Forest Cover Change, Households’ Livelihoods, Trade-Offs, and Constraints Associated with Plantation Forests in Poor Upland-Rural Landscapes: Evidence from North Central Vietnam
Bibliographic record
Abstract
Vietnam’s forests have experienced a notable transformation over the past 20 years from net deforestation to reforestation and expanding forests. Continued reforestation that aims to achieve further economic and environmental benefits remains a national priority and strategy. We explore the current status of plantation forests and highlight possible means to facilitate their expansion in the uplands of Vietnam. We employ mixed method triangulation to empirically explore plantation forests and their economic role in household livelihood, to quantify trade-offs between plantation forests and shifting cultivation, and to assess the constraints on plantation forest expansion in Nghe An province, north-central Vietnam. Results show that forest in the study area expanded by 406,000 ha (71.1%) between 1990 and 2016. Plantation forests increased by nearly 500% (from 32,000 ha to 190,000 ha), while natural forests expanded by 48.1% (from 538,000 ha to 797,000 ha). Plantation forests contributed an average of 35.1 percent of total household income in wealthier households and 27.9 percent of income in poor households. Switching from shifting cultivation to plantation forests would increase total household income and average carbon stock but decrease food provision. Total Economic Value would be higher for plantation forest scenarios if increased carbon stocks in plantations can be monetized. This carbon income might drive conversion of shifting cultivation to plantation forests. Constraints on further expansion of plantation forest are low external cooperation, education, market stability, and agroforestry extension services. Our empirical results inform national plantation forest development, sustainable upland livelihood development, and climate change mitigation programs to ultimately facilitate forest transition and improve the resilience and sustainability of socio-ecological systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".