Development of a Multi-Criteria Analysis Decision-Support Tool for the Sustainability of Forest Biomass Heating Projects in Quebec
Bibliographic record
Abstract
Residual forest biomass for heating is an alternative to fossil fuels that is in line with global greenhouse gas emission reduction targets. Even if the opportunities and the benefits of such projects may be important, one should not neglect the barriers and potential impacts of these projects regarding their sustainability. The decision support tool developed and presented in this paper will help guide and support public decision makers in selecting the best project and improving its sustainability. A reliable and relevant weighting method is determined, based on the use of the Analytic Hierarchical Process multi-criteria decision analysis method, allowing the integration of stakeholders and the consideration of their views and opinions. This choice, combined with the privileged use of quantifiable qualitative data, allows the use of the tool in a preliminary phase of the project development and enables the evaluation of the project and its sustainability from a social acceptability perspective. The tool was applied to two fictional scenarios to demonstrate its ability to guide decision making and to highlight the differentiation of weights and scenarios through both weighting and evaluation methods.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".