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Indigenous peoples’ participation in public policy and planning is ascribed in numerous international and national legal instruments as essential to the realisation of their self-determination. This study examines how the Akwé: Kon guidelines (AK) can promote effective indigenous peoples participation in environmental management, especially during environmental impact assessment (EIA). Special focus is drawn on the Finnish context, home of the Sámi indigenous people. The study applies an effectiveness review package by Lee and Colley (1999), supplemented by interview and questionnaire surveys, to analyse how effective the AK have been. It was found that although they were useful in promoting further interaction of the Sámi with authorities, the AK did not address their most fundamental political and legal grievances. This leaves room for EIA policy and practice, in Finland and all other jurisdictions with indigenous peoples, to consider how they can more effectively harness the potentialities in AK.

Complex social processes introduce difficulties to validating causal parameters and identifying the correct system structure in modelling. Policy impact assessment for sustainability transitions should therefore not expend too many resources modelling any single set of assumptions about the world. Furthermore, keeping models relatively simple allows more effective communication and stakeholder collaboration. This paper presents an exploratory system dynamics model of urban mode choice. We demonstrate that, despite structural and parametric uncertainty, it is possible to rank alternative policy approaches and identify high-leverage uncertainties as targets of policy action or further analysis. We also show how different narrative theories of change can have drastically different or unintuitive outcomes for the same intervention. Simulation can benefit both impact assessment and the further scrutiny and refinement of change narratives. We argue that the following methodological choices and their synergies made our modelling approach effective: exploratory modelling, focus on endogeneity, coarse resolution and avoidance of abstract variables.