Solving problems in social-ecological systems: definition, practice and barriers of transdisciplinary research

Abstract

Translating policies about sustainable development as a social process and sustainability outcomes into the real world of social-ecological systems involves several challenges. Hence, research policies advocate improved innovative problem-solving capacity. One approach is transdisciplinary research that integrates research disciplines, as well as researchers and practitioners. Drawing upon 14 experiences of problem-solving, we used group modeling to map perceived barriers and bridges for researchers' and practitioners' joint knowledge production and learning towards transdisciplinary research. The analysis indicated that the transdisciplinary research process is influenced by (1) the amount of traditional disciplinary formal and informal control, (2) adaptation of project applications to fill the transdisciplinary research agenda, (3) stakeholder participation, and (4) functional team building/development based on self-reflection and experienced leadership. Focusing on implementation of green infrastructure policy as a common denominator for the delivery of ecosystem services and human well-being, we discuss how to diagnose social-ecological systems, and use knowledge production and collaborative learning as treatments.

Fig. 1

Simple cause–effect relationships shown as causal loop diagrams (CLD). The variable at the tail of the arrow causes a change to the variable at the head. A plus sign indicates that the variable at the tail and the variable at the head of the arrow change in the same direction, while a minus sign indicates that the variables at the tail and head change in opposite directions. Thus, if the variable at the tail increases, the variable at the head decreases and vice versa. The letter R in the middle of a loop indicates that the loop is reinforcing, causing either a systematic growth or decline. The letter B indicates that the loop is balancing and moves the system towards equilibrium. Thus, (i) The rain irrigates the soil, which is needed for the grass to grow. Another effect of the rain is that my hair becomes wet. The growth of the grass and the wet hair seem to be correlated due to the same cause but the grass does not grow because my hair is wet. Even if the phenomena are statistically correlated, the cause–effect relationship is not sound. (ii) A cause–effect relationship with two counteracting factors acting on effect 1

Fig. 2

a Causal loop diagram (CLD1) that resulted from group modeling of the question “What are the impediments to the development of a transdisciplinary research agenda?, based on 14 case studies of problem-solving. b Causal loop diagram (CLD2) resulted from group modeling of the question “What factors influence functional TDR team development?”, based on 14 case studies of problem-solving

Fig. 3

Cycle of re-enforcing transdisciplinary problem-solving capacity of a team of researchers, playing both individually and in concert like in jazz, from different disciplines and stakeholders, relevant to a particular issue