Scaling GESI-responsive Nature-based Solutions in the Hindu Kush Himalaya: An operational guide
Bibliographic record
Abstract
Nature-based Solutions (NbS) have a huge potential to support biodiversity, climate, and sustainable development objectives, but their implementation has been rather slow and fragmented. There are policy, governance, coordination, design, and financial barriers to wider-scale adoption of NbS. This operational guide elaborates on the key actions that are necessary to scale NbS. NbS in the Hindu Kush Himalaya (HKH) region are vital for addressing the complex environmental and social challenges that are being exacerbated by climate change. The region, characterised by its diverse ecosystems and unique sociocultural contexts, has been witnessing a growing interest in NbS, such as in the form of reforestation, eco-disaster risk reduction, sustainable agriculture, naturebased tourism, community-based watershed management, wetland restoration, and springshed management. These solutions have shown promise in enhancing biodiversity, improving water security, and supporting livelihoods. However, challenges remain in scaling these solutions effectively across the HKH due to limited financial resources, varying levels of institutional capacity, and the need for more comprehensive policies (Mehta et al., 2023). Collaborative efforts, capacity building, and innovative financing mechanisms are crucial for the successful implementation and scaling of NbS in this ecologically and culturally rich region. The insights on the actions for scaling NbS have been drawn from published literature and stakeholder consultations in Bangladesh, Bhutan, India, and Nepal as part of a process to facilitate wider scale adoption of NbS through the Himalayan Resilience Enabling Action Programme (HI-REAP). HI-REAP is a nine-year programme (2023–2031) being implemented by the International Centre for Integrated Mountain Development (ICIMOD), funded by the United Kingdom International Development under its Climate Action for a Resilient Asia (CARA) programme; its aim is to ‘deliver a major shift on resilience in Asia – moving from incremental and isolated investments to building systems resilience to climate change in Asia that unlocks action and finance needed for transformational adaptation’. HI-REAP is being implemented in four countries – Bangladesh, Bhutan, India, and Nepal – and is building on knowledge and learning from China.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.019 |
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".