Scaling up uncertain environmental evidence

This project aims to facilitate quality evaluation of scientific-based evidence for environmental management under uncertainty. Incomplete knowledge and conflicting objectives turn environmental management into complex decision problems. Quantitative assessments promote transparent production of evidence. Quality of evidence depends on qualitative aspects of uncertainty (the epistemic situation) like weak bases for inference, low confidence in model predictions, or extent of subjective (expert) knowledge.

It is not enough to assure quality of evidence based on quality of underlying empirical studies. We propose to merge principles of quantitative risk assessment, treatment of knowledge-based uncertainty and statistical interference into a decision theory for quality evaluation of scientific-based evidence under uncertainty.

We will: 

1. clarify the meaning of quality of evidence,
2. integrate qualitative and quantitative uncertainties into quantitative assessments and
3. test the theory's feasibility to evaluate quality of evidence.

The decision theory will be tested on a case study on evidence based management of pollination ecosystem services in agricultural landscapes, considering effects on farmers and societal risks, and for which data and models are available. Testing will be made on evidence from three management problems with decreasing strength of the epistemic situation created by extrapolating from the scale of observations in space to a landscape and into the future.