Energy and exergy assessment with updated Reistad estimates: A case study in the transportation sector of Bangladesh
Bibliographic record
Abstract
Abstract Transportation sector is one of the core parts of modern civilization. Proper utilization of energy and exergy in this sector is necessary to ensure energy loss and environmental sustainability. Increasing exergy efficiency will reduce carbon emissions from this sector. Since 1970, Reistad estimates have been widely used to determine the energy and exergy efficiencies of this sector. However, the modern transport sector has undergone significant changes in recent decades. Hence, it is necessary to apply new Reistad estimates in determining the energy and exergy efficiencies. This is the first study to apply updated Reistad estimates to explore the energy and exergy efficiencies in the transportation sector of Bangladesh based on the data from 2000 to 2017. The overall exergy efficiency is significantly lower than the energy efficiencies as it ranges from 27.7% to 30.0%. Efficiencies are lower as the maximum portion of input exergy is lost to the environment. The road subsector needs major improvements as it is responsible for major amount of exergy loss. A comparison is made between conventional and updated estimates which highlights that the updated estimates provide more accurate results. Thus, it is recommended to apply updated Reistad estimates in determining the energy and exergy efficiencies of the transport sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".