Financing Sustainable Landscapes through Innovative International Economic Law and Governance Instruments
Bibliographic record
Abstract
This article examines innovative ways to promote investment and financing of sustainable landscape initiatives in international law. It argues that increased flows of investment and finance for sustainable landscapes must be guided by a clear and comprehensive legal framework; better and more appropriate knowledge and technologies; more informed decision-making; and improved governance at all levels. The article considers concerns and opportunities to support the financing of sustainable forestry and land-use programs, especially in developing countries. It reviews the key contributions of international investment instruments to sustainability landscapes financing, in light of recent decisions concerning International Investment Agreements (iias) by arbitral panels convened to hear disputes under the rules of the International Centre for Settlement of Investment Disputes (icsid) or the United Nations Commission on International Trade Law (uncitral). It then proposes innovative ways that investment and financing rules might foster more effective implementation of innovative financing instruments, such as Landscape Funds in developing 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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".