In the context of transportation systems, System Dynamics (SD) modeling is especially appropriate because it serves to reveal underlying system structures and the transition dynamics arising from these structures. Furthermore, SD can assist in developing experimental transport tools to explore various transport policies and provide a platform for learning about transport problems [72
]. In this respect, several SD models have been designed to conceptualize and analyze policies regarding the uptake of AFVs [2
]. While these models all vary in their scope, focus, and assumptions about technological innovation [76
], they all capture AFV development, diffusion, and adoption. None of the models developed so far, however, allow for the exploration of multi-level interactions and how these influence the system’s dynamics. Therefore, we develop a model that considers the multi-level nature of the e-mobility transition process, using an entity-based SD model. Entity-based models can provide more effective explanations by decomposing problems into various entity types [89
3.1. Model Description
This section presents a high-level description of the model. Figure 1
depicts a stylized overview of the model. We focus here on the most important feedback loops in this model, as depicted in Figure 1
. The list of variables and equations of the model, as well as the stylized overview of the entity types, are provided in the Supplementary Materials (S1, S2 and S3)
. Overall, the main feedback loops arise from the various cause-effect relationships between EV adoption, EV-related subsidies, perceived EV legitimacy, learning, and relative availability of charging points.
First, the balancing Subsidy Dependence loop (B.1) reflects the pivotal role of subsidies in the diffusion of e-mobility: this loop runs from Subsidies via Public Charging Points and EV adoption rate to the number of EV Adopters and back to Subsidies. When EV Adopters increase, the number (and size) of subsidies declines. When subsidies decline, another loop reinforces B.1, via the decreasing Price Value of EV (i.e., the price of EV relative to ICEV) that reduces the Intention Rate to Buy EV, which in turn affects the EV purchase intention rate and so forth.
Second, the balancing Regime Resistance
loop (B.2) is about the (potential) adopters’ perceived legitimacy of the e-mobility system. When the number of EV Adopters increases to a certain threshold, ICEV manufacturers are likely to invest in improving their products to prohibit further loss of their customers; this is known as the sailing-ship effect, for example, [41
]. This effect is in line with studies that observed how incumbent firms, representing the dominant regime, actively resist the diffusion of niche innovations, see, for example, [93
]. Therefore, the Perceived Legitimacy of EVs may decrease (or grow slower than initially anticipated) and thus demotivate Entrepreneurial Activities, which in turn reduces EV Production Rate (across all EV manufacturers). As such, this may decrease the EV adoption rate when this loop is dominant (i.e., when the total number of people with an intention to buy an EV is higher than the number of EVs produced).
Perceived Legitimacy is also part of the reinforcing loop R.1, called the Legitimization loop. Accordingly, an increasing number of EV Adopters enhances the Perceived Legitimacy of EVs, which in turn stimulates Entrepreneurial Activities and thereby the EV production and adoption rates. Moreover, Entrepreneurial Activities are also likely to lead to more Public Charging Points, which also motivates more people to adopt EVs—similar to how “word-of-mouth” works.
There are several learning loops in the model, which are all reinforcing in nature and capture the influence of experience on the EV adoption rate. In the Performance Learning loop (R.2), the EV adoption rate positively affects learning and, thereby, Knowledge Development and Diffusion, which in turn—after a time delay—positively affects EV Performance and Effort Expectancy as well as Hedonic Expectancy. These two factors influence the Intention Rate to Buy EVs, and so forth.
The Entrepreneurial Learning loop (R.3) involves the effect of Knowledge Development and Diffusion on the Perceived Legitimacy of EVs, which in turn affects the Entrepreneurial Activities. The latter stimulates the EV Production Rate and the growth in Public Charging Points, which together positively affect the EV adoption rate. Via enhanced Learning, this loop feeds back to Knowledge Development and Diffusion.
In the reinforcing Price Learning loop (R.4), a higher EV adoption rate leads to more learning—for example, with regard to manufacturing and economies of scale and scope. As a result, the Price Value of EVs is likely to increase, which in turn motivates more people to buy an EV, and so forth.
The balancing Availability of Charging Points loop (B.3) is about the effect of changes in the number of EV Adopters on the expectancy of facilitating conditions (i.e., the available public charging points): for example, when the number of EV Adopters increases rapidly (everything else being unchanged), the expectancy regarding the availability of charging points per EV declines. As a result, the Intention Rate to Buy EVs declines, affecting the Purchase Intention Rate, and so forth.
Finally, the Return on Knowledge Investment loop (R.5) reflects the reinforcing effect from Resource Mobilization (e.g., for investing in more efficient Charging Points respectively EV design and development) on Knowledge Development and Diffusion. The latter improves the Guidance of Search for solutions, which in turn positively affects the Resources being mobilized.
3.3. Experimental Setup
We adopt an experimental setup to explore the workings of the model, to investigate how the macro-and micro-level variables and interactions influence the e-mobility transition. To do so, we zoom out/in on the simple structure of EV adoption by adding macro-and micro-level variables of the selected entities to explore the overall dynamic behavior. The simulations in the remainder of this paper especially serve to explore the effects of different age groups, income levels and urbanization levels on EV adoption. In the remainder of this section, these experiments are described in detail. Table 1
provides an overview of our base run and the scenarios developed. In future work, other scenarios can be developed to examine the effect of adding other entities. In this paper, we focus on demonstrating the added value of our modeling approach.
The base run
: We start our experiments by running a selected part of the overall model, the EV Purchasers entity type, which includes only four main stocks: Potential Adopters of EV, Potential Adopters with Intention to Buy EV, EV Adopters, and Dissatisfied EV Adopters. That is, this run concerns our base case, which excludes influence from the variables associated with TIS and UTAUT on the diffusion and adoption of EVs (see Figure 1
Scenario 1: This scenario serves to make sense of the effects on the e-mobility transition development as a result of including the TIS macro-level variables.
Scenario 2: In the second scenario, we also consider the effects of the UTAUT entities and variables on EV Purchase Intention Rate. Note that in this scenario, we also trace the interactions and mutual influences of macro-level variables of TIS and micro-level variables of UTAUT on e-mobility development.
Subsequently, we continue with a set of sub-scenarios (i.e., 2-1, 2-2, and 2-3) that focus on the differential effects on the system’s dynamics as a result of various attributes of (potential) EV purchasers. These scenarios serve to illustrate the potential insights arising from a simulation model that incorporates both the micro and macro-level dimensions of the e-mobility transition. Accordingly, we differentiate EV purchasers in terms of income, age, and urbanization level. These attributes are selected because they are among the most important moderators in EV adoption [116
]. As such, we model different classes of potential purchasers to adopt EV, based on various timings and different decision rules. This means each group of EV purchasers attaches different weights to the UTAUT variables that influence their decision to purchase an EV. In the following paragraphs, we describe each of these scenarios. Table 2
and S4 in Supplementary Materials
provide more details about each scenario.
. In this scenario, we explore the effect of varying income levels on EV adoption. As such, we change the weights of the UTAUT variables for different groups of potential purchasers. The main assumption in estimating these weights is that people with higher income levels are more likely to buy an EV at an earlier point in time [117
], as the initially high relative price of EV matters less to this group. Performance and Effort Expectancy, Hedonic Expectancy, and Facilitating Conditions Expectancy are arguably more important to this group. Notably, we assume people with an income of over 50,000 € annually to be “early adopters” of EVs.
. In this scenario, we investigate the influence of age on EV adoption. Here, we assume that the likelihood of buying an EV is greater for young or middle-aged groups [119
]. We posit that the higher the age is, the higher the importance of Performance and Effort Expectancy will be. However, a higher age will decrease the importance of Hedonic Expectancy and Facilitating Conditions because the (average) distance traveled per day will decrease. Therefore, the importance of public charging points and facilitating conditions will decrease [116
]. We also assume that there is no relationship between the purchaser’s age and price sensitivity. As such, we assume that older and younger people attach the same weight, as in the base run, to the price of EV. In scenario 2-2, we assume that potential adopters aged between 18 and 49 will be the first interested in buying an EV. Note that we keep the demographical distribution fixed over the course of a simulation (i.e., nobody “ages” in our model).
. In this scenario, we use the urbanization level as an indication of population density, and the average distance traveled per day as the key indicator of the mobility pattern. That is, we assume that the higher the urbanization level, the higher the density. As a result, the average daily distance traveled per day decreases. As such, to estimate the weights of the UTAUT for different groups, scenario 2-3 assumes that by increasing density, the importance of Performance and Effort Expectancy, Hedonic Expectancy, and Facilitating Conditions Expectancy decreases—because drivers spend less time driving. However, the importance of EV Value Price in determining EV adoption increases. Finally, we assume that the early adopters of EVs are especially people living in high-density cities [121