ANALYSIS OF FOREST CONSERVATION PERFORMANCE OF MAJOR FORESTED COUNTRIES: AN APPLICATION USING TOPSIS AND WASPAS
Bibliographic record
Abstract
Countries hosting extensive forest areas, particularly those encompassing a significant proportion of the world’s forests, play a critical role in global biodiversity, environmental stability, and economic systems. Within this framework, the forest conservation performance of nine nations—Russia, Brazil, Canada, the USA, China, the Democratic Republic of Congo (DRC), Indonesia, India, and Peru—representing 65% of global forest cover, was evaluated using the 2024 Forest Environmental Performance Index (EPI-F) criteria through the WASPAS (Weighted Aggregated Sum Product Assessment) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methodologies. Results indicated that rankings derived from WASPAS and TOPSIS diverged only for China and India. Furthermore, average forest conservation performance scores were computed using both approaches. According to WASPAS, India, China, and Peru exceeded the average, whereas TOPSIS identified India, China, Peru, and Indonesia as above-average performers. Consequently, a joint evaluation of both methods suggests that Russia, Brazil, Canada, the USA, the DRC, and Indonesia, whose forest conservation performances fall below the average, should reinforce their conservation policies to more effectively support global environmental integrity, biodiversity preservation, and economic sustainability. Moreover, sensitivity and comparative analyses confirmed the suitability of WASPAS and TOPSIS within the EPI-F framework for assessing these countries’ forest conservation performance. Regarding limitations, the study exclusively employed data from 2024. Future research may benefit from longitudinal analyses spanning multiple years and incorporating additional multi-criteria decision-making (MCDM) techniques to broaden the methodological comparison.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".