Stakeholder engagement in adaptation interventions: an evaluation of projects in developing nations
Bibliographic record
Abstract
Institution-oriented, top-down and community-oriented, bottom-up stakeholder approaches are evaluated for their ability to enable or constrain the implementation of adaptation in developing nations. A systematic review approach is used evaluate the project performance of 18 adaptation projects by three of the Global Environment Facility's (GEF) adaptation programmes (the Strategic Priority for Adaptation (SPA), the Special Climate Change Fund (SCCF), and the National Adaptation Programs of Action (NAPA)) according to effectiveness, efficiency, equity, legitimacy, flexibility, sustainability, and replicability. The ten SPA projects reviewed performed highest overall, especially with regards to efficiency, legitimacy, and replicability. The five SCCF projects performed the highest in equity, flexibility, and sustainability, and the three NAPA-related projects were the highest-performing projects with regards to effectiveness. A comparison of top-down and bottom-up approaches revealed that community stakeholder engagement in project design and implementation led to higher effectiveness, efficiency, equity, flexibility, legitimacy, sustainability, and replicability. Although low institutional capacity constrained both project success and effective community participation, projects that hired international staff to assist in implementation experienced higher overall performance. These case studies also illustrate how participatory methods can fail to genuinely empower or involve communities in adaptation interventions in both top-down and bottom-up approaches. It is thus crucial to carefully consider stakeholder engagement strategies in adaptation interventions.Policy relevanceWhile adaptation is now firmly on the policy and research agenda, actual interventions to reduce vulnerability and enhance resilience remain in their infancy, and there is limited information on the factors that influence the successful implementation of adaptation in developing areas. Engaging stakeholders in assessing vulnerability and implementing adaptation interventions is widely regarded to be an important factor for adaptation implementation and success. However, no study has evaluated the effects of stakeholder engagement in the actual implementation of adaptation initiatives. Effective stakeholder engagement is challenging, especially in a developing nation setting, due to high levels of poverty, inadequate knowledge on adaptation options, weak institutions, and competing interests to address more immediate problems related to poverty and underdevelopment. In this context, this article documents and characterizes stakeholder engagement in adaptation interventions supported through the GEF, examining how top-down or bottom-up stakeholder approaches enable or constrain project performance.
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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 | 0.188 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".