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

ii

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

iii

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.

2

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 &

3

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

4

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).

10

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

14

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.

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.

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

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).

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

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

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

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

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.

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

24

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

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.

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

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

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

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.

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

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