The Road to 2030: Combining Life Cycle Assessment and Multi-Criteria Decision Analysis to Evaluate Commuting Alternatives in a University Context
Bibliographic record
Abstract
Institutions are increasingly being challenged to reduce the environmental impacts of daily commuting, while balancing complex and often conflicting sustainability goals. This study addressed the limitations of carbon-centric assessments by proposing a framework that integrated life cycle assessment (LCA) with multi-criteria decision analysis (MCDA) to evaluate seven prospective commuting alternatives for 2030, using a Portuguese university as a case study. Utilizing the PROMETHEE method across 16 environmental criteria, the analysis revealed that active mobility offered the most balanced and sustainable outcomes, consistently performing the best across all impact categories. In contrast, the electrification of private vehicles, although it reduced greenhouse gas emissions, was identified as the least favorable option, due to significant trade-offs in areas such as resource depletion and water use, as well as other environmental burdens. Public transport scenarios, particularly those involving electric bus systems, showed intermediate performance. In this context, the proposed LCA–MCDA framework provides policymakers and institutions with a comprehensive decision-support tool to navigate environmental trade-offs, promote low-impact mobility strategies, and meet evolving sustainability reporting requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".