Search for: All records

Abstract Although considerable research over the past two decades has examined collective learning in environmental governance, much of this scholarship has focused on cases where learning occurred, limiting our understanding of the drivers and barriers to learning. To advance knowledge of what we call the “collective learning continuum,” we compare cases of learning to cases where learning was not found to occur or its effects were “blocked.” Through semi‐structured interviews with key stakeholders in science‐policy forums in the Colorado River Basin, a large and complex river basin in western North America, we examine differences and patterns that explain moments of learning, blocked learning, or non‐learning, drawing insights from the collective learning framework. Our results find various factors that influence learning, blocked learning, and non‐learning. We discover technical and social factors as common drivers of both learning and blocked learning. In contrast, we find more structural factors associated with non‐learning. At the same time, the cases reveal insights about the role of political factors, such as timing, legal constraints, and priorities, which are underdeveloped in the collective learning framework. Overall, these findings advance theoretical knowledge of the collective learning continuum and offer practical insights that may strengthen the coordination of science and management for effective governance within the Basin. 

In scarcely a decade, a “labification” phenomenon has taken hold globally. The search for innovative policy solutions for social problems is embedded within scientific experimental-like structures often referred to as policy innovation labs (PILs). With the rapid technological changes (e.g., big data, artificial intelligence), data-based PILs have emerged. Despite the growing importance of these PILs in the policy process, very little is known about them and how they contribute to policy outcomes. This study analyzes 133 data-based PILs and examines their contribution to policy capacity. We adopt a policy capacity framework to investigate how data-based PILs contribute to enhancing analytical, organization, and political policy capacity. Many data-based PILs are located in Western Europe and North America, initiated by governments, and employ multi-domain administrative data with advanced technologies. Our analysis finds that data-based PILs enhance analytical and operational policy capacity at the individual, organizational and systemic levels but do little to enhance political capacity. It is this deficit that we suggest possible strategies for data-based PILs. 

Abstract The way in which public policies are composed may lead to conflicts that manifest in an extended policymaking duration. This paper explores the associations between policy composition and the relative duration for policies to be adopted in 15 U.S. state legislatures. We treat policy passage duration as an indicator of policy conflicts in the legislative process. We adapt the institutional grammar tool (IGT) to examine how 168 oil and gas development policies are composed and gauge the association between the content of these policies and the speed of their adoption. We find policies that are more stringent, contain more constitutive rules, target issues related to oil and gas operations or tax and finance take relatively longer to pass. These findings offer theoretical insights into the relationships between policy composition and policy adoption duration. They also provide methodological insights on measuring policy design components using a semi‐automated application of the IGT.