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Policy Adoption and Policy Intensity: Emergence of Climate Adaptation Planning in U.S. States
Author(s) -
Rai Saatvika
Publication year - 2020
Publication title -
review of policy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.832
H-Index - 45
eISSN - 1541-1338
pISSN - 1541-132X
DOI - 10.1111/ropr.12383
Subject(s) - ideology , political science , public economics , vulnerability (computing) , public administration , agency (philosophy) , scholarship , adaptation (eye) , government (linguistics) , politics , political economy , economics , business , sociology , psychology , law , computer security , computer science , social science , linguistics , philosophy , neuroscience
Abstract The United States is experiencing growing impacts of climate change but currently receives a limited policy response from its national leadership. Within this policy void, many state governments are stepping up and taking action on adaptation planning. Yet we know little about why some states adopt State Adaptation Plans (SAPs), while others do not. This article investigates factors that predict the emergence of SAPs, both in terms of policy adoption and policy intensity (goal ambitiousness). Applying the diffusion of innovation theory, I consider the relative influence of internal state characteristics, regional pressures, and test for conditional effects between government ideologies and severity of the problem. The results show interesting differences between predictors that influence policy adoption and ambitiousness. States are more motivated to adopt a policy when faced with greater climate vulnerability, have more liberal citizenry, and where governments have crossed policy hurdles by previously passing mitigation plans. The intensity of policies and goal setting, moreover, is more likely to be driven by interest group politics and diffuse through policy learning or sharing information among neighboring states in Environmental Protection Agency regions. These findings support an emerging scholarship that uses more complex dependent variables in policy analysis. These variables have the potential to differentiate symbolic from substantive policies and capture finer information about predictors of importance.