Empirical Analysis of “GHG Emission Avoidance” Calculation for Selection of Sustainable Environmental Projects
Bibliographic record
Abstract
Innovative environmental projects, selected for co-financing, must contribute to the green recovery of Europe’s economy and maintain the momentum in its transition to climate neutrality. However, measuring overall sustainable environmental efficiency of a project is a complex matter. The “GHG emission avoidance” indicator, which began to appear in the award criteria of the environmentally oriented tenders of the European cohesion policy for the Programme period 2021-2027, enables the allocation of funds based on the achieved environmental benefits. The purpose of this paper is to present theoretical background and practical calculation of the “GHG emission avoidance” indicator. An absolute and relative indicator is analysed, with which investors quantitatively evaluate the potential for avoiding emissions, generated in the case of project implementation. The research part of this article is presented through case study of a project in the sector of glass, ceramics and building materials in energy-intensive industry, in which contribution of the selected sustainable project to the EU vision for a climate-neutral economy is demonstrated.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".