NATIONAL GHG EMISSIONS PROJECTIONS FROM INDUSTRIAL PROCESSES – METAL INDUSTRY
Bibliographic record
Abstract
The EU ETS is a policy tool used to promote investment in clean, low-carbon technologies and has placed climate change on the companies' agenda, by putting a price on carbon.The EU ETS cap and trade concept were the first policy tool used and implemented in the world for the greenhouse gas emissions (GHG).Metals process sector is the largest industrial source of greenhouse gases, with steel as the main culprit.Traditional methods of extracting iron from it or require a carbon-based reductant produce large quantities of CO2.The proposed methodology for achieving emission forecasts for GHG emissions is based on historical data from the National Emissions Inventory (NIGHGE) between 1989 -2015, and, on the forecasts of macroeconomic indicators considered in the strategies of the Romanian Government and policies adopted for the economic and social development of the country.In the paper one analyzes the GHG emissions from industrial processes for Romanian for iron and steel sector and GHG emissions projections for this sector until 2035 will be presented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".