Motor Vehicle Carbon Emission Reduction Strategy Using the PROMETHEE Approach: A Case Study in Higher Education

Rapid industrial expansion worldwide and the heavy dependence on finite energy resources have intensified the release of greenhouse gases, which in turn accelerates global warming and amplifies diverse environmental deterioration [1]. From the pre-industrial period around 1850 up to the year 2022, the mean concentration of atmospheric carbon dioxide (CO₂) increased markedly, rising from about 285 ppm to roughly 419 ppm [2]. By 2050, global emissions of greenhouse gases are expected to rise by roughly 50%, driven predominantly by carbon dioxide released from the continued reliance on non-renewable energy sources [3, 4]. Without the implementation of effective strategies or technologies aimed at reducing or controlling CO₂ emissions, global atmospheric CO₂ levels—as well as both surface and ocean temperatures—will continue to rise. The resulting global warming driven by greenhouse gases has already produced substantial ecological and societal impacts, including accelerated species extinction, declining biodiversity, prolonged droughts, severe flooding, widespread forest fires, ocean acidification, melting ice sheets in polar regions, and continuous sea-level rise [5, 6].

Without the implementation of effective strategies or technologies aimed at reducing or controlling CO₂ emissions, global atmospheric CO₂ levels—as well as both surface and ocean temperatures—will continue to rise. The resulting global warming driven by greenhouse gases has already produced substantial ecological and societal impacts, including accelerated species extinction, declining biodiversity, prolonged droughts, severe flooding, widespread forest fires, ocean acidification, melting ice sheets in polar regions, and continuous sea-level rise [7].

Under the Paris Agreement, countries pledged to prevent global temperatures from increasing beyond 2℃, while also striving to constrain the rise to no more than 1.5℃ [4]. As of February 2021, 124 nations had formally declared their intention to achieve carbon neutrality and realize net-zero emissions by either 2050 or 2060 [2, 8]. Meeting the objectives outlined in the Paris Agreement and advancing sustainable development demands a two-pronged strategy: lowering CO₂ emissions and extracting CO₂ already present in the atmosphere. Achieving net-zero or even negative carbon levels requires an integrated set of social, economic, environmental, and technological interventions.

Carbon neutrality denotes the attainment of net-zero carbon emissions, in which all direct and indirect releases of carbon dioxide or other greenhouse gases associated with a nation, organization, product, activity, or individual over a defined period are fully counterbalanced through carbon-offsetting or carbon-removal measures [9]. To achieve carbon neutrality, the Intergovernmental Panel on Climate Change (IPCC), through its special report on limiting global warming to 1.5℃, highlighted the critical need to curtail and ultimately eliminate fossil-fuel use, expand the deployment of renewable energy, improve energy-efficiency measures, and apply these strategies within urban environments [10, 11]. Furthermore, realizing net-zero emissions and advancing sustainable development requires strengthening efforts to remove or sequester carbon within both terrestrial and marine ecosystems [12, 13]. A range of regions, nations, and urban areas have adopted diverse initiatives designed to strengthen carbon sequestration or removal as part of their efforts to achieve carbon-neutral targets [14-16]. Nevertheless, achieving net-zero carbon emissions remains a highly demanding objective, accompanied by numerous substantive obstacles [1].

A comprehensive evaluation of higher education's carbon emissions is urgently needed, necessitating the use of integrated methods such as Multi-Criteria Decision Making (MCDM). This method systematically addresses the framework related to carbon emissions and the factors that influence them, which form the basis for selecting optimal reduction alternatives. Recent study findings emphasize the importance of MCDM in formulating carbon emission reduction strategies for higher education institutions. The study conducted by Angelo et al. [17] shows that MCDM is an effective method for improving decision-making in planning by simultaneously evaluating multiple sustainability dimensions of the environmental impacts associated with various development alternatives. This integration allows for the selection of policies that reduce emissions while promoting social equity and economic viability, all crucial issues [17].

Campus sustainability and its effects on the environment are becoming more and more important in densely populated higher education institutions. Without incentives and with just measures, universities can cut carbon emissions by about 560 tons annually [18]. However, the university's yearly emission reductions are projected to rise to 800 to 1,045 tons when incentives and interventions are combined. The significant institutions, such as universities, utilize a huge quantity of energy daily [19]. In addition, one of the few examples in the literature documenting energy usage in university buildings in tropical climes is the Paricarana Campus of the Federal University of Roraima (UFRR), Brazil [20]. From 2015 to 2017, the research examined the energy usage of 30 national universities, 9 national universities of science and technology, 17 private universities, and 16 private universities of science and technology in Taiwan. The four categories of institutions released 1518, 1350, 760, and 557 kg-CO₂e/person/year of greenhouse gases per student [21]. Moreover, the research employed a stochastic impact model with population, wealth, and technology regression (STIRPAT) to create a carbon emission forecast model for university buildings in a hot and humid location, using Guilin as an example. There were three scenarios: baseline, low-carbon, and ultra-low-carbon. The results indicated that the latter two scenarios had substantial potential for reducing carbon emissions. Emissions are predicted to reach a peak of 17,227.97 tCO₂ in 2028 under the low-carbon scenario. According to the ultra-low-carbon scenario, emissions are expected to peak earlier in 2021 and then drop to 13,999.60 tCO₂ in 2035, a 16.1% decrease [22, 23].

In a key contribution, researchers [24] examined the significance of demand-side mitigation strategies in achieving climate goals. The paper underscores the importance of understanding non-financial barriers to behavior modification for the effectiveness of policy measures designed to reduce carbon footprints. This perspective aligns with the need for comprehensive evaluation approaches such as MCDM that consider diverse stakeholder perspectives. This research demonstrates that behavioral insights can be successfully integrated into MCDM frameworks to increase community engagement and acceptance of low-carbon programs. Furthermore, a recent study in the journal Sustainability examined the effectiveness of several urban carbon reduction initiatives using system dynamics methodology. Akbari et al. [25] argued that utilizing MCDM techniques can substantially improve the robustness of policy proposals by taking into account multiple factors and stakeholder preferences. This study underscores the importance of incorporating MCDM to formulate effective methods for sustainability. The authors present case studies demonstrating how cities have effectively implemented this approach, resulting in measurable reductions in carbon emissions. Furthermore, Colapinto et al. [26] examined how a region can utilize the MCDM framework to evaluate the trade-offs between economic development and environmental sustainability. The results showed that regions that applied this technique were able to manage complex decision-making environments more effectively while ensuring efficient and equitable solutions. This study highlighted that MCDM can enhance participatory decision-making processes, enabling stakeholders from various sectors to share their views on development initiatives.

MCDM is a concept commonly used in planning that allows decision-makers to evaluate the ranking of various solutions based on multiple, often conflicting criteria. Development programs designed to reduce carbon emissions always involve considerations between economic feasibility, social equity, environmental impact, and technical feasibility [27]. MCDM is the primary approach employed to assess the sustainability of a nation or region within the energy sector. Issues with numerous objectives can be addressed through the MCDM technique. Various solutions can be implemented for energy management and preparatory declarations based on the weighted average, outranking methods, critical concern scenarios, fuzzy logic, and integrations [28, 29]. MCDM has undergone substantial advancements since the early 1960s, encompassing cutting-edge methodologies and robust algorithms (computational tools) [30]. A study by Gazi et al. [31] was undertaken on synergistic techniques for sustainable hospital site selection in Saudi Arabia, utilizing the Spherical Fuzzy MCDM algorithm. The aim was to create a model for choosing a PhD supervisor from the available possibilities at the academic institution. A hybrid MCDM framework was employed to select supervisors according to the criteria favored by students in an interval-valued intuitive fuzzy (IVIF) context.

There are various MCDM models, including the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) [29, 32-34]. Each of these approaches has its own advantages that depend on the specific context and objectives of the decision-making process. Decision Criteria Making (DCM) encompasses all possible strategies undertaken by stakeholders within a framework to ensure that the chosen strategies align with societal goals.

This study uses the PROMETHEE methodology to combine qualitative and quantitative aspects of the existing carbon emission reduction strategy landscape. This article is further enhanced by using data to evaluate carbon emission reduction strategy scenarios in higher education. This study examines appropriate decision-making in carbon emission reduction in higher education institutions within the framework of sustainable development for university institutions.

Based on the results of the questionnaire that was distributed to five respondents, the alternatives were obtained, which are presented in Table 1.

Table 1. Criteria for carbon emission reduction strategies at universities

Moreover, from the results of distributing the questionnaire, alternatives were also obtained, which are presented in Table 2.

After the distribution of the PROMETHEE questionnaire was carried out, the results of the respondents' assessment of the alternatives that had been determined were obtained. After the respondents' assessments were obtained, the first step that would be taken was to calculate the alternative preference value.

Table 2. Alternatives in carbon emission reduction strategies

Table 3. Respondents’ assessments of the alternatives

After the alternatives were determined, a questionnaire was distributed to five respondents. The results of the assessments against the criteria and alternatives are in Table 3.

In this stage, the preference value will be calculated by comparing one alternative with another. This calculation will be done by subtracting the value of the first alternative from the second alternative, then continuing by calculating the preference value based on the preference type that will be used, based on Formula (3), namely the usual criteria. The calculation results can be seen in the following Table 4.

Table 4. Alternative preference values

Table 5. Multicriteria preference index

As stated in the preference value tables above, the calculation is performed using the example of subtracting A1 and A2 from No. 1, where 5 – 5 = 0. From the calculation results, a value of 0 indicates that d ≤ 0. If d ≤ 0, then H(d) = 0. If d > 0, then H(d) = 1. Therefore, it can be concluded that the results of A1 and A2 obtained H(d) = 0, meaning there is no preference between A1 and A2 for that criterion. This value indicates that A1 is not better than A2, or both are considered equivalent. Then, the subsequent data calculations are carried out in the same manner.

After calculating the alternative preference values, the multicriteria preference index is calculated. Table 5 shows the overall multicriteria preference index obtained from the calculation using Eq. (8). For example, the calculation for A1 and A2 is as follows:

$\varphi(a, b)=\frac{1}{5} \times(0+1+1+1+0)=0.6$

In calculating Leaving Flow, it will be done based on the multicriteria preference index of each alternative by calculating horizontally, where the calculation results are as follows:

(A1) = 17 - 1 × (0.6 + 0.4 + 0.2 + 0 + 0 + 0) = 0.2

(A2) = 17 - 1 × (0 + 0.2 + 0.2 + 0 + 0 + 0) = 0.066

(A3) = 17 - 1 × (0.2 + 0.6 + 0.2 + 0 + 0 + 0.2) = 0.2

(A4) = 17 - 1 × (0.4 + 0.6 + 0.6 + 0 + 0 + 0.2) = 0.3

(A5) = 17 - 1 × (0.6 + 0.8 + 0 + 0.4 + 0 + 0.4) = 0.36

(A6) = 17 - 1 × (0.6 + 0.8 + 0 + 0.4 + 0 + 0) = 0.3

(A7) = 17 - 1 × (0.4 + 0.8 + 0.6 + 0.2 + 0 + 0) = 0.33

Based on these calculations, the Leaving Flow calculation for Alternative 1 yields a value of 0.2. Alternative 2 yields a value of 0.066. Alternative 3 yields a value of 0.2. Alternative 4 yields a value of 0.3. Alternative 5 yields a value of 0.36. Alternative 6 yields a value of 0.3. And Alternative 7 yields a value of 0.33.

After obtaining the Leaving Flow values for the seven alternatives, the Entering Flow calculation will be performed.

In calculating Entering Flow, it will be done based on the Multicriteria Preference Index of each alternative by adding it up vertically, where the calculation results are as follows:

(A1) = 17 – 1 × (0.6 + 0.4 + 0.2 + 0 + 0 + 0) = 0.2

(A2) = 17 – 1 × (0 + 0.2 + 0.2 + 0 + 0 + 0) = 0.066

(A3) = 17 – 1 × (0.2 + 0.6 + 0.2 + 0 + 0 + 0.2) = 0.2

(A4) = 17 – 1 × (0.4 + 0.6 + 0.6 + 0 + 0 + 0.2) = 0.3

(A5) = 17 – 1 × (0.6 + 0.8 + 0 + 0.4 + 0 + 0.4) = 0.36

(A6) = 17 – 1 × (0.6 + 0.8 + 0 + 0.4 + 0 + 0) = 0.3

(A7) = 17 - 1 × (0.4 + 0.8 + 0.6 + 0.2 + 0 + 0) = 0.33

Based on these calculations, the Entering Flow value for Alternative 1 is 0.36. Alternative 2 is 0.7. Alternative 3 is 0.3. Alternative 4 is 0.26. Alternative 5 is 0. Alternative 6 is 0. And Alternative 7 is 0.13.

After the Entering Flow values for the seven alternatives are obtained, the Net Flow calculation will be performed to determine the ranking of the selected alternatives.

After calculating the Leaving Flow and Entering Flow, the next step is to calculate the Net Flow value obtained from the subtraction of the Leaving Flow and Entering Flow values, as follows:

(A1) = 17 – 1 × (0 + 0.2 + 0.4 + 0.6 + 0.6 + 0.4) = 0.36

(A2) = 17 – 1 × (0.6 + 0.6 + 0.6 + 0.8 + 0.8 + 0.8) = 0.7

(A3) = 17 – 1 × (0.4 + 0.2 + 0.6 + 0 + 0 + 0.6) = 0.3

(A4) = 17 – 1 × (0.2 + 0.2 + 0.2 + 0.4 + 0.4 + 0.2) = 0.26

(A5) = 17 – 1 × (0 + 0 + 0 + 0 + 0 + 0) = 0

(A6) = 17 – 1 × (0 + 0 + 0 + 0 + 0 + 0) = 0

(A7) = 17 – 1 × (0 + 0 + 0.2 + 0.2 + 0.4 + 0) = 0.13

Based on the calculation results, it is known that the Net Flow calculation results for Alternative 1 obtained a value of -0.1667. Alternative 2 obtained a value of -0.6333. Alternative 3 obtained a value of -0.1000. Alternative 4 obtained a value of 0.0333. Alternative 5 obtained a value of 0.3667. Alternative 6 obtained a value of 0.3000. Alternative 7 obtained a value of 0.2000.

Based on the results of the Net Flow calculation, the ranking order of the seven alternatives can then be determined to determine the selected alternative, namely as follows in Table 6.

Table 6. Alternative ranking order

Of the seven alternatives that have been determined, there are three highest alternatives, namely Alternatives 5, 6, and 7. Where Alternative 5 is implementing a policy for students to be able to walk within the campus area, Alternative 6 is using public transportation, and Alternative 7 is creating a one-day program without motorized vehicles on campus. The main alternative that will be proposed as the best strategy for climate mitigation from carbon emissions produced by motorized vehicles is Alternative 5 which is ranked first, where the alternative proposes to implement a policy for students to be able to walk during activities within the campus area, both short and long distances, so that by implementing the policy, the movement of motorized vehicles will rarely occur or even not occur at all in the campus area, except when the vehicle has just arrived or left the campus area. With this policy, it is hoped that it can reduce carbon emissions produced by motorized vehicles at the university.

This alternative implies that Pancasila University is expected to provide space for pedestrians by creating a pedestrian walkway. This proposal, in addition to reducing motorized vehicle use, can also prevent accidents caused by vehicle traffic. By providing a dedicated space for pedestrians, it will provide comfort to the academic community, enabling them to implement this alternative to achieve the research objective of climate mitigation from motorized vehicle carbon emissions.

Based on the results of this study, one alternative policy that could be implemented is to encourage students to walk around campus. This policy is expected to reduce reliance on private motorized vehicles, especially for short-distance mobility. In addition to directly helping reduce carbon emissions, this policy can also strengthen a culture of sustainable transportation on campus. Implementation can be done by providing safe, comfortable, and shaded pedestrian paths and limiting motorized vehicle access to certain areas. The second and third alternatives can also be implemented, namely, using public transportation and establishing a one-day car-free program on campus. If implemented consistently by the entire academic community, this policy has the potential not only to reduce carbon emissions but also to maintain and even improve previously achieved environmental sustainability scores.