Nature-based Solutions to Address Global Societal Challenges: Benefiting People and Nature
Bibliographic record
Abstract
The global crises of climate change, biodiversity loss, and land degradation have been catching the attention of governments, communities, and organizations worldwide, underscoring the urgent need for integrated and scalable solutions. Nature-based Solutions (NbS) provide a transformative approach to addressing these challenges while delivering benefits for both people and nature. Defined as “actions to protect, manage, and restore natural or modified ecosystems, which address societal challenges, effectively and adaptively, providing human well-being and biodiversity benefits”. NbS have demonstrated their capacity to generate multi-dimensional impacts. For instance, mangroves avert USD 57 billion in annual flooding damages, NbS can provide one-third of the climate mitigation needed to meet the Paris Agreement goals, and the global benefits of ecosystem services from NbS focused on climate are estimated at USD 170 billion annually. These figures underscore the economic, ecological, and societal value of integrating NbS into sustainable development strategies.IUCN has been at the forefront of advancing NbS for over two decades, developing the IUCN Global Standard for Nature-based Solutions to guide their design, implementation, and evaluation. This standard, comprising 8 criteria and 28 indicators, ensures that NbS are effective, equitable, and adaptable to diverse contexts. The potential of NbS to address global societal challenges—including climate change, biodiversity loss, and ecosystem degradation—will be explored, with a focus on how NbS can advance the objectives of the three Rio Conventions (UNFCCC, CBD, and UNCCD). These solutions also align with many international frameworks such as the Paris Agreement, the Kunming-Montreal Global Biodiversity Framework (KMGBF), and the Sustainable Development Goals (SDGs), which also foster resilient and sustainable communities.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".