Background
Complex environmental decision-making has triggered the development of a wide variety of decision support frameworks and tools, including in the context of climate change and peatland management. Often, the decision support tools consist of a user-friendly web-based or computer-aided front-end with complex coupled models and data at the back-end. The complexity of these coupled models is related to the fact that multiple aspects, dimensions and perspectives influence decision-making that are hard to capture in one holistic, all-encompassing model and dataset. Geographic information system (GIS) and multi-criteria decision analysis (MCDA) are increasingly used to make different information sets (e.g. peatland vulnerability or resilience maps and economic development scenarios) compatible and comparable to identify synergies and trade-offs in space and time between alternative policy interventions, management practices and alternative courses of action under climate change. Although these decision-support tools aim to be generally applicable, they are typically developed for specific contexts and target groups, which can reduce the accessibility and application of the tools. The European Water Framework Directive has, for example, been a driving force behind the development of more participatory decision-support tools, resulting in important lessons learned. Besides accurate representations of the scientific evidence related to causal (adverse outcome) pathways, increasing attention is paid to the role of risks and uncertainties in such decision-support frameworks and tools.
Activity Outline
- Use multiple online and offline data and modelling platforms to put the data, knowledge, information, and models coming out of Can-Peat together in a meaningful and impactful way
- Ensure accessibility to the new data and knowledge
- Increase uptake and wide-spread applications of the project's results in actual policy and decision-making
- Develop a decision support framework following a facilitated, structured demand-driven process, involving both scientists and stakeholders (e.g. municipal sector, indigenous communities) as potential end-users of the framework or sets of tools