Sustainability Assessment of Renewable Energy in the United States, Canada, the European Union, China, and the Russian Federation
Bibliographic record
Abstract
Biomass is the oldest resource of energy for humans, and its use could slow the depletion rate of fossil fuel reserves and enable the development of sustainable energy and the chemical industry. The sustainability of the replacement of natural gas, crude oil, and coal with corn-based bioethanol was assessed by using the ethanol equivalents (EE) of fossil fuel resources used in the United States, Canada, the European Union, China, and the Russian Federation in 2008–2014. The calculations were based on first generation corn-based bioethanol technology as commercially practiced in the United States in 2008. Based on the EE 2.3 values, the required volume of corn and the corresponding size of land were calculated and compared with the actual lands used for corn production, which is only enough to replace one-sixth of the fossil fuel resources in the United States, European Union, and China and practically insufficient in Canada and the Russian Federation. Until the utilization of electricity becomes practical and economical in aviation, biomass-based liquid fuels could be the sustainable alternative. The assessment of the replacement of natural gas, crude oil, and coal-based energy with renewable energy in these countries in 2008–2014 shows that a significant increase in the renewable energy portfolio is required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".