Evaluation of Sustainable Forest and Air Quality Management and the Current Situation in Europe through Operation Research Methods
Bibliographic record
Abstract
Forests cover 30 percent of the Earth’s land surface, almost four billion hectares, and they are necessary to sustain human health, economic growth, and environmental health. Approximately 25 percent of the global population depends on forests for food and work. The world population is expected to reach 9.6 billion by 2050. Therefore, there is a need for urgent action plans at all levels to ensure sustainable forest management and policy collaboration among all stakeholders, in order for forests to continue to serve our ecosystem and life in the future. The study compares 30 countries using 15 indicators related to forest and air quality. This was performed with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje, meaning Multi-Criteria Optimization and Compromise Solution), which are among the most used multi-criteria decision-making methods in the literature. According to the analysis results, Denmark, Luxembourg, Lithuania, and Germany are the best performing countries in terms of indicators, whereas Slovakia, Estonia, Turkey, Latvia, Chile, and Canada are the worst performing. The paper aims to present the current situation of some developed and developing countries and compare them to each other in terms of forest and air quality indicators. In addition, the article aims to inform all stakeholders and raise awareness to achieve the Sustainable Development Goals (SDGs) and Global Forest Goals of the United Nations Strategic Plan for Forests 2017–2030 targets.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".