Coastal area rehabilitation for climate change adaptation: The key role of mangroves in Nationally Determined Contributions
Bibliographic record
Abstract
With a coastline of more than 90,000 km – the second longest after Canada – it is in Indonesia’s interests to protect its coastal areas from climate change impacts. The continued existence and preservation of extensive coastal vegetation like mangroves and seagrasses is a nature-based solution for successful adaptation to climate change. A coastal area rehabilitation/restoration agenda for climate change adaptation must be able to increase areas’ resilience in overcoming rising sea levels, waves, coastal erosion, flooding and inundation, so the resilience of communities, particularly fishing communities living in coastal areas, can be enhanced. Communities’ social cohesion, economic opportunities, and institutional capacity must also improve. Information and funding flows must be transparent for all stakeholders, so adaptation agenda decision making and implementation can be carried out effectively, efficiently and equitably.This paper demonstrates efforts to bundle adaptation and mitigation measures to secure optimum outcomes in coastal area rehabilitation/restoration, as recommended in the Paris Agreement. It proposes adopting a responsive adaptation cycle so adaptive measures in these strategic coastal areas can commence immediately, and be monitored and evaluated. In this regard, emissions mitigation scenarios linked to adaptation measures can be considered to facilitate the achievement of 2030 Nationally Determined Contribution (NDC) targets and Sustainable Development Goals (SDGs).
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".