Trustworthy and Accountable Decision-Support Frameworks for Biodiversity - A Virtual Labs based Approach

Lead Research Organisation: UK Centre for Ecology & Hydrology
Department Name: Pollution (Lancaster)


The nature of science is changing, particularly in its relationship to decision-making and policy formulation. In essence, science is becoming more complex with questions becoming broader in scope and with the consequent need to span disciplines and achieve integration between scientific disciplines and socio-economic concerns. Given this complexity, levels of uncertainty are increasing and there is a need to make decisions in the face of such uncertainties. In addition, we are seeing that the stakes are high in scientific discourse and there is an urgency associated with decision-making. Many observers refer to this as a period of post-normal science but, whatever terminology one adopts, it is clear that we need new tools to support science and decision-making given this complexity, uncertainty, importance and urgency. These statements apply strongly to the environmental sciences and in particular to issues related to biodiversity and its relationships with economics and society. The Dasgupta Review highlights the criticality of nature for our economies, livelihoods and wellbeing, our failures in managing nature to date and the huge risks associated with this. Crucially, it calls for transformative change in the way we think, act and measure success, seeing our economies as fundamentally embedded and interlinked with nature. This resonates with statements from the post-normal science literature calling for a fundamental rethink about the approaches and tools we use for decision-making related to science.

In response to these challenges, we will deliver a transformative approach to embedding biodiversity values in decision-making by integrating novel perspectives around the economics of biodiversity with virtual labs (collaborative, cloud-based environments supporting transparent science). As a starting point, we will build a comprehensive evidence base to support economics of biodiversity decision-making within virtual labs, thus: i) facilitating the necessary integration of data and analyses around biodiversity and its economic and non-monetary benefits, values and costs; ii) promoting an approach that is collaborative and open, both critical components in supporting the necessary dialogue between disciplinary experts and stakeholders, and supporting collective reasoning around uncertainties. We will extend virtual labs by adding trustworthy and accountable decision-making capability, through decision-support frameworks. These frameworks will be informed by a systems thinking approach, building on the integration offered by virtual labs, and promoting an understanding of interactions and feedback. This will enable deeper analyses of co- or incidental benefits or other synergies associated with biodiversity and socio-economic activity, which we see as crucial in supporting improved decision-making in this area. The work will be evaluated through two complementary case studies, investigating co-benefits between: i) biodiversity and renewable energy in the planning and operation of solar parks; ii) biodiversity and agricultural production in land use decision-making. Note that we seek a flexible approach to the design of decision-support frameworks, where they can be specialised for different contexts and scales with commonalities and variabilities emerging from the case studies.

The research is fundamentally transdisciplinary in nature and we have a consortium with internationally leading expertise in science, data science and social science (see Part I). We adopt an agile approach to the research, an approach that can achieve the necessary cross-disciplinary dialogue, as well as enabling tighter integration of stakeholders in the co-design of solutions. We have a rich set of project partners supporting this process, and have already engaged with our partners in co-design activities in preparation for this proposal.


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