Scholars have undertaken much conceptual development of “polycentric” climate governance (PCG). Yet, there has been limited empirical examination of whether this descriptive, analytical, and normative concept can aid climate change mitigation; it may even undermine our efforts in certain contexts. Thus this special issue examines the empirical realities of PCG. Building from a shared definition of the concept, the constitutive articles analyze an exploratory range of systems, across policy styles, governance levels, and types of actors. Here we consolidate the findings of the articles by identifying five key themes that are drawn from across the special issue, for consideration in future research. These themes are operationalization of PCG systems; voluntary action; temporality; power; and, crucially, effectiveness in mitigating climate change. Our findings provide evidence from a wide range of contexts, from which we build to propose new research streams on this topic.

Much existing empirical research on polycentric climate governance (PCG) systems examines small- N examples. In response, we aim to advance studies of PCG by exploring, and reflecting on, the use of large- N data sets for analyzing PCG. We use Python (a programming language) to create a novel data set from the United Nations’ Global Climate Action Portal. This method allows us to quantify key variables for 12,568 businesses located in Organization for Economic Co-operation and Development countries: the number of businesses’ climate commitments, their progress toward meeting those commitments, and businesses’ memberships in “more polycentric” networks via transnational climate initiatives (TCIs). Our analysis of these data reveals that greater interconnectedness may strengthen climate policy performance, since businesses with memberships in TCIs more commonly achieved their commitments. Additional research using these data, and/or similar methods, could be conducted on climate governance and on other areas of international environmental governance, such as mining and oil production.

In this forum, we highlight a discord in strategies around climate change policy and politics. On one hand, there is widespread concern for the pursuit of climate policy stability : stability in the design of policy and institutions, but particularly making policy and institutional development irreversible . However, much recent literature has revived an insistence on the inevitability of political conflict for pursuing the often large transitions needed to mitigate and adapt to accelerating climate change. Here, addressing climate change requires conflict, to weaken the power of incumbent actors that have blocked ambitious climate policy enactment for decades. Scholarship deploying each perspective tends to explicitly accept the need for radical sociotechnical transformations to address the climate crisis, but each entails radically different approaches to how to pursue decarbonization. The article outlines a research agenda focused on thinking about how these two apparently contradictory dynamics in climate politics interact, to advance our understanding of what sorts of strategies might open up political space for rapid transformations.

In 1992 the United Nations identified twenty-four “Annex II” states as being “developed” and holding the greatest responsibility for reducing emissions. Since then, the ambitions of these states toward mitigating climate change have varied significantly. This article is the first to employ fuzzy-set qualitative comparative analysis (fsQCA) to analyze climate policy variation among the Annex II developed states. The presence of a left-wing government is shown to be sufficient for ambitious climate policy, as is having high GDP per capita in conjunction with close links to the EU and few political constraints. The analysis highlights Austria’s surprisingly unambitious climate policy, which is explained, following elite interviews, by the state’s unique social partnership governance model and unusual fuel tourism industry. Overall, fsQCA proves a useful method for examining variables in combination and for case study selection, although limited by the number of variables it can assess.