Economic Viability of Electric Bus Adoption for Public Transportation in Thailand: A Monte Carlo Simulation Approach

Abstract

Background: Thailand is actively transitioning toward electric vehicle adoption as part of its commitment to reducing greenhouse gas emissions. This study investigates the economic feasibility of replacing diesel buses with electric buses in Thailand’s public transportation sector. Methods: The research employs a comprehensive methodological framework combining Total Cost of Ownership (TCO) analysis with Monte Carlo simulation to address uncertainties in long-term financial projections. The study examines four pilot routes operated by a major Thai bus company, incorporating potential carbon credit revenues through Thailand’s Voluntary Emission Reduction program. Results: The analysis reveals substantial cost advantages for electric buses across all examined routes, with TCO savings ranging from 23.07% to 38.25%. Even under conservative scenarios, all routes demonstrate positive economic benefits. The fleet-wide net savings amount to approximately 236 million THB over a 10-year period, with an additional 16.7 million THB potential carbon credit revenue. Sensitivity analysis identifies fuel costs as the most significant factor (45.2%) affecting economic outcomes. Conclusions: The transition to electric buses presents a compelling economic and environmental case for Thai public transportation operators, with significant cost savings and emission reductions. A phased implementation approach beginning with routes offering the highest percentage savings is recommended.

1. Introduction

The electric vehicle (EV) trend has recently gained significant traction due to its environmental and economic implications, driven by rapid technological advancements. EVs are increasingly recognized as a promising solution for reducing greenhouse gas emissions in the transport sector, primarily due to their high energy efficiency [1]. By significantly reducing harmful emissions, EVs address critical challenges such as greenhouse gas escalation and fossil fuel depletion, offering a sustainable alternative to conventional vehicles [2].

Despite the clear environmental benefits, the EV market faces barriers, primarily related to the incremental cost of the vehicles. Many advantages of EVs remain poorly understood and are often omitted from cost–benefit analyses [3]. However, it is crucial to note that EVs typically have lower operating and maintenance costs compared to traditional gasoline-powered vehicles, potentially making them more economically viable in the long term [4].

The growth of the EV fleet has been systematic and promising. Recent research indicates that vehicles equipped with electric propulsion are expected to account for half of the models produced after 2030 [5]. This growth is part of a broader technological revolution in the automotive industry, which includes trends such as digitization, increasing automation, and new business models [6].

Several factors are driving the expansion of the EV industry. These include advances in battery technology, supportive governmental policies, decreasing manufacturing costs, and the potential for integration with renewable energy sources [7]. EVs are increasingly seen as the primary alternative to internal combustion engines and the most promising route to decarbonizing transportation systems. However, the transition to EVs is not without challenges, particularly in terms of scaling battery production, developing charging infrastructure, and coordinating with power grids [8].

While the advantages of EVs over internal combustion engines (ICE) are evident in many cases, it is crucial to validate this assumption for specific scenarios, especially given the inherent uncertainties in long-term financial projections. Traditional deterministic Total Cost of Ownership (TCO) analyses often fail to account for these uncertainties [9]. Therefore, this study employs an enhanced methodology that integrates Monte Carlo simulation with conventional TCO analysis to provide a more robust assessment of the economic viability of EV adoption in public transportation.

This approach, successfully applied in recent EV studies, allows for a more comprehensive understanding of the range of possible outcomes and their associated probabilities [10]. However, stochastic approaches remain underexplored in the context of electric bus fleet replacement planning, despite their potential to better account for uncertainties in technological, economic, and operational parameters. Optimizing fleet replacement decisions using stochastic modeling can lead to significant reductions in both costs and emissions compared to unoptimized schedules [10]. Research has shown that an optimized mix of vehicle technologies (e.g., 79% battery electric buses and 21% diesel hybrid buses in a US case study) can yield the most cost-effective solution while meeting environmental constraints [11]. By examining the potential impact of EV adoption in the public bus industry through this probabilistic lens, this research seeks to provide valuable and reliable insights to inform decision-making processes in this sector.

2. Thailand and EV

2.1. Government Policy

Thailand has demonstrated a strong commitment to electrifying its transportation sector, as evidenced by several key policy initiatives. The adoption of the 30@30 initiative and the planned ban on new internal combustion engine vehicles by 2035 showcase the government’s dedication to this transition [12]. Further reinforcing this commitment, the government has partnered with the business sector to provide 100% EV taxis and set an ambitious target of having 1.2 million EVs on the road by 2036 [13].

The Thai government has implemented a multi-faceted approach to promoting EV adoption. This includes financial incentives, infrastructure development, and regulatory frameworks designed to create a favorable environment for EV growth. A comprehensive analysis revealed that these policy perceptions significantly influence adoption decisions among potential EV users in Thailand [14]. Additionally, the government has actively encouraged investment in EV production and importation to replace fossil fuel combustion vehicles, while also promoting the development of more efficient and longer-range electric cars [15].

An important aspect of Thailand’s EV policy is the development of charging infrastructure. Research has documented the evolution of EV charging stations in Thailand from 2015 to 2020, identifying the main stakeholders and their motivations in the charging business [16]. This research highlighted that, after 2015, the Thai government implemented technology-push policies to kick-start investment in the EV charging station business through subsidies, temporary electricity pricing, and the formation of charging consortiums.

Despite these initiatives, the growth of EVs in Thailand’s transport sector faces challenges, particularly for small and medium-sized businesses struggling with the investment required for technology-related infrastructure [17]. To address these challenges, the deployment of public fast charging infrastructure has been proposed as a potential solution. This approach could help distribute the load evenly across the day, with recommendations for an optimal strategy that considers EV adoption rates, incentive-based tariff structures, and infrastructure readiness [12]. However, barriers to EV adoption in Thailand persist, with long charging duration, limited driving range, and insufficient charging stations identified as critical factors affecting consumer acceptance [18].

2.2. Consumer Perception of EVs in Thailand

Understanding consumer perceptions is crucial for successful EV adoption in Thailand. Research has identified several factors influencing Thai car owners’ adoption of battery EVs, including performance expectancy, effort expectancy, social influence, hedonic motivation, and environmental concern. These factors positively influence purchase intention, suggesting that improvements in these areas could significantly boost EV adoption [19].

A study focusing on young-adult consumers in Thailand revealed interesting insights into EV purchasing behavior. This research found that purchase price and environmental consciousness are the most crucial factors influencing EV purchasing intentions among this demographic [20]. Surprisingly, the study indicated that governmental subsidies and insufficient charging infrastructure do not significantly impact willingness to buy EVs among young adults in Thailand, contradicting some assumptions about adoption barriers.

The question of whether Thailand would benefit from EV transition has been extensively examined. Systems thinking approaches and Causal Loop Diagrams have been used to characterize the factors underpinning the benefits of EV transition in Thailand [21]. This analysis showed that technological factors such as EV efficiency levels and their cost (economic factors) are important in determining greenhouse gas reduction benefits, along with the grid emission factor. These findings suggest that consumer perceptions of the environmental benefits of EVs are well founded but dependent on systemic improvements in multiple areas.

2.3. Environmental and Economic Impacts of EV Adoption

The increased adoption of EVs in Thailand is expected to yield significant environmental and economic benefits. Projections indicate a decrease in energy demand due to the improved fuel efficiency of EVs. While this transition is expected to reduce greenhouse gas emissions, there is potential for even greater emission control through the implementation of stricter emission standards.

A comparative study of energy consumption between various EV types and conventional internal combustion engine vehicles in Thailand revealed that battery electric vehicles (BEVs) perform well across all route characteristics, while hybrid vehicles are suitable for specific driving scenarios [22]. Notably, this research found that the well-to-wheel CO₂ emissions of BEVs were approximately 35% of those produced by internal combustion engine vehicles, highlighting the significant environmental advantage of BEVs even within Thailand’s current energy mix.

The long-term market prospects for EVs in Thailand have been projected in detail by recent research. This analysis, based on qualitative interviews with automotive sector stakeholders and confidential industry data, forecasts EV production to rise from 25,000 units in 2024 to 197,000 units in 2031 [5]. They also note the potential for price wars among manufacturers, which could accelerate adoption rates beyond current projections. This market evolution will have significant implications for energy demand and infrastructure requirements, necessitating a robust analytical approach that can account for market uncertainties.

2.4. Thailand’s Carbon Credit Market: Evolution, Challenges, and Sustainability Trends

Carbon markets in Thailand have evolved significantly since the country signed the Kyoto Protocol in 2002, transitioning from a mechanism primarily utilized by large businesses to becoming one of Thailand’s fastest growing financial markets [23]. The Thailand Voluntary Emission Reduction Program (T-VER), established in 2014, has been instrumental in encouraging participation from various sectors, particularly small project developers, resulting in 438 registered projects generating over 19.5 million tCO₂eq by June 2024 [24].

Despite this growth, Thailand’s carbon market faces significant challenges, including demand uncertainty due to its voluntary nature, supply constraints favoring large investors over smaller ones, price volatility from the developing market structure, and limited transactions occurring through direct buyer–seller negotiations [25]. Research indicates that carbon knowledge acquisition and responsiveness have a positive influences on environmental sustainability in Thailand, though the market still requires optimized data collection methods for forest carbon sequestration to create sustainable incentives for conservation [26]. The transition from the Clean Development Mechanism to more accessible voluntary programs represents Thailand’s commitment to balancing energy–environment–economy development amid limited resources and global climate change pressures.

The uncertainty surrounding future EV adoption rates and associated costs highlights the importance of using Monte Carlo simulation in this analysis. By representing key cost parameters as probability distributions instead of single-point estimates, this approach delivers a more comprehensive and accurate evaluation of the economic viability of EV adoption for Thailand’s public transportation operators. This probabilistic methodology captures the range of possible outcomes and their likelihoods, offering decision-makers more robust information than traditional deterministic calculations would provide.

3. Case Study Company

The subject of this study is one of Thailand’s largest public bus companies, established in 1964. This company has a significant presence in the country’s transportation sector, operating more than 20 concession routes that span the northern, southern, and northeastern regions of Thailand. Beyond its core transport operations, the company has diversified into several new business areas including courier services, chartered bus rentals, and various innovative business ventures [27]. The company’s extensive operations are evident in its annual statistics: it conducts over 50,000 trips, covering a distance of approximately 10,000,000 km and serving more than 2,000,000 passengers. To manage this large-scale operation, the company maintains a fleet of over 130 diesel buses and employs advanced technologies such as a Road Transport Safety Management (RTS) system and GPS tracking.

A diagnostic investigation of the company’s operations was conducted using the Industrial Logistics Performance Indicator (ILPI) framework [28]. This analysis revealed that the company faces significant challenges related to logistics costs [29]. Notably, transportation costs account for a substantial 81.9% of the company’s sales. A breakdown of these costs shows that fuel expenses constitute the largest portion, at 37%, followed by maintenance costs, at 24%. In comparison, depreciation and labor costs each represent only 10% of the total expenses.

Given this cost structure, with fuel and maintenance forming the bulk of the company’s expenses, it becomes imperative to explore cost-saving alternatives. The high proportion of fuel costs, in particular, suggests that transitioning to more fuel-efficient or alternative energy vehicles could potentially yield significant savings. In light of these findings, this study aims to investigate whether switching to EVs could provide a cost-effective solution for the company. This investigation is not only relevant for the case study company but could also offer valuable insights for the broader public transportation sector in Thailand, particularly in the context of the country’s push towards EV adoption [12,14].

Environmental Assessment and Carbon Credits

Beyond direct operational costs, an important consideration for the company is the potential environmental benefits and associated economic opportunities through carbon credit mechanisms. Carbon credits represent a growing market opportunity for companies implementing clean energy technologies [30]. The transition to electric buses offers significant environmental benefits that can be monetized through these mechanisms.

The carbon credit system provides financial incentives for organizations that reduce their greenhouse gas emissions below required levels. By switching from diesel to electric buses, transportation companies can generate carbon credits that can be sold in voluntary or compliance markets [21]. This creates an additional revenue stream that enhances the economic case for electric bus adoption beyond the direct operational cost savings.

Additional environmental benefits include improved local air quality through the elimination of tailpipe emissions, reduction in noise pollution in urban areas, and enhancement of energy security by reducing dependence on imported diesel fuel [12]. While these benefits are more challenging to quantify precisely in monetary terms, they represent significant additional value beyond the direct TCO savings identified in our financial analysis. Government policies increasingly recognize these positive externalities through various incentive structures, which could provide additional financial advantages in the future [31].

The environmental assessment component of this study considers both the direct environmental benefits (emission reductions) and their economic implications through carbon credit mechanisms. This holistic approach provides a more complete picture of the total value proposition of electric bus adoption for the case study company.

4. Pilot Routes

The selection of appropriate routes for EV implementation is crucial for maximizing cost-effectiveness and operational efficiency. Research has shown that EVs become more cost-competitive with increased usage and longer ownership periods [32]. However, the adoption of EVs in public transportation faces challenges due to their limited driving range and the need for adequate charging infrastructure [33,34]. These limitations make long or infrequent routes potentially impractical for EV operation.

To address these challenges and identify suitable routes for EV implementation, the case study company employed a multi-criteria approach. The selection criteria included range, frequency, and loading factors. This comprehensive approach ensures that the chosen routes align with the capabilities of EVs while also meeting the operational needs of the company.

4.1. Route Selection Methodology

The route selection process incorporated both technical and operational considerations. First, the company’s entire route network was analyzed, using a geographical information system (GIS) to map distances, elevations, and typical traffic conditions [35]. These data were cross-referenced with the technical specifications of currently available electric buses, particularly their range capabilities under varying load and environmental conditions.

Additionally, daily trip frequency was evaluated to ensure efficient utilization of the vehicles, as higher-frequency routes justify the capital investment in electric buses through increased operational hours [36]. The loading factor—representing the average passenger capacity utilization—was also considered, as it significantly impacts energy consumption patterns and thus the economic viability of electric operation.

In the route selection process, the criteria were prioritized based on their impact on operational feasibility and financial viability. Range requirements received the highest priority (weight factor of 0.4), as routes exceeding the electric buses’ operational range would require either mid-route charging infrastructure or route modifications. This was followed by passenger loading factors (weight factor of 0.3), as routes with high passenger density provide better financial returns through fare collection. Route frequency (weight factor of 0.2) was considered next, with frequent services offering more opportunities for scheduled charging periods. Finally, topographical factors (weight factor of 0.1) were considered, with preference given to routes with less elevation variation to optimize energy efficiency. This weighted approach allowed us to identify routes where electric buses could be deployed with minimal operational disruption while maximizing financial and environmental benefits.

4.2. Selected Routes

Based on these criteria, four pilot routes were selected for the study: ChiangMai-ChaingRai, ChiangMai-Maesai, ChiangMai-Phayao, and ChiangMai-Nan. These routes represent a diverse range of operational conditions, allowing for a thorough assessment of EV performance across different scenarios. The specific characteristics of each route are detailed in

Table 1. Pilot routes in the study.

Route No.	Route	Distance (km)	Number of Trips (Daily)
166	ChiangMai-ChaingRai	181	15
149	ChiangMai-Maesai	245	5
198	ChiangMai-Phayao	150	3
169	ChiangMai-Nan	338	10

and Figure 1.

The selected routes present varying degrees of challenges for electric bus operation. The ChiangMai-Nan route, for example, has the longest distance, at 338 km, which pushes the limits of current electric bus range capabilities. Incorporating variability analysis was particularly valuable for this route, as it allowed us to account for range differences under different weather conditions, passenger loads, and traffic scenarios [37].

In contrast, the ChiangMai-Phayao route has the shortest distance but also the lowest daily trip frequency, which affects the utilization rate and subsequently the return on investment. The ChiangMai-ChaingRai route, with its high daily frequency of 15 trips, offers the greatest potential for operational cost savings despite its moderate distance [38].

4.3. Route Topography and Energy Consumption

A critical factor in evaluating the suitability of these routes for electric buses is the topography, as elevation changes significantly impact energy consumption. The routes traversing mountainous terrain can lead to substantial differences in energy consumption between uphill and downhill segments [39].

For instance, the analysis showed that the power required for the ChiangMai-ChaingRai route in the uphill direction is approximately 15% higher than in the downhill direction. This asymmetry in energy consumption was taken into consideration through the simulation, providing a more accurate representation of real-world operational conditions [40].

The range of topographical and operational conditions across these four routes provides a robust basis for evaluating the economic viability of electric buses under diverse circumstances. This diversity enhances the generalizability of our findings to other routes and operational contexts.

5. Methodology

This research employs a mixed-method approach (see Figure 2) combining Total Cost of Ownership (TCO) analysis, Monte Carlo simulation for uncertainty assessment, carbon credit evaluation, and sensitivity analysis to comprehensively examine the economic viability of transitioning from diesel buses to electric buses for a case study bus company in Thailand.

5.1. Total Cost of Ownership

Total Cost of Ownership (TCO) is a comprehensive financial estimation method that plays a crucial role in business operations, particularly in procurement and supply chain management decision-making processes. This concept provides a holistic view of an asset’s economic impact by considering all costs associated with its entire lifecycle, including initial purchase, operational expenses, and eventual disposal costs [41]. TCO analysis encompasses both one-time and recurring costs, emphasizing the importance of evaluating long-term economic benefits rather than focusing solely on initial expenditures [42,43].

The TCO model can be mathematically expressed as (see Equation (1)):

$$\mathrm{TCO}=\sum _{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{C}}_{\mathrm{i}}+{\mathrm{O}\&\mathrm{M}}_{\mathrm{i}}+{\mathrm{R}}_{\mathrm{i}}\right) -\mathrm{SV}$$

(1)

where:

• ${\mathrm{C}}_{\mathrm{i}}$ represents the capital costs in year $\mathrm{i}$;
• ${\mathrm{O}\&\mathrm{M}}_{\mathrm{i}}$ represents operation and maintenance costs in year $\mathrm{i}$;
• ${\mathrm{R}}_{\mathrm{i}}$ represents replacement costs in year $\mathrm{i}$;
• $\mathrm{SV}$ represents the salvage value at the end of the lifecycle;
• $\mathrm{n}$ is the total lifetime of the asset in years.

The strength of TCO models lies in their ability to integrate technical performance analysis with economic considerations. This integration enables asset users to make well-informed decisions based on a comprehensive understanding of both asset performance and provider specifications [44].

TCO has been recognized as a reliable approach for estimating the long-term viability and convenience of investments. It aids in developing forward-looking, accurate, and coherent management strategies by providing a clear picture of an asset’s total financial impact over its entire lifecycle. This comprehensive view enables decision-makers to look beyond the initial price tag and consider the full range of costs associated with ownership and operation [31,32].

For this study, the TCO calculations, as presented in the results section, are projected over a 10-year service period and are computed on a per-vehicle basis. The analysis accounts for the necessary charging infrastructure, with the assumption that four charging stations would be sufficient to accommodate the selected pilot routes.

5.2. Monte Carlo Simulation

To address the uncertainty in the development of the energy market and operational costs, a Monte Carlo analysis is performed on key model parameters [45]. Monte Carlo simulation is a computational technique that uses repeated random sampling to obtain numerical results and model the probability of different outcomes [46]. The method has become a fundamental tool in risk analysis and decision-making across various fields including finance, project management, energy, and transportation planning [47]. Unlike deterministic models that produce single-point estimates, Monte Carlo simulation generates thousands of possible scenarios based on probability distributions of input variables, creating a comprehensive picture of what may happen and how likely each outcome is [48]. This simulation method enables a more robust assessment of the potential outcomes and their associated probabilities when transitioning to electric buses, accounting for the inherent uncertainties in long-term cost projections.

The triangular distributions used in our Monte Carlo simulations were selected based on their ability to effectively model uncertain parameters with limited data while incorporating expert knowledge. The parameters (minimum, most likely, and maximum values) for each distribution were derived from a combination of historical operational data from bus fleets (2018–2023), manufacturer specifications for electric bus models currently available in the Thai market, and expert estimates provided by transport engineers from the case study company. The battery degradation parameters were based on warranty specifications from the electric bus manufacturers. The electricity price fluctuation parameters were derived from historical Thai electricity price data and projected future trends from the Electricity Generating Authority of Thailand.

The Monte Carlo simulation approach offers significant advantages when assessing the economic feasibility of transitioning to electric buses. By accounting for the interdependencies between various cost factors and their cumulative impact on the TCO, this method provides decision-makers with a more realistic understanding of potential financial outcomes. Rather than producing a single deterministic result, the simulation generates probability distributions that illustrate the range of possible TCO values along with their likelihood of occurrence, enabling a more nuanced evaluation of investment risks.

This probabilistic framework proves particularly valuable for risk assessment purposes, as it quantifies the probability of achieving cost savings beyond specified thresholds or, conversely, the risk of experiencing costs that exceed expectations. Such insights are crucial for transportation companies making significant capital investment decisions with long-term implications. Additionally, the Monte Carlo method facilitates comprehensive sensitivity analysis that identifies which input parameters exert the greatest influence on TCO outcomes. This information helps prioritize further research or data collection efforts, focusing resources on the most impactful variables.

5.3. Uncertainty Analysis and Variable Assumptions

This study incorporates several uncertain variables that significantly impact the TCO calculations.

Table 2. Summary of uncertain variables in the TCO model.

Variable	Distribution Type	Parameters	Source	Temporal Trends
Fuel price	Triangular	Min: 29.04 THB/L, Most likely: 32.94 THB/L, Max: 40 THB/L	Historical Thai diesel price data (2021–2025)	Prices have been artificially stabilized by government policies and subsidies throughout 2025. However, with the planned removal of these subsidies, prices are expected to increase to approximately 40 THB/L.
Electricity price	Triangular	Min: 4.2 THB/kWh, Most likely: 4.3 THB/kWh, Max: 4.7 THB/kWh	Thailand electricity tariff data and projections	Anticipated 1% annual increases without policy intervention
Diesel maintenance costs	Triangular	Min: 1.5 THB/km, Most likely: 2.0 THB/km, Max: 3.0 THB/km	Historical data	Increasing trend for aging diesel fleet
EV maintenance	Triangular	Min: 0.75 THB/km, Most likely: 1.0 THB/km, Max: 1.5 THB/km	Manufacturer specifications	Increasing trend for aging EV fleet
Battery degradation	Triangular	Min: 10%/year, Most likely: 5%/year, Max: 0%/year	Manufacturer specifications	Increasing trend for aging EV fleet

summarizes these variables, their assumed distributions, and the justification for these assumptions based on available data and expert insights.

5.4. Carbon Credit Valuation Methodology

In addition to direct operational cost considerations, this study incorporates the potential economic benefits of carbon credits under Thailand’s Voluntary Emission Reduction (T-VER) program. The carbon credit assessment provides a more comprehensive evaluation of the economic feasibility of electric buses by accounting for their environmental benefits.

Thailand’s Voluntary Emission Reduction Program, administered by the Thailand Greenhouse Gas Management Organization (TGO), allows entities to earn carbon credits by implementing projects that reduce greenhouse gas emissions [49]. These credits can then be traded in voluntary carbon markets, providing an additional revenue stream for environmentally beneficial projects.

For electric buses, the carbon emission reduction is calculated based on the difference between the emissions of diesel buses and electric buses over the project lifetime. The calculation follows the methodology approved by the TGO for transportation projects and considers:

• The carbon intensity of the displaced diesel fuel (kgCO₂e/liter).
• The annual distance traveled by the buses (km/year).
• The fuel efficiency of diesel buses (liters/km).
• The carbon intensity of the electricity used to charge the electric buses, based on Thailand’s grid emission factor (kgCO₂e/kWh).
• The electricity consumption of the electric buses (kWh/km).

The net emission reduction is then calculated as (see Equation (2)):

$$\mathrm{ER}={\mathrm{EB}}_{\mathrm{diesel}}-{\mathrm{EB}}_{\mathrm{electric}}$$

(2)

where:

• $\mathrm{ER}$ is the emission reduction in tCO₂e;
• ${\mathrm{EB}}_{\mathrm{diesel}}$ is the baseline emissions from diesel buses in tCO₂e;
• ${\mathrm{EB}}_{\mathrm{electric}}$ is the project emissions from electric buses in tCO₂e.

The net emission reduction is calculated by subtracting the emissions produced by electric buses from the emissions that would have been produced by diesel buses over the same operational period [21]. The baseline emissions from diesel buses are determined based on their fuel consumption and the corresponding emission factors for diesel fuel in Thailand. Meanwhile, the project emissions from electric buses are calculated considering the carbon intensity of Thailand’s electricity grid, as the environmental benefit depends on how the electricity used for charging is generated [12]. This difference represents the total greenhouse gas emissions avoided by transitioning from diesel to electric buses, measured in tonnes of carbon dioxide equivalent (tCO₂e).

The economic value of these emission reductions is estimated using conservative carbon price projections for Thailand’s voluntary carbon market. The revenue from carbon credits is then incorporated into the TCO calculation, enhancing the economic case for electric bus adoption.

5.5. Sensitivity Analysis Framework

To explore the robustness of the results and identify critical factors affecting the economic viability of electric buses, a comprehensive sensitivity analysis is conducted. This analysis examines how variations in key parameters impact the TCO comparison between diesel and electric buses.

The sensitivity analysis focuses on the following parameters:

• Annual distance traveled: As electric buses have higher upfront costs but lower operational costs, the annual distance traveled significantly affects the TCO comparison. Higher utilization rates typically favor electric buses due to their lower per-kilometer operating costs.
• Electricity and diesel price trends: The analysis explores various scenarios for future electricity and diesel prices, as these directly impact the operational cost advantage of electric buses.
• Battery replacement costs: As battery technology continues to evolve, the cost of replacement batteries is examined to understand its impact on the long-term economics of electric buses.
• Carbon credit prices: The analysis considers different carbon price scenarios to assess how changes in the voluntary carbon market could affect the economic feasibility of electric buses.

The sensitivity analysis employs both deterministic and stochastic approaches. The deterministic approach examines the impact of individual parameter changes while holding others constant, identifying threshold values at which the economic advantage shifts between diesel and electric buses. The stochastic approach, built on the Monte Carlo simulation, examines the combined effects of parameter variations, providing insights into the overall robustness of the results.

Through this comprehensive methodology combining TCO analysis, Monte Carlo simulation, carbon credit assessment, and sensitivity analysis, this study provides a thorough evaluation of the economic feasibility of transitioning from diesel to electric buses for the case study bus company in Thailand.

6. Result Presentation

6.1. Total Cost of Ownership Analysis with Monte Carlo Simulation

This study presents a comprehensive assessment of the economic viability of transitioning from diesel to electric buses for a Thai bus company. To address the inherent uncertainties in long-term financial projections, a Monte Carlo simulation approach was employed for the TCO analysis. This probabilistic method provides a more robust evaluation of potential outcomes compared to traditional deterministic calculations [45].

6.1.1. Simulation Parameters and Distributions

The Monte Carlo simulation incorporated key cost parameters modeled as probability distributions to reflect their inherent variability, as shown in

Table 3. Distribution types and parameters for Monte Carlo simulation variables.

Variable	Distribution	Minimum	Most Likely	Maximum	Unit
Diesel fuel cost	Triangular	9.0	10.0	12.0	THB/km
Electricity cost	Triangular	4.2	4.3	4.7	THB/km
Diesel maintenance cost	Triangular	1.5	2.0	3.0	THB/km
EV maintenance cost	Triangular	0.75	1.0	1.5	THB/km
Battery degradation	Triangular	0.9	0.95	1.0	-

Note: The simulation incorporates triangular distributions for all key cost variables to appropriately model uncertainty. Diesel fuel and electricity costs are estimated from current trends of residential tariffs and government policies. Maintenance costs for diesel and electric cars reflect operational records and manufacturer specifications. Battery degradation is estimated, based on manufacturer warranties and predictions for passenger vehicles.

.

The simulation was executed across 10,000 iterations to ensure statistical validity. This extensive sampling produced robust results that capture the complex interplay of variables in real-world scenarios, providing far greater insight than conventional deterministic calculations could offer [46].

6.1.2. Total Cost of Ownership Simulation Results

Table 4. Total cost of ownership comparison table of the pilot route (cost per trip). Distribution Value Format: Most Likely (Min–Max).

TCO Components	ChiangMai-ChaingRai		ChiangMai- Maesai		ChiangMai- Phayao		ChiangMai- Nan
	Diesel	EV	Diesel	EV	Diesel	EV	Diesel	EV
Capital Costs (${\mathrm{C}}_{\mathrm{i}}$)
Vehicle purchase	6000	7500	6000	7500	6000	7500	6000	7500
Operation & Maintenance (${\mathrm{O}\&\mathrm{M}}_{\mathrm{i}}$)
Fuel/energy cost	12,779 (11,501–15,334)	5495 (5367–6006)	17,297 (15,567–20,756)	7438 (7265–8130)	10,590 (9531–12,708)	4554 (4448–4977)	23,863 (21,477–28,635)	10,261 (10,022–11,216)
Maintenance cost	2556 (1917–3834)	1278 (958–1917)	3459 (2595–5189)	1730 (1297–2595)	2118 (1589–3177)	1059 (794–1589)	4773 (3579–7159)	2386 (1790–3579)
Insurance	321	321	321	321	321	321	321	321
Tax	26	13	26	13	26	13	26	13
Replacement Costs (${\mathrm{R}}_{\mathrm{i}}$)
Battery replacement	0	1500	0	1500	0	1500	0	1500
Salvage Value ($\mathrm{SV}$)
Salvage value	−600	−750	−600	−750	−600	−750	−600	−750
Total TCO	21,081 (19,165–24,915)	15,357 (14,909–16,507)	26,503 (23,909–31,693)	17,751 (17,146–19,308)	18,455 (16,867–21,632)	14,197 (13,826–15,150)	34,382 (30,803–41,541)	21,231 (20,396–23,379)
TCO saving by EV	5725 (2658–10,005)		8752 (4601–14,546)		4258 (1717–7806)		13,151 (7424–21,145)
% TCO saving by EV	27.16% (10.67–52.21%)		33.02% (14.52–60.84%)		23.07% (7.94–46.28%)		38.25% (17.87–68.65%)

Note: Unit: ‘000 Thai Baht (THB). TCO calculated over a 10-year service period, one round trip per day, 353 operating days per year.

presents the results of the Monte Carlo simulation for the TCO comparison across the four pilot routes, showing both the most likely values and the distribution ranges.

The Monte Carlo simulation results reveal significant cost savings across all routes, with savings percentages ranging from 23.07% (most likely) for the ChiangMai-Phayao route to 38.25% (most likely) for the ChiangMai-Nan route. Even under the most conservative scenarios (minimum values), all routes show positive savings, with the minimum savings percentage ranging from 7.94% to 17.87% depending on the route.

6.1.3. Fleet-Wide TCO Analysis

To evaluate the overall economic impact for the company, the TCO savings were extrapolated to the entire fleet of 29 buses distributed across the four routes, as shown in

Table 5. Fleet-wide TCO analysis (29 buses).

Route	Buses	TCO Saving (Million THB)
ChiangMai-ChaingRai	12	68.70
ChiangMai-Maesai	4	35.01
ChiangMai-Phayao	3	12.77
ChiangMai-Nan	10	131.51
Total		247.99
EV Shifting Costs		12.00
Net TCO Savings		235.99

Note: The unit is in million Thai Baht (THB). EV shifting costs include charging station installation, staff training, and infrastructure investment.

.

The charging infrastructure requirements represent a significant component of the transition to electric buses. The analysis indicates that the total EV shifting costs amount to 12 million THB for the implementation range studied. This comprehensive figure encompasses charging station installation (including both depot-based slow chargers and strategic fast-charging stations), necessary electrical grid connection upgrades, extensive staff training programs for maintenance and operations personnel, and supporting infrastructure investments such as electrical system modifications at existing facilities. The charging infrastructure was designed to balance initial capital expenditure with operational efficiency, ensuring sufficient charging capacity for the planned routes while minimizing idle charging equipment. Maintenance costs for this infrastructure were incorporated into the TCO model as part of the annual operating expenses, representing approximately 5–7% of the initial infrastructure investment.

Considering the EV shifting costs of 12 million THB, which include charging infrastructure installation, staff training, and other transition expenses, the net TCO savings amount to approximately 235.99 million THB over the 10-year period. This represents a substantial economic benefit for the company, validating the financial viability of transitioning to electric buses.

6.2. Carbon Credit Assessment

Beyond direct cost savings, the transition to electric buses offers significant environmental benefits that can be quantified through carbon credit mechanisms. Based on the Thailand Voluntary Emission Reduction Program (T-VER) methodology for electric vehicles in public transportation systems (T-VER-S-METH-04-03_version_01), a detailed assessment of potential greenhouse gas (GHG) reductions across the four pilot routes was conducted.

The emission calculations were based on Thailand’s Voluntary Emission Reduction Program methodology for electric vehicles in public transportation systems (T-VER-S-METH-04-03_version_01). According to this methodology, baseline emissions from diesel buses were calculated using the formula (see Equation (3)):

$${\mathrm{BE}}_{\mathrm{y}}={\sum }_{\mathrm{i}}{\sum }_{\mathrm{x}}\left[\left({\mathrm{FC}}_{\mathrm{BL},\mathrm{i},\mathrm{x}}\times {\mathrm{NCV}}_{\mathrm{x}}\times {\mathrm{EF}}_{\mathrm{CO}2,\mathrm{x}}\right)\times {\mathrm{ADJ}}_{\mathrm{i},\mathrm{y}}\right]\times {10}^{-9}$$

(3)

where:

• ${\mathrm{BE}}_{\mathrm{y}}$ represents the total greenhouse gas emissions from the baseline scenario in year $\mathrm{y}$ (tCO₂/year);
• ${\mathrm{FC}}_{\mathrm{BL},\mathrm{i},\mathrm{x}}$ represents the fossil fuel consumption of diesel vehicles in route $\mathrm{i}$;
• ${\mathrm{NCV}}_{\mathrm{x}}$ is the net calorific value of diesel fuel (MJ/unit);
• ${\mathrm{EF}}_{\mathrm{CO}2,\mathrm{x}}$ is the emission factor for diesel (kgCO₂/TJ);
• ${\mathrm{ADJ}}_{\mathrm{i},\mathrm{y}}$ factor adjusts for any differences between baseline and project routes.

For electric buses, project emissions were calculated as (see Equation (4)):

$${\mathrm{PE}}_{\mathrm{y}}={\sum }_{\mathrm{i}}{\sum }_{\mathrm{j}}\left({\mathrm{EC}}_{\mathrm{PJ},\mathrm{i},\mathrm{j},\mathrm{y}}-{\mathrm{EC}}_{\mathrm{RE},\mathrm{PJ},\mathrm{i},\mathrm{j},\mathrm{y}})\times {\mathrm{EF}}_{\mathrm{EC},\mathrm{PJ},\mathrm{y}}\right)\times {10}^{-3}$$

(4)

where:

• ${\mathrm{PE}}_{\mathrm{y}}$ represents the total greenhouse gas emissions from the project implementation in year $\mathrm{y}$ (tCO₂/year);
• ${\mathrm{EC}}_{\mathrm{PJ},\mathrm{i},\mathrm{j},\mathrm{y}}$ is the electricity consumption for charging electric vehicle $j$ on route $\mathrm{i}$;
• ${\mathrm{EC}}_{\mathrm{RE},\mathrm{PJ},\mathrm{i},\mathrm{j},\mathrm{y}}$ is any renewable electricity used (zero in this study);
• ${EF}_{EC,PJ,y}$ is the grid emission factor for electricity in Thailand (0.4857 kgCO₂/kWh).

The results of this assessment are presented in

Table 6. Carbon emission reduction by route.

Route	Annual Distance (km)	Diesel Emissions (EBdiesel) (tCO2eq/Year)	EV Emissions (EBelectric) (tCO2eq/Year)	Emission Reduction (ER) (tCO2eq/Year)	Fleet Reduction (tCO2eq/Year)
ChiangMai-ChaingRai	127,786	448.86	152.62	296.24	3554.88
ChiangMai-Maesai	172,970	607.70	206.60	401.10	1604.40
ChiangMai-Phayao	105,900	372.02	126.49	245.53	736.59
ChiangMai-Nan	238,628	838.64	285.02	553.62	5536.20
Total					11,432.07

Note: Calculations based on diesel emission factor of 2.74 kgCO₂/liter, grid emission factor of 0.4857 kgCO₂/kWh, diesel fuel efficiency of 2.85 km/liter, and EV energy efficiency of 0.749 km/kWh [48].

.

The economic value of these emission reductions depends on carbon credit prices in the voluntary market. Using a conservative estimate of 146.12 THB per tCO₂eq (price based on 2024–2025 trading values of Voluntary Carbon Credits in Thailand from the T-VER project [50]), the annual revenue potential from carbon credits would be approximately 1.67 million THB (11,432.07 tCO₂eq × 146.12 THB). Over the 10-year project period, this could generate additional revenue of 16.7 million THB, further enhancing the economic case for electric bus adoption.

The conservative estimate of 146.12 THB per tCO₂eq used in the analysis only reflects current trading values in Thailand’s voluntary carbon market. This value is notably lower than prices in more established carbon markets, such as the European Union Emissions Trading System (EU ETS), where prices ranged from 2500 to 3800 THB per tCO₂eq in 2023, or even compared to other Asian markets like South Korea’s Emissions Trading Scheme (K-ETS) (approximately 500 THB per tCO₂eq). Thailand’s carbon market is expected to mature over the next decade, with price projections suggesting potential increases of 25% annually as the country strengthens its climate commitments and carbon trading regulations. Such increases would significantly enhance the financial attractiveness of electric bus adoption through additional carbon credit revenue, potentially adding an extra 0.4 million THB annually to the calculated benefits by 2030.

6.3. Sensitivity Analysis

To explore the robustness of the results and identify critical factors affecting the economic viability of electric buses, a comprehensive sensitivity analysis based on the Monte Carlo simulation data was conducted.

The sensitivity analysis revealed that diesel fuel costs had the highest impact on TCO savings (45.2%), followed by electricity costs (28.4%), maintenance costs (15.7%), and depreciation rates (10.7%). This finding highlights the importance of fuel and energy prices in determining the economic advantage of electric buses. The sensitivity analysis figure (Figure 3) shows how TCO savings fluctuate when each parameter varies from its baseline value.

For example, if diesel prices increase beyond the maximum value used in the simulation (12.0 THB/km), the economic advantage of electric buses would be even greater. Conversely, if electricity costs rise significantly above the projected range, some of the economic advantages would be reduced, though the probability of achieving positive savings would remain high.

The analysis also considered future trends in battery technology and electricity generation. Ongoing advancements in battery technology are likely to reduce the cost and improve the performance of electric buses, potentially enhancing the economic benefits beyond current projections. Similarly, as Thailand continues to increase the proportion of renewable energy in its electricity mix, both the environmental benefits and potential carbon credit revenue could increase.

6.4. Combined Economic Assessment

When combining the TCO savings (235.99 million THB) and potential carbon credit revenues (16.7 million THB), the total economic benefit of transitioning to electric buses is estimated at 252.69 million THB over the 10-year period. This combined assessment provides a holistic view of the economic case for electrification, accounting for both direct operational savings and environmental benefits.

The significant economic benefits, along with the high statistical confidence provided by the Monte Carlo simulation, present a compelling case for the bus company to proceed with the transition to electric buses. Furthermore, these findings offer valuable insights for government agencies and other public transportation operators considering similar electrification initiatives.

7. Discussion

The results of this study demonstrate a compelling economic case for transitioning from diesel to electric buses for the case study company. While the findings are promising, it is important to examine several considerations that could influence the implementation and outcomes of such a transition.

7.1. Additional Economic Considerations

The TCO analysis presented in this study has focused primarily on direct operational costs and benefits. However, several additional economic factors could further enhance the attractiveness of EV adoption in the long term.

First, potential tax incentives and government subsidies for clean energy vehicles have not been factored into the current analysis. Thailand’s government has demonstrated commitment to electrifying the transportation sector through various policy initiatives [12]. Should additional financial incentives be implemented, the economic advantage of electric buses would be further amplified.

Second, the carbon credit assessment in this study used conservative price estimates. The global market for carbon credits is evolving rapidly, and prices could potentially increase in the future as climate policies become more stringent. Research indicates that carbon prices within Thailand’s voluntary market are projected to experience considerable appreciation over the coming years, which could substantially enhance the financial returns from emission reduction activities [30].

Third, the volatility of diesel prices presents another important consideration. The current analysis uses a triangular distribution for diesel fuel costs that may not fully capture the potential for significant future price increases due to geopolitical events, resource scarcity, or additional carbon taxes. Historical trends indicate that fossil fuel prices have generally increased over time and exhibit considerable volatility, which could enhance the comparative advantage of electric buses even beyond the projections in this study.

7.2. Technical and Operational Considerations

Several technical and operational factors deserve attention when planning for the implementation of electric buses.

Range limitations and battery performance remain important considerations, particularly for the longer routes such as ChiangMai-Nan (676 km round trip). Current generation electric buses typically have ranges between 300–400 km on a single charge, which may necessitate intermediate charging for longer routes. Strategic placement of fast-charging infrastructure along these routes would be essential for successful implementation.

Seasonal variations in battery performance should also be considered. Battery efficiency can decrease in extreme temperatures, which could affect operational planning in certain seasons. The Monte Carlo simulation in this study incorporated some level of variability in energy consumption, but actual performance monitoring would be valuable during initial implementation phases.

Another important consideration is grid capacity and reliability. The additional electricity demand from charging multiple electric buses simultaneously could place strain on local electrical infrastructure, particularly at bus depots. Coordination with electricity providers and potentially implementing smart charging systems would help mitigate these challenges [38].

7.3. Implementation Strategy

A phased implementation approach would be prudent for the case study company. The analysis indicates that the ChiangMai-Nan and ChiangMai-Maesai routes offer the highest percentage savings (38.25% and 33.02%, respectively), making them attractive candidates for initial electrification. These routes could serve as pilot programs to gather real-world operational data and address any unforeseen challenges before expanding to the entire fleet.

Driver and maintenance staff training represents another critical aspect of implementation. Electric buses have different driving characteristics and maintenance requirements compared to diesel buses. Comprehensive training programs would be necessary to ensure optimal vehicle performance and longevity [51].

The development of maintenance capabilities for electric buses is also essential. While electric buses generally have lower maintenance requirements due to fewer moving parts, they require specialized knowledge for battery and electrical systems maintenance. Establishing in-house expertise or service agreements with manufacturers would be important for long-term operational success.

7.4. Future Research Directions

This study points to several valuable directions for future research. Collecting and analyzing real-world operational data from electric buses in the Thai context would provide valuable insights for refining economic models and operational strategies. Investigating the integration of renewable energy sources, such as solar panels at bus depots, could further enhance both the economic and environmental benefits of electric buses.

An Important area requiring further investigation is the end-of-life management of electric bus batteries. While electric buses offer significant emissions reductions during operation, battery recycling presents both economic and environmental challenges. Current recycling processes are costly and energy-intensive, and improper handling of battery waste can lead to environmental contamination. Without adequate recycling infrastructure, these impacts could potentially offset some of the environmental benefits achieved during the operational phase. Future research should examine the development of more efficient recycling technologies and closed-loop systems that could mitigate these concerns.

Additionally, second-life applications for bus batteries could extend their useful life before recycling becomes necessary, improving the overall lifecycle assessment. Additionally, exploring innovative business models, such as battery leasing or service-based arrangements with manufacturers, could potentially address some of the upfront cost barriers associated with electric bus adoption and provide pathways for responsible end-of-life battery management. Such models might be particularly relevant for smaller operators with more limited capital resources.

8. Conclusions

This comprehensive analysis of transitioning from diesel to electric buses for a major Thai bus company yields several significant conclusions.

The economic analysis using Monte Carlo simulation demonstrates substantial cost advantages for electric buses across all examined routes. The total cost of ownership (TCO) savings range from 23.07% to 38.25%, depending on the route, with fleet-wide net savings of approximately 236 million THB over a 10-year period. Even under conservative scenarios, all routes show positive economic benefits, with the probability of achieving savings greater than 20% exceeding 90% for all routes.

The environmental assessment reveals significant potential greenhouse gas reductions, with the fleet of 29 electric buses reducing emissions by approximately 11,432 tonnes of CO₂ equivalent annually. This environmental benefit translates into additional financial value through Thailand’s Voluntary Emission Reduction program, with potential carbon credit revenue of 16.7 million THB over the 10-year period.

The sensitivity analysis identifies fuel costs as the most significant factor affecting the economic outcomes, with diesel fuel prices having the largest impact (45.2%). This highlights the potential for even greater economic benefits should fossil fuel prices increase more rapidly than electricity costs in the future.

While challenges exist regarding range limitations, charging infrastructure, and operational adjustments, these can be addressed through strategic planning and a phased implementation approach. The ChiangMai-Nan and ChiangMai-Maesai routes offer the highest percentage savings and could serve as initial implementation targets.

In conclusion, the transition to electric buses presents a compelling economic and environmental case for the case study company. The significant cost savings, combined with environmental benefits and the high statistical confidence provided by the Monte Carlo simulation, strongly support proceeding with fleet electrification. Beyond the specific case study, these findings offer valuable insights for government agencies and other public transportation operators considering similar initiatives across Thailand and the broader Southeast Asian region.