Integrated Scenario Planning and Multi-Criteria Decision Analysis Framework with Application to Forest Planning
Bibliographic record
Abstract
This paper explores approaches concerning complex forest planning challenges, such as restoration after large-scale disturbances and under climate change. It introduces a new framework that integrates qualitative scenario planning with quantitative multi-criteria decision analysis. This framework allows stakeholders without background in forestry to express their preferences as a set of scenarios that are further assessed for specific forest management goals and activities using multi-criteria models. The assessment of the modelled scenarios created a common understanding for the stakeholders and experts to compare trade-offs between several management options and needed policy choices. The framework was applied in the case study of forest restoration following insect disturbance in British Columbia, Canada. The framework enabled structured stakeholder groups’ interactions such as industry, business associations, local and regional governments, and non-governmental organizations to identify potential restoration options. Different community futures were envisioned by two scenarios: one resembling current conditions and standard practices, while another promoting diversification of the forestry sector. The results indicated that each of the scenarios leads to different consequences for the community measured by levels of economic benefits, total harvest volumes and harvest flows over time. The results also show that the developed framework linking scenarios and multi-criteria decision analyses proved crucial to broaden the discussion on relevant species mixes and management practices, and their implications for the community and policy development.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".