Economic Analysis of Sustainable Transportation Transitions: Case Study of the University of Saskatchewan Ground Services Fleet
Bibliographic record
Abstract
The global transport sector of the world economy contributes about 15% of Greenhouse Gas (GHG) emissions in the world today, and this must be drastically curbed. To reduce GHG emissions and achieve carbon neutrality, the University of Saskatchewan’s Office of Sustainability has directed a green energy transition for the institution in recent years, leading diverse sustainability projects and agendas due to the importance of curbing climate change and advancing sustainability. However, there is a strong need to pursue deep decarbonization within the campus, and the university transport system is a critical operational area that the Sustainability Office has considered for decarbonization to achieve the net-zero agenda of the university. The motivation for this work stems from the directive of the Sustainability Office to transform the campus vehicle fleet as an identified area for curbing GHG emissions and meet the University agenda. This study was organized in partnership with the Sustainability Office and involved an economic benefit analysis of the campus fleet (consisting of 91 ICE vehicles) to determine if it was economically or financially feasible to transition from Internal Combustion Engines (ICEs) or PVs (Petrol Vehicles) to Electric Vehicles (EVs). The analysis used RETScreen Expert (a software for analyzing renewable energy technology projects) to model diverse transition scenarios. The variables of Payback Period (PBP), cash flow projections, savings made from transitioning (fuel cost savings and energy cost savings), benefit–cost ratio, and GHG emission reduction potential were analyzed. The findings revealed that the GHG emissions from the campus fleet could be reduced by 100%, resulting in the removal of 298.1 tCO₂ from the environment. The fleet manager could save $CAD 129,049 (88.9%) in fuel costs, and the return on investment could be achieved in year 5 but could be reduced to year 2 if the vehicles were put into constant and active use, eliminating idle times. Lastly, the Sustainability Office would achieve a GHG reduction revenue of CAD 14,906. These findings show that pursuing sustainable transport transitions in the transportation transition for a university campus is financially and economically viable and should be pursued vigorously. The contribution of this work provides examples and evidence to advance policy recommendations to aid the effective and efficient transitioning of the transportation sector, specifically for communities at the scale of university campuses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".