Effects of economic globalization and trade on forest transitions: Evidence from 76 developing countries
Bibliographic record
Abstract
Current forest recovery efforts in developing countries are different from previous efforts in developed countries, especially since the rise of economic globalization in the 1980s. Therefore, forest transition theory should now consider factors relating to industrialization, urbanization, and globalization. While previous studies have mainly focused on the variable trade of primary sector products, this study applies a more holistic research perspective and discusses, more widely, the links between trade, adjustment of trade structure, FDI, and forest transition. The results suggest that the total export value has a significant negative effect on forest area and volume, while the percentage of non-primary products has a significant positive impact on forest volume and density in the 76 developing countries studied. These results indicate that a country or region may improve the forest resource conditions by upgrading the export structure through the development of export-oriented manufacturing and service industries during the process of global industrial restructuring. This demonstrates the need to consider the overall global economic situation of a country when exploring the effects of economic globalization on forest transitions. In addition, this study attempts to address extant concerns regarding the quality of forest transitions by moving beyond the analysis of forest coverage to explore changes in both forest area and forest volume.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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".