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RENEWABLE PORTFOLIO STANDARDS IN THE PAST, PRESENT, AND FUTURE:
ADOPTION, EFFECTIVENESS, AND POST ADOPTION
by
SOJIN JANG
B.S., Indiana University Bloomington, 2009
M.P.A., Sungkyunkwan University, 2012
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Public Affairs Program
2018
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This thesis for the Doctor of Philosophy degree by
Sojin Jang
has been approved for the
Public Affairs Program
by
Todd Ely, Chair
Christopher Weible, Advisor
Deserai Crow
Sanya Carley
Date: May 12, 2018
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Jang, Sojin (Ph.D., School of Public Affairs)
Renewable Portfolio Standards in the past, present, and future: Adoption, Effectiveness, and Post
adoption
Thesis directed by Professor Christopher Weible
ABSTRACT
Renewable Portfolio Standards (RPS) are state-level policies that mandate a certain
amount of renewable energy production. Despite the absence of federal law that requires the
reduction of CO2 emissions or increase in the electricity generation from renewable energy
sources, states have voluntarily adopted and implemented them with expectations for economic,
environmental, and political benefits. Over two decades of RPS history have spawned the
copious literature on RPS adoption and effectiveness. This dissertation advances the existing
RPS literature by providing quantitative and qualitative analyses of RPS adoption, effectiveness,
and post adoption decisions in an attempt to answer the question on the policy processes
surrounding state-level RPS: Why and how are state-level RPS policies adopted and revised? To
what extent has RPS met its policy goals?
In regards to RPS adoption (Chapter two), this dissertation pays attention to the roles of
crisis events and RPS adoption decisions in similar states on a state’s likelihood of RPS adoption.
As for the effectiveness of RPS in state energy markets (Chapter three), this dissertation
evaluates the effectiveness of RPS in achieving renewable energy generation expansion and
electricity price stability. Electricity price stability is often touted as the economic benefits of
RPS. Yet, less is known about the effectiveness of RPS on electricity price stability while
existing RPS evaluation literature has focused on RPS’s effectiveness in renewable energy
expansion and environmental effects (e.g., Carley, 2009; Fischlein & Smith, 2013; Prasad &
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Munch, 2012; Sekar & Sohngen, 2014). Thus, this dissertation adds knowledge to the existing
RPS effectiveness literature by comparing RPS’s effectiveness in renewable energy expansion
and electricity price stability. Further, post RPS adoption decisions are studied using semi-
structured interviews with state legislators in West Virginia and Oregon (Chapter four).
Although considerable research has been accomplished to explain RPS adoption and
effectiveness, less is known about the revisions of RPS after adoption. Through the interviews,
this dissertation explores why and how RPS has taken divergent paths in West Virginia and
Oregon.
There are three major lessons from this dissertation. First, a state is more likely to adopt
RPS if it directly experienced large scale weather-related crisis events, and politically and
economically similar states had already adopted RPS. Crisis events have been known as a major
driver for policy change (Birkland, 2007; Boushey, 2012; Kingdon, 1995; Sabatier & Weible,
2007b). Yet, policy adoption literature has paid limited attention to the roles of crisis events in
triggering policy adoption. This dissertation reveals that one of the ways that states respond to
directly experienced crisis events is to adopt a state-level renewable energy policy, RPS. Further,
extant RPS adoption studies exclusively focused on the effects of RPS adoptions in neighboring
or ideologically similar states as external factors (Carley & Miller, 2012; Carley, Nicholson-
Crotty, & Miller, 2016; Matisoff, 2008; Yi & Feiock, 2012). This dissertation shows that states
also imitate RPS adoptions in not only ideologically similar states but also economically similar
states. Energy or environmental policy often incurs high economic and political costs, and the
patterns of RPS imitation across states reflect concerns on such costs around RPS adoption and
implementation. Second, this dissertation may be one of the very first studies to examine RPS’s
effectiveness on electricity price stability. The duration of RPS increases renewable energy
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generation in a state, and stabilizes electricity prices. However, a binary approach to RPS
adoption is not a statistically significant indicator for either renewable energy generation or
electricity price stability. Third, RPS is inextricably linked with politics and economy. Within
this context, political ideology serves as the basis of RPS adoption and revision decisions by
shaping the perceptions of policy effectiveness and costs associated with RPS implementations.
The form and content of this abstract are approved. I recommend its publication.
Approved: Christopher Weible
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TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION
II. CRISIS OR CONFORMITY:
EXPLAINING PATTERNS IN THE ADOPTION OF RPS ............................................ 8
Abstract ....................................................................................................................... 8
Introduction ................................................................................................................. 8
RPS through the lens of Diffusion of Innovation Theory ................................................. 9
Roles of crises in RPS adoption ................................................................................ 11
Horizontal diffusion of RPS adoption....................................................................... 16
Controls - Other internal determinants of RPS adoption .......................................... 18
Analytical methods and Dependent variables ................................................................. 24
Analysis........................................................................................................................... 25
Conclusion and Policy Implications ............................................................................... 31
III. EVALUATING RENEWABLE PORTFOLIO STANDARDS:
HAVE THEY KEPT THEIR ENERGY AND ECONOMIC PROMISES?................... 35
Abstract ........................................................................................................................... 35
Introduction ..................................................................................................................... 35
Overview of RPS goals ................................................................................................... 37
Evaluation Framework .................................................................................................... 40
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Dependent variables .................................................................................................. 40
Independent variables ............................................................................................... 41
Analysis........................................................................................................................... 49
Conclusion and Policy Implications ............................................................................... 57
IV. AFTER THE ADOPTION OF RENEWABLE PORTFOLIO STANDARDS:
GO GREENER OR BACK TO GREY?..........................................................................60
Abstract ........................................................................................................................... 60
Introduction ..................................................................................................................... 61
Policy revision after adoption ......................................................................................... 62
Post RPS adoption..................................................................................................... 64
Case selection.................................................................................................................. 66
Data collection ................................................................................................................ 68
Interview Questions .................................................................................................. 69
Analysis........................................................................................................................... 70
Overview of RPS in West Virginia and Oregon ....................................................... 72
Cross-case analysis of RPS revisions in West Virginia and Oregon ........................ 75
Conclusion and Policy Implications ............................................................................... 85
V. CONCLUSION ............................................................................................................... 90
RPS adoption in Chapter 2 .............................................................................................. 91
RPS effectiveness in Chapter 3 ....................................................................................... 94
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RPS revision after adoption in Chapter 4 ....................................................................... 95
Overall Chapter Summary .............................................................................................. 97
Limitations ...................................................................................................................... 98
Future studies ................................................................................................................ 100
Summary of lessons ...................................................................................................... 101
REFERENCES ................................................................................................................... 103
APPENDIX
A. Estimation Results for Model 1: Changes in probabilities for imitation .................115
B. Estimation Results for Model 2: Changes in probabilities for imitation .................116
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LIST OF TABLES
TABLE
2.1. Comparison of geographic and policy dimensions of crisis .................................................. 15
2.2. Measurement strategies for independent variables ................................................................ 22
2.3. Dyadic data structure ............................................................................................................. 25
2.4. Descriptive statistics .............................................................................................................. 26
2.5. Estimation results – RPS adoption using dyadic Probit......................................................... 30
3.1. Measurement of electricity price stability.............................................................................. 41
3.2. Descriptive statistics .............................................................................................................. 49
3.3. Correlation between RPS and renewable energy capacity .................................................... 50
3.4. Estimation results – renewable energy generation ................................................................. 53
3.5. Estimation results – electricity price stability ........................................................................ 56
4.1. Interview questions ................................................................................................................ 69
4.2. Interview codebook ................................................................................................................ 71
4.3. Analysis of RPS revision decisions in West Virginia and Oregon ........................................ 83
5.1. Chapter summaries................................................................................................................. 97
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LIST OF FIGURES
FIGURE
1.1. RPS adoption timeline ............................................................................................................. 5
4.1. Post adoption decisions .......................................................................................................... 66
4.2. RPS adoption year, Population rank, and GSP rank of candidate states
for case study ......................................................................................................................... 68
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LIST OF ABBREVIATIONS
EERS: Energy Efficiency Resource Standards
MGPO: Mandatory Green Power Option
PBF: Public Benefit Funds
RPS: Renewable Portfolio Standards
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CHAPTER I. INTRODUCTION
Climate change is one of the most pressing environmental issues that have deeply
penetrated environment, economy, and politics at various levels. Although some scientists still
have dissenting views on the consequences of climate change, they generally have acknowledged
human activities as a major contributor to climate change (Boykoff, 2007; Cavallo & Noy, 2010;
IPCC, 2001; Klein, Nicholls, & Thomalla, 2004; O’Brien, O’Keefe, Rose, & Wisner, 2006;
Schipper & Pelling, 2006; van Aalst, 2006). Recently, the rate of climate change has become
much faster and the acceleration in climate change is largely attributed to the rapid increase in
CO2 emissions driven by the use of carbon-based energy since the Industrial Revolution (EPA,
2015; Sekar & Sohngen, 2014).1 Increase in CO2 emissions and accelerated climate change can
alter the economic, environmental, and social conditions of both major CO2 emitting countries
and relatively low emitting countries because of the cumulative and trans-boundary nature of
CO2.
Many countries around the world have promoted the use of renewable energy and/or set
CO2 reduction goals at the national level as they acknowledge a close relationship between the
emissions from fossil fuel combustion and climate change (Sovacool & Barkenbus, 2007). The
use of renewable energy is of particular importance in the U.S. because it is the second biggest
CO2 emitter in the world and also the 11th
in CO2 emissions per capita.2 In the U.S. from 1990 to
2013, fossil fuel combustion for electricity generation accounts for 37% of CO2 emissions. The
second largest CO2 emitting source is transportation, accounting for 31% of CO2 emissions (EPA,
1 http://www3.epa.gov/climatechange/science/causes.html
2 Except for Luxembourg, all 9 countries with higher CO2 emissions per capita are the Middle East
countries that heavily rely their economy on oil and gas production (Qatar with the highest CO2 emissions
per capita followed by Trinidad and Tobago, Kuwait, Brunei Darussalam, Aruba, Luxembourg, United
Arab Emirates, Oman, Saudi Arabia, and Bahrain). In case of Luxembourg, OECD (2013) notes that the
low tax on road fuels is largely responsible for high CO2 emission per capita in Luxembourg.
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2016). Electricity generation is the major source of CO2 emissions in the U.S., thereby calling for
the substantial reduction of greenhouse gas emissions from electricity generation (Duane, 2010).
Nevertheless, the U.S. has long been criticized for the passive responses against climate change
because of the absence of the national level climate change action and President George W.
Bush’s rejection of the implementation of Kyoto Protocol. More recently, Trump administration
announced the withdrawal from the Paris Agreement on climate change mitigation, and proposed
the repeal of the Clean Power Plan, initially designed for reductions in greenhouse gas emissions
from electricity generations.
Despite the federal government’s inactive response to anthropogenic climate change, a
number of state governments have been committed to the reduction of CO2 emissions through
the adoption of renewable energy policy. Among various policy options and programs, one of the
most adopted is the Renewable Portfolio Standards (RPS), which set the target amount of
renewable energy production within a specific year. Iowa first adopted the RPS in 1983 and it
has been widely adopted by 29 states and Washington D.C. as of 2015. The RPS mandates the
certain portion of electricity coming from renewable energy sources such as solar, wind,
geothermal heat, wave or tidal energy, and organic matter. Among these sources, the most
widely used is wind and solar energy. State RPS policies are distinguished from one another in
terms of policy design and implementation. These include the selection of target utilities,
resource eligibility, applicability (geographic, industrial coverage), flexibility in account
balancing, tradability of renewable energy credits (REC) and designation of administrative
responsibilities for RPS target, RPS compliance monitoring and penalties for noncomplying
entities (T. Berry & Jaccard, 2001). The adoption of RPS policy is expected to bring
environmental, social and economic benefits through renewable energy production (T. Berry &
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Jaccard, 2001; Bird, Chapman, Logan, Sumner, & Short, 2010; Cory & Swezey, 2007; Gonzalez,
2007). Increases in the electricity costs in the short term and the need for large scale investment
in renewable energy production are subject to public criticism. In spite of such challenges, more
than half of states have already adopted RPS, and more states are expected to adopt RPS or make
their renewable energy production goals more stringent. All 29 state RPS policies are different
and operate in different contexts. The variability of RPS policies is found throughout the various
components of RPS including the size and timing of renewable energy production, target utilities,
presence of cost cap, types of eligible resources and facilities, tradability of renewable energy
credits, compliance monitoring, and noncompliance penalty (T. Berry & Jaccard, 2001). Yet,
what best represents and characterizes state RPS policies are the stringency of RPS predicated
upon the size and timing (target year) of renewable energy production.
States adopt RPS policies for various internal and external reasons. Recently,
governments and businesses began to increase the use of renewable energy as a response to
environmental problems and extreme weather events. Climate change is a long term and slowly
developing phenomenon that cannot be directly felt. But, people and media often interpret
specific weather-related crisis events as related to climate change and learn the need for taking
actions against climate change through experiences with those events. Although such
interpretation is scientifically invalid, this is how humans perceive the threat of climate change
and motivate themselves and others to engage in climate change mitigation or environmental
protection measures. Although extant policy change literature has extensively discussed the roles
of crisis events in explaining the factors for policy change, extant studies have paid less attention
to crisis events as internal and external drivers of policy adoption. Further, recent diffusion
scholars raised questions on the neighboring effects as a factor for policy diffusion and discussed
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the need for exploration of external factors beyond neighboring effects (Baybeck, Berry, &
Siegel, 2011; Shipan & Volden, 2012; Volden, Ting, & Carpenter, 2008). In an attempt explore
additional factors of policy diffusion across states, this dissertation examines the effects of policy
adoptions in states that share similar political, economic, and energy market conditions.
Studies on RPS adoption merit attention not only for the widespread adoption across
states but also it opens up the avenues for the evaluation of RPS and examination of policy
decisions after adoption. Although the RPS is primarily a policy for renewable energy production,
it is not the sole purpose of the RPS adoption. State policymakers promote and adopt the RPS not
only for its immediate goal of renewable energy production, but also for its long term economic
goals. To date, RPS evaluation literature has predominantly focused on the effectiveness of RPS
policies in achieving renewable energy expansion and CO2 emissions reductions (Carley, 2009;
Fischlein & Smith, 2013; Prasad & Munch, 2012; Sekar & Sohngen, 2014). However, there is a
dearth of research on the effectiveness of RPS policies in promoting electricity price stability,
which is one of the main goals of RPS policies. When RPS supporters discuss the adoption and
expansion of RPS, they advocate such long term benefits of RPS for its positive and clean policy
image. In this vein, policy scholars are tasked with probing into the extent to which RPS fulfilled
the promised goals. The evaluation of RPS effectiveness provides critical insights into RPS for
scholars and policymakers in states with and without RPS in different ways. RPS evaluation
information serves as an indicator for the progress that the policy has made to date. States
considering RPS adoption may refer to RPS effectiveness studies in making their future policy
decision.
Recently, however, the proliferation of RPS lost its momentum in 2014. Ohio first rolled
back its RPS by freezing the renewable energy production goals for two years. In 2015, Kansas
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revised their mandatory RPS into voluntary policy, and West Virginia repealed its RPS. Yet, a
number of existing RPS states further heightened their renewable energy production goals.
Vermont recently adopted the RPS in 2015 and it is marked by its stringent goals of 75% of
electricity from renewable energy sources by 2032. California, Hawaii, and Oregon amended
their RPS in a direction that increases the renewable energy production to 50% or more of total
electricity generation. However, the divergent paths of RPS policies after adoption are less
studied and such recent trends are difficult to be captured in the quantitative RPS study that
analyzes a large-N data spanning about two decades. Hence, a detailed case study is needed to
explore the drivers that shaped and differentiated the direction of policy changes after initial
adoption.
As of 2015, 29 states and Washington D.C. have mandatory RPS. Figure 1 illustrates the
states adopting mandatory RPS in a chronological order.
Figure 1.1. RPS adoption timeline
This dissertation is composed of five chapters. This present Chapter 1 presents an
overview of the dissertation and topic. Chapters two, three, and four provide the empirical
analysis of the different aspects of RPS. Chapter five offers the summary of findings from the
empirical analysis chapters.
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Specifically, the three empirical chapters (2, 3, and 4) are created to answer the following
research questions:
What factors explain patterns in the adoption of state RPS policies? (Chapter two)
Chapter 2 explores the drivers of RPS policy adoption across 46 states from 1998 to 2009. To
examine the RPS stringencies and its driving factors, this chapter uses dyadic analysis to
examine the policy imitation across states. This chapter contributes to the existing RPS literature
by examining the effects of internal and external crisis events on RPS adoption. As for diffusion
of RPS policies across states, this chapter examines the factors for policy imitation. This chapter
finds that a state with frequent weather-related hazard experiences is more likely to adopt a RPS
policy. Further, state may take cues from politically and economically similar states.
To what extent have RPS policies achieved their intended energy and economic goals?
(Chapter three)
Chapter 3 examines the factors associated with the effectiveness from 1998 to 2012. This chapter
assesses the energy and economic effects of RPS. It analyzes the effectiveness of RPS and other
energy policies on renewable energy generation and electricity price stability. This chapter finds
that an RPS policy is positively correlated with renewable energy generation. Further, it has been
effective in reducing electricity prices over time, contrary to the renewable energy opponents’
concerns on the increases in electricity prices.
What factors shaped the state RPS policies after adoption? How have these factors
contributed to policy decisions after adoption? (Chapter four)
Chapter 4 explores the revision of RPS after adoption using semi-structured interviews and
literature review. Due to the dynamic nature of RPS and full latitudes in RPS policy design, state
policymakers have continuously amended their RPS to cater to states’ needs and conditions. The
study of RPS policy decisions after adoption provides a detailed account of contributors to recent
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RPS amendments that evolved in opposite directions through the comparative case study of West
Virginia and Oregon. West Virginia repealed the RPS in 2015, whereas Oregon passed the bill in
2016 that introduced 50% of utilities from renewable energy by 2040 and the banning of coal
from electricity generation by 2035. This chapter finds that state government ideology shapes the
views on environmental and economic issues and effectiveness of RPS, and it ultimately guides
the directions of policy revisions. Moreover, RPS repeal in West Virginia was led by state
legislators while local government and citizen-led effort for renewable energy expansion spurred
the proposal and passage of RPS expansion bill in Oregon.
This dissertation explores state governments’ effort for renewable energy expansion
through RPS adoption and revision, and its impact on state-level energy production and energy
economy. By employing quantitative and qualitative approaches to understand policy processes
and effectiveness of RPS, this dissertation provides insights into how state governments have
responded to weather-related crisis events with RPS and imitated RPS adoption decisions in
other states. Further, the evaluation of RPS’s effectiveness on renewable energy expansion and
electricity price stability would reveal RPS’s contribution as well as areas for improvement in
terms of meeting the goals promised to citizens. Finally, interviews with state legislators provide
explanations of why and how radically different types of RPS revisions took place in West
Virginia and Oregon. This dissertation ends with overall findings, limitations, and future study
tasks.
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CHAPTER II. CRISIS OR CONFORMITY: EXPLAINING PATTERNS IN THE
ADOPTION OF RPS
Abstract
In the U.S., states with renewable portfolio standards (RPS) set annual RPS schedules
that require a certain share of electricity sales to come from renewable energy. States adopt RPS
policies for various reasons inside and outside states. Recently, governments and businesses
began to increase the use of renewable energy as a response to environmental problems and
extreme weathers. This chapter analyzes the drivers of RPS adoption from 1998 to 2009 through
the diffusion of innovation theory using dyadic analysis. This chapter found that a state with
frequent weather-related hazard experiences is more likely to adopt a RPS policy. As for external
factors for RPS adoption, a state may take cues from politically and economically similar states.
Introduction
RPS policies are one of the most widely adopted climate change mitigation measures at
state level (Engel, 2006). Climate change is a long term and slowly developing phenomenon that
cannot be directly felt. But, people and media often interpret specific weather-related crisis
events as climate change and learn the need for taking actions against climate change through
experiences with those events. Although such interpretation is scientifically invalid, this is how
humans perceive the threat of climate change and motivate themselves and others to engage in
climate change mitigation or environmental protection measures. In fact, extant policy change
literature has extensively discussed the roles of crisis events in explaining the factors for policy
change (Birkland, 2007; Boushey, 2012; Kingdon, 1995; Nohrstedt & Weible, 2010; Sabatier &
Weible, 2007a). However, policy innovation and diffusion studies have paid less attention to
crisis events as a driver of policy adoption.
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As for the diffusion of policy innovation, extant studies heavily focused on the effect of
policy decisions in neighboring states to the likelihoods of policy adoption in a state (Shipan &
Volden, 2012). Although more recent policy diffusion studies began to probe into the effects of
ideologically similar states, a state may take cues from the policy decisions of other states that
share similar policy adoption motivations and contexts where policy is adopted and implemented.
Thus, this chapter investigates the possibility that a state refers to other states that have similar
internal conditions or determinants relevant to RPS adoption besides geographical and
ideological proximity to existing adopters. In sum, this chapter seeks to contribute to existing
RPS adoption and diffusion literature by introducing two new important internal and external
indicators for policy adoption, crisis events and shared internal conditions across states.
This chapter examines the drivers of RPS adoption from 1998 to 2009 through the
theoretical lens of diffusion of innovation using dyadic analysis. Dyadic analysis allows for the
pairwise analysis of the effect of other states’ conditions and similarities in internal determinants
on the adoption decisions between two states in a paired observation. This chapter found that a
state with frequent weather-related hazard experiences is more likely to adopt a RPS policy, and
a state may take cues from politically and economically similar states.
RPS through the lens of Diffusion of Innovation Theory
The diffusion of innovation theory offers a way to explain the timing and the design of
policies adopted across multiple jurisdictions. The theory allows for the concrete explanation of
policy adoption and diffusion mechanisms.3 In the diffusion of innovation theory, an innovation
is defined as “a program or policy which is new to the states adopting it, no matter how old the
program may be or how many other states may have adopted it” (Walker, 1969, p. 881). Policy
3 This chapter considers the diffusion of innovation as a theory. Theories facilitate the understanding of related
concepts or variables based on the set of assumptions or propositions and also seek to explain the causal
mechanisms between interrelated concepts (Kiser & Ostrom, 2000; Ostrom, 2010; Weible & Nohrstedt, 2012).
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innovation yields financial and political costs (Rose-Ackerman, 1980) and states have often been
judged according to the relative speed with which they have accepted new ideas (Walker, 1969).
Any pattern of successive adoptions of a policy across similar government units can be called
diffusion (Eyestone, 1977, p. 441). Rogers (1995) presented the S-shaped curve to describe an
incremental learning process that the increased availability of policy information and reduced
uncertainties with a new policy over time may increase the likelihood of policy adoptions across
states. A premise of policy diffusion is that the early policy adopters affect the policy decisions
of potential adopters. More concretely, policy diffusion indicates policy decisions that are
“systemically conditioned by prior policy choices made in other [jurisdictions]” (Simmons,
Dobbin, & Garrett, 2006, p. 787). Interdependent decision making is critical to understand policy
diffusion, and policy decisions continue to interact with one another (S. M. Brooks, 2007).
In the context of RPS adoption and diffusion across states, the diffusion of innovation
theory provides the explanation for the examination of state’s internal characteristics that make
states more capable of adopting the RPS relative to other states. Further, different policy
diffusion mechanism can help RPS scholars to examine why the RPS has been widely spread
across states without pressure from the federal government. For this reason, copious RPS
literature delved into RPS adoption directly and indirectly through the theoretical lens of
diffusion of innovation (Carley & Miller, 2012; Carley et al., 2016; Chandler, 2009; Huang,
Alavalapati, Carter, & Langholtz, 2007; Matisoff, 2008; Nicholson-Crotty & Carley, 2015; Yi &
Feiock, 2012).
Yet, the determinants of RPS are not consistent across studies and common findings on
the internal determinants of RPS is limited to the roles of state wealth (Carley et al., 2016;
Chandler, 2009; Huang et al., 2007; Upton & Snyder, 2015; Yi & Feiock, 2012), and CO2
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emissions or fossil fuel production (Carley & Miller, 2012; Carley et al., 2016; Lyon & Yin,
2010; Upton & Snyder, 2015) on RPS adoption. These limited consistencies in findings imply
that RPS studies should explore determinants that are germane to the immediate need and
operation of RPS rather than focusing on general indicators of state’s internal conditions.
Accordingly, the following sections are devoted to the discussions of effects of crises and
existing relevant policies on the RPS adoption in an attempt to unearth the RPS adoption
determinants that directly call for the switch to renewable energy and facilitate the operation of
RPS.
Roles of crises in RPS adoption
Crises, such as large scale disasters, often play critical roles in triggering major or radical
policy changes (Nohrstedt & Weible, 2010). Further, politicians often rely on a sense of crisis to
catalyze the achievement of policy goals that they support (Keeler, 1993). Policy scholars have
long advocated for the roles of crises in facilitating policy change as in multiple streams
framework, punctuated equilibrium and advocacy coalition framework. In multiple streams
framework, Kingdon (1995) used the term focusing events to describe the role of abrupt and
infrequent events, such as crises or disasters, on the agenda setting. Focusing events increase
attention to policy issues, which policy actors seek to place on the agenda by giving rise to media
and public awareness of society’s problems (Birkland, 2007). Focusing events are viewed as
exogenous shocks to the political system and draw political attention to a policy problem that has
been overlooked or neglected in punctuated equilibrium theory (Boushey, 2012). In the advocacy
coalition framework, shock or crisis event may contribute to the redistribution of political
resources, disrupt the existing power structure among coalitions, and also promote major policy
change (Sabatier & Weible, 2007a). In spite of the important roles of focusing events or crises
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for policy change as explicated in multiple streams, punctuated equilibrium and advocacy
coalition framework, how focusing events or crises trigger the adoption of a new policy has not
been extensively examined in the policy adoption and diffusion studies.
Keeler (1993, pp. 440-442) discussed mechanisms of how a crisis creates the contexts for
policy change. First, the political party can gain advantage in suggesting a new policy as it
emphasizes the role of policy as a solution or response to a crisis. Second, a crisis contributes to
a sociopolitical environment favorable for governance that can add weight to the passage of
reforms. Third, a crisis could trigger a social mobilization to advocate the need for reform. In
particular, first and second description of the role of crisis speaks to the adoption of RPS as a
climate change mitigation measure. Despite the arguments around the drivers of climate change,
scientists began to reach a consensus on the CO2 emissions from human activities as a major
contributor to climate change (Boykoff, 2007; Cavallo & Noy, 2010; IPCC, 2007; Klein et al.,
2004; O’Brien et al., 2006; Schipper & Pelling, 2006; van Aalst, 2006). Among the various types
and levels of environmental risks driven by climate change, weather-related hazards are one of
the most frequently discussed impacts of climate change. Warming climate is associated with the
long term trends of weather-related hazards such as storms, floods, drought, and extreme
temperatures (N. Brooks & Adger, 2003; IPCC, 2007; Klein et al., 2004; Mileti, 1999; Mirza,
2003; O’Brien et al., 2006; Schipper & Pelling, 2006; van Aalst, 2006).
In fact, people do not realize the immediate need for taking actions against climate
change or severity of climate change largely because climate slowly develops over long term and
cannot be directly experienced (Moser & Dilling, 2011). However, people learn the signs and
impacts of climate change through experiences in large scale weather-related hazards. Similarly,
Weber (2006) posited that catastrophic impacts of climate change and globally experienced
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climate change impacts amplify people’s reactions to climate change risk. In other words, people
often need to see or feel something to believe the need for action. By the same token, Ray,
Hughes, Konisky and Kaylor (2017) found that individual experiences with extreme weather
events are positively associated with civic support for climate adaptation policy. If there is one
way that people learn the severity of climate change and need for taking actions for climate
change mitigation, it would be through frequent large-scale weather related hazards that may
disrupt their normal lives. Scientifically, or within the environmental and natural science
discipline, we cannot link each specific hazard event to climate change as a driver of the event
since climate change is more about the long term trends and patterns of weather systems.
However, in the real world and media, the way non-scientist average people understand climate
change and weather-related hazards event is not quite consistent with scientist’s views. Rather,
often times, people perceive that individual weather-related crisis event that we are experiencing
now is directly related to climate change as portrayed in the headlines from news media as
follows:
Experts: Climate Change May Make Northeast Winter Storms Worse (Thomas, 2015)
CBS Boston. Feb 12, 2015
Scientist Link Hurricane Harvey’s Record Rainfall to Climate Change (Fountain, 2017)
New York Times. Dec 13, 2017
Climate change, extreme weather already threaten 50% of U.S. military sites (Roth, 2018)
USA Today. Jan 31, 2018
In turn, weather-related crisis events have increasingly served as a signal for the need to
take action for climate change mitigation. Recently, insurance companies began to show interest
in renewable energy as a growing number of scientific evidences suggest that the increases in
frequency and magnitude of natural disasters are attributed to the explosive growth in the use of
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conventional energy sources (Bull, 2001). In fact, the House of Representatives in Hawaii
drafted a bill that explicitly asks for the federal government and states to switch from fossil fuels
to renewable energy as a response to various environmental and economic problems triggered by
fossil fuel uses (Hawaii, 2017). The bill describes the increasing number and scale of hurricanes
affected by warming air and discusses the renewable energy as a solution to the climate-related
crisis.
However, not much is known about the nuanced differences of crises and their roles in
policy change. Accordingly, Nohrstedt and Weible (2010) suggested a typology of crisis with
respect to its geographical and policy attributes. In their typology, the dimension of geographic
proximity denotes the proximity of origin of event to the jurisdiction.4 Another dimension of
typology, policy proximity indicates the extent to which a crisis is relevant to the policy of
subsystem’s focus. Based on these criteria, four types of crises in the context of policy change
were identified as following: immediate crisis (close geographic proximity, close policy
proximity), geographic-proximate crisis (close geographic proximity, distant policy proximity),
policy-proximate crisis (distant geographic proximity, close policy proximity), and vicarious
crisis (distant geographic proximity, distant policy proximity) as summarized in Table 2.1.
4 In the original text, the authors used the term “policy subsystem” instead of jurisdiction. Policy subsystem is
defined as “a set of policy participants and territorial and substantive scopes” (Weible 2006, p. 98). I replaced it with
jurisdiction because this chapter exclusively focuses on the territorial scope.
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15
Table 2.1. Comparison of geographic and policy dimensions of crisis
Close geographic proximity Distant geographic proximity
Close policy proximity Immediate Crisis Example: Hurricane Katrina for
the Louisiana crisis
management subsystem
Policy-Proximate Crisis
Example: 9/11 terrorist attacks for
European security subsystems
Distant policy
proximity Geographic- Proximate Crisis
Example: Southern California
wildfires for the California public
health subsystem
Vicarious Crisis
Example: Swine-flu crisis for
counterterrorism subsystems
Source: Nohrstedt & Weible (2010), p. 21
Yet, not all people directly experience severe weather extremes in their local areas or
states. The influence of hazards experiences in other states, policy-proximate crisis, should also
play a role in the RPS adoption since policymakers and citizens learn about the severity of
hazards and its relationship with climate change through media and social media networks
although they are remote from the origin of such hazards.5
Thus, the following hypotheses are suggested:
Immediate Crisis Hypothesis: Weather-related crisis events inside a state will increase the
likelihood of RPS policy adoption.
Policy-Proximate Hypothesis: Weather-related crisis events outside a state will increase the
likelihood of RPS policy adoption.
Among different types of crisis events, this chapter focused on coastal and ice storms,
hurricane, drought, flood, freezing, snow, and tornado as these are largely known to be
associated with climate change in the long run (N. Brooks & Adger, 2003; IPCC, 2007; Klein et
al., 2004; Mileti, 1999; Mirza, 2003; O’Brien et al., 2006; Schipper & Pelling, 2006; van Aalst,
5 This chapter exclusively focuses on the comparison of roles of immediate crisis and policy-proximate crisis
because crises that are distant in terms of policy proximity (e.g., geographic-proximate crisis and vicarious crisis)
are very broad and difficult to limit the scope of distant policy proximity.
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16
2006). One may argue the need for counting wildfire toward climate-related hazards. However,
this chapter does not consider wildfire as climate-related hazards largely due to the fact that
humans activities are directly accountable for the starting of 84% of wildfires in the U.S. from
1992 to 2012 (Balch et al., 2017). Following the occurrence of disaster events, two-year window
for changes in public behaviors or policies open (Mason, 2006; OECD, 2004). By the same logic,
crisis events in each state are measured by the number of emergency or disaster declaration by
FEMA in the past two years. Specifically, immediate crisis is measured by crisis events
occurring inside a state, and policy-proximate crisis is measured by crisis events outside a state.
Horizontal diffusion of RPS adoption
When a state faces risks that can be addressed with a new policy, it tends to take cues
from other state’s policy decisions (Balla, 2001). By taking cues from other states that share
similar characteristics, a state can make expectations about the performance of a new policy,
thereby making a policy adoption decision with more certainty on the effect of policy. Among
different state characteristics, policy decisions in geographically neighboring states are the most
widely employed measure to explain horizontal diffusion since the inception of diffusion theory
to date. The rationale for referring to neighboring states’ policy decision is that neighboring
states would share political ideology, beliefs, customs, economy and culture. Although
geographic neighbors can provide policy lessons to potential adopters, sometimes their influence
is exaggerated (Shipan & Volden, 2012). Geographically adjacent states do not always share
these similarities and they may refer to policy decisions in states that share specific attributes
other than geographic borders. Therefore, this chapter investigates policy imitation by directly
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17
measuring the effects of states that share demographic, economic, environmental, and political
conditions on potential adopters’ policy decisions.6
Imitation Policy imitation occurs as potential adopters refer to policy decisions in similar states
(Karch, 2007). The imitation mechanism is based on contextual similarities across states, and the
process of imitation provides the evidences of how a policy would work in a state and how a
state responds to a new policy. The study of the imitation mechanism contributes to the policy
diffusion literature by offering a hypothetical account of similar policy environment as a driver
of policy diffusion (Karch, 2007). In explaining policy imitation beyond neighboring states,
Grossback, Nicholson-Crotty and Peterson (2004) pioneered the measuring and analyzing the
effect of policy adoptions in ideologically similar states on potential adopters. They argued that
ideological information signals the potential responses of the electorate and other policy elites to
a new policy. Volden (2006)’s study on the adoption and diffusion of the Children’s Health
Insurance Program pioneered the inquiry of the impact of policy adoption in states that share
similar internal attributes in addition to the emulation of successful policies. In the testing of
Similar States Hypotheses, his study found that policy diffusion can be explained by similarities
in political ideologies, per capita income, managed care structures, and budgetary considerations
whereas no statistically significant effect of geographic neighbors is found.
6 Other horizontal diffusion mechanisms include coercion, learning, and competition. Although diffusion by
coercion across the same levels of governments are possible, coercion by upper level government is more
commonplace as the upper level government implements the measures to incentivize policy adoption or penalize
non adoption (Frances S. Berry & Berry, 2007; Shipan & Volden, 2008). For this reason, coercion is often described
as a vertical influence (Frances S. Berry & Berry, 2007; Shipan & Volden, 2008, 2012). In describing learning as a
diffusion mechanism, Shipan and Volden (2008) and Karch (2007) emphasized the roles of “successful” policies in
other jurisdictions for the policy adoption in a jurisdiction that has not yet adopted the policy. Policy competition as
a driver for policy diffusion involves economic spillover (Shipan & Volden, 2008) or economic competition
between states for businesses and tax revenues (F. J. Boehmke & Witmer, 2004). States may engage in the
competition to attract private investments and businesses for potential growth in employment and tax revenue
whereas they try to avoid attracting undesirable groups (Karch, 2007).
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18
Most RPS studies that examine horizontal diffusion rely on policy adoption in
neighboring states (Carley & Miller, 2012; Chandler, 2009; Matisoff, 2008) or ideologically
similar states (Carley et al., 2016; Chandler, 2009). However, such geographical proximity or
ideological similarity may not be the only factors that states consider when adopting policies.
States may refer to the policy decisions in other states that share similar economic,
environmental, energy, and risk attributes as they would adopt RPS for various reasons. Thus, it
is plausible that states would refer to decisions made by other states facing common challenges
or characteristics to imitate the policy options as well as to predict the policy feasibility in a
similar policy environment. In dyadic analysis that directly compares two states in the same
observation, horizontal diffusion factors are simply calculated as the absolute difference in the
internal determinants between two states.
Similar States Hypothesis: A state is more likely to adopt RPS if other states that share similar
conditions have RPS.
Controls - Other internal determinants of RPS adoption
Net metering Net metering policies allow private utility industries to connect their renewable
energy generators to the power grid and sell the power generated from the renewable energy
sources for monetary benefit. As of 2015, 44 states have net metering policies. The purpose of
net metering and RPS is to promote the use of renewable energy sources for electricity
generation. However, net metering is limited in its scope, focusing on small scale generation,
thus spurs the consumers’ renewable energy production to only a limited extent (Duscha, Held,
& Rio, 2016; Forsyth, Pedden, & Gagliano, 2002). Carley et al. (2016) and Yi and Feiock (2012)
found that the existence of net metering policies are likely to promote the adoption of RPS. A
state with a net metering policy may experience the increase and promotion of renewable energy
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19
production at relatively low cost and small scale in advance, thereby increasing the likelihood of
RPS adoption.
Political ideology For political factors associated with state policy adoption, political ideology of
state legislatures and governors are often employed to explain the relationship between politics
and policy adoption. Historically, liberal states are more likely to be favorable to the adoption of
policies that are purposed to expand social welfare (William D. Berry, Ringquist, Fording, &
Hanson, 1998; Fellowes & Rowe, 2004) or environmental actions (Carley & Miller, 2012;
Chandler, 2009; Lyon & Yin, 2010; Yi & Feiock, 2012) whereas conservative states are more
amenable to economic development policy. Liberal state government ideology is expected to
have positive correlations with RPS.
Population growth Demographic information, such as the size of state, education levels, and
ethnicity of state citizens, can shape policy adoption decisions (Rogers, 1995). Traditional policy
innovation literature focuses on the “size (population)” of state as an explanatory variable for
policy innovation (V. Gray, 1973; Walker, 1969). In general, these researchers hypothesize that
a larger (populous) state is more likely to adopt a new policy (Walker, 1969).
In terms of state population size and the RPS adoption, more rapidly growing states are
under pressure for additional electricity generation to meet the increasing demand. RPS adoption
literature suggests that the extent to which states need to generate additional electricity is largely
dependent on the change in the demand. The change in the electricity demand is better captured
by change in the population rather than state’s total population. For this reason, existing RPS
studies tend to estimate the effect of population growth or change measure (M. J. Berry, Laird, &
Stefes, 2015; Carley & Miller, 2012; Carley et al., 2016; Chandler, 2009; Huang et al., 2007).
But, only M. J. Berry et al. (2015) found statistically significant positive correlation between
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20
population change and the likelihood of RPS adoption. Similar to previous studies’ assumptions
on the effect of population growth on RPS, population growth would be positively linked with
RPS.
Fossil fuel consumption One of the important indicators that create the pressure for or against
the renewable energy production would be CO2 emission levels. Scholars have used various
measures such as CO2 intensity and air pollutants (Matisoff, 2008), nonattainment index and
emissions from electric generation (Lyon & Yin, 2010), coal-based energy consumption for
electricity generation (M. J. Berry et al., 2015) and CO2 emissions per capita (Carley et al., 2016).
One of the RPS goals is to reduce CO2 emission levels by increasing the renewable energy
production. RPS is often described as a measure to reduce CO2 emissions and slow down the
anthropogenic climate change. By logical sense, states with high CO2 emissions should adopt
stringent RPS policies to reduce CO2 emissions from electricity generation. However, high CO2
emissions from power generation signal that a significant share of state economy is driven by the
extraction of fossil fuel sources as well as the production and sales of electricity generation from
fossil fuel sources. Thus, empirical studies of RPS adoption often found the negative association
between CO2 emissions or coal dependence and the likelihood of RPS adoption (M. J. Berry et
al., 2015; Carley et al., 2016; Matisoff, 2008). I expect the negative association between the
likelihood of RPS adoption and goals.
State wealth Another critical indicator for policy adoption is state wealth since states with more
resources can afford the costs and risks associated with the adoption of new policy (Walker,
1969). State wealth has been consistently positively related to the likelihood of RPS adoption
(Carley et al., 2016; Chandler, 2009; Huang et al., 2007; Upton & Snyder, 2015; Yi & Feiock,
2012). However, state wealth is an ambiguous concept that needs additional specification. The
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21
expansion of renewable energy production is a costly decision that calls for additional
investment in electricity infrastructure and bearing of short-term increase in the electricity
production costs. In this light, affluent states are more capable of supporting the RPS, and state
citizens are more likely to afford the price tag attached to renewable energy production.
Electricity price States with RPS tend to experience the increase in electricity prices for
expansion of renewable energy production (Bird et al., 2010). Notwithstanding the unavoidable
costs associated with renewable energy production, renewable energy production has positive
impacts on the energy supply as it promotes the energy security through the diversification of
energy sources as well as the stable energy prices in the long term (T. Berry & Jaccard, 2001).
Fischer (2009) also discussed that RPS can either increase or decrease the electricity price,
largely depending on the elasticity of renewable energy supply. In this vein, the mixed findings
on the effect of electricity price on RPS adoption are not surprising because of the discrepancy
between short term and long term effects. Lyon and Yin (2010) did not find statistically
significant correlation between electricity price and RPS adoption whereas Carley et al. (2016)
and Berry et al. (2015) found positive influence of electricity prices on RPS adoption and
stringency measures. The expected relationship between electricity price and RPS is inconclusive,
yet it is worth examining how a state taps into renewable energy to respond to changing
electricity prices.
Renewable energy potential Renewable energy potential indicates the amount of electricity that
can be generated from renewable energy given renewable energy sources and technical
advancement (Lopez, Roberts, Heimiller, Blair, & Porro, 2012). Renewable energy potentials are
directly relevant to the state’s capacity to produce renewable energy. Studies that looked into the
effect of renewable energy sources in each state focused exclusively on solar and wind energies
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22
since the majority of renewable energy sources utilized in the U.S. is solar and wind energies (M.
J. Berry et al., 2015; Lyon & Yin, 2010; Yi & Feiock, 2012). Although these past studies use
different indicators for renewable energy potentials and exhibit differences across the types of
renewable energy potentials that affect the RPS adoption, they still found in common that the
potential for renewable energy substantially affect the RPS adoption.
Duration RPS duration is introduced in the analytical models since more states have adopted
RPS over time. I control for the duration of RPS by counting the number of years since RPS was
first adopted in the U.S. The number of states adopting RPS has increased gradually, and states
accumulate knowledge and information on RPS from existing RPS states. The RPS duration
variable is employed to account for such information accumulation and increasing adoption
trends over time.
Table 2.2 presents the measurement strategy for internal determinants and horizontal
diffusion factors for the testing of hypotheses.
Table 2.2. Measurement strategies for independent variables
Independent variables Measurement Source
Internal determinants
Immediate crisis:
Weather-related hazards in
statei
Counts of weather-related hazards event
declared as emergency or disaster by FEMA
in statei
FEMA
(2016)7
Policy-proximate crisis:
Weather-related hazards in
statej
Counts of weather-related hazard events
declared as emergency or disaster by FEMA
in statej
FEMA
(2016)
Net metering Dichotomous variable indicating the existence
of net metering in a state
DSIRE
(2017)
7 Some may view FEMA declared emergency or disaster as politicized measure of crisis events. Nevertheless, these
are still one of the reliable measures for crisis events since FEMA is responsible for the nationwide management of
large-scale crisis events and has kept a historical record of large-scale crisis events.
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23
Table 2.2. Measurement strategies for independent variables cont’d
Independent variables Measurement Source
State government ideology State government ideology score (0-100) in
statei
Berry et al.
(1998)
% CO2 emissions per capita
from electricity generation
CO2 emissions per capita from electricity
generation in statei (metric tons)
EIA (2016)
Solar-wind potential Integrated measure of technical potentials for
wind and solar energy generation (GW) in
statei
Lopez et al.,
(2012)
Population growth A percentage change in population relative to
previous year’s population in statei
U.S. Census
Bureau
(2016)
Real GSP per capita Real GSP per capita in statei U.S. Census
Bureau
(2016)
Electricity price Annual average electricity price for total
electric industry in statei
EIA (2016)
Duration of RPS in statei
Number of years with RPS since 1997 DSIRE
(2017)
Horizontal diffusion
Ideological similarity between
statei and statej
Difference in state gov’t ideology between
statei and statej
Berry et al.
(1998)
Similarity in per capita CO2
emissions from electricity
generation between statei and
statej
Difference in per capita CO2 emissions from
electricity generation between statei and statej
EIA (2016)
Similarity in renewable energy
potential between statei and
statej
Difference in renewable energy potential
between statei and statej
Lopez et al.,
(2012)
Similarity in population
between statei and statej
Difference in population
between statei and statej
U.S. Census
Bureau
(2016)
Similarity in real GSP per
capita between statei and statej
Difference in real GSP per capita
between statei and statej
U.S. Census
Bureau
(2016)
Similarity in energy price
between statei and statej
Difference in energy price between statei and
statej
EIA (2016)
Neighbor Border sharing between statei and statej in the
same observation
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Analytical methods and Dependent variables
Event History Analysis (EHA) has been widely used for policy innovation and diffusion
studies as it allows for the simultaneous analysis of internal and external determinants in the
same model (Berry & Berry, 1990). However, the conventional state-year EHA has limitations in
analyzing the effect of horizontal diffusion as it does not allow for the comparison between
previous adopters and potential adopters. On the other hand, dyadic EHA directly compares the
policy decisions in two different states in a dyadic pair. More simply put, a researcher can
compare a policy decision in a specific state with each respective state, rather than compare a
decision in a state with average of all decisions made across all states.
Recently, policy diffusion scholars have increasingly used dyadic EHA for more refined
study of diffusion (Carley et al., 2016; Gilardi & Füglister, 2008; Nicholson-Crotty & Carley,
2015; Volden, 2006). Volden (2006) first employed dyadic approach to investigate different
types of policy diffusion in his study of Children’s Health Insurance Program. A dyadic EHA
allows the researcher to estimate whether the policy in statei (follower or laggard) changes to
align with the policy in statej (leader) while controlling for the internal determinants of each state
and the shared characteristics (Carley et al., 2016). By directly comparing two states in a pair, the
researcher can compare not only policy adoption decisions but also internal determinants of each
state in a pair.
Since dyadic EHA approach is used to analyze the alignment of policy decisions in statei
and statej, the construction of DV is different from conventional EHA approach. The DV in
dyadic analysis is coded “1” if statei adopts in year t and statej in the same observation have RPS
in or before t-1. Table 2.3 presents the DV and structure of dyadic analysis. Unlike in the state-
year EHA approach where a state is dropped from analysis once it adopts a new policy, dyadic
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25
pair is dropped once statei and statej both adopted RPS and dyadic DV is coded 1. Thus, dyadic
pairs after the occurrence of policy imitation will be dropped since no other imitation
opportunities exist. In other words, once DV is recorded as “1” in year t, the rest of observations
after t will be dropped.
Table 2.3. Dyadic data structure
IDij Statei Statej Year Adoptioni Adoptionj Dyadic DV
1 AZ CT 2004 0 1 0
1 AZ CT 2005 0 1 0
1 AZ CT 2006 1 1 1
2 AZ DE 2004 0 0 0
2 AZ DE 2005 0 0 0
2 AZ DE 2006 1 0 0
Using Probit, this chapter examines the drivers of RPS adoption across 46 states from
1998 to 2009.8 One may raise a concern on the time frame of this research that ends in 2009.
Since 2009, Vermont is the only state that adopted RPS in 2015. Therefore, the study of RPS
adoption until 2009 would still offer valuable lessons for the indicators of RPS adoption. All
independent variables are lagged by one year, and standard errors are clustered by state dyads.
Analysis
Table 2.4 presents the descriptive statistics of variables used for the model estimations.
The first three rows exhibit data characteristics of three dependent variables. Further, the table
provides the overall summary of crisis event count, relevant policies, socioeconomic factors as
well as horizontal diffusion factors.
The mean of imitation variable from 1998 to 2009 is 0.03 with the standard deviation of
0.18. States experience 2.33 large scale climate-related hazards in the past two years on average
8 Following the same logic with Carley et al. (2016), Iowa is excluded from this chapter because the RPS adopted in
Iowa in 1983 is different from the current RPS adopted by other states. Further, MA, ME, and NV are dropped from
statei since they are the first RPS adopters in 1997 and they do not have leader states from which they can imitate
their policy decisions.
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26
with minimum frequency as low as 0 incident and maximum frequency as high as 12 incidents
every year. Net metering is measured using binary variable. The mean of net metering policy
variable is 0.39 with the standard deviation of 0.49. Real GSP per capita ranges from $28,368 to
$69,973.
Table 2.4. Descriptive statistics
Variables Obs Mean Std. dev Min Max
DV
Imitation 10141 0.03 0.18 0 1
Crisis
Crisis inside a state (t-1, t-2) 10141 2.33 1.80 0 12
Crisis outside a state (t-1, t-2) 10141 2.03 1.80 0 11
Internal factors
Net metering 10141 0.39 0.49 0 1
Population growth 10141 1.07 1.13 -5.72 8.13
Real GSP per capita 10141 41578.82 7560.98 28368 69973
State gov’t ideology 10141 44.21 22.24 6.51 90.79
CO2 per capita from electricity 10141 15.15 17.86 0.01 97.26
Solar wind potential 10141 3271.80 2421.68 10.05 22466.8
Electricity price 10141 6.72 1.85 3.87 14.74
External factors
Years with first RPS in the U.S. 10141 6.14 3.35 1 12
Neighbor 10141 0.75 0.26 0 1
Difference in population (logged) 10141 1.19 0.86 0 4.27
Difference in real GSP per capita 10141 9021.88 6844.37 0 37540
Difference in state gov’t ideology 10141 27.68 19.32 0 84.78
Difference in CO2 per capita 10141 10.78 16.68 0 95.63
Difference in solar wind potential 10141 3515.80 3963.26 0..59 22456.75
Difference in electricity price
10141 2.80 2.63 0 23.59
This chapter provides the empirical analysis of RPS adoption in models 1 and 2 as
summarized in Table 2.5. Models 1 and 2 analyze the likelihood of RPS adoption in statei
following RPS adoption decision in statej. Model 1 analyzed 21,481 observations, which include
49 statej as leader states and 46 statei as follower states. However, Boehmke (2009) raised a
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27
concern that dyadic analysis may yield less than accurate findings since late adopters appear to
follow early adopters’ decisions even when they are not necessarily engaged in interdependent
decision making processes merely because of the large data. His recommended solution for such
bias is to limit the analysis to the pairs that have a possibility of sharing the same policy decision.
Thus, states that never adopted RPS are excluded from statej in model 2, leaving 10,125
observations with 28 statej and 46 statei in model 2. Results are relatively consistent across two
models except for the loss of statistical significance and reversed directions in electricity price
and difference in state government ideology variables in model 2. While the directions of
coefficients for population growth and solar-win potential remain constant across the analytical
models, their statistical significances have improved in model 2.
Since all internal determinants are measured using raw numbers except for net metering
and EERS policy variables, the interpretations of estimation results for weather-related hazards,
state government ideology, per capita CO2 emissions, solar-wind potential, population growth,
real GSP per capita, and electricity price in Probit analysis are based on the effect of one unit
increase in these indicators on the predicted probability of RPS adoption in statei following statej.
The interpretation of net metering would involve the effect of existence of net metering in statei
on the predicted likelihood of RPS adoption in statei following statej. Horizontal diffusion
variables are interpreted differently from internal determinants. Neighbor variable indicates
whether statei and statej are geographically neighboring states. The estimation of the rest of
horizontal diffusion variables captures the correlation between the absolute differences in
indicators and the predicted likelihood of RPS adoption. In other words, the negative value of
estimated coefficients for horizontal diffusion variables indicate that statei is more likely to adopt
RPS following statej if statei and statej, share similar internal characteristics (i.e., the smaller the
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28
differences between the two states are, the more likely statei is to imitate statej’s RPS adoption
decision). Appendices A and B provides the summary of changes in probabilities for models 1
and 2, respectively.
First, large scale weather-related crisis events inside a state are positively related with the
likelihood of RPS adoption. However, policy- weather-related crisis events in other states come
into effect in an opposite direction. A state is less likely to adopt RPS if the counts of large scale
weather-related hazards in other state (statej) in a dyadic pair increase. The direction and
magnitude of association between crises and RPS adoption indicates that direct experience with
relevant crisis positively influences the adoption of RPS, whereas the negative relationship
between weather crisis in other states and a state’s RPS adoption decision raises the question on
the effect of policy-proximate crisis. Specifically, the probability of RPS adoption for a statei
with twelve hazards events in the past two years (maximum) is 2.38% more than that for the one
with no hazard events in the past two years.
Second, in terms of internal conditions, net metering, wealth, and liberal state
government ideology is positively linked with the likelihood of RPS adoption. In other words, a
state with net metering policy, high real GSP per capita, and liberal state government is more
likely to adopt RPS policy. Also, a state with high renewable energy potential is more likely to
adopt RPS in model 2. On the other hand, per capita CO2 emissions from electricity generation is
negatively linked with the likelihood of RPS adoption. From this result, it can be speculated that
the presence of large fossil fuel industries in a state may present challenges or oppose RPS
adoption. The effect of electricity price on RPS adoption appears to be mixed. In model 1, a state
that pays high electricity price is less likely to adopt RPS, but the relationship between electricity
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29
price and RPS adoption is no longer significant in model 2. Population growth and solar-wind
potential are positively related to RPS adoption in model 2.
Third, state considering the adoption of RPS is likely to take cues from existing RPS
states that share similar economic conditions. Neighboring state is not a statistically significant
indicator of RPS adoption after controlling different types of similar states. As for the effects of
horizontal diffusion factors, a state is more likely to adopt RPS if other states that share
similarities in per capita real GSP per capita and/or CO2 emissions from electricity generation
had already adopted RPS. Also, similar state government ideologies increase the likelihood of
shared RPS adoption decisions in model 1. Intuitively, $1,000 less difference in real GSP per
capita between statei and statej increases the probability of RPS policy imitation by 0.02%.
Similarly, 1% less difference in per capita CO2 emissions from electricity generation is
associated with 0.05% higher probability of RPS policy imitation in two states in a dyadic pair.
However, estimated coefficients for differences in solar-wind potential, and electricity price
exhibit positive signs, indicating the negative relationship between these similarities and RPS
adoption decisions. The positive coefficients for these variables indicate that a state with less
renewable energy potential tends to refer to RPS adoption decisions in other states with higher
renewable energy potential. Moreover, a state with low electricity price is likely to imitate RPS
adoption decisions in states with high electricity prices.
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30
Table 2.5. Estimation results – RPS adoption using dyadic Probit
Variables DV: RPS adoption in statei and statej
Model 1 Model 2
Crisis
Crisis inside a state 0.0544*** 0.0511***
(0.0129) (0.0150)
Crisis outside a state -0.0483*** -0.0301**
(0.0130) (0.0149)
Internal factors
Net metering 0.147** 0.369***
(0.0618) (0.0720)
Population growth 0.0169 0.0434*
(0.0218) (0.0226)
Real GSP per capita (in thousands $) 0.0191*** 0.0260***
(0.0036) (0.0045)
State gov’t ideology 0.00925*** 0.0153***
(0.00127) (0.00165)
CO2 emissions per capita from electricity -0.00694* -0.0139**
Generation (%) (0.00412) (0.00610)
Solar wind potential (in thousands GW) 0.0077 0.0053***
(0.0088) (0.0017)
Electricity price -0.0560*** 0.0212
(0.0124) (0.0179)
External factors
Years with first RPS in the U.S. 0.109*** 0.137***
(0.00971) (0.0127)
Neighbor 0.0913 0.109
(0.0891) (0.103)
Difference in population (logged) 0.0102 -0.0455
(0.0326) (0.0396)
Difference in real GSP per capita -0.0123*** -0.0075*
(0.0039) (0.0045)
Difference in state gov’t ideology -0.00414*** 0.000691
(0.00129) (0.00152)
Difference in CO2 emissions per capita -0.0299*** -0.0153**
from electricity generation (0.00347) (0.00658)
Difference in solar-wind potential 0.0270*** 0.0128*
(0.0067) (0.0071)
Difference in electricity price 0.0536*** 0.0289***
(0.00871) (0.00943)
Constant -3.782*** -5.188***
(0.165) (0.267)
Observations
Pseudo R-squared
21,481
0.1633
10,141
0.2261 Standard errors in parentheses *** p
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31
[Note] Model 1 analyzed 21,481 observations, which include 49 statej as leader states and 46
statei as follower states. For the purpose of robustness check, states that never adopted RPS are
excluded from statej in model 2, leaving 10,125 observations with 28 statej and 46 statei in model
2.
Conclusion and Policy Implications
Many countries have promoted the use of renewable energy and/or set CO2 reduction
goals at the national level as they acknowledge a close relationship between the emissions from
fossil fuel combustion and climate change (Sovacool & Barkenbus, 2007). In tune with national
and global trends, scholars have advanced the explanation of RPS adoption drivers over the last
decade. Scholars have reached some consensus on a few of internal determinants of RPS
adoption, such as state wealth (Carley et al., 2016; Chandler, 2009; Huang et al., 2007; Upton &
Snyder, 2015; Yi & Feiock, 2012) and state government ideology (Carley & Miller, 2012;
Chandler, 2009; Lyon & Yin, 2010; Yi & Feiock, 2012). Yet, there remains much to be studied
in terms of roles of crises, institutional contexts, and diffusion mechanisms. This chapter is set
out to examine the roles of direct and indirect crisis event experiences in the adoption of state
RPS policies. Further, I investigated whether states refer to other states’ RPS adoption decisions
that share similar internal conditions other than geographic borders.
Although the theories of policy change explicitly discuss the role of focusing events or
crisis in facilitating the context for policy change, the diffusion of innovation theory has paid
limited attention to the effects of crisis events on the policy adoption. Using the crisis typology
suggested by Nohrstedt and Weible (2010), this chapter finds that immediate crises represented
by large scale weather-related hazards increases the likelihood of RPS adoption decision and the
size of RPS goals, whereas weather-related hazards experiences in other states negatively affect
the likelihood of RPS adoption. Because of the intangible nature of climate change, people tend
to downplay the importance of climate change mitigation and the use of clean energy.
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Scientifically, we cannot attribute the occurrence of each of these large scale hazards to climate
change. However, this is how non-scientists and average citizens perceive and interpret the crisis
experiences. This is more about human behavior and perception rather than the hard
environmental and natural science. In the real world and our daily lives, crisis events signal the
urgent need for solutions. Further, citizens and policymakers are able to turning this crisis
experiences into opportunity for better future as they respond to crises by adopting a renewable
energy policy. Therefore, in this chapter, I find that a directly experienced crisis event plays
significant role as an internal factor for policy adoption. However, the negative relationship
between crisis events outside a state and RPS adoption can be traced to the nature of large-N data
for dyadic analysis. Only 342 dyadic pairs, equivalent to 18.24% of total dyadic pairs, have
shared their RPS policy adoption decisions, while 81.76% of dyadic pairs did not engaged in the
imitation of RPS policies. The multiplicative and time-dependent nature of dyadic data
drastically increased the number of pairs with dependent variables coded 0, marginalizing or
introducing bias to the effects of external variables. Another possible reason for the negative
coefficients for external crisis variable arises from the processing of information that occurred
outside a state. Except for few hazards events that capture national level attention such as
Hurricanes Katrina, Sandy, and Harvey, state governments may not have concrete information
on the severity or frequency of large scale climate related hazards that took place in other states.
In fact, the negative coefficient of external crisis opens up the avenue for additional research on
the specific types or attributes of crisis events that offer lessons to other states.
For the study of policy diffusion, scholars have largely relied on the effect of policy
adoptions in neighboring states. Although neighboring effects is the most frequently examined
variable for the analysis of policy diffusion across multiple states, recent diffusion scholars
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raised questions on the neighboring effects as a factor for policy diffusion and discussed the need
for exploration of nuanced external or diffusion factors beyond neighboring effects (Gilardi,
2016; Shipan & Volden, 2012). Due to technological advancement and better access to
information, state government may also look to other states that similar internal conditions or
grapple with common issues. In this chapter, I found that politically and economically similar
states are likely to share RPS adoption decisions. In fact, renewable energy policy is not just
about using clean energy sources and reducing CO2 emissions from electricity generation. They
are highly politicized and have an impact in local and state economy. Because the adoption of
renewable policy incurs political and economic costs, it makes more sense for potential policy
adopters to take cues from the policy decisions in other states that are politically or economically
similar and reduce the uncertainties in costs associated with the policy. In doing so, state
policymakers make better-informed policy decisions. On the other hand, a state with the low
levels of solar-wind potential and electricity price imitates RPS policy adoption decisions in
other states with high solar-wind potential and electricity price. This implies that a state with
fewer renewable energy sources and more fossil fuel supplies takes proactive measures for the
switch to clean energy sources by imitating RPS policy adoption in states with more favorable
conditions for renewable energy productions.
This chapter has several limitations. First, policy design elements of RPS, such as penalty
for noncompliance and types of renewable energy allowed, are not taken into account in this
chapter. Thorough analysis of RPS adoption that consider various aspects of RPS besides
renewable energy production schedule would provide more concrete knowledge of policy
adoption. Second, the number of observations for dyadic dataset is conspicuously higher than
that for the dataset for traditional EHA due to the multiplicative nature of dataset in dyadic
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analysis, resulting in numerous observations with 0 for the dependent variables. Accordingly, the
statistical power of dyadic analysis is limited due to the rarity of policy adoption or imitation
activities where the dependent variable is coded 1. Third, sole reliance on secondary data is not
sufficient to answer how each state sets their annual RPS goals and overcomes resistance from
the utility industries. Many states struggled with the implementation of RPS during early RPS
years because of the multiplicity of interests and conflicts around RPS such as local resistance to
the renewable energy generating facilities and pressure from stakeholders for particular
renewable energy sources (Rabe & Mundo, 2007). However, various interests and conflicts
around RPS and its goals are less likely to be identified by the analysis of state-level secondary
data. Therefore, future studies may consider a mixed-method approach to RPS adoption and
goals for the exploration of various contextual factors that shape sates RPS policies specific to
each state.
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CHAPTER III. EVALUATING RENEWABLE PORTFOLIO STANDARDS: HAVE
THEY KEPT THEIR ENERGY AND ECONOMIC PROMISES?
Abstract
Renewable portfolio standards (RPS) are adopted by numerous states in the U.S. to achieve
various goals including energy, environmental, and economic goals. To date, RPS evaluation
literature has predominantly focused on the effectiveness of RPS policies in achieving renewable
energy expansion and CO2 emissions reduction. However, there is a dearth of research on the
effectiveness of RPS policies in promoting electricity price stability, which is one of the main
goals of RPS policies. Accordingly, this chapter examines whether RPS has contributed to the
improv