NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

State of the States 2010: 

The Role of Policy in Clean 

Energy Market Transformation

Elizabeth Doris and Rachel Gelman

Technical Report

NREL/TP-6A20-49193

January 2011
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Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

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Contract No. DE-AC36-08GO28308

State of the States 2010:

The Role of Policy in Clean 

Energy Market Transformation

Elizabeth Doris and Rachel Gelman

Prepared under Task No. IGST.0005

Technical Report

NREL/TP-6A20-49193

January 2011
NOTICE

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iii

Acknowledgments

This report was produced with funding from, and in coordination with, the Department of 

Energy (DOE) Weatherization and Intergovernmental Program in the Office of Energy 

Efficiency and Renewable Energy. 

The report content and design was greatly aided by input from Marguerite Kelly, Lori 

Bird, Kate Young, Doug Arent, Joyce McLaren, Sarah Busche, and Eric Lantz of NREL; 

Eleni Pelican of DOE; and Terry Schultz, an independent applied statistician. 

In addition to these reviewers, we appreciate the assistance of Michelle Kubik, Mary 

Lukkonen, and NREL’s Technical Communications Office in providing editing support. 

Of course, any remaining errors are the responsibility of the authors. 

NREL’s Clean Energy Policy Analyses (CEPA)

The CEPA suite of analyses and activities explore clean energy development and policy 

implementation at the regional, state, and local levels and disseminate that information to 

interested stakeholders. The activities gauge the effectiveness of and interactions between 

clean energy policies, provide insight into regional activities, investigate the interactions 

between local and state-level policies, and convene leading thought leaders to develop 

innovative regional, state, and local clean energy policies. The goal is to provide 

information to decision makers, researchers, and other stakeholders regarding the status 

of, barriers to, and possibilities for increased energy efficiency and renewable energy 

development at various levels of governance. For more information, see 

http://www.nrel.gov/cepa/. This report focuses primarily on energy use in electricity and 

buildings. For more information on transportation policies at the state and local level, 

please see the Alternative Fuels Data Center: http://www.afdc.energy.gov/afdc/. 
iv

Executive Summary

The increase in the use of state policy to drive energy efficiency and renewable energy 

market transformation is leading to extensive research on determining the best policies 

and policy designs to achieve this goal. In recent years, numerous best practice and state 

policymaker guidebooks have emerged to inform and assist the development of effective 

policy (e.g., DOE 2009; EPA 2008; LBNL 2009). In addition, there is growing interest 

in quantifying the connection between policies and development. To date, much of this 

work has been specific to wind resource development (Bohn and Lant 2009; Menz and 

Vachon 2005) or has focused specifically on renewable portfolio standard (RPS) 

effectiveness (Carley 2009). 

This report uses statistical methods to better quantify the connection between a broad 

array of energy efficiency and renewable energy (collectively known as clean energy) 

policy and actual reductions in energy use and increases in renewable resource 

development. Using a multi-faceted dataset including policies, socioeconomic factors, 

and electricity information, ordinary least-squares regression is used to identify 

relationships between policy implementation and development. For energy efficiency, 

these methods led to an adjusted R2 of 46% for residential energy use and an adjusted R2

of 67% for commercial energy use. For renewable resource development, an adjusted R2

of 43% to 63% was achieved depending on the technology (Table ES-1). 

Table ES-1. Percent of Variation Between States Explained by Models (Adjusted R2) 

R2 is the proportion of variation among states in their capacity or generation that is 

explained by the regression analysis and is an indicator of how well the model fits the 

data. It ranges from 0.0 to 1.0, and an R2 value of 1.0 means that the regression model fits 

the data perfectly. As shown in Table ES-1, approximately 58% of the variation in the 

states’ 2009 photovoltaic capacity is explained by the 2008 policies and additional 

socioeconomic variables chosen for the regression.

The adjusted R2 modifies the R2 slightly by adjusting for the number of independent 

variables in the model. The adjusted R2 only increases when a new independent variable 

improves the regression model more than what would be expected by chance alone. It is 

important to note that the adjusted R2 does not indicate that the independent variables 

cause the variation in the renewable energy capacity or generation among states; it is 

simply a measure of how well the model fits the data.

2009 Capacity/Generation vs 2008 Policies 2009 Capacity/Generation vs 2007 Policies

(One Year Lag) (Two Year Lag)

Technology Capacity (MW) Generation (MWh) Capacity (MW) Generation (MWh)

Biomass 43.7% 46.7% 49.8% 52.6%

Geothermal 47.2% 47.4% 49.8% 50.5%

Photovoltaic 58.2% 63.3%

Wind 47.5% 45.7% 45.5% 43.6%

Impact of Policy on Efficiency

Commercial 67.5%

Residential 46.1%
v 

While consistent with similar reports, the seemingly low adjusted R2 values reflect both 

the lack of policy information available for incorporation into the statistical analysis and 

the potential for factors not tested here to drive clean energy development. The 

methodological limitations include:

• The relatively small sample size (50 states plus the District of Columbia) 

limits the observations to a maximum of 51 for any regression. 

• The time series data for policies dates back to only 2007. 

• The policy terms can vary from state to state, and the differences are difficult 

to reflect in this type of analysis. Additionally, not all policies are designed to 

spur in-state development but rather target regional growth, and this 

methodology is specifically designed to identify policy impacts on in-state 

development. 

• It is possible that omitted variable bias would cause the independent variable 

(capacity/generation) to be correlated with the error term, therefore distorting 

the coefficients estimated in the analysis and producing inconsistent estimates. 

This methodology attempts to capture omitted variable bias by including more 

variables then necessary and reducing down to an optimal regression equation. 

However, macroeconomic variables (including changes in demographics) 

could have impacted energy use and capacity development and 

unintentionally been omitted from the analysis. 

• The dependent variable in this analysis is absolute renewable capacity or 

generation, not the percent of total capacity or generation that is a renewable 

resource. RPS policies typically target growth of the percent of total 

generation that is composed of renewable energy and may be a better metric 

to test in the future.

• Biomass and geothermal projects typically require a construction schedule in 

excess of two years, and therefore the effects of a policy would have 

corresponding longer lag. 

Generally, results from this analysis align with the existing literature (Bohn and Lant 

2009; Carley 2009; Menz and Vachon 2005), especially in the conclusions that policy in 

concert with other macroeconomic factors is connected with renewable energy 

development. The findings of this report expand the current body of work to include a 

more detailed evaluation of the connections between energy use and energy efficiency 

policies as well as a broader review of renewable energy policies and renewable energy 

resources. That is, the current body of literature is primarily focused on the development 

of wind resources and RPSs; this work reviews a broader array of clean energy resources 

and policies, as well as certain macroeconomic factors (e.g., electricity price and 

population). Several broad conclusions can be drawn from this work: 

• Policy alone does not explain variability in state clean energy growth. When 

other variables (including population, electricity price, and number of years a 

policy is in place) were incorporated into the analysis, the results indicated 

better explanation of the variation between state clean energy developments. 
vi 

• It appears from the methodology used that the current set of policies is 

targeted more at influencing wind and solar development than development of 

biomass and geothermal renewable resources. This indicates that state 

policies, while broadly applicable across renewable energy resources, may not 

be usable by developers of those resources because the policies do not meet 

the resources’ needs. Furthermore, biomass and geothermal can require a 

substantially longer timeline for development than wind and solar projects 

(especially distributed generation projects), and it may take more time before 

the results of incentives are visible in increased generation capacity.

• Where significant relationships were found, mixes of policies explain growth 

best, indicating that an environment for investment in clean energy through 

implementation of a suite of policies may be more effective at driving clean 

energy development than those that choose a single or small number of 

mechanisms. 

• Policies are more connected with clean energy development the longer they 

are in place, indicating that policy longevity (and resulting market certainty 

for investors) may be an important aspect of effectiveness. 

This research provides another piece in understanding how policy interacts with market 

development of clean energy. Additional policy experience and research are necessary to 

develop further understanding of these relationships. As policies are in place for longer 

periods of time, their impacts on clean energy development will become clearer since it 

takes time to develop a clean energy project once the environment is established for its

development. Further research using more refined data inputs or alternative quantitative 

methods to better connect policy and clean energy development could help refine the 

understanding of clean energy development and the role of policy. 
vii 

Table of Contents

List of Figures...........................................................................................................................................viii

List of Tables.............................................................................................................................................viii

1 Introduction ........................................................................................................................................... 1

Clean Electricity .........................................................................................................................2

Evolution of the State of the States Report ................................................................................3

2 Methodology.......................................................................................................................................... 5

Energy Efficiency ......................................................................................................................6

Renewable Energy .....................................................................................................................7

3 Results .................................................................................................................................................11

Energy Efficiency ....................................................................................................................12

Biomass ....................................................................................................................................13

Geothermal ...............................................................................................................................14

Photovoltaics ............................................................................................................................15

Wind .........................................................................................................................................17

4 Discussion...........................................................................................................................................18

References .................................................................................................................................................20
viii

List of Figures

Figure 1. Continuum of clean energy market transformation ............................................. 2

Figure 2. Sample of possible policy levers for developing clean energy ........................... 3

List of Tables

Table ES-1. Percent of Variation Between States Explained by Models (Adjusted R2) ... iv

Table 1. Definitions of Terms Used in the Statistical Analysis .......................................... 5

Table 2. State Policy Options and Typical Application to Renewable Resources ............. 8

Table 3. Percent of Variation Between States Explained by Models (Adjusted R2

) ........ 12

Table 4. Impact of Policy on Residential Efficiency: Residential Consumption/Capita 

(2008 Incentives) .............................................................................................................. 12

Table 5. Impact of Policy on Commercial Efficiency: Commercial Consumption/GSP 

(2008 Incentives) .............................................................................................................. 13

Table 6. Impact of Policy on Biomass Power Development (2008 Incentives) ............... 14

Table 7. Impact of Policy on Biomass Power Development (2007 Incentives) ............... 14

Table 8. Impact of Policy on Geothermal Power Development (2008 Incentives) .......... 15

Table 9. Impact of Policy on Geothermal Power Development (2007 Incentives) .......... 15

Table 10. Impact of Policy on PV Development (2008 Incentives) ................................. 16

Table 11. Impact of Policy on PV Development (2007 Incentives) ................................. 16

Table 12. Impact of Policy on Wind Power Development (2008 Incentives) .................. 17

Table 13. Impact of Policy on Wind Power Development (2007 Incentives) .................. 17
1 

1 Introduction

Significant efforts have been made to document success stories and lessons learned from 

past and recent policy implementation. Case studies reviewing the anecdotal evidence of 

policy effectiveness are common; they provide important insights and inform the growing 

field of literature on policy design practices (e.g., DOE 2009; Hurlbut 2008; Lantz and 

Doris 2009; LBNL 2009; Wiser et al. 2007; Wiser et al. 2002). 

The general understandings resulting from these case studies and other experience 

regarding effective policy actions have been compiled into best practices and step-by-step 

guides to assist policymakers in their efforts to develop policies and programs tailored to 

their state clean energy goals. The Environmental Protection Agency (EPA) published a 

state and local guide to action that outlines a strategy for developing energy efficiency 

and renewable energy through planning and policy implementation and provides lessons 

learned for 16 commonly used policies (EPA 2008). The Lawrence Berkeley Laboratory 

has an extensive list of downloadable case studies on energy efficiency and renewable 

energy project and policy implementation (LBNL 2009). The U.S. Department of Energy 

(DOE) also provides case studies and examples of energy efficiency and renewable 

energy projects (DOE 2009). The DOE Solar Market Transformation program, through 

the Solar America Cities project, has compiled design best practices at the state and local 

level and developed a local policymakers’ guidebook (SAC 2009). 

Less common, but growing in number, are quantitative evaluations of policy 

effectiveness. To date, most of the research has focused on wind energy development. 

Menz and Vachon (2005) looked at the connection between wind resource development 

and various state policies, finding supporting evidence that some mix of mandatory rules 

and regulations [i.e., renewable portfolio standards (RPS), fuel generation disclosure, and 

mandatory green power purchasing] is associated with increased wind energy 

development. Bohn and Lant (2009) found that states with standardized siting and 

permitting procedures have a strong association with increased wind resource 

development. This work also looked at factors beyond policy and resource availability 

that drive the development of wind, finding that population distribution, electricity 

demand, and access to transmission are determinates in resource development. More 

generally relating RPS to renewable energy generation, Carley (2009) found that there is 

a relationship between the number of years an RPS is in place and higher generation from 

renewable sources but not the existence of the policy alone. 

In this context, the aim of this research is to augment and build on traditional case studies

and narrower quantitative analyses to develop a quantitative understanding of policy 

impacts using statistical and empirical methods, as well as to open the door for more 

thorough analyses of policy options, inform future policy development, and ultimately 

optimize the market share of renewable energy resources. Ideally, the outcomes will be 

useful for policymakers to elect policies that will work within their context to meet the 

goal of increased clean energy development. The remainder of this introduction describes 

the concepts behind our definitions of clean energy as well as outlines how this 

streamlined version of the State of the States (SOS) differs from earlier versions. 

Following that, Chapter 2 presents the methodology used for evaluating policy 
2 

effectiveness, Chapter 3 summarizes the results, and Chapter 4 presents a discussion of 

the findings as well as next steps. 

Clean Electricity 

Clean electricity is defined by the entire spectrum of non-extractive technologies for 

meeting the nation’s electricity needs. This report is limited to the discussion of policies 

addressing clean electricity and does not include a discussion of clean fuels for the 

transportation sector. Clean electricity and fuels together represent the range of clean 

energy options. “Clean energy” comprises the entire spectrum of non-extractive 

technologies for meeting the nation’s energy needs. The concept exists on a continuum 

(Figure 1), from conservation of energy as a behavioral change to energy efficiency 

measures, which both minimize the amount of energy used to meet the need. Finally, 

renewable energy technologies meet the remaining need for energy. 

Figure 1. Continuum of clean energy market transformation

Capturing all of these opportunities for clean energy adoption requires multiple policy 

levers over time. Some of these levers are represented in Figure 2. 
3 

Figure 2. Sample of possible policy levers for developing clean energy

State policymakers have many policy options aiming at the development of clean energy 

resources, including various rules, regulations, and incentives. 

For full descriptions of the policies currently used by states, please see the DSIRE 

glossary, available at: http://www.dsireusa.org/glossary/. 

Evolution of the State of the States Report

There are three primary differences between the 2010 and previous (Brown and Busche

2008; Doris et al. 2009) SOS reports:

• Targeting of statistical analyses to specific policies and resources

• Widening of data used to evaluate policy effectiveness and more graphical 

display of that data in other CEPA documents 

• Transitioning of extensive context factor discussion to other documents in the 

CEPA series. 

This work aims to look more critically at the development of renewable energy resources 

and the role of policy. Earlier versions of the report (Brown and Busche 2008; Doris et al. 

2009) attempted an en masse approach, lumping together all state energy policies and 

clean electricity generation. Drawing results from these analyses was challenging because 

of the number of policies and other factors contributing to the development of clean 

energy, the limited experience with clean energy policies over time, and variability 

among state policies even within the same general policy mechanism. 

This year, while still looking toward the big picture of assisting policymakers in decision

making across policy and resource options, the methodology is more targeted towards

resources and specific policies that emerged in previous years’ efforts and the literature 

as likely effective policies. While this may lead to overlooking some lesser used policies, 

Energy Efficiency Renewable Energy 

Behavior 

Change

Education

Transmission 

Policies

Rebates

Feed-In-Tariffs

RPS

Energy 

Efficiency 

Resource 

Standards

Weatherization

Grants

Interconnection Access

Net 

Metering

Tax 

Incentives
4 

it has the benefit of more clearly identifying the connection between specific policies and 

resource development. In addition to targeting the analyses to specific technologies, this 

methodology integrates multiple factors external to policy, including population, 

restructuring, state gross domestic product, competing energy costs, and length of time 

policy has been in place. This methodology is designed to give policymakers a clearer 

view of the role of policy within the existing context. 

For standardization purposes in earlier reports, data used were limited to U.S. Energy 

Information Administration (EIA) renewable energy generation data. This year, a broader 

dataset was used to more accurately reflect the existence of distributed generation 

resources, including capacity and generation, which EIA does not track as closely due to 

methodological challenges. These datasets include Larry Sherwood’s “U.S. Solar Market 

Trends” (2009), Solar Energy Industries Association’s (SEIA’s) “US Solar Industry Year 

in Review 2009” (2009), Geothermal Energy Association’s (GEA’s) “U.S. Geothermal 

Power Production and Development Update: April 2010” (2010), and American Wind 

Energy Association’s (AWEA’s) “Year End 2009 Market Report” (2010a). Because the 

methodology has been targeted to specific policies and technologies, the different 

collection methodologies from the various datasets is less of a concern than it has been in 

previous years. Previous reports included extensive tables and graphics of available data 

from EIA. In addition to using this diverse dataset this year, those data are presented in a 

highly visual way in the CEPA State Energy Data Book, available at: 

http://www.nrel.gov/cepa. 
5 

2 Methodology

Policies fall into several general categories (e.g., incentives and mandates) but are 

uniquely applied to different situations and implemented in different ways, creating 

challenges in generalizing their success at driving the clean energy market. In addition, 

different policies are applied to different types of clean energy technologies. The 

methodology for these analyses splits clean energy resources into energy efficiency and 

renewable energy technologies, primarily because the metric for success differs between 

the two. Energy efficiency improvements in the commercial sector are measured by 

commercial energy use (EIA 2010b) normalized for economic change by gross state 

product in the same year. Energy efficiency improvements in the residential sector are 

measured by residential energy use normalized for population in the same year. 

Renewable energy improvements are measured by increased energy supply and are 

therefore measured by increased capacity and generation of electricity from renewable 

resources. It is crucial to note that this methodology tests only in-state development and 

some policies are designed to target regional growth. The regional clean energy impacts 

of these policies will not be directly captured by this methodology. 

In this methodology we focus on individual policies as they are currently being 

implemented and, to a limited extent, combinations of policies. The methodology will not 

result in recommendations for altering policy implementation strategies but instead 

reflects clean energy development impacts of the current mix of state policies. 

Table 1 summarizes the definitions of statistical terms used in this analysis. 

Table 1. Definitions of Terms Used in the Statistical Analysis

 A measure of the amount of variation about the mean 

explained by the model, adjusted for the number of 

independent variables

 Increases only when the additional variable in question 

improves the model more than what would be expected 

by chance

 Linear regressions often require a constant (a

y-intercept term) to be present

 The change in the dependent variable per a one-unit 

change in the value of the independent variable

 The probability of obtaining a test statistic at least as 

extreme as the one observed

P-Value  At the 5% significance level, a p-value of less than 

0.05 means that the observed result cannot be ascribed 

to chance alone

Error Term  An estimate of the unobservable statistical error

Beta

Constant

Adjusted R2
6 

Energy Efficiency1

In this analysis, the dependent variable for commercial energy efficiency is commercial 

energy use normalized for economic changes by the gross state product in the same year, 

and the dependent variable for the residential sector is residential energy use normalized 

for population in the same year. For both commercial and residential energy use, it was 

assumed that state population demographics had not materially shifted from 2008 to 

2009. The independent variables in this analysis are the existence of: 

1. High efficiency (equal to or better than ASHRAE 90.1 2004) statewide 

commercial building code (OCEAN 2010) 

2. High efficiency (equal to or better than IECC 2006) statewide residential building 

code (OCEAN 2010) 

3. Average commercial electricity price in 2009 (EIA 2010d) 

4. Average residential electricity price in 2009 (EIA 2010d) 

5. Energy Efficiency Resource Standard (EERS) (ACEEE 2010) 

6. State personal tax incentives (DSIRE 2010b) 

7. State rebates (DSIRE 2010b) 

8. State loans (DSIRE 2010b) 

EERS data, state personal tax incentives, state rebates, and state loans appear in both the 

commercial and residential energy efficiency models, while building codes and electricity 

prices correspond to the dependent variable being tested. Only commercial and 

residential energy consumption were used as a proxy for overall building efficiency; the 

industrial sector was omitted because it consumes energy in a different way, through 

industrial processes as well as building efficiency.

The general form of the two models estimated can be written as:

Commercial Energy Use/Gross State Product (GSP) = β0 + β1High Commercial 

Building Code + β2 Commercial Electricity Price + β3EERS + β4 Personal Tax 

Incentives + β5Rebates + β6 Loans + ε

Residential Energy Use/Capita = β0 + β1High Residential Building Code + β2

Residential Electricity Price + β3EERS + β4 Personal Tax Incentives + β5Rebates + β6

Loans + ε

Every independent variable in this energy efficiency analysis was expected to have a 

negative impact on energy use (lower commercial energy use/GSP and lower residential 

energy use/capita), and therefore, coefficients β1 through β6 were expected to be 

significant at the 5% level and negative. Regardless of whether a state had all four 

incentives/standards or not, it was kept in the dataset (since every state consumes energy 

in the commercial and residential sectors), resulting in 51 observations in each model. 

 

1 For a broader look at the impact of policy and different factors on the development of energy efficiency 

resources, see ACEEE’s Scorecard for Energy Efficiency (http://www.aceee.org). 
7 

The coefficients were estimated using ordinary least-squares (OLS) methodology. The 

variables were analyzed for multicollinearity through a bivariate correlation table. 

Intercorrelations were found not to be a concern, and a test of the variance inflation 

factors (VIF) was conducted as well, which is commonly used as an indicator of the 

severity of multicollinearity (Neter et al. 1996). A VIF value in excess of 10 is an 

indication that multicollinearity is influencing the least squares estimates, and the average 

electricity price (both residential and commercial) is the only variable that exceeded this 

threshold, albeit only slightly. Given the lack of multicollinearity based on this test, the 

analysis proceeded with the original variables, taking into consideration that the 

electricity price variable may be inflating the variance of the estimated coefficients. 

Renewable Energy 

Two dependent variables for each renewable technology [wind, photovoltaics (PV), 

geothermal, and biomass] were used: end-of-year 2009 cumulative capacity (MW) and 

2009 annual generation (MWh). For PV, only capacity data was utilized because the 

state-by-state generation data that EIA classifies as “solar” is over 90% concentrated 

solar power, which is not included in this analysis due to its limited geographic 

application. Capacity data for wind is from the AWEA’s “Year End 2009 Market Report”

(2010a), PV capacity data is from Larry Sherwood’s “U.S. Solar Market Trends 2009” 

(2010), geothermal capacity data is from GEA’s “U.S. Geothermal Power Production and 

Development Update” (2010), and biomass capacity data is from EIA’s “Electric Power 

Monthly” (2010). Generation data for biomass, geothermal, and wind are gathered from

Form EIA-923 (EIA 2010a). Discrepancies between capacity and generation may stem 

from a number of factors, including but not limited to: varying data reporting regulations 

and data collection methods, ranges of efficiencies, plant outages, and power purchase 

agreements. The independent variables vary based on the typical technology to which the 

policy applies. Table 2 summarizes the policies that serve as the independent variable in 

comparison to the dependent renewable resource variable. In addition to tests comparing 

the currently available policy information and capacity and generation data, time lag 

analyses were completed for several policies (the independent variable being 2007 policy 

existence) to measure if the length of time a policy has been in place is correlated with 

increased renewable energy generation. 
8 

Table 2. State Policy Options and Typical Application to Renewable Resources

In previous editions of this study, correlations and t-tests were used to identify 

relationships between policy and renewable energy resource build-out. While valid in 

their own ways, these methods are highly susceptible to outliers. A test that ranks data 

would be more robust to outliers, giving less weight to outlying data. Because the data 

are ordinal (megawatts of capacity, gigawatt-hours of generation) from two samples 

(states with a policy, states without a policy), non-parametric testing is used in this 

analysis, meaning that the samples are not required to come from any specific distribution 

(Navidi 2010). The Wilcoxon Rank-Sum Test (also called the Mann-Whitney test) was 

chosen as it tests the chance of obtaining a higher observation in one population versus 

the other. For this analysis, this translates to testing whether a state with a certain policy 

is likely to have higher renewable energy capacity/generation than a state without the 

policy. 

Initial attempts at utilizing the Wilcoxon Rank-Sum Test failed to identify a subset of 

variables explaining the connections between policy and renewable energy development. 

This is likely the result of too many extraneous or confounding variables included in the 

set of variables. Variables outside of the ones being tested (policy versus renewable 

energy resource) may lead to a spurious relationship between the test variables. This is 

called a “Type 1 Error,” or an erroneous conclusion that the dependent variable (capacity 

or generation) is affected by the independent variable (policy implementation); in other 

words, a “false positive” (Navidi 2010). Therefore, the outcomes of these tests are not 

presented here but rather are considered with a collection of other analyses and built on in 

future years as more data becomes available. 

PV Wind Geothermal Biomass

Access Laws x

Bonds

Construction and Design 

Contractor Licensing x x x x

Corporate Tax Incentives x x x

Equipment Certification x

Generation Disclosure

Grants x

Industry Support x x x

Interconnection x x

Line Extension Analysis x x

Loans x x

Net Metering x x

Personal Tax Incentives x x

Production Incentives x x x x

Property Tax Incentives x x

Rebates x

RPS x x

RPS with Solar Set Aside x

Sales Tax Incentives x x

Voluntary and Mandatory 

Green Power x x
9 

Regression models for each of the renewable energy technologies were structured in the 

same way as the energy efficiency models shown earlier (definitions for variables can be 

found at http://www.dsireusa.org): 

Wind Capacity/Wind Generation = β0 + β1Contractor Licensing + β2 Corporate Tax 

Incentives + β3 Industry Support + β4 Interconnection + β5Net Metering + β6

Production Incentives + β7 RPS + β8Sales Tax Incentives + β9 Green Power + β10

Average Electricity Price + β11 Freeing The Grid (FTG) Interconnection Grade2 β12 + 

Number of Policies + β13Population + β14Restructuring + β15RPS Effective Years + ε

PV Capacity = β0 + β1Access Laws + β2 Contractor Licensing + β3Corporate Tax 

Incentives + β4 Equipment Certification + β5 Grants + β6 Interconnection + β7 Line 

Extension Analysis + β8 Net Metering + β9 Personal Tax Incentives + β10Production Tax 

Incentives + β11Property Tax Incentives + β12Rebates + β13RPS + β14RPS with Solar 

Disclosures + β15 Sales Tax Incentives + β16Green Power + β17 Average Electricity Price

+ β18FTG Interconnection Grade

3 + β19Number of Policies + β20 Population + β21

Restructuring + β22RPS Effective Years + ε

Geothermal Capacity/Geothermal Generation = β0 + β1Contractor Licensing + β2

Industry Support + β3Line Extension Analysis + β4 Loans + β5 Personal Tax Incentives

+ β6 Production Tax Incentives + β7 Average Electricity Price + β8 Number of Policies + 

β9 Population + β10Restructuring + β11RPS Effective Years + ε

Biomass Capacity/Biomass Generation = β0 + β1Contractor Licensing + β2 Corporate 

Tax Incentives + β3 Industry Support + β4 Loans + β5 Production Tax Incentives + β6

Property Tax Incentives + β7 Average Electricity Price + β8 Number of Policies + β9

Population + β10Restructuring + β11RPS Effective Years + ε

The coefficients were estimated using OLS methodology. A bivariate correlation table 

was constructed for each model, and the resulting intercorrelations were found not to be a 

concern. A VIF test was conducted on each model. Forbiomass, geothermal, and wind 

(both capacity and generation), average electricity price and the number of policies were 

of concern in terms of intercorrelations. Both variables had a VIF in excess of 10 and 

were therefore dropped from the model. For PV capacity, the average electricity price, 

net metering, and the number of policies all exhibited a VIF in excess of 10 and were 

removed from the model. 

 

2 Refers to the Network for New Energy Choices report, “Freeing the Grid: Best and Worst Practices in 

State Net Metering Policies and Interconnection Procedures.” (NNEC 2009).
10 

As each model was run, variables that were not significant to the model and that degraded 

the R2 were removed until the model was left with the most efficient set of variables to 

explain renewable energy build-out for each technology. If a variable was not significant 

at the 0.05 level but improved the R2, it remained in the model. 
11 

3 Results 

The analysis produced several unexpected results, and while they may appear to indicate 

that certain policies are not associated with clean energy development, further research 

with refined datasets is necessary. It should also be noted that there are methodological 

challenges in this type of quantitative analysis. Specifically in this exercise, the 

limitations include:

• The relatively small sample size (50 states plus the District of Columbia) 

limits the observations to a maximum of 51 for any regression. 

• The time series data for policies dates back to only 2007. 

• The policy terms can vary from state to state, and the differences are difficult 

to reflect in this type of analysis. Additionally, not all policies are designed to 

spur in-state development but rather target regional growth.

• It is possible that omitted variable bias would cause the independent variable 

(capacity/generation) to be correlated with the error term, therefore distorting 

the coefficients estimated in the analysis and producing inconsistent estimates. 

This methodology attempts to capture omitted variable bias by including more 

variables then necessary and reducing down to an optimal regression equation. 

However, macroeconomic variables (including changes in demographics) 

could have impacted energy use and capacity development and 

unintentionally been omitted from the analysis.

• The dependent variable in this analysis is absolute renewable capacity or 

generation, not the percent of total capacity or generation that is a renewable 

resource. RPS policies typically target growth of the percent of total 

generation that is composed of renewable energy and may be a better metric 

to test in the future.

• Biomass and geothermal projects typically require a construction schedule in 

excess of two years, and therefore the effects of a policy would have 

corresponding longer lag. 

As a result of these limitations, results from this analysis should be taken in hand with 

other quantitative and qualitative work in the area of clean energy policy and its relation 

to clean energy technology development. 

Table 3 shows that in general, this methodology produces an adjusted R2 of 46% for 

residential sector state energy use and 67% for commercial sector state energy use. For 

renewable energy resources, policy and the other macroeconomic factors tested produce 

an adjusted R2 of 43%–63% for capacity and generation, depending on the measure and 

the renewable technology being tested. 
12 

Table 3. Percent of Variation Between States Explained by Models (Adjusted R2) 

The remainder of the results section is structured to show detailed results by resource 

evaluated. For the energy efficiency evaluations, only a one-year time lag of the policies 

(2008) produced meaningful results, so only those are presented. For each renewable 

technology, the first table presents results using a one-year time lag with 2008 incentive 

data, and the second table presents the results of the two-year time lag analysis (e.g., 

incentives available in 2007 compared to current capacity or generation). 

Energy Efficiency

Tables 4 and 5 are the results from the energy efficiency analysis. In this analysis only 

the one-year time lag data led to meaningful results, so only those are presented here. 

Table 4 shows that in the residential analysis, none of the incentives (e.g., personal tax 

incentives, rebates, and loans) were significant in explaining energy use per capita. There 

was a relationship with high efficiency residential building codes with a p-value of 0.072, 

making it significant only at the 0.10 level. A state having an EERS is also more likely to 

exhibit lower energy use per capita. Residential electricity price had a significant, 

negative relationship with per capita energy use. Of all the rules, incentives, and 

macroeconomic factors tested, this methodology can explain only 46.1% of the variation 

in residential state energy use. 

Table 4. Impact of Policy on Residential Efficiency: Residential Consumption/Capita (2008 

Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

In the commercial sector, the model is able to explain 67.5% of the variation between 

states consumption/GSP. There is a relationship between high efficiency commercial 

building codes, EERS, higher commercial electricity price, and reduced commercial 

2009 Capacity/Generation vs 2008 Policies 2009 Capacity/Generation vs 2007 Policies

(One Year Lag) (Two Year Lag)

Technology Capacity (MW) Generation (MWh) Capacity (MW) Generation (MWh)

Biomass 43.7% 46.7% 49.8% 52.6%

Geothermal 47.2% 47.4% 49.8% 50.5%

Photovoltaic 58.2% 63.3%

Wind 47.5% 45.7% 45.5% 43.6%

Impact of Policy on Efficiency

Commercial 67.5%

Residential 46.1%

Variable Beta P-Value

Constant 101.8 0.000

High Efficiency Residential Building Code -5.1 0.072 *

Average Residential Electricity Price (2009) -1.8 0.000

Energy Efficiency Resource Standard (EERS) -5.6 0.047

Adjusted R2 0.461

Number of Observations 51

*Not significant at the 5% level.
13 

consumption. However, the existence of personal tax incentives is associated with

increased commercial energy use in the model. It is not clear why personal tax incentives 

would affect commercial energy use, but it is possible that the existence of those 

incentives indicates a prioritization of residential energy use reduction. Further

refinements of the datasets and additional data could increase the understanding of why 

these variables are significantly correlated with commercial energy efficiency. 

Table 5. Impact of Policy on Commercial Efficiency: Commercial Consumption/GSP 

(2008 Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

Biomass

Very little biomass capacity has come online in recent years, so finding relationships 

between current policies and increased development was a challenge. In addition, state 

policies do not target biomass development in the same way that wind and solar are 

targeted. In general, biomass development is impacted by factors outside of the policy 

arena, including feedstock availability and the ability to meet emissions criteria. That 

being said, this initial analysis into the policies that may be related to biomass 

development is a first step and indicates that further analysis and refinement of the data is 

necessary to better understand the relationship between policy and biomass resource 

development. 

Only contractor licensing and population are associated with higher capacity and 

generation from biomass resources, though only population was significant at the 0.05% 

level. A time lag of two years for loan programs was also associated with higher biomass 

levels, though not at the 0.05 significance level. Corporate and property tax incentives 

appear to be a deterrent to biomass development, but those results are not statistically 

significant and more targeted datasets in subsequent research may illuminate this 

relationship more clearly. 

Variable Beta P-Value

Constant 2,029.5 0.000

High Efficiency Commerical Building Code -104.7 0.042

2008 Personal Tax Incentives 154.4 0.010

Average Commercial Electricity Price (2009) -48.8 0.000

Energy Efficiency Resource Standard (EERS) -193.8 0.000

Adjusted R2 0.675

Number of Observations 51
14 

Table 6. Impact of Policy on Biomass Power Development (2008 Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

Table 7. Impact of Policy on Biomass Power Development (2007 Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

Geothermal

Similar to biomass, very little geothermal capacity has come online in recent years, and 

finding relationships between the current portfolio of policies and an increase in 

geothermal development is difficult. Much of the geothermal capacity in the United 

States was brought online decades ago, spurred by incentives that have since expired. 

Like biomass, policies specifically targeting geothermal do not currently exist in the same 

way that there are policies targeting wind and solar production. While this initial effort at 

identifying connections between policy and geothermal resource development may 

illuminate the relationships, further research and data refinement to better connect 

policies that are truly targeting geothermal development are necessary to better 

understand the relationship between geothermal development and policy implementation. 

Industry support, both in 2008 and 2007, is negatively associated with electricity 

development from geothermal sources. Contractor licensing, loans, personal tax, and 

production incentives are all positively associated. The number of years an RPS is 

Capacity (2009) Generation (2009)

Variable Beta P-Value Beta P-Value

Constant 144.6 0.023 671.5 0.014

2008 Contractor Licensing (Yes/No) 88.6 0.297 *

2008 Corporate Tax (Yes/No) -77.4 0.223 * -381.7 0.159 *

2008 Property Tax (Yes/No) -74.0 0.281 * -372.0 0.204 *

2009 Population 29.1 0.000 136.5 0.000

Adjusted R2 0.437 0.467

Number of Observations 50 (1) 50 (1)

* Not significant at the 5% level.

(1) District of Columbia is removed: zero capacity and generation and none of the policies being tested.

Capacity (2009) Generation (2009)

Variable Beta P-Value Beta P-Value

Constant 123.0 0.087 * 475.5 0.120 *

2007 Contractor Licensing (Yes/No) 116.8 0.157 * 401.0 0.252 *

2007 Corporate Tax (Yes/No) -103.7 0.094 * -519.9 0.050

2007 Loans (Yes/No) 85.6 0.177 * 457.4 0.092 *

2007 Property Tax (Yes/No) -119.8 0.062 * -513.5 0.061 *

2009 Population 28.6 0.000 129.4 0.000

Adjusted R2 0.498 0.526

Number of Observations 49 (1) 49 (1)

* Not significant at the 5% level.

(1) District of Columbia and Wyoming are removed: zero capacity and generation and none of the policies being tested.
15 

effective is not a significant variable in any of the biomass or geothermal regression 

models, but it does appear significant in every wind and solar model. An interpretation 

and implications of these results are discussed in the following chapter. 

Table 8. Impact of Policy on Geothermal Power Development (2008 Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

Table 9. Impact of Policy on Geothermal Power Development (2007 Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

Photovoltaics

Compared to biomass and geothermal, policies appear to play a stronger role in the PV

models, potentially because more policies are tailored toward the development of PV

resources. Policies currently available, including corporate tax incentives, equipment 

certification, grants, interconnection, sales tax, and green power all appear to have a 

negative relationship with PV capacity. However, as stated above, the number of years an 

RPS is positively associated with PV resource development, implying that it is not the 

existence of a policy but rather the length of time the policy is in place that drives PV

development. Contractor licensing, production incentives, rebates, access laws, and 

personal tax incentives are all positively associated with PV capacity.

Capacity (2009) Generation (2009)

Variable Beta P-Value Beta P-Value

Constant -130.4 0.042 -1,016.8 0.034

2008 Contractor Licensing (Yes/No) 232.4 0.034 1,172.4 0.035

2008 Industry Support (Yes/No) -185.1 0.039 -851.2 0.062 *

2008 Loans (Yes/No) 450.3 0.322 *

2009 Population 33.6 0.000 172.3 0.000

Adjusted R2 0.472 0.474

Number of Observations 46 (1) 46 (1)

* Not significant at the 5% level.

(1) Delaware, Indiana, South Dakota, West Virginia, and District of Columbia are removed: zero capacity and generation and none of the 

policies being tested.

Capacity (2009) Generation (2009)

Variable Beta P-Value Beta P-Value

Constant -225.3 0.004 -1,167.4 0.003

2007 Contractor Licensing (Yes/No) 160.5 0.163 * 809.4 0.161 *

2007 Industry Support (Yes/No) -220.5 0.032 -1,105.6 0.032

2007 Personal Tax (Yes/No) 178.2 0.043 926.1 0.037

2007 Production Incentives (Yes/No) 218.7 0.081 * 1,019.9 0.104 *

2009 Population 31.4 0.000 162.2 0.000

Adjusted R2 0.498 0.505

Number of Observations 44 (1) 44 (1)

* Not significant at the 5% level.

(1) Kentucky, Illinois, Georgia, Indiana, South Dakota, West Virginia, and the District of Columbia are removed: zero capacity 

and generation and none of the policies being tested.
16 

Table 10. Impact of Policy on PV Development (2008 Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

Table 11. Impact of Policy on PV Development (2007 Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

PV Capacity (2009)

Variable Beta P-Value

2008 Contractor Licensing (Yes/No) 65.0 0.027

2008 Equipment Certification (Yes/No) -68.2 0.226 *

2008 Grants (Yes/No) -53.2 0.011

2008 Interconnection (Yes/No) -41.1 0.081 *

2008 Production Incentives (Yes/No) 86.3 0.016

2008 Rebates (Yes/No) 35.2 0.146 *

2008 Sales Tax (Yes/No) -37.9 0.060 *

2009 Population 8.8 0.000

RPS Effective (Number of Years) 2.7 0.226 *

Adjusted R2 0.582

Number of Observations 51

* Not significant at the 5% level.

PV capacity includes grid-tied only.

Solar generation from EIA not used because it is over 90%

concentrating solar power (CSP).

PV Capacity (2009)

Variable Beta P-Value

Constant -28.2 0.265 *

2007 Access Laws (Yes/No) 30.0 0.181 *

2007 Contractor Licensing (Yes/No) 47.9 0.081 *

2007 Corporate Tax Incentives (Yes/No) -71.6 0.005

2007 Grants (Yes/No) -41.1 0.054 *

2007 Green Power (Yes/No) -33.0 0.189 *

2007 Interconnection (Yes/No) -24.3 0.313 *

2007 Personal Tax Incentives (Yes/No) 72.1 0.003

2007 Production Incentives (Yes/No) 43.6 0.155 *

2007 Rebates (Yes/No) 27.4 0.225 *

2007 Sales Tax (Yes/No) -37.5 0.081 *

2009 Population 9.8 0.000

RPS Effective (Number of Years) 3.3 0.163 *

Adjusted R2 0.633

Number of Observations 51

* Not significant at the 5% level.

PV capacity includes grid-tied only.

Solar generation from EIA not used because it is over 90%

concentrating solar power (CSP).
17 

Wind

Wind regression results (Tables 11 and 12) are similar to the existing literature in terms

of the policies that are shown to influence development. Contractor licensing, state 

production incentives, and having a grade of “C” or better from the “Freeing the Grid...” 

report (NNEC 2009) are negatively associated with wind resource build-out. Industry 

support and the number of years an RPS was effective are both positively associated with 

wind power development. 

Table 12. Impact of Policy on Wind Power Development (2008 Incentives)

Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

 Table 13. Impact of Policy on Wind Power Development (2007 Incentives)

 Sources: BEA 2010; DSIRE 2010c; EIA 2010d; Ocean 2010

Capacity (2009) Generation (2009)

Variable Beta P-Value Beta P-Value

FTG Interconnection Good Grade -944.6 0.004 -1,970.2 0.005

2008 Contractor Licensing (Yes/No) -895.5 0.039 -1,845.7 0.042

2008 Industry Support (Yes/No) 538.6 0.118 * 1,126.5 0.119 *

2008 Production Incentives (Yes/No) -627.2 0.218 * -1,113.1 0.296 *

2009 Population 141.3 0.000 282.1 0.000

RPS Effective (Number of Years) 103.3 0.002 212.1 0.003

Adjusted R2 0.475 0.457

Number of Observations 49 (1) 49 (1)

* Not significant at the 5% level.

(1) Alabama and Mississippi have been removed: zero capacity and 

generation and none of the policies being tested.

Capacity (2009) Generation (2009)

Variable Beta P-Value Beta P-Value

FTG Interconnection Good Grade -793.1 0.014 -1,661.3 0.015

2007 Contractor Licensing (Yes/No) -769.9 0.075 * -1,605.1 0.077 *

2007 Production Incentives (Yes/No) -621.5 0.190 * -1,121.8 0.259 *

2009 Population 144.1 0.000 289.5 0.000

RPS Effective (Number of Years) 119.2 0.001 244.4 0.001

Adjusted R2 0.455 0.436

Number of Observations 49 (1) 49 (1)

* Not significant at the 5% level.

(1) Alabama and Mississippi have been removed: zero capacity and 

generation and none of the policies being tested.
18 

4 Discussion 

Generally, results from this analysis align with the existing literature (Bohn and Lant 

2009; Carly 2009; Menz and Vachon 2005), especially in the conclusion that policy, in 

concert with other macroeconomic factors, is connected with renewable energy 

development. The findings of this work expand the current body of research to include a 

more detailed evaluation of the connections between energy use and energy efficiency 

policies as well as a broader review of renewable energy policies and renewable energy 

technologies. That is, the current body of literature is primarily focused on the 

development of wind resources and RPSs, and this work reviews a broader array of clean 

energy resources and policies, as well as certain macroeconomic factors (e.g., electricity 

price and population). Several broad conclusions can be drawn from this analysis: 

• Policy alone does not explain variability in state clean energy growth. When 

other variables (including population, electricity price, and number of years a 

policy is in place) were incorporated into the models, the results better 

explained the variation among state clean energy development. 

• It appears from this methodology that the current set of policies is more 

targeted at influencing wind and solar development than developing biomass 

and geothermal resources. This indicates that state policies, while broadly 

applicable across renewable energy technologies, may not be usable by 

developers of those technologies because the policies do not meet the 

technologies’ needs. For example, a rebate program with a capacity limit of 

5 kW may be available to geothermal project developers, but because 

electricity generation from geothermal resources is commonly on a larger, 

multi-megawatt scale, the rebate program may not provide enough of an 

incentive to drive development. Policies, even if applicable to a wide range of 

technologies, often are designed to promote one or two specific resources—in 

this case, wind and solar are targeted far more frequently than geothermal and 

biomass. Programs tailored to the specific needs of the technology may be 

more beneficial to renewable energy development. 

• Where significant relationships were found, mixes of policies explained

growth best. For example, a wide variety of policies contributes to PV

development across states. These policies, along with non-policy factors, 

explain variation among states in wind growth. This may indicate that the 

specific policies in place are less important than the grouping of policies. In 

other words, state policymakers that create an environment for investment in 

clean energy by implementing a suite of policies may be more effective at 

driving clean energy development than those that choose a single or small 

number of mechanisms. 

• Policies are more connected with clean energy development the longer they 

are in place. The methodology presented here compared current development 

with current policies as well as those policies that were put in place in the 

previous two years. Findings indicate that connections are more pronounced 

when the time lag is incorporated. This indicates that policy longevity is an 
19 

important aspect of effectiveness. Furthermore, due to more intensive 

construction requirements for geothermal and biomass projects, a longer study 

period may be needed to quantify policy impacts on the development of these 

technologies. 

While the methodology applied in this report produced valuable results, as previously

discussed, an evolution of this methodology in subsequent reports may better handle the 

unique nature of this type of data. The current method of choice, OLS regression, 

assumes normality of data, and performing OLS on the ranks of data may result in a 

better fitting model. Not only would it address the issue of high-leverage values and data 

normality, but this adjustment would also address the clear outliers in both total 

renewable energy installed capacity and generation, California and Texas. 

This research provides another piece in the understanding of how policy interacts with 

market development of clean energy. Further experience with policies and research are 

necessary to better understand these relationships. As policies are in place for longer 

periods of time, their impacts on clean energy development will become clearer, as it 

takes time to develop clean energy projects once the environment is established for their 

development. 
20 

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14. ABSTRACT (Maximum 200 Words)

This report builds on the emerging body of literature seeking to identify quantitative connections between clean 

energy policy and renewable energy. The methods presented test the relationships between a broad set of policies 

and clean energy resources (energy efficiency, biomass, geothermal, solar, and wind). Energy efficiency findings are 

an initial foray into this type of analysis and indicate significant connections between reduced energy use and 

buildings codes, energy efficiency resource standards (in some cases), and electricity price. Renewable energy 

findings specify that there is most often a relationship between state policies and solar and wind development, 

indicating that while policies might apply to a wide variety of renewable resources, further tailoring of policy specifics 

to resource needs may lead to increased development of a wider variety of renewable energy resources. Further 

research is needed to refine the connections between clean energy development and policy, especially in the area of 

the impact of the length of time that a policy has been in place.

15. SUBJECT TERMS

clean energy; policy; renewable energy; energy efficiency; biomass; geothermal; photovoltaics; PV; wind; ordinary 

least-squares; OLS; United States

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