Indirect Emissions Analysis ▪ 1 

 

 

 

 EPA/600/R-08-087

 July 2008 

ANALYSIS OF INDIRECT EMISSIONS 

BENEFITS OF WIND, LANDFILL GAS, 

AND MUNICIPAL SOLID WASTE 

GENERATION 

By
 

Ezra D. Hausman, Jeremy Fisher, and Bruce Biewald 

Synapse Energy
 

22 Pearl Street 

Cambridge, MA 02139 

Order No.: EP07C000079 

Project Officer 

Joseph F. DeCarolis 

Air Pollution Prevention and Control Division 

National Risk Management Research Laboratory
 

Research Triangle Park, NC 27711
 
 

 

Notice 

The U.S. Environmental Protection Agency, through its Office of Research and Development, 

funded and managed the research described here under Order Number EP07C000079 to 

Synapse Energy Economics. It has been subjected to the Agency‘s peer and administrative 

review and has been approved for publication as an EPA document. 

Foreword 

The U. S. Environmental Protection Agency (EPA) is charged by Congress with protecting the 

Nation's land, air, and water resources. Under a mandate of national environmental laws, the 

Agency strives to formulate and implement actions leading to a compatible balance between 

human activities and the ability of natural systems to support and nurture life. To meet this 

mandate, EPA’s research program is providing data and technical support for solving 

environmental problems today and building a science knowledge base necessary to manage our 

ecological resources wisely, understand how pollutants affect our health, and prevent or reduce 

environmental risks in the future. 

The National Risk Management Research Laboratory (NRMRL) is the Agency's center for 

investigation of technological and management approaches for preventing and reducing risks 

from pollution that threaten human health and the environment. The focus of the Laboratory's 

research program is on methods and their cost-effectiveness for prevention and control of 

pollution to air, land, water, and subsurface resources; protection of water quality in public water 

systems; remediation of contaminated sites, sediments and ground water; prevention and control 

of indoor air pollution; and restoration of ecosystems. NRMRL collaborates with both public and 

private sector partners to foster technologies that reduce the cost of compliance and to anticipate 

emerging problems. NRMRL's research provides solutions to environmental problems by: 

developing and promoting technologies that protect and improve the environment; advancing 

scientific and engineering information to support regulatory and policy decisions; and providing 

the technical support and information transfer to ensure implementation of environmental 

regulations and strategies at the national, state, and community levels. 

This publication has been produced as part of the Laboratory's strategic long-term research plan. 

It is published and made available by EPA's Office of Research and Development to assist the 

user community and to link researchers with their clients. 

 Sally Gutierrez, Director 

 National Risk Management Research Laboratory 

Indirect Emissions Analysis ▪ i 
Abstract 

A number of techniques are introduced to calculate the hourly indirect emissions benefits 

of three types of renewable resources: wind energy, Municipal Solid Waste (MSW) 

combustion, and Landfill Gas (LFG) combustion. These techniques are applied to each of 

the EPA’s eGRID subregions in the continental United States in order to derive hourly, 

seasonal, or annual (as appropriate) coefficients for use in evaluating the indirect 

emissions benefits of such projects in each region. 

For wind power impacts, simulated wind project power profiles are derived using publicly 

available wind data scaled to a typical turbine height for new wind projects and 

transformed through a power curve for a proxy turbine. The results for one region are 

compared to the output of an existing project, although the limitations of this type of 

comparison, as well as the limitations of representing large areas of the country with 

single proxy curves, are discussed. LFG and MSW are found to have flat, “always on” 

profiles based on the limited data available. 

The regional, hourly power profiles for each type of resource are combined with the 

hourly indirect emissions coefficients to yield annual indirect emissions benefits for each 

type of resource for each eGRID subregion. 

For each GWh of renewable energy produced each year, the indirect CO2 emissions 

benefit is found to be between 600 and 1100 tons of CO2 depending on the region, with 

coal-dependent regions having the highest indirect emissions benefit. The indirect NOx 

and SO2 emissions benefit also depends on the regional fuel mix, as well as the 

stringency of environmental regulation in each region. These indirect emissions benefits 

vary between 500 and 5,000 pounds of NOx per GWh, and between 200 and 1,300 

pounds of SO2 per GWh. With some exceptions, these results are robust regardless of 

whether the renewable resource has a base load profile (such as MSW and LFG) of 

output which varies diurnally and seasonally (such as wind). However, as the calculated 

benefits do vary in some cases depending on the analytical approach, care must be used 

in selecting an appropriate calculation methodology for each application. 

This report was submitted in fulfillment of Order Number EP07C000079 under the 

sponsorship of the EPA. 

Indirect Emissions Analysis ▪ ii 
Table of Contents
 

Notice............................................................................................................................................................. i
 

Foreword....................................................................................................................................................... i
 

Abstract........................................................................................................................................................ ii
 

List of Tables ............................................................................................................................................. iv
 

List of Figures ............................................................................................................................................ v
 

Acronyms and Abbreviations............................................................................................................... vii
 

List of Appendices ................................................................................................................................. viii
 

1. Introduction and Summary of Conclusions ................................................................................... 1
 

2. Operating Data on Renewable Energy Technologies.................................................................. 6
 

A. Wind Generation: Existing and Synthetic Projects........................................................6
 

Data Sources for Synthetic Generation........................................................................7
 

Generation of Synthetic Wind Power Data ..................................................................8
 

Comparing Empirical and Simulated Wind Generation............................................10
 

B. Landfill Gas .......................................................................................................................14
 

C. Municipal Solid Waste.....................................................................................................14
 

3. Hourly generation and emissions data ......................................................................................... 15
 

Characterization of emission rates..............................................................................16
 

Data filtering....................................................................................................................16
 

4. Emissions Displacement Methods ................................................................................................. 17
 

A. Limiting Cases ..................................................................................................................18
 

B. Hourly Average Emissions Rate (HAER) .....................................................................19
 

C. Emissions Slope Factor ..................................................................................................22
 

D. Empirical Incremental Emissions Rate (EIER) ............................................................29
 

E. Load-Following Incremental Emissions Rate (LFIR) ..................................................30
 

F. Flexibility-Weighted Hourly Average Emissions Rate (FW-HAER)...........................31
 

G. Comparison of Indirect Emissions Rates .....................................................................35
 

5. Indirect Emissions Results............................................................................................................... 37
 

6. Conclusions.......................................................................................................................................... 44
 

Appendix A: .............................................................................................................................................. 46
 

Appendix B: .............................................................................................................................................. 48
 

Appendix C: .............................................................................................................................................. 52
 

Appendix D: .............................................................................................................................................. 56
 

Indirect Emissions Analysis ▪ iii 
 

List of Tables 

Table 1. Seasonal and annual emissions slope factors and R-squared 

values for CO2. ..................................................................................24
 

Table 2. Seasonal and annual emissions slope factors and R-squared 

values for SO2....................................................................................25
 

Table 3. Ozone season and non-ozone season emissions rate 

slopes for NOx...................................................................................26
 

Table 4. Guide to the summary figures...............................................................37
 

Table 5. Annual indirect emissions reduction rates associated with an 

incremental GWh of wind energy in each of the eGRID 

subregions for 2005.. .........................................................................39
 

Table 6. Annual indirect emissions reduction rates associated with an 

incremental GWh of landfill gas of MSW generation in each of 

the eGRID subregions for 2005.. .......................................................40
 

Table 7. “Shape impact” for indirect emissions calculations for each 

pollutant in each eGrid region............................................................40
 

Indirect Emissions Analysis ▪ iv 
List of Figures 

Figure 1. Indirect emissions results for CO2, NOx, and SO2 for wind, 

municipal solid waste and landfill gas generation projects................4
 

Figure 2. Wind power curve for a GE 1.5se wind turbine.................................9
 

Figure 3. Proxy wind turbine power curve plotted against histograms of 

raw and scaled wind speeds at a sample site in the Midwest.........10
 

Figure 4. Comparison of power output distribution at sample wind 

power site in the Midwest with predicted output based on 

scaled wind data from a nearby meteorological station ..................12
 

Figure 5. Comparison of hourly synthetic and observed wind power 

time series at nearby locations in the Midwest................................13
 

Figure 6. Cumulative distribution of the hourly deviation between 

observed and synthetic generation time series...............................13
 

Figure 7. Distribution of CO2 emission rates for fossil generating plants 

by reported fuel type .......................................................................18
 

Figure 8. Representations of CO2 hourly average emissions rate 

(HAER) in New England .................................................................21
 

Figure 9. Determination of the regional emissions slope factor in the 

RFCE (Reliability First/Central) region............................................23
 

Figure 10. Bifurcation of the NOx emissions slope factor for the RFCE 

region..............................................................................................23
 

Figure 11. Regional slope factors for CO2 .......................................................27
 

Figure 12. Seasonal and regional slope factors for SO2 ..................................27
 

Figure 13. Regional slope factors for NOx .......................................................28
 

Figure 14. Flexibility coefficient vs. generator size in New England and 

California.........................................................................................33
 

Figure 15. Hourly dispatch and flexibility coefficient for three plants of 

differing capacities in the FRCC subregion.....................................34
 

Figure 16. Comparison of the distribution of indirect emissions 

coefficients for CO2, SO2, and NOx based on methods 

described in the text........................................................................36
 

Indirect Emissions Analysis ▪ v 
Figure 17. Annual indirect CO2 emissions impact associated with one 

incremental GWh of wind energy in each of the eGRID 

subregions for 2005. .......................................................................41 

Figure 18. Annual indirect NOx emissions impact associated with one 

incremental GWh of wind energy in each of the eGRID 

subregions for 2005. .......................................................................41 

Figure 19. Annual indirect SO2 emissions impact associated with one 

incremental GWh of wind energy in each of the eGRID 

subregions for 2005. .......................................................................42 

Figure 20. Annual indirect CO2 emissions impact associated with one 

incremental GWh of landfill gas or municipal solid waste 

energy in each of the eGRID subregions for 2005..........................42 

Figure 21. Annual indirect NOx emissions impact associated with an 

incremental GWh of landfill gas or municipal solid waste 

energy in each of the eGRID subregions for 2005..........................43 

Figure 22. Annual indirect SO2 emissions impact associated with an 

incremental GWh of landfill gas or municipal solid waste 

energy in each of the eGRID subregions for 2005..........................43 

Indirect Emissions Analysis ▪ vi 
AWEA American Wind Energy Association 

BTU British Thermal Unit 

CO2 Carbon Dioxide 

DOE U.S. Department of Energy 

eGRID Emissions & Generation Resource Integrated Database 

EIER empirical incremental emission rate (defined in this study) 

EPA United States Environmental Protection Agency 

FW-HAER flexibility-weighted hourly average emission rate (defined in this study) 

GE General Electric 

GW gigawatt 

GWh gigawatt-hour 

HAER hourly average emission rate (defined in this study) 

kW kilowatt 

kWh kilowatt-hour 

LFG landfill gas (generating resource) 

LFIR load-following incremental emissions rate (defined in this study) 

mmBTU million British thermal units 

MSW municipal solid waste (generating resource) 

MW megawatt 

MWh megawatt-hours 

NCDC National Climatic Data Center 

NERC North American Electric Reliability Corporation 

NOAA National Oceanic and Atmospheric Administration 

NOx mono-nitrogen oxides (NO and NO2) 

NREL National Renewable Energy Laboratory 

SO2 sulfur dioxide 

WBAN Weather Bureau Army Navy 

Acronyms and Abbreviations 

Indirect Emissions Analysis ▪ vii 
List of Appendices 

Appendix A 

Identification and map of eGRID subregions 

Appendix B 

Synthetic wind power time series (hourly percent of capacity) for each eGRID subregion, 

represented as color intensity plots 

Appendix C 

Summary tables of raw and scaled wind speed and synthetic wind power time series 

Appendix D 

Color intensity maps of hourly indirect CO2, NOx and SO2 emissions rates for each 

eGRID subregion, using several alternative calculation methodologies 

Indirect Emissions Analysis ▪ viii 
 

 

 

1. Introduction and Summary of Conclusions 

The purpose of this report is to develop, analyze, and report upon methods of quantifying 

the indirect emissions benefit associated with renewable energy generation resources. In 

particular, the goal is to conduct a national assessment of indirect emissions reductions 

from wind, LFG and MSW by NERC subregion.1

 Because conventional electricity 

generation resources can differ widely in their emissions characteristics, the 

determination of exactly which conventional resource or resources would be running but 

for the contribution of the renewable resource would be the ideal first step in calculating 

indirect emissions benefits. If we knew which resources were “displaced” by the 

renewable resource, we could simply multiply the emission rate of those resources by the 

total number of megawatt-hours (MWh) displaced, accounting for line losses, and an 

exact answer would be obtained. The answer thus obtained would vary from region to 

region, and from hour-to-hour within any given region, depending on current and 

expected market conditions. 

Unfortunately, in large, interconnected, security-constrained2

 electricity markets, 

determining which units would be displaced in any given hour would require perfect 

information and a complex dispatch model. Even under such ideal circumstances, the 

analysis would be prohibitively time-consuming and complex for each hour. Thus our goal 

in the current study is to develop and apply proxy methods that, while imperfect, are 

applicable to and useful for the general estimation of the indirect emissions benefits for 

renewable energy projects anywhere in the continental United States. Over the course of 

this report, we develop and apply these methods to estimate the indirect emissions 

benefits of three kinds of renewable energy projects, for carbon dioxide (CO2), sulfur 

dioxide (SO2) and oxides of nitrogen (NOx), in each of the 22 eGRID3

 subregions of the 

continental United States. 

An “indirect emissions benefit” may be a real reduction in total emissions in a region or, 

under cap-and-trade regulation such as that in much of the United States for the 

pollutants SO2 and NOx, it may be an opportunity to release emissions allowances for 

some other use. The calculation methods explored in this report are appropriate for either 

application, but the impacts of these scenarios are quite different in terms of their ultimate 

effect on pollutant emissions. In the case of cap-and-trade regulation, displacing air 

emissions provides an opportunity for the allowance holder to sell or bank valuable 

emissions allowances. There may also be a societal benefit in terms of reducing the 

market price of emissions allowances, thereby reducing this component of the cost of 

1 EPA RFQ # RFQ-OH-07-00015, as posted at: 

https://www.fbo.gov/?s=opportunity&mode=form&id=556118954b3728ae31326b103396bcf6&tab=core&_cview=0. 

2

 “Security constrained” refers to the need for system operators to dispatch generating units to meet load while respecting 

the loading on transmission lines and interfaces within their operating limits. Because of these security constraints, 

generating units cannot simply be dispatched in merit order, from least expensive to most expensive running costs. In 

many cases more expensive units must be run out of merit order due to transmission transfer limits. 

3 The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive inventory of environmental 

attributes of electric power systems. (http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html.) Definitions and a 

map of the eGRID subregions are shown in Appendix A. eGRID subregions are roughly coincident with NERC (North 

American Electric Reliability Corporation) subregions. 

Indirect Emissions Analysis ▪ 1 
 

 

 

 

 

electricity. Finally, there may ultimately be a total emissions reduction if the lower cost 

and greater availability of allowances permits regulators to tighten the total emissions 

cap. 

This report is organized as follows: 

• In Section 2, we develop and present operating characteristics of wind, landfill gas, 

and municipal solid waste energy resources in each of the eGRID subregions of the 

United States. We develop hourly operating profiles for a “typical” such resource in 

each region, normalized to produce one GWh4

 of electricity per year. 

• In Section 3, we collect and present data on conventional power plants in each 

eGRID region based on the EPA’s Acid Rain database.5

 We characterize the data 

and discuss our approach to data filtering and error handling, in preparation for 

characterizing the indirect emissions benefits associated with changes in dispatch. 

• In Section 4, we implement and evaluate a number of approaches to applying the 

filtered and cleaned EPA data for the purposes of calculating hourly avoidable 

emissions factors. These factors can then be applied to renewable resource 

production profiles to calculate annual indirect emissions benefits. In addition, we 

present hourly indirect emissions results for each pollutant, region, and calculation 

approach under consideration, and we discuss which approaches make sense for 

which kinds of analyses. 

• In Section 5 we apply the indirect emissions calculation methods to determine 

indirect emissions benefits associated with each resource type under consideration 

for each region and pollutant. 

• Finally, in Section 6, we discuss our results and present suggestions for further 

research in this area. 

Fundamental to the analysis in this report, all of which is based on retrospective data, is 

the primary focus on short-term, operational response of the electric system to the 

presence of new renewable energy resources. This analysis is thus most appropriate for 

analyzing the impact of a relatively small quantity of new resources during the first few 

years of operation, during which the displaced resources are likely to be the most flexible, 

load-following units on the system. Over the long term, it is likely that the capacity mix of 

an electric system will be altered as a result of the addition of new renewable energy 

resources. Specifically, new generating capacity investments may be avoided or 

deferred, or existing capacity may be retired, until the system returns to equilibrium in 

terms of the balance of base load and load-following resources. Ultimately, it may be 

expected that the proportion of flexible units will be restored to what it would have been 

without the new resource. In this sense, as it matures and is incorporated in larger 

quantities, renewable energy technology will ultimately displace more base load capacity 

4 A GWh (gigawatt-hour) is one thousand megawatt-hours, approximately the amount of power that could be produced by 

one of the largest power plants in the United States in one hour. For perspective, the largest new single wind turbines 

are about 3 MW in capacity but produce, on average, about one MWh of electricity per hour. Thus one of the largest 

wind turbines would be expected to produce about nine GWh of electricity each year. In general, wind projects have 

more than one wind turbine, and can have up to 200 or more. 

5

 Available from the EPA Clean Air Markets data website, at http://camddataandmaps.epa.gov/gdm/ 

Indirect Emissions Analysis ▪ 2 
 

 

on the system. While we touch on this important issue in some parts of this report, it is 

not a primary focus.6 

The indirect emissions benefit calculated for any kind of renewable energy resource 

depends on a number of questions: 

•	 Where is the resource located? 

•	 What is the pollutant of interest? 

•	 Is the time period of interest historical, in the near future, or several years in the 

future? 

•	 Is the resource base load, dispatchable, or intermittent and nondispatchable? 

•	 If intermittent and nondispatchable, what is the expected hourly and seasonal profile 

of the resource? 

Because of the wide range of applications reflected in the possible answers to these 

questions, the best method to use in calculating indirect emissions benefits can vary with 

the application. In this report we present a number of methods, and apply them to three 

types of renewable resources and three pollutants in 22 regions of the United States. 

Even this level of detail is almost certainly insufficient for precise analysis of the 

emissions benefits of individual projects. However, it leads to illuminating and useful 

results that will allow for a far more detailed understanding of the issues and a better first 

approximation of the indirect emissions benefits than has previously been available. 

We find: 

•	 The indirect emissions benefits of renewable energy for all pollutants vary 

significantly by region, and that these differences can be quantified and applied in 

calculating the indirect emissions reductions. 

•	 For each GWh of renewable energy produced each year, the indirect CO2 emissions 

benefit is found to be between 600 and 1000 tons of CO2, with coal-dependent 

regions having the highest indirect emissions benefit. 

•	 For each GWh of renewable energy produced each year, the indirect NOx emissions 

benefit varies between 500 and 5,000 pounds of NOx. 

•	 For each GWh of renewable energy produced each year, the indirect SO2 emissions 

benefit varies between 200 and 1,300 pounds of SO2. 

•	 These results are robust regardless of whether the renewable resource has a base 

load profile (such as MSW and LFG) of output which varies diurnally and seasonally 

(such as wind). 

•	 Different calculation approaches are appropriate for different types of resources and 

different applications. 

6

 For a thorough treatment of this issue, see 2005 Synapse report Using Electric System Operating Margins and Build 

Margins: Quantification of Carbon Emission Reductions Attributable to Grid Connected CDM Projects, available at 

http://www.synapse-energy.com/ 

Indirect Emissions Analysis ▪ 3 
•	 More research is needed to establish which methods of calculating indirect emissions 

changes most accurately reflect real-world dispatch over a range of timescales and 

system conditions. 

Of the methods introduced here, we find the flexibility-weighted hourly average emission 

rate (FW-HAER) to be the most appropriate for near-term (<3 year) projections of indirect 

emissions impact from wind resources, because it best represents the response of the 

electric system to rapid fluctuations in system load. We find the seasonal slope factor 

approach to be most appropriate for calculating the indirect emissions impact from base 

load resources such as municipal solid waste and landfill gas electricity generation. 

The annual indirect emissions results for these resources based on 2005 data, assuming 

one GWh of energy was produced by each over the course of the year, are shown in 

Figure 1. With some notable exceptions, the indirect CO2 and NOx emissions benefits of 

the different renewable energy resources is generally similar; this benefit diverges most 

dramatically by resource type for SO2. This divergence probably reflects the fact that in 

many regions, base load resources like MSW and LFG can displace coal, which emits 

SO2, while wind initially primarily replaces gas, which does not emit SO2. However, with 

increasing penetration and geographic diversity of wind generation it is likely that this 

resource will begin to displace more base load coal, and the indirect emissions impact of 

wind generation will be closer to that of MSW and LFG. Displaced Tons of CO2 per GWh 

1,200 

1,000 

800 

600 

(a) 

400 

200 

0 

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC
 

Wind MSW/LFG 

 

Figure 1. Indirect emissions results for (a) CO2; (b) NOx; and (c) SO2 for wind, municipal solid 

waste (MSW) and landfill gas (LFG) generation projects. (Continued on next page) 

Indirect Emissions Analysis ▪ 4 
(c) 

(b) 

Wind MSW/LFG 

Displaced Pounds of SO2 per GWh Displaced Pounds of NOx per GWh 

4,000 

2,000 

0 

Wind MSW/LFG 

14,000 

12,000 

10,000 

8,000 

6,000 

6,000 

5,000 

4,000 

3,000 

2,000 

1,000 

0 

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI NYLI

NYUP NYUP

RFCE RFCE

RFCM

RFCW

RMPA

RFCM

RFCW

RMPA

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC
 

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC
 

Figure 1 (Continued) 

Indirect Emissions Analysis ▪ 5 
 

 

 

2. Operating Data on Renewable Energy Technologies 

In order to determine the indirect emissions reduction impacts of electricity production 

from renewable energy sources, it is necessary to first establish what kinds of 

conventional resources they are displacing. As a first step, in this section of the report we 

explore the operational characteristics of three types of renewable energy resources: 

wind power generation, municipal solid waste combustion, and landfill gas. We develop 

profiles for these resources representative of each eGRID subregion of the continental 

United States7

 based on historic data and operating characteristics of these resources. 

(The eGRID subregions are identified and mapped in Appendix A.) Once we have 

determined their hourly output profiles over the course of a historic year, we can begin an 

assessment of which resources they would have been likely to displace. 

In the section that follows, we will explore corresponding operational and dispatch 

characteristics of conventional resources. Following that we combine the results of these 

research tasks, characterizing renewable energy output and assessing displaceable 

conventional resources, to make a final assessment of which fossil resources would have 

been likely to be displaced by renewables in each eGRID subregion. Finally, we use the 

results of this combined analysis to determine factors representing the indirect emissions 

benefit for each subregion. 

A. Wind Generation: Existing and Synthetic Projects 

The American Wind Energy Association (AWEA) estimates that there are approximately 

8.3 gigawatts (GW) of installed wind capacity in the United States as of the 4th quarter of 

2007, with another 4.1 GW under construction.8

 Texas and California dominate the 

current market, but there are underutilized regions throughout the Midwest. Some states 

with the greatest wind potential, such as the corridor stretching from North Dakota south 

through Kansas, have yet to significantly tap this resource. In general, there are 

extensive opportunities to expand the rapid growth in wind generation, resulting in the 

further displacement of fossil fuel generation and decreased emissions of greenhouse 

gases and other pollutants. This large resource potential motivates the need to 

understand in more detail the emissions benefits associated with renewable energy 

projects in each region. 

Ideally, to characterize the output profiles for new wind generation projects, we would 

draw empirical wind power output datasets from a broad sampling of existing projects 

around the country. Unfortunately, wind generation projects are not required to publicly 

report hourly generation, and thus there are limited opportunities to calculate expected 

emissions reductions from existing resources. In addition, “real” wind projects present 

numerous hurdles that make them problematic as a source of research data for a broadbased study of operational characteristics. For example, there is generally no uniformity, 

and often no available information, on the technical details of existing resources. With 

7 eGRID subregions are almost coincident with NERC subregions. We chose to use eGRID subregions for this analysis 

as this is the system used by EPA for emissions reporting. 

8 American Wind Energy Association, 2007. Updated information can be found at http://www.awea.org/projects/. 

Indirect Emissions Analysis ▪ 6 
 

 

operational data available on few projects, what may appear to be regional differences in 

operating characteristics would be heavily influenced by unknown differences in 

technology, turbine size, turbine height, vintage, and specific local conditions associated 

with each individual project. In addition, the geographical coverage of available wind 

power output data is extremely limited. In fact, we were only able to find one source for 

such data, from the U.S. Department of Energy’s National Renewable Energy Laboratory 

(NREL). This dataset covered only a small number of projects with limited geographic 

distribution. Even with this source we were asked not to present specific, identifiable 

details or operational data to avoid disclosure of proprietary information, limiting its 

usefulness for our study. 

Our solution to these limitations is to simulate wind power projects based on 

meteorological data gathered at locations where wind projects are likely to be built in 

each region, determined based on the quality of the wind resource. While wind power 

data are scarce, wind speed data are abundant and readily available at reasonably high 

resolution throughout the United States. Near-surface hourly wind speed time series data 

can be scaled to reflect a typical turbine height, and then converted with the help of a 

“power curve”9

 into corresponding simulated wind power output time series. We have 

attempted to perform a comparison of one of our simulated wind power time series with 

the output of a nearby physical wind-power project; however, the results are difficult to 

interpret. The wind power and the wind data are not from exactly the same location or 

height, and even such small differences can be extremely significant in determining the 

output of a resource. The technology and vintage represented by our power curve is also 

unlikely to be well matched to the actual technology and vintage of the specific wind 

turbines for which we have data. Even so, this approach provides a useful means of 

evaluating the potential impacts of wind energy production on emissions. The data 

sources and comparison results are described in more detail below. 

Data Sources for Synthetic Generation 

Over 1700 meteorological stations collect hourly data throughout the United States and 

territories for the National Climate Data Center (NCDC) of the National Oceanic and 

Atmospheric Administration (NOAA). Our primary source of wind data for generating 

synthetic wind power time series was NCDC’s Weather Bureau Army Navy (WBAN) 

stations, distributed throughout the United States. To estimate the temporal 

characteristics of potential wind power generation, and thus expected indirect emissions 

reductions for each eGRID subregion, we needed to determine likely locations for new 

wind generation projects. We did this by reviewing the highest resolution available 50­

meter wind speed estimates in each eGRID subregion, as cataloged by the NREL,10 to 

determine where favorable wind resources were likely to be located based on average 

wind speeds at 50-meter hub heights. We assumed that the minimum economic wind 

speed threshold for a wind power project would be at Class 4 wind speeds, or winds of 

9 A “power curve” is a graph which relates wind speed to expected power production for a specific wind turbine technology 

and configuration. Actual power output data from wind turbines generally exhibit considerable scatter around the 

expected power curve, but this remains the best means of characterizing this relationship. 

10 USDOE NREL Wind and Hydropower Electricity Program, 2007. Available online at: 

http://www.eere.energy.gov/windandhydro/windpoweringamerica/wind_maps.asp 

Indirect Emissions Analysis ▪ 7 
0 16.25 WS = WS ∗ i, j i, j

WS j

15.7-16.8 miles per hour (mph). Once the area was selected in this manner, the closest 

high-quality NCDC station wind data location was selected for each subregion. Twentytwo NCDC meteorological stations near these locations were selected as proxies to 

represent the temporal pattern of wind generation expected at these sites. The locations 

of the sites and summary wind characteristics from the selected NCDC WBAN stations 

may be found in Appendix C. 

It must be noted at the outset that the geographic granularity of the wind data is far from 

satisfactory. Wind conditions can change significantly over spatial scales as small as a 

few kilometers or less, so representing each eGRID subregion with a single site entails a 

considerable simplification. We have tried to choose sites which are reasonably 

representative of promising wind power locations for each region, understanding that 

considerably more research and analysis would be needed to pick a truly representative 

site, if indeed a single site can stand in for a large region. The same can be said for solid 

waste and landfill gas projects considered in our analysis: there is simply not enough 

information available to confidently predict the behavior of all potential future sites. This is 

an inevitable shortcoming of attempting to represent site-specific characteristics on a 

national scale, and it should be kept in mind by the reader in interpreting our results. 

Generation of Synthetic Wind Power Data 

WBAN meteorological stations are typically located at local and regional airports, or near 

rural towns, at 10 meters (33 ft) above ground level. Due to surface friction, wind speeds 

near the ground are significantly lower than speeds at expected 50-100 m windmill hub 

heights. In addition, the WBAN stations collect meteorological data in irregular intervals, 

with increments of approximately one hour. To create hourly synthetic time series, we 

used the simple average of the two measurements surrounding each hour. Next we 

normalized wind speeds to Class 4 speeds by scaling the time series for each station to 

an average speed of 16.25 mph, as follows: 

(1) 

Where: 

WSi,j = Adjusted wind speed during hour i and station j 

WS0

i,j = Unadjusted wind speed during hour i and station j 

and WS j = Average unadjusted wind speed at station j over all hours. 

Wind scales non-linearly with height, increasing in speed as the influence of ground-level 

friction decreases. However, we assume that a proximal Class 4 wind site would have 

similar temporal characteristics to the meteorological station, and thus our approximation 

is to scale the WBAN wind data linearly to reflect Class 4 characteristics. This approach 

encompasses a host of decisions, design parameters, and operational characteristics into 

a single scalar that represents, roughly, the decisions made by the builder of a wind 

power facility to optimally exploit a local resource. 

Indirect Emissions Analysis ▪ 8 
 

 

To convert the scaled wind data into synthetic wind power data, it was necessary to 

select a proxy technology with an appropriate wind power conversion function. We used 

a wind power curve based on the General Electric (GE) 1.5se (1.5 MW nameplate 

capacity) wind turbine to estimate potential generation associated with the wind time 

series at each selected meteorological station. The power curve, shown in Figure 2, is 

determined by a number of factors including the area swept by the blades, the “pitch” or 

angle of the blades (which can be variable in different wind conditions), and 

characteristics of the turbine gearbox. Wind turbines require a minimum amount of wind 

to begin generating power, known as the cut-in speed, and are designed to stall, or even 

brake, at higher wind velocities (the cut-out speed) to avoid damage.11 Once again, we 

do not know a priori what technology or set of parameters would be most appropriate for 

a wind turbine in each area, but by scaling the winds to match a proxy turbine we 

generate a series that preserves temporal variability while representing a project which is 

optimized to local conditions. 

Figure 2. Wind power curve for a GE 1.5se (1.5 MW) wind turbine. 

Source: Http://www.gepower.com/prod_serv/products/wind_turbines/en/downloads/ge_15_brochure.pdf 

The GE 1.5se proxy turbine is a common mid-sized turbine used on-shore; larger 

turbines are generally used offshore due to high wind conditions. The design tower height 

for these turbines is 54.7 meters (m), with 35.25 m blades sweeping an area of 3,904 m2

. 

The cut-in speed is 4 meters per second, and the cut-out speed is 25 meters per second. 

To generate the wind power time series, we relied upon a GE-supplied power curve 

which was imported as a look-up table for our analysis; the look-up table indexes the 

power curve and returns an estimated generation value for each hour given the 

calculated wind speed at turbine height. 

11 Tony Burton, David Sharpe, Nick Jenkins, Ervin Bossanyi, Wind Energy Handbook, John Wiley & Sons, 2001. 

Indirect Emissions Analysis ▪ 9 
20% 1800 

2% 

4% 

6% 

8% 

10% 

12% 

14% 

16% 

18% 

Percent of hours 

Raw Wind Speed

Scaled Wind Speed

Power Curve 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

Power output from 1.5 MW generator

(kW) 

0% 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 

Wind speed (mph) 

0 

Q = a

b−cWS 1+ e

 

For purposes of our analysis, we approximated the GE wind power curve with a sigmoid 

function: 

(2)

Where Q is output (kW) and WS is wind speed (m·s-1). The best fit to the GE curve in 

these units was obtained with parameter values a=1500, b=8.1, and c=0.9. The curve 

was truncated to zero at the cut-out speed of 25 m·s-1 The resulting curve is shown in 

Figure 3, along with reported and scaled wind data. 

Figure 3. Proxy wind turbine power curve plotted against histograms of raw and scaled wind speeds at a 

sample site in the Midwest. “Scaled” wind speeds are adjusted to reflect class 4 winds at a turbine height of 

50-100 meters using Equation 1. 

Comparing Empirical and Simulated Wind Generation 

As noted earlier, we were able to find one source of wind power data for comparison with 

our synthetic power profiles. The wind power time series that we were able to obtain 

represent twelve operational projects associated with the NREL wind farm monitoring 

program. The NREL data cover twelve sites including two in Minnesota, one in Iowa, one 

in Oklahoma, four in Texas, and four in Oregon. Total capacities ranged from 25 MW to 

230 MW, and the turbines ranged from 0.6 to1.6 MW each. While NREL was able to 

make 2005 production records from these projects available for research purposes, siteidentifying information remains confidential. The turbines represent primarily plain and 

low-ridge wind farms; the farms in Iowa and Minnesota are distributed throughout active 

agricultural land, while the Oregon, Oklahoma, and Texas sites partially overlap grazing 

lands. 

Indirect Emissions Analysis ▪ 10 
To test whether our simulated wind generation time series were realistic and 

representative of the output profile for a physical wind project at the same location, we 

selected an NREL wind project from a midwestern U.S. location and compared the 

recorded output with the simulated production time series generated using wind data 

from a nearby location. The test project is in a region with Class 4 and Class 5 wind 

speeds, running 145 turbines of 750 KW each, for a total capacity of 108 MW. A nearby 

WBAN meteorological station was located, and wind records for this location extracted 

from the NCDC data. The wind data were transformed through the power curve (Equation 

2) to generate hourly power output. 

Such a comparison has a number of complications, including: 

•	 Gaps in wind data. In the case of the selected series, there are 84 gaps larger than 

2 hours in duration, and 9 gaps longer than 24 hours in duration. The longest gap is 

nearly a week in duration. We have no way of knowing whether these were neglected 

because they were zero, or they were very high, or they simply represent random 

data errors, Thus it is impossible to determine what effect their omission may have on 

our analysis. 

•	 Gaps in power output data. The output time series for the installed project has 552 

missing hours in the sample year (6% of all hours) which were removed from the 

analysis. As with the missing wind data, it is impossible to determine what effect their 

omission may have on our analysis. 

•	 Granularity of wind data. Wind speed data are taken approximately once per hour, 

meaning that variations in wind speed within each hour are unknown. In addition, the 

wind data show recording artifacts, such as certain values that rarely appear in the 

record including any measurements between approximately 0.5 and 2.75 mph. We 

have not attempted to perform any smoothing to compensate for this. 

•	 Representativeness of wind site. While the wind data are taken from a 

meteorological station in proximity to the turbine, the station is clearly not measuring 

winds identical to those that drive the turbine. This lack of correspondence to actual 

values is exemplified by the average wind speed in the meteorological record of only 

9.71 mph; to scale to the Class 4 average of 16.25 mph winds, we multiply all values 

by 1.67, consistent with Equation 1. In addition, a wind power installation can contain 

hundreds of turbines, each responding to highly localized conditions and each 

subject to maintenance schedules and other operational constraints which are not 

captured in the power curve. 

•	 Idealization of power curve transformation. Each hour’s scaled wind speed is 

transformed into an hour of power output assuming a perfect generator response to 

steady winds, neglecting any effects such as generator inertia which may dampen 

the response. Wind speeds are implicitly assumed to hold steady for exactly one hour 

before instantly changing to the next hour’s value, again with an instantaneous 

response from the turbine. 

The comparison of simulated and observed power output is shown in Figure 4. In this 

analysis we take the maximum recorded output for the year as the maximum output for 

the station, although this value is only about 90% of the nameplate capacity of the 

Indirect Emissions Analysis ▪ 11 
 

 

installation. This assumption presumably corrects for scheduled maintenance and forced 

outages which would affect some subset of turbines at all times. 

The simulated output curve shows an exaggerated peak at 5%-10% of capacity relative 

to the recorded power output series, and a peak at the 100% output level which is absent 

in the observed data. These peaks likely reflect the granularity issues identified above for 

the wind records, which can reflect just one or two observations for each hour while the 

measured power output reflects inevitable variations in wind speed and output over the 

course of each hour. For the simulated project the overall capacity factor over the course 

of the study period is 32.8%, while for the observed project the overall capacity factor is 

30.3%. 

0% 

5% 

10% 

15% 

20% 

25% 

30% 

0% 

5% 

10% 

15% 

20% 

25%

30%

35% 

40% 

45% 

50% 

55% 

60% 

65%

70%

75% 

80% 

85% 

90% 

95%

100% 

Percent of maximum output 

Percent of hours 

Simulated Generation 

Observed Generation 

Figure 4. Comparison of power output distribution at sample wind power site in the Midwest with predicted 

output based on scaled wind data from a nearby meteorological station. Output levels for observed 

generation are relative to the maximum recorded output for the year. 

The simulated and observed generation, as well as the difference between the two, are 

shown on color charts in Figure 5. These charts represent each hour of the year as a cell 

on a 24-hour by 365-day matrix, with each cell color-coded according the scheme shown. 

Figure 6 shows a cumulative distribution of the hourly deviation between the two, in 

percentage points. The simulated output exhibits a pronounced diurnal cycle which is not 

present in the project data. This discrepancy could be due to any number of factors, 

including light winds which blow through the evening at hub-heights which do not appear 

in the NCDC dataset (NCDC does not record wind speeds less than 3 mph at ground 

level) or more sensitive wind turbines with lower stalling speeds than the proxy turbine, 

which would produce a generally flatter distribution of power generation throughout the 

day. This discrepancy may also simply reflect the difference in the location of the two 

sites. The two time series are within 20 percentage points of each other during 

approximately 65% of the 6,434 hours recorded in both datasets. 

Indirect Emissions Analysis ▪ 12 
 

 

Figure 5. Comparison of hourly synthetic and observed wind power time series at nearby locations in the 

Midwest. Each plot shows 8,760 hours in 2005, with hour of the day on the vertical axis and day of the year 

on the horizontal. Missing data shown in black. Top: Observed wind power output (% of maximum); Middle: 

Synthetic wind power output (% of capacity) based on scaled wind speed data; Bottom: Hourly deviation of 

synthetic from observed time series. 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

100% 

Difference in Percent of Maximum Output 

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Non-Exceedence Hours (%) 

Figure 6. Cumulative distribution of the hourly deviation (in absolute percentage points relative to maximum 

output) between observed and synthetic generation time series. 

Indirect Emissions Analysis ▪ 13 
 

 

 

 

While there are clearly significant discrepancies between these two datasets, we feel that 

the NCDC meteorological dataset is a reasonable, if by no means ideal, resource for 

estimating temporal patterns and potential offsets from projects which might be built in 

each eGRID region. 

The hourly synthetic output generated for each of the eGRID subregions is shown in 

Appendix B as 24-hour by 365-day color intensity plots. Monthly summary tables of raw 

and scaled wind speeds, as well as synthetic wind power, may be found in Appendix C. 

The hourly data are available in electronic format upon request. 

B. Landfill Gas 

To determine the operational and dispatch characteristics of landfill-gas-fired generating 

facilities, we contacted several landfill gas (LFG) project developers.12 We received 

hourly generation data for several landfill gas projects from Granger Electric Company 

and Granger Energy, LLC, (“Granger”) which together own and operate six landfill gas 

projects in Michigan, and 10 LFG projects in Indiana, Ohio, and Pennsylvania. 

According to Granger, the generation profile of LFG projects generally does not differ 

significantly by time or season.13 The primary factors that impact the energy output of 

LFG projects have to do with ambient conditions. The warmer and the wetter the climate, 

the higher the methane content of the landfill gas, such that more power can be produced 

from this resource. 

High temperatures also affect the plant’s output by generally reducing the temperature 

gradient that drives the turbine. This temperature effect is minimal for the reciprocating 

engines which are used by Granger. Gas turbines appear to be the predominant choice 

for LFG projects by other companies. Gas turbine outputs are more susceptible to 

variation in temperature. This characteristic largely mirrors the behavior of base load 

generating plants, and system operators can reasonably expect a constant and 

predictable stream of energy output from these facilities. Thus we conclude that the 

fossil-fuel-fired generation displaced by landfill gas in any region of the country will be 

similar in operational and emissions characteristics to typical base load resources in that 

same region. 

C. Municipal Solid Waste 

Municipal Solid Waste (MSW) generators, also known as waste-to-energy facilities, are 

currently operating in 27 states and burning roughly 95,000 tons of garbage each day, 

generating roughly 2.5 GW of electricity.14,15 Even with this large volume of output, the 

12 Landfill gas project operators were identified by Rachel Goldstein of the EPA Landfill Methane Outreach Program: 

http://www.epa.gov/lmop/. 

13 Granger does have one project which operates as peaking generation, but this appears to be an anomaly and does not 

affect our conclusions about general LFG generation characteristics. 

14 New York State Department of Environmental Conservation, http://www.dec.ny.gov/chemical/8979.html. 

15 Because some portion of the fuel supply for MSW generators is generally fossil-based, there is some dispute over 

whether this resource should be considered “renewable” or not. However, as a number of state programs include MSW 

as a renewable energy source (see dsireusa.org for updated details), we treat it as a renewable resource for the 

purposes of this report. 

Indirect Emissions Analysis ▪ 14 
 

 

facilities are not fuel-limited; our research and discussions with facility operators reveals 

that waste-to-energy plants run at high capacity factors of 85% and above. As with landfill 

gas projects, these characteristics suggest that they serve as base load capacity, and 

that the fossil generation they are likely to displace is also base load capacity. Thus we 

conclude that the fossil generation displaced by MSW in any region of the country will be 

similar in operational and emissions characteristics to typical base load resources in that 

same region. 

3. Hourly generation and emissions data 

In this section we characterize the operational, dispatch, and emissions characteristics of 

conventional power plants in order to predict the change in dispatch and emissions 

resulting from the presence of new renewable energy resources. 

Electricity grids are often characterized as having a range of resource types including 

base load, intermediate, and peaking resources. These resources respond differently to 

variations in load over different timescales: put simply, base load resources run most 

hours regardless of load levels, intermediate resources ramp up and down frequently in 

response to changes in system load, while peaking resources run only during the highest 

load periods. However, real-world electric grids are more complicated than this, and realworld resources have complex operating constraints and practices depending on factors 

including heat rate, ramping capability, demand and price in the energy and ancillary 

service markets, availability of competing resources, local transmission constraints, 

environmental constraints, operator or dispatcher discretion, and even the warrantee 

and/or service contracts on specific pieces of generating equipment. It is thus impossible 

to predict with specificity which resources would respond to perturbations in load or 

available low-cost energy. However, historical data on unit operations can lend insight 

into which units are more or less likely to be displaceable at any point in time.16 To the 

extent that these operational data are coupled with emissions characteristics, they can be 

used to estimate the likely displaceable emissions at any point during a historical year. 

Such an approach would be more refined than using a simple average emission rate at 

any point in time to represent displaceable emissions, because it would be more sensitive 

to the actual dispatch characteristics of individual generating plants. 

To implement this approach, we obtained generation and emissions data for United 

States fossil-fuel powered generating plants for 2005 from the Hourly Emissions 

database of the EPA Clean Air Markets Programs.17 The database consists of hourly 

reported generation, heat rate, and total emissions of CO2, SO2, NOx, and mercury for all 

fossil fuel-fired generators with nameplate capacities above 15 MW. These data form the 

basis of all of the displaceable emissions analysis reported here. We use the hourly 

16 It is common to think of generating resources as responding only to changes in electricity demand; however, certain 

intermittent renewable resources, such as wind generation, change the load on fossil resources in a very similar manner 

to variations in load. Thus we treat the availability of energy from such resources in the same manner that we would a 

reduction in energy demand from consumers. 

17 EPA, 2007. Data Sets and Published Reports: Emissions Data Prepackaged Data Sets. Online at 

http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=prepackaged.select 

Indirect Emissions Analysis ▪ 15 
 

generation data to characterize the operational characteristics of generating units in each 

NERC subregion, and we use the associated emissions data to characterize the indirect 

emissions benefit that would be associated with displacing the output of each generating 

unit. 

Characterization of emission rates 

Hourly emissions are reported in the Clean Air Markets database for CO2, NOx, and SO2. 

Within the database, the data records include fields to indicate whether the reported 

emissions are measured or calculated. Calculated emissions are based on the 

composition of the fuel, the unit’s heat rate, and the output of the unit. In general, it 

appears that most of the emissions data in the database for 2005 are measured. 

Pollutant emission rates are reported in the hourly emissions database as tons per million 

British thermal units (mmBtu) for CO2, and pounds per mmBtu for NOx and SO2. For the 

purposes of this analysis it is necessary to consider emissions in units of mass per MWh 

output. However, while emission rates per unit of heat input may be fairly constant, 

emission rates per unit of energy output can vary considerably—this variation is due to 

changes in the efficiency (or “heat rate”) of a generating unit with different output levels. 

For purposes of this analysis, we will use the overall average emissions rate for each unit 

as derived from the EPA hourly emissions database, unless otherwise noted. 

Data filtering 

The EPA emissions data contain anomalous features which are indicative of either 

reporting or calculation errors. While the hourly generation data are used to characterize 

generator behavior and identify times at which the generator is operational, emission 

rates could be misleading if anomalous data were included. Therefore, data are removed 

from the emission rate analysis according to the following rules: 

• Generation, CO2, or heat rate is reported as zero. 

•	 The reported heat rate is in the top 99.5th or bottom 0.5th percentile (for a record with 

8,760 non-zero hourly values, this would remove the 44 highest and 44 lowest 

values). 

• CO2 emission rate is above 2.5 tons of CO2 per megawatt-hour (tCO2 MWh-1) or less 

than zero. 

We did not filter on the basis of NOx or SO2 measurements as we had no basis for 

judging reasonable emission rate limits. 

Indirect Emissions Analysis ▪ 16 
 

 

4. Emissions Displacement Methods 

In order to calculate the indirect emissions benefits associated with power production 

from renewable resources, it is necessary to determine which power plants will have their 

output curtailed as a result of the availability of the new resource. This need arises from 

the recognition that the emission rates of power plants which will be running in either 

case (with and without the new resource) do not affect the calculation; nor do those of 

power plants that would not be running in either case. In fact, it is only a small number of 

plants, as few as one marginal resource in any given dispatch interval, that will determine 

the relevant emission rate. The difficulty comes in identifying which plant or plants to 

consider, and thus in identifying the appropriate emissions rate or rates for each interval. 

There are numerous factors that affect which power plants operate, are committed to 

operate, or are held in reserve at any point in time. These factors include: 

•	 Offer price or short-run marginal cost; 

•	 Constraints such as ramp rates, minimum up and down times, and other operating 

restrictions; 

•	 Transmission or other dispatch constraints; 

• Maintenance schedules; 

•	 Unplanned outages and/or deratings; and 

• Environmental constraints. 

Unfortunately, most of this information is not available from public sources for most 

resources. Using only the information which we have available from the EPA database 

(i.e., hourly generation, heat rate, and emissions), it is impossible to reconstruct all of the 

factors that guide dispatch and to determine the precise marginal emissions rate in effect 

for any particular hour. 

Our solution is to infer operational characteristics from the suite of units in each region 

based on their patterns of operation and emissions over the course of the study year. 

There is a broad range of possible approaches to interpreting the data in this manner. 

Some of these approaches use emissions characteristics aggregated over a season or 

year, and some are more directly focused on hourly analysis.18 Each approach carries its 

own implicit model about system operation and response to new resources, and each 

may be more appropriate than others to address the impact of certain kinds of resources 

or over specific time scales. In this analysis, we present five methods, ranging in 

simplicity, design, and conceptual model, and explore the implications of each. These 

methods are: 

18 Earlier investigations based on extracting hourly performance information from the same EPA database used here: 

•	 Connors, S., K. Martin, M. Adams, E. Kern, and B Asiamah-Adjei. 2005. “Emissions Reductions from 

Solar Photovoltaic (PV) Systems” Publication MIT LFEE 2004-003 Report. 

•	 Berlinski, M. and S. Connors, “Economic and Environmental Performance of Potential Northeast 

Offshore Wind Energy Resources: Final Report”, Report to the Offshore Wind Collaborative Pilot 

Research Projects, January 2006. 

Indirect Emissions Analysis ▪ 17 
 

 

• Hourly Average Emissions Rate (HAER); 

• Annual (and seasonal) emissions slope factor (Slope Factor); 

• Empirical Incremental Emissions Rate (EIER); 

• Load-Following Rate Incremental Emissions Rate (LFIR); and 

• Flexibility-Weighted Hourly Average Emissions Rate (FW-HAER). 

A. Limiting Cases 

Before delving into each method in detail, it is useful to characterize the most extreme 

emission scenarios to bound the results of our analysis, particularly with respect to CO2. 

In producing a single MWh of electricity, power plants fueled by coal (the most carbonintensive fossil fuel) typically produce somewhat over one ton of CO2; modern plants 

fueled by natural gas produce about 0.6 tons of CO2 per MWh.19 The exact emission rate 

for each plant depends on technology, age, maintenance, and specific fuel (i.e., some 

coals have a higher carbon content than others for the same energy output.) The ranges 

rates by fuel type. 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

Tons per MWh 

0% 

5% 

10% 

15% 

20% 

25% 

30% 

35% 

Percent of plants 

Gas 

Oil 

Coal 

Figure 7. Distribution of CO2 emission rates for fossil generating plants by reported fuel type.
 

Note that reported primary fuel is not a precise categorization, as some units are capable of 

switching fuels and some plants may actually have units which burn different primary fuels.
 

The difference between emission rates from coal and gas essentially bounds the range of 

outcomes in accounting for the displaced emissions associated with renewable 

http://www.eia.doe.gov/cneaf/electricity/page/co2_report/co2emiss.pdf;. Values in the EPA database confirm these numbers: 

the median emission rate for a coal plant is 1.024 tCO2 MW-1, while for a gas plant it is 0.697 tCO2 MW-1
 

of reported hourly emission rates for gas, coal, and oil-fired plants in the EPA database 

are shown in Figure 7. While this figure highlights some of the anomalous data in the 

database, such as coal plants which report unrealistically low CO2 emissions, it does 

show that the reported data are generally consistent with the expected range of emission 

Indirect Emissions Analysis ▪ 18 

19
 

∑CO2 (t) HAERt =

∑ MW (t) 

 

 

 

 

generation. Renewable resources cannot displace more carbon emissions than the 

emission rate of an inefficient coal plant on a per-MWh basis; nor is it likely that they will 

displace less CO2 for each MWh generated than the amount emitted by an efficient 

combined cycle gas plant. This is because other low-carbon sources of power (such as 

nuclear and hydro) have low running costs and are unlikely to be displaced.20,21 In 

general, the result will lie somewhere between these two extremes. 

For NOx, the story is somewhat more complicated. NOx emissions tend to be bifurcated 

into ozone-season emissions (May through September), when emissions are regulated 

and many power plants are operating emissions control technology; and non-ozone 

season emissions, when they are not. As will be shown below, in some areas this 

bifurcation is more pronounced than in others. However, NOx emission rates generally 

range from zero to perhaps five or six pounds per MWh, with ozone season rates 

generally one or two pounds below non-ozone season rates. 

SO2 emission rates depend upon the fuel used for generation, with natural gas having no 

sulfur (and therefore producing no SO2) and certain types of coal having the most. Thus if 

only gas generation is displaced, there is no displacement of SO2 emissions. For this 

reason, we would expect SO2 to have perhaps the largest regional displaceable 

emissions differences of all of the pollutants considered, with ranges from zero up to 

about eight pounds per MWh for regions that rely on coal resources for load following 

capacity. 

To narrow these ranges, it is necessary to find a means to identify which types of plants 

are more likely to be displaced in any given region. All of the methods described in this 

section are designed to refine the estimate of displaced emissions based on the 

observed operational characteristics of the system as deduced from the EPA database. 

For clarity, we will develop the displacement methodologies with primary reference to 

carbon dioxide emissions since it is the primary anthropogenic cause of global warming. 

However, results will be presented for all three of the pollutants considered here. 

B. Hourly Average Emissions Rate (HAER) 

One of the most conceptually straightforward methods for estimating displaced emissions 

is to use a simple hourly average. This method calls for adding the aggregate emissions 

from an eGRID subregion (for example, in tCO2) for each hour of the study period and 

dividing this by aggregate fossil generation (MWh) for the same hour: 

(3)

20 As noted earlier, displaced emissions are not necessarily equivalent to avoided emissions, as emissions policies and 

other factors can come into play in determining total emissions in any given area. 

21 There are exceptions to this statement – in some cases storage hydropower may be the most flexible resource 

available to follow load. However, storage hydro is energy-limited resource, so total generation from such a facility will 

not be affected by the presence of a new renewable resource. 

Indirect Emissions Analysis ▪ 19 
 

 

Where t denotes a particular hour, and the summations are over all of the generating 

units in the region of interest. 

Equation 3 produces the average emissions rate across all fossil units for a specific 

region and hour, with the implicit assumption that every fossil plant running during that 

hour has an equal probability of being displaced. This is clearly not the case on an hourto-hour basis: certain units have limited ramping capability and other operational and 

economic constraints which dictate that they only be used as base load resources. 

However, this method is fairly straightforward to understand and apply, and as noted 

earlier we cannot know for sure which units would be displaced in any hour. In addition, 

as the market matures with higher levels of renewables penetration, it is reasonable to 

imagine that units throughout the supply curve may ultimately be displaced. Finally, this 

method may be the most appropriate for base load renewables such as landfill gas or 

municipal solid waste combustion, for which output is stable and predictable. Thus it may 

be reasonable to quantify displaced emissions using this approach if more of a long-term 

or base load impact is under consideration for any of these reasons. 

Figure 8 shows selected representations of the variations in HAER for CO2 in the New 

England (NEWE) eGRID subregion. The first representation is a timeline of hourly 

variations, demonstrating that the CO2 emissions rate fluctuates between about 0.65 and 

0.95 tCO2 per MWh, with an average of 0.73 tCO2/MWh and a standard deviation of 0.05 

tCO2/MWh. the second panel from the top shows a three-week period in the summer in 

more detail. The second representation (third from top) is a histogram of hourly HAER 

values. The final representation (bottom panel) recasts the dataset as a color intensity 

image of HAER, with day of year (DOY) in 2005 along the horizontal and the hour of the 

day on the vertical. The green / yellow band across the top and bottom of the image 

indicates that the evening hours have higher average emission rates, probably reflecting 

the lower contribution of natural gas units during these hours. 

We can see from these graphs that there is significant diurnal variability to the data over 

a fairly narrow range, with higher hourly average emission rates at night. This reflects the 

low position in merit order for coal plants with higher CO2 rates, which dominate the 

lower-usage hours at night, but also the fact that these same large plants strongly 

influence the overall average, in combination with lower-emitting gas plants, even during 

peak times. 

Indirect Emissions Analysis ▪ 20 
0.62

0.64

0.66

0.68 

0.7

0.72

0.74

0.76

0.78 

0.8

0.82

0.84

0.86

0.88 

0.9

0.92

0.94

0.96

0.98 

1 

Hourly Average Emissions Rate (HAER) in New England (NEWE) 

0.6 

0.7 

0.8 

0.9 

1 

HAER (T CO2 /MWh) 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 

0.6 

0.7 

0.8 

0.9 

1 

HAER (T CO2 /MWh) 

07/18 07/20 07/22 07/24 07/26 07/28 07/30 08/01 08/03 08/05 08/07 

0 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

1800 

Number of Hours in 2005 

Emissions Rate Bin (tCO2 / MWh) 

Hourly Average Emissions Rate (HAER) in New England (NEWE) 

Hour 

5 

10 

15 

20 

50 100 150 200 250 300
 350
 

Day of Year (2005) 

0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
 

Figure 8. Representations of CO2 Hourly Average Emissions Rate (HAER) in New England. Top: full record, 

with detail shown for three weeks in the summer of 2005; Middle: Histogram of hourly emissions rates 

showing distribution throughout the year; Bottom: 24x365 color representation of emission rates, showing 

diurnal cycle and seasonal variations. 

Indirect Emissions Analysis ▪ 21 
 

C. Emissions Slope Factor 

The emissions slope factor is an empirical estimate of the marginal emissions rate based 

on the assumption that the observed linear relationship between emissions and electricity 

output from fossil fuel units is the best indicator of the system’s response to a change in 

load. This factor can be calculated as a simple regression relationship over an entire 

year, or on a seasonal basis. Because of seasonal differences in dispatch and operating 

constraints, there may be additional value in performing this analysis on a seasonal 

basis. For the time period under consideration, total emissions and total fossil generation 

for each subregion are summed for each hour, based on the EPA database. When the 

hourly pairs of emissions/output data points for a subregion are plotted on a scatter plot, 

the slope of the regression line represents an empirical measure of the dependence of 

emissions on total electricity output in units of mass of pollutant emitted per MWh 

produced. If the correlation is high, this slope provides a reasonable estimate of the 

regional, seasonal avoidable emissions factor for a change in load or for the addition of 

emissions-free energy. This metric makes no specific assumptions about which generator 

will be displaced by a change in system load, but instead relies on a robust diagnostic of 

the overall system response to variations in load. 

One interesting feature of this approach is that the line of best fit rarely has a y-intercept 

of zero. This may seem counterintuitive, as zero generation would surely be associated 

with zero emissions. However, zero generation is clearly a domain which is not of interest 

for this analysis, and the very lowest generating units on the supply curve are likely to 

have emissions characteristics that diverge from those of units that are more likely to be 

load-following. Were we to force the regression lines to cross through the origin, we 

would obtain a much poorer estimate of the emission factor in each region. 

Figure 9 illustrates the application of this method for emissions in the Reliability 

First/Central (RFCE) subregion. All three pollutants of interest are shown. CO2 and SO2 

emissions can be characterized as having a linear dependence on total MW output, with 

R2

 values of 0.98 and 0.81, respectively. 

The NOx emissions data (Figure 10) do not lend themselves to this simple relationship. 

Readily apparent from the Figure are two different trends. These trends represent the 

emissions in the ozone season, when most generating units are required to operate 

ozone control technology, and the non-ozone season when they are not.22 Finally, there 

appears to be an up-tick in NOx emissions during very high load hours during the ozone 

season. This up-tick may represent units that disengage their NOx controls to increase 

output during very high load hours. 

22 Complicating this analysis is the fact that many units were not required to report NOx output during the non-ozone 

season in 2005. 

Indirect Emissions Analysis ▪ 22 
450,000
 

400,000
 

tCO2/MWh y = 8.5754x + 89172 

R2

 = 0.8079 

Emissions (see legend for units) 

lbs NOx/MWh 350,000 lbs SOx/MWh

300,000 

250,000 

200,000 

150,000 

y = 1.5361x + 23825 

R2

 = 0.2525 

100,000 

50,000 

y = 0.7615x + 1731 

R2

 = 0.9842 

0
 

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
 

MW Output 

Figure 9. Determination of the regional Emissions Slope Factor in the RFCE (Reliability First/Central) region. 

Total emissions of CO2, NOx and SO2 vs. MW output for each hour of 2005 are shown. A linear line of best 

fit is calculated for each pollutant, and the slope of this fit determines the annual slope factor for the region. 

The bifurcation of the NOx data reflects the differential operations of pollution control equipment during the 

ozone (summer) vs. non-ozone seasons.

120,000 

y = 3.5035x + 1414.5 

R2

 = 0.9314 100,000 O3 Season
 

Non-O3 Season

Total NOx Emissions (lbs) 

80,000 

60,000 

40,000 y = 1.8629x + 1626.8 

R2

 = 0.9592 

20,000 

0 

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 

MW Output 

Figure 10. Bifurcation of the NOx emissions slope factor (pounds of NOx per MW output) for the RFCE 

region. Slopes for the ozone season (May through September) and non-ozone season hours are shown. 

The difference reflects the required operation of ozone control technology during the ozone season. Also 

apparent are excess NOx emissions during high-load hours during the ozone season. 

Indirect Emissions Analysis ▪ 23 
 

CO2 Slope Factor R-Squared 

 Region Spring Summer Fall Winter Annual Spring Summer Fall Winter Annual 

AZNM 0.59 0.53 0.56 0.55 0.58 0.95 0.97 0.94 0.91 0.96 

CAMX 0.56 0.50 0.53 0.48 0.52 0.91 0.99 0.95 0.92 0.97 

ERCT 0.69 0.59 0.64 0.67 0.66 0.94 0.99 0.99 0.97 0.97 

FRCC 0.73 0.69 0.73 0.72 0.74 0.98 0.99 0.99 0.99 0.99 

MROE 0.80 0.77 0.83 0.96 0.83 0.94 0.97 0.95 0.94 0.95 

MROW 0.97 0.88 0.93 0.91 0.95 0.98 0.99 0.98 0.94 0.98 

NEWE 0.63 0.65 0.61 0.68 0.65 0.94 0.98 0.97 0.88 0.95 

NWPP 0.82 0.71 0.78 0.70 0.82 0.97 0.95 0.90 0.87 0.95 

NYCW 0.66 0.55 0.63 0.64 0.56 0.83 0.97 0.98 0.91 0.93 

NYLI 0.55 0.48 0.61 0.51 0.53 0.84 0.90 0.97 0.82 0.92 

NYUP 0.69 0.66 0.60 0.76 0.66 0.95 0.97 0.92 0.95 0.95 

RFCE 0.78 0.69 0.75 0.81 0.76 0.99 0.99 0.98 0.98 0.98 

RFCM 0.81 0.67 0.75 0.93 0.71 0.81 0.97 0.91 0.94 0.93 

RFCW 0.95 0.85 0.89 0.92 0.89 0.98 0.99 0.99 0.99 0.99 

RMPA 0.74 0.70 0.86 0.89 0.79 0.85 0.92 0.90 0.93 0.90 

SPNO 0.93 0.93 0.96 1.08 0.97 0.94 0.97 0.99 0.97 0.97 

SPSO 0.70 0.60 0.66 0.65 0.66 0.87 0.98 0.97 0.89 0.94 

SRMV 0.66 0.60 0.64 0.64 0.61 0.94 0.97 0.94 0.89 0.96 

SRMW 0.93 0.86 0.95 0.99 0.94 0.99 0.99 0.99 0.98 0.99 

SRSO 0.83 0.70 0.79 0.80 0.80 0.99 0.98 0.97 0.96 0.98 

SRTV 0.81 0.76 0.87 0.88 0.84 0.97 0.97 0.98 0.97 0.97 

SRVC 0.94 0.79 0.88 0.90 0.85 0.97 0.99 0.98 0.99 0.98 

These figures and statistics suggest that the empirical slope factor approach is a welldefined and reasonable way to characterize the variation of emissions with load in this 

region, especially if some care is taken in subdividing the load to isolate meaningful 

trends. Unfortunately, the regression relationships are not as constrained for all pollutants 

and all regions. Figure 1 shows the seasonal and annual slope factors for CO2, and Table 

2 for SO2, for each eGRID region. Table 3 shows analogous results for NOx separated 

into the ozone and non-ozone seasons. Also shown are the corresponding R-squared 

metrics indicating the strength of the regression relationship underlying each slope factor. 

The R-squared values for CO2 are uniformly quite high, indicating strong confidence in 

these calculated slopes. For NOx the relationships are also generally quite strong, but for 

SO2 the relationships range from very strong to extremely weak. This diagnostic should 

be taken into account in applying this approach. 

In most cases, the differences between the seasons are small, and some of these 

relationships require significant error bars. As a rule we apply seasonal slopes in 

performing the emissions analysis, but it is important to keep in mind that we do so with 

more confidence in some areas and for some pollutants than for others. 

Summary results for the Slope Factor method for all eGRID subregions, including factors 

calculated for CO2 (tons/MWh), SO2 (lbs/MWh), and NOx (lbs/MWh) are displayed in 

Figure 11, Figure 12, and Figure 13, respectively. 

Table 1. Seasonal and annual emissions slope factors (left) and R-squared values (right) for CO2. Spring = 

March-May; Summer = July-August; Fall = September-November; Winter = January, February, and 

December. 

Indirect Emissions Analysis ▪ 24 
SO2 Slope Factor R-Squared 

 Region Spring Summer Fall Winter Annual Spring Summer Fall Winter Annual 

AZNM 0.29 0.19 0.34 0.07 0.06 0.06 0.10 0.11 0.00 0.00 

CAMX 0.31 0.03 0.33 0.16 0.18 0.05 0.01 0.10 0.03 0.07 

ERCT 1.49 0.51 0.80 1.52 0.86 0.32 0.16 0.26 0.27 0.25 

FRCC 5.68 5.08 5.30 5.70 5.19 0.94 0.92 0.93 0.79 0.91 

MROE 4.65 3.59 4.18 6.20 4.15 0.70 0.68 0.34 0.79 0.47 

MROW 6.59 4.36 5.06 5.22 4.95 0.82 0.82 0.75 0.74 0.80 

NEWE 3.04 3.26 1.87 4.33 2.98 0.38 0.78 0.27 0.63 0.40 

NWPP 2.15 1.07 1.25 0.97 1.85 0.74 0.28 0.09 0.05 0.47 

NYCW 1.51 1.01 1.35 2.07 0.99 0.47 0.70 0.77 0.83 0.51 

NYLI 2.94 1.59 3.01 3.05 2.07 0.33 0.54 0.75 0.55 0.50 

NYUP 6.11 5.98 4.93 7.00 6.14 0.84 0.88 0.76 0.79 0.85 

RFCE 12.99 6.39 8.58 9.73 8.58 0.87 0.79 0.78 0.85 0.81 

RFCM 7.40 4.77 6.85 9.68 5.87 0.72 0.78 0.79 0.72 0.72 

RFCW 13.66 10.38 12.02 14.73 11.00 0.76 0.90 0.92 0.93 0.84 

RMPA 0.06 1.00 2.03 0.82 1.11 0.00 0.19 0.49 0.15 0.19 

SPNO 5.91 7.02 7.84 7.26 6.59 0.45 0.85 0.90 0.79 0.79 

SPSO 1.52 1.03 1.52 2.13 1.43 0.21 0.43 0.53 0.41 0.38 

SRMV 1.41 1.53 1.53 1.91 1.52 0.35 0.62 0.32 0.36 0.47 

SRMW 6.69 6.58 6.64 9.02 7.08 0.72 0.83 0.89 0.70 0.79 

SRSO 8.75 5.26 7.28 9.14 7.57 0.81 0.64 0.67 0.75 0.77 

SRTV 6.56 6.36 7.19 9.23 7.07 0.45 0.70 0.72 0.72 0.66 

SRVC 10.84 7.29 9.76 10.24 8.60 0.90 0.89 0.90 0.92 0.89 

Table 2. Seasonal and annual emissions slope factors (left) and R-squared values (right) for SO2. 

Indirect Emissions Analysis ▪ 25 
NOx Slope Factor R-Squared 

Region 

AZNM 

CAMX 

ERCT 

FRCC 

MORE 

MROW 

NEWE 

NWPP 

NYCW 

NYLI 

NYUP 

RFCE 

RFCM 

RFCW 

RMPA 

SPNO 

SPSO 

SRMV 

SRMW 

SRSO 

SRTV 

SRVC 

Ozone 

season 

0.79 

0.20 

0.83 

2.56 

1.37 

3.26 

1.10 

2.17 

2.08 

1.88 

1.61 

1.86 

2.06 

2.10 

1.73 

4.06 

2.21 

1.68 

1.17 

1.40 

1.80 

1.61 

Non-Ozone 

Season 

0.92 

0.62 

0.76 

2.63 

1.93 

4.43 

1.34 

2.37 

1.70 

2.24 

2.15 

3.50 

3.29 

5.18 

2.61 

5.56 

2.58 

1.51 

3.55 

2.26 

4.38 

3.60 

Ozone 

season 

 0.54 

 0.21 

 0.88 

 0.95 

 0.77 

 0.90 

 0.83 

 0.88 

 0.89 

 0.82 

 0.93 

 0.96 

 0.87 

 0.95 

 0.55 

 0.88 

 0.94 

 0.71 

 0.50 

 0.80 

 0.77 

 0.96 

Non-Ozone 

Season 

 0.28 

 0.42 

 0.59 

 0.90 

 0.64 

 0.85 

 0.52 

 0.61 

 0.52 

 0.74 

 0.82 

 0.93 

 0.71 

 0.87 

 0.67 

 0.85 

 0.74 

 0.66 

 0.67 

 0.67 

 0.73 

 0.90 

Table 3. Ozone season and Non-Ozone season emissions rate slopes for NOx. Ozone season = May 

through September. 

Indirect Emissions Analysis ▪ 26 
Seasonal CO 2 Emissions Rate Slope 

0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 

Spring (MAM) Summer (JJA) Fall (SON) 

Winter (DJF) Annual 

AZNMCAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNOSPSO

SRMV

SRMW

SRSO

SRTV

SRVC 

Seasonal SO 2 Emissions Rate Slope 

0 2 4 6 8 10 12 14 16 

AZNMCAMX

ERCT

FRCC

MROE

NYUP

RFCE

RFCM

RFCW

RMPA

SPNOSPSO

SRMV

SRMW

SRSO

SRTV

SRVC 

Spring (MAM) 

Summer (JJA) 

Fall (SON) 

Winter (DJF) 

Annual 

MROW

NEWENWPP

NYCW

NYLI

Figure 11. Regional slope factors for CO2 (tons/MWh) 

Figure 12. Seasonal and regional slope factors for SO2 (lbs/MWh) 

Indirect Emissions Analysis ▪ 27 
0 1 2 3 4 5 6 

AZNMCAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNOSPSO

SRMV

SRMW

SRSO

SRTV

SRVC 

Seasonal NOx Emissions Rate Slope 

Ozone Season Not Ozone Season 

Figure 13. Regional slope factors for NOx (lbs/MWh) 

Indirect Emissions Analysis ▪ 28 
CO2 (t) −CO2 (t −1) EIERt = MW (t) − MW (t −1) 

D. Empirical Incremental Emissions Rate (EIER) 

The previously discussed methods for estimating emissions factors rely upon the 

empirical dependence of total emissions on total output over a predefined period, which 

could be a year, a season, or any other time period. As a result, they produce emissions 

factors characteristic of the selected time period, but not indirect emissions factors at an 

hourly level. However, it is possible to take this analysis to its limit and assess the hourly 

slope factor by dividing the change in total emissions from one hour to the next by the 

change in total output for the same hour. This approach results in the empirical 

incremental emissions rate (EIER), which may be expressed mathematically as follows: 

(4)

The implicit model underlying this approach is that units which are not marginal or 

displaceable in any hour will have constant output and emissions, so that the metric will 

be sensitive only to units which change their output from hour to hour. This theoretically 

weights the metric by the change in output for these “marginal” plants. In contrast, the 

hourly average emission rate approach was weighted by the total output of each unit, 

even for units whose output is insensitive to changes in load. 

While the EIER has the attractive feature that it is a direct estimator of the dependence of 

change in emissions on change in total generation for each hour, there are certain 

considerations which must be made in its application. First, the estimator does not 

distinguish between units that change their output because they are in a normal ramping 

cycle, for example in anticipation of daily changes in load from those that change their 

output in response to shorter term load fluctuations. This may be problematic as the 

former may be large units and exhibit large changes in load, so they are likely to 

dominate the metric during the hours when their output varies. Next is a numerical 

stability issue: if the change in total generation is small, the calculated EIER could be 

dominated by reporting and round-off errors in the emissions data. Thus we choose 

(somewhat arbitrarily) to eliminate hours from consideration if the change in total 

generation in the region from the previous hour is less than 100 MW. In this case we 

carry forward the EIER from the previous hour. 

Finally, the EIER can be less than zero in some hours. This phenomenon occurs when 

total reported generation output in some region rises or falls during an hour by an amount 

greater than the 100 MW threshold, but total reported emissions changes in the other 

direction. This is not necessarily reporting error; it may well be that changes in dispatch, 

due to transmission constraints or some other consideration, have enough of an impact 

on emission rates to offset the effect of changing total output. This instability, and the 

wide scatter in the hourly results for this approach, cast some doubt on the value of this 

approach for predicting hour-to-hour indirect emissions benefits associated with 

renewable energy resources. 

Indirect Emissions Analysis ▪ 29 
 

 

E. Load-Following Incremental Emissions Rate (LFIR) 

The load-following incremental emissions rate is our name for a hybrid of the hourly 

average emissions rate and the empirical incremental emissions rate introduced by 

Stephen Connors in a number of studies of avoided emissions in the United States. 23,24 

The implicit model underlying the LFIR is that units which change their output in the same 

direction that load is changing during any dispatch period can be treated as load 

following. Thus the LFIR approach is designed to yield the average emission rate of the 

units that change output in the direction of changing load during each hour under 

consideration. However, as Connors notes,25 units which are held for spinning reserves 

or automatic generation control may actually be load following units, and would be 

missed by the first criterion. Thus the list of “load following” units must include any unit 

whose last change in output has been in the direction of the change in total load, even if 

subsequent hours have produced only small changes in output (less than 2.5% of their 

maximum.) Finally, Connors examines the range of unit behavior and finds that any unit 

whose output is between 55% and 90% of its capacity is likely to be providing spinning 

reserve service, and designates these units as load following as well.26 

Following this logic, we find that a large proportion of generating units on the system 

most of the time would be designated as “load following” in the 2005 EPA database; in 

many regions, one-third to one-half of all resources earn this designation more than 50% 

of the time. This brings the effect of this approach close to that of the HAER approach 

based on all units running, despite the significantly more complicated analysis required. 

At the same time, because the units (and hence emission rates) may change from hourto-hour even when total load is changing only slightly, this method suffers from even 

greater numerical instability than the EIER approach. 

Connors describes the logic for selecting load following units as “excessively inclusive” 27 

in designating too large a proportion of available units in each hour. To remedy this, he 

weights each unit’s contribution for each hour by its MW change in output from the 

previous hour. This method may serve to mitigate some of the distortion from large base 

load resources that happen to make it into the list of “load-following” units by 

coincidentally ramping along with load, but only for the hours in which they do not happen 

to be ramping. For the hours when they are, they will clearly dominate the calculated 

average, as their changes in load will be much greater than that for smaller, truly loadfollowing units. 

In summary, we find that the LFIR approach suffers from many of the same distortions as 

both the EIER approach, and the HAER approach. The LFIR is numerically unstable, 

producing emissions rates which are highly and unrealistically variable throughout the 

year, while at the same time it gives undue weight to large units which are likely to have 

23 Connors et al. (2005). 

24 Berlinski, and Connors (2006). 

25 Ibid., p.1-4.
 

26 Connors et al., 2005, Table 1.1, p. 1-6.
 

27 Ibid, p. 1-10. 

Indirect Emissions Analysis ▪ 30 
Nramping,i Fi = Noperating,i

∑(FiERi,t) FW-HAERt =

∑Fi

larger changes in output from hour to hour but are not necessarily likely to be loadfollowing. Although we have calculated and tabulated the estimated indirect emissions 

benefits using the LFIR for comparison, we conclude that it does not produce a useful 

metric for estimating displaced emission factors on electricity grids. 

F. Flexibility-Weighted Hourly Average Emissions Rate (FW-HAER) 

The final method considered, FW-HAER is also based on the premise that only certain 

units are likely to be displaced by variable output renewable energy resources. This 

approach attempts to identify load following units based on their operational behavior 

over the course of the entire study period, rather than during individual hours as with the 

LFIR method. The FW-HAER approach assigns to each unit a score based on how 

readily it appears to shift output; this score is then used as a weighting coefficient in 

calculating the indirect emissions coefficient for each hour. 

While it is impossible to determine exactly which units would be load following at any 

given time given the complexities of dispatch in real electric systems, it is reasonably 

straightforward to determine which tend to behave in a more or less flexible manner. 

Large base load units are generally scored lower than smaller units. Units that appear to 

spend a lot of time ramping in response to load during the year, or that are often found 

partially dispatched, are ranked as highly displaceable whenever they are running. 

To capture these dynamics, the FW-HAER approach tracks how often a plant is in the 

process of ramping relative to the number of hours it operates during the year, as shown 

in Equation 5: 

(5) 

Where F is the flexibility coefficient and i refers to an individual unit. A “ramping” hour is 

defined as any hour in which the change in the unit’s output is greater than or equal to 

2.5% of its maximum capacity; by dividing the number of ramping hours by the total 

number of hours in which the unit operates, we obtain a unit flexibility coefficient which 

represents the proportion of its operating hours which appear to be in “ramping” mode. 

Once the flexibility coefficient of each unit on the system is calculated, the indirect 

emissions rate for each hour of operations is calculated as shown in Equation 6:

(6)

Where ERi,t is the emission rate for unit i during hour t and the summations are over all 

units operating during that hour, and Fi is as in Equation 5. Again, this method does not 

weight by either unit size or by the change in output in any given hour. Only units which 

appear to be more flexible in their output over the year have increased influence on the 

displaceable emissions rate. This approach avoids giving undue weight to large, inflexible 

Indirect Emissions Analysis ▪ 31 
units, and also exhibits substantially less volatility than methods that are based on hourly 

changes in output. 

The FW-HAER approach has several advantages for estimating the emissions impacts of 

variable output resources. It does a much better job than any other approach in 

narrowing the list of candidate units that dominate the indirect emissions calculation. 

Unlike the other approaches considered here, the FW-HAER avoids giving undue weight 

to large, inflexible units. FW-HAER is much more numerically stable than either the EIER 

approach or the LFIR approach, yielding a much smaller range of displaceable emissions 

rates through the year. Finally, because of this stability and more refined identification of 

the most flexible units in any region, it does the best job of differentiating regions by their 

true indirect emissions rates. These features are demonstrated in the figures below. 

Figure 14 shows the relationships among flexibility coefficient, generator size, and 

number of operating hours for (a) New England and (b) California. The size of each circle 

represents the observed number of operating hours in 2005. In both regions, we see that 

the very highest flexibility coefficients are attributed to small units which run infrequently; 

when running, these may also be the first units displaced. In California, it is apparent that 

the largest units are not displaceable; in New England, there does not appear to be much 

of a relationship between unit size and flexibility. 

Figure 15 shows hourly generation profiles for three sample plants in Florida, exhibiting 

typical profiles for low, medium, and high flexibility coefficients. 

Indirect Emissions Analysis ▪ 32 
(a) (b) 

Figure 14. Flexibility coefficient vs. generator size in New England (NEWE) and California (CAMX). Each circle represents a single generegion, and the size reflects total operating hours in 2005. Maximum size is 8760 hours. 

Indirect Emissions Analysis ▪ 33 
200 

W) 

100 

Generation (M

(a) 

Jan 

0 

200 

Generation (MW) 

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 

100 

07/28 

0 

07/31 08/03 08/06 08/09 08/12 

1000 

W) 

500 

Generation (M

(b) 

Jan 

0 

1000 

Generation (MW) 

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 

500 

07/28 

0 

07/31 08/03 08/06 08/09 08/12 

100 

W) 

50

Generation (M

(c) 

Jan 

0 

100 

Generation (MW) 

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 

50 

07/28 

0 

07/31 08/03 08/06 08/09 08/12 

Figure 15. Hourly dispatch and flexibility coefficient (F) for three plants of differing capacities in the FRCC 

subregion. Each lower graph is a summer detail of the 2005 profile shown above. (a) Polk power plant, F = 

0.08; (b) Manatee gas plant, F = 0.55; (c) Debray peaker, F = 0.86. 

Indirect Emissions Analysis ▪ 34 
 

 

G. Comparison of Indirect Emissions Rates 

It is instructive to compare the implications for indirect emission rates from each 

estimation approach. Figure 16 displays these results in three panels, for CO2, SO2, and 

NOx, respectively. Each panel is organized vertically by region, with indirect emission 

rates on the horizontal axis. For each region, the range (single standard deviation around 

the mean) of the hourly indirect emission rates may be read and compared for each of 

the calculation approaches considered. Methods depicted are the empirical incremental 

emission rate (EIER, solid line), the hourly average emission rate (HAER, angle 

brackets), and the flexibility-weighted average emission rate (FW-HAER, shaded 

rectangle).28 Also shown is the annual (CO2, SO2) or seasonal (NOx) slope factor for 

each region, which is shown as a point as it is not an hourly value but a metric calculated 

from the annual or seasonal data. 

The graphs in Figure 16 illustrate the variations among regions, and within each region 

between the different calculation approaches. For example, the leftmost panel illustrates 

that for CO2, there is general agreement among the different calculation approaches in all 

regions, although the slope factor tends to suggest a lower indirect emissions rate than 

other methods. The HAER and the FW-HAER approaches both yield values in a fairly 

tight range and are generally in agreement with each other, while the EIER approach 

regions such as California and New England, which rely more heavily on gas, have 

indirect emissions rates of perhaps 0.6 to 0.8 tCO2 per MWh. 

Color intensity plots for each hourly method for each pollutant, and for each region are 

provided in Appendix D. These plots display the diurnal and seasonal variations in each 

displacement factor derived using each approach. The data are also available in 

electronic format. 

yields a much broader range of hourly values reflecting its numerical instability. It is also 

clear from Figure 16 that certain regions, such as the Midwest areas that rely heavily on 

coal generation, have indirect emissions rates of over 1.0 to 1.2 tCO2 per MWh, while 

28 We have not plotted the single-standard-deviation range based on the LFIR approach in Figures 14-16 because it is far 

too broad for this scale, reflecting the numerical instability of this approach. 

Indirect Emissions Analysis ▪ 35 
(a) (b) 

Figure 16. Comparison of the distribution (mean +/- one standard deviation) of indirect emissions coefficients for (a) CO2, (b) SO2 and (c) NO

described in the text. Line: empirical incremental emission rate (EIER); Angle brackets: hourly average emission rate (HAER); Shaded rectan

hourly average emission rate (FW-HAER). Also shown as round marks are the annual (for CO2 and SO2) or seasonal (for NOx; dark = ozone factors for each region. 

Indirect Emissions Analysis ▪ 36 
5. Indirect Emissions Results 

In this section we present our assessment of what the annual indirect emissions 

reductions of NOx, SO2, and CO2 would have been in each of the 22 eGRID subregions 

in 2005, had an incremental renewable energy project (wind, landfill gas, or municipal 

solid waste) produced one gigawatt-hour (GWh) of energy over the course of the year. 

As discussed earlier, we find that landfill gas and municipal solid waste have identical 

base load energy profiles. Thus we have combined the analyses for these two resources 

into a single set of results. 

All of the results presented here are based on a one-to-one displacement of fossil 

generation, assuming emissions-free generation from the renewable resource. While 

beyond the current scope, it would improve precision to incorporate line losses, as well 

as consideration of internal energy use at power plants. In general, renewable generation 

sites are remote from load centers, meaning that more than one MW of generation is 

required to serve one MW of load. Fossil generation sites may be near to or remote from 

load. However, all fossil units have some parasitic load—again, more than one MW must 

be produced in order to deliver one MW onto the system. The extent to which these 

cancel out in any given region, or the adjustments that should be made to accommodate 

them, is beyond the scope of this study. 

The calculated indirect emissions results are presented graphically as outlined in Table 4. 

The results are also tabulated in Table 5 (wind projects) and Table 6 (landfill gas and 

municipal solid waste.) 

Table 4. Guide to the summary figures. 

Figure Resource Type Pollutant Methods Regions 

Figure 17 Wind CO2 

HAER, EIER, FWHAER All eGRID regions 

Figure 18 Wind NOx HAER, EIER, 

FW-HAER All eGRID regions 

Figure 19 Wind SO2 

HAER, EIER, 

FW-HAER All eGRID regions 

Figure 20 MSW, LFG CO2 

HAER, seasonal and 

annual slope factors All eGRID regions 

Figure 21 MSW, LFG NOx HAER, Seasonal Slope 

Factor All eGRID regions 

Figure 22 MSW, LFG SO2 

HAER, Seasonal and 

Annual Slope Factors All eGRID regions 

The results show significant regional differences in the indirect emissions of all pollutants, 

no matter what calculation method is used. They also show that the calculation method 

applied makes a large difference in some regions, while in other regions these 

differences are less significant. This leads to the question of which method is most 

appropriate to use under what conditions. 

In our judgment, the FW-HAER best captures the short-term operational behavior of 

power plants because it reflects observations of short-term changes in output for 

individual plants which respond flexibly to system perturbations. We recommend this 

approach in particular for shorter-term indirect emissions benefits for CO2, in response to 

Indirect Emissions Analysis ▪ 37 
variable output, non-dispatchable resources such as wind power. The regional FW-HAER 

values presented in this paper can be applied for this purpose, where the definition of 

short-term depends on the application but may be the first two or three years of a new 

wind resource lifetime. 

Over the longer term, and with greater penetration of wind resources, it is reasonable to 

assume that the system will adjust by displacing resources lower down in the dispatch 

order, ultimately restoring the utilization rate of load-following capacity to accommodate 

fluctuations in both load and wind resource output. In addition, NOx and SO2 are both 

regulated under regional or national cap-and-trade regimes which make hourly indirect 

emissions analysis less relevant for these pollutants. Thus for longer-term indirect CO2 

analysis, or for NOx and SO2, we recommend using either HAER or the emissions slope 

factor. These approaches are based on the longer-term response to perturbations over 

the full resource portfolio, as opposed to reflecting primarily the marginal unit or units. 

These two approaches (HAER and slope factor) give generally similar results. The 

primary difference between them is that the HAER size-weights base load fossil 

resources running at any given hour, giving the most weight to large units regardless of 

their flexibility in response to changes in output. Conversely, the slope factor method 

tends to deemphasize continually operational resources regardless of size. As we find it 

unlikely that the largest and lowest running cost resources would ever be displaced by 

renewable energy projects, we prefer the slope factor approach for those regions and 

pollutants for which the slope factor is well defined. 

For landfill gas and municipal solid waste resources, the most likely type of generators to 

be displaced are intermediate units, which are more costly to run than base load but 

which do not have the flexibility of load-following resources. In this case, the best method 

would again be either HAER or the slope factor approach, and we would again give slight 

preference to the slope factor approach if the slope is well-defined. 

Finally we note that most of our results, especially those for CO2, are relatively insensitive 

to hourly or seasonal variations in wind power generation. Replacing the calculated wind 

profiles with a flat power output profile (such as that characteristic of landfill gas or 

municipal solid waste) results in small changes in the calculated indirect emissions 

impact over the course of a year. Table 8 shows this result as the percent change in the 

total annual indirect emissions impact for wind resources if the hourly profile is replaced 

by a flat profile, typical of an LFG or MSW facility. The small changes suggest that 

indirect emission factors and wind output profiles are generally poorly correlated, and in 

many cases little is gained from the effort to apply realistic profiles. 

However, some of these differences are larger than others and probably important—for 

example, in some regions the impact of using a realistic wind-based shape on indirect 

NOx emissions is as high as 10%. This different probably reflects seasonal differences in 

wind strength which are correlated with seasonal differences in the operation of NOx 

control equipment. Whether the effort required to establish realistic wind profiles pays 

sufficient dividends in this type of analysis to be justified probably depends on the specific 

application. 

Indirect Emissions Analysis ▪ 38 
Table 5. Annual indirect emissions reduction rates associated with an incremental GWh of wind energy in each of the eGRID subreg

methods shown are: HAER: Hourly average emission rate; FW-HAER: Flexibility-weighted hourly average emission rate; EIER: Emp

emissions rate; LFIR: Load-following incremental rate; Slope factor: Indirect emission rate based on seasonal relationship between 

across all hours in each season. For CO2 and SO2 the seasonal slope factors are based on conventional 3-month seasons; for NOx 

the ozone and non-ozone regulation seasons. 

eGrid 

Region 

2005 Avoided CO2 (tons/GWh) 2005 Avoided NOx (lbs/GWh) 2005 A

HAER EIER LFIR FW-HAER 

Slope 

Factor HAER EIER LFIR FW-HAER 

Slope 

Factor HAER EIE

AZNM 844 573 591 738 943 2,768 1,335 1,260 1,641 927 1,782 1,3

CAMX 670 497 496 619 892 1,238 259 287 411 508 1,138 2

ERCT 845 606 596 684 1,040 1,058 863 877 785 775 3,920 8

FRCC 845 694 689 757 994 2,645 2,811 2,757 1,675 2,584 4,857 2,8MROE 1,071 803 807 1,054 1,114 2,981 1,902 2,346 4,380 1,810 7,678 1,9MROW 1,094 885 884 1,060 1,079 4,456 3,407 3,388 4,461 4,101 6,887 3,4

NEWE 728 588 637 711 780 1,177 1,004 944 913 1,253 4,177 1,0NWPP 981 716 657 741 1,076 3,267 2,400 2,101 2,310 2,547 2,546 2,4

NYCW 800 616 657 717 742 1,821 2,073 957 1,206 1,846 1,452 2,0NYLI 662 530 621 687 744 1,623 2,277 1,160 1,317 2,158 4,204 2,2NYUP 731 664 698 875 816 2,016 1,991 1,864 2,172 1,967 6,277 1,9RFCE 854 724 731 911 896 3,062 2,691 2,485 3,039 3,009 13,596 2,6RFCM 1,059 835 773 1,072 959 3,194 3,277 3,057 2,988 2,912 9,363 3,2RFCW 954 885 854 948 906 3,579 3,852 3,496 3,435 4,219 12,250 3,8

RMPA 980 661 612 734 1,056 2,963 1,675 1,011 1,805 2,377 1,848 1,6

SPNO 1,066 912 906 1,040 1,074 4,060 4,019 3,203 3,955 4,966 6,949 4,0

SPSO 905 618 629 735 1,062 2,642 2,662 2,610 2,151 2,466 3,912 2,6

SRMV 765 659 653 686 1,027 1,638 1,912 1,900 1,407 1,616 2,693 1,9SRMW 996 905 900 1,001 966 2,664 2,415 2,489 2,717 2,839 6,739 2,4SRSO 960 818 826 914 997 2,769 2,300 2,504 2,919 2,089 11,308 2,3

SRTV 1,005 856 882 987 974 3,755 3,270 3,252 3,708 3,499 9,004 3,2

SRVC 1,035 955 837 974 933 3,349 3,177 2,685 3,405 3,029 11,289 3,1

Indirect Emissions Analysis ▪ 39 
Table 6. Annual indirect emissions reduction rates associated with an 

incremental GWh of landfill gas of MSW generation in each of the eGRID 

subregions for 2005. Calculation methods shown are: HAER: Hourly average emission rate; Slope factor: Indirect emission rate based on seasonal 

relationship between emissions and output across all hours in each season. For CO2 and SO2 the seasonal slope factors are based on conventional 3­

month seasons; for NOx the slopes are defined for the ozone and non-ozone regulation seasons. 

Table 7. “Shape impact” for indirect emissions in each eGrid region. The shape impact is the emissions if an hourly wind profile is replaced wprofile with the same total output. 

eGrid 

Region 

2005 Avoided 

CO2 (tons/GWh) 

2005 Avoided 

NOx (lbs/GWh) 

2005 Avoided 

SO2 (lbs/GWh) 

HAER 

Slope 

Factor HAER 

Slope 

Factor HAER 

Slope 

Factor 

AZNM 838 943 2,718 901 1,710 223 CAMX 667 893 1,226 476 1,129 206 ERCT 852 1,042 1,069 775 3,981 1,080 

FRCC 859 995 2,632 2,582 4,806 5,442 

MROE 1,086 1,117 2,996 1,764 7,834 4,649 MROW 1,098 1,080 4,459 4,022 6,891 5,307 NEWE 732 777 1,138 1,232 4,140 3,119 

NWPP 982 1,078 3,274 2,472 2,553 1,362 NYCW 795 740 1,739 1,887 1,339 1,482 

NYLI 660 740 1,549 2,110 4,103 2,645 

NYUP 736 812 2,004 1,917 6,306 6,003 

RFCE 862 898 2,904 2,829 13,720 9,424 

RFCM 1,058 958 3,103 2,787 9,380 7,164 RFCW 956 905 3,303 3,900 12,187 12,688 

RMPA 980 1,055 2,952 2,278 1,830 974 SPNO 1,069 1,075 4,088 5,012 6,949 7,005 SPSO 909 1,062 2,653 2,464 3,958 1,549 SRMV 764 1,025 1,660 1,628 2,665 1,593 SRMW 999 970 2,458 2,482 6,773 7,226 

SRSO 961 997 2,654 1,976 11,269 7,601 

SRTV 1,004 975 3,366 3,175 8,927 7,327 SRVC 1,032 932 3,096 2,790 11,168 9,529 

eGrid 

Region 

CO2 Shape 

Impact 

NOx Shap

Impact 

HAER 

Slope 

Factor HAER 

Slo

Fa

AZNM -0.7% -0.1% -1.8% 

CAMX -0.4% 0.0% -0.9% ERCT 0.9% 0.2% 1.0% 

FRCC 1.7% 0.1% -0.5% MROE 1.5% 0.3% 0.5% 

MROW 0.3% 0.1% 0.1% 

NEWE 0.5% -0.4% -3.3% NWPP 0.2% 0.2% 0.2% 

NYCW -0.7% -0.2% -4.5% NYLI -0.3% -0.5% -4.6% NYUP 0.6% -0.4% -0.6% RFCE 0.9% 0.2% -5.1% RFCM -0.1% -0.1% -2.8% RFCW 0.1% -0.2% -7.7% RMPA 0.0% -0.1% -0.4% SPNO 0.3% 0.1% 0.7% 

SPSO 0.4% 0.1% 0.4% 

SRMV -0.1% -0.2% 1.3% SRMW 0.3% 0.5% -7.7% -1SRSO 0.1% 0.0% -4.2% SRTV -0.1% 0.1% -10.4% SRVC -0.3% 0.0% -7.6% 

Indirect Emissions Analysis ▪ 40 
Indirect Emissions Analysis ▪ 41 

Tons CO2 per GWh 

HAER EIER FW-HAER 

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC 

1,200 

1,000 

800 

600 

400 

200 

0 

 Pounds NOx per GWh 

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC

3,500 

4,000 

4,500 

5,000 

3,000 

2,500 

2,000 

1,500 

1,000 

500 

0 

HAER EIER FW-HAER 

Figure 17. Annual indirect CO2 emissions impact associated with one incremental GWh of wind energy in each of the 

eGRID subregions for 2005. 

Figure 18. Annual indirect NOx emissions impact associated with one incremental GWh of wind energy in each of the 

eGRID subregions for 2005. 
Indirect Emissions Analysis ▪ 42 

Pounds SO2 per GWh 

HAER EIER FW-HAER 

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC 

16,000 

14,000 

12,000 

10,000 

8,000 

6,000 

4,000 

2,000 

0 

Tons CO2 per GWh 

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC 

800 

1,000 

1,200 

600 

400 

200 

0 

HAER Seasonal Slope Factor Annual Slope Factor 

Figure 19. Annual indirect SO2 emissions impact associated with one incremental GWh of wind energy in each of the 

eGRID subregions for 2005. 

Figure 20. Annual indirect CO2 emissions impact associated with one incremental GWh of landfill gas or municipal 

solid waste energy in each of the eGRID subregions for 2005. 
 Pounds NOx per GWh 

6,000 

5,000 

4,000 

3,000 

2,000 

1,000 

0 

HAER Seasonal Slope Factor 

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC

 

Pounds SO2 per GWh 10,000 

8,000 

6,000 

4,000 

2,000 

0 

HAER Seasonal Slope Factor Annual Slope Factor 

AZNM

CAMX

ERCT

FRCC

MROE

MROW

NEWENWPP

NYCW

NYLI

NYUP

RFCE

RFCM

RFCW

RMPA

SPNO

SPSO

SRMV

SRMW

SRSO

SRTV

SRVC 

12,000 

14,000 

16,000 

Figure 22. Annual indirect SO2 emissions impact associated with an incremental GWh of landfill gas or municipal solid 

waste energy in each of the eGRID subregions for 2005. 

Indirect Emissions Analysis ▪ 43 

Figure 21. Annual indirect NOx emissions impact associated with an incremental GWh of landfill gas or municipal 

solid waste energy in each of the eGRID subregions for 2005. 
 

6. Conclusions 

In this analysis we have produced and applied a number of approaches for calculating 

indirect emissions impacts and provided a general discussion of which might be more 

appropriate under different circumstances. We have calculated indirect emissions 

benefits associated with CO2, NOx, and SO2 for wind, landfill gas, and municipal solid 

waste electricity generating resources, in each of the 22 eGRID subregions of the 

continental United States. 

The indirect emissions benefit calculated for any kind of renewable energy resource 

depends on a number of questions: 

•	 Where is the resource located? 

•	 What is the pollutant of interest? Is there a cap and trade system in place for this 

pollutant? 

•	 Is the time period of interest historical, in the near future, or several years in the 

future? 

•	 Is the resource base load, dispatchable, or intermittent and nondispatchable? 

•	 If intermittent and nondispatchable, what is the expected hourly and seasonal profile 

of the resource? 

•	 We have found that there are important regional differences in indirect emissions 

which can be quantified in ways that will be useful for estimating the emissions 

benefits of renewable energy projects in each region. 

•	 Because of the wide range of applications reflected in the possible answers to the 

questions posed above, the best method to use in calculating indirect emissions is 

almost certainly application-specific. We believe that these methodologies provide 

useful guidance for many applications, and a better understanding of the issues for 

approximation of indirect emissions benefits than has previously been available. 

•	 The results presented here could be validated by a modeling analysis, using a full 

electric system dispatch model to simulate actual indirect emissions under realistic 

unit commitment and dispatch conditions. The results could then be compared to 

those that would be derived using each of the methods applied here. This 

comparison would provide an empirical basis for establishing which approach is most 

consistent and accurate in predicting indirect emissions in any particular region or 

under specific circumstances, at least in the context of the model. 

•	 In summary, we have found that: 

•	 The indirect emissions benefits of renewable energy for all pollutants vary 

significantly by region, and these differences can be quantified and applied in 

calculating indirect emissions impacts; 

•	 Different calculation approaches are appropriate for different types of resources and 

different applications; 

Indirect Emissions Analysis ▪ 44 
•	 In general, hourly profiles of renewable energy resources make only a modest 

difference in quantifying annual indirect emissions benefits; 

•	 More research is needed to establish which methods of calculating indirect emissions 

benefits most accurately reflect real-world dispatch over a range of timescales and 

system conditions. 

•	 Finally, we note again that the geographic coverage of this analysis was by necessity 

coarse, in the interest of presenting results for the entire continental United States, 

particularly with respect to wind resources. For the analysis to be applied to calculate 

or predict indirect emissions impacts for specific projects, the tables of hourly 

avoidable emissions data in Appendix D could be applied directly to site-specific 

operational and meteorological data. However, given the modest impact of 

differences in hourly profiles noted above, the annual results presented here are 

likely to be sufficient for most purposes. 

Indirect Emissions Analysis ▪ 45 
Appendix A: 

Identification and map of eGRID subregions 

Indirect Emissions Analysis ▪ 46 

Appendix A 
 

 

 

Figure A-1. Map of eGRID subregions. Subregions in Alaska and Hawaii are not considered in this analysis. 

Acronyms are defined below. Source of figure: https://www.energystar.gov/istar/pmpam/help/eGRID_Subregion_Map.htm. 

AZNM Arizona and New Mexico RFCE Reliability First - East 

CAMX California RFCM Reliability First - Michigan 

ERCT ERCOT (Texas) RFCW Reliability First - West 

FRCC FRCC (Florida) RMPA Rocky Mountain region 

MROE Midwest Reliability - East SPNO Southwest Power Pool - North 

MROW Midwest Reliability - West SPSO Southwest Power Pool - South 

NEWE New England SRMV SERC - Mississippi Valley 

NWPP Northwest SRMW SERC - Midwest 

NYCW New York City/ Westchester SRSO SERC - South 

NYLI New York - Long Island SRTV SERC - Tennessee Valley 

NYUP New York - Upstate SRVC SERC - Virginia/Carolina 

Indirect Emissions Analysis ▪ 47 

Appendix A 
Appendix B: 

Color intensity plots of synthetic wind power time series (hourly percent of 

capacity) for each eGRID subregion. Each grid shows hour of the day on 

the vertical axis and days of the year on the horizontal axis. Color scale 

for all charts is shown below and at the bottom of the last page. 

These data were obtained by scaling publicly available ground-level 

meteorological data to a typical turbine height and class 4 wind strength 

(Equation 1 in the text) and then transforming into wind power output 

using Equation 2. 

The hourly data are available in electronic format upon request. 

Indirect Emissions Analysis ▪ 48 

Appendix B 
Indirect Emissions Analysis ▪ 49 

Appendix B 
Indirect Emissions Analysis ▪ 50 

Appendix B 
Indirect Emissions Analysis ▪ 51 

Appendix B 
6 

8 

13 

5 11 7 

15 14 9,10 19 16 12 

17 21 22 

2 

3 20 

1 

18 

4 

Appendix C: 

Summary tables of raw and scaled wind speed and synthetic wind power 

time series. Monthly summaries are shown for each eGRID subregion. 

The hourly data are available in electronic format upon request 

The proxy wind turbine locations are shown below, overlaid on map of all 

U.S. WBAN stations. 

NERC WBAN 

Index Subregion Number Location Latitude Longitude 

1 AZNM 23055 GUADALUPE PASS, TX 31.5 -104.49 

2 CAMX 23187 SANDBERG, CA 34.44 -118.43 

3 ERCT 23042 LUBBOCK, TX 33.4 -101.49 

4 FRCC 12844 WEST PALM BEACH, FL 26.41 -80.06 

5 MROE 14898 GREEN BAY, WI 44.31 -88.07 

6 MROW 24012 DICKINSON, ND 46.48 -102.48 

7 NEWE 14739 BOSTON, MA 42.22 -71.01 

8 NWPP 24150 LIVINGSTON, MT 45.42 -110.27 

9 NYCW 94789 NEW YORK, NY 40.4 -73.48 

10 NYLI 94789 NEW YORK, NY 40.4 -73.48 

11 NYUP 4725 BINGHAMTON, NY 42.13 -75.59 

12 RFCE 93730 ATLANTIC CITY, NJ 39.28 -74.28 

13 RFCM 94860 GRAND RAPIDS, MI 42.53 -85.31 

14 RFCW 14848 SOUTH BEND, IN 41.43 -86.2 

15 RMPA 24018 CHEYENNE, WY 41.1 -104.49 

16 SPNO 23065 GOODLAND, KS 39.22 -101.41 

17 SPSO 23047 AMARILLO, TX 35.13 -101.43 

18 SRMV 12916 NEW ORLEANS, LA 29.59 -90.15 

19 SRMW 93822 SPRINGFIELD, IL 39.51 -89.41 

20 SRSO 13874 ATLANTA, GA 33.38 -84.26 

21 SRTV 13893 MEMPHIS, TN 35.04 -89.59 

22 SRVC 93729 CAPE HATTERAS, NC 35.14 -75.37 

Indirect Emissions Analysis ▪ 52 

Appendix B 
Monthly average interpolated wind speed (mph) 

from representative WBAN station in each NERC subregion 

Jan Feb Mar Apr May Jun July Aug Sept Oct 

AZNM 19.63 21.52 22.01 20.21 16.42 15.69 15.94 13.56 14.98 16.82 

CAMX 14.08 13.99 13.37 14.91 14.43 14.16 12.23 11.38 11.39 13.43 

ERCT 10.61 10.36 13.24 14.15 12.49 13.87 10.74 9.23 10.37 9.69 FRCC 10.49 8.74 8.99 10.33 8.44 8.85 8.97 7.67 9.07 10.69 MROE 9.23 8.34 9.12 9.59 9.27 8.04 6.98 6.44 6.41 7.36 

MROW 11.78 10.66 13.27 14.23 12.74 10.95 10.83 10.56 10.52 11.14 

NEWE 12.32 10.86 12.21 11.29 11.53 9.75 10.45 9.18 9.69 12.67 NWPP 15.95 14.12 15.85 12.57 12.15 11.63 9.35 10.30 12.52 12.35 

NYCW 12.33 11.71 13.01 12.40 10.97 10.28 9.42 9.75 10.31 12.10 

NYLI 12.33 11.71 13.01 12.40 10.97 10.28 9.42 9.75 10.31 12.10 

NYUP 8.11 7.23 8.37 8.35 8.00 6.72 7.00 7.22 7.36 7.58 

RFCE 8.15 8.77 10.40 10.72 8.30 8.17 6.85 6.26 6.94 9.58 

RFCM 9.49 8.23 8.90 10.23 8.68 7.90 7.36 7.32 8.32 7.98 

RFCW 9.23 8.40 9.26 10.53 8.58 8.08 7.05 6.11 7.19 6.94 

RMPA 11.03 11.36 13.99 13.03 11.14 11.01 9.86 9.80 9.70 10.07 

SPNO 10.20 9.73 13.26 14.24 12.76 12.91 12.76 9.74 12.28 11.23 

SPSO 11.87 12.04 13.01 14.29 11.71 14.10 12.50 10.55 13.01 11.86 

SRMV 8.94 8.79 8.39 9.93 7.47 6.12 7.08 5.45 8.33 8.57 

SRMW 9.86 9.37 11.32 10.93 9.26 7.52 6.34 5.74 6.35 7.60 

SRSO 8.92 8.72 10.08 8.96 7.71 7.66 5.82 7.03 7.61 8.48 

SRTV 9.75 8.60 10.39 9.35 7.38 7.03 7.10 6.64 7.04 6.81 

SRVC 10.60 9.51 11.27 12.02 9.59 8.65 7.44 7.25 9.04 10.18 

Indirect Emissions Analysis ▪ 53 

Appendix C 
Monthly average interpolated representative wind speed 

scaled to Class 4 (16.25 mph average) for each NERC subregion 

Jan Feb Mar Apr May Jun July Aug Sept Oct 

AZNM 38.64 42.36 43.33 39.78 32.32 30.89 31.38 26.70 29.48 33.12 

CAMX 28.04 27.86 26.63 29.68 28.74 28.19 24.35 22.67 22.68 26.74 

ERCT 21.49 20.98 26.82 28.65 25.29 28.09 21.76 18.69 21.00 19.62 

FRCC 13.75 11.45 11.79 13.55 11.06 11.61 11.76 10.06 11.90 14.02 

MROE 8.36 7.55 8.26 8.68 8.40 7.28 6.33 5.83 5.80 6.66 

MROW 16.50 14.93 18.60 19.94 17.85 15.34 15.18 14.80 14.74 15.61 

NEWE 14.79 13.03 14.66 13.55 13.83 11.70 12.54 11.02 11.63 15.20 

NWPP 18.86 16.70 18.74 14.87 14.37 13.75 11.06 12.18 14.81 14.60 

NYCW 18.14 17.23 19.14 18.24 16.13 15.12 13.86 14.35 15.16 17.80 

NYLI 23.51 22.32 24.81 23.64 20.91 19.60 17.95 18.59 19.64 23.07 

NYUP 14.24 12.68 14.69 14.65 14.04 11.80 12.28 12.67 12.91 13.30 

RFCE 13.85 14.91 17.68 18.22 14.10 13.88 11.64 10.63 11.79 16.28 RFCM 18.69 16.23 17.54 20.16 17.11 15.58 14.51 14.43 16.40 15.73 RFCW 16.96 15.44 17.02 19.35 15.76 14.85 12.96 11.23 13.21 12.75 RMPA 20.79 21.40 26.36 24.56 20.99 20.76 18.58 18.47 18.28 18.97 SPNO 13.88 13.23 18.03 19.36 17.36 17.55 17.36 13.24 16.70 15.26 SPSO 21.61 21.92 23.68 26.02 21.31 25.66 22.76 19.20 23.67 21.59 SRMV 12.18 11.97 11.43 13.53 10.18 8.34 9.64 7.43 11.34 11.67 

SRMW 20.22 19.22 23.23 22.42 19.00 15.42 13.01 11.78 13.03 15.59 SRSO 12.60 12.31 14.24 12.65 10.89 10.83 8.21 9.92 10.75 11.98 

SRTV 13.77 12.14 14.68 13.21 10.42 9.93 10.03 9.38 9.95 9.62 

SRVC 15.10 13.55 16.06 17.13 13.67 12.33 10.61 10.33 12.89 14.51 

Indirect Emissions Analysis ▪ 54 

Appendix C 
Monthly average calculated wind power output from 1.5 MW proxy turbine 

in each NERC subregion (dropout at 56 mph) 

Jan Feb Mar Apr May Jun July Aug Sept Oct 

AZNM 883.49 919.73 910.01 823.61 879.14 1056.20 1102.16 988.58 990.65 901.21 

CAMX 971.55 941.26 865.85 1048.52 1101.11 1005.35 924.58 835.30 803.95 906.92 

ERCT 680.73 670.23 929.82 902.22 899.96 1121.26 769.51 577.06 692.23 604.10 

FRCC 286.16 166.17 190.93 268.04 137.00 198.17 193.28 149.79 199.06 267.63 

MROE 58.62 23.10 51.41 64.85 47.93 28.74 14.36 11.58 17.25 16.27 MROW 439.92 343.82 573.12 655.90 518.31 386.22 329.69 343.77 336.63 391.38 

NEWE 338.89 213.95 272.13 241.16 286.37 119.42 156.53 107.23 138.02 363.12 

NWPP 572.73 479.22 574.52 402.75 358.02 323.25 185.49 229.19 368.41 354.81 

NYCW 546.35 504.41 589.69 577.93 406.64 330.33 276.92 288.96 336.61 572.02 

NYLI 822.24 767.69 848.34 849.13 677.99 606.39 531.39 556.41 649.84 812.71 

NYUP 335.36 324.15 368.73 377.29 294.03 158.21 171.78 197.17 225.59 261.58 

RFCE 356.54 394.50 540.16 584.68 341.90 271.12 169.22 164.24 237.97 498.45 

RFCM 638.43 477.22 605.68 721.99 549.50 423.01 363.23 354.43 452.75 446.94 

RFCW 506.60 413.12 549.15 606.09 457.05 350.10 281.30 208.01 304.92 264.16 

RMPA 644.99 659.34 895.00 835.90 664.56 643.43 566.71 526.45 522.83 602.56 

SPNO 290.58 217.16 515.08 587.51 515.45 497.59 490.45 233.00 451.34 366.46 

SPSO 752.59 766.10 815.82 878.05 735.24 1004.26 859.04 625.42 912.29 697.96 

SRMV 202.22 177.89 160.16 283.73 114.13 67.34 106.24 62.86 207.91 207.15 

SRMW 677.87 624.12 797.53 796.78 637.27 390.96 274.41 204.13 305.80 480.58 

SRSO 253.66 227.19 310.51 235.63 127.92 139.60 91.58 92.51 97.90 210.36 

SRTV 258.66 200.36 335.47 242.64 97.73 87.17 94.79 87.37 108.31 100.74 

SRVC 353.12 226.16 407.72 500.17 228.59 172.78 90.20 88.40 237.99 312.39 

Indirect Emissions Analysis ▪ 55 

Appendix C 
Figure Title Page 

D-1 Hourly Average CO2 Emissions Rate 56 

D-2 Empirical Incremental CO2 Emissions Rate 59 

D-3 Flexibility-Weighted Hourly Average CO2 Emissions 

Rate 62

D-4 Load Following Incremental CO2 Emissions Rate 65 

D-5 Hourly Average NOx Emissions Rate 68 

D-6 Empirical Incremental NOx Emissions Rate 71 

D-7 Flexibility-Weighted Hourly Average NOx Emissions 

Rate 74

D-8 Load Following Incremental NOx Emissions Rate 77 

D-9 Hourly Average SO2 Emissions Rate 80 

D-10 Empirical Incremental SO2 Emissions Rate 83 

D-11 Flexibility-Weighted Hourly Average SO2 Emissions 

Rate 86

D-12 Load Following Incremental SO2 Emissions Rate 89 

Appendix D: 

Color intensity maps of hourly indirect CO2, NOx and SO2 emissions rates 

for each eGRID subregion, using several alternative calculation 

methodologies. 

Each grid shows hour of the day on the vertical axis and days of the year 

on the horizontal axis. Color scale is shown at the bottom of each page. 

The indirect emissions metrics shown are: 

The data are available in electronic format upon request. 

Indirect Emissions Analysis ▪ 56 

Appendix D
 
Hourly Average CO2

 Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-1: Hourly Average CO2 Emissions Rate(tons per MWh) 

Indirect Emissions Analysis ▪ 57 

Appendix D
 
 

Hourly Average CO2 Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-1 (continued): Hourly Average CO2 Emissions Rate 

Indirect Emissions Analysis ▪ 58 

Appendix D
 
Hourly Average CO2

 Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-1 (continued): Hourly Average CO2 Emissions Rate (continued) 

Indirect Emissions Analysis ▪ 59 

Appendix D
 
Empirical Incremental CO2

 Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-2: Empirical Incremental CO2 Emissions Rate (tons per MWh) 

Indirect Emissions Analysis ▪ 60 

Appendix D
 
Empirical Incremental CO2

 Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

 D-2 (continued): Empirical Incremental CO2 Emissions Rate 

Indirect Emissions Analysis ▪ 61 

Appendix D
Empirical Incremental CO2

 Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

 D-2 (continued): Empirical Incremental CO2 Emissions Rate 

Indirect Emissions Analysis ▪ 62 

Appendix D
Empirical Incremental CO2

 Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

 D-2 (continued): Empirical Incremental CO2 Emissions Rate 

Indirect Emissions Analysis ▪ 63 

Appendix D
Flexibility Weighted Hourly Average CO2

 Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-3: Flexibility-Weighted Hourly Average CO2 Emissions Rate 

(tons per MWh) 

Indirect Emissions Analysis ▪ 64 

Appendix D
 
Flexibility Weighted Hourly Average CO2

 Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-3 (continued): Flexibility-Weighted Hourly Average CO2 Emissions Rate 

Indirect Emissions Analysis ▪ 65 

Appendix D
 
SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-3 (continued): Flexibility Weighted Hourly Average CO2 Emissions Rate 

Indirect Emissions Analysis ▪ 66 

Appendix D
 
Connor Incremental CO2

 Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-4: Load-Following Incremental CO2 Emissions Rate (tons per MWh) 

Indirect Emissions Analysis ▪ 67 

Appendix D
 
Connor Incremental CO2

 Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

 D-4 (continued): Load-Following Incremental CO2 Emissions Rate 

Indirect Emissions Analysis ▪ 68 

Appendix D
Connor Incremental CO2

 Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (tons/MWh) 

0.6 0.7 0.8 0.9 1 1.1 1.2 

D-4 (continued): Load-Following Incremental CO2 Emissions Rate 

Indirect Emissions Analysis ▪ 69 

Appendix D
 
Hourly Average NOX Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-5: Hourly Average NOx Emissions Rate (pounds per MWh) 

Indirect Emissions Analysis ▪ 70 

Appendix D
 
Hourly Average NOX Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-5 (continued): Hourly Average NOx Emissions Rate 

Indirect Emissions Analysis ▪ 71 

Appendix D
 
Hourly Average NOX Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-5 (continued): Hourly Average NOx Emissions Rate 

Indirect Emissions Analysis ▪ 72 

Appendix D
 
Empirical Incremental NOX Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-6: Empirical Incremental NOx Emissions Rate (pounds per MWh) 

Indirect Emissions Analysis ▪ 73 

Appendix D
 
Empirical Incremental NOX Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-6 (continued): Empirical Incremental NOx Emissions Rate 

Indirect Emissions Analysis ▪ 74 

Appendix D
 
Empirical Incremental NOX Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-6 (continued): Empirical Incremental NOx Emissions Rate 

Indirect Emissions Analysis ▪ 75 

Appendix D
 
 

Flexibility Weighted Hourly Average NOX Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-7: Flexibility-Weighted Hourly Average NOx Emissions Rate 

(pounds per MWh) 

Indirect Emissions Analysis ▪ 76 

Appendix D
 
Flexibility Weighted Hourly Average NOX Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-7 (continued): Flexibility-Weighted Hourly Average NOx Emissions Rate 

Indirect Emissions Analysis ▪ 77 

Appendix D
 
Flexibility Weighted Hourly Average NOX Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

 

D-7 (continued): Flexibility-Weighted Hourly Average NOx Emissions Rate 

Indirect Emissions Analysis ▪ 78 

Appendix D
 
Connor Incremental NOX Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-8: Load-Following Incremental NOx Emissions Rate (pounds per MWh) 

Indirect Emissions Analysis ▪ 79 

Appendix D
 
Connor Incremental NOX Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-8 (continued): Load-Following Incremental NOx Emissions Rate 

Indirect Emissions Analysis ▪ 80 

Appendix D
 
Connor Incremental NOX Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

D-8 (continued): Load-Following Incremental NOx Emissions Rate 

Indirect Emissions Analysis ▪ 81 

Appendix D
 
Hourly Average SOX Emissions Rate

 

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

D-9: Hourly Average SO2 Emissions Rate (pounds per MWh) 

Indirect Emissions Analysis ▪ 82 

Appendix D
 
Hourly Average SOX Emissions Rate

 

Emissions Rate (lbs/MWh) 

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

D-9 (continued): Hourly Average SO2 Emissions Rate 

Indirect Emissions Analysis ▪ 83 

Appendix D
 
Hourly Average SOX Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

D-9 (continued): Hourly Average SO2 Emissions Rate 

Indirect Emissions Analysis ▪ 84 

Appendix D
 
Empirical Incremental SOX Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

 

D-10: Empirical Incremental SO2 Emissions Rate (pounds per MWh) 

Indirect Emissions Analysis ▪ 85 

Appendix D
 
Empirical Incremental SOX Emissions Rate

 

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

D-10 (continued): Empirical Incremental SO2 Emissions Rate 

Indirect Emissions Analysis ▪ 86 

Appendix D
 
Empirical Incremental SOX Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

D-10 (continued): Empirical Incremental SO2 Emissions Rate 

Indirect Emissions Analysis ▪ 87 

Appendix D
 
Flexibility Weighted Hourly Average SOX Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

2 4 6 8 10 12 14 

D-11: Flexibility-Weighted Hourly Average SO2 Emissions Rate 

(pounds per MWh) 

Indirect Emissions Analysis ▪ 88 

Appendix D
 
Flexibility Weighted Hourly Average SOX Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

2 4 6 8 10 12 14 

D-11 (continued): Flexibility-Weighted Hourly Average SO2 Emissions Rate 

Indirect Emissions Analysis ▪ 89 

Appendix D
 
Flexibility Weighted Hourly Average SOX Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

2 4 6 8 10 12 14 

D-11 (continued): Flexibility-Weighted Hourly Average SO2 Emissions Rate 

Indirect Emissions Analysis ▪ 90 

Appendix D
 
Connor Incremental SOX Emissions Rate

AZNM 

5 

10 

15 

20 

CAMX 

5 

10 

15 

20 

ERCT 

5 

10 

15 

20 

FRCC 

5 

10 

15 

20 

MROE 

5 

10 

15 

20 

MROW 

5 

10 

15 

20 

NEWE 

5 

10 

15 

20 

NWPP 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

D-12: Load-Following Incremental SO2 Emissions Rate (pounds per MWh) 

Indirect Emissions Analysis ▪ 91 

Appendix D
 
Connor Incremental SOX Emissions Rate

NYCW 

5 

10 

15 

20 

NYLI 

5 

10 

15 

20 

NYUP 

5 

10 

15 

20 

RFCE 

5 

10 

15 

20 

RFCM 

5 

10 

15 

20 

RFCW 

5 

10 

15 

20 

RMPA 

5 

10 

15 

20 

SPNO 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

D-12 (continued): Load-Following Incremental SO2 Emissions Rate 

Indirect Emissions Analysis ▪ 92 

Appendix D
 
Connor Incremental SOX Emissions Rate

SPSO 

5 

10 

15 

20 

SRMV 

5 

10 

15 

20 

SRMW 

5 

10 

15 

20 

SRSO 

5 

10 

15 

20 

SRTV 

5 

10 

15 

20 

SRVC 

50 100 150 200 250 300 350 

5 

10 

15 

20 

Emissions Rate (lbs/MWh) 

0 5 10 15 

D-12 (continued): Load-Following Incremental SO2 Emissions Rate 

Indirect Emissions Analysis ▪ 93 

Appendix D