies for renewable energy

Abstract

Climate protection is a collective project. However, most previous research on people’s pro-climate behavior ignores the collective dimension, looking at personal private-sphere behavior and considering personal cost-benefit predictors only. The present paper transcends this individualistic perspective by addressing behaviors that target collective transformation (i.e., financial investments in renewable energy projects and the acceptance of renewable energy policies) and predictors of collective cognition and motivation (i.e., social identity). Combining insights and methods from economics and psychology, the current research investigates if collective pro-environmental motivation (e.g., pro-environmental ingroup norms, collective climate efficacy beliefs) can add to the explanation of investment decisions and the acceptance of policies for renewable energies, also beyond personal psychological and economic factors. Results from a multi-country survey (31 European countries, N = 18,037), including a discrete choice experiment, showed that collective pro-environmental motivation was positively correlated with the acceptance of green energy policies and negatively correlated with discounting of future benefits (money discount rate) in investment decisions for renewable energies. Importantly, collective pro-environmental motivation remained a significant predictor of policy acceptance and the discount rate after controlling for personal pro-environmental motivation. Furthermore, the associations between collective pro-environmental motivation and our outcome measures were stronger for respondents who highly identified with their group compared to low identifiers. Our (correlational) results are one of the first to show that collective psychological factors are a unique predictor of green investment behavior and acceptance of green policies. From an applied perspective, our findings suggest that interventions should target agentic social identities with norms supporting pro-environmental behavior to increase acceptance of and participation in the transformation towards carbon neutrality, particularly for persons with low personal pro-environmental motivation.

Citation: Marder F, Masson T, Sagebiel J, Martini C, Quaas M, Fritsche I (2023) Discounting the future: The effect of collective motivation on investment decisions and acceptance of policies for renewable energy. PLOS Clim 2(6): e0000173. https://doi.org/10.1371/journal.pclm.0000173

Editor: Abdul Rehman, Henan Agricultural University, CHINA

Received: February 6, 2023; Accepted: April 24, 2023; Published: June 5, 2023

Copyright: © 2023 Marder et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data that support the findings of this study are openly available in Zenodo at http://doi.org/10.5281/zenodo.3524917. The codes can be retrieved from: https://git.idiv.de/fm58hufi/discounting-the-future.git.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Scientific forecasts show that the ecological, social and economic consequences of continued global warming will be dramatic [1]. Previous calls to action to stop global warming were ineffective or insufficient. Why is this the case? Perhaps, the wrong actions and the wrong levers of action were addressed: Fighting a large-scale social problem, such as climate change requires collectives to act and societies to transform. However, ignoring the collective and transformative nature of climate action, environmental behavioral sciences and interventions have long been focusing on explaining and changing private (consumption) behavior, instead of behaviors supporting collective ecological system change. Also, they addressed climate action as the result of a personal decision of individuals, instead of considering the impact of collective cognition and motivation (i.e., social identity) [2–4]. Before we show how, in the present research, we introduced the collective dimension in climate action research, we explain why ignoring the collective dimension might have been wrong-headed and insufficient.

First, the urgency and scale of global environmental degradation require the immediate transformation of societies’ production and consumption systems. Specifically, dramatic changes in the infrastructural, economic, and legal boundary conditions of individuals’ behavior are needed to enable large-scale changes in private environmentalism across different societal milieus and groups. This is because current structures often discourage or disable pro-environmental behavior options as ecologically sustainable products or services are not offered or only at high personal costs in terms of money, effort, or safety (e.g., biking is often perceived as dangerous in car-crowded cities, and frequent public transport connections are often missing in rural areas). At the same time, dynamics of free-riding and commons dilemma situations [5–7] require regulations and prohibitions to induce people to make personally costly contributions to the common (environmental) good [8,9]. As a consequence, understanding and changing individuals’ environmental behavior needs a focus on structural changes [10]. This does not mean, however, that investigating and supporting pro-environmental action in individuals is not important. The opposite is true. It is just pivotal to look at the relevant types of action. Thus, instead of limiting the focus to private consumption, behavioral sciences urgently need to understand when, how, and why individuals support or oppose societal and economic transition processes. These actions may include the passive acceptance of green policy measures (e.g. increased taxes on fossil fuels), but also more active behaviors like participation in collective environmental projects, such as investment in renewable energy sites. In the realm of economic behavior, much more than through individual pro-environmental consumption, a person might be able to effectively support the transformation towards carbon-neutrality by investing money in green businesses. In other words, behavioral sciences are now needed to explain individuals’ actions that are directed on changing the system, and not just their personal environmental behaviors. This is why the present research seeks to explain the psychological and economic drivers of both the acceptance of environmental policies and personally costly investment decisions in green businesses, such as financial investments in renewable energy projects.

There is a second reason why the current focus on personal behavior decisions is insufficient. It refers to an inaccurate conception of individuals’ behavior as a solely personal decision that is driven by personal cost-benefit analyses, personal morals, and personal capabilities. If environmental action would be a solely personal decision, probably, people would never start to act. This is because the current large-scale environmental crises that burden people [11,12] did not emerge, and cannot be solved by, an individual’s action alone. In the global North, it even does not threaten most individuals’ current personal well-being, but that of the many generations of people to come. Obviously, environmental crises such as climate change, are solely collective, but not personal, problems. So, why should people act? We propose that they do so, nevertheless, because their basic psychic design implies that humans think and act as group members instead of idiosyncratic and isolated persons [13,14]. That is, people act upon collective problems on the ground of their identification with, and their perception of, a collective they categorize themselves as [15]. Collectives may refer to groups from different levels of inclusiveness, ranging from small activist groups to very inclusive social categories (e.g. generational or national groups; [16]). Then, group members’ environmental cognition and action depend on whether they consider their group as being in favor of pro-environmental action and as having the capabilities to significantly affect environmental crises [17]. Recently, such theorizing on collective pro-environmental motivation has been introduced to the study of pro-environmental behavior [15,18,19]. Building on the Social Identity Approach [13], this work indicates that collective pro-environmental motivation may be an important, but sometimes overlooked factor in transition processes towards carbon neutrality [20].

The present research aims to shed light on the question, of how the human capacity to think and act as social group members uniquely shape people’s efforts to mitigate large-scale environmental crises. Extending previous work, we target environmental behaviors that are more directly related to structural changes, namely acceptance of environmental policies and the subjective discount rate in investment decisions for renewable energies. The discount rate is an important factor to consider in investment behavior as it represents the time preference for consumption and reflects the opportunity cost of a specific investment, such as an investment in a renewable energy project. A high discount rate would result in a lower present value of future benefits from the investment, making it less attractive to private or public investors. In contrast, a low discount rate would increase the present value of future benefits and make the investment more appealing. The subjective discount rate can have a significant impact on the pace and success of the transformation towards a carbon-neutral future, as it determines the perceived value and feasibility of investments in green businesses. Economic research on (subjective discount rates in) investment in renewable energy projects has mainly focused on the role of markets and incentive-based policies, for example how to design feed-in tariffs to induce efficient investments into renewable electricity generation [21,22]. However, less is known about the effects of collective psychological factors on investment decisions. Bringing together economic and psychological research, the present work aims to provide novel and interdisciplinary insights into how collective pro-environmental motivation may affect the investment behavior and the acceptance of policies for renewable energies and—as a consequence—may increase private engagement for the transformation towards carbon neutrality.

Social identity and pro-environmental behavior

Psychological research investigating the cognitive and motivational drivers of people’s pro-environmental behavior has tended to focus on personal beliefs and motivation, such as personal environmental attitudes, perceived personal behavior costs or (personal) self-efficacy beliefs. However, we need to consider collective cognition and motivation as well, i.e. the switch from the personal ‘I’ to the collective ‘we’, if we aim to understand and support people’s pro-environmental behavior [15,19,23,24]. Recently, environmental psychology has started to investigate the effects of collective motivation on pro-environmental conduct. In line with the Social Identity Approach [13], this work proposes that–if certain conditions are met–individuals think and act in terms of their group membership (social identity) when appraising and responding to environmental problems. This self-categorization as a group member increases the importance of collective motivation for pro-environmental behavior.

But how exactly does group membership affect environmental appraisal and behavior? Models of collective pro-environmental action, such as the Social Identity Model of Pro-Environmental Action (SIMPEA; [15]), describe three key factors that influence how group members respond to perceived environmental crisis: ingroup norms and goals, collective efficacy beliefs, ingroup identification. Specifically, SIMPEA proposes that individuals are more likely to act in a pro-environmental manner if the norms and goals of their group support such behavior, particularly for members who are highly identified with their group. Similarly, collective environmental efficacy beliefs, i.e. the perception that the ingroup is capable (or not) to achieve its pro-environmental goals, should affect pro-environmental action. If the group is perceived as agentic and capable to achieve its pro-environmental goals, group members, especially high identifiers, should be more motivated to engage in pro-environmental action. However, collective factors may also influence how individuals appraise environmental issues. For example, social identities may increase or decrease acceptance of anthropogenic climate change, depending on whether (or not) climate change denial is perceived as prototypical for the salient group [25].

A growing body of research has shown that collective pro-environmental motivation can foster people’s pro-environmental behavior, albeit less work has been carried out regarding the effects of collective motivation on appraisal processes (see [17,18], for recent reviews). For example, increasing the salience of their political identity reduced acceptance of anthropogenic climate change and climate action intentions among self-identified political right-wingers [26]. Similarly, environmental ingroup norms, i.e. norms supportive or not supportive of pro-environmental behavior, were found to affect pro-environmental action intentions across different behavioral domains, including mobility behavior, energy-saving behavior, recycling or sustainable food choice [27–29]. Importantly, the effects of ingroup norms on action intentions were stronger for individuals highly identified with their group compared to low identifiers [30,31]. Corroborating these findings, meta-analytic results indicated that a stronger endorsement of a social identity with clear climate-protective norms was associated with higher behavioral intentions to fight climate change or self-reported climate-protective behavior [32]. Finally, strong beliefs about the ingroup’s capability to mitigate climate change increased climate-protective private consumption behavior as well as climate activist behavior [33–35]. Notably, the effects of collective pro-environmental motivation on pro-environmental action are not limited to groups inherently related to environmental issues (e.g. environmental activist groups) but were also observed for broader social categories (e.g., community identification; [36]). This suggests that social identities may provide a point of entry for interventions to foster pro-environmental action across different social contexts. The majority of the studies on collective pro-environmental motivation and pro-environmental behavior, however, have targeted private consumption behaviors or activist behavior (Fritsche et al., 2018). In contrast, fewer studies have investigated the effects of collective pro-environmental motivation on economic behavior, such as decisions about investment in green businesses or acceptance of green, but relatively costly policy measures [37]. Applying the social identity perspective to the study of green investment behavior may be a timely endeavor, as raising investment in green businesses can be considered a key strategy to facilitate the transformation towards carbon neutrality.

Economic research on investment behavior for renewable energies

From the economics perspective, an investment into a renewable energy project is profitably if its present value exceeds the costs of the investment. This present value depends on the cash flow of the project. A large literature asks how to design economic instruments that increase the cash flow in order to set the correct investment incentives (reviewed in [21,38]). In addition, the present value of a renewable energy project depends on the discount rate applied to future payments. In more psychological terms, a subjective discount rate represents the (reduced) present value people assign to investment outcomes they expect only for the future, not for today. As an example, imagine the choice between receiving €100 today or €100 in one year. If the discount rate is 5%, the €100 somebody receives in one year is worth less to this person today, or in other words, €100 in one year is equivalent to €95.23 today (€100/(1+0.05) = €95.23). The larger the subjective discount rate, the less favorable an investment becomes. Higher discount rates thus make investments with long-term payouts or benefits, such as benefits for future generations, substantially less attractive. As a consequence, the subjective discount rate could be a crucial factor influencing support for private and public investments for the transformation towards carbon neutrality. While there is a growing body of literature showing that individual discount rates are shaped by personal and contextual circumstances [39,40], much less is known about how social identities and collective motivation affect discount rates and, hence, investment strategies.

Present research

The present research investigates the effects of personal and collective pro-environmental motivation on efforts to support the transformation towards carbon neutrality. Previous work in environmental psychology has often focused on private consumption behaviors (e.g., recycling, private mobility behavior) and personal-level variables when predicting pro-environmental behavior (e.g., personal attitudes; [41]). The present research extends these studies by testing how collective pro-environmental motivation (e.g., perceived ingroup norms supportive of pro-environmental action, collective environmental efficacy beliefs) may influence behaviors that are more directly related to changes in our production and consumption patterns. Specifically, we examine if collective pro-environmental motivation can uniquely add to the explanation of investment decisions and acceptance of policies for renewable energies. We use investment in renewable energy projects as a key possibility for individuals to contribute to the transformation towards carbon neutrality. The key parameter for private or public decision-making in such climate-related investments is the subjective discount rate [42–45] which converts future payoffs into a present-day equivalent value. The subjective discount rate thereby takes into account the time value of money and other factors.

Using data from a multi-country survey in 31 European countries (N = 18,037), we test if personal pro-environmental motivation (H1a) and collective pro-environmental motivation (H1b) are negatively associated with subjective money discount rate in a choice experiment on investment in renewable energy projects and positively associated with acceptance of green energy policies (personal motivation: H2a, collective motivation: H2b). In line with social identity theory, we also examine if the effects of collective pro-environmental motivation on discount rate (H3a) and policy acceptance (H3b) are stronger for participants with a strong identification with their group compared to low identifiers. Although the primary focus of the present research is on collective pro-environmental motivation, we explore if the expected correlations between collective pro-environmental motivation and our two outcome variables remain significant after controlling for personal pro-environmental motivation. In other words, we examine if collective motivation can uniquely add to the explanation of investment decisions and policy acceptance. Our research is unique as it is the first study that combines choice experiments and psychological research methods to investigate investments in renewable energy projects with a focus on time preferences. There exist only few studies that use choice experiments to investigate time preferences [46,47] and, to our knowledge, there is no study linking discount rates to collective and individual motivation items. Our study is thus novel, being the first intertemporal choice experiment, to explain time preferences with psychological items.

Materials and methods

Survey and participants

We use data from an online multi-country survey collected in the ECHOES Horizon 2020 project (echoes-project.eu; [48]). The survey region covered 31 European countries (EU 27, Norway, Switzerland, Turkey, UK) and the online questionnaire was administered by a market research company. All survey materials were presented to the participants in their native language and monetary values were translated from Euros into an equivalent value of the national currency, where applicable. About 600 respondents were recruited in each target country using quota sampling methods to ensure that the samples were representative concerning income, age and gender. The total sample amounted to 18,037 completed questionnaires. Participants received a compensation of €5 after completing the questionnaire. Table 1 presents a summary of the socio-demographic indicators of the survey sample.

thumbnail
Download:
Table 1. Respondent socio-demographic characteristics.

https://doi.org/10.1371/journal.pclm.0000173.t001

Questionnaire and measurement of psychological variables

The questionnaire included information on respondents’ socio-demographic situation, their decisions in a choice experiment to invest in renewable energy projects, as well as items on respondents’ pro-environmental and energy-related attitudes, beliefs, personal norms and behaviors (and behavioral intentions). Participants were also asked to answer a number of group-related items on energy norms, efficacy beliefs and behaviors as well as their social identification for different social ingroups (see [48], for the full survey). For this, participants were randomly assigned to respond to group-based questions that referred to one out of three social ingroups: their municipality (N = 5919), their country (N = 6007), or Europe (N = 6111). For the current research, we use items on personal pro-environmental motivation and group pro-environmental motivation as predictor variables. Our central outcome measures are the subjective money discount rate (see description of the choice experiment below) and the acceptance of green energy technologies. If not indicated otherwise, all items were measured on five-point scales, ranging from 1 = “strongly disagree” to 5 = “strongly agree”.

Acceptance of green technologies was assessed with one item (‘I would accept energy policies that protect the environment even when these induce higher costs, e.g., policies that increase the prices of fossil fuels.’). This variable will henceforth be called Acceptance. Personal pro-environmental motivation includes two items on personal norms to save energy and to support the energy transition (example item: ‘I feel a personal obligation to support energy policies that support the energy transition.’), a single item on environmental self-identity (‘Acting pro-environmentally is an important part of who I am.’) as well as a graphical measure of inclusion of nature in self (adapted from [49]), a single item on self-efficacy beliefs to support the energy transition (‘As an individual, I can do a lot to support the energy transition.’) and two items on climate change beliefs (‘Most scientists say that the world’s temperature has slowly been rising over the past 100 years. Do you think this has been happening?’, ranging from 1 = “No, definitely not” to 5 = “Yes, definitely”; ‘Assuming that the world’s temperature is rising, do you think this is caused mostly by natural causes, about equally by natural causes and human activity, or mostly by human activity?‘, ranging from 1 = “Mostly by natural causes”to 3 = “Mostly by human activity“). We z-standardized all eight items and combined them into a single measure of personal pro-environmental motivation (Cronbachs α = .80), henceforth called personal motivation index (PMI).

Items measuring collective pro-environmental motivation refer to the salient ingroup (municipality, national, or EU). Collective pro-environmental motivation includes two items on perceived injunctive ingroup norms to save energy and to support the energy transition (example item: ‘Many people in [my municipality, the country I live in, the EU] would support it if I used less energy, e.g., using public transport instead of a personal car, turning off lights when leaving the room, using technical appliances which help to save energy.’), two items on perceived descriptive ingroup norms to save energy and to support the energy transition (example item: ‘A growing number of people in [my municipality, the country I live in, the EU] try to save energy, e.g., using public transport instead of a personal car, turning off lights when leaving the room, using technical appliances which help to save energy.’), and a single item on collective efficacy beliefs to support the energy transition (‘We as people in [my municipality, the country I live in, the EU] can act together to achieve the energy transition.’). We z-standardized all items and averaged them into a single measure of collective pro-environmental motivation (Cronbachs α = .79), henceforth called collective motivation index (CMI). Finally, social identification, i.e. identification with the salient ingroup, was assessed with one item (‘How much do you see yourself as a citizen of [your municipality, the country you live in, Europe]?’, ranging from 1 = “not at all” to 5 = “very much”). This variable will henceforth be called ID. The summary of these variables are depicted in Table 2.

thumbnail
Download:
Table 2. Summary statistics of the respondent-specific variables.

https://doi.org/10.1371/journal.pclm.0000173.t002

The choice experiment

The ECHOES survey incorporated a discrete choice experiment (DCE) to examine preferences for community renewable energy (CRE) projects. A DCE is a research method used to study the preferences of individuals. It is a type of stated preference study, which is used to measure how individuals would choose among different options. The method involves presenting respondents with a series of hypothetical choices between two or more options, where each option is defined by a set of attributes. The respondents are asked to indicate which option they would choose in each scenario.

Within the ECHOES’ DCE, the respondents were presented with two hypothetical investment opportunities in eight different scenarios. In each scenario, respondents could choose to invest in a wind park or solar farm, with the investment levels, holding time and other attributes of the options varying between scenarios. A third ‘opt-out’ option was also provided in each scenario, allowing respondents not to invest. The order of the scenarios was randomized, and the survey included three blocks of eight scenarios for a total of 24 choice scenarios. An example choice card is depicted in Fig 1. The experimental design uses the D-efficiency criteria with Bayesian priors for creating choice sets. More information about the statistical design of the DCE can be found in [50].

thumbnail
Download:
Fig 1. Example choice card (Source: [48]).

https://doi.org/10.1371/journal.pclm.0000173.g001

The levels of the holding periods vary between 5, 10 and 15 years. To calculate the profit we use the profit rate (0%, 5%, 10%, 20% or 50%) and the investment level which were randomly assigned. The investment levels—€100, €500, €1000, €2000, or €5000 —, were not varied between the scenarios in order to simplify the choice tasks for the respondents. In Table 3 we describe all attributes and list their levels. Further, the survey included a treatment that told respondents that a local government, national government, or EU official had endorsed the investment opportunities. Each treatment was shown to one-quarter of the respondents in each country, with the remaining respondents seeing only a briefing explaining the investment opportunities.

thumbnail
Download:
Table 3. Attribute levels and description.

https://doi.org/10.1371/journal.pclm.0000173.t003

Econometric model: Empirical model based on random utility theory

Our model assumes that people maximize utility over time [51]. Utility in a broad sense depends on individual-level factors, both tangible economic variables, such as the amount and timing of monetary payoffs, and personal behavior of self-efficacy beliefs. It further includes variables that capture collective cognition and motivation relevant to the decision-making situation. Specifically, utility is a function of observable characteristics of the investment alternatives, in particular the profit rate, the project length, the investment volume, the visibility of the renewable energy project, and the administrator of the project, as specified in the choice experiment. Moreover, the parameters of the utility function are modeled as functions of observed individual and collective motivations of the respondents. The main aim of the paper is to analyze how the respondents’ preferences are shaped by these latter variables.

In denotes the investment, which is independent of the choice alternative j but varies with respondent n, with In∈{100,500,1000,2000,5000} Euros. The profit rate is πj∈{0,0.05,0.10,0.20,0.50}, and is one of the attributes changing with choice alternatives.

After the specified holding period for the choice alternative, Tj∈{5,10,15} years have passed, the project delivers the cash flow In(1+πj). The utility from cash flow and other characteristics of the renewable energy investment Xnj at the end of the investment period Tj is described by the following utility function (1) with α>0, and where the β is a vector of parameters indicating the marginal utility of other investment-specific characteristics Xj. As these are categorical variables, they enter linearly in the log of utility. The utility derived from the investment accrues Tj periods into the future, whereas the decision is made at present. Thus utility is expressed as a present value, which is obtained by applying the subjective (annual) utility discount rate δ. Taking logs and adding an independently and identically distributed random component εnj, we obtain the model (2)

Applying the model to the data from the choice experiment allows us to identify the model parameters. We model the utility discount rate as a function of individual and collective motivation indicator variables, which we summarize in the variable Yn. The utility discount rate becomes: (3)

The effects of the social and psychological variables Yn on the utility discount rate δn are empirically identified by the estimated parameters for the interaction between these variables and the holding period Tn, leading to (4)

To facilitate interpretation, we convert the utility discount rate δn into a money discount rate, dividing it by the estimated coefficient for log profit, α. We thus obtain the money discount rate (5)

In the choice experiment, respondents choose repeatedly between two hypothetical investment alternatives. We assume that the alternatives are mutually exclusive and the respondent chooses either one of the two investment alternatives j∈{1,2} or chooses not to invest (opt out) j = 0.

In this setting, the parameters from this utility function can be estimated using a conditional logit model. Assuming that εnj is Extreme Value Type I (Gumbel) distributed, we obtain the logit probability (6)

As only differences in utility matter, the model can only be identified if the error variance is normalized. The normalization implies that the estimated parameters are confounded with the scale of the error variance so that the parameters have arbitrary values which cannot be directly interpreted. However, by dividing the subjective utility discount rate by the coefficient of the log profit α, the scale parameters drop out and we can interpret money discount in units of % of profit per year. We are particularly interested in the subjective money discount rate, ρn, which has been identified to be a key variable in decision-making related to climate change, as pointed out in the introduction.

Results

In Models 1–5 (Table 4) we estimate Conditional Logit models using the DCE data. The dependent variable is the choice made by the respondents. The models include alternative-specific constants (ASC_A and ASC_B), which show the preferences for investment options A and B (i.e. respondent decides to invest in the energy project) over the opt-out alternative (i.e. respondent decides not to invest in the energy project). We also entered alternative-specific variables (Profit, Holding period, Visible installation, Community admin, Utility admin) and respondent-specific variables in the analysis (personal motivation index, collective motivation index, ID, group assignment: municipality, country, EU). We are in particular interested in the ratio of coefficients of the variable Holding period and ln(Profit) which we can interpret as the money discount rate, i.e. one of our central outcomes. Specifically, we aim to examine the impact of respondent-specific variables on the money discount rate, by analyzing interaction effects between the variable Holding period and the respondent-specific variables (personal motivation index, collective motivation index, ID, group assignment). For testing our hypotheses, we included the two-way interaction term of Holding period and personal motivation index in Model 1 (H1a), the two-way interaction term of Holding period and collective motivation index in Model 2 (H1b), as well as all two-way and three-way interaction terms of Holding period, collective motivation index and ingroup identification (ID) in Model 4 (H3). For exploring if collective pro-environmental motivation uniquely predicts the money discount rate, we included personal motivation index, collective motivation index and their two-way interaction terms with Holding period in Model 3.

thumbnail
Download:
Table 4. Results of the conditional logit models.

https://doi.org/10.1371/journal.pclm.0000173.t004

Probability to invest in energy project

First, we analyzed respondents’ choices to invest or not invest in the proposed energy project. We observe a consistent preference for the opt-out alternative over an investment in the project A and a consistent preference for the opt-out alternative over an investment in the project B, ceteris paribus, evidenced by the significant negative regression coefficients for the variables ASC_A and ASC_B (Models 1–5). Overall, 27% of the choices were project A, 30% project B and 43% opt-out. We also find that higher profit rates, non-visible installation (vs. visible installation) and community-based administration (vs. administration by a utility company or public authority) of the energy site increased probability of investment in the energy project. These results are in line with previous findings on private investments in renewable energy projects [50].

(Money) discount rate

From the coefficients of the variable Holding period and ln(Profit) in Models 1–5, we can derive the money discount rates by applying Eq (5). We expect that the discount rate is negatively associated with personal pro-environmental motivation (H1a) and collective pro-environmental motivation (H2a). The results of Models 1 and 2 support our assumptions. Specifically, we find a negative interaction effect of Holding period and personal motivation index (coefficient of Hold*PMI) in Model 1, indicating that higher levels of personal pro-environmental motivation are associated with a lower money discount rate (see Fig 2A). The ratio of the coefficient of Hold*PMI and the coefficient of ln(Profit) describes the impact of an increase in the personal motivation index by one unit on the money discount rate (seeTable 5). Given that the mean value of the personal motivation index is zero, the mean money discount rate across all respondents is 2.00% per year. In other words: €100 in one year is equivalent to €98.00 today (€100/(1+0.02) = €98.00). Further, increasing the personal motivation index by one unit decreases the mean money discount rate by 0.91%. Similarly, results also reveal a negative interaction effect of Holding period and collective motivation index in Model 2 (coefficient of Hold*CMI), showing that a stronger collective pro-environmental motivation is related to a lower money discount rate (see Fig 2B). The mean money discount rate here is 1.99% and decreases by 0.7% with an increase of the collective motivation index by one unit.

thumbnail
Download:
Fig 2. A: Money discount rate and personal motivation index.

B: Money discount rate and collective motivation index. (Shaded areas indicate the 95% confidence intervals).

https://doi.org/10.1371/journal.pclm.0000173.g002

thumbnail
Download:
Table 5. Calculated money discount rates.

https://doi.org/10.1371/journal.pclm.0000173.t005

Next, we explored if the negative association between collective pro-environmental motivation and the money discount rate will remain stable after controlling for the effects of personal pro-environmental motivation. Results of Model 3 indicate that including the interaction effect of personal motivation index and Holding period (Hold*PMI) did not change the interaction effect of collective motivation index and Holding period (see Fig 3). Put differently, the negative relationship between collective pro-environmental motivation and the money discount rate remained robust after controlling for personal pro-environmental motivation. The results of Models 2 and 3 support our assumption that a stronger collective pro-environmental motivation is associated with a lower money discount rate. Building on the Social Identity Approach, we expect that the negative relationship between collective pro-environmental motivation and the money discount rate is stronger for participants who are highly identified with their group compared to low identifiers (H3a). The results of Model 4 support this assumption, revealing a statistically significant three-way interaction effect of Holding period, collective motivation index and ID (coefficient of Hold*CMI*ID). Inspection of the simple slopes (see Fig 4) showed that the negative association between collective pro-environmental motivation and the money discount rate was stronger for high identifiers (+1SD) than for respondents with low levels of ID (-1SD). Specifically, high identifiers exhibited a lower money discount rate compared to low identifiers when collective pro-environmental motivation was high. However, we found no difference in money discount rate between high and low identifiers for low levels of collective pro-environmental motivation. Finally, we also tested if the negative correlation between money discount rate and collective pro-environmental motivation changed for different salient ingroups (municipality, country, EU). Results of Model 5 showed no significant interaction effects of Holding period, collective motivation index and the dummy variables for the type of salient identity (coefficients of Hold*CMI*Municipal and Hold*CMI*Country). This suggests that the negative relationship between collective motivation and money discount rate can be generalized across different forms of collectives.

thumbnail
Download:
Fig 3. Money discount rate, personal motivation and collective motivation index.

(Shaded areas indicate the 95% confidence intervals).

https://doi.org/10.1371/journal.pclm.0000173.g003

thumbnail
Download:
Fig 4. Money discount rate, collective motivation index and group identification.

(Shaded areas indicate the 95% confidence intervals).

https://doi.org/10.1371/journal.pclm.0000173.g004

Acceptance of green energy policies

Table 6 presents the results of a linear mixed model to investigate the relationships between policy acceptance, our second outcome measure, and the respondent-specific variables. The fixed effects in this model are represented by the coefficients of the independent variables personal motivation index, collective motivation index, and ID, as well as the interaction term of collective motivation index and ID. These coefficients represent the average effect of each variable on policy acceptance across all groups. The random effect in this model is represented by the Survey country variable. This variable accounts for the fact that the data was collected from multiple groups (countries) and that the variation within each group may be different from the variation across groups. The inclusion of random effects in this model helps to account for the non-independence of observations within groups and leads to more accurate estimates of the fixed effects of our independent variables. We expected that policy acceptance is positively associated with personal pro-environmental motivation (H1b) and collective pro-environmental motivation (H2b). We also expect that the correlation between policy acceptance and collective motivation is stronger for high identifiers compared to low identifiers (H3b). In line with H1b and H2b, the results of Model 6 (Table 6) indicate significant positive relationships between personal motivation index and acceptance of green energy policies (coefficient of PMI) as well as between collective motivation index and policy acceptance (coefficient of CMI). Although the correlation between personal motivation index and policy acceptance is stronger, collective pro-environmental motivation can uniquely add to the explanation of policy acceptance. Furthermore, we found a significant interaction effect of collective motivation index and ID (coefficient of CMI*ID). Inspection of the simple effects (see Fig 5) revealed that the correlation between collective motivation index and policy acceptance is stronger when ID is high (+1SD) than for low levels of ID (-1SD). Results of Model 6 thus support H3b.

thumbnail
Download:
Fig 5. Policy acceptance, collective motivation index and group identification.

(Shaded areas indicate the 95% confidence intervals).

https://doi.org/10.1371/journal.pclm.0000173.g005

thumbnail
Download:
Table 6. Results of the linear mixed model.

https://doi.org/10.1371/journal.pclm.0000173.t006

Discussion

Given the urgency of the ecological transformation of whole societies, it is important to determine when and why citizens are ready to support systemic changes by accepting green policies and by investing their money in green businesses. The collective nature of effectively coping with large-scale environmental crises suggests that such support cannot be fully explained as a personal decision people make on the ground of their perceived personal costs, benefits, and capabilities [15,23]. Instead, support for a green transformation might be better understood as an individual’s expression of collective action. That is, people support–personally costly–systemic changes towards ecological sustainability when they define themselves as a member of a collective that has collectively shared pro-environmental norms and goals and appears to be agentic in initiating collective action and effectively contributing to fighting environmental crises [17]. The current research supports this novel look at individuals’ pro-environmental action: Collective motivation to protect the environment, indicated by people’s perception of pro-environmental collective norms and collective efficacy, predicted both people’s acceptance of green energy policies and lower discounting of future gains in hypothetical green energy investment decisions. While personal motivation (sense of personal obligation to protect the environment and personal pro-environmental identity) predicted these pro-environmental behaviors as well (as suggested by previous results; e.g., [32,41,52]) the effects of collective motivation remained present when controlling for the effect of personal motivation. That is, collective motivation predicted support of the transformation independent of personal motivation. At the same time, controlling for personal motivation effects reduced the effects of collective motivation. This suggests that part of the collective motivation effect could be mediated via people’s personal sense of pro-environmental obligation and identity. In other words, perceived collective norms and efficacy might affect people’s pro-environmental behavior by changing the personal attitudes that then drive pro-environmental action. Our results are in line with other studies showing that collective motivation can foster pro-environmental behavior, either directly [30] or through changes in personal pro-environmental motivation [35,53]. For example, previous results indicate that a strong sense of identification with an energy community initiative was positively associated with sustainable energy behavior and behavior intentions [54].

As a further indication that the effects of norms and collective efficacy are also truly collective, we found that the effects were stronger in people who indicated higher identification with their salient ingroup. Obviously, it needs identified group members to make collective motivation factors work. Groups may not just have the power and magnitude to bring about significant pro-environmental change through societal transformation but they also provide identified members with a sense of agency in the face of collective problems causing personal helplessness, and they validate their actions as being appropriate. This is why, in our study across 31 different European countries, not just very large and highly powerful collective identities, such as “EU Citizens”, had the observed motivating effects, but also smaller groups, such as the people in one’s own country or municipality. Obviously, just thinking about the self in terms of some collective strengthens people’s motivation to support pro-environmental systemic change (see [55] for similar results in the context of energy community initiatives). This transcends earlier research showing that ingroup norms affect group members’ environmental behavior only when they were highly identified with their group [30,31,56]. The current results show that ingroup identification is a crucial boundary condition for collective motivation factors more broadly, including collective efficacy beliefs, to affect people’s pro-environmental behavior, as proposed in the social identity model of pro-environmental action [15].

Economic analysis usually takes preferences as given. This is true in particular for the discount rate, which is often assumed to be a constant, independent of time and circumstances also in the analysis of climate change mitigation policies [57]. Our study provides evidence that “personal circumstances” affect the discount rate. Specifically, personal and collective pro-environmental motivations influenced the discount rate people applied to renewable energy investment decisions in a choice experiment. Our study thus may help to inform the analysis of climate policies and renewable energy transition with endogenously changing preferences [58]. To our knowledge, there exists only limited work on individual discount rates in the context of climate policies [47] combined a DCE on energy efficiency investments with a method to identify individual discount rates. Similar to our results, they find a strong influence of individual discount rates on willingness to pay (in their case for energy-efficient home appliances). They also find several socio-demographic variables to influence the individual discount rate but do not investigate psychological factors [59] investigate psychological factors affecting the willingness to invest in socially responsible investments (which includes an environmental component) but do not investigate individual discount rates. They find strong links between the willingness to invest and attitudinal variables (warm glow, social norms). To that end, we can conclude that other studies find similar patterns between investment decisions, individual discount rates and psychological factors. Yet, our study is unique in linking individual discount rates directly to group motivational factors.

To increase investments in renewable energy projects and other environmental projects, policymakers can initiate marketing campaigns that aim to increase collective and individual motivations and strengthen collective experiences. Through this channel, the average social discount rate may reduce, facilitating the willingness to invest.

Our study has a few limitations: First, there is a debate about statistical challenges from integrating indicator variables such as self-reported measures of perceptions and attitudes in discrete choice models. There are two reasons to expect endogeneity in these variables [60]. First, indicator variables are often measured on Likert scales, which can lead to measurement error. Second, the dependent variable in a choice experiment and an indicator variable can be simultaneously caused by a third unobserved variable, leading to a correlation between the error term and the indicator variable. Such a setting can lead to bias in the estimated parameters. The literature suggests different approaches to deal with such endogeneity issues. The first issue, measurement error could be handled by hybrid choice models, which model the endogenous variables as explanatory variables for a latent variable [61]. Yet, these models cannot capture the bias caused by simultaneity. Other approaches used to deal with endogeneity in choice models are control function approaches and instrumental variable regressions [62]. However, all these approaches can cause new issues, which may be more severe than the biases caused by measurement error and simultaneity. We, therefore, decided not to use them in this paper.

Second, our experiment does not allow us to separate the discount rate for environmental benefits from the discount rate of money because we do not vary the timing of the environmental benefit. In a future study, it would be interesting to investigate time preferences for money vis-à-vis time preferences for environmental benefits. For example, a choice experiment could include an attribute describing after how many years the investment will create positive externalities in terms of CO2 mitigation.

Third, the generalizability of our results needs to be done with care. On the one hand, our sample is not representative for the 30 European countries. For example, our samples have higher education degrees than the country averages. On the other hand, the choices made in the choice experiment are hypothetical and may suffer from hypothetical bias [63]. Respondents likely overstate their willingness to invest, i.e. they would be less likely to do so in real life.

Conclusion

Pursuing rapid societal transformation towards ecological sustainability requires citizens’ support. Obviously, environmentalism has definitely entered the stage where it is no longer sufficient to consider private consumption and lifestyle behavior as the individuals’ contribution to saving the environment. Instead, now this is about supporting systemic, collective changes. This further illustrates that pro-environmental action is basically collective in nature and is motivated on the ground of collective cognition. The present study provides evidence for the crucial role of collective motivation in explaining individuals’ support of an ecological transformation of societies, although the correlational nature of our data requires conceptual replications in experimental or longitudinal studies to provide clear causal evidence. On the more methodological side, our study shows that insights from psychology can meaningfully contribute to our understanding of economic decision-making, thus opening up a new perspective for fruitful interdisciplinary collaboration.

Supporting information

S1 Fig. Histograms of personal (A) and collective (B) motivaion indices.

https://doi.org/10.1371/journal.pclm.0000173.s001

(TIF)

S1 Table. Items of personal motivation index.

https://doi.org/10.1371/journal.pclm.0000173.s002

(DOCX)

S2 Table. Items of collective motivation index.

https://doi.org/10.1371/journal.pclm.0000173.s003

(DOCX)

Acknowledgments

We thank participants of the Breathing Nature Conference 2022 in Leipzig for helpful discussion.

References

  1. 1. IPCC. Climate change 2022: Impacts, adaptation and vulnerability: summary for policymakers. Geneva: IPCC; 27 February 2022.
    • 2. Bamberg S, Möser G. Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of Environmental Psychology. 2007;27:14–25.
    • 3. Hamann K, Masson T. Kollektives nachhaltiges Handeln und Psychologie. In: Genkova P, editor. Handbuch Globale Kompetenz: Grundlagen–Herausforderungen–Krisen. Wiesbaden: Springer Fachmedien Wiesbaden; Imprint Springer; 2020. p. 1–16. https://doi.org/10.1007/978-3-658-30684-7_35–1
      • 4. Lange F, Dewitte S. Measuring pro-environmental behavior: Review and recommendations. Journal of Environmental Psychology. 2019;63:92–100.
      • 5. Gordon HS. The Economic Theory of a Common-Property Resource: The Fishery. Journal of Political Economy. 1954;62:124–42.
      • 6. Hardin G. The tragedy of the commons. Science. 1968;162:1243–8. pmid:17756331
      • 7. Stavins RN. The Problem of the Commons: Still Unsettled after 100 Years. American Economic Review. 2011;101:81–108.
      • 8. Bronnmann J, Stoeven MT, Quaas M, Asche F. Measuring Motivations for Choosing Ecolabeled Seafood: Environmental Concerns and Warm Glow. Land Economics. 2021;97:641–54.
      • 9. Quaas MF, Meya JN, Schenk H, Bos B, Drupp MA, Requate T. The social cost of contacts: Theory and evidence for the first wave of the COVID-19 pandemic in Germany. PLoS One. 2021;16:e0248288. pmid:33740007
      • 10. Becker E, editor. Soziale Ökologie: Grundzüge einer Wissenschaft von den gesellschaftlichen Naturverhältnissen. Frankfurt: Campus Frankfurt / New York; 2006.
        • 11. Clayton S, Karazsia BT. Development and validation of a measure of climate change anxiety. Journal of Environmental Psychology. 2020;69:101434.
        • 12. Salomon E, Preston JL, Tannenbaum MB. Climate change helplessness and the (de)moralization of individual energy behavior. J Exp Psychol Appl. 2017;23:15–28. pmid:28165276
        • 13. Reicher S, Spears R, Haslam SA. The Social Identity Approach in Social Psychology. In: Wetherell M, Mohanty C, Mohanty CT, editors. The SAGE handbook of identities. Los Angeles, Calif., London: SAGE; 2010. p. 45–62. https://doi.org/10.4135/9781446200889.n4
          • 14. Turner JC. Rediscovering the social group: A self-categorization theory. Oxford: Basil Blackwell; 1987.
            • 15. Fritsche I, Barth M, Jugert P, Masson T, Reese G. A social identity model of pro-environmental action (SIMPEA). Psychol Rev. 2018;125:245–69. pmid:29265852
            • 16. Lickel B, Hamilton DL, Wieczorkowska G, Lewis A, Sherman SJ, Uhles AN. Varieties of groups and the perception of group entitativity. J Pers Soc Psychol. 2000;78:223–46. pmid:10707331
            • 17. Masson T, Fritsche I. We need climate change mitigation and climate change mitigation needs the ‘We’: a state-of-the-art review of social identity effects motivating climate change action. Current Opinion in Behavioral Sciences. 2021;42:89–96.
            • 18. Fritsche I, Masson T. Collective climate action: When do people turn into collective environmental agents? Curr Opin Psychol. 2021;42:114–9. pmid:34130199
            • 19. Fielding KS, Hornsey MJ. A Social Identity Analysis of Climate Change and Environmental Attitudes and Behaviors: Insights and Opportunities. Front Psychol. 2016;7:121. pmid:26903924
            • 20. Bamberg S, Rees JH, Schulte M. Environmental protection through societal change. In: Clayton SD, Manning C, editors. Psychology and climate change: Human perceptions, impacts, and responses. London, San Diego, Cambridge, Oxford: Academic Press; 2018. p. 185–213. https://doi.org/10.1016/B978-0-12-813130-5.00008–4
              • 21. Requate T. Green tradable certificates versus feed-in tariffs in the promotion of renewable energy shares. Environ Econ Policy Stud. 2015;17:211–39.
              • 22. Lancker K, Quaas MF. Increasing marginal costs and the efficiency of differentiated feed-in tariffs. Energy Economics. 2019;83:104–18.
              • 23. Barth M, Masson T, Fritsche I, Fielding KS, Smith JR. Collective responses to global challenges: The social psychology of pro-environmental action. Journal of Environmental Psychology. 2021;74:101562.
              • 24. Schulte M, Bamberg S, Rees J, Rollin P. Social identity as a key concept for connecting transformative societal change with individual environmental activism. Journal of Environmental Psychology. 2020;72:101525.
              • 25. Bliuc A-M, McGarty C, Thomas EF, Lala G, Berndsen M, Misajon R. Public division about climate change rooted in conflicting socio-political identities. Nature Clim Change. 2015;5:226–9.
              • 26. Unsworth KL, Fielding KS. It’s political: How the salience of one’s political identity changes climate change beliefs and policy support. Global Environmental Change. 2014;27:131–7.
              • 27. Bamberg S, Rollin P, Schulte M. Local mobility culture as injunctive normative beliefs–A theoretical approach and a related measurement instrument. Journal of Environmental Psychology. 2020;71:101465.
              • 28. Barth M, Jugert P, Fritsche I. Still underdetected–Social norms and collective efficacy predict the acceptance of electric vehicles in Germany. Transportation Research Part F: Traffic Psychology and Behaviour. 2016;37:64–77.
              • 29. Masson T, Jugert P, Fritsche I. Collective self-fulfilling prophecies: group identification biases perceptions of environmental group norms among high identifiers. Social Influence. 2016;11:185–98.
              • 30. Masson T, Fritsche I. Adherence to climate change-related ingroup norms: Do dimensions of group identification matter? Eur. J. Soc. Psychol. 2014;44:455–65.
              • 31. Terry DJ, Hogg MA, White KM. The theory of planned behaviour: self-identity, social identity and group norms. Br J Soc Psychol. 1999;38 (Pt 3):225–44. pmid:10520477
              • 32. Vesely S, Masson T, Chokrai P, Becker AM, Fritsche I, Klöckner CA, et al. Climate change action as a project of identity: Eight meta-analyses. Global Environmental Change. 2021;70:102322.
              • 33. Hamann KRS, Reese G. My Influence on the World (of Others): Goal Efficacy Beliefs and Efficacy Affect Predict Private, Public, and Activist Pro‐environmental Behavior. Journal of Social Issues. 2020;76:35–53.
              • 34. Hornsey MJ, Fielding KS. A cautionary note about messages of hope: Focusing on progress in reducing carbon emissions weakens mitigation motivation. Global Environmental Change. 2016;39:26–34.
              • 35. Jugert P, Greenaway KH, Barth M, Büchner R, Eisentraut S, Fritsche I. Collective efficacy increases pro-environmental intentions through increasing self-efficacy. Journal of Environmental Psychology. 2016;48:12–23.
              • 36. Goedkoop F, Sloot D, Jans L, Dijkstra J, Flache A, Steg L. The Role of Community in Understanding Involvement in Community Energy Initiatives. Front Psychol. 2021;12:775752. pmid:35222148
              • 37. Berneiser JM, Becker AC, Loy LS. Give Up Flights? Psychological Predictors of Intentions and Policy Support to Reduce Air Travel. Front Psychol. 2022;13:926639. pmid:35992431
              • 38. Requate T. Dynamic incentives by environmental policy instruments—a survey. Ecological Economics. 2005;54:175–95.
              • 39. Voors MJ, Nillesen EEM, Verwimp P, Bulte EH, Lensink R, van Soest DP. Violent Conflict and Behavior: A Field Experiment in Burundi. American Economic Review. 2012;102:941–64.
              • 40. Matousek J, Havranek T, Irsova Z. Individual discount rates: a meta-analysis of experimental evidence. Exp Econ. 2022;25:318–58.
              • 41. Klöckner CA. A comprehensive model of the psychology of environmental behaviour—A meta-analysis. Global Environmental Change. 2013;23:1028–38.
              • 42. Stern N. The Economics of Climate Change: Cambridge University Press; 2014.
                • 43. Nordhaus WD. A Review of the Stern Review on the Economics of Climate Change. Journal of Economic Literature. 2007;45:686–702.
                • 44. Quaas MF, van Soest D, Baumgärtner S. Complementarity, impatience, and the resilience of natural-resource-dependent economies. Journal of Environmental Economics and Management. 2013;66:15–32.
                • 45. Drupp MA, Freeman MC, Groom B, Nesje F. Discounting Disentangled. American Economic Journal: Economic Policy. 2018;10:109–34.
                • 46. Cubitt RP, Read D. Can intertemporal choice experiments elicit time preferences for consumption? Exp Econ. 2007;10:369–89.
                • 47. Newell RG, Siikamäki J. Individual Time Preferences and Energy Efficiency. American Economic Review. 2015;105:196–200.
                • 48. Reichl J, Cohen J, Kollmann A, Azarova V, Klöckner C, Royrvik J, et al. International survey of the ECHOES project. Zenodo. 2019;1.
                • 49. Psychology of sustainable development. New York, NY: Springer Science + Business Media; 2012.
                  • 50. Cohen JJ, Azarova V, Kollmann A, Reichl J. Preferences for community renewable energy investments in Europe. Energy Economics. 2021;100:105386.
                  • 51. McFadden D. Conditional logit analysis of qualitative choice behavior. In: Frontiers in econometrics. New York [u.a.]: Academic Press, 1974; 1974.
                    • 52. Udall AM, Groot JIM de, Jong SB de, Shankar A How I See Me-A Meta-Analysis Investigating the Association Between Identities and Pro-environmental Behaviour. Front Psychol. 2021;12:582421. pmid:33796041
                    • 53. Hoppe A, Fritsche I, Chokrai P. The “I” and the “We” in Nature Conservation—Investigating Personal and Collective Motives to Protect One’s Regional and Global Nature. Sustainability. 2023;15:4694.
                    • 54. Sloot D, Jans L, Steg L. Can community energy initiatives motivate sustainable energy behaviours? The role of initiative involvement and personal pro-environmental motivation. Journal of Environmental Psychology. 2018;57:99–106.
                    • 55. Sloot D, Jans L, Steg L. In it for the money, the environment, or the community? Motives for being involved in community energy initiatives. Global Environmental Change. 2019;57:101936.
                    • 56. Fielding KS, Terry DJ, Masser BM, Hogg MA. Integrating social identity theory and the theory of planned behaviour to explain decisions to engage in sustainable agricultural practices. Br J Soc Psychol. 2008;47:23–48. pmid:17535461
                    • 57. Hänsel MC, Drupp MA, Johansson DJA, Nesje F, Azar C, Freeman MC, et al. Climate economics support for the UN climate targets. Nature Clim Change. 2020;10:781–9.
                    • 58. Mattauch L, Hepburn C, Spuler F, Stern N. The economics of climate change with endogenous preferences. Resource and Energy Economics. 2022;69:101312.
                    • 59. Gutsche G, Ziegler A. Which private investors are willing to pay for sustainable investments? Empirical evidence from stated choice experiments. Journal of Banking & Finance. 2019;102:193–214.
                    • 60. Budziński W, Czajkowski M. Endogeneity and Measurement Bias of the Indicator Variables in Hybrid Choice Models: A Monte Carlo Investigation. Environ Resource Econ. 2022;83:605–29.
                    • 61. Ben-Akiva M, McFadden D, Train K, Walker J, Bhat C, Bierlaire M, et al. Hybrid Choice Models: Progress and Challenges. Marketing Letters. 2002;13:163–75. :1020254301302.
                    • 62. Guevara CA. Critical assessment of five methods to correct for endogeneity in discrete-choice models. Transportation Research Part A: Policy and Practice. 2015;82:240–54.
                    • 63. Haghani M, Bliemer MCJ, Rose JM, Oppewal H, Lancsar E. Hypothetical bias in stated choice experiments: Part I. Macro-scale analysis of literature and integrative synthesis of empirical evidence from applied economics, experimental psychology and neuroimaging. Journal of Choice Modelling. 2021;41:100309.