Lam, D., E. Hinz, D. Lang, M. Tengö, H. von Wehrden, and B. Martín-López. 2020. Indigenous and local knowledge in sustainability transformations research: a literature review. Ecology and Society 25(1):3. https://doi.org/10.5751/ES-11305-250103

Implementing education for sustainable development (ESD) in higher education institutions (HEIs) is critical to facilitating a transition toward sustainable development. However, little is known about the specific implementation processes that lead to the institutionalization of sustainability curricula in HEIs. This meta-study and cluster analysis uses 131 international case studies to shed light on six distinct implementation patterns: (1) collaborative paradigm change, (2) bottom-up, evolving institutional change, (3) top-down, mandated institutional change, (4) externally driven initiatives, (5) isolated initiatives, and (6) limited institutional change. A cluster comparison reveals two distinct implementation phases: ESD can be implemented from the bottom-up, from the top-down, or both, and the impetus can stem from manifold external or internal stakeholders. To achieve more comprehensive ESD implementation, open communication among all stakeholders should be facilitated and feedback as well as reflection encouraged. Maintaining a unified vision statement and active participation of all stakeholders fosters a sense of ownership in ESD implementation and ensures that it will be long-lasting. Collaboration between isolated ESD initiatives and various stakeholders leads to shared knowledge and resources. Strong informal collaboration and communication can compensate for a lack of formalized leadership support from the top. Moreover, thorough planning that involves creating a strategy with detailed steps, and balancing shared responsibilities among internal stakeholders further enables fuller implementation of ESD. This analysis represents a first synthesis of small-N case studies and facilitates a better understanding of sustainability curriculum implementation patterns, which are shared in different contexts. Most HEIs and practitioners can benefit from these findings by reflecting on the specific implementation pattern with which the most overlap is found and focusing on this pattern’s most pertinent drivers.

Hanspach, J., T. Hartel, A. I. Milcu, F. Mikulcak, I. Dorresteijn, J. Loos, H. von Wehrden, T. Kuemmerle, D. Abson, A. Kovács-Hostyánszki, A. Báldi, and J. Fischer. 2014. A holistic approach to studying social-ecological systems and its application to southern Transylvania. Ecology and Society 19(4): 32.http://dx.doi.org/10.5751/ES-06915-190432

Artificial intelligence (AI) holds significant potential to advance Sustainable Development Goals by enabling data-driven insights and optimizations. In this analysis, we review 792 articles that explore AI applications in Sustainable Development Goal-related research. The literature is organized along two dimensions: (1) the disciplinary spectrum, from natural sciences to the humanities, and (2) the focus, distinguishing economic from socioecological content. Deep learning and supervised machine learning were the most prominently applied algorithms for forecasting and system optimization. However, we identify a critical gap: only a few studies combine advanced AI applications with deep sustainability expertise. Sustainability needs to strike a balance between contextualization and generalizability to provide tangible knowledge that will lead to responsible change. AI must play a central role in this process. While expectations for AI’s transformative role in sustainable development are high, its full potential remains to be realized.