Forest Load Capacity and Carbon Emissions in the World's Largest Forest Nations: An <scp>EKC</scp> ‐Based Assessment for Sustainable Management
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".