Conference Paper

NREL/CP-6A2-45971

July 2009

The Role of State Policy in 

Renewable Energy Development 

Elizabeth Doris, Sarah Busche,

Stephen Hockett, and Joyce McLaren 

Presented at the American Society of Mechanical Engineers 

(ASME) Third International Conference on Energy Sustainability

San Francisco, California

July 19-23, 2009
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(ASE), a contractor of the US Government under Contract No. DE-AC36-08-GO28308. Accordingly, the US 

Government and ASE retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any 

warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or 

usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not 

infringe privately owned rights. Reference herein to any specific commercial product, process, or service by 

trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, 

recommendation, or favoring by the United States government or any agency thereof. The views and 

opinions of authors expressed herein do not necessarily state or reflect those of the United States 

government or any agency thereof.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information 

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/ordering.htm

Printed on paper containing at least 50% wastepaper, including 20% postconsumer waste
1 

THE ROLE OF STATE POLICY IN RENEWABLE ENERGY DEVELOPMENT

Elizabeth Brown Doris 

National Renewable 

Energy Laboratory

Golden, CO, USA

Sarah Busche 

National Renewable 

Energy Laboratory 

Golden, CO, USA

Stephen Hockett

National Renewable 

Energy Laboratory 

Golden, CO, USA

Joyce McLaren Loring

National Renewable 

Energy Laboratory 

Golden, CO, USA

ABSTRACT

State policies can support renewable energy development 

by driving markets, providing certainty in the investment 

market, and incorporating the external benefits of the 

technologies into cost/benefit calculations. Using statistical 

analyses and policy design best practices, this paper quantifies 

the impact of state-level policies on renewable energy 

development in order to better understand the role of policy on 

development and inform policy makers on the policy 

mechanisms that provide maximum benefit. The results include 

the identification of connections between state policies and 

renewable energy development, as well as a discussion placing 

state policy efforts in context with other factors that influence 

the development of renewable energy (e.g. federal policy, 

resource availability, technology cost, public acceptance). 

INTRODUCTION

There are success stories of how policy has resulted in 

increased development in specific situations, as well as a field 

of literature on policy design practices. However, the 

generalization of lessons learned in specific cases and the 

application of design practices to inform effective policy design 

and implementation in other jurisdictions has not been 

accomplished. It is necessary to fill the gaps between case 

studies and a quantitative understanding of policy impact, as 

increased interest in renewable energy is resulting in a growing 

list of policies for promoting renewable energy. This research 

establishes the importance of quantitative understanding of 

generalized policy impact to inform state policy makers of the 

opportunities and limitations of policy in developing renewable 

energy resources.

Three primary elements form the results of this work: 1) 

understanding the current status of renewable electricity 

development at the state level; 2) identifying policies, and 

elements within policies, that lead to renewable energy 

development; and, 3) identifying and defining the broader 

contextual factors that influence renewable energy development

in order to place the policy discussion within the broader 

context.

 The paper begins with an overview of development trends 

at the state level. Those quantitative trends are then used in 

statistical analyses that aim to link policy implementation and 

actual development. The discussion then moves to contextual 

factors other than policy that affect renewable energy 

development, and concludes with the presentation of overall 

next steps for research to better understand the role of policy in 

renewable energy development and inform state policy makers 

on the impact of policies to promote renewable energy within 

individual state contexts. 

QUANTITATIVE TRENDS IN RENEWABLE ENERGY 

DEVELOPMENT

This section summarizes the status of electrical generation 

from renewable resources in the United States Department of 

Energy’s Energy Information Administration (EIA) state data 

from 2006. The goal of the review is to provide state policy 

makers with a variety of metrics to inform their understanding 

of current and historical clean energy market penetration in 

their state, relative to other states. Due to space limitations, 

only an overview of the complete analysis is given here. 

The states were ranked in several ways to account for 

differences in resource availability amongst the states and to 

highlight various factors. Rankings were done for 2006 

generation data based on generation (in MWh), generation from 

renewable resources as a percentage of total generation, 

generation per capita, and generation per gross state product 

(GSP). Furthermore, indication of the ‘most improved’ states 

are provided by ranking the states according to the change in 

capacity from 2001 to 2006, using the same catagories, i.e. 

generation (in MWh), generation from renewable resources as a 

percentage of total generation, generation per capita, and 

generation per GSP. To reflect resource differences among 

states and to address the challenges of understanding how states 

take advantage of available local resources, the rate of change 

in renewable generation is presented by individual resource,

with the exception of solar, due to the insufficiency of data. 
2 

The metric used is percentage increase over time. The 

strength of this metric is that it lends more weight to growth in 

states reporting little or no renewable energy in the beginning 

year. While the actual improvements may be small in terms of 

actual capacity development, they represent large strides in the 

transition to a clean energy economy. In addition to other 

factors, the size and economic context of the state can be a 

large determinate of the level of renewable energy 

development. To begin to address the contextual differences 

between states in a quantitative way, state renewable energy 

generation is normalized for population and gross state product 

to address economic contexts of individual states. 

The definition of renewable energy used here includes 

biomass, geothermal, hydroelectric, solar (central), and wind, 

as defined and tracked by the EIA. Also included are 

distributed solar capacity data as tracked by the Interstate 

Renewable Energy Council (Sherwood 2008).

2006 Renewable Energy Generation Trends 

Table 1 displays the EIA-collected data for gridconnected renewable electricity generation for the top five 

states in 2006, in total megawatt hours (MWh). The dataset 

includes generation from biomass, geothermal electricity, nondistributed solar, and wind. Distributed solar data are not 

collected by EIA, so are not included in this table.1

 

Considering all renewable resources in the dataset, 

Washington ranks first with nearly 72 terawatt hours (TWh). 

Large-scale hydroelectric generation resources are more 

developed than most renewable resources and are removed 

from the dataset in Table 2 to better illustrate the development 

of other renewable resources at the state level. When 

hydroelectric resources are not included, California becomes 

the highest ranked with 24 TWh, and generates more than three 

times the renewable generation of any other state. 

Nonhydroelectric renewable generation in Arizona, Missouri, 

Alaska, and Delaware was less than 100,000 MWh in 2006.

Table 1. Total On-Grid Renewable Energy Generation

(2006)

Rank State MWh

1 Washington 84,510,138

2 California 71,937,993

3 Oregon 39,720,153

4 New York 29,951,143

5 Idaho 11,941,587

 1 Distributed solar capacity is tracked by IREC USA and those data are 

referenced later in this report. 

Table 2. Total Non-hydroelectric Renewable Electricity 

Generation (2006)

Rank State MWh 

1 California 23,890,613

2 Texas 7,833,733

3 Florida 4,372,475

4 Maine 3,974,084

5 Alabama 3,905,741

Percentage of Total Generation

Percentage of total in-state generation is a normalizing 

metric to add context to the state progress toward renewablebased electricity development. Table 3 presents the renewable 

percentages including hydroelectric resources for the top 5 

states, and Table 4 presents percentages without hydroelectric. 

When hydroelectric is included, northwestern states generate 

more than three-quarters of in-state generation from renewable 

resources. Large-scale hydroelectric developments are the 

primary contributors to this generation. Removing large-scale 

hydroelectric from consideration – in order to focus on 

developing markets – no state produces more than 25% of 

electricity from renewable resources, and most states generate 

less than 5%.

Table 3. Percentage of Total State Electricity Generation: 

All Renewable Resources (2006)

Rank State

% Total State 

Generation

1 Idaho 89.2%

2 Washington 78.1%

3 Oregon 74.5%

4 South Dakota 49.7%

5 Maine 49.1%

Table 4. Percentage of Total State Electricity Generation: 

Non-hydroelectric Renewable Resources (2006)

Rank State

% Total State 

Generation

1 Maine 23.63%

2 California 11.02%

3 Vermont 6.35%

4 Minnesota 5.74%

5 Iowa 5.40%

Generation per Capita

Generation per capita is another normalizing metric to 

gain insight into trends. States with smaller populations and 

large renewable generation top this list. When all renewable 

resources are considered, hydroelectric resource use in the 

northwestern states launches Washington, Montana, and 

Oregon to more than 10 MWh of generation per person (Table 

5). When those resources are removed, Maine has the highest 

generation per person at 3 MWh/capita, with the vast majority 

of states generating less than 1 MWh per capita (Table 6). 
3 

Table 5. Renewable Electricity Generation (2006): 

MWh/Capita

Rank State MWh/Capita

1 Washington 13.257

2 Montana 11.253

3 Oregon 10.761

4 Idaho 8.158

5 Maine 6.276

Table 6. Non-hydroelectric Renewable Electricity 

Generation (2006): MWh/Capita

Rank State MWh/Capita

1 Maine 3.022

2 Wyoming 1.480

3 Alabama 0.851

4 Iowa 0.826

5 Vermont 0.725

Generation per Gross State Product (GSP)

Normalizing for economic context provides further 

insights into renewable electricity generation. Tables 7 and 8

normalize generation using gross state product (GSP), a 

traditional measure of state economic output. Similar to 

population analyses, states with relatively small output and high 

renewable generation will top this list. To rank higher, more 

economically productive states would need to generate a larger 

amount of renewable-based electricity. 

Table 7. Renewable Generation per Gross State 

Product (MWh/$M, 2006 GSP)

Rank State MWH/$M 

1 Montana 329.63

2 Washington 287.91

3 Oregon 262.52

4 Idaho 239.28

5 Maine 175.68

Table 8. Non-hydroelectric Renewable Generation per 

Gross State Product (MWh/$M, 2006 GSP)

Rank State MWH/$M

1 Maine 84.60

2 Wyoming 25.68

3 Alabama 24.32

4 Iowa 19.80

5 Vermont 18.58

Changes in Renewable Energy Generation 

Development by Resource (2006 and 2001-2006)

This section presents resource-specific renewable energy 

development at the state level, as well as changes between 2001 

and 2006. All data, with the exception of solar, is presented 

from EIA data available in the 2006 Renewable Energy Annual 

(EIA 2008). 

Attempting to rank state resource development while 

qualitatively normalizing resource availability factors, this 

section provides individual resource tables to identify states 

leading in specific resource development in 2006 as well as 

highlighting growth within recent years. This “most improved” 

ranking system intends to identify states that have excelled at 

individual resource development, but whose accomplishments 

may be overlooked when mixed in with all states and all 

resources. It is a way of identifying and recognizing state 

efforts in developing economically feasible in-state resources. 

For each resource, a ranking was done for 2006 

generation in MWh, as well as for “most improved” 2001-2006 

for:

• Total Generation

• Percentage of Total In-State Generation

• Generation per Capita

• Generation per Gross State Product

Summary points resulting from this analysis follow: 

Biomass 

Biomass sources can be defined as agricultural crops and 

residues; dedicated energy crops (herbaceous and tree species); 

forestry products and residues; residues and byproducts from 

food, feed, fiber, wood, and materials processing plants 

[sawdust from sawmills, black liquor (a byproduct of paper 

making), cheese whey (a byproduct of cheese-making 

processes), and animal manure]; post-consumer residues and 

wastes, such as fats, greases, oils, construction and demolition 

wood debris and other urban wood waste, municipal solid 

wastes/wastewater, and landfill gases (Milbrandt 2008). The 

EIA definition includes landfill gas/MSW biogenic, wood, and 

derived fuels (2003a, 2008). 

California generated the most biomass-based electricity in 

2006, followed by Florida, Maine, Alabama and Georgia. In 

total, 19 states produced more than 1 million MWh from 

biomass-based electricity. Generation was not reported or 

reported as zero for eight states and all of the territories. 

Recent developments of biomass-based electricity are occurring 

in the central and southern United States, where there is a 

wealth of resource (Milbrandt 2005). 

Kentucky has experienced the largest increase in total 

electric generation from biomass during the period studied, 

followed by Nebraska and South Carolina. All other states with 

documented generation from biomass sources increased 

generation by less than 100% or demonstrated negative growth 

during this period. Kentucky also experienced a substantially 

larger increase in percent of biomass-based electricity used than 

any other state between 2001-2006. Nineteen of the 44 states 

showed positive improvements for this metric. 

Kentucky increased biomass generation per capita by a 

factor of 56.5, an unprecedented rate. Twenty states 
4 

experienced an increase in per capita electricity generation 

from biomass sources, while twenty-three states experienced a 

decrease. Kentucky also leads the states in the generation per 

gross state product (GSP), with six other states also making 

positive improvements during the period.

Hydroelectric

The northwestern states of Washington, California, 

Oregon produced the most conventional hydroelectricity in 

2006. The northeastern states of Massachusetts, New Jersey 

and Connecticut experienced the most growth in 

hydroelectricity from 2001 to 2006, although the mature status 

of the market results in fewer large growth states. Northeastern 

states may also rank high on the growth list because of 

relatively small market penetration in 2001 as compared to 

2006. Northwestern state generation also increased in this time 

period, possibly as a result of efficiency gains in generation or 

expansion of facilities. In general, hydroelectric generation 

increases kept pace with population growth. In some states, 

however, economic growth outstripped hydroelectric 

production increases during the five-year period.

 

Geothermal 

Data collection on geothermal is limited to large-scale 

generation in this dataset, and therefore there is no direct 

geothermal included. According to the EIA data, only four 

states generated electricity from geothermal resources during 

the study period (Table 19). 

Table 19. Geothermal Generation (2006)

Rank State MWh

1 California 12,821,434

2 Nevada 1,343,711

3 Hawaii 212,276

4 Utah 190,608

This is not a comprehensive list of states with resources, 

but the only states with reported generation. Of these, Utah 

experienced the greatest increase in generation during the 

period, with nearly 25% more MWh generated in 2006 than in 

2001. 

Only Nevada and Utah made positive gains in increasing 

the percentage of in-state generation from geothermal sources 

during these five years. Utah experienced the largest increase in 

generation per capita, while Hawaii and Nevada saw decreases. 

All four states experienced a decrease in geothermal electricity 

generation per GSP during the 2001 to 2006 period, indicating 

that economic growth outstripped geothermal electricity 

production increases during these five years.

Distributed Solar

EIA does not report data on capacity from distributed 

solar electricity production, primarily photovoltaics (PV). 

However, data was drawn from recent literature providing onand off-grid capacity installation estimates by state for 2007 

(Sherwood 2008). 

California is the leading state for PV capacity installations 

(328.8 MWdc), with six times the capacity than subsequent 

states of New Jersey (43.6 MWdc), Arizona (18.9 MWdc), 

Nevada (18.8 MWdc), New York (15.4 MWdc), and Colorado 

(14.6 MWdc) follow in installed capacity. All but six states 

have less than 5 MW installed. 

Wind 

Renewable electricity generation from wind has increased 

dramatically between 2001 and 2006, as a result of market and 

policy changes, as well as technology development, availability, 

and increasing volatility in traditional fossil markets. In 

addition to expansion of generation in states, 11 states that had 

no wind-based generation in 2001 had developed generation by 

20062

. 

Texas, California, Iowa and Minnesota rank highest in 

terms of reported wind generation in 2006. Considering 

percent change, South Dakota and Nebraska experienced the 

largest increase in total generation between 2001-2006, while 

Vermont and Alaska were the only two states experiencing a 

decrease in wind generation during this period. Twenty-three 

states increased the portion of total in-state electricity generated 

by wind during the period. Of these, South Dakota shows the 

most improvement with an increase of more than 170-fold. 

Of the 23 states that increased wind generation per capita, 

South Dakota and Nebraska experienced substantially larger 

increases than all other states. Two states, Vermont and Alaska, 

experienced a decrease in wind generation per capita. Twentythree states increased generation per capita between 2001-2006, 

with South Dakota experiencing the largest increase, at more 

than 12,000%. Per capita wind generation in both Vermont and 

Alaska decreased during the period.

Summary of Trends

The following trends in renewable resource-based electricity 

development were observed:

• Hydroelectric resources provided the largest portion of 

renewable energy development in the United States in 

2006. However, the share of hydroelectric is shrinking due 

to growth in development of other renewable energy 

resources and maximization of the larger-scale 

hydroelectric resources. 

• Between 2001 and 2006, wind resource represents the 

largest growth in renewable generation nationwide. 

 

2 There are three states (Idaho, Montana, and New Jersey) in which wind 

generation reporting began in 2006. The improvements and successes of these 

states should not be ignored; but, because there is no base-year generation with 

which to compare the 2006 data, they are not considered in the “most 

improved” analysis. However, they are likely to be most improved in later-year 

datasets. 
5 

• Growth in electricity from biomass is primarily occurring 

in the southeastern areas of the United States, coincident 

with resource availability.

• Renewable energy growth during this period was generally 

outstripped by population growth and economic growth as 

measured by gross state product (GSP). 

• According to EIA data, between 2001 and 2006:

o 24 states increased electricity generation from 

biomass resources, 

o 23 states from wind electricity production,

o 4 states from geothermal electricity production, 

and

o 2 states from large-scale solar electricity 

production.

Data and method limitations in identifying trends:

• In general, the EIA dataset is considered the most 

comprehensive source for electricity generation 

information in the United States and it is the primary 

source for trends information in this report (with noted 

exceptions). There are a number of challenges in collecting 

renewable electricity generation at the state level, but those 

are not the focus here. Instead, the strength of the dataset 

as a nationwide comparable source regarding definitions 

and data collection techniques are the reasons for its use. 

• Data for distributed solar electricity resource development 

are limited by lack of collection by EIA. (Solar PV data are 

the only presented in this report that are not from the EIA. 

Data presented are installed capacity for 2007, collected by 

the Interstate Renewable Energy Council using established 

methodologies described in Sherwood 2008). 

• Data on renewable-based electricity generation in the U.S. 

territories is limited. EIA data were supplemented with 

direct contact to territory energy offices, but the authors 

received no additional data. 

• Most recent data are from 2006. Significant market 

changes between 2006 and 2008 are expected to have an 

impact on renewable energy generation and will be 

reported in later versions of NREL’s State of the States

report. 

• “Most Improved” rankings provide information on the 

largest growth rates between 2001 and 2006, leading to 

heavier weighting of states that began the development of 

the particular renewable resource in that time frame. The 

purpose is to acknowledge the challenge of early-stage 

development. The analysts are considering alternative and 

additional methods for future reports. 

THE ROLE OF POLICY IN RENEWABLE ENERGY 

DEVELOPMENT: STATISTICAL ANALYSES

The variation in the relative importance of the factors 

leading to renewable energy development makes identifying 

generally effective policy mechanisms at the state level 

challenging. The value of using quantitative methods to explore

the role of polices in development is that of supporting 

maximum impact of government intervention for development 

of renewable energy. The information in this section is a highlevel statistical analysis to aid the understanding of the 

connection between policy and renewable energy development.

Several steps were taken to refine the data before 

conducting the analyses. First, policies that impact renewable 

energy were identified and defined (i.e. tax incentives, 

equipment certification, green power purchasing, portfolio 

standards, etc.). Next, best practices in policy design were 

identified and their applicability to this study defined. Most 

state policies were considered in the analyses simply if they 

were being implemented. However interconnection and netmetering policies, can be designed in a way that discourages 

renewable energy growth; thus only those that receive a ranking 

of C or better according to the Network for New Energy 

Choices definition (NNEC, 2008) are considered in this 

analysis. 

The list of policies considered is drawn from the Database 

of State Incentives for Renewables and Efficiency (DSIRE 

2008), and definitions are compiled from the DSIRE database 

and select other resources.

Several sets of statistical analyses were conducted to 

identify relationships between the implementation of policy and 

actual development. The first set identifies correlations between 

the individual policies designed to support renewable energy 

development and the development trends presented in the 

previous sections of this paper. 

A statistical T-test is used, which compares the means of 

renewable energy generation (or capacity, etc.) for states that 

have a particular policy or set of policies implemented, and 

states that do not. The output of the test shows whether the 

difference in the means is statistically significant at a specified 

level (.05 or 0.1 level) of analysis. If the means are 

significantly different, then the null hypothesis - that the 

implementation of a policy and the actual generation of 

renewable energy are not related - may be rejected. The output 

also provides a “t-value” which indicates how confidently you 

can reject the null hypothesis.

The second set of analyses compares the same 

development trends with combinations of policies categorized 

using the tenants of market transformation theory. Market 

transformation studies the effects of policy and other integrated 

factors in transforming markets. It is typically discussed in 

relation to energy efficiency technologies, however it provides 

useful insight into the role of policy in transforming other 

markets. Market transformation focuses on creating a 

sustainable market place using low cost policies that (1) 

restructure the market by removing barriers or (2) make 
6 

technologies more accessible (through, for example, financial 

incentives that can eventually be withdrawn). 

To prepare for the second set of analyses, renewable 

energy policies were categorized as either market preparation 

(barrier reduction) policies or as policies that improve the 

accessibility of technologies. The categorization is shown in 

Table 20, below.

Table 20. Categorization of Policies

According to Tenants of Market Transformation

Market Preparation (Barrier Reduction) Policies

Contractor Licensing 

Equipment Certification

Generation Disclosure 

Interconnection

Land Access

Line Extension Analysis

Net Metering

Public Benefit Fund w/RE

Renewable Portfolio Standard

Voluntary/Mandatory Green 

Power

Technology Accessibility Policies

Corporate Tax Incentives

Grants

Loans

Personal Tax Incentives

Property Tax Incentives

Rebates

RE Production Incentives

Sales Tax Incentives

Correlation analysis is used to indicate whether an 

increase in the number of policies of a specified type is related 

to an increase in actual generation. Thus, this analysis indicates 

whether there is a significant correlation between increased 

levels of renewable energy generation and 1) the 

implementation of market preparation policies as group, 2) the 

implementation of market transformation policies as a group, 

and/or the total number of policies implemented. 

The third set of analyses combines policies into portfolios, 

based on identified policy best practices and understandings of 

policy interactions from the literature. The goal is to investigate 

the effectiveness of particular combinations of policies that are 

theorized to complement each other to result in more effective 

policy solutions3

. The policy portfolios investigated are:

Portfolio 1: RPS + line extension analysis + interconnection 

standards + green power purchasing 

Portfolio 2: Tax incentives + line extension analysis + 

interconnection standards

Statistical Analyses Results

Before discussing the results of the analyses, it is 

important to note that causation between policy and generation 

 3 See Hurlbut, 2008 and Cory, et. al, 2009. More information about the 

background research and understandings that lead to the selection of these 

portfolios will be provided in NREL’s upcoming State of the States 2009 report. 

The identification of effective portfolios is a topic of on-going NREL research. 

cannot be assumed. In other words, the results of these

analyses do not necessarily prove that the policies were the 

direct cause of increased renewable energy generation. The 

statistical analyses only show that states that have implemented 

certain policies or groups of policies have significantly more 

renewable energy generation than those that have not 

implemented those policies.

The results of the first set of analyses indicate significant 

relationships between the implementation of some individual 

state policies and the level of in-state renewable energy-based 

electricity generation. 

Higher total renewable energy generation is significantly 

related to the implementation of the following individual 

policies:

 

• RPS

• Production incentive

• Generation disclosure

• Interconnection

• Land Access

The results of the second set of analyses indicate a 

significant correlation between states implementing market 

preparation (barrier reduction) policies and high levels of

renewable energy development (for both non-hydro renewable 

generation and total renewable energy generation). A 

significant correlation was also found between the 

implementation of high numbers of total policies from both 

categories and high levels of development. However, no 

correlation was found between the implementation of only 

technology accessibility policies and high levels of 

development. 

These results illustrate the importance of implementing

market preparation/barrier reduction policies. They suggest 

that incentive policies (technology accessibility policies) alone 

do not lead to significant renewable energy development, and 

that they must be combined with barrier-reduction policies in 

order to make a significant difference in the development of 

renewable energy. 

 The findings of the third set of analyses indicate that the 

concept of policy portfolios warrants more research. No 

relationship was found for the combination of policies defined 

in portfolio 2 (tax incentives, line extension analysis, and 

interconnection). However, T-test analyses indicate that the 

combination of policies defined in Portfolio 1 (RPS, line 

extension, interconnection and green power purchases policies) 

is significantly related to higher levels of non-hydroelectric 

renewable energy generation.

It is interesting to note that the relationship between this 

combination of policies and the level of generation is stronger 

than for the individual policies on their own, and that the 
7 

relationship is specifically with non-hydroelectric renewable 

generation, rather than all renewable fuel types. This supports 

the theory that the policies defined in Portfolio 1 are an 

effective combination of policies to support the development 

of non-hydro renewable energy. 

Table 21 provides the statistical values for the results reported 

here. 

FACTORS INFLUENCING RENEWABLE ENERGY 

DEVELOPMENT

The generation trends discussed in the initial section of 

this paper result from a variety of interwoven factors that 

influence renewable energy development. The primary focus of 

this study is the role of policy in renewable energy development 

at the state level (described above). Because the benefits of 

renewable energy are primarily a public good, policy can be a 

major driver for development of resources (e.g., DOE 2008, 

Bezdek 2007, McLaren Loring 2006). However, the 

importance of other drivers, and their interactions with policy, 

cannot be overlooked. What follows is a list of contextual 

factors that also influence renewable energy development. As 

made evident in their descriptions, many of the issues overlap 

and intertwine.

• Resource availability. One of the most obvious

influencing factors affecting renewable energy 

development is resource availability. If a physical resource 

is not available, development cannot progress. In the case 

of renewable resources, however, a more relevant question 

is that of the economic feasibility of tapping the resource. 

States that import electricity or the fuels to produce 

electricity, as well as states that have high electricity costs, 

may have more incentive to develop the local renewable 

resources. This factor is inexorably linked to the issues of 

technology availability and cost.

• Technology cost. Even in areas of excellent resource, 

technology price can be the limiting factor in development. 

As resource availability decreases, the cost of developing 

incremental units of a technology becomes more 

expensive, even if technology cost remains constant. This 

is the link between resource availability and technology 

cost. Policies can bring down costs through incentives or 

by encouraging research and development. Cost of 

renewable technologies, as well as those they compete with 

in also linked to the broader economic context. 4

• Economic context. The broader economic context may 

influence the development of renewable energy in several 

ways. High costs of traditional generation may make the 

economics of renewable energy more favorable (e.g., DOE 

2008 and Bezdek 2007). States with higher gross state 

products may choose to direct more funds to clean energy 

development, even when issues such as the high cost of 

importing fossil fuels or electricity is not a driving factor. 

The ability to pay for clean energy is tied to the willingness 

to pay, which is an issue of social acceptance of the 

technologies.

• Social acceptance/opposition. The level of public support 

can greatly influence renewable energy development. It has 

been demonstrated that organized opposition efforts with 

strong leadership can seriously hinder renewable energy 

development. Opposition may focus around issues such as 

aesthetics, effects on wildlife, or land and water use. Early 

involvement of relevant stakeholders and attention to a 

 

4 In the solar industry, concern surrounding silicon (a key element in module 

production) shortages drove market prices up in the early years of the century 

(Gartner 2005). Wind technology suffered similar price increases in 2006, with 

demand for wind as well as competing steel (a key element in tower 

construction) leading to turbine shortages drove prices higher and reduced 

development (DOE 2007).
8 

democratic decision making process can ease public 

concerns around renewable energy projects (McLaren 

Loring 2006). The leadership of an influential champion 

who identifies mutually beneficial opportunities and rallies 

public support is also valuable (EST 2006, McLaren 

Loring 2006). 

• Ownership structure. The ownership structure of 

proposed projects can affect whether owners are able to 

obtain necessary financing and take advantage of 

incentives. The ownership structure may even affect the 

acceptability of the project in the public view; locally 

owned and community-owned projects may be more 

favorably received (McLaren Loring 2006). 

• Financing. The design and availability of financing 

mechanisms can great affect renewable energy project 

development. There are several common barriers to 

obtaining financing for renewable energy projects. 

Financing institutions may view new or rapidly developing 

technologies as overly risky; project developers may be 

new in the market and, thus, have little credit worthiness; 

and financing fees and administrative procedures may be 

prohibitive to small developers. Policy measures can 

address these barriers and make financing available to a 

broader spectrum of projects representing a variety of 

ownership structures (UNEP 2008, Goldman 2007). 

 

Given that many of the above contextual factors are 

interrelated and site specific, it is difficult to determine their 

relative impact on the development of renewable energy. The 

presence or absence of one factor can intensify or nullify the 

importance of other factors. In addition, the interactions 

involve continuous feedback mechanisms. Making changes in 

one factor generally impacts other factors. For example, 

lowering technology costs through research may change the 

economic viability of tapping the resource; or public opposition 

to projects may be lessened if financing mechanisms encourage 

community ownership schemes. 

 Quantifying the impacts of the various factors is

challenging, and there are limits to the value of generalizing. 

The value of quantification is in developing a better 

understanding the costs and benefits of policies and informing 

policy makers regarding the potential impact of policies. The 

renewable energy market is rapidly expanding (DOE 2008), 

and state policy makers are working to implement policies with 

quantifiable impacts. Without understanding the role of policy 

in development at the state level (and possibly the site level), 

the impact of policies cannot be accurately projected.

 

Concluding that the complications of quantifying the 

interaction of these contextual factors are too challenging to 

overcome denies policy makers a potentially valuable level of 

understanding of the influences on development. While the 

uncertainties should not be oversimplified, understanding the 

roles in different contexts contributes to effective policy design. 

OVERVIEW OF RESULTS AND NEXT STEPS

The results of this report show that there is a quantifiable 

connection between state-level policy and renewable energy 

development. The connection is made more complex by the 

contextual factors within which policies are set, including 

resource availability, technology cost, economic context, public 

acceptance, and ownership and financing structures. 

The following observations of this research suggest many 

areas of future research that would aid in the a better 

understanding of the role of state policy in renewable energy 

development – those suggestions conclude each of the 

observations. 

• There is a quantified connection between policy and 

renewable energy development. Understanding the 

details of the connection to better inform policy 

development at the state level is the primary next step.

• In addition to policy, there are many other factors 

driving the development of renewable energy 

resources at the state level. Better understanding the 

role of each of these factors, and the way they are 

manifested in specific states, will provide insight and 

understanding into the development of renewable 

energy resources. Continued investigation of the 

various factors influencing renewable energy 

development will be included in forthcoming reports. 

• Research on policy best practices is currently designbased, not results-based. Further investigation into 

policy outcomes and better understanding of policy 

design elements that are applicable across state 

contextual factors are critical to informing the 

development of state policies that are more effective in 

increasing renewable energy. In addition, 

methodologies to better understand the connection 

between policy design and differences in overall 

impact are being developed. Results-based policy 

design and policy portfolios will be addressed in 

forthcoming research.

Follow-on research will refine and expand this study to provide

further understanding of the: 

• current state of renewable energy development; 

• impacts of policy on recent development;

• impacts of contextual factors affecting renewable 

energy development;

• impacts of individual policies and policy portfolios; 

The DOE-funded, NREL-implemented State Clean Energy 

Policies Analysis (SCEPA) project, as well as future versions of 

this report, will build on and develop next steps. The project 
9 

teams appreciate input and participation by stakeholders. More 

information can be found on the SCEPA website: 

http://www.nrel.gov/applying_technologies/scepa.html. 

ACKNOWLEDGEMENTS

The report was produced with funding from and in coordination 

with the Department of Energy (DOE) Weatherization and 

Intergovernmental Program (WIP) in the office of Energy 

Efficiency and Renewable Energy (EERE). During the review 

process, the authors benefited from extensive comments from a 

wide variety of stakeholders.

REFERENCES

Beck, F. and E. Martinot. (2004). “Renewable energy policies 

and barriers.” Encyclopedia of Energy. Ed C. J. 

Cleveland. Academic Press/Elsevier Science. 

http://www.martinot.info/Beck_Martinot_AP.pdf

Accessed on 6/13/08

Bezdek, R. (2007). “Renewable Energy and Energy Efficiency: 

Economic Drivers for the 21st Century.” Prepared for 

the American Solar Energy Society. URL: 

http://www.greenforall.org/resources/renewableenergy-and-energy-efficiency-economic

Bird, L., M. Bolinger, T. Gagliano, R. Wiser, M. Brown, and B. 

Parson. (2005). “Policies and market factors driving 

wind power development in the United States.” 

Energy Policy, 33, pp. 1397-1407.

Blumstein, C., Goldstone, S., and Lutzenhiser, L. (2000). “A 

theory-based approach to market transformation.” 

Energy Policy, 28, p. 137-144.

Bolinger, M., Wiser, R., Milford, L, Stoddard, M, and Porter, K. 

(2001). „Clean energy funds: an overview of state 

support for renewable energy.” Berkeley, CA: 

Lawrence Berkeley National Laboratory. p. viii.

http://www.osti.gov/energycitations/servlets/purl/783499-

FqqXYE/native/783499.PDF

Brown, E., P. Quinlan, H. Sachs, and D. Williams. (2004). “Tax 

Credits for Energy Efficiency and Green Buildings: 

Opportunities for State Action.” Report: ACEEEE021. American Council for an Energy-Efficient 

Economy: Washington, D.C.

Brown, E., K. Cory, D. Arent. (2007). “Understanding and 

Informing the Policy Environment: State Level 

Renewable Fuel Standards.” NREL/TP-640-41075. 

NREL: Golden, Co

[CEE] Consortium for Energy Efficiency. ND. “Market 

transformation primer.” Consortium for Energy 

Efficiency. http://www.cee1.org/cee/mt-primer.php3

Accessed on 6/18/08

Clement, D., M. Lehman, J. Hamrin, R. Wiser. (2005). “Draft 

report: international tax incentives for renewable 

energy: lessons for public policy.” Center for Resource 

Solutions. Prepared for Energy Foundation China 

Sustainable Energy Program. http://www.resourcesolutions.org/lib/librarypdfs/IntPolicyRenewable_Tax_Incentives.pdf (p. 20-24). Accessed 

on 6/20/08

Clemmer, S., D. Donovan, A. Nogee, and J. Deyette. (2001). 

“Clean Energy Blueprint: A Smarter National Energy 

Policy for Today and the Future.” UCS, ACEEE, 

Tellus Institute. Published by UCS: Cambridge, MA

Cory, K.; Couture, T.; Kreycik, C. (2009). Feed-in Tariff Policy: 

Design, Implementation, and RPS Policy Interactions. 

23 pp.; NREL Report No. TP-6A2-45549.

Cory, K and B. Swezey. (2007). “Renewable Portfolio 

Standards in the States: Balancing Goals and 

Implementation Strategies.” NREL Report TP 640-

41409. NREL: Golden, CO.

Darby, S. (2006). “The Effectiveness of Feedback on Energy 

Consumption: A Review for DEFRA of the literature 

on metering, billing and direct displays.” 

Environmental Change Institute at the University of 

Oxford. Oxford, England. 

[DSIRE Description] Database of State Incentives for 

Renewables and Efficiency. DSIRE: Glossary Web 

Page: 

http://www.dsireusa.org/glossary/glossary.cfm?&Curre

ntPageID=8&EE=1&RE=1. Accessed July 2008.

Egan, C. and E. Brown. (2001). “An Analysis of Public 

Opinion and Communication Campaign Research on 

Energy Efficiency and Related Topics.” ACEEEA013. ACEEE: Washington, D.C.

DOE. (2008). Renewable Energy Data Book. URL: 

http://www1.eere.energy.gov/maps_data/pdfs/eere_dat

abook_091208.pdf

DOE. (2007). U.S. Department of Energy, Annual Report on 

U.S. Wind Power Installation, Cost, and Performance 

Trends: 2006, p. 9.

[EIA] U.S. Department of Energy, Energy Information 

Administration. (2008). “Renewable Energy Annual 

2006.” DOE/EIA-0603(2006). Washington, D.C., 

DOE EIA. URL: 

http://www.eia.doe.gov/cneaf/solar.renewables/page/re

a_data/rea_sum.html

[EPA] United States Environmental Protection Agency. (2008). 

“Advancing Clean Energy Funds: Options for 

Administration and Funding.” Prepared for EPA by 

Optimal Energy. EPA: Washington, D.C. URL: 

http://www.epa.gov/cleanenergy/documents/clean_ene

rgy_fund_manual.pdf

EPA. (2007). EPA’s Clean Energy Guide to Action. USEPA: 

Washington, D.C. URL: 

http://www.epa.gov/cleanenergy/documents/gta/guide

_action_full.pdf

[EPA CHP] United States Environmental Protection Agency 

Combined Heat and Power. (2008). Interconnection 

standards. Combined Heat and Power Partnership. 

http://www.epa.gov/chp/statepolicy/interconnection.html accessed on 6/3/08
10

[EST] Energy Savings Trust. (2006). “What are energy 

champions and how can they benefit your 

organization?” Fact sheet. URL: 

http://www.energyadvicecentre.co.uk/uploads/docume

nts/housingbuildings/in_house_energy_experts_onepa

ge.pdf

Gartner, J. (2005). “Silicon Shortage Stalls Solar.” Wired 

Magazine. March 28. URL: 

http://www.wired.com/science/planetearth/news/2005/

03/67013

Geller, H. and Nadel, S. (1994). “Market transformation 

strategies to promote end-use efficiency.” American 

Council for an Energy-Efficient Economy. 

http://www.aceee.org/pubs/e941.htm

Accessed on 6/18/08

Goldman, D. P.; McKenna, J. J.; Murphy, L. M. (2007). 

Overcoming Barriers to Renewable Energy Project 

Financing. Solar Today. , July/August 2007; pp. 34-37; 

NREL Report No. JA-110-42948.

Gouchoe S., Everette, V., Haynes, R. (2003). “State Incentives 

for Renewable Energy: Case Studies of Program 

Effectiveness,” North Carolina Solar Center, North 

Carolina State University, U.S. Department of Energy, 

Office of Energy Efficiency and Renewable Energy, 

2003. 

http://www.dsireusa.org/documents/PolicyPublications

/Case_Studies_Paper_ASES_2003.pdf accessed on 

6/20/08. 

Haynes, R. and C. Whitaker (2007). “Connecting to the Grid: A 

Guide to Distributed Generation Interconnection 

Issues.” IREC: North Carolina. 

Hurlbut, D. (2008). State Clean Energy Practices: Renewable 

Portfolio Standards. 23 pp.; NREL Report No. TP670-43512.

Komor, Paul. (2004). “Renewable Energy Policy.” Prepared for 

the Diebold Institute for Public Policy Studies. 

Published by iUniverse. 

[IEE] “Distributed Energy and Electric Reliability – Fact 

Sheet.” Institute for Electrical Engineers. 

http://www.nrel.gov/docs/fy03osti/33405.pdf Accessed 

on 6/3/08

[IREC] Interstate Renewable Energy Council. “Model 

Interconnection Standards” (IREC MR-I2005). URL: 

http://www.irecusa.org/fileadmin/user_upload/Connect

Docs/IC_Model.pdf. Accessed July 1, 2008.

Kettles, Colleen. (2008). Florida Solar Energy Research and 

Education Foundation, Inc. Personal communication 

with Elizabeth Brown, August 3. 

Mann, R. F. and M. L. Hymel. (2006). “Getting into the act: 

enticing the consumer to become “green” through tax 

incentives.” bepress Legal Series. Working paper 

1068. 

http://law.bepress.com/cgi/viewcontent.cgi?article=50

83&context=expresso

Accessed on 6/23/08

McLaren Loring, J. (2006). “Wind energy planning in England, 

Wales and Denmark: Factors influencing project 

success.” Energy Policy, Volume 35 Issue 4.

McNeill, D. and W. Wilkie. (1979). “Public Policy and 

Consumer Information: Impact 

of the New Energy Labels.” Journal of Consumer 

Research. Volume 6, Issue 1: P1-11. 

Milbrandt, A. (2008). Personal communication to Elizabeth 

Brown. September 18, 2008. 

Milbrandt, A. (2005). “A Geographic Perspective on the 

Current Biomass Resource Availability in the United 

States.” NREL: Golden, CO. URL: 

http://www.nrel.gov/docs/fy06osti/39181.pdf

Mosey, G. and C. Kreycik. (2008). “State Clean Energy 

Practices: Renewable Fuel Standards.” NREL Report 

No. TP-670-43513.

[NNEC] Network for New Energy Choices. (2008). “Freeing 

the Grid 2008.” Forthcoming. NNEC: New York, New 

York. 

[PEW] PEW Center on Global Climate Change. (2008a.) Public 

Benefit Funds. http://www.pewclimate.org/node/5849

accessed on 6/3/08

PEW. (2008b). Renewable Portfolio Standards. 

http://www.pewclimate.org/what_s_being_done/in_the

_states/rps.cfm accessed on 6/3/08

[Tax] Taxadmin.org. State Sales Tax Table. URL:

http://www.taxadmin.org/FTA/rate/sales.html

Accessed: July 3, 2008 

[UNEP]. United National Energy Program and (2008). Public 

Finance Mechanisms to Mobilise Investment in 

Climate change Mitigation.

http://www.osti.gov/bridge. 

Wellinghoff, J. (2007). “Remarks from the WGA Annual 

Summit: Renewable Development, The Western U.S. 

and Landscapes.” URL: 

http://www.ferc.gov/EventCalendar/Files/2007092712

0026-WGA%20Summit%20Renewables.pdf

Wiser, R., M. Bolinger, and G. Barbose. (2007). “Using the 

Federal Production Tax Credit to build a durable 

market for wind power in the United States.” The 

Electricity Journal, 20(9), pp. 77-88.

Wiser, R., M. Bolinger, L. Milford, K. Porter, and R. Clark. 

(2002). “Innovation, renewable energy, and state 

investment: case studies of leading clean energy 

funds.” Ernest Orlando Lawrence Berkeley National 

Laboratory. LBNL-51493.

http://eetd.lbl.gov/EA/EMS/reports/51493.pdf

accessed on 6/27/08 (p. 14).

Wiser, R. and S. Pickle. (1997). “Financing Investments in 

Renewable Energy: The Role of Policy Design and 

Restructuring.” Ernest Orlando Lawrence Berkeley 

National Laboratory. University of California 

Berkeley. 

http://eande.lbl.gov/ea/EMS/reports/39826.pdf

Accessed on 6/27/08
11

[WPA] Wind Powering America. (2008). Wind Powering 

America Web site: “Factors Influencing Wind 

Development.” URL: 

http://eere.energy.gov/windandhydro/windpoweringam

erica/ne_economics_determine.asp

York, D. and M. Kushler. (2005). “ACEEE's 3rd National 

Scorecard on Utility and Public Benefits Energy 

Efficiency Programs: A National Review and Update 

of State-Level Activity.” ACEEE-u054. ACEEE: 

Washington, D.C.
F1147-E(10/2008) 

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents 

should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a 

currently valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

July 2009

2. REPORT TYPE

Conference Paper

3. DATES COVERED (From - To)

 

4. TITLE AND SUBTITLE

Role of State Policy in Renewable Energy Development

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

E. Doris, S. Busche, S. Hockett, and J. McLaren 

5d. PROJECT NUMBER

NREL/CP-6A2-45971 

5e. TASK NUMBER

IGST.9000

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/CP-6A2-45971 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

State policies can support renewable energy development by driving markets, providing certainty in the investment 

market, and incorporating the external benefits of the technologies into cost/benefit calculations. Using statistical 

analyses and policy design best practices, this paper quantifies the impact of state-level policies on renewable 

energy development in order to better understand the role of policy on development and inform policy makers on the 

policy mechanisms that provide maximum benefit. The results include the identification of connections between state 

policies and renewable energy development, as well as a discussion placing state policy efforts in context with other 

factors that influence the development of renewable energy (e.g. federal policy, resource availability, technology cost, 

public acceptance).

15. SUBJECT TERMS

state policy; renewable energy; RE investment; RE markets; policy makers; federal policy; RE technologies; resource 

availability; policy design; renewable portfolio standard; RPS

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18