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.

The concept of resilience gained increased attention in sustainability science, with a notable spike from 2014 onwards. However, resilience is a multifaceted concept with no unanimous definition, making applications in the context of sustainability, a similarly multifarious term, a challenge. Here, we examine the use of resilience in well-cited sustainability literature in the period from 2014 to 2018. Based on our analysis, resilience as a concept proves its analytical strength through a diverse set of frameworks, indicators, and models, while its usefulness as boundary object is less clear. Most of the examined publications do not cite one of the well-established resilience definitions as a conceptual basis. The normativity of resilience is often implicit and rarely critically questioned, and strong participatory approaches are lacking. A multivariate statistical full-text bibliographic analysis of 112 publications reveals four distinct research clusters with partial conceptual proximity but hardly any overlap. While the majority of publications consider human well-being as an integral factor in their research, some research marginalizes this concept. Resilience to climate change dominates the discourse in the literature investigated, which signifies a need to broaden research efforts to other equally pressing—but in terms of the concept, widely neglected—sustainability challenges.

The current coronavirus outbreak may provide an illustrative analogy for sustainability challenges, exemplifying how challenges such as climate change may become wicked problems demanding novel and drastic solution attempts.

The global catastrophic risk (GCR) and existential risk (ER) literature focuses on analyzing and preventing potential major global catastrophes including a human extinction event. Over the past two decades, the field of GCR/ER research has grown considerably. However, there has been little meta-research on the field itself. How large has this body of literature become? What topics does it cover? Which fields does it interact with? What challenges does it face? To answer these questions, here we present the first systematic bibliometric analysis of the GCR/ER literature. We consider all 3437 documents in the OpenAlex database that mention either GCR or ER and use bibliographic coupling (two documents are considered similar when they share many references) to identify 10 distinct emergent research clusters in the GCR/ER literature. These clusters align in part with commonly identified drivers of GCR, such as advanced artificial intelligence (AI), climate change, and pandemics or discuss the conceptual foundations of the GCR/ER field. However, the field is much broader than these topics, touching on disciplines as diverse as economics, climate modeling, agriculture, psychology, and philosophy. The metadata reveal that there are around 150 documents published on GCR/ER each year, the field has highly unequal gender representation, most research is done in the United States and the UK, and many of the published articles come from a small subset of authors. We recommend creating new conferences and potentially new journals where GCR/ER-focused research can aggregate, making gender and geographic diversity a higher priority, and fostering synergies across clusters to think about GCR/ER in a more holistic way. We also recommend building more connections to new fields and neighboring disciplines, such as systemic risk and policy, to encourage cross-fertilization and the broader adoption of GCR/ER research.