The Roadmap to Achieving Climate Neutrality in Türkiye: A Comprehensive Analysis of Long-Term Forestry Strategies
Bibliographic record
Abstract
In 2021, Türkiye ratified the Paris Agreement and committed to achieving climate neutrality by 2053. As mandated by the agreement, Türkiye submitted its first Nationally Determined Contribution (NDC) and has been conducting simulations to identify alternatives to establish its Long-Term Strategy (LTS). Our study focused on the LULUCF (Land Use, Land Use Change, and Forestry) sector of Türkiye, mainly focusing on forestry. Our analysis shows that the forests in Türkiye offset approximately 8-10% of the country's total greenhouse gas emissions in 2021, down from over 20% in 2014. This reduction in offset percentage is due to a drop in the removal rate of forests over the last ten years. To achieve climate neutrality, this trend of reduction must be reversed. Recent inventory data shows that forest management is the central activity, with afforestation and other land use activities contributing less than 1%. However, when analyzing their effectiveness, it is important to consider the co-benefits of mitigation policies and measures. Our study concluded that Türkiye should prioritize forest management, including wildfire prevention and improved use of wood products, by investing in research and innovation. The forest products industry should also enhance the added value of wood products and embrace circularity to reduce raw material demand. By reducing the harvest rate, the carbon stock and increment of forests can be enhanced. Acceleration is needed towards achieving sectoral targets to achieve a climate-smart forestry perspective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".