Evaluation of Factors for Adoption of Alternative-Fuel-Based Vehicles

Abstract

The transportation industry significantly contributes to greenhouse gas (GHG) emissions. Federal and provincial governments have implemented strategies to decrease dependence on gasoline and diesel fuels. This encompasses promoting the adoption of electric cars (EVs) and biofuel alternatives, investing in renewable energy sources, and enhancing public transit systems. There is a growing focus on enhancing infrastructure to facilitate active transportation modes like cycling and walking, which provide the combined advantages of decreasing emissions and advancing public health. In this paper, we propose a System Dynamics simulation model for evaluating factors for the adoption of alternative-fuel vehicles such as EVs, biofuel vehicles, bus, bikes, and hydrogen vehicles. Five factors—namely, customer awareness, government initiatives, cost of vehicles, cost of fuels, and infrastructure developments—to increase the adoption of alternative-fuel vehicles are studied. Two scenarios are modeled: A baseline scenario that follows the existing trends in transportation (namely the use of gasoline vehicles), Scenario 1, which prioritizes greater adoption of electric vehicles (EVs) and biofuel-powered vehicles, and Scenario 2, which prioritizes hydrogen fuel-based vehicles and improves biking culture. The simulation findings show that all scenarios achieve reductions in GHG emissions compared to the baseline, with Scenario 2 showing the lowest emissions. The proposed work is useful for transport decision makers and municipal administrators in devising policies for reducing overall GHG emissions, and this also aligns with Canada’s net zero goals.

1. Introduction

Climate change causes the continuous transition of weather patterns from tropical regions to polar ones. This is a global hazard that has already begun to impact a diverse array of industries. In addition, the gradual decline in ecological systems has resulted in the loss of biological diversity, as the survival and existence of a significant number of species have been in peril due to the change in climate [1]. This is due to the fact that temperature ranges that were considered to be desirable have fluctuated. An overwhelming body of scientific evidence suggests that human activities—in particular, the combustion of fossil fuels such as coal, oil, and gas—are a contributor to the levels of atmospheric carbon dioxide and other greenhouse gases in the environment. This, in turn, contributes to the increase in the average temperature of the Earth’s surface. As a consequence of global warming, severe climatic changes are anticipated to occur, and many of these changes are expected to have a negative impact on human communities [2]. Anthropogenic climate change is projected to go on for a significant number of centuries, according to projections. When it involves climate change, our accomplishments are putting us in danger, and the consequences that could emerge from this could be disastrous [3]. The usage of fossil fuels, which include coal, oil, and natural gas, is widespread, having historically been an essential element in the growth of both the economy and the industrial sector. As a consequence of this dependence, however, a variety of complex issues have arisen, including pollution, adverse impacts on health, and weakening of the economy.

There is a limited amount of fossil fuels readily available on our planet. Additionally, conventional energy sources are considered to be contributors to the release of greenhouse gases. Alternative fuels and energy sources will need to be developed before the fossil fuels are fully depleted, which is inevitable. A significant amount of research is being conducted all over the world in order to find alternative fuels that have the potential to meet our energy requirements in the present as well as in the future without contributing to any additional climate change in the planet [4]. The transportation industry significantly contributes to greenhouse gas (GHG) emissions in Canada. Environment and Climate Change Canada reports that transportation accounts for around 22% of the country’s total emissions, equating to around 150–170 million equivalent tons of ${\mathrm{CO}}_2$ annually. The role of this sector is primarily influenced by road transportation, encompassing passenger vehicles, light-duty trucks, and heavy-duty trucks. These cars release carbon dioxide and other pollutants during combustion, constituting a significant component of the nation’s total emissions.

Through the implementation of the Clean Fuel Regulations, which were approved by Canada in June 2022, fuel importers and manufacturers are obligated to gradually lower the amounts of carbon emissions that are produced by gasoline and diesel. By encouraging the development of fuels that are less harmful to the environment and the use of technology that is more environmentally friendly, this policy helps to mitigate the negative effects that transportation fuels have on the environment [5].

The federal government established the Clean Fuels Fund with the goal of encouraging the production and distribution of fuels that are derived from renewable sources. The government has committed nearly USD 1.8 billion to the expansion of the biofuels industry, beginning in January 2025 [6]. Established in 2016, the Electric Vehicle and Alternative Fuel Infrastructure Deployment Initiative has the objective of building a network of hydrogen refueling stations in key cities, natural gas refueling stations along important freight passageways, and fast chargers for electric vehicles from coast to coast. This infrastructure development is absolutely necessary in order to facilitate the widespread adoption of alternative-fuel vehicles [7].

In an effort to reduce emissions in the transportation and logistics industry, the Green Freight Program provides incentives that are not repayable for the purposes of recharging vehicles and purchasing environmentally friendly alternative-fuel vehicles. This program will help to achieve a reduction in emissions on a global scale by promoting the utilization of environmentally friendly technology in the transportation of goods [8].

In order to incentivize emission reductions across a variety of businesses, Canada has implemented a carbon price system by which carbon is priced. The federal carbon tax has been increasing at a rate of USD 10 per ton since 2018, and it is expected to reach USD 50 per ton in 2022. This has been the case since the tax was first implemented. For both customers and businesses, this policy encourages the use of technology and energy sources that are less damaging to the environment [9].

In this paper, we investigate factors that play a critical role in the adoption of alternative-fuel vehicles. A System Dynamics simulation approach is used to model the relationship between various factors. The rest of the paper is organized as follows: Section 2 presents a literature review, which is followed by System Dynamics modeling in Section 3. Section 4 presents the case study followed by discussion in Section 5. Finally, we conclude the paper in Section 6.

2. Literature Review

Throughout the years, a variety of methods have been developed with the goal of increasing the output, cost-effectiveness, and durability of road transportation systems [10]. Examples include the development of alternative fuels and the use of public transport, biking, carsharing, electric and hybrid vehicles, etc. In this section, we will present commonly used approaches for evaluating factors for the adoption of alternative fuels.

2.1. Multicriteria Decision Making

Borghetti et al. [11] performed evaluations of six alternative fuels for urban and interurban buses (i.e., BEV, FCEV, D, CNG, LNG, and HEV) in the Italian context, using a set of criteria regarding environmental impact, vehicle life cycle, and costs. AHP, ELECTRE I, and WSM were integrated in a single method to generate the best compromise solution among the choice of alternatives, with this decision-making process involving highly skilled senior Italian PTC managers.

Kulkarni et al. [12] presented a methodological framework for evaluating multicriteria strategies for working energy alternatives in India, including central grid and grid extension, solar home systems, and microgrids. Multiple empirical data sources were used to build the model utilizing the Analytic Hierarchy Process (AHP). MATLAB was used to evaluate and compare these multiple criteria utilizing Saaty’s AHP and Fuzzy Sets approach. The investigation included environmental and cost scenarios. Final energy generation options were ranked using the AHP and Fuzzy logic. The results show that the microgrid is the best centralized energy generation option and may solve the problem of energy shortage.

Deniz & Zincir [13] performed an environmental and economical assessment of alternative marine fuels, namely methanol, ethanol, liquefied natural gas, and hydrogen, using the AHP. The results indicated that liquefied natural gas was the most suitable alternative fuel for ships. Methanol and ethanol are not suitable alternative fuels for ships. Hydrogen can be used as ship fuel in the future.

Dan et al. [14] studied all possible factors affecting the feasibility of potential alternative vehicles through the DEMATEL (Decision-Making Trial and Evaluation Laboratory) and found that the most essential factor influencing the feasibility of AFVs is infrastructure availability, followed by charging/refueling time, environmental impacts, and vehicle upfront costs.

Masuk et al. [15] evaluated several alternative fuels to gasoline by means of analyzing their availability, engine tests, toxic element emissions, price, etc. Moreover, a selection criterion for alternative fuels was presented, with several measuring scales provided that were useful for selecting different fuels. Ethanol is said to cause 80% less CO emissions if properly blended with gasoline.

Sengupta and Cohan [16] analyzed greenhouse gases, regulated emissions, and energy use in a transportation (GREET) model for every vehicle alternative. Cost per kilometer was computed using first purchase price added to fuel and maintenance expenses. The results show that HEVs can achieve 36% lower GHG emissions. BEVs and PHEVs offer still further emission reductions. CNG sedans and trucks could only offer 11% emission reductions.

Haghshenas et al. [17] analyzed transportation sustainability in the historic city of Isfahan by using a System Dynamics model. Nine urban sustainable transportation indicators were selected from the study database, three for each main environmental, economic, and social sustainability group. The relevant metrics were combined using a composite index. The model results indicated that transit network development is the most important policy for Isfahan sustainability.

Hileman and Stratton [18] found that economic sustainability, environmental concerns, and the pursuit of energy diversity drove the interest in alternative jet fuels. It was found that only synthetic liquid alternative fuels are compatible with current aircraft fleets, thus offering immediate prospects. F-T and HEFA fuels are currently being certified for use as jet fuel when blended with conventional jet fuel up to 50-50. Yet economically and environmentally sustainable, large-scale production of biomass feedstocks remains a key challenge.

Chang et al. [19] used DEMATEL-based analytical networks to evaluate AFVs from a sustainable development standpoint. The results show that pricing, additional value, user approval, hazardous substance reduction, and dematerialization are the most important criteria. Price matters most since it may increase alternative-fuel-vehicle appeal and user approval. Furthermore, energy utilization is predicted to greatly impact alternative-fuel-vehicle sustainability. These findings have implications for automakers and governments constructing alternative-fuel vehicles.

Tsita & Pilavachi [20] examined alternative fuels for the Greek road transportation industry utilizing the Analytic Hierarchy Process (AHP). Seven distinct fuel mode alternatives—namely, internal combustion engines (ICEs) combined with petroleum and blends of first- and second-generation biofuels, fuel cells, hybrid vehicles, plug-in hybrids, and electric vehicles—were considered. An examination of alternative-fuel options was conducted based on economic and regulatory considerations. To assess each alternative fuel, one baseline scenario and ten alternative scenarios with varying weight factors for each criterion were investigated. The results revealed that internal combustion engines utilizing a blend of first- and second-generation biofuels represent the most appropriate alternative fuels for the Greek road transport industry.

2.2. Simulation

Shepherd [21] assessed the advantages and disadvantages of System Dynamics (SD) as a modeling approach in the transportation sector. Their list included 12 advantages of the approach, highlighting its suitability for strategic challenges and its potential as a valuable instrument for facilitating policy research and decision-making in the transportation sector. This methodology facilitates the seamless integration of transport models with other sectors, including health, environment, and the economy, while considering temporal delays and feedback mechanisms across many scales.

Sayyadi & Awasthi [22] proposed a System Dynamics (SD) model to analyze sustainable transportation policies. Loh & Bellam [23] investigated Singapore’s probable paths to achieving net zero goals while boosting energy security based on current and future energy plans. Using System Dynamics, current and projected policies can be examined to provide new energy solutions to increase energy security. Findings suggest that Singapore should aim for >80% renewable energy by 2050.

Neuling & Kaltschmitt [24] examined four biokerosene manufacturing techniques in northern Germany using two biomass feedstocks. Data from a comprehensive simulation was used to assess these conversion processes for technical, economic, and environmental factors. Mass and energy balances, kerosene production costs, and GHG emissions for the examined conversion routes were the main findings. The criteria results were varied, yet biomass feedstock supply was found to have a major impact. A more environmentally friendly and economically effective feedstock supply implies more promising biokerosene based on these factors.

Wen et al. [25] predicted and compared Beijing’s industrial carbon emissions in ten policy scenarios and made emission-cutting recommendations. Their System Dynamics simulation suggested that energy structure, carbon intensity, and heavy energy consumption organizations are essential elements, and numerous factors tend to affect industrial carbon emissions more. The simulation results led to low industrial carbon emissions in Beijing.

To achieve net zero greenhouse gas (GHG) emissions by 2050, Canada aims for 35% of new heavy-duty trucks sold to be zero-emission trucks by 2030 and for this percentage to rise to nearly 100% by 2040. Redick et al. [26] used System Dynamics simulation and a stock and flow model to estimate the adjustments needed to transition from diesel to battery electric and fuel cell electric vehicles in Alberta. With current BEV and FCEV vehicles and infrastructure, the "35% by 2030" aim is not feasible.

2.3. Fuzzy Set Theory

Oztaysi et al. [27] addressed the alternative-fuel technology selection challenge of a US utility company using interval-valued, intuitionistic fuzzy sets (IVIFSs). Linguistic data were used to build a multi-expert, multi-criteria, decision-making (MCDM) system. Extended-range natural gas vehicles were found to be ideal for the utility company.

Mukherjee [28] performed selection of alternative fuels for sustainable urban transportation under multi-criteria, intuitionistic fuzzy environments. Intuitionistic fuzzy set (IFS) similarity measurements are used to obtain optimal alternatives. Accurate data are hard to collect; hence, intuitionistic fuzzy data are used to communicate uncertainty. Attribute weights may be known, partially known, or unknown. Normalizing the average score functions of the intuitionistic fuzzy data for the criterion is performed to determine the unknown weights. Various challenges are handled with algorithms and numerical demonstrations.

Alonso-Villar et al. [29] assessed the technical, economic, and environmental feasibility of alternative-fuel, heavy-duty vehicles in Iceland. AFLEET and GREET were used to calculate the life cycle emissions and total cost of ownership of 10 heavy-duty powertrains. The technical viability of battery electric and hydrogen trucks was assessed in terms of battery/tank-required capacity for representative fuel efficiency values. The results indicated that battery electric trucks have the greatest environmental and economic benefits, but the limited range of current battery technology limits their use in delivery trucks. Regional trucks benefit from hydrogen and compressed natural gas paths, but their high life cycle costs and feedstock capacity limit their use. Icelandic resources may meet the 100% alternative-fuel, heavy-duty fleet energy demand.

2.4. Environmental Impact Assessment

Bicer & Dincer [30] compared life cycle assessments of electric, hybrid, and internal combustion engine vehicles fueled by hydrogen, gasoline, and other fuels. For comprehensive comparison and environmental impact assessment, vehicles using gasoline, diesel, liquefied petroleum gas, methanol, compressed natural gas, hydrogen, and ammonia; hybrid electric vehicles using gasoline and electricity; and electric-only vehicles were considered. From raw material extraction to vehicle disposal, process-based life cycle evaluation was used. Electric and plug-in hybrid electric vehicles were found to be more toxic as a result of their manufacture and maintenance. Due to their high energy density and low fuel usage, hydrogen vehicles emerged as the greenest option.

Zorpas et al. [31] investigated the effects of alternative fuels (hydrogen, natural gas, bio-ethanol, and bio-gas) by analyzing various factors that were most likely to impact the environmental, economic, and social sectors, in order to assess the viability of improving their percentage relative to conventional fuels. The results demonstrated that the economic criteria of alternative fuels have a significant advantage over traditional fuels, while the comparative assessment derived from a combination of properties with optimal values demonstrated hydrogen to be the ’superior’ alternative-fuel source.

Kim et al. [32] investigated alternative fuels like LNG, hydrogen, ammonia, methanol, ethanol, biofuel, synthetic fuel, battery-generated power, and others for maritime vessels to achieve the aim of GHG emission reduction by 50% in 2050 compared to 2008; while other alternative fuels may not emit GHGs, their capital and operational costs may be significant. They concluded that alternative maritime fuels must be assessed for environmental impact, danger to humans, and business value.

Kuimov & Plotnikov [33] calculated the efficiency of alternative fuels for minimizing economic loss and pollution. Theoretical studies validate the calculation method’s functionality. The authors compared work fuel injection equipment and diesel as a whole using adjusted mean values of effective ${\eta }_e$ and ${\eta }_i$ efficiency measures. They also offered ways to preserve the law of heat flow in diesel engine cylinders for alternate fuels and clean diesel fuel.

2.5. Life Cycle Analysis

Ashnani et al. [34] examined the environmental implications of road vehicle fuel and technology life cycles and compared cleaner solutions with traditional fuels/technologies. A thorough fuel life cycle assessment (LCA) was performed on petrol, diesel, CNG, EV, FCV, and biodiesel vehicles. The effects of vehicle technologies on climate change, air quality, and energy resource depletion were discussed. Vehicle and fuel policies were deemed successful when they reduced emissions and allowed the market to find the best option.

McKenzie & Durango-Cohen [35] provided a life cycle assessment of the costs and greenhouse gas emissions of transit buses that utilized a hybrid input–output model to compare ultralow sulfur diesel to hybrid diesel–electric, compressed natural gas, and hydrogen fuel-cell alternatives. It was found that alternative-fuel buses cut emissions and operational costs but raise life cycle expenses. Alternative fuel bus infrastructure was crucial to life cycle emission comparisons.

Bilgili [36] assessed biogas, dimethyl ether, ethanol, liquefied natural gas, liquefied petroleum gas, methanol, ammonia, and biodiesel as alternative fuels and monitored their environmental impacts during the life cycle. SimaPro V9.0.0.49 and ReCiPe 2008 V1.09 were used. Effects on health, environment, resource use, emission inventories, and societal costs were investigated. Biogas performed best in the short, medium, and long term, while methanol, ammonia, and biodiesel performed worst. Biogas was classified as the most sustainable fuel despite production, transmission, and storage issues.

2.6. Economic Feasibility Analysis

Ahmadi & Kjeang [37] performed a life cycle assessment of hydrogen fuel cell passenger vehicles (FCVs) in different Canadian provinces. Results were provided for three alternative hydrogen production methods, namely electrolysis, thermochemical water splitting, and steam methane reforming of natural gas, and these were compared against conventional gasoline vehicles as a reference case. Significant reductions in greenhouse gas and criteria air contaminant emissions were predicted from all three hydrogen production methods in all four provinces, except for electrolysis in Alberta, where most electricity is generated from fossil fuels.

Mitkidis et al. [38] showed that Greece has the resources to enable indigenous and perhaps commercial production of second-generation biofuels in order to meet the targets of the European Union’s “Renewable Energy Directive”. Following a review of the biofuels market in Greece and investigating the availability of second-generation resources, a market analysis was conducted. Economic feasibility for production was also analyzed. Given suitable technological pathways and feedstocks that minimize overall supply chain and production costs along with strong policies, second-generation biofuels could be an attractive choice.

Mathur et al. [39] examined the economic feasibility and carbon footprint of alternative fuels for sustainable agriculture. Biomass-based fuels offer promising results, but cost may be an issue.

2.7. Research Gaps

Based on the review of the above studies, the following research gaps were identified:

• Not many studies focusing on modeling net zero emission goals for transportation exist for Canada.
• Lack of System Dynamics models that analyze biofuels, hydrogen, and EV emissions together in a Canadian context.
• Lack of integration of public buses and bikes into the System Dynamics model to reduce GHG emissions.
• Not enough research on comparisons of various factors responsible to help increase the adoption of alternative-fuel-based vehicles, public buses, and bikes.
• Limited data on customer acceptance, behavior, and alternative-fuel adoption challenges.
• Insufficient economic models that analyze the long-term costs, benefits, and market dynamics of switching from gasoline to other fuels in Canada.
• The need for more dynamic models to address uncertainty and quick technology advancements.
• Not enough studies that address geographic variations and social and cultural issues, especially for different socioeconomic and indigenous groups in Canada.

3. System Dynamics Modeling

The objective of the System Dynamics model is to simulate factors that affect the adoption of alternative-fuel vehicles. Three types of alternative-fuel-based vehicles that have become popular in recent times are considered in our study: electric vehicles [40], biofuel-based vehicles [41,42], and hydrogen-fuel-based vehicles [43,44,45,46]. In addition, we included gasoline vehicles due to their extensive usage and role in curbing pollution levels. Two other modes of transportation that were considered are public buses (in our simulation, hybrid public buses due to data constraints) and bikes, which have also gained popularity in recent times and are proven to have zero emissions. Based on our review of the literature (Section 2), we identified the following five factors for our study:

1.

Customer awareness: This includes customer knowledge of the technology—for example, the various methods of charging a vehicle in the case of electric vehicles or the different blends used in biofuels, understanding the rebates and incentives that are being provided, understanding the importance of switching to alternative fuels to help reduce emissions, learning about the risks and benefits of this technology, understanding the importance of public transportation and biking in terms of their benefits on health and the environment, and knowledge on where to look for more information about these vehicles.

2.

Government initiatives: This includes government-given incentives while purchasing the vehicles, such as reduced upfront costs, reduced registration fees compared to those for traditional gasoline vehicles, more tax credits for using alternative-fuel-based vehicles, more incentives for developing infrastructure facilities, mandates for automakers, stricter emission regulations for gasoline vehicles, the creation of promotional advertisements and programs to motivate people and teach them the benefits of sustainable transportation, more funding, research, and development as concerns improving the infrastructure and technology.

3.

Fuel price: This includes the price of fuels, e.g., whether there are lower electricity prices compared to gasoline prices for EV adoption, the price differences of biofuels compared to gasoline, how the cost of blending biofuels with traditional fuels impacts the overall fuel price, and the cost of hydrogen fuel for hydrogen vehicle adoption.

4.

Cost of vehicles: This includes the costs of different vehicle types (e.g., EVs), the cost of batteries, different models of a vehicle, the cost of biofuel vehicles, the cost of engine modification expenses if needed, the cost of hydrogen-fuel-based vehicles, the cost of bikes, higher-end models, electric bikes that are more expensive, and the reduced cost of public bus fares.

5.

Infrastructure: This includes transportation infrastructure for the expansion of public charging networks in highways, in cities, and in workplaces, the installation of home charging infrastructure, upgrading existing fuel distribution networks to accommodate biofuels, improving the grid to handle increased electricity demand from EVs, ensuring the compatibility of fuel pumps and storage tanks with various biofuel blends, investing in infrastructure for the production of green hydrogen from renewable energy sources, building a network of hydrogen refueling stations strategically located along major transportation corridors, the construction of dedicated bike lanes and paths, the installation of bike parking facilities and repair stations, expanding and improving bike-sharing systems, the expansion of bus routes and the increased frequency of service, improvements to bus stops and terminals, and the increase in accessibility and convenience.

Figure 1 presents a causal loop diagram showing the relationship between various factors. Causal loop diagrams, also known as CLDs, are an effective tool for visualizing the interdependencies and feedback processes that are present inside a system. Using a CLD, one can identify the cause-and-effect links that exist between various system variables, thereby drawing attention to feedback loops that have an effect on the behavior of the system over time.

It can be seen in Figure 1 that there is one reinforcing loop (R1) and three balancing loops (B1, B2, B3). Reinforcing Loop R1 involves variables such as clean grids for EVs, charging infrastructure for EVs, and EV adoption. Balancing Loop B1 illustrates the role of government initiatives in increasing biofuel adoption to reduce greenhouse gas (GHG) emissions in sustainable energy systems. Balancing Loop B2 illustrates the role of government initiatives in increasing hydrogen fuel adoption to reduce greenhouse gas (GHG) emissions in sustainable energy systems. Balancing Loop B3 illustrates the role of government initiatives in increasing EV adoption and reducing the use of gasoline vehicles over time in order to reduce greenhouse gas (GHG) emissions in sustainable energy system initiatives.

Figure 2 presents a stock flow diagram. It can be seen that there are six vehicle types (stocks), namely, EVs, biofuel vehicles, bikes, hydrogen vehicles, public buses, and gasoline vehicles, and five factors (customer awareness, government initiatives, fuel price, cost of the vehicle, and infrastructure). The objective is to explore how different modes of transportation and vehicle technologies like EVs, biofuel vehicles, hydrogen vehicles, gasoline vehicles, public buses, and bikes interact with key factors that influence their growth, like government initiatives, customer awareness, infrastructure developments, and the prices of fuels and vehicles, and observe how greenhouse gas emissions decrease over time to meet net zero goals. The scope of this simulation is to find system boundaries. The temporal boundary of this simulation is 25 years. The aim is to perform a "what if" scenario for the years 2024–2050. The geographical boundary of this simulation is Canada.

There are numerous tools available for System Dynamics simulation. We used Insight Maker to develop the stock flow diagram. Insight Maker is an innovative, free-of-charge, web 2.0-based, multi-user, general purpose, online modeling and simulation environment. It is fully implemented in JavaScript, promotes online sharing, and is collaborative in nature [47].

The input data (

Table A1. Factors and inputs for different vehicle types.

Vehicle Type	Factor (Converter)	Input	Units
Electric Vehicles	Government_Initiatives_EV	0.1(=0), 0.7(=25)	-
	Charging_Infrastructure_EV	0(=0), 1(=25)	-
	Customer_Awareness_EV	0(=0), 0.7(=25)	-
	FuelPrice_EV	0.18(=0),	$/
		0.19(=25)	KWH
	Cost_EV	112,934(=0),	$
		53,990(=25)
Biofuel Vehicles	Government_Initiatives_BV	0(=0), 0.3(=25)	-
	Infrastructure_BV	0(=0), 0.5(=25)	-
	Customer_Awareness_BV	0(=0), 0.5(=25)	-
	FuelPrice_BV	0.2(=0), 0.99(=25)	$/L
	Cost_BV	34,407(=0),	$
		22,900(=25)
Hydrogen Vehicles	Government_Initiatives_Hydrogen_Vehicle	0(=0), 0.3(=25)	-
	Infrastructure_Hydrogen_Vehicle	0(=0), 0.7(=25)	-
	Customer_Awareness_Hydrogen_Vehicle	0.1(=0), 1(=25)	-
	FuelPrice_Hydrogen_Vehicle	28.28(=0), 5.7(=25)	$/L
	Cost_Hydrogen_Vehicle	100,000(=0),	$
		54,350(=25)
Bikes	Government_Initiatives_Bike	1(=0), 1(=25)	-
	Infrastructure_Expansion_Bike	1(=0), 1(=25)	-
	Customer_Awareness_Bike	1(=0), 1(=25)	-
	Cost_Bike	360(=0),	$
		1100(=25)
Gasoline Vehicles	Government_Initiatives_GV	1(=0), 1(=25)	-
	Customer_Awareness_GV	1(=0), 1(=25)	-
	FuelPrice_GV	1.52(=0), 3.25(=25)	$/L
	Cost_GV	15,000(=0),	$
		11,500(=25)
Public Buses	Government_Initiatives_Public_Bus	0(=0), 1(=25)	-
	Expansion_Infrastructure_Public_Bus	0(=0), 1(=25)	-
	Customer_Awareness_Public_Bus	0(=0), 0.7(=25)	-
	Fare_Public_Bus	6(=0), 4(=25)	$

) and the equations used for the stock flow diagram can be found in Appendix A.

3.1. Scenario Analysis

The objective of scenario analysis is to assess model performance under different scenarios. Three scenarios are considered—one baseline and two futuristic. The baseline scenarios considers the as-is situation and serves as the reference point for all subsequent comparisons. Two futuristic scenarios are considered: in the first scenario, EVs and biofuel vehicles are projected to grow, and in the second scenario, hydrogen vehicles continue to grow. We then compare the results.

1.

Baseline scenario (Business-as-usual): In the baseline scenario, gasoline vehicles usage remains high, and future predictions are in line with historical data [48]. The values for the baseline scenario between 2024 and 2050 were selected based on historical data trends and projections. We ran this model over a timeline of 25 years, from 2024 to 2050, to analyze how the model behaved over time and what the future for transportation in Canada would be without significantly improving the current system and integrating alternative fuels and sustainable transportation into it. We ran a simulation and found that the output for alternative-fuel-based vehicles, gasoline vehicles, public buses, and bikes change over time. And we also found the total GHG emissions by the year 2050.

2.

Scenario 1 (EVs highest by 2050): In this scenario, EVs are expected to grow due to their present degree of maturity, supporting infrastructure, and popularity.

3.

Scenario 2 (Hydrogen vehicles highest by 2050): In Scenario 2, strategic emphasis is placed on the zero tailpipe emissions of hydrogen vehicles and bikes, given their ability to provide clean energy, as well as advantages like faster refueling and longer range, which are essential. Bikes enhance this goal by providing an ultra-low-emission option meant to reduce urban congestion and pollution. Gasoline vehicles are assumed to be almost phased out due to their high greenhouse gas emissions.

3.2. Sensitivity Analysis

Sensitivity analysis was used to evaluate how changes in input parameters affect the output behavior of a model. We used the one-at-a-time (OAT) sensitivity analysis method, which modifies one parameter at a time while keeping all other parameters unchanged [49,50]

4. Case Study

In order to test the proposed System Dynamics model, a case study was conducted for Canada using open source data. Input data were collected from various websites such as that of the Government of Canada, vehicle inventory websites, blogs, polls, research articles, and fuel price websites. The temporal boundary of this model was 25 years. The simulation was run from 1999 to 2024. The outputs of the model are the stocks of EVs, biofuel vehicles, hydrogen vehicles, gasoline vehicles, public buses, bikes, and total GHG emissions. The results of the simulation were compared with historical data for validation. The error margin (in percentage) was found to be less than 1%, which is negligible (

Table 1. Results of model validation.

Name	Historical Data Output (2024)	Model Output (2024)	Error %
Gasoline Vehicles	634,000,000	637,714,633	0.59%
EVs	1,978,449	1,974,832	0.18%
Biofuel Vehicles	908,100	908,112	0.0013%
Hydrogen Vehicles	1040	1044	0.38%
Bikes	811,000	811,893	0.11%
Public Buses	84,199	84,354	0.18%
Total GHG Emissions	24,982	24,782	0.80%

). Therefore, the model created was successfully validated.

4.1. Baseline Scenario Results

Table 2. Summary of inputs and outputs in the baseline scenario.

Name	2024 (Input)	2050 (Output)
EVs	1,978,449	3,929,546
Biofuel Vehicles	908,100	1,292,017
Hydrogen Vehicles	1040	18,188
Gasoline Vehicles	634,000,000	801,995,561
Public Buses	84,199	98,408
Bikes	811,000	1,207,123
Total GHG Emissions	24,982	61,443

presents the results for the baseline scenario. We observe that the number of gasoline vehicles increases exponentially. By the end of 2050, the total number of gasoline vehicles increases to 802 million from 644 million initially in 2024. Figure 3 presents the results in terms of GHG emissions for the baseline scenario. The initial GHG emissions in 2024 is 24,982 MTCO2. The GHG emissions continue to increase over time and become 61,443 MTCO2 by the end of 25 years.

4.2. Scenario 1 Output

Table 3. Summary of inputs and outputs in Scenario 1.

Name	2024 (Input)	2050 (Output)
EVs	1,978,449	50,153,921
Biofuel Vehicles	908,100	28,972,747
Hydrogen Vehicles	1040	3,367,070
Gasoline Vehicles	634,000,000	0
Public Buses	84,199	293,938
Bikes	811,000	1,113,925
Total GHG Emissions	24,982	4783

presents the results for Scenario 1. It can be seen that gasoline cars almost become extinct at the end of 25 years. This change emphasizes how alternative fuels, mostly EVs and other developing technologies, are progressively replacing conventional internal combustion engine (ICE) vehicles. There are 1,978,449 EVs in use initially (2024), and this number increases to 50,153,921 units by 2050. This accelarating adoption rate is due to rising consumer acceptance, technology improvements, and supporting government measures. This consistent increasing trend suggests that, over the simulation period, EVs start to account for a sizable share of the total vehicle fleet. Starting at roughly 908,100 units at 2024, biofuel vehicles expand to almost 28,972,747 units. Although this increase is noteworthy, the fast spread of electric vehicles somewhat offsets it. According to the trend, biofuel technologies acquire popularity but do not rule the market as much as electric vehicles do. This significant cut matches the decline in gasoline-powered cars and the increase in greener alternative-fuel-based vehicles. This implies that, over the simulation timeline, switching from conventional ICE vehicles to EVs, biofuel, and hydrogen alternatives greatly reduces transportation-related carbon emissions (Figure 4).

4.3. Scenario 2 Output

Table 4. Summary of inputs and outputs in Scenario 2.

Name	2024 (Input)	2050 (Output)
EVs	1,978,449	5,832,253
Biofuel Vehicles	908,100	2,558,810
Hydrogen Vehicles	1040	73,869,204
Gasoline Vehicles	634,000,000	0
Public Buses	84,199	1,059,204
Bikes	811,000	5,497,933
Total GHG Emissions	24,982	2320

summarizes the change in the six different types of vehicles over the 2024–2050 simulation period. With almost 634 million units, gasoline vehicles rule the market. But their population collapses to almost zero over the 25-year simulation period by the end of 2050. This rapid drop points to a clear shift away from conventional internal combustion engines, most likely resulting from changing consumer preferences for greener alternatives, stricter policies against gasoline usage, and technical changes. Over the simulation period, bicycles range in number from 811,000 to 5,497,933 units. According to the trend, active transportation like biking remains a crucial part of a diversified and sustainable transportation system. Public bus use increases from about 84,199 to 1,059,204 units over 25 years. This growth reflects ongoing investments in transit infrastructure and policies encouraging public transportation for the purposes of lowering emissions. Along with the modest but significant increases in bike and public bus use, this decline corresponds with the sharp drop in gasoline vehicles and the concurrent rise in cleaner alternatives, including EVs, hydrogen vehicles, and biofuels. The total change emphasizes the possibility for significant emission reductions when several low-emission technologies and transportation choices become popular together (Figure 5).

4.4. Sensitivity Analysis Results

Table 5. Ranking of sensitivity analysis results by importance.

Name	Customer Awareness	Government Initiatives	Cost of Vehicle/Bike	Fuel Cost of Vehicle	Fare Public Buses	Infrastructure Developments
EVs	5	4	1	2	-	3
Biofuel Vehicles	5	4	1	2	-	3
Hydrogen Vehicles	5	4	1	2	-	3
Gasoline Vehicles	2	3	1	4	-	3
Bikes	2	4	1	-	-	3
Public Buses	4	2	-	-	1	3

presents the sensitivity analysis results of six alternative-fuel vehicle types according to different factors. The ranking of factors for each vehicle type ranges from 1 (Highest) to 5 (Lowest). Sensitivity analysis is performed with the Insightmaker tool for a timeline of 25 years. The number of runs is set to 50. It can be seen that the cost of vehicle/fare receives the highest rating for EVs, biofuel vehicles, hydrogen vehicles, public buses, bikes, and gasoline vehicles.

5. Discussion

In this section, the effects of the three scenarios in terms of the future greenhouse gas emission profile of Canada are addressed.

Influenced by historical data trends, the baseline scenario shows that the transportation sector is mostly dependent on gasoline vehicles; hence, it produces higher greenhouse gas (GHG) emissions. Although other alternative-fuel-based vehicles, including electric vehicles (EVs), public buses, bikes, biofuel vehicles, and hydrogen vehicles, also show an increase in growth, their total integration into the current transportation system is still rather low. This result emphasizes the slow progress of current infrastructure and customer awareness when government support or technological changes are not implemented drastically.

Driven by EVs’ increasing popularity and the viability of replacing gasoline with biofuels, Scenario 1 emphasizes a move towards electric and biofuel vehicles. Rapid decarbonization policies show great promise, as seen by the significant reduction in GHG emissions when gasoline vehicles almost completely disappear by 2050. Although at a slower rate than EVs and biofuels, bikes, public transportation, and hydrogen vehicles also see growing interest and acceptance. This situation underlines how much coordinated government policy measures, technological developments, and growing consumer awareness can change market dynamics.

Emphasizing hydrogen vehicles and bikes in Scenario 2 increases the shift to zero-emission technologies faster, while the strong increase in bike use highlights the attractiveness of zero pollution, low-cost mobility, and health benefits; hydrogen vehicles’ fast growth supports fuel cell invention and infrastructure expansion. EVs, biofuel vehicles, and public buses also see significant growth. Gasoline vehicles are again limited to almost zero. From personal mobility to public transportation, this scenario emphasizes the transforming power of methodologically supporting several low-emission solutions.

Sensitivity studies across several contexts show that adoption is driven mostly by cost considerations. The most important factors for EVs, biofuel vehicles, and hydrogen vehicles are their initial costs, as well as their running fuel consumption. Regarding public buses and bikes, the cost of fare and bicycle rates, respectively, are shown to have a great influence on user acceptance and thus increase their growth and demand. Although they are phased out in later scenarios, gasoline vehicles remain extremely sensitive to vehicle costs and consumer awareness. These results show how strongly taxes, subsidies, and general market incentives can affect consumer decisions.

Altogether, the findings show that cost reductions, technological developments, and strong government policy interventions can greatly reduce GHG emissions and rebuild the transportation sector as concerns sustainable modes. The great cost sensitivity of all vehicle types emphasizes the need of financial levers as accelerators for vehicle adoption, such as high taxes on gasoline-fueled cars and targeted subsidies and rebates for alternative-fuel vehicles. Ultimately, these situations highlight the need for inclusive plans combining consumer involvement, government policy frameworks, and technological advancement to accelerate the change to low-carbon transportation systems.

6. Conclusions

In this paper, we presented a System Dynamics simulation model to investigate factors affecting the adoption of alternative-fuel-based vehicles. Five factors (customer awareness, government initiatives, fuel price, cost of vehicles, and infrastructure) and six vehicle types (EVs, biofuel vehicles, bikes, hydrogen vehicles, public buses, and gasoline vehicles) were considered. Three scenarios were considered: the baseline, or as-is scenario, higher electric vehicle and biofuel vehicle use (Scenario 1), and increased hydrogen and bike usage (Scenario 2). The results from the analysis of the scenarios indicate that significantly increasing customer perception and government initiatives, improving infrastructure, and reducing the cost needed to fuel the vehicles, and also the cost to purchase alternative-fuel-based vehicles, can drive the growth of such vehicles.

From the results of the sensitivity analysis, it was found that the most influential factor was cost, i.e., cost of the vehicle, cost of fuel, cost of the bike, and cost of fare for a public bus. Emphasizing these elements will enable Canada to quickly reach its net zero target and help accelerate the acceptance of new alternative-fuel technologies.

Future work will involve the following:

• Investigating upcoming technologies such as driverless vehicles, enhanced battery storage systems, and developments in smart grids to evaluate their possible effects.
• Applying Monte Carlo simulations to explore input uncertainties. This would enable the creation of probabilistic outcomes, offering more robust insights.
• Including other vital issues like public health and social equality in addition to greenhouse gas emissions. This comprehensive approach will allow for more informed decision-making that matches environmental sustainability and helps the community.
• Integrating context-specific factors applicable to urban and rural dynamics.