Nature-based Solutions in Bangladesh: Evidence of Effectiveness for Addressing Climate Change and Other Sustainable Development Goals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many lower-income countries are highly vulnerable to the impacts of natural disasters and climate change, due to their geographical location and high levels of poverty. In response, they are developing climate action plans that also support their sustainable development goals, but conventional adaptation approaches such as hard flood defenses can be expensive and unsustainable. Nature-based solutions (NbS) could provide cost-effective options to address these challenges but policymakers lack evidence on their effectiveness. To address this knowledge gap, we focused on Bangladesh, which is exceptionally vulnerable to cyclones, relative sea-level rise, saline intrusion, floods, landslides, heat waves and droughts, exacerbated by environmental degradation. NbS have been implemented in Bangladesh, but there is no synthesis of the outcomes in a form accessible to policymakers. We therefore conducted a systematic review on the effectiveness of NbS for addressing climate and natural hazards, and the outcomes for other sustainable development goals. Research encompasses protection, restoration and participatory management of mangroves, terrestrial forests and wetlands, as well as conservation agriculture and agro-forestry, but there is an evidence gap for urban green infrastructure. There is robust evidence that, if well-designed, these NbS can be effective in reducing exposure to natural disasters, adapting to climate change and reducing greenhouse gas emissions while empowering marginalized groups, reducing poverty, supporting local economies and enhancing biodiversity. However, we found short-term trade-offs with local needs, e.g. through over-harvesting and conversion of ecosystems to aquaculture or agriculture. To maximize NbS benefits while managing trade-offs, we identified four enabling factors: support for NbS in government policies; participatory delivery involving all stakeholders; strong and transparent governance; and provision of secure finance and land tenure, in line with international guidelines. More systematic monitoring of NbS project outcomes is also needed. Bangladesh has an opportunity to lead the way in showing how high quality NbS can be deployed at landscape scale to tackle sustainable development challenges in low to middle income countries, supporting a Green Economic Recovery. Our evidence base highlights the value of protecting irreplaceable natural assets such as mangroves, terrestrial forests and wetlands, and the non-market benefits they deliver, in national planning policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it