Assessing GHG Emissions, Ecological Footprint, and Water Linkage for Different Fuels
Bibliographic record
Abstract
Currently, transport is highly dependent on fossil fuels and responsible for about 23% of world energy-related GHG (greenhouse gas) emissions. Ethanol from sugar cane and corn emerges as an alternative for gasoline in order to mitigate GHG emissions. Additionally, deeper offshore drilling projects such as in the Brazilian Pre-Salt reservoirs and mining projects of nonconventional sources like Tar Sands in Canada could be a solution for supplying demand of fossil fuels in the short and midterm. Based on updated literature, this paper presents an assessment of GHG emissions for four different fuels: ethanol from sugar cane and from corn and gasoline from conventional crude oil and from tar sands. An Ecological Footprint analysis is also presented, which shows that ethanol from sugar cane has the lowest GHG emissions and requires the lowest biocapacity per unit of energy produced among these fuels. Finally, an analysis using the Embodied Water concept is made with the introduction of a new concept, the "CO(2)-Water", to illustrate the impacts of releasing carbon from underground to atmosphere and of the water needed to sequestrate it over the life cycle of the assessed fuels. Using this method resulted that gasoline from fossil fuels would indirectly "require" on average as much water as ethanol from sugar cane per unit of fuel energy produced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".