Technical Report

NREL/TP-6A2-45639

July 2009

Renewable Electricity Benefits 

Quantification Methodology: 

A Request for Technical 

Assistance from the California 

Public Utilities Commission 

Gail Mosey and Laura Vimmerstedt
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

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

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-6A2-45639

July 2009

Renewable Electricity Benefits 

Quantification Methodology: 

A Request for Technical 

Assistance from the California 

Public Utilities Commission 

Gail Mosey and Laura Vimmerstedt

Prepared under Task No. IGST.8107
NOTICE

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iii

List of Acronyms

BAU business as usual

BenMAP Environmental Benefits Mapping and 

Analysis Program 

BLM U.S. Department of the Interior, 

Bureau of Land Management 

C-R concentration-response 

CARB California Air Resources Board

CEC California Energy Commission 

COBRA Co-Benefits Risk Assessment Model

CPUC California Public Utilities Commission

CSP concentrating solar power 

DOE U.S. Department of Energy 

DOER Massachusetts Division of Energy Resources 

EEPS Energy Efficiency Portfolio Standard

eGRID Emissions & Generation Resource 

Integrated Database

EIA Energy Information Administration

EPA U.S. Environmental Protection Agency 

ERC Environmental Review Commission of the 

North Carolina General Assembly

GREET Greenhouse Gases, Regulated Emissions, and

Energy Use in Transportation 

GSP gross state product 

I-937 State of Washington Initiative 937

I-O input-output 

IPM Integrated Planning Model 

IRP integrated resource plan

JEDI Jobs and Economic Development Impacts Model 

MARKAL Market Allocation Model 

MW megawatts

NE MARKAL New England Market Allocation Model

NEMS National Energy Modeling System 

NESCAUM Northeast States for Coordinated 

Air Use Management

NPV net present value 

NREL National Renewable Energy Laboratory 

NYSERDA New York State Energy Research and 

Development Authority 

O&M operations and maintenance 

PM particulate matter

PPA power purchase agreement

PV photovoltaics 

ReEDS Regional Energy Deployment System 

REMI Regional Economic Models, Inc. 
iv

RFP request for proposal 

RGGI Regional Greenhouse Gas Initiative 

RPO regional planning organization 

RPS renewable portfolio standard

TAP technical assistance project

TEPC Texas Energy Planning Council 

TREIA Texas Renewable Energy Industries Association

VOC volatile organic compound 

WRAP Western Regional Air Partnership
v 

Executive Summary

The California Public Utilities Commission (CPUC) requested assistance in identifying 

methodological alternatives for quantifying the benefits of renewable electricity. The context is 

the CPUC’s analysis of a 33% renewable portfolio standard (RPS) in California—one element of 

California’s Climate Change Scoping Plan.1

Economic Impact

 The information would be used to support 

development of an analytic plan to augment the cost analysis of this RPS (which recently was 

completed). The National Renewable Energy Laboratory (NREL) has responded to this request 

by developing a high-level survey of renewable electricity effects, quantification alternatives, 

and considerations for selection of analytic methods. This report addresses economic effects and 

health and environmental effects, and provides an overview of related analytic tools. Economic 

effects include jobs, earnings, gross state product, and electricity rate and fuel price hedging. 

Health and environmental effects include air quality and related public-health effects, solid and 

hazardous wastes, and effects on water resources.

The implementation of an RPS typically has associated economic impacts that include jobs, 

earnings, and gross state product (GSP). Some industries are impacted more directly than others, 

depending on the source of materials and labor used in the construction and operation of a 

renewable energy power-generation facility, but those direct impacts typically filter through the 

economy through indirect2 and induced3 impacts. Assuming that an RPS works as intended, the 

economic development impacts can vary by state depending on how property taxes, income 

taxes, and excise taxes figure into the state’s fiscal mix.4

Two important considerations for measuring economic impact within a state are whether the RPS 

allows for the eligibility of out-of-state renewable resources, and the share of in-state resources 

(local share) used for manufacturing of materials and the construction and operation of the 

renewable-energy facilities. These factors help determine how much of the economic impact will 

occur in-state vs. out-of-state. State policy makers might have to prioritize between local 

economic growth and minimizing ratepayer impacts if the most cost-effective renewable 

resources are in a neighboring state. Extending RPS eligibility to out-of-state resources can 

reduce ratepayer impacts, but it involves letting another state realize the local economicdevelopment benefits associated with those resources (Hurlbut 2008).

 

 

1 The California Climate Change Proposed Scoping Plan was approved at the December 12, 2008, meeting of the California Air 

Resources Board. The document still is titled “Climate Change Proposed Scoping Plan,” but now is considered final. The word 

“Proposed” consequently was dropped from the document’s name as used in this document. The document is available at 

http://www.arb.ca.gov/cc/scopingplan/document/scopingplandocument.htm (accessed April 22, 2009). 2 Indirect impacts refer to the changes in inter-industry purchases resulting from changes in direct final demand (i.e., purchases of 

goods and services).

3 Induced impacts refer to the changes that occur in household spending as household income increases or decreases as a result of 

the direct and indirect effects from changes in direct final demand (i.e., purchases of goods and services).

4 A comprehensive assessment is beyond the scope of this analysis, but anecdotal results are indicative of what can happen. The 

West Texas community of McCamey, where Texas’ first wind boom took place in 2001 and 2002, is one example. In 2000, the 

local property-tax base funding the McCamey Independent School District was just more than $328 million in total assessed 

valuation. By 2006, with 760 MW of new wind power in operation on nearby mesas, the tax base had increased by more than 

$1 billion. Additionally, taxable sales receipts in the area doubled from 2002 to 2005. See School and Appraisal Districts’ 

Property Value Study, various years, available at http://www.cpa.state.tx.us/taxinfo/proptax/ (accessed April 23, 2009). 
vi 

The current body of analysis includes an assortment of studies, both on the state and national 

RPS level, which estimate economic impacts of an RPS. Many of these studies include economic 

impact as a component of a bigger picture to measure overall impacts. The studies can be divided 

into two main types: (1) those that use input-output (I-O) models of the economy; and (2) those 

that use simpler, largely spreadsheet-based analytical (in some cases econometric) models 

(Kammen et al. 2004). A simplifying assumption in several of the models is to attribute all 

renewable energy development over the impact period to the RPS rather than attempting to factor 

out the development that would have occurred without an RPS. 

There is a wide variety of approaches to take in evaluating the economic impact of an RPS. 

Analysis methodology can range from creating a spreadsheet to employing several discrete 

models evaluating specific impacts or even to the development of a more comprehensive, 

complex model. There are several considerations when deciding on an approach, including the 

key impacts to measure, data availability, and important considerations of the analysis (e.g., 

gross versus net impacts; in-state (local share) versus out-of-state renewable resources and 

manufacturing availability to meet RPS goals; and the allocation of renewable energy 

development attributed to the RPS). 

Health and Environmental Effects

Renewable electricity development to meet an RPS will have a variety of health and 

environmental effects including changes in releases of pollutants to the air, water, or land; and 

the health or environmental implications of those releases. Among the many health and 

environmental effects, methods to quantify health effects of air pollution are especially welldeveloped. 

Our overview of the growing set of state-level analytic studies on health and environmental 

effects of renewable electricity generation in the United States reveals that diverse methods have 

been used, including a number of different computer models. Different methods and models have 

distinct advantages and disadvantages, and the choice of a particular method or model will 

depend on the purpose of the study. 

Development of the scope of an analysis of health and environmental effects of an RPS is a 

significant undertaking. The scope must be tailored to the primary purpose of the study. Scoping 

decisions include selection of the analytic period, spatial and temporal scale, technology life 

cycle, and electric generation scenarios. The study must focus on selected environmental releases 

and effects of those releases.
vii 

Table of Contents

List of Acronyms .......................................................................................................................... iii

Executive Summary ...................................................................................................................... v

Table of Contents ........................................................................................................................ vii

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

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

I. Introduction ............................................................................................................................... 1

II. Background .............................................................................................................................. 1

California .................................................................................................................................... 2

III. Economic Impact .................................................................................................................... 2

IV. Electricity Rates and Fuel Price Hedging ............................................................................ 4

Models Employed in the Studies ................................................................................................ 5

Economic Summary .................................................................................................................. 17

V. Health and Environmental Effects ....................................................................................... 18

California .................................................................................................................................. 21

United States Department of Energy ........................................................................................ 23

Northeast States for Coordinated Air Use Management ........................................................... 25

Regional Greenhouse Gas Initiative ......................................................................................... 25

Texas ......................................................................................................................................... 27

VI. Overview of Analysis and Effects ........................................................................................ 28

Step 1. Establish Scope ............................................................................................................. 29

Step 2. Estimate Generation Sources ........................................................................................ 30

VII. Air Quality: Air Emission, Air Quality, and Health and Environmental Effects......... 31

Step 3. Estimate Emissions ....................................................................................................... 31

Step 4. Estimate Concentrations ............................................................................................... 33

Step 5. Estimate Health and Environmental Outcomes ............................................................ 33

Solid Waste / Hazards and Hazardous Materials ...................................................................... 34

Water Resources ....................................................................................................................... 35

Land Use ................................................................................................................................... 36

Health and Environmental Effects Summary ........................................................................... 37

VIII. Conclusions and Recommendations ................................................................................ 37

References .................................................................................................................................... 38

Appendix: Bibliography of RPS Studies Described in Economic and Fuel Diversity Sections

 .............................................................................................................................................. 42
viii

List of Figures

Figure 1. States that have renewable portfolio standards ............................................................... 2

Figure 2. Sample results ................................................................................................................ 26

List of Tables

Table 1. Sampling of Studies that Address Economic Impacts from an RPS ................................ 4

Table 2. Sampling of Studies that Address Fuel Price and/or Electricity Price Hedging Impacts of 

an RPS .................................................................................................................................... 5

Table 3. Sampling of Models Employed in Studies Profiled .......................................................... 5

Table 4. Select Jobs and Earnings Results from NYSERDA Study ............................................. 12

Table 5. Cumulative Impacts for Construction and Operation Periods, RPS Versus BAU 

Portfolios from Black and Veatch Study .............................................................................. 13

Table 6. Comparison of Benefits, Texas RPS Proposals (More Likely Scenario) ....................... 14

Table 7. Summary of State Health and Environmental Analyses of RE Generation ................... 20

Table 8. Scoping Plan Appendix H, Table H-4 ............................................................................ 22

Table 9. Scoping Plan Appendix H, Table H-7 ............................................................................ 22

Table 10. Scoping Plan Appendix H, Table H-8 .......................................................................... 23

Table 11. New Jersey Clean Energy Program Sample Results .................................................... 24

Table 12. Illinois Evaluation Results ............................................................................................ 25

Table 13. Comparison of Various Models .................................................................................... 27

Table 14. California Energy Commission’s 2007 Environmental Performance Report of 

California’s Electrical Generation System, Summary Table on Cooling Water .................. 36

Table 15. Additional Analysis Questions ..................................................................................... 37
1 

I. Introduction

The California Public Utilities Commission (CPUC) submitted a Technical Assistance Project 

(TAP) Request for national laboratory assistance to the U.S. Department of Energy (DOE), 

identifying different options for methods of quantifying the benefits of renewable electricity. The 

core of this request is as follows.

TAP Request: Develop a methodology to quantify the benefits of an RPS 

[renewable portfolio standard]. We already have a methodology to quantify 

avoided costs of natural gas fuel purchases and avoided investments in fossil 

infrastructure. We would like to develop a methodology to quantify other 

benefits, such as fuel diversity/hedging value, health benefits/avoided criteria 

pollutants, other environmental benefits, and economic development (even though 

that isn’t technically a benefit). We would like this work to be within the context 

of the current cost analysis CPUC staff are for a 33% RPS (you can view our 

work plan here: http://www.cpuc.ca.gov/NR/rdonlyres/1C8F102F-53A6-486EA11E-BAD5DA3FBB9B/0/33_Percent_by_2020_RPS_Draft_Workplan.DOC).

Goal: The near-term goal is to evaluate the additional benefits of the CA RPS so 

that the current cost-benefit analysis will be more inclusive of all the benefits. The 

long term goal is to guide decision-makers on the appropriate RPS targets. (CPUC 

2009) 

The California Public Utilities Commission intends to use this information to develop a 

methodology for a more comprehensive analysis of a 33% renewable portfolio standard (RPS), 

extending beyond the cost analysis to include other effects. The increase to 33% from a 20% 

RPS is part of California’s Climate Change Scoping Plan (California Air Resources Board 2008). 

In this report, the NREL has responded to CPUC’s request with a high-level survey of renewable 

electricity effects, quantification alternatives, and considerations for selection of analytic 

methods. This report does not select or implement analytic methods. Section III addresses 

economic effects: jobs, earnings, and gross state product. Section IV addresses electricity and 

fuel price hedging. Section V addresses health and environmental effects that include air quality 

and related public health effects, solid and hazardous wastes, and effects on water resources.

II. Background

State renewable portfolio standards have emerged as one of the most important policy drivers of 

renewable energy capacity expansion in the United States (Wiser et al. 2007). Collectively, these 

state policies now apply to roughly 50% of U.S. electricity load, and hold the prospect of having 

substantial impacts on electricity markets, ratepayers, and local economies. Renewable portfolio 

standards require that a minimum amount of renewable energy is included in each retail 

electricity supplier’s portfolio of electricity resources. This is accomplished by establishing 

numeric targets for renewable energy supply, which generally increase over time. To date, 28 

U.S. states, along with the District of Columbia, have adopted such standards (Figure 1) (Chen et 

al. 2008). 
2 

Figure 1. States that have renewable portfolio standards

California

California’s RPS was established in 2002 under Senate Bill 1078 and was accelerated in 2006 

under Senate Bill 107. It is one of the most ambitious renewable energy standards in the country. 

The RPS program requires electric corporations to increase procurement from eligible renewable 

energy resources by at least 1% of their retail sales annually, until they reach 20% by 2010. The 

California RPS considers wind, solar, biopower, small hydro (less than 30 MW), geothermal, 

ocean wave, ocean thermal, and tidal current as eligible power-generation sources and, to be 

counted in the RPS, the power must be actual energy delivered. The CPUC and the California 

Energy Commission (CEC) recommend 33% renewables by 2020 as a key strategy to reducing 

greenhouse gas emissions, and the governor’s Executive Order S-14-08 (California State 2008) 

made it an official goal for state agencies.

III. Economic Impact

The implementation of an RPS typically has associated economic impacts that include jobs, 

earnings, and gross state product (GSP). Some industries are impacted more directly than others, 

depending on the source of materials and labor used in the construction and operation of a 

renewable energy power-generation facility, but those direct impacts typically filter through the 

economy through indirect5 and induced6

 

5 Indirect impacts refer to the changes in inter-industry purchases resulting from changes in direct final demand (i.e., purchases of 

goods and services).

impacts. Assuming that an RPS works as intended, the 

6 Induced impacts refer to the changes that occur in household spending as household income increases or decreases as a result of 

the direct and indirect effects from changes in direct final demand (i.e., purchases of goods and services).
3 

economic development impacts can vary by state depending on how property taxes, income 

taxes, and excise taxes figure into the state’s fiscal mix.7

Two important considerations for measuring economic impact within a state are whether the RPS 

allows for the eligibility of out-of-state renewable resources, and the share of in-state resources 

(local share) used for manufacturing of materials and the construction and operation of the 

renewable-energy facilities. In either case, economic-impact leakages to other states could occur. 

State policy makers might have to prioritize between local economic growth and minimizing 

ratepayer impacts if the most cost-effective renewable resources are in a neighboring state. 

Extending RPS eligibility to out-of-state resources can reduce ratepayer impacts, but it involves 

letting another state realize the local economic-development benefits associated with those 

resources (Hurlbut 2008).

 A study of Pennsylvania, for example, 

found that if an RPS succeeded in increasing renewable energy use to 10% of total electricity 

demand by 2025, the impact on jobs, income, and economic output would be significantly 

greater than that expected if load growth and unit retirements during that same time instead were 

met by fossil-fuel expansion (Pletka et al. 2004).

The current body of analysis includes an assortment of studies, both on the state and national 

RPS level, which estimate economic impacts of an RPS. Many of these studies include economic 

impact as a component of a bigger picture to measure overall impacts. The studies can be divided 

into two main types: (1) those that use input-output (I-O) models of the economy; and (2) those 

that use simpler, largely spreadsheet-based analytical (in some cases econometric) models 

(Kammen et al. 2004). A simplifying assumption in several of the models is to attribute all 

renewable energy development over the impact period to the RPS rather than attempting to factor 

out the development that would have occurred without an RPS. 

Several studies which specifically address economic impacts are listed in Table 1. Some of the 

newer, more comprehensive studies are described below for consideration in developing a 

California-specific methodology (full references to these studies can be found in the bibliography 

presented in Appendix A) (Chen et al. 2008). In the description below, some background is 

provided to frame the discussion, high-level results are presented, and analytical methods are 

described. In most cases these descriptions are taken directly from the respective report. Some 

descriptions are more detailed than others depending on relevance and availability of 

information. Assumptions for local share are addressed when available because of their direct 

relevance to the question posed by the TAP, but other assumptions are not discussed as they vary 

widely among the studies, the states and the RPS structure. The purpose of this section is to 

present different methodological approaches and give examples of results. The results reported in 

these studies are not directly comparable because of the varying assumptions and methodologies 

of each study. In developing an approach to evaluate the impacts of an RPS in California, it will 

be of key importance to review the studies in more depth to determine the direction, 

assumptions, and approach for a California-specific methodology. 

 

7 A comprehensive assessment is beyond the scope of this analysis, but anecdotal results are indicative of what can happen. The 

West Texas community of McCamey, where Texas’ first wind boom took place in 2001 and 2002, is one example. In 2000, the 

local property-tax base funding the McCamey Independent School District was just more than $328 million in total assessed 

valuation. By 2006, with 760 MW of new wind power in operation on nearby mesas, the tax base had increased by more than 

$1 billion. Additionally, taxable sales receipts in the area doubled from 2002 to 2005. See School and Appraisal Districts’ 

Property Value Study, various years, available at http://www.cpa.state.tx.us/taxinfo/proptax/ (accessed April 23, 2009). 
4 

Table 1. Sampling of Studies that Address Economic Impacts from an RPS

State Produced by

Publish 

Year Jobs

Gross 

State 

Product 

(GSP) Income

Reviewed 

Below?

AZ AZ PIRG Education Fund 1998 x x x No

AZ American Solar Energy Society 2005 x x No

CO Union of Concerned Scientists 2004 x x x Yes

MI Next Energy 2007 x x x No

NC La Capra, GDS and Sustainable 

Energy Advantage

2006 x Yes

NE Union of Concerned Scientists 2001 x x x No

NH University of New Hampshire 2007 x No

NJ The State University of NJ 

(Rutgers)

2004 x x x No

NY KEMA, Inc. and Econ. 

Development Research Group

2008 x x x Yes

PA Black and Veatch 2004 x x x Yes

TX Union of Concerned Scientists 2005 x x Yes

WA Union of Concerned Scientists 2006 x x x Yes

WI Union of Concerned Scientists 2006 x x x Yes

IV. Electricity Rates and Fuel Price Hedging

Two types of hedging or risk mitigation are addressed in the literature most prominently: 

electricity rate and fuel price hedging. Although the existing body of analysis is not as robust for 

hedging impacts from an RPS as for economic impact, there are several studies which address 

the topic (listed in Table 2). Several of the studies which cover electricity or fuel price hedging 

are the same as those that cover economic impacts, and are discussed below as noted in Table 2. 

An additional study—which does not address economic impact but does estimate a hedge 

adder—includes a natural gas price adder to correct for “downward bias” of EIA forecasts and 

also models electric rate impacts of “NG price spike” sensitivity scenario. This study was 

conducted for Colorado by Public Policy Consulting and also is described below. 
5 

Table 2. Sampling of Studies that Address Fuel Price and/or 

Electricity Price Hedging Impacts of an RPS

State Produced by

Publish 

Year

Wholesale 

price

Gas 

price

Hedge 

adder

CO Public Policy Consulting* 2004 x

CO Union of Concerned Scientists* 2004 x x

NH University of New Hampshire 2007 x x

NY Center for Clean Air Policy 2003 x x

NY NY State Department of Public 

Service, Sustainable Energy 

Advantage and La Capra Associates

2004 x

NY ICF Consulting 2003 x x 

NY Potomac Economics 2005 x

PA Black and Veatch* 2004 x

TX Union of Concerned Scientists* 2005 x x

*Discussed further below

Models Employed in the Studies

Several different models were employed in the studies profiled. Table 3 lists some of the models 

and includes a description and some pros and cons for each model. 

Table 3. Sampling of Models Employed in Studies Profiled

Model 

Name Availability Model Description Pros and Cons

IMPLAN 

(Impact 

Planning) 

Software available for 

purchase through 

MIG & Associates 

(http:// 

www.implan.com/) 

IMPLAN is an economic impact assessment software system. IMPLAN allows the 

user to develop models of local economies 

to estimate a wide range of economic 

impacts from a one-time or sustained 

increase in economic activity in a given 

geographic area.

Pro: Effective and widely used tool to 

estimate economic impact.

Cons: Learning curve associated with 

the software; cost associated with 

acquiring the software. Must use this 

software with an estimate of the 

increase in economic activity in a 

particular industry.

JEDI

(Jobs and 

Economic 

Development 

Impact) 

Available for free 

download at http:// 

www.nrel.gov/ 

analysis/jedi/ 

about_jedi.html

JEDI is an input-output model which 

estimates the economic impact—including 

jobs, earnings, and output—to the 

economy from the construction and 

operation of a renewable energy powergenerating facility. There are also versions 

of JEDI for biofuels facilities. JEDI 

estimates project development and on-site 

impacts, manufacturing and supply chain 

impacts, and induced impacts.

Pros: Stand-alone, user-friendly, Excelbased model is available for free 

download. Minimal learning curve 

associated with learning how to use 

the model. Provides default cost input 

assumptions which user easily can 

override. Local share can be explicitly 

stated as a model input. Output is 

clear and concise.

Cons: Renewable energy–project 

specific; assumptions must be made

about the ramp-up of particular 

renewable energy technologies and 

the related size of facilities to be 

constructed. Reports gross, rather 

than net, impacts.
6 

Model 

Name Availability Model Description Pros and Cons

NEMS 

(National 

Energy 

Modeling 

System)

For more information 

see http:// 

www.eia.doe.gov/ 

oiaf/aeo/ overview/

NEMS is a computer-based, energyeconomy modeling system of U.S. energy 

markets for the midterm period through 

2025. NEMS projects the production, 

imports, conversion, consumption, and 

prices of energy. The results are subject to 

assumptions on macroeconomic and 

financial factors, world energy markets, 

resource availability and costs, behavioral 

and technological choice criteria, cost and 

performance characteristics of energy 

technologies, and demographics. NEMS 

was designed and implemented by the 

Energy Information Administration (EIA) of 

the U.S. Department of Energy (DOE).

NEMS is a model run by DOE and 

output is made available in the Annual 

Energy Outlook. Output from NEMS can 

be used as inputs for economic 

modeling. 

Pros: Comprehensive framework for 

modeling energy usage across all 

sectors in the United States.

Cons: Does not include sufficient 

geographic detail to model many 

issues of renewable electricity 

generation. Extreme cases with high 

renewable electricity penetration are 

difficult to model.

RIMS 

(Regional 

InputOutput 

Modeling 

System)

Software available for 

purchase through 

Bureau of Economic 

Analysis 

(http://www.bea.gov/ 

regional/rims) 

RIMS II multipliers attempt to estimate how 

much a one-time or sustained increase in 

economic activity in a particular region will 

be supplied by industries located in the 

region. RIMS II provides six types of 

multipliers: Final-demand multipliers for 

output, earnings, employment, and value 

added; and direct-effect multipliers for 

earnings and employment. The multipliers 

can be ordered for any user-defined region 

or for any RIMS II industry.

IMPLAN and RIMS are multiplier-based 

models so the pros and cons for 

IMPLAN apply to RIMS.

Colorado (2004) (Electricity Rate Hedging Only)

Public Policy Consulting (PPC) prepared this report to estimate the effect that an RPS will have 

on the retail rates of the affected Colorado utilities. Using the RPS requirements contained in 

HB 1273, introduced in the 2004 Colorado General Assembly, the report first estimates the 

amount of renewable energy that would be obtained by Colorado’s two investor-owned utilities 

over the next 20 years. The analysis then compares the cost of renewable energy with new fossilfueled generation to estimate the effect that the RPS requirement will have on retail electric rates.

The report examines the degree to which renewable resources can act as a hedge against price 

fluctuations is the natural gas market. PPC found that renewable energy sources can save 

consumers money by acting as a “hedge” against spikes in natural gas prices. Renewable sources 

such as wind will result in consumer savings of $0.52 to $0.75 per month (in addition to other 

savings) in years when natural gas prices spike as they did in 2000 and 2003. 

In preparing this report PPC employed two methodologies. First, it conducted extensive research 

of the research literature on energy-price forecasts, on renewable portfolio standards (including 

studies from other states that estimated the rate impact of an RPS), and on the current state of 

generation technologies. Next, the 20-year future of retail electric prices for Colorado’s investorowned utilities were modeled, estimating the avoided cost against which the estimated future 

costs of renewable energy can be compared. The report computes the difference between the 

future price of renewables (assumed to be wind power, for analytic purposes) and the future cost 

of electricity produced with fossil fuels (assumed to be advanced combined-cycle natural gasfired generation). 

In addition to offering point estimates of the impact of renewable portfolio standard on retail 

electric rates, the report also examines the sensitivity of the analysis to certain key variables, 

including the cost of natural gas and changes in federal tax policy. Finally, the report simulates 
7 

the change in electric prices caused by spikes in natural gas prices, similar to those that occurred 

in 2000 and 2003. This last analysis enables measurement of the “hedge” value of some 

renewable resources. 

Colorado (2004)

Union of Concerned Scientists (UCS) evaluated Colorado’s Amendment 37 in 2004. At that 

time, Colorado’s Amendment 37 provided the first opportunity for citizens in any state to vote on 

these policies directly. Amendment 37 passed overwhelmingly, and established a renewable 

energy standard requiring the state’s utilities with more than 40,000 customers to generate or 

acquire renewable energy equal to at least 3% of retail sales by 2007, increasing to 6% in 2011 

and to 10% in 2015, and then remaining at 10% each year thereafter.

8 The amendment also 

established a funding mechanism for solar energy, giving a rebate to building owners who install 

solar energy systems. This is similar to funding mechanisms established in many of the state 

renewable energy funds.9

Under the most-likely scenario that primarily utilizes renewable-energy technology cost 

projections from the Department of Energy’s national labs, UCS found that by 2025 Amendment 

37 would result in the following economic benefits for Colorado. 

• $236 million in savings on consumer electricity and natural gas bills resulting mostly 

from reduced natural gas demand and reduced natural gas prices resulting from the 

increase in renewable energy availability.

• 2,000 new jobs in manufacturing, construction, operation, maintenance, and other 

industries. 

• $70 million in additional income and $50 million increase in gross state product. 

• $709 million in new capital investment. 

• $15 million in income to rural landowners from wind power land leases. 

• $107 million in new property tax revenues for local communities.10

For the macroeconomic analysis, UCS used the Impact Analysis for Planning (IMPLAN)11

model and specific data on Colorado’s economy to estimate the macroeconomic impacts 

(employment, income, gross state product) of the Colorado renewable energy standard.12

 

8 The full text of the renewable energy standard ballot initiative can be viewed at 

MRG 

& Associates conducted this portion of the analysis. IMPLAN is an input-output (I-O) model that 

identifies interactions between all sectors of the economy. Input-output models can show how 

expenditures for installing, manufacturing, operating, and maintaining renewable energy 

technologies and related equipment not only directly benefit the industries engaged in these 

activities, but also indirectly benefit businesses that provide inputs (i.e., goods, services) to these 

industries. This type of model also can show the benefits of workers spending the income earned 

http://www.dsireusa.org/documents/Incentives/CO26R.htm (accessed April 23, 2009). 9

 Fifteen states have enacted renewable energy funds which provide financial resources for renewable energy development. 10 Results are in cumulative net present value in 2002 dollars, using a 7% real discount rate. Job results are for the year 2020. 11 IMPLAN is a software program which can be purchased from MIG & Associates. For more information, visit 

http://www.implan.com/ (accessed April 23, 2009). 12 The analytical approach used in this analysis is similar to that used by Geller, DeCicco, and Laitner (Energy Efficiency and Job 

Creation, 1992, Washington DC: American Council for an Energy-Efficient Economy). 
8 

from these direct and indirect activities, and the impact of changes in consumer energy bills. 

There were four main steps to completing the macroeconomic analysis. 

• First, total expenditures were estimated for installing, manufacturing, operating, and 

maintaining renewable energy technologies that are projected to be developed to meet the 

Colorado renewable energy standard and for coal and natural gas power plants that would 

have been developed without the standard. 

• Second, the expenditures were broken down and allocated to the industries that would 

directly supply the equipment, labor, and services for these technologies. 

• Third, the detailed expenditures were multiplied by the estimated local share of 

equipment, labor, and services that could be supplied by Colorado businesses and 

matched to the appropriate sectors in the IMPLAN model to calculate the direct and 

indirect macroeconomic impacts in Colorado. 

• Finally, the macroeconomic impacts of changes in consumer energy bills in Colorado 

were calculated. 

The key assumptions and data sources for the macroeconomic analysis include the following. 

• The expenditure breakdown for the construction and operation and maintenance of 

renewable and conventional power plants was based on data from actual projects 

collected from a variety of sources, including state and federal agencies, renewable 

energy developers, and utilities. The expenditure breakdown and local share data on wind 

projects—the technology that benefits most under the renewable standard13—was based 

on inputs used in NREL’s Jobs and Economic Development Impacts (JEDI) Model.14

• The Union of Concerned Scientists used data from the IMPLAN model to estimate the 

local share of expenditures for specific industries, with a few exceptions. It assumed that 

33% of the manufacturing for the wind and solar technologies installed in Colorado 

would be produced by businesses located in the state. It does not include any jobs or 

economic development from Colorado manufacturers exporting equipment to other states 

or countries. If Colorado is able to attract renewable energy manufacturers to produce 

equipment for facilities in the state and for export, then the jobs and income from the 

standard would increase significantly.

• Additionally, it was assumed that 25% of the fuel expenditures for coal and 50% of the 

fuel expenditures for natural gas are spent on Colorado-based resources and therefore 

stay in Colorado. This is based on data from EIA (EIA 2009a) and a recent study by 

NREL (Tegen 2004). 

North Carolina (2006)

At its January 24, 2006, meeting, the Environmental Review Commission (ERC) of the North 

Carolina General Assembly requested that the North Carolina Utilities Commission

(Commission) undertake a review of the potential costs and benefits of enacting an RPS in North 

Carolina. The Commission retained a team of consultants consisting of GDS Associates, Inc., 

 

13 Considering the wind resource in Colorado, in areas having the best wind resources wind energy can compete on a lowest-cost 

resource basis with fossil fuels on a long-term basis. 14 For more information about the JEDI model, see http://www.nrel.gov/analysis/jedi/about_jedi.html. 
9 

Sustainable Energy Advantage, LLC, and La Capra Associates, Inc. (La Capra Team). As 

discussed in detail below, the key findings of the analysis are as follows. 

• North Carolina should have sufficient renewable resources within the state to meet a 5% 

RPS requirement for new renewable generation. A 5% RPS would increase average retail 

electricity rates by less than 1% and would be accompanied by net job-creation and 

property-tax benefits.

• The state would have difficulty meeting a more-aggressive 10% RPS with only new 

renewable resources located within North Carolina. A 10% RPS focused solely on 

generation supply only would be achievable by the inclusion of larger hydroelectric 

generation and the development of wind in both the western part of the state and at offshore locations. A 10% RPS met only with new renewable generation would increase 

average retail electricity rates by at most 3.6% in the tenth year.

• Inclusion of energy efficiency as an eligible RPS resource in addition to larger 

hydroelectric generation and wind in the western part of the state would enable the state 

to achieve a 10% RPS and could dramatically reduce the cost of an RPS. If energy 

efficiency was permitted to comprise 25% of an expanded resources RPS portfolio, for 

example, both a 5% RPS and a 10% RPS reasonably could be expected to produce total 

electric cost savings for consumers of about half a billion dollars over 20 years.15

A number of steps were necessary to establish the framework of the analysis. These steps include 

the following. 

• Establish different policy scenarios to investigate, and include different RPS targets and 

eligible resources. Issues that were addressed in designing appropriate scenarios 

included:

o The treatment of existing resources;

o The time frame to be covered by the study; 

o The RPS target or targets to model; and 

o The types of resources that should be included. 

• Estimate resource potential (renewable energy and energy efficiency) within the state and 

the costs associated with each type of resource.

• Develop renewable resource supply curves, assuming that most suppliers would not be 

utility-owned facilities but instead would be contracted through long-term power 

purchase agreements (PPAs). The expectation is that lower-cost resources will be 

developed first. 

• Estimate North Carolina’s future electric-supply expansion needs based on the state’s 

utility-filed integrated resource plans (IRPs). This is called the utility’s portfolio.

• From the supply curves, determine the mix of resources (renewable, energy efficiency, 

conventional generation) that would fulfill each of the RPS scenarios, and meet future 

capacity and energy growth. 

 

15 This is calculated in net present value (NPV) over 20 years using a discount rate of 10%.
10 

• Compare the costs of the alternative RPS portfolios with that of the utilities’ portfolios. 

• Conduct similar comparisons for sensitivity tests.

New York (2008)

As an aside, this recent report by KEMA, Inc. and Economic Development Research Group for 

New York State Energy Research and Development Authority (NYSERDA) seems to be the 

most comprehensive with regard to economic impact analysis of the reports discussed herein, 

considering direct and indirect impacts as well as impacts associated specifically with various 

renewable technologies on the facility level.

This report provides an assessment of the total economic benefits that result from NYSERDA’s 

Renewable Portfolio Standard Main Tier program. The analysis used a discrete set of economic

measures that covered the facility’s construction phase and over the life of the facility. The construction phase was estimated to last for three years (herein referred to as short term) and the 

“over the life of the facility” phase was estimated to last 20 years (herein referred to as long

term).

The economic benefits or effects of these measures were analyzed at two levels—the direct 

benefits or effects in the economy resulting from the facilities were calculated, and the indirect or 

multiplier effects were modeled (using an IMPLAN I-O model) throughout other sectors of the 

economy. These benefits include direct project benefits as well as the indirect—or multiplier 

induced16

• First three competitive solicitations (RFP 916, RFP 1037, RFP 1168) 

—benefits to New York’s economy. The results were modeled for the three scenarios. 

One scenario covered the progress to date of the RPS Main Tier and Maintenance Resources 

Program, as well as two potential scenarios for NYSERDA’s Main Tier1 RPS Program. The 

analysis interval was extended over the 20-year life of a facility. The three scenarios are as 

follows. 

• 25% RPS goal by 2013 using the post-energy efficiency portfolio standard (EEPS)

(NYPSC 2008)17

• 30% RPS goal by 2015 using the post-EEPS load forecast

 load forecast

Direct benefits reported by developers of wind-, hydropower-, and biofuels-generation facilities

include both short-term and persistent long-term impacts. Short-term impacts primarily result

from construction jobs and compensation to municipalities, abutting property owners, and some 

others. Long-term impacts are jobs tied to facility operations and maintenance (O&M); state and 

municipal revenues (as taxes or payments in lieu of taxes); payments to land owners for land 

leases; fuel purchases for biofuel facilities; in-state spending on equipment, supplies, and 

services; and other annual O&M expenses.

The economic measures were estimated and reported by the developers in response to the second 

and third solicitations issued by NYSERDA for the RPS Main Tier program. KEMA, Inc. 

 

16 NYSERDA’s reference to indirect impact is different from that used by I-O models. For the purposes of the NYSERDA 

analysis, the indirect impacts include both the wage-spending effects (termed induced), and the supplier transaction (the 

traditional definition of indirect) effects.

17 In June 2008, New York enacted an Energy Efficiency Portfolio Standard that seeks to reduce projected energy use by 15% by 

2015 (or “15 x 15”).
11 

verified the estimates’ accuracy, assessed them for credibility, and extrapolated economic 

measures for facilities from the first competitive solicitation (which lacked developers’ estimates

of economic benefits). The direct and indirect effects were calculated for the short-term and 

long-term economic measures.

Short-term measures were:

• Jobs lasting up to three years such as construction, planning, and engineering; 

• Payments to municipalities that do not persist over the life of the facility;

• Payments to abutting landowners or others that could be affected by the facility but which

are not receiving payments from hosting the facility on their land; and 

• Initial equipment or one-time capital expenditures (such as turbines or repowered 

upgrade equipment). 

Long-term measures, which are tied to the life of the facility’s operations, include: 

• Payroll; 

• Number of jobs and their duration (described as job years)

18

• Taxes or payments in lieu of taxes to state and municipalities; 

; 

• Fuel purchases (for biofuels); 

• Land leases; and

• Other O&M in-state spending on equipment, supplies, and services. 

Select jobs and earnings results are presented in Table 4. The results shown in Table 4 confirm 

that energy-sector jobs are well paying (the average reflects the initial presence of construction 

payroll as well). Note that the lesser average annual compensation per job among the indirect 

jobs created reflects, in part, the large role that household spending (by RE facility workers 

spending their wages in New York) exerts in the added economic value. Households tend to 

purchase goods and services from lower-wage sectors, such as retail.

 

18 The reference to job years is customary when discussing employment changes over a time span. Three construction jobs that 

are in effect for one year are the equivalent of three job years. A single job that continues for three years also represents three job 

years.
12 

Table 4. Select Jobs and Earnings Results from NYSERDA Study

First 3 

Solicitations 25% by 2013 30% by 2015

Direct Annual Jobs Created in NYS from Main Tier RPS

Short-term Jobs 677 857 1,764

Long-term Jobs 223 279 600

Average Annual Over Facility Life Worker Compensation

Direct Job Years 6,492 8,298 19,607

Direct Payroll $501,788,643 $635,533,210 $1,481,422,272 

Avg. Compensation per Direct 

Job $77,293 $76,589 $75,556 

Indirect Job Year Impact 16,184 20,230 45,201

Indirect Payroll Impact $860,000,000 $1,070,000,000 $2,331,000,000 

Avg. Compensation per Indirect 

Job $53,139 $52,892 $51,570 

Total Job Years 22,676 28,528 64,808

Pennsylvania (2004)

Black and Veatch analyzed the potential economic impacts of renewable energy development in 

Pennsylvania spurred by a RPS. These impacts include direct and indirect differences in the jobs, 

income, and gross state output associated with the alternative expansion plans. The model used 

in the study is the Regional I-O Modeling System (RIMS II model), developed and maintained 

by the Bureau of Economic Analysis.19

To estimate the direct impact that an RPS would have on electricity costs, an economic model 

was constructed to measure the 20-year (2006–2025) cost of providing 10% of the electricity

consumed in Pennsylvania from renewable energy sources. This involved comparing the cost of 

generating electricity under the RPS with the costs that would be avoided (avoided fuel, O&M 

costs, capacity costs) due to the RPS. In essence, these avoided costs represent the benefit or 

value of the RPS. Ignoring secondary costs and benefits, the avoided costs represent the 

maximum that consumers could pay for electricity and still be no worse off than in the BAU 

case. In other words, if the costs of the RPS are less than the BAU avoided costs resulting from 

the program, it is an indication that the RPS would have direct cost-of-power benefits to

consumers as compared to a BAU case. Conversely, if the RPS costs are greater than the BAU

avoided costs, it is an indication that the direct cost of the RPS does not result in direct electricity 

savings to consumers, although the RPS program still could be beneficial when secondary costs 

and benefits are considered.

 The study developed a hypothetical least-cost portfolio 

of renewable energy technologies that would likely be developed to meet the RPS requirement 

that 10% of energy be supplied from new renewable energy by 2015. The study analyzed two 

scenarios: Business-as-usual (BAU) and Renewable Portfolio Standard (RPS). Wind energy and 

biomass were estimated to contribute more than 80% of the energy. The remainder is composed 

of hydro, digester gas, and landfill gas generation projects, and a small amount of solar 

photovoltaic generation. 

 

19 For additional information on RIMS, visit http://www.bea.gov/regional/rims. 
13 

The economic impacts of the RPS portfolio were compared to a “business as usual” case of

building all fossil fuel resources. The results are as follows.

• The analysis revealed that over a 20-year span the RPS portfolio would cost $1.23 billion 

more than the BAU case on a present-value basis. Relatively speaking, this cost is 

minimal.

• When spread over all retail electric customers, this increase in cost would result in an 

increase in electric rates of only $0.036 per kilowatt hour, or about $0.29 per month, for 

the average residential customer.

• The RPS portfolio would result in $10.1 billion more in gross state output over 20 years 

than would the BAU portfolio. 

• The RPS portfolio would provide a $2.8 billion advantage in earnings and generate about 

85,000 more job-years over 20 years than would the BAU portfolio.

• A review of recent studies revealed that there is strong evidence for fossil-fuel price and 

consumption decreases as a result of renewable energy development. This analysis 

revealed that even a 1% reduction in fossil fuel prices would lead to a $140 million 

reduction in annual fossil fuel expenditures for power generation, or 50% of the RPS cost 

premium in 2015. 

Additional results are presented in Table 5. 

Table 5. Cumulative Impacts for Construction and Operation Periods,

RPS Versus BAU Portfolios from Black and Veatch Study

Output Impact

Earnings 

Impact

Employment 

Impact

RPS $15,468,918,425 $4,736,305,108 129,439

BAU $5,391,459,876 $1,897,570,828 44,272

Difference $10,077,458,549 $2,838,734,280 $85,167 

Texas (2005)

In 1999 Texas enacted its RPS—requiring 2,000 megawatts (MW) of new renewable energy 

capacity by 2009—as part of legislation that restructured the state’s electricity market. By 2005, 

the Texas RPS had become one of the most effective and successful in the nation. The state is 

ahead of its annual requirement schedule with nearly 1,200 MW of new renewable energy 

already installed. Given the success of the existing law and the state’s vast renewable energy 

potential, at least two proposals have been made to increase the state’s standard. The Texas 

Renewable Energy Industries Association (TREIA) and a coalition of Texas environmental 

organizations are advocating for a long-term 20% by 2020 RPS, with 1% of the requirement set 

aside for distributed resources like solar energy and farm-based technologies.20

 

20 TREIA also recommended a shorter-term expansion of the current RPS to be adopted by the Texas Legislature in 2005, 

requiring 10,000 MW of renewable energy capacity (500 MW from distributed renewable resources) by 2015. This shorter-term 

goal is not analyzed in the UCS report.

The Texas 

Energy Planning Council (TEPC) is recommending a more modest increase of the standard to 

5,000 MW by 2015 (500 MW from non-wind renewable resources), with a goal of 10,000 MW 
14 

by 2025. The Union of Concerned Scientists project that the TEPC proposal would yield 

approximately 8% renewable energy in 2025.

The Union of Concerned Scientists analyzed the costs and benefits of increasing the current 

Texas RPS based on the proposals made by TREIA and the TEPC, using the Energy Information 

Administration’s (EIA) National Energy Modeling System (NEMS). Under the more likely 

scenario that primarily utilizes renewable energy technology cost projections from the 

Department of Energy’s national laboratories, UCS found that both the 20% proposal and the 

10,000 MW proposal would result in significant new benefits for Texas’ economy and its 

environment (Table 6). Under the 20% proposal economic development and environmental 

benefits would be much greater, because this proposal stimulates more renewable energy

development—a total of 17,820 MW by 2025. 

Table 6. Comparison of Benefits, Texas RPS Proposals

(More Likely Scenario) 

20% by 2020 

RPS

10,000 MW by 

2025 RPS

Consumer Benefits

Electric Bill Savings $4.6 billion $5 billion

Natural Gas Bill Savings $1 billion $0.5 billion

Total Energy Bill Savings $5.6 billion $5.5 billion

Economic Benefits

New jobs created 38,290 19,950

New capital investment $9.4 billion $4.7 billion

Biomass energy revenues $542 million $197 million

School tax revenues** $1.1 billion $628 million

Wind power land lease 

royalties $154 million $111 million

Note: Results are in cumulative net present value 2002$ using a seven percent real 

discount rate. Job results are for the year 2025. 

*Biomass is called out as an impact because of its contribution to the proposed renewable 

portfolio mix. 

**The impact on school tax revenues were analyzed as an impact of interest in this study

The Union of Concerned Scientists used the IMPLAN model and specific data on the Texas 

economy to estimate the macroeconomic impacts (employment, income, gross state product) of 

the Texas RPS. The macroeconomic analysis was completed by MRG & Associates.21

• First, total expenditures were estimated for installing, manufacturing, operating, and 

maintaining renewable energy technologies that are projected to be developed to meet the 

 Four main 

steps were performed to complete the macroeconomic analysis (these steps are presented above 

in the profile for Colorado in the study by UCS but are repeated here for ease of reference). 

 

21 The analytical approach used in this analysis is similar to that used by Geller, DeCicco, and Laitner (Energy Efficiency and Job 

Creation, 1992, American Council for an Energy Efficient Economy).
15 

Colorado renewable energy standard and for coal and natural gas power plants that would 

have been developed without the standard. 

• Second, the expenditures were broken down and allocated to the industries that would 

directly supply the equipment, labor, and services for renewable and conventional energy 

technologies. 

• Third, the detailed expenditures were multiplied by the estimated local share of 

equipment, labor, and services that could be supplied by Colorado businesses and 

matched to the appropriate sectors in the IMPLAN model to calculate the direct and 

indirect macroeconomic impacts in Colorado. 

• Finally, the macroeconomic impacts of changes in consumer energy bills in Texas were 

calculated.

The Union of Concerned Scientists also adopted several key assumptions for the macroeconomic 

analysis. The expenditure breakdown for the construction and operation and maintenance of 

renewable and conventional power plants was based on data from actual projects collected from 

a variety of sources, including state and federal agencies, renewable energy developers, and 

utilities. The expenditure breakdown and local share data on wind projects—the technology that 

benefits most under the RPS22

—were based on inputs used in the NREL Jobs and Economic 

Development Impacts (JEDI) Model.23

Increasing the Texas RPS to the levels analyzed in this study would create a large market for 

renewable energy in the state, which would help attract businesses that manufacture technologies 

and components and provide services. Implementation of the current standard already has 

resulted in an estimated 2,500 direct jobs from Texas businesses supplying wind towers; blades; 

and development, construction, and transportation services (The SEED Coalition and Public 

Citizen’s Texas Office 2002). Based on this information, the analysis made the following 

assumptions about the share of expenditures and manufacturing that could be supplied by local 

businesses. 

 Data from the IMPLAN model was used to estimate the 

local share of expenditures for specific industries, with a few key exceptions. 

• 100% of the towers and blades for wind turbines 

• 33% of other wind turbine components and solar photovoltaic panels 

• 100% of solar water-heating collectors

• 100% of natural gas fuel expenditures and 52% of coal fuel expenditures (based on data 

from EIA) (EIA 2009b) 

Also completed was a sensitivity analysis that assumed that 100% of other wind turbine 

components and solar photovoltaic panels are manufactured in the state. The analysis did not 

include any jobs or economic development from Texas manufacturers exporting equipment to 

other states or countries. If Texas is able to attract renewable energy manufacturers to produce 

equipment for facilities in the state and for export, the jobs and income from the standard would 

increase significantly.

 

22 Considering the wind resource in Texas, in areas that have the best wind resources wind energy can compete on a lowest-cost 

resource basis with fossil fuels on a long-term basis. 23 For more information about the JEDI model, see http://nrel.gov/analysis/jedi/
16 

Washington (2006)

The Union of Concerned Scientists analyzed the costs and benefits of the renewable energy and 

energy efficiency provisions of Initiative 937 (I-937) in the State of Washington, which would 

establish a renewable energy standard requiring the state’s largest electric utilities to supply 15% 

of their electricity sales from eligible renewable resources by 2020. Under the expected case, 

which primarily utilizes cost and performance projections from industry experts—the U.S. 

Department of Energy and the Northwest Power and Conservation Council—UCS found that, by 

2025, I-937 would result in economic benefits including: 

• 2.9%, or $1.13 billion, in savings on consumer electricity bills resulting mostly from 

reduced natural gas demand and reduced natural gas prices, resulting from the increase in 

renewable energy availability;

• 2,000 new jobs in manufacturing, construction, operation, maintenance, and other 

industries; 

• $138 million in additional income and a $148 million increase in gross state product; 

• $2.9 billion in new capital investment; 

• $30 million in income to rural landowners from wind-power land leases; and

• $167 million in new property tax revenues or payment in lieu of taxes for local 

communities.24

This analysis uses a spreadsheet model to estimate the cost and benefits of Washington’s I-937 

ballot initiative. These impacts are calculated by analyzing the interaction between renewable

energy and energy efficiency supply and policy-driven demand in a competitive wholesale 

market. The Tellus Institute and the Institute for Lifecycle Energy Analysis initially developed

the modeling approach on behalf of the NW Energy Coalition for a 2003 report that examined a 

policy similar to I-937 (Lazarus et al. 2003). UCS updated the model to reflect current conditions 

in the electric power industry, and to match the provisions included in I-937. To calculate the 

macroeconomic impacts (employment, income, gross state product) of I-937, UCS used the 

Impact Analysis for Planning (IMPLAN) model, with data specific to Washington. 

The intent of the analysis is to measure relative effects, not absolute effects. Many different 

factors influence utility-rate levels, revenue requirements, and resource costs, and UCS 

attempted to measure only the effects of I-937. Additionally, UCS makes the general assumption 

that the energy efficiency and renewable resource development that occurs after I-937 takes 

effect is attributable to the initiative. Therefore, UCS compares I-937 compliance with a 

reference case in which no further energy efficiency and renewable resource investments are 

made after 2009. It is not unreasonable to expect that some amount of energy efficiency and 

renewable resource development would take place in the absence of additional policy support. 

The level of development under I-937, however, would be predictable and assured for energy 

efficiency and for renewable energy; without I-937, the outlook is highly uncertain and hard to 

predict. The primary focus of the analysis is to examine the overall costs and benefits to 

consumers of the level of renewable energy and energy efficiency that is required by I-937. 

 

24 Results are in cumulative net present value 2005 dollars using a 4% real discount rate. Job results are for the year 2025.
17 

The UCS analyzes the range of costs and benefits under an expected case that primarily utilizes

renewable energy cost and performance projections based on information from industry 

experts—the Department of Energy’s national labs that study renewable energy technologies, 

and the EIA—as well as data on energy efficiency and avoided cost of power generation from 

the NPCC’s Fifth Power Plan. Additionally, UCS analyzes several sensitivities to determine the 

effects of I-937 on consumers under more adverse and pessimistic conditions.

Wisconsin (2003)

Using an updated version of a model developed for the University of Wisconsin and the 

Wisconsin Division of Energy in 2003, the Union of Concerned Scientists examined the costs 

and benefits of increasing Wisconsin’s renewable standard to 10% by 2015. It was found that the 

10% standard would provide significant economic and environmental benefits, and would help to 

protect consumers from rising natural gas and electricity prices.

The analysis uses a relatively simple and transparent spreadsheet model to project the costs,

renewable generation mix, and carbon dioxide (CO2) emission reductions of increasing 

Wisconsin’s renewable portfolio standard. These impacts are calculated in the model by

analyzing the interaction of renewable energy supply and policy-driven demand in a competitive 

wholesale market. This modeling approach initially was developed under contract to the 

Massachusetts Division of Energy Resources (DOER) in 2000. Two key features in this 

modeling approach are (1) the incremental forward contract clearing market, which assumes that 

the renewable energy market is a market for long-term contracts that clears annually, and (2) a 

renewable generation premium, which builds the energy supply curve for each renewable 

generator based on the required premium over the commodity market value that is necessary to 

bring it on-line (i.e., to meet its levelized revenue requirement). 

This study specifically addresses the local share issue by assuming that only 33% of the 

manufacturing for wind and solar technologies installed in Wisconsin is produced by businesses 

located in the state, and does not include any jobs or economic development that would result 

from Wisconsin-based manufacturers exporting equipment to other states or countries. If 

Wisconsin is able to attract renewable energy manufacturers that will produce equipment both 

for use in the state and for export, jobs and income generated by the renewable portfolio standard 

would increase significantly.

Economic Summary

There is a wide variety of approaches to take in evaluating the economic impact of an RPS. 

Analysis methodology can range from creating a spreadsheet to employing several discrete 

models evaluating specific impacts or even to the development of a more comprehensive, 

complex model. There are several questions to consider when deciding on an approach, but the 

following seem to be the most relevant through discussions with CPUC: 

• What are the key impacts to measure?

• What type of data is available for input? (This could be a factor in cost and scope of the 

analysis.) 
18 

• What are the important aspects that the analysis should consider? For example: 

o Gross versus net impacts;

o In-state (local share) versus out-of-state resources to meet RPS goals, including 

source of electric power and manufacturing capabilities; and

o The allocation of renewable energy development attributed to the RPS (i.e., some 

renewable energy development would likely have occurred without the RPS. How 

much will the RPS take credit for?).

V. Health and Environmental Effects

This section first reviews literature and applications of the analysis of health and environmental 

effects of renewable electricity generation, and then offers an overview of the analysis of 

selected effects. Estimation of health and environmental effects of electricity generation has a 

long history and a variety of applications. Integrated Resource Planning (IRP) approaches have 

expanded criteria for electricity generation capacity expansion planning beyond considerations of 

cost and electric system performance.25 Extensive literature about theories and methods for 

estimating these effects has been developed over the years to support IRP and other applications, 

and as a subject of basic environmental economic research on externalities.26 Analyses that 

supported IRP emphasized quantification of air emission effects, although other effects 

sometimes were included qualitatively (EIA 1995). In Europe, the ExternE project has developed 

methods to quantify electric-sector externalities27—including environmental impacts, global 

warming impacts, and accidents—in great detail (ExternE 2009). Substantial related analysis and 

research also has occurred in the context of “co-benefits” analysis of greenhouse gas 

mitigation,28

In the United States, state and regional analyses of costs and benefits of renewable generation 

generally have not included holistic treatment of health and environmental effects, but some do 

include one step towards estimating health and environmental effects of renewable electricity 

generation: air-emissions effects estimates. Most recent analyses have been performed as part of 

RPS evaluations, other renewable generation deployment efforts, or greenhouse gas mitigation 

and prevention of global climate change also is an important effect. Effects of air 

emissions are the most well quantified, and economic valuation of effects of air pollution 

mitigation has well-developed methods and completed studies. 

 

25 Although many states moved away from IRP due to regulatory reform, analysis of environmental effects undertaken for IRP 

remains relevant to this discussion. A general introduction to IRP is Swisher, J.N.; Jannuzzi, G.dM.; Redlinger, R.Y. (1997). 

Tools and Methods for Integrated Resource Planning, United Nations Environment Programme.

http://uneprisoe.org/IRPManual/IRPmanual.pdf (accessed April 23, 2009). 26 Some of the more recent reviews include: Schleisner, L. (2000). “Comparison of Methodologies for Externality Assessment.” 

Energy Policy 28:1127–1136; Sundqvist, T.; Söderholm, P. (2003). “Pricing Environmental Externalities in the Power Sector.” 

Ecological Economics 46:333–350; Stirling, A. (1998). “Valuing the Environmental Impacts of Electricity Production: A Critical 

Review of Some ‘First-Generation’ Studies.” Energy Sources 20:267–300. 27 Externalities are societal costs not included in the monetary cost of electricity. Environmental impacts or effects include all 

effects, regardless of whether their costs are included. 28 The IPCC Fourth Assessment Report, Working Group III, summarizes co-benefits, primarily those associated with airpollution reduction, including human health, agricultural production, ecosystems, and avoided air pollution control costs.

http://www.ipcc.ch/pdf/assessment-report/ar4/wg3/ar4-wg3-chapter11.pdf (section 11.8) (accessed April 23, 2009). The U.S. 

EPA has pursued a long-term co-benefits analysis project, again primarily focused on air-pollution effects. 

http://www.epa.gov/ies/ (accessed April 23, 2009).
19 

policies. Although this review identified qualitative consideration of diverse effects of RPS,29

The U.S. EPA has substantial tools, data, resources, and expertise that can support states and 

regions in estimating effects of renewable electricity generation. Some EPA methods to estimate 

air-emissions reduction effects have been developed through a series of congressionally 

mandated periodic studies of costs and benefits of Clean Air Act Amendments of 1990 (EPA 

2009a). The EPA has developed tools for screening benefits (such as Co-Benefits Risk 

Assessment Model (COBRA)), and for valuing certain air emissions effects (BenMAP) (EPA 

2009b). EPA had a variety of resources for states related to clean energy, and is developing a 

guide to evaluation of the multiple benefits of clean energy technologies, which will address the 

topic of this report in considerable detail (EPA 2009c). 

it 

seems that primarily emissions effects have been quantified.

Table 7 summarizes recent studies and methods, and identifies which of these were selected for 

further elaboration below. To identify relevant studies, we used personal knowledge of the DOE 

Clean Energy and Air Quality Integration Initiative, internet searches, targeted searches on the 

EPA website, LBNL 2007, and personal communication with national experts. Studies identified 

as potentially including environmental effects in LBNL 2007 are all included in this table (Wiser

et al. 2007). This analysis focuses on selected studies that appeared to utilize methods of greatest 

potential interest to California.

 

29 For example, following is a quote from the New York Public Service Commission findings about the RPS that notes various 

effects, none of which appear to be quantified except the emissions. 

In general, the proposed action will have numerous potential benefits, including reduced air emissions for nitrogen 

oxide (NOx), sulfur dioxide (SO2), greenhouse gases and particulates; increased energy diversity and security and 

economic development; opportunities for more distributed generation; and greater customer choice by virtue of 

expanding the mix of available options to include energy alternatives that promote a cleaner, healthier environment. It 

may also have potential adverse effects on land use, aquatic and terrestrial resources, community character, culturally

and visually sensitive resources, and air emissions.

http://www3.dps.state.ny.us/pscweb/WebFileRoom.nsf/0/85D8CCC6A42DB86F85256F1900533518/ 

$File/301.03e0188.RPS.pdf?OpenElement (accessed April 23, 2009).
20 

Table 7. Summary of State Health and Environmental Analyses of RE Generation

AnalysisProduced 

By Year

In 

Review

Reports 

Quantitative 

Air Emissions 

Effects

Reports 

Other 

Quantitative 

Health or 

Environmental 

Effects

Reviewed 

Below Model Notes

CA CARB 2008 No Yes Yes Yes Several Effects of Scoping Plan; RPS part 

of overall estimate http:// 

CA/ 

OR/ 

WA

www.arb.ca.gov/cc/scopingplan/

document/appendix2.pdf

Tellus 2004 Yes No No No NEMS Raw NEMS results would have 

emissions-related data, but these 

were not published. http:// 

CO 

www.ef.org/westcoastclimate/

B_Tellus_Turning_Corner.pdf

UCS 2004 Yes No No No NEMS Raw NEMS results would have 

emissions-related data, but these 

were not published. http:// 

DC/ 

VA/ 

MD/ 

NJ 

www.ucsusa.org/assets/

documents/clean_energy/ 

co_ballot_res_report_-_final.pdf

DJ 

Consulting 

Multiple 

studies 

No Yes No Yes 

(DOE)

TMM DOE-supported project developed 

“time-matched marginal” method for 

estimating emissions avoided due 

to EE and RE. 

DC/VA/MD: Ongoing;

NJ: http://www.nrel.gov/ 

applying_technologies/pdfs/ 

41173.pdf

IL Shaw Multiple 

studies 

No Yes No Yes 

(DOE)

Power 

World

DOE-supported project developed 

dispatch-model-based method for 

estimating emissions avoided due 

to energy efficiency and renewable 

energy. Initial project: http:// 

www.erc.uic.edu/PDF/ 

Clean_Energy_Development.pdf

Follow-up project: Ongoing 

IN Peter 

Boerger

2006 Yes No Report not found

NESC

AUM 

NESCAUM Ongoing No Yes Yes Yes Several Northeast States for Coordinated 

Air Use Management (NESCAUM) 

has ongoing projects with MA, MD, 

and NY; http://www.nescaum.org/

NY NY DPS, 

NYSERDA, 

Grace, 

LaCapra

2003–

2004 

Yes Yes No No GEMAPSEmissions outputs from dispatch 

model: http://www.dps.state.ny.us/ 

rps/RPS-COST-STUDY-II-VolumeA-2-27-04rev1.pdf

NY CCAP, NY 

GHG Task 

Force

2003 Yes Yes No IPM Report not found

RGGI ICF 2004–

2007

No Yes No Yes IPM http://www.rggi.org/about/history/ 

modeling

RI Tellus 2002 Yes No NEMS Report not found; raw NEMS 

results would have emissionsrelated data

TX UCS 2005 Yes No No No NEMS Raw NEMS results would have 

emissions-related data, but these 

were not published; http:// 

www.ucsusa.org/assets/ 

documents/clean_energy/ 

texas_res_report-02-05_final.pdf

TX ESL Multiple 

studies 

No Yes No Yes eGRIDbased Texas legislature mandated 

reporting on air-emissions effects of 

energy efficiency and renewables

UT Synapse OngoingNo Yes 

(DOE)

Ongoing project 
21 

California

Objective

The California Air Resources Board performed an analysis of the public health and environmental effects of the Scoping Plan, as required by AB32 (CARB 2008a). The RPS was analyzed 

in combination with other elements of the Scoping Plan, and some results were reported 

separately for the RPS.

Methodology

The CARB methodology for performing the required analysis is described in Appendix H of the 

Scoping Plan. The analysis targets effects on air quality, water quality and supply, land 

resources, biological resources, and waste disposal and hazardous waste. These effects were used 

to examine the Scoping Plan’s effect on public health due to changes in air quality. The analysis 

also examined how these effects might manifest themselves at a local level.

Effects were estimated through comparison of a business-as-usual scenario to a scenario in 

which the Scoping Plan was implemented. The sectors characterized in each scenario are 

transportation, electricity and natural gas, water, industry, recycling and waste management, 

forests, high global warming potential gases, and agriculture. The business-as-usual scenario 

describes anticipated changes that are expected to occur over the analysis period due to changes 

in population and changes in regulations and laws. The Scoping Plan Implementation scenario 

describes changes, relative to business-as-usual, that are expected due to the Scoping Plan. 

For each of the targeted effects (air quality, water quality and supply, land resources, biological 

resources, waste disposal, and hazardous waste), the analysis estimates quantitative or qualitative 

effects attributable to Scoping Plan measures. The RPS is found to have effects on each of the 

targeted environmental effects; however only effects on air quality are quantified in detail. 

CARB emission factors are used to estimate air emissions from electricity generation (Scoping 

Plan, Appendix H, p. H-34). The effect of the RPS on air emissions presumably is estimated 

from the difference between the business-as-usual and Scoping Plan Implementation cases, 

multiplied by the emission factor for generator type. The method used to estimate health effects 

from the emissions effects was similar to an existing CARB method (CARB 2008b). 

Results

The following tables, copied from Scoping Plan Appendix H (CARB 2008b), summarize the 

quantitative results of the air emissions and air quality–related health effects analysis. The RPS is 

a line item in the emissions results, but its emissions effects are combined with those of other 

measures in the health effects analysis, and so the exact health effects of the RPS are not 

quantified. 
22 

Table 8. Scoping Plan Appendix H, Table H-4 

Table 9. Scoping Plan Appendix H, Table H-7 
23 

Table 10. Scoping Plan Appendix H, Table H-8 

Land resource effects of new renewable facilities are qualitatively described, and consideration 

of these effects at the project level is recommended (Scoping Plan, Appendix H, p. H-52). Water 

effects of the Scoping Plan are not judged significant overall; Scoping Plan, Appendix H, p. H66, considers individual generation technology water usage. Biological resource effects are noted 

as a siting issue for new facilities, and specific effects of renewable generation technologies are 

described (Scoping Plan, Appendix H, p. H-79). Waste disposal and hazardous waste effects of 

the Scoping Plan are not judged to be significant, and individual renewable generation 

technology effects on wastes are qualitatively presented (Scoping Plan, Appendix H, p. H-88). 

United States Department of Energy

The Department of Energy has supported a wide variety of state projects to demonstrate air 

quality benefits of renewable energy and energy efficiency technologies through its Clean 

Energy and Air Quality Integration Initiative. This section summarizes quantitative methods that 

have been used or are being developed in projects in Washington, D.C.; Virginia; Maryland; 

New Jersey; Illinois; and Utah. Additionally, DOE support has contributed to the development of 

the NESCAUM and Texas analyses, which are summarized in separate sections.

Objective

The state projects reviewed here have the objective of quantifying environmental and economic 

effects of renewable energy and energy efficiency.

Methodology

Time-Matched Marginal (TMM)

The ongoing project in the Washington, D.C., metropolitan region and completed project to 

evaluate New Jersey’s Clean Energy Program (NREL 2007a, NREL 2007b), both supported by 

DOE, used a time-matched marginal (TMM) method to estimate air emissions effects of electric-
24 

sector renewables and efficiency. This method uses historic plant-level data to estimate on an 

hourly basis which fossil generators are most likely to be displaced by renewable generation.30

Illinois

The DOE contributed to Illinois’ evaluation of its Sustainable Energy Plan (NREL 2007c), and 

an ongoing project contributes to the state’s evaluation of its Climate Action Plan. The methods 

for these project use electric-sector dispatch models to estimate differences in emissions between 

scenarios with and without the policy measures. The ongoing project is intended to produce 

methodology that can be applied to other states. 

Utah

An ongoing DOE-supported project will develop cost/benefit metrics for Utah to quantify effects 

of energy efficiency and renewable energy, including changes in water use and air emissions 

(criteria and CO2); economic valuation of environmental effects of these changes; and health, 

energy security, and energy-reliability effects.

Results

TMM

Sample results from evaluation of New Jersey’s Clean Energy Program are shown in Table 11, 

which is taken from the detailed project report (Jacobson et al. 2006). Results are reported for 

nitrogen oxide (NOx) because of the project goal of identifying emissions reductions that would 

support ozone reduction. 

Table 11. New Jersey Clean Energy Program Sample Results

 

30 For additional information about the method, please contact Debra Jacobson at djconsultingllc@earthlink.net. 
25 

Illinois

Sustainable Energy Plan evaluation results are summarized in Table 12 (NREL 2007c). The final 

report from this project has not been published. 

Table 12. Illinois Evaluation Results

Utah

Results are not complete.

Northeast States for Coordinated Air Use Management

Objective

Northeast States for Coordinated Air Use Management (NESCAUM) undertakes studies with 

states that examine the multiple effects of efficiency and renewable energy. NESCAUM has 

ongoing studies with Maryland, Massachusetts, and New York that assess energy, economic, and 

health effects of renewable electricity at varying levels of detail. Studies in Maryland and 

Massachusetts include a screening-level analysis of health effects, whereas the study in New 

York includes a greater level of detail.

Methodology

Northeast States for Coordinated Air Use Management uses the New England Market Allocation 

(MARKAL) model (NESCAUM 2009) to develop reference case and policy-intervention case 

scenarios in the electricity sector, with different levels of renewable electricity generation. 

Outputs from NE MARKAL include air emissions for nitrogen oxide (NOx), sulfur dioxide 

(SO2), carbon dioxide (CO2), and mercury (Hg). The Maryland and Massachusetts studies are 

primarily NE MARKAL scenario analyses, but include screening-level analysis of health 

benefits using the COBRA tool. The New York study will be more comprehensive, including 

Regional Economic Models, Inc. (REMI) economic analysis, air quality analysis, and BenMAP 

valuation. 

Results

Results for the Maryland study were to be published by April 2009. Results for the New York 

study are anticipated during summer 2009. 

Regional Greenhouse Gas Initiative

Objective

The primary purpose of this analysis was to assess effects of the Regional Greenhouse Gas 

Initiative (RGGI) program on electric-sector capacity expansion, generation, and costs. Carbon 

dioxide, nitrogen oxide, sulfur dioxide, and mercury emissions were estimated.
26 

Methodology

This analysis used the Integrated Planning Model (IPM) model to produce state-level results for 

a wide variety of cases, including numerous reference and RGGI cases.

Results

Sample results are shown below from the October 11, 2006, RGGI package scenario (RGGI 

2009). 

Figure 2. Sample results

CO2 Emissions [Million Tons]

2006 2009 2012 2015 2018 2021 2024

MA 25 23 24 25 24 24 24

CT 10 12 12 11 11 12 13

ME 1 1 2 1 1 1 1 

NH 8 8 5 5 5 5 5 

RI 1 2 2 1 1 1 1 

VT - 0 0 0 0 0 0 

NY 53 52 51 52 53 53 55

DELMARVA 9 10 10 9 9 8 8 

NJ 18 17 17 18 19 19 20

Total RGGI Emissions 125 123 123 123 124 123 126

Total Emissions at Affected Plants 121 118 118 119 120 119 122

Eastern Interconnect without RGGI Em 2,216 2,307 2,446 2,570 2,619 2,732 2,849

Total Eastern Interconnect Emissions 2,342 2,430 2,568 2,693 2,743 2,856 2,974

Total Canadian Emissions 65 66 67 72 80 89 97

NOX Emissions [Thousand Tons]

2006 2009 2012 2015 2018 2021 2024

MA 17 16 14 12 12 12 13

CT 5 6 5 3 3 3 3 

ME 0 0 0 0 0 0 0 

NH 7 6 4 4 4 4 4 

RI 0 0 0 0 0 0 0 

VT - 0 0 0 0 0 0 

NY 43 37 34 28 27 28 28

DELMARVA 13 13 12 11 10 7 7 

NJ 19 9 9 10 9 10 10

Total 104 87 78 68 66 66 67

SO2 Emissions [Thousand Tons]

2006 2009 2012 2015 2018 2021 2024

MA 36 17 20 17 17 16 15

CT 10 5 6 6 6 3 3 

ME - - - - 0 0 0 

NH 50 6 4 4 4 4 4 

RI - - - - - - -

VT - - - - - - -

NY 109 86 92 80 75 57 54

DELMARVA 93 69 52 53 51 16 11

NJ 70 30 19 17 13 12 12

Total 368 214 193 177 166 108 100

MER Emissions [Tons]

2006 2009 2012 2015 2018 2021 2024

MA 0.24 0.26 0.16 0.16 0.16 0.14 0.13

CT 0.18 0.14 0.09 0.09 0.09 0.09 0.09

ME - - - - - - -

NH 0.13 0.03 0.03 0.03 0.03 0.03 0.03

RI - - - - - - -

VT - - - - - - -

NY 0.72 0.62 0.53 0.52 0.53 0.50 0.46

DELMARVA 0.22 0.14 0.08 0.07 0.06 0.05 0.03

NJ 0.39 0.11 0.10 0.10 0.08 0.08 0.07

Total 1.9 1.3 1.0 1.0 1.0 0.9 0.8
27 

Texas

Objective

The Texas legislature mandates reporting on the emissions effects of renewable electricity 

generation. Texas Energy Systems Laboratory has developed a reporting system that meets this 

requirement (ESL 2009a). 

Methodology

The methodology relies upon historical dispatch of generation to identify which fossil generators 

are likely to be displaced by renewable electricity generation. This method was developed by the 

EPA based on Emissions & Generation Resource Integrated Database (eGRID) data.

Results

Results are published in a report series that is available on the Energy Systems Lab website (ESL 

2009b). 

Table 13. Comparison of Various Models

Model 

Name Availability Model Description Pros and Cons

BenMAP Available from EPA:

http://www.epa.gov/air/

benmap/

Calculates human health benefits 

of air quality changes.

Pros: Provides a simple, integrated 

framework for valuation of many 

health effects.

Cons: Cannot use air emissions as 

inputs.

COBRA Not generally available Screening tool developed by EPA 

to estimate value of air quality 

changes.

Pros: Simple framework for screening 

benefits.

Cons: Not generally available in all 

regions.

eGRID Available from EPA: 

http://www.epa.gov/

cleanenergy/energyresources/egrid/

index.html

Comprehensive set of historical 

data on emissions from electric 

generators.

Pros: Based on actual emissions data. 

Includes most electric generators. 

Organized by major categories.

Cons: Does not project future emissions 

or future dispatch. Only includes 

major air emissions. Does not include 

data relevant for marginal analysis.

GE 

MAPS 

Software or consulting 

can be purchased from 

GE: http://

www.gepower.com/

prod_serv/products/ 

utility_software/en/

downloads/10320.pdf

Electricity market simulation 

software package. Includes 

generation, power flow, load, and 

transmission databases.

Pros: Supports analysis of spot prices or 

locational marginal prices (LMP), 

shadow prices, determination and 

evaluation of transmission congestion, 

environmental compliance, generation 

siting, asset evaluation, and revenue 

stream projection.

Cons: Complicated model requires 

considerable investment to run. 

Includes only major air emissions.

IPM Proprietary model owned 

by ICF. http:// 

www.epa.gov/

Linear programming model of the 

electric sector in the United 

States. Deterministically forecasts 

capacity expansion, dispatch, and 

emission control to meet constraints of energy demand, 

environmental compliance, 

transmission, dispatch, and 

reliability.

airmarkt/ 

progsregs/epa-ipm/

index.html

Pros: Primary tool used to estimate 

policy effects on SO2, NOx, CO2, and 

Hg in electric power sector.

Cons: Does not include sufficient 

geographic detail to model many 

issues of renewable electricity 

generation.
28 

Model 

Name Availability Model Description Pros and Cons

NE 

MARKAL 

Developed by 

NESCAUM: http://

www.nescaum.org/

topics/modeling

Energy system model including 

technology-based representation 

of each step in the energy supply 

chain, from resource extraction 

through conversion processes to 

end-use devices meeting enduser demands.

Pros: Comprehensive representation of 

energy system. Can be linked with 

other MARKAL models. NESCAUM 

has developed analyses using 

MARKAL outputs with other models, 

such as REMI and BenMAP.

Cons: Does not include sufficient 

geographic detail to model many 

issues of renewable electricity 

generation.

NEMS — See models table in economic 

section of this report.

— 

Power 

World

Software or consulting 

can be obtained from 

Power World: http://

www.powerworld.com/

Simulates power flow in an 

electrical grid.

Pros: Analysis produces electric 

generation that is feasible within 

transmission system. 

Cons: Complicated model requires 

considerable investment to run. 

Includes only major air emissions.

TMM Proprietary method of 

RSG Consulting: 

http://www.rsginc.com

Estimates time-matched marginal 

emissions rates from electricity 

generation.

Pros: Can be used to estimate avoided 

emissions attributable to renewable 

generation without running a dispatch 

model, based on historical data.

Cons: Not publicly available at this time. 

Not appropriate for substantially 

changed situations.

For pollutants subject to a cap, such as sulfur dioxide, mercury, and in some areas nitrogen 

oxide, an RPS is likely to reduce allowance prices but not emissions (unless an emission level is 

quite high). Any health effects beyond those already ensured by air quality regulations would be 

from non-capped pollutants. Because of caps, reported emissions reductions must be interpreted 

carefully. Some of the supporting documentation for the New York RPS analysis notes modeled 

emission reductions (New York State 2004), for example, but whether these are achieved in 

practice for all pollutants depends on the cap and trade regulations and associated markets. The 

importance of estimating actual emission changes—and not just theoretical ones achieved if 

renewables substituted for emitting generators—often is overlooked or ignored (IRFC 2006).31

VI. Overview of Analysis and Effects

Thus, if air quality regulations already ensure a certain level of protection through a cap, then the 

value of renewable generation could be in reducing the cost of compliance with those 

regulations, rather than enhancing health and environmental protections. 

Having briefly reviewed the related literature and applications in the United States, next is an 

overview of analysis and effects. Renewable electricity generation offers a different set of 

environmental effects relative to fossil or nuclear generation. The most significant differences 

are in air emissions and air quality, solid waste and hazardous materials, water resources, and 

land use (with associated possible effects on biological and recreational resources),32

 

31 The negative effects of emissions from fossil generation, for example, often are sited as a benefit of renewable generation, 

without actually showing that these emissions and associated effects would be reduced under current regulatory frameworks. 

 each of 

which is introduced below. Of these, air emissions differences are most likely to result in 

32 These categories of environmental effects are based on California Environmental Quality Act categories.
29 

measurable changes in health outcomes in the general public. Occupational health effects also 

are likely to be measurably different among the different generation options. 

All quantification of health and environmental effects relies upon two initial analytic design 

steps, which are discussed here before a consideration of specific effects.

• Step 1. Establish the overall analytic scope with respect to beginning, interim, and ending 

year of analysis period; spatial and temporal scale (geographic location of generation and 

sub-annual time slices for generation, geographic scope, and specificity; and sub-annual 

time slices for air quality analysis); specifications of electric generation scenarios; 

portions of technology life cycle; and specific health or environmental effects for 

analysis.

• Step 2. Estimate generation sources under each scenario in terms of type, location, 

capacity, and generation. 

For each of these steps, estimation methods must be selected.

Step 1. Establish Scope

The following discussion presents each scoping element from step 1, with options and 

considerations for method selection. 

Analysis Period

This already might be defined by the scope of the study. Considerations in selecting the analysis 

period could include availability of data sets for the initial year, analysis periods used in other 

related analyses, and policy-related dates. It might be desirable to extend the analysis period 

beyond policy-related end dates—at least for portions of the analysis—if interesting policy 

effects are expected to continue. For example, air quality changes at full implementation of the 

RPS could persist and continue to affect health outcomes thereafter.

Spatial and Temporal Scale

The spatial and temporal scale should be selected with the analytic endpoint in mind. For airquality-related health outcomes, for example, the spatial and temporal scales used to identify 

electricity generation should not constrain the spatial scales that can be used in the air quality and 

health analysis. This means that the spatial and temporal data about electricity generation should 

be at least as high-resolution as the air quality modeling framework. An 8,760-hour per year 

generation profile by generation source with latitude and longitude is likely to be more than 

adequate, but if less generation data is retained it might need to be matched with requirements of 

the dependent analyses with respect to specific seasons or larger geographic areas.

Technology Life Cycle

Air emissions, solid and hazardous wastes, and water and land resource effects occur at other 

stages of the technology life cycle and not just during generation.33

 

33The U.S. Life Cycle Inventory Database provides data on material flows for a wide variety of basic processes and is used in 

performing life cycle assessments. 

 A scoping decision is needed 

on life-cycle stages to include. Including other life cycle stages (besides generation) could be 

especially important for certain technologies, therefore a reasonable option could be to include 

different life-cycle stages for different technologies and types of effect. Life-cycle assessments 

http://www.nrel.gov/lci/ (accessed April 24, 2009).
30 

of generation technologies including coal, natural gas, and biopower are available.34 Additionally, the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation35

Step 2. Estimate Generation Sources

(GREET) 

Model includes full fuel-cycle emissions and energy estimates. Although it is not as extensive as 

a full life-cycle assessment, it can be used to estimate certain upstream emissions and energy 

effects. If the CPUC decided to include effects of some upstream processes, GREET or related 

analyses could be useful.

Specifications of Electric Generation Scenarios

The overall renewable generation targets are defined in the policy (business-as-usual with 20% 

RPS, 33% RPS) but the exact generation sources are not set by the policy, so there could be 

several side cases exploring different generation mixes. Following are some examples.

• Aggressive distributed generation case that emphasizes rooftop photovoltaics (PV) (likely 

to reduce land use change)

• Aggressive wind case (likely to show different emissions reductions as compared to the 

other mixes) 

• Aggressive renewable baseload case that assumes high availability of enhanced/ 

engineered geothermal systems, concentrating solar power, and biopower (can displace 

imported coal-fired baseload plants)

• Aggressive regional renewables case that assumes high penetration of renewables 

throughout the Western Interconnect

If it is undesirable to have a great number of side cases, displaced fossil emissions could be 

bounded by two cases, one with all renewables displacing peak load and one with all renewables 

displacing baseload. In the peak load case, adequate ancillary services should be taken into 

account. If renewables in the core case displace mainly peak or baseload, then a single side case 

could be selected that focuses on the other condition. 

It is understood that a capacity expansion model was not used to estimate the generation sources. 

Capacity expansion modeling could be used to optimize the mix of generation sources.36 Also

understood is that, for a few selected cases (probably two), the Plexos model (Plexos Solutions 

2009) will be used to develop detailed production cost and power flow results. 

 

34 A search on “life cycle” in the NREL publications database will yield an extensive list of life-cycle assessments of both 

renewable and fossil-fuel technologies. http://www.nrel.gov/publications/ 35 Argonne National Laboratory maintains GREET. http://www.transportation.anl.gov/modeling_simulation/GREET/ 36 Many of the challenges in modeling large-scale renewable energy deployment are addressed in the Regional Energy 

Deployment System (ReEDS) model. If further analysis of the generation mix is feasible, it could be valuable to use ReEDS.
31 

VII. Air Quality: Air Emission, Air Quality, and Health and 

Environmental Effects

Air emissions changes are likely to be the single most significant quantifiable effect of electricity 

generation.37 Air emissions can affect global climate, human health, and other environmental 

endpoints. Quantifying greenhouse gas emissions effects on global climate is probably beyond 

the scope of analysis—although reduced greenhouse gas emissions likely will be a significant 

effect of RPS and one of the core benefits that the CPUC already is analyzing. Quantifying 

human health and environmental effects can be undertaken through the following general steps, 

and quantifying other environmental effects of air emissions also could be pursued38

• Step 3. Estimate emissions of the selected air pollutants and their precursors under each 

scenario.

 (steps 1 and 

2 are discussed above). 

• Step 4. Estimate concentrations of the air pollutants under each scenario. 

• Step 5. Estimate health and environmental outcomes under each scenario.

Step 3. Estimate Emissions

Electricity generation contributes to criteria and toxic air-pollution problems. The Climate 

Change Scoping Plan relied on the California Air Resources Board methodology for air pollution 

and health effects estimation (CARB 2006). For this analysis, an alternative scope and 

methodology could be developed or the CARB method could be used. An alternative scope 

could include additional pollutants, although it is understood that this is unlikely. Following is a 

brief discussion of pollutants. 

Particulate matter and ozone are the criteria pollutants with the greatest health effects in 

California. Particulate matter was analyzed in the Climate Change Scoping Plan, and is likely to 

have the greatest health effect.39

 

37 U.S. EPA’s 2003–2008 Strategic Plan is the most recent for which societal costs and benefits were summarized for different 

environmental goals. Monetized benefits of air quality programs are significantly greater than those that can be monetized for 

other environmental media. This summary provides a good overview of which environmental effects can be most readily 

quantified and which cannot. 

 One alternative is to extend the scope to ozone or, if ozone is 

not included, a simplifying assumption could be considered that the effect of ozone is unlikely to 

exceed that of PM. However, the validity of such an assumption would require further 

evaluation. Another possible direction in which to extend the scope is to include toxic air 

pollution (and related water and hazardous waste issues). 

http://www.epa.gov/ocfo/plan/2003sp.pdf (accessed April 24, 2009). 38 ExternE includes an excellent general overview of the assessment of impact pathways. 39 A CARB fact sheet on particulate matter and ozone highlights the likely greater health impact from fine particulate matter, 

relative to ozone, in California, and this relative ranking probably is the same nationally. http://www.arb.ca.gov/msprog/ 

ordiesel/documents/pm_03fs.pdf. 
32 

For example, mercury or other heavy metals could be considered for analysis. Quantification of 

effects of air toxics poses significantly greater analytic challenges than for particulate matter or 

ozone (IEI 2003).40,41

If particulate matter is selected for analysis, then emissions of both primary particulate matter 

(by size class PM2.5 and PM10) and particulate matter precursors (sulfur and nitrogen oxides) 

ideally would be estimated for each generation source. If ozone also is selected, then emissions 

estimates for its precursors—nitrogen oxide and volatile organic compounds (or reactive organic 

gases)—would be most important.42

For the selected emissions, an emission factor is needed for each generation source, control 

technology type, and life-cycle stage. We recommend using emission factors from CARB or 

EPA, where available.43 If other life-cycle stages besides generation are included, then some 

emission factors from other sources might be required.44

If the desired emission factors already are used in the capacity expansion model, or if plantspecific emission factors are used in the Plexos model, then emissions estimates could be 

included in these model results. If the Plexos model has detailed, plant-specific factors that take 

into account load, start-up and shut-down emissions, and specific control technology, then these 

could be more accurate. If emissions results from these models do not use the desired emissions 

factors, then generation estimated from these model results (by generator type, control 

technology type, location, and time period) can be multiplied by emission factors. In either case, 

the desired result is emissions by generator type (coal, gas, bio), perhaps by control technology 

type, location (lat/long or region), time period (hour or aggregate), and chemical species 

(primary PM, NOx, SO2). Including control technology types gives a better idea of how much 

 Future emission factors should 

incorporate available data on new generators or for old generators with new control technologies 

installed in future years. If CARB has emission factors for future years that it uses for long-term 

planning, these could be incorporated. If not, then future emission factors in IPM could be used. 

Another source is the five regional planning organizations (RPOs) across the United States that 

are tasked with evaluating and compiling air quality emissions data to address haze and related 

issues (EPA 2009d). The Western Regional Air Partnership (WRAP) compiles such data for the 

western United States, including California. WRAP provides annual emission estimates for point 

sources in its region for 2002 and 2018 that include anticipated growth and control technologies 

(WRAP 2009). Temporal allocation of these emissions can be done using standard EPA 

weighting factors or regional- or plant-specific data, if available (EPA 2009e).

 

40 This table identifies quantification of air toxics as a major limitation of the first prospective analysis, which was a 

comprehensive estimate of benefits and costs, and explains that the second prospective analysis focuses on a case study 

quantifying effects of benzene as an initial step towards addressing air toxics. The analytic plan discusses emissions and 

ecological estimates of mercury, but it appears that health effects will not be estimated.

41 EPA takes a risk-based approach to air toxics that seeks to direct resources towards the greatest risks. If air toxics were to be 

addressed, similar approaches could be used to focus on the most significant pollutants and pathways to health and environmental

effects. We mention mercury because it would be a likely pollutant of concern in the electric sector, but if air toxics are included 

then a systematic screening is recommended. A general EPA fact sheet on air toxics risk is found at

http://www.epa.gov/ttn/atw/3_90_022.html (accessed April 24, 2009). California’s list of air toxics is found at 

http://www.oehha.ca.gov/air/allrels.html (accessed April 24, 2009). Of those listed, a select few are of particular concern in 

electricity generation. http://www.arb.ca.gov/energy/powerpl/guidocfi.pdf (p. 54) (accessed April 24, 2009);

ftp://ftp.arb.ca.gov/carbis/research/hs2002/Nazaroff/Nazaroff1.pdf (p. 8) (accessed April 24, 2009). 42 Good background information on criteria air pollutants is available at http://www.epa.gov/air/urbanair/ 43 EPA’s comprehensive electronic emissions factor database is http://www.epa.gov/ttn/chief/software/fire/ 44 U.S. Life Cycle Inventory Database, http://www.nrel.gov/lci/
33 

generation might have other opportunities for emissions control. To estimate upstream effects, 

capacity by type from the capacity-expansion model would be needed. All these results would 

ideally be indexed by year as well, because emission factors most likely will change over time.

Step 4. Estimate Concentrations

To estimate health impacts, ambient emissions must be converted to atmospheric concentrations. 

To accurately estimate particulate matter (PM) and ozone (O3) concentrations, a photochemical 

air quality model must be applied to the region of interest, combining available data on emissions 

and meteorological conditions. These models can estimate the concentrations of primary 

pollutants that are emitted directly into—and transported in—the atmosphere, as well as 

secondary pollutants which are formed in the atmosphere from precursor emissions. Particulate 

matter can be emitted directly or can be formed in the atmosphere from sulfur dioxide and 

nitrogen oxides. Secondary PM is the greatest component of PM that is less than 2.5 microns in 

diameter (PM2.5). This contributes more significantly to adverse human health impacts than 

does PM10, or PM that is less than 10 microns in diameter. 

Ozone pollution is not emitted directly into the atmosphere, but instead is formed from 

photochemical reactions between nitrogen oxides and volatile organic compounds (VOCs). Use 

of photochemical models to estimate both transport and transformation in the atmosphere ensures 

that ambient concentration estimates include both primary and secondary pollutants for better 

approximation of associated health impacts. The CARB method describes air quality modeling 

performed for the scoping study. NREL recommends using CARB methods for air quality 

modeling. For ecological effects of air emissions, deposition of pollutants on the land or water 

might need to be estimated. If the CPUC wishes to analyze these effects, we recommend using 

CARB or EPA methods.

Step 5. Estimate Health and Environmental Outcomes

A variety of health effects of air pollutants has been identified—ranging from premature death to 

restricted activity days—and a scoping decision could select which ones to include. The CARB 

method used in the Climate Change Proposed Scoping Plan (CARB 2008) specified certain 

health outcomes, and a reasonable option would be to select the same health effects. Additional 

research that might suggest adding new health outcomes could be evaluated further, especially if 

there are any differences between what CARB has chosen and the EPA Second Prospective 

study of costs and benefits of the Clean Air Act Amendments of 1990 (IEI 2003). 

Health effects typically are estimated using a concentration-response (C-R) function, which 

quantifies the change in health outcome to be expected per unit change in pollutant concentration, and the baseline incidence of health effects. Each pollutant will have a different C-R 

function, and different studies or aggregations of studies imply different functions based on 

geography and demography. The function chosen should reflect the underlying population 

exposed to the atmospheric concentrations determined through air quality modeling. There seems 

to be no compelling reason to pursue changes in the CARB method from the scoping study, but 

we recommend that this study conform to the current practice of CARB or EPA. Selection of 

appropriate C-R functions is subject to considerable scientific discussion in the EPA analytic 

plan for the Second Prospective Study, because selection of different C-R functions significantly 

changes health-effects results.
34 

EPA has identified ecological effects of air pollutants that are most quantifiable. These include 

acidification of lakes and streams, eutrophication caused by excessive nitrogen, mercury 

contamination, and direct toxic effects of ozone on plants (EPA 2007). Quantification methods 

are less well-defined for ecological effects than for health effects, and economic valuations are 

much less, but quantification nonetheless could be of interest to the CPUC.45

Solid Waste / Hazards and Hazardous Materials

In addition to the air quality and health effects introduced above, there are likely to be significant 

differences between renewable generation and other generation sources in the areas of solid 

waste and hazardous materials. Solid and hazardous wastes for most renewable technologies are 

produced outside the generation stage of the life cycle; in contrast, for other technologies they 

are produced during all life cycle stages. Therefore the scoping question related to life cycle is 

especially relevant to these wastes. Health and environmental outcomes associated with solid and 

hazardous waste are extremely diverse. If the CPUC wishes to pursue these, we recommend 

further consideration by other experts and use of a risk-based approach.46

• Step 3. Estimate quantities of each type of solid and hazardous waste produced under 

each scenario.

After the two initial 

steps, the following steps could be pursued for solid and hazardous wastes: 

• Step 4. Estimate resulting concentrations of hazardous substances in the environment. 

• Step 5. Estimate health or environmental outcomes attributed to the solid and hazardous 

waste.

Within the solid and hazardous waste category, three specific waste types to consider for 

inclusion are coal wastes, nuclear wastes, and certain photovoltaic wastes.

Coal Wastes

The coal generation life cycle produces a variety of solid and hazardous wastes, including during 

mining and combustion. Many of these environmental effects could occur outside of California 

because of the limits on in-state coal combustion. California, however, relies on substantial 

quantities of out-of-state coal generation. The CPUC has indicated an interest in considering the 

inclusion of out-of-state environmental issues, and coal wastes could be included. Life-cycle 

assessments of coal, GREET estimates, as well as pollutant-specific analyses (e.g. EPA’s work 

on mercury) (EPA 2009f) would provide good sources for such analysis. 

Nuclear Wastes

Similar to coal generation, nuclear generation produces solid and hazardous wastes during 

several stages of its life cycle, with many—but not all—of the effects likely to occur out of state 

because the fuel life cycle is not confined to California. Some of these issues could be less 

amenable to quantitative analysis by the CPUC, however, because they depend upon resolution 

of major national political issues such as development of a permanent nuclear-waste repository, 

or they depend on risk valuation where little consensus exists (for example, “Are low-

 

45 A study that used the ExternE methodology in Germany, for example, found external costs associated with global warming and 

health effects were about an order of magnitude greater than ecosystem costs (Friedrich, & Bickel 2001). 46 U.S. EPA’s 2003–2008 Strategic Plan includes quantitative and qualitative health and environmental effects of solid and hazardous waste. This summary could be a good starting point. http://www.epa.gov/ocfo/plan/2003sp.pdf
35 

probability, high-consequence risks of nuclear socially acceptable?”), or depend on success of 

new generations of nuclear technology. We understand that new nuclear capacity is not likely to 

be part of any of the scenarios analyzed. In time, however, pursuit of a higher RPS might reduce 

or postpone installation of new nuclear capacity. If the CPUC wishes to include nuclear waste 

within its analysis, we recommend further evaluation of nuclear issues be conducted by other 

experts, with consideration that nuclear-waste effects might best be addressed qualitatively.

Photovoltaic Wastes

Certain photovoltaic materials contain toxic metals and others require use of hazardous gases 

during manufacturing, which can generate occupational hazards or hazardous wastes. Although 

hazardous materials in finished photovoltaic systems do not pose a significant risk to installers or 

the general public, ultimate disposal or reuse of these systems also could raise solid and hazardous waste issues (TSES 2008).47

Water Resources

The CPUC will need to decide whether to include these issues 

in its analysis.

Renewable generation has different water-resource effects than those of fossil and nuclear 

generation. Photovoltaic and wind generation do not use water during the generation stage of the 

life cycle, except for nominal amounts in cleaning. Concentrating solar thermal power, 

geothermal, and biopower can use cooling water although water-minimizing cooling systems are 

envisioned—especially for CSP, which is very likely to be located in desert environments. 

Water-minimizing cooling systems could increase costs, therefore including this as the basic 

technology characteristic would be important, particularly for CSP.48

Life-cycle assessments are a source of information about water usage for the technology 

configuration specified, although they do not always analyze different technology alternatives 

that could reduce water usage. California’s assessment of water effects for the Scoping Plan 

refers to the California Energy Commission’s 2007 Environmental Performance Report of 

California’s Electrical Generation System (CEC 2008). The summary table on cooling water 

from that report is shown below (Table 14). The report also notes the poor quality of existing 

water data, and indicates that regulatory changes allow CEC to begin collecting better data for 

2009. NREL has reviewed data regarding water usage in electricity generation and found it to be 

insufficiently detailed with regard to water-usage categories, unclear or inconsistent regarding 

definitions of water use and water-quality data, and inconsistent regarding data collected for 

different technology types.

 Hydropower and ocean 

current generation use moving water directly, and hydropower of course requires dams which 

alter water flows.

Additionally, water usage that diverts liquid water to the atmosphere—thermal effects of water 

usage—are important. Renewable generation is less likely to rely on cooling water than is fossil 

generation, therefore its increased penetration under an aggressive RPS might reduce thermal 

pollution. Although renewable generators that do use cooling water and discharge it to a river or 

stream (most likely geothermal or biopower) might face the thermal pollution issue, each 

 

47 Also see a draft of another document at http://www.nrel.gov/pv/thin_film/docs/summary_esh_from_bnl_all_techs_draft.doc. 

Accessed April 23, 2009. 48 NREL’s Solar Advisor Model can be used to estimate CSP costs with hybrid or dry cooling. NREL uses these estimates in its 

ReEDS model.
36 

renewable plant is unlikely to be as large as a single nuclear or coal-fired facility, which reduces 

the local impact. Furthermore, these renewable generation technologies are unlikely to be located 

on the coast, and therefore would not use once-through cooling with seawater. Quantitative 

estimates could be made of relative cooling and other water usage of different generation technologies. Agricultural wastes can be used to generate electricity through anaerobic digestion, 

possibly reducing the water-quality effects of conventional waste-management practices. The 

CPUC will have to determine which water issues to consider in the analysis, and how to quantify 

these effects.

Table 14. California Energy Commission’s 2007 Environmental Performance Report of California’s 

Electrical Generation System, Summary Table on Cooling Water

Land Use

Generation technologies differ substantially in the quantity and type of land use required. Land 

use per megawatt by generation type and land-use type is feasible to quantify (Denholm and 

Margolis 2008). The compatibility of renewable generation with other simultaneous uses, 

however, is not always well understood. For example, technologies such as wind do not require 

exclusion of plants and animals from the entire site, but the effect on the quality of habitat for 

animal and plant species in an area, the cumulative effect, and the mitigation options are not well

characterized. The U.S. Department of the Interior, Bureau of Land Management (BLM) has 

completed a Programmatic Environmental Impact Statement for wind generation 

(U.S. Department of the Interior BLM 2005), and the BLM is completing one for solar (PEIS 

2009) and geothermal. These documents include substantial information about renewable energy 

environmental impacts, especially effects on land resources.
37 

Health and Environmental Effects Summary

Scoping questions to analyze health and environmental effects of an RPS include determination 

of the analytic period, spatial and temporal scale, technology life cycle, and electric generation 

scenarios. The scope with regard to environmental releases (for example, air emissions) and 

their effects (for example, specific health effects attributable to human exposure to ambient air) 

must also be selected. The type, cost, and quality of methods will depend upon the scope and 

may also influence scoping decisions. We recommend careful attention at the scoping stage to 

the ultimate questions that the analysis is intended to answer. Table 15 summarizes questions 

that could be answered with additional electricity scenario and air quality analyses. 

Table 15. Additional Analysis Questions

Adding This to Analytic Scope . . . Makes it Possible to Answer This . . .

RPS-only analysis (versus scoping study that 

included RPS along with other measures)

Is RPS more or less effective at improving air quality and 

health, as compared to other Scoping Plan measures?

Different levels of RPS How do effects change at different RPS levels? Is there a 

different RPS level that appears more optimal or more costeffective from a health perspective?

Electric generation side cases What are the air quality and health effects associated with 

specific types of renewable generation? How different are 

air quality and health effects likely to be if the actual 

renewable generation installed differs significantly from 

expected generation shares?

Secondary PM, ozone, other additional pollutants What are the (new pollutant–related) air quality and health 

effects of the RPS?

VIII. Conclusions and Recommendations

CPUC requested technical assistance to understand the overall effects of renewable electricity 

generation, alternatives to quantify these effects, and considerations for determining an analytic 

approach. This report documents technical assistance that NREL provided in response to this 

request, especially with regard to economic effects, price hedging, and health and environmental 

effects. Conclusions and recommendations from this process include:

1. Economic, health, and environmental effects span a very broad range of specific effects, and 

these may vary substantially. Although it may be clear in some cases which effects are likely to 

be important, there may be other important effects that are not readily apparent. Therefore, 

careful thought is needed to scope studies of the effects so that the study design will not exclude 

consideration of important effects.

2. Prior analyses have been conducted in a number of states that provide references and 

experiences to inform California’s situation. Some common methods and practices can be seen 

across these efforts, but no single approach or set of methods and tools predominates. 

Additional, more detailed, and better organized reporting of state-level analyses could facilitate 

learning from experience.

3. Methods to analyze economic, health, and environmental effects vary widely in cost and 

quality, and are in many cases limited by availability of data. A concerted effort to advance the 

state-of-the-art of methodology for states could improve these analyses and provide better 

information for state decision-makers.
38 

References 

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Form. Unpublished communication to National Renewable Energy Laboratory. 

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October 2008. http://www.arb.ca.gov/cc/ scopingplan/document/psp.pdf (pp. 86–95). Accessed 

April 23, 2009. 

CARB (2008b). Climate Change Proposed Scoping Plan. (Appendix H). Published October 

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CARB (2006). Quantification of the Health Impacts and Economic Valuation of Air Pollution 

from Ports and Goods Movement in California. 

http://www.arb.ca.gov/planning/gmerp/march21plan/appendix_a.doc. Accessed April 24, 2009. 

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California’s Electrical Generation System. Published January 2008. 

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California Public Utilities Commission (CPUC) (2009). Technical Assistance Project 

Application for Assistance. January 2, 2009. San Francisco, California. Submitted to Cathy 

Iverson (DOE), James Ferguson (DOE), and Misty Conrad (NREL) by Jaclyn Marks (CPUC). 

California State (2008). Executive Order S-14-08. Available online at 

http://gov.ca.gov/executive-order/11072/Accessed April 23, 2009. 

Chen, C.; Wiser, R.; Mills, A.; Bolinger, M. (2008). Weighing the Costs and Benefits of State 

Renewable Portfolio Standards in the United State: A Comparative Analysis of State-Level 

Policy Impact Projections. Berkeley, CA: Ernest Orlando Lawrence Berkeley National 

Laboratory. http://eetd.lbl.gov/ea/EMS/reports/61580.pdf (number of states with RPS updated by 

NREL). Accessed April 23, 2009. 

Denholm, P.; Margolis, R. (2008). “Land-Use Requirements and the Per-Capita Solar Footprint 

for Photovoltaic Generation in the United States.” Energy Policy, 36(9).

Friedrich, R.; Bickel, P. (2001). Estimation of External Costs Using the Impact-PathwayApproach. http://www.externe.info/. Accessed April 23, 2009. 

Energy Information Administration (EIA) (2009a). Colorado State Energy Profile. 

http://tonto.eia.doe.gov/state/state_energy_profiles.cfm?sid=CO. Accessed April 23, 2009. 

EIA (2009b). Natural Gas Production and Use by Texas. 

http://www.eia.doe.gov/cneaf/coal/statepro/imagemap/tx1p1.html. Accessed April 23, 2009. 
39 

EIA (1995). Electricity Generation and Environmental Externalities: Case Studies. 

http://www.eia.doe.gov/cneaf/electricity/external/external.pdf. Accessed April 23, 2009. 

Energy Systems Laboratory (ESL) (2009a). About Senate Bill 5. Web site sponsored by Texas 

A&M. http://esl.eslwin.tamu.edu/senate-bill-5.html Accessed April 24, 2009. 

ESL (2009b). Background and 2008 Report. Web site sponsored by Texas A&M. 

http://esl.eslwin.tamu.edu/reports-2.html The 2008 report is found at 

http://esl.eslwin.tamu.edu/docs/documents/HARC-2008-Report-08-27-2008-final.pdf. Accessed 

April 24, 2009.

Environmental Protection Agency (EPA) (2009a). Benefits and Costs of the Clean Air Act. 

http://www.epa.gov/air/sect812/. Accessed April 23, 2009. 

EPA (2009b). Environmental Benefits Mapping and Analysis Program (BenMAP). 

http://www.epa.gov/air/benmap/. Accessed April 23, 2009. 

EPA (2009c). Clean Energy State Programs. http://www.epa.gov/cleanenergy/energyprograms/state-and-local/state.html. Accessed April 23, 2009. 

EPA (2009d). Regional Planning Organizations. http://epa.gov/visibility/regional.html. Accessed 

April 24, 2009. 

EPA (2009e). Emissions Modeling Clearinghouse Temporal Allocation. 

http://www.epa.gov/ttn/chief/emch/temporal/. Accessed April 24, 2009. 

EPA (2009f). Mercury. http://www.epa.gov/mercury/. Accessed April 23, 2009. 

EPA (2007). Assessing the Effects of the Clean Air Act Amendments of 1990 on Ecological 

Resources: Updated Literature Review and Terrestrial Case Study Approach. 

http://www.epa.gov/air/sect812/mar07/eco_assessment.pdf. Accessed April 24, 2009. 

Externalities of Energy (ExternE) (2009). The Methodology for Generating External Costs. 

http://www.externe.info/. Accessed April 23, 2009. 

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

Report. NREL/TP-670-43512. Golden, CO: National Renewable Energy Laboratory. 

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Indiana Regulatory Flexibility Committee (IRFC). (2006). The Public Health Impact of a 

Renewable Electricity Standard (RES) in Indiana. Published September 26, 2006. 

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April 24, 2009. 

Industrial Economics Incorporated (IEI) (2003). Benefits and Costs of the Clean Air Act 1990 - 

2020: Revised Analytical Plan For EPA's Second Prospective Analysis. Published May 2003. 

http://www.epa.gov/air/sect812/1990-2020/mainbody51203.pdf, Table 9-1, pp. 9–23. Accessed 

April 24, 2009. 

International Energy Agency (IEA) presentation. Young, K. (2009). Interaction between Water 

Issues and Energy Efficiency & Renewable Energy.
40 

Jacobson, D.; O'Connor, P.; High, C.; Brown, J. (2006). Final Report on the Clean Energy/Air 

Quality Integration Initiative Pilot Project of the U.S. Department of Energy's Mid-Atlantic 

Regional Office. 138 pp.; NREL Report No. TP-710-40477; DOE/GO-102006-2354. 

Kammen; Kapadia; Fripp (2004). Putting Renewables to Work: How Many Jobs Can the Clean 

Energy Industry Generate? Published by University of California Berkeley, April 2004. 

rael.berkeley.edu/old-site/renewables.jobs.2006.pdf. Accessed April 23, 2009. 

Lazarus, M.; Lazar, J.; Hammerschlag R. (2003). Economics of a Washington Energy Portfolio 

Standard: Effects on Ratepayers. Seattle, WA: Northwest Energy Coalition. 

National Renewable Energy Laboratory (NREL) (2007a). New Jersey - A Leader in Fighting 

Pollution. U.S. DOE Clean Energy and Air Quality Integration Initiative Fact Sheet Series. 

(2007). 2 pp.; NREL Report No. FS-670-41173; DOE/GO-102007-2399. 

NREL (2007b). Eastern States Harness Clean Energy to Promote Air Quality. U.S. DOE Clean 

Energy and Air Quality Integration Initiative (Brochure). (2007). 8 pp.; NREL Report No. BR840-42143; DOE/GO-102007-2500. 

NREL (2007c). Illinois - High-Level Commitment Key to Air Quality Success. U.S. DOE Clean 

Energy and Air Quality Integration Initiative Fact Sheet Series. (2007). 2 pp.; NREL Report No. 

FS-670-42165; DOE/GO-102007-2503. 

New York Public Service Commission (NYPSC) (2008). Order Establishing Energy Efficiency 

Portfolio Standard and Approval Programs. http://www.dps.state.ny.us/Case_07-M-0548.htm. 

Accessed May 20, 2009. 

New York State (2004). Emissions Comparison. Published March 2, 2004. 

http://www.dps.state.ny.us/rps2/EMISSIONS_7_COMPARISON_3-2-04.pdf. Accessed April 

24, 2009. 

Northeast States for Coordinated Air Use Management (NESCAUM) (2009). The MARKAL 

Model. http://www.nescaum.org/topics/modeling. Accessed April 23, 2009. 

Pletka, R.; Wynne, J.; Abiecunas, J.; Scupham, S.; Lindstrom, N.; Jacobson, R.; Stevens, B. 

(2004). Economic Impact of Renewable Energy in Pennsylvania. Overland Park, KS: Black and 

Veatch. http://www.bv.com/Downloads/Resources/Reports/PA_RPS_Final_Report.pdf. 

Accessed April 23, 2009. 

Plexos Solutions (2009). The Plexos Model. Web site at http://www.plexossolutions.com/. 

Accessed April 24, 2009. 

Regional Greenhouse Gas Initiative (RGGI) (2009). Modeling Results. 

http://www.rggi.org/about/history/modeling. Accessed April 24, 2009. 

Solar Energy Development Programmatic Environmental Impact Statement (PEIS). (2009). 

Programmatic Environmental Impact Statement (PEIS) to evaluate utility-scale solar energy 

development. http://www.solareis.anl.gov/ Accessed April 23, 2009. 
41 

Tegen, S. (2004). Statewide Economic Impacts of Wind Energy Compared with Coal and 

Natural Gas. NREL/CP-500-35979. Washington, DC: American Wind Energy Association; 

Omni Press.

The SEED Coalition and Public Citizen’s Texas Office (2002). Renewable Resources: The New 

Texas Energy Powerhouse. http://www.citizen.org/documents/ 

Tx%20Energy%20Powerhouse.pdf. Accessed April 23, 2009. 

United States Department of the Interior, Bureau of Land Management (2005). Final 

Programmatic Environmental Impact Statement on Wind Energy Development on BLMAdministered Lands in the Western United States (EIS). http://windeis.anl.gov/documents/fpeis/. 

Accessed April 23, 2009. 

Western Regional Air Partnership (WRAP) (2009). Stationary Sources Joint Forum. 

http://www.wrapair.org/forums/ssjf/pivot.html. Accessed April 24, 2009. 

Wiser R.; Namovicz, C.; Gielecki, M.; Smith, R. 2007. “The Experience with Renewable 

Portfolio Standards in the United States.” Electricity J., 20(4), pp. 8–20. 
42 

Appendix: Bibliography of RPS Studies Described in

Economic and Fuel Diversity Sections 

Binz, R. 2004. The Impact of the Renewable Energy Standard in Amendment 37 on Retail 

Electric Rates in Colorado. Denver, CO: Public Policy Consulting. Available at 

http://rbinz.com/Files/Binz%202004%20Colo%20RES%20Report.pdf. Accessed April 24, 2009. 

Clemmer, S., Grace, B. and K. Cory (UCS). 2003. A Study to Evaluate the Impacts of Increasing 

Wisconsin’s Renewable Portfolio Standard. Madison, Wisconsin: Wisconsin Division of Energy. 

Available at: http://energytaskforce.wi.gov/docview.asp?docid=2. Accessed April 24, 2009. 

Deyette, J.; Clemmer, S. (UCS). 2006. The Washington Clean Energy Initiative: Effects of I-937 

on Consumers, Jobs and the Economy. Cambridge, MA: Union of Concerned Scientists. 

Available at http://www.ucsusa.org/clean_energy/solutions/renewable_energy_solutions/thewashington-clean-energy.html. Accessed May 13, 2009. 

Deyette, J. and S. Clemmer (UCS). 2005. Increasing the Texas Renewable Energy Standard: 

Economic and Employment Benefits. Cambridge, MA: Union of Concerned Scientists. Available 

at http://www.ucsusa.org/clean_energy/solutions/renewable_energy_solutions/increasing-thetexas.html. Accessed April 24, 2009. 

Deyette, J.; Clemmer, S. 2004. The Colorado Renewable Energy Standard Ballot. Union of 

Concerned Scientists. Initiative: Impact on Jobs and the Economy. Boston, MA: Union of 

Concerned Scientists. Available at http://www.ucsusa.org/clean_energy/solutions/

renewable_energy_solutions/the-colorado-renewable-energy.html. 

KEMA, Inc. and Economic Development Research Group, Inc., 2008. NYSERDA Main Tier 

RPS Economic Benefits Report. Prepared for The New York State Energy Research and 

Development Authority. Available at http://www.edrgroup.com/images/stories/ 

Energy/NSERDA-RPS-Main-Tier-Econ-Benefits.pdf Accessed April 24, 2009. 

LaCapra, GDS and Sustainable Energy Advantage (LaCapra). 2006. Analysis of a Renewable 

Portfolio Standard for the State of North Carolina. Prepared for the North Carolina Utilities 

Commission. Available at http://www.ncuc.commerce.state.nc.us/reps/NCRPSReport12-06.pdf. 

Pletka, R.; Wynne, J.; Abiecunas, J.; Scupham, S.; Lindstrom, N.; Jacobson, R.; Stevens, B.

(B&V). 2004. Economic Impact of Renewable Energy in Pennsylvania. Overland Park, Kansas: 

Black and Veatch. Available at http://www.bv.com/Downloads/Resources/Reports/

PA_RPS_Final_Report.pdf. Accessed April 24, 2009. 

Union of Concerned Scientists (UCS). 2006. Fact Sheet: Increasing Wisconsin’s Renewable 

Portfolio Standard Will Create Jobs and Help Stabilize Energy Bills. Cambridge, Massachusetts: 

Union of Concerned Scientists. Available at http://www.legis.state.wi.us/lc/comtmats/

old/07files/ab0346_20080111102159.pdf. Accessed May 13, 2009.
F1147-E(10/2008) 

REPORT DOCUMENTATION PAGE Form Approved

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1. REPORT DATE (DD-MM-YYYY)

July 2009

2. REPORT TYPE

Technical Report

3. DATES COVERED (From - To)

 

4. TITLE AND SUBTITLE

Renewable Electricity Benefits Quantification Methodology: 

A Request for Technical Assistance from the California Public 

Utilities Commission

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

G. Mosey and L. Vimmerstedt

5d. PROJECT NUMBER

NREL/TP-6A2-45639

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IGST.8107

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National Renewable Energy Laboratory

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NREL/TP-6A2-45639

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NREL

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National Technical Information Service

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The California Public Utilities Commission (CPUC) requested assistance in identifying methodological alternatives for 

quantifying the benefits of renewable electricity. The context is the CPUC’s analysis of a 33% renewable portfolio 

standard (RPS) in California—one element of California’s Climate Change Scoping Plan. The information would be 

used to support development of an analytic plan to augment the cost analysis of this RPS (which recently was 

completed). NREL has responded to this request by developing a high-level survey of renewable electricity effects, 

quantification alternatives, and considerations for selection of analytic methods. This report addresses economic 

effects and health and environmental effects, and provides an overview of related analytic tools. Economic effects 

include jobs, earnings, gross state product, and electricity rate and fuel price hedging. Health and environmental 

effects include air quality and related public-health effects, solid and hazardous wastes, and effects on water 

resources.

15. SUBJECT TERMS

NREL; renewable energy; RE; California Public Utilities Commission; CPUC; economic benefits; health benefits; 

environmental benefits; renewable portfolio standard; RPS; renewable electricity; Gail Mosey; Laura Vimmerstedt

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18