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Pursuing integrated research and decision-making to advance action on the sustainable development goals (SDGs) fundamentally depends on understanding interactions between the SDGs, both negative ones (“trade-offs”) and positive ones (“co-benefits”). This quest, triggered by the 2030 Agenda, has however pointed to a gap in current research and policy analysis regarding how to think systematically about interactions across the SDGs. This paper synthesizes experiences and insights from the application of a new conceptual framework for mapping and assessing SDG interactions using a defined typology and characterization approach. Drawing on results from a major international research study applied to the SDGs on health, energy and the ocean, it analyses how interactions depend on key factors such as geographical context, resource endowments, time horizon and governance. The paper discusses the future potential, barriers and opportunities for applying the approach in scientific research, in policy making and in bridging the two through a global SDG Interactions Knowledge Platform as a key mechanism for assembling, systematizing and aggregating knowledge on interactions.

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Large-scale land acquisitions (LSLAs) have become a major concern for land use sustainability at a global scale. A considerable body of case studies has shown that the livelihood outcomes of LSLAs vary, but the understanding of factors and processes that generate these livelihood outcomes remains controversial and fragmented in terms of cases, contexts, and normative orientations. Therefore, this study presents a meta-analysis of case studies and applies the archetypes approach developed in global change research to analyse the configurations of factors and processes that generate different livelihood outcomes in LSLA situations. The analysis is based on 44 systematically selected studies covering 66 cases in 21 countries in Africa, Latin America, Southeast Asia, and Eastern Europe. The results show that LSLAs affect rural livelihoods through a small set of archetypical configurations. Adverse livelihood outcomes arise most frequently from processes of (1) enclosure of livelihood assets, (2) elite capture, (3) selective marginalisation of people already living in difficult conditions, and (4) polarisation of development discourses, and less frequently from (5) competitive exclusion, (6) agribusiness failure, and (7) transient jobs. The processes are activated in specific configurations of social-ecological factors. Moving beyond diagnosis, the paper identifies archetypical potentials for safeguarding or enhancing sustainable livelihoods in LSLA target regions at multiple levels of decision-making. Finally, we analyse how contextual factors modify these general insights. This paper helps to advance the archetypes methodology for use in global change research that aims at integral analysis of recurrent patterns expressed in local manifestations. The results can be used to better link local case studies with regional and global inventories of the global land rush.

The UN 2030 Agenda for Sustainable Development stresses the fundamental role science should play in implementing the 17 Sustainable Development Goals endorsed by the global community. But how can and should researchers respond to this societal demand on science? We argue that answering this question requires systematic engagement with the fundamental normative dimensions of the 2030 Agenda and those of the scientific community-and with the implications these dimensions have for research and practice. We suggest that the production of knowledge relevant to sustainable development entails analytic engagement with norms and values through four tasks. First, to unravel and critically reflect on the ethical values involved in sustainability, values should increasingly become an empirical and theoretical object of sustainability research. Second, to ensure that research on social-ecological systems is related to sustainability values, researchers should reflect on and spell out what sustainability values guide their research, taking into account possible interdependencies, synergies, and trade-offs. Third, to find common ground on what sustainability means for specific situations, scientists should engage in deliberative learning processes with societal actors, with a view to jointly reflecting on existing development visions and creating new, contextualized ones. Fourth, this implies that researchers and scientific disciplines must clarify their own ethical and epistemic values, as this defines accountability and shapes identification of problems, research questions, and results. We believe that ignoring these tasks, whether one is in favor or critical of the 2030 Agenda, will undermine the credibility and relevance of scientific contributions for sustainable development.

2019.Archetype analysis in sustainability research: meanings, motivations, and evidence-based policy making. Ecology and Society 24(2):26.ABSTRACT. Archetypes are increasingly used as a methodological approach to understand recurrent patterns in variables and processes that shape the sustainability of social-ecological systems. The rapid growth and diversification of archetype analyses has generated variations, inconsistencies, and confusion about the meanings, potential, and limitations of archetypes. Based on a systematic review, a survey, and a workshop series, we provide a consolidated perspective on the core features and diverse meanings of archetype analysis in sustainability research, the motivations behind it, and its policy relevance. We identify three core features of archetype analysis: recurrent patterns, multiple models, and intermediate abstraction. Two gradients help to apprehend the variety of meanings of archetype analysis that sustainability researchers have developed: (1) understanding archetypes as building blocks or as case typologies and (2) using archetypes for pattern recognition, diagnosis, or scenario development. We demonstrate how archetype analysis has been used to synthesize results from case studies, bridge the gap between global narratives and local realities, foster methodological interplay, and transfer knowledge about sustainability strategies across cases. We also critically examine the potential and limitations of archetype analysis in supporting evidence-based policy making through context-sensitive generalizations with case-level empirical validity. Finally, we identify future priorities, with a view to leveraging the full potential of archetype analysis for supporting sustainable development.

Developed to be interconnected by design, the 17 sustainable development goals (SDGs) and their 169 targets have attracted a growing scientific community committed to exploring the systemic interactions inherent to the 2030 Agenda. Understanding which SDGs influence one another (positively or negatively) is critical to prioritize and implement policies that maximize synergies between goals while navigating trade-offs. In this way, the need for informed decision-making urgently requires knowledge of context-specific SDG interactions. Drawing on an extensive literature review (including scientific reports and scholarly articles), we collected, synthesized,