Scaling Nature-Based Solutions (NbS): Lessons from Global Progress and Indonesia’s Path to Sustainability
Notice bibliographique
Résumé
Abstract Nature-based Solutions (NbS) have emerged as a transformative approach to addressing climate change, biodiversity loss, and sustainable development. Despite growing global recognition, the implementation of NbS remains uneven, particularly in countries like Indonesia, which possess high ecological potential but face several challenges. This study critically reviewed the global recognition and implementation progress of NbS in achieving international sustainability and climate agendas, with a particular focus on identifying key enabling factors and hindering risks, and deriving globally informed lessons tailored to Indonesia’s context. This study presents a comprehensive systematic review of 219 peer-reviewed articles from the Web of Science (WoS) and Scopus, conducted following the PRISMA 2020 protocol. The review identifies three primary typologies of NbS—Intrinsic, Hybrid, and Artificial—each associated with specific ecosystem types and functions. It maps their alignment with major global agendas, including the Sustainable Development Goals (SDGs), the Paris Agreement, Nationally Determined Contributions (NDCs), the Sendai Framework, the Nature-Positive 2030 initiative, the Kunming-Montreal Global Biodiversity Framework (GBF), and the Climate Resilient Development Pathway (CRDP). Intrinsic NbS, such as forests, wetlands, and peatlands, dominate the literature due to their critical roles in carbon sequestration, biodiversity conservation, and disaster mitigation. Findings reveal a significant research gap in Indonesia, with only four studies explicitly linking NbS to national climate commitments of the NDC. Key barriers to implementation include fragmented governance, lack of standardized frameworks, limited stakeholder engagement, and insufficient financial mechanisms. Conversely, enabling factors such as regulatory reforms, green financing instruments (e.g., green sukuk, ecological fiscal transfers), and the integration of traditional ecological knowledge (TEK) offer promising pathways for scaling up NbS. This study contributes a novel typological framework and a synthesis of enabling and hindering factors contextualized for Indonesia. It underscores the need for localized, evidence-based NbS strategies that are aligned with global frameworks yet tailored to national socio-ecological realities. By bridging global lessons with local contexts, the findings provide a strategic foundation for policymakers, researchers, and practitioners to enhance the effectiveness, scalability, and equity of NbS in achieving climate resilience and sustainable development. Graphical Abstract This graphical abstract provides a concise and visually engaging summary of the study, which systematically reviews the global and Indonesia-specific progress of Nature-based Solutions (NbS) in achieving climate and sustainability goals. The visual begins by categorizing NbS into three typologies—Intrinsic, Hybrid, and Artificial—each illustrated with a representative short description to distinguish their ecological and engineered characteristics. The central panel outlines the systematic literature review process, using the PRISMA framework, and highlights the screening and selection of 219 articles from the Web of Science (WoS) and Scopus databases. A four-key aspect is being reviewed concerning various ecosystem types. On the right panel, the abstract further maps the alignment of NbS with major global agendas, including the Sustainable Development Goals (SDGs), the Paris Agreement, Nationally Determined Contributions (NDCs), the Sendai Framework, Nature-Positive 2030, the Kunming-Montreal Global Biodiversity Framework (GBF), and the Climate Resilient Development Pathway (CRDP). These are depicted through recognizable icons, reinforcing the relevance of NbS in international policy frameworks. At the bottom panel, a timeline and global map illustrate the temporal trends and geographical spread of NbS implementation. The findings reveal an underrepresentation of Indonesian contexts in the implementation of NbS. A chart at the corner summarizes the key enabling factors (e.g., stakeholder engagement, financial incentives) and hindering risks (e.g., regulatory barriers, fragmented governance). This visual synthesis highlights the pressing need for localized, evidence-based strategies to scale up NbS in Indonesia. By identifying typologies, ecosystem types, and global lessons learn of key enabling or hindering risks, the study offers actionable insights for researchers, practitioners, and policymakers. It aims to bridge knowledge gaps, inform national climate strategies, and enhance Indonesia’s contribution to global sustainability and climate resilience targets through NbS implementation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,009 | 0,012 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».