Social cost-benefit analysis of different types of buses for sustainable public transportation
Bibliographic record
Abstract
The transportation sector holds significant importance within a nation, constituting a considerable share of its total energy consumption. In developing nations, the predominant use of non-renewable energy sources like natural gas and petroleum-based fuels is notably observed in the transportation domain. Both public and private vehicles predominantly operate on fossil fuels, giving rise to concerns such as depleting national energy resources and escalating environmental impacts. Consequently, addressing these challenges calls for a shift towards cleaner and more sustainable transportation options. Therefore, the purpose of this research is to conduct a comprehensive ex-ante social cost-benefit analysis of various types of buses for public transportation. Specifically, the types of buses considered include: (i) solar buses, (ii) electric buses, (iii) hydrogen buses, and (iv) diesel buses. This study investigates the feasibility of each type of bus by determining its overall benefits and costs. The economic, environmental, and social impacts are determined and monetized which are used to calculate the net present values (NPV). The solar bus was found to have a comparatively higher NPV of 79.46 Million PKR which demonstrates that the associated overall benefits are higher as compared to the costs. On the contrary, the hydrogen bus was found to have a comparatively lower NPV of −5.87 Million PKR which depicted that considerably higher costs surpassed its benefits. Finally, an exhaustive sensitivity analysis was performed to investigate the impacts of critical system parameters on the feasibility of the examined buses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".