Despite substantial focus on sustainability issues in both science and politics, humanity remains on largely unsustainable development trajectories. Partly, this is due to the failure of sustainability science to engage with the root causes of unsustainability. Drawing on ideas by Donella Meadows, we argue that many sustainability interventions target highly tangible, but essentially weak, leverage points (i.e. using interventions that are easy, but have limited potential for transformational change). Thus, there is an urgent need to focus on less obvious but potentially far more powerful areas of intervention. We propose a research agenda inspired by systems thinking that focuses on transformational 'sustainability interventions', centred on three realms of leverage: reconnecting people to nature, restructuring institutions and rethinking how knowledge is created and used in pursuit of sustainability. The notion of leverage points has the potential to act as a boundary object for genuinely transformational sustainability science.

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Current political trends and scholarly research increasingly promote collaborative and participatory governance in multi-level systems as a way to more sustainable and effective environmental policy. Yet empirical fi ndings as well as conceptual works from different academic fi elds remain ambiguous about this claim. This paper explores whether and to what extent the existence of multiple levels of governance affects the ability of participatory decision-making to deliver high quality environmental policy output and to improve implementation and compliance. To this end, fi ndings from the literature on multi-level governance, public participation and policy implementation as well as on complex systems are integrated in fi ve sets of hypotheses. In order to put these to a 'plausibility probe', a meta-analysis of 47 case studies from Northern America and Western Europe is conducted. These cases provide qualitative insights and allow for some generalization in the form of correlation analysis. The study fi nds that, predominantly, environmental preferences of the involved actors determine the environmental outputs (and outcomes) of decision-making. Further, face-to-face, but not mere two-way, communication appears to positively infl uence the ecological standard of decisions. The analysis also suggests that a highly polycentric governance system comprising many agencies and levels of governance yields higher environmental outputs than rather monocentric governance. However, correlations between governance effectiveness and decision-making scale, as well as policy delivery and institutional fi t to ecosystem, could not be identifi ed. The paper concludes by outlining pathways for more systematic comparative research on these pressing research questions. Copyright

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In the face of apparent failures to govern complex environmental problems by the central state, new modes of governance have been proposed in recent years. Network governance is an emerging concept that has not yet been consolidated. In network governance, processes of (collective) learning become an essential feature. The key issue approached here is the mutual relations between network structure and learning, with the aim of improving environmental management. Up to now, there have been few attempts to apply social network analysis (SNA) to learning and governance issues. Moreover, little research exists that draws on structural characteristics of networks as a whole, as opposed to actor-related network measures. Given the ambiguities of the concepts at stake, we begin by explicating our understanding of both networks and learning. In doing so, we identify the pertinent challenge of individual as opposed to collective actors that make up a governance network. We introduce three learning-related functions that networks can perform to different degrees: information transmission, deliberation, and resilience. We address two main research questions: (1) What are the characteristics of networks that foster collective learning in each of the three dimensions? To this end, we consider SNA-based network measures such as network size, density, cohesion, centralization, or the occurrence of weak as opposed to strong ties. (2) How does collective learning alter network structures? We conclude by outlining a number of open issues for further research.