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1. Introduction

The challenges of urban mobility currently faced result from decisions made throughout history, especially in the first half of the 20th century, influenced by economic, political, and cultural factors [1]. Currently, efforts are focused on implementing advanced propulsion technologies, including fully electric and hybrid vehicles, which emerge as strategic alternatives for reducing pollutant gas emissions and increasing energy efficiency [2].

According to the International Energy Agency, major global metropolises account for more than two-thirds of primary energy demand and 70% of greenhouse gas (GHG) emissions. Within this scenario, the transport sector stands out as one of the main contributors to global emissions, representing 23% of GHG emissions associated with energy consumption. Additionally, this sector is the largest consumer of petroleum-based products, accounting for 64.5% of petroleum consumption in 2014 [3].

The increasing concentration of carbon dioxide in the atmosphere reinforces these concerns. CO₂ levels have risen from 278 parts per million (ppm) to 396 ppm in 2014, with projections indicating a growth between 25% and 90% by 2030, which could result in a 3 °C rise in global temperature by the end of the century [4]. This scenario brings alarming consequences for life on Earth, such as reduced agricultural productivity, increased food insecurity risks, and the spread of diseases associated with climate change.

The dependence on fossil fuels and increasing urban traffic has generated environmental, social, and economic impacts, affecting the quality of life in cities and public health. Thus, the search for more sustainable and efficient transportation solutions has become an urgent necessity. In this context, electric buses emerge as a promising alternative, offering significant advantages over conventional diesel-powered vehicles. In addition to reducing GHG emissions, these vehicles contribute to lower noise pollution and improved air quality, promoting healthier urban environments.

University environments play a fundamental role in research and the implementation of sustainable solutions, serving as strategic spaces to promote the adoption of new technologies in urban transportation. However, before expanding these solutions to cities, it is essential to assess the feasibility of electrifying university fleets. Besides the environmental benefits, adopting electric buses can provide economic advantages, such as lower operational costs and fostering technological innovation in the transport sector.

Despite its advantages, the transition to electric buses faces complex challenges, including adequate infrastructure, financial feasibility, and acceptance by the academic community. Implementing this model requires strategic planning, involving studies on energy efficiency, operational costs, and charging infrastructure, to ensure a successful and economically viable transition.

Several studies across Brazil have investigated the technical and economic feasibility of replacing diesel-powered buses with electric models, employing comparable methodological approaches. At the University of Campinas (UNICAMP), in collaboration with CPFL Energia and BYD, an electric bus powered by photovoltaic energy generated on campus was implemented as part of the “Living Laboratory for Electric Mobility”. With a 324 kWh battery capacity, the vehicle underwent initial testing and driver training phases, followed by a gradual integration into operational routes tailored to energy demands. Data collection and continuous monitoring supported research into urban electric mobility performance and optimization [5].

In Cuiabá-MT, an economic-financial assessment was conducted using indicators such as Net Present Value (NPV), Internal Rate of Return (IRR), discounted payback, and total cost of ownership (TCO), with calculations supported by the Brazilian Energy Research Office (EPE) feasibility tool. Findings revealed a 19.17% reduction in TCO when transitioning from diesel to electric buses, primarily due to long-term operational expenditures (OPEX), with an estimated investment return period of seven years [6].

A structured deployment framework was proposed by Machado (2024) [7] for the implementation of electric buses on route “188 TICEN—UFSC” in Florianópolis. The methodology emphasized a phased transition, involving the selection of a suitable route, contextual analysis of user demand, scenario definition, and technical specification assessment of available electric bus models in the Brazilian market. Despite the high initial capital expenditure, the study concluded that a gradual implementation strategy is key to facilitating the transition, particularly in diverse urban contexts that require adaptive planning.

A similar approach was applied by Santana et al. (2021) [8] in Goiânia, using the EPE simulator to assess the replacement of combustion buses with electric alternatives. The results demonstrated feasibility at both micro and macro scales, underscoring the importance of integrating sociocultural factors and public acceptance into the decision-making process for successful urban mobility electrification.

In Curitiba, Guenther and Padilha (2016) [9] conducted a comparative analysis focusing on energy consumption, pollutant emissions, and operational costs. Although electric buses presented higher upfront investment, their operation over a ten-year period proved more economically advantageous. The study also highlighted the superior energy efficiency of electric motors, contributing not only to financial savings but also to lower environmental impacts, provided adequate charging infrastructure is available.

Collectively, these studies demonstrate convergence in analytical tools—particularly the EPE simulator—and reinforce the long-term economic and environmental benefits of adopting electric mobility in Brazil’s urban transport sector. The gradual and context-sensitive transition approach emerges as a consistent recommendation to ensure technical viability, cost-effectiveness, and public integration.

Given the expansion of electric mobility and the need to reduce the environmental impact of public transportation, this article aims to assess the feasibility of implementing electric buses in the internal transport system of the Federal University of Paraíba (UFPB). The research provides a comprehensive analysis considering technical, economic, and environmental aspects, contributing to a better understanding of the benefits and challenges of electrifying university transport fleets.

To address this issue in a structured manner, this study focuses on five main aspects that influence the adoption of electric mobility at the university. These aspects are essential for understanding the feasibility and challenges of fleet electrification and allow for a detailed analysis of the environmental, economic, and social impacts of this transition. The main highlights of the study include: (i) Sustainability and energy efficiency: Evaluation of the reduction in GHG emissions and noise pollution, promoting a more sustainable university campus. (ii) Detailed economic analysis: Comparison between the operational costs of electric and diesel buses, evaluating financial feasibility and potential return on investment. (iii) Infrastructure and implementation challenges: Analysis of the need for charging stations, adaptation of UFPB’s infrastructure, and the technical feasibility of the project. (iv) Impact on the academic community: Consideration of the acceptance by students and staff and the potential to encourage sustainable mobility practices within and beyond the campus. (v) Future projections and expansion: Analysis of trends in the sustainable transport sector and the potential expansion of the electric fleet beyond the university campus.

These aspects will be addressed in the article, providing a solid foundation for decision-making and facilitating the replication of this model in other urban and academic contexts. To ensure a structured and clear approach, this study is organized into five sections. Section 1—Introduction presents the context of electric mobility, the relevance of university transport electrification, the study’s objective, its key highlights, and the article’s structure. Section 2—Electric Public Transport discusses the fundamentals of electric public transport, highlighting its operation, environmental and economic impacts, and national and international implementation examples. In Section 3—Performance Evaluation: Methodology and Indicators, the criteria for comparative analysis between electric and diesel buses are detailed, including energy efficiency, operational costs, and GHG emissions. Section 4—Results and Discussion presents and interprets the study’s key findings, comparing the performance of electric and conventional buses, while discussing the implications of the results for the feasibility of fleet electrification. Finally, Section 5—Conclusions and Future Work summarizes the main findings of the study, highlighting the benefits of university transport electrification, concluding with proposals for future research and potential areas for further study.

2. Electric Public Transport

This section presents the fundamentals of electric public transport, addressing its operation, main components, and benefits compared to traditional combustion models. The environmental and economic impacts of this technology are discussed, including the reduction of greenhouse gas (GHG) emissions and operational costs. Additionally, a life cycle analysis of electric buses is conducted, highlighting their energy efficiency and sustainability. Finally, national and international experiences in electric mobility for public and university transportation are explored, emphasizing the applicability of this model in the context of UFPB.

2.1. Fundamentals of Electric Public Transport

According to [10], fully electric vehicles use electricity stored in internal batteries to power the traction or propulsion motor. These batteries can be recharged by connecting the vehicle to external chargers or the electrical grid. This is the most common model among electric buses, and its evolution is directly linked to advancements in battery technology.

According to [11], the most common types of batteries used in electric vehicles are lithium-ion, nickel-metal hydride, and lead–acid batteries. In fully electric vehicles, the main components include powertrains, batteries, auxiliary systems, and power converters and inverters, which are detailed in Table 1.

Since there is no combustion during operation, these vehicles emit zero greenhouse gases and significantly lower amounts of particulate matter, which originates only from friction between mechanical parts and tire contact with the ground. They have fewer components than conventional vehicles, which also simplifies and reduces maintenance costs.

It is estimated that the lifespan of a battery in electric vehicles ranges from 8 to 10 years, during which approximately 20% of its maximum charge capacity is lost. The authors further state that these batteries can be repurposed for applications requiring lower energy demands, such as battery banks for solar energy storage [12].

2.2. Economic and Sustainability Indicators

The adoption of electric buses represents a promising alternative to reduce the environmental and economic impacts of public transportation. Regarding sustainability, electric motors can significantly contribute to reducing air pollution, minimizing the emission of both local and global pollutants. According to IPEA (2016) [13], the main vehicular pollutants that directly affect human health include particulate matter (PM), nitrogen oxides (NOx), and sulfur dioxide (SO₂), which are often present in soot emitted by exhaust systems, brakes, tires, and road surfaces. Meanwhile, global pollutants such as carbon dioxide (CO₂) are responsible for exacerbating the greenhouse effect and global warming, highlighting the urgency of adopting cleaner alternatives for urban transportation.

Studies indicate that replacing conventional fleets with electric vehicles can lead to significant reductions in CO₂ emissions. In an analysis of the environmental impact of urban transportation electrification, the potential variations in CO₂ emissions resulting from replacing urban buses with electric models were quantified [10]. The authors point out that urban buses and minibuses account for 9% of emissions from the road transport sector, equivalent to approximately 19.08 million tons of CO₂. Using data from the Ministry of the Environment on diesel consumption for these vehicles, the study concluded that these emissions correspond to 25.114 million MWh of diesel consumption [10,14].

Based on these values, the Ministry of Science, Technology, and Innovation (MCTI) estimated that CO₂ emissions from each MWh of electricity generation in Brazil amounted to 0.0653 tons, considering the energy mix in operation at the time. By multiplying this emission factor by the energy consumption of urban buses and minibuses, the total CO₂ generated for charging electric vehicles would be approximately 1.64 million tons. Thus, when comparing environmental impacts, replacing conventional vehicles with electric ones could result in a reduction of 17.44 million tons of CO₂, representing a 91.4% decrease in emissions for the year 2012 [15].

Beyond reducing air pollution, electric motors create quieter urban environments, as they generate less noise than internal combustion engines. This aspect is crucial for mitigating noise pollution, a recurring problem in major urban centers. Studies suggest that excessive urban noise not only affects hearing but can also lead to sleep disorders, cardiovascular problems, and psychological conditions, including headaches, anxiety, depression, and cognitive difficulties in children [16]. Thus, the electrification of public transport can play a crucial role in enhancing urban quality of life, reducing prolonged exposure to harmful noise levels [16].

Regarding economic indicators, one key factor that may encourage the replacement of diesel buses with electric models is the difference in fossil fuel and electricity prices. According to [17], oil prices are expected to rise in the coming decades due to increasing extraction costs in non-conventional regions and rising global demand. As petroleum supply becomes more constrained, frequent price hikes may make diesel an increasingly expensive input for public transport operators [17].

Conversely, the authors state that although electricity prices may also fluctuate, the expected increase will likely be less pronounced than that of petroleum. Furthermore, carbon policies and environmental regulations will likely introduce additional costs for fossil fuels, further encouraging transport electrification. Even though electric bus acquisition still represents a high initial investment, projections indicate that this cost will decline in the coming years. According to Bloomberg New Energy Finance (2018) [18], electric bus prices are expected to equalize with diesel models by 2030. This shift will be driven primarily by the declining cost of batteries, which accounted for 26% of the vehicle’s total price in 2016 but are expected to drop to just 8% by 2030.

Given this scenario, the electrification of public transport not only reduces environmental impacts but also emerges as an economically competitive alternative in the medium and long term. The combination of lower pollutant emissions, public health benefits, and the expected decrease in electric vehicle costs reinforces the need to expand discussions on transitioning to a sustainable and energy-efficient mobility model.

2.3. Emission and Life Cycle Indicators

A Life Cycle Assessment (LCA) consists of a comprehensive study of the mass and energy consumption of a given product, identifying its environmental impacts from raw material extraction, through its use, to the final disposal of its waste throughout the entire process. In other words, it is a “cradle-to-grave” analysis (Figure 1). This method has been employed to evaluate the integration of different vehicle types into urban transportation fleets, comparing various powertrains and fuel sources. Such methodology prevents zero-exhaust-emission technologies, like electric vehicles (EVs), from being generalized as having no environmental impact.

This study focuses on greenhouse gas (GHG) emissions, measured in gCO₂eq, and their applications in transportation planning. The goal is to provide planners and decision-makers with more effective tools to optimize energy consumption and emission reductions in public bus transportation systems. The study compares diesel-powered and battery electric buses (BEBs), analyzing their powertrain emissions. Hydrogen fuel cell buses and other fuels, such as natural gas (GNV) and hydrotreated vegetable oil (HVO), were excluded due to the maturity of these technologies for buses in Brazil.

The metrics and data used to calculate GHG emissions are described in Table 2 and Table 3, detailing input parameters used in calculations for the university bus fleet, including route data (bus type, quantity, average mileage, weight, average yield) and emissions data.

The 2016 Mascarello GranMidi bus, designed for urban and intermunicipal use, exhibits structural characteristics typical of medium-sized models, featuring a carbon steel metal frame and a diesel-powered chassis (Table 4). Although precise emission data from its manufacturing process are not publicly available for this specific model, its environmental impact can be estimated based on Life Cycle Assessment (LCA) studies of similar buses, taking into account the production, maintenance, and disposal and/or recycling phases.

The emission factors for ICE buses were obtained from the JEC Well-To-Wheels Reports, which provide CO₂ equivalent emission values for heavy-duty diesel engines, including buses. The data is a result of a collaborative study by the Joint Research Center of the European Commission (JRC), the European Council for Automotive R&D (EUCAR), and CONCAWE (CONservation of Clean Air and Water in Europe) [20]. The CO₂ equivalent emissions reported for ICE buses are as follows: fuel production: 12.8 gCO₂eq/t·km, fuel combustion: 50.2 gCO₂eq/t·km, and total ICE emissions: 63.0 gCO₂eq/t·km.

For internal combustion engine buses (ICEBs), the development of the emission factor within the LCA framework considers two fundamental stages: fuel production, which includes its manufacturing, transportation, and storage, and fuel use, corresponding to its combustion by the vehicle. Thus, the greenhouse gas (GHG) emissions from combustion engine buses are determined as the sum of the emissions from the production stage and the use (combustion) stage, as shown in Equation (1):

(1) $G E E_{I C E B} = (G E E_{p r o d u c t i o n} + G E E_{u s e}) \times P ⃗$

where GEE_ICEB represents the total greenhouse gas emissions from an ICE bus, expressed in CO₂eq/km; GEE_production and GEE_use refer to the emissions from fuel production and combustion, respectively (gCO₂eq/ton·km); and P is the total weight of the bus, in tons.

The LCA process for battery electric buses (BEBs) follows a similar methodology but consists of two main stages: (i) Energy extraction and transportation—mining of energy sources and their transportation to power plants. (ii) Electricity transmission and consumption—energy transfer from the grid to the bus and its use for traction [21].

Thus, the GHG emissions for BEBs (measured from well-to-wheel) are calculated as the sum of emissions from mining and electricity use, as expressed in Equation (2):

(2) ${G E E}_{B E B} = (\sum_{e} P i * {(G E E}_{m i n i n g} + {G E E}_{u s e}) \times η$

where GEE_BEB represents the total GHG emissions generated by the energy supply chain or national electricity mix, measured in (gCO₂eq/km), GEE_mining and GEE_use refer to the GHG emissions per kWh from energy extraction and electricity consumption, respectively, and η represents the energy efficiency of the electric bus (km/kWh).

The GEE_extraction emission factor follows the methodology proposed by [22], who conducted a comprehensive review of 167 LCA studies evaluating GHG emissions from different energy sources. GEE_use for BEBs was not considered in this study since battery electric buses do not generate direct exhaust emissions [23].

The composition of Brazil’s national electricity mix was considered to account for the emissions associated with electricity generation. The Brazilian electricity grid is predominantly based on renewable sources, with the following distribution: hydropower: 58.9%, wind power: 13.2%, biomass: 5.2%, solar power: 7.0%, coal and derivatives: 4.9%, petroleum and derivatives: 2.7%, nuclear energy: 2.0%, natural gas: 5.3% (includes domestic production and imports) [24].

Energy usage varies from country to country, depending on economic development, geopolitical conditions, resource availability, climate, industrial profile, and other relevant factors [25].

This issue is also relevant in battery production and recycling emissions for BEBs. According to [26], the manufacturing process of lithium-ion batteries accounts for 45% to 60% of total emissions. The study concludes that the carbon footprint of battery production strongly depends on the energy mix of the manufacturing country. The same report estimates that battery production emissions range from 150 to 200 kgCO₂eq/kWh, particularly in countries where fossil fuels account for 50–70% of electricity generation, such as China, the main supplier of lithium-ion batteries used in Brazil.

In this study, Chinese manufacturing emissions were considered, adopting a 200 kgCO₂eq/kWh emission factor. A battery pack of 3000 cycles was assumed, with an efficiency of 0.775 kWh/km and a range of 200 km per charge.

Additionally, an average battery degradation rate of 0.008% per cycle was used, based on established vehicle models listed in Table 4. Thus, the total production impact per kilometer traveled is a function of the battery life cycle. In this study, the resulting impact was 58.71 gCO₂eq/km, assuming a total lifespan of 528,000 km.

For the recycling and disposal phase, only the battery of the battery electric bus (BEB) is considered for its first useful life (500,000 km). It is estimated that this process accounts for approximately 5 to 10% of total emissions. Battery recycling remains a challenge, as less than 50% of the materials from lithium batteries are currently recovered globally [27]. Advanced reuse and recycling technologies may help reduce this environmental footprint in the future.

Many studies have explored the reuse of these batteries, as they still retain approximately 80% of their initial capacity (plug-in 400 kWh) [28]. However, the market for battery reuse and recycling remains highly uncertain [26].

During the manufacturing process, it is estimated that approximately 40 to 45% of total CO₂ emissions are generated, with the production of lithium-ion batteries being the primary contributor to the environmental impact [29]. The extraction and refining of materials such as lithium, cobalt, and nickel significantly contribute to these emissions [30].

Research indicates that manufacturing emissions of an urban bus also depend on the energy matrix of its origin. A medium- to large-sized bus results in emissions ranging from 40 to 60 tons of CO₂ equivalent (tCO₂e), considering the production of the body, chassis, engine, and battery of the electric bus [26,31,32].

Maintenance of electric buses accounts for between 5 and 15% of total CO₂ emissions over their lifetime, as these vehicles have fewer moving parts and require fewer mechanical interventions than traditional buses [33]. However, for a future study, an extended useful life (20 years) could be considered, and we would then add battery replacement over the total useful life of the bus, which would increase this share of emissions [28].

For combustion buses, preventive measures such as oil, filter, tire, and brake pad changes were considered during the maintenance phase, which represents 10 to 20% of overall emissions [34].

2.4. Electric Mobility: Advances in Public and University Transportation

The transition to electric bus fleets has been driven in various parts of the world as a strategy to reduce pollutant gas emissions, improve energy efficiency, and modernize urban systems. The implementation of electric buses has shown significant positive impacts, promoting environmental sustainability and reducing operational costs in the medium and long term. Different cities and institutions have adopted various approaches to enable this transition, including tax incentives, public–private partnerships, and integration with renewable energy sources. The following sections present successful cases of electric bus deployment and the main challenges that still need to be overcome.

China leads the global electric bus market, accounting for a significant portion of the worldwide fleet. Between 2015 and 2021, the country experienced substantial growth, reaching approximately 700,000 units in operation and over 1000 models available for sale [35]. One of the most widely used models, the K9 BEB, has a range of over 400 km at a constant speed of 40 km/h. Meanwhile, models equipped with lithium batteries for fast charging can reach distances exceeding 150 km, with 50% of these units surpassing 260 km of autonomy [21].

In Europe, the electrification of public transport has progressed significantly, driven by the European Green Deal, which aims to make the continent carbon neutral by 2050. In 2019, the European electric bus fleet reached approximately 4000 units, including battery-powered models, plug-in hybrids, IMC trolleybuses, and fuel cell buses [36]. The transport sector accounted for 15% of net CO₂ emissions in Europe in 2018, with 11% originating from road transport [37]. At the municipal level, Gijón, Spain, incorporated six electric buses into its fleet in 2025 as part of a plan to gradually replace diesel vehicles with electric and hybrid models by 2027. The direct benefits of this transition include a 50% reduction in CO₂ emissions, 16 h operational autonomy, and lower maintenance costs due to the simpler structure of electric motors [38].

Outside Europe, the island of Barbados, in the Caribbean, adopted a public transportation modernization plan in 2020, incorporating 33 electric buses in Bridgetown. The project aims to reduce dependence on fossil fuels and encourage the use of renewable energy sources for vehicle recharging. As a result, there was a 30% reduction in fossil fuel consumption, an improvement in public transportation quality, and a decrease in operational costs [39].

In Latin America, Santiago, Chile, stands out as one of the leading examples of public transport electrification. Since 2018, its electric fleet has grown progressively, surpassing 800 electric buses in operation. This initiative was made possible through a partnership between the Metbus operator, the energy company Enel X (Rome, Italy), and the manufacturer BYD (Shenzhen, China), resulting in a 70% reduction in operational costs compared to diesel buses, as well as a significant decrease in CO₂ emissions and noise pollution [34]. Following this trend, Bogotá, Colombia, established itself as a reference by acquiring 406 electric buses in 2020, becoming one of the largest fleets in Latin America. As part of a transportation modernization plan, this initiative led to a 60% reduction in GHG emissions, improved air quality, and lower operational costs in the medium term [39,40].

The electrification of public transport in Brazil has been advancing, albeit at a slower pace compared to other Latin American countries. São Paulo leads the sector, with 201 electric buses in operation in 2025 [41]. The city government established a plan to gradually replace diesel buses by 2038, with the goal of reducing 50% of public transport emissions by 2030. The adopted public concession model enables this transition through subsidies and tax incentives [42]. In Curitiba, electric bus testing began in 2023, prioritizing high-demand routes. The project is supported by research institutions and private companies, and initial results indicate a 60% reduction in energy consumption compared to diesel models [5].

In the academic sector, electric mobility has also been implemented. The Federal University of Santa Catarina (UFSC) developed eBus, a 100% solar-powered electric bus, which has been transporting the academic community between the Trindade Campus and Sapiens Parque since 2017. The vehicle completes five daily trips, covering over 5000 km per month, without charging passengers. In addition to being a sustainable mode of transport, eBus serves as a research laboratory for electric mobility and energy efficiency studies [2]. Similarly, the State University of Campinas (UNICAMP) introduced an electric bus powered by solar energy in 2020, as part of the Campus Sustentável project, in partnership with CPFL Energia. The model contributes to reducing the university’s carbon footprint while promoting research on sustainable mobility [42].

In the study conducted by Fidelis (2021) [43], sustainable urban mobility alternatives for the UFPB Campus I were analyzed. Through bibliographic research across various university campuses, the author examined the mobility situation at UFPB and emphasized the need for alternatives that are safe, accessible, and of high quality. However, the acceptance of this non-traditional model, such as the electric model, depends not only on the availability of adequate infrastructure but also on the commitment of external stakeholders, who play a crucial role in the implementation and maintenance of the system.

Despite the numerous advantages associated with fleet electrification, challenges remain that hinder the adoption of this system. In [10], three main obstacles are identified: (1) high initial investment costs, (2) scalability and operational flexibility, and (3) limited experience with this technology.

Although the total cost of ownership of an electric bus may be lower than that of a diesel model, many cities face budget constraints for initial investments, even with government subsidies. Additionally, the reduced range of electric buses can be a challenge for long-distance routes or continuous 24 h operations.

A viable alternative to overcome these challenges is the implementation of electric buses in university campuses, where distances are shorter and travel times are reduced. In addition to contributing to sustainable mobility, this approach allows universities to serve as test centers, analyzing the feasibility of electrification for future large-scale applications.

3. Performance Evaluation: Methodology and Indicators

This section presents the methodology adopted to evaluate the performance of electric buses in comparison to diesel models, considering technical, economic, and environmental indicators. The analysis is based on the definition of energy efficiency parameters, operational costs, and environmental impact, focusing on the estimation of greenhouse gas (GHG) emissions throughout the vehicles’ life cycle. Additionally, criteria are established for comparing energy consumption and the economic feasibility of fleet electrification at UFPB, allowing for the identification of the benefits and challenges of implementing this technology within the university context.

3.1. Context and University Transportation at UFPB

UFPB was established by State Law 1366 on 2 December 1955, initially under the name University of Paraíba, following the merger of several higher education institutions. With its federalization, approved by Law N°. 3835 on 13 December 1960, it was renamed Federal University of Paraíba (UFPB), incorporating the university structures of João Pessoa and Campina Grande. It operates as a multi-campus institution, comprising: Campus I, in João Pessoa; Campus II, in Areia; Campus III, in Bananeiras; and Campus IV, in Mamanguape and Rio Tinto [44].

Campus I has three hubs, with the main headquarters structured into thirteen academic centers, namely: Center for Exact and Natural Sciences; Center for Humanities, Letters, and Arts; Center for Medical Sciences; Center for Health Sciences; Center for Applied Social Sciences; Center for Education; Center for Technology; Center for Legal Sciences; Center for Biotechnology; Center for Technology and Regional Development; Center for Communication, Tourism, and Arts; Center for Informatics; and Center for Renewable Alternative Energies.

The Lynaldo Cavalcanti unit, located in the Mangabeira neighborhood, houses the Center for Informatics (CI) and the Center for Technology and Regional Development (CTDR). Additionally, the unit located in Santa Rita municipality accommodates the Department of Legal Sciences and the Center for Legal Sciences (DCJ/CCJ).

In 2021, the university administration implemented free transportation, aiming to facilitate the mobility of the academic community within the university (Figure 2). Currently, UFPB’s university transport operates across three routes: (1) Internal route: Runs throughout the day and night, covering 17 loops within the main campus. (2) Route between the Center for Humanities, Letters, and Arts (CCHLA) and the Lynaldo Cavalcanti unit (CI/CTDR) in the Mangabeira neighborhood. (3) Route between the main campus and Santa Rita municipality, linking the Center for Legal Sciences (CCJ) to the Department of Legal Sciences (DCJ/CCJ).

These routes connect the university headquarters to the Lynaldo Cavalcanti and Santa Rita units, ensuring accessibility for students, professors, and staff. According to [45], in 2022, UFPB had approximately 33,000 active students, distributed among 130 undergraduate programs and 137 graduate programs, in addition to a vast faculty and administrative staff that complement the academic community. Given this scenario, the daily movement on campus results in a significant flow of motor vehicles, contributing to traffic congestion and exacerbating air pollution issues.

The bus provided for these trips has a capacity of 30 seated passengers (Figure 3). According to [46], the model is old, does not have air conditioning, includes an accessible entrance for wheelchair users, and operates approximately 21 trips per day between 6:40 a.m. and 10:00 p.m., covering the route between the main campus (CCHLA) and the Lynaldo Cavalcanti unit (CI/CTDR) in the Mangabeira neighborhood, as shown in Table 2.

Between the main campus, departing from the Center for Legal Sciences (CCJ), and the Santa Rita unit at the Department of Legal Sciences (DCJ/CCJ), two trips are made daily in each direction. Table 3 provides a detailed itinerary of these trips, including departure times, destinations, and corresponding distances. Meanwhile, Table 4 presents the technical characteristics of the bus used at UFPB.

However, it is not possible to determine the exact number of daily users of the transport service. The highest demand occurs during student arrival and departure times (8:00 a.m. and 5:00 p.m.). Due to the variety of class schedules, the passenger flow fluctuates throughout the day.

Additionally, the Federal University of Paraíba (UFPB) operates two photovoltaic solar power plants with a total installed capacity of 536.8 kWp. The first plant, inaugurated in August 2021 at the Technology Center (CT), has a capacity of 295 kWp and comprises 672 solar panels covering approximately 1500 m² [47]. The second plant, located at the Center for Technology and Regional Development (CTDR) and inaugurated in 2023, has an installed capacity of 241.8 kWp, consisting of 372 solar modules rated at 650 W each. This second facility is notable for its artificial intelligence-driven solar tracking system, which optimizes energy capture throughout the day [48].

Based on available technical data and employing classical estimation methods that consider an average solar irradiance of 5.2 kWh/m²/day for João Pessoa and a Performance Ratio (PR) of 0.75, the following average energy outputs were estimated: the CT power plant yields an average of 1150.5 kWh/day, equivalent to 34,515.0 kWh/month and 414,180 kWh/year. Meanwhile, the CTDR facility generates an average of 1131.6 kWh/day, totaling 33,948.7 kWh/month and 407,384.4 kWh/year. Therefore, the combined estimated annual output of the two units is 821,564.4 kWh, representing a significant contribution to energy compensation through distributed generation for the university.

The energy produced by the photovoltaic plants installed on UFPB campuses can be strategically utilized to support the implementation of an intercampus university transportation system using electric vehicles, thereby promoting synergy between sustainable mobility and distributed energy generation. With an estimated annual production exceeding 821 MWh, the CT and CTDR plants offer significant potential to power a fleet of electric buses or vans, reducing reliance on fossil fuels and lowering greenhouse gas emissions associated with the transportation of students and staff. Furthermore, utilizing a proprietary charging infrastructure powered by renewable sources aligns with the principles of energy sustainability and technological innovation, reinforcing the university’s autonomy and amplifying the positive impact of its investments in solar energy. This integration represents a practical application of institutional decarbonization policies and positions UFPB to attract new funding through public calls focused on electric mobility and smart cities.

These initiatives reinforce UFPB’s commitment to sustainability and energy efficiency, reducing operational costs and promoting smarter energy management on campus. This photovoltaic infrastructure could play a strategic role in the electrification of the university’s fleet, allowing electric buses to be powered by clean and renewable energy, minimizing dependence on the conventional electrical grid, and enhancing the overall sustainability of the project.

3.2. Methodology for Obtaining Indicators

Figure 4 presents the methodology for converting operational data, fuel properties, and energy consumption of electric buses (EBs) into comparable values. The implementation of EBs in UFPB’s internal transport system requires an efficacy and efficiency analysis in comparison to diesel buses (DBs), which is carried out in Stage A. For comparison, five EB models are evaluated, considering their performance relative to the reference model.

The definition of these indicators is essential to enable comparisons between different vehicle types, ensuring that performance evaluation is conducted using standardized metrics and defined criteria [49]. All the necessary information for developing these indices is presented in Table 5, where diesel properties can be found in [50] and energy tariff costs in [51].

This systematic approach enables the evaluation of operational performance, allowing EBs to be assessed under the same route conditions as DBs, analyzing the technical and economic impact of their implementation. The detailed development of the indicators is described in the following subsection.

3.3. Performance Indicators

The structuring of energy indicators for evaluating EB performance is defined in Stage A (Figure 5), consisting of two sub-stages: (i) Sub-stage A.1 focuses on the development of indicators representing energy equivalence (kWh_e) during EB operation and the equivalent distance (km_e) it can travel. (ii) Sub-stage A.2 focuses on operational cost calculations, considering maintenance costs and fuel expenses—diesel for DBs and electricity for EBs—quantifying refueling costs for both models. These are expressed as diesel cost (D_C), expressed in USD/L, for diesel, obtained from local sources, and energy cost (E_C), expressed in USD/kWh, for electricity in the same region.

The development of both sub-stages occurs simultaneously and is interconnected through the indicator representing the Average Fuel Consumption of Diesel Bus (ACDB), given in kWh/km. This indicator, formulated in sub-stage A.1 (Figure 6) and represented as (kWh/km)_OD, is fundamental for determining energy equivalence and travel distance for EBs, as it is based on operational conditions and diesel properties analysis.

The ACDB indicator allows relating the distance traveled to the energy consumed, expressed in terms of fossil fuel equivalence, determining the required equivalent energy (kWh_e). Based on the Electric Bus Energy Consumption (EBEC), given in (kWh/km)_E, the equivalent travel distance for EBs (km_e) is calculated. This formulation is defined using division and multiplication operators, represented by A and B, respectively.

Similarly, Sub-stage A.2 (Figure 7) describes the development of cost indicators. These indicators are directly related to fuel and electricity costs (D_C and E_C, respectively). As a result of this formulation, cost indicators Diesel Bus Cost Index (D_BCI) and Electric Bus Cost Index (E_BCI), both expressed in USD/km, are obtained, where D_CBI relates to DBs and E_BCI to EBs. The cost (USD) is calculated by multiplying the distance each vehicle travels by its respective index, referred to as Total Cost of Diesel Buses (T_CDB) for DBs and Total Cost of Electric Buses (T_CEB) for EBs.

The definition of these indicators enables the evaluation of EB implementation based on technical and economic criteria, ensuring comparison in terms of energy and cost under the given operational conditions. Additionally, a comparative analysis of EB models with the DB model is conducted through the Index Variation (I_V), given by Equation (3):

(3) $I_{V} = \frac{(P_{E B} {- P}_{D B})}{P_{D B}} * 100 [%]$

where P_EB represents parameters related to the electric bus and P_DB is the reference parameter for the diesel bus. The performance indicators analysis also includes the development of the Energy Performance Index (EPI), which evaluates the relationship between the battery capacity of EBs and the equivalent energy required for the route. The objective is to determine the fraction of the EB battery capacity that can supply the equivalent energy for the journey.

3.4. Methodology to Evaluate the Cost-of-Living Analysis

The electrification of UFPB’s internal transportation fleet requires careful planning from technical, environmental, social, and economic perspectives. While this transition offers social benefits—such as noise reduction—as well as technical and environmental advantages—including lower operational costs and reduced pollutant emissions—it is essential to ensure financial sustainability. This involves estimating the economic impact by comparing the costs associated with each technology [21,52,53].

In this context, Figure 8 illustrates the process used to evaluate the technological transition from a diesel bus (DB) to an electric bus (EB) from an economic perspective.

The methodology described is a modification of the tool developed by the Energy Research Company (ERC), which is based on a bottom-up modeling approach. This approach, which details the technological structure of energy conversion and use based on user-defined specifications, considers the replacement of a diesel bus with a battery electric bus of equivalent capacity [55].

The adopted approach enables the evaluation of the substitution between propulsion technologies by using the financial break-even point; that is, the balance between fixed and variable costs for each alternative. This analysis indicates the extent to which the adoption of the EB represents a financially advantageous investment project [54].

3.4.1. Theorical Foundation

The methodological framework proposes a step-by-step structure that separates the assessment of fixed and variable costs, which constitute capital expenditure (CAPEX) and operational expenditure (OPEX), respectively. It details their components along with financing models, based on the Constant Amortization System (CAS), which is advantageous due to its reduced total cost [56], through a comprehensive cash flow analysis. The criteria used to define the parameters for each step are presented in Table 6.

It is important to note that, in the case of an EB, the CAPEX includes not only the cost of vehicle acquisition but also the cost of charging infrastructure.

Based on this cash flow, the feasibility of implementing EB can be assessed using standard economic metrics [60]. Evaluating the technological transition from a DB to an EB from an economic standpoint requires the use of appropriate financial metrics, such as: (i) Internal Rate of Return (IRR); (ii) discounted payback (DP); (iii) Net Present Value (NPV); (iv) total cost of ownership (TCO); and (v) break-even point (BEP) [52,54]. These metrics, detailed in Table 7, enable a long-term financial assessment of the proposed technology, allowing for a direct comparison of the investment requirements between the EB and the DB [61].

The mathematical framework used to conduct the economic feasibility analysis in this study will be presented in the following sections. Its purpose is to describe the modifications made to the methodology outlined in Figure 8.

3.4.2. Mathematical Foundation

This section aims to detail the financial mathematical expressions used to quantify the economic feasibility analysis, considering factors such as the time value of money, interest rates, investment amortization, and cash flow projections.

In general terms, for each bus model S, where $S \in {E B, D B}$ , a cash flow $F_{S} (t)$ is defined over the year $t = 0$ to $t = n_{u}$ (with n_u representing the useful life), there is an associated set of costs—CAPEX and OPEX—as well as a residual value (resale value) at the end of the analysis horizon n_u. Additionally, considering the CAS model, the analysis incorporates the Weighted Average Cost of Capital (WACC) methodology, as developed in [64].

The costs associated with CAPEX are directly related to the initial investment in vehicle acquisition and the cost of the charging infrastructure. As previously mentioned, the infrastructure for electric vehicle charging equipment still needs to be implemented, whereas the diesel refueling system is already in operation. Therefore, in the initial year ( $t = 0$ ), the CAPEX in the cash flow, associated with own costs, is defined by Equation (4).

(4) $F_{S} (0) = - ϕ \cdot C A P E X_{S} [\frac{$}{y e a r_{0}}]$

where, for EB and DB,

${C A P E X_{E B} = - (C o s t o f E l e c t r i c B u s + C o s t o f C h a r g i n g I n f r a s t r u c t u r e) C A P E X_{D B} = - (C o s t o f D i e s e l B u s)$

It is worth noting that, at the end of the useful life ( $t = n_{u}$ ), the salvage value (SV) of both models is applied to the initial CAPEX and returned to the cash flow, as detailed in Equation (5).

(5) $S V_{S} (n_{u}) = + C A P E X_{S} . α_{S} [\frac{$}{y e a r_{n_{u}}}]$

where

α_{S}

(%) represents the resale value as a percentage of the initial investment.

In terms of costs associated with OPEX, in addition to operational expenses related to energy consumption (for EB) and fuel consumption (for DB), there are also maintenance costs for both types of vehicles. It is important to note that, for all these components, an annual increase in the prices of energy, fuel, and maintenance is considered over time. In general terms, OPEX is defined by Equation (6)

(6) $O P E X_{S} (t) = - F u e l / E n e r g y_{S} (t) + M a i n t e n c e_{S} (t) [\frac{$}{y e a r}]$

where, for both EB and DB,

$\begin{matrix} O P E X_{E B} (t) & = {- (E n e r g y C o n s u m p t i o n (0) ⏟}_{\frac{k W h}{y e a r}} . {E l e c t r i c F e e (t) ⏟}_{\frac{$}{k W h}} + {M a i n t e n c e_{E B} (t) ⏟}_{\frac{$}{y e a r}}) O P E V_{D B} (t) \\ = {- (F u e l C o n s u m p t i o n (0) ⏟}_{\frac{L}{y e a r}} . {F u e l C o s t (t) ⏟}_{\frac{$}{L}} + {M a i n t e n c e_{D B} (t) ⏟}_{\frac{$}{y e a r}}) \end{matrix}$

As described in [60], energy consumption depends on the power of the vehicle charger and the amount of time required to charge the bus, as well as the number of days the electric bus is in operation. Similarly, fuel consumption depends on the fuel price, distance traveled, fuel efficiency, and the number of operational days. In summary, both are described by:

$\begin{array}{l} E n e r g y C o n s u m p t i o n (0) = \\ = {(P o w e r o f c h a r g e r) ⏟}_{k W} . {(T i m e t o c h a r g e) ⏟}_{h o u r s} . {(O p e r a t i o n a l d a y s i n a y e a r) ⏟ [\frac{k W h}{y e a r}]}_{\frac{d a y s}{y e a r}} F u e l C o n s u m p t i o n (0) \\ = {(O p e r a t i o n a l d a y s i n a y e a r) ⏟}_{\frac{d a y s}{y e a r}} . {(\frac{D i s t a n c e}{f u e l e f f i c i e n c y}) ⏟}_{L} [\frac{L}{y e a r}] \end{array}$

and considering an average annual increase rate for both electricity (

g_{e l e}

) and diesel (

g_{f u e l}

) prices,

$\begin{array}{l} E l e c t r i c F e e (t) & = E l e c t r i c F e e (0) . {(1 + g_{e l e})}^{t} F u e l C o s t (t) \\ = F u e l C o s t (0) . {(1 + g_{f u e l})}^{t} [\frac{$}{k W h (E B) o r L (D B)}] \end{array}$

The CAS system, as used in [61], ensures that the amortization installments remain equal (or constant) throughout the entire financing period. The value of each installment is calculated by dividing the principal by the number of payments. Both the interest and the total payment amounts decrease over time, as the outstanding balance is reduced by the fixed amortization installments [65,66,67].

Considering the application focused on financing a diesel bus and an electric bus, some variable definitions described in [66] are adapted to meet the objectives of this study. The steps for calculating the proposed system are outlined in Table 8, where $ϕ$ (%) is the equity share, representing the proportion of own capital, r_d (%) is the cost of debt, $(1 - ϕ)$ (%) is the financed share and $n_{f}$ is number of financing years (where k is defined as the index ranging from 1 to n_f).

The construction of the cash flow enables the projection of a project’s economic feasibility by accounting for financial inflows and outflows, such as the costs associated with the project—CAPEX and OPEX—and the financing system through which it is paid, which, in this case, is the CAS model [59,69].

The cash flow ( $F_{S}$ ) formulation is defined by listing the previously discussed cost outflows (excluding the resale value at the end). In this case, considering both the financing period and the useful life of the project, defined over the year $t = 0$ to $t = n_{u}$ , the general cash flow formulation is subject to the following conditions:

$\begin{array}{c} F_{S} (t) = {- ϕ . C A P E X_{S} - O P E X_{S} (t) - Π_{S, k} - O P E X_{S} (t) - O P E X_{S} (n_{u}) + S V_{S} (n_{u}) w h e n t = 0 1 \leq t, k \\ \leq n_{f} n_{f} + 1 \leq t \leq n_{u} - 1 t = n_{u} \end{array}$

The presented cash flow formulation highlights how revenues and expenses will behave over the analyzed period. However, this analysis is limited to assessing inflows and outflows within a single evaluated project. To determine the feasibility between two proposed projects—particularly in cases involving the transition between motorization technologies—it is necessary to assess the incremental cash flow [70].

According to [71], the calculation of incremental cash flow to assess the economic feasibility of implementing an electric bus is described by Equation (12)

(12) $N P V_{S} = C a s h f l o w o f E B - C a s h f l o w o f D B [$]$

As previously described, the assessment of the economic feasibility of transitioning between propulsion systems for UFPB’s internal transportation is conducted using the economic metrics presented in Table 2. Therefore, considering the financing model and the established cash flow conditions, the NPV, which is a modification of [72], is calculated using Equation (13), where

$N P V_{S} = C a s h f l o w o f E B - C a s h f l o w o f D B [$]$

thus,

(13) $N P V_{S} = \sum_{t = 0}^{n_{u}} \frac{F_{S} (t)}{{(1 + W A C C)}^{t}} [$]$

In this context, instead of using a single attractivity rate, the Weighted Average Cost of Capital (WACC) is applied. This rate, calculated using Equation (14), is used in conjunction with financing models and represents a company’s average cost of capital across all funding sources. It serves as a common method for determining the required rate of return expected by investors [64,73].

(14) $W A C C = r_{e} ϕ + r_{d} (1 - ϕ) (1 - T)$

where r_e (%) is the cost of equity—the minimum return expected by the investor—

ϕ

(%) is the equity share, representing the proportion of own capital, r_d (%) is the cost of debt,

(1 - ϕ)

(%) and T (%) represent the marginal tax rate, which is based on established tax regulations.

According to [72], the formulation of the IRR is developed when the NPV becomes zero; that is, when the discount rate equals the present value of cash inflows and outflows at a given point. Thus, as shown in Equation (15), the IRR is implicitly calculated by

(15) $\sum_{t = 0}^{n_{u}} \frac{F_{S} (t)}{{(1 + I R R)}^{t}} = 0$

where, as defined in [67],

$\begin{array}{l} i f {I R R \geq W A C C & \to T h e i n v e s t m e n t i s e c o n o m i c a l l y a t t r a c t i v e I R R < W A C C \\ \to T h e i n v e s t m e n t s h o u l d b e r e j e c t e d \end{array}$

As described in Table 2, the DP aims to identify the year t_DP in which, considering the minimum rate WACC and within the project’s useful life, the recovery of the invested capital occurs through the investment’s cash flow. For this purpose, based on the considerations presented in [53], the calculation of the t_DP is given by Equation (16).

(16) $t_{D P} = \{t_{d p} | \sum_{t = 0}^{t_{D P}} \frac{F_{S} (t)}{{(1 + W A C C)}^{t}} \geq 0\} [y e a r]$

where t_DP is the first year in which the cumulative sum becomes non-negative.

As described in [74,75], the total cost of ownership (TCO) aims to calculate the overall cost of acquiring and operating a product or system over time, including financial factors. Originally, Equation (17) defines the calculation of the nominal TCO, given by

(17) $T C O_{S, n o m i n a l} = C A P E X_{S} + \sum_{t = 1}^{n_{u}} O P E X_{S} (t) - S V_{S} (n_{u}) [$]$

However, as described in [76] it is necessary to apply the WACC rate to future costs in order to equate them with present-day values. As a result, considering the previously defined cash flow conditions, the calculation of the TCO is described by Equation (18).

(18) $T C O_{S, N P V} = {ϕ . C A P E X_{S} ⏟}_{w h e n t = 0} + \sum_{t, k = 1}^{n_{f}} \frac{O P E X_{S} (t) + Π_{S, k}}{{(1 + W A C C)}^{t}} + \sum_{{t = n}_{f} + 1}^{n_{u} - 1} \frac{O P E X_{S} (t)}{{(1 + W A C C)}^{t}} - \frac{S V_{S} (n_{u})}{{(1 + W A C C)}^{n_{u}}} [$]$

The final metric used to assess the financial viability of both projects is the break-even point (BEP). This metric enables a comparison between the two technologies (EB and DB) through the accumulated cost (A_C) of the cash flow, allowing for the identification of the year in which this accumulated cost reaches zero [77]. The calculation of the nominal A_c is given by Equation (19).

(19) ${A C}_{S, n o m i n a l} (t) = - \sum_{t = 0}^{n_{u - 1}} F_{S} (t) + S V_{S} (n_{u}) [$]$

Meanwhile, in terms of the WACC rate, Equation (20) is used,

(20) ${A C}_{S, N P V} (t) = - \sum_{t = 0}^{n_{u} - 1} \frac{F_{S} (t)}{{(1 + W A C C)}^{t}} + \frac{S V_{S} (n_{u})}{{(1 + W A C C)}^{n_{u}}} [$]$

thus, in order to identify the BEB year in which the project costs become equal, the following condition must be met:

${C_{c}}_{D B, N P V} (B E P) = {C_{c}}_{E B, N P V} (B E P)$

3.5. Performance and Analysis of GHG Emissions

The selection of electric bus models follows technical and operational feasibility criteria, ensuring market representativeness and suitability for urban and university transportation. The vehicles were chosen based on commercial availability, energy efficiency, autonomy, operational cost, and passenger capacity, allowing for a balanced comparative analysis among different electric propulsion configurations.

The selection of the analyzed models was based not only on their commercial availability and technical characteristics but also on their relevance to urban and university transportation scenarios. This selection allows for the evaluation of different approaches to fleet electrification, considering variables such as energy efficiency, autonomy, and operational costs. Thus, the results obtained help formulate strategies for optimizing vehicle selection in future electrification initiatives.

Additionally, priority was given to models that incorporate widely adopted technologies in the sector, such as lithium-ion batteries and energy regeneration systems, essential elements for assessing performance and cost-effectiveness. To ensure that the results can be extrapolated to different urban and academic scenarios, the selection was based on technical data provided by manufacturers, operational performance reports, and academic studies on sustainable transportation.

Based on these criteria, five EB models were selected to be compared with the reference DB model. The selection of these vehicles was based on previous studies, such as those presented by [18], which details the main models, manufacturers, and technical specifications of EBs available on the market. Table 9 presents the list of selected models, ensuring compatibility with the operational characteristics of the reference model.

Based on the technical specifications available regarding battery size and the distance that each EB model can travel, the EBEC index for each model was obtained. This index is used in the formulation of the technical and economic indicators presented earlier.

Within the presented life cycle analysis (Figure 9), the BEB electric bus achieved the lowest emission result in gCO₂eq. The usage emissions of ICEBs, particularly from combustion, still represent the highest emission index, even when using 10% biodiesel in the fuel composition. Possible future increases in the biodiesel blend could lead to near-zero combustion emissions from an LCA perspective, provided that biofuels improve and vehicle lifespan considerations are addressed.

Electricity generation is a key factor in the life cycle of BEBs. Regions with predominantly low-emission energy sources, such as Brazil, are more suitable for electric vehicle adoption. Economic studies and charging technologies associated with the simultaneous use of renewable sources, such as photovoltaic energy, are expected to gradually facilitate the market penetration of BEBs in Brazil [78,79,80,81].

In the production phase, battery manufacturing remains a major contributor to emissions. There is still a need for greater efficiency in battery production processes and increased use of low-emission electricity, primarily sourced from renewables. Additionally, changes in certain chemical components used in battery production could further reduce emissions.

In this case study, we considered a lifespan of 10 years for the BEB, equivalent to 500,000 km. Since the vehicle’s weighted range does not exceed the number of cycles expected for the original battery, battery replacement was excluded from the maintenance phase. The results indicate that the BEB’s emissions are lower than those of the I_CEB. However, in a sensitivity analysis for a 20-year period—estimated as the vehicle’s end-of-life—maintenance-related emissions for the BEB would be 40% to 50% higher than those of the ICEB.

The battery recycling process for vehicles is still in its infancy in the Brazilian market. Even globally, there are no regulations requiring 100% recycling. The most common practice is pyrometallurgy, which can extract only cobalt, nickel, and copper [26]. The current volume of end-of-life vehicle batteries is still too low to drive a viable reuse market. Additionally, economic factors and user perceptions often lead to a preference for new batteries over reused ones.

As an opinion leader, UFPB aims to achieve carbon neutrality in its fleet by aligning its planning with the Paris Agreement and the 2030 Agenda for Sustainable Development, thereby mitigating its climate impact. Under Brazil’s Nationally Determined Contributions (NDC) commitments at COP 26, greenhouse gas (GHG) emissions must gradually decrease over the coming years to reach a 43% reduction by 2030 and achieve neutrality by 2060 [82].

Consequently, replacing the existing I_CEB combustion bus (with 10% biodiesel) with a corresponding electric model would result in a 43.33% reduction in CO₂eq emissions, meeting the 43% reduction target by 2030 in the LCA scenario. Achieving neutrality by 2060 for BEBs is only viable under the LCA scenario if mass adoption of renewable electricity sources, process efficiency improvements, and awareness regarding vehicle battery reuse are promoted. In a sensitivity analysis considering emissions from a renewable energy source (photovoltaic systems), which is the most accessible option for UFPB, the proposed LCA scenario could lead to a 52.52% reduction.

4. Results and Discussions

In this section, the results obtained from the methodology and indicators defined for evaluating the performance and economic feasibility of EBs compared to the reference DB are analyzed. The data is discussed from technical, economic, and environmental perspectives, emphasizing energy efficiency, operational costs, and greenhouse gas (GHG) emissions throughout the vehicle life cycle.

4.1. Results of Indicators Analisys

The comparative analysis of five electric bus models and one reference diesel model reveals significant advantages of electric vehicles regarding operational costs and environmental impact. As illustrated in Figure 10 and detailed in Table 10 and Table 11, the key performance indicators obtained include average mileage per charge (km_e), energy equivalence (kWh_e), cost indices (D_CBI, E_BCI), and aggregated total cost values (T_CEB, T_CDB).

The results indicate that the diesel model presented a total cost (T_CDB) of USD 48.91, significantly higher than the most expensive electric model analyzed (BYD Double Decker, with T_CEB of USD 19.63). Among the electric buses, the Optare Solo EV stood out as the most efficient alternative, recording the lowest total cost (T_CEB of USD 9.6) and the highest autonomy (km_e of 800.83). This combination suggests a highly advantageous cost-benefit ratio, balancing operational savings and extended range without frequent recharging.

One of the key factors for the economic feasibility of fleet electrification is the I_V index, which represents the percentage variation in operational costs between electric buses and the diesel model. This index quantifies the cost savings generated by electrification, highlighting the reduction in expenses related to energy consumption and maintenance. Negative I_V values indicate lower operational costs for electric buses compared to diesel models. The more negative the I_V, the greater the cost savings from fleet replacement.

For example, the Optare Solo EV recorded a I_V of −80.38%, meaning its operational costs are 80.38% lower than those of the diesel bus. Even the BYD Double Decker, which showed the lowest savings among the electric models analyzed, still achieved a 59.87% reduction in operational costs compared to the reference model. These data reinforce that, although the initial acquisition cost of electric buses may represent a significant investment, recurring savings throughout the vehicle’s life cycle can offset this difference, making electrification financially viable in the medium and long term.

This combination suggests a highly beneficial cost-benefit ratio, balancing operational savings and increased flexibility in operations, which are crucial factors for implementing electric transport in urban and university settings. Other electric models, such as the Solaris Urbino 8.9 and Evopro Modulo C88e, also showed significant reductions in operational costs, surpassing the diesel model in energy efficiency. Even the BYD Double Decker, which recorded the highest cost among the electric models analyzed, still showed a total cost 59.87% lower than the diesel model, indicating that electrification can provide recurring financial benefits, even for larger vehicles.

The Energy Efficiency of Electric Buses (EEEB) is also highlighted as a strategic advantage. The Operational Cost Index of the diesel bus was USD 0.27/km, while all electric models analyzed presented lower values for the electric bus Operational Cost Index, demonstrating a lower cost under operational conditions.

Furthermore, the cost difference (negative in most cases) indicates that although the initial implementation requires significant investment, the recurring financial benefits make this transition highly rewarding. As previously highlighted, the Optare Solo EV presented the highest cost reduction (80.38%), but even the smallest reduction (59.87% in the BYD Double Decker) still represents a significant economic advantage compared to the reference model.

Another relevant aspect of the analysis is the equivalent distance traveled by electric models. The lowest value found was −36.2% relative to the original route (184 km), indicating that all analyzed electric models can cover the required distance with ease, maintaining energy efficiency and lower fueling costs. This factor is crucial for the feasibility of fleet electrification in university environments, where shorter distances favor the autonomy of electric vehicles.

From an environmental impact perspective, the analyzed electric buses exhibit significantly lower GHG emissions compared to diesel models. This is critical for reducing the carbon footprint of public transportation and aligns with global environmental commitments, such as the Paris Agreement and the Sustainable Development Goals (SDGs).

The data analyzed indicate that replacing diesel fleets with electric buses can result in significant reductions in operational costs, energy consumption, and environmental impact. In addition to the superiority of electric buses in energy efficiency and cost savings, their applicability in university settings appears even more viable, considering:

Shorter and predictable routes, maximizing battery autonomy.

Integration with renewable energy sources, such as UFPB’s photovoltaic power generation, reducing operational and maintenance costs, which are fundamental for the financial sustainability of the university transportation system.

Beyond operational savings, university fleet electrification brings long-term environmental and economic benefits. GHG emission reductions not only improve local air quality but also contribute to achieving global sustainability goals, such as those outlined in the Paris Agreement.

Thus, despite implementation challenges, university fleet electrification proves to be a viable and sustainable solution. Models like the Optare Solo EV and Solaris Urbino 8.9 have significant potential for university environments, combining autonomy, low cost, and energy efficiency.

4.2. Results of Cost-of-Living Analysis

The conditions established to evaluate the economic transition between diesel and electric bus technologies involve defining a range of parameters related to operation and equipment acquisition, service life, energy tariffs and fuel prices, financing models, among others. All these parameters are incorporated into the previously described equations, with the initial objective of individually assessing the resulting cash flow of each technology and, subsequently, evaluating the incremental cash flow using the defined economic metrics.

As a result of the literature review, the parameters required for this analysis are presented in Table 12, considering the reference of Mascarello Gran Mini 2016 diesel bus and Optare Solo EV electric bus model. The costs associated with both electric and diesel buses were obtained through market surveys, particularly using data provided by the online platform Mercado Livre, which lists recent commercial models such as the 2019 Mascarello Urbano equipped with air conditioning. The costs related to the charging infrastructure for electric vehicles, as well as operational estimates concerning daily distance traveled, charger power, charging time, and operational lifespan, were primarily based on studies conducted by the Brazilian Energy Research Office (EPE).

Regarding financial parameters associated with the cost of capital, such as the cost of equity, recent historical data on Brazil’s benchmark interest rate (Selic), as provided by the Central Bank of Brazil, were used. The capital structure, cost of debt, and marginal tax rate followed standard references for Brazil, as indicated by the Teleco portal, which specializes in financial and tax structures.

The annual growth rate of electricity costs employed in the financial model was based on projections published by the EPE, which forecast an average annual growth of 2.1% in energy consumption over the next decade. Conversely, the annual increase in diesel costs was derived from official historical data provided by the National Agency of Petroleum, Natural Gas and Biofuels (Agência Nacional do Petróleo, Gás Natural e Biocombustíveis—ANP), which maintains updated historical series on fuel prices in Brazil.

The values established for the operational parameters were based on the daily operation of the UFPB bus, along with data provided for the electric bus, which supported the selection of the charger and charging time, as reported by [53]. Additionally, maintenance cost estimates were derived from the diesel bus operation, if electric buses incur, on average, 25% lower maintenance costs. Financial parameters were obtained from the study conducted by [54,83].

Based on the defined boundary conditions, it was possible to assess the feasibility of replacing diesel bus technology with electric buses, as illustrated in Figure 11.

The figure presents the individual cash flows for each technology, with the electric bus represented in blue and the diesel bus in red. On the right side, the incremental cash flow between the two technologies is shown, reflecting the potential modernization of UFPB’s transport system through the adoption of electric buses. The corresponding results are presented in Table 13.

The initial investment cost for the electric bus (year 0) is higher than that of the diesel bus, resulting in a more negative initial cash flow. However, over time, the electric bus demonstrates significantly lower operational costs. From the 10th year onward, its annual cash flows become less negative or even positive, signaling long-term economic benefits.

The “Incremental Approach” graph on the right illustrates the difference in cash flow between the electric and diesel buses (EB–DB). While the initial year shows a negative incremental value—reflecting the higher upfront cost of the electric bus—the electric option begins to recover this disadvantage from the first year. By year 4, the incremental cash flow turns consistently positive, clearly favoring the electric bus in economic terms.

A key financial indicator, the Internal Rate of Return (IRR), is 36.92%, significantly exceeding the Weighted Average Cost of Capital (WACC) of 9.19%. This result confirms the project’s high financial attractiveness and viability. According to standard investment criteria, a project is considered acceptable when IRR ≥ WACC.

Additionally, the NPV is approximately USD 57,709.07, indicating positive economic value generated by choosing the electric bus over the diesel alternative during the ten-year analysis period.

The discounted payback period is 4 years, at the same time of BEP, which is considered reasonable for sustainable infrastructure investments. This short recovery period further strengthens the case for the electric bus, as the investment is recouped quickly and followed by substantial cost savings.

4.3. Limitations and Risks in the Implementation of Electric Buses

Despite being a key governmental strategy to promote more sustainable urban mobility, the implementation of EBs faces several challenges that hinder large-scale deployment. Technologically, the current limitations of battery technology are a major constraint. Low energy density restricts the driving range of electric buses, requiring either larger and heavier batteries or more frequent charging [85].

Economically, high upfront costs remain a significant barrier. This includes not only the higher price of electric buses compared to diesel models but also the substantial investments needed for charging infrastructure and power grid upgrades. Mass electrification increases demand on the electrical grid, requiring careful planning to prevent overloads and service disruptions [86].

From an environmental perspective, the benefits of electric buses can be reduced if the electricity used for charging comes from fossil fuels. Moreover, emissions from battery production and disposal must be closely monitored to avoid unintended environmental impacts [86].

In response, several strategies have been adopted in pilot projects worldwide. These include raising public awareness of electric vehicles, investing in robust charging infrastructure, implementing smart energy systems for off-peak charging, and encouraging innovation in vehicle design and materials. Addressing these barriers with targeted actions can make the electrification of public transport more viable and accelerate its adoption.

5. Conclusions and Futures Perspectives

This section presents the conclusions of the study, summarizing the key findings from the performance evaluation of electric buses compared to traditional models in promoting the transition to sustainable university transportation. This study assessed the feasibility of implementing electric buses for internal transportation at UFPB, considering technical, economic, and environmental aspects. The results demonstrate that transitioning to this technology can provide significant benefits, particularly in reducing greenhouse gas (GHG) emissions, improving energy efficiency, lowering operational costs, and enhancing the quality of life of the academic community. Furthermore, the presence of two photovoltaic solar power plants on campus represents a strategic advantage for energy self-sufficiency, making fleet electrification both sustainable and economically viable.

The comparative analysis between electric and diesel buses highlighted that electrification of the university fleet can reduce GHG emissions by up to 85.90% when powered by photovoltaic energy. This shift not only reinforces UFPB’s environmental commitments but also positions the institution as a model of innovation and sustainability for other universities. Additionally, replacing conventional buses with electric models can lead to significant reductions in operational costs throughout the vehicle life cycle, cutting expenses on fuel and maintenance.

The financial indicators—positive NPV, IRR significantly higher than the WACC, and a short payback period—reinforce the case for adopting electric vehicles over conventional diesel buses. The differences observed in relation to the traditional EPE methodology, particularly the inclusion of annual incremental changes in diesel and electricity prices, offer a more realistic perspective on the economic advantages of electric buses. Nevertheless, the findings remain conceptually aligned with the outcomes typically reported by the institution.

When compared to the traditional methodology adopted by EPE, the results remain consistent, with minor deviations. These differences are mainly attributed to two factors: (i) the application of annual growth rates for fuel and energy costs in this study, whereas EPE assumes constant prices; and (ii) variations in the WACC and initial cost assumptions, reflecting project-specific conditions and implementation contexts.

However, despite its advantages, electric fleet implementation faces challenges that must be addressed to ensure feasibility. The high initial cost of vehicle acquisition and charging infrastructure remains a significant barrier, requiring alternative financing models, government incentives, and strategic partnerships. Another crucial factor is community acceptance, as adopting this new technology requires user engagement and adaptation to a new internal transportation model.

UFPB’s experience can be strengthened by national and international examples that demonstrate that public transport electrification is viable when there is structured planning, financial support, and adequate infrastructure. Cities such as Santiago (Chile) and Bogotá (Colombia) have achieved significant reductions in operational costs and pollutant emissions by investing in fleet electrification. In Brazil, cities like São Paulo and Curitiba have advanced in adopting this technology, while universities such as UNICAMP and UFSC have already demonstrated the feasibility of solar-powered electric buses for internal transportation. These cases reinforce the potential for replicating the model at UFPB.

Based on the study’s findings, further exploration is recommended regarding the integration of electric mobility with photovoltaic energy generation, including the feasibility of energy storage in batteries to maximize the use of electricity generated internally. Additionally, future research could focus on optimizing the vehicle charging system, considering UFPB’s energy consumption profile and smart charging strategies to reduce costs and improve fleet efficiency.

The efficiency of fleet electrification is directly linked to the availability of charging infrastructure and battery management. Future studies should explore the implementation of smart charging stations integrated with renewable energy sources, such as UFPB’s existing solar photovoltaic generation. Additionally, the investigation of overnight charging strategies and fast-charging systems could optimize vehicle autonomy and reduce peak energy demand. These analyses will be essential for improving operational planning and ensuring greater efficiency in the university’s electrical system.

The adoption of new transportation technologies in a university setting depends on community acceptance and engagement. Therefore, future studies should include qualitative and quantitative surveys, through questionnaires and interviews with students, faculty, and staff. This assessment will help identify perceptions, concerns, and suggestions regarding fleet electrification, as well as evaluate user adoption levels and potential incentives to encourage sustainable mobility practices. The data collected will provide valuable insights to adjust the system implementation and develop strategies that promote widespread adoption.

The transition to an electric fleet requires in-depth analysis of economic feasibility and financing strategies. Future research should investigate financing models and public–private partnerships, considering government subsidies and tax incentives for the purchase of electric vehicles and installation of charging infrastructure. Additionally, it is essential to analyze the long-term financial impacts of fleet electrification, considering operational cost reductions, maintenance savings, and the institutional value of sustainability initiatives. These studies will help develop internal policies and financial strategies to ensure project viability while minimizing budgetary impacts on the university.

The adoption of electric buses on UFPB’s campus represents a concrete solution to the challenges of sustainable mobility, aligning with global targets, such as the Paris Agreement and the Sustainable Development Goals (SDGs). In addition to reducing GHG emissions and improving energy efficiency, electrification of university transportation can drive research and technological innovations, consolidating UFPB as a leader in developing sustainable mobility solutions and contributing to accelerating the energy transition.

Author Contributions

Conceptualization: I.B., S.A.S.d.S., E.T.C.S., G.B.S., J.J.G.L. and O.H.A.J., investigation and simulation I.B., S.A.S.d.S., E.T.C.S., G.B.S., J.J.G.L. and O.H.A.J., writing and final editing: I.B., S.A.S.d.S., E.T.C.S., G.B.S., J.J.G.L. and O.H.A.J. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors would like to thank to the Federal University of Latin American Integration (UNILA) and the Federal Rural University of Pernambuco (UFRPE) for financial supporting and facilities, as well as Coordination for the Improvement of Higher Education Personnel (CAPES). This research was partially supported by the FACEPE agency (Fundação de Amparo a Pesquisa de Pernambuco) throughout the project with references APQ-0616-9.25/21 and APQ-0642-9.25/22. O.H.A.J. was funded by the Brazilian National Council for Scientific and Technological Development (CNPq), grant numbers 407531/2018- 1, 303293/2020-9, 405385/2022-6, 405350/2022-8 and 406662/2022-3.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AD	Author Database
AI	artificial intelligence
ANN	Artificial Neural Network
ARIMA	Autoregressive Integrated Moving Average
ATBLS	Adaptive Time-shifting Broad-Learning System
AUTOML	Auto-Machine Learning
ACDB	Average Consumption of Diesel Buses
AC	accumulated cost
BESS	Battery Energy Storage Systems
BEP	break-even point
BEB	battery electric bus
BLS	Broad-Learning System
BMA	Bayesian Model Averaging
BMLR	Bootstrap Multiple Linear Regression
BMS	Battery Management System
BNN	Bayesian Neural Network
BP	Bibliography Portfolio
BPNN	Back Propagation Neural Network
CAPSNET	Capsule Neural Network
CAS	Constant Amortization System
CAPEX	capital expenditure
CCHLA	Center for Human Sciences, Letters and Arts
CCJ	Center for Legal Sciences
CI/CTDR	Computer Science Center/Center of Technology and Regional Development
CNG	Compressed Natural Gas
CO₂	Carbon Dioxide
CPFL Energia	Companhia Paulista de Energia de Luz
DB	diesel bus
D_C	diesel cost
DCJ	Department of Legal Sciences
D_BCI	Diesel Bus Cost Index
DP	discounted payback
EB	electric bus
E_C	energy cost
EPI	Energy Performance Index
EBEC	Electric Bus Energy Consumption
EEEB	Energy Efficiency of Electric Buses
E_BCI	Electric Bus Cost Index
GHG	greenhouse gas
HVO	hydrotreated vegetable oil
I_V	Index Variation
I_CE	internal combustion engine
IRR	Internal Rate of Return
km	Kilometer
kWh	Kilowatt-hour
kW_p	Kilowatt-peak
LCA	life cycle analysis
NPV	Net Present Value
OPEX	operational expenditure
SV	salvage value
TCO	total cost of ownership
T_CDB	Total Cost of Diesel Buses
T_CEB	Total Cost of Electric Buses
WACC	Weighted Average Cost of Capital

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 Flowchart of methodology for obtaining life cycle emission indicators.

Figure 2 Route taken by UFPB’s circular bus.

Figure 3 UFPB’s internal circular bus [46].

Figure 4 Flowchart of methodology for obtaining equivalent indicators for EBs.

Figure 5 Flowchart of methodology for Stage A.

Figure 6 Equation development for obtaining energy equivalence indicators.

Figure 7 Development of cost indicators D_CBI and E_BCI.

Figure 8 Methodology for cost-of-living analysis. Adapted from [54].

Figure 9 Results obtained for life cycle analysis (LCA) for UFPB buses.

Figure 10 Development of cost indicators IOD and IOE.

Figure 11 Methodology for cost-of-living analysis.

Table 1

Information main components of fully electric vehicles.

Component	Description
Powertrain	Responsible for converting the electrical energy stored in the batteries into mechanical energy. During braking, it recovers part of the kinetic energy and converts it back into electricity.
Battery	Stores electrical energy. Lithium-ion batteries are the most widely used due to their high energy density and operational efficiency.
Auxiliary Systems	In an electrified vehicle, each auxiliary system, such as the power steering system, may require an independent small electric motor to function correctly, as their rotational speeds and nominal power differ
Power Converters and Inverters	Inverters convert the direct current (DC) from the batteries into alternating current (AC) and adjust the voltage for the equipment. Converters regulate voltage to ensure the proper functioning of these systems.

Table 2

Internal route and route between main campus (CCHLA) and Lynaldo Cavalcanti unit (CI/CTDR).

Time	Departure	Destination	Route
06:40 a.m.	Internal route (two loops within the main campus)		6.4 km
07:20 a.m.	CCHLA (Principal Campus)	CI/CTDR	6.4 km
08:00 a.m.	CI/CTDR	CCHLA (Principal Campus)	6.9 km
09:20 a.m.	Internal route (two loops within the principal campus)		6.4 km
10:20 a.m.	Internal route (two loops within the principal campus)		6.4 km
11:20 a.m.	Internal route (two loops within the principal campus)		6.4 km
12:20 p.m.	CCHLA (Principal Campus)	CI/CTDR	6.4 km
01:00 p.m.	CI/CTDR	CCHLA (Principal Campus)	6.9 km
02:20 p.m.	Internal route (two loops within the principal campus)		6.4 km
03:20 p.m.	Internal route (two loops within the principal campus)		6.4 km
04:20 p.m.	Internal route (two loops within the principal campus)		6.4 km
05:20 p.m.	Internal route (two loops within the principal campus)		6.4 km
06:20 p.m.	CCHLA (Principal Campus)	CI/CTDR	6.4 km
07:00 p.m.	CI/CTDR	CCHLA (Principal Campus)	6.9 km
08:20 p.m.	Internal route (one loop within the principal campus)		3.2 km
09:30 p.m.	CCHLA (Principal Campus)	CI/CTDR	6.4 km
10:00 p.m.	CI/CTDR	CCHLA (Principal Campus)	6.9 km
Total Distance per Day			107.6 km/day

Table 3

Route between main campus (CCJ) and Santa Rita campus (DCJ/CCJ).

Time	Departure	Destination	Route
06:30 a.m.	CCJ (Principal Campus)	DCJ/CCJ (Santa Rita)	19.4 km
01:00 p.m.	DCJ/CCJ (Santa Rita)	CCJ (Principal Campus)	18.8 km
04:50 p.m.	CCJ (Principal Campus)	DCJ/CCJ (Santa Rita)	19.4 km
10:20 p.m.	DCJ/CCJ (Santa Rita)	CCJ (Principal Campus)	18.8 km
Total Distance per Day			76.4 km/day

Table 4

Characteristics of circular bus.

Model/Year	Capacity	Fuel	Gross Weight (kg)	Fuel Efficiency (km/L)	Engine Power (hp)
Gran-Midi Mascarello/2016	30 person	Diesel/S10 *	14,000	3.0	19.4 km

Note: * Based on MPV Law 647/2014 [19], which mandates the addition of biodiesel to diesel fuel, the National Petroleum Agency (ANP) became responsible for regulating the volumetric participation of biodiesel in diesel (B10: 90 vol.% diesel, 10 vol.% biodiesel).

Table 5

General information operating conditions, diesel properties, and economic parameters.

Operating Conditions
Route Distance [km]	Vehicle Consumption [km/L]		Number of Loops [loops]
184	4.5		21
Diesel Properties
Density [kg/L]		Lower Heating Value [kWh/kg]
0.846		11.83
Economic Parameters
Diesel Cost [USD/L]		Energy Cost [USD/kWh]
1.19		0.102

Table 6

Categorization of CAPEX and OPEX costs, and CAS model [57,58,59].

Category	Description
CAPEX	Refers to expenditures that require substantial up-front investment, such as the acquisition of equipment when it involves a one-time purchase.
OPEX	Refers to ongoing and recurring expenses that are fully recognized as deductions within the fiscal year in which they are incurred.
CAS	In this model, the principal is repaid through equal amortization installments, resulting in decreasing total payments over time. Therefore, the total interest paid by the end of the financing period is lower compared to other repayment systems.

Table 7

Description of metrics for economic feasibility assessment [60,62,63].

Metric	Metric Description
IRR	Metric related to the return generated by the activity over a specific period.
DP	Considers the payback period for the invested capital by establishing a maximum timeframe for investment recovery, considering a discount rate or required rate of return.
NPV	Compares the initial investment with the present value of the project’s future cash flows, considering the time value of money.
TCO	Indicates the actual investment required for the acquisition of assets by the company.
BEP	It represents the level of sales required for a company to cover all its costs, that is, the point at which profit equals zero.

Table 8

Description of metrics for economic feasibility assessment. Adapted from [67,68].

Step	Description	Equation
1°	Define the total amount to be financed (P).	$P_{S} = (1 - ϕ) . C A P E X_{S}$	(7)
2°	Calculate the amortization value (A).	$A_{S} = P_{S} / n_{f}$	(8)
3°	Calculate the annual interest on the outstanding balance (AR).	$A R_{S, k} = D B_{S, k - 1} . r_{d}$	(9)
4°	Determine the payment for the period by adding the amortization to the calculated interest ( $Π$ ).	$Π_{S, k} = A_{s} + A R_{S, k}$	(10)
5°	Calculate the final debit balance by subtracting the amortization from the previous debit balance (DBk).	$D B_{S, k} = D B_{S, k - 1} - A_{S}$	(11)

Table 9

Top manufacturers, models, and EB specifications.

Model	Size	Size of Battery	Distance	Energy Consumption
Model	[m]	[kWh]	[km]	[kWh/km]
BYD Double Decker	10.2–12	345	330	1.045
Solaris Urbino 8.9	8.9	160	200	0.8
Optare Solo EV	10.8–12	138	270	0.511
Evopro Modulo C88e	9.5	84	120	0.7
DCGT Temsa MD9	9.3	200	230	0.869

Table 10

Results obtained from the indicators for analyzed EB models.

Model	EBEC [kWh/km]	km_e[km]	E_PI[-]	E_BCI [USD/km]	I_V[%]	T_CEB [USD]
BYD Double Decker	1.04	391.6	0.84	0.11	−59.87	19.63
Solaris Urbino 8.9	0.8	511.53	0.39	0.08	−69.28	15.03
Optare Solo EV	0.51	800.83	0.79	0.05	−80.38	9.6
Evopro Modulo C88e	0.7	584.6	0.21	0.07	−73.12	13.15
DCGT Temsa MD9	0.87	470.91	0.49	0.09	−66.63	16.32

Table 11

Results obtained from the indicators for reference DB model.

Model	kWh_e	ACDB	D_CBI [USD/km]	T_CDB [USD]
Diesel Bus	122.77	0.67	0.27	48.91

Table 12

Description of general, operational, and financial parameters [53,54,83,84].

Bus Parameters	Electric Bus	Diesel Bus
Bus cost (BRL)	85,078.13	148,886.73
Charging infrastructure cost (BRL)	27,951.39 *	-
Salvage value (%)	20	66
Maintenance cost per km (BRL/km)	0.227	0.304
Energy/Fuel efficiency (kWh/km)(km/L)	0.511	1.35 **
Initial electricity/fuel cost (BRL/kWh)(BRL/L)	0.102	1.196
Electricity/diesel cost growth rate (% per year)	2.1	13.04 ***
Service life	10	5
Operational parameters
Operational days per year	252
Distance per day (km)	184
Charger power (kW)	42
Charging time (hours)	~4
Financial parameters
Equity portion (%)	20
Cost of equity (% per year)	14.25 ****
Cost of debt (% per year)	12
Financing period (electric bus, years)	5
Financing period (diesel bus, years)	10
Marginal tax rate (%)	34

Dolar quotation: BRL 1.00 is equal to USD 5.76 at 31/03. * Charging is considered exclusively in garage. ** 30% of value is considered in relation to efficiency of a diesel engine. *** Average rate of percentage differences over the past 10 years is considered. **** Selic rate as of 20 March 2025 is considered.

Table 13

Results obtained from cost-of-living analysis.

WACC	9.19%
IRR	36.92%
NPV	USD 57,709.07
DP	4 years
Analysis based on a single bus unit	Diesel bus	Electric bus	Difference in TCO₁₀
TCO in year 10	USD 504,754.86	USD 392,552.08	USD 112,202.78D

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Abstract

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Sustainable mobility in university environments presents both a challenge and an opportunity to reduce environmental impact and promote energy efficiency. This study assesses the feasibility of implementing electric buses in the internal transportation system of the Federal University of Paraíba (UFPB), considering environmental, economic, and operational aspects. The analysis demonstrates that transitioning to this model can lead to a significant reduction in greenhouse gas (GHG) emissions, noise pollution mitigation, and optimization of operational costs throughout the vehicle’s life cycle. The study examines technical, structural, and financial factors, emphasizing the necessary infrastructure, academic community acceptance, and the economic viability of the project, as well as the strategic advantage of integrating the electric fleet with photovoltaic energy generation. The key highlights of this research include: (i) Sustainability and energy efficiency, emphasizing a reduction of up to 52.52% in CO₂ emissions when vehicles are powered by photovoltaic energy in an LCA context, alongside improvements in air quality and noise pollution mitigation. (ii) Economic feasibility analysis, comparing operational and maintenance costs between electric and conventional diesel buses, evaluating the financial viability and potential return on investment. (iii) Infrastructure and implementation challenges, addressing the need for charging stations, adaptation of UFPB’s infrastructure, and financing models, including government subsidies and strategic partnerships. (iv) Impact on the academic community, analyzing student and staff perceptions and acceptance of fleet electrification and the promotion of sustainable practices. (v) Future projections and replicability, exploring trends in the sustainable transportation sector, as well as the potential expansion of the electric fleet and its integration with energy storage systems. The results indicate that adopting electric buses at UFPB can position the institution as a benchmark in sustainable mobility, serving as a replicable model for other universities and contributing to carbon emission reduction and modernization of university transportation.

Details

Title

Sustainable Mobility: Analysis of the Implementation of Electric Bus in University Transportation

Author

Borne Ivonete¹

; Souza Sara Angélica Santos de¹

; Carniatto Silva Evelyn Tânia²

; Soares Gabriel Brugues³

; Gimenez Ledesma Jorge Javier⁴

; Ando Junior Oswaldo Hideo⁵

¹ Graduate Program in Civil and Environmental Engineering (PPGECAM), Technology Center (CT), Federal University of Paraiba (UFPB), João Pessoa 58051-900, PB, Brazil; [email protected] (I.B.); [email protected] (S.A.S.d.S.)
² Graduate Program in Energy Engineering in Agriculture (PPGEA), Western Paraná State University (UNIOESTE), Cascavel 85819-110, PR, Brazil; [email protected]
³ Interdisciplinary Graduate Program in Energy and Sustainability (PPGIES), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil; [email protected] (G.B.S.); [email protected] (J.J.G.L.)
⁴ Interdisciplinary Graduate Program in Energy and Sustainability (PPGIES), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil; [email protected] (G.B.S.); [email protected] (J.J.G.L.), Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, PE, Brazil
⁵ Interdisciplinary Graduate Program in Energy and Sustainability (PPGIES), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil; [email protected] (G.B.S.); [email protected] (J.J.G.L.), Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, PE, Brazil, Smart Grid Laboratory (LabREI), Center for Alternative and Renewable Research (CEAR), Federal University of Paraiba (UFPB), João Pessoa 58051-900, PB, Brazil

First page

2195

Publication year

2025

Publication date

2025

Publisher

MDPI AG

e-ISSN

19961073

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.3390/en18092195

ProQuest document ID

3203194190