Assessing the Potential Economic and Poverty Effects of the National Greening Program¹
Bibliographic record
Abstract
Over the years, deforestation in the Philippines resulted in significant reduction in forest cover. Between 1990 and 2013, the Philippines has lost 3.8 million hectares of its forest. This study carries out a quantitative assessment of the potential economic and poverty impacts of the NGP using a computable general equilibrium (CGE) model. In the assessment, a CGE model is specified, calibrated and used to simulate three scenarios: (i) a baseline or a business-as-usual scenario that incorporates the current forest deterioration in the Philippines; (ii) a full NGP scenario which implements a reforestation program that halts and reverses the reduction in the country’s forest cover; and (iii) a partial NGP scenario where only half of the 1.5 million hectare target reforestation is achieved. The assessment indicates that the NGP will result in an improvement in the overall output of the economy. The production of agricultural crops (palay, coconut, sugar, and other agriculture) improves, as well as the processing of these crops into food. Reforestation increases the effective supply of productive land in the country. The factor markets for labor, capital, and land are affected favorably as the overall output of the economy improves. The improvement in factor efficiency decreases the cost of production, which lowers the consumer price of commodities. Food prices decline as agricultural production improves. Lower income groups benefit from declining consumer food prices as their food consumption share in their total expenditure is larger compared to households in higher income groups. Higher household incomes and lower consumer prices lead to reduced poverty. Also, those in extreme poverty benefit the most. Income distribution also improves over time as indicated by a declining GINI coefficient.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".