Challenges and prospects of Local Adaptation Plans of Action (LAPA) initiative in Nepal as everyday adaptation
Bibliographic record
Abstract
Impacts of climate change, manifested in different forms, are integrally linked with context-specific socio-economic, political, and environmental stressors. Dealing with climatic risks, in most parts, requires understanding these mundane location-specific stressors exacerbated by climate variability and change. In large part, the discussion about dealing with impending threats from climate change has relied on policy objectives hatched at the global and national levels. Despite the fact that these policy objectives are responsible for a wide range of actions at the local levels, they often struggle to incorporate the voices of local communities. With the goal of integrating bottom-up and top-down approaches in climate adaptation and connecting them to local development, the Local Adaptation Plans of Action (LAPA) initiative in Nepal makes a promising case. However, little is known about the institutional barriers and enablers of local adaptation initiatives and how they are affected by the political nature of climate adaptation. Using Nepal's LAPA as a case study and relying on a preliminary field visit, analysis of LAPA documents, and interviews with stakeholders, we reveal several obstacles local communities face that limit their ability to adapt. These obstacles include regular challenges such as insufficient financial resources and the lack of implementation support, to more specific ones such as less recognition of local knowledge and power differences among institutions and between local-level stakeholders having varying interests, power, and views. Our results show gender-based differences on a few key issues. By building on the local knowledge, enhancing local capacity, and by fostering interaction among different actors having unequal power relationships, local efforts such as LAPA can increase the ownership of adaptation policy objectives both at global and local levels. Most importantly, this paper reveals the struggle in linking identified options for dealing with climate change with everyday practices of managing risk and uncertainty.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".