Deforestation in Legal Amazon: A Panel Data Analysis of Potential Interferers
Bibliographic record
Abstract
The knowledge on the significant factors that lead to environmental changes can be an attractive tool for directing priority actions of management, sustainability and impact minimization. In this regard, this work suggests the use of panel data analysis in environmental assessments, proposing a panel data regression model for the context of the Amazon forest, aiming to evaluate the role of primary activities over deforestation in Legal Amazon between 1988 and 2018. For this, the deforested areas in Legal Amazon were assessed regarding the potential explanatory variables: (i) area intended for soybean cultivation; (ii) area intended for palm oil cultivation; (iii) cattle ranching; and (iv) firewood and wood extraction. The model developed in this work evidenced cattle ranching and palm oil cultivation as significant factors for the increase of deforested areas, as well as the contribution of other factors besides primary activities in Amazon deforestation from 1988 to 2018. These results are in accordance with the literature, evidencing the applicability and assertiveness of the proposed method. This approach can help decision-makers of several other fields of environmental management. Additionally, this work also assessed the evolution of deforestation rates from 1988 to 2018, as well as possible regionalities and temporal trends in Legal Amazon deforestation. Statistically significant upward trends in deforestation rates in Amazonas, Mato Grosso, Pará, and Rondônia since 2012 were noticed. The spatial homogeneity in deforestation reinforces the need for effective oversight in Amazon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".