A Conceptual System Dynamics Model of Fuel Cell Electric Vehicle Adoption in Indonesia

The transition to a sustainable transportation system requires a comprehensive understanding of technology, policy, and market behaviour dynamics. Fuel Cell Electric Vehicle (FCEV) has emerged as one of the new potential solutions with its characteristics of low emissions, high efficiency, and short refuelling time. However, FCEV adoption in the world, moreover in Indonesia, is still very limited due to structural challenges such as high costs, lack of hydrogen infrastructure, and low public awareness. This study explores the system structure that influences FCEV adoption in Indonesia using the System Dynamics approach. This study focuses on developing system diagrams and causal loop diagrams (CLD) as conceptual representations of complex systems. The modelling process is carried out through actor analysis, identification of key variables, and compilation of cause-and-effect relationships that form feedback loops. The modelling results show a significant influence of fiscal policy, infrastructure readiness, and consumer perceptions on the dynamics of FCEV adoption. This model provides a strong conceptual foundation to support the formulation of system-based policies in accelerating FCEV penetration in Indonesia, as well as being the initial stage for the development of dynamic models based on policy scenarios. 

Introduction

Indonesia is among the countries most vulnerable to the impacts of climate change, mainly due to its geographical location in the tropics and its status as the world’s largest archipelago. The increasing concentration of greenhouse gases (GHG) in the atmosphere has caused global temperatures to rise significantly over the past century (International Energy Agency (IEA), 2021). These environmental changes are directly impacting Indonesia through rising sea levels, shifting weather patterns, and more frequent natural disasters such as floods and droughts. One of the major contributors to GHG emissions is the transportation sector, which accounted for approximately 27% of the national total in 2019 (Kementerian Energi dan Sumber Daya Mineral Republik Indonesia, 2020), primarily due to the rapid growth of fossil-fuel-based motor vehicles, which numbered over 140 million in 2022 (Badan Pusat Statistik, 2022).

This situation triggers the urgent need for Indonesia to implement more sustainable transportation solutions. In line with the country’s commitment to the Paris Agreement and its target to reduce GHG emissions by 29% by 2030 (Prepres. No. 22, 2017), the adoption of low-emission vehicles such as Electric Vehicles (EVs)—including Battery Electric Vehicles (BEVs) and Fuel Cell Electric Vehicles (FCEVs)—has become a strategic priority (Prepres. No. 55, 2019). While BEVs have gained significant traction in recent years, FCEVs are a promising alternative, especially for long-distance transport and commercial use, due to their longer driving range and much faster refuelling time (Canoet al., 2018; Manoharanet al., 2019).

FCEVs operate using hydrogen fuel cells, which generate electricity through an electrochemical reaction that only produces water vapor and heat as byproducts (Sazali, 2020). This makes them an environmentally friendly alternative to internal combustion engine vehicles (ICEVs), emitting considerable amounts of CO₂ and other pollutants (Ajanovic & Haas, 2021). Despite these advantages, FCEV adoption globally, particularly in Indonesia, remains limited due to several challenges. These include high vehicle and infrastructure costs, limited hydrogen refuelling stations, and the absence of strong government incentives specifically for FCEVs (Shahzad & Iqbal Cheema, 2024).

While the Indonesian government has introduced policies such as Presidential Regulation No. 55/2019 to promote electric vehicles, most focus has been on BEVs. Despite being included in the broader low-carbon emission vehicle (LCEV) strategy, FCEVs have not yet received the same level of support. Furthermore, public awareness and consumer readiness for hydrogen-based vehicles remain low, and infrastructure development is still very early. As of 2024, there is only one operational hydrogen refuelling station in the country.

Despite that, Indonesia has significant potential to support hydrogen-based transportation due to its abundant renewable energy resources, which could be used to produce green hydrogen (Kementerian Energi dan Sumber Daya Mineral Republik Indonesia, 2017). The combination of environmental need, national policy goals, and untapped natural resources presents a strong rationale for exploring how FCEVs could be more widely adopted in Indonesia (Aditiya & Aziz, 2021).

Several international studies have applied system dynamics as a modelling framework to explore the dynamics of clean energy adoption (Gaoet al., 2024; Liet al., 2022; Yanget al., 2015). However, most of these studies focus on BEVs (Setiawanet al., 2022) or broader policy issues without deeply understanding the unique challenges and opportunities surrounding FCEVs in Indonesia. Furthermore, few models explicitly consider the complexity of multi-actor interactions, feedback loops, and behavioural dynamics in the FCEV ecosystem. A clear research gap exists in understanding how policy, infrastructure, market readiness, and consumer perception interact systemically in Indonesia's FCEV adoption context.

This study aims to fill that gap using a System Dynamics approach to explore the adoption scenario of FCEVs in Indonesia. Specifically, the research develops a conceptual system model using actor analysis, system diagramming, and causal loop diagrams (CLDs). These tools identify key variables and feedback loops influencing FCEV adoption and support the design of more robust and sustainable policy pathways. The findings will provide theoretical insights and practical policy recommendations to support Indonesia’s transition toward low-emission transportation.

Literature Review

Fuel Cell Electric Vehicle (FCEV) Technology

Fuel Cell Electric Vehicles (FCEVs) are zero-emission vehicles that use hydrogen as the primary fuel. Hydrogen is stored in a high-pressure tank and converted into electricity through an electrochemical reaction with oxygen inside a fuel cell stack. The electricity produced powers an electric motor, while the only byproduct of the reaction is water vapor (Manoharanet al., 2019). This technology offers a clean and sustainable alternative to conventional internal combustion engine vehicles (ICEVs) and hybrid electric vehicles (HEVs) and shares the emission-free advantage with battery electric vehicles (BEVs).

An FCEV typically consists of the following main components: (1) a hydrogen tank that stores hydrogen under high pressure, (2) a fuel cell stack where the chemical reaction takes place to generate electricity, (3) an electric motor that converts the electrical energy into mechanical motion, (4) a battery that stores additional energy from regenerative braking and supports the power demand during acceleration, (5) a thermal management system that ensures optimal operating temperature of the fuel cell, and (6) a power control unit that manages the distribution of electricity from both the fuel cell and the battery to the motor (Manoharanet al., 2019).

FCEVs offer significant advantages over ICEVs and HEVs regarding energy efficiency and environmental impact. ICEVs convert only about 20%–30% of fuel energy into motion, while HEVs improve this to 30%–40% using electric and internal combustion power. BEVs provide the highest efficiency at 70%–90%, followed by FCEVs at approximately 50%–60% (Canoet al., 2018). Despite not matching BEVs in efficiency, FCEVs offer the flexibility to support a broader hydrogen-based energy ecosystem that spans transportation, power generation, and industrial applications.

In terms of performance, FCEVs provide several functional advantages. Vehicles such as the Toyota Mirai and Hyundai Nexo can travel over 600 km on a single refuelling, exceeding most BEVs, whose ranges generally fall between 300 km and 500 km (Toyota, 2021) (Hyundai, 2022). Furthermore, FCEVs can be refuelled in 3–5 minutes, offering a user experience similar to conventional refuelling. BEV charging times can range from 30 minutes to several hours, depending on the charging infrastructure and battery capacity (BloombergNEF, 2020).

From a sustainability perspective, FCEVs and BEVs are both emission-free, but BEVs still rely on electricity grids that may use fossil fuel sources. In contrast, FCEVs powered by green hydrogen—produced from renewable energy—can be considered among the cleanest technologies available (Olabiet al., 2021). This underscores the long-term potential of FCEVs in supporting global carbon neutrality goals.

Globally, several automakers have committed to FCEV technology. Toyota pioneered mass production with the launch of the Toyota Mirai in 2014, offering a range of 500 km and quick refuelling. By 2021, Toyota had sold more than 10,000 units of the Mirai across markets like Japan, the United States, and Europe (Toyota, 2021). Hyundai introduced the Nexo in 2018, capable of up to 666 km of range and aiming for 110,000 annual sales by 2025 (Hyundai, 2022). Honda also developed the Clarity Fuel Cell in 2016, with a 589 km range, although production ceased in 2021 to focus on next-generation hydrogen technologies. BMW and Toyota have also announced a collaboration to mass-produce FCEVs by 2028, reflecting a multi-pathway approach to decarbonization (International Energy Agency (IEA), 2023).

Strong government policies in several developed countries support these developments. The European Union has outlined a hydrogen strategy targeting at least 6 GW of green hydrogen capacity by 2024 and 40 GW by 2030 (European Commission, 2020). Germany has allocated €9 billion to hydrogen technology development, aiming for 5000 hydrogen refuelling stations and 5 million hydrogen vehicles by 2030 (International Energy Agency (IEA), 2023). California leads the U.S. in FCEV adoption through the California Fuel Cell Partnership. At the same time, Japan targets 200,000 FCEVs and 320 hydrogen stations by 2025, with subsidies and strategic events such as the Tokyo 2020 Olympics promoting hydrogen use (Toyota, 2021).

In Indonesia, FCEV adoption faces considerable challenges. There is only one commercial hydrogen refuelling station in Senayan, Jakarta. But still, the country has abundant renewable resources such as solar, wind, hydro, biomass, and geothermal energy that can be harnessed for green hydrogen production. However, high infrastructure costs remain a significant barrier. According to the Indonesian Hydrogen Roadmap (2023) published by the Ministry of Energy and Mineral Resources (ESDM), the initial focus of hydrogen development will be on industrial and power sectors, rather than transportation. This implies that unless supported by large-scale investment or public-private initiatives, FCEVs are unlikely to become a national priority soon.

The Indonesian government has prioritized BEVs through various programs and incentives to support the transition to clean energy in transportation (Perpres No. 55, 2019; Perpres No. 74, 2021). In contrast, FCEVs still lack specific regulatory frameworks and financial support. Overcoming these barriers requires more substantial government intervention, increased public awareness, and collaborative efforts from industry and academia. Despite these challenges, FCEVs hold long-term potential in long-range transport and heavy-duty applications where BEVs may face limitations (Kementerian Energi dan Sumber Daya Mineral (ESDM), 2023).

System Dynamics

System Dynamics (SD) is a methodology that allows researchers to understand how a system behaves over time and to analyse the underlying causes of that behaviour (Pruyt, 2013). It focuses on exploring system structures and feedback loops, rather than only developing simulation models or static diagrams. Initially introduced by Forrester (1985), system dynamics offers a process-oriented approach that enables the modelling of complex, dynamic systems with interrelated components (Featherston & Doolan, 2013).

This study uses system dynamics to explore the interaction between various stakeholders and key variables that influence the adoption of Fuel Cell Electric Vehicles (FCEVs) in Indonesia. The purpose is to model the system behaviour by identifying variables, feedback loops, and system boundaries, which later form the basis for conceptual diagrams such as system diagrams and causal loop diagrams (CLD) (Pruyt, 2013).

Every system consists of elements and the relationships between them. A model is a simplified representation of the system, built from variables and their interconnections. In system dynamics, variables are categorized into five types: stocks, flows, auxiliaries, parameters, and constants. Stocks accumulate over time and are influenced by the difference between inflow and outflow. Flows are the rates of change that affect the stock levels. Auxiliaries, parameters, and constants provide additional support for modelling logic and mathematical expressions (Pruyt, 2013).

The structure of a system—including the types of variables, their relationships, feedback loops, and underlying equations—determines the model's behaviour. Therefore, any fundamental behaviour change must be derived from a change in system structure, such as feedback mechanisms or parameter values. Since models represent real-world systems, model structure modifications should correspond to real-life interventions (Pruyt, 2013).

One of the central features of system dynamics is the concept of feedback loops, which occur when a series of cause-and-effect relationships eventually return to affect the original variable. There are two feedback loops: (1) Positive (Reinforcing) Feedback Loops: These loops amplify change. This creates a cumulative effect, potentially resulting in exponential growth or decline. (2) Negative (Balancing) Feedback Loops: These loops counteract change. An increase in a variable lead to actions that eventually decrease that variable, restoring equilibrium. They are stabilizing forces within the system. When multiple feedback loops are interconnected, they form a feedback system that determines the system’s long-term behaviour, usually called a Causal Loop Diagram (CLD) (Pruyt, 2013).

CLDs are used to visualize the qualitative structure of the system. They help identify feedback loops, define the polarity (positive or negative) of relationships, and show the direction of causality between variables. A positive causal relationship occurs when an increase in variable A causes an increase in variable B, or a decrease in A causes a decrease in B. Conversely, a negative causal relationship occurs when an increase in A causes a decrease in B, or vice versa. The polarity does not imply value judgments (e.g., good or bad), but rather the direction of influence. CLDs play a central role in mapping how system variables interact. They are the foundation of conceptual modelling before moving to quantitative modelling using Stock Flow Diagrams (Pruyt, 2013). An example of CLD used in this research is illustrated in Fig. 2, which is based on stakeholder analysis and policy instruments affecting FCEV adoption.

Before developing a CLD, a system diagram is created to provide a high-level representation of the system's operation. This diagram includes: (1) Actor Analysis: Identifies stakeholders such as government agencies, automakers, consumers, hydrogen suppliers, and infrastructure providers, (2) External Factors: Includes external drivers such as global energy trends, market readiness, and international commitments, (3) Policy Instruments: Maps regulatory tools and incentives that influence the system. These components are integrated into a system diagram that visualizes the significant elements affecting FCEV adoption. This serves as a bridge between stakeholder mapping and the development of causal loops, enabling a deeper understanding of system behaviour in a structured manner.

Bass Model and New Technology Adoption

Adopting new technology is one of the significant challenges in industrial development, especially in cases where the technology is still in its early stages, such as Fuel Cell Electric Vehicles (FCEVs). To understand how a new technology diffuses through a market, one of the most widely used models is the Bass Diffusion Model, introduced by Frank M. Bass in 1969. This model has been commonly applied in analysing innovation diffusion, forecasting the demand for new products, and designing marketing strategies for technological innovations (Bass, 1969).

The Bass model is built upon two primary factors that influence the adoption of innovation:

• Coefficient of Innovation ($p$): Reflects the portion of adopters who are influenced by external factors such as advertisements, promotions, and governmental policies. These are considered early adopters who adopt the technology regardless of whether others have done so.

• Coefficient of Imitation ($q$): Represents the adoption influenced by social factors such as word of mouth, peer influence, or observable use by others. These are later adopters who are influenced by the number of previous adopters.

The fundamental equation of the Bass model is as follows:

$$\begin{array}{r}\frac{dN\left(t\right)}{dt}=p(M-N\left(t\right))+q\frac{N\left(t\right)}{M}(M-N\left(t\right))\end{array}$$

where $N\left(t\right)$is the cumulative number of adopters at time, $M$ is the total market size, p and q are the innovation and imitation coefficients, respectively.

This equation illustrates that adoption at any point in time depends on both external marketing efforts and internal social influences. Early adopters respond to direct incentives or information campaigns, while later adopters are more responsive to the growing number of existing users.

The Bass model has been applied to various industries such as telecommunications, renewable energy, electric vehicles, and software. It provides insight into the likely trajectory of product adoption and helps identify bottlenecks in diffusion. Studies by Meade and Islam (2006) applied the Bass model using historical data to project adoption curves, allowing companies to refine their market penetration strategies. These applications suggest that knowing the relative values of p and q helps decision-makers to design targeted marketing interventions.

In the context of electric vehicles, Sonnenscheinet al. (2019) used the Bass model to predict the shift from internal combustion engine vehicles to electric vehicles. The findings indicated that a higher value of p accelerates early adoption, especially when coupled with government incentives and strong marketing efforts. Conversely, a higher q value reflects strong peer influence and faster uptake once the technology gains a foothold in the market.

Furthermore, the model has been applied to forecast the diffusion of renewable energy technologies such as solar panels and battery storage. Haoet al. (2021) showed that the coefficient of innovation (pp) becomes more dominant in markets with high government incentives. Meanwhile, the imitation effect (q) is more significant in markets with strong social networks. These dynamics were also demonstrated in policy analysis studies on electric vehicle adoption in Indonesia by Setiawanet al. (2022), reinforcing the model’s usefulness in forecasting and policy formulation.

Given the nature of FCEVs as a new and underdeveloped technology in Indonesia, the Bass model can serve as a strategic tool to understand how adoption may evolve under different scenarios. It suggests that early market penetration will likely rely heavily on external drivers such as government incentives, infrastructure readiness, and promotion campaigns (linked to p). Over time, adoption can accelerate through network effects and user experiences, contributing to imitation-driven adoption (linked to q).

Although the model is valid, it also has limitations. It does not explicitly consider nonlinear effects, policy disruptions, or competition among multiple technologies. Despite these limitations, the Bass model remains a fundamental framework for understanding and estimating the diffusion process of new technologies, including hydrogen-based mobility solutions.

In the Indonesian context, more empirical data is required to calibrate p and q specifically for FCEVs. Moreover, strategic efforts such as fiscal incentives, awareness campaigns, and demonstration projects can be aligned with Bass model insights to enhance market readiness and reduce uncertainty in FCEV adoption (Ziegler & Abdelkafi, 2023).

Methodology

This study applies a qualitative System Dynamics approach to explore the adoption of Fuel Cell Electric Vehicles (FCEVs) in Indonesia by developing a conceptual model that captures the dynamic interactions among key variables, actors, and policies. The modelling process consists of three primary stages: actor analysis, system diagram development, and CLD construction. The actor analysis (Table I) aims to identify stakeholders involved in the FCEV ecosystem and assess their respective roles, interests, resources, and levels of influence. These include government agencies, automotive manufacturers, hydrogen infrastructure providers, research institutions, and consumers. The relationships and positions of these actors are synthesized in a stakeholder matrix, which helps map power dynamics and possible policy support or resistance (Setiawanet al., 2022).

Table I. Actor Analysis

Actor	Role/Responsibility	Problem perception	Goal	Interest
Government	Policymaker, provider of fiscal and regulatory incentives	Low adoption of eco-friendly vehicle technologies	Promote the adoption of clean vehicle technologies	Reduce GHG emissions; stimulate economic growth
Automotive industry	Developer and manufacturer of vehicles	Difficulty in transitioning to new technologies	Innovate and respond to market demand	Financial profit; alignment with global market trends
Consumer (ICEV/HEV User)	Buyer of fossil-fuel-powered vehicles	Eco-friendly vehicles are too expensive	Access to affordable and practical vehicles	Lower purchase cost, fuel availability
Consumer (Current EV User - BEV)	Users of electric vehicles	Limited range, long charging time and lack of charging infrastructure	Improve vehicle efficiency and convenience	Lower operational costs; extended driving range
Hydrogen station provider	Developer of hydrogen refuelling infrastructure	High cost of building and maintaining hydrogen stations	Expand hydrogen infrastructure network	Financial return from hydrogen infrastructure development
Hydrogen Producer	Producer and distributor of hydrogen fuel	High cost of hydrogen production	Increase efficient hydrogen production	Financial gains; utilization of natural energy resources

The insights from actor analysis are then integrated into a system diagram (Fig. 1) that outlines the broader structure influencing FCEV adoption. The diagram includes problem owners, policy goals, instruments, and external factors, providing a structured overview of how various system elements are interconnected. This system diagram acts as a bridge toward more detailed dynamic modelling and ensures alignment between stakeholder interactions and policy mechanisms.

Fig. 1. System diagram of FCEV technology adoption in Indonesia.

Building on this structure, a causal loop diagram (Fig. 2) is developed to identify the feedback relationships and interdependencies within the system. The CLD highlights reinforcing and balancing feedback loops, showing how public awareness, infrastructure readiness, vehicle pricing, and policy incentives interact over time. Reinforcing loops reflect growth dynamics, while balancing loops capture the resistance or limiting behaviours that may slow adoption. This methodology provides a comprehensive system-based framework to understand FCEV adoption's underlying mechanisms and identify strategic leverage points for effective policy design.

Fig. 2. Causal loop diagram of FCEV technology adoption

Results and Discussion

The development of a conceptual model using a System Dynamics approach offers a structured lens to explore the complexities surrounding the adoption of Fuel Cell Electric Vehicles (FCEVs) in Indonesia. The results are presented through a system diagram (Fig. 1) and a causal loop diagram (Fig. 2), which are analytical tools to visualize how various components interact over time. These diagrams are instrumental in understanding not only the static structure of the adoption ecosystem but also its dynamic behaviour, particularly the reinforcing and balancing feedback mechanisms that influence policy outcomes and market responses.

The system diagram (Fig. 1) provides a comprehensive view of the FCEV adoption ecosystem, placing the government at the centre as the problem owner. The government’s primary objective is to reduce greenhouse gas emissions from the transportation sector, and this is pursued through a range of policy instruments including fiscal incentives, regulatory interventions, public awareness programs, and infrastructure development strategies. The diagram illustrates the relationships between external factors—such as vehicle and hydrogen pricing, refuelling infrastructure readiness, and technological performance—and internal system variables such as policy intervention, consumer behaviour, and industrial readiness. It also reflects the multiple stakeholder interactions that characterize the Indonesian context, involving ministries, automotive manufacturers, hydrogen suppliers, and end users. As a conceptual map, the system diagram defines the boundary and scope of the problem, while serving as a foundation for deeper behavioural analysis using feedback modelling.

Building upon the system structure, a causal loop diagram (Fig. 2) is constructed to capture the feedback loops that drive or constrain the dynamics of FCEV adoption. The CLD reveals reinforcing and balancing feedback structures, which collectively shape the country's technology diffusion trajectory.

Among the reinforcing loops, the Social Influence Loop (R1) describes how increased FCEV usage enhances public visibility and awareness, encouraging further adoption. This effect is amplified by word-of-mouth, testimonials, and public promotion, contributing to a cycle of accelerating user acceptance. The Infrastructure Investment Loop (R2) explains that demand for hydrogen refuelling stations increases as more users adopt FCEVs. This demand incentivizes further infrastructure investment, improving accessibility and convenience, which reinforces adoption growth. The Emission Regulation Loop (R3) focuses on the role of government intervention through emission-based taxation and regulation of ICEVs and HEVs. Consumers are nudged toward cleaner alternatives such as FCEVs as these conventional vehicles become less economically attractive. Two additional loops, the HEV Push Loop (R4) and ICEV Push Loop (R5), represent policy mechanisms that increase the operational costs or restrict the use of HEVs and ICEVs, further enhancing the market position of FCEVs through relative advantage.

In contrast, a balancing loop—the BEV Competition Loop (B1)—introduces resistance. It reflects the potential for Battery Electric Vehicles (BEVs), which currently receive more favourable policies and infrastructure support, to become more attractive to consumers than FCEVs. This competition can suppress demand growth for FCEVs, especially without sufficient hydrogen infrastructure or differentiated policy support.

The interplay of these feedback loops demonstrates that the success of FCEV adoption in Indonesia depends not solely on technological readiness or isolated policy incentives but on the synchronization of multiple efforts. These include policy harmonization across sectors, consistent infrastructure development, public education, and stakeholder alignment. In the early phases of adoption, balancing forces such as infrastructure limitations, cost barriers, and inter-technology competition may dominate. However, once targeted interventions strengthen reinforcing loops like R1 (social visibility) and R2 (infrastructure investment), the system may transition toward self-reinforcing growth.

In summary, the causal structure shown in Fig. 2 reveals several potential leverage points for effective policy design. These include coordinated investment in hydrogen stations, simultaneous promotion of public knowledge, and the gradual phasing out of conventional vehicle advantages. By understanding these dynamics, policymakers can better craft strategies to transition Indonesia’s transportation sector toward cleaner, hydrogen-based mobility solutions.

Conclusion

This study presents a conceptual model using a System Dynamics approach to explore the adoption pathway of FCEVs in Indonesia. The model was developed through structured actor analysis, a system diagram that outlines the ecosystem of stakeholders and policy instruments, and a causal loop diagram (CLD) that reveals the feedback mechanisms shaping system behaviour. Together, these tools comprehensively understand how different variables interact and influence FCEV adoption over time.

The system diagram positions the government as the central problem owner, aiming to reduce CO₂ emissions from the transport sector through strategic intervention. It illustrates how various components—pricing mechanisms, infrastructure availability, consumer awareness, and institutional roles—are interlinked. This structure supports identifying enablers and bottlenecks in the system, which are further explored in the feedback loops presented in the CLD.

The CLD highlights five reinforcing loops (R1–R5) and one balancing loop (B1). Among the reinforcing dynamics, social influence (R1) and infrastructure development (R2) emerge as critical loops that, when activated together, can create a self-reinforcing diffusion process. Increasing public awareness and visibility of FCEVs boosts social acceptance, while the concurrent expansion of hydrogen refuelling stations strengthens accessibility, enhancing the appeal of adoption. The reinforcing impact of emission regulations and fiscal disincentives on HEVs and ICEVs (R3–R5) further shifts the market structure toward FCEVs, showing that policy instruments can play a central role in accelerating the transition.

However, a significant balancing loop (B1) illustrates the competitive tension from Battery Electric Vehicles (BEVs), which may inhibit FCEV growth if policy and infrastructure development remain more favourable to BEVs. This loop reflects a realistic challenge in Indonesia’s current EV landscape, where BEV adoption is already actively supported.

Insights from the system model suggest that a successful transition toward FCEVs requires synchronized and multi-dimensional efforts. Infrastructure readiness must be advanced in parallel with adoption campaigns to prevent public disillusionment. Financial incentives and regulatory instruments should be carefully structured to balance the attractiveness of different low-emission technologies, rather than unintentionally crowding out one pathway. Furthermore, inter-ministerial coordination and consistent stakeholder engagement are essential to ensure that efforts across sectors are aligned and mutually reinforcing.

Overall, this conceptual model demonstrates the importance of viewing FCEV adoption not as an isolated technological shift but as a systemic transformation. It reveals key leverage points and interdependencies that can inform future policy design, investment strategies, and simulation-based modelling. The insights generated here offer a foundation for further empirical validation and scenario exploration, which can guide Indonesia’s long-term transition toward a sustainable, hydrogen-based mobility future.

Conflict of Interest

Conflict of Interest: The authors declare that they do not have any conflict of interest.