Forest Load Capacity and Carbon Emissions in the World's Largest Forest Nations: An <scp>EKC</scp> ‐Based Assessment for Sustainable Management
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT The 2030 Sustainable Development Goals (SDGs) emphasize the crucial role of forests in regulating the global climate. This study investigates the relationship between the forest load capacity factor (F‐LCF), which measures the biocapacity of a country's forests relative to human demand, alongside per capita income, urbanization, and CO 2 emissions. A panel of the 10 largest forest nations from 1992 to 2021 is analyzed using a cross‐sectionally augmented autoregressive distributed lag (CS‐ARDL) model, and the Environmental Kuznets Curve (EKC) hypothesis (an inverted‐U trajectory of environmental impacts as income grows) is tested by comparing short‐ and long‐term income elasticities. The EKC pattern is confirmed for the whole sample and for Russia, Brazil, Canada, the United States, and Australia, but not for China, India, Indonesia, Peru, and the Democratic Republic of Congo. The results show that higher F‐LCF levels reduce CO 2 emissions, while rising GDP and urbanization amplify them. These findings underscore the importance of sustainable forest management for achieving the climate target (SDG‐13) and protecting terrestrial ecosystems (SDG‐15) and call for tailored policies that reflect the forest dynamics of individual countries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it