Contributions of Nature-Based Solutions to Reduce Peoples’ Vulnerabilities to Climate Change across the Rural Global South
Bibliographic record
Abstract
Nature-based solutions (NbS) —i.e. working with and enhancing nature to address societal challenges— feature with increasing prominence in responses to climate change, including in the adaptation plans of the most vulnerable nations. Although evidence for the effectiveness of NbS for adaptation is growing, there is less evidence on whether and how NbS reduce vulnerability to climate change in the Global South, despite this region being home to most of the world’s most climate-vulnerable people. To address this, we analysed the vulnerability-reduction outcomes of 85 nature-based interventions in rural areas across the Global South, and factors mediating their effectiveness, based on a systematic map of peer-reviewed studies encompassing a wide diversity of ecosystems, climate impacts, intervention types and institutions. We applied an analytical framework based on social-ecological systems and climate change vulnerability, coding studies with respect to six pathways of vulnerability reduction: social and ecological exposure, sensitivity, and adaptive capacity. We find widespread effectiveness of NbS in the dataset with 95% providing positive outcomes for climate change adaptation. Overall, nature-based interventions reduced vulnerability primarily by lowering ecosystem sensitivity to climate impacts (73% of interventions), followed by reducing social sensitivity (43%), reducing ecological exposure (37%), and/or increasing social adaptive capacity (34%), ecological adaptive capacity (18%) and reducing social exposure (12%). With an analysis of mediating factors, we show that vulnerability-reduction effectiveness was affected as much by social and political factors as by technical considerations. Indeed configurations of existing and introduced formal and informal institutions appear central to the efficacy and distributive effects of the studied interventions. We conclude that attention to the distinct pathways through which vulnerability is reduced can help maximise the benefits of NbS and that to be successful, careful consideration is required on their applicability to particular circumstances as well as their social dimensions.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".