Bibliographic record
Abstract
The main aims of this study were to describe the method for calculating the cost-effectiveness of environmental measures, including the determination of the sensitivity on some of the parameters used for the cost-effectiveness calculations, and to gather and store information on the costs and effects of environmental measures. Defined in this study as the costs per unit avoided emission, the cost-effectiveness is relatively high when the costs per unit are low. To calculate cost-effectiveness a calculating model and database built in Excel were used. The total emission reduction of the package of measures analysed in this study is approximately 9 billion acid equivalents in 2020 compared to 1995. The average cost-effectiveness of this emission reduction is about 200 Euro per 1000 acid equivalents. Measures applied to traffic contribute half of the total emission reduction, contrasted the highest average costs for this group. However, these traffic measures also reduce VOC, fine particles and carbon monoxide. Measures taken by industry are contrasted to the lowest average costs, amounting to a quarter of the total emission reduction in 2020. Halving or doubling the interest rate or depreciation period leads to a considerable change in costs, but hardly influences the sequence of measures on the cost-effectiveness curve for NOx. Comparison of volume measures with technical measures based on only direct costs has limited value. The indirect costs and benefits appear to have more influence on volume measures.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".