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State of Wisconsin Department of Administration Division of Energy Focus on Energy Public Benefits Evaluation

Economic Policy Analysis—Final Report

March 10, 2004

Evaluation Contractor: PA Government Services Inc.

Prepared by: Mike Sherman, Lisa Petraglia, and Glen Weisbrod Economic Development Research Group, Inc. Boston, Massachusetts Bryan Ward, Carmen Best, and David Sumi PA Government Services Inc. Middleton, Wisconsin

This report is the property of the State of Wisconsin, Wisconsin Department of Administration, Division of Energy, and was funded through the Wisconsin Focus on Energy Program.

State of Wisconsin Department of Administration Division of Energy Focus on Energy Public Benefits Evaluation

Economic Policy Analysis—Final Report

March 10, 2004 © PA Knowledge Limited 2004

Liaison Contact: Dr. David Sumi PA Government Services Inc. 2711 Allen Boulevard, Suite 200 Middleton, Wisconsin 53562 Tel: +1 608 827 7820 Fax: +1 608 827 7815 E-mail: [email protected]

Prepared by: Mike Sherman, Lisa Petraglia, and Glen Weisbrod Economic Development Research Group, Inc. Boston, Massachusetts

Bryan Ward, Carmen Best, and David Sumi PA Government Services Inc. Middleton, Wisconsin

Acknowledgement: Ralph Prahl contributed critical review and analysis. Prahl & Associates Madison, Wisconsin

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Economic Policy Analysis—Final Report 3/10/2004

PREFACE

This report is presented to the Wisconsin Department of Administration (DOA) as part of the overall evaluation of the Focus on Energy (Focus) set of energy efficiency programs funded by Wisconsin utility ratepayers and administered by the DOA statewide.

The mission of Focus is to develop and operate a range of sustainable energy efficiency and renewable energy programs. In partnerships with consumers, utilities, businesses, nonprofit organizations and government at all levels, these programs are intended to:

• Reduce the amount of energy used per unit of production in Wisconsin while improving energy reliability.

• Enhance economic development and make Wisconsin firms more competitive.

• Reduce the environmental impacts of energy use.

• Expand the ability of markets to deliver energy efficient and renewable goods and services to consumers and businesses.

• Deliver quantified financial returns on public investments in energy improvements.

This policy analysis report is one in a series of program impact evaluations of the two years of Focus on Energy programs. Other reports cover:

1. Energy Use Impacts (including electricity and natural gas use and costs).

2. Economic Development Impacts (including flows of dollars in the economy).

3. Environmental Impacts (including pollutant emissions).

4. Non-energy Impacts (including quality of life, safety, and third party costs).

5. Benefit-Cost Analysis (comparing net benefits and costs from alternative perspectives).

Among these lines of evaluation, the first four deal with impact analysis, and accordingly they assess various program-related impacts compared to what would have occurred if the Focus on Energy program did not exist. They are not intended to represent benefit-cost studies and as such do not include any additional calculations regarding impacts of alternative uses of program funds. However, the fifth line of analysis is the benefit-cost analysis, which does examine the economic valuation of all of the preceding impacts, net of the alternative uses of the funds (referred to as ”opportunity costs”).

This report specifically builds upon and extends findings of the Economic Development Benefits: Interim Economic Impacts Report (March 31, 2003) to examine a topic area of particular policy interest—the distribution of energy and economic development impacts on various elements of the economy.

Focus on Energy Vision

That the people of Wisconsin will make sound

energy efficiency and renewable energy investments

that result in sustained economic growth and a healthy

environment for current and future generations.

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TABLE OF CONTENTS

Preface i

1. Executive Summary 1–1

2. Introduction 2–1 2.1 Purpose of this Report 2–1 2.2 Methodology 2–1 2.3 Overall Economic Development Impacts 2–2 2.4 Interpretation of Economic Development Impacts 2–3

3. Policy Issue: How are economic benefits from Focus distributed among urban, rural, and semi-rural counties? 3–1 3.1 Findings 3–2 3.2 Program Participants 3–6 3.3 Trade Allies by County Type 3–20

4. Policy Issue: How does Focus affect industry in Wisconsin? 4–1 4.1 Findings 4–2 4.2 Direct Economic Impacts 4–3 4.3 Overall Economic Development Impacts 4–11 4.4 Comparison of Economic Impacts to Baseline Patterns and

Trends 4–16

5. Policy Issue: What types of job and income opportunities does Focus create? 5–1 5.1 Findings 5–1 5.2 Occupational Patterns 5–1 5.3 Pay Levels 5–7

APPENDIX A: Comparison of Economic Impact Studies A–1 A.1 Evolution of Economic Impact Models and their Use for Energy

Program Analysis A–1 A.2 Annotated Bibliography and Findings from Economic Impact

Studies of Energy Programs A–5 A.3 Bibliography of Additional Economic Impact Studies of Energy

Programs A–13

APPENDIX B: Participation and Energy Bill Savings—All Counties B–1

APPENDIX C: Two Approaches to Estimating Total Energy Consumption by Wisconsin County C–1

APPENDIX D: Primary Incorporated Updates to the Draft Report D–1

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1. EXECUTIVE SUMMARY

This report is presented to the Wisconsin Department of Administration (DOA) as part of the overall evaluation of the Focus on Energy (Focus) set of energy efficiency programs funded by Wisconsin utility ratepayers and administered by the DOA throughout the state. Prior reports have covered a wide range of issues including impacts on energy use, environmental pollution, non-energy impacts, economic development impacts, low-income population impacts, and benefit-cost analysis. This report builds upon and extends the analysis of economic development impacts. While other reports have examined the overall magnitude of program effects on development of the Wisconsin state economy, this report extends that economic development analysis to examine how the program has affected the distribution of benefits across parts of the state, the implications for job development, and specific industry impacts. Specifically, we examine three economic policy questions:

1. How are economic benefits distributed among urban, semi-rural, and rural counties of Wisconsin?

2. How does Focus affect industry in Wisconsin?

3. What type of job and income opportunities does Focus create?

To address these questions, Economic Development Research Group, Inc. (EDRG), with assistance from PA Government Services Inc. (PA Consulting Group), reviewed a variety of program data including:

• Geographic information about program participants, trade allies, and energy impacts.

• Projections of industry growth and decline in Wisconsin through 2011.

• REMI model analysis of Focus on Energy’s impacts on statewide economic growth.

• Implications of Focus on Energy non-energy benefits for the Wisconsin economy.

The analysis process involved a detailed examination of how Focus on Energy affects the Wisconsin economy. The budget for Focus is small compared to the Wisconsin economy as a whole; however, the decision to implement Focus causes specific and notable changes in the flow of dollars within the state’s economy and changes in household and business operating costs. While there can be some winners and losers associated with any changes in the flow of spending in the economy, the key impact of Focus is to reduce the cost of living and the cost of doing business in Wisconsin. The economic development impact analysis focuses on the dynamics of economic growth and change over time—specifically, how the program increases the competitiveness and attractiveness of Wisconsin as a place to live and locate a business and reduces the “leakage” of dollars now flowing out of the Wisconsin economy while increasing the flow of dollars to in-state producers.

It should be noted that the economic impacts projected were based on actual spending levels and implemented projects for the first 18 months of program operations and projected program budgets out ten years. The analysis was conducted in February–March 2003, prior to completion of the state of Wisconsin’s biennial budget for 2003–2005. In that budget, the funding for Focus was reduced by approximately 20% in the fiscal year 2003–2004 and 40% in the fiscal year 2004–2005. At this reduced level of funding, the economic impacts of Focus will not reach the projected levels. However, depending on policy directions taken by Focus,

1. Executive Summary. . .

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the distribution of those benefits and the sectors of the economy impacted, and the types of jobs created, will be consistent with what is presented here.

The findings of this report are organized around the three economic policy questions cited above. These questions and the corresponding study findings are summarized below.

How are the economic benefits of the program distributed among urban, semi-rural, and rural counties of Wisconsin?

Overall, 84% of households and 83% of businesses in Wisconsin are eligible for participation in Focus on Energy. They span every county in the state. The program has succeeded in getting over 150,000 residential participants since the beginning of the program, coming from every county in the state. It has also succeeded in serving over 2,000 commercial and 227 industrial businesses, spanning 66 different counties (or 92% of Wisconsin counties). Recurring annual cost savings from first year participants alone exceeded $8.7 million for residential participants and $7.3 million for business participants.

The average residential participant saved $52 in their cost of living, which reflects a mix of small average savings from purchases of compact fluorescent light bulbs and much larger average savings from weatherization and heating/cooling projects. In general, the location distribution of residential participants mirrors that of the state’s population—indicating a relatively equitable distribution of program benefit. However, initial rates of participation and average cost savings per participant were slightly higher in urban areas. The average business participant saved $7,958 in annual business operating costs, reflecting the fact that some of the business projects involved major refrigeration and industrial process projects. Commercial and industrial participants were located in both metropolitan and rural areas. At the time of this analysis, business participants were slightly more concentrated in metropolitan areas than the overall distribution of eligible commercial and industrial firms across the state.

Trade allies, those providing energy services and products, were relatively evenly distributed—suggesting that access to such services was also well distributed. However, the volume of energy saving with which allies from urban areas assisted, far exceeded those of both semi-rural and rural allies. For the business programs, over 90% of the energy savings involved the assistance of an urban ally, even though urban allies only make up 63% of active allies. This is likely the result of urban allies being able to offer a more comprehensive set of energy efficiency related products and services.

How does Focus affect the industry mix in the state?

The Focus on Energy program reaches all sectors of the economy: households, commercial and industrial businesses, as well as government and nonprofit agencies. It affects the growth of these different sectors of Wisconsin’s economic base. The results of the economic analysis modeling, which tracks all of the short-term impacts and forecasts longer-term implications for Wisconsin’s economy, shows that economic effects of Focus on Energy grow over time. Focus supported $46 million of business sales in Wisconsin in its first year, and this is projected to grow to $224 million per year by the tenth year. However, this growth is not distributed equally across all sectors of the economy. The impacts on each sector are largely dependent on program design and implementation.


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Certain effects are directly attributable to the program design and implementation. These include: reduced business operating costs, reduced household living costs, price effects, and reduced leakage of spending from the state’s economy.

In addition, the program also promotes other changes in the flow of dollars as a consequence of its own spending on services and the alliances it creates with businesses that supply energy services and products (trade allies).

The commercial and industrial programs serve to reduce business operating costs and this effect continues to grow over time as past participants continue to save money and they are joined by a continuing stream of new participants. The aggregate effect is to improve Wisconsin’s economic competitiveness by continuing to reduce the cost of doing business in Wisconsin relative to other states. This allows existing businesses to increase profits and expand markets and makes Wisconsin a more attractive place for business location and expansion. The economic model forecasts how these cost changes affect Wisconsin’s economic growth over time. While the greater business competitiveness aids in retention and expansion of manufacturing output in Wisconsin, job increases are limited in the manufacturing sector because there is also a trend towards greater productivity with fewer workers in many manufacturing industries.

The Residential Programs also serve to decrease the cost of living for participating households, freeing up disposable income on a recurring basis for the life of the installed energy efficiency measures. Over time, the aggregate statewide cost savings for Wisconsin residents grows as past participants continue to save money and a continuing stream of new participants joins them. The aggregate effect is to help to make Wisconsin a more attractive place to live, increasing population. The additional disposable income increases spending on consumer purchases, which causes growth particularly in the retail and service sectors.

Since the Residential Program accounts for over half of total cost savings to date, the overall Focus program impact on Wisconsin’s employment is particularly high for retail and service sectors of the economy. This is largely because residential participants who reduce energy costs end up redirecting much of their spending to purchases of consumer goods and services. An additional reason is that the program directly benefits growth of the business services sector, particularly energy services businesses.

Focus on Energy, by reducing energy use and encouraging development of renewable power, serves to decrease the “leakage” of dollars flowing out of the state’s economy for purchases of coal used in generating electricity. Instead, the program directs more dollars towards in-state suppliers of energy services and manufacturers of heating and cooling systems, thermostat controls, and motors—all of which have a disproportionately high concentration in Wisconsin. It also supports the long-term growth of renewable power equipment suppliers in the state. As a result, Wisconsin’s economy grows more over time and additional jobs are created. There are small losses of jobs in utilities and coal-handling industries, which are more than offset by growth in jobs in technology-driven energy services and electrical equipment industries.

The program also promotes the flow of dollars by spending money on staff, equipment, marketing, and financial incentives to promote energy efficiency—$22 million in the first year. The money spent on operations supports staff jobs, energy-efficiency equipment sales, and energy and marketing services within the state. The money spent on financial incentives also leverages additional private investment in purchases of energy-saving and renewable energy


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equipment. It also leverages additional trade ally activities to strengthen their activity promoting and installing energy-efficient equipment. Allies come from a variety of industries, and while certain industries are assisting with greater proportions of the energy savings, it is largely a factor of the type of measures with which they are able to assist.

The overall economic growth impacts from Focus on Energy are the changes in patterns of industry growth and associated jobs and wages that result from:

• The response of various industries to the direct effects noted above.

• The pattern of indirect industry growth impacts to suppliers of the directly affected industries.

• The induced industry growth due to respending of additional worker wages.

The magnitude and industry mix that results from the indirect and induced impacts is critically dependent on the magnitude and industry mix of the direct effects.

The total industry impacts of the program (not including farm, forestry and fishing, or government) were estimated to be $224 million and 2,435 new jobs. The key findings of the industry impacts after 10 years are cited below:

• Manufacturing accounts for the largest share of the total statewide output impact—22%, though only 5% of the total job impact.

• Retail accounts for 12% of the output impact and 24% of the employment impact.

• Business services accounts for 20% of the output impact and 29% of the employment impact.

These findings are more valuable when compared to a ten-year trend in the state’s economy without the program. The key findings from this comparison are cited below.

18% (+459) of the Focus on Energy job impact by 2011 is in the industries forecast to be declining, which may reduce the magnitude of losses that would otherwise be occurring in those industries.

21% (+518) of the job impact is in industries classified as basically stagnant, which may create additional growth in industries that would otherwise have little or no growth.

49% (+1199) of the job impact is in industries that are already forecast to be growing. This larger impact reflects the strong program impacts on retailing and service industries, both of which fall into this growth class. This result also reflects the positive program impact on technology development, which is another source of economic growth.

The support of growing industry is consistent with sound economic development strategies.


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In what ways does Focus expand various types of occupation and income opportunities for Wisconsin residents?

Focus supported 630 jobs in Wisconsin in its first year and this is projected to grow to over 2,700 jobs by the tenth year. The economic analysis model shows that Focus on Energy is supporting job growth in all occupational groups, spanning skilled and unskilled jobs in white-collar and blue-collar occupations. However, this effect is not distributed equally across all sectors of the economy. Focus on Energy is directly supporting growth of jobs in business services and technical occupations, but also creating even more jobs in sales and service occupations as a consequence of households respending their added disposable income.

By comparing the total job effects of Focus against the current job mix in Wisconsin, we can see that the program is expanding jobs in professional occupations as well as sales and service occupations. Overall, the mix of jobs that it is supporting is disproportionately white-collar occupations—both skilled and semi-skilled.

The job impacts of Focus on Energy are concentrated in the medium wage category. This reflects the programs’ impact on business and professional services (including energy services). There are proportionately fewer jobs in the high wage category (more than 15% higher than the statewide average wage). This reflects the relatively modest representation of (high-paying) manufacturing job impacts that would be expected if there were a greater participation by industrial customers.

It is common for economic development policy to try to promote forms of economic growth that expand demand for jobs in leading technology industries with above-average pay or replace lost jobs in lower paying, less highly skilled job categories. Focus on Energy is actually succeeding in supporting the growth of a wide range of jobs with generally average pay.

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2. INTRODUCTION

This chapter discusses: (1) the purpose of this report, (2) its analytic methodology, (3) overall economic development impacts, and (4) their interpretation for policy analysis.

2.1 PURPOSE OF THIS REPORT

Prior evaluation reports have examined the overall economic impacts of Focus on Energy’s first year results and extrapolations of those results over future years. This was done by bringing together four elements: (1) analysis of data from the first eighteen months of Focus on Energy’s operations, (2) development of reasonable expectations for assessing the future consequences of those initial results, (3) compilation of baseline data about the nature of Wisconsin’s economy and its reliance on out-of-state energy fuels, and (4) application of a statewide economic model to assess how Focus on Energy’s operations, energy savings and participation patterns affect the overall level of business activity, jobs, and income in the state of Wisconsin.

The purpose of this report is to examine what those overall economic impacts mean in terms of the distribution of benefits. The report is organized around three economic policy questions:

1. How are economic benefits distributed among urban, semi-rural, and rural counties of Wisconsin?

2. How does Focus affect industry in Wisconsin?

3. What type of job and income opportunities does Focus create?

2.2 METHODOLOGY

A variety of data was compiled and analyzed to answer the three economic policy questions stated above including:

1. Geographic distribution of program participants, trade allies, and energy impacts at the county level. Estimates of energy consumption for each county. Classification of each county based on Beale codes, which are based on population density and proximity to metropolitan areas.

2. Tracking of the direct effects of Focus on Energy on changing: a. Spending and income patterns of households, businesses, and government in the

state. b. Savings in household cost of living and cost of doing business in the state. c. Shifts in the share of purchases going to in-state versus out-of-state suppliers of

energy, fuels, manufactured equipment, and services. d. Additional environmental and non-energy impacts that directly affect household

and business operating costs. e. Changes, if any, in prices for energy, labor, or capital equipment.

2. Introduction. . .

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3. Historical and projected future patterns of business and occupational mix in Wisconsin’s economy.

4. Application of an economic simulation model for Wisconsin, to assess how the above-cited direct effects will lead to changes over time in costs, prices, economic competitiveness, attraction of capital investment, local purchasing patterns and inter-industry sales and purchasing patterns. The results are projections of Focus on Energy’s impacts on various industries, occupations, and wage rates over the 2001 to 2011 period.

The data and analyses presented in this report are based on both the tracking of direct program effects for participants and trade allies and the indirect effects on the economy. The indirect effects were determined using the REMI model for Wisconsin. This is a forecasting and policy impact simulation model that forecasts how Wisconsin’s economy will change over the next 20 years under alternative scenarios. This model was used because it calculates changes in the cost of living and cost of doing business and forecasts how they lead to other changes in the state’s economic competitiveness and subsequent economic growth in the long term. It also calculates how changes in supply and demand for labor, equipment, and energy can further affect prices, costs, and local purchasing patterns over time.

The workings of the REMI economic model and its application for this study are described in a separate report on overall economic impacts: Focus on Energy Statewide Evaluation, Economic Development Benefits: Interim Economic Impacts Report, March 31, 2003. In addition, Appendix A to this report contains a literature review that explains key differences in various types of economic analysis processes and also provides findings from earlier studies conducted in other states.

It should be noted that the economic impacts projected were based on projected program budgets for the first ten years of program operations. The analysis was conducted in February–March 2003, prior to completion of the state of Wisconsin’s biennial budget for 2003–2005. In that budget, the funding for Focus was reduced by approximately 40 percent. At this reduced level of funding the projected economic impacts will not reach the levels projected. However, depending on policy directions taken by Focus, the distribution of those benefits and the sectors of the economy impacted, and the types of jobs created, will be consistent with what is presented here.

2.3 OVERALL ECONOMIC DEVELOPMENT IMPACTS

The Interim Report (cited above) presented measures of the current and expected future economic impacts of Focus on Energy Residential, Business, and Renewable Energy Programs, based on the first ten years of program operations (2001–2011). It defined and presented results in terms of the overall magnitude of statewide impacts on jobs created, business sales, gross regional product (value added), and personal income. Table 2-1 summarizes those overall economic impacts.


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Table 2-1. ECONOMIC DEVELOPMENT IMPACTS FOR ALL FOCUS ON ENERGY PROGRAMS

Year First Year

Fifth Year

Tenth Year

Sum of 10 years

Impact Without Market Effects

Job Years 582 1,667 2,401 17,243 Sales generated (million) $43 $125 $190 $1,322 GRP (Value Added)* (million) $24 $78 $123 $824 Disposable income generated** (million) $11 $63 $127 $613 Impact With Market Effects

Job Years 630 1,774 2,778 18,956 Sales generated (million) $46 $135 $224 $1,483 GRP (Value Added)* (million) $26 $85 $146 $934 Disposable income generated** (million) $11 $66 $149 $779

* GRP = Gross Regional Product, reflecting net personal incomes to households and net profit income to businesses. ** Disposable income reflects both earned income and household savings in energy costs resulting from program participation. NOTE: All dollar amounts are in 2001 constant dollars.

2.4 INTERPRETATION OF ECONOMIC DEVELOPMENT IMPACTS

2.4.1 Distinguishing Economic Development Benefits from Other Impact Measures

When we refer to economic development impacts, we are referring to how the program causes changes in Wisconsin’s economy compared to what would happen without the program. It is important at the outset to distinguish economic development from other types of impacts. Some aspects of energy, environmental, and other non-energy impacts cause changes in the flow of dollars as measured in this report. However, there are other aspects of those impacts that are not reflected in the analysis of economic development impacts in this report. They include aspects of safety, security, reliability, health, and other facets of quality of life—which either lack estimates of how they affect the economy, or have policy importance beyond their mere effect on the flow of dollars in the economy.

It is also important to distinguish the coverage of factors covered under economic development impact analysis from those covered in a traditional benefit-cost analysis. A benefit-cost study can include any type of benefit that can be put into dollar terms (based on either actual flows of money or willingness-to-pay studies), whereas economic development analysis considers only effects on the actual flow of dollars. On the other hand, a traditional benefit-cost study does not encompass localized (in this case, state-level) impacts on economic competitiveness, on economic diversification, or on shifts in activity between this state and other states. An economic development impact analysis can consider all of these other types of impacts. In a national view of societal impacts, many of these state-level impacts disappear. However, these factors are often of great importance for state legislatures that are validly concerned about focusing their spending in ways that also help strengthen their own states’ economies.

Another important difference is that a benefit-cost study considers program spending as a cost that is subtracted from program benefit, while an economic development impact analysis traces how program spending can affect additional jobs and business activity. These two perspectives are not at odds. There can be a very real public policy interest in how a program


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creates local jobs and income that is different from the policy issue of whether the long-term benefits outweigh the required public outlay of funds. Both types of analysis have a place.

2.4.2 Adopting a Point of Reference

When we look at the economic impacts of energy programs, we have to carefully distinguish three different perspectives concerning net impacts: (1) When measuring net energy impacts, we do attempt to measure the change in energy use that is beyond what would have otherwise happened if the program did not exist. However, we do not assume that without the program, there would be any alternative program or policy instituted to affect energy use. (2) When measuring economic development impacts, we measure the flows of dollars in the economy that are associated with the operation of the program and its net energy impacts. Since we are trying to understand the nature of the dollar flows in the economy that are associated with the program, we do not assume that there would be any alternative program or policy instituted to affect energy use. However, (3) when measuring benefit-cost analysis components, then we do net out the effects of spending on this program since similar effects would have similarly occurred with any alternative disbursement of state program funds into the economy.

The above three perspectives are interdependent. First, it is important to accurately estimate net energy impacts before even starting to measure dollar flows in the economy. Second, it is important to be sure that we are correctly tracking the full flow of dollars in the economy. Some recent economic impact studies have measured jobs created in the energy efficiency industry and dollars of cost savings for participants, but have not fully tracked how these programs can also affect economic diversification, and “import substitution” (local content of products purchased). Finally, it is important that the impacts on the flow of dollars are understood before comparing program benefits and costs. This is important because benefit-cost findings measure impacts relative to an alternative scenario for use of program funds and the findings can thus vary depending on the nature of those assumptions. Without first looking at the nature of program direct impacts on the economy, we cannot know whether a small benefit-cost ratio means that: (a) the program has very little impact on the economy, or (b) the program has a large economic impact, but alternative uses of the funds could create similar effects.

The tracking of economic development impacts presented in this report is based on calculations of net energy impacts associated with Focus on Energy, while tracking the full economic effects of program spending without adjustment for how the money was being used prior to the creation of the Focus program. Funds for Focus were primarily redirected from Wisconsin utilities to Focus on Energy in an effort to preserve energy efficiency efforts in the state. The utilities had been collecting funds for these energy efficiency programs since the mid-1980s, but the programs were perceived to be threatened by pending deregulation of the utilities. Efforts to prepare an alternative economic scenario reflecting impacts of the myriad efficiency programs of the 31 participating utilities would be prohibitively expensive. If 31 utilities each operated their own energy efficiency efforts, we would not expect to see economic benefits comparable to those generated by the Focus comprehensive statewide programs, because Focus provides economies of scale in program design and implementation that could not be otherwise achieved. However, the analysis covered in this report is not intended to compare the current program against either past programs or hypothetical alternative spending programs. Rather, it is intended to describe how Focus


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program design and implementation are affecting Wisconsin’s economy and to identify the pattern of those impacts and associated policy implications.

2.4.3 Relationship to Different Types of Programs

It is important to recognize that Focus on Energy actually encompasses several different types of programs, each of which leads to a very different form of economic development impact.

• The Business Programs and Residential Energy Efficiency Programs are both designed to achieve energy efficiency by promoting the purchase of energy efficient products and equipment. This is accomplished by educating businesses and households on the economic viability of the energy efficient products and equipment and/or through improving their economic viability with incentives.

• The Focus on Energy Renewable Energy program is intended to stimulate the production of electricity in Wisconsin using nonfossil fuel sources—such as wind, solar energy, and biomass energy. The Renewable Energy program does not reduce energy usage but instead substitutes new forms of in-state electricity generation. While customers may not necessarily see any energy cost savings, the additional in-state generation capacity can reduce the outflow of money from Wisconsin that is now going for purchases of fossil fuels (e.g., coal and natural gas) and substitute spending on local businesses involved in development and installation of renewable energy equipment. Some forms of renewable generation (wind and solar) also add a benefit of decreased emissions-related costs. Biomass generation does produce emissions but has the added benefit of using in-state resources (farm waste, waste water products) that would otherwise produce no economic benefits to Wisconsin.

• Some of the funds spent on the Business, Residential, and Renewable Energy Programs are aimed at promoting long-term market effects, by encouraging the growth of energy efficient and renewable energy equipment manufacturers, suppliers, and installers within the state. As such, they support long-term economic growth of those industries in the state that is beyond the beneficial economic effect of household and business cost savings.

• The Low-income Programs provide comprehensive energy-efficiency services and pay a portion of eligible customers’ utility bills. The latter program elements do not reduce energy costs but merely change who is paying the bill and hence have little effect on growing the state economy except insofar as they affect household safety and bill payment costs. In addition, they clearly serve an important equity and social service function that is outside of the economic development impact analysis. Economic development impacts of the Low-income Programs are examined in a separate report from the rest of the economic development impacts, and, hence, are excluded from the analysis in this report.

While the primary objectives of these program elements may differ, they all have some effects on the economy, either by shifting purchasing patterns, saving energy, and/or providing other non-energy benefits. The same economic analysis framework is applied for each program. However, it should be noted that the programs that are specifically designed to save money (by saving energy) emerge with the greatest magnitude of economic benefits, while programs whose primary objectives are other than energy savings (e.g., promoting alternative energy


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generation) have a relatively smaller impact on the economy. Since economic development is only one portion of total program benefit, this does not mean that these programs are any less important in terms of either overall social goals or cost-effectiveness.

The remainder of this report examines how the program design and mix of program elements affect the nature of direct benefits and overall economic development impacts—in terms of impacts on urban/rural areas within Wisconsin, industry sector in the economy, and types of job and income opportunities created.

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3. POLICY ISSUE: HOW ARE ECONOMIC BENEFITS FROM FOCUS DISTRIBUTED AMONG URBAN, RURAL, AND SEMI-RURAL COUNTIES?

This chapter examines the impacts of Focus on Energy (Focus) in terms of the distribution of the economic benefits among urban, rural, and semi-rural counties in the state. In the economic development report, these benefits are represented as changes in jobs, business sales, disposable income, and gross state product. However, the REMI model used to determine the magnitude of these impacts does not provide a distribution of these benefits by geographic region. Therefore, as an alternative, we will report on the distribution of two of the key drivers of these economic impacts: (1) the households and businesses that have participated in Focus and benefit from savings on their energy bills, and (2) the businesses that provide energy efficiency goods and services and benefit from increased sales through projects they have implemented for participants. This analysis is conducted on data representing Focus activity through September 2003.

While this alternative does provide a reasonable picture of the distribution of the economic impacts of Focus, it should be recognized that there are issues with this approach. For example, there are differences in the intensity of energy use between regions; difference in the distribution of the industries that benefit from Focus such as the trade allies (which are represented in this analysis), and manufacturers of the equipment purchased for energy efficiency projects such as high-efficiency motors and HVAC systems. In addition, while these are inputs into the model, it does not provide any reflection of the economic multiplier benefits realized by Focus. As the following chapters demonstrate, the economic multiplier benefits are significant. The distribution of direct economic benefits that drive those multipliers provide little or no information about the distribution of the economic multiplier benefits.

Focus is largely a statewide program with the exception of the nonparticipating utilities. Therefore, there is some expectation that the economic benefits of Focus would be realized throughout the state.

Exploration of the geographic distribution requires the determination of the appropriate geographic level for the analysis. The choice to explore the distribution of economic benefits across urban and rural regions of the state was driven by WDOA’s interest in this analysis and the legislated mandate of rural economic development. Research was done around the issues of (1) using urban areas and urban clusters as defined by the Census Bureau, (2) county level, and (3) zip code level. After a review of these options, the decision was made to pursue the analysis at the county level—with counties designated as urban, semi-rural, or rural.

Prior to presenting the distributions by urban, semi-rural, and rural county types, information is provided about how Wisconsin counties were assigned a county type and how estimates of the numbers of eligible participants were derived.

The information presented in this chapter on program participation and energy bill savings are based on the detailed data provided by the Residential and Business program administrators on the energy efficiency improvements that they have implemented through the Focus on Energy. It is based on data as it existed in their tracking systems as of September 30, 2003. The energy bill savings reported are based on evaluated annual gross energy savings. It should be noted that for the economic analysis the energy bill savings used was based on

3. Policy Issue: How are economic benefits from Focus distributed…

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evaluated net annual energy savings. It should also be noted that the annual energy savings are realized every year over the life of the improvements made.

3.1 FINDINGS

Overall, 84% of households and 83% of businesses in Wisconsin are eligible for participation in Focus on Energy. Eligible participants are located in every county in the state. Over 200,000 of Wisconsin’s households have participated since the program began in the spring of 2001. These participants represent every county in the state. Focus has also served over 2,500 commercial and over 340 industrial businesses. Recurring annual cost savings from first year participants alone exceeded $8.7 million for residential participants and $7.3 million for business participants. And, as of September 2003, the recurring annual cost savings was over $13 million for residential participants and over $14 million for business participants.

The average residential participant saved $53 in their cost of living, which reflects a mix of small average savings from purchases of compact fluorescent light bulbs and much larger average savings from weatherization and heating/cooling projects. There were residential participants in all 72 counties.

The average industrial business participant saved over $18,000 and the average commercial business participant saved over $1,700 in annual business operating costs (excluding those only purchasing CFLs). There have been business participants in all Wisconsin counties except one.

Direct economic benefits to Focus participants are reaching all counties in the state. Participants in urban counties are receiving a slightly higher level of the direct economic benefits as compared to the semi-rural and rural counties. The distribution of benefits to counties through Focus programs is reasonably close to the distribution of eligible participants for all sectors (commercial, industrial, and residential). Residential participants in urban counties make up 81% of all residential participants and 82% of the energy savings, while they make up 78% of the eligible population. Commercial participants in urban counties make up 78% of all commercial participants and 76% of the energy savings, while they are 72% of the eligible population. Industrial participants in urban counties make up 75% of all industrial participants and 71% of the energy savings, while they are 62% of the eligible population.

The distributions of benefits to trade allies are more skewed toward allies in urban counties than are the benefits to participants, especially when examining the distribution of assisted energy savings to available trade allies.

Possible causes for the higher concentration of benefits in urban counties include:

• Participants

− A concentration of marketing and recruiting efforts in urban areas counties.

− Energy savings from energy efficiency measures implemented in rural counties realize less energy savings than similar projects in urban counties.

− Length of time to develop energy efficiency projects in the commercial and industrial sectors.

− Latent effects of Pilot program.

− Costs are higher to recruit and support participants in rural areas.


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− Rural counties are more likely to have significant portions covered by nonparticipating utilities, further decreasing the density of the eligible population.

− Energy intensity of eligible participants is lower in rural counties.

• Trade allies

− Trade allies in rural areas may not have enough demand for energy efficiency products and services to justify making it a component of the products and/or services that they provide.

− Latent effects of Pilot program.

− Costs are higher to recruit and support participants in rural areas.

It is important to note that programs already have a significant array of objectives that they must balance, e.g. resource acquisition, market development, equitable distribution of benefits (utility territory, senate and assembly districts, urban/rural), maximization of environmental benefits, and maximization of benefit-cost ratio. The primary question is whether the disparities noted are significant enough to warrant some action to increase rural trade ally participation. Many of these objectives can be at odds with each other—e.g., resource acquisition versus market development and maximization of a benefit/cost ratio versus equitable distribution of benefits across urban and rural counties. Nonetheless, program managers and program administrators should work to prioritize the various objectives so that it is understood what is an acceptable level of performance for each objective.

While Focus program administrators are encouraged to reach all areas of the state, they are also expected to manage their programs efficiently and sometimes these two objectives may be at odds. It takes more program resources and staff time to work with rural or semi-rural areas, and opportunities for energy efficiency are often not as great as they are for businesses that are located in urban areas.

A directive to program administrators to increase efforts in rural counties to rectify any inequities in the distribution of benefits could reduce the economic impacts realized by the state as a whole.

3.1.1 County Type

Counties were assigned a type of urban, semi-rural, or rural based on a coding system created by the Economic Research Service of the U.S. Department of Agriculture that assigns counties into one of ten categories based on their population density, urban population, and proximity to an urban area. Table 3-1 below shows a description of the nine codes that apply to Wisconsin, lists the counties included in each category, and has the codes grouped by county type. Twenty-five of Wisconsin’s 72 counties are classified as urban. These 25 urban counties are home to approximately 72% of Wisconsin’s population and make up around 30% of its land area. The 17 counties classified as rural are home to just over 9% of Wisconsin’s population and make up around 25% of its land area.


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Table 3-1. WISCONSIN COUNTIES (By County Type and Category Code)

County Type

Category Code

Category Code Description Counties Included

Percent of Total

Population

Percent of Land Area

Urban 1 County in metro area with 1 million population or more

Kenosha, Milwaukee, Ozaukee, Pierce, St. Croix, Washington, Waukesha

32.6% 5.58%

2 County in metro area of 250,000 to 1 million population

Brown, Columbia, Dane, Douglas, Iowa, Kewaunee, Oconto 15.4% 10.90%

3 County in metro area of fewer than 250,000 population

Calumet, Chippewa, Eau Claire, Fond du Lac, La Crosse, Marathon, Outagamie, Racine, Rock, Sheboygan, Winnebago

24.1% 13.51%

Semi-rural 4

County in metro area of fewer than 250,000 population

Dodge, Jefferson, Manitowoc, Portage, Sauk, Walworth, Wood 10.0% 9.25%

6

Nonmetro county completely rural or less than 2,500 urban population, not adj. to metro area

Barron, Door, Dunn, Grant, Green, Green Lake, Jackson, Langlade, Lincoln, Marinette, Monroe, Polk, Richland, Rusk, Shawano, Taylor, Vernon, Washburn, Waupaca

11.2% 29.40%

7

Nonmetro county with urban population of 2,500-19,999, adjacent to a metro area

Ashland, Crawford, Juneau, Oneida 1.8% 6.46%

Rural 8

Nonmetro county completely rural or less than 2,500 urban population, adj. to metro area

Adams, Bayfield, Buffalo, Burnett, Clark, Lafayette, Marquette, Menominee, Pepin, Trempealeau, Washara

3.6% 14.52%

9 County in metro area of fewer than 250,000 population

Florence, Forest, Iron, Price, Sawyer, Vilas 5.9% 10.39%

TOTAL 5,363,675

people 54,314 sq mi

Source: USDA Economic Research Service (ERS) 2003 Rural Urban Continuum Codes.

Figure 3-1 below provides a map of Wisconsin’s counties, shaded to identify their county type. A review of the map shows that that the classification of counties is largely consistent with what someone familiar with the state might expect: half of Wisconsin’s counties are semi-rural, 8 of the 17 rural counties are located in the northernmost portion of the state; 7 of the 25 urban counties are in the Milwaukee area or the corridor between Milwaukee and Chicago, with the other urban counties being those containing Wisconsin’s larger cities and or have high rates of commuting to those metropolitan areas—e.g., Dane County (Madison), La Crosse County (La Crosse), Brown County (Green Bay)—or are located close to Minneapolis, Minnesota—e.g., Pierce and St. Croix Counties.


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Figure 3-1. WISCONSIN COUNTIES (By County Type)

3.1.2 Eligibility to Participate in Focus on Energy

As alluded to earlier, not all Wisconsin residents or businesses are eligible to participate in Focus. Only those households or businesses in a participating utilities’ territory are eligible. While investor owned utilities (IOU’s) were required to participate in Focus, municipal utilities and cooperatives were not. Currently there are 31 utilities participating in Focus on Energy. These 31 utilities serve approximately 84% of Wisconsin’s 2.1 million households and approximately 82% of Wisconsin’s ~280,000 businesses. However, the semi-rural and rural counties are more likely to be served by a municipal or a cooperative utility, and, therefore, homes and businesses located in urban counties are more likely to be located in a participating utility territory with 90% of households in urban counties being eligible, while only 65% of households located in rural counties are eligible. Similarly, 92% of businesses in urban counties are eligible, while only 66% of businesses in rural areas are eligible.

The rest of this chapter provides discussion about the households and businesses that had participated in Focus as of September 2003, and compares the distribution of participants against the distribution of eligible participants. There are no further discussions or comparisons of state population of households or businesses.


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For the commercial and industrial sectors the estimates of eligible participants was based on two efforts.

1. The number of businesses in participating utility territories in each county were estimated by determining the proportion of businesses in the state of Wisconsin Department of Workforce Development Standard Name and Address Program (SNAP) covered by Wisconsin’s unemployment insurance law. It was determined, based on geographic location, whether each business was in a territory of a utility participating in the Focus on Energy program. Then, for each industry (at the two-digit Standard Industrial Classification [SIC] level) in each county, the proportion of the businesses that were in a participating utility territory was determined. This proportion was then applied to the number of businesses in each corresponding industry/county as reported by Dunn and Bradstreet to arrive at an estimate of the number of businesses eligible to participate in each industry in each county.

2. The second step was to identify the industries targeted by the Business Program administrator. This analysis resulted in the identification of 26 of the 82 two-digit SIC codes as being targeted by the industrial programs and 27 of the 82 two-digit SIC codes as being targeted by the commercial programs, with 10 industries (as identified by the two-digit SIC code) being targeted by both the industrial and commercial programs. The 26 codes identified as being targeted by the industrial programs account for about 36% of Wisconsin businesses, while the 27 codes identified as being targeted by the commercial programs account for about 79% of Wisconsin businesses. The number of eligible participants was then estimated by summing the eligible participants in each county for each of the industries identified as being targeted by the program administrator.

For the residential sectors, the estimate of the number of eligible households for each county was arrived at by determining the proportion of the area of each census block group that was within the boundaries of a utility participating in Focus on Energy. This proportion was then applied to the population of that census block group to estimate the number of participating households within the block group. These block group estimates were then aggregated to the county level.

3.2 PROGRAM PARTICIPANTS

3.2.1 Residential Programs

As of September 30, 2003, over 230,000 households are saving energy through participation in Focus on Energy. This represents about 13% of eligible Wisconsin households. The average annual energy bill savings for these households was $53, for a total of $12 million dollars in annual energy bill savings. The vast majority of the residential participants are households that purchased ENERGY STAR® products from retail stores in Wisconsin.

The distribution of energy bill savings from Residential Programs by county type is reasonably close to the distribution of eligible participants. Of the $12 million dollars in annual energy bill savings being realized by participating households, 82% is being realized in urban counties, while only 74% of the eligible participants are in urban counties (see Table 3-2). The greater proportion of savings in urban counties is partially due to a slightly higher rate of participation of households in urban counties at 14% of eligible participants compared to 12% of households in semi-rural counties and 9% of households in rural counties. This is


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compounded by a slightly higher average annual energy bill savings being realized by participants in urban counties at $53 per participating household in urban counties versus $52, and $48 for semi-rural and rural households respectively.

Table 3-2. RESIDENTIAL PARTICIPATION AND ENERGY BILL SAVINGS (By County Type)

County Type Eligible

Participants Participants

Annual Energy Bill

Savings Rate of

Participation

Average Annual Energy Bill Savings per Participant

Urban 78 % 81% 82 % 14% $53

Semi-rural 18 % 16% 16 % 12% $52

Rural 4 % 3% 2 % 9% $48

Program Total 1,761,395 230,119 $12 Million 13% $53 Appendix B includes the above information by county.

Figure 3-2 shows that urban counties have attracted a level of participation that is slightly higher than their proportion of the eligible population and a level of energy savings that is even higher. The reverse is true for the semi-rural and rural counties.

Figure 3-2. RESIDENTIAL PROGRAMS: ELIGIBILITY, PARTICIPATION, AND ENERGY BILL SAVINGS

(By County Type)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Urban Semi-Rural Rural

Per

cen

tag

e o

f To

tal

Eligible Participants Participants Annual Energy Bill Savings

Appendix B includes the above information by county.

In Figure 3-4, Residential per Capita Energy Savings, energy bill savings are shown as “per capita” to show differences in the intensity of savings relative to the population in the county. The per capita value is derived by summing the annual energy bill savings from all participants in a county and dividing that sum by the number of eligible participants in that county. Since energy bill savings of residential participants is one of the key factors in generating economic impacts, this map provides a view of how the impact from that economic


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driver is distributed by county. The comparison of the percentage of counties that fall into each of the five per capita energy bill savings categories shows that the urban counties are very highly concentrated in the upper end of the range with 55% of the 20 urban counties in the highest category (per capita energy bill savings of greater than $5 per household) while there are no rural counties in the highest category and 50% of the 16 rural counties are in the lowest two categories (per capita energy bill savings of less than $3 per household).

Figure 3-3. INTENSITY OF RESIDENTIAL ENERGY BILL SAVINGS (By County Type*)

$0-3.75 $3.76-5.50

$5.51-6.75

$6.76-8 >$8

0%5%

10%15%20%25%30%35%40%45%

RURAL URBAN SEM I-RURAL

* Intensity of residential energy bill savings is portrayed in terms of the program impact on total residential energy bill savings in the county, divided by the total number of eligible households living in the county. Heights of the graphs show the percentage of all counties falling into each of the five categories (from low to high savings per eligible household). Appendix B includes the above information by county.


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Figure 3-4. Residential Per Capita Energy Savings

DANE

CLARK

POLK RUSK

DOUGLAS

DODGE

ROCK

TAYLOR

LINCOLN

ASHLAND

JUNEAU

CHIPPEWA

BURNETT

PORTAGE

LANGLADE

BROWN

FOND DU LAC

LAFAYETTE

EAU CLAIRE

OUTAGAMIE

CRAWFORD

TREMPEALEAU

JEFFERSON

WALWORTH

MANITOWOC

WINNEBAGOCALUMET

WASHINGTON

MENOMINEE

KENOSHA

MILWAUKEE

PRICE

VILAS

GRANT

IRON

ONEIDA

SAWYER

BAYFIELD

SAUK

DUNN

IOWA

MARATHON

FOREST

WOOD

MARINETTE

OCONTO

JACKSON

BARRON

MONROE

VERNON

ADAMS

SHAWANO

GREEN

PIERCEDOOR

BUFFALO

COLUMBIA

ST. CROIX

WAUPACA

WASHBURN

RICHLAND

WAUSHARA

WAUKESHA

FLORENCE

RACINE

PEPIN

LA CROSSE

SHEBOYGAN

MARQUETTE

KEWAUNEE

GREEN LAKE

OZAUKEE

Wisconsin Focus on Energy Commercial Programs Per Capita* Energy Bill Savings by County

The map above portrays the annual energy savings realized by projectsimplemented through programs targeted at commercial sector businesses as of September 30, 2003. Electric and gas savings have been valued at the average cost of gas and electricity for commercial businesses in Wisconsin and summed for all projects within each county and divided by the number of eligible commercial businesses in that county.

* The unit of population is commercial customers in industries targeted by the agricultural and commercial business programs in participating utility territories.

Per Capita Annual Energy Bill Savings by County

Map Produced by: PA Government Services and Patrick Engineering Incof The Focus on Energy Evaluation Team. November, 2003.

$0

$1 - $9.00

$7.01 - $17.50

$17.51 - $26.00

$26.01 - $38.00

> $38.00


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3.2.2 Business Programs—Commercial Sector

As of September 30, 2003, over 2,500 organizations (including schools, churches, and government agencies) and commercial businesses are saving energy through participation in Focus on Energy. These businesses represent one percent of the eligible commercial entities in Wisconsin. The average annual energy bill savings for these businesses was $1,746. In aggregate, participants in this program achieved annual energy bill savings of $7.3 million dollars for the state of Wisconsin. The majority of participants were active in the Small Retail and Services program or the Schools program. Participants came from 71 of Wisconsin’s 72 counties.

Each type of county was responsible for a similar relative proportion of energy bill savings compared to their proportion of participants, with urban counties showing slightly higher relative savings and participation. Organizations and commercial businesses in urban counties account for 78% of Focus participants in this sector and account for 72% of eligible participants (see Table 3-3). These participants in urban counties have realized 76% of the energy bill savings in this sector. Nonurban counties, while lagging in participation, had greater per participant energy savings, which allowed them to capture a greater proportion of the energy bill savings compared to their proportion of participants.

Table 3-3. COMMERCIAL PARTICIPATION AND ENERGY BILL SAVINGS (By County Type*)


Participants Participants Annual Energy

Bill Savings Rate of

Participation

Average Annual

Energy Bill Savings per Participant

Urban 72% 78% 76% 2% $1,721

Semi-rural 23% 20% 21% 1% $1,840

Rural 6% 2% 3% 1% $1,751

Program Total 160,033 2,629 $7,261,747 1% $1,746 Commercial participants is from the Business Program.mdb Query”Count Number of Participants2” A pivot table of the same data is in the GIS mapping workbook for November 03. * Columns not adding up to 100% are due to rounding. Appendix B includes the above information by county.

Figure 3-5 shows that the influence of participant location had little bearing on the level of energy savings achieved in the commercial sector, but that nonurban counties had slightly lower rates of participation and savings compared to their proportion of the eligible population.


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Figure 3-5. COMMERCIAL PROGRAM: ELIGIBILITY, PARTICIPATION, AND ENERGY BILL SAVINGS

(By County Type)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Urban Semi Rural Rural



In Figure 3-7, Commercial per Capita Energy Savings, energy bill savings are shown as “per capita” to show differences in the intensity of savings relative to the number of eligible commercial businesses in the county. The “per capita value” is derived by summing the annual energy bill savings from all participants in a county and dividing that sum by the number of eligible participants in that county. Since energy bill savings of commercial participants is one of the key factors in generating economic impacts, this map provides a view of how the impact from that economic driver are distributed by county. The comparison of the percentage of counties that fall into each of the five per capita energy bill savings categories shows that the rural counties are more concentrated in the lower end of the range with 29% of the 17 rural counties in the lowest category (per capita energy bill savings under $12 per eligible commercial participant). The highest category is still dominated by urban and semi-rural counties, but rural counties were represented. Semi-rural counties had their greatest representation in the moderate energy savings per capita. This distribution is presented graphically in Figure 3-6.


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Figure 3-6. INTENSITY OF COMMERCIAL SECTOR ENERGY BILL SAVINGS (By County Type*)

>0 >=12.01>=20.01

>=30.01>=50.01

0%

5%

10%

15%

20%

25%

30%

35%

RURAL SEM I-RURAL URBAN

* Intensity of commercial energy bill savings is portrayed in terms of the program impact on total commercial energy bill savings in the county, divided by the total number of eligible commercial business establishments located in the county. Heights of the graphs show the percentage of all counties falling into each of the five categories (from low to high savings per eligible commercial business). Appendix B includes the above information by county.


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Figure 3-7. Commercial Per Capita Energy Savings

DANE

CLARK

POLK RUSK

DOUGLAS

DODGE

ROCK

TAYLOR

LINCOLN

ASHLAND

JUNEAU

CHIPPEWA

BURNETT

PORTAGE

LANGLADE

BROWN

FOND DU LAC

LAFAYETTE

EAU CLAIRE

OUTAGAMIE

CRAWFORD

TREMPEALEAU

JEFFERSON

WALWORTH

MANITOWOC

WINNEBAGOCALUMET

WASHINGTON

MENOMINEE

KENOSHA

MILWAUKEE

PRICE

VILAS

GRANT

IRON

ONEIDA

SAWYER

BAYFIELD

SAUK

DUNN

IOWA

MARATHON

FOREST

WOOD

MARINETTE

OCONTO

JACKSON

BARRON

MONROE

VERNON

ADAMS

SHAWANO

GREEN

PIERCEDOOR

BUFFALO

COLUMBIA

ST. CROIX

WAUPACA

WASHBURN

RICHLAND

WAUSHARA

WAUKESHA

FLORENCE

RACINE

PEPIN

LA CROSSE

SHEBOYGAN

MARQUETTE

KEWAUNEE

GREEN LAKE

OZAUKEE

Wisconsin Focus on Energy Commercial Programs Per Capita* Energy Bill Savings by County

The map above portrays the annual energy savings realized by projectsimplemented through programs targeted at commercial sector businesses as of September 30, 2003. Electric and gas savings have been valued at the average cost of gas and electricity for commercial businesses in Wisconsin and summed for all projects within each county and divided by the number of eligible commercial businesses in that county.

* The unit of population is commercial customers in industries targeted by the agricultural and commercial business programs in participating utility territories.



$0

$1 - $9.00

$7.01 - $17.50

$17.51 - $26.00

$26.01 - $38.00

> $38.00


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3.2.3 Business Programs—Industrial Sector

Since the program’s inception, 362 industrial businesses have implemented energy saving measures and represented 54 different counties. Overall, urban counties accounted for 74% of all industrial participants, while semi-rural counties accounted for 21% and rural counties represented 5% of industrial participants. Figure 3-10 shows the “per capita” savings by county in the Focus industrial programs.

As of September 30, 2003, over 362 industrial businesses had started saving energy through participation in Focus on Energy. These businesses represent less than one percent of the eligible industrial businesses in Wisconsin. The average annual energy bill savings for these businesses was $18,981. In aggregate, participants in this program achieved annual energy bill savings of $6.9 million. The majority of participants were active in the General Industrial program. Participants represented 54 of Wisconsin’s 72 counties. It is not surprising that a number of counties are not represented given the relatively small number of participants. Most of the counties not represented are rural and semi-rural counties. Sixty percent of rural counties, 73 percent of semi-rural counties, and 88 percent of urban counties were represented. The smaller representation from rural counties did not hinder their energy bill savings.

Rural counties were responsible for a considerably higher proportion of energy bill savings thanks to one participant in Price county. Their energy savings also lifted the per participant average to $18,981. Otherwise, urban counties maintained a similar proportion of participants (74%) to energy bill savings (71%). Semi-rural counties suffered in proportional energy bill savings, mostly in response to the proportional gain by rural counties.

Table 3-4. INDUSTRIAL PARTICIPATION AND ENERGY BILL SAVINGS (By County Type)



Annual Energy Bill

Savings Rate of

Participation

Average Annual


Urban 69% 74% 71% 0.5% $18,313

Semi-rural 25% 21% 14% 0.4% $12,725

Rural 6% 5% 15% 0.5% $53,387

Program Total 71,740 362 $6.9 Million 0.5% $18,981 Ten additional participants, who accounted for 0.61% of energy savings, were in the database, but did not have sufficient information to assign to a county, they are not included. Appendix B includes the above information by county.

Figure 3-8 shows that urban counties have attracted a level of participation that is higher than their proportion of the eligible population; however, the level of energy savings is not commensurate with the level of participation. The inverse is true in rural counties, where they have attracted a nearly equal proportion the eligible population but one participant has significantly increased the per capita energy savings. Semi-rural counties attracted a smaller portion of the eligible population and had the lowest rates of energy savings.


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Figure 3-8. INDUSTRIAL PROGRAM: ELIGIBILITY, PARTICIPATION, AND ENERGY BILL SAVINGS

(By County Type)

0%

10%

20%

30%

40%

50%

60%

70%

80%




In Figure 3-10, Industrial per Capita Energy Bill Savings, energy bill savings are shown as “per capita” to show differences in the intensity of savings relative to the eligible industrial businesses in the county. The “per capita value” is derived by summing the annual energy bill savings from all participants in a county and dividing that sum by the number of eligible participants in that county. Since energy bill savings of industrial participants is one of the key factors in generating economic impacts, the map (Figure 3-10) provides a view of how the impact from that economic driver are distributed by county. Figure 3-9 provides a comparison of the percentage of counties that fall into each of the five per capita energy bill savings categories. It shows that rural and semi-rural counties are more concentrated in the lower end of the range with 50% of the 30 semi-rural counties in the lowest category (per capita energy bill savings under $18 per eligible industrial participant). The high percentage of rural and semi-rural counties in the lower energy bill savings categories is partially driven by the counties with no participants. Counties from all three county types are represented in the highest energy bill savings category, with over 24% of urban counties in the highest category and only one percent in the rural counties. (The large per capita energy savings in rural counties is driven by energy savings by a single participant in Price county.)


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Figure 3-9. INTENSITY OF INDUSTRIAL SECTOR ENERGY BILL SAVINGS (By County Type*)

$0-18 $18.01-29

$29.01-50

$50.01-130

>$130

0%

10%

20%

30%

40%

50%

60%

SEM I-RURAL RURAL URBAN

* Intensity of industrial energy bill savings is portrayed in terms of the program impact on total industrial energy bill savings in the county, divided by the total number of eligible industrial business establishments located in the county. Heights of the graphs show the percentage of all counties falling into each of the five categories (from low to high savings per eligible industrial business). Appendix B includes the above information by county.


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Figure 3-10. Industrial Per Capita Energy Bill Savings

PRICE

RUSK

SAUK

MARATHON

DOUGLAS

WOOD

ASHLAND

CHIPPEWA

BURNETT

PORTAGE BROWN

FOND DU LAC

EAU CLAIRE

OUTAGAMIE

JEFFERSON

WALWORTH

WINNEBAGO

RACINE

LA CROSSE

SHEBOYGAN

OZAUKEE

DANE

CLARK

VILAS

POLK

BAYFIELD

DUNN

FOREST

DODGE

ROCK

MARINETTELINCOLN

BARRON

JUNEAU

VERNON

ADAMS

SHAWANO

GREEN

LANGLADE

PIERCEDOOR

COLUMBIA

ST. CROIX

WAUPACA

RICHLAND

WAUSHARA

WAUKESHA

TREMPEALEAU

MANITOWOC

PEPIN

CALUMET

WASHINGTON

KENOSHA

MILWAUKEE

GRANT

ONEIDA

SAWYER

IRON

IOWA

TAYLOR

OCONTO

JACKSON

MONROE

BUFFALO

WASHBURN

LAFAYETTE

CRAWFORD

FLORENCE

MARQUETTE

MENOMINEE

KEWAUNEE

GREEN LAKE

Wisconsin Focus on Energy Industrial ProgramsPer Capita* Energy Bill Savings by County

The map above portrays the annual energy savings realized by projects implemented through programs targeted at industrial sector businesses as of September 30, 2003. Electric and gas savings have been valued at the average cost of gas and electricity for industrial businesses in Wisconsin and summed for all projects within each county and divided by the number of eligible industrial businesses in that county.

*The unit of population is industrial customers in industries targeted by the industrial programs in participating utility territories.



$0

$0.01 - $18.00

$18.01 - $29.00

$29.01 - $50.00

$50.01 - $130.00

> $130.00


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3.2.4 Business Programs—By Energy Consumption

A more telling comparison between counties is the energy savings the business program relative to the county energy use rather than relative to eligible population. This is particularly important for the industrial sector, since there are some very large industrial businesses located in rural and semi-rural areas. Precise data on energy consumption is not available on a county level. To derive an estimate of the annual energy consumption in MMBTU per county we applied the energy intensity indices as reported by the Wisconsin Division of Energy to the relevant data by sector.1 This value was adjusted to reflect the proportion of customers eligible to participate in the program, but does not any differences in the intensity of firms that may or may not be eligible within the county.

This alternative perspective indicates that rural counties are achieving high levels of energy savings compared to their proportion of energy consumption, while urban counties are falling short. However, as noted previously participation rates are more favorable in urban counties.

Table 3-5. BUSINESS PROGRAM PARTICIPATION AND ENERGY BILL SAVINGS COMPARED TO ENERGY CONSUMPTION

(By County Type)

This data is summarized in: the Excel files: F:\WDOA-Statewide\CrossCuttingEval\UrbanRural\Participants: Avoided Costs by county type Res-Com-Indust -09-03.xls and Eligible - Actual Participants by county type Res-Com-Indust 09-03.xls (the sources for generating the data are included in those spreadsheets).

As demonstrated visually in Figure 3-9, the majority of energy consumed in the state is in urban counties, but these counties have not saved a similar proportion of energy, even though they had higher rates of participation in the Focus program. Semi-rural counties out- saved their proportion of energy consumed, with lower rates of participation than would have been expected. Rural counties, with the help of one large project far exceeded its expected proportion of energy savings compared to total consumption, but they too fell short of their rightful share of participants.

1 Commercial energy intensity is measured in BTU per employee, Industrial by value added of manufacturing, and Agriculture by acres in production. See Appendices C and D for further details on the source of the data and the values derived.



Annual Energy Bill

Savings

Distribution of Energy

Consumption

Average Annual


Urban 71% 76% 74% 82% $4,545

Semi-rural 24% 21% 18% 16% $4,299

Rural 6% 3% 9% 2% $14,478

Program Total 231,773 3,165 $14,132,884 673 TBTU $4,775


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Figure 3-9. BUSINESS PROGRAM: ENERGY CONSUMPTION, ENERGY BILL SAVINGS ELIGIBILITY, AND PARTICIPATION

(By County Type)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%


Per

cent

of S

tate

Energy Consumption Annual Energy Bill Savings Eligible Participants Participants

3.2.5 Renewable Energy Program

As of September 30, there were a total of 55 participants, 38 of which completed energy producing projects. Participants represented 24 counties. The installed projects included 30 photovoltaic installations, 4 wind machines, and 1 solar space-heating project. One large hydroelectric project in an urban county will contribute 600 kW of capacity and contributes to the uneven distribution of energy generation compared to participation. There are issues with accurately identifying eligible participants so the proportion of eligible participants and the rate of participation by county type have not been provided.

Table 3-5. RENEWABLE ENERGY PARTICIPATION AND ENERGY BILL SAVINGS (By County Type)

County Type All Participants

Energy Production Participants

Annual Retail Value of Energy Produced*

Average Annual Retail Value of Energy

Produced per Participant

Urban 62% 56% 96% $15,244 Semi-rural 31% 42% 4% $810 Rural 7% 6% 0.05% $153 Total 55 38 $317,166 $8,810 * The cost savings are based on the retail energy rates for residential customers, although the participants are a mix between residential, commercial, and industrial customers.


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3.3 TRADE ALLIES BY COUNTY TYPE

A. RESIDENTIAL—RETAIL/NON-RETAIL

The ENERGY STAR Products program is driven by promotion of ENERGY STAR products through retail outlets throughout the state. The promotions typically offer a reward to retail customers for purchasing ENERGY STAR qualified products. These rewards may be offered at the counter at the time of purchase, or may require the purchaser to send in a form to claim the reward. Retail establishments that have participated in ENERGY STAR products promotions sponsored by Focus on Energy are defined as “active allies.” The “targeted allies” are all of the businesses in the sectors that represent the majority of “active allies.” Of the retailers for which their SIC was known (~65%), over 90% are represented by the five industrial sectors shown below Table 3-6. There are 2,976 businesses in Wisconsin in these five industrial sectors. Approximately 31% of those businesses have participated in an ENERGY STAR Products promotion, resulting in over 277,185 million BTUs of energy savings for program participants. The distribution of active allies is significantly higher in semi-rural counties at 31% as compared to the 16% of targeted allies. However, the proportion of assisted energy savings is strongly skewed to active allies in urban counties, accounting for 90% of assisted energy savings, while they are only 64% of the active allies. Active allies in rural counties make up 5% the active allies, but less than 1% of the assisted energy savings.

Table 3-6. RESIDENTIAL PROGRAM ALLIES—RETAIL

Location Distribution of Targeted* Allies

Distribution of Active Allies

Rate of Participation

(Active/Targeted)

Proportion of Assisted

Energy Savings

Urban 81% 64% 24% 90%

Semi-rural 16% 31% 61% 10%

Rural 4% 5% 43% 0%

Grand Total 2,976 914 31% 277,185 MMBTU

* This reflects over 90% of the savings from the ENERGYSTAR reward program * Targeted retail allies include the four-digit SIC codes of: 5311 Department stores 5712 Furniture Stores 5251 Hardware Stores 5722 Household Appliance Stores 5211 Lumber and other building materials

The nonretail active allies for the Residential Program are those that are providing services through programs such as Wisconsin ENERGY STAR Homes (which promotes the building of energy efficient homes) and Home Performance with ENERGY STAR (which promotes energy efficiency improvements for existing homes). These programs account for approximately 20% of the total energy savings for the residential sector. As might be expected, the trade allies for these programs tend to be in the building trades (see list of targeted ally industries listed below Table 3-7). Again, the distribution of active allies is skewed towards active allies in urban counties. Also, urban allies are responsible for a much higher proportion of assisted energy savings (87%) relative to the proportion of active allies (72%).


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Table 3-7. RESIDENTIAL PROGRAM ALLIES—NONRETAIL

Location Distribution of Targeted Allies

Distribution of Active Allies


(Active/Targeted)

Proportion of Assisted

Energy Savings

Urban 61% 72% 17% 87%

Semi-rural 32% 24% 11% 12%

Rural 7% 4% 8% 1%

Grand Total 7,509 1,109 15% 118,853 MMBTU

* Targeted allies include the four-digit SIC codes of: 1521 General Building Contractors Single Family Homes 1542 Nonresidential Construction, Nec (not elsewhere classified) 1711 Plumbing Heating and Air Conditioning 1742 Plastering Drywall, Acoustical and Insulation Work

B. BUSINESS PROGRAM ALLIES (COMMERCIAL AND INDUSTRIAL)

The following tables represent active allies that had sufficient information in the database to identify their location by county. Allies with this information, and allies overall, were involved with about 5 per cent of the energy savings as of September 30, 2003.

Of the ~4960 proposals that have been implemented since the beginning of the program, an ally identification of “0” was provided for over 3,500 or approximately 70% of these projects, accounting for over 94% of the energy savings for business participants. This indicates that the largest portion of energy savings in the business program is not coming with the assistance of allies. These records may represent purchases of lighting equipment, retail activity, or omissions in reporting. The allies that are assisting are predominantly from urban areas as demonstrated in Table 3-8.


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Table 3-8. BUSINESS PROGRAMS ALLIES

Location

Distribution of Targeted

Allies

Distribution of Active

Allies


(Active/Targeted)

Proportion of Assisted Energy

Savings*

Distribution of Annual Energy Consumption

Urban 70% 69% 2% 89% 82%

Semi-rural 23% 30% 2% 11% 16%

Rural 6% 1% 0.34% 0.03% 2%

Grand Total 14,489 263 2% 68,971 MMBTU 673 T BTU Source: Targeted Allies: BUS AlliesBUS ALLIES - TargetSIC-Mar2003 - UPDATE-12-8-2003.xls; Active Allies count and energy savings:Business Programs.mdb Query: “Implemented Measures - Details2_Ally Savings”; Annual Energy Consumption: County Stats E-Consumption.mdb Query - Consumption by County and Sector – Eligible *WI allies only assisted with about 5% of the total energy savings, the rest were independent, or not properly recorded 30 Allies were not from WI, they assisted with less than one percent of total energy savings. Targeted SIC Codes Include: 7629 Electrical and Electronic Repair Shops, NEC 5063 Electrical Apparatus and Equipment Wiring Supplies, and Construction Materials 1731 Electrical Work 8711 Engineering Services 5083 Farm and Garden Machinery and Equipment 1521 General Building Contractors Single Family Homes 5084 Industrial Machinery and Equipment 5211 Lumber and other building materials 8742 Management Consulting Services 1711 Plumbing Heating and Air Conditioning 1761 Roofing Siding and Sheetmetal Work

According to this distribution, allies in urban counties are acquiring a significantly larger proportion of the benefits from sale of energy efficiency products and services. Considered on a per-ally basis, urban allies on average assisted with about 340 MMBTU while rural allies assisted with an average of only 8 MMBTU. This may be a result of the availability of such services in urban areas.

C. RENEWABLE ENERGY PROGRAM ALLIES

Allies in the Renewable Energy Program assist in the installation of renewable energy equipment. Not all projects require the assistance of an ally, some are self-installed, and some are research grants that require no installation. Still others have incomplete record of the allies that assisted.

For the 38 projects completed before September 30, 2003, eight allies assisted with the completion of 28 projects. Allies for the wind machines, two PV, and one solar space-heating project were not available in the database. Three PV systems were self-installed. The following table shows the projects completed by allies from the various county types and the resulting capacity expansion for which they were responsible. The high proportion of assisted savings in urban counties was primarily the result of the large hydroelectric project installed in one urban county by a firm from another urban county. There are issues with accurately identifying eligible participants, so the proportion of targeted allies and the rate of participation by county type have not been provided.


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Table 3-9. ALLIES FOR THE RENEWABLE ENERGY PROGRAM

Location Number of

Allies Percent of Projects Proportion of Assisted

Energy Production

Urban 38% 10% 93%

Semi-rural 38% 53% 1%

Rural 21% 11% 0.16% Self-installed or No ally reported 26% 5%

Total 8 38

The eight allies came from the following counties: Bayfield, Dane, Dunn, Door, Marathon, Portage, Richland, and Vilas.

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4. POLICY ISSUE: HOW DOES FOCUS AFFECT INDUSTRY IN WISCONSIN?

This chapter examines impacts of the first year of Focus on Energy in terms of how they affect the growth of different industries within Wisconsin’s economic base. Following the introduction is a brief summary of the findings presented in more detail later in the chapter. Following the findings section are three sections that discuss: (1) direct economic impacts (2) overall economic development effects, and (3) comparison of economic development impacts to current business patterns and expected future trends.

At the outset, it is important to note that Focus on Energy was designed to reach all sectors of the economy. The Business Programs directly reach commercial and industrial businesses, as well as farms, government, and nonprofit organizations. The Residential Program directly reaches households. The Renewable Energy Program is open to all types of applicants. Yet, despite this broad outreach, various industry segments can be affected by Focus on Energy in different ways. For instance, rates of participation and energy cost savings can vary among segments of the economy, as a consequence of program design and implementation features (such as the particular types of energy-saving measures being promoted, the form of financial incentives, and marketing patterns). Various segments of the economy can also experience different levels of growth impact, depending on the nature of changes in business and household spending patterns (such as amount and the types of products and services being purchased and the portion of spending going to in-state businesses).

Focus on Energy, by reducing energy use and encouraging development of renewable power, serves to decrease the “leakage” of dollars flowing out of the state’s economy for purchases of coal used in generating electricity. Instead, the program directs more dollars towards in-state suppliers of energy services and manufacturers of heating and cooling systems, thermostat controls, and motors—all of which have a disproportionately high concentration in Wisconsin. It also supports the long-term growth of renewable power equipment suppliers in the state. As a result, Wisconsin’s economy grows more over time and additional jobs are created. There are small losses of jobs in utilities and coal-handling industries, which are more than offset by growth in jobs in technology-driven energy services and electrical equipment industries.

The Residential Program also serves to decrease the cost of living for participating households, freeing up disposable income on a recurring basis for the life of the installed energy efficiency measures. Over time, the aggregate statewide cost savings for Wisconsin residents grows as past participants continue to save money and a continuing stream of new participants joins them. The aggregate effect is to help to make Wisconsin a more attractive place to live, increasing population. The additional disposable income increases spending on consumer purchases, which causes growth, particularly in the retail and service sectors.

The commercial and industrial programs serve to reduce business operating costs and this effect also continues to grow over time as past participants continue to save money and a continuing stream of new participants joins them. The aggregate effect is to improve Wisconsin’s economic competitiveness by continuing to reduce the cost of doing business in Wisconsin relative to other states. This allows existing businesses to increase profits and expand markets and also makes Wisconsin a more attractive place for business location and expansion. The economic model forecasts how these cost changes affect Wisconsin’s economic growth over time. While the greater business competitiveness aids in retention and expansion of manufacturing output in Wisconsin, job increases are limited in the

4. Policy Issue: How does Focus affect industry in Wisconsin?. . .

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manufacturing sector because there is also a trend towards greater productivity with fewer workers in many manufacturing industries.

Overall, it shows that economic effects of Focus on Energy grow over time. Focus supported $46 million of business sales in Wisconsin in its first year and this is projected to grow to $224 million per year by the tenth year. However, this growth is not distributed equally across all sectors of the economy. Key findings about the distribution of impacts are listed below.

The process of tracking and modeling economic impacts of Focus on Energy was described in a separate report, Focus on Energy Statewide Evaluation, Economic Development Benefits: Interim Economic Impacts Report, March 31, 2003. While that report did discuss the magnitude of overall impacts on Wisconsin’s economy, this chapter extends that analysis to examine more closely the distribution of those overall impacts on various industries within Wisconsin.

4.1 FINDINGS

Key findings in response to the question, “How does Focus affect industry in Wisconsin?”

• Since the Residential Programs account for over half of total cost savings to date, the overall Focus program impact on Wisconsin’s employment is particularly high for retail and service sectors of the economy. This is largely because residential participants who save energy costs end up redirecting much of their spending to purchases of consumer goods and services. An additional reason is that the program directly benefits growth of the business services sector and particularly energy services businesses.

• It is expected that these economic impacts will evolve as programs mature. For instance, in the first full year of program operation, significant portions of Business Program impacts were concentrated among school facilities. In a period of tight state and local budgets, this concentration can be seen as a prudent and beneficial use of Public Benefit funds. As Focus programs reach out to more industries in the future, the nature of the economic benefits may also shift with larger benefits on the state’s industrial competitiveness.

• Focus reduces the dollars leaking out of Wisconsin’s economy; by the tenth year of the program (at current budget levels) this amount is expected to be over $20 million annually.

• In the first year of Focus, participation by schools accounted for 15% of all projects and 23% of cost savings created by participation in Focus Business Programs.

• The manufacturing sector accounted for only ~8% of the projects, but realized over 40% of the savings created by participation in Focus Business Programs.

• Hardware stores dominated the participation and assisted savings generated by active allies in the ENERGY STAR Products program.

• As might be expected, the “Electrical Work” and “Plumbing, Heating and Air Conditioning” sectors played significant roles in both the Residential nonretail Programs and Business Programs.


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• Manufacturing accounts for the largest share of Focus’ total statewide economic output impact—22%, though only 5% of the total job impact. The high impact on manufacturing output reflects the program impact on increasing the cost competitiveness of this sector as well as the redirection of some business and household purchases towards energy-efficient electrical equipment and machinery manufactured in Wisconsin. The smaller job impact is due to the fact that Wisconsin manufacturing has a high value of output per worker.

• Retail accounts for 12% of the output impact and 24% of the employment impact. The effect on output is attributable to the large size of the Residential Program, which causes participating households to experience an increase in their disposable income, which they then spending on retail, entertainment, and personal services. The larger job impact is due to the high labor-intensity of retailing. There is also an impact on wholesaling as a result of the increased retail sales.

• Business services accounts for 20% of the output impact and 29% of the employment impact. This classification includes energy-related services, which are supported by the Business Programs’ marketing and incentive features.

4.2 DIRECT ECONOMIC IMPACTS

The direct effects are factors directly attributable to the program design and implementation. They can be classified into four categories: (a) reduced business operating costs, (b) reduced household living costs, (c) price effects, and (d) reduced leakage of spending from the Wisconsin economy. This section discusses the nature of each of these impact categories in terms of the extent to which these impacts occur and the way in which they affect some industries more than others.

4.2.1 Reduced Business Operating Costs

By reducing energy consumption, one of the key direct economic effects of Focus on Energy is to reduce operating costs for businesses. The participation rates and extent of cost savings differed among industries. Table 4-1 shows the distribution of first year Business Programs participants by industry classification, and its associated shares of total projects and business program energy savings. It shows that participants came from nearly all elements of the economy—including farms, manufacturing plants, commercial activities, office buildings, and government facilities.

However, it is also clear that the business program was implemented with some targeting in the first year of the program. Schools made up a notably large share of the program’s first year participants (15% of all projects and 23% of all cost savings). In a period of tight state and local budgets, the concentration on reducing operating costs of schools through heating and cooling improvements can be seen as a prudent and beneficial use of Public Benefit funds. It is unlikely, though, that this concentration on schools will continue indefinitely. Accordingly, the analysis of long-term economic development impacts assumed that business program participants will gradually move towards being more representative of the overall mix of players in the economy.

The other large concentration of business projects was in retail establishments, which accounted for 28% of all first-year projects. However, most of these retail projects involved


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more modest measures, such as lighting changes, that produce more modest energy cost savings. Accordingly, retailers accounted for just 3% of the program’s overall energy cost savings.

The opposite was true for manufacturing, which accounts for 8.7% of the projects but over 42% of the business program’s overall energy cost savings. Moreover, it was particularly true for furniture and paper companies, which accounted for just 0.7% of the projects, but over 16% of the business program’s overall energy cost savings.

Table 4-1. BUSINESS PROGRAMS: DISTRIBUTION OF FIRST YEAR PARTICIPATION (Sorted by Industry Sector)

Industry Sector Percent of Cost Savings Percent of Projects Agriculture–Forestry–Fishing 1.8 2.4 Apparel & other textile products 3.1 0.9 Auto repair, services, parking 0.4 4.5 Bldg materials & garden supplies 1.2 1.5 Business services 4.4 1.5 Construction 0.3 2.6 Education 22.5 15.4 Electric, gas & sanitary services 1.6 0.4 Electronic and electric equipment 2.2 0.5 Engineering & management 0.1 1.2 Financial institutions 0.7 1.3 Food and kindred products 2.5 0.7 Furniture and fixtures 1.1 0.1 Government 1.7 0.9 Health services 0.2 2.5 Hotels and other lodging 0.7 0.6 Industrial machinery & equipment 7.7 1.4 Instruments and related products 0.2 0.1 Insurance 3.7 8.5 Legal services 0.1 0.8 Local passenger transit 0.0 0.2 Lumber and wood products 3.4 0.2 Membership organizations 1.9 1.7 Miscellaneous manufacturing 0.0 0.2 Miscellaneous repair services 0.0 0.5 Movies, Amusement, Rec. 0.2 0.8 Paper and allied products 11.7 0.4 Personal services 0.8 3.3 Primary & fab. metal industries 7.8 2.5 Printing and publishing 1.1 1.0 Real estate 1.0 1.8 Retail–Apparel and accessories 0.1 0.8 Retail–Auto dealers & gas stations 0.2 2.6 Retail–Eating and drinking estabs 1.1 7.2 Retail–Foods stores 1.7 3.2 Retail–Furniture and home stores 0.3 1.8 Retail–General merchandise 0.0 0.2 Retail–Miscellaneous 0.9 12.3 Rubber and plastics products 0.0 0.3 Social services 0.4 1.3 Stone, Clay, and glass products 0.0 0.3 Transportation by air 0.1 0.1


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Industry Sector Percent of Cost Savings Percent of Projects Transportation equipment 0.2 0.1 Trucking and warehousing 0.1 0.6 Wholesale trade–durable goods 4.1 3.7 Wholesale trade–nondurables 1.0 0.9 Unclassified establishments 5.4 4.6

Total 100.0 100.0

These examples illustrate how the business program’s effects on business operating cost savings are distributed among all sectors of the economy, but skewed towards industries that have the greatest opportunity for major cost savings.

Overall, new participants in the business program saved $1.2 million of business operating costs in its first year and this amount is expected to increase to over $3.2 million per year for new participants in subsequent years. Since these energy savings continue year after year, the net effect is cumulative. Even after adjusting for equipment failure and loss of use over time, the net operating cost savings for Wisconsin businesses is still projected to exceed $26 million per year (in constant year 2001 dollars) by the tenth year. This reduction in business operating costs can lead to increased cost competitiveness for those Wisconsin industries that serve broader regional, national, and international markets. They can also lead to increased profitability and reinvestment for other industries. These subsequent economic development impacts are discussed later in this chapter.

4.2.2 Reduced Cost of Living

The Residential Program led to energy cost savings that effectively lowered costs of living for participating residents. These household cost savings affect the economy differently from business cost savings. The reduction in the cost of living frees up additional money that households spend on other consumer purchases—such as food, clothing, recreation, etc. In this way, the Residential Programs particularly support growth of the food products, retail, and consumer services sectors of the economy. In addition, the lower cost of living can also make Wisconsin a more attractive location for people to live, which can serve to attract more population and business.

New participants in the Residential Program saved nearly $2 million in their cost of living in the first year, and as the program gears up, this amount is expected to exceed $10 million per year for new participants in subsequent years. Since these energy savings also continue year after year, the net effect is cumulative. Even after adjusting for equipment failure and loss of use over time, the net savings for Wisconsin households is still projected to exceed $84 million per year (in constant year 2001 dollars) by the tenth year. Besides saving in energy costs, households can receive more modest additional savings in expenses associated with air pollution, safety, and comfort. All of these cost impacts affect consumer spending as noted above. There are also subsequent economic development impacts on households as a result of job and income impacts which are discussed later in this chapter.

4.2.3 Reducing Leakage of Dollars from the State Economy

By reducing electricity and natural gas purchases, another direct economic effect of Focus on Energy is to reduce imports of coal and natural gas from out-of-state suppliers. In regional economics, this is referred to as a reduction in “leakage” of dollars flowing out of the state’s economy. Instead, money is redirected towards increased purchases of energy-saving


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equipment and services as well as consumer spending on local consumer goods and services. Most of the money spent on business purchases of services (including energy related services) and consumer spending stays within the state. In addition, since Wisconsin has a disproportionately large concentration of industries manufacturing electronic controls, HVAC systems and motors, much of the spending on energy savings equipment also stays within the state.

This redirection of spending from energy to other purchases translates into a reduction in sales for electric and gas utilities in Wisconsin, compared to what would otherwise have occurred. However, since these still are regulated industries in Wisconsin and much of the transmission, distribution, billing, and operations cost is fixed, our analysis indicated that most of the loss would be in purchases of fuels from out-of-state suppliers rather than losses of jobs for utilities.

The REMI model was used to trace how Focus on Energy directly shifts the pattern of purchases of products and services from out-of-state suppliers (referred to as “imports” into the state) and the corresponding rate of change in purchases from in-state suppliers (referred to as “self-supply”). The displacement of spending that was formerly going out-of-state for purchases of energy fuels amounts was approximately $726,000 in the first year. (That includes a saving of $222,200 from Business Programs and $459,600 from Residential Programs during 2002, with another $44,200 from the Renewable Energy Program starting in 2003.) With continued growth of new participants and continued energy savings from previous-year participants, the net displacement is projected to continue to grow cumulatively to reach a level of $21.8 million per year by the tenth year of the program. This effect is illustrated in Figure 4-1 below.

Figure 4-1. REDUCTION IN DOLLARS ANNUALLY LEAKING OUT OF WISCONSIN’S ECONOMY (Projected by Year 10)

Simulation forecasts with the REMI economic model indicate the share of total statewide business and consumer spending that flows to suppliers located within the state. This proportion, referred to as the “regional purchase coefficient,” is forecast to increase from the


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current level of 52.7% to a level of 56.6% by the end of the tenth year (2011)—an indicator of strengthened business resulting from Focus on Energy.

4.2.4 Promoting Other Changes in the Flow of Dollars

Focus on Energy directly affects Wisconsin’s economy in other ways. The program itself spends money on staff, equipment, marketing, and financial incentives to promote energy efficiency—totaling $22 million in the first year. The money spent on operations supports staff jobs, energy-efficiency equipment sales, and energy and marketing services within the state. It also leverages additional trade ally activities to strengthen their activity promoting and installing energy-efficient equipment.

In the first year, there was $0.37 of additional private business spending on energy efficiency for every total dollar spent running the business program. This is the net additional cost paid by business participants after receiving incentives. In subsequent years, this value is expected to rise to $0.41. For the Residential Program’s first year, every total dollar spent on running the program led to $0.26 of additional private household spending on energy efficiency. This value is expected to rise to $0.54 in subsequent years. In this way, money spent on financial incentives also leverages additional private investment in purchases of energy-saving and renewable energy equipment, in addition to the previously discussed elements of business and household cost savings and reduction in economic leakage.

In these ways, Focus on Energy acts to shift business and household spending patterns in the state to support greater sales for firms manufacturing energy efficient equipment, firms providing supporting energy services, and agencies promoting these programs. Looking at the types of industries assisting the program can provide additional insight into the benefits of shifting spending patterns toward energy efficient equipment and services. Principal among the beneficiaries are the program’s “trade allies.” Trade allies are not participants, but are businesses that sell the energy efficiency goods and services offered by the programs. These include businesses that manufacture, distribute, sell, install, or service heating, ventilation, and air conditioning (HVAC) products and services, compact fluorescent bulbs, and other efficient lighting products, energy-efficient motors, and electronic controls, as well as other program-eligible appliances, equipment, and building services. To evaluate which allies were benefiting from the program it was necessary to first distinguish between allies that were actively supporting the program and those that could potentially become involved:

• Active allies are businesses that are registered with, and providing services directly through, Focus on Energy programs. As of September 30, 2003, there were 280 active allies working with Focus Business Programs and over a thousand involved with Focus Residential Programs.

• Targeted allies were defined based on the types of allies that are currently participating in the program. Allies were sorted by four-digit SIC code. Those SIC codes that made up 1% or greater of the existing allies, were considered to be a targeted SIC. The total number of each business type was extracted from Business Patterns 2001, U.S. Census. With this process, 14,489 potential “targeted allies” were identified across the state, including businesses located inside and outside the Focus on Energy participating utility service territories.


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Many types of industries are actively supporting Focus on Energy activities. To determine what types of industries are involved, active allies were sorted by their four-digit SIC codes. Businesses with multiple SIC categories were classified based on their primary SIC.

In the Residential Program, there are two types of active allies: (1) retail allies and (2) “other” allies. Active retail allies participate directly in promotions of ENERGY STAR products. Active “other” allies are implementing energy efficiency measures through other Focus programs—such as Home Performance with ENERGY STAR and Wisconsin ENERGY STAR Homes.

4.2.5 Trade Allies by SIC Code

A. RESIDENTIAL PROGRAM—TRADE ALLIES

Hardware stores dominate the distribution of allies and assisted energy savings for the Residential Program retail sector trade allies. This sector is primarily made up of Ace Hardware and Hardware Hank stores. The “Miscellaneous General Merchandise” and “Lumber and Other Building Materials” sectors combine with hardware stores to represent about 88% of the assisted energy savings for which active retail allies were responsible. The “Miscellaneous General Merchandise” sector is almost entirely represented by Sam’s Club stores and the “Lumber and Other Building Materials” sector is represented by Home Concept, Menard’s, Home Depot, and with some participation from more local stores such as NEU’s Building Center and Guyer’s Builder Supply.

Table 4-2. INDUSTRIES WITH GREATEST PROPORTION OF RESIDENTIAL PROGRAM RETAIL ACTIVE ALLIES* OR SAVINGS

Four-digit SIC Code SIC Description

Distribution of Allies with Known SIC

Codes

Percent of Energy Savings for Allies with

Known SIC

5251 Hardware Stores 33% 37% 5311 Department Stores 10% 23% 5712 Furniture Stores 5% 12% 5211 Lumber and other Building Materials 7% 10% 5722 Household Appliance Stores 16% 9%

Other 28% 9% Total 780 246,947 MMBTU

Nonretail allies active in the Residential Program are made up primarily of the construction trades. Plumbing Heating and Air Conditioning is the sector that accounts for more than half of the active allies and just over 50% of the assisted energy savings. General Building Contractors make up over 20% of the nonretail allies and a similar level of the assisted energy savings. While the Plastering Drywall, Acoustical and Insulation Work sector only accounts for 2% of the allies, they account for 20% of the assisted energy savings.


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Table 4-3. INDUSTRIES WITH GREATEST PROPORTION OF RESIDENTIAL PROGRAM NONRETAIL ACTIVE ALLIES* OR SAVINGS

Four-digit SIC Code SIC Description

Distribution of Allies with Known SIC

Codes

Percent of Energy Savings for Allies with

Known SIC

1711 Plumbing Heating and Air Conditioning 52% 51% 1521 General Building Contractors 23% 21%

1742 Plastering Drywall, Acoustical and Insulation Work 2% 20%

1542 Nonresidential Construction, NEC 5% 3%

Other 18% 7% Total 613 96,795 MMBTU

* Active allies include those from the commercial and industrial programs with sufficient information to identify their type of industry and their location.

B. BUSINESS PROGRAM—TRADE ALLIES (COMMERCIAL AND INDUSTRIAL)

There are roughly forty different types of industry that have assisted with Focus on Energy. Table 4-4 shows both the types of industry that had the greatest proportion of allies, and those industries responsible for the greatest proportion of energy savings. The proportion of energy savings serves as a rough proxy for the benefits that are accruing to those types of business. For the most part, industries with the most participating allies also were responsible for a large portion of the assisted energy savings. Some industries, like wholesale electric apparatus and equipment, were responsible for a greater portion of energy savings than their proportion of allies. These differences are due to the nature of the equipment and services each industry is providing.

Table 4-4. INDUSTRIES WITH GREATEST PROPORTION OF BUSINESS PROGRAMS ACTIVE ALLIES* OR SAVINGS

Allies By Industry Classification

SIC Description Count of Allies (75% of Active Allies)

Ally Assisted Energy Savings (90% of WI active ally assisted

energy savings)

1711 Plumbing Heating and Air Conditioning 44.93% 21.81%

5063

Electrical Apparatus and Equipment Wiring Supplies, and Construction Materials

13.04% 34.06%

1731 Electrical Work 12.56% 6.09%

5083 Farm and Garden Machinery and Equipment 3.38% 0.51%

1521 General Building Contractors Single Family Homes

2.42% 0.30%

5211 Lumber and Other Building Materials 1.93% 0.05%

5075 Warm Air Heating and Air-Conditioning Equipment and Supplies

0.97% 0.11%

5999 Misc. Retail stores 1.45% 1.76%


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Allies By Industry Classification

SIC Description Count of Allies (75% of Active Allies)

Ally Assisted Energy Savings (90% of WI active ally assisted

energy savings) Other 19.32% 35.32% Total 263 68,971 MMBTU

Source: Active Allies count and energy savings: AllAllies2.mdb - Query "BUS_Esavings of Active Allies w- 4_SIC by County” * Active allies include those from the commercial and industrial programs with sufficient information to identify their type of industry and their location.

Figure 4-2. BUSINESS PROGRAMS ACTIVE ALLIES AND RESPECTIVE ENERGY SAVINGS

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Plumbing Heating and

A i r C ondit ioning

Elect r i cal Appar atus

and Equipment Wir i ng

Supplies, and

Constr uction Mater i al s

Elect r i c a l W o r k Far m and Gar den

Machiner gy and

Equipment

Gener al Bui ld ing

Contr actor s Si ngl e

Fami ly Homes

Lumber and Other

Bui ld ing Mater i a l s

War m A i r H eating and

A i r -Condit ioning

Equipment and Suppli es

msc. Retai l s tor es

Proportion of Allies Proportion of Energy Savings

C. RENEWABLE ENERGY PROGRAM—TRADE ALLIES

Allies in the Renewable Energy Program assist in the installation of renewable energy equipment. Not all projects require the assistance of an ally, some are self-installed and some of the projects are research grants that require no installation. Still others may have incomplete record of the allies that assisted.

The types of allies that assisted with the Renewable Energy Program came from seven four-digit SIC categories. The types of allies are similar to those assisting in other programs. Here too, Plumbing, Heating, and Air Conditioning make up a large portion of the participating allies. One trade ally was responsible for nearly all of the energy savings (a hydroelectric generation system). Lumber and Building Material Dealers had the next largest portion of energy savings, also similar to the other programs.


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Table 4-5. DISTRIBUTION OF RENEWABLE ENERGY PROGRAM ALLIES (By SIC Code)

4 Digit SIC SIC Description Proportion of Active Allies (100% of Allies)

Proportion of Energy Savings Achieved (95% of all energy savings)

1711 Plumbing, Heating, and AC 25% 0.05% 5063 Electrical Apparatus (Wholesale) 13% 98% 5083 Farm and Garden Machinery (Wholesale) 13% 0.30% 8721 Accounting Auditing and Bookkeeping 13% 0.02% 8731 Commercial Physical Research 13% 0.16% 1731 Electrical Work 13% 1.05% 5211 Lumber and Other Building Materials

Dealers 13% 0.36%

Total 8 12,240 MMBTU Source: RENEW Allies with SIC.xls (this has the manually updated SIC codes using the ZAPDATA “Company Profile” feature)

4.3 OVERALL ECONOMIC DEVELOPMENT IMPACTS

The overall economic growth impacts of Focus on Energy are changes in patterns of industry growth and associated jobs and wages—which reflect (a) the differing growth response of various industries to the above-cited direct effects on costs and spending patterns, (2) the pattern of indirect industry growth impacts due to additional rounds of impacts on suppliers to the directly affected industries, and (c) the pattern of induced industry growth due to subsequent rounds of respending of additional worker wages. The magnitude and industry mix pattern of indirect and induced impacts depend critically on magnitude and industry mix pattern of the direct effects.

These overall impacts to industry growth are best understood by examining the consequences of maintaining the program for a period of time. That is desirable because the annual cost savings from program participants in any one-year actually continue for many subsequent years and total annual savings accumulate as the program continues to enroll new participants each year. This causes increasingly significant impacts on the magnitude and mix of spending changes and subsequently leads to business attraction impacts that evolve with improved cost competitiveness and the development of trade ally industries. Many of the economic growth impacts unfold over a period of time. They do not take place overnight. In order to appropriately trace the evolution of such impacts over a period of time, this study utilized the REMI economic forecasting and policy analysis simulation model for Wisconsin (developed by Regional Economic Models, Inc.).

The economic model was used to forecast how the state economy would change year by year over a period of ten years, with a scenario in which Focus on Energy is continued over that time period and with a comparison scenario in which there is no Focus on Energy nor any alternative to it (which is equivalent to assuming that the state keeps the program funds in an interest-bearing bank account). The difference between these two scenarios effectively represents the economic development impact of the program. The overall impacts were presented and explained in the earlier report, Focus on Energy Statewide Evaluation, Economic Development Benefits: Interim Economic Impacts Report, March 31, 2003. This


4–12


section focuses not on overall impacts, but rather, on how the program leads to differential effects on various economic sectors.

The overall impact of Focus on Energy is to make Wisconsin’s total business output $224 million per year higher in the tenth year than would have occurred without the program. With that growth comes an additional 2,435 private sector jobs and 2,778 total jobs. Table 4-6 summarizes the mix of job and business output impacts by major economic sector, based on results of the REMI economic model. A more detailed breakdown for private, nonfarm industries is shown in Table 4-7. Key findings from both tables and their interpretation is as follows:

• Manufacturing accounts for the largest share of the total statewide output impact—22%, though only 5% of the total job impact. The high impact on manufacturing output reflects the program impact on increasing the cost competitiveness of this sector as well as the redirection of some business and household purchases towards energy-efficient electrical equipment and machinery manufactured in Wisconsin. The smaller job impact is because Wisconsin manufacturing has a high value of output per worker.

• Retail accounts for 12% of the output impact and 24% of the employment impact. The effect on output is attributable to the large size of the Residential Program, which causes participating households to experience an increase in their disposable income, which they then spending on retail, entertainment, and personal services. The larger job impact is due to the high labor-intensity of retailing. There is also an impact on Wholesaling because of the increased retail sales.

• Business services accounts for 20% of the output impact and 29% of the employment impact. This classification includes energy-related services, which are supported by the business program’s marketing and incentive features.

• The additional impacts on transportation, construction, finance, and other services are attributable to increased spending by both households (due to disposable income growth) and businesses (due to expansion of activity).


4–13


Table 4-6. IMPACTS ON THE ECONOMY BY MAJOR INDUSTRY (Tenth Year, Output in Constant 2001 Dollars)

Major Industry Name Output Impact ($mil)*

Percent of Output

Job Impact

Percent of Jobs

Business Services $45 20.1% 702 29.2%

Finan, Ins, Real Estate $23 10.4% 116 4.8%

Manufacturing $50 22.2% 125 5.2%

Other Services $27 12.0% 544 21.2%

Retail $27 11.9% 569 23.7%

Trans, Constr, Util $38 17.0% 296 12.3%

Wholesale $14 6.4% 83 3.5%

Total Private Nonfarm $224 100.0% 2435 100.0%

Farm, Forestry, Fishing 23

Government 320

Total All Sectors 2778


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Table 4-7. IMPACTS ON THE ECONOMY BY DETAILED INDUSTRY (Tenth year, output in Constant 2001 Dollars)

Detailed Industry Sector Output ($ Mil) Jobs

Air Transportation 1.148 8

Apparel Mfg 0.534 2

Auto Repair Services 4.287 32

Banking 4.577 33

Chemical Products Mfg 0.169 0

Communication 4.752 16

Construction 23.354 226

Credit & Finance 3.815 25

Eating & Drinking 5.552 128

Education 2.357 57

Electric Equip Mfg 19.139 35

Food Products Mfg 5.968 14

Furniture Mfg 0.396 3

Hotels 0.158 5

Insurance 2.479 27

Leather Products Mfg 0.041 0

Local Public Transit 0.605 14

Lumber 1.808 16

Machinery Mfg 15.584 17

Medical Services 3.110 46

Mining 0.209 1

Misc Business Services 44.802 702

Misc Professional Services 5.154 85

Motor Vehicles Mfg 0.959 2

Movies, Amusem & Rec 3.028 77

Nonprofit Org 4.882 145

Other Manufacturing 0.133 1

Other Transport 0.335 4

Paper Mfg 2.576 7

Personal Services& Repair 2.286 62

Petro Products Mfg 0.185 0

Prim & Fabric Metals Mfg 1.065 5

Printing 2.460 22

Private Household 0.158 14

Public Utilities 4.601 13

Railroad 0.115 0

Real Estate 12.371 31

Rest of Retail 20.948 439


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Detailed Industry Sector Output ($ Mil) Jobs

Services to Agri–For–Fish 0.558 21

Stone, Glass, Clay Mfg 1.037 5

Textile Mfg 0.060 0

Trucking 2.947 14

Wholesale 14.152 83

Total Private Non-farm 224.851 2435

Looking more closely at output impact on the manufacturing sector, Figure 4-3 illustrates how the distribution of industry growth looks different depending on whether it is viewed in terms of business output or jobs. Key findings are that:

• Some of the industries that are saving significant operating costs—such as wood and paper—account for a significant share of the impact on business output, but a smaller share of the impact on jobs.

• Some of the industries that benefit from redirected spending towards energy efficiency—such as electrical equipment and machinery—have the opposite situation. They account for a small share of the impact on business output but a larger share of the impact on jobs.

• Food products is the other major industry in Wisconsin that accounts for a significant share of both output and job growth impacts. The growth impact on this industry is attributable to both increased cost competitiveness as well as increased consumer spending (freed up by the reduction in energy costs).

• Other industries that are saving operating costs—such as metal products and apparel manufacturing—account for only a small share of the impact on business output and jobs. This is most likely because these are generally more mature and stagnant (or declining) industries, which are in some cases losing market share to lower cost competition from abroad. In such cases, operating cost savings in Wisconsin can have a more muted impact on business growth since energy cost improvements alone may not be enough to turn around the overall competitiveness issues facing these business sectors in Wisconsin.


4–16


Figure 4-3. COMPARISON OF OUTPUT AND JOB IMPACTS FOR MANUFACTURING SECTOR (Tenth Year)

0% 5% 10% 15% 20% 25% 30% 35% 40%

Other Manufact

Stone,Clay,Etc

Fab Metals

Machinery

Elec Equip

Motor Vehicles

Food Prod

Apparel

Paper

Printing

Wood & Furn % of Jobs

% of Output

Altogether, the variation in business output and employment growth impacts shown in Figure 4-3, along with the associated discussion above, serves to underscore the fact that different aspects of energy cost savings and spending changes can affect various industries. The economic model also shows that industry responses to those cost and spending changes may also differ depending on the cost structure and existing competitive position of the industry.

4.4 COMPARISON OF ECONOMIC IMPACTS TO BASELINE PATTERNS AND TRENDS

To understand and interpret the industry mix of jobs supported by Focus on Energy, it is useful to compare: (1) the Focus program ten year job impacts with (2) the state’s job mix as of 2001 when Focus on Energy was first started, and (3) projections of the state’s job mix ten years later (as of 2011, not incorporating any impact of Focus on Energy). Figure 4-4 illustrates this comparison for major sectors of the economy. It shows that:

• The share of total services sector jobs in the general economy is expected to increase over the next ten years (2001–2011), but the share of additional jobs supported by Focus on Energy is even larger for that sector of the economy.

• The share of total jobs in the retail and transportation & construction industry groups is expected to be generally stable over the next ten years. However, their share of total jobs in those industries created by Focus on Energy is larger than either current or projected patterns in the general economy.


4–17


• The share of total jobs in the manufacturing, wholesaling and finance & insurance industry groups is expected to decline over the next ten years. This actually reflects slower growth or stagnation in these industries, while other sectors of the economy are growing fast. In addition, increasing labor productivity (output/worker) in manufacturing and wholesaling makes jobs grow at a slower rate than industry output. Focus has a lesser impact on these industry groups than others. The share of total jobs created by Focus on Energy in these industry groups is smaller than their current or projected future shares of total jobs in the general economy.

Figure 4-4. COMPARISON OF FOCUS DRIVEN JOB IMPACTS BY INDUSTRY TO BASELINE 2001 AND PROJECTED 2011 DISTRIBUTION OF JOB GROWTH WITHOUT FOCUS

(Portion of Total Private, Nonfarm Jobs in Wisconsin)

0% 5% 10% 15% 20% 25% 30% 35%

Other Services

Business Serv

Trans-Constr-Util

Finan-Ins-Real Est

Retail

Wholesale

Manufacturing

State 2001

State 2011

Progam Impact

The data on Focus impacts and baseline economic profiles and projections was also examined at a more detailed 53-sector industry level. Using the pre-program (year 2001) economic profile and the base case (no program) forecasts (for year 2011), we calculated a “base case” expectation of the percentage change in employment over the 2001–2011 period, as predicted by the REMI model assuming no impact of Focus on Energy. From that information, we classified all Wisconsin industries into five industry growth classes:

• High growth (over 10% increase in employment)

• Small growth (2% to 10% increase in employment)

• Stagnant (forecast change +/-2%)

• Small loss (-2% to 10% loss of employment)

• High loss (over -10% loss of employment).


4–18


Using this classification, we transformed the distribution of Focus program job impacts by industry (shown earlier in Tables 4-2 and 4-3) into a breakdown of Focus program job impacts by growth class. This effectively shows us the extent to which Focus on Energy is: (1) helping to reduce job losses in declining industries, (2) helping to grow stagnant industries and/or (3) helping to further expand jobs in industries that are already forecast to be growing.

The result is shown in Figure 4-5. The length of the bars indicate the program impact in terms of the number of additional jobs over and above what would otherwise be the existing in those industry classes in the year 2011. The figure shows that Focus is effectively working to create additional jobs across the spectrum of growing, stagnant, and declining industries. Key findings are listed below:

• It is notable that fully 18% (+459) of the Focus on Energy job impact by 2011 is in the industries forecast to be declining. The positive impact on jobs in those industries can be interpreted as a reduction in the magnitude of losses that would otherwise be occurring in those industries. This reflects program support for some manufacturing industries that are otherwise projected to be declining.

• Another 21% (+518) of the job impact is in industries classified as basically stagnant. This can be interpreted as creating additional growth in industries that would otherwise have little or no growth.

• The other category is industries that are already forecast to be growing even without Focus on Energy. Slightly under half of the overall program impact (+49%, reflecting 1,199 jobs) is projected to occur in those industries. This larger impact reflects the strong program impacts on retailing and service industries, both of which fall into this growth class. This result also reflects the positive program impact on technology development, which is another source of economic growth.

Figure 4-5. IMPACTS ON JOB GROWTH BY INDUSTRY GROWTH CLASS (Tenth Year)

0 200 400 600 800 1000 1200

Hi Loss (> -10%)

Sm Loss(-2 to 10%)

Stagnant (+/-2%)

Sm Growth (2 to 10%)

Hi Growth (> +10%)


4–19


This raises the question of whether this expected impact is consistent with good economic development policy. Focus on Energy was never designed to be a comprehensive economic development program by itself, although it was intended to be supportive of economic development goals. In general, economic developers simultaneously seek multiple goals: (1) to attract and expand the share of jobs in industries that are expected to be growing in the future, (2) to retain and protect existing jobs in mature industries, and (3) to support entrepreneurship and appropriate new technology startups. The results shown in Figure 4-5 can be interpreted as indicating that Focus is indeed supporting this first goal. The finding shown earlier in Tables 4-1 and 4-3 indicate that Focus is also supporting the second goal by reducing operating costs and helping to support a modest number of additional jobs in the more mature industries. Finally, the Renewable Energy Program can be supportive of the third goal of helping new technology businesses, although it is too early to document or estimate any of those effects.

5–1


5. POLICY ISSUE: WHAT TYPES OF JOB AND INCOME OPPORTUNITIES DOES FOCUS CREATE?

This chapter examines the economic impacts of Focus on Energy in terms of their effect on the types of jobs, skill levels, and pay rates for workers in the state. It is organized into two parts to examine job impacts in two ways: (1) by type and skill level of jobs, and, (2) by wage levels. Both sections examine how impacts of Focus of Energy are different from existing patterns and trends.

5.1 FINDINGS

Focus supported 630 jobs in Wisconsin in its first year, and this is projected to grow to over 2,700 jobs by the tenth year. The economic analysis model shows that Focus on Energy is supporting job growth in all occupational groups, spanning skilled and unskilled jobs in white-collar and blue-collar occupations. However, this effect is not distributed equally across all sectors of the economy. Focus on Energy is directly supporting growth of jobs in business services and technical occupations, but also creating even more jobs in sales and service occupations because of households respending their added disposable income. By comparing the total job effects of Focus against the current job mix in Wisconsin, we can see that the program is expanding jobs in professional occupations as well as sales and service occupations. Overall, the mix of jobs that it is supporting is disproportionately white-collar occupations—both skilled and semi-skilled.

• Focus on Energy affects an extremely wide range of job types. The largest numbers of job impacts are in the individual categories of managerial and administrative, computer operator, teacher, retail sales, clerical, building services, protective services, and construction trades, with over 100 jobs created for each of these job types.

• White-collar jobs account for two-thirds of the Focus on Energy impacts with a near equal split between skilled and semi-skilled positions.

• Compared to the distribution of highly skilled jobs versus semi-skilled jobs that currently exist in Wisconsin, Focus has a disproportionate impact on semi-skilled jobs.

• Job impacts are concentrated in the medium wage category. This reflects the significant impact on business and professional services (including energy services).

• Job impacts are under-represented in the high wage category. This reflects the relatively modest representation of (high-paying) manufacturing job impacts, which is attributable to the high share of households and commercial (as opposed to industrial) program participants.

5.2 OCCUPATIONAL PATTERNS

In general, job impacts follow because of Focus on Energy impacts on business sales growth. Whenever a business grows in the volume of sales activity, there is normally a corresponding increase in employment. However, the magnitude of the change in jobs differs dramatically among industries, because each industry has a different mix of needs for equipment, materials, and workers. For instance, as noted in the prior chapter, retail and service

5. Policy Issue: What types of job and income opportunities does Focus create?. . .

5–2


industries are more labor intensive than manufacturing industries and hence account for a larger share of job impacts than business output (sales volume) impacts.

Another key issue is that skill and pay level are not the same across industries. Some technology-driven manufacturing and service industries provide a significant amount of jobs for highly paid skilled workers, while other industries such as retailing and wholesaling rely more heavily on less highly paid semi-skilled workers.

Each industry has its own unique combination of occupational skill needs. Job growth impact of Focus on Energy are calculated in the REMI economic model by considering the combination of industrial growth opportunities, their occupational requirements, and Wisconsin’s existing and projected future workforce skills and pay levels. This process is important in ensuring that the projected impact on jobs are realistic—which means that the industry growth projections are generally consistent with workforce skills and wage levels.

As discussed in the previous chapter, economic growth impacts of Focus on Energy are best understood by examining the consequences of maintaining the program for ten years. That is desirable because the cost savings from program participation accumulate over time, leading to more significant impacts on the magnitude of spending changes, business attraction from improved competitiveness, and development of trade ally industries and market changes over a period of time. Many of the economic growth impacts unfold over time. They do not take place overnight. Accordingly, this chapter is consistent with the prior chapter in examining differences in the state economy in year 10, compared to what would otherwise be expected.

The economic projections for Focus on Energy impact is an increasing number of additional jobs in Wisconsin—amounting to 2,778 more jobs present by the tenth year. All figures on occupational impacts that are shown in this chapter cover 2,755 private and public sector jobs, which excludes the impacts on 23 additional farm jobs. Table 5-1a shows a detailed projection of the occupational impacts of Focus on Energy, based on an analysis of 85 occupations in the REMI model. Overall, it shows that Focus on Energy ultimately affects an extremely wide range of job types, reflecting the broad industry impacts previously noted in Chapter 4, Table 4-1. The largest numbers of job impacts are in the individual categories of managerial and administrative, computer operator, teacher, retail sales, clerical, building services, protective services, and construction trades (each with impacts exceeding 100 jobs).


5–3


Table 5-1a. IMPACTS ON WHITE-COLLAR JOBS BY DETAILED OCCUPATION (Tenth Year)

Category Occupation Number of Jobs

Managerial & administrative 202 Management support 87 Engineers 27 Architects & surveyors 2 Life scientists 2 Computer, math & operational research 159 Physical scientists 3 Social scientists 5 Social, recreation & religious workers 42 Judges, other judicial workers 1 Lawyers 7 Teachers, librarians, counselors 103 Health diagnosing 3 Health assessment & treating 26 Writers, artists & entertainers 40

Pro

fess

iona

l Wor

kers

(7

34 jo

bs, 2

7% o

f tot

al)

All other professional workers 24

Health technicians & technologists 31

Engineering & science technologists & technologists 30

Whi

te-c

olla

r S

kille

d (8

51 jo

bs, 3

1% o

f tot

al

Tec

hnic

al W

orke

rs

(117

jobs

, 4%

of t

otal

)

Technologists, except health, engineering & science 56

Cashiers 81 Counter and rental clerks 16 Insurance sales workers 3 Real estate agents, brokers & appraisers 2 Salespersons, retail 102 Securities & financial services sales workers 5 Marketing & sales worker supervisors 47 Travel agents 1

Sal

es W

orke

rs

(382

jobs

, 14%

of t

otal

)

All other sales & related workers 126 Adjustors, investigators & collectors 37 Communications equipment operators 6 Computer operators & peripheral equipment operators 6 Financial records processing 47 Information clerks 44 Mail clerks & messengers 8 Post clerks & mail carriers 0 Material receiving, scheduling, dispersal & distribution 89 Record processing, except financial 26 Secretary, stenography & typists 77

Whi

te-c

olla

r S

emi-s

kille

d (9

31 jo

bs, 3

4% o

f tot

al)

Cle

rical

Wor

kers

(5

49 jo

bs, 2

0% o

f tot

al)

Other clerical & administrative support workers 209


5–4


Table 5-1b. IMPACTS ON BLUE-COLLAR JOBS BY DETAILED OCCUPATION (Tenth Year)

Category Occupation Number of Jobs Cleaning & building services, except private households 127 Food prep and service 132 Health service 23 Personal service 58 Private household workers 11 Protective service 151 S

ervi

ce W

orke

rs

530

jobs

19

% o

f tot

al

All other service workers 28

Motor vehicle operators 66

Rail transportation workers 0

Water transportation & related workers 0

Material moving equip operators 16

Blu

e-co

llar

Sem

i-ski

lled

(615

jobs

, 22%

of t

otal

)

Tra

nspo

rt

Wor

kers

85

jobs

3%

of t

otal

All other transportation & material moving operators 2

Blue-collar worker supervisors 37

Construction trades 111

Oil and gas extraction 0

Mining, quarry & tunneling 0 Con

stru

ctio

n

& E

xtra

ctio

n 15

3 jo

bs

6% o

f tot

al

All other extraction & related workers 5

Communication equipment mechanic, installation & repair 1 Electronics equip mechanic, installation & repair 9 Machinists & related mechanic, installation & repair 28 Vehicle & mobile equip mechanic & repair 28 Other mechanic, inst & rep 30 Assemblers, precision 3 Food workers, precision 4 Inspect, testers & graders precision 13 Metal workers, precision 5 Printing workers, precision 1 Text, apparel & furniture workers, precision 2 Woodworkers, precision 3

Pre

cisi

on E

quip

men

t Wor

kers

13

3 jo

bs

5% o

f tot

al

Other precision workers 5 Chemic plant & system operator 0 Electric power generation plant operator & distribution & disp 1 Gas & petroleum plant & systems 0 Stationary engineers 0 Water & waste treatment system operator 3 All other plant & system operators 5 Num ctrl mach tool operator, metal & plastic 0 Combination machine tool setters & operators 0 Machine tool operators, metal & plastic 6 Metal fabrication mach operators 1 Metal & plastic process machine operators 1 Print, binding & related workers 7 Textile and related operators 4 Woodworking mach operators 2

Blu

e-co

llar

Ski

lled

(466

jobs

, 17%

of t

otal

)

Mac

hine

and

Sys

tem

Ope

rato

rs

80 jo

bs

3% o

f tot

al

Other machine setters & operators & tenders 41


5–5


To better focus on the patterns of impact, Figure 5-1 summarizes the mix of job impacts in terms of combinations of blue-collar/white-collar and skilled/semi-skilled. Most notably, it shows that white-collar jobs account for two-thirds of the Focus on Energy impacts, with a near equal split between skilled and semi-skilled positions.

Figure 5-1. OCCUPATION MIX OF FOCUS ON ENERGY JOB IMPACTS

White Collar Skilled31%

White Collar Semi Skilled

34%

Blue Collar Skilled13%

Blue Collar Semi-Skilled

22%

To further interpret these results, it is useful to compare the Focus on Energy job impacts to the current mix of occupations in Wisconsin. The results are provided in Table 5-2 and illustrated graphically in Figure 5-2. They show the following results:

• Among white-collar skilled jobs, Focus on Energy is projected to provide a slightly larger (than existing) share of new jobs for professional workers, and the same (as existing) share of new jobs for technical workers. This may reflect positive program impacts on the business program promoting technologically advanced manufacturing and services with indirect effects on financial and insurance industries.

• Among white-collar semi-skilled jobs, Focus on Energy is projected to provide a slightly larger (than existing) share of new jobs for sales and clerical worker. This may reflect increased consumer spending on retail and services, due to Residential Program impacts on the cost of living and widespread participation of commercial establishments in the Business Program.

• Among blue-collar semi-skilled jobs, Focus on Energy is projected to provide a slightly larger (than existing) share of new jobs for service workers, but a smaller (than existing) share of new jobs for transport workers.

• Among blue-collar skilled jobs, Focus on Energy is projected to provide a slightly larger (than existing) share of new jobs for construction occupations, but a smaller (than existing) share of new jobs for precision crafts, machine and system operators. This reflects the modest program impact on manufacturing jobs, which was noted earlier in Chapter 4.

Altogether, these results indicate the new jobs supported because of Focus on Energy represent a higher share of white-collar jobs and a lower share of blue-collar jobs than now exists in the overall statewide economy. However, the largest impact is on semi-skilled rather


5–6


than highly skilled jobs. The policy interpretation of this finding depends on whether there is an interest in seeing more growth of higher skill technology jobs or providing a broader mix of skilled and semi-skilled jobs in the future.

If there is a particular policy goal to affect the mix of job types, then it is important to note that this job mix can be affected by program design. The relative size of the Residential Program compared to that of the Business Program, as well as the commercial/industrial mix within the Business Program, affect this outcome.

Table 5-2. OCCUPATION MIX OF EXISTING JOBS AND FOCUS ON ENERGY JOB IMPACTS

Existing Statewide Jobs Jobs Created by Focus on Energy

Professional workers 25.9% 26.6% Technical workers 4.3% 4.3% White-collar Skilled 30.3% 31.0% Sales workers 11.6% 13.9% Clerical workers 18.2% 19.9% White-collar Semi-skilled 29.8% 33.8% Construction & Extraction workers 5.1% 5.6% Precision workers 6.6% 4.8% Machine & System operators 5.1% 2.6% Blue-collar Skilled 16.8% 13.0% Service workers 18.7% 19.2% Transport workers 4.4% 3.1% Blue-collar Semi-skilled 23.1% 22.3% Total—All Occupations 100.0% 100.0%

Figure 5-2. OCCUPATION MIX OF EXISTING JOBS AND FOCUS ON ENERGY JOB IMPACTS

0% 10% 20% 30% 40%

White CollarSkilled

White CollarSemi Skilled

Blue CollarSkilled

Blue CollarSemi-Skilled

FOE IMPACT %

EXISTING ANYWAY


5–7


5.3 PAY LEVELS

Each of the industries discussed in Chapter 4 has a different mix of occupations and pay scales. As a consequence, the pattern of program impacts on industries also leads to impacts on the mix of pay levels for new jobs.

Table 5-3 ranks the various industries in terms of their average annual income to workers in Wisconsin, listed from highest to lowest. The annual income levels range from approximately $83,000 per year at the high end to approximately $10,000 per year at the low end of the range. The middle group consists of industries where the average annual pay is between $32,000 and $42,000 annually. (All figures are in 2001 dollars). It is clear from this analysis that many of the higher paying jobs are in the manufacturing and credit/finance/banking sector, while many of the lower paying jobs are in retail and service sectors.

Table 5-3. CLASSIFICATION OF IMPACT INDUSTRIES BY WAGE CLASS

High Wage: wage is more than 15% higher than statewide average wage.

Instruments Mfg., Public Utilities, Motor Vehicles & Transport Equip. Mfg., Railroads, Chemicals Mfg., Paper Mfg., Machinery & Computer Mfg., Communications, Primary & Fabricated Metals Mfg., Electric Equipment, Wholesale, Credit & Finance, Textiles, Stone/Glass/Clay Products, Medical Services, Food Products Mfg, Insurance, Air Transport, Banking, Printing.

Medium Wage: wage is within +/-15% of statewide average wage.

Mining, Furniture Mfg., Construction, Leather Products Mfg., Misc. Professional Services, Trucking, Miscellaneous Manufacturing, Lumber Products Mfg, Apparel Mfg., Miscellaneous Business Services.

Low Wage: wage is more than 15% under statewide average wage.

Education, Auto Repair & Services, Nonprofit Organizations, Local Transit, Retailing, Agriculture/Forestry/Fishing Services, Amusements & Recreation Services, Hotels, Personal Services & Repair, Eating & Drinking Establishments, Private Household Services, Real Estate.

Using this classification, Figure 5-3 shows the wage distribution of Focus jobs impacts. This is compared to the wage class distribution of the overall Wisconsin economy. The results indicate that:

• Job impacts are disproportionately concentrated in the medium wage category. This reflects the significant impact on business and professional services (including energy services).

• Job impacts are under-represented in the high wage category. This reflects the relatively modest representation of (high-paying) manufacturing job impacts, which is attributable to the high share of households and commercial (as opposed to industrial) program participants.


5–8


Figure 5-3. WAGE MIX OF EXISTING JOBS AND FOCUS ON ENERGY JOB IMPACTS

Low Wage

Medium Wage

High Wage

Wisconsin

Focus on Energy

34.3%55.8%

9.9%39.5%

21.6%38.8%

0%

10%

20%

30%

40%

50%

60%

Economic development policy may seek to promote forms of economic growth that expand demand for jobs in leading technology industries with above-average pay, or it may seek to replace lost jobs in lower paying, less highly skilled job categories. The results of Figure 5-3 indicate that Focus on Energy is actually succeeding in supporting the growth of a wide range of jobs with generally average pay. The interpretation of this finding depends on clarifying the specific economic development goals. In any case, though, it should be clear that program design and marketing can affect the nature of the job impacts. This means that there are opportunities for further “fine tuning” of the program design and marketing to further achieve those goals.

A–1


APPENDIX A: COMPARISON OF ECONOMIC IMPACT STUDIES

A.1 EVOLUTION OF ECONOMIC IMPACT MODELS AND THEIR USE FOR ENERGY PROGRAM ANALYSIS

A.1.1 Motivation

Economic impact models assess how current or planned programs and policies can affect the economy of a region, state, or nation. Such models are widely used and accepted around the nation. Nearly all of the states use such models for some of their policy analysis; many have used them for assessing economic impacts of energy efficiency, renewable energy, and energy pricing policies. Their usefulness for energy program evaluation comes from their ability to assess how alternative scenarios involving program size, participation, and targeting and energy effects can affect the economy. Some of the evaluations (such as the Wisconsin Focus on Energy studies) calculate not only immediate impacts on the economy, but also expected changes in the development of the economy over time. These latter changes are sometimes also referred to as “economic development impacts.” There is an important distinction between measuring economic development impacts and conducting economic benefit-cost analysis, which was explained in Chapter 1 of this report.

This appendix provides an overview of the different types of economic impact studies and their implications for the types of programs and impacts that can be assessed. After those distinctions are made clear, there is an annotated bibliography of prior studies and their findings on the economic impacts of energy programs. It is clear from that bibliography that there are substantial differences in the types of program elements being measured and the types of impacts being measured.

A.1.2 Input-Output Models

In general, input-output (I-O) tables provide a means for identifying the inter-industry linkages, which show how purchases of goods and services in one industry lead to spending and purchases of goods and services in other industries. The direct impacts of energy-related expenditures are the purchases made to buy goods or services from specific industries. These, in turn, lead to indirect impacts on spending for "factor inputs" (other goods and services) in supplier industries. The workers hired as a result of the direct and indirect impacts provide additional income, which leads to further consumer spending for consumer goods and services, which is the induced impact. For any given type of spending within the state, some of the recipients of the direct, indirect, and induced spending will be within the state and some will be outside of the state. The extent of spending going to firms and individuals outside of the state is known as leakage. The percentage of overall purchases occurring within the state (i.e., not leakage) is known as the regional purchase coefficient (RPC). Employment and income multipliers are built on the basis of the inter-industry linkages and leakage/RPC values for the affected industries.

Input-output models have been applied to assess the impacts of energy efficiency and renewable energy programs over a period of 20 years. Most of these studies used one of two input-output modeling tools RIMS (developed by the U.S. Department of Commerce) or IMPLAN (originally developed by the U.S. Department of Interior and now offered by a private firm). Applications of RIMS include studies for Nebraska, Florida, Wisconsin, and New York. Applications of IMPLAN include reports for Sacramento, Central Illinois, California, Ohio,

A: Comparison of Economic Impact Studies. . .

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Oklahoma, four Midwest states, and the nation. Applications using other I-O models include reports for California, the Pacific Northwest, British Columbia, Spain, and China. Most of these studies were conducted over the 1980–1995 period, after which dynamic simulation models became more popular.

A major limitation of input-output models by themselves has been that economic impacts of energy policies may come from any combination of: (a) changes for spending patterns, (b) changes in personal and business income, and/or (c) shifts in prices affecting productivity and economic competitiveness. I-O models are designed foremost to address the implications of spending changes. They provide no internal basis for estimating how programs that affect local prices and costs of goods and services will ultimately affect the competitive position, and therefore the relative pattern of economic growth or decline of an area.

To address those issues, it is necessary to supplement the I-O model with some assumptions about how businesses would react to cost changes. The arbitrary rule most often used with I-O models to evaluate economic impacts of energy efficiency programs has been that any savings in energy costs will trigger an identical expansion in spending by those parties receiving the energy savings. This is a convenient but incorrect assumption. In reality, a small change in productivity and relative costs of doing business may trigger much larger expansion or contraction of some highly competitive and footloose industries. The same relative change in business costs for other "captive" local industries may trigger little or no change in volume of business activity (and merely a shift in prices). That is why it is important to evaluate how energy-related policies can affect the relative competitive position of various local industries, and the extent to which changes in that position will affect the retention, attraction and expansion of various local industries over time.

A.1.3 Dynamic Simulation Models

A major limitation of I-O models is that they are fundamentally accounting tables, which trace how expenditure flows affect the economy without any time dimension for tracing how impacts evolve and play out over time. This includes price and cost effects—the fact that financing energy efficiency programs can positively or negatively affect energy prices and costs of doing business, which can ultimately affect the cost competitiveness of local industry and lead to changes in expansion and attraction of population and business over time. Shifts in business productivity resulting from energy efficiency programs can similarly affect business cost competitiveness and national market shares for industries. Yet, another consideration is the shifting mix of population and business characteristics in the state, which can also change the nature of energy program impacts over time. Yet, another time factor is the differential between the short-term impact of installation of energy efficiency and long-term employment impacts of maintaining that efficiency.

A policy analysis and forecasting simulation model combines an input-output model with an additional ability to forecast shifts in prices, competitiveness factors, and business attraction over time. The REMI model (developed by Regional Economic Models, Inc.) is the most well-known and widely used policy analysis and forecasting model in the United States. Applications of the REMI model for assessment of energy efficiency, renewable energy, and energy pricing policies include reports for California, Wisconsin, Iowa, Wyoming, Massachusetts, and New Jersey. Other applications using the REMI model to assess impacts of regulatory changes and shifts in energy fuels and technologies were reports for Maine, Missouri, Illinois, Michigan, Connecticut, Vermont, New Jersey, Florida, New York, and the Midwest. Another policy analysis and forecasting model, known as the REAL model


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(developed by Regional Economics Applications Laboratory of the University of Illinois), has also been applied in studies of the impacts of clean energy technologies for ten Midwestern states.

These dynamic simulation models provide an important added ability to trace economic impacts of shifts over time in technology, business cost competitiveness, and productivity, and then forecast additional shifts in business attraction/expansion (i.e., economic development) over time. This cannot be done by I-O models. In some cases, these additional factors are not significant, but in other cases, these models can demonstrate how public policy impacts can have cumulative growth effects over periods of 5 to 20 years. For that reason, this type of model is most applicable for scenarios affecting business competitiveness.

A.1.4 Brief Review of Selected Other Studies

Evaluations of the economic impact of energy conservation and efficiency programs have a long and checkered past. The early studies, conducted over 1979–1987, were simple and straight applications of input-output models to trace the economic impacts of proposed spending on energy efficiency programs. These include studies for California (California Energy Commission, 1979), Long Island (Buschsbaum et al., 1979), Pacific Northwest (Charles River Associates, 1984), the Midwest (Laitner, 1984), and Iowa (Macke and Associates, 1985; Kegel and Laitner, 1987). At that time, there was no major energy efficiency or conservation program spending in those states. Rather, those studies focused on evaluating the linkages of petroleum, natural gas, electricity, and coal spending on the state economies. For each type of energy spending, those studies estimated state impacts of hypothetical energy conservation programs, with hypothetical results, by studying the associated labor intensity, profit margins, and flow of dollars for other factor inputs of the energy industries. For those studies, "leakages" associated with spending dollars flowing to out-of-state suppliers were estimated on the basis of data on available expenditure estimates and state trend data on prices and energy use.

In the early 1990s, there was a series of more sophisticated studies of the employment and income impacts of energy efficiency programs, which relied upon the IMPLAN and RIMS-II input-output models to estimate the potential future job impacts associated with the hypothetical situation where investment (spending) is focused on electric efficiency instead of traditional energy supply sources. These include Florida (Krier et al., 1993), Minnesota (Economic Research Associates, 1993), British Columbia (Jaccard & Sims, 1991), Ohio (Laitner et al., 1994), and New York (New York State Energy Planning Board, 1994). For these reports, much of the study work actually concerned the definition and construction of the bundle of energy efficiency policies that would be feasible for the state or province. Once that was done, spending on energy efficiency was then allocated over selected SIC codes and a synthetic I-O model (IMPLAN or RIMS-II in most cases) was then used to generate estimates of leakage and overall multiplier impacts on jobs in the supply area. Since each of these studies utilized a static I-O approach, there was still not a full accounting of employment effects of shifts in energy prices and business productivity.

The City of Austin Study (Megdal and Rammaha, 1992) was notable because it developed the data for energy conservation multipliers based on use of a local survey to profile local energy conservation of service providers, rather than synthetic constructs. An important contribution of this study was that data for energy conservation multipliers were not all synthetic, but rather built upon a local survey to profile local energy conservation service


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providers and “trade allies.” In addition, the I-O model was used not just to estimate impacts of increasing energy efficiency spending, but also to account for offsetting increases in energy rates to pay for that spending in the current year and in future years. A later study for Iowa (Hagler Bailly, 1995) also surveyed utilities to obtain highly detailed information on their technology vendors and suppliers as a basis for developing multipliers and regional purchase coefficients.

The 1990 study “Impacts of the SCE/SDG&E Merger” (Weisbrod and Moses, 1984), sponsored by the California Public Utility Commission provided a first approach to the use of a dynamic economic model for forecasting impacts of energy prices and policies on a regional economy. This study utilized two different analytic approaches to predict the employment and income impacts of shifts in utility spending, prices, efficiency programs, and community support programs in the San Diego area. One approach was to use the RIMS-II Input-Output model. The other approach was to use the REMI policy simulation model. In both cases, the model inputs and assumptions were modified based on data collected on utility program spending patterns and the specific locations of suppliers and contractors. The study found that short-term impacts were essentially similar for the REMI and RIMS models, but that long-term impacts of alternative scenarios produced by REMI showed significant changes over the 1990–2000 study period. A parallel application of the national-level INFORUM model, which also incorporates general equilibrium concepts in an integrated forecasting and simulation model, is described in Moscovich, 1994.

Subsequent policy simulation studies over the 1994–2002 time period have used the REMI model to assess economic implications of long-term cost savings benefits of energy programs, which can continue for a long period of time and can grow as the use and value of the equipment technologies persists and expands over time. They have also traced implications of the fact that benefits may extend over a longer period of time than the payment of costs, which are incurred earlier on. They have also traced how economic development impacts of energy programs are changed by effects on the business cost-competitiveness of an affected area. This includes studies of Iowa (Hagler Bailly, 1995), Wisconsin (Clemmer, 1994), Wyoming (Black & Veatch, 1997), and Massachusetts (Division of Energy Resources, 2002). The Wisconsin Focus on Energy evaluation reports (Economic Development Research Group, 2003) continue to refine this approach through development of increasingly sophisticated measures of impacts accruing to government, private industry and households as a result of the shifting flow of expenditures, income, productivity gains, and financial effects of social/environmental benefits.


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A.2 ANNOTATED BIBLIOGRAPHY AND FINDINGS FROM ECONOMIC IMPACT STUDIES OF ENERGY PROGRAMS

Applied Technology Research Corporation. 1995. “Economic and Fiscal Impacts of the Energy Rated Homes of Louisiana Program,” prepared for Louisiana Department of Natural Resources. Baton Rouge, Louisiana.

Economic Impacts of ERHL Program: Over a period of 1995–2005, increase in output of all Louisiana firms by the end of the 10-year period under the high case scenario would total $1.3 billion (1989$), while in low case the cumulative total would be about $159 million (1989$). The net increase in personal income of Louisiana employees’ totals about $54 million (1989$) after ten year of program activity, while under the low case scenario the increase is about $6.8 million (1989$). The cumulative totals in net increase would be about $520 million (1989$) in the high case and $65 million (1989$) in the low case. Under the high case, new jobs added to the Louisiana economy would total 2,843 by the year 2005 and, under the low case, the increase would be 355. Thus, the total economic benefit based on earnings measures, at the end of the ten year program in (1989$) would be $857 million, while under the low case, benefits would total about $107 million.

Blois, T.E., Cunningham, S.R., & Lott, W.F. 1996. “Electric Industry Restructuring an Economic Impact Study.” Connecticut Center for Economic Analysis. Storrs, Connecticut.

Economic and Tax Impact of Certain Restructuring Proposals for State Tax Policy on Electric and Gas Utilities: Based on 13 scenario analysis, the tax proposal has a negative tax impact as business do not generate enough new activity to create tax collections that offset the direct loss of the presently collected taxes. The key economic variables that show positive results in a time period of 1998–2017 are: (1) real disposable personal income; (2) real exports to other states and foreign countries—average changes in exports ranges between 20–45 million 1987 dollars; (3) private non farm employment—changes in private non farm employment ranges between 200–700 jobs; (4) manufacturing—average changes in total manufacturing employment is between 5–150 jobs; (5) overall cost of production—average % [percent] change in the relative profitability of manufacturing, ranges from as low as 10% to as high as 35%. However, most scenarios have a negative impact on total employment within Connecticut and gross state product. Average changes in total employment goes minus 500–2000 jobs, and average changes in gross state product goes below 20–60 millions 1987 dollars.

Black & Veatch and Planning Information Corporation. 1997. “Study of the Potential Economic Impacts of Electric Restructuring on the State of Wyoming.” Cheyenne, Wyoming.

Potential Impact of Electric Restructuring on Electricity Prices and the Economy in Wyoming over the 1998–2007 time horizon: The economic analysis under the Flash-cut scenario indicates that electricity prices under the Flash-cut scenario would result in 0.5% increase in jobs, 0.4% in personal income, and 0.5% increase in Wyoming revenue. However there will be also 0.1% reduction in population. However, these effects would dissipate over the early years of the forecast period. The economic analysis under the Phased retail access scenario, would peak in 2001, with increase in by .25% in jobs, by .21% in personal income, and Wyoming revenue by .2%.


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Central Illinois Energy. “Central Illinois Energy Cooperative Economic Impact Analysis.” Canton, Illinois.

Economic Impact of Energy Cooperative: The construction of the CIEC will have a very positive impact on the local regional economy. During the construction phase, there will be an increase in total business activity of $117 million, including personal earning of $34 million and additional job creation of 1,609 job years. In the operational phase, the plant will have an overall economic impact of $78 million with an increase in earning of $17 million and support 929 jobs.

Clemmer, S. 1994. “The Economic Impacts of Renewable Energy Use in Wisconsin,” directed by Wichert, Don. Wisconsin Energy Bureau.

Investing in renewable energy instead of fossil fuels in Wisconsin could save the state’s residents about $700 million in avoided environmental regulations on emissions, translating to $250 million in personal income.

Department of Administration. Division of Energy and Intergovernmental Relations Wisconsin Energy Bureau. 1994. “The Economic Impacts of Renewable Energy Use in Wisconsin.” Wisconsin.

Economic Impacts of Wisconsin’s Renewable Energy Resources Against Imported Fossil Fuel Use and Investment: The renewable energy use under the accelerated growth scenario generates 62,234 job-years of employment, $1.2 billion in payroll, and 4.6 billion in business output during a time period of 1992–2010 (1989$). However, without environmental costs, investments in renewable energy still generate a net increase of 48,202 job-years of employment, $931 million in payroll, and $3.7 billion in output. In the low growth scenario, the cost of investment in renewable energy is less than investing in fossil fuels; however, the net economic impacts for electricity generation are ten times lower

Division of Energy Resources Massachusetts. 2002. “The 2000 Energy Efficiency Activities.” Commonwealth of Massachusetts.

Impact of Energy Efficiency Programs on the Commonwealth’s Economy: In the year 2000, employment increased by 1,183 jobs, and generated disposable income from the net employment by $48 million.

Efficiency Vermont. 2001. “Efficiency Vermont 2001 Annual Report.”

Based on collective $14 million investment in efficiency in 2001, the economic value of all resources saved as a result of Efficiency Vermont activities is estimated to be $24.7 million, thus resulting in a net saving of $10.7 million.

Elliot, R. N. 1994. “Electricity Consumption and the Potential for Electric Energy Savings in the Manufacturing Sector.” American Council for an Energy-efficient Economy. Washington, DC/Berkeley, California.

Electricity Consumption in Manufacturing Industries: In 1991, manufacturing industries consumed slightly over 15 squads of all form of energy of which 16% was electricity. The electricity purchases by manufacturing industries in 1991 represented almost two-third of


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the total manufacturing energy purchases, totaling over $55 billion. In spite of the size of the electricity expenditure, electricity cost represents only 1.2% of the value of the manufacturing goods. However, there are a few industries like aluminum and industrial gases were electricity purchases are more than 20% of the value of the manufactured products. Many opportunities exist for improving the electrical energy efficiency of manufacturing industries as a whole.

Fountain, R. 2000. “Economic Impact of SMUD Energy Efficiency Programs.” Real Estate Land Use Institute. California Sate University, Sacramento.

Economic Impact from SMUD 1997–2001 Energy efficiency Programs: The total employment created by the commercial and industrial programs over the entire period of 1997–2030 is about 2,946. Years 2003–2008 are the peak years of employment at 175 employees per year. The economic benefits discounted at present value over the period of 1997–2030 in total output equals approx. $185 million, value added of $107 million, personal income of about $62 million, and employee compensation totals $51 million.

Geller, H., DeCicco J., Laitner, S. 1992. “Energy Efficiency And Job Creation: The Employment and Income Benefits from Investing in Energy Conserving Technologies Economic Research Associates.” Eugene, Oregon.

Indirect Economic Impact of High Efficiency Energy Strategy: Based on input-output analysis of high efficiency scenario for all end-use sectors of the economy vs. reference business-as-usual scenario, the high efficiency scenario leads to more jobs, higher personal income, and marginally higher GDP throughout 1995–2010. The net efficiency gains approximate about 293,000 new jobs by 1995, 471,000 new jobs by 2000, and 1.1 million by year 2010. The net income increase (in 1990$) by 1995 equals 6.6 billion, 10.7 billion by 2000, and 29 billion by year 2010. Most sectors gain jobs and additional income, in particular, construction, retail trade, and services industries. However, refining, coal mining, gas utilities, oil & gas extraction, and electric utilities industries project substantial loss in jobs and income. About 200,000 jobs could be lost in the five energy sectors by 2010, in the high efficiency scenario.

Hagler Bailly Consulting, Inc. 1995. “The Economic Impact of Energy Efficiency Programs and Renewable Power For Iowa,” prepared for Iowa Department of Natural Resources. Des Moines, Iowa.

The Analysis of Iowa’s Economy and Economic Competitiveness: In the energy efficiency program, based one-time spending of $80 million leads to a creation of accumulated 2029 job-years, $144 million (1994$) of increased disposable income in the state and 480 million (1994$) increase in gross state product. That averages to 200 jobs/year and $14 million/year of income over the period. On Biomass Energy production, in an aggressive scenario, a 1% of Iowa’s electricity generated from switch-grass could lead a net growth as high as 315 jobs/year of employment and $5.5 million/year of additional disposable income. If 15% of Iowa’s electrical power is generated, there could be a net growth of 4,725 jobs/year or 94,500 job-years of employment over 20 years.

Hickman J.E. 1995. “Economic Opportunities Through Energy Efficiency: An Alternative Analysis.” Kansas City, Montana.


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Impact of Energy-efficiency Policies on the State’s Utility Industry: Based on a detailed structural model, this study analyzes three scenarios for Missouri State’s public utility industry impact. In the first scenario, using the residential and commercial energy savings for economic impact result in a less positive increase of 561 jobs between 1995–2000, and $75 million in income. The second scenario, assuming that all of the increased commercial and residential construction costs are debt financed shows an initial boost in employment, GSP, and less in RDI between 1995–2015 as the construction industry reacts to construction demands. In the third scenario, where half of the costs are debt financed, and half are met by cutting construction cost elsewhere project a higher employment of 190 in the year 2000, and a higher real disposable income of $48 million between 1995–2000. Thus, there is a likelihood of significant economic impact of enacting energy-efficiency policies on public utility industry.

Institute For Prospective Technological Studies. 1998. “The Socio-economic Impact of Renewable Energy Projects in Southern Mediterranean Countries.” Seville.

In a socio-economic impact study of Algeria, Morocco, Egypt, and Tunisia, it was found that the social impact of electrification is very positive, especially on education, security, and migration. The economic impact is less clear-cut.

Iowa Department of Natural Resources. 2001. “Building Energy Management Annual Report 2001.” Des Moines, Iowa

Since 1986, Iowa’s building energy management programs have saved $134.7 million in total energy costs and save almost $20 million annually in taxpayer dollars.

Kegal, J. And Laitner, D. 1987. “Community Energy Choices: The Goal Programming Concept as An Economic Development Assessment Tool for Utility/Community-based Energy Management Programs.”

Impact Upon Grainbin Goals: Based on six grainbin energy management goals of 20 or more job-years, $100,000 or more in utility bills savings, an investment cost of $300,000 or less, new economic activity of $1.5 million in a payback period of less than 5 years, the goal programming analysis upon grainbin goals has following impacts: 20 job-years, $130,000/year of energy savings, investment cost of $300,000, economic activity of $2.8 million, and payback period within 4 years.

Krier, B., Goodman, I. and Kelley-Detwiler, P. 1993. “Employment Impacts of Electricity Efficiency in Florida,” prepared for the Florida Energy Office. The Goodman Group.

Employment Impact form Investments in Electricity Efficiency against Traditional Supply sources: Based on different cost/benefit criteria, under the Total Resource Cost (TRC) test, DSM investments totaled $7.9 billion (NPV) and with the Rate Impact Measure (RIM) test, DSM investments are $1.9 billion. In a period of 1993–2033, investment in the TRC test program produces 86,600 NPV person-years of employment, as compared with 65,100 displaced from avoided supply. The RIM test produces 27,000 NPV person years of employment as compared with 15,400 from avoided supply. The bulk employment under both the programs occurs in high wage industrial sectors; construction, manufacture of machinery, and high skill services. Because of cost effectiveness of the DSM programs, additional employment benefits and lower electricity bill savings increases employment gains from TRC test by 35,400 NPV and RIM test by 17,700 NPV jobs. Thus,


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the total employment generated by investment in the DSM programs net of employment displaced from avoided supply in a time interval of 1993–2033 is 56,000 NPV jobs for the TRC test and 29,000 for the RIM test program.

Laitner, S. et al. 1995. “Energy Efficiency and Economic Development in the Midwest.” American Council for an Energy-efficient Economy. Washington, DC/California.

Energy Efficiency Scenario Analysis and Economic Benefits in the four-state Midwest Region: In 1992, the region spent approximately $70.8 billion on energy consumption, i.e. 8.2% of the region’s combined GSP while US as a whole spent only 7.8%. Based on the energy efficiency scenario, the region’s energy requirements in 2010 would be 7% below the 1992 consumption level, and energy related carbon emission would be reduced by 26%. A $104 billion investment in energy efficiency technologies between 1995–2010 would yield cumulative energy savings of $183 billion 1990 dollars. The net employment in the region would increase from 3,000 jobs in 1995 to 205,000 jobs by 2010. The employment benefits from energy efficiency investment could reduce unemployment level in 2010 by nearly 1%.

Laitner, S., Goldberg, M. 1996. “Colorado’s Energy Future: Energy Efficiency and Renewable Energy Technologies as an Economic Development Strategy.”

The report compares a business-as-usual strategy and an alternative strategy with accelerated investments in energy efficiency and renewable energy resources. The alternative strategy would support a net increase of 8,400 jobs adding $171 million in new wage and salary income (1996$). Those jobs are equivalent to the employment supported directly and indirectly by about 67 small manufacturing plants.

Macke, D.M. 1985. “Energy And Economic Impacts.” Lincoln, Nebraska.

Energy and Economic Impacts: Based on the estimates provided by the I-Save Residential Conservation Program, an investment of $74.7 million dollars in conservation measures will result in an annual dollar saving of $13.9 million. The annual energy saving will create $15.0 million in additional spending, jobs, and economic activity within Iowa.

Macke and Associates. 1986. “Energy Efficiency Impacts and the Iowa Economy,” prepared for the Iowa Energy Policy Council.

Economic Returns of the IOWA Building Improvements Program: Based on a 4-year payback scenario, over the 20-year period the Iowa economy will gain $1.182 billion in activity. In the same 20-year period, under 5-year and 6-year payback scenario there will be a net increase in economic activity of $63 and $41 million. In a three-year program, $40 million-improvement program appears to sustain 1,000 job years. Additionally, over a 10-year timeframe, 400 job-years are supported through the higher income effect of the program.

Massachusetts Energy Efficiency Council. 1992. “The Energy Efficiency Industry and the Massachusetts Economy.” Boston, Massachusetts.

The Massachusetts energy efficiency industry is an important part of the Massachusetts economy with over 750 companies and 15,000–20,000 employees. Because energy efficiency is labor-intensive, Massachusetts utility DSM program created 2,350 energy


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efficiency jobs in 1991, and the electricity cost saving produced by those programs created over 700 additional jobs. Massachusetts energy efficiency industry has potential to serve regional, national, and international markets, reduce manufacturing and utility cost, and help clean up the environment.

Megdal, L., and Rammaha, H. 1992. "The Development of a Local Electric Energy Efficiency Economic Impact Model for Use in Integrated Resource Planning." Proceedings of the ACEEE Summer Study 8.127–133. American Council for an Energy Efficient Economy. Washington, DC.

Economic Impact of the City of Austin’s 1989 DSM Investment: Based on $4.3 million investment in energy efficiency programs in 1989–90 generates a net present value of $3.7 million in income over a period of 1991–2009, creates 75 jobs in 1990 and 10 jobs in Austin for 1991–2009. Under the more optimistic scenario of rate impact and energy efficiency investments not requiring a decrease in expenditures for other goods and services, the City’s 1989–90 investment generates a net present value of $15.9 million in income over the investment life and creates 514 jobs in 1990 and 20 additional jobs in Austin for 1991–2009.

Moscovitch, E. 1994. "DSM in the Broader Economy: The Economic Impact of Utility Programs." The Electricity Journal.

The Energy Foundation Study on DSM Impacts: Based on the general equilibrium model of the national economy, the energy foundation study over the 20 years period from 1990–2010 offers significant environmental improvements and economic gains. As compared with annual avoided cost of $60 billion, the DCM program would result in a net saving of $30 billion. By the year 2010, the program could reduce electricity generation by 27%, construction of new generating capacity by 50%, total electricity cost by 18% ($82 billion 2010 dollars), lower emission levels, and increase annual consumption net of electricity by $45 billion. The cumulative benefits by the 2010 are $74 billion. In the employment area, there is a loss of 467,000 jobs in utilities, business services, metal production, coal mining, construction, and whole trade. Countering the job losses, there is a total increase of 469,00 jobs in gaining industries.

New York State Energy Research And Development Authority. 2002. “New York Energy $martSM Program Evaluation and Status Report” prepared for System Benefits Charge Advisory Group.

New York Energy $martSM Program: The program will result in anticipated energy bill savings for program participants by $119.1 million of which $102 million will be from electricity savings. The anticipated co-funding and leverage investment equals $617.7 million and as of June 2001, $201 million have been committed to NYSERDA’s New York Energy Smart finds. Anticipated jobs sustained or created will be 2,311.

Regional Economics Applications Laboratory. “Job Jolt The Economic Impacts of Repowering the Midwest: The Clean Energy Development Plan for the Heartland,” prepared for the Environmental Law and Policy Center. Chicago, Illinois.

Economic Impact of Clean Energy Development Plan: Implementing the energy efficiency plan will generate 140,900 job, personal income of $3.2 billion, and business output by $12.7 billion in a time period of 2000–2010. The renewable energy plan will generate


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68,400 jobs, and business output of $6.7 billion in the same period. In total the Repowering The Midwest Clean Energy Development Plan would create 209,000 jobs across the ten-state Midwest region, up to $5.5 billion in additional worker income, and $19.4 billion in increased economic activity in a time period of 2000–2010.

Scott Allen. 1992. “Energy Conservation Job Bonanza.” Article in Boston Globe December 10, 1992 issue.

Energy efficiency conservations in Massachusetts created about 3,000 jobs during 1991. The national utility conservation market is expected to be worth $5 billion within a few years. In all, the report estimates that about 750 to 1,500 firms provide conservation products or services having employment ranging form 1 person to 3,800 people who work for Sylvania in Danvers.

Smith, P.R., DeCotis, P.A., Michael, K.S. “Linking Market-based Energy Efficiency Programs to Economic Growth, Sustainable Development and Climate Change Objectives.” New York State Energy Research And Development Authority. New York.

Economic and Environmental Benefits of Building Energy Efficiency Programs: The economic impacts of the energy efficiency programs are driven primarily by reducing energy expenditures. In the 2003, 2,295 new jobs are created because of these programs. The state’s total output in 2003 is estimated to increase by approx. $419 million (1997$), GSP by $139 million (1997$), and personal income by $217 million (1997$).

Tellus Institute. 2000. “Cost-effective Energy Efficiency Opportunities in the Utah Economy” prepared for Utah Public Service Commission.

Report Findings: Three DSM alternatives were evaluated: energy efficiency, energy conservation, and load management. Based on the total estimated resources costs of $367 million in 2001–2019 time period, the present value of projected electricity saving was estimated at $1.65 billion (2000$) by 2019. The projected net benefit is $1.28 billion.

“The Impacts on Wisconsin’s Economy of Reducing Greenhouse Gas Emissions through Investments in Energy Efficiency.” Report 4 of the Wisconsin Greenhouse Gas Emission Reduction Cost Study.

Economic Impacts of Wisconsin Electric End-use Energy Efficiency and Fuel Switching Investments in 2010: The cost effective investments in energy efficient technologies by Wisconsin residents, businesses, and farmers will create 8,526 new jobs, $490 million in disposable income (1992$) and $41 million (1992$) in gross state product by 2010.

The Law and Water Fund of the Rockies. 1996. “How The West Can Win, A Blueprint for a Clean & Affordable Energy Future.”

The alternative strategy would lead to a net gain of over 12,000 jobs and add $180 million annually to the region’s wage base, with no reductions in coal jobs through 2010.


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“The New Mother Lode: The Potential for More Efficient Electricity Use in the Southwest.” 2002. A Report in the Hewlett Foundation Energy Series. Boulder, Colorado.

Energy Efficiency Use in the Southwest States of Arizona, Colorado, Nevada, New Mexico, Utah, and Wyoming: Based on the high efficiency scenario, a total investment of $9 billion in efficiency measures during 2003–2020 (2000$) would have significant economic impact. Regional employment will increase by 58,4000 jobs, consumers and business savings by $28 billion net or about $4,800 per current household in the region. Regional personal income would increase by $1.34 billion. However, regional GSP declines by $560 million due to declining capital investments. In Arizona, employment will increase by 24,100 jobs and personal income by $550 million and GSP will decline by $230 million. In Colorado, employment will increase by 12,200 jobs, personal income by $280 million, and GSP will decline by $100 million. In New Mexico, employment will increase by 6,900 jobs; personal income by $130 million and GSP will decline by $110 million. In Nevada, employment will increase by 6,300 jobs personal income by $180 million, and GSP will decline by $90 million. In Utah, employment will increase by 6,300 jobs, personal income by $160 million and GSP by $50 million. Moreover, in Wyoming, employment will increase by 2,000, personal income by $40 million, and GSP will decline by $60 million.

University of Colorado. 1998. “Colorado Renewable Energy Industry, Current Economic Impact and Growth requirements,” prepared for the Colorado Office of Energy Conservation.

Colorado renewable Energy Industry will create 1,900 jobs, generate business activity worth $118.8 million and personal income of $113 million.

Weisbrod, G., and X. Lin, 1996. “The Economic Impact of Generating Electricity From Biomass in Iowa: A General Equilibrium Analysis.” Boston, Massachusetts.

Economic Impact in Generating Electricity from Biomass in Iowa: Based on five scenario analyses, the economic impact in terms of GDP, income and employment indicate that there would be a net growth of as high as 373 jobs per year of employment, $7 million (1994$) of disposable income, and $9 million of GRP in scenario-analysis 1; the GRP increases by $5–$13 million (1994$) for different years, annual real disposable income is up by $4–$10 million (1994$) and estimated annual employment ranges from 230 to 530 jobs in scenario-analysis 2–4; and finally in scenario-analysis 5, there would initially be a net growth of 3,732 jobs, $74 million per year of additional disposable income, and $90 million per year of GRP.


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A.3 BIBLIOGRAPHY OF ADDITIONAL ECONOMIC IMPACT STUDIES OF ENERGY PROGRAMS

Buchsbaum, S. et al. 1979. “Jobs and Energy.” Council on Economic Priorities. New York.

California Energy Commission. 1979. “The Comparative Effects of Energy Technologies on Employment.”

Economic Research Associates. 1993. “Energy Efficiency and Minnesota Jobs: The Employment Impacts of Electric Utility Demand-side Management Programs,” prepared for the Izaak Walton League of America. Minneapolis, Minnesota.

Elliot, R. Neal. 1993. “Energy Efficiency in Industry and Agriculture: Lessons form North Carolina.” American Council For An Energy-efficient Economy. Washington, D.C./Berkeley, California.

Geller, H., DeCiccio, J., and Laitner, S. 1992. “Energy Efficiency and Job Creation: The Employment and Income Benefits of Investing in Energy Conserving Technologies.” American Council for an Energy Efficient Economy (ACEEE). Washington, D.C.

Goodman Group. 1992. “A Comparison of the Employment Creation Effects of the A.S. Hartman Cove Generating Station and Maine Demand Side Management.” Boston, Massachusetts.

Jaccard, M. and Sims, D. 1991. "Employment Effects of Electricity Conservation: The Case of British Columbia,” Energy Studies Review 3:1, pages 35–44.

Kegal, J., Laitner, S. “Community Energy Choices: The Goal Programming Concepts as an Economic Development Assessment Tool for Utility/Community-based Energy Management Programs.”

Laitner, S. 1984. “Region VII Economic Indicators Study” for Nebraska Energy Office.

Laitner, S. et al. 1994. “Energy Efficiency as an Investment in Ohio's Economic Future,” prepared for the Campaign for an Energy Efficient Ohio. American Council for an Energy-efficient Economy. Washington, D.C./California.

Levy, Lloyd. 1997. “Economic Impacts of Electric Industry Restructuring: A Wyoming Study.” Kansas City, Missouri.

Macke, Donald M. 1986. “Regional Economic Indicators Analysis Update.” Nebraska.

National Wind Coordinating Committee: Wind Energy and Economic Development Workshop and Work Group Meeting. March 15–16, 2001. “Study Synopses And Biographies.” Minneapolis, Minnesota.

New York State Energy Planning Board. 1994. “New York State Energy Plan, Vol. III: Supply Assessments.” Pages 606–621.


A–14


Lin, Xiannuan, and Karen R. Polenske. 1993. “ Input-Output Anatomy of China’s Energy-demand Change, 1981–1987.” Department of Urban Studies and Planning Massachusetts Institute of Technology. Cambridge, Massachusetts.

Lin, Xiannuan, with Karen R. Polenske and Kelly Robinson. 1993. “Economic Impact Analysis in U.S. State and Local Air-pollution Control Agencies: A Survey.” Department of Urban Studies and Planning. Massachusetts Institute of Technology. Cambridge, Massachusetts.

Polenske, Karen R., and Xiannuan Lin. 1993. “Conserving Energy to Reduce Carbon-dioxide Emissions in China.” Department of Urban Studies and Planning. Massachusetts Institute of Technology. Cambridge, Massachusetts.

Thornton, S. 2001. “Economic Impact Analysis of Mandatory Energy Performance Standards for Specific Product Classes” prepared for the Ministry for the Environment. PA Consulting Group Inc.

B–1


APPENDIX B: PARTICIPATION AND ENERGY BILL SAVINGS—ALL COUNTIES

The following tables list all counties according to rates of participation and energy bill savings.

Table B-1. ALL COUNTIES PARTICIPATION (Rates of Participation)

Participation Residential Commercial Industrial

County Type County Name Total Number of

Households

Eligible Participants

Actual Participants


Total Number of

Commercial Firms


Actual Participants


Total Number

of Industrial

Firms


Actual Participants


RURAL ADAMS 7,900 2,520 309 12% 409 271 2 1% 191 109 1 1%

SEMI-RURAL ASHLAND 6,718 4,950 419 8% 818 709 32 5% 325 234 3 1%

SEMI-RURAL BARRON 17,851 9,115 807 9% 2,201 1,116 14 1% 1,126 577 2 0%

RURAL BAYFIELD 6,207 3,837 322 8% 718 590 4 1% 305 256 2 1%

URBAN BROWN 87,295 87,295 10,272 12% 7,816 7,816 81 1% 3,502 3,502 23 1%

RURAL BUFFALO 5,511 2,374 171 7% 823 619 1 0% 491 390 0 0%

RURAL BURNETT 6,613 3,487 126 4% 718 462 3 1% 323 172 1 1%

URBAN CALUMET 14,910 13,028 2,591 20% 1,120 951 18 2% 630 546 5 1%

URBAN CHIPPEWA 21,356 13,564 4,452 33% 2,389 1,797 10 1% 1,303 812 7 1%

SEMI-RURAL CLARK 12,047 7,799 633 8% 1,839 1,212 17 1% 1,211 625 4 1%

SEMI-RURAL COLUMBIA 20,439 15,890 1,838 12% 2,864 1,888 16 1% 1,304 853 2 0%

SEMI-RURAL CRAWFORD 6,677 4,210 365 9% 847 783 5 1% 452 409 0 0%

URBAN DANE 173,484 154,704 25,237 16% 15,606 13,960 183 1% 6,625 5,796 19 0%

SEMI-RURAL DODGE 31,417 27,768 3,590 13% 3,109 2,666 44 2% 1,713 1,514 5 0%

SEMI-RURAL DOOR 11,828 6,178 777 13% 1,912 1,020 11 1% 702 351 2 1%

URBAN DOUGLAS 17,808 14,885 2,275 15% 1,573 1,462 9 1% 576 522 6 1%

SEMI-RURAL DUNN 14,337 8,904 724 8% 1,552 1,249 4 0% 878 632 1 0%

B: Participation and Energy Bill Savings—All Counties. . .

B–2




Households


Actual Participants


Total Number of

Commercial Firms


Actual Participants


Total Number

of Industrial

Firms


Actual Participants


URBAN EAU CLAIRE 35,822 30,944 4,268 14% 3,610 3,406 24 1% 1,415 1,273 7 1%

RURAL FLORENCE 2,133 1,756 44 3% 160 30 0 0% 70 19 0 0%

SEMI-RURAL FOND DU LAC 36,931 36,540 4,842 13% 3,418 3,300 53 2% 1,632 1,587 10 1%

RURAL FOREST 4,043 4,043 240 6% 378 378 2 1% 173 173 1 1%

SEMI-RURAL GRANT 18,465 10,565 1,200 11% 2,919 1,690 15 1% 1,716 853 0 0%

SEMI-RURAL GREEN 13,212 11,717 1,812 15% 1,070 815 33 4% 536 420 1 0%

SEMI-RURAL GREEN LAKE 7,703 6,844 859 13% 1,846 1,686 9 1% 976 835 0 0%

SEMI-RURAL IOWA 8,764 8,754 1,170 13% 1,258 1,258 20 2% 756 752 0 0%

RURAL IRON 3,083 2,385 162 7% 332 327 4 1% 130 128 0 0%

SEMI-RURAL JACKSON 7,070 981 132 13% 787 84 1 1% 386 45 0 0%

SEMI-RURAL JEFFERSON 28,205 22,670 2,889 13% 2,650 2,004 29 1% 1,334 1,050 6 1%

SEMI-RURAL JUNEAU 9,696 2,814 416 15% 1,154 351 7 2% 575 212 1 0%

URBAN KENOSHA 56,057 56,057 4,238 8% 3,288 3,288 279 8% 1,264 1,264 5 0%

SEMI-RURAL KEWAUNEE 7,623 7,163 807 11% 967 934 10 1% 577 561 0 0%

URBAN LA CROSSE 41,599 37,142 3,331 9% 3,975 3,834 30 1% 1,630 1,536 13 1%

RURAL LAFAYETTE 6,211 3,563 530 15% 1,037 539 10 2% 681 305 0 0%

SEMI-RURAL LANGLADE 8,452 8,452 593 7% 939 939 11 1% 445 445 2 0%

SEMI-RURAL LINCOLN 11,721 11,553 933 8% 1,172 1,166 11 1% 531 531 1 0%

SEMI-RURAL MANITOWOC 32,721 13,227 2,157 16% 2,973 1,033 26 3% 1,386 717 7 1%

URBAN MARATHON 47,702 44,419 8,585 19% 4,765 4,564 31 1% 2,305 2,153 16 1%

SEMI-RURAL MARINETTE 17,585 16,835 1,296 8% 1,775 1,700 19 1% 789 733 2 0%

RURAL MARQUETTE 5,986 2,535 374 15% 665 518 3 1% 339 252 0 0%

RURAL MENOMINEE 1,345 1,289 127 10% 57 57 1 2% 38 38 0 0%


B–3




Households


Actual Participants


Total Number of

Commercial Firms


Actual Participants


Total Number

of Industrial

Firms


Actual Participants


URBAN MILWAUKEE 377,729 377,729 38,096 10% 22,552 22,552 513 2% 8,053 8,053 24 0%

SEMI-RURAL MONROE 15,399 11,207 577 5% 1,848 1,539 11 1% 977 703 0 0%

SEMI-RURAL OCONTO 13,979 8,064 1,075 13% 1,356 806 18 2% 693 400 1 0%

SEMI-RURAL ONEIDA 15,333 15,211 1,635 11% 2,049 2,046 10 0% 721 718 0 0%

URBAN OUTAGAMIE 60,530 46,720 9,484 20% 6,235 5,185 66 1% 2,777 2,200 18 1%

URBAN OZAUKEE 30,857 25,246 4,487 18% 3,006 2,445 48 2% 1,501 1,260 6 0%

RURAL PEPIN 2,759 1,916 141 7% 473 409 1 0% 267 218 1 0%

URBAN PIERCE 13,015 3,977 210 5% 1,676 860 6 1% 953 579 1 0%

SEMI-RURAL POLK 16,254 8,143 368 5% 1,968 1,378 12 1% 1,053 655 2 0%

SEMI-RURAL PORTAGE 25,040 23,160 2,867 12% 2,352 2,244 23 1% 1,078 975 7 1%

SEMI-RURAL PRICE 6,564 2,802 277 10% 742 492 3 1% 352 191 3 2%

URBAN RACINE 70,819 70,819 8,046 11% 5,078 5,078 232 5% 2,292 2,292 8 0%

SEMI-RURAL RICHLAND 7,118 2,100 478 23% 859 118 3 3% 477 68 2 3%

URBAN ROCK 58,617 54,207 12,088 22% 4,900 4,726 182 4% 2,083 1,935 12 1%

SEMI-RURAL RUSK 6,095 3,369 845 25% 702 445 10 2% 416 201 3 1%

SEMI-RURAL SAUK 6,095 16,065 2,228 14% 2,795 1,955 20 1% 1,339 980 1 0%

RURAL SAWYER 6,640 4,683 348 7% 973 857 1 0% 371 326 0 0%

SEMI-RURAL SHAWANO 15,815 9,376 1,367 15% 1,631 755 25 3% 859 511 2 0%

URBAN SHEBOYGAN 43,545 34,843 4,218 12% 3,749 2,952 32 1% 1,673 1,269 14 1%

URBAN ST. CROIX 43,545 13,911 768 6% 2,460 1,483 9 1% 1,339 777 3 0%

SEMI-RURAL TAYLOR 7,529 734 221 30% 854 59 3 5% 477 40 0 0%

RURAL TREMPEALEAU 10,747 2,786 383 14% 1,585 381 4 1% 997 232 2 1%

SEMI-RURAL VERNON 10,825 4,360 578 13% 1,462 851 6 1% 861 451 2 0%


B–4




Households


Actual Participants


Total Number of

Commercial Firms


Actual Participants


Total Number

of Industrial

Firms


Actual Participants


RURAL VILAS 9,066 8,794 1,007 11% 1,276 946 2 0% 439 337 1 0%

SEMI-RURAL WALWORTH 34,522 31,729 2,795 9% 3,675 3,274 72 2% 1,624 1,451 6 0%

RURAL WASHBURN 6,604 3,684 451 12% 911 626 5 1% 431 281 0 0%

URBAN WASHINGTON 43,842 38,827 5,051 13% 3,474 2,919 35 1% 1,808 1,594 5 0%

URBAN WAUKESHA 135,229 128,673 21,920 17% 13,171 12,446 93 1% 6,504 6,188 20 0%

SEMI-RURAL WAUPACA 19,863 14,440 1,959 14% 2,510 1,741 40 2% 1,236 917 2 0%

RURAL WAUSHARA 9,336 6,153 724 12% 889 762 7 1% 410 314 2 1%

URBAN WINNEBAGO 61,157 55,533 7,892 14% 4,854 4,566 46 1% 2,088 1,991 24 1%

SEMI-RURAL WOOD 30,135 9,379 1,622 17% 3,086 665 9 1% 1,398 451 4 1%

Grand Total 2,089,130 1,761,395 230,119 13% 192,660 160,033 2,629 1.6% 88,888 71,740 331 0.5%

Table B-2. ALL COUNTIES SAVINGS (Savings Rates)

Savings Residential Commercial Industrial

County Type County Name Annual Energy Cost Savings

Participants Savings/ Participant

Annual Energy Cost Savings




RURAL ADAMS $10,881 309 $35 $5,044 2 $2,522 $3,094 1 $3,094

SEMI-RURAL ASHLAND $35,703 419 $85 $65,927 32 $2,060 $19,362 3 $6,454

SEMI-RURAL BARRON $30,310 807 $38 $12,588 14 $899 $7,177 2 $3,589

RURAL BAYFIELD $22,512 322 $70 $11,754 4 $2,939 $3,971 2 $1,985

URBAN BROWN $585,895 10,272 $57 $322,071 81 $3,976 $430,605 23 $18,722

RURAL BUFFALO $8,145 171 $48 $65 1 $65 $0 0 $0


B–5









RURAL BURNETT $5,053 126 $40 $25,432 3 $8,477 $16,699 1 $16,699

URBAN CALUMET $129,093 2,591 $50 $43,329 18 $2,407 $18,605 5 $3,721

URBAN CHIPPEWA $213,182 4,452 $48 $84,534 10 $8,453 $165,747 7 $23,678

SEMI-RURAL CLARK $27,691 633 $44 $41,913 17 $2,465 $15,299 4 $3,825

SEMI-RURAL COLUMBIA $98,762 1,838 $54 $43,056 16 $2,691 $22,381 2 $11,191

SEMI-RURAL CRAWFORD $13,150 365 $36 $212,867 5 $42,573 $0 0 $0

URBAN DANE $1,468,897 25,237 $58 $1,378,043 183 $7,530 $199,707 19 $10,511

SEMI-RURAL DODGE $217,075 3,590 $60 $115,850 44 $2,633 $27,578 5 $5,516

SEMI-RURAL DOOR $36,682 777 $47 $12,259 11 $1,114 $8,911 2 $4,456

URBAN DOUGLAS $157,113 2,275 $69 $192,278 9 $21,364 $83,270 6 $13,878

SEMI-RURAL DUNN $33,621 724 $46 $9,695 4 $2,424 $2,085 1 $2,085

URBAN EAU CLAIRE $268,432 4,268 $63 $142,990 24 $5,958 $145,690 7 $20,813

RURAL FLORENCE $2,916 44 $66 $0 0 $0 $0 0 $0

SEMI-RURAL FOND DU LAC $338,010 4,842 $70 $105,357 53 $1,988 $195,035 10 $19,504

RURAL FOREST $12,387 240 $52 $1,056 2 $528 $6,771 1 $6,771

SEMI-RURAL GRANT $71,746 1,200 $60 $33,754 15 $2,250 $0 0 $0

SEMI-RURAL GREEN $98,697 1,812 $54 $34,780 33 $1,054 $10,312 1 $10,312

SEMI-RURAL GREEN LAKE $48,379 859 $56 $6,969 9 $774 $0 0 $0

SEMI-RURAL IOWA $65,708 1,170 $56 $14,453 20 $723 $0 0 $0

RURAL IRON $5,195 162 $32 $7,684 4 $1,921 $0 0 $0

SEMI-RURAL JACKSON $4,742 132 $36 $2,153 1 $2,153 $0 0 $0

SEMI-RURAL JEFFERSON $139,538 2,889 $48 $104,727 29 $3,611 $76,599 6 $12,767

SEMI-RURAL JUNEAU $20,216 416 $49 $11,927 7 $1,704 $4,528 1 $4,528

URBAN KENOSHA $223,398 4,238 $53 $143,745 279 $515 $24,157 5 $4,831

SEMI-RURAL KEWAUNEE $40,147 807 $50 $19,571 10 $1,957 $0 0 $0


B–6









URBAN LA CROSSE $213,645 3,331 $64 $104,672 30 $3,489 $325,909 13 $25,070

RURAL LAFAYETTE $30,313 530 $57 $26,953 10 $2,695 $0 0 $0

SEMI-RURAL LANGLADE $26,748 593 $45 $34,653 11 $3,150 $7,833 2 $3,917

SEMI-RURAL LINCOLN $45,643 933 $49 $54,668 11 $4,970 $404 1 $404

SEMI-RURAL MANITOWOC $101,883 2,157 $47 $40,524 26 $1,559 $8,231 7 $1,176

URBAN MARATHON $245,880 8,585 $29 $65,065 31 $2,099 $332,291 16 $20,768

SEMI-RURAL MARINETTE $70,301 1,296 $54 $33,086 19 $1,741 $25,924 2 $12,962

RURAL MARQUETTE $12,604 374 $34 $7,713 3 $2,571 $0 0 $0

RURAL MENOMINEE $4,884 127 $38 $10,626 1 $10,626 $0 0 $0

URBAN MILWAUKEE $2,124,663 38,096 $56 $1,211,970 513 $2,363 $315,684 24 $13,154

SEMI-RURAL MONROE $45,106 577 $78 $26,484 11 $2,408 $0 0 $0

SEMI-RURAL OCONTO $48,532 1,075 $45 $22,362 18 $1,242 $0 1 $0

SEMI-RURAL ONEIDA $63,007 1,635 $39 $43,256 10 $4,326 $0 0 $0

URBAN OUTAGAMIE $515,176 9,484 $54 $204,075 66 $3,092 $994,158 18 $55,231

URBAN OZAUKEE $248,884 4,487 $55 $71,254 48 $1,484 $146,749 6 $24,458

RURAL PEPIN $13,123 141 $93 $8,044 1 $8,044 $1,594 1 $1,594

URBAN PIERCE $8,888 210 $42 $9,016 6 $1,503 $5,090 1 $5,090

SEMI-RURAL POLK $13,620 368 $37 $59,925 12 $4,994 $20,501 2 $10,250

SEMI-RURAL PORTAGE $166,738 2,867 $58 $175,569 23 $7,633 $308,300 7 $44,043

SEMI-RURAL PRICE $14,214 277 $51 $2,343 3 $781 $938,475 3 $312,825

URBAN RACINE $367,537 8,046 $46 $97,405 232 $420 $165,057 8 $20,632

SEMI-RURAL RICHLAND $30,292 478 $63 $27,985 3 $9,328 $3,313 2 $1,656

URBAN ROCK $634,479 12,088 $52 $572,864 182 $3,148 $75,121 12 $6,260

SEMI-RURAL RUSK $37,005 845 $44 $54,769 10 $5,477 $23,243 3 $7,748

SEMI-RURAL SAUK $111,266 2,228 $50 $43,529 20 $2,176 $69,631 1 $69,631


B–7









RURAL SAWYER $13,557 348 $39 $6,149 1 $6,149 $0 0 $0

SEMI-RURAL SHAWANO $58,790 1,367 $43 $22,337 25 $893 $3,622 2 $1,811

URBAN SHEBOYGAN $233,611 4,218 $55 $41,809 32 $1,307 $785,032 14 $2,372

URBAN ST. CROIX $30,359 768 $40 $13,678 9 $1,520 $19,481 3 $6,494

SEMI-RURAL TAYLOR $9,938 221 $45 $14,142 3 $4,714 $0 0 $0

RURAL TREMPEALEAU $16,478 383 $43 $11,579 4 $2,895 $11,211 2 $5,606

SEMI-RURAL VERNON $23,553 578 $41 $13,601 6 $2,267 $8,272 2 $4,136

RURAL VILAS $51,002 1,007 $51 $4,681 2 $2,340 $12,372 1 $12,372

SEMI-RURAL WALWORTH $131,319 2,795 $47 $184,925 72 $2,568 $79,922 6 $13,320

RURAL WASHBURN $19,441 451 $43 $16,879 5 $3,376 $0 0 $0

URBAN WASHINGTON $270,995 5,051 $54 $96,351 35 $2,753 $31,831 5 $6,366

URBAN WAUKESHA $1,009,279 21,920 $46 $276,497 93 $2,973 $282,829 20 $14,141

SEMI-RURAL WAUPACA $108,950 1,959 $56 $42,820 40 $1,071 $6,392 2 $3,196

RURAL WAUSHARA $30,985 724 $43 $16,276 7 $2,325 $4,861 2 $2,430

URBAN WINNEBAGO $422,420 7,892 $54 $254,489 46 $5,532 $125,263 24 $5,219

SEMI-RURAL WOOD $97,511 1,622 $60 $30,846 9 $3,427 $244,956 4 $61,239

Grand Total $12,155,897 230,119 $53 $7,261,747 2,629 $2,762 $6,871,138 331 $20,759

C–1


APPENDIX C: TWO APPROACHES TO ESTIMATING TOTAL ENERGY CONSUMPTION BY WISCONSIN COUNTY

This report did not address the question of total energy consumption by county for one key reason: the difficulty of finding current, Wisconsin specific, energy consumption data that can be applied on a county level, even though the value of energy consumption data was recognized as an important component of the comparison of urban and rural benefits. Nonresidential estimation of energy consumption posed the greatest challenge.

Two approaches outlined below represent two ways to roughly estimate relative energy consumption in different counties. Both approaches are based on average energy intensities and variation in economic structure in each county, and are therefore sensitive to both the size and types economic activity in the county. This will provide a rough measure of relative total consumption between counties, but it does not reflect differences that may exist in the average intensities of industries or households found in different counties. Neither approach considers energy efficiency changes that have occurred over time.

RESIDENTIAL ENERGY CONSUMPTION

Residential consumption data can be standardized across the state, which will only loosing some variation that may exist from north to south. Utilities provide data on total energy sales down to the village level, it is not disaggregated by residential or commercial and industrial, there do not appear to be any other consumption data sources that can be manipulated to estimate average residential consumption on a county level. Based on data in the 2003 Wisconsin Energy Statistics preliminary report total residential end use in 2002 was 430.8 trillion BTU. The total number of households in Wisconsin in the same year was 21,446,000. 2 Therefore, the average Wisconsin household in consumed 20 MMBTU in 2002. For each county the number of eligible households can be multiplied by the average energy consumption to determine the total market potential in that county.

2 2003 Wisconsin Energy Statistics, Wisconsin Residential Energy Use, by Type of Fuel, 1970–2002, page 12; Wisconsin Population, Number of Households and Total and Per Capita Personal Income, 1970–2002 page 125.

C: Two Approaches to Estimating Total Energy Consumption by Wisconsin County. . .

C–2


NONRESIDENTIAL ENERGY CONSUMPTION

In the commercial and industrial sectors, there is a wide variation in energy consumption. Energy intensity is also commonly measured in different units. The trial is to get energy intensity measurements that will work with the best information on they types and size of industry on a county level.

Sector Level Consumption

In the 2003 report on Wisconsin Energy Statistics three different energy efficiency indices were used for sector level intensities: commercial (MMBTU per employee), industrial (MMBTU per $1000 of manufacturing value added) and agricultural (MMBTU per acre).

One approach for determining sector level county energy consumption would be to simply multiply these energy efficiency indices by the available data in each sector. Wisconsin Labor Market Information (LMI) provides industry level data on employment by county3. Manufacturing value added by county is available through the Wisconsin Department of Commerce, but it is from 1997.4 County level information on land in agricultural production is available through the 1997 United States Agricultural Census.5 This measure can provide an estimate of energy consumption that is sensitive to the sector and size of economic activity in a county.

Industry Level Consumption

A more sensitive, but possibly not more accurate, method is to focus on industry specific energy intensities and apply those to existing data on the prevalence of those industries in each county. This method requires moving beyond Wisconsin specific data. The marriage of two data sources: industry energy consumption data at the census region level and industry employment data at a county level; can provide an estimate of energy consumption that is sensitive to the type and size of economic activity in a county.

National surveys on commercial and manufacturing energy consumption are available at the census region level. For both sectors, the reported data must be manipulated before it can be applied to the county level employment data. All consumption rates were translated to a per employee measure of energy intensity so it is compatible with our best available data on industry employment at the county level: U.S. Census Bureau’s County Business Patterns for 2001.

Manufacturing

The 1998 Manufacturing Energy Consumption Survey (MECS) for the Midwest census region reports total energy consumption for a selection of NAICS codes. Energy intensity, by employee was not provided. This value had to be calculated by first determining the total

3 Wisconsin Labor Market Information is available for 2002 at: http://www.dwd.state.wi.us/lmi/data.htm

4 The Wisconsin Department of Commerce has profiles of each county in the state, which includes value added by manufacturing from 1997. The reports were prepared in 2000 and are available in PDF format. 2001 data will not be available until spring of 2004. This is available on line at: http://www.commerce.state.wi.us/MT/MT-COM-4300.html

5 The 1997 Agricultural Census has data on the total acres that are in production by county, these are also provided on-line at: http://www.nass.usda.gov/census/census97/highlights/wi/wi.htm


C–3


number of employees for these same NAIC codes in the same census region6 by using the Census County Business Patterns on-line data on employment data by industry for the same year. The total energy consumption was divided by the total number of employees reported for the Midwest census region by industry to determine the energy intensity per employee for each NAICS code. These intensities were transferred to the appropriate SIC categories to make them comparable to Focus data.

This approach, while not providing a Wisconsin specific measure of energy intensity, provides industry level detail that will also be sensitive to the relative size of the industry in a specific county. The following table shows the average total energy consumption per employee.

Table C-1. Energy Intensity and Efficiency Change for Participating Firms Based on MECS

Two digit SIC* SIC Description Energy Intensity

MMBTU/ Employee 20 Food and kindred products 992 23 Apparel and other textile products 65 24 Lumber and wood products 286 25 Furniture and Fixtures 247 27 Printing and publishing 135 29 Petroleum and coal products 47,312 30 Rubber and miscellaneous plastics products 254 32 Stone, clay, glass, and concrete products 1,914 33 Primary metal industries 849 34 Fabricated metal products 335 35 Industrial Machinery and equipment 246 36 Electrical and electronic equipment 161 37 Transportation equipment 346 38 Instruments and related products 117

Man

ufac

turin

g

39 Miscellaneous manufacturing industries 108 51 Wholesale trade–nondurable goods 868

54 Food stores 1,010

56 Apparel and accessory stores 65

57 Furniture, home furnishings and equipment stores 247

73 Business services 419

Non

-M

anuf

actu

ring

80 Health services 108

Grand Total 725 *The per employee energy consumption is only for those industries that were included in the MECS survey. SIC codes above 39 are generally not considered manufacturing, but some sub sectors of these SIC codes have been re classified as manufacturing in the NAICS system. ** This energy intensity measure considers all energy consumption of industries listed not just electricity and natural gas.

6 The reports for individual states in the Midwest Census region had to be aggregated to develop the census wide employment estimates.


C–4


Commercial

The Commercial Building Energy Consumption Survey (CBECS) tracks energy use in different types of commercial building use, they do not directly correspond to industrial coding systems like SIC (Standard Industrial Classification) or NAIC (North American Industrial Classification). Therefore, the building uses must be translated into industrial codes to match the available data on industry by county. Like the manufacturing survey, results are available at the census region level, but unlike the manufacturing survey, the data included in the survey allows for calculation of the energy intensity per employee. The following table shows the proposed match of primary building application to SIC code, as well as the estimated energy intensity per employee.

Table C-1. Energy Intensity for Midwest Commercial Industries CBECS

Primary Building Activity (Plus)a SIC Included Average Energy Intensity in MMBTU per employee*

Education 822, 821, 829, 835 91 Food sales 54, 541-546, 549, 592 256* Food service 581, 896 129 Health care (inpatient) 806 130

Health care (outpatient) 803, 804, 809 41

Laboratory 807, 873 277 Lodging 701, 704 239 Mercantile 551, 552, 555, 557, 559, 521, 523, 525,

526, 531, 562, 566, 569, 572, 573, 591, 593, 594, 596, 599, 784

79

Office/Professional 47, 48, 67,81,87,731,732, 733, 734, 735,736, 738, 839, 861, 862, 863, 864, 865, 869, 801, 802,60-65, 91-99, 919

40

Other 102 Public assembly 651, 792, 794, 799, 823, 841, 791, 793 126 Public order and safety 832, 921 103*

Religious worship 866 109 Service 373, 725, 737, 753, 754, 762, 763, 764,

769, 722, 723, 724, 726, 729, 501, 553, 554, 571, 721, 431

96

Skilled nursing 805, 836 103 Warehouse (nonrefrigerated and refrigerated)

Wholesale Trade Industries 50-51 139

a Primary Building Activity (PBA), Public Access Data File 1, 2, 15, 1999 CBECS Midwest *These values were calculated with fewer than 20 buildings in the sample.

Agriculture

No clear alternative to the acreage intensity reported by DOE in the Wisconsin Energy Statistics and the 1997 Agricultural Census data exists. Specific research on dairy operations could provide a realistic per head estimate of energy intensity and the 1997 Agricultural Census would have information on the number of dairy cattle in a county. This may be valuable given the high concentration of Focus activity, about 80% of all participants in the agriculture program, in this type of activity.


C–5


County Economic Structure

After all of the energy intensities have been established for all sectors, the County Business Patterns data7 will be used to determine the number of employees by industry in a given county. The latest data for County Business Patterns is reported in NAICS codes (data before 1998 is also available by SIC code). Once again, given the difference in the coding type all of the efficiency indices will have to be in NAIC format to calculate energy consumption and then turned back into SIC codes to be allow for industry level comparisons of program achievements. If total market potential for the county, without regard to industry is sufficient, then the last switch of codes would not be necessary.

7 County Business Patterns is available on CD for $50, or can be accessed on line from the following Web site: http://censtats.census.gov/cbpnaic/cbpnaic.shtml

D–1


APPENDIX D: PRIMARY INCORPORATED UPDATES TO THE DRAFT REPORT

There are four primary updates pending on the Economic Policy Analysis-Draft Report, issued October 3, 2003:

Update Change From Draft Report

• The direct impacts to urban and rural counties will include energy intensity for Commercial/Ag and Industrial analysis.

• The analysis for Residential will remain the same. Analysis by energy intensity does not provide new information since the energy intensity measure is based on household.

Previously, all analysis was based on populations of firms and households.

Commercial EI will be based on number of employees, for Ag based on acres in agricultural production, and for Industrial, based on value added dollars.

• Counties will be classified based on the 2003 Urban Rural Continuum Codes.

Previously based on the 1993 Urban Rural Continuum Codes

• Energy savings achieved as of September 2003 will be included in the direct impacts.

Previously based on June 2003 energy savings

• Information on the distribution of CFL impacts for Business Programs will be included in the direct impacts.

Previously only CFL impacts for Residential were available.

These changes were proposed in response to comments on the draft and improvements in the data available.

Energy intensity will be included in the direct impact analysis for urban and rural counties. This will be based on a methodology that can provide very rough sector level energy consumption data for each county in the state. More detailed industry level estimates were explored, but the uncertainty in the final value did not seem to justify the additional work required. Neither method would have been time specific since the data is derived from several sources, but both reflect the differences in the types of economic activity found in Wisconsin counties.

For nonresidential consumption, the 2003 report on Wisconsin Energy Statistics offers three different energy efficiency indices to derive sector level intensities: commercial (166.7 MMBTU per employee), industrial (8.9 MMBTU per $1000 of manufacturing value added) and agricultural (2.3 MMBTU per acre).

These indices will be multiplied by available data on employment, value added, and acreage for each sector. Wisconsin Labor Market Information (LMI) provides industry level data on employment by county8. Manufacturing value added by county is available through the

8 Wisconsin Labor Market Information is available for 2002 at: http://www.dwd.state.wi.us/lmi/data.htm

D: Primary Incorporated Updates to the Draft Report. . .

D–2


Wisconsin Department of Commerce, but is from 1997.9 County level information on land in agricultural production is available through the 1997 United States Agricultural Census.10 This measure can provide an estimate of energy consumption that is sensitive to the sector and size of economic activity in a county, but does not reflect actual energy consumption for any specific year.

This method has served to account for 92% of the state’s total energy consumption as reported in the 2003 report on Wisconsin Energy Statistics. The only sector that could account for less than 95% was manufacturing, which accounted for 88.4% of 2002 consumption. This is partially due to the lack of county level value added data for all “industrial” activity as defined in Wisconsin Energy Statistics.

Counties will be reclassified based on the 2003 Urban Rural Continuum codes. The 1993 Urban Rural Continuum Codes were recently updated based on the 2000 Census, and over 25 counties in Wisconsin were modified, and 9 moved from their previous grouping of urban, semi-rural, or rural. This newer data will provide a more accurate comparison between counties and county types.

Energy savings as of September 2003 will be included to provide the most current view of the program as possible.

In the draft report, the energy savings from compact fluorescent lighting upgrades made through the Business Program participants were not included because of the lack of geographical data to assign the savings to a specific county. Since the issuance of the draft, this information has become available, and will be included in the direct impact analysis.

9 The Wisconsin Department of Commerce has profiles of each county in the state, which includes value added by manufacturing from 1997. The reports were prepared in 2000 and are available in PDF format. 2001 data will not be available until spring of 2004. This is available on-line at: http://www.commerce.state.wi.us/MT/MT-COM-4300.html 10 The 1997 Agricultural Census has data on the total acres that are in production by county, these are also provided on-line at: http://www.nass.usda.gov/census/census97/highlights/wi/wi.htm

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