Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Scaling up uncertain environmental evidence

Quality assurance in ecosystem service predictions

This three-year project started 2014 and is financed by the Swedish Research Council FORMAS focus on qualitative aspects of knowledge and how to consider these in decision making under uncertainty. A special interest is given to the process in which knowledge is produced to support environmental decision problems in which model-based simulations are used as replacement of the lack of empirical studies.

Project summary

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.


A conceptual model of the methodological framework to integrate quantitative and qualitative uncertainty and evaluate quality in evidence. Graphics.

The image shows a conceptual model of the methodological framework to integrate quantitative and qualitative uncertainty and evaluate quality in evidence, here based on characteristics of the epistemic (i.e. strength in knowledge) situation in the background knowledge and performance measures of the assessment.

A decision theory for quality evaluation of scientific-based evidence under uncertainty

We propose to merge principles of quantitative risk assessment, treatment of knowledge-based uncertainty and statistical inference 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.


An illustration of the “scaling up” experiment.

This is an illustration of the “scaling up” experiment. Issues related to quality in knowledge will be explored for three scales corresponding to changing (more demanding) epistemic situations in background knowledge. The first is to produce evidence on the outcome from a field experiment (A), the second to produce evidence on the management effects on the environmental system on a scale larger than the field experiment (from A to B), and the third to produce evidence on the outcome of management actions on the performance of the environmental system in the future (from A to B to C).

People involved

Ullrika Sahlin
Centre for Environmental and Climate Science

Niklas Vareman –
Department of Philosophy

Maj Rundlöf –
Department of Biology

Ola Olsson –
Department of Biology

Jörgen Ripa –
Department of Biology


Evenmang under framtidsveckan 2021 Under veckan kommer olika aspekter av att fatta beslut under osäkerhet tas upp och il...
Join the Swedish Society for Risk Sciences and the Nordic chapter of the Society for Risk Analysis’ upcoming webinar ent...
This year’s version of Bayes@Lund will be a series of online talks. Matthew Kay, Assistant professor Computer Science an...