Forest land use change effects on biodiversity ecosystem services and human well-being: A systematic analysis
Bibliographic record
Abstract
Deforestation in the form of forest land use change (FLUC) increases the emission of greenhouse gases, disrupts the water cycle, dries the soil, and reduces the growth of plant products. This has a direct effect on the well-being of local communities whose livelihoods depend on the forest and threatens biodiversity. The systematic review aimed to analyze the studies conducted on the effects of FLUC on biodiversity ecosystem services (BECS) and human well-being (HWB) of local communities. The study utilized a qualitative content analysis (QCA) based on a deductive approach, which reviewed 114 scientific documents, particularly research articles, selected by searching keywords through a purposeful sampling method. The FLUC indicators in the two groups of dominant morphology (intensity, scale, pattern, and usage) and recessive morphology (function, property rights, and management mode) had 172 repetitions in the articles. Moreover, the BECS criteria (regulating, provisioning, supportive, and cultural services) and HWB (items related to Maslow's hierarchy of needs, subjective well-being, objective well-being, and preferences) had 125 and 148 repetitions, respectively. Results confirm the relationship and effects of FLUC on BECS and HWB, which emphasizes the mutual role of these variables in social, economic, and environmental studies in future research programs. An increase in FLUC can decline the performance and structure of BECS and have a negative impact on the HWB of those communities who depend on forest. Findings are presented in the form of a model that provides a comprehensive understanding of the relationships between FLUC, BECS, and HWB for relevant decision makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".