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Additional External Costs Analysis and Environmental CBA

2017· article· en· W2757637095 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLife Cycle Costing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceRisk analysis (engineering)Business

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.224
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it