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Cover; Endorsement; Half Title; Series Information; Title Page; Copyright Page; Dedication; Table of contents; List of figures; List of tables; Acknowledgments; Part I Foundations; 1 Introduction; Environmental Policymaking and Politics; The Politics of Environmental Policymaking; Plan of the Book; Part I: Foundations; Part II: The Environmental Policymaking System and Climate Policy; Notes; References; 2 A Framework for Environmental Policymaking; Introduction; Markets and Governments; Environmental Policy and Governance; The Environmental Policymaking Process

Within the Advocacy Coalition Framework (ACF), policy-oriented learning is understood as a change in policy beliefs. Additional work has noted that belief reinforcement, not just belief change, is also a potential policy learning outcome. Yet, little work has attempted to reconcile how learning could involve both belief change and belief reinforcement. In this article, I propose a policy-oriented learning model where policy beliefs – deep core, policy core, or secondary aspects – are understood as having a distribution with a central tendency (that is, the belief) as well as variance (that is, certainty associated with the belief). With policy beliefs considered as distributions, learning can be understood as changes in beliefs (that is, a change in the central tendency) as well as changes in certainty (that is, variance), and conversely, a decrease in belief uncertainty would constitute belief reinforcement. Using data from a deliberative forum that brought together various stakeholders including experts, natural resource managers, and the public to discuss environmental issues impacting coastal communities, I explore policy-oriented learning as changes in concern regarding several key issues before and after the forum. Additionally, I examine the association between concern following the forum and self-reported learning. I find support for the proposed policy-oriented learning model as shown by significant changes in average concern as well as average variance among participants across several of the issues discussed. In this way, the article makes a theoretical contribution to the ACF literature by testing the use of distributions to assess policy learning.

AbstractUnder a polycentric approach to climate change, action is taken at different scales and across all levels of government and sectors of society. Some scholars have argued that such an approach is the best lens to view the governance of climate change and that a polycentric approach has advantages in addressing collective‐action problems. However, taking a polycentric approach would require public support for action at multiple scales. The issue of climate change is polarized across political beliefs and cultural worldviews and little research has examined how the public views climate action at one level of government relative to others as well as relative to actions by the private sector and by individuals. Using an original survey of the US public from October 2017, I explore who it is that the public thinks should "do more" about climate change and the role that the cultural worldviews posited by cultural theory—hierarchical, egalitarian, individualist, and fatalist—plays in shaping those opinions. Overall, I find support for multiple actors doing more to address climate change, but with differences in support between egalitarians and individualists for actors overall and for the federal government in particular.

Policy learning is an important concept in the study of policymaking, yet it is difficult to model and empirically estimate. Additionally, work on policy learning has not fully drawn from the work on information processing in the policy process. In this paper, I propose a model of policy learning that incorporates the Advocacy Coalition Framework's notion of policy‐oriented learning and the theory of disproportionate information processing within a Bayesian learning framework. Policy learning through Bayesian updating occurs as individuals adjust their prior beliefs in light of new information, and in the approach posited here, learning is a function of the strength of prior beliefs and the weight given to new information. Additionally, learning is thought to occur only when subsequent beliefs move in the direction of the information. Then, I demonstrate the policy learning model using public opinion data about Yucca Mountain, a proposed repository site for nuclear waste. Finally, I conclude with suggesting ways in which the policy learning model can be incorporated into current policy learning theories and frameworks.

AbstractMany of the leading theories of the policy process are aimed at providing insights into the factors that make policy change more (or less) likely. In general, policy change is seen as a result of shifting dynamics within policy subsystems. However, building on theories of policy feedback and interest mobilization, this article examines whether policy change, apart from being an effect of subsystem dynamics, can be a cause of shifting dynamics as latent actors are motivated to participate in the subsystem as a result of policy change. Two hypotheses regarding post‐policy change mobilization are developed and tested using data on participation in congressional hearings concerning the management of nuclear waste. The findings suggest that policy change can activate latent policy actors, specifically those actors that view themselves as "losing" as a result of the policy change. These results point to the need for scholars to examine the potential impacts of post‐policy change dynamics on policy development.

Issue definitions, the way policy issues are understood, are an important component for understanding the policymaking process. Research on issue definitions has been divided between a macro level that examines collective issue definitions and a micro level focusing on the ways in which policy actors frame policy issues. This article develops a model of issue definitions that assumes issues are multidimensional, competition exists among policy actors in defining issues, and that collective issue definitions can be understood as the aggregation of individual issue definitions. This model is then estimated using quantitative text analysis. While various approaches to text analysis and categorization have been used by scholars, latent Dirichlet allocation (LDA), a specific type of topic modeling, is used to estimate issue definitions. Using LDA, witness testimony taken from Congressional hearings that occurred from 1975 to 2012 about the issue of used nuclear fuel (UNF) is examined and seven distinct dimensions of the UNF debate are estimated. The construct validity of these dimensions is checked by testing them against two major policy changes that occurred in the UNF domain. I conclude with a discussion of the strengths and weakness of topic modeling, and how this approach could be used to test hypotheses drawn from several of the major policymaking theories.

Over the last two decades many alternate theories of the policy process have been developed. This essay covers recent scholarship (from 2008 to 2010) regarding the major policy process theories. In addition, several recent trends in research are discussed including; the use of narrative in policy theory, issues that cross multiple subsystems, bureaucracy in the policy process, and synthesizing multiple theories and frameworks. Adapted from the source document.

Abstract
Social science studies of weather and natural hazards have examined in depth the sources of information individuals use in response to a disaster. This research has primarily focused on information sources in isolation and as they relate to severe weather. Thus, less research has examined how individuals use information acquisition strategies during routine times. This paper addresses this limitation by examining patterns of routine weather information source usage. Using three unique survey datasets and latent class analysis, we find that weather information source usage can be summarized by a limited number of coherent classes. Importantly, our results suggest that weather information types, or classes, are generally consistent across datasets and samples. We also find demographic determinants, particularly age, help to explain class membership; older respondents were more likely to belong to classes that are less reliant on technology-based information sources. Income and education also were related to more complex or comprehensive information use strategies. Results suggest that the prevalent view of single-source information usage in previous research may not be adequate for understanding how individuals access information, in both routine and extreme contexts.

Using survey data collected from residents of counties along the South Atlantic and Gulf Coasts of the United States, we use innovative compositional data analysis techniques to examine individuals' assignment of responsibility for hurricane preparedness across federal, state, and local officials as well as among household residents and their community. We find that the public assigns limited responsibility for hurricane preparedness to governments. Rather, respondents, especially conservatives and those with low trust in government, view individuals themselves as responsible for preparedness. Our results emphasize the role of ideology and the individualistic culture of American politics. These results also have implications for scholars who study individual attribution responsibility in multi-level systems and who may assume that individuals will assign responsibility to one of the various levels of government; however, focusing on disaster preparation in particular, our study shows that a significant number of individuals may not assign responsibility to government at any level.

ObjectivePolitical parties provide an important function for a public that is generally seen as not consistently ideological by guiding its thinking about policy issues. In this article, we examine whether the four cultural worldviews derived from grid‐group cultural theory—hierarch, egalitarian, individualist, and fatalist—are predictive of partisan attachments and perceptions of climate change risk among the public.MethodsData come from an original survey of the U.S. public conducted in 2011 and 2012. We use regression analysis to examine partisanship and perceived climate change risk; ordered logit to examine party attachment; and mediation analysis to examine the mediating effect of partisanship on cultural worldviews and climate change risk.ResultsWe find that the group‐oriented cultural types (egalitarians and hierarchs) are more likely to have stronger party attachments than the nongroup‐oriented cultural types (individualists and fatalists). In addition, we find that the mediation effect of party is more pronounced among the group‐oriented types than the nongroup oriented.ConclusionGrid‐group cultural theory is predictive of both partisanship and policy attitudes and partisanship mediates the influence of cultural worldviews on climate change risk, particularly for those types with group orientations.

Climate change poses significant risks to individuals and societies, yet the issue of climate change is politically and culturally polarized. However, views of climate change may also be driven by living in a community, such as a coastal area, that is likely to face several climate change-related impacts. In this paper, we use a unique survey of nearly 500 residents of South Carolina to examine views regarding the existence of climate change and the risks it poses. Specifically, we draw on the cultural theory of risk to explore the role of values compared to the role of place – living in a coastal county – on views about climate change. Overall, we find that cultural worldviews, particularly egalitarianism, is the largest driver of views about climate change, but living in a coastal county impacts concern about sea-level rise and flooding.