Factors Affecting Forest Area Changes in Cambodia: An Econometric Approach
Bibliographic record
Abstract
Clarifying factors affecting forest area changes is critical to implementing REDD+ scheme properly. We analyzed some socio-economic factors and clarified their relationships with deforestation in Cambodia for the period of 2002 to 2010. A panel data analysis was conducted for 18 provinces, while six other provinces were deleted from the list because only a small amount of their land was forested. Time effects, cross-sectional dependence, serial correlation in idiosyncratic errors, and heteroskedasticity were tested, and robust variance matrix estimations were obtained to solve the problems of heteroskedasticity and serial correlation. The model estimation results showed that population, gross agricultural production and large-scale plantation development have negative impacts on forest area changes. On the other hand, the impacts of rice cultivation, gross industrial production, household income and house floor area by household were found not to be significant. Overall, however, the results indicated that forests in Cambodia still face pressure from the increases in population, agriculture production, and the enlargement of land development. As the increase in productivity of agriculture gives a better use of current agricultural land and lessens the pressure on forest, intensifying agriculture is important. It is also important to develop industry and other economic ventures to grow national economy while not imposing pressure on forest. This research reminds decision makers to use discretion when developing large-scale plantations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".