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Incentivizing Energy Efficiency: Explaining Local Commitment to Energy Efficiency

in Cities with Municipal-Owned Utilities

Hongtao Yi Askew School of Public Administration and Policy

Florida State University Phone:1-850-980-2315

Email: hy08d@my.fsu.edu

David S. T. Matkin Askew School of Public Administration and Policy

Florida State University dmatkin@fsu.edu

Richard C. Feiock

Askew School of Public Administration and Policy Florida State University

rfeiock@fsu.edu

Abstract: Municipal-owned utilities perform an important role in local government efforts to implement energy-efficiency strategies and practices. Utility ownership provides unique opportunities for local governments to involve citizens in “green” initiatives. This paper seeks to understand why some governments with municipal-owned utilities invest resources on energy-efficiency strategies while others do not. We investigate this question by examining the use of energy-efficiency initiatives by municipal-owned utilities in American local government. We argue that spending on energy-efficiency strategies is affected by (1) cost considerations, (2) city political and fiscal conditions, and (3) citizen pressures. With data collected for the 145 municipal-owned utilities in cities with populations over 25,000, we test these relationships with a Heckman Selection Model. We find that the decision to adopt an energy-efficiency program is associated with siting difficulties and available generation capacity. The level of energy-efficiency expenditures is associated with a jurisdiction’s form of government and the availability of local retail partners. The implications of these findings for theory and practice are discussed in conclusion.

Key Words: Energy Efficiency; Demand-side Management; Municipal-Owned Utilities

Currently under review in Energy Policy

Paper presented at the annual meeting of the American Political Science Association Annual Meeting, Seattle WA, September 1-4, 2011.

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Incentivizing Energy Efficiency

Introduction Energy efficiency and climate protection are among the most complex and

challenging policy areas in modern governance, and efforts to improve energy

efficiency and conservation take increasingly prominent positions on the policy

agendas of international, national, state and local governments. The popularity of

energy-efficiency programs comes, in part, from the various societal benefits that are

anticipated and promoted by policy advocates. For example, energy efficiency

strategies are thought to have the potential to (1) reduce the emission of greenhouse

gases and thereby mitigate global climate change, (2) reduce costs associated with

maintaining reliable energy supplies for peak periods of electrical power demand, (3)

reduce or eliminate the economic and political costs of siting new energy

infrastructure, and (4) induce energy-efficient technological innovations (Brennan,

2010). In addition, as a result of the recent economic downturn and the fiscal stress

that many U.S. cities have experienced, local government policy makers have

increasingly turned to energy efficiency as a mechanism to ease financial constraints

(Francis and Feiock, 2010).

Local governments, however, are not equally equipped to implement

energy-efficiency strategies. The presence of municipal-owned utilities may play a

critical role in the availability and desirability of various policy options. But utility

ownership may also create incentives to resist or delay the use of energy-efficiency

strategies. This is because owning a municipal utility can be a double-edged sword.

On the one hand, owning a utility can possibly slow down a city’s commitment to

climate protection, due to the high costs that are often associated with

climate-protection strategies (Krause, 2010). On the other hand, owning a utility can

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present an opportunity for local governments to involve citizens in efforts to pursue

“green” initiatives. For example, cities with municipal-owned utilities can directly

engage in efforts to inform citizens of how much they can save in their monthly bills

through improving their energy efficiency, inform citizens of their green-house gas

emissions, and sponsor strategies to reduce those emissions though various rebate

programs (Byrne et al., 2007). If city officials intend to pursue energy-efficiency

programs, owning a utility can provide distinct advantages. However, not all cities

are committed to climate protection and energy efficiency, and not all cities own

their own utilities.

Even among those cities with municipal-owned utilities, there is significant

variation in the use of energy-efficiency programs. Some governments with

municipal-owned utilities spend millions of dollars on energy-efficiency programs

while others don’t spend anything. This begs the question of why some cities with

municipal-owned utilities spend financial resources on energy efficiency while

others do not? We will answer this question by investigating the underlying factors

behind the use of energy-efficiency incentives.

Energy Efficiency at the Local Level

Energy-efficiency programs attempt to reduce the overall electricity

consumption by creating incentives for consumers to engage in actions to make

existing buildings and equipment more efficient (e.g., proper insulations, smart

climate controls) or by creating an incentive to substitute more efficient equipment

for less efficient equipment. For example, equipment replacement incentives often

include high-efficiency appliances, high-efficiency lighting systems, and

high-efficiency heating and air conditioning (HVAC) systems. Local governments

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and municipal-owned utilities promote energy efficiency by providing incentives, in

the forms of rebates and loans, for residents, commerce and industries to buy

energy-efficient equipment and engage in load management programs.

Several streams of literature inform this study. First, studies of utility-operated

energy-efficiency programs are found in the literature on demand-side management.

Demand-side management includes “the planning, implementation, and monitoring

of utility activities designed to encourage consumers to modify patterns of electricity

usage, including the timing and level of electricity demand” (EIA, 2011).

Utility-operated energy-efficiency programs are one type of demand-side

management program. Demand-side management can significantly improve energy

efficiency through information audits, labeling, rebate, loans and leasing,

performance contracting, load switching, direct-load controls, time-of-use pricing

and use of smart metering (Nadel and Geller, 1996; Strbac, 2008).

Studies of demand-side management programs are generally concerned with the

cost and effectiveness of those programs (Loughran and Kulick, 2004; Auffhammer

et al., 2008; Wirl, 2000; Hill and Brown, 1995; Hobbs, 1991), the design of

incentives (Eto et al., 1998), and the impact of demand-side programs on greenhouse

gas emissions (Garg et al., 2011). Most studies focus on investor-owned utilities, but

a few have studied the energy-efficiency programs of municipal-owned utilities. For

example, Wilson et al. (2008) reported on a survey that examined the challenges and

opportunities for energy-efficiency programs in rural electric co-operatives and small

municipal utilities. Wilson et al. (2008) systematically summarized the differences

between investor and municipal ownership of utilities in their institutional structure,

ownership form, customers and demand, revenue systems, and the role of state

regulation. The findings suggest that the major benefits of demand-side management

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programs are in improved customer relations and public image, and that local

partnerships are salient to the success of demand-side management programs. The

work of Wilson et al. (2008) is based on a case study of policy efforts in a single

state (Minnesota), but their analysis suggests the potential contribution from

quantitatively evaluating the reasons behind the use of energy-efficiency programs

by governments with municipal-owned utilities across the U.S.

The major difference between investor-owned utilities and municipal-owned

utilities is that investor-owned utilities are typically regulated by state public

utilities/service commissions, while municipal-owned utilities have much more

discretion in making decisions related to rate designing and energy efficiency

(Wilson et al., 2008; Bachrach et al., 2004). The state green energy policies, such as

renewable portfolio standards and energy efficiency resource standards, among other

policy tools, generally apply to investor-owned utilities, instead of municipal utilities.

The “unregulated” or “less regulated” nature of municipal utilities poses interesting

questions as to why and how they make commitment to energy efficiency with much

less regulation from upper level government.

An emerging literature on the local climate actions is tied to research on

energy-efficiency programs in several ways. Local climate protection involves the

use of inventories, reduction strategies and monitoring of greenhouse gases (ICLEI,

2011). Research shows that energy-efficiency programs are among the most

common policy instruments that local officials consider in their efforts to address

local climate protection (Francis and Feiock, 2010; Krause, 2010). Also, local

government decisions to adopt local climate policies are driven by localized policy

benefits such as air pollution reduction and economic benefits (Betsill, 2001). The

factors that are expected to influence local climate policy adoption include:

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• expectations of cost savings (Kousky and Schneider, 2003; Lindseth, 2004),

• community wealth (Van Liere and Dunlap, 1981; Scott and Willits, 1994; Brody et al. 2003; Tang and Brody, 2009; Tang et al., 2009),

• economic development opportunities (Kousky and Schneider, 2003;

Lindseth, 2004; Engel and Orbach, 2008),

• co-benefits such as air quality improvement, public health protection and traffic congestion alleviation (Kousky and Schneider, 2003; Lindseth, 2004),

• the presence of policy entrepreneurs (Kousky and Schneider, 2003; Engel

and Orbach, 2008),

• citizen demand (Betsill, 2001; Bassett and Shandas, 2010),

• state mandates (Berke et al., 1996),

• climate risks (including coastal distance, population density and hazard damage) (Brody et al., 2008; Zahran et al., 2008),

• climate stress variables such as light transportation, emissions per capita,

carbon intensive industry (Brody et al., 2008; Tang et al., 2010),

• and the influence of neighboring governments (Krause, 2010).

In studies of local climate action, counties or large cities are typically included

in the sample frame, and the role of municipal-owned utilities in local climate

actions and energy efficiency is emphasized. Understanding the circumstances under

which cities with their own utilities will commit to energy-efficiency strategies and

at what level they commit to those strategies is also of practical values to local

officials who wish to coordinate their efforts for climate protection and energy

efficiency.

Explaining Local Energy Efficiency Effort

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Several factors are thought to be associated with the efforts of municipal-owned

utilities to engage in energy-efficiency programs. These include: energy supply-side

conditions, the characteristics of municipal-owned utilities sales, the institutional

form and fiscal capacity in the city, the presence and nature of relevant

public-private partnerships, and various citizen characteristics. The use of

energy-efficiency programs by municipal-owned utilities may be affected by (1) a

variety of cost considerations, (2) political and fiscal conditions in the city, (3) the

cooperation of external private partners, and (4) pressures from the citizens.

Cost Considerations and Supply-Side Conditions

Utility services perform an important role in balancing between energy supply

and energy demand. As such, supply-side factors (i.e., the scarcity or abundance of

energy) are likely to directly affect local government decisions to invest in

energy-efficiency programs. This is particularly relevant when energy-efficiency

programs are viewed as viable alternatives to efforts to increase energy supply, such

as with the siting and development of new power plants (Nadel and Geller, 1996)

and the expansion of existing power plants. In addition to possibly reducing financial

costs by delaying or reducing the need for additional energy supply,

energy-efficiency programs are favorably viewed because they can defer the

environmental costs of building and operating additional power plants (Hirst et al.,

1996).

Supply-side challenges are manifested in several ways. First, difficulties in

siting new energy infrastructure are driving forces behind government efforts to

implement demand-side management. For example, a 2010 proposal for a biomass

power station in Tallahassee met substantial neighborhood opposition and split

environmental constituencies, leading to the proposal’s ultimate rejection.

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Common forms of energy infrastructure include power plants, wind farms,

transmission lines, and petroleum refineries, all of which require significant land. In

the 1980s, the number of baseload power plants steadily increased to meet the rising

demand for energy (Hirst et al., 1996). That growth was made possible by relatively

affordable land and low levels of conflict among stakeholders involved in siting

decisions, and much has changed since then. In recent decades, siting new energy

infrastructures has become increasingly difficult (Casper and Wellstone, 1981;

Halvorsen, 1999). A recent public opinion survey found the majority of Americans

oppose the location of coal, natural gas, and nuclear power plants in their area

(Ansolabehere and Konisky, 2009).

The siting difficulty sometimes presents itself with NIMBY

(not-in-my-back-yard) issues, with the siting of new power plants delayed by local

environmental groups as well as neighborhood and homeowner organizations. For

example, Michigan's first large coal-fired power plants since 1984 was delayed by

local citizen groups with the help of the Sierra Club, and a citizens group has sued to

stop state environmental regulators from issuing air quality permits for new plants

until they regulate carbon dioxide emissions (Michigan Business, 2008). Similar

stories have been repeated throughout the county. For example, Loftis (2010)

documents the mobilization of local environmental groups protests against the

establishment of new power plant in west Texas.

Siting issues are different for each community and can vary depending on the

community and the type of infrastructure. If there is a desire to increase energy

supply by enlarging transmission capacities, the barriers often lie in the siting of

transmission lines. For communities that plan to develop new power plants, the

problems are to site both the plant and the transmission lines. The political and

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financial pressures against the expansion of energy infrastructure in order to increase

the supply of energy is likely to create electoral and administrative incentives to shift

policy efforts away from supply-side solutions for energy needs and toward efforts to

reduce energy demand.

Another supply-side factor to consider is whether cities generate their own

electricity. Unlike many investor-owned utilities, municipal-owned utilities often do

not generate their own electricity. Instead, they purchase electricity from a wholesale

electricity market and from other power generators in order to satisfy the demands of

its jurisdiction. A municipal-owned utility with electricity generation has a greater

stake in balancing the supply and demand of the electricity, therefore, making it

more likely to implement a demand-side management program. By contrast, a

municipal-owned utility without electricity generation is faced with less risks and

costs involved in keeping the energy demand stable and below the supply capacity,

because the risks and costs are partially shifted to consumers. Municipal-owned

utilities without generation are retailers of electricity and can avoid the high peak

demand price by shifting the cost to the consumers. Even if they refrain from this,

considering the political risks, the cost difference between peak and normal period is

larger for municipal-owned utilities with generation capacities than those without

generation capacities. Furthermore, the generation capacity of municipal-owned

utilities are much smaller compared with the large generators in the wholesale

market and thus are vulnerable to fluctuations in the demand. Utilities without

generation capacity are insulated from risks by connecting to a larger and more

stable electricity network. We test these relationships with the following hypotheses

(stated in their alternative form).

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Hypothesis 1: As new energy infrastructure becomes more difficult to site, municipal-owned utilities will be more likely to engage in energy-efficiency programs.

Hypothesis 2: Municipal-owned utilities with their own electricity generation

function will be more likely to engage in energy-efficiency programs. Sale Characteristics

Electricity is needed by all sectors of a local economy (i.e., residential, industrial

and commercial). The proportion of the electricity consumed by each sector,

however, differs widely among utilities. Some municipal-owned utilities sell the vast

majority of their electricity to residential properties, while others have more balanced

portfolios of residential, industrial and commercial sales. This is important because

the electricity sold to industrial and commercial facilities is generally more profitable,

because these customers have fewer accounts and thereby less operating expenses

associated with account management. Furthermore, it is argued that public

ownership of the electric utilities would favor the residential customers

overindustrial customers (Kwoka, 1996) with residential customers paying less and

industrial customers paying more compared with customers of investor-owned

utilities. This suggests that municipal-owned utilities subsidize residential customers

with profits generated from the industrial accounts. A higher percentage of

residential accounts for a municipal-owned utility means it has more customers to

serve than to extract, leaving less slack resources for the utilities to spend on

energy-efficiency programs. In addition, savings-to-expenditures ratios are lower for

residential demand-side management programs than for commercial or industrial

programs (Joskow and Marron, 1992).

Municipal-owned utilities that generate their own electricity use that power to

meet the needs of its own jurisdiction and sell the excess production to other

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jurisdictions. The resale of the electricity can provide significant revenues for

municipal-owned utilities with excess capacity. The potential to sell excess

electricity has implications for decisions to pursue energy-efficiency programs.

Utilities with excess electrical capacity may be less likely to invest in

energy-efficiency programs in order to reduce local demand. This is because their

jurisdiction has a lower risk of exceeding electrical capacity resulting in blackouts or

the need to purchase expensive electricity from other utilities. Although utilities with

excess electrical capacity might also be more likely to invest in energy-efficiency

programs in order to capitalize on possible arbitrage opportunities through selling

their electricity to other jurisdictions or corporations. We test these relationships with

the following hypotheses (stated in their alternative form).

Hypothesis 3: The more dependent a municipal-owned utility is on the residential electricity sales, the less it will spend on energy-efficiency programs.

Hypothesis 4: The more a municipal-owned utility’s revenue is derived from the

resale of electricity to other utilities, the less it will spend on energy-efficiency programs.

City Government and Energy-Efficiency Programs

While municipal-owned utilities are generally more independent from city

management than conventional city departments, their administration is not wholly

independent of policy choices made by city governments. Municipal-owned utilities

provide a direct benefit to their cities in the form of payments and contributions.

Specifically, these payments and contributions are made in property-like taxes,

payments in lieu of taxes, and transfers to the general funds. According to the data of

American Public Power Association, a median municipal-owned utility contributed

4.7 percent of its electric operating revenues to its local community. This means that

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the managers of those utilities must consider issues of political feasibility and

city-level budgetary considerations in addition to issues of profitability.

Previous research demonstrates that one of the most important local political

institutions is the form of government. In the U.S., there are two major municipal

government types: mayor-council and council-manager government forms. Extant

research concludes that a key difference between the two forms lies in the ways they

respond to different interests and prioritize different policy issues (Clingermayer and

Feiock 2001). Mayor-council form of government has been observed to be more

open to organized interest group influences, while council-manager form of

government tends to be more insulated from some of these pressures (Lubell et al.,

2008; Sharp et al., 2011). The influence of the forms of government on local

government use of energy-efficiency programs results from their different

approaches to the options in the supply of energy generation. Because officials in

cities with mayor-council form of government tend to be highly concerned about

electoral outcomes (e.g., reelection), they are more easily influenced by constituency

interest groups opposition to new energy infrastructures. Faced with a choice

between supply-side and demand-side strategies, officials in mayor-council cities are

anticipated to choose politically safe energy-efficiency programs. By contrast, the

career ambitions of city managers are likely to favor efficiency and economic

development considerations in infrastructure decisions. Thus mayor-council

governments may be less likely to invest on new energy infrastructures that may

crowd out energy-efficiency program efforts.

Local government fiscal conditions also has implications for energyprograms.

Energy-efficiency programs require varying levels of direct expenditures (e.g.,

purchase of energy-efficient light bulbs for trade-in programs) or indirect

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expenditures (e.g., tax incentives). Governments experiencing fiscal stress are likely

to have limited resources to devote to energy-efficiency programs. Even though

municipal-owned utilities are considered proprietary enterprises and, as such, are

generally expected to be financially self-sufficient, the financial systems of

municipal propriety enterprises and general government activities are closely

connected. General governments often subsidize enterprise functions, especially

public transit enterprises, but electrical utilities are more self-sufficient and

frequently subsidize general government functions, usually through charges for

general government overhead. When cities are in fiscal deficit or have increases in

liabilities, city officials may be more likely to transfer utility revenues toward

general government functions rather than energy-efficiency programs (Bachrach et

al., 2004). Therefore, we expect that officials in cities in strong fiscal condition

spend more on energy-efficiency programs than officials in cities in poor fiscal

condition.We test these relationships with the following hypotheses (stated in their

alternative form).

Hypothesis 5: Cities with mayor-council form of government are likely to spend more on energy-efficiency programs than those with a council-manager form of government.

Hypothesis 6: The fiscal condition of a city government is positively associated with

their level of spending on energy-efficiency programs.

Public-Private Partnerships and Energy Efficiency

Modern trends in government administration have moved from a focus on direct

provision of public goods to a greater reliance on public-private partnerships in

policy implementation (Kettl, 1993). This trend is well demonstrated in the area of

energy efficiency. Local utilities often provide incentives for the consumers to buy

energy-efficient appliances, light bulbs and equipment. However, public utilities

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have to rely on local retail stores to provide these items (Wilson et al., 2008). If a

utility provider lacks these partners, it can significantly impede their efforts to

implement energy-efficiency programs, because the limited supply of private

providers (e.g., retail stores or retail stores of necessary capacity) may significantly

reduce the effectiveness of incentive programs. As such, decisions to engage in

energy-efficiency programs often require careful consideration into the availability

of qualified private partners. In addition, private providers of energy-efficiency

solutions are likely to function as interest groups for various energy-efficiency

programs. As such, they may lobby for energy-efficiency expenditures. Therefore,

we anticipate a positive relationship between private providers of energy-efficiency

solutions and municipal-owner utility expenditures for energy-efficiency programs.

We test this relationship with the following hypothesis (stated in its alternative

form).

Hypothesis 7: The presence of local private partners for energy-efficiency products is positively associated with local energy-efficiency program expenditures.

Citizen Characteristics and Energy-Efficiency Programs

Environmental interests are active participants in local politics. The

environmental benefits of energy-efficiency programs attract the support of local

environmental groups in several ways. First, the expected reduction in emissions of

the greenhouse gases resulted by the implementation of demand-side management

programs are consistent with the climate protection theme of the environmental

movement. Second, if demand-side options are seen as substitutive to supply-side

deployment of new energy infrastructures, the promotion of demand-side alternatives

will be highly attractive to environmental interest groups since they may mitigate the

need to build additional power plants or transmission lines.

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Citizens’ ideological positions are also likely to affect the use of

energy-efficiency programs. Liberal ideology is generally associated with

pro-environmental policy positions, and it could be expected that governments with

more liberally oriented populations will favor the adoption and implementation of

energy-efficiency programs (Sharp et al., 2011). Even if liberal residents do not

actively lobby for the use of energy-efficiency programs, officials in more liberal

communities may view energy-efficiency programs as a tool to improve customer

relations and public image (Wilson et al., 2008). We test these relationships with the

following hypotheses (stated in their alternative form).

Hypothesis 8: The greater the number of environmental interest groups in a city, the more the city will spend on energy-efficiency programs.

Hypothesis 9: The higher the percentage of citizens in a city who vote for

Democrat-party candidates, the more the city will spend on energy-efficiency programs.

Methodology

We examine the population of cities with populations of 25,000 or more

residents in 2000 that operate a municipal-owned utility. The decision to investigate

energy-efficiency programs in cities with municipal-owned utilities reflects their

significant different from the decision in cities that don’t own their own utilities.

The sample is limited to cities with populations over 25,000 is due to data

availability. Information on municipal-owned utilities is based on reports included in

the Energy Information Agency (EIA) Form-861 on the Demand Side Management

programs. EIA conducts an annual survey of the operations and activities of the

electricity utilities by asking them to fill Form-861, generating a rich data set of

utility revenues, sales, and demand side management. This procedure generates a

sample of 145 cities.

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Dependent Variable

The dependent variable, Energy Efficiency Expenditure (EEEXP), measures the

annual incentive payment for energy efficiency. The data is coded from the 2007

version of EIA Form-861, which collects annual information on electric utility

characteristics, operations, revenues, generations and demand side management. The

2007 data is used because it is the most comprehensive EIA demand-side

management data available over the last ten years. The summary statistics show

that municipal-owned utilities spend $261,000 on energy-efficiency programs per

year, with the largest annual spending $9 million (Huntsville, AL).

Energy Supply Variables

Two variables measuring the characteristics of energy supply in the MOUs are

Siting Difficulty (SITINGDIFF) and Net Generation (NETGEN). Siting Difficulty

measures the level of difficulty in siting new energy infrastructure, like power plants

and transmission lines. There are multiple indicators of siting difficulty in the

literature (Vajjhala and Fischbeck, 2007), including economic indicators, geographic

indicators, construction indicators and respondents assessments. A survey based

measure of siting difficulty collected through surveys conducted by Vajjhala and

Fischbeck (2007) is used. Their survey elicits expert opinions from siting experts and

professionals across the U.S. regarding the siting difficulties and constraints in their

given state. Siting difficulties are rated on a 10-point scale, where 1 was the easiest

and 10 was the hardest. The weighted average of all respondents was used as the

measurement of siting difficulty in this study. The scores indicate that Florida has the

highest score of 8.08, making it the hardest state to site new energy facilities and

Ohio is the easiest state to site a new utility project, with a score of 5.69.

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Net Generation measures whether an MOU has its own generation capacity. It is

measured with a dummy variable, with 1 indicating an MOU has its own generation

capacity and 0 indicating an MOU does not have its own generation capacity. The

data on Net Generation is gathered from EIA Form-861 (2007).

Sales Characteristics Variables

Two variables measure the sales characteristics of municipal-owned utilities.

Percent Residential Sales (RESISALE) measures the percentage of electricity sales

from the residential sector to the total sales. Both residential sales and total sales data

were obtained from EIA Form-861 (2007). Percent Residential Sales was calculated

for each municipal-owned utility by dividing residential sales over total sales.

Another variable is Resale Revenue (RESALEREV), which measures the revenue

received for the sale of wholesale power to other electric utilities. The data on Resale

Revenue was also obtained from EIA Form-861 (2007).

City Government Variables

Two city government variables are Form of Government (FOG) and Fiscal

Capacity (FISCAL). Form of Government measures the institutional structure of the

executive branch in the municipal government. A city government dummy variable

was coded as 1 if the city has a mayor-council form of government, and was coded

as 0 if the city has a council-manager form of government based on International

City Management Association (ICMA) surveys in 1991, 1996, 2001 and 2006.

Missing survey data was supplemented by searches of websites of these governments.

Fiscal Capacity variable is measured by per capita difference between the city’s

general revenue and general expenditure. The city fiscal data were obtained from

Census (2000).

Local Retail Partners and CitizenVariables

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The Local Retail Partner (REPART) variable is measured with a dummy

variable, with 1 indicating there is 1 or more local energy-efficiency retailers within

5 miles of the city center, and 0 indicating there is 0 retail stores within the 5 mile

range. The local retailers include chained stores like Home Depot and Best Buy, as

well as local-based stores. The logic behind using a dummy variable instead of

continuous variable is that once the supply of private partners passes a minimum

threshold, the decision on energy efficiency might not be dependent on the

oversupply of retailers. The data is retrieved from Department of Energy’s Energy

Star programs.

Two variables measure citizens’ environmental orientation. Environmental

Nonprofits (ENVNPO) measures the number of nonprofit environmental

organizations in the county where the city is located as of September 2007.

Nonprofit organizations are defined as organizations that are required to file

Form-990 with Internal Revenue Service. Data on nonprofit status were retrieved

from National Center for Charitable Statistics.

In addition, we include another measure for the citizen’s ideological orientations,

the Percent Voting Democrat (VOTEDEM) variable. This variable measures the

percent of citizens voting for the Democratic Party in the Presidential election in a

county in 2004.

Control Variables

A set of variables are also included as controls in the statistical analysis.

Household Median Income (INCOME) measures the median household income in a

city. Percent White (WHITE) and Percent Black (BLACK) measure the percentage of

white and black population in a city separately. Peak Energy Consumption Days

(PEAK) measures the sum of cooling degree days and heating degree days. This

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variable is an indicator of the energy consumption intensity in the city. The control

variables were obtained or calculated based on the Census data (2000). Explanations

of the variable measurement and data sources are presented in Table 1.

Table 1 here

Method

Considering the data generation procedures on the dependent variable, whether a

utility responds to EIA Form-861 is probably nonrandom. The relative completeness

of the dataset on the dependent variable does not guarantee its randomness in

selection into the sample frame. Therefore, we decided to estimate the Heckman

Selection Model where the selection equations uses the energy supply variables to

predict whether a utility will have a demand side program, and the outcome

equations uses sales characteristics variables, city government variables, local retail

partners, citizen characteristics variables and other control variables to predict the

level of demand side management expenditures. The reason for using the energy

supply variables in the selection equation is that if a municipal-owned utility is faced

with the constraint from the supply side, it does not have many alternatives other

than implementing the energy-efficiency programs. This means that the supply side

constraints dominate the decision in implementing the demand-side programs. The

Heckman Selection Model is specified as follows.

Selection Equation: Pr(DSM=1) = Ф(γ0 + γ1 SITINGDIFF + γ2 NETGEN) Outcome Equation:

EEEXP = β0 + β1 RESISALE + β2 RESALEREV + β3 REPART + β4 FOG + β5 FISCAL + β6 ENVNPO + β7 VOTEDEM + β8 INCOME + β9 WHITE+ β10 BLACK + β11 PEAK

In the selection equation, an indicator variable DSM is used as dependent

variable, with 1 indicating that there are energy-efficiency programs in the city and 0

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indicating no such programs in the city. The 30 cities that have implemented

energy-efficiency programs are coded as 1, and theother 115 cities are coded as 0.

Both two-step procedures and Maximum Likelihood Estimation (MLE) were applied

to estimate the model. Given that the two methods generate similar results, we use

MLE as our estimation strategy. Table 2 reports the descriptive statistics for all the

variables.

Table 2 here

Results and Discussion

Table 3 reports the results of the Heckman Selection Model for the

energy-efficiency expenditures. The Wald test (χ2(11 d.f.) = 28.50) confirms that

coefficients of the outcome equation are significantly different from zero. To test the

validity of the choice of the Heckman Selection Model, we need to test the

hypothesis that the selection and outcome equations are independent. A Likelihood

Ratio test of Independent Equations was performed, and the test statistics fails to

achieve significance at the 0.05 level (χ2(1 d.f.) = 0.01). This indicates that the

standard regression analysis applied to the outcome equation could have yielded

biased results.

The first results to note are the significant effects of Siting Difficulty and Net

Generation in the selection model. The coefficient of Siting Difficulty is positively

significant at the 0.05 level (p-value = 0.035). This indicates that the more difficult

the new energy infrasture’s siting decisions, the more likely the municipal-owned

utility is going to start an energy-efficiency program. The effect of Net Generation is

positively significant at the 0.01 level (p-value = 0.001). This indicates that city with

its own generation capacity is more likely to start a DSM program. This confirms our

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hypotheses that the supply side constraints are the major driving forces underlying

the decision to develop energy-efficiency programs at the first place.

The sale characteristics of municipal-owned utilities present complicated results.

On the one hand, the coefficient of Resale Revenue was negatively significant

(p-value=0.08), consistent with our hypothesis. This means that the more revenue a

city can get from selling electricity to other utilities, the less likely it will spend on

energy efficiency. On the other hand, the variable Percent Residential Sales was not

statistically significant.

Table 3 here

The effects of city characteristics on the level of local energy-efficiency

expenditures are also noticeable. The coefficient of Form of Government is

positively significant (p-value = 0.008, consistent with our hypothesis. Cities with

mayor-council form of government invest more on energy efficiency, if the cities

have already started the demand-side management program. However, the

coefficient of the Fiscal Capacity failed to achieve significance.

Consistent with our hypothesis, the coefficient of Retail Partners is positively

significant (p-value=0.041), indicating that local energy-efficiency expenditure is

dependent on the presence of local private partners. The effects of citizen

characteristics are surprising. The coefficient of Percent Voting Democrat was

negatively significant, contrary to our hypothesis that cities with more Democratic

Party voters spend more on energy efficiency. To the contrary, the negative

coefficient means that cities with more citizens voting for Republican Party spend

more on energy-efficiency projects. A possible explanation for this relationship is

that energy efficiency might be a cast as different from other green policy issues and

thus support does not follow traditional ideological differences. Given that

22  

conservative ideology promotes savings for government and individuals, energy

efficiency could be a policy option that attracts more conservative supporters.

Several control variables also show significant results, with Household median

income and percentage black population having positively significant coefficients,

and Peak Energy Consumption Days having negatively significant coefficient.

Conclusions

Energy-efficiency is a policy option increasingly implemented as a

multi-purpose solution to the environmental and economic sustainability in the local

communities. For local government, owning a municipal utility provides special

opportunities as well as challenges in implementing energy-efficiency strategies. To

date, few studies attempted to understand the role of municipal-owned utilities in the

implementation of energy-efficiency in the community and why there are

pronounced differences in commitment to energy-efficiency across communities. We

provide some preliminary answers to these questions by examining the driving

forces for the adoption and implementation of energy-efficiency incentives in local

governments with municipal-owned utilities.

With data collected for 145 municipal-owned utilities in cities with population

over 25,000, we test these relationships with a Heckman Selection Model. We find

that whether a city has energy-efficiency programs is determined by the energy

supply factors, including siting difficulties of new energy infrastructure and whether

municipal-owned utilities have generation capacities. More stringent regulations

over land use regarding energy infrastructure might lead local communities to

demand-side options of energy efficiency. Municipal-owned utilities are also more

inclined to implement energy-efficiency programs if they own generation capacities.

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We also find that the level of local energy-efficiency expenditure is associated with

the resale revenues, the form of government in the city and the availability of local

retail partners. These findings reveal an important connection between utility

decisions and the larger community for which it is serving. The commitment to

energy-efficiency in the municipal-owned utilities is affected by local politics as well

as economics. A city’s political structure can shape the decisions of the public

utilities, and public-private partnership is very important for some municipal-owned

utilities to successfully implement its energy-efficiency strategies.

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Table 1: Summary of Variables and Predicted Relationships

Variable Explanation Predicted Signs Source

Dependent Variable Energy Efficiency Expenditure (Outcome Unit: Thousand Dollars N/A EIA, 2008

DSM(Selection equation) Dummy, 1: with energy-efficiency

programs; N/A EIA, 2008

Independent Variables

Outcome Equation 

Resale Revenue Unit: Thousand Dollars - EIA, 2008

Percent Residential Sales Unit: Percentage of Residential Sale of Electricity in the Total Sale - EIA, 2008

Form of Government Dummy, 1:Mayor Council; 0: Council Manager + ICMA, 1991,

1996, 2001, 2006Fiscal Capacity

Unit: Thousand Dollars Per Capita + Census, 2000

Local Retail Partners Dummy: 1: with no less than 1 local retail partners; 0: with no local retail partners + Energy Star, 2011

Environmental Non-Profits Counts + NCCS, 2007

Percent Voting Democrat Unit: Percentage of county population voted for Democratic Party in 2004 + State and County

Databook, 2007 Percent White Unit: Percentage of White Population in a

city N/A Census, 2000

Percent Black Unit: Percentage of Black Population in a city N/A Census, 2000

Household Median Income Unit: Thousand Dollars N/A Census, 2000

Peak Energy Consumption Days

Unit: Number of Days that Require Heating and Cooling in a city N/A Census, 2000

Selection Equation

Siting Difficulty An Index Score Measuring the Difficulties in Siting Energy Facitilies + Vajjhala &

Fischbeck, 2007 Net Generation Unit: Megawatts + EIA, 2008

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Table 2 Descriptive Statistics

Variable Obs. Mean SD Min Max Energy Efficiency Expenditure 145 216.124 905.074 0 9400 Resale Revenue 145 14183.2 39903.5 0 207259 Percent Residential Sales 145 0.394 0.107 0.086 0.637 Form of Government 145 0.372 0.485 0 1 Fiscal Capacity 145 -0.009 0.227 -0.815 0.705 Local Retail Partners 145 0.772 0.421 0 1 Environmental Non-Profits 145 41.724 85.545 1 457 Percent Voting Democrat 145 44.441 12.459 11.6 75.2 Percent White 145 0.743 0.168 0.162 0.958 Percent Black 145 0.143 0.169 0.001 0.780 Household Median Income 145 38205.28 10877.07 21180 90377 Peak Energy Consumption Days 145 5490.448 1420.688 2434 10106 Siting Difficulty 145 6.649 0.709 5.69 8.08 Net Generation 145 0.524 0.501 0 1

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Table 3 Heckman Selection Model of Local Energy Efficiency Expenditures Coefficient Standard Errors Outcome Equation Resale Revenue -0.009* (0.005) Percent Residential Sales 667.07 (3062.72)

Form of Government 1634.65*** (616.65) Fiscal Capacity -568.95 (1144.63) Local Retail Partners 1650.10** (809.45) Environmental Non-Profits -0.01 (2.96) Percent Voting Democrat -77.23** (30.54) Percent Black 7056.99* (3833.34)

Percent White 589.56 (4586.53) Household Median Income 0.05* (0.03) Peak Energy Consumption Days -0.75** (0.35) Constant 3049.72 (4342.65) Selection Equation Siting Difficulty 0.37** (0.18) Net Generation 0.93*** (0.28) Constant -3.89*** (1.18) Rho 0.05 (0.56) Sigma 1253.91*** (163.89) Lambda 66.23 (707.40) LR test of Independent Equations χ2(1 d.f.) = 0.01 - Wald Test of Model Fit χ2(11 d.f.) = 28.50*** - Observations Censored = 115

Uncensored = 30 * p < 0.10; ** p < 0.05; *** p < 0.01 (two-tailed)