Evaluation of the Sustainable Forest Management Performance in Forestry Enterprises Based on a Hybrid Multi-Criteria Decision-Making Model: A Case Study in China
Bibliographic record
Abstract
Sustainable Forest Management (SFM) can fully use forest resources and improve the economic, environmental, and social sustainability of forest areas. Forestry enterprises play a crucial role in the implementation of SFM. However, the previous literature on SFM pays little attention to the subject of forestry enterprises. This paper aims to extend research on SFM from a macro perspective to the micro level of forestry enterprises. Taking the Triple Bottom Line (TBL) as a theoretical framework and the Montreal Process Criteria and Indicators (MP C&Is) as a basis, this paper constructs an indicator system to evaluate the performance of SFM of forestry enterprises from economic, social, and environmental aspects. Then, we apply the hybrid Multi-Criteria Decision-Making (MCDM) methods, i.e., the Best–Worst Method (BWM) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, to construct the methodological system for SFM performance evaluation of forestry enterprises. Through a questionnaire survey of 68 academics and researchers, we determine the weights of the SFM indicators and select a representative forestry enterprise as a case study. The effectiveness of this SFM performance evaluation model is then demonstrated through its application to the case study of forestry enterprises in China. Through the application of the model, this paper evaluates the enterprise’s SFM performance over the five-year period 2017–2021 and proposes appropriate policy recommendations and improvements. It is found that environmental factors are the primary factors of SFM in forestry enterprises. Forestry enterprises should not only pay attention to economic benefits but also to the use of forest resources and the protection of forest ecosystems to better achieve SFM.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".