Criteria for Assessing the Sustainability of Logging Operations—A Systematic Review
Bibliographic record
Abstract
Abstract Purpose of Review The comprehensive assessment of timber and fuelwood harvesting operations through the consideration of the three pillars of sustainability: Economic, social, and environmental has not received much attention. The use of criteria can significantly improve impact assessment. Therefore, the objective of this review paper is to compile and analyze the most commonly used criteria and indicators for each dimension of sustainability in logging operations over the last 6 years. This review provides an overview of these criteria for different harvesting machines, geographical areas, slope classes, time periods, types of research, and silvicultural treatments. Recent Findings The environmental pillar was the most studied (46%), followed by the economic pillar (38%). Productivity was the most investigated criterion (15%). On the one hand, productivity is linked to the environmental and social pillars, as it is related to the level of greenhouse gas emissions, energy consumption, and the employment rate. However, productivity is mainly used as a criterion of financial interest, as it is most often studied in combination with costs. In addition to productivity, the other most frequently examined criteria were costs (10%), soil nutrients (9.5%), and soil compaction (9%). The social dimension was the least studied pillar (16%). This may be due to a lack of knowledge of social sustainability issues in this area. Summary Sustainability is achieved when all three dimensions are balanced. The results of this review show an imbalance, with economic and environmental aspects being weighted more heavily than social aspects. Balancing all three dimensions typically requires an assessment of trade-offs. This review provides a comprehensive summary of the criteria that have been studied to date and can be used as a checklist and guideline for future sustainability assessments of harvesting operations.
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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 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.034 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".