Bridging practice and science in socio-environmental systems research and modelling: A design science approach

A strong and close connection between science and practice in socio-environmental systems (SES) research and modelling is warranted to face complex and interdisciplinary socio-environmental challenges around issues such as sustainability and climate change. However, significant gaps and inadequate knowledge flow between the scientific and practical aspects of SES exist. This paper highlights several areas that require improvement, including reducing the lag time between scientific solutions and practical implementation, making academic research more relevant to practitioners and decision makers, enhancing the transfer and translation of scientific outputs into practice, improving the integration of practical studies and local knowledge into academia, and addressing the complexity of real-world problems more effectively. To bridge these gaps, we advocate for adopting a design science research (DSR) approach in socio-environmental research. DSR is a problem-solving paradigm that creates applied artifacts such as models, methods, and design theories to enrich knowledge and provide practical solutions. By applying DSR, we can reduce the time between identifying a problem and implementing a solution while facilitating the transfer and translation of research findings into practical applications. We demonstrate the value of artifacts in extracting solutions from practical studies and local knowledge and making them applicable to a broader range of socio-environmental research. DSR emphasises the design of solutions and learning about complex problems through the process of solving them. We demonstrate the merits of the DSR approach application in SES through two case studies to provide a first trial of DSR's practicality, value and challenges. Although DSR is not yet widely recognised or applied in socio-environmental research, this paper encourages its adoption as an overarching approach that complements traditional methods in SES research. By strengthening the connection between practice and science, DSR has the potential to address the existing gaps and improve the effectiveness of socio-environmental research and modelling. The paper therefore advocates that, for complex SES problems, DSR offers ways to not only strengthen but also escalate the overall value of SES modelling and practice to addressing earth's grand challenge problems. It contributes directly to the Joint Special Issue on good modelling practices, including developing Findable, Accessible, Interoperable and Reusable (FAIR) artifacts.