Calculation of Monetary Values of Environmental Impacts from Emissions and Resource Use The Case of Using the EPS 2015d Impact Assessment Method
Bibliographic record
Abstract
<p>Monetary values of environmental impacts from emissions and from use of natural resources help in understanding the environmental significance of human activities. It is however a complicated and time consuming task to determine these values, and the values are easy to uncritically accept without understanding the many ways they may be determined, the many preferences they may represent and the different contexts for which they may be relevant.</p><p>This article aims at increasing the usefulness of monetary valuation and decreasing some of its shortcomings by demonstrating a way to model and calculate monetary values of environmental impacts from emissions and use of natural resources, highlight subjective choices that have to be made in modelling and calculations, and discuss how some of them influence the values assessed.</p><p>The method we use is based on the principles of the EPS default impact assessment method, which comply with the requirements of the ISO 14044 life cycle assessment standard.</p><p>Monetary values for 98 endpoint category indicators are determined, and calculations of characterization factors are demonstrated for CO<sub>2</sub>, N<sub>2</sub>O, CH<sub>4</sub>, and NO<sub>x</sub>.</p>Two methodological choices have proven particularly important for the values obtained. One is the long term perspective and intergenerational equity. The other is the approach to uncertainty. Both is important for what is included in the assessments and to what extent.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".