Additional External Costs Analysis and Environmental CBA
Bibliographic record
Abstract
The sustainable development requires policies and measures which negative impacts would not be spilled over on another area or has trends that pose severe or irreversible threats to future quality of life. The environmental costs-benefits analysis (CBA) as well as multi criteria analyse are the most common used methods for the decision making processes including the approved methodology for quantifying external costs especially regarding air quality. Since the reducing one type of external cost generates another external cost due to fact that the problem is only shifted from the one area to the another CBA is not enough for the decision making process because external cost of a future implemented measure isn't considered. By the usage of Life-cycle costing (LCC), a tool which evaluates the costs of an new installed asset imposed trough the adopted policy or measure throughout its life cycle, it is possible beside the common costs for conducting CBA include also the end-of-life and disposal costs as the new installed asset's external costs too. These costs have to be calculated and added to the cost side of CBA before comparing to the benefits. So, for the purpose of decision making process of the retrofitting existing thermal power plants with DeSOx such calculation has been done as a case study for one thermal power plant in Bosnia and Herzegovina highlighting overall costs and benefits of the DeSOx installation.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".