Integrating Environmental and Social Life Cycle Assessment for Sustainable University Mobility Strategies

Abstract

Universities play a critical role in shaping sustainable mobility strategies, especially in urban contexts where the institutional transport system can influence environmental and social outcomes. This study integrates Environmental and Social Life Cycle Assessment (E-LCA and S-LCA) to evaluate the current university transport system from internal combustion engines, diesel, and compressed natural gas (CNG), focusing on the operation and maintenance phases. Also, it compares seven scenarios, including electric, renewable sources, and biodiesel technologies. Environmental impacts were assessed using the ReCiPe 2016 midpoint method, which considers the following impact categories: Global Warming Potential (GWP); Ozone Formation, Human Health (OfHh); Ozone Formation, Terrestrial Ecosystem (OfTe); Terrestrial Acidification (TA); and Fine Particulate Matter Formation (FPmf). The sensitivity analysis explores scenarios to assess the effects of technological transitions and alternative energy sources on the environmental performance. Social impacts are assessed through a Social Performance Index (SPI) and Aggregated Social Performance Index (ASPI), which aggregates indicators such as safety, travel cost, punctuality, accessibility, and inclusive design. Accessibility emerged as the lowest indicator (ranging from 0.61 to 0.67), highlighting opportunities for improvement. Our findings support decision-making processes for integrating sustainable transport strategies into a University Mobility Plan, emphasizing the importance of combining technical performance with social inclusivity.

1. Introduction

Higher education institutions (HEIs) play a crucial role in advancing sustainability through academic research, education, and training, as well as through institutional commitments and practices [1]. Academic institutions are also essential for establishing evaluation criteria for transit buses, incorporating social, environmental, economic, technological, and mobility-related considerations [2]. Sustainable transportation aims to ensure that transportation systems meet the economic, social, and environmental needs of society while minimizing their negative impacts [3,4]. In this context, the sustainability assessment of transport services, including public transit, considers environmental, socio-economic, and technological dimensions innovations [5] and their sustainable impact on safety, health, and equity [6].

For environmental transport sustainability analysis, the Environmental Life Cycle Assessment (E-LCA) is a method used to analyze a product’s life cycle and then establish environmental impacts, from the production of raw materials to transport, use, disposal, reuse, and vehicular emissions [7]. In addition, E-LCA generates useful information for decision-makers through the evaluation of direct and indirect impacts associated with bus manufacturing and operations of transportation systems [8,9,10,11]. Recently, Rodrigues da Silva et al. [12] applied E-LCA to evaluate the environmental impacts associated with university campuses, specifically considering the broader environmental implications of transportation systems from a life cycle perspective, part of broader efforts to incorporate sustainability into HEIs.

E-LCA studies on public transit primarily focus on comparing fuel options, such as diesel, diesel–electric (hybrid), compressed natural gas (CNG), and fully electric buses, as well as evaluating other advanced technologies [11,13,14]. The combustion process generates air pollutants, such as carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), nitrogen dioxides (NO₂), nitrogen monoxide (NO), and particulate matter (PM_2.5), among others. In transportation, these emissions, along with fuel consumption, are referred to as direct emissions because they are directly linked to the primary purpose of the system [15]. This includes not only the direct emissions from vehicle operation but also the full energy-cycle emissions involved in fueling the vehicle [16].

The evolution of E-LCA on HEIs has been influenced by the social pillar of sustainability and technological advancements [17], viewed through a life cycle perspective incorporating the assessment of social impacts [18,19,20] and integrating Social Life Cycle Assessment (S-LCA) [20,21]; Qiu et al. [22] incorporate life cycle thinking into a continuous-improvement framework by means of a structured E-LCA methodology to evaluate the environmental impacts of products and services. Some higher education institutions operate their own transportation systems, offering an ideal context for comprehensively assessing the environmental performance of these systems.

Numerous sustainable transport strategies implemented in university settings begin with a comprehensive assessment of current mobility conditions [23]. These initiatives have increasingly been explored through the lens of Mobility as a Service (MaaS) and modal shift strategies. MaaS is an innovative approach that aims to transform transportation systems into more sustainable and efficient services for users by integrating multiple transport modes and services into a single digital platform. In university settings MaaS holds significant potential as a scheme for effectively shifting demand away from private vehicles and fostering more sustainable travel behavior by facilitating access to multimodal alternatives and promoting environmentally conscious mobility choices [24,25,26,27].

Modal shift strategies serve as a foundation for the planning and management of mobility in university settings, aiming to reduce car dependency and promote greater use of eco-friendly transport options, thereby encouraging sustainable transportation behaviors, such as e-bikes, car-sharing, taxis, walking, and cycling [28]. According to Guido et al. [29], the Home University Travel Plan (HUTP) focuses on initiatives that enhance the mobility of academic communities through sustainable and inclusive practices. This aligns with broader institutional sustainability goals. Case studies from the University of Thessaloniki (Greece) and the University of Calabria (Italy) demonstrate how HUTPs can encompass actions such as promoting public transport, shared mobility services, and the adoption of electric vehicles. These measures contribute to decreasing reliance on private vehicles, reducing single-occupancy trips, and mitigating congestion during peak travel periods.

However, as noted by Rodrigues da Silva et al. [12], these strategies vary significantly. The analysis of sustainable transport policies indicates that it can be of value to shift the choice mode of campus commuters towards more sustainable transport practices for students, which requires social indicators, such as accessibility [30], security of transport modes [12], safety [7], and resource efficiency [31]. According to a report by Klimi [32], mobility requirements are closely tied to issues of accessibility, gender, and transportation modes, while in non-urban areas, factors such as income and travel time play a significant role in transport decisions, through the formulation and implementation of environmental regulations [33]. In this context, university transport services, including shared mobility options, play a key role in implementing these strategies. By offering accessible, affordable, and low-emission alternatives, these services encourage a shift in user behavior, thereby supporting the transition toward a more efficient and sustainable mobility system within the university setting [12,34].

HEIs play an essential role in achieving the Sustainable Development Goals (SDGs) [35], and in that sense, many university campuses have implemented environmental protection strategies [36], leveraging their roles in education, research, and the transfer of knowledge to society and governance, acting as catalysts for social change [37]. Assessment and monitoring frameworks serve as effective instruments for understanding and enhancing the contribution of academic activities toward the achievement of the SDGs [38] and provide a valuable framework for identifying targeted strategies tailored to university campuses, which is urgently needed to mitigate the adverse impacts of unsustainable transportation practices on carbon emissions, local air quality, campus health, and institutional budgets. Furthermore, these strategies play a key role in fostering a culture of sustainability within academic communities [39]. By aligning campus mobility initiatives with the SDGs, HEIs can systematically address environmental challenges, such as greenhouse gas (GHG) emissions and air pollution, while simultaneously tackling social issues, including public health, accessibility, and financial sustainability. This strategic alignment not only supports global sustainability objectives but also strengthens the role of universities as catalysts for transformative change at local and regional levels [18].

The Autonomous University of Mexico State (UAEMEX) is a public university whose developing program (2021–2025) [40] describes the university transport “Potrobus” as a strategy to address the students’ transport requirements while also ensuring the implementation of sustainable practices that contribute to environmental protection. The Potrobus is free for students (users) and is a reliable service that reduces the number of cars in academic spaces; it includes the implementation of diesel and low-emission vehicles powered by natural gas, with features for accessibility and infrastructure improvements. The Potrobus has 17 routes, of which 6 are shared with the Potro Green Bus (CNG units), according to data from the 2021–2025 Institutional Developing Program [40]; in 2019, it served 10,276 users, a number that decreased to 6331 the following year due to the COVID-19 pandemic.

As part of its institutional commitment to sustainability and climate change mitigation, the UAEMEX implements a range of strategies aimed at neutralizing its carbon footprint. Among these initiatives, the promotion of sustainable university transportation stands out as a key measure to reduce GHG emissions associated with daily commuting by the university community. This strategy encourages the adoption of low-carbon mobility options, including university-operated collective transport, bicycle-sharing systems, the use of electric vehicles, and the development of safe routes for walking and cycling [41]. Alanis et al. [23] recently conducted a study assessing the social perception of the mobility system at UAEMEX through a survey of the university community. The research evaluated five key dimensions: accessibility, employment, mobility and connectivity, safety and public health, and livability. The results indicated that 22% of respondents rely on the Potrobus service, a transportation system powered by diesel and CNG, specifically designed to offer safe, efficient, and affordable mobility for students within and around the campus.

Building on this context, the present study contributes to the field of sustainable mobility by employing an innovative approach that integrates Environmental and Social Life Cycle Assessments to evaluate university transport strategies. To further strengthen this line of research, it would be relevant to examine mobility planning initiatives in other comparable university settings and to assess the effectiveness of proposed interventions in reducing environmental impacts, enhancing social inclusion, and influencing travel behavior, particularly by monitoring the outcomes of implemented sustainable mobility measures [29]. Unlike previous studies focused mainly on environmental aspects, this dual perspective framework enables a comprehensive analysis by also considering social dimensions of the academic community. Furthermore, the proposed methodology of E-LCA and S-LCA serves as a practical and replicable tool for other HEIs seeking to align their mobility policies with the SDGs, thus supporting evidence-based decision-making for the development of sustainable and socially inclusive campuses. Regarding future research developments, it would be relevant to assess mobility planning initiatives in other comparable university contexts and to evaluate the effectiveness of the proposed interventions in terms of reducing environmental impact, enhancing social inclusion, and influencing travel behavior, particularly through the monitoring of outcomes resulting from the implementation of sustainable mobility measures.

The objective of this study has three major contributions: 1) to evaluate the current environmental impacts of the university transport service (diesel and CNG); 2) to compare the energy scenarios in the operation phase for reducing environmental impacts with electric, renewable sources, and biodiesel technologies; and 3) to study social impacts associated to users of the university transport service, with all of the above from a sustainable vision towards the Sustainable University Mobility Plan.

2. Materials and Methods

This study was conducted according to the ISO 14040 [42] and 14044 [43] standards. In concordance, the E-LCA phases were 1) goal and scope definition, 2) inventory analysis, 3) impact assessment, and 4) interpretation, analyzing the environmental and social dimensions of the sustainability assessment. For the goal and scope definition, the outcome of this phase is the same for both the environmental and the social dimensions of the assessment.

2.1. Goal and Scope Definition

The objective was to evaluate the potential environmental impacts of the transport Potrobus powered by diesel (Bus 1) and the Potro green bus fueled by CNG (Bus 2). The target beneficiaries of this analysis were students because it is a free transport service and will positively improve the public’s environmental and social awareness. The scope of this analysis, as shown in Figure 1, is gate to gate or wheel to wheel; it was considered the use or operation by varying the energy source for its functioning between diesel and CNG, and the maintenance required for one year of service. For S-LCA, additional transport services were analyzed (private car, motorcycle, taxi, and urban bus), focusing primarily on university transport services powered by diesel (Bus 1) and CNG (Bus 2), within the users’ impact category.

2.2. System Boundary and Functional Units

The system boundary of university transport services included Bus 1 for diesel and Bus 2 for CNG, and social indicators, as shown in Figure 1. The stages that fall outside the system boundaries are fuel production, bus manufacturing, transportation of maintenance supplies, and end of life. Bus 1 and Bus 2 cover fuel use (including diesel production, electricity generation, and charging infrastructure). The evaluated Bus 1 was a Volksbus^® and Bus 2 was a Dina^®, both Euro 6 [44], with a service frequency of every 25,000 km for Bus 1 or 5000 engine operating hours for Bus 2; the maintenance supplies and spare parts include lubricating oil, tires, spark plug, filters (air and fuel), coolant, battery, and electric energy.

The functional unit for the environmental analysis was person-kilometer (pkm) [45]; the reference flow was adjusted to 164 passengers during off-peak hours, with a total of 4 rounds, two trips per day, with a daily travel distance of 174.16 km and 180 days operating during the year. Data obtained experimentally of the route named Ixtlahuaca involved timing the journey, recording the mileage of a round trip, and counting the passenger flow. The distance was calculated using Google Earth based on the bus route from Ixtlahuaca to the university main campus in Toluca for both the outbound and return trips, with an average distance of 87.08 km per trip.

2.3. Life Cycle Inventory (LCI)

2.3.1. Operation Phase

The inventory analysis for this stage, as shown in

Table 1. Inventory analysis per functional unit (pkm); operation stage.

University Transport Services	Data Inventory	Inputs	Outputs	Source
Bus 1	Diesel	1.69 × 10−4 kg	-	Experimental
	CO2	-	6.30 g	[47]
	CO	-	0.12 g
	NOX	-	0.03 g
	PM2.5	-	9.53 × 10−5 g
	NO	-	0.02 g
Bus 2	CNG	4.84 × 10−3 L	-	Experimental
	CO2	-	5.92 g	[48]
	CO	-	7.93 × 10−3 g
	NOX	-	5.37 × 10−3 g
	NO	-	5.0 × 10−3 g
	PM2.5	-	6.10 × 10−6 g
	NO2	-	1.22 × 10−4 g

, includes the consumption of diesel and CNG fuels as the primary inputs associated with the operation of university transport services. Fuel efficiency was determined experimentally; fuel consumption was measured over a complete route at three different hours with passenger loads in one week, taking into consideration the data provided by drivers. The average results were considered with yielding values of 3.17 km/L for Bus 1, and 1.26 km/L for Bus 2, compared with SEMARNAT and INECC [46]. The outputs were calculated using emission factors derived from burning fossil fuels in buses, considering the release of harmful pollutants like CO₂, CO, NOx, and PM_2.5 [47,48]. These sources provided standardized emission factors that, when combined with the measured fuel consumption data, enabled the estimation of pollutant outputs associated with the operation of diesel and CNG buses under typical university transport conditions.

2.3.2. Maintenance Phase

During the maintenance phase, data were gathered through interviews with drivers and technicians. This analysis focuses on inputs associated with preventive maintenance carried out in workshops, while corrective maintenance (such as repairs resulting from defects or accidents) was excluded. For Bus 1 the main preventive maintenance periodicity is every 25,000 km. The used lubricating oil was 15W40; for the coolant it was considered the mixture of coolant and demineralized water. The tire size was 11R22.5, electric energy from the workshop was taken with the electricity bills, and washing water was considered; for both buses it was considered a lead battery. The filter and spark plugs were made of aluminum alloy, with service lives of 500 and 1000 h, respectively. The difference for Bus 2 is the main preventive maintenance periodicity (every 500 h); see

Table 2. Inventory analysis per functional unit (pkm); maintenance stage.

University Transport Services	Inputs		Outputs		Source
Bus 1	Lubricating oil	6.23 × 10−6 kg	Lubricating oil	6.23 × 10−6 kg	Experimental
	Aluminum alloy	5.60 × 10−7 kg
	Aluminum alloy	1.40 × 10−8 kg	-	-
	Water, ultrapure	7.66 × 10−7 kg	-	-
	Ethylene glycol	3.28 × 10−7 kg	-	-
	Tap water	4.90 × 10−4 kg	-	-
	Synthetic rubber	2.82 × 10−6 kg	-	-
	Electricity	2.82 × 10−6 kg	-	-
	Battery, lead	6.23 × 10−6 kg	-	-
Bus 2	Lubricating oil	1.29 × 10−5 kg	Lubricating oil	1.29 × 10−5 kg	Experimental
	Aluminum alloy	5.60 × 10−7 kg	-	-
	Aluminum alloy	1.40 × 10−8 kg	-	-
	Water, ultrapure	3.58 × 10−6 kg	-	-
	Ethylene glycol	1.53 × 10−6 kg	-	-
	Tap water	4.90 × 10−4 kg	-	-
	Synthetic rubber	2.82 × 10−6 kg	-	-
	Electricity	1.62 × 10−3 kg	-	-
	Battery, lead	7.65 × 10−6 kg	-	-

. The data quality and uncertainty characterization are addressed in

Table 3. The pedigree matrix criteria selected for LCI and mean scores: 1, low uncertainty; 2, moderately low uncertainty; 3, moderate uncertainty; 4, moderately high uncertainty; and 5, high uncertainty.

Criteria for LCI	Bus 1. Fuel Efficiency	Bus 1. Maintenance	Bus 2. Fuel Efficiency	Bus 2. Maintenance
Reliability	1.5	2.5	1.5	2.8
Completeness	3	1.8	3	1.8
Temporal correlation	1	1	1	1
Geographical correlation	1	1	1	1
Technological correlation	1	1	1.5	2

with a pedigree matrix.

2.3.3. Pedigree Matrix for LCI

The pedigree method allows for quantitative uncertainty analysis even when empirical variability data is not available. It facilitates the assessment of not only parameter uncertainty but also nonparametric uncertainties related to the technical quality, methodological choices, and epistemic limitations inherent in a dataset. In Ref. [49] based on the criteria of Qin et al. [50], see Table 3, the uncertainty of the experimental data specifically relates to fuel efficiency and maintenance requirements for Bus 1 and 2.

2.4. Life Cycle Impact Assessment

For the impact assessment phase, the environmental impact assessment was carried out using the software SimaPro PhD 10.2.0.2 [51]. The method for evaluating E-LCA was ReCiPe 2016 Midpoint (H) V1.09/World (2010) H selecting the next midpoint impacts [52] Global Warming Potential (GWP) (kgCO₂eq), Ozone Formation, Human Health (OfHh) (kg NOx eq), Fine Particulate Matter Formation (FPmf) (kg PM_2.5 eq), Ozone Formation, Terrestrial Ecosystem (OfTe) (kg NOx eq), and Terrestrial Acidification (TA) (kg SO₂ eq) [13,21,53]. The impacts categories were evaluated at the midpoint level (i.e., environmental mechanisms) due to their wide geographical coverage, from a hierarchist cultural perspective (i.e., impact assessment is realized through current scientific evidence, and the potential future impact is determined from a risk-aware perspective) (Bicer & Dincer, 2018) [16], and the used database was ecoinvent 3.10 [54]. The environmental impact reported is the carbon footprint of such mobility in terms of kg of CO₂ per student [5,55].

2.5. Interpretation

Within the decarbonization of the transportation sector, electricity and renewable alternative sources reduce global warming by being low GHG emitters when using photovoltaic panels for energy generation [56]. Sustainable mobility practices reduce electricity consumption for these components or utilize renewable energy, which is likely to have a positive impact on the system’s life cycle footprint. The sensitivity analysis objective was to assess scenarios for reducing environmental impacts: electric bus, a hybrid energy source system considering 50% electric by country mix and 50% solar photovoltaic generation, and powered entirely by a solar photovoltaic source. For the electric scenario, the electricity energy mix corresponds to the national grid mix of Mexico as available in the ecoinvent database [57].

The analysis also included scenarios with biodiesel–diesel blends at varying proportions: 10% biodiesel and 90% diesel (designated as B-10), 15% biodiesel in diesel (B-15), 20% biodiesel in diesel (B-20), and 25% biodiesel in diesel (B-25), ensuring the accurate interpretation of the environmental and technical implications of this study. Then the seven scenarios proposed include S1 (electric), S2 (electric and renewable), S3 (renewable), and biodiesel mixtures: S4 (B-10), S5 (B-15), S6 (B-20), and S7 (B-25), as shown in Figure 2. The ecoinvent model was as follows: Fatty acid methyl ester {RoW}| treatment of used vegetable cooking oil, purified, esterification | Cut-off, U.

It is important to acknowledge that the E-LCA conducted in this study relies extensively on experimental and operational data, which inherently involve sources of uncertainty. These uncertainties may arise from measurement errors, variability in fuel consumption under different operational conditions, estimations associated with maintenance activities, and assumptions regarding the energy mix employed [58]. Such uncertainties can considerably affect the accuracy and robustness of the impact results, particularly in comparative assessments, such as the present case.

Specifically, input data uncertainties may be linked to data quality, the type and version of databases used, data representativeness, system boundaries, and the temporal validity of data. Methodological uncertainties, in turn, may emerge from decisions regarding allocation methods, selection of impact assessment methods, characterization factors, software tools, and other modeling assumptions [58,59]. Addressing these uncertainties through systematic approaches, such as sensitivity analysis, scenario analysis, or Monte Carlo simulations, would enhance the transparency and reliability of the results and support more informed decision-making in future studies [60].

2.6. Social Life Cycle Assessment (S-LCA)

For the impact assessment of social sustainability, the reference scale approach proposed by the United Nations’ Guidelines for S-LCA of Products and Organizations [61] was adopted. These guidelines identify users as a key stakeholder group for analysis. Additionally, the Handbook for Product Social Impact Assessment (PSIA) includes several stakeholder groups (categories) that align with those presented in the framework [61]. To assess both positive and negative impacts, a five-point scale from 0 to 1.0 was employed, evaluated by means of specific data (see Table 4).

The social impact weighting method was employed to examine the feasibility of implementing sustainability policies on the university campus to obtain Social Performance Indexes (SPIs) [20]. The SPIs were derived from five social impact subcategories: accessibility, safety, travel cost, punctuality, and inclusive design. Each indicator was evaluated based on scenario-specific data and scored using predefined performance criteria. To account for this, priority indices were derived from stakeholder interviews, where respondents ranked each subcategory according to its perceived importance within the respective impact category. To achieve these objectives, participants received the questionnaire through Google Forms, and to obtain the stratified sample size, the data reported in the Statistical Agenda was used [62], resulting in 372 students divided into ten academic spaces, considering the previous study of [23].

The new SPI scale ranged from 1 to 5, allowing for both discrete and continuous values. To summarize the social performance of the assessed transport service in a single score, an Aggregated Social Performance Index (ASPI) was also calculated. This index combines the weighted performance scores of the individual social impact indicators into a single composite measure, providing an overall assessment of social performance [63].

Table 4. Label assessment framework in S-LCA, adapted from [20,63].

Color *	Original Scale	New Scale		Label
	Continuous	Discrete	Continuous
	1.0–0.81	5	5.0–4.3	Very positive
	0.8–0.61	4	4.2–3.4	Positive
	0.6–0.41	3	3.3–2.6	Neutral
	0.4–0.21	2	2.5–1.8	Negative
	0.2–0	1	1.7–1.0	Very negative

* Colors range from green to red, representing performance levels for each indicator.

Table 4. Label assessment framework in S-LCA, adapted from [20,63].

Color *	Original Scale	New Scale		Label
	Continuous	Discrete	Continuous
	1.0–0.81	5	5.0–4.3	Very positive
	0.8–0.61	4	4.2–3.4	Positive
	0.6–0.41	3	3.3–2.6	Neutral
	0.4–0.21	2	2.5–1.8	Negative
	0.2–0	1	1.7–1.0	Very negative

* Colors range from green to red, representing performance levels for each indicator.

Ostovar [64] proposed that the methodology for social performance assessment ensures a comprehensive evaluation of the university’s mobility system, covering transportation options, including university buses 1 and 2 (diesel and CNG), private cars, motorcycles, and the urban bus service. Specifically, this research seeks to understand how students, particularly campus commuters (users), perceive and respond to policies related to accessibility, safety, travel costs, punctuality, and inclusive design (see

Table 5. Modes of transport used at the university and classifications of social impact subcategories by impact categories for users.

Modes of Transport	Stakeholder	Subcategory	Indicator
Bus 1, bus 2, private car, motorcycle, taxi, and urban bus	Users	Accessibility	Access points
			Number of passengers
		Safety	Accidents
		Travel cost	Amount of money paid per trip
		Punctuality	Punctuality
		Inclusive design	Inclusive design

); these indicators were selected based on studies reported by [19,65,66]. According to Gompf et al. [19], in the case of the user’s stakeholder group, most indicators focus on accessibility, safety, and convenience (punctuality). Additionally, Alanis et al. [23] reported on social performance measures of a university mobility service, in which the survey questions were organized into five categories: accessibility, jobs, mobility and connectivity, safety and public health, and livability. These dimensions reinforce the relevance of indicators such as accessibility and safety. The overlap in selected categories supports the validity of our indicator framework within the context of the university transport system.

In the context of university mobility, the accessibility subcategory refers to key elements, such as the number and distribution of access points, which are essential for allowing users to enter and exit the transport system with ease, thereby enhancing its attractiveness and usability. Another relevant factor is the number of passengers, which indicates the system’s capacity and demand, offering insights into how well it meets users’ needs and preferences. While the university’s transportation system aims to incorporate features that improve accessibility in its buses, bus stops still pose daily challenges for individuals with reduced mobility. Moreover, accessibility has been widely discussed in the literature, with several authors emphasizing the importance of access points and passenger flow in evaluating transportation services [67,68,69,70].

The safety subcategory in the university mobility system encompasses both the perceived and actual safety experienced by users. A key indicator within this dimension is the number of accidents, which serves as a measurable parameter to evaluate the system’s performance in protecting users from harm. Monitoring accident data not only reflects the operational safety of the transport service but also contributes to understanding users’ trust and confidence in the system [71,72].

The travel cost describes the amount of money paid per trip; the affordability of the service is a fundamental factor for users, especially for vulnerable or low-income groups, as it directly affects their capacity to access mobility options. Several studies have highlighted that when transport is perceived as unaffordable, users may resort to less safe or more polluting alternatives or may reduce their academic or social participation. Therefore, integrating travel cost considerations into university mobility planning is essential to ensure a fair and accessible transport system for the entire campus community [72,73,74]. According to Yang et al. [71], social benefits may be transformed into economic benefits for users, derived from the availability and quality of the transport infrastructure.

Punctuality measures the reliability of the service from the user’s perspective, as delays or deviations from scheduled times can significantly impact their daily activities and overall satisfaction. In university transport systems, punctuality is especially important given the strict academic schedules of students and staff. It affects user trust, influences commuter behavior, and plays a key role in optimizing service performance. As research shows, considering punctuality as a core indicator supports more efficient, equitable, and user-centered campus mobility planning [12,75,76].

Inclusive design evaluates the extent to which the transport system incorporates inclusive design principles and ensures that the needs of all users, including those with disabilities or reduced mobility, are considered, contributing to the system’s social equity and accessibility. This goes beyond basic accessibility by promoting a transport experience that is safe, comfortable, and dignified for everyone. Key elements include accessible vehicles (e.g., low-floor buses, ramps), properly designed and signposted stops, priority seating, and clear visual and auditory information. In the university context, adopting inclusive design is essential to ensure equal access to educational opportunities, as transportation is often a gateway to full participation in campus life [19,77].

3. Results and Discussion

3.1. Life Cycle Assessment: University Transport

The operation of Bus 1, powered by diesel, was identified as the stage with the highest environmental impact, as reported in [13,78,79]. As shown in Figure 3, for Bus 1, the operation had an environmental contribution of 100%, and maintenance in the range 1.3–37.4%, in all the impacts studied [80]. In Bus 2, operation contributes to a range of 20.4–72.8%, and maintenance to 1.4–39.2%. For example, an E-LCA conducted on the metropolitan transport system in Shanghai demonstrated that 92.1% of the total CO₂ emissions resulted from the system’s operations stage [81].

Among the potential environmental impacts that are most reported, related to the operation of different energy sources (diesel and CNG), see

Table 6. Impact categories for operation and maintenance stages of university transport using different fuels.

Impact Category	Unit	Bus 1 (Diesel)			Bus 2 (CNG)
		Operation	Maintenance	Total	Operation	Maintenance	Total
Global Warming	kg CO2 eq	8.14 × 10−3	1.04 × 10−3	9.18 × 10−3	5.92 × 10−3	1.07 × 10−3	6.99 × 10−3
Ozone Formation, Human Health	kg NOx eq	8.12 × 10−5	2.00 × 10−6	8.32 × 10−5	1.66 × 10−5	2.09 × 10−6	1.87 × 10−5
Fine Particulate Matter Formation	kg PM2.5 eq	3.78 × 10−6	1.41 × 10−6	5.20 × 10−6	3.65 × 10−7	1.48 × 10−6	1.85 × 10−6
Ozone Formation, Terrestrial Ecosystems	kg NOx eq	1.66 × 10−4	2.15 × 10−6	1.68 × 10−4	3.48 × 10−5	2.26 × 10−6	3.70 × 10−5
Terrestrial Acidification	kg SO2 eq	1.77 × 10−5	3.14 × 10−6	2.08 × 10−5	2.64 × 10−6	3.33 × 10−6	5.96 × 10−6

, was global warming, for Bus 1 (9.18 × 10⁻³ kgCO₂ eq), and for Bus 2 (6.99 × 10⁻³ kg CO₂ eq), per functional unit. The impact contribution emitted throughout the life cycle of each technology, representing between 94.3 and 89.6% of CO₂, 4.3–9% of methane (CH₄), and 1.3% nitrous oxide (N₂O) [46]. The combustion of CNG generates less CO₂ per unit of energy compared to diesel, but its calorific value is lower, which can reduce this advantage. In addition, the natural units have the limitation of not finding fuel as easily available as diesel. Among GHG, CNG can represent a more favorable climate alternative to diesel, if CH₄ leaks are properly controlled. In addition, it offers an advantage by reducing the generation of N₂O to some extent. CNG offers lower levels of GHG emissions than the diesel bus during combustion (exhaust gases) [46].

Regarding Ozone Formation, Human Health, and Terrestrial Ecosystems, Bus 2 emits less NO_x compared to Bus 1, 1.87 × 10⁻⁵ kg NO_x eq and 8.32 × 10⁻⁵ kg NO_x eq, respectively, in both stages.

Bus 1 has more Fine Particulate Matter Formation in combustion (5.20 × 10⁻⁶ kg PM_2.5 eq); its maintenance is less frequent, reducing the generation of waste with fine particles from the wear of components. The combustion of CNG generates less PM_2.5 in the exhaust (3.65 × 10⁻⁷ kg PM_2.5 eq), but its maintenance involves greater wear on valves and engine components, leading to increased particle emissions due to friction and material degradation, particularly under frequent or intensive use [82]. In both dual-fuel internal combustion engines (ICEs) using diesel and CNG, raw PM_2.5 emissions are significantly lower compared to conventional diesel engines, even after catalytic treatment. In the case of dedicated CNG engines, the combustion process itself does not generate PM_2.5 emissions; the only source of PM_2.5 in these engines is related to the presence of lubricating oil within the combustion chamber [83]. Maintenance in Bus 2, compared with the operation stage, represents significant environmental impacts in all the studied impact categories, mainly in Fine Particulate Matter Formation (1.48 × 10⁻⁶ kg PM_2.5 eq) and Terrestrial Acidification (3.33 × 10⁻⁶ kg SO₂ eq), as shown in Figure 2, due to the release of particles due to the wear of components and handling of filter and lubricant residues that can contribute to acidification.

3.2. Sensitivity Analysis (E-LCA)

The gathered data indicate that energy independence is the primary factor in assessing alternative vehicles, followed by the importance of energy availability and safety. Energy efficiency ranks third, highlighting the necessity for new buses powered by alternative fuels [2]. Vehicles powered by diesel and gasoline are the second-largest source of GHG emissions, accounting for 27% of the total GHG output. To mitigate and manage air pollution in metropolitan regions, numerous cities around the globe have initiated or planned efforts to replace traditional diesel buses used in public transportation with electric or fuel cell buses [8].

Early replacement of older buses that are still operational should be included among the key measures to support the transition toward cleaner and more sustainable bus fleets [13,14]. The assessment of seven alternative scenarios (S1–S7), which represent diverse fuel and technology pathways for university transport systems, offers valuable guidance for this transition. These scenarios can inform public procurement strategies and foster institutional initiatives aligned with broader sustainability and climate policy goals. As part of the strategies to evaluate the sustainability of university transport, different scenarios were proposed, as shown in Figure 4; scenario S3, corresponding to the use of renewable energies, such as solar photovoltaic, reduces the carbon footprint by 96.7%, followed by S2 by 80.1% and S1 by 63.5%. These results were compared with the findings of Razy-Yanuv & Meron [13], who reported that electric buses, which demonstrated the best overall environmental performance, reduce air pollution costs and noise by 94% and 73%, respectively, compared to Euro 5 diesel buses, by 78% and 44% compared to Euro 6 diesel, by 70% and 45% compared to CNG buses, and by 72% and 28% compared to hybrid buses.

Although electric buses face several technical, operational, economic, and environmental challenges, they also offer the greatest potential benefits. While electric buses deliver higher economic returns compared to CNG buses, both technologies provide comparable social benefits. However, in terms of local environmental impact, CNG buses perform significantly better than conventional diesel buses [13,21]. For the electric buses it was considered to have an energy consumption of 1.006 kWh/km [46]. It can be observed, as shown in Figure 3, that the university can reduce its carbon footprint by 96% by implementing an electrification of the mobility system.

While electric vehicles offer lower emissions during use, their overall environmental impact remains debated due to the resource-intensive battery production, challenges in battery disposal, and the carbon intensity of electricity used for charging [84,85]. Moreover, the limited capacity of existing power grids to support widespread electric vehicle adoption raises concerns about infrastructure readiness. These factors highlight the importance of assessing from a full life cycle perspective rather than focusing solely on their operational benefits [86,87,88].

Biodiesel has emerged as a promising alternative fuel to reduce dependence on fossil fuels. In addition to lowering anthropogenic emissions, it also holds the potential to stimulate the local economy [89]. A strategy that has been studied in institutions is the production of biodiesel from the waste cooking oil generated in university food courts from bifunctional heterogeneous catalysts [90,91,92]. When this waste is not properly managed, it poses a significant threat to the environment and public health by polluting bodies of water, clogging drainage systems, and producing toxic emissions if improperly incinerated or dumped. In this context, valorizing waste oil makes it possible to recognize its energy potential and transform it from an environmental problem into a sustainable solution [93], as well as an opportunity to evaluate conditions for a circular economy system in the university.

Biodiesel blends greatly reduced the GWP (kgCO₂eq) when compared to diesel [9,89]; therefore, a higher biodiesel blend leads to a lower impact on GWP. The biodiesel blends had a reduction in GWP between 7.06 and 17.61%, and as shown in Figure 5, this significant reduction is primarily due to the higher oxygen content and the lower carbon content in biodiesel blends, which produce lower CO emissions compared to conventional diesel fuel. The reduction in CO emissions with biodiesel is explained by several factors: increased engine speed, high load conditions, higher oxygen concentration, and lower carbon content [94]. This reduction can be attributed to the availability of excess oxygen and the more complete combustion of the dual fuel blends [95]. A similar reduction percentage was reported by Tibesigwa et al. [96] and Loo et al [94].

The environmental contribution of PM_2.5 was between 2.42% and 5.43%, as shown in Figure 5; when pure biodiesel and its blends undergo combustion, they produce less soot than conventional diesel fuel [94]. This significant reduction is primarily due to the higher oxygen content and the lower carbon content of biodiesel blends, which enhance the combustion process and reduce particulate emissions. As a result, biodiesel can contribute to improved air quality and reduced environmental impact compared to traditional diesel fuel.

B-20 is a commonly used biodiesel blend, as it represents a favorable balance between cost, emissions reduction, cold-weather performance, and compatibility with conventional diesel engines [89]. Most biodiesel users purchase B-20 or lower blends from their regular fuel distributors or specialized biodiesel suppliers. Additionally, regulated fleets that use biodiesel blends of 20% or higher can qualify for biodiesel fuel use credits under the Energy Policy Act of 1992.

To systematically evaluate the environmental performance of university transport alternatives, seven scenarios (S1 to S7) were defined and analyzed using the E-LCA approach; see

Table 7. Environmental impact categories for university transport service scenarios by energy source per functional unit (pkm).

Energy Source	Scenario	Impact Category
		GWP	OfHh	FPmf	OfTe	TA
		kg CO2 eq	kg NOx eq	kg PM2.5 eq	kg NOx eq	kg SO2 eq
Diesel		8.14 × 10−3	8.12 × 10−6	3.78 × 10−6	1.66 × 10−4	1.77 × 10−5
CNG		5.92 × 10−3	1.66 × 10−5	3.65 × 10−7	3.48 × 10−5	2.64 × 10−6
Electric *	S1	2.98 × 10−3	5.55 × 10−6	3.91 × 10−6	5.91 × 10−6	8.60 × 10−6
Electric and renewable **	S2	1.63 × 10−3	3.14 × 10−6	2.26 × 10−6	3.33 × 10−6	4.96 × 10−6
Renewable	S3	2.71 × 10−4	7.23 × 10−7	6.14 × 10−7	7.58 × 10−7	1.33 × 10−6
B-10 ***	S4	7.56 × 10−3	6.85 × 10−5	3.58 × 10−6	1.40 × 10−4	1.56 × 10−5
B-15	S5	7.28 × 10−3	6.85 × 10−5	3.62 × 10−6	1.39 × 10−4	1.56 × 10−5
B-20	S6	6.99 × 10−3	6.84 × 10−5	3.65 × 10−6	1.39 × 10−4	1.56 × 10−5
B-25	S7	6.71 × 10−3	6.84 × 10−5	3.69 × 10−6	1.39 × 10−4	1.56 × 10−5

* By country mix; ** solar photovoltaic; *** biodiesel blends.

. Each scenario represents a different energy source or operational strategy for the university bus fleet. The environmental impact categories assessed include GWP, OfHh, FPmf, OfTe, and TA, based on the ReCiPe 2016 midpoint method. This structured scenario analysis enables a comparative understanding of the environmental burdens associated with each alternative. The results show a progressive reduction in environmental impacts as the scenarios transition from fossil fuels to cleaner energy sources (solar photovoltaic), with S2 and S3 achieving the lowest emissions across all categories.

3.3. Social Impact Assessment: Users Stakeholder

As part of the sustainable analysis, the S-LCA made it possible to identify and evaluate the social impacts associated with the life cycle of the transport service. The analysis of social impacts in the transportation services used by UAEMEX students, the diesel and CNG buses, had good results in the subcategory of safety (0.95 and 0.88), travel cost (0.84 and 0.8), punctuality (0.81 and 0.80), and inclusive design (0.78 and 0.74), respectively, as shown in Figure 6.

Accessibility was the lowest subcategory for the university transport (Bus 1 and 2) (0.67 and 0.61), evaluated through indicators such as the number of access points and the volume of passengers served, due to the limited infrastructure on the routes. To enhance accessibility within university and urban transport systems, several targeted actions should be considered; expanding the coverage of existing bus routes can ensure that underserved areas within and around the campus are connected, thereby increasing the inclusivity of the service. Investments in inclusive infrastructure, such as low-floor buses, ramps, tactile paving, and audible stop announcements, would improve access for individuals with disabilities, older adults, and other vulnerable groups. Extending service hours, particularly during early mornings, evenings, and weekends, would accommodate the diverse schedules of students, faculty, and staff, enhancing equity and service usability. These strategies contribute to the broader goal of promoting sustainable and socially inclusive mobility [97,98,99].

For the case study and all transport services, the individual SPI for the five social impact subcategories were as follows (see

Table 8. Social Performance Indexes for each transport university service by impact subcategory.

Impact Subcategory	Transport Services						SPI
	Bus 1	Bus 2	Private Car	Motorcycle	Taxi	Urban Bus
Accessibility	4	4	5	5	4	4	4.3
Safety	5	5	5	5	5	3	4.7
Travel cost	5	5	4	5	3	4	4.3
Punctuality	5	5	5	5	4	4	4.7
Inclusive design	4	4	4	3	4	3	3.7
Mean	4.5	4.5	4.5	4.4	3.9	3.5	4.2
SPI by service	4.6	4.6	4.6	4.6	4.0	3.6	ASPI	4.3

Colors range from green to red, representing performance levels for each indicator, see Table 4.

): accessibility (4.3), safety (4.7), travel cost (4.3), punctuality (4.7), and inclusive design (3.7), from which an ASPI of the transport university services of 4.3 was obtained. The obtained ASPI of 4.3 is in the range of very positive performance and the slightly positive performance range, whose limit is the value of 4.2. The subcategories that most contribute to the very positive performance index were accessibility, safety, travel cost, and punctuality. Regarding the SPI by service, the urban bus scored the lowest (3.6), particularly in the categories of safety and inclusive design.

The main barriers to public transport usage were identified as long waiting times and congestion, whereas active mobility was mainly hindered by long distances, inadequate infrastructure, and unfavorable weather conditions. Public transportation was primarily chosen for its affordability and reduced travel time, while active modes were preferred for their cost savings and associated health benefits. To effectively promote sustainable mobility on campus, it is essential to encourage active transportation modes [100], invest in the development of efficient mass transit systems, and foster awareness through workshops and conferences aimed at engaging both students and staff. Addressing this issue is essential for advancing the Sustainable Development Goals achievement, particularly SDG 10 (Reduced Inequalities) and SDG 11 (Sustainable Cities and Communities), by ensuring that sustainable transport systems are accessible, affordable, and equitable for all.

The integration of E-LCA and S-LCA provides a comprehensive understanding of transport strategies in a university campus [6]. While the environmental E-LCA highlights key differences across alternative scenarios (S1–S7), the S-LCA complements these findings by evaluating social dimensions such as accessibility, safety, travel costs, punctuality, and inclusive design. For instance, the scenarios demonstrate significant advantages beyond mere reductions in environmental burdens. These cleaner technologies contribute to improved air quality by substantially lowering harmful emissions, such as particulate matter and greenhouse gases, which in turn positively impact public health outcomes among campus users and surrounding communities. Additionally, the adoption of such fuels often fosters greater social acceptance due to growing awareness of environmental and health benefits, enhancing the overall sustainability profile of the university’s transportation system. On the other hand, scenarios that prioritize expanded route coverage and the implementation of an inclusive infrastructure, such as accessible bus stops, ramps, and real-time service information, achieve higher scores in social indicators related to accessibility and the reduction in inequalities. These improvements are especially crucial for vulnerable groups, including individuals with disabilities, low-income students, and those residing in underserved areas, ensuring equitable access to essential mobility services. By employing a dual approach that integrates E-LCA and S-LCA, this study highlights the critical need to balance environmental efficiency with social equity [101]. This holistic perspective [102] enables the design of sustainable transport solutions that are not only ecologically responsible but also socially inclusive, thereby advancing the university’s broader goals of sustainability and community well-being. The findings of this study align with and contribute to the achievement of the SDGs, particularly SDG 9 (Industry, Innovation, and Infrastructure), SDG 10 (Reduced Inequalities), and SDG 11 (Sustainable Cities and Communities). Notably, SDG target 11.2 calls for access to safe, affordable, accessible, and sustainable transport systems for all, especially vulnerable groups [103].

3.4. Integrating Sustainable Transport Strategies into a University Mobility Plan

The strategies analyzed encompass fleet electrification, the use of renewable energy sources and biodiesel blends, expansion of route coverage with CNG, and the implementation of inclusive infrastructure and design to enhance accessibility and promote user equity. In relation to the operation of the analyzed scenarios (S1 to S7) and the social impact subcategories (accessibility, safety, travel cost, punctuality, and inclusive design), the results underscore the importance of promoting a greater number of eco-friendly university transport units. Such initiatives not only contribute to reducing environmental impacts but also enhance the social performance of transport services. This improvement is particularly critical in terms of user safety and ensuring accessibility for all segments of the university population, including those with reduced mobility or other special needs. Expanding the fleet of sustainable buses equipped with inclusive design features and prioritizing reliable, safe, and affordable transport options will help create a more equitable and environmentally responsible campus mobility system.

Currently, the transport system comprises 47 operational units, of which 38 run on diesel and 9 are powered by CNG. This distribution reflects the institution’s ongoing efforts to diversify its fleet with more sustainable alternatives while maintaining service coverage and efficiency [104]. It is important that the university develops a policy framework for a Sustainable University Mobility Plan (SUMP) [105], aiming to evaluate the feasibility of transitioning existing vehicle fleets towards more sustainable alternatives, while also promoting environmental awareness campaigns targeted at encouraging behavioral changes among users, fostering a culture of sustainable transportation both within and beyond the university context. In this way, the life cycle-based environmental assessment supports the formulation of actionable strategies to promote sustainable mobility transitions at both local and institutional levels. Consequently, mobility management becomes a crucial approach for designing and implementing strategies that ensure efficient mobility while addressing social equity, environmental sustainability, and energy conservation objectives [29].

Scenarios S4 to S7, which involve the use of biodiesel produced from waste cooking oil, represent a promising institutional strategy that could contribute to the circular economy by valorizing waste generated within the university itself [90], thus improving air and water quality and mitigating public health risks associated with improper oil disposal. The use of B-20 offers several advantages and challenges [14]. On the positive side, it requires only minor infrastructure upgrades and contributes to the reduction in GHG emissions. However, biodiesel is not readily available in all regions, and its cost per diesel gallon equivalent is generally higher compared to conventional fuels.

The case of the biodiesel plant at Mexico City’s Central de Abasto [106], which currently produces approximately 1350 L of biodiesel per day using technology developed by the National Polytechnic Institute, demonstrates the feasibility of scaling up such initiatives with public support. The biodiesel is blended with diesel in proportions ranging from B-5 to B-20 and is used in vehicles such as passenger buses. Establishing a pilot biodiesel production facility within the university could serve as a model for integrating sustainable fuels into institutional fleets while simultaneously promoting environmental awareness and fostering community engagement. These actions would contribute to advancing the broader goals of energy transition and sustainable mobility policies while delivering co-benefits in terms of environmental protection, public health improvement, and climate change mitigation.

4. Conclusions

This study presents an innovative and integrated approach by combining E-LCA and S-LCA to analyze sustainable university transport alternatives. The transport service called “Potrobus”, offered by the UAEMEX, constitutes a sustainable mobility strategy that seeks to facilitate the safe, free of charge, and efficient transfer of the university community (students) and thereby minimizes the polluting emissions associated with transport. As part of the bus life cycle assessment, the operation stages (use and maintenance) for diesel and CNG fuel were studied. The use of CNG is an alternative that reduces the impact categories: Global Warming Potential (27.2%), Ozone Formation, Human Health, and Terrestrial Ecosystems (79.6%), Fine Particulate Matter Formation (90.3%), and Terrestrial Acidification (85.1%). The maintenance of units that operate with CNG has a greater impact compared to its use, particularly in the formation of fine particles (1.48 × 10⁻⁶ kgPM₂.₅ eq/pkm) and Terrestrial Acidification (3.33 × 10⁻⁶ kg SO₂ eq/pkm), due to the emissions generated during the activities of inspection, substitution of materials, and disposal of waste, especially under conditions of frequent and intensive use. The environmental impact of the carbon footprint for Bus 1 (diesel) was 9.18 × 10⁻³ kg CO₂ eq/pkm, and for Bus 2 (CNG), it was 6.99 × 10⁻³ kg CO₂ eq/pkm. On the sensitivity analysis, the effect of electric by country mix consumption and renewable sources, such as solar photovoltaic, was studied; scenario S1 reduces the carbon footprint 96.7%, followed by S2 by 80.1% and S3 by 63.5%; the last being a cleaner mobility alternative, aimed at the decarbonization of transport and the improvement of environmental quality in cities.

Likewise, another alternative studied at UAEMEX was the production of biodiesel from waste cooking oil. Higher biodiesel blends (B-25) lead to a lower impact on Global Warming Potential, with the reductions in GWP ranging between 7.06% and 17.61%. PM_2.5 particulate emissions were between 2.42% and 5.43% of reduction compared to diesel. The use of B-20 offers several advantages and challenges, representing a favorable balance between cost, emissions reduction, cold-weather performance, and compatibility with conventional diesel engines. Moreover, it constitutes a promising institutional Sustainable University Mobility Plan, which could contribute to the circular economy by valorizing waste generated within the university.

A Social Life Cycle Assessment was carried out in the categories of students and the Potrobus transport service, identifying that accessibility is the lowest subcategory of Social Performance Indexes (0.67 and 0.61), due to the limited infrastructure in the current routes, evaluating for the best safety, travel cost, punctuality, and inclusive design. Among all transport services, the urban bus showed the lowest SPI score (3.6). Therefore, promoting a greater number of eco-friendly university transport units to improve overall service performance represents a vital step toward establishing a sustainable and socially equitable mobility system within the university community.

This dual perspective provides a more comprehensive understanding of both the environmental and social performance of the current transport system, and the proposed clean energy scenarios significantly reduce environmental impacts while contributing to public health improvements and greater social acceptance. Conversely, scenarios that expand route coverage and promote an inclusive infrastructure stand out in terms of accessibility and the reduction in inequalities. These findings provide valuable insights for institutional decision-making and for shaping public policies on sustainable mobility. Furthermore, including economic indicators in future analyses could enhance this integrated framework by incorporating a third dimension, a triple bottom line approach, allowing for the simultaneous assessment of environmental, social, and financial outcomes. This comprehensive approach provides a scalable and replicable planning tool for other university campuses aiming to develop a sustainable and inclusive mobility system.