Forest sector models for tropical countries - A case study of Colombia
Bibliographic record
Abstract
The evolution of Forest Sector Models (FSMs) since the 1960s has marked a significant advancement in forest economics and policy analysis. However, this development is limited to North America and Europe's nations; tropical countries, crucial for biodiversity, carbon storage, and deforestation , face a notable scarcity of FSMs, often attributed to the limited and fragmented nature of their forest sector data. The importance of unprocessed wood and sources of wood supply are also distinct in tropical countries. We address these issues by introducing a comprehensive framework to build FSMs tailored for tropical countries whose national accounts are aligned with United Nations standards. We demonstrate the applicability of our framework by constructing the Colombian Forest Sector Model (CFSM), a structural econometric partial equilibrium model. The CFSM includes five markets grouped in two market sub-models: one for unprocessed wood (firewood and industrial wood) linked to a forest plantations simulator, and other for manufactured wood products (wood, furniture, and pulp & paper). The model consists of 32 behavioral equations, explaining supply, consumption, exports and imports, and prices for consumption and trade for each market, plus 18 summation and market-clearing identities. Model estimation is based on 41 years (1975–2015) of data collected, organized, and transformed through a meticulous process. Rigorous validation confirms the CFSM's robustness and reliability. The model's application is demonstrated by estimating wood availability and impacts under several plantation expansion scenarios, and the monetary effects of expanding Colombia's wood products industry. The paper opens new frontiers of research in FSMs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".