A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Energy, Economic, and 

Environmental Benefits of 

the Solar America Initiative 

S. Grover 

ECONorthwest 

Portland, Oregon 

Subcontract Report 

NREL/SR-640-41998 

August 2007 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

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Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337

Subcontract Report 

NREL/SR-640-41998 

August 2007 

Energy, Economic, and 

Environmental Benefits of 

the Solar America Initiative 

S. Grover 

ECONorthwest 

Portland, Oregon 

NREL Technical Monitor: R. Margolis 

Prepared under Subcontract No. ADJ-7-77253-01 
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Acknowledgments 

This report was prepared by ECONorthwest's Portland, OR office for the National Renewable Energy 

Laboratory. Dr. Stephen Grover was the project manager for the analysis and primary author of this 

report. Questions regarding the report should be directed to him by e-mail at grover@portland.econw.com

or by phone at (503) 222-6060. ECONorthwest staff members John Boroski, Alec Josephson, Jonny Holz, 

Rob Wyman, and Jessica Brown assisted with the analysis and preparation of this report. 
TABLE OF CONTENTS

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

2. Energy Benefits ..................................................................................................................................... 2

3. Economic Benefits................................................................................................................................. 5

IMPLAN Model ....................................................................................................................................... 8

Economic Benefit Estimation Results .................................................................................................... 12

4. Environmental Benefits ....................................................................................................................... 21

Emissions Reduced By SAI.................................................................................................................... 21

Health Benefits ....................................................................................................................................... 22

5. Additional Energy Benefits ................................................................................................................. 24

6. Summary and Conclusions .................................................................................................................. 27

7. References ........................................................................................................................................... 28

Appendix A: Detailed IMPLAN Model Results.......................................................................................A-1

Appendix B: IMPLAN Model Technical Detail....................................................................................... B-1 
1. INTRODUCTION 

The President’s Solar America Initiative (SAI) was launched in January 2006 as part of the 

administration’s Advanced Energy Initiative. The SAI is being led by the U. S. Department of Energy 

(DOE) Solar Energy Technologies Program (SETP), with the National Renewable Energy Laboratory 

(NREL) providing analytical and technical support. The SAI has a goal of installing 5-10 GW of 

photovoltaic (PV) systems in the U.S. by 2015 and 70-100 GW of PV systems in the U.S. by 2030. To 

make PV cost competitive with other energy resources, this requires that the installed cost of PV fall from 

approximately $8/Wdc in 2005 to $3.3/Wdc in 2015 and $2.5/Wdc in 2030. 

This report presents estimates of the potential benefits should the SAI PV installation goals be achieved. 

For this analysis, the areas researched include energy, economic, and environmental benefits. For the 

purposes of estimating these benefits, we assume that the SAI meets its PV installation goals. Other issues 

– such as estimating consumer demand for solar and determining the optimal market mechanism that will 

drive installation cost reductions – are important, but outside the scope of this project. 

Throughout this report, benefits are calculated for both high and low scenarios for the SAI goals based on 

the target installation costs and amount of PV installed: 

• The Low Scenario assumes that 5 GW of PV is installed by 2015 at $3.3/Wdc and 70 GW is 

installed by 2030 at $2.5/Wdc 

• The High Scenario assumes that 10 GW of PV is installed by 2015 at $3.3/Wdc and 100 GW of 

solar is installed by 2030 at $2.5/Wdc. 

To estimate the potential SAI benefits given these scenarios, ECONorthwest completed the following: 

• The IMPLAN input-output model was used to estimate the economic impacts associated with the 

manufacture, installation, and operation of PV systems that would meet the SAI installation goals 

• Existing model results and secondary research findings were used to estimate emissions and 

associated health benefits that will result with PV installations 

• Benefit factors taken from secondary sources were used to estimate additional environmental and 

energy benefits created by the SAI. 

The remainder of this report is organized as follows. The Energy Benefits section provides an estimate of 

the amount of electricity generation that will be provided by the SAI if its goals are met as well as 

estimates of the amount of natural gas displaced. Following this, the Economic Benefits section presents 

estimates of the economic benefits assuming that the SAI installation goals are achieved and builds on the 

energy results provided in the previous section. The Environmental Benefits section presents a discussion 

of environmental benefits and includes emissions reductions and health benefits that could be achieved 

with PV installations that meet the SAI goals. This is followed by an Additional Benefits section that 

presents additional information on energy-related benefits that will result from the SAI, including 

information on possible additional energy benefits such as blackout prevention, price effects, emergency 

power, and reduced transmission and distribution (T&D) losses. Finally, all of the estimated benefits are 

summarized in the Summary and Conclusions section. Additional IMPLAN model results are included in 

Appendix A and a technical discussion of the IMPLAN model is included in Appendix B. 

 Page 1 
 Page 2 

2. ENERGY BENEFITS

Achieving the SAI goals for PV installation will result in a significant amount of electricity generation 

resources that will displace generation from more traditional sources. The first step in estimating the 

benefits of the SAI is to determine the amount of electricity generation that will result in both the high 

and low scenarios. The PV energy estimates are then used to determine the economic and environmental 

benefits in subsequent sections of this report. 

Table 1 shows the estimated annual PV electricity generation that would result if the SAI achieved its PV 

installation goals in both the high and low scenarios. These results are based on an average annual solar 

capacity factor of 19 percent estimated by NREL based on national solar insolation data1

. Actual 

insolation levels will vary based on location or orientation. In comparison, the Energy Information 

Administration (EIA) reports that in 2004, 397 MW of PV capacity was installed throughout the country, 

producing 579,000 annual MWh, for a capacity factor of approximately 17 percent2

. 

Based on these assumptions, the SAI is projected to produce 10–20 TWh/yr of PV electricity generation 

in 2015 and 110–170 TWh/yr in 2030 based on the installed PV capacity in the high and low case 

scenarios. The EIA projects total U.S. electricity generation to be 4,713 TWh/yr in 2015 and 5,788 

TWh/yr in 20303

. Attaining the SAI targets would therefore result in PV displacing approximately 0.2–

0.4 percent of total U.S. electrical generation in 2015 and 2–3 percent of total generation in 2030. In terms 

of residential use, the EIA’s Residential Energy Consumption Survey indicates that average U.S. 

household electricity consumption is about 10,000 kWh/yr (EIA 2001). Under the SAI, PV would 

therefore generate enough electricity to power 1–2 million homes in 2015 and 10–20 million homes in 

2030. 

Table 1: Estimated SAI PV Generation and Total U.S. Electricity Generation

Installed PV PV Generation Total U.S. PV Percent of

Scenario Capacity (GW) (TWh/yr) Generation (TWh/yr) Total Generation

2015 Low 5 8.3 4,713 0.2%

2015 High 10 16.6 4,713 0.4%

2030 Low 70 116.5 5,788 2.0%

2030 High 100 166.4 5,788 2.9%

Natural Gas Displaced 

One of the primary benefits of the SAI will be the displacement of natural gas used to generate electricity. 

PV generation is well suited to offset a portion of peak electricity demand, which is often met by natural 

gas power plants, as PV output is highly correlated with peak demand on the electricity system. 

 

1

 Margolis (2006) bases this analysis of location-specific capacity factors from NREL’s PVWatts PV simulation program 

available at: http://rredc.nrel.gov/solar/codes_algs/PVWATTS/version1/. 

2

 EIA Renewable Energy Annual 2004. Tables 11 and 12. 

3

 EIA Annual Energy Outlook 2007 (Early Release) Data for Figure 5 available at: 

http://www.eia.doe.gov/oiaf/aeo/electricity.html 
 Page 3 

Natural gas fuel savings were estimated for each of the SAI capacity scenarios using the following 

assumptions: 

1. At relatively low penetration levels (less than 10 percent of total electricity generation), PV 

would displace fossil fuel generation on a one-to-one basis (Denholm and Margolis 2006). 

2. Transmission and distribution (T&D) losses for fossil fuel generation are assumed to average 

about 7 percent based on several studies reviewed for this report. This reflects electricity that is 

lost as it travels through the wires from generating plants to end-users. Due to these losses, fossil 

fuel plants must generate 7 percent more electricity than PV systems to provide end-users with an 

equivalent amount of electricity. 

3. Seventy-five percent of the PV generation would displace natural gas generation, as estimated by 

Connors et al. (2005) in a recent analysis of the load-shape following characteristics of the U.S. 

electricity generation system. (The other 25 percent of PV generation would mainly displace coal 

generation.) 

4. The average heat rate for all natural gas plants offset by PV generation is estimated to be 7,100 

BTU/kWh in future years4

. Heat rates for natural gas plants vary greatly and depend on the type 

and vintage of the plant. While a typical natural gas combustion turbine (NGCT) has a heat rate 

of 11,100 BTU/kWh, new natural gas combined cycle (NGCC) plants in California have a much 

lower heat rate of 7,100 BTU/kWh (Margolis 2006). The EIA expects the heat rate of new NGCC 

plants to continue to decline over time, to as low as 6,333 BTU/kWh in 20155

. Since natural gas 

offset by PV will be from a mix of old and new NGCC and NGCT plants, an average heat rate of 

7,100 BTU/kWh is assumed for this analysis. 

Based on these assumptions, the projected natural gas savings under the SAI are roughly 0.05–0.1 

quadrillion BTU (quads) in 2015 and 0.5–1.0 quads in 2030. The EIA projects that total U.S. natural gas 

consumption will be 26 quads in 2015 and 27 quads in 2030.6

 Thus, PV generation due to the SAI is 

expected to displace 0.2–0.4 percent of natural gas consumption in 2015 and 2–4 percent of natural gas 

consumption in 2030. 

Another way to view the benefits of reduced natural gas consumption is to consider the potential impact 

on liquefied natural gas (LNG) imports. LNG imports to the U.S. increased 700 percent between 1997 

and 2005, and much of this demand has been driven by increased natural gas consumption for electricity 

generation.7

 During this same period, natural gas use for electricity generation increased by 1.8 quads 

(43 percent). 

In the future, increasing use of natural gas use for electricity generation is expected to drive further 

increases in LNG imports. LNG imports are projected to rise from approximately 0.5 quads in 2003 to 

3 quads in 2015 and to 4 quads in 20308

. By offsetting a significant amount of natural gas generation, the 

 

4

 The heat conversion rate provides a measure of thermal efficiency (BTU per kWh) for converting heat input to electricity 

output. 

5

 EIA Annual Energy Outlook 2006. 

6

 EIA Annual Energy Outlook 2007 (Early Release) Data for Figure 3 available at: 

http://www.eia.doe.gov/oiaf/aeo/consumption.html 

7

 EIA. Natural Gas: U.S. Data. Available at: www.eia.doe.gov/oil_gas/natural_gas/info_glance/natural_gas.html. 

8

 Ibid. 
 Page 4 

SAI could displace the equivalent of 2–3 percent of LNG imports by 2015 and 12–24 percent of LNG 

imports by 2030. 

Natural Gas Plants Avoided 

Since the SAI will result in PV installations that help provide electricity during peak demand periods, the 

need for constructing new natural gas peaking plants will be reduced. The following assumptions were 

used to determine the number of new natural gas plants avoided due to the SAI: 

1. The average effective load carrying capacity (ELCC) of PV was assumed to be 60 percent (i.e., 

on average, 60 percent of installed PV capacity is available during peak generation periods) based 

on research by Perez et al (2006). Thus, each 1 GW of PV would displace the need for 0.6 GW of 

natural gas peaking generation capacity. 

2. On average, new natural gas plants will be sized at 250 MW in the future. EIA data indicates that 

over the next few years, utilities plan to build natural gas plants in the 170–215 MW size range9

. 

After that, the EIA projects that typical new plant sizes will continue to grow10. 

Under the SAI scenarios, PV systems would eliminate the need for 3–6 GW of new gas generating 

capacity through 2015 and 42–61 GW of new capacity through 2030. EIA projects that 48 GW of new 

natural gas electricity generation capacity will be required between 2005 and 2015, and 124 GW of new 

natural gas electricity generation capacity will be required between 2005 and 2030.11 Given the forecasted 

demand, the estimated PV installations would be the equivalent of 6–13 percent of projected new natural 

gas capacity between 2005 and 2015, and 34–49 percent of new natural gas capacity between 2005 and 

2030. Stated differently, attaining the SAI goals would avoid the construction of 12–24 natural gas plants 

by 2015 and 170–240 plants by 2030. 

The following section uses these energy results to estimate the potential economic benefits should the SAI 

goals be achieved. 

 

9

 EIA Electric Power Annual 2004. Table 2.5 

10 EIA Annual Energy Outlook 2006. 

11 Ibid. 
 Page 5 

3. ECONOMIC BENEFITS

Achieving the SAI’s installation goals will stimulate a significant amount of economic activities as 

resources are directed toward solar-related industries. This activity will be offset to some degree by 

decreased economic activity in other sectors, however, as the new PV resources displace more 

conventional power sources. 

The gross impacts of the SAI only account for activities related to PV construction, installation and 

maintenance, and do not consider the forgone spending in other energy sectors. For this analysis, 

ECONorthwest also compared the estimated impacts of the SAI to a counterfactual scenario in which 

most of the electricity that would be generated by PV is instead assumed to be provided by natural gas 

plants. The difference in economic impacts between the SAI scenario and the counterfactual scenario is 

referred to as the net impact of the SAI. For example, if a net impact of 100 new jobs is reported, this 

means that the SAI would result in 100 more jobs than would have occurred relative to the case where the 

SAI is not achieved and electricity is provided instead by natural gas generation. Both the gross and net 

impacts of the SAI are described in this section of the report. 

SCENARIO ASSUMPTIONS

As with the energy benefits described in the previous section, the economic benefits were estimated using 

both a high and low scenario derived from the SAI goals: 

• The Low Scenario assumes that 5 GW of PV is installed by 2015 at $3.3/Wdc and 70 GW is 

installed by 2030 at $2.5/Wdc 

• The High Scenario assumes that 10 GW of PV is installed by 2015 at $3.3/Wdc and 100 GW of 

solar is installed by 2030 at $2.5/Wdc. 

The energy benefits described in the last section were also used in the economic analysis for both the high 

and low scenarios. 

The first step in estimating the economic impacts of the SAI is to determine the manufacturing and O&M 

costs (expenditures) associated with installing the new PV capacity. Table 2 summarizes the construction 

and O&M costs for 1 MW of PV installed at the different price points assumed for 2015 ($3.3/Wdc) and 

2030 ($2.5/Wdc), and these costs are used as inputs into the estimation of economic benefits. PV costs are 

allocated as 70 percent in manufacture and 30 percent in installation.12 This allocation remains fixed but 

the total system cost decreases over time as both the manufacturing and installation costs are assumed to 

decrease proportionately with price. PV maintenance costs are also assumed to decrease in direct 

proportion to system costs, decreasing from $35/kW in 2005 to $10.94/kW in 2030.13

 

12 Installation costs also include design, site preparation, permitting, and other soft costs. Cost allocation based on information 

included in Solar Energy Technologies Program Multi-Year Program Plan 2007-2011. U.S. Department of Energy. Department 

of Energy Efficiency and Renewable Energy. 2006. 

13 Based on 2005 annual O&M costs of $42/kW for residential systems and $28/kW for commercial systems; $35/kW is the 

average of these values. Ibid. 
Table 2: PV Installation and O&M Cost Assumptions 

Cost Component

1 MW 

$3.30/Wdc

2015

1 MW 

$2.50/Wdc

2030

PV Manufacture (70%) $2,310,000 $1,750,000

PV Installation (30%) $990,000 $750,000

PV O&M ($35 decreasing to 

$10.94/kW annually) $14,440 $10,940

For each scenario, the economic benefits are grouped into three distinct categories based on spending 

activity: 

• Construction benefits include both the manufacture and installation of PV systems 

• Operations and Maintenance (O&M) refer to maintenance costs plus the energy savings that 

result from the PV systems 

• Research and Development (R&D) refers to the benefits resulting from that portion of the annual 

SAI budget that is spent on research through private (non-governmental) research agencies. 

Table 3 below shows the key assumptions for the SAI and counterfactual scenarios as they pertain to each 

of these activity areas. 

Table 3: SAI Scenario and Counterfactual Assumptions 

Activity SAI Assumption Counterfactual Assumption

Construction Dollars spent on PV manufacture 

and installation.

Equivalent $ spending on natural gas plant 

construction (based on 60% assumed ELCC), with 

remainder spent on household goods and 

services.

O&M

$35 to $10.94/kW annual cost for 

PV maintenance. Households 

receive $0.097/kWh in savings for 

PV generation.

Utilities lose $0.097 in revenue for every kWh 

generated from PV.

R&D Spending 50% of annual SAI budget goes to 

private sector research.

R&D dollars spent on other Federal government 

programs.

Additional assumptions are shown in Table 4 and all values are reported as 2007 dollars. Natural gas 

construction costs are set at $0.67/Wdc and do not vary over time. The O&M costs for natural gas plants 

are included in the retail price of electricity sold from these plants. For this analysis we assume that the 

retail price does not change over time (other than for inflation) in order to develop a conservative estimate 

of economic benefits. The effect of relaxing this assumption and allowing for price increases is discussed 

later in this report. 

 Page 6 
 Page 7 

To convert the installed capacity of the PV systems to a natural gas kWh value, the same parameter 

assumptions are used that were discussed in the preceding Energy Benefits section of this report. To 

determine the amount of PV power generated, we apply a capacity factor of 19 percent based on average 

solar insolation in the United States. Given a 19 percent capacity factor, 1 GW of PV is assumed to 

produce 1,664 GWh of electricity annually. The kWh produced by PV is assumed to offset electricity 

purchased from utilities on a one-to-one basis (after adjusting for T&D losses). We also assume a 60 

percent ELCC in our calculations to determine the amount of power generation offset by the SAI 

installations. 

Finally, in the economic benefit calculations, the electricity bill savings are all allocated to household 

spending even though some of the PV systems will likely be installed in the commercial sector. This is 

done under the assumption that any savings achieved in the commercial sector will eventually be passed 

on through to households through lower prices. It also simplifies the input-output modeling, as we do not 

need to identify which specific commercial sectors will install solar.14 

Table 4: Natural Gas Plant and Generation Assumptions 

For the construction impacts, a numerical example will help illustrate how the net impacts were 

calculated. In the analysis, solar installations are assumed to have an effective load carrying capacity 

(ELCC) of 60 percent relative to a new natural gas plant. That is, every gigawatt installed reduces the 

need to construct 600 MW of natural gas generating resources. A 1 GW PV plant at $3.5/Wdc would cost 

$3.5 billion and displace the need to build 600 MW of natural gas generation at a cost of $402 million 

(600 MW @ $0.67/Wdc). Since the natural gas plant costs less than the PV installations, the remaining 

portion ($3.1 billion) needs to be accounted for in the input-output model so that both scenarios assume 

 

14 Different commercial sectors will have potentially significant differences in benefits from PV depending on the energy 

intensity of that industry. Efforts to capture these effects on production efficiencies (and subsequent results on production costs 

and retail prices of goods) requires the use of more sophisticated general equilibrium models rather than the static IMPLAN 

model. Because the potential production gains through PV are omitted from this analysis, the economic benefit estimates should 

be considered conservative. 

Assumption Value Source

Natural Gas Plant Construction Cost $0.67/Wdc University of Chicago study on nuclear 

power (2004), adjusted to 2007 $.

PV Capacity Factor 19 percent

Provided by NREL based on locationspecific capacity factors used in the PV Watts 

simulation program

Transmission & Distribution Loss Factor 7 percent

Average based on U.S. Climate Change 

Technology Program (2003) and Connors et. 

al. (2005)

PV Effective Load Carrying Capacity 60 percent Perez et. al. (2006)

Electricity Price $0.097 kWh EIA, 2005 average residential electricity 

price, adjusted to 2007 $.
 Page 8 

total spending of $3.5 billion. In the counterfactual scenario, we assume that the remaining $3.1 billion is 

spent by U.S. households following historical purchase patterns. 

In this example, assume that the PV spending results in 15,000 jobs while spending in the natural gas 

counterfactual scenario results in 12,000 jobs. In this case, the net impact of the PV installations is the 

difference between the two scenarios, or 3,000 jobs (15,000 – 12,000). 

Presenting the economic impacts in terms of net benefits results in numbers that are—by definition—

lower than gross impact values, and in many cases lower than the gross impacts that have been reported in 

other studies. For example, the 2001 study The Work That Goes Into Renewable Energy published by the 

Renewable Energy Policy Project (REPP) found that there are approximately 35 jobs associated with the 

manufacture and installation of 1 MW of PV. We used these numbers to help develop the PV cost input 

values for our analysis. However, the final net benefit results reported here are lower than the REPP 

results, as they incorporate the natural gas counterfactual scenario presented above. In contrast, the REPP 

study did not incorporate any counterfactual information, and therefore the employment figures from that 

report reflect gross rather than net employment effects. 

IMPLAN MODEL

The IMPLAN input-output model is used to estimate the SAI economic benefits based on the assumptions 

described above. Input-output analysis employs specific terminology to identify the different types of 

economic impacts resulting from economic activities. Expenditures made as a result of the SAI affect the 

U.S. economy directly, through domestic purchases of goods and services, and indirectly, as those 

purchases, in turn, generate purchases of intermediate goods and services from other, related sectors of 

the economy. In addition, the direct and indirect increases in employment and income enhance overall 

economy purchasing power, thereby inducing further consumption- and investment- driven stimulus. This 

cycle continues until the spending eventually leaks out of the U.S. economy as a result of taxes, savings, 

or purchases of non-locally produced goods and services or “imports.” Throughout this report, results are 

presented for the direct, indirect, and induced effects described above. 

Within this overall framework, the IMPLAN model reports the following economic impacts: 

• Output is the value of production by industries for a specified period of time. Output can be also 

thought of as the value of sales including reductions or increases in business inventories. 

• Personal Income represents the total payments to workers (wages) and business owners 

(proprietor and corporate income). Together, wages and business income are often referred to as 

personal income. Corporate income represents net business income or profits. These may be 

reinvested or paid as dividends to shareholders. Income excludes payments from one industry to 

another for the purchase of intermediate goods, and is often used as a measure of the value added 

during production.15

• Job impacts include both full and part time employment. 

• Tax revenues for various state and local taxing jurisdictions. 

 

15 Alternatively, value added is measured as total output less purchases of intermediate goods and services. In either case, the 

measure of value added will be the same. However, to the extent that owners of corporations live outside the relevant study area, 

including corporate income will tend to overestimate the measure of value added in production. 
The results for Output, Personal Income, and Jobs are presented in the main body of the report while 

detailed for these categories plus Tax Revenues are included in Appendix A. Additional technical detail 

on the IMPLAN model is provided in Appendix B. 

GROSS ECONOMIC BENEFITS

This section describes the gross economic benefits that could be expected from the SAI, and does not take 

into account the changes in economic activity that would have occurred in other sectors (e.g., due to 

natural gas plant construction) if the goals of the SAI were not achieved. The tables in this section present 

selected analysis results from the input-output model runs. To keep the results to a manageable level, only 

selected years (2015 and 2030) are reported in the main report for both the high and low scenarios. For 

each year, the results for the construction, O&M, and R&D activities are reported. 

Note that NREL assumes that 1 GW of PV capacity will likely be installed by 2015, and that 15 to 20 

GW of PV capacity will be installed by 2030 even without the SAI. In this report, only the new PV 

installations that are caused by the SAI are used to calculate the economic impacts. In Table 5 (and all 

subsequent tables), a column labeled “Total Installed Capacity” shows the total PV installations for a 

particular year and includes capacity that is expected as part of the baseline. A second column labeled 

“SAI Induced Annual Installed Capacity” reflects only the PV installations that are assumed to result 

directly from the SAI. It is the capacity shown in this second column that is used to calculate the 

economic benefits shown in the rest of the table. 

Table 5 shows the gross benefits of the PV system construction (manufacture and installation) for 2015. 

Again, these results do not adjust for impacts that would have otherwise occurred in the counterfactual 

scenario, which assumes natural gas plant construction. The construction results reflect the impacts that 

occur only during the construction period, which is assumed to be a single year. 

For the high scenario, a cumulative total of 10 GW of new PV is installed by 2015. This construction will 

result in a gross increase in economic output of $27 billion during the construction period. This will also 

result in a gross increase in personal income of $8.7 billion over the same period, and total full-time and 

part-time jobs would increase by 162,890. 

The bottom of Table 5 shows the same gross results in the construction phase assuming the low scenario 

of 5 GW of installed PV by 2015. In the low scenario, the SAI results in a gross increase of economic 

output of $8.2 billion and a gross increase in personal income of $2.6 billion. This also results in a gross 

increase in jobs of 49,370. 

Table 5: Annual Gross Construction Impacts (2015) 

Construction Gross 

Impacts

Total Annual 

Installed 

Capacity In 2015

SAI Induced 

Annual Installed 

Capacity In 2015

Direct Indirect Induced Total

High Scenario

Output 2.74 2.37 $7,989,362,000 $9,024,295,000 $10,087,769,000 $27,101,426,000

Personal Income 2.74 2.37 $2,486,000,000 $3,056,905,000 $3,158,265,000 $8,701,170,000

Jobs 2.74 2.37 36,500 51,440 74,950 162,890

Low Scenario

Output 1.02 0.72 $2,421,746,000 $2,735,456,000 $3,057,818,000 $8,215,020,000

Personal Income 1.02 0.72 $753,560,000 $926,613,000 $957,337,000 $2,637,510,000

Jobs 1.02 0.72 11,060 15,590 22,720 49,370

Table 6 shows the gross impacts of PV O&M activities in 2015 for both the high and low SAI scenarios. 

The O&M impacts account for maintenance costs as well as money saved on electricity bills due to the 

PV systems. For the high scenario, O&M activities result in a gross increase in total economic output of 

 Page 9 
$3.6 billion annually based on the 10 GW of installed PV capacity. Personal income also shows a gross 

increase of over $1.1 billion annually and an additional 27,510 jobs are added due to the SAI in 2015. 

The low scenario results are also shown in Table 6 for 2015. Since the low scenario is based on half of the 

installed PV capacity (5 GW rather than 10 GW in 2015), the economic impacts are also reduced by 50 

percent relative to the high scenario, with slight differences due to rounding. 

Table 6: Cumulative Gross O&M Impacts (2015) 

O&M Gross Impacts 

Total 

Cummulative 

Installed 

Capacity in 2015

SAI Induced 

Cummulative 

Installed 

Capacity in 2015

Direct Indirect Induced Total

High Scenario

Output 10.00 9.00 $1,399,807,000 $961,320,000 $1,301,392,000 $3,662,519,000

Personal Income 10.00 9.00 $453,592,000 $302,021,000 $407,449,000 $1,163,062,000

Jobs 10.00 9.00 11,860 5,980 9,670 27,510

Low Scenario

Output 5.00 4.00 $622,136,000 $427,253,000 $578,397,000 $1,627,786,000

Personal Income 5.00 4.00 $201,596,000 $134,232,000 $181,089,000 $516,917,000

Jobs 5.00 4.00 5,270 2,660 4,300 12,230

Note: Table shows annual impacts in 2015 based on cumulative SAI PV installations. 

The gross economic impacts resulting from the SAI R&D funding are shown in Table 7 and are based on 

the annual funding of $148 million. Of this budget, 50 percent is assumed to go to private research 

entities in the U.S. Funding is assumed to be constant between the high and low scenarios. The spending 

on solar R&D through the SAI results in a gross increase in economic output of $235 million with 

personal income increasing by $101 million. The R&D funding also results in a gross increase of 1,780 

jobs. 

Table 7: Annual Gross R&D Impacts (2015) 

R&D Gross Impacts Annual R&D 

Spending Direct Indirect Induced Total

High Scenario $148,000,000 

Output $73,634,000 $47,076,000 $114,377,000 $235,087,000

Personal Income $48,979,000 $16,121,000 $35,809,000 $100,909,000

R&D Impacts - Gross 600 330 850 1,780

Low Scenario $148,000,000 

Output $73,634,000 $47,076,000 $114,377,000 $235,087,000

Personal Income $48,979,000 $16,121,000 $35,809,000 $100,909,000

Jobs 600 330 850 1,780

The combined gross economic benefits for 2015 across all activities are shown in Table 8. Additional 

detail on individual years is provided in Appendix A. 

 Page 10 
 Page 11 

Table 8: Gross Economic Benefit Summary (2015) 

Economic Benefit 

Category (Gross 

Impacts)

Construction O&M R&D Total

2015 High Scenario

Output $27,101,426,000 $3,662,519,000 $235,087,000 $30,999,032,000

Personal Income $8,701,170,000 $1,163,062,000 $100,909,000 $9,965,141,000

Jobs 162,890 27,510 1,780 192,180

2015 Low Scenario

Output $8,215,020,000 $1,627,786,000 $235,087,000 $10,077,893,000

Personal Income $2,637,510,000 $516,917,000 $100,909,000 $3,255,336,000

Jobs 49,370 12,230 1,780 63,380

Table 9 shows the annual gross construction impacts for 2030 for both the high and low SAI scenarios 

and assumes that PV costs have fallen to $2.5/Wdc. For the high scenario, a cumulative total of 100 GW 

of new PV is installed by 2030. This construction results in a gross increase of over $67 billion in 

economic output and personal income has a gross increase of $21.5 billion. The PV construction activity 

also results in a gross increase of 403,920 jobs in 2030. 

The low scenario construction impacts are greater than the high scenario impacts, as the low scenario 

installs more capacity in the last year of construction (2030) compared to the high scenario, and 

construction impacts are shown for a single year. In the low scenario for 2030, output increases by almost 

$73 billion, personal income increases by $23.4 billion, and jobs show a gross increase of 438,690.16 

Table 9: Annual Gross Construction Impacts (2030) 

Construction Gross 

Impacts

Total Annual 

Installed 

Capacity In 2030

SAI Induced 

Annual Installed 

Capacity In 2030

Direct Indirect Induced Total

High Scenario

Output 10.40 7.77 $19,810,956,000 $22,377,245,000 $25,014,306,000 $67,202,507,000

Personal Income 10.40 7.77 $6,164,452,000 $7,580,107,000 $7,831,446,000 $21,576,005,000

Jobs 10.40 7.77 90,510 127,560 185,850 403,920

Low Scenario

Output 10.31 8.44 $21,516,485,000 $24,303,706,000 $27,167,792,000 $72,987,983,000

Personal Income 10.31 8.44 $6,695,151,000 $8,232,681,000 $8,505,656,000 $23,433,488,000

Jobs 10.31 8.44 98,300 138,540 201,850 438,690

The 2030 O&M economic benefits are shown in Table 10 and reflect all of the benefits of PV O&M 

spending and electricity bill savings for the cumulative installations through 2030. In the high scenario, 

the O&M benefits total over $31.7 billion in gross economic output and $10 billion in increased personal 

income. The cumulative employment impact is a gross increase of 237,300 jobs. 

In the low scenario, the O&M impacts remain very significant with a gross increase in economic output of 

$21.8 billion and personal income increasing by $6.8 billion annually. The low scenario also results in a 

gross increase in jobs of 163,150 based on the cumulative installed capacity of 70 GW of PV by 2030. 

 

16 For 2030, the impacts in the low scenario are greater than the high scenario due to growth rates used for this analysis. In this 

particular year, the incremental amount of PV added in 2030 in the low scenario is greater than the PV capacity added in the high 

scenario in 2030 (8.44 GW and 7.77 GW respectively). 
Table 10: Cumulative Gross O&M Impacts (2030) 

O&M Gross Impacts

Total 

Cummulative 

Installed 

Capacity in 2030

SAI Induced 

Cummulative 

Installed Capacity in 

2030

Direct Indirect Induced Total

High Scenario

Output 100.00 80.00 $12,162,729,000 $8,390,215,000 $11,190,494,000 $31,743,438,000

Personal Income 100.00 80.00 $3,866,276,000 $2,636,991,000 $3,503,604,000 $10,006,871,000

Jobs 100.00 80.00 101,970 52,190 83,140 237,300

Low Scenario

Output 70.00 55.00 $8,361,876,000 $5,768,273,000 $7,693,465,000 $21,823,614,000

Personal Income 70.00 55.00 $2,658,065,000 $1,812,930,000 $2,408,728,000 $6,879,723,000

Jobs 70.00 55.00 70,110 35,880 57,160 163,150

Note: Table shows annual impacts in 2030 based on cumulative SAI PV installations. 

The combined benefits for each activity area for 2030 are summarized in Table 11. 

Table 11: Gross Economic Benefit Summary (2030) 

Economic Benefit 

Category (Gross 

Impacts)

Construction O&M Total

2030 High Scenario

Output $67,202,507,000 $31,743,438,000 $98,945,945,000

Personal Income $21,576,005,000 $10,006,871,000 $31,582,876,000

Jobs $403,920 $237,300 $641,220

2030 Low Scenario

Output $72,987,983,000 $21,823,614,000 94,811,597,000 $ 

Personal Income $23,433,488,000 $6,879,723,000 30,313,211,000 $ 

Jobs 438,690 163,150 $ 601,840

NET ECONOMIC BENEFITS

This section describes the net economic benefits that could be expected from the SAI, and takes into 

account (i.e., subtracts) the changes in economic activity that would have occurred in other sectors if the 

SAI were not implemented. For each year (2015 and 2030), the results for the construction, O&M, and 

R&D activities are reported. Detailed benefit estimates for all years (2005-2030) for these categories are 

included in Appendix A. 

Table 12 shows the net benefits of the PV system construction (manufacture and installation) for 2015. 

Again, these results reflect impacts over and above what would have happened in the counterfactual 

scenario, which assumes natural gas plant construction. The construction results reflect the impacts that 

occur only during the construction period, which is assumed to be a single year. 

For the high scenario, a cumulative total of 10 GW of new PV is installed by 2015. This construction will 

result in a gross increase in economic output of $8.5 billion during the construction period. This will also 

result in a gross increase in personal income of $2.9 billion over the same period. 

For the net job impacts, the direct impacts for PV installation are a negative 17,850, meaning that there 

are more direct jobs initially created by the construction of natural gas plants combined with the spending 

from households than from the PV manufacture and installations occurring in 2015. However, when the 

indirect and induced effects of construction spending are taken into account, then the overall net job 

impact is positive. The overall effect is a net increase of 28,940 jobs over the employment that would 

occur in the counterfactual scenario. 

 Page 12 
 Page 13 

The bottom of Table 12 shows the same net results in the construction phase assuming the low scenario of 

5 GW of installed PV by 2015. In the low scenario, the SAI results in a net increase of economic output 

of $2.5 billion and a net increase in personal income of $879 million over the counterfactual scenario. 

This also results in a net increase in jobs of 8,770. 

Table 12: Annual Net Construction Impacts (2015) 

Construction Net 

Impacts

Total Annual 

Installed 

Capacity In 2015

SAI Induced 

Annual Installed 

Capacity In 2015

Direct Indirect Induced Total

High Scenario

Output 2.74 2.37 $988,946,000 $3,922,413,000 $3,610,641,000 $8,522,000,000

Personal Income 2.74 2.37 $327,745,000 $1,443,688,000 $1,130,363,000 $2,901,796,000

Jobs 2.74 2.37 -17,850 19,960 26,830 28,940

Low Scenario

Output 1.02 0.72 $299,771,000 $1,188,967,000 $1,094,462,000 $2,583,200,000

Personal Income 1.02 0.72 $99,347,000 $437,613,000 $342,637,000 $879,597,000

Jobs 1.02 0.72 -5,410 6,050 8,130 8,770

Table 13 shows the net impacts of PV O&M activities in 2015 for both the high and low SAI scenarios. 

The O&M impacts are based on the entire installed PV capacity in 2015 and include maintenance costs as 

well as money saved on electricity bills due to the PV systems. 

For the high scenario, O&M activities result in a net increase in total economic output of $518 million 

annually based on the 10 GW of installed PV capacity.17 Personal income also shows a net increase of 

over $361 million annually and an additional 15,330 jobs are added due to the SAI in 2015. 

The low scenario results are also shown in Table 13 for 2015. Since the low scenario is based on half of 

the installed PV capacity (5 GW rather than 10 GW in 2015), the economic impacts are also reduced by 

50 percent relative to the high scenario, with slight differences due to rounding. 

Table 13: Cumulative Net O&M Impacts (2015) 

O&M Net Impacts

Total Cummulative 

Installed Capacity 

in 2015

SAI Induced 

Cummulative 

Installed Capacity 

in 2015

Direct Indirect Induced Total

High Scenario

Output 10.00 9.00 -$141,982,000 $263,195,000 $397,401,000 $518,614,000

Personal Income 10.00 9.00 $141,430,000 $95,235,000 $124,429,000 $361,094,000

Jobs 10.00 9.00 9,840 2,540 2,950 15,330

Low Scenario

Output 5.00 4.00 -$63,103,000 $116,976,000 $176,622,000 $230,495,000

Personal Income 5.00 4.00 $62,857,000 $42,327,000 $55,302,000 $160,486,000

Jobs 5.00 4.00 4,370 1,130 1,310 6,810

Note: Table shows annual impacts in 2030 based on cumulative SAI PV installations. 

The economic impacts resulting from the SAI R&D funding are shown in Table 14 and are based on the 

annual funding of $148 million. Of this budget, 50 percent is assumed to go to private research entities in 

the U.S. The net impacts are shown relative to the counterfactual scenario that assumes the SAI funding is 

spent on other Federal programs. Funding is assumed to be constant between the high and low scenarios. 

 

17 The negative direct effect for output is the result of lost utility revenues due to reduced power sales. This is made up in the 

indirect and induced effects as households use the money saved on their electricity bills to purchase other goods and services.
The spending on solar R&D through the SAI results in a net increase in economic output of $118 million 

with personal income increasing by $64 million. The R&D funding also results in a net increase of 910 

jobs. 

Table 14: Annual Net R&D Impacts (2015) 

R&D Net Impacts Annual R&D 

Spending ($M) Direct Indirect Induced Total

High Scenario $148,000,000 

Output $73,634,000 $47,076,000 -$2,704,000 $118,007,000

Personal Income $48,979,000 $16,121,000 -$846,000 $64,254,000

Jobs 600 330 -20 910

Low Scenario $148,000,000 

Output $73,634,000 $47,076,000 -$2,704,000 $118,007,000

Personal Income $48,979,000 $16,121,000 -$846,000 $64,254,000

Jobs 600 330 -20 910

The combined net economic benefits for 2015 across all activities are shown in Table 15. Additional 

detail on individual years is provided in Appendix A. 

Table 15: Net Economic Benefit Summary (2015) 

Net Economic Benefit 

Category (Net 

Impacts)

Construction O&M R&D Total

2015 High Scenario

Output $8,522,000,000 $518,614,000 $118,007,000 $9,158,621,000

Personal Income $2,901,796,000 $361,094,000 $64,254,000 $3,327,144,000

Jobs 28,940 15,330 910 45,180

2015 Low Scenario

Output $2,583,200,000 $230,495,000 $118,007,000 $2,931,702,000

Personal Income $879,597,000 $160,486,000 $64,254,000 $1,104,337,000

Jobs 8,770 6,810 910 16,490

Figure 1 shows how the net gains in economic output are distributed across sectors for the construction, 

O&M, and R&D phases combined in 2015. The Manufacturing and Construction sectors show large 

increases due to the manufacture and installation of PV systems. Conversely, the Utility sector shows a 

net loss in economic activity due to lower electricity sales revenue resulting from the increasing amount 

of electricity provided by PV. 

 Page 14 
Figure 1: 2015 Net Output 

Construction

Manufacturing

Services

Other

Utilities

-$2,000,000,000

-$1,000,000,000

$0

$1,000,000,000

$2,000,000,000

$3,000,000,000

$4,000,000,000

$5,000,000,000

$6,000,000,000

$7,000,000,000

$8,000,000,000

Sector

2015 Output - High 2015 Output - Low

Figure 2 shows how the net change in jobs is distributed across industry sectors. As with economic 

output, the Manufacturing and Construction sectors show large increases in employment due to the 

increased production and installation of PV systems. The other sectors show relatively small changes in 

net employment. Note that while the Utility sector shows a large drop in economic output in Figure 1, a 

relatively smaller decrease in employment shown in Figure 2. This is due to the fact that the utility sector 

is not as labor intensive as other industries and that much of the lost economic output shown in Figure 1 is 

due to fuel purchases (or other non-labor inputs) rather than wages. 

 Page 15 
Figure 2: 2015 Net Jobs 

Manufacturing

Services

Utilities

Other

Construction

-5,000

0

5,000

10,000

15,000

20,000

25,000

30,000

Sector

2015 Jobs - High 2015 Jobs - Low

Table 16 shows the construction impacts for 2030 for both the high and low SAI scenarios and assumes 

that PV costs have fallen to $2.5/Wdc. In 2030, total installed capacity is 100 GW for the high scenario. 

The additional PV construction results in a net increase of $20.5 billion in economic output relative to 

counterfactual scenario and personal income has a net increase of almost $7 billion. The PV construction 

activity also results in a net increase of 69,280 in 2030. 

The low scenario construction impacts are greater than the high scenario impacts for 2030, as the low 

scenario installs more capacity in this particular year compared to the high scenario (8.44 GW in the low 

scenario compared with 7.77 GW in the high scenario for 2030). In the low scenario for 2030, net output 

increases by $22 billion, personal income increases by $7.5 billion, and jobs show a net increase of 

75,230. 

Table 16: Annual Net Construction Impacts (2030) 

Construction Net 

Impacts

Total Annual 

Installed Capacity 

In 2030

SAI Induced 

Annual Installed 

Capacity In 2030

Direct Indirect Induced Total

High Scenario

Output 10.40 7.77 $2,352,777,000 $9,499,047,000 $8,651,896,000 $20,503,720,000

Personal Income 10.40 7.77 $718,510,000 $3,502,286,000 $2,708,602,000 $6,929,398,000

Jobs 10.40 7.77 -43,300 48,300 64,280 69,280

Low Scenario

Output 10.31 8.44 $2,555,328,000 $10,316,822,000 $9,396,739,000 $22,268,889,000

Personal Income 10.31 8.44 $780,366,000 $3,803,798,000 $2,941,786,000 $7,525,950,000

Jobs 10.31 8.44 -47,030 52,450 69,810 75,230

 Page 16 
The 2030 O&M economic benefits are shown in Table 17 and reflect all of the benefits of PV O&M 

spending and electricity bill savings for the cumulative installations through 2030. In the high scenario, 

the O&M benefits total over $3.7 billion in net economic output and $2.8 billion in increased personal 

income. The cumulative employment impact is a net increase of 129,120 jobs over the counterfactual 

scenario. 

In the low scenario, the O&M impacts remain very significant with a net increase in economic output of 

$2.6 billion and personal income increasing by $1.9 billion annually. The low scenario also results in a 

net increase in jobs of 88,770 based on the cumulative installed capacity of 70 GW of PV by 2030. 

Table 17: Cumulative Net O&M Impacts (2030) 

Note: Table shows annual impacts in 2030 based on cumulative SAI PV installations. 

O&M Net Impacts 

Total Cummulative 

Installed Capacity 

in 2030

SAI Induced 

Cummulative 

Installed Capacity 

in 2030

Direct Indirect Induced Total

High Scenario

Output 100.00 80.00 -$1,542,062,000 $2,184,665,000 $3,155,011,000 $3,797,614,000

Personal Income 100.00 80.00 $1,091,503,000 $798,895,000 $987,867,000 $2,878,265,000

Jobs 100.00 80.00 84,060 21,620 23,440 129,120

Low Scenario

Output 80.00 55.00 -$1,060,168,000 $1,501,957,000 $2,169,070,000 $2,610,859,000

Personal Income 80.00 55.00 $750,408,000 $549,240,000 $679,158,000 $1,978,806,000

Jobs 80.00 55.00 57,790 14,860 16,120 88,770

The combined net benefits for each activity area are summarized in Table 18 for 2030. 

Table 18: Net Economic Benefit Summary (2030) 

Net Economic Benefit 

Category (Net 

Impacts)

Construction O&M Total

2030 High Scenario

Output $20,503,720,000 $3,797,614,000 $24,301,334,000

Personal Income $6,929,398,000 $2,878,265,000 $9,807,663,000

Jobs 69,280 129,120 $198,400

2030 Low Scenario

Output $22,268,889,000 $2,610,859,000 $24,879,748,000

Personal Income $7,525,950,000 $1,978,806,000 $9,504,756,000

Jobs 75,230 88,770 $164,000

Figure 3 shows how the combined economic output estimates from the construction, O&M, and R&D 

activities for 2030 are distributed across economic sectors. Compared with the chart for 2015, the 

magnitude of the changes in output are much greater due to the much higher levels of PV installed in 

2030 relative to 2015. As in 2015, the Manufacturing sector shows the largest net gains in economic 

output due to the increase in the manufacture and installation of PV systems. The Services and 

Construction sectors also see significant net gains in economic output due to the SAI relative to the 

counterfactual scenario. The Utility sector sees a significant decrease in economic output from the lower 

electricity sales that result from the increase in PV capacity by 2030. 

 Page 17 
Figure 3: 2030 Net Output 

Construction

Utilities

Other

Services

Manufacturing

-$15,000,000,000

-$10,000,000,000

-$5,000,000,000

$0

$5,000,000,000

$10,000,000,000

$15,000,000,000

$20,000,000,000

$25,000,000,000

Sector

2030 Output - High 2030 Output - Low

Figure 4 shows how the net change in jobs is distributed across industry sectors in 2030. The Services 

sector shows the largest gains in employment due to the SAI, followed by Construction and 

Manufacturing. Note that Services shows a large increase in employment relative to the change in output 

shown in Figure 3. The large increase in employment is due to the labor-intensive jobs that are included 

in the Service sector. 

 Page 18 
Figure 4: 2030 Net Jobs 

Construction

Manufacturing

Utilities

Services

Other

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

Sector

2030 Jobs - High 2030 Jobs - Low

All of the preceding economic benefit estimates assume a constant electricity price that has been adjusted 

for inflation but does not account for any additional price increases during the analysis period. If the 

electricity price does increase over time, then the economic benefits of the SAI will increase as customers 

with PV systems receive a greater benefit through reduced electricity bills. In the benefit framework 

discussed here, an increase in price will effect the benefit estimates only through the O&M benefits, as 

the Construction and R&D phases are unaffected by changes in the electricity prices. 

The economic benefits are estimated assuming an electricity price of $0.0966/kWh. If this is increased by 

1 cent to $0.1066/kWh, then the O&M jobs impacts are expected to increase by 8 to 9 percent. Similarly, 

a 1-cent increase in price will also increase personal income by 6 to 7 percent while the impact on 

economic output is expected to increase by 3 percent. This relationship is relatively consistent across 

years. 

Selected price sensitivity examples are shown in Table 19 for the low scenario and the relative effects, in 

percentage terms, are the same in the high scenario (not shown). While only a 1-cent change is shown, the 

relationship is linear and each 1-cent change in the price of electricity will increase the net economic 

output by 3 percent, personal income by 6 to 7 percent, and jobs by 8 to 9 percent. 

 Page 19 
Table 19: Effect of a 1-Cent Electricity Price Change on O&M Benefits 

O&M Economic 

Benefit Category

Electricity Price 

$0.0966/kWh

Electricity 

Price 

$0.1066/kWh

Percent 

Change

2015 Low Scenario

Output $230,495,000 $237,013,000 3%

Personal Income $160,486,000 $170,023,000 6%

Jobs 6,810 7,370 8%

2030 Low Scenario

Output $2,610,859,000 $2,700,478,000 3%

Personal Income $1,978,806,000 $2,109,935,000 7%

Jobs 88,770 96,360 9%

 Page 20 
 Page 21 

4. ENVIRONMENTAL BENEFITS

This section of the report describes the major environmental and health benefits that could be expected as 

a result of attaining SAI solar generation goals, and thereby reducing fossil fuel-fired electricity 

generation. In particular, we focus on the impacts of reducing CO2, NOX and SO2 emissions. Our analysis 

of these benefits utilizes the energy calculations described in the Energy Benefits section of this report 

that show the amount of displaced electricity generation that will result should the SAI PV installation 

goals be achieved. 

The U.S. Environmental Protection Agency (EPA) reports that electricity generation is currently the 

largest industrial source of air emissions in the United States. Fossil fuel-fired power plants are 

responsible for 40 percent of man-made carbon dioxide (CO2) emissions, 23 percent of the nation’s 

nitrogen oxide (NOX) emissions, and 67 percent of sulfur dioxide (SO2) emissions.18 These emissions 

contribute to the formation of smog and haze, and are associated with a wide range of health problems. In 

addition, greenhouse gases (e.g., carbon dioxide) emitted by fossil fuel-fired power plants increase the 

risk of climate change. In comparison, the emissions from solar electricity generation are negligible as no 

fossil fuels are combusted to create electricity. 

EMISSIONS REDUCED BY SAI 

The emissions offset or reduced by new PV capacity are calculated by taking the amount of fossil fuel 

generation offset by PV and multiplying by the average emissions rates for those fuels. A wide range of 

fossil fuel emission rates for CO2, NOX and SO2 was found in the literature. Most of these studies, 

however, were regionally based and were affected by local emissions regulations and trading schemes, 

unique plant types and emission cleaning technologies, and/or utilized highly refined regional economic 

dispatch models. For this analysis, we elected to use broader and more generic emission rates based on 

national generating plant data rather than use factors that were derived from a region-specific study. 

Table 20 shows two sets of emission rates that could be applied for this analysis. One set of rates was 

calculated by EPA using national generating plant data from the eGRID 2000 database.19 A second set of 

rates was calculated by ECONorthwest using the most current eGRID data available. These rates are 

based on plant net generation before entering the grid, which subtracts plant auxiliary power consumption 

(e.g., for scrubbers, cooling fans, turbine auxiliaries) from gross generation.20 

The subsequent results for emissions avoided and associated health impacts are based on the more current 

eGRID 2006 emission rate estimates. These emission rates are generally lower than the 2000 rates, and 

likely reflect more efficient generating plants and growing adoption of emissions cleaning technologies to 

meet local and federal air quality standards.21

 

18 www.epa.gov/cleanenergy/emissions.htm 

19 See www.epa.gov/cleanenergy/natgas.htm and www.epa.gov/cleanenergy/coal.htm. 

20 Plants with negative net generation were not considered in the analysis. Plant gross generation and on-site power consumption 

are not provided in the eGRID 2006 data. The emission rates are based on total plant heat input, total emissions, and net 

generation. 

21 The ECONorthwest emission rates are based on calculated average heat conversion rates of approximately 9,900 Btu/kWh for 

natural gas plants and 10,500 Btu/kWh for coal plants. These heat rates are based on plant heat input and net generation. 
 Page 22 

Table 20: Average Emission Rates (lbs/MWh) 

CO2 NO X SO2 CO2 NOX SO2

EPA, eGRID 2000 1,135 1.70 0.10 2,249 6.00 13.00

ECONorthwest, eGRID 2006* 1,169 0.69 0.02 2,164 3.50 10.30

*2004 plant level data

Natural Gas Coal

Source

Table 21 shows the emissions that would by avoided, by fuel type, for the 2015 and 2030 PV capacity 

scenarios. The SAI is projected to reduce annual CO2 emissions by 69 to 100 million tons in 2030. 

Annual NOX emissions would be reduced by 68,000 to 99,000 tons, and SO2 emissions would be reduced 

by 126,000 to 184,000 tons. These results assume that PV generation would replace fossil fuel generation 

on a one-to-one basis, and that 75 percent of the fuel displaced would be natural gas and 25 percent would 

be coal. PV systems only generate power during daylight hours and in many regions of the country they 

would reduce emissions from natural gas peaking units, as these are used in many regions to meet peak 

(usually daytime) electricity demand22. Some areas of the country, however, remain heavily coaldependent, and meet increases in peak demand by increasing output at “spinning reserve” and “standby” 

plants that operate at less than full capacity. 

Table 21: Emissions Avoided by Displaced Fuel (Annual Tons) 

Installed PV

Scenario Capacity (GW) CO2 NO X SO2 CO2 NO X SO2 CO2 NO X SO2

2015 Low 5 3,122,822 1,843 40 1,926,942 3,117 9,172 5,049,765 4,960 9,212

2015 High 10 7,026,350 4,147 90 4,335,621 7,012 20,636 11,361,970 11,160 20,726

2030 Low 70 42,938,805 25,345 551 26,495,459 42,853 126,111 69,434,264 68,198 126,662

2030 High 100 62,456,444 36,865 801 38,538,849 62,332 183,434 100,995,293 99,197 184,235

Natural Gas Coal Total

HEALTH BENEFITS

Table 22 shows the estimated health benefits that would result from reducing emissions from fossil-fired 

power generation. Health impacts were related to total emissions of NOX and SO2, which react with other 

chemicals to form fine particulate matter (PM2.5) in the air. To estimate health benefits from reduced 

emissions, we used benefit estimates developed by EPA23 and the Clean Air Task Force24 in their 

analyses of the Clear Skies Act and alternative regulatory scenarios. In those studies, very detailed air 

quality models were utilized to estimate PM formation based on NOX and SO2 emissions. Since we did 

not have access to these air quality models for this analysis, we analyzed the assumed NOX and SO2 

reductions in reported in these studies and found that they were highly correlated with the estimated 

health impacts. For this study, we applied the emissions/health relationships we discerned in the EPA 

studies to extrapolate health benefits from the NOX and SO2 emission reductions due to the SAI (

than from PM formation, which is the method EPA and the Clean Air Task Forc

rather 

e used). 

 

22 Connors, Stephen with Edward Kern, Michael Adams, Katherine Martin and Baafour Asiamah-Adjei. National Assessment of 

Emissions Reduction of Photovoltaic (PV) Power Systems. Prepared for US EPA Air Pollution Prevention and Control Division. 

2005. 

23 United States Environmental Protection Agency. Technical Addendum: Methodologies for the Benefit Analysis of the Clear 

Skies Act of 2003. September 2003. 

24 Abt Associates Inc. Power Plant Emissions: Particulate Matter-Related Health Damages and the Benefits of Alternative 

Emission Reduction Scenarios. Prepared for Clean Air Task Force. Boston MA. June 2004. 
As shown in the table, the emissions reductions resulting from the SAI will have a positive impact on a 

range of respiratory and cardio health issues. In particular, the reduced emissions are expected to have a 

significant effect on the amount of workdays lost or restricted as shown at the bottom of the table. Note 

that lost and restricted workdays are not included in the economic benefits discussed in the previous 

section and therefore reflect an additional economic benefit resulting from the SAI. 

The EPA studies note that there do not appear to be any PM thresholds beyond which health problems 

increase significantly. Instead, there is limited evidence that for PM, health effects may actually be greater 

at lower levels of exposure, and lower at higher levels of ambient PM. While the emission reductions in 

our analysis are significantly below those analyzed in the Clear Skies studies, we did not adjust the health 

impact estimates up or down to account for potential ambient PM threshold issues, which is still a focus 

of ongoing air quality research. 

Table 22: Health Impacts Due to Reduced Emissions 

Scenario 2015 Low 2015 High 2030 Low 2030 High

Installed PV Capacity (GW) 5 10 70 100

Cases Reduced 

Mortality 22 49 300 437

Chronic Bronchitis 15 34 206 300

Heart Attacks 36 81 493 717

Hospital Admissions - Respiratory

Chronic Lung, less Asthma (20-64) 1 2 14 21

Asthma (0-64) 2 4 25 36

Pneumonia (65+) 7 17 102 148

Chronic Lung (65+) 1 2 13 18

Total 11 25 153 223

Hospital Admissions - Cardiovascular

All Cardiovascular (20-64) 4 8 51 74

All Cardiovascular (65+) 5 12 73 106

Total 9 20 124 180

Emergency Room Visits for Asthma 24 53 324 471

Acute Bronchitis 35 78 479 697

Lower Respiratory Symptoms 397 894 5,462 7,945

Upper Respiratory Symptoms 319 718 4,387 6,381

Work Loss Days 2,538 5,710 34,894 50,755

Minor Restricted Activity Days 17,439 39,239 239,791 348,787

 Page 23 
5. ADDITIONAL ENERGY BENEFITS

In addition to the benefits described in the prior sections, the SAI has the potential to create additional, 

ancillary energy benefits that are less direct than the ones described earlier. For this analysis, we reviewed 

the current literature to identify the additional benefit areas that are relevant to the SAI. The potential 

benefits are discussed below along with a discussion of the potential magnitude of these benefits should 

the SAI goals be achieved. 

Price Impacts 

There have been several recent studies that show a link between increased renewable energy deployment 

and lower natural gas prices. This is an important benefit to consider as small price decreases can result in 

very large benefits given the large number of electricity customers that will be affected. 

Wiser et al. (2005) reviewed several studies to assess how renewable energy development (to meet 

proposed renewable portfolio standards) could affect natural gas prices in the U.S. These studies, 

conducted by the EIA, the Union for Concerned Scientists (UCS) and the Tellus Institute indicate that 

increased use of renewable energy or higher levels of energy efficiency would put downward pressure on 

natural gas prices. From these studies, Wiser concludes that each 1 percent reduction in natural gas 

demand due to an increased supply of renewable energy or enhanced energy efficiency would lead to 

long-term average wellhead price reductions of between 0.8 and 2 percent. Wiser also concludes that in 

the short-term, the average consumer would not see reductions in their electricity bills; they would 

probably remain the same. While natural gas prices would fall somewhat, the cost of meeting national 

renewable portfolio standards of 10 to 20 percent renewable energy would be quite high, and will remain 

high in the short-term. That said, Wiser et al. (2004b) notes that increased renewable energy use would 

help to hedge natural gas price volatility due to supply shocks. 

The Union of Concerned Scientists (UCS, 2003) also reports that increased use of renewable energy 

would cause a reduced natural gas demand and prices. For each 1 percent of renewable energy added to 

total electricity capacity in the U.S., the price of natural gas would fall between .45 and .60 percent, 

depending on how much renewable energy is installed. The UCS indicates that there would be a 

decreasing rate of gas price reduction as total generating capacity increases. 

If the mid-point of Wiser et al’s elasticity range is used (1.4), the expected effect of a 1 percent increase in 

the quantity of renewable energy would be a 1.4 percent decrease in natural gas prices. Recall that if the 

SAI goals are met, PV will displace 0.2 to 0.4 percent of forecasted natural gas demand in 2015 and 2 to 4 

percent of natural gas demand in 2030. With the 1.4 inverse elasticity, the change in consumption would 

result in a similar decrease in natural gas prices of 0.3 to 0.6 percent in 2015 and 3 to 6 percent in 2030. 

This simple example obviously does not account for a host of other factors that will impact natural gas 

prices during the same period. Nevertheless, given the increasing U.S. demand for natural gas and the 

large number of natural gas customers, any decrease in natural gas prices due to the SAI will have wide 

reaching impacts. 

T&D Benefits 

Two primary costs associated with the transmission and distribution of electricity are the capital and 

operating costs of the infrastructure (e.g., lines, substations) and electricity that is lost as it travels through 

the wires from generating plants to end users (i.e., efficiency losses). Transmission and distribution costs 

can vary greatly depending on locational factors specific to a particular region (e.g., the dispersion of 

electricity users, geographic constraints that affect capacity expansions, plant locations) and time of day 

electricity use (i.e. demand patterns). An effective means for reducing these costs is to increase the use of 

 Page 24 
distributed energy resources, which do not rely on the T&D infrastructure. As this suggests, the SAI PV 

installations may produce significant benefits in terms of reduced T&D losses and stress on the existing 

T&D infrastructure. 

In its planning study for the California Public Utilities Commission (CPUC), the Energy and 

Environmental Economics Group (E3, 2004) note that average electric transmission costs range widely 

from $1.16/kW-year to $18.81/kW-year in California, depending on the utility and geographic zone that 

is analyzed. These costs are for the construction of transmission and distribution infrastructure. Thus, on 

average, every kW of load that could be reduced by greater transmission efficiency is worth 

approximately $10.00 in savings per year in California. 

The U.S. Climate Change Technology Program (2003) estimates that in the next decade, the demand for 

electricity transmission capacity will greatly exceed the construction of infrastructure. While line-miles 

are only projected to increase by 6 percent, demand is expected to increase by more than 20 percent. The 

amount of electricity that must be pushed through existing lines will increase, causing increased energy 

losses and transmission congestion. As a result, grid reliability will likely decrease, resulting in more 

transmission failures and blackouts. 

In 1995, the U.S. transmission and distribution system had energy losses of 7.2 percent (Climate Change 

Technology Program), and other studies find similar rates ranging from 5 to 10 percent. Connors et al. 

(2005) estimated average line losses to be between 5 and 12 percent of net generation by comparing EIA 

data on net generation to retail electricity sales (the difference between the two is presumed to equal line 

losses). In another California study, Itron (2005) estimated that over the entire year, line loss costs range 

from $0.00 to $0.12 per kWh. 

Based on these studies, we use 7 percent to account for T&D losses in our economic impact analysis as 

this is approximately the midpoint of the range of estimates found in the literature. 

Blackout Prevention / Power Reliability 

Consumers place a high value on the reliability of their electricity supply, and many authors have noted 

the potential reliability benefits of developing more distributed electrical generating systems (e.g., Young 

2004, Itron 2005, Hadley et al. 2003). In particular, studies have focused on the causes and economic 

impacts of power blackouts, and the potential role that PV generation could play in mitigating blackouts. 

Blackouts are typically caused by one or more of the following factors: unusually high demand, often 

related to extreme weather; equipment failure; high power transfer activity (which can lead to line 

overloads); and human/operator error. Gellings and Yeager (2004) estimated the annual cost of power 

blackouts in the US to be about $100 billion. Due to the unpredictability of blackout occurrences (i.e., 

frequency, location, duration, precise causes), however, this study does not try to quantify total “blackout 

prevention benefits” that could be attributable to PV generation under the SAI. That said, information is 

presented here that shows that the economic costs of blackouts are high, and that PV installations that lead 

to increased system stability could have significant economic benefits. 

Several studies have also analyzed the August 2003 blackout that lost 62,000 MW of power and affected 

over 50 million people in the Northeast region of the country. ICF (2003) used preliminary information 

about the blackout and extrapolated findings from previous analyses to estimate the economic costs of 

this outage to be between $7 and $10 billion. Perez et al. (2004) subsequently analyzed the precise causes 

of the blackout, and estimated that the availability of less than 500 MW of PV resources would have been 

sufficient to prevent the cascading blackout. More specifically, Perez et al. found that regionally dispersed 

PV could have reduced the need for the large national power transfers that were occurring, and alleviated 

 Page 25 
conditions contributing to the eventual blackout. They also note that in the case of the 2003 Northeast 

blackout, PV effective capacity was particularly high in the region (due to little cloud cover) and was 

directly related to high air conditioning demand (the fundamental cause of the blackout). The authors 

conclude that in some situations, PV can act as a natural offset or hedge against blackout causes, and that 

PV (and other distributed generation sources) can work in conjunction with and in parallel to gridsupplied power to improve system reliability. 

 Page 26 
6. SUMMARY AND CONCLUSIONS

Based on our analysis, achieving the SAI PV installation goals will result in a wide range of significant 

benefits: 

• The SAI is expected to produce significant economic benefits over and above what would be 

achieved in the counterfactual scenario where the SAI generation is instead supplied by 

conventional sources. If the 2015 SAI goals are reached, economic output will have a net increase 

of $2.9 to 9.1 billion and 16,000 to 45,000 new jobs will be created. By 2030, the SAI results in 

an increase of $25 billion in economic output and 165,000 to 199,000 new jobs. 

• The economic benefits assume that the price of electricity remains constant (except for inflation) 

throughout the analysis period. Most predictions indicate the price of electricity will increase, 

however, particularly for electricity generated by natural gas, which is the most likely source to 

be offset by the SAI. When price increases are considered in the analysis, a 1-cent increase in 

electricity prices will increase the net economic output from PV O&M by 3 percent. Similarly, 

every 1-cent increase in electricity prices will also increase the PV O&M personal income benefit 

by 6 to7 percent and the net number of new jobs by 8 to 9 percent annually. 

• The SAI PV installations will also have a significant effect in reducing emissions. If the 2015 SAI 

goals are met, emissions of CO2 will decrease by 5 to 11 million tons annually, NOX emissions 

will decrease by 5,000 to 11,200 tons annually, and SO2 emissions will fall by 9,200 to 20,700 

tons annually. By 2030, the SAI will result in decreased CO2 emissions of 69 to 100 million tons 

annually, decreased NOX emissions by 68,000 to 99,000 tons annually, and decreased SO2

emissions by 126,000 to 184,000 tons annually. These emissions reductions will continue each 

year the PV equipment remains installed. 

• The reduced emissions will have a corresponding influence on health benefits in addition to 

reductions in global warming and acid rain. In 2015, the reduced emissions should result in a 

decrease in mortality of 22 to 49, with this number increasing to 300 to 437 deaths avoided 

annually by 2030. The improved health benefits due to the SAI is also expected to reduce the lost 

number of work days, with lost work days falling by 2,500 to 5,700 annually in 2015 and 

decreasing 35,000 to 51,000 annually in 2030. 

• Increasing the deployment of PV through the SAI will reduce demands on natural gas, which in 

turn will reduce the price of natural gas. If the inverse price elasticity of 1.4 is used, then the SAI 

PV installations will have the effect of reducing natural gas prices by 0.3 to 0.6 percent in 2015 

and 3 to 6 percent in 2030. This has potentially far reaching effects, as even a small reduction in 

the price of natural gas will be felt by all customers that rely on natural gas for electricity 

generation. 

• Finally, several studies have researched the potential benefits of using PV and other distributed 

energy resources to reduce the risk of blackouts. Large regional blackouts are expensive and 

disruptive to the economy and have been estimated to cost the U.S. $100 billion annually. 

Research suggests that 500 MW of PV may have been enough to avoid this blackout. The ability 

of PV to prevent specific blackouts will depend on very specific information on where the PV 

installations are installed and their ability to relieve pressure on the high stress points on the grid. 

While this level of detail is outside the scope of this study, it appears that there will be some 

potential benefit for blackout prevention should the SAI PV goals be achieved. 

 Page 27 
7. REFERENCES

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Benefits of Alternative Emission Reduction Scenarios.” Prepared for Clean Air Task Force. Boston MA. 

June 2004. 

American Council for an Energy-Efficient Economy (ACEEE). “Natural Gas Price Effects of Energy 

Efficiency and Renewable Energy Practices and Policies.” Report Number E032. Washington, D.C., 

2003. 

Awerbuch, Shimon. “Determining the Real Cost: Why Renewable Power is More Cost-Competitive than 

Previously Believed.” Renewable Energy World, James and James (Science Publishers) Ltd., April 2003. 

Bolinger, Mark and Ryan Wiser and W. Golove. “Accounting for Fuel Price Risk: Using Forward Natural 

Gas Prices Instead of Gas Price Forecasts to Compare Renewable to Natural Gas-Fired Generation.” 

Lawrence Berkeley Laboratory, 2003. 

Connors, Stephen and Edward Kern, Michael Adams, Katherine Martin and Baafour Asiamah-Adjei. 

“National Assessment of Emissions Reduction of Photovoltaic (PV) Power Systems.” Prepared for United 

States Environmental Protection Agency, Air Pollution Prevention and Control Division. 2005. 

Denholm, P. and R. Margolis. “Very Large-Scale Deployment of Grid-Connected Solar Photovoltaics in 

the United States: Challenges and Opportunities.” Prepared for the American Solar Energy Society Solar 

2006 Conference, Denver, CO, July 2006. NREL/CP-620-39683. National Renewable Energy 

Laboratory. April 2006. 

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the Evaluation of California Energy Efficiency Programs.” Prepared for the Public Utilities Commission 

of the State of California. October 2004. 

Energy Information Administration. “Impacts of a 10-percent Renewable Portfolio Standard.” Report 

Number SR/OIAF/2002-03. 2002 and 2003. 

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www.eia.doe.gov/emeu/recs/recs2001/enduse2001/enduse2001.html. 

Energy Information Administration. “Electric Power Annual 2004.” 2005. 

Energy Information Administration. “Renewable Energy Annual 2004.” June 2006. 

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www.eia.doe.gov/oil_gas/natural_gas/info_glance/natural_gas.html. 

Energy Information Administration. “Annual Energy Outlook 2007 (Early Release)” Data for Figure 5. 

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Energy Information Administration. “Annual Energy Outlook 2007 (Early Release)” Data for Figure 3. 

Available at: www.eia.doe.gov/oiaf/aeo/consumption.html. 

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Environmental Protection Agency. “Technical Addendum: Methodologies for the Benefit Analysis of the 

Clear Skies Act of 2003.” September 2003. 

Gellings, C. W., and Yeager, K. “Transforming the Electric Infrastructure.” Physics Today. December 

2004. 

Hadley, S.W. and J.W. Van Dyke, W.P. Poore, T.K. Stovall. “Quantitative Assessment of the Distributed 

Energy Resource Benefits.” U.S. Department of Energy Oak Ridge National Laboratory. May 2003. 

Hoff, T.E., Perez, R., Margolis, R.M. “Maximizing the Value of Customer-Sited PV Systems Using 

Storage and Controls.” Proceedings of the ASES 34h Annual Conference. 2005. 

ICF Consulting. “The Economic Cost of the Blackout–An issue paper on the Northeastern Blackout, 

August 14, 2003”. Issue Paper. Fall 2003. 

“Interim Opinion on E3 Avoided Cost Methodology.” Decision 05-04-024 before the Public Utilities 

Commission of the State of California. April 7, 2005. 

Itron, Inc. “CPUC Self-Generation Incentive Program Preliminary Cost-Effectiveness Evaluation 

Report.” Submitted to the California Public Utilities Commission: Energy Division. September 2005. 

Itron, Inc. “Framework for Assessing the Cost-Effectiveness of the Self-Generation Incentive Program.” 

Prepared for the California Public Utilities Commission. March 2005. 

Margolis, Robert. “The Solar America Initiative: Energy, Economic and Environmental Benefits.” Draft 

White Paper Prepared for the National Renewable Energy Laboratory. September 2006. 

Perez, R. and R. Margolis, M. Kmiecik, M. Schwab, M. Perez. “Update: Effective Load Carrying 

Capability of Photovoltaics in the United States.” Prepared for the American Solar Energy Society Solar 

2006 Conference, Denver, CO, July 2006. NREL/CP-620-40068. National Renewable Energy 

Laboratory. June 2006. 

Perez, R., Kmiecik, M., Hoff, T., Herig, C., Letendre, S., Williams, J., and Margolis, R. “Availability of 

Dispersed Photovoltaic Resource During the August 14th 2003 Northeast Power Outage.” Proc. ASES 

Natl. Conference. 2004. 

Union of Concerned Scientists. “Renewable Energy can help Alleviate Natural Gas Crisis: A National 

Renewable Electricity Standard Conserves Natural Gas, Reduces Natural Gas Prices, and Can Save 

Consumers and Businesses Money.” Fact Sheet, June 2003. 

U.S. Climate Change Technology Program. “Technology Options for the Near and Long Term.” Section 

1.3.2: Transmission and Distribution Technologies. Page 34. November 2003. 

Wiser, Ryan and Mark Bollinger and Matthew St. Clair. “Easing the Natural Gas Crisis: Reducing 

Natural Gas Prices through Increased Deployment of Renewable Energy and Energy Efficiency.” 

Lawrence Berkeley National Laboratory: Environmental Energy Technologies Division. 2005. 

Wiser, Ryan and Mark Bollinger and Matthew St. Clair. “Putting Downward Pressure on Natural Gas 

Prices: The Impact of Renewable Energy and Energy Efficiency.” Lawrence Berkeley National 

Laboratory. 2004(a). 

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Wiser, Ryan and Mark Bollinger. “The Value of Renewable Energy as a Hedge Against Fuel Price Risk.” 

Lawrence Berkeley National Laboratory. 2004(b). 

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Capacity Costs of Electricity Distribution.” Energy: The International Journal. Volume 19(12): 1213-

1218. 1994. 

Young, William. “Assuring Power in a Disaster and Providing Energy Security.” Proceedings of ASES 

Annual Conference. July 2004. 

 Page 30 
APPENDIX A: DETAILED IMPLAN MODEL RESULTS

The following tables show the annual net benefits (relative to the counterfactual scenario) for each of the 

IMPLAN benefit categories of output, personal income, jobs, and tax revenues. For these tables, the total 

impacts are shown which are the sum of the direct, indirect, and induced effects discussed in the main 

body of the report. 

For each year, the construction impacts are calculated based only the PV installations done in that year. 

Similarly, the R&D impacts reflect only the R&D spending for the individual year. For O&M expenses, 

these continue to accrue for each the PV system is installed. Therefore, the economic impacts are 

calculated based on the total installed capacity for that year. For example, the O&M benefits in 2015 are 

calculated based on the total installed capacity of 5 GW (low scenario) and 10 GW (high scenario). 

 Page A-1 
Table 23: Annual Construction Impacts - High Scenario 

Year

Total Annual 

Installed Capacity 

(GW) - High

SAI Induced 

Installed Capacity 

(GW) - High

Construction 

Total Output

Construction 

Total Personal 

Income

Construction 

Total Jobs

2005 0.11 0.11 $1,009,491,000 $348,085,000 3,450

2006 0.15 0.15 $1,251,959,000 $431,356,000 4,290

2007 0.21 0.20 $1,552,160,000 $534,336,000 5,310

2008 0.29 0.28 $1,923,660,000 $661,604,000 6,580

2009 0.40 0.38 $2,383,135,000 $818,782,000 8,140

2010 0.55 0.51 $2,951,074,000 $1,012,754,000 10,070

2011 0.76 0.69 $3,652,613,000 $1,251,931,000 12,450

2012 1.04 0.94 $4,518,531,000 $1,546,564,000 15,390

2013 1.44 1.28 $5,586,461,000 $1,909,128,000 19,020

2014 1.99 1.75 $6,902,320,000 $2,354,770,000 23,460

2015 2.74 2.37 $8,522,000,000 $2,901,796,000 28,940

2016 2.99 2.57 $9,038,166,000 $3,076,249,000 30,680

2017 3.27 2.78 $9,585,255,000 $3,261,040,000 32,530

2018 3.58 3.01 $10,165,089,000 $3,456,772,000 34,490

2019 3.91 3.26 $10,779,599,000 $3,664,077,000 36,560

2020 4.27 3.52 $11,430,825,000 $3,883,629,000 38,760

2021 4.67 3.81 $12,120,921,000 $4,116,131,000 41,090

2022 5.10 4.13 $12,852,168,000 $4,362,333,000 43,560

2023 5.58 4.47 $13,626,979,000 $4,623,016,000 46,160

2024 6.10 4.84 $14,447,899,000 $4,899,021,000 48,930

2025 6.67 5.23 $15,317,622,000 $5,191,221,000 51,850

2026 7.29 5.66 $16,238,995,000 $5,500,542,000 54,950

2027 7.96 6.13 $17,215,025,000 $5,827,962,000 58,230

2028 8.70 6.63 $18,248,885,000 $6,174,512,000 61,710

2029 9.51 7.18 $19,343,936,000 $6,541,273,000 65,380

2030 10.40 7.77 $20,503,720,000 $6,929,398,000 69,280

 Page A-2 
Table 24: Annual Construction Impacts – Low Scenario 

Year

Total Annual 

Installed Capacity 

(GW) - Low

SAI Induced 

Installed Capacity 

(GW) - Low

Construction 

Total Output

Construction 

Total Personal 

Income

Construction 

Total Jobs

2005 0.11 0.11 $1,009,491,000 $348,085,000 3,450

2006 0.14 0.13 $1,111,096,000 $382,823,000 3,800

2007 0.17 0.16 $1,222,532,000 $420,860,000 4,180

2008 0.21 0.19 $1,344,662,000 $462,470,000 4,600

2009 0.27 0.23 $1,478,411,000 $507,944,000 5,050

2010 0.34 0.28 $1,624,756,000 $557,587,000 5,550

2011 0.42 0.34 $1,784,734,000 $611,717,000 6,090

2012 0.52 0.41 $1,959,426,000 $670,656,000 6,680

2013 0.65 0.49 $2,149,958,000 $734,731,000 7,310

2014 0.82 0.60 $2,357,491,000 $804,273,000 8,010

2015 1.02 0.72 $2,583,200,000 $879,597,000 8,770

2016 1.19 0.85 $2,982,955,000 $1,015,284,000 10,130

2017 1.39 1.00 $3,444,452,000 $1,171,852,000 11,690

2018 1.62 1.18 $3,977,201,000 $1,352,498,000 13,500

2019 1.89 1.39 $4,592,181,000 $1,560,922,000 15,570

2020 2.21 1.63 $5,302,050,000 $1,801,375,000 17,980

2021 2.57 1.93 $6,121,415,000 $2,078,765,000 20,750

2022 3.00 2.27 $7,067,122,000 $2,398,748,000 23,950

2023 3.50 2.68 $8,158,602,000 $2,767,845,000 27,630

2024 4.09 3.15 $9,418,263,000 $3,193,563,000 31,890

2025 4.77 3.71 $10,871,949,000 $3,684,560,000 36,810

2026 5.56 4.38 $12,549,465,000 $4,250,809,000 42,460

2027 6.49 5.16 $14,485,171,000 $4,903,800,000 49,000

2028 7.57 6.08 $16,718,693,000 $5,656,770,000 56,540

2029 8.83 7.16 $19,295,710,000 $6,524,967,000 65,220

2030 10.31 8.44 $22,268,889,000 $7,525,950,000 75,230

 Page A-3 
Table 25: Annual O&M Impacts - High Scenario 

Year

Total 

Cummulative 

Installed Capacity - 

High 

SAI Induced 

Cummulative 

Installed Capacity - 

High 

O&M Total 

Output

O&M 

Total Personal 

Income

O&M Total 

Jobs

2005 0.44 0.44 $51,601,000 $28,364,000 980

2006 0.59 0.59 $64,067,000 $35,934,000 1,270

2007 0.80 0.79 $79,920,000 $45,781,000 1,650

2008 1.09 1.07 $100,077,000 $58,597,000 2,160

2009 1.49 1.45 $125,720,000 $75,302,000 2,840

2010 2.04 1.96 $158,361,000 $97,109,000 3,740

2011 2.79 2.65 $199,955,000 $125,619,000 4,930

2012 3.84 3.60 $253,016,000 $162,960,000 6,540

2013 5.28 4.88 $320,800,000 $211,949,000 8,660

2014 7.26 6.63 $407,511,000 $276,331,000 11,520

2015 10.00 9.00 $518,614,000 $361,094,000 15,330

2016 12.99 11.57 $657,780,000 $460,551,000 19,630

2017 16.26 14.35 $805,054,000 $566,828,000 24,250

2018 19.84 17.36 $961,035,000 $680,454,000 29,230

2019 23.75 20.62 $1,126,362,000 $802,008,000 34,590

2020 28.02 24.14 $1,301,720,000 $932,103,000 40,340

2021 32.69 27.96 $1,487,844,000 $1,071,407,000 46,540

2022 37.79 32.08 $1,685,518,000 $1,220,635,000 53,220

2023 43.37 36.55 $1,895,586,000 $1,380,562,000 60,420

2024 49.47 41.39 $2,118,949,000 $1,552,014,000 68,170

2025 56.14 46.62 $2,356,573,000 $1,735,897,000 76,520

2026 63.42 52.29 $2,609,495,000 $1,933,169,000 85,520

2027 71.38 58.42 $2,878,827,000 $2,144,876,000 95,220

2028 80.09 65.05 $3,165,756,000 $2,372,139,000 105,680

2029 89.60 72.23 $3,471,563,000 $2,616,166,000 116,950

2030 100.00 80.00 $3,797,614,000 $2,878,265,000 129,120

 Page A-4 
Table 26: Annual O&M Impacts – Low Scenario 

Year

Total 

Cummulative 

Installed Capacity - 

Low

SAI Induced 

Cummulative 

Installed Capacity - 

Low

O&M Total 

Output

O&M 

Total Personal 

Income

O&M Total 

Jobs

2005 0.44 0.44 $51,601,000 $28,364,000 980

2006 0.58 0.57 $62,239,000 $34,908,000 1,240

2007 0.75 0.73 $73,872,000 $42,315,000 1,530

2008 0.96 0.93 $86,674,000 $50,748,000 1,870

2009 1.23 1.16 $100,848,000 $60,404,000 2,280

2010 1.57 1.44 $116,624,000 $71,516,000 2,760

2011 1.99 1.78 $134,268,000 $84,353,000 3,310

2012 2.51 2.19 $154,089,000 $99,244,000 3,970

2013 3.16 2.68 $176,444,000 $116,575,000 4,770

2014 3.98 3.28 $201,750,000 $136,805,000 5,700

2015 5.00 4.00 $230,495,000 $160,486,000 6,810

2016 6.19 4.85 $275,635,000 $192,987,000 8,230

2017 7.58 5.85 $328,036,000 $230,966,000 9,890

2018 9.20 7.02 $388,893,000 $275,353,000 11,830

2019 11.09 8.41 $459,593,000 $327,246,000 14,100

2020 13.30 10.05 $541,752,000 $387,925,000 16,790

2021 15.87 11.97 $637,251,000 $458,889,000 19,940

2022 18.87 14.24 $748,284,000 $541,900,000 23,620

2023 22.38 16.92 $877,405,000 $639,015,000 27,960

2024 26.46 20.07 $1,027,585,000 $752,652,000 33,060

2025 31.23 23.79 $1,202,294,000 $885,634,000 39,030

2026 36.80 28.16 $1,405,567,000 $1,041,274,000 46,060

2027 43.29 33.32 $1,642,109,000 $1,223,457,000 54,320

2028 50.86 39.40 $1,917,403,000 $1,436,732,000 64,010

2029 59.69 46.56 $2,237,837,000 $1,686,431,000 75,400

2030 70.00 55.00 $2,610,859,000 $1,978,806,000 88,770

 Page A-5 
Table 27: Annual R&D Impacts – High and Low Scenarios 

Year Annual R&D 

Spending R&D Total Output

R&D 

Total Personal 

Income

R&D Total 

Jobs

2005 $148,000,000 $118,007,000 $64,254,000 910

2006 $148,000,000 $118,007,000 $64,254,000 910

2007 $148,000,000 $118,007,000 $64,254,000 910

2008 $148,000,000 $118,007,000 $64,254,000 910

2009 $148,000,000 $118,007,000 $64,254,000 910

2010 $148,000,000 $118,007,000 $64,254,000 910

2011 $148,000,000 $118,007,000 $64,254,000 910

2012 $148,000,000 $118,007,000 $64,254,000 910

2013 $148,000,000 $118,007,000 $64,254,000 910

2014 $148,000,000 $118,007,000 $64,254,000 910

2015 $148,000,000 $118,007,000 $64,254,000 910

 Page A-6 
Table 28: Annual Tax Impacts - Construction 

Year

Total Federal 

Taxes - 

Construction

Total State and 

Local Taxes - 

Construction

Total Taxes - 

Construction

Total Federal 

Taxes - 

Construction

Total State and 

Local Taxes - 

Construction

Total Taxes - 

Construction

2005 $73,543,590 $14,513,980 $88,057,570 $73,543,590 $14,513,980 $88,057,570

2006 $91,206,530 $18,077,390 $109,283,920 $80,944,540 $16,043,450 $96,987,990

2007 $113,074,630 $22,517,600 $135,592,230 $89,061,180 $17,735,580 $106,796,760

2008 $140,135,530 $28,050,970 $168,186,500 $97,956,490 $19,607,990 $117,564,480

2009 $173,603,900 $34,947,640 $208,551,540 $107,697,600 $21,680,230 $129,377,830

2010 $214,971,580 $43,544,600 $258,516,180 $118,355,740 $23,974,120 $142,329,860

2011 $266,068,490 $54,262,860 $320,331,350 $130,006,010 $26,513,840 $156,519,850

2012 $329,135,610 $67,628,090 $396,763,700 $142,727,110 $29,326,390 $172,053,500

2013 $406,912,310 $84,297,110 $491,209,420 $156,600,830 $32,441,870 $189,042,700

2014 $502,740,750 $105,090,860 $607,831,610 $171,711,330 $35,893,820 $207,605,150

2015 $620,688,860 $131,035,690 $751,724,550 $188,144,040 $39,719,710 $227,863,750

2016 $658,277,500 $139,275,510 $797,553,010 $217,257,850 $45,966,490 $263,224,340

2017 $698,117,570 $148,034,680 $846,152,250 $250,867,840 $53,196,130 $304,063,970

2018 $740,341,760 $157,346,050 $897,687,810 $289,666,740 $61,563,350 $351,230,090

2019 $785,090,470 $167,244,460 $952,334,930 $334,453,730 $71,247,260 $405,700,990

2020 $832,512,150 $177,767,090 $1,010,279,240 $386,150,750 $82,455,110 $468,605,860

2021 $882,763,780 $188,953,420 $1,071,717,200 $445,821,260 $95,426,950 $541,248,210

2022 $936,011,280 $200,845,430 $1,136,856,710 $514,691,860 $110,440,450 $625,132,310

2023 $992,430,090 $213,487,800 $1,205,917,890 $594,177,350 $127,817,200 $721,994,550

2024 $1,052,205,610 $226,928,020 $1,279,133,630 $685,909,390 $147,929,340 $833,838,730

2025 $1,115,533,850 $241,216,630 $1,356,750,480 $791,769,670 $171,207,730 $962,977,400

2026 $1,182,621,900 $256,407,330 $1,439,029,230 $913,927,970 $198,151,110 $1,112,079,080

2027 $1,253,688,540 $272,557,320 $1,526,245,860 $1,054,886,330 $229,336,870 $1,284,223,200

2028 $1,328,964,960 $289,727,320 $1,618,692,280 $1,217,529,520 $265,433,320 $1,482,962,840

2029 $1,408,695,320 $307,982,060 $1,716,677,380 $1,405,183,360 $307,214,260 $1,712,397,620

2030 $1,493,137,530 $327,390,310 $1,820,527,840 $1,621,681,990 $355,575,390 $1,977,257,380

High Scenario Low Scenario

 Page A-7 
Table 29: Annual Tax Impacts - O&M 

Year

Total Federal 

Taxes - O&M

Total State and 

Local Taxes - 

O&M

Total Taxes - 

O&M

Total Federal 

Taxes - O&M

Total State and 

Local Taxes - 

O&M

Total Taxes - 

O&M

2005 -$3,249,310 $1,509,210 -$1,740,100 -$3,249,310 $1,509,210 -$1,740,100

2006 -$4,802,490 $1,781,260 -$3,021,230 -$4,665,430 $1,730,420 -$2,935,010

2007 -$7,011,170 $2,099,080 -$4,912,090 -$6,480,560 $1,940,210 -$4,540,350

2008 -$10,137,550 $2,464,930 -$7,672,620 -$8,779,820 $2,134,820 -$6,645,000

2009 -$14,544,140 $2,878,580 -$11,665,560 -$11,666,830 $2,309,120 -$9,357,710

2010 -$20,731,720 $3,335,520 -$17,396,200 -$15,267,720 $2,456,420 -$12,811,300

2011 -$29,390,800 $3,824,490 -$25,566,310 -$19,735,690 $2,568,080 -$17,167,610

2012 -$41,472,160 $4,323,570 -$37,148,590 -$25,256,790 $2,633,060 -$22,623,730

2013 -$58,283,630 $4,795,070 -$53,488,560 -$32,056,750 $2,637,360 -$29,419,390

2014 -$81,621,910 $5,177,610 -$76,444,300 -$40,409,230 $2,563,310 -$37,845,920

2015 -$113,953,120 $5,375,250 -$108,577,870 -$50,645,820 $2,388,990 -$48,256,830

2016 -$147,269,960 $6,487,760 -$140,782,200 -$61,711,670 $2,718,600 -$58,993,070

2017 -$183,622,700 $7,533,260 -$176,089,440 -$74,821,060 $3,069,590 -$71,751,470

2018 -$223,267,260 $8,502,900 -$214,764,360 -$90,347,640 $3,440,800 -$86,906,840

2019 -$266,481,150 $9,386,860 -$257,094,290 -$108,733,220 $3,830,150 -$104,903,070

2020 -$313,565,400 $10,174,060 -$303,391,340 -$130,500,110 $4,234,260 -$126,265,850

2021 -$364,846,510 $10,852,230 -$353,994,280 -$156,265,700 $4,648,100 -$151,617,600

2022 -$420,678,480 $11,407,680 -$409,270,800 -$186,759,830 $5,064,430 -$181,695,400

2023 -$481,445,240 $11,825,130 -$469,620,110 -$222,845,100 $5,473,460 -$217,371,640

2024 -$547,563,070 $12,087,680 -$535,475,390 -$265,541,070 $5,861,940 -$259,679,130

2025 -$619,483,400 $12,176,600 -$607,306,800 -$316,052,580 $6,212,360 -$309,840,220

2026 -$697,695,620 $12,071,080 -$685,624,540 -$375,803,560 $6,501,930 -$369,301,630

2027 -$782,730,380 $11,748,080 -$770,982,300 -$446,476,550 $6,701,210 -$439,775,340

2028 -$875,162,990 $11,182,220 -$863,980,770 -$530,059,650 $6,772,730 -$523,286,920

2029 -$975,617,070 $10,345,320 -$965,271,750 -$628,901,880 $6,668,810 -$622,233,070

2030 -$1,084,768,760 $9,206,400 -$1,075,562,360 -$745,778,530 $6,329,420 -$739,449,110

High Scenario Low Scenario

 Page A-8 
Table 30: Annual Tax Impacts - R&D 

Year

Total Federal 

Taxes - R&D

Total State and 

Local Taxes - 

R&D

Total Taxes - 

R&D

Total Federal 

Taxes - R&D

Total State and 

Local Taxes - 

R&D

Total Taxes - 

R&D

2005 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2006 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2007 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2008 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2009 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2010 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2011 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2012 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2013 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2014 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

2015 -$2,975,790 $2,840,140 -$135,650 -$2,975,790 $2,840,140 -$135,650

High Scenario Low Scenario

 

 Page A-9 
 Page B-1 

APPENDIX B: IMPLAN MODEL TECHNICAL DETAIL

This appendix provides a detailed discussion of the modeling tools and analysis methods used to estimate 

economic impacts. This appendix begins with a discussion of what economic impacts are and how they 

can be measured using an input-output modeling framework. It then discusses the limitations of inputoutput analysis, with recommendations on when an input-output model should be used. This appendix 

concludes with a discussion of the IMPLAN modeling software, which is a widely used model for 

estimating economic impacts. 

ECONOMIC IMPACTS

Economic impacts are changes in economic activity as a result of some initial change in the economy. 

Although the initial stimuli can vary, economic impacts are typically measured as changes in output (or 

sales), income (value added), and jobs: 

• Output is the broadest measure of economic activity. It represents the total value of production 

or, alternatively, business revenues. Output includes the costs of materials and labor, net business 

income (profits), and indirect business taxes. 

• Income represents the total payments to workers (wages) and business owners (proprietor and 

corporate income). Together, wages and business income are often referred to as personal 

income. Corporate income represents net business income or profits. These may be reinvested or 

paid as dividends to shareholders. Income excludes payments from one industry to another for the 

purchase of intermediate goods, and is often used as a measure of the value added during 

production.25

• Jobs represent the number of additional jobs gained or lost as a result of some economic activity. 

Job impacts are the most popular measure of economic impacts because they are easy to 

understand.

Economic impacts often lead to changes in government revenues and expenditures. These fiscal impacts

occur as changes in output, income, and jobs, lead to changes in the regional tax base and demand for 

government services. 

Gross Versus Net Economic Impacts 

Simply citing the economic impacts that occur as a result of some activity would produce an upper bound 

estimate of economic impacts. This upper bound estimate is often referred to as a measure of the “gross” 

economic impacts. Gross economic impacts offer a perspective on the magnitude of overall economic 

impacts that can be traced back to the activity. Gross economic impacts, however, do not accurately 

reflect the creation of new jobs or income as they do not take into account the jobs or income creation that 

would have occurred in absence of the activity being analyzed. 

This problem is addressed by analyzing the “net” economic impacts of a given activity. An analysis of the 

net economic impacts requires that only economic stimuli that are new or additive to the local economy 

 

25 Alternatively, value added is measured as total output less purchases of intermediate goods and services. In either case, the 

measure of value added will be the same. However, to the extent that owners of corporations live outside the relevant study area, 

including corporate income will tend to overestimate the measure of value added in production. 
be counted. To do this, the impact analysis must include a “base case” or “counterfactual” scenario that 

describes what would have happened in the absence of a change in the economy. 

Establishing a base case affects an analyst’s ability to properly identify cause-and-effect relationships. 

Attributing effects to causes, and doing so only once (i.e., avoiding double counting) is essential to an 

evaluation of net economic impacts. In impact analysis, this base case scenario is typically implemented 

by positing a counterfactual argument that only counts spending that “but for” the project or activity 

would not have occurred. 

To get a better understanding of how this base case scenario affects the impact analysis, the three 

components of the counterfactual argument related to a net impact analysis of a new business activity, for 

example, are described below: 

1. Net impacts include expenditures by non-local sources that would have gone to non-local 

businesses but for the local presence of the new business. (The new business is an “exporter” of 

goods and services. In this sense, the new business brings in new spending to the community.) 

2. Net impacts include expenditures by local sources that would have been spent outside of the local 

economy but for the local presence of the new business. (This is called “import substitution.” The 

new business keeps local spending in the community.) 

3. Net impacts would make deductions for expenditures by local sources that would have gone to 

other local businesses but for the new business. (This is a form of “direct substitution.” The new 

business may actually divert spending away from other local businesses. This spending should 

not be included in the impact analysis.) 

Net impacts are often considerably smaller than gross impacts, but provide a truer picture of the benefits 

from a stimulus. Using a net impact approach, the analyst is better able to answer the following question: 

How much better off is the local economy because of the activity or project relative to the base case 

alternative? 

At the outset, one should decide whether the question being posed for analysis requires that net or gross 

impacts be determined. A common mistake is to use the results from a gross impact analysis to answer a 

question about the benefits or improvement to an economy due to a project or activity. This often leads to 

unrealistic claims about economic impacts. 

FEATURES OF INPUT-OUTPUT MODELS

Input-output models serve two general purposes. First, the input-output framework is useful for 

organizing information about the structure of a regional economy. Using standard accounting 

conventions, input-output models describe the flow of commodities between producing and consuming 

sectors, the flow of income between businesses and institutions, and the trade in commodities between 

regions. In this manner, the input-output modeling framework can be used for descriptive purposes. For 

instance, researchers can evaluate the relative importance of various industry sectors to the local 

economy, e.g., the number of jobs or purchases from other local industries. 

Once the information on the various transactions within an economy has been gathered and organized 

using the input-output framework, the data can be manipulated using a special field of mathematics called 

matrix algebra. This phase of input-output modeling produces “multipliers” and allows researchers to use 

the input-output model for prescriptive purposes. For example, a researcher can estimate the “ripple” 

effect that a change in one sector has on the entire economy. 

 Page B-2 
 Page B-3 

Economic Linkages 

An input-output model begins with a transactions table that provides a reasonably comprehensive 

description of an economy and linkages between economic sectors. Table B-1 shows a hypothetical 

transactions table for an economy with three industry sectors. For exposition purposes, the table has been 

divided into four quadrants. 

Table B-1: A Transactions Table for a Hypothetical Economy 

INTERMEDIATE DEMANDS FINAL DEMANDS

 Buyer

Seller Agriculture Manufacturing Services

Household 

Demand

Other 

Demand Exports

Total 

Outputs

Agriculture $600 $100 $400 $700 $100 $1,300 $3,200

Manufacturing $200 $700 $800 $1,400 $600 $300 $4,000

Services $200 $400 $1,500 $1,600 $200 $1,000 $4,900

Wages $900 $600 $1,000

Other Earnings $500 $400 $600 NONMARKET TRANSFERS

Imports $800 $2,200 $1,200

Total Inputs $3,200 $4,000 $4,900

FINAL 

PAYMENTS

The top left quadrant of the transactions table describes the production relationships between industries in 

the economy. This portion of the table describes the way raw materials and intermediate goods are 

provided by some industries (“sellers”) and used by other industries (“buyers”) to produce final goods and 

services. Because industries are buying from other industries, it describes the intermediate demands for 

goods and services. 

The upper right quadrant of the transactions table shows who consumes the final goods and services. In 

this simple hypothetical transactions table, the final demand sectors consist of households, others,26 and 

exports. In more sophisticated input-output models, the final demand sectors are more extensive and 

explicitly identified in the model. 

By combining the top two quadrants of the transactions table, researchers can trace the flow of goods and 

services from producing industries to other industries and final consumers. The sum of each row shows 

the total output or production of each industry. For instance, the value of total output produced by the 

agriculture sector is $3,200. Of this amount, $1,10027 is used in production by other industries. The 

remainder is sold to final consumers—households consume $700 worth of agricultural output; 

government and businesses consume $100 worth of agricultural output; and $1,300 of the total 

agricultural output is sold to entities outside of the region. The rows in the transactions table describe the 

“forward linkages” in an economy. 

The lower left quadrant of the transactions table describes the transactions between businesses and the 

suppliers of factors of production. Labeled final payments, this section of the input-output model depicts 

 

26 “Other demand” consists of government and business spending, including additions to business inventories. 

27 This amount represents the sum of the intermediate demands across all sectors, i.e., $600 agriculture, $100 manufacturing, and

$400 services. 
 Page B-4 

the flow of income from businesses to households (wages), others,28 and imports. The income 

components, excluding imports, are the elements of value added. 

The left side of the transactions table shows the purchases and payments by industries to other industries 

and other institutions within the economy. The sum of the entries for each column represents the total 

purchases by that industry. The agricultural sector shows large intra-sector purchases ($600) from others 

within the agriculture sector, e.g., to produce milk the dairy must buy significant quantities of feed. Firms 

in the agriculture sector will also hire workers and pay wages ($900). In addition, firms will pay taxes and 

earn profits ($500), and purchase imported goods and services ($800). From this example, we can trace 

all of the purchases for each sector of the economy. The columns in the transactions table describe the 

“backward linkages” in the economy. 

As discussed previously, the input-output model is based on a double-entry accounting convention where 

inputs must be equal to outputs. Because government and businesses are implicitly included in Other 

Demand (government purchases, capital investment and inventories) and Other Earnings (taxes and 

profits), inputs must be equal to outputs. The agriculture sector of this economy produces $3,200 in 

output. To do this, the agriculture sector must purchase $3,200 in inputs from other industries, 

households, government, businesses and foreigners. The input-output model, thus, is a balanced or 

symmetrical model because total sales (outputs) of each industry equal the total purchases (inputs) by 

each industry. 

In order to emphasize the relationships among businesses and institutions, the lower right hand quadrant 

of the transactions table labeled “Non-Market Transfers” has been left blank. This section of the 

transactions table contains information regarding inter-institutional transfers or non-market financial 

flows between households, businesses, government and foreigners. 

Economic Multipliers 

The transactions table provides a reasonably comprehensive description of the economy at a given point 

in time. Using annual data, it shows how much an industry produces, what it purchases, and to whom the 

final output is sold. It also distinguishes local from non-local sources of spending and production. In this 

form, the transactions table fully describes the linkages between the various sectors of the economy, but 

has no predictive or analytical capabilities. 

The information contained in the transaction table can be expanded and then manipulated using matrix 

algebra to construct multipliers that measure the total impacts from a change in final demand on all 

industries in an economy. It is through this matrix operation that the input-output model is converted from 

a descriptive to a prescriptive model. 

Economic impact multipliers allow researchers to follow the initial change in economic activity as it 

“ripples” through each industry sector. For any given type of change in economic activity, the impacts on 

the economy can be reported on one of three levels: 

1. Direct impacts represent the initial change in final demand for the industry sector(s) in question. 

Direct impacts describe the changes in economic activity for sectors that first experiences a 

change in demand because of a project, policy decision, or some other stimuli. 

 

28 “Other earnings” includes small business income, corporate profits, and indirect business taxes paid to government. 
2. Indirect impacts represent the response as supplying industries increase output in order to 

accommodate the initial change in final demand. These indirect beneficiaries will then spend 

money for supplies and services, which results in another round of indirect spending, and so on. 

3. Induced impacts are generated by the spending of households who benefit from the additional 

wages and business income they earn through all of the direct and indirect activity. The increase 

in income, in effect, increases the purchasing power of households. 

The following example illustrates how these types of impacts affect overall economic activity. Suppose 

that a new manufacturing facility comes to a region. The direct impacts would consist of the value of 

output produced at the facility, and the number of employees working at the facility and their payroll. 

In order to operate, the manufacturing facility will purchase a host of goods and services including, for 

example, spare parts and equipment, repair services, electricity, water and sewer, etc. This spending 

generates the first round of indirect impacts. Suppliers and vendors to the manufacturing facility will also 

have to purchase additional goods and services. The local special trade contractor hired to repair a 

component on the manufacturing floor will purchase welding equipment and gases, lease equipment, and 

fuel their vehicles. This spending leads to another round of indirect impacts. 

The direct and indirect increases in employment and income enhance the overall purchasing power in the 

economy. Workers at the manufacturing facility will use their income to purchase groceries or take their 

family to the theater. Workers at businesses who supply the manufacturing facility will do the same. This 

spending will generate induced economic impacts for workers and businesses in other sectors of the 

economy. 

This cycle of spending does not go on forever. It continues until the spending eventually leaks out of the 

local economy as a result of taxes, savings, or purchases of non-locally produced goods and services or 

“imports”. As discussed previously, we can now see how the definition of the study area affects the 

impact analysis. A larger study area will have greater economic linkages between businesses and 

institutions, and a smaller propensity to import. As a result, the initial economic stimulus filters 

throughout the economy more than it would for a smaller study area, and the multipliers are larger than 

they would be for a smaller study area. 

LIMITATIONS OF INPUT-OUTPUT MODELING

The input-output modeling framework for economic impact analysis has grown in popularity. Much of 

this growth is due to significant improvements in computer technology that now make it possible to 

quickly perform the complex matrix operations. Some of this growth is due to improvements in 

government data collection efforts. Lastly, the growth in input-output modeling has been fueled by the 

desire of policy makers, industry officials, and others to obtain information that will help them to better 

understand and respond to economic change. 

Like many quantitative tools, input-output models rely on a set of assumptions. Indeed, without 

simplifying assumptions it would be impossible for researchers to model something as complex and 

dynamic as a regional economy. The use of simplifying assumptions, however, also imposes certain 

limitations on the use of input-output modeling. These limitations should be fully understood and guide 

its use. 

 Page B-5 
Input-Output Modeling—Static Models 

Input-output models are static models in that they measure the flow of inputs and outputs in an economy 

at a point in time. With this information and the balanced accounting structure of an input-output model, 

an analyst can: 1) describe an economy at one time period, 2) introduce a change to the economy, and 

then 3) evaluate the economy after it has fully accommodated that change. 

This type of analysis is called “partial equilibrium” analysis. Input-output models are just one of many 

economic models that fall under the rubric of partial equilibrium analysis. The logic of partial equilibrium 

analysis is straightforward: take a snapshot of an economy, posit a change to the economy, and then take 

another snapshot to measure what happened. Measurement in this sense is really a before and after 

comparison. 

Partial equilibrium analysis permits comparison of the economy at two points in time, but yields little 

information about how the economy actually moves from one equilibrium to the next. In fact, in partial 

equilibrium analysis, other than the initial economic stimulus, the researcher assumes that all other 

relationships in the economy remain the same. 

Their point-in-time construction and the assumption that nothing else changes, make static input-output 

models very different than dynamic models. Dynamic models have feedback effects that allow the events 

of one year to change the linkages in future years. In so doing, dynamic models simulate the expected 

long-term changes in the structure of the economy. Contrary to dynamic models, static models assume 

that there are no changes in wage rates, input prices, and property values. In addition, underlying 

economic relationships in input-output models are assumed constant, i.e., there are no changes in the 

productivity of labor and capital, and no changes in population migration or business location patterns. 

The assumptions and their implications for input-output modeling are discussed below. 

Fixed Production Relationships 

Input-output models are a representation—as reported in the transactions table—of economic 

relationships that exist at a moment in time. For industries, this means that input-output models are based 

on production relationships that are fixed. This assumption results in: 

• Constant Returns to Scale means that an industry’s production function is linear, and an 

increase in output requires all inputs to increase proportionately. If the demand for milk doubled, 

for instance, then the demand for all of the inputs used to produce milk would also double. In the 

long run, production processes exhibit economies and diseconomies of scale that vary with the 

level of output. An industry with scale economies would be able to double production without 

necessarily doubling all inputs. 

• Fixed Commodity Input Structure means that input-output models do not allow changing input 

prices to affect the production decisions of businesses. Input-output models assume that changes 

in an economy will affect the output of industries but not the mix of inputs that they use. Using 

the previous example, dairies respond to the increase demand for milk by simply increasing 

production of milk. Input-output models, in effect, ignore possible changes in the prices of inputs 

used to produce milk. Depending on the size of study area and the economic stimulus, an increase 

in demand for output could cause one or more input prices to increase. If the increase in demand 

for milk caused the wage of dairy workers to increase, then economic theory suggests that dairy 

farmers would have an incentive to substitute other inputs for labor. 

 Page B-6 
 Page B-7 

No Supply Constraints 

Input-output models show how local industries respond to some initial change in final demand, but 

assume that supplies of raw materials and intermediate goods are unlimited, i.e., perfectly elastic. Under 

an assumption of no supply constraints, an industry simply responds to a change in final demand by 

increasing output, and it increases output by acquiring inputs that are readily available at current prices. 

Sector Homogeneity 

An industry consists of businesses producing goods and services—these are called commodities in inputoutput modeling. Businesses can produce more than one type of commodity, i.e., they produce a primary 

commodity, but can also produce secondary commodities or by-products. 

In input-output modeling, industry sectors are assumed to be homogenous. That is, all businesses within 

an industry sector 1) produce commodities in fixed proportions and 2) produce identical commodities that 

are perfectly substitutable. Using the previous example, an increase in demand for milk will cause dairy 

farms to increase production of milk as well as other by-products such as buttermilk, cottage cheese, and 

sour cream. If the demand for milk doubled causing milk production to double, then the output of byproducts will also double. In addition, dairy farms are assumed to produce milk that is perfectly identical 

across farms. 

Input-Output Modeling—Practical Considerations 

Apart from the limitations imposed by the static nature of input-output models, there are also some very 

practical considerations that should also guide their use. These practical considerations are discussed 

below. 

Lag Between Data Collection and Modeling 

Input-output models can be constructed for almost any geographic region. Typically, their structure is 

based on a national input-output model29 that is then combined with national and regional economic data 

to tailor the model to a specific study area. However, there is often a lag between actual data collection 

and incorporation of that data into the modeling software. With this “implementation” lag, changes in the 

structure of an economy—such as improvements in technology, changes in demand, and changes in 

regional trade patterns—will affect the multipliers and make the results less reliable. Obviously, inputoutput models constructed with the most current data available will provide the most accurate results. 

Time 

Economic impacts occur over time. As discussed previously, depending on their occurrence, impacts can 

be categorized as direct, indirect, and induced. These impacts are far from instantaneous. The implications 

for impact analysis are two-fold. 

First, sometimes the effects of a large project can span several decades. The direct hires and payment of 

wages and benefits will also span that period of time. In this context, the researcher must consider the fact 

that inflation erodes purchasing power over time. If economic impacts are to be reported accurately, each 

 

29 The U.S. Bureau of Economic Analysis constructs national benchmark input-output accounts every five years. The most 

current version available is the 1997 benchmark accounts. BEA estimates that the 2002 benchmark accounts will be completed 

by the summer of 2007. 
 Page B-8 

dollar needs to be presented in terms of its economic value today. Economists must use a base year when 

conducting input-output analysis. All transactions that occur after that base year should be discounted by 

some factor to account for expected changes in the relative value of the dollar. 

The inflation assumptions that are built into an input-output analysis can have a profound impact on its 

results. Underestimating inflation by just one percent will inflate the net present value of a multi-million 

dollar project by a wide margin. 

Second, the indirect and induced impacts take time to filter through the economy. Researchers use 

economic multipliers calculated in input-output analysis as a mathematical short cut for providing an 

estimate of final impacts. These final impacts are generated as spending cycles between businesses, 

consumers, governments and foreigners. This multiplier process takes time. 

THE IMPLAN INPUT-OUTPUT MODELING SOFTWARE

One of the most common software packages used to conduct input-output analyses is IMPLAN (IMpact 

analysis for PLANning). IMPLAN was developed by the US Forest Service in cooperation with the 

Federal Emergency Management Agency and the Bureau of Land Management to assist federal agencies 

in their land and resource management planning. Since 1993, IMPLAN has been maintained and 

distributed by the Minnesota IMPLAN Group, Inc.30 Currently there are over 1,500 public and private 

users of the IMPLAN modeling software. 

Applications of IMPLAN by the US Government, public agencies, and private firms span a wide range of 

projects. Examples include new factories, resorts, proposals for developing coal mines, and harvesting 

timber. IMPLAN can also be applied to a variety of policy issues. Predicting the effects of a tourism 

marketing campaign or measuring the importance of an existing industry on a local community are 

common examples. 

The Structure of the Input-Output Model in IMPLAN 

IMPLAN uses a commodity/industry accounting framework that corresponds closely to that used in the 

Bureau of Economic Analysis “Input-Output Study of the U.S. Economy,” and those recommended by 

the United Nations. 

IMPLAN Database 

IMPLAN uses a large database of regional and national data to forecast economic activity. The main 

sources of data are: 

• US Bureau of Economic Analysis 1997 Benchmark I/O Accounts 

• US Bureau of Economic Analysis Output Estimates 

• US Bureau of Economic Analysis REIS Program 

• US Bureau of Labor Statistics Covered Employment and Wages or ES202 data 

• US Bureau of Labor Statistics Consumer Expenditure Survey 

• US Census Bureau County Business 

• US Census Bureau Decennial Census and Population Surveys 

• US Census Bureau Economic Censuses and surveys 

 

30 For additional information, see the Minnesota IMPLAN Group’s website at www.implan.com. 
 Page B-9 

• US Department of Agriculture Crop and Livestock Statistics 

• US Geological Survey 

In IMPLAN, the process that develops county-level input-output models generates coefficients that are 

internally consistent, in that county data sum to state totals and state data sum to national totals. This 

generally is not the case with survey-based input-output models, which limits their applicability to largescale projects that affect a number of interrelated regions.31

Sectors 

A sector consists of industries that produce similar products or services. IMPLAN breaks an economy 

down to 509 separate industry sectors based on the North American Industrial Classification System 

(“NAICS”). 

Final Demands 

Final demand is sum of all purchases of goods and services for final consumption within an economy. In 

the IMPLAN model, final demands are in producer prices and are allocated among industry sectors. In 

addition, final demands are adjusted or “margined” to reflect the transportation, wholesale, and retails 

costs of getting products from industries to consumers. 

The IMPLAN model has the following major categories of final demand: 

• Personal Consumption Expenditures. The largest component of final demand comes from 

household spending. Households consume a wide variety of goods and services, including food, 

energy, housing, and transportation. They also use some of their personal income to pay taxes, 

save for the future, pay debts, or purchase new housing. In IMPLAN, households are 

disaggregated by income levels to account for different spending patterns across income levels. 

• Federal Government Purchases. Government purchases are broken down into two categories: 

military and non-military. Military expenditures include any purchases made in the interest of 

national defense. Non-military expenditures include all other purchases made by the federal 

government for the remaining services it provides. 

• State and Local Government Purchases. State and local government purchases are also broken 

down into two categories: education and non-education. Spending on public education goes 

primarily to compensate teachers, but also includes things like textbooks and supplies. Noneducation spending includes anything not spent for public education such as police, fire and 

emergency services, and state-sponsored healthcare. 

• Inventory Purchases. Inventories accumulate anytime an industry fails to sell all of its output in 

a given year. Goods can be sold out of inventory any time sales exceed production. Industries 

rarely sell exactly what they produce each year, so this category is a widely used tool for 

reconciling economic activities. 

 

31 Arguably, however, an input-output model estimated from survey data has more accurate coefficients, because the survey can 

be customized to the problem at hand. In contrast, IMPLAN derives its coefficients using a combination of the national inputoutput survey model and local activity data; conceivably, this will produce somewhat different results from a direct, local survey. 

Given the difficulty and expense of input-output surveys, however, the disadvantages of the IMPLAN approach are slight. 
• Capital Formation. A large component of productive capability is capital. Industries use varying 

quantities of capital depending on the nature of goods and services they provide. The 

manufacturing sector, for example, tends to require large investments in property, plant, and 

equipment for the goods it produces. This category of final demand contains all spending on 

capital equipment. 

• Foreign Exports. Just as some economies must import goods and services from outside their 

borders, other economies sell a significant portion of their output overseas. Demand for final 

goods and services that come from beyond a region’s borders falls into this category. Although 

the consumption happens elsewhere, input-output analysis is concerned with where the goods and 

services are produced. 

Types of impacts 

IMPLAN reports economic impacts as measured by changes in output, incomes (value added), jobs and 

taxes. The value added or income measure is broken out into four categories. These measures of 

economic impacts consist are: 

• Output: The total value of the production of a sector is its output. For most sectors, output 

approximately equal to sales. The notable exceptions are government and the trade sectors. The 

output of government sectors is approximately equal to revenues. For the trade sector, which 

consists of firms that buy goods and re-sell them, output is roughly the difference between what 

they sell goods for and what they paid to procure them. The trade sector consists of wholesalers 

and retailers. 

• Value Added: This is a measure of the value added to the economy by a sector. It equals the sum 

of the wages, proprietor income, other income, and indirect business taxes. 

• Wages represent the total cash and non-cash compensation of workers on payroll. This includes 

the value of benefits. 

• Proprietor Income, sometimes called small business income, is the amount earned by selfemployed workers and the working owners of small businesses. 

• Other Income counts all other sources of income. The largest source of income is usually rents, 

but it may also include royalties, dividends, and corporate profits. 

• Indirect business taxes are the excise and sales taxes paid by individuals to businesses. 

• Employment: The total number of payroll employees, including part time workers. The selfemployed are not counted, however, their earnings are captured under proprietor income. 

• Taxes: Total Federal, state, and local tax revenues. 

Modeling

The process of modeling in IMPLAN involves three steps: 

1. Creation of study area database; 

2. Customization of IMPLAN coefficients; and 

3. Estimating the impact of an activity on the model of the study area economy. 

 Page B-10 
 Page B-11 

The IMPLAN model allows substitution and incorporation of primary data at each stage of the modelbuilding process, greatly increasing the model’s accuracy and flexibility. In addition to being able to 

directly modify the IMPLAN database statistics, the user can alter import and export relationships, utilize 

modified input-output functions, and change industry groupings. IMPLAN allows the creation of 

aggregate models consisting of industries grouped together for a specific purpose. 

The key to input-output analysis is the construction of the input-output or transactions table, which shows 

the flow of commodities from each of a number of producing industries to all consuming industries and 

final demand (ultimate consumers). Given that many industries produce more than one commodity, 

production information is often tabulated on an industry-by-commodity basis into a “Make” matrix, 

containing the value of commodities produced by different industries, and a “Use” matrix, containing the 

value of commodities used by each industry in the production process. These matrices are combined to 

produce the input-output transactions table showing each industry buying and selling from other 

industries. 

From these industry flows, two other structural tables are developed: (1) a table of technical coefficients 

or direct requirements and (2) a table of direct and indirect coefficients or total requirements. The entries 

in the former are interpreted as the dollar value of the minimal requirements from each of the contributing 

industries in order for each producing industry to produce one dollar’s worth of output. The entries in the 

latter table are to be interpreted as the amount of output from the contributing industries required, both 

directly and indirectly, to deliver one dollar’s worth of the producing industry’s output to final demand. 

Customizing the IMPLAN Coefficients 

The process of customizing the IMPLAN model does not stop with the development of the study area 

databases. Part of the expertise of input-output practitioners is in the customization of the model 

coefficients. Depending on the type of analysis, this enables the analyst to: 

• Vary structural, technological, and/or trade factors within the model. For instance, the user may 

add or remove sectors from the model, or change the size of an industry, or the user may change 

production functions, or make changes in commodity imports and exports. 

• Exclude expenditures that do not generate current economic activity, such as depreciation and 

amortization. 

• Exclude expenditures that are known to occur outside the local economy. The IMPLAN model 

contains purchasing assumptions32 for each industry sector that are specific to the study area. 

Instead of relying entirely on these purchasing assumptions, the analyst can identify and remove 

spending that is known to occur outside of local economy. 

The IMPLAN system permits a sector-by-sector breakout of transportation, wholesale, and retail margins, 

and allows the user to over-ride these margin assumptions using primary source data if available. For 

instance, instead of the estimated retail margin embedded in the IMPLAN model, the analyst can use 

actual retail margins for the activity. 

 

32 These purchasing assumptions are called “Regional Purchase Coefficients.” They specify the ability of local suppliers to meet 

or satisfy a change in demand for a good or service. 
Estimating Multipliers 

The last step in building the model is to estimate the multipliers. Multiplier analysis is used to estimate 

the regional economic impacts resulting from a change in final demand. Impacts can be in terms of direct 

and indirect effects (commonly known as Type I multipliers), or in terms of direct, indirect, and induced 

effects (Type II and Type III multipliers). 

More specifically, direct effects are production changes associated with the immediate effects of final 

demand changes. Indirect effects are production changes in backward-linked industries caused by the 

changing input needs of directly affected industries. Induced effects are the changes in regional household 

spending patterns caused by changes in household income—generated from the direct and indirect effects. 

IMPLAN calculates two types of multipliers for each of the five impact measures. The first output 

multiplier represents the value of production, from indirect and direct effects, required from all sectors by 

a particular sector in order to deliver one dollar’s worth of output. The second output multiplier adds in 

the induced requirements. The size of the multiplier is not a measure of the amount of activity or the 

importance of a given industry for the economy. It is an estimation of what would happen if that 

industry’s sales to final demand increased or decreased. In other words, output multipliers can be used to 

gauge the interdependence of sectors; the larger the output multiplier, the greater the interdependence of 

the sector on the rest of the regional economy. 

Performing Impact Analysis 

Once the input-output model is built, impact analysis can be performed on the model. There are two 

general types of impact analysis. At a very simple level, an analyst can evaluate economic changes using 

industry sectors contained in the IMPLAN model. In this case, business revenues or employment are used 

to model changes in demand in the relevant industry sector. 

In some cases, such as the entry of a new business, the industry sector may not be represented in the 

IMPLAN model of that region. This type of analysis is more complicated and requires the use of 

expenditure and revenue data to develop a spending pattern (or “production function”) that is specific to 

the activity. Under this type of analysis: 

• The direct effects are based on output, employment and personal income data supplied by the 

client. 

• The indirect effects are measured by identifying changes in output for each industry from which 

goods and services are purchased. What is specified as direct impacts in this model are more 

precisely described as the first round of indirect impacts. Subsequent rounds of indirect impacts 

occur as vendors and suppliers purchase goods and services from other businesses, who will also 

need to buy goods and services. The indirect impacts of the activity are what the IMPLAN model 

reports as direct and indirect impacts in this model. 

• The induced impacts are based, in part, on estimates of the direct personal income generated in 

production. This data is fed into a consumption function specific for that household income group 

and region. What is specified as direct impacts in this model are actually the first round of 

induced impacts, so what is reported as induced impacts are the total impacts from this model 

plus the induced impacts from the model inter-industry expenditures. 

The IMPLAN model is perhaps the most popular input-output modeling system in use today. Of course, 

any software is susceptible to the “garbage in—garbage out” phenomenon. Thus, the accuracy of its 

 Page B-12 
results is dependent on the quality of the data used in the modeling process as well as the skills of the 

analyst in conducting the analysis. 

 Page B-13 
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