Towards a new index for environmental sustainability based on a DALY weighting approach
Bibliographic record
Abstract
Abstract Composite indicators are synthetic indices that are used to rank country performances in specific policy areas. Many do, however, suffer from methodological difficulties. Specific difficulties linked to indices for environmental sustainability are analyzed through the illustration of several sets. The most critical issues are linked with a poor analytical framework and a lack of common unit for the aggregation. Some measure directly the state of the environment while other use proxies such as pressure or response indicators or even a mix of these. A new composite index for environmental sustainability was developed in the EU project EPSILON, which aimed at assessing European regional sustainability for policy decision making related to the improvement of regional sustainability. Indicators are expressed according to a coherent framework issuing from the ‘driving force–pressure–state–impact–response’ approach with an innovative weighting scheme derived from human health impact assessment based on disability adjusted life years (DALYs). Results are compared with a more conventional aggregation technique based on an equal weighting coupled to various normalization techniques. Copyright © 2008 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".