Environmental impact assessment of renewables and conventional fuels for different end use purposes
Bibliographic record
Abstract
In this study, we present a comparative environmental impact assessment of renewables and conventional fossil fuels for electricity and hydrogen generation. The conventional fossil fuels investigated in this study are coal, oil, and natural gas. Renewables considered in this study are geothermal, hydropower, ocean, solar, and wind energies. Furthermore, nuclear and biomass energies are taken into consideration while assessing environmental impact and performances. Environmental impact criteria considered in this study are CO2, NOx, and SO2 emissions, land use, water consumption, water quality of discharge, solid waste and ground contamination, and biodiversity. For comparison purposes, all collected data are normalised and ranked between 0 and 3 while 0 giving highest negative environmental impact and 3 giving lowest negative environmental impact. Our results showed that overall, in terms of both electricity and hydrogen production, oceans give the highest rankings (2.71 for electricity and 2.73 for hydrogen). Coal has the lowest rankings in terms of environmental impact (0.26 for electricity and 0.30 for hydrogen).
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 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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".