QR is an innovative technique, originating from Artificial Intelligence (AI) that involves non-numerical description of systems and their behaviour, preserving all the important behavioural properties and distinctions. QR technology is of great importance for developing, strengthening and further improving education and training on topics dealing with systems and their behaviours. It is well known that an essential part of modern education and training involves the comprehension of systems and their behaviours. That is, being able to distinguish a system from the environment in which it operates, to identify the parts that it is made of, and to predict or explain its behaviours. Research in the cognitive sciences has shown that when learners have a causal model of system behaviour, they are better able to apply their knowledge to new situations (Schumacher and Gentner, 1988; Bredeweg & Winkels, 1998). QR models are a way to develop such causal models, because they capture the fundamental aspects of a system or mechanism, while suppressing much of the irrelevant detail. This approach makes expert knowledge available to non-experts for direct use in educational and applied contexts. An important advantage of QR over other techniques like expert or knowledge-based systems is that QR transfers not just predictions based on expert knowledge, but also makes this knowledge explicit, allowing its transfer to others. Hence, our approach will help reconcile the conflicting interests of stakeholders and facilitate restoration and sustainable development throughout Europe.