● Participation and implementation monitoring, including participation quality tracking, adoption rates, compliance indicators, and community feedback mechanisms. ● Spatial and landscape monitoring, including displacement or leakage risks and accessibility constraints. ● Outcome monitoring through panel data and equity analysis, including benefit distribution, income and food security indicators, and behavioural adoption over time. ● Final performance and learning assessments, including governance legitimacy review, unit-economics and equity audits, and lessons-learned and adaptation tracking. DISCUSSION Applying this assumption-testing approach to NbS highlights how systematically examining socioeconomic assumptions early in project design can strengthen framing, governance, and management choices. By surfacing assumptions at the outset, issues that would otherwise emerge only during implementation, often once design decisions are already fixed, can be identified and addressed earlier. This creates space to adjust theories of change, refine safeguard strategies, and align management procedures with on-the-ground realities before social challenges become operational constraints. More broadly, Synergy’s work with project developers and investors indicates that teams often face uncertainty when anticipating behavioural dynamics, participation, or governance conditions. A structured review of assumptions helps navigate this uncertainty and supports a more grounded understanding of the social and political factors shaping project feasibility. While context matters, early integration of social expertise appears to contribute to more realistic expectations and more balanced designs that consider both ecological and social dimensions. Several factors may influence how this approach is applied across different contexts. NbS projects often operate in complex socio‑ecological settings, where understanding local dynamics and governance environments requires time and resources. This reflects broader findings that many enabling‑condition assumptions are difficult to assess and highly context‑specific. In addition, developers frequently work within layered safeguard requirements that can fragment analysis and limit attention to the issues most relevant to each landscape. There are also limits to what NbS can reasonably address. These interventions alone cannot overcome structural drivers such as poverty or weak state capacity, and recognising these boundaries is important for shaping realistic project pathways. Despite these challenges, the approach remains adaptable across NbS types because it centres on making project assumptions explicit and testable. Its effectiveness, however, will depend on access to local expertise, institutional flexibility, and the ability to embed adaptive management throughout the project lifecycle. 82
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