States’ Perspectives on EPA’s Roadmap 

to Incorporate 

Energy Efficiency/Renewable Energy in 

NAAQS State Implementation Plans: 

Three Case Studies 

Final Report to 

the U.S. Environmental Protection Agency 

Prepared by 

NESCAUM 

December 31, 2013 

(Revised May 22, 2014)
Members of Northeast States for Coordinated Air Use Management 

Arthur Marin, Executive Director 

Northeast States for Coordinated Air Use Management

Anne Gobin, Bureau Chief 

Connecticut Department of Energy and Environmental Protection, Bureau of Air Management 

Marc Cone, Bureau Director 

Maine Department of Environmental Protection, Bureau of Air Quality 

Christine Kirby, Division Director 

Massachusetts Department of Environmental Protection, Bureau of Waste Prevention 

Craig Wright, Acting Director 

New Hampshire Department of Environmental Services, Air Resources Division 

William O’Sullivan, Director 

New Jersey Department of Environmental Protection, Office of Air Quality Management 

David Shaw, Director 

New York Department of Environmental Conservation, Division of Air Resources 

Douglas L. McVay, Chief 

Rhode Island Department of Environmental Management, Office of Air Resources 

Elaine O’Grady, Director 

Vermont Department of Environmental Conservation, Air Quality & Climate Division 

 
ii

States’ Perspectives on EPA’s Roadmap 

to Incorporate 

Energy Efficiency/Renewable Energy in 

NAAQS State Implementation Plans: 

Three Case Studies 

Final Report 

to the U.S. Environmental Protection Agency 

Prepared by 

NESCAUM 

December 31, 2013 

(Revised May 22, 2014) 
iii

STATES’ PERSPECTIVES ON EPA’S ROADMAP 

TO INCORPORATE 

ENERGY EFFICIENCY/RENEWABLE ENERGY IN 

NAAQS STATE IMPLEMENTATION PLANS: 

THREE CASE STUDIES 

FINAL REPORT 

TO THE U.S. ENVIRONMENTAL PROTECTION 

AGENCY 

Project Managers 

 Allison Guerette, NESCAUM 

 Leah Weiss, NESCAUM 

Principal Contributors 

 Report: NESCAUM 

 Appendix A: Massachusetts Department of Environmental Protection 

 Appendix B: New York State Department of Environmental Conservation 

 

 Appendix C: Maryland Department of the Environment

Appendix D: NESCAUM 

 
iv

Acknowledgments 

NESCAUM gratefully acknowledges the funding and program support provided by the 

U.S. Environmental Protection Agency (EPA). Specifically, NESCAUM thanks: Julie 

Rosenberg, Niko Dietsch, Robyn DeYoung, Denise Mulholland, and Gabrielle Stevens, 

from the Office of Atmospheric Programs (OAP), Climate Protection Partnerships 

Division; Greg Greene, Chris Stoneman, Karl Pepple, and Angie Shatas, from the Office 

of Air Quality Planning and Standards (OAQPS); Robert McConnell and John Moskal 

from EPA Region 1; Gavin Lao and Kirk Wieber from EPA Region 2; and Megan Goold, 

Andrew Kreider, Todd Ellsworth, Gregory Beacoat, and Ellen Schmidt, from EPA 

Region 3. 

NESCAUM also thanks the following individuals: 

William Space, Sharon Weber, and Christine Kirby, Massachusetts Department of 

Environmental Protection 

Robert D. Bielawa, P.E., and Scott Griffin, New York State Department of 

Environmental Conservation 

Tad Aburn, Husain Waheed, Kathleen Wehnes, and Carolyn Jones, Maryland 

Department of the Environment 

Ken Colburn, Chris James, and John Shenot, Regulatory Assistance Project 

Printed: May 2014
v

TABLE OF CONTENTS 

Acknowledgments.............................................................................................................. iv

Executive Summary ........................................................................................................... vi

1. Introduction ................................................................................................................ 1-1

1.1. Opportunity for Including Energy Efficiency and Renewable Energy in State 

Implementation Plans.................................................................................. 1-1

1.2. Challenges to Including Energy Efficiency and Renewable Energy in State 

Implementation Plans.................................................................................. 1-2

1.3. Report Overview ......................................................................................... 1-3

1.4. Project Overview ........................................................................................ 1-3

2. Summary of State Roadmap Case Studies ................................................................. 2-1

2.1. Massachusetts: The Baseline Pathway ........................................................ 2-1

2.2. New York: The Control Strategy Pathway ................................................. 2-2

2.3. Maryland: The Weight-of-Evidence (WOE) Pathway ............................... 2-3

3. States’ Perspectives and Suggestions on key Policy considerations ......................... 3-1

3.1. Location of Energy Programs and Emissions Reductions .......................... 3-1

3.2. Acceptable Level of Detail on Energy Programs and Associated Emissions 

Reductions................................................................................................... 3-2

3.3. Acceptance of Energy Models, Tools, and Quantification Methodologies 3-2

4. Summary .................................................................................................................... 4-1

Appendix A: Massachusetts Case Study, The Baseline Pathway 

Appendix B: New York Case Study, The Control Strategy Pathway 

Appendix C: Maryland Case Study, The Weight-of-Evidence Pathway 

Appendix D: EE/RE in SIPs Policy Issues, Memorandum to EPA from NESCAUM on 

Behalf of the Case Study States, March 27, 2013 

 
vi

Executive Summary 

Air quality planners are interested in promoting the multi-pollutant air quality 

benefits of energy efficiency (EE) and renewable energy (RE) programs. EE and RE 

prevent emissions from occurring by reducing demand for energy from emitting sources. 

EE also prevents emissions associated with conversion losses, plant use, and transmission 

and distribution losses.1 EE and RE programs have the potential to reduce the operation 

of the dirtiest energy generation sources that tend to run during unhealthy air quality 

episodes. 

EE and RE have long-standing roles in helping to meet energy demand in the 

Northeast, Mid-Atlantic, and across the United States. Air quality planners are interested 

in assessing the air quality impacts and benefits of existing and new EE and RE programs 

and appropriately accounting for and incorporating their benefits into State 

Implementation Plans (SIPs), which detail how states plan to attain and maintain National 

Ambient Air Quality Standards (NAAQS). 

With this interest in mind, the Northeast States for Coordinated Air Use 

Management (NESCAUM) worked with the U.S. Environmental Protection Agency 

(EPA), the Massachusetts Department of Environmental Protection (MassDEP), the New 

York State Department of Environmental Conservation (NYSDEC), and the Maryland 

Department of the Environment (MDE) to pilot case studies of EPA’s Roadmap for 

Incorporating Energy Efficiency/Renewable Energy Policies and Programs into State 

and Tribal Implementation Plans (the Roadmap).

2

 The Roadmap clarifies and provides 

details on three new pathways for including EE and RE in SIPs: (1) baseline emissions 

forecast (baseline pathway); (2) control strategy quantification (control strategy 

pathway); and (3) weight-of-evidence (WOE pathway). A fourth pathway, innovative and 

emerging measures, was the basis for EPA’s previous EE in SIPs guidance, issued in 

2004. 3

The project’s goals were to highlight opportunities to include EE and RE in SIPs 

and to provide real-world examples and lessons learned in incorporating EE and RE in 

SIPs. Each state applied one of the three new Roadmap pathways and described its 

process, results, and policy questions in the case studies that are included in this report’s 

appendices. 

MassDEP’s case study reviews how it might represent its statutory requirement of 

“all cost-effective EE” using EPA’s baseline pathway.

4

 MassDEP’s case study reviews 

Massachusetts’ recent experience developing load projections to support analysis of 

 

1

 According to the U.S. Energy Information Administration’s Annual Energy Review 2011, every one unit 

of energy demand results in three units of energy consumed due to conversion losses, plant energy use, and 

transmission and distribution losses. Conversion losses occur as fuel is turned into mechanical energy to 

turn electric generators, in particular at steam-electric power plants. Transmission and distribution losses 

(also called line losses) occur as electricity is transported from power plants to end-use consumers. See: 

http://www.eia.gov/totalenergy/data/annual/pdf/aer.pdf.

2

 See: http://www.epa.gov/airquality/eere/pdfs/EEREmanual.pdf. 

3

 See: http://www.epa.gov/ttn/caaa/t1/memoranda/ereseerem_gd.pdf.

4

 For the Massachusetts Green Communities Act of 2008 and Massachusetts’s requirement to implement 

“all cost effective EE”, see: https://malegislature.gov/Laws/SessionLaws/Acts/2008/Chapter169. 
vii

potential changes to the Regional Greenhouse Gas Initiative (RGGI) program and shows 

what can be achieved through sustained collaboration among regulators from multiple 

state agencies and electric grid operators. MassDEP found that its utility commission and 

its independent system operator have valuable information on energy programs and their 

impacts on electricity demand. However, MassDEP noted a lack of consistent methods to 

quantify and forecast EE program savings, particularly with regard to timing and location 

of emissions reductions, which would be important for non-carbon dioxide pollutants. 

MassDEP also documented the lack of consistent methodology for determining and 

documenting the degree to which EE is captured in load forecasts. 

NYSDEC’s case study provides a hypothetical SIP submission using the control 

strategy pathway. NYSDEC’s case study illustrates the magnitude of energy programs 

needed to achieve meaningful emissions reductions on a scale that noticeably affects 

ozone levels, and it underscores the potential for bundling EE and RE measures for 

inclusion in a SIP. NYSDEC had questioned whether EE/RE emissions reductions 

obtained through the control strategy pathway would be able to meet all four of the SIP 

credit criteria (permanent, enforceable, quantifiable, and surplus); NYSDEC chose to 

account for uncertainties associated with the quantifiable criterion by applying a discount 

factor to its calculated emissions reductions for one of the three measures that were 

quantified. NYSDEC’s case study also brought forward a question as to whether EPA 

would accept particular quantification tools for SIP crediting purposes. NYSDEC is using 

the NE-MARKAL energy model and would use such an analysis for a future SIP. Based 

on discussions to date with EPA, it is understood that any analytical tools used in a SIP 

would need to be rigorously and appropriately applied and their use would be judged in 

the context of the SIP. NYSDEC’s case study also underscores the importance of 

bundling EE and RE measures for inclusion in a SIP, as a small individual program 

would not achieve the magnitude of emissions reductions needed to significantly lower 

ozone levels. 

MDE’s case study is a status report of completed and planned work for its 2015 

ozone SIP using an approach based on the WOE pathway. As written, EPA’s WOE 

pathway allows states to assess likely attainment based on analysis using a variety of 

models of varying rigor, and does not allow for SIP crediting. Maryland’s 2015 SIP will 

include EE and RE as part of a broader, rigorous multi-pollutant planning analysis. The 

case study describes a multi-pollutant planning process that combines traditional air 

quality assessment tools with less traditional assessment tools. Due to the rigor of its 

analysis, MDE created what it calls an “expanded WOE approach,” and will request 

that EPA allow the measures quantified in that analysis to be included in its SIP. EPA 

and MDE plan to continue discussion on this expanded approach as the analysis 

proceeds. 

This pilot project provided an opportunity for the three states to gain a deeper, 

working understanding of the Roadmap. It allowed them to engage with various EPA 

offices regarding key policy considerations, including: 
viii

• Promoting state and regional consistency in the development and 

review/approval of SIPs that incorporate EE and RE 

• Addressing the challenge of determining location of emissions reductions 

from EE and RE programs, including using an approach similar to how 

mobile and area source programs are assessed credit for SIP purposes 

• Clarifying expectations for how states demonstrate that EE and RE 

programs benefit the nonattainment area, as discussed in the August 2013 

Frequently Asked Questions (FAQ) document 5

 

• Promoting consistency in quantification methods, including the 

assessment of EE evaluation, monitoring and verification (EM&V) 

protocols 

• Briefing the states on the U.S. Department of Energy (DOE) State and 

Local Energy Efficiency (SEE) Action Network and the DOE Uniform 

Methods project,6

 including timelines, expected outcomes, and 

applicability of these efforts to quantifying EE and RE in SIPs 

 The three states would like to continue discussion on some of these policy 

considerations. They are interested in maintaining ongoing discussion and dialogue with 

EPA as these case studies are introduced to other states, and as they and other states 

prepare SIPs that quantify EE and RE programs.

 

5

 See: http://www.epa.gov/airquality/eere/pdfs/eerefaqAug2013.pdf 

6

 See: http://www1.eere.energy.gov/seeaction/ and 

http://www1.eere.energy.gov/office_eere/de_ump_about.html. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 1-1 

1. INTRODUCTION 

1.1. Opportunity for Including Energy Efficiency and Renewable 

Energy in State Implementation Plans 

Energy efficiency (EE) and renewable energy (RE) offer new and unique 

opportunities to improve air quality. As states are faced with fewer traditional air 

pollution control options that are cost-effective and can achieve significant criteria 

pollutant reductions, many EE and RE programs have the benefit of being “on the books” 

or “on the way,” yielding multi-pollutant benefits such as reductions in nitrogen oxides, 

sulfur dioxide, and carbon dioxide, and avoiding emissions of the dirtiest electric 

generating units (EGUs) during unhealthy air quality episodes. Moreover, EE programs 

achieve additional benefits by avoiding energy losses from plant use, conversion, and 

transmission and distribution.7

EE has long helped in meeting energy demand in the Northeast and Mid-Atlantic, 

and other states and regions. The American Council for an Energy Efficient Economy 

estimates that, since 1970, about three-quarters of the new demand for electricity has 

been met by EE rather than energy generation.8

 Over the past several years, air regulators 

in the Northeast and Mid-Atlantic states have started examining ways to assess the air 

quality benefits of EE and RE and incorporate them into State Implementation Plans 

(SIPs), which detail how states plan to attain and maintain the National Ambient Air 

Quality Standards (NAAQS). Air regulators have become more active participants in 

regional and national dialogues with energy regulators, electricity generators, and system 

operators, and have engaged in multi-pollutant analysis exercises that integrate energy 

and air quality programs.9,

10

 

The Environmental Protection Agency’s (EPA’s) Roadmap for Incorporating 

Energy Efficiency/Renewable Energy Policies and Programs into State and Tribal 

Implementation Plans (the Roadmap) provides states with guidance for including EE and

RE programs and technologies in SIPs.11 The Roadmap provides three new pathways for 

including EE/RE in SIPs: (1) baseline emissions forecast (“baseline pathway”); (2) 

control strategy quantification (“control strategy pathway”); and (3) weight-of-evidence 

(“WOE pathway”). The Roadmap also expands upon a fourth pathway, voluntary and 

 

7

 According to the U.S. Energy Information Administration’s Annual Energy Review 2011, every one unit 

of energy demand results in three units of energy consumed due to conversion losses, plant energy use, and 

transmission and distribution losses. Conversion losses occur as fuel is turned into mechanical energy to 

turn electric generators, in particular at steam-electric power plants. Transmission and distribution losses 

(also called line losses) occur as electricity is transported from power plants to end-use consumers. See: 

http://www.eia.gov/totalenergy/data/annual/pdf/aer.pdf.

8

 See: http://aceee.org/research-report/e121.

9

 Regional and national dialogues on EE include the Northeast Energy Efficiency Partnership’s (NEEP’s) 

Evaluation, Measurement, and Verification (EM&V) Forum (http://neep.org/emv-forum/), Department of 

Energy’s (DOE’s) Uniform Methods Project for Determining Energy Efficiency Program Savings 

(http://www1.eere.energy.gov/office_eere/de_ump.html), and the State and Local Energy Efficiency (SEE) 

Action Network (http://www1.eere.energy.gov/seeaction/). 

10 For information on multi-pollutant planning, see: http://www.nescaum.org/topics/multi-pollutantprograms.

11 See: http://www.epa.gov/airquality/eere/pdfs/EEREmanual.pdf.
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 1-2 

emerging measures, which was the basis for EPA’s 2004 EE in SIPs guidance. 12 In 

August 2013, EPA released a Frequently-Asked-Questions (FAQ) document, providing 

further clarification about the inclusion of EE and RE in SIPs.13

 

1.2. Challenges to Including Energy Efficiency and Renewable Energy 

in State Implementation Plans 

Pilot states have noted that one hurdle to including EE and RE in SIPs is the 

enforceability of these programs. Unlike traditional air control programs, typically 

enforced through air permits or self-certification compliance, EE and RE programs are 

typically under the purview of public utility commissions (PUCs) or state energy offices. 

Enforceability is further complicated because EE and RE programs are not implemented 

at the source (i.e., the EGU), and thus the resulting emissions benefits may be realized at 

any EGU on the electricity grid, including out-of-state locations. A PUC or state energy 

office can only assure that the energy savings from the EE or RE program occurred. 

Quantifying and characterizing energy savings from EE and RE programs for SIP 

crediting purposes is more complex than tracking emissions reductions from traditional 

controls on EGUs. First, meaningful reductions from EE and RE programs require 

implementation of many small measures (a bundled approach). Second, quantifying the 

energy savings from these measures requires realistic and transparent assumptions about 

the average energy savings, usage, and lifespan of each measure. Third, the data and 

information about energy savings are often housed within state PUCs or the utilities 

(where the utility acts as program administrator). Evaluation, measurement, and 

verification (EMV) of energy savings resulting from EE and RE programs is typically 

performed by the program administrator or PUC, and such processes differ by state.14

Air quality planners will likely find that energy savings data are available at an 

appropriate level of disaggregation for use in SIP planning. However, translating energy 

savings into emissions reductions introduces further complexity for air quality planners.15

In the Roadmap, EPA outlines potential methodologies for estimating the emissions 

reductions from a unit of energy saved, which range from using a multiplier of regional 

annual, average non-baseload emissions rates (the average emission rate for the EGU’s 

that could be displaced by an EE or RE program) to assessing hourly emissions 

reductions through dispatch modeling. The average non-baseload emissions factor 

approach may be too simplistic for SIP purposes, and the dispatch modeling approach 

may be too burdensome as air agencies do not have access to these models, they are 

costly to run, and may be at an inappropriate level of granularity for SIP purposes. 

In addition, there are currently efforts underway to provide appropriate 

methodologies and tools for translating energy savings into emissions reductions for SIP 

purposes. For example, EPA’s Avoided Emissions and Generation Tool (AVERT) 

quantifies hourly emissions reductions aggregated at the regional, state, and county 

levels, using statistical analysis of historical dispatch.16 Also, the Northeast and Mid-

 

12 See: http://www.epa.gov/ttn/caaa/t1/memoranda/ereseerem_gd.pdf.

13 See: http://www.epa.gov/airquality/eere/pdfs/eerefaqAug2013.pdf 

14 See: www.raponline.org/document/download/id/6680.

15 Ibid. 

16 For more information on AVERT visit: www.epa.gov/avert 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 1-3 

Atlantic states are engaging with their regional system operators with the goal of 

developing a methodology to determine average annual marginal emissions rates for peak 

and off-peak hours for summer and non-summer seasons. 

1.3. Report Overview 

This report describes a pilot project by three volunteer states to apply three of the 

four Roadmap pathways to state EE and RE programs, and summarizes key policy 

considerations identified by the states. The remainder of Section 1 describes project 

background, goals, partners and their roles, and timeline. Section 2 provides a summary 

of the states’ case study processes, findings, and recommendations. Section 3 highlights 

the state perspective on the outstanding issues the states have identified for EPA. Section 

4 concludes with a review of key issues and a list of resource links and contacts. The case 

studies, as developed by the states, are contained in the appendices. 

1.4. Project Overview 

In October 2012, the Northeast States for Coordinated Air Use Management 

(NESCAUM) began working with EPA three volunteer states—Maryland, 

Massachusetts, and New York—to pilot the Roadmap. Each state applied one of the 

pathways to state energy programs and recorded its process, results, and policy questions 

in a case study. The project goals were to highlight opportunities to include EE and RE in 

SIPs, identify obstacles to incorporating EE and RE in SIPs, and provide real-world 

examples, lessons learned, and questions about the Roadmap. 

EPA participants included: 

• The Climate Protection Partnerships Division (CPPD) in the Office of 

Atmospheric Programs (OAP)the Office of Air Quality Planning and 

Standards (OAQPS), which is responsible for SIP-related rules and 

guidance and authored the Roadmap; 

• OAP’s Clean Air Markets Division (CAMD), which runs and maintains 

EGU-based forecasting tools such as the Integrated Planning Model (IPM) 

and the Emissions & Generation Resource Integrated Database (eGRID); 

• The Office of Research and Development (ORD), which develops and 

uses energy models; and 

• EPA Regions 1, 2, and 3, which review SIPs and will determine the 

approvability of EE/RE-related SIP submissions. 

The Regulatory Assistance Project (RAP) provided technical and policy 

assistance on an as-needed basis. NESCAUM consulted with its Energy Efficiency-Air 

Quality Workgroup, which is comprised of Northeast and Mid-Atlantic state air 

regulatory staff and is working towards common methodologies for quantifying EE and 

RE emissions benefits. 

Each state assessed a different Roadmap pathway. The Massachusetts Department 

of Environmental Protection (MassDEP) applied the baseline pathway to its statutory 

requirement to implement all cost-effective EE. The New York State Department of 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 1-4 

Environmental Conservation (NYSDEC) applied the control strategy pathway to a 

hypothetical combined heat and power (CHP) program and to planned initiatives to 

incentivize solar and increase EE in public buildings. The Maryland Department of the 

Environment (MDE) is applying the WOE pathway to the energy programs included in 

its Greenhouse Gas Reduction Act Plan and has developed a comprehensive analytical 

approach that it calls an “expanded WOE approach.” Two of these case studies are 

hypothetical, as neither Massachusetts nor New York currently have an ozone attainment 

SIP obligation. Maryland’s ozone SIP is due in 2015, and its analytical efforts are 

ongoing. 

Throughout the project, NESCAUM coordinated and facilitated routine 

discussions among the three states and the various participating EPA offices. These 

discussions promoted ongoing information exchange, including training opportunities in 

relevant topics. For example: 

• NESCAUM provided EPA with training on the NE-MARKAL energy 

model and its ability to quantify emissions reductions from EE and RE 

programs; 

• EPA created materials and provided training to educate states on the 

magnitude of EE and RE programs needed to achieve significant 

emissions reductions.17 

• EPA provided briefings for MDE and MassDEP on EPA’s analysis of EE 

and RE programs included in the U.S. Department of Energy (DOE) 

Energy Information Administration’s (EIA’s) Annual Energy Outlook 

(AEO) demand forecast for power sector modeling;18 and 

• EPA provided an overview and tutorial of the AVERT tool and responded 

to state concerns about the EGU-level of AVERT’s results. 

As the states began working with the Roadmap, they identified key policy 

considerations and questions, predominately stemming from the complexity of capturing 

and documenting the energy savings and emissions benefits of EE and RE. NESCAUM, 

EPA, and the States worked together to address each of these questions through a series 

of meetings and discussions. This document is intended to document that experience and 

help other states learn from progress in the pilot states to date. 

 

18 For information about EPA’s analysis of the EE/RE included in the AEO, see: 

http://www.epa.gov/statelocalclimate/state/statepolicies.html.
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 2-1 

2. SUMMARY OF STATE ROADMAP CASE STUDIES 

2.1. Massachusetts: The Baseline Pathway 

The baseline pathway addresses emissions reductions from EE and RE by 

incorporating them directly into baseline emissions projections. To use this pathway, 

states must understand what is included in a chosen EGU baseline, account for the impact 

of EE and RE programs that are not already included in the EGU baseline, and then 

incorporate this electricity sales baseline into the SIP emissions baseline. 

MassDEP examined how it would apply the baseline pathway to include 

Massachusetts Department of Public Utilities’ (DPU’s) statutory requirement to 

implement “all cost-effective EE” in a SIP.19 The full case study is provided in Appendix 

A. 

First, MassDEP reviewed program data from the DPU; MassDEP found that 

program data were readily available and that acceptable methodologies are in place in 

Massachusetts to estimate the energy savings from EE and RE programs and project 

these savings into the future.20 Then it reviewed existing energy baselines embedded in 

state, regional, and national modeling efforts. This included examining data from EIA’s 

AEO, the Eastern Regional Technical Advisory Committee’s (ERTAC’s) Electric 

Generating Utility Growth Model, and ISO-New England’s load forecast.21 MassDEP 

also examined the process of quantifying the emissions benefits of the DPU’s EE 

requirement and how a load forecast might be adjusted if necessary to incorporate these 

benefits. MassDEP cited as a model recent experience working with ISO-New England to 

develop load forecasts deemed acceptable for use during the recent review of the RGGI 

program.

22

MassDEP found that there is no standard methodology across the New England 

states for determining whether the benefits of EE and RE programs are included in the 

load forecasts embedded in electric sector models. The lack of standardized methodology 

could lead to significant double counting or under-reporting of the benefits of EE and RE 

programs. MassDEP recommends that EPA and DOE work together to reduce the 

complexity of implementing this pathway by including all “on-the-books” state EE and 

RE programs in the state energy forecasts that are developed at the national level, with 

requirements for the use of consistent and transparent EMV. MassDEP stresses the 

 

19 For the Massachusetts Green Communities Act of 2008 and Massachusetts’s requirement to implement 

“all cost effective EE”, see: https://malegislature.gov/Laws/SessionLaws/Acts/2008/Chapter169. 

20 For an example of established methodology for quantifying and forecasting energy savings, see: 

http://emp.lbl.gov/sites/all/files/lbnl-5803e.pdf.

21 See: http://www.eia.gov/forecasts/aeo/, http://www.ertac.us/index_egu.html, and http://www.isone.com/trans/celt/index.html. 

22 For a review of the RGGI process to understand the ISO-NE forecast, see: Evolution of an EnergyEfficient Forecast: Building a model that works across states and programs, by Gordon van Welie, Public 

Utilities Fortnightly, January 2013. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 2-2 

importance of ongoing work by EPA and DOE to develop a national methodology to 

estimate emissions reductions from EE and RE.23 

MassDEP also recommends that EPA provide more specificity as to the level of 

detail on EE and RE programs that would be required of states for SIP purposes. This 

could address the location and timing of emissions reductions if necessary, and the 

characterization of load in energy forecasts. Clarification from EPA on these factors will 

be necessary for states to evaluate available baselines for use in this pathway. 

2.2. New York: The Control Strategy Pathway 

The control strategy pathway is intended for “on the way” EE and RE programs. 

The intent is to quantify these nontraditional programs in a manner similar to traditional 

air quality control programs and to demonstrate that the programs meet the four SIP 

criteria (permanent, enforceable, quantifiable, and surplus). 

NYSDEC applied the control strategy pathway to three programs: a hypothetical 

CHP program, a planned initiative to incentivize solar energy (NY-Sun), and an initiative 

to increase EE in public buildings (Build Smart NY). NYSDEC used EPA’s CHP 

Calculator tool for the CHP analysis, and worked with NESCAUM in using the NEMARKAL energy model to analyze the associated energy and emissions changes 

resulting from implementing NY-Sun and Build Smart NY. The full case study is 

provided in Appendix B. 

This case study highlights how both program design and the technique for 

quantifying energy savings and emissions benefits of EE and RE programs should be 

factored into determining appropriate SIP credit. It also provides EPA with a concrete 

example of how a state can use an energy model to quantify the emissions benefits of an 

EE or RE program. The NE-MARKAL model is the Northeast-specific version of 

MARKAL,24 which represents the energy infrastructure of the northeastern U.S., 

including energy demand and supply in the power generation, commercial, industrial, 

residential, and transportation sectors.25 

For purposes of SIP crediting, NYSDEC chose to discount the CHP scenario by a 

factor of 20 percent to account for uncertainties as well as for potential discrepancies in 

calculated and real program performance and emissions benefits. When evaluating EPA 

guidance and deciding which quantification methodology to use for the NY-Sun and 

Build Smart NY initiatives, NYSDEC chose what it felt was the most sophisticated 

analytical approach to quantify emission reductions. Consequently, NYSDEC deemed 

that an emissions reduction discount was not necessary for those initiatives. NYSDEC 

requests that EPA provide states and EPA regional offices with guidance on determining 

appropriate discount factors for EE and RE programs during SIP development exercises. 

 

23 For example, the U.S. DOE is currently developing a framework and protocols for determining energy 

efficiency program savings through the Uniform Methods Project.. See: 

https://www1.eere.energy.gov/office_eere/de_ump.html. 

24 For information on the MARKAL model, see: Loulou, R., G. Goldstein, and K. Noble, The MARKAL 

Family of Models, Energy Technology Systems Analysis Programme (ETSAP), October 2004. See 

http://www.iea-etsap.org/web/index.asp.

25 See: http://www.nescaum.org/topics/ne-markal-model. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 2-3 

NYSDEC confirmed that the magnitude of EE and RE programs needed to 

achieve meaningful results could be significant. Because individual measures could result 

in relatively small emissions reductions, NYSDEC would likely bundle measures for 

inclusion in a future SIP. In its August 2013 FAQ document, EPA refers states to its 

“Guidance on Incorporating Bundled Measures in State Implementation Plans,” was 

originally intended for emerging and voluntary measures.26 NYSDEC would like to work 

with EPA during future SIP development to clarify how this guidance is appropriate for 

use with the control strategy pathway. 

This case study also indicates that determining SIP credit for EE and RE—

including choosing appropriate discount factors—will likely be done on a case-by-case 

basis until more standardized emission quantification protocols are available to states. It 

is therefore important that any case-specific decisions made on SIP crediting be reviewed 

and shared nationally. This would foster regional consistency in SIP reviews and provide 

states with access to approved approaches. 

2.3. Maryland: The Weight-of-Evidence (WOE) Pathway 

The WOE pathway allows states to use a variety of analytical methods to 

document how state programs will maintain or reduce emissions without the need for 

otherwise required air quality modeling. EPA does not allow for crediting of EE and RE 

programs using this pathway, but intends to consider the results of this pathway when 

reviewing SIP submittals. States have used WOE in the past when there is no clear 

quantification methodology or when resource constraints prevent states from conducting 

a more rigorous analysis. 

Maryland Department of Environment (MDE) chose to apply the WOE pathway 

while also significantly modifying it to include a robust technical approach that combines 

traditional air quality modeling with less traditional assessment tools. This approach 

expands upon the “expanded weight-of-evidence” approach developed by the Ozone 

Transport Commission and outlined in a June 17, 2011 letter to EPA.27 Because MDE 

plans to conduct and submit a rigorous technical analysis for its ozone SIP, it asks EPA to 

provide guidance on allowing SIP credit for such an expanded WOE approach. The full 

case study is provided in Appendix C. 

MDE describes its WOE assessment—which will be used to assess EE and RE 

programs for its ozone SIP that is due in 2015—in Chapter 5 of its Greenhouse Gas 

Emissions Reduction Act Plan.28 MDE will assess multi-pollutant benefits, including 

changes in emissions of nitrogen oxides, sulfur dioxide, fine particulates, air toxics, and 

greenhouse gases, for the EE and RE programs, and will analyze their impacts for 

attaining the ozone NAAQS. MDE anticipates that its analysis will be completed in late 

2014. 

 

26 See: http://www.epa.gov/ttn/oarpg/t1/memoranda/10885guideibminsip.pdf, August 2005. 

27 Letter to Chet Wayland and Scott Mathias, EPA/OAQPS, from the Ozone Transport Commission. June 

17, 2011. See: http://www.otcair.org/upload/Interest/Modeling/OTC%20Expanded%20Weight-ofEvidence%20Letter%20and%20Recommendation.pdf. 

28 For MDE’s Greenhouse Gas Emissions Reduction Act Plan, see: 

http://climatechange.maryland.gov/publications/greenhouse-gas-emissions-reduction-act-plan/. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 2-4 

MDE is using a series of linked models for its multi-pollutant assessment that 

include: NE-MARKAL, the Regional Economic Models, Inc. (REMI), EPA’s 

Community Multi-scale Air Quality (CMAQ) model, and EPA’s Environmental Benefits 

Mapping and Analysis Program (BenMAP). MDE requests that EPA clarify how results 

generated from this expanded approach could be credited. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 3-1 

3. STATES’ PERSPECTIVES AND SUGGESTIONS ON 

KEY POLICY CONSIDERATIONS 

These case studies achieved much in moving states towards incorporating EE and 

RE into SIPs. There are still some outstanding policy considerations, as well as 

corresponding suggestions, from MassDEP, NYSDEC, and MDE as a result of their 

experiences. These considerations can be categorized as follows: (1) location of energy 

programs and emissions reductions; (2) acceptable level of detail on energy programs and 

associated emissions reductions; and (3) acceptance of energy models, tools, and 

quantification methodologies. 

3.1. Location of Energy Programs and Emissions Reductions 

Pilot states noted that attributing energy savings and emissions reductions from 

EE and RE programs to specific EGUs is not achievable with any degree of certainty, 

especially when considering a portfolio of programs that are implemented across a 

county, state, or region. Both the Roadmap and the August 2013 FAQ document indicate 

that the location of emissions reductions with respect to a non-attainment area is 

important, and begin to provide states with options for estimating the location of EE/RE 

program impacts. 

Over the course of the project, the states noted the inherent challenge of assigning 

emissions reductions to particular EGUs or other geographic locations because they have 

no way of guaranteeing that emissions reductions will occur at a particular EGU or within 

a particular non-attainment area. The states’ interpretation of EPA’s FAQ document is 

that assigning location to emissions reductions appears to provide some flexibility for 

states if there is no EGU in the nonattainment area or if the modeled emissions reductions 

occur outside the nonattainment area, provided that states demonstrate air quality benefits 

to the area. States’ understanding is that if the EE/RE program were to perform as 

anticipated but the projected emissions reductions were not to occur in the specified area, 

then any further action would depend on the state’s attainment status; the state may be 

required to implement contingency measures. 

The states also interpret the FAQ document to allow flexibility only for certain 

types of SIPs. In the FAQ, EPA indicates that it is constrained with respect to locationspecific emissions due to a court order. The states believe this could be ameliorated by 

allowing methodologies that generalize the location of emissions reductions, such as an 

approach that is similar to how area and mobile source programs are credited within SIPs. 

In quantifying SIP credit for area and mobile source programs, states are not required to 

specify the exact geographic location of impact of those sources. States use models such 

as MOVES, which rely on general emissions factors and, as appropriate, apply rule 

effectiveness and rule penetration discounting factors. States recommend that EPA 

consider this type of approach for EE and RE programs, with the acknowledgement that 

states are ultimately responsible for achieving the emissions reductions detailed in their 

SIPs. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 3-2 

3.2. Acceptable Level of Detail on Energy Programs and Associated 

Emissions Reductions 

The Roadmap and the August 2013 FAQ document mention the magnitude of EE 

and RE needed to achieve meaningful emissions reductions. The pilot states noted that 

accounting for the energy savings and emissions reductions of many individual programs 

is complex. States noted that they would benefit from guidance on acceptable methods of 

estimating energy savings and emissions reductions of bundled programs, as well as easy 

access to available technical information on the energy savings associated with sample 

EE and RE measures. States also noted that DOE has expertise developing related EE 

measurement protocols, which could be useful for achieving SIP quantification 

objectives.29

The FAQ document describes how states can bundle the impacts of EE and RE 

programs so that the total effect of all the measures are considered, thus increasing the 

likelihood that the desired air quality results will be achieved. EPA’s “Guidance on 

Incorporating Bundled Measures in State Implementation Plans”30 addresses bundling of 

measures for voluntary and emerging measures for mobile and area sources and the 

application of a discount factor. For purposes of SIP development, the states expressed 

interest in clarifying guidance on the acceptability of this tool for other pathways and the 

determination of appropriate discount factors. 

Another consideration for States is understanding the level of detail on the 

individual EE and RE programs that EPA regional offices will expect in their SIP 

submittals. The pilot states indicated that they intend to rely upon their energy offices, 

PUCs, and utilities for this evaluation, measurement, and verification (EM&V) 

information. States noted that this approach is consistent with EPA’s FAQ document, 

which indicates that air quality regulators must ensure that rigorous, credible, and 

transparent EM&V is performed by utility commissions. The Massachusetts case study 

observes that states would benefit from guidance on how to evaluate existing EM&V 

practices and whether this information must be included in the SIP. 

The three states expressed interest in continued and expanded coordinated efforts 

among EPA offices, particularly with the regions, to promote consistency in evaluating 

SIP submittals with regards to the level of program detail and rigor of analyses. This 

could take the form of trainings and a set of criteria developed before states submit their 

first SIPs incorporating EE and RE programs. The state perspective is that these criteria 

would greatly facilitate the inclusion of EE and RE in SIPs. 

3.3. Acceptance of Energy Models, Tools, and Quantification 

Methodologies 

EPA’s FAQ document indicates that guidelines are being developed to help states 

select appropriate models and tools, in particular those that use capacity factors, historical 

hourly emissions rates, and energy modeling approaches. The FAQ document identifies 

 

29 For example, see DOE’s Uniform Methods Project 

(https://www1.eere.energy.gov/office_eere/de_ump.html) and the SEE Action Network project 

(http://www1.eere.energy.gov/seeaction/). 

30 See: http://www.epa.gov/ttn/oarpg/t1/memoranda/10885guideibminsip.pdf.
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 3-3 

NE-MARKAL as a model that states could use in quantify the emissions benefits of EE 

and RE programs. NYSDEC expressed interest in working with EPA to clarify 

expectations for crediting the results of this type of analysis during any future SIP 

development process to ensure acceptability. Moreover, the states requested that EPA 

continue discussions about this model and other models as they are introduced for SIP 

quantification purposes. The three states recommended that these discussions include 

additional training for the regional offices, and guidance for states on how these models 

may best be used to quantify EE and RE programs in SIPs. 

State and EPA participants in the pilot also noted that the Northeast and MidAtlantic states and regional EPA offices are currently engaged in an effort with regional 

system operators to develop a methodology to determine average annual marginal 

emissions rates for peak and off-peak hours (rather than hourly emissions rates) for 

summer and non-summer seasons. If this effort is successful, states may be interested in 

using this new approach for their SIPs. At that time, states anticipate reviewing the 

methodology with EPA. 

To address the considerations raised here, States expressed interest in continuing 

to work with EPA and in maintaining discussions begun through the Roadmap project 

and expand them to include other states. Their perspective is that this would be helpful 

as new, credible, documented quantification approaches are introduced for use in SIPs. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 4-1 

4. SUMMARY 

The Roadmap provides states with opportunities to incorporate the multi-pollutant 

benefits of EE and RE programs into their SIPs. is The States are interested in exploring 

the pathways established in the Roadmap, recognizing that EE and RE programs can 

reduce emissions from the dirtiest energy generation sources that tend to run during 

unhealthy air quality episodes. 

This project is the first application of the EPA Roadmap with state-based case 

studies. It has provided an opportunity for states and EPA, working in partnership, to 

uncover fundamental benefits and policy considerations around including EE and RE in 

SIPs. States noted that EPA participated in this effort with an unprecedented level of 

engagement, and this effort has thus built the foundation for continued discussions with 

the volunteer states and other states interested in including EE and RE in SIPs. 

The states are interested in continuing to work with EPA to clarify acceptable 

approaches for estimating the location of emissions reductions, discuss the balance 

between flexibility and documented, credible SIP submittals, and establish the 

importance of evaluating future analytical tools. States also stressed the importance of 

working with state and national energy offices in the promotion of more standardized 

protocols for quantifying the energy savings of EE programs and for translating the 

energy savings from both EE and RE programs into emission reductions. In particular, 

the pilot project provided an opportunity for states to engage with various EPA offices 

regarding key policy considerations, including: 

• Promoting state and regional consistency in the development and 

review/approval of SIPs that incorporate EE and RE 

• Addressing the challenge of determining location of emissions reductions 

from EE and RE programs, including using an approach similar to how 

mobile and area source programs are assessed credit for SIP purposes 

• Clarifying expectations for how states demonstrate that EE and RE 

programs benefit the nonattainment area, as discussed in the August 2013 

Frequently Asked Questions (FAQ) document 

• Promoting consistency in quantification methods, including the 

assessment of EE evaluation, monitoring and verification (EMV) 

protocols 

• Briefing the states on the U.S. Department of Energy (DOE) State and 

Local Energy Efficiency (SEE) Action Network and the DOE Uniform 

Methods project,31 including timelines, expected outcomes, and 

applicability of these efforts to quantifying EE and RE in SIPs 

 

31 See: http://www1.eere.energy.gov/seeaction/ and 

http://www1.eere.energy.gov/office_eere/de_ump_about.html. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 4-2 

The three states would like to continue discussion on some of these policy 

considerations. They are interested in maintaining ongoing discussion and dialogue with 

EPA as these case studies are introduced to other states, and as they and other states 

prepare SIPs that quantify EE and RE programs. 
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page 4-3 

 

For more information about the Roadmap Case Study Project, please contact: 

Allison Guerette, NESCAUM, aguerette@nescaum.org 

Leah Weiss, NESCAUM, lweiss@nescaum.org 

William Space, Massachusetts Department of Environmental Protection, 

william.space@state.ma.us 

Robert D. Bielawa, P.E., New York State Department of Environmental 

Conservation, rdbielaw@gw.dec.state.ny.us 

Tad Aburn, Maryland Department of the Environment, gaburn@mde.state.md.us 

Niko Dietsch, U.S. EPA, Dietsch.Nikolaas@epa.gov 

For a copy of the Roadmap and supporting documents, see: 

http://www.epa.gov/airquality/eere/manual.html

For a copy of the Roadmap Frequently-Asked-Questions document, see: 

http://epa.gov/airquality/eere/pdfs/eerefaqAug2013.pdf
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page A-1 

Appendix A: Massachusetts Case Study 

The Baseline Pathway 
1

Massachusetts Case Study- The Baseline Pathway

Introduction

Massachusetts is a leader among states implementing energy efficiency (EE) programs. The 

American Council for an Energy Efficient Economy ranked Massachusetts first in the nation for 

its energy efficiency programs in 2011 and 2012, and Massachusetts has directed more than 80% 

of its share of the revenue generated by the Regional Greenhouse Gas Initiative allowance 

auctions toward energy efficiency. Massachusetts is currently implementing numerous EE 

programs that reduce electricity, natural gas, and oil consumption, with associated reductions in 

emissions and ambient air pollution levels. This case study1reviews how EE programs might be 

incorporated into State Implementation Plans (SIPs) using the baseline approach described in the 

U.S. Environmental Protection Agency’s (EPA’s) Roadmap for Incorporating Energy Efficiency/ 

Renewable Energy into SIPs/TIPs.

2

In the Roadmap, EPA describes the baseline pathway as best suited for “on the books” policies 

that states wish to address in SIPs. Consistent with this guidance, Massachusetts evaluated 

options for using the baseline pathway approach to incorporate its EE programs into SIPs. A 

suite of Massachusetts EE policies are “on the books,” in that they are being implemented 

pursuant to orders of the Massachusetts Department of Public Utilities, and in that their 

continuation will be necessary to ensure ongoing compliance with the Massachusetts Green 

Communities Act of 2008 requirement that “electric and natural gas resource needs shall first be 

met through all available energy efficiency and demand reduction resources that are cost 

effective or less expensive than supply.”

3

A significant challenge to including these policies into SIPs arises from the need to integrate 

projected EE effects with a broader modeling effort directed at establishing an emissions 

baseline. There is currently no standard method for determining whether reductions attributable 

to a specific EE policy are implicitly reflected in load forecasts embedded in electric sector 

models. A second set of challenges arises from the need to connect energy savings with reduced 

levels of ambient air pollution, especially if benefits must be correlated with pollution events that 

occur at specific times and locations. This case study describes these challenges, and how they 

may be addressed, in more detail. While some options for addressing these challenges may 

represent departures for regulating the electric sector (which has traditionally been regulated 

through requirements to install controls directly on emissions sources), they share some 

 

1 Massachusetts does not currently have a relevant SIP obligation; this case study is intended to inform the 

development of future SIPs, but the specific programs discussed in this case study are not intended for inclusion in 

any particular SIP at this time.

2

http://www.epa.gov/airquality/eere/manual.html.

3 M.G.L. c. 25, § 21(a). 
2

characteristics with strategies that have been used to control emissions from mobile sources 

(such as transit programs that indirectly reduce vehicle use by reducing demand).

Massachusetts’ statutory requirement for procuring all cost effective EE resources has 

significantly raised the importance of the evaluation, monitoring, and verification (EM&V) of 

the results of those programs. In addition to the general need to ensure that resources are being 

deployed efficiently, the application of the cost-effectiveness test requires detailed consideration 

of savings resulting from each measure implemented. For example, dozens of EM&V reports

published since 2010 are available on the Massachusetts Energy Efficiency Advisory Council’s 

web page.4These reports are used to evaluate program performance and inform future 

investment decisions. To ensure national consistency in calculated EE savings, simplify analysis 

for other states, and ensure that credited EE savings are realized, EPA must draw on these and 

similar efforts of other states and the US Department of Energy (DOE) to standardize the process 

of measuring, verifying, and projecting EE savings.5 This will ensure that a ton of pollution 

saved through energy efficiency is consistently valued around the country, as EPA requires now 

for other control strategies.

6

Massachusetts has experience estimating emissions impacts of EE measures. For example:

 Massachusetts’ Regional Greenhouse Gas Initiative regulations include a provision that 

addresses voluntary purchases of renewable energy by retiring allowances corresponding 

to estimated emission reductions resulting from those purchases.7Similarly, as part of its 

NOx Allowance Trading Program, Massachusetts has allocated allowances to EE projects 

using a static formula that estimates potential emission reductions based on MWh 

savings.

 The Massachusetts Clean Energy and Climate Plan for 2020 includes estimates of the 

impacts of EE policies on greenhouse gas (GHG) emissions in Massachusetts.8

 Massachusetts and other states implementing the Regional Greenhouse Gas Initiative 

have worked to ensure load forecasts used for electric system modeling fully reflect 

efficiency spending in the region. Several other states have used data provided by 

Massachusetts to estimate impacts of their programs. This effort is discussed in detail 

below in the section titled RGGI Experience.

 

4

See http://www.ma-eeac.org/. 

5 The Uniform Methods Project is an example of this type of effort. More information is available at 

http://www1.eere.energy.gov/office_eere/de_ump_about.html.

6

For example, EPA publishes standard emission factors for use in a number of contexts, and establishes detailed 

monitoring requirements in regulation for key parameters such as emissions from power plants that are subject to 

cap-and-trade programs.

7

310 CMR 7.70(5)(c)1.b. requires the use of an emission factor to estimate avoided CO2 emissions.

8

See http://www.mass.gov/eea/docs/eea/energy/2020-clean-energy-plan.pdf, pp. 18 - 19.
3

 Studies of program benefits (“avoided cost studies”) prepared for New England electric 

utilities, gas utilities, and other efficiency program administrators and state energy offices 

include estimates of avoided nitrogen oxide (NOX), sulfur dioxide (SO2), and carbon 

dioxide (CO2) emissions that are occurring as a result of energy efficiency investments.9

One of the key questions that Massachusetts would need to address if including EE in a SIP is 

the level of detail with which EE policies should be specified. On the one hand, the broad 

requirement to capture all cost effective EE represents a specific, enforceable, “on the books” 

policy that should be sufficient to support inclusion in a SIP baseline. In fact, a baseline 

emissions projection that did not include this requirement would not be complete. On the other 

hand, it may be necessary in some cases to provide information about specific measures expected 

to deliver the anticipated reductions for at least three reasons: 

 In order to determine whether anticipated reductions are likely to occur and persist over 

time, EPA will need sufficiently detailed technical descriptions of the measures that are 

driving the reductions.

 For SIPs that address National Ambient Air Quality Standards (NAAQS) that have 

relatively short averaging times (i.e., one or eight hour standards), analyses of individual 

measures may be necessary to establish the relationship between EE measures and 

pollutant levels at times and locations where exceedances are most likely to occur.

 Analysis of specific measures may be useful or necessary to avoid double counting of EE 

impacts that may be captured in load forecasts that do not explicitly detail whether EE 

measures are included.

This case study includes technical discussion of these issues, describes how Massachusetts 

recently approached a similar problem as part of a recent review of the Regional Greenhouse Gas 

Initiative program, and proposes that EPA adopt an approach to the inclusion of EE in SIP 

emissions baselines that draws on Massachusetts’ experience implementing and evaluating 

comprehensive EE programs over time.

Technical Discussion of EE Quantification and EGU Modeling: Estimating Energy Savings

In order to establish an emissions baseline that fully reflects the impacts of EE policies and

measures, Massachusetts needs to address two related technical challenges: 

(1) The impacts of the EE policies and measures must be specified with enough certainty 

and detail to support analysis of air quality impacts. For example, a measure directed at 

air conditioner efficiency would only be appropriate for inclusion after analysis of 

 

9

See http://www.ma-eeac.org/Avoided%20Costs.html.
4

uncertainty regarding any rebound effects,10 and of the fact that air conditioners only 

operate in the summer months in Massachusetts. 

(2) The impact of the efficiency measure must be evaluated in the context of a 

comprehensive forecast of electric load, for inclusion in a model of the electric sector. 

The primary challenge here is that, while models generally represent individual electric 

generating units (EGUs), or at least categories of EGUs, they generally do not represent 

individual loads in the same way. Instead, loads targeted by EE measures are often 

represented only in that their operation is included in aggregate load forecasts, often 

derived based on historical trends. In other words, EE measures change variables that are 

not explicitly represented in electric sector models, making them much more difficult to 

integrate than, for example, emissions control technologies installed at specific EGUs.11

These challenges will require significant technical effort to adequately address.

Much work has been accomplished on the first of these challenges. For example, extensive 

EM&V work has allowed the use of “deemed savings” to specify impacts of measures for which 

extensive experience exists, such as light bulb replacement programs.12 Similarly, studies that 

specify savings in kilowatt-hours saved per dollar invested (kWh/$) terms are increasingly

viewed as reliable indicators of likely energy savings from future EE investments. While less 

well-developed, studies that separate EE impacts on electric system capacity needs (which reflect 

demand on hot summer days) from impacts on total energy demand may form a basis for 

beginning to address the need to specify the timing of EE impacts. And studies that consider 

impacts on locational electricity prices implicitly acknowledge that impacts of EE measures on 

emissions may depend to some degree on where the impacts occur. These issues are addressed in 

additional detail below.

Measuring the impact of an EE program or measure on electricity consumption represents a 

significant technical and conceptual challenge.13 The key conceptual issue arises from the need 

to isolate program or measure impacts that cannot be directly measured. While the amount of 

energy used by a particular load can be measured directly, measuring the impact of replacing a 

given load with a more efficient version can only be determined indirectly, by making 

assumptions about the amount of electricity that would have been used if the load had not been 

 

10 Rebound effects occur when users respond to lower operating costs of efficient appliances by using them more.

11 This situation is somewhat similar to the challenge of modeling impacts of transit programs on motor vehicle 

emissions, in that the effect of the programs on the emission sources is indirect.

12 As described by EPA at http://www.epa.gov/statelocalclimate/state/activities/measuring-savings.html: “To 

evaluate programs that target simpler efficiency measures with well–known and consistent performance 

characteristics, a deemed savings approach may be appropriate. This method involves multiplying the number of 

installed measures by an estimated (or deemed) savings per measure, which is derived from historical evaluations. 

Deemed savings approaches may be complemented by on–site inspections.”

13 Transportation programs may represent a useful model for understanding these challenges, as they also rely in 

some cases on strategies that reduce emission indirectly, without directly regulating the relevant emission source 

(e.g., transit programs).
5

replaced.14 Myriad technical challenges arise from the need to understand what actually occurs 

when, for example, an efficient appliance is installed in a home or a business. Does the appliance 

perform as represented by the manufacturer? Does use of the appliance have indirect impacts on 

other loads? While challenges remain, much progress has been made in this area, primarily 

because of the need to ensure that funding is justified by realized savings.

For example, the following table provides an example of the number and range of EE measures 

that may be available for a given end-use sector, in this case low-income residences in 

Massachusetts.15 As discussed below, in addition to successful implementation experience, 

Massachusetts has an extensive record of careful evaluation of the impacts of such programs.

EE Measures Used in Low-Income Residences in MA

Numerous studies provide information that is used to estimate the impact of measures such as 

those listed in the above table. For example, the Massachusetts Energy Efficiency Advisory 

 

14 The challenge of determining the appropriate consumption baseline for comparison is even more challenging at 

the program level, where free-riders (i.e., consumers who would have implemented the efficiency measure anyway, 

absent the EE program) and spillover (i.e., cases in which consumers who do not participate directly in the EE 

program adopt a measure) complicate the analysis.

15 The table is taken from 2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas Energy Efficiency 

Plan, p.178, available at http://www.ma-eeac.org/Three%20Year%20Plans.html. 
6

Council website lists 24 residential studies published in 2012 alone, ranging from WiFi 

Programmable Controllable Thermostat Pilot Program Evaluation, to Consumer Survey Results

to Home Energy Services Pre-Weatherization Initiative: Interim Evaluation Findings Memo.

16

Massachusetts Program Administrators plan to spend nearly $70 million measuring program 

impacts over the next three years (2013 – 2015).17 These measurement efforts have allowed the 

Massachusetts Public Utilities Commission and ISO New England (ISO-NE) to be confident that 

planned reductions in energy use will occur, and they should be sufficient, if referenced in a SIP, 

to allow EPA to reach the same conclusion without the need for additional evaluation. However, 

in the longer term, EPA should continue to work with DOE to publish consistent and replicable 

methods for quantifying EE program savings, and require the use of these methods in SIP 

submittals.

18

Projections beyond the three-year periods covered by Massachusetts’ efficiency plans and ISONE’s forward capacity market are more challenging, but are essential. The challenge arises from 

the impracticality of specifying individual measures in detail more than three years into the 

future. Longer-term forecasts are essential because they are needed to plan capital investments to 

ensure adequate electricity supply and meet environmental goals. As states have gained 

experience in recent years planning and evaluating EE programs, the following method has been 

used by EPA and others to project EE savings a decade or more into the future:19

1. Drawing on EM&V and short-term planning experience, estimate average production 

costs of energy savings, expressed in dollars invested per kilowatt-hour saved.20

2. Project future production costs considering the estimate described above and any 

variables likely to affect future production costs (such as product efficiency standards 

that will take effect within the time period covered by the plan).

3. Develop an estimate of planned investment levels over a longer time period. For 

example, Massachusetts’ ongoing statutory commitment to realize all cost effective EE 

savings provides a basis for such an estimate.

4. For each future year, multiply the projected production cost by the estimated spending 

level to determine the projected EE savings for that year.

 

16 These studies are available at http://www.ma-eeac.org/EMV.html. 

17 See 2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas Energy Efficiency Plan, p.30, 

available at http://www.ma-eeac.org/Three%20Year%20Plans.html.

18 For example, EPA’s existing processes for demonstrating equivalency in the context of emission inventories 

development could serve as a model.

19 For example, see the Lawrence Berkeley National Laboratory report titled The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025 (2013), available at 

http://emp.lbl.gov/sites/all/files/lbnl-5803e.pdf.

20 In practice this will likely be expressed in terms of “first year” savings, and $/kWh estimates will be higher than 

retail electricity costs, even though per kWh costs will generally be low over the lifetime of the measure. 
7

In response to requests by Massachusetts and other states, ISO-NE recently integrated this 

method into its load forecasting process. This change is discussed in more detail below in the 

section titled RGGI Experience.

For some purposes the timing of EE impacts may be nearly as important as the total magnitude. 

For example, while air conditioning needs in New England are less than in warmer locations, 

they contribute significantly to peak summer loads, which are highly correlated with summer 

ozone pollution events. States that wish to reflect such timing effects in SIP baselines would 

need to document them explicitly in SIPs, which may justify more sophisticated analysis than the 

four-step process described above.21

Technical Discussion of EE Quantification and EGU Modeling: Estimating Emissions Impacts

While much progress has been made in specifying the energy impacts of EE programs, 

integration of these impacts in models that forecast EGU emissions remains a challenge. As 

discussed above, the key difficulty arises from the fact that, even prior to the explicit 

specification of EE impacts, these models include load forecasts that implicitly reflect some level 

of EE deployment. EPA documents include a description of how states might proceed if the 

fraction of EE savings were known, but acknowledge reliance on an analysis that “lacking better 

information, assumes that the growth rates derived from the US Energy Information 

Administration’s Annual Energy Outlook (AEO) forecast implicitly account for a continuation of 

50 percent of historical levels of reported energy savings.”22 Unfortunately, however, there does 

not appear to be any widely applicable method for estimating the relevant percentage in 

particular cases, so this guidance may be of limited use to states. If possible, states and EPA 

should take steps to ensure that “on the books” EE policies are reflected in baseline forecasts, 

instead of introducing an adjustment at a later stage.

Several examples are provided below of modeling platforms that could be used in future SIP 

submittals. Note their similarity with regard to how load forecasts are used to develop emissions 

baselines:

 

21 Examples of efforts in this area include ISO-NE’s estimates of EE impacts of generation capacity needs (see, for 

example, the “Summer EE Peak Impacts” on slide 18 of the presentation at http://www.isone.com/committees/comm_wkgrps/othr/enrgy_effncy_frcst/2013frcst/index.html), and discussion of “coincidence 

factors” on page 17 of the 2013 – 2015 Massachusetts Technical Reference Manual for Estimating Savings from 

Energy Efficiency Measures (available at http://www.ma-eeac.org/TRMs.html.) Coincidence factors describe the 

degree to which EE savings are correlated in time with peak loads. The fact that coincidence factors are used by 

ISO-NE to forecast EE impacts on generation needs demonstrates their suitability for use in planning efforts such as 

SIPs.

22 Roadmap, p. J-9.
8

 The Integrated Planning Model (IPM) is the modeling tool most often used by EPA to 

model EGU emissions in the SIP context. According to EPA: Developed by ICF 

Consulting, Inc. and used to support public and private sector clients, IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power 

sector. It provides forecasts of least-cost capacity expansion, electricity dispatch, and 

emission control strategies for meeting energy demand and environmental, transmission, 

dispatch, and reliability constraints. IPM can be used to evaluate the cost and emissions 

impacts of proposed policies to limit emissions of sulfur dioxide (SO2), nitrogen oxides 

(NOx), carbon dioxide (CO2), and mercury (Hg) from the electric power sector.23

 States are currently collaborating as the Eastern Regional Technical Advisory Committee 

(ERTAC) to develop an alternative to IPM.24 This model is explicitly intended for use in 

the SIP context, and is expected to have capabilities similar to those of IPM. The 

advantage of the ERTAC model will be greater transparency; states are dissatisfied with 

the fact that, because IPM is a proprietary model developed and operated by a private 

contractor, states and other stakeholders have limited ability to independently verify that 

IPM appropriately represents relevant aspects of the EGU sector.

 According to NESCAUM: The NE-MARKAL initiative, which began in 2003 through 

collaboration between NESCAUM and the U.S. EPA Office of Research and 

Development, has resulted in the development of a MARKet ALlocation (MARKAL) leastcost optimized linear programming (LP) model tailored specifically to the energy 

infrastructure of the Northeast. NE-MARKAL is a data-rich analytical framework for 

examining energy policy options and their resultant impact on energy services in the 

region. The model serves as the centerpiece of the integrated policy analysis framework 

developed at NESCAUM that aids in developing a comprehensive understanding of 

technology, economic, environmental and public health consequences of air quality and 

climate initiatives.25

For the purposes of this case study, the important similarity among these modeling platforms is 

that they all allow users to customize electricity load forecasts. Therefore, as long as states or 

EPA are able to develop load forecasts that reflect EE programs, these forecasts can be reflected 

in baseline modeling.

As an alternative to adjusting load forecasts explicitly, as discussed above, it may be possible to 

adjust emissions baselines in SIP submittals using default or case-specific emission factors. This 

could be appropriate if, for example, EPA were to provide states with a baseline emissions 

 

23 See http://www.epa.gov/airmarkt/progsregs/epa-ipm/.

24 See http://www.ertac.us/#.

25 NESCAUM is Northeast States for Coordinated Air Use Management. NESCAUM is described at 

http://www.nescaum.org/about-us/overview/, and NE-MARKAL is described at http://www.nescaum.org/topics/nemarkal-model .
9

forecast derived using IPM, and a state concluded that this baseline emissions forecast did not 

fully reflect planned state EE investments. Instead of re-running IPM, the state could develop an 

appropriate emission factor (i.e., in lbs/MWh) and use it to adjust EPA’s emissions forecast. This 

process could be improved if EPA were to develop a procedure for producing such emission 

factors as part of the modeling process, for example through sensitivity analysis of multiple IPM 

runs using different load forecasts. Such adjustments, however, could introduce additional 

complexity and uncertainty to the modeling process, and may not be the preferred method for 

reflecting on-the-books EE programs in baseline models.

A second challenge arises from the need, for some pollutants, to specify when and where impacts 

occur. Even for measures for which the location and timing of impacts may be reasonably well 

characterized, EGU models may not include sufficient resolution to project impacts. To cite an 

obvious example, EE measures that target commercial air conditioners in the Boston area may 

not have much impact on ambient wintertime levels of particulate pollution in western 

Massachusetts, even if the particulate pollution includes a significant contribution from EGUs. 

Models vary in their ability to reflect these dynamics.

RGGI Experience

The Regional Greenhouse Gas Initiative (RGGI) is a cap and trade program for carbon dioxide 

emissions from power plants in nine northeastern states. As part of a recent program review, the 

RGGI states, including Massachusetts, modeled electric sector emissions through 2020 using 

IPM. To support this modeling effort, it was necessary for each state to provide (or approve) a 

projection of electric load.

One option available to states was to use load forecasts provided in the US Energy Information 

Administration’s Annual Energy Outlook (AEO), as is common for electric sector modeling 

using IPM. This option was rejected for two reasons: (1) states did not believe that AEO 

forecasts were reflective of planned EE investments in the region, and (2) states believed that 

regional ISOs would be a more reliable source of information, as they require a detailed 

understanding of future loads to fulfill their function of ensuring reliable operation of the electric 

grid over the long term. In particular, all New England states, including representatives of the 

Massachusetts Department of Energy Resources, began reviewing ISO-NE’s load forecasts to 

determine whether they would be suitable for use in the RGGI modeling effort.

While all New England states, including Massachusetts, eventually decided to use ISO-NE’s 

load forecasts without adjustment, this occurred only after ISO-NE responded to state input and 
10

developed a load forecast reflecting state EE investments. The following aspects of this process 

are relevant:26

 ISO-NE produces numerous forecasts using varied assumptions regarding variables such 

as fuel prices and weather conditions, as would be expected, given their mandate to 

ensure that the electric grid will operate reliably under reasonably anticipated stresses. 

Among the assumptions that vary across these scenarios are assumptions about the 

impacts of EE investments on load.

 ISO-NE’s wholesale electric market includes a forward capacity market, in which entities 

willing to commit to deliver energy to the electric grid three years in the future are paid 

for making such a commitment. By reducing demand from particular loads, EE projects 

increase the amount of energy available for delivery to other loads, therefore increasing 

the overall capacity of the system to serve all connected loads at times when demand is 

highest. Therefore, EE projects are allowed to participate in the forward capacity market 

in direct competition with generators. In order to participate in the forward capacity 

market, EE program administrators must provide projections of electricity savings that 

are sufficiently reliable and detailed to allow ISO-NE to be confident that the savings will 

be realized as projected. These projections are incorporated into load forecasts and, 

because of the financial incentive provided by the forward capacity market, are believed 

to reflect most energy efficiency investment in the region.

 Prior to 2012, ISO-NE’s load forecasts did not include any energy efficiency investments 

beyond the three-year period covered by the forward capacity market. However, after 

extensive consultations with states, ISO-NE began publishing a 10-year load forecast 

reflecting ongoing EE investments, beyond the three-year period reflected in the forward 

capacity market. 27

Initially, before ISO-NE revised its load forecast as described above, Massachusetts and other 

New England states were unwilling to rely on ISO-NE’s load forecast beyond the three-year 

period covered by the forward capacity market. Instead, states completed the first round of RGGI 

program review modeling in 2011 by adjusting ISO-NE load forecasts downward. Each state 

determined the amount of the adjustment by dividing a state-specific estimate of future levels of 

annual EE investment (in dollars) by a state-specific production cost for EE savings (in dollars 

per kilowatt-hour). However, after review of ISO-NE’s revised load forecasts and their similar 

treatment of EE investment, Massachusetts and other states decided to rely on ISO-NE’s load 

 

26 For more information, see Evolution of an Energy-Efficient Forecast: Building a model that works across states 

and programs, by Gordon van Welie, Public Utilities Fortnightly, January 2013.

27 In some cases, ISO-NE’s calculations include a “Budget Uncertainty Rate” to account for the possibility that 

forecasted spending levels are not realized. For a Massachusetts-specific example, see ISO-NE’s Final 2013 Energy 

Efficiency Forecast 2016 – 2022, available at http://www.isone.com/committees/comm_wkgrps/othr/enrgy_effncy_frcst/2013frcst/iso_ne_final_ee_forecast_2016_2022.pdf (see 

slide 46).
11

forecasts for use in the final IPM modeling conducted for the RGGI program review.

28 The 

figure below illustrates the effect of reflecting EE investments in ISO-NE’s load forecasts.29

ISO-NE Annual Load Forecast (GWh)

This experience provides a useful framework for developing load forecasts that reflect EE 

investments. If it is necessary, in a given situation, to use a load forecast that is believed not to 

fully reflect EE investment, then such a forecast may be adjusted downward using EE spending 

and production cost estimates. However, a preferable approach is to work directly with an entity 

with expertise and responsibility in the area of load forecasting, such an ISO or state energy 

office, to reach a forecast that is viewed by all participants as reflective of EE investments. One 

relevant aspect of the RGGI experience is that, while ISO-NE considered information about 

specific measures being implemented by the states, forecasts beyond the three-year time frame of 

the forward capacity market are based on general commitments to fund EE programs, not 

commitments to continue investing in the same specific measures. 

Conclusions

Massachusetts strongly supports EPA’s efforts to provide pathways that states can use to include 

electric EE programs in SIPs. This case study supports two key conclusions that should inform 

 

28 Illustrative load growth forecasts are available on slide 6 of the presentation available at 

http://www.rggi.org/docs/ProgramReview/RGGI%20DRAFT%20Reference%20Case%20Assumptions%20071212v

6.pdf. 

29 See slide 6 of the presentation available at http://www.isone.com/committees/comm_wkgrps/othr/enrgy_effncy_frcst/2013frcst/iso_ne_final_ee_forecast_2016_2022.pdf. The 

red line includes only EE reflected in the three-year-ahead forward capacity market.
12

the development of SIPs that include EE policies using the baseline approach described in the 

Roadmap, and other similar efforts:

1. Including EE policies via the baseline pathway should not require more specificity in 

SIPs than is necessary to provide an adequate level of assurance that EE savings reflected 

in the SIP will be realized. Traditionally, EGU policies included in SIPs have directly 

regulated EGUs through enforceable caps on emissions from specific facilities, or 

through cap-and-trade programs that control aggregate emissions from a set of facilities. 

In contrast, EE policies do not directly regulate EGUs. While this concept may represent 

a departure for the EGU sector, the transportation sector includes many examples of SIPapproved policies that reduce emissions from vehicles without directly regulating them, 

such as transit programs.30 EPA should treat EE policies similarly; as long as a state can 

demonstrate the connection between enforceable investment commitments and reductions 

through a robust modeling program, these policies should be acceptable for inclusion in 

SIP emission baselines. 

2. To ensure national consistency in calculated emission reductions, simplify analysis, and 

ensure that programs realize credited EE savings, EPA should collaborate with DOE and 

states to standardize the process of measuring, verifying, quantifying and projecting EE 

savings. Massachusetts’ recent experience working with other states and ISO-NE to 

embed future EE investments in ISO-NE’s load forecast, and subsequently using these 

forecasts to project electric sector emissions in the RGGI region, represents a useful 

model for how states and EPA can incorporate EE savings in future SIP emission 

baselines.

 

30 See http://www.epa.gov/OMS/stateresources/policy/pag_transp.htm for examples of policies that may be included 

in SIPs even though realization of reductions requires behavior changes that cannot always be directly enforced.
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page B-1 

Appendix B: New York Case Study 

The Control Strategy Pathway 
Page 1 of 4

NYSDEC FINAL DRAFT REPORT

EPA PILOT PROJECT

ENERGY EFFICIENCY / RENEWABLE ENERGY IN STATE IMPLEMENTATION PLANS

December 19, 2013

In July 2012 the U.S. Environmental Protection Agency (EPA) released guidance entitled 

“Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and Programs into 

State and Tribal Implementation Plans.” The goal of this “Roadmap” is to facilitate the use of 

Energy Efficiency / Renewable Energy (EE/RE) emissions reduction strategies in air quality plans, 

and to provide methodologies that could be used by states to account for emission reductions 

from EE/RE programs in State and Tribal Implementation Plans (SIPs/TIPs). 

New York has a strong interest in incorporating EE/RE programs into future SIPs. Air quality 

modeling conducted by DEC indicates that the 2008 ozone National Ambient Air Quality 

Standard (NAAQS) will be difficult to attain, so it will be necessary to consider all potential 

sources of emission reductions, including energy efficiency and renewable energy programs. 

Since EE/RE programs are vigorously being implemented in New York State, it is in our best 

interest to receive SIP credit for the resulting criteria pollutant reductions. 

The Roadmap describes four SIP pathways that are available to states as they consider which 

approach to adopt for incorporating policies and programs in SIPs. The four pathways are:

1. Baseline emissions projection pathway;

2. Control strategy pathway;

3. Emerging/voluntary measures pathway; and

4. Weight of evidence (WOE) determination pathway.

The New York State Department of Environmental Conservation (DEC) utilized the Roadmap’s 

control strategy pathway as part of the pilot project and in formulating the enclosed

hypothetical SIP submission, focusing on three EE programs currently being conducted in New 

York State: the installation of combined heat and power systems; the NY-Sun Initiative; and the 

Public Buildings Initiative (or “Build Smart NY”). 

Combined heat and power is a form of distributed generation that creates electric 

power and thermal energy at or near the source, rather than from separate units at 

separate locations. It is being considered as an alternative compliance measure for 

EPA’s recent update to the boiler maximum achievable control technology (MACT) 

regulation. 

The NY-Sun Initiative began on April 19, 2012 as a public/private partnership seeking to 

drive growth in the solar industry. The program’s initial goal was to install 60 MW of 
Page 2 of 4

photovoltaic (PV) capacity in 2012 and 120 MW in 2013, for a total statewide installed 

photovoltaic capacity of 239 MW by the end of 2013. Recently, the program was 

provided with additional funding through 2023 to further attract private investment in 

solar photovoltaic systems, enable the sustainable development of a robust solar power 

industry in New York, create well-paying skilled jobs, improve the reliability of the 

electric grid, and reduce air pollution over the next decade. On December 9, 2013, it 

was announced that the largest NY-Sun solar project, a 1.56 megawatt rooftop 

installation in New York City, was completed. $30 million is available through NY-Sun’s 

competitive PV Program to stimulate other large-scale solar and biogas projects in New 

York City and the Hudson Valley.

Build Smart NY was established through an Executive Order to improve EE in stateowned buildings. It dictates that, by April 1, 2020, the average energy usage of state 

buildings by reduced by at least 20 percent from a baseline of the 2010/2011 fiscal year. 

The upgrade projects include new lighting fixtures, heating and ventilating systems, 

electric motors, automated energy management systems, fuel cells, and solar power 

installations, while beneficiaries include schools, colleges, police and fire stations, 

municipal buildings, transit facilities, public housing, libraries, and wastewater 

treatment plants. 

DEC utilized various tools to estimate the emission reductions resulting from these EE 

programs, but primarily relied upon the Northeast Market Allocation (NE-MARKAL) energy 

model run by the Northeast States for Coordinated Air Use Management (NESCAUM). 

Additional EE/RE program details were obtained from official EE/RE program reports posted on 

public websites. As needed, additional information would be available from the various 

administering groups (i.e., New York State Energy Research & Development Authority 

(NYSERDA), New York Power Authority (NYPA), and Long Island Power Authority (LIPA)). The 

New York Independent System Operator (NYISO) and the New York State Department of Public 

Service (NYSDPS) could also assist in providing supply-side data. The DEC regularly collaborates 

with NYSERDA, the NYISO, and NYSDPS on a myriad of workgroups and committees, and the 

advancement of incorporating energy efficiency in SIPs, to the extent practicable, will be an 

extension of the already established relationships. 

Emission reductions can be quantified through future-year emission modeling, but models such 

as NE-MARKAL are not forecast tools and real-world reductions can therefore vary from those 

projected by the model. Some uncertainty is inherent in any modeling tool. To mitigate the 

possible over-estimation of future reductions, DEC would propose to discount the projected 

emission reductions from the CHP initiative in its hypothetical submission. Previous EPA 

guidance suggested starting with a 20% discount, but recognizes variability due to models used 

and respective inputs and assumptions. Discounting emissions from the NY-Sun or Build Smart

NY initiatives would be discussed as a potential option at such time that a real SIP would be

submitted.
Page 3 of 4

Overall, DEC found that EPA’s Roadmap is very well written and fully supports the use of the 

four pathways to obtain EE/RE SIP credit. A few potential challenges are noted as follows:

Emissions reductions occurring outside boundaries of nonattainment areas

o DEC feels this issue could be an issue for states wishing to use the control 

strategy pathway. According to EPA’s August 27, 2013 EE/RE in SIPs FAQ 

document and their interpretation of the Clean Air Act (Act), EE/RE policies and 

programs can help states meet attainment and maintenance SIP requirements 

for areas that are designated nonattainment, or have attained with an approved 

maintenance SIP, even when the emissions reductions resulting from EE/RE are 

projected to occur outside the boundaries of nonattainment areas, as long as 

they benefit the nonattainment area’s air quality. EPA believes the CAA requires 

emissions reductions that apply to Reasonable Further Progress (RFP) plans 

come from sources of emissions located within the boundaries of nonattainment 

areas. Therefore, in order to make such demonstrations for either SIP type, 

states would need to quantify their emission reductions through using the most 

sophisticated (and costly) quantification approach ... energy models. 

Bundling of measures

o The three programs analyzed in this hypothetical submission resulted in 

emissions reductions that were not insignificant, but also not at a level that 

would largely reduce ozone concentrations individually. Because of their 

relatively small magnitude, these measures would very likely be bundled 

together if they were to be included in a SIP through the control strategy 

pathway (or any of the four pathways). This topic is addressed in EPA’s EE/RE in 

SIPs FAQ document and is recommended by EPA in certain cases. EPA states 

that “bundling together relatively small-scale or local SIP measures can be 

beneficial if individually these measures would be difficult or resource-intensive 

to quantify or verify in the SIP.” 

o In addition to the Roadmap, DEC referred to the August 2005 EPA guidance 

entitled “Incorporating Bundled Measures In A State Implementation Plan (SIP)” 

for additional information to see if bundling would be an option when using the 

control strategy pathway. Page 6 of that guidance states that “Bundled 

measures should include only those measures that are considered to be 

voluntary or emerging measures, or traditional measures too small-scale to be 

typically included in a SIP.” Traditional emissions reductions measures are 

defined as measures that individually have small amounts of emissions 

reductions and typically would not be included in a SIP. Traditional emissions 

reductions measures differ from voluntary and emerging measures in that they 

are enforceable against an individual source and can be reliably and replicably 

measured or determined. Some uncertainty remains as to the likelihood that 

EE/RE measures could be, or would be bundled when using the control strategy 
Page 4 of 4

pathway when considering both sets of EPA guidance. Perhaps the guidance 

could be amended to address this uncertainty. 

Cap and Trade programs

o In order to receive SIP credit, all emissions reductions, including those resulting 

from EE/RE measures using the Control Strategy pathway, must be permanent, 

enforceable, quantifiable, and surplus. As part of the “surplus” criteria, a state 

must demonstrate emission reductions are not used for other CAA requirements 

(e.g., under a cap and trade program). If an EE program causes several EGUs that 

are part of a cap and trade program to scale back the amount of electricity they 

generate and, therefore, reduce overall emissions, then, absent additional 

limitations, it may be difficult to show that these reductions meet the “surplus” 

criteria for crediting the measure. Per EPA guidance, one acceptable way of 

achieving additional emission reductions from EE and RE measures in the 

presence of a cap and trade program is through the retirement of allowances 

commensurate to the emissions expected to be reduced by the EE measures. 

Another way is to clearly demonstrate that emissions will decrease in the area 

despite the cap and trade program and the ability of plants to sell more 

electricity to other areas. This demonstration will likely entail a detailed analysis 

of electricity dispatch and allowance markets to determine the specific impact of 

the measures on the system. While not insurmountable, these issues could be 

problematic for states with limited budgets and staff.

Because of the emerging nature of incorporating EE/RE programs in SIPs, DEC expects that it 

will work closely with EPA Region 2 staff to deal with any questions concerning application of 

the Roadmap for official SIP submissions. For now, DEC feels this hypothetical SIP submission is 

a suitable representation of what a future submission would be comprised of under the 

Roadmap’s Control Strategy pathway. 

In conclusion, DEC found that EPA’s Roadmap is very well written and should be followed when 

considering including energy programs in SIPs. Having the option of the four delineated 

pathways is helpful because states vary in the emphasis they put on EE programs and how far 

along they are in program development and implementation. Larger EE programs could take 

advantage of the Baseline or Control Strategy pathways, whereas smaller or less-developed 

programs can benefit from the Emergency/Voluntary Measures or WOE pathways. The ability 

to utilize the different pathways is also helpful depending on how close the state is to meeting 

the NAAQS. The pathway chosen may also depend on whether a number of programs could be 

bundled together that would significantly impact ozone levels and, at the same time, would 

prove to be quantifiable, surplus, enforceable, and permanent. Energy modeling support from 

NESCAUM was extremely helpful to DEC in quantifying future reductions, so a similar 

methodology would be recommended for other states, especially if the Control Strategy 

pathway is desired.
December 19, 2013 Hypothetical Draft For Discussion Purposes Only

EE.0 Energy Efficiency/Renewable Energy Measures

EE.1 Introduction

Energy efficiency (EE) and renewable energy (RE) policies and programs

represent a real opportunity for improving air quality as part of a multipollutant emissions reduction strategy. EE/RE programs also have the 

potential to reduce regional haze, thereby increasing visibility, as well as 

reduce air toxics and greenhouse gas (GHG) emissions. This submittal 

represents New York’s first incorporation of EE and RE programs in any

State Implementation Plans (SIPs). 

The following energy programs included in the “hypothetical SIP” are 

current energy efficiency and/or renewable energy programs in the State 

of New York: 

NY-Sun initiative 

Build Smart NY 

Combined Heat and Power (CHP)

The DEC used EPA’s “Roadmap for Incorporating Energy 

Efficiency/Renewable Energy Policies and Programs into State and Tribal 

Implementation Plans” for the development of this section of the 

“hypothetical SIP.” 

EE.2 EPA’s “Roadmap for Incorporating Energy Efficiency/Renewable 

Energy Policies and Programs into State and Tribal Implementation 

Plans”

In July 2012, EPA issued guidance entitled “Roadmap for Incorporating 

Energy Efficiency/Renewable Energy Policies and Programs into State 

and Tribal Implementation Plans”. The roadmap includes charts and 

tables for decision makers to consider in weighing which of the four 

pathways to pursue for incorporating EE/RE policies into SIPs. Each 

pathway is appropriate for a specific set of circumstances and has its own 

documentation and analytical provisions. For this hypothetical SIP 

submission, the DEC is using the “Control Strategy” pathway which is 

appropriate for the situation where a state is contemplating the adoption of 

new EE/RE policies before it submits its SIP.

This hypothetical SIP attempts to adhere to the control strategy pathway 

tasks of the Roadmap by:

Demonstrating that the EE/RE policies and programs are 

permanent
December 19, 2013 Hypothetical Draft For Discussion Purposes Only

Estimating the magnitude of the emissions reductions

Demonstrating that the EE/RE policies and programs are surplus 

and not accounted for as part of another pathway or mechanism

Ensuring that the EE/RE policies and programs are traditionally 

federally enforceable

 

EE.3 “Hypothetical SIP” EE/RE Measures 

NY-Sun Initiative1

The NY-Sun Initiative was launched on April 19, 2012 and is a dynamic 

public-private partnership intended to drive growth in the solar industry 

and make solar technology more affordable for all New Yorkers. It is 

expected to attract significant private investment in solar photovoltaic 

systems, enable the sustainable development of a robust solar power 

industry in New York, create well-paying skilled jobs, improve the reliability 

of the electric grid, and reduce air pollution over the next decade. 

As part of a balanced statewide renewable energy policy, NY-Sun will 

install twice the customer-sited solar photovoltaic (PV) capacity added 

during 2011 in 2012, and quadruple that in 2013. This policy promotes 

clean energy innovation and protects the environment while cutting 

dependence on foreign oil.

The NY-Sun Initiative brings together and expands existing programs 

administered by the New York State Energy Research and Development 

Authority (NYSERDA), Long Island Power Authority (LIPA), and the New 

York Power Authority (NYPA), to ensure a coordinated, well-funded solar 

energy expansion plan. 

The NY-Sun Initiative increases financial incentives for large, commercialsized photovoltaic (PV) projects and expands incentive programs for 

small-to-medium residential and commercial systems. It also provides 

additional funding for its competitively-bid solar program for larger-scale 

and aggregated systems that currently focuses on businesses, colleges 

and universities, and other large buildings located in New York City, 

Westchester, and the lower Hudson Valley.

The NY-Sun Initiative introduces a balance-of-system (BOS) program, 

where NYSERDA and NYPA will work with private and public partners 

across New York State to standardize and streamline procedures for 

permitting and interconnection, and development and training. It will build 

 

1

Source: http://ny-sun.ny.gov/ on December 10, 2012
December 19, 2013 Hypothetical Draft For Discussion Purposes Only

on the BOS advancements made by the City University of New York 

(CUNY) and the efforts underway in the PV Manufacturing Consortium.

Build Smart NY (also referred to as the NY Public Buildings 20%

Initiative)

2

Build Smart NY is an ambitious program for aggressively pursuing energy 

efficiency in New York State government buildings while advancing 

economic growth, environmental protection, and energy security in New 

York State out to 2020. It was established through Executive Order No. 

88, “Directing State Agencies and Authorities to Improve the Energy 

Efficiency of State Buildings.”

The Build Smart NY agenda calls for accelerating EE improvements to 

public buildings, highlighting the multiple benefits that include saving 

taxpayer dollars, improving the environment, and creating jobs. The 

upgrade projects include new lighting fixtures, heating and ventilating 

systems, electric motors, automated energy management systems, fuel 

cells, and solar power installations, while beneficiaries included schools, 

colleges, police and fire stations, municipal buildings, transit facilities, 

public housing, libraries, and wastewater treatment plants. 

In support of Build Smart NY, NYPA will finance $450 million in costeffective EE projects over the next four years, with a goal to reduce 

energy consumption in state buildings by 20 percent. NYPA also intends 

to finance an additional $350 million over the next four years to provide EE

financing and technical services to county and local governments and 

schools, helping to lower local government costs for taxpayers. 

Combined Heat and Power (CHP) Conversion

In exploring opportunities for partnership to promote investment in CHP, 

New York State Government, the United States Department of Energy and 

the EPA has identified the EPA pilot project as one of two opportunities for 

immediate partnership. An overall goal is to provide a model for the 

country, where best practices and policies can be replicated to support 

increasing investment in CHP systems and industrial energy efficiency.

The DEC has chosen a hypothetical facility in the Bronx as its primary 

case study for including EE and RE in SIPs. The facility has four boilers 

that burn heavy oil with a total firing capacity of 370 million British thermal 

units per hour (MMBtu/hr) that were theoretically replaced with eight CHP, 

natural gas-fired combustion turbines rated at 13,500 kilowatts (kW) each.

 

2

Source: http://www.governor.ny.gov/press/070212-cuomo-energy-efficient on December 10, 2012
December 19, 2013 Hypothetical Draft For Discussion Purposes Only

EE.4 Emissions Quantification

In order to quantify emissions reductions from the NY- Sun Initiative and 

the Build Smart NY program, DEC used the Northeast Market Allocation 

energy model (NE-MARKAL). 

The core NE-MARKAL database used for this analysis had a 2002 base 

year. The reference case that was used was developed, calibrated, and 

approved by DEC and NYSERDA for a previous analytical effort 

conducted in 2009 through 2010. The Northeast States for Coordinated 

Air Use Management (NESCAUM) reviewed the model constraints from 

that reference case and made some updates for the purposes of this 

analysis. Energy price projections were updated to be consistent with the 

2012 Annual Energy Outlook, and characterized fine particulate matter 

(PM2.5) and volatile organic compounds (VOC) emissions factors for all 

sectors in the model.3

NY-Sun Initiative

The NY-Sun Initiative was represented in the NE-MARKAL model by 

setting the lower bound on customer-sited commercial and residential 

solar PV capacity to 50 megawatts (MW) in 2011, 119 MW by 2012, and 

239 MW from 2013 through 2029.4 Energy efficiency and other 

technology deployment in this model run were constrained to isolate the 

energy and emissions results associated with the program’s target for 

solar PV deployment. As a result, solar technology deployment was solely 

responsible for all modeled energy and emissions changes.

Build Smart NY

The Build Smart NY Initiative was represented in the NE-MARKAL model 

by establishing an upper-bound on the total amount of fuel consumed by 

public buildings. The upper bound was derived by applying a 20 percent 

decrease in energy demand from public buildings. Public building energy 

demand was isolated using the ratio of public building floor space to all 

commercial floor space in New York State.5 This decrease in demand 

was applied starting in 2011 and then linearly interpolated, reaching full 

implementation by 2020, and remaining constant afterwards. This run is 

called “New York Public Buildings 20 Percent Initiative -a”.

 

3 The updated emissions factors were based on the 2011 release of the US 9 Region MARKAL model. 

NOx emissions had been characterized.

4 NE-MARKAL produces results in three year increments. Results for model year 2029 are an average of 

the results for years 2028-2030.

5

Public buildings in New York State account for five percent of the overall commercial floor space.
December 19, 2013 Hypothetical Draft For Discussion Purposes Only

CHP Conversion

For this hypothetical scenario, DEC used EPA’s CHP Calculator tool to 

gauge the level of emission reductions that would result from the 

installation of a CHP system. In order to take SIP credit for a real-world 

CHP installation, DEC would need to engage with the New York 

Independent System Operator (NYISO) and/or the New York State 

Department of Public Service (NYSDPS) to determine the impact of the 

subject facility’s reduced reliance on an electric generating unit (EGU) (or 

multiple EGUs), and the related impact on pollutant emissions from the 

EGU(s). Since this was not practical at this time, DEC would propose to 

discount the emissions reductions projected by EPA’s CHP calculator by 

20 percent, or another negotiated amount. 

EE.5 Energy Modeling Emission Reduction Results

NY-Sun Initiative

The emissions results of the NY-Sun Initiative are presented in the 

appendix. Included are buildings sector and power sector emissions 

summaries.

Modeled building sector emissions relative to the reference case show a 

modest decline due to the displacement of a small amount of natural gas 

usage by thermal solar electric devices. During the 2002 to 2029 

modeling timeframe, carbon dioxide (CO2) and nitrogen oxides (NOx) are 

projected to decline in the buildings sector, while other emissions are 

essentially unchanged.

Emissions effects were seen particularly in the power sector, where 

emissions declined over the modeled timeframe, especially for SO2 and 

NOx. The emissions effects are attributable to the backing-off on 

conventional fossil fuel generation resources due to decreased load from 

the buildings sector.

The table below represents the net cumulative change in emissions from 

the power and buildings sectors for the NY Sun Initiative.

Cumulative Change 2002-2029 (thousand tons):

2002 2005 2008 2011 2014 2017 2020 2023 2026 2029

2002-

2014

2002-

2029

CO2* 0.0 0.0 -0.1 -1.5 -2.2 -2.1 -2.0 -0.7 -0.3 0.0 -3.8 -8.9

NOx 0.0 -0.1 0.0 -1.5 -2.9 -2.3 -1.3 -0.4 0.7 0.8 -4.5 -7.1

PM2.5 0.0 0.0 0.0 -0.4 -0.1 -0.4 -0.5 -0.2 -0.2 -0.2 -0.5 -2.1

SO2 0.0 0.0 -0.1 -6.0 -2.4 -3.3 -1.5 -0.5 0.9 1.7 -8.5 -11.2

VOC 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.17

* CO2 in Million Tons
December 19, 2013 Hypothetical Draft For Discussion Purposes Only

Build Smart NY

The emissions results of the Public Buildings Initiative are presented in the 

appendix.

The initial modeling indicates a modest decrease in emissions in the 

buildings sector, countered by a slight increase in emissions from the 

power sector. In the second analysis, where energy efficiency is deployed 

first, changes in the buildings sector emissions are negligible, while the 

overall decline in electricity demand-- as a result of EE in the buildings 

sector--leads to decreased power sector emissions across all pollutants.

In the initial analysis, the buildings sector appears to experience sectorwide emissions reductions as more efficient non-emitting electrical devices 

displace natural gas equipment. However, the greater use of electrical 

devices in the buildings sector leads to a modest increase in the demand 

for electricity and results in increased emissions from the power sector. 

Sulfur dioxide in the power sector declines due to the slight displacement 

of coal-fired generation by natural gas, as mentioned above. 

After accounting for the trade-off in emissions between the buildings and 

power sectors, net emissions from the public buildings scenario decrease 

across all emissions indicators in both sets of runs. The magnitude of net 

emissions reductions is similar for both iterations of the analysis.

The table below represents the net cumulative change in emissions from 

the power and buildings sectors for the Public Buildings Initiative.

Cumulative Change (thousand tons):

2002 2005 2008 2011 2014 2017 2020 2023 2026 2029

2002-

2014

2002-

2029

CO2* 0.0 0.0 0.0 -0.1 -0.8 -0.8 -0.7 -0.6 -0.6 -0.1 -0.9 -3.7

NOx 0.0 -0.1 0.0 0.0 -0.7 -1.1 -0.2 0.1 0.1 1.0 -0.8 -0.9

PM2.5 0.0 0.0 0.0 0.0 0.0 -0.1 -0.2 -0.3 -0.3 -0.2 -0.1 -1.1

SO2 0.0 0.0 -0.1 -1.9 -1.7 -1.5 -0.3 0.5 0.2 1.8 -3.7 -2.9

VOC 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1

* CO2 in Million Tons

EE.6 CHP Conversion Emissions Reductions

EPA’s Combined Heat and Power (CHP) Emissions Calculator compares 

the anticipated carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), 

carbon dioxide equivalent (CO2e), sulfur dioxide (SO2), and nitrogen oxide 

(NOx) from a CHP system to those of a separate heat and power system. 
December 19, 2013 Hypothetical Draft For Discussion Purposes Only

The calculator uses fuel-specific CO2, CH4, and N2O emissions factors 

used in the EPA Greenhouse Gas Reporting Program, region-specific 

transmission and distribution loss values, and data from Emissions & 

Generation Resource Integrated Database (eGRID) 2012. The calculator 

also presents estimated emissions reductions as metric tons of carbon 

equivalent and emissions from passenger vehicles, as shown below.

The DEC acknowledges that the resulting estimates of environmental 

benefits of CHP generated by the calculator are appropriate for 

educational and outreach purposes only. However, the calculator allows 

users to specify up to 26 additional CHP system characteristics for those 

who are interested in more accurate emissions estimates. Given the time 

and staff constraints of the EE in SIPs Pilot Project, the DEC modified the 

26 additional CHP characteristics to the best of its ability. Therefore, the 

DEC believes the estimates would be adequate for SIP credit purposes 

when applying a discount, tentatively set at 20 percent. 

The CHP Emissions Calculator estimates a 50.41 ton per year reduction in 

NOx from 2012 levels in 2018. A 20 percent discount reduces the estimate 

to 40.3 tons per year. A rough conversion to ozone season day converts 

to 0.11 tons (221 lbs) per day.

In order to take SIP credit for a real-world CHP installation, DEC would 

need to further engage with NYISO and/or NYSDPS to determine the 

impact of the subject facility’s reduced reliance on an EGU, and the 

related impact on pollutant emissions from the EGU. These estimations 

would serve as appropriate supporting materials to submit to EPA.

Also bear in mind that these estimates are for a single hypothetical CHP 

conversion. In all likelihood, the DEC would bundle projected CHP 

projects when considering for inclusion in an actual SIP.

EE.7 Appendices

DEC has attached the NE-MARKAL modeling results performed by 

NESCAUM. This modeling work represents a thorough analysis of the 

NY-Sun and Build Smart NY program impacts on energy consumption and 

pollutant emissions through 2029. Also attached in the appendix is the 

results generated by the CHP Emissions Calculator.

DEC would also submit the April 19, 2012 press release announcing the 

NY-Sun program, and information from the program’s official website. 

Other potential resources are NYSERDA’s Solar Technologies webpage, 

NYPA’s Solar Market Acceleration Program webpage (once further 

developed), and LIPA’s webpage containing information on solar energy 

systems.
December 19, 2013 Hypothetical Draft For Discussion Purposes Only

For the Build Smart NY program, DEC would also submit the December 

28, 2012 press release and executive order announcing the program, and 

information from the program’s official website—notably the August 2013 

Build Smart NY 2013 Baseline Energy Performance Report and the 

September 2013 Build Smart NY Executive Order 88 Guidelines.




























Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page C-1 

Appendix C: Maryland Case Study 

The Weight-of-Evidence Pathway 
- 1 -

Appendix C – The Maryland Pilot Project

TABLE OF CONTENTS

Summary of the Maryland Pilot Project _________________________ 2

Background________________________________________________________________ 2

Air Pollution Transport______________________________________________________ 3

SIP Credit for Innovative, “Non-Traditional Programs” __________________________ 3

Expanded Weight-of-Evidence _________________________________ 4

Modeling the Benefits of EE/RE Programs _______________________ 5

The Maryland/NESCAUM Modeling Platform __________________________________ 5

Analyzing Regional Transport Benefits from EE/RE Efforts in States 

Upwind of Maryland _________________________________________ 7

The Ozone Transport Commission’s EE/RE Initiative ____________________________ 7

Additional Efforts in Maryland to Quantify the Emission Reduction 

Potential of EE/RE Programs __________________________________ 8

Preliminary Estimates of EE/RE SIP Benefits ____________________ 9

Relationship Between the Pilot Project and Maryland’s SIP Submittals

__________________________________________________________ 12

ADDENDUMS _____________________________________________ 13

ADDENDUM 1: Expanded Weight-of-Evidence Explained _______________________ 13

ADDENDUM 2: Multi-Pollutant Planning in Maryland and the MARKAL, CMAQ, 

BenMAP, REMI Modeling Platforms _________________________________________ 14

ADDENDUM 3: Report on Emission Reduction Efforts of RGGI States and 

Recommendations for Guidelines under Section 111(d) of the Clean Air Act _________ 26

ADDENDUM 4: EPA Modeling of Benefits from Regional EE/RE Efforts ___________ 42

ADDENDUM 5: Maryland’s Efforts Based On OTC Energy Efficiency Status Report 

(December 19, 2012)________________________________________________________ 50

ADDENDUM 6: Maryland’s Efforts to Quantify GHG (and other) Emission Reduction 

Benefits from EE/RE Programs ______________________________________________ 52

ADDENDUM 7: Overview of 2015 Ozone SIP-related Multi-Pollutant Analysis Work_ 55

A pilot project to use ―expanded weight-of-evidence‖ to include emission 

reductions and air quality benefits from local and regional energy 

efficiency and renewable energy (EE/RE) efforts as part of Maryland 

State Implementation Plans for the ozone and fine particulate standards
- 2 -

Summary of the Maryland Pilot Project

The Maryland pilot project is actually more than a pilot project. It is the preliminary 

work Maryland is doing to build credit for energy efficiency and renewable energy 

(EE/RE) programs into the ozone State Implementation Plan (SIP) required for the 

Baltimore nonattainment area by June of 2015. The effort builds off of work that 

Maryland originated in it’s 2010 ozone SIP and the States efforts on the Maryland 

Greenhouse Gas Emission Reduction Act of 2009 (GGRA). The GGRA requires a State 

SIP to reduce greenhouse gas emissions by 25% by 2020.

The Maryland pilot is also linked to Maryland’s efforts to develop all future SIPs using a 

multi-pollutant framework to look at all pollutants whenever a single pollutant SIP is 

being developed. Maryland’s multi-pollutant planning approach is discussed later.

The Maryland pilot is a critical part of efforts in Maryland (and builds off of earlier work) 

to try and better address some of the uncertainties associated with the SIP and attainment 

demonstration process, specifically the modeling and future year projections. This 

uncertainty analysis is captured in the SIP process under something called the Weight-ofEvidence (WOE) concept.

Unfortunately, the WOE concept has somehow been misinterpreted as an option to 

pursue ... ―when you really can’t demonstrate attainment‖. Maryland strongly disagrees 

with this interpretation and believes that explicit analyses of uncertainty should be a 

mandatory element of all SIPs. Maryland considers its effort ―expanded‖ weight of 

evidence as it goes beyond what is included in EPA guidance, and more explicitly 

addresses all of the uncertainties associated with building a plan that tries to predict the 

future.

Maryland’s ozone SIP for the 75 ppb standard is due in June of 2015. This pilot project 

and other preliminary analyses being developed for the SIP represent the very earliest 

work Maryland has completed to develop that SIP. In the second half of 2014, Maryland 

will be completing the next phase of the work to develop the 2015 ozone SIP and will be 

taking a draft SIP, including the credit for EE/RE programs, through a stakeholder 

process to seek additional feedback from interested parties. In early 2015, Maryland will 

finalize the proposed SIP and take the final proposal through the more formal public 

comment and hearing process required by law. The final SIP will be submitted to EPA in 

June of 2015.

Background

Since the early 1990s, the Maryland Department of the Environment (MDE) has been 

developing State Implementation Plans (SIPs) for ground level ozone, fine particles and 

other air pollutants. The State has adopted a very large number of regulatory programs to 

meet the requirements of the Clean Air Act (CAA). High profile state regulatory 

initiatives include the Maryland Healthy Air Act (power plants), the Maryland Clean Car 
- 3 -

Program (mobile sources) and numerous other control programs developed regionally 

through the Ozone Transport Commission (OTC).

Despite these aggressive regulatory efforts, Maryland has struggled to attain both the 

ozone and fine particle standards. To continue to make progress in cleaning the air, 

Maryland has pushed very hard in two additional areas that are critical to lowering both 

ozone and fine particle air pollution in the State. These two priority areas are:

 Reducing air pollution that is transported into Maryland from upwind 

states, and 

 Implementing effective ―non-traditional‖ control programs to further 

reduce local emissions without the traditional ―command-and-control‖ 

regulatory driver.

This pilot project looks at how one of those non-traditional program areas, effective 

energy efficiency and renewable energy (EE/RE) programs, can both help clean the air 

and be included in SIPs as creditable programs.

Air Pollution Transport 

Approximately 70 percent of Maryland’s ground level ozone problem originates in 

upwind states. Maryland works in partnership with the University of Maryland College 

Park (UMD) and other universities to implement one of the East Coast’s most 

comprehensive air quality research programs. This effort uses both modeling and 

measurements to look at air quality in general, but focuses to a greater extent on the 

transport of air pollution into the Mid-Atlantic region and Maryland. 

The research platform includes numerous measurement efforts, including aircraft, 

balloons, mountaintop monitors, LIDAR and continuous wind profilers that look 

specifically at transported air pollution. This research shows that for the new 75 parts per 

billion (ppb) ozone standard, incoming ozone is already above the standard at certain 

times.

The primary purpose of this pilot program is not air pollution ―transport‖, although one 

element of the pilot does look at the potential benefits in Maryland from the 

implementation of effective EE/RE programs in states that are upwind of Maryland.

For additional information on the MDE research program and the State’s efforts on 

transport see: 

 http://www.mde.state.md.us/programs/Air/AirQualityMonitoring/Pages/Mountain

top.aspx

SIP Credit for Innovative, “Non-Traditional Programs”
- 4 -

Over the past 15 years, MDE has been working to include non-traditional control 

programs in the air quality planning process and in the SIP. In the 1990s, MDE worked 

to link the States aggressive efforts on ―Smart Growth‖ to the SIP. More recently, in the 

2010 time frame, Maryland included benefits from a package of non-traditional programs 

in the States ozone SIPs for the Washington, Baltimore and Philadelphia 8-hour ozone 

nonattainment areas.

The package of non-traditional programs included some EE/RE efforts, as well as other 

voluntary and incentive-based initiatives like the regional Clean Air Partners program, 

the air quality forecasting program, and a preliminary effort designed to use teleworking 

and carpooling to reduce vehicle emissions on forecasted bad (code orange and code red) 

air quality days.

An important aspect of the 2010 effort was the use of ―expanded weight-of-evidence‖ as 

the best tool for building in the benefits of non-traditional programs into the SIP.

Expanded Weight-of-Evidence

Because of Maryland’s problems with pollutant transport, the State has been very careful 

about how credit for non-traditional programs should be included in SIPs. Maryland does 

not believe that an upwind state (or Maryland) should be allowed to receive SIP credit for 

a non-traditional program until all common sense traditional regulatory programs have 

been implemented in those states. This concept is at the heart of the ―expanded weightof-evidence‖ idea.

Addendum 1 provides additional detail on expanded weight-of-evidence and the process 

that a state should use to demonstrate that it is already implementing all feasible 

traditional controls including nonattainment RACT and any new regulatory control 

programs shown to be effective by stakeholders or EPA.

EPA’s current guidance allows for the use of a weight-of-evidence demonstration to 

support the modeling based attainment demonstration required under the CAA. States 

are allowed, actually encouraged by EPA, to submit additional technical and policy 

analyses (weight-of-evidence) that further demonstrates why the control programs in the 

SIP are likely to provide for attainment by the dates mandated in the law. By allowing 

states to submit additional weight-of-evidence, EPA is recognizing that the modeling 

demonstration is uncertain, and encouraging additional analyses to address these 

uncertainties where possible.

Two examples of such supplemental analysis are described below

 Using measured aloft ozone data to ground truth modeling – In 2010, 

MDE used it’s data from aloft measurements made by aircraft and 

ozonesondes to show that the models where not correctly capturing aloft 

ozone concentrations and that this indicated that the models were 
- 5 -

underpredicting the ozone reduction potential from power plant controls 

in upwind areas. The air monitoring data has proven this to be true.

 As part of this pilot, and the next SIP, Maryland plans to use projected 

reduced energy consumption from EE/RE programs to augment 

traditional projections of emissions growth using business as usual 

projection methodologies. Recent data on both EE/RE programs and the 

increased use of natural gas clearly show that there is a large degree of 

uncertainty in this area as business-as-usual projections for the energy 

generation sector made just 5 years ago have proven to be heavily 

influenced by recent market trends.

Maryland plans to submit a comprehensive expanded weight-of-evidence demonstration 

that will include three basic elements:

1. Traditional analysis of benefits from regulatory programs that are onthe-books - A baseline demonstration showing the attainment status 

when all feasible traditional control programs are implemented in 

Maryland;

2. Transport analysis - A demonstration that combines the controls in the 

above number 1 with all possible regional transport controls that 

Maryland believes are required or could be compelled under the CAA; 

and 

3. Non-traditional control program analysis - A demonstration that 

combines the controls in the above 1 and 2 with projected benefits that 

can be achieved by non-traditional, non-regulatory control programs.

This pilot project focuses on the EE/RE piece of number 3. Addendum 1 provides 

additional information on Maryland’s Expanded Weight-of-Evidence approach.

Modeling the Benefits of EE/RE Programs

The Maryland/NESCAUM Modeling Platform

Maryland has been working with NESCAUM to build a modeling system or framework 

to support the State’s efforts on multi-pollutant planning and to look at the benefits of 

EE/RE strategies. The modeling system is designed to look at multiple pollutants at the 

same time and to look at benefits and costs using a multi-pollutant approach. The 

modeling system will be enhanced over time and is intended to be the primary tool that 

Maryland will use to analyze the air quality benefits from EE/RE efforts and to do multipollutant planning. The package of models is intended to be used in an expanded weightof-evidence analysis, which will supplement, but not replace, the SIP-quality modeling 

required under the attainment demonstration provisions of the CAA. 
- 6 -

The basic building block in the modeling system is the NE-MARKAL model. NEMARKAL can be used to estimate the energy and emission reduction benefits associated 

with EE/RE strategies. The modeling system also includes the following:

 The CMAQ (Community Multi-Scale Air Quality) model to estimate changes in 

air quality associated with reduced emissions

 The BenMAP (Benefits Mapping and Analysis Program) model to estimate the 

health benefits associated with lower concentrations of air pollution and

 The REMI (Regional Economic Models, Inc.) model to estimate economic costs 

and benefits associated with the strategies being analyzed

MARKAL (an acronym for MARKet ALlocation), or NE-MARKAL is a mathematical 

model of the energy system of one or several regions that provides a technology-rich 

basis for estimating energy dynamics over a multi-period horizon. The NE-MARKAL 

model is a linear programming model, similar to the U.S. Department of Energy’s 

National Energy Modeling System (NEMS) in that it covers multiple energy demand 

sectors including residential and commercial buildings, transportation, and the industrial 

sector, as well as the supply side power generation sector.

The NE-MARKAL model provides a tool to estimate how EE/RE programs in Maryland 

will reduce energy consumption and how that reduced energy consumption will reduce 

emissions of greenhouse gases, nitrogen oxide (NOx), sulfur dioxide (SO2) and mercury.

The current EE/RE programs being modeled using NE-MARKAL, CMAQ, BenMAP and 

REMI framework include:

 The Regional Greenhouse Gas Initiative (RGGI)

 The EmPower Maryland energy conservation program

 The Maryland renewable portfolio standard program

 Light-duty vehicle GHG standards

 EV technology deployment

 Vehicle miles traveled reductions consistent with Maryland transit and ―smart 

growth‖ initiatives

 Low-carbon imports

Addendum 2 provides additional information on the NE-MARKAL model and the other 

models being used as part of this pilot.

Some of the preliminary results from Maryland and NESCAUM’s early work with the 

modeling platform are provided later in this document.
- 7 -

Analyzing Regional Transport Benefits from EE/RE Efforts in States 

Upwind of Maryland

This pilot project will also begin to look at the air quality benefits that Maryland could 

see if effective EE/RE programs are implemented in upwind states. Again, on bad air 

days, approximately 70% of Maryland’s air quality problem originates in upwind states.

EPA has initiated some modeling to estimate how EE/RE efforts in states that are upwind 

of Maryland will reduce energy consumption and regional emissions of NOx and SO2. 

Maryland will be using this work and supplemental analyses that builds from the EPA 

work to model (using CMAQ) the reduced concentrations of ozone and fine particles 

associated with reduced transport because of effective EE/RE efforts upwind of 

Maryland. To the extent that resources are available, Maryland plans to also look at two 

additional regional EE/RE program scenarios to try and capture a highly optimistic 

(upper bound) and a less optimistic (lower bound) projection of the energy and emission 

reduction benefits associated with the quickly evolving growth of EE/RE programs 

across the East.

Maryland is hoping to partner with EPA to link this effort to the preliminary information 

that is available on the options states have to implement the new GHG reduction 

requirements for existing power plants under Section 111(d) of the CAA. One of the 

options that many states are considering is a ―system‖ approach (i.e. a system of emission 

reduction that is focused on the electricity system as a whole) where limits at power 

plants coupled with aggressive efforts to implement EE/RE programs can cost-effectively 

reduce GHG emissions from the power generation sector.

Maryland is a member of the Regional Greenhouse Gas Initiative (RGGI). RGGI is the 

countries first GHG cap-and-invest reduction program. It has helped dramatically reduce 

GHG emissions from existing power plants in the 9 state RGGI region (more than a 40% 

reduction) between 2005 and 2012. Addendum 3 includes the recent RGGI comments on 

Section 111(d) and includes discussion of a ―system-wide‖ approach for Section 111(d) 

and the critical role that EE/RE programs have played in RGGI’s emission reduction 

successes. 

Addendum 3 also provides more detail on the earlier EPA modeling conducted to look at 

the benefits from regional EE/RE efforts.

This piece of the Maryland effort is designed to analyze and demonstrate that aggressive 

regional EE/RE programs or cost-effective ―system‖ based approaches to implement 

Section 111(d) will not only reduce GHG emissions, but they will also help reduce the 

transport of criteria pollutants and help states develop ―Good Neighbor‖ SIPs.

The Ozone Transport Commission’s EE/RE Initiative

At its Fall meeting in 2012, the OTC finalized a policy paper designed to reduce NOx 

emissions in the Ozone Transport Region (OTR) by promoting common sense, costeffective energy retrofits at large commercial buildings.
- 8 -

The policy paper entitled ―Promoting Deep Energy Retrofits of Large Commercial

Buildings to Reduce Nitrogen Oxide Emissions in the Ozone Transport Region‖ was 

adopted on November 15, 2012. It was developed by the Energy Efficiency Workgroup

of the OTC’s Stationary and Area Source (SAS) Committee.

The workgroup decided to initially focus on the ozone reduction potential from profitable 

―deep energy retrofits‖ of commercial buildings. The policy paper

(http://www.otcair.org/upload/Documents/Meeting%20Materials/Commercial%20Buildi

ng%20Energy%20Efficiency%20Status%20Report.pdf) includes the following:

1. Estimates of the magnitude of NOx emission reductions possible in the OTR 

through profitable deep energy retrofits of large commercial buildings, and

2. A list of several low-cost policy strategies that jurisdictions in the OTR could 

pursue to promote profitable NOx reductions (including strategies that some 

jurisdictions are already pursuing).

The OTC policy paper indicates that the NOx emission reduction potential from this costeffective initiative is large (approximately 36,000 tons of potential NOx reductions each 

year).

Partly because OTC works with state air quality agencies, and energy efficiency

strategies are typically pursued by state and local energy agencies, the strategies listed in 

the policy paper are not subjected to an in depth analysis. Rather, they are presented as 

options which air divisions may discuss with their respective states’ energy divisions, for 

further evaluation and possible implementation. 

The OTC SAS Committee continues to work on implementation of this initiative. 

Addendum 4 provides more detail on the OTC EE/RE initiative. Maryland will be 

including this initiative in its efforts to include EE/RE programs in a multi-pollutant air 

quality planning process and the next round of SIPs.

Additional Efforts in Maryland to Quantify the Emission Reduction 

Potential of EE/RE Programs 

In 2009, the Maryland General Assembly adopted the Greenhouse Gas Emission 

Reduction Act (GGRA). The law was sponsored by Maryland’s Governor. The law 

requires the State to adopt and implement a plan to achieve a 25% reduction in 

greenhouse gas (GHG) emissions between 2006 and 2020. The plan was required to be 

finalized by the end of 2012. The plan includes a large number of EE/RE efforts being 

developed and implemented in Maryland. Examples of these programs include:

 The RGGI

 EmPOWER Maryland 

 The Maryland RPS program

 Clean car initiatives
- 9 -

 Electric vehicle initiatives

 Green building initiatives

 Lead by example efforts

Because of this law, MDE has worked in partnership with the Maryland Energy 

Administration, the Maryland Public Service Commission, the Maryland Department of 

Transportation, the Maryland Department of Planning, the Maryland Department of 

Housing and Community Development and other State and local partners to generate the 

best possible emission reduction estimates possible for the Maryland EE/RE programs. 

The primary focus of this effort has been GHG emission reductions. MDE plans to 

continue to refine this work and plans to focus more on the NOx, SO2 and mercury 

reduction estimates that can be built from the baseline energy work used to estimate the 

GHG emission reductions.

This effort will eventually blend with NE-MARKAL driven modeling work discussed 

earlier in this document. The current effort with the NE-MARKAL platform was 

conducted with the preliminary work, completed in 2011, to develop the GGRA plan.

Preliminary Estimates of EE/RE SIP Benefits

The Maryland plan to implement the GGRA includes a chapter on multi-pollutant 

benefits. The work conducted to develop this chapter was based on preliminary data and 

information, and to a certain extent, should be viewed as a demonstration project for how 

to link states GHG emission reduction efforts with CAA SIP requirements that focus on 

other pollutants.

As part of this work NESCAUM ran the MARKAL and BenMAP models and contracted 

with the University of Maryland (UMD) to run CMAQ and Towson University Regional 

Economic Studies Institute (RESI) to run REMI. The complexity of the models and the 

time and expense needed to contract with experienced modelers is an important concern 

for agencies considering this approach. The following illustrations depict select output 

and results from the preliminary work conducted by Maryland and NESCAUM.

Figures 1 and 2 summarizes the potential emission reductions from a selected set of 

EE/RE initiatives (described earlier) for carbon dioxide (CO2), NOx, SO2 and mercury 

that resulted from the preliminary NE-MARKAL modeling effort.

Figure 1 summarizes the power sector results. 

Figure 2 summarizes the transportation sector results.
- 10 -

MARKAL-modeled Power Sector Emissions Under the GGRA Case Through 2020

Figure 2 MARKAL-Modeled Transportation Sector Emissions Underthe GGRA Case 

Through 2020

Figures 3 and 4 summarize the potential for the EE/RE efforts to reduce ambient 

concentrations and exposure to both fine particulate and ozone air pollution. 
- 11 -

Figure 3 - CMAQ Output - Difference Between Average 24-hour Mean PM2.5 

Calculated for the GGRA and Reference Cases – Maryland

Figure 4 - CMAQ Output – Difference Between Average Maximum Daily 8-Hour 

Average Ozone Calculated for Control and Reference Cases – Maryland
- 12 -

Figure 5 summarizes the health benefit information developed as part of the NEMARKAL/BenMAP exercise that was part of the initial Maryland/NESCAUM effort.

BenMAP Output – Health Impact Incidence and Valuation, Change from Base to GGRA 

Case, by State for Ozone

The economic analysis piece of this effort is still evolving. REMI findings from the 

preliminary work include the following:

• Over the short-term (5-10 years during technology transition), there are large 

benefits due to increased spending and investment in new technologies

• Subsequent loss of fuel sector jobs/wages could lead to negative trend in output if 

the Maryland economy is not ―re-tooled‖ to fit with new opportunities (e.g. clean 

tech sectors)

• Complementary incentive or subsidy programs could be considered with GGRA 

implementation (e.g. MD Clean Energy Incentive Tax)

Relationship Between the Pilot Project and Maryland’s SIP Submittals

Maryland is working with NESCAUM on this pilot project to look at how EE/RE 

programs can be included in SIPs by including the potential EE/RE emissions and air 

quality benefits in the weight of evidence piece of the attainment demonstration required 

as part of the SIP. Maryland will continue to investigate means to ensure that we are not

predicting reductions that were already taken out of the foretasted emissions.

Incidence Valuation (millions $)

Mortality 

(All Cause)

Morbidity

Mortality Morbidity

State

(Abbrev.)

Acute Respiratory SymptomsEmergency Room Visits, RespiratoryHospital Admissions, RespiratorySchool Loss Days

CT - 52 - - 15 - 35 0.2 - 0.3 0.0

DC - 260 - 0 - 1 76 - 181 1.0 - 1.4 0.0

DE - 643 - 1 - 3 201 - 479 2.5 - 3.5 0.1

MA - 12 - - 3 - 8 0.1 0.0

MD 3 - 5 6,853 3 - 6 3 - 20 2,107 - 5,020 24.9 - 35.1 0.6 - 0.7

ME - (84) - - (53) – (22) (0.6) – (0.4) 0.0

NH - 3 - - 1 - 3 0.0 0.0

NJ 1 1,806 1 - 2 1 - 6 542 - 1,292 7.0 - 9.9 0.2

NY 2 3,731 3 - 6 2 - 10 1,095 - 2,613 12.2 - 17.2 0.3 - 0.4

PA 2 - 3 2,939 1 - 3 2 - 13 873 - 2,083 13.8 - 19.4 0.3

RI - - - - 2 - 5 0.0 0.0

VA 1 2,151 1 - 2 2 - 9 676 - 1,613 6.7 - 9.4 0.2 - 0.3

VT - (16) - - (10) – (4) (0.1) 0.0
- 13 -

The EE/RE pilot project, which will conclude in 2015, is just a small piece of a larger 

effort in Maryland to build EE/RE programs into the air quality planning process using a

multi-pollutant framework. Maryland included the preliminary estimates of the potential 

multi-pollutant benefits as part of the 2012 GGRA plan. In 2015, Maryland will submit 

updates to the State’s SIP for ground level ozone. This plan will also include estimated 

multi-pollutant emissions reductions, air quality and public health benefits from EE/RE 

programs. Finally, in the 2018 to 2020 time frame, Maryland will work on a third phase 

of this effort linked to a new ozone standard, fine particulate and regional haze.

Maryland’s approach is to continue to develop the suite of tools being used in the multipollutant planning framework and to improve the analyses of the EE/RE benefits each 

time the State updates a clean air plan.

ADDENDUMS

ADDENDUM 1: Expanded Weight-of-Evidence Explained 

Maryland’s expanded weight of evidence approach was started on February 16, 2010, 

with a White Paper titled ―What is the Role of ―Weight of Evidence‖?. This white paper 

put forward the concept that the attainment modeling should be considered as part of a 

weight of evidence document that would also include trends analysis, sensitivity 

modeling and other scientific research. In addition, the White Paper also posed six 

questions in an effort to generate discussion on this expanded weight of evidence 

approach. One of questions was ―Should several different, but plausible, estimates of 

growth in the future year emissions be part of the attainment demonstration?‖ This 

particular question is important, as it relates directly to climate change programs which 

are challenging the business as usual assumptions about future energy consumption and 

growth related to vehicle use.

As a result of this white paper, an OTC workgroup was formed. After several iterations 

the workgroup agreed upon a set of recommendations for an Expanded Weight of 

Evidence for Attainment Demonstration1. These recommendations were then sent to 

EPA Office of Air Quality Planning and Standards (OAQPS) for their consideration and 

possible use in revised Guidance on the Use of Models and Other Analyses for 

Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. 

The OTC workgroup basically recommended a two step weight of evidence process be 

implemented as follows:

 Basic weight of evidence to include the traditional data analysis and 

other supplemental (modeling, analysis, etc) information and 

 Expanded weight of evidence option, which is only possible after all 

feasible traditional controls (RACT, stakeholder or EPA suggestions) 

have been implemented. The reason for including this condition was to 
- 14 -

stop states from skipping over controls that others have been forced to 

implement. 

The expanded weight of evidence provisions would allow states to do the following: 

demonstrate through modeling how additional regional controls in other states would 

help with attainment, use a range of potential future design values for a probability of 

attainment, use statistical (meteorological adjusted ozone) and other (inventory, 

sensitivity, etc.) analyses, and use of non-traditional programs (Smart Growth, Energy 

Efficiency, Renewable Energy/Renewable Portfolio Standards, etc.) with both optimistic 

and pessimistic assumptions.

In addition, OTC included an option that called for the affected state to work with EPA to 

determine if a mid-course review and consultation would be required under the expanded 

weight of evidence provisions.

(1)(http://www.otcair.org/upload/Documents/Correspondence/OTC%20Expanded%20Weight-ofEvidence%20Letter%20and%20Recommendation.pdf)

ADDENDUM 2: Multi-Pollutant Planning in Maryland and the MARKAL, CMAQ, 

BenMAP, REMI Modeling Platforms

Maryland is implementing multi-pollutant planning in a three-phase approach that 

corresponds with major policy implementation schedules. Each phase builds on previous 

analysis and integrates co-benefits derived from reducing emissions.

The initial phase of MPAF is currently under development. Maryland is incorporating the 

MPAF as part of the Greenhouse Gas Reduction Plan. The Plan supports legislative 

requirements to reduce greenhouse gas emissions by 25 percent from a 2006 baseline by 

2020. The major sources of greenhouse gases, transportation and electricity generation, 

are also sources of ozone precursors and particle-forming emissions. Initial analysis 

supports the multi-pollutant approach by identifying co-benefits of ozone and particle 

reduction associated with lowering greenhouse gas emissions.

In 2015, Maryland will submit an ozone State Implementation Plan to the U.S. 

Environmental Protection Agency. This plan will identify policies and programs needed 

to ensure compliance with the ozone national ambient air quality standard. Over the last 

30 years, Maryland has worked to identify and correct emissions sources that contribute 

to elevated ozone levels. Recent research has associated warming climate trends with 

increased potential for elevated ozone levels. We believe that by identifying synergistic 

effects, we can identify those policies and programs that yield the greatest benefits in 

terms of emissions reductions and that are economically sound.

In the 2020 time frame, Maryland will begin the third phase of MPAF with the submittal 

of the Fine Particle State Implementation Plan. Fine particles are formed from some of 

the same compounds that contribute to ozone formation. Building on knowledge gained 

from our greenhouse gas and ozone control analysis, the State will apply the MPAF in its 
- 15 -

efforts to identify the most effective controls for fine particles. By linking the co-benefits 

of reducing greenhouse gases, ozone and fine particles, we believe that we will have 

designed a process that maximizes pollution reduction in a cost-effective framework. 

MULTI-POLLUTANT PLANNING

Multi-pollutant planning is a process that identifies the air quality co-benefits of select 

policy options. Maryland’s approach to multi-pollutant planning is to reduce emissions 

through an integrated process that maximizes the co-benefits of reduction policies. This 

process allows for multi-sector analysis and estimates environmental, public health, 

economic and energy benefits of policies designed to reduce criteria pollutants, toxics 

and greenhouse gases. The approach, developed by the Northeast States for Coordinated 

Air Use Management (NESCAUM), is the Multi-Pollutant Analytical Framework 

(MPAF). Maryland is working with NESCAUM to customize elements of the MPAF to 

address climate change and criteria pollutant reduction goals through selected policy 

options.

The MPAF consists of three broad areas of activity: Visioning, Processing and Analysis, 

and Data / Results. The process is illustrated below.

VISIONING

Visioning is the process to identify a suite of policies and technologies that support clean 

air and climate change goals. This process is labor intensive and requires close attention. 

The policy scenarios may be derived from regulatory requirements, long-range planning

exercises or the desire to evaluate the implementation of policy/technology combinations 

in the context of desired outcomes. The products from the visioning process are the 

development of a set of a reference case (the baseline conditions) and a set of policies to 

evaluate in comparison to the reference case.

PROCESSING & ANALYSIS
- 16 -

The process and analysis process of the MPAF consists of four models, each designed to 

provide a set of results focused on key areas of analysis: energy, air quality, public health 

and the economy. Generally, the models are run in sequence. Output from the MARKAL 

model feeds CMAQ and REMI. Output from CMAQ feeds BenMAP.

MARKAL (an acronym for MARKet ALlocation), or NE-MARKAL1is a 

mathematical model of the energy system of one or several regions that provides a 

technology-rich basis for estimating energy dynamics over a multi-period horizon. The 

NE-MARKAL model is a linear programming model, similar to the U.S. Department of 

Energy’s National Energy Modeling System (NEMS) in that it covers multiple energy 

demand sectors including residential and commercial buildings, transportation, and the 

industrial sector, as well as the supply side power generation sector.2

MARKAL computes energy balances at all levels of an energy system: primary 

resources, secondary fuels, final energy, and energy services. MARKAL is a vertically 

integrated model of the entire energy system. The model aims to supply energy services 

at minimum global cost by simultaneously making equipment investment and operating 

decisions and primary energy supply decisions, by region. For example, in MARKAL, if 

there is an increase in residential lighting energy service (perhaps because more people 

build houses in a community), either existing generation equipment must be used more 

intensively or new generation equipment must be installed. The choice of generation 

equipment (type and fuel) incorporates analysis of both the characteristics of alternative 

generation technologies and the economics of primary energy supply. 

MARKAL computes an inter-temporal partial equilibrium on energy markets. The 

quantities and prices of the various fuels and other commodities are such that at those 

prices the suppliers produce exactly the quantities demanded by the consumers. Further, 

this equilibrium has the property that the total surplus is maximized over the whole 

horizon. Investments made at any given period are optimal over the horizon as a whole.

The basic components in a MARKAL model are specific types of energy or emission 

control technology. Each is represented quantitatively by a set of performance and cost 

characteristics. A menu of both existing and future technologies is input to the model. 

Both the supply and demand sides are integrated, so that one side responds automatically

to changes in the other. MARKAL is a ―Least-cost‖ model that selects that combination 

of technologies that minimizes total energy system cost. 

 

1 NESCAUM, with the assistance of the International Resources Group (IRG), has developed a Northeast U.S. version of the 

MARKAL model based on regional data and in cooperation with energy and air quality divisions of 11 Northeast states and the 

District of Columbia. This planning tool allows for the analysis of a range of transportation, energy, air quality, and climate programs 

with a time horizon of 30 years and a focus on the cost and environmental implications of key program design elements.

2 As opposed to NEMS, however, NE-MARKAL is state-based and regionally specific, with increased regional detail beyond what is 

currently available in national energy models. Each northeast jurisdiction is represented as its own region within the model and can be 

analyzed independently or as a part of the regional collective. Thus, the model is particularly good at demonstrating the benefits of 

regional cooperation and of flexible implementation of air quality and climate programs.

NE-MARKAL is similar to the Integrated Planning Model (IPM), which has been used for several national regulatory program 

assessments by the U.S. EPA. IPM does have significant regional detail with respect to the power sector; however, the MARKAL 

model is multi-sector – as opposed to IPM which only covers the power generation sector – and, thus, is capable of analyzing intersector tradeoffs among emission reduction programs that may be more or less cost-effective than single sector focused programs. 

Furthermore, IPM requires that projections for electricity demand be provided exogenously while MARKAL determines the demand 

endogenously, weighting it against conservation, fuel switching and other options available to the model.
- 17 -

MARKAL does not require -- or permit -- an a priori ranking of abatement measures as 

an input to the model. The model chooses the preferred technologies and provides the 

ranking as a result. Indeed, the choice of abatement measures often depends upon the 

degree of future abatement that is required.

Typically, a series of model runs is made examining a range of alternative futures (over 

30 years). The model requires as input projections of energy service demands -- room 

space to be heated or vehicle-miles to be traveled, for example -- and projected resource 

costs. Then, a reference case is defined in which, for example, no measures are required 

to reduce emissions. Reference case estimates of end-use energy service demands (e.g., 

car, commercial truck, and heavy truck road travel; residential lighting; steam heat 

requirements in the paper industry) are developed by the user on the basis of economic 

and demographic projections, for each region in a multi-region formulation of the model. 

In addition, the user provides estimates of the existing stock of energy related equipment, 

and the characteristics of available future technologies, as well as new sources of primary 

energy supply and their potentials.

A series of runs is then made with successive reductions in emissions: emissions 

stabilized at present levels, for example, then reduced by 10 percent, 20 percent, etc., by 

some future date before being stabilized.

In each case, the model will find the least expensive combination of technologies to meet 

that requirement -- up to the limits of feasibility -- but with each further restriction the 

total energy system cost will increase. Thus, the total future cost of emission reductions is 

calculated according to how severe such restrictions may become. These can be plotted as 

continuous abatement cost curves. In addition, the marginal cost of emission reduction in 

each time period is determined.

This is of special interest in establishing abatement policy because it can be interpreted as 

the amount of carbon tax that would be needed to achieve this level of abatement.

Some uses of MARKAL are to:

 Identify least-cost energy systems

 Identify cost-effective responses to restrictions on emissions

 Perform prospective analysis of long-term energy balances under different scenarios

 Evaluate new technologies and priorities for R&D

 Evaluate the effects of regulations, taxes, and subsidies

 Project inventories of greenhouse gas emissions

 Estimate the value of regional cooperation
- 18 -
- 19 -

MARKAL outputs emissions and expenditures data. The emissions data is sent to the air 

quality model and the expenditure data is sent to the economic model. Processing 

emissions data is a 2-step procedure that passes data through SMOKE through CMAQ. 

The results are expressed in terms of air quality benefits.

The Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System3is 

primarily an emissions processing system designed to create gridded, speciated, hourly 

emissions for input into a variety of air quality models like CMAQ. SMOKE supports 

area, biogenic, mobile (both on-road and non-road), and point source emissions 

processing for criteria, particulate, and toxic pollutants. For biogenic emissions modeling, 

SMOKE uses the Biogenic Emission Inventory System, version 2.5 (BEIS2) and version 

3.09 and 3.14 (BEIS3). SMOKE is also integrated with the on-road emissions model 

MOBILE6 and MOVES.

The Community Multi-scale Air Quality (CMAQ) modeling system has been designed 

to approach air quality as a whole by including state-of-the-science capabilities for 

modeling multiple air quality issues, including tropospheric ozone, fine particles, toxics, 

acid deposition, and visibility degradation. In this way, the development of CMAQ 

involves the scientific expertise from each of these areas and combines the capabilities to 

enable a community modeling practice. CMAQ was also designed to have multi-scale 

 

3 The sparse matrix approach used throughout SMOKE permits rapid and flexible processing of emissions data. Rapid processing is

possible because SMOKE uses a series of matrix calculations rather than a less-efficient sequential approach used by previous 

systems. Flexible processing comes from splitting the processing steps of inventory growth, controls, chemical speciation, temporal 

allocation, and spatial allocation into independent steps whenever possible. The results from these steps are merged together in the 

final stage of processing using vector-matrix multiplication. This means that individual steps (such as adding a new control strategy, 

or processing for a different grid) can be performed and merged without having to redo all of the other processing steps.
- 20 -

capabilities so that separate models were not needed for urban and regional scale air 

quality modeling.

The target grid resolutions and domain sizes for CMAQ range spatially and temporally 

over several orders of magnitude. With the temporal flexibility of the model, simulations 

can be performed to evaluate longer-term (annual to multi-year) pollutant climatologies 

as well as short-term (weeks to months) transport from localized sources. With the 

model's ability to handle a large range of spatial scales, CMAQ can be used for urban and 

regional scale model simulations. By making CMAQ a modeling system that addresses 

multiple pollutants and different spatial scales, CMAQ has a "one atmosphere" 

perspective that combines the efforts of the scientific community.

To implement multi-scale capabilities in CMAQ, several issues, such as scalable 

atmospheric dynamics and generalized co-ordinates that depend on the desired model 

resolution are addressed. Meteorological models may assume hydrostatic conditions for 

large regional scales, where the atmosphere is assumed to have a balance of vertical 

pressure and gravitational forces with no net vertical acceleration on larger scales. 

However, on smaller scales such as urban scales, this assumption cannot be made. A set 

of governing equations for compressible non-hydrostatic atmospheres is available to 

better resolve atmospheric dynamics at smaller scales. These non-hydrostatic equations 

are more appropriate for finer regional scale and urban scale meteorology. Because 

CMAQ is designed to handle scale dependent meteorological formulations and a large 

amount of flexibility, CMAQ's governing equations are expressed in a generalized 

coordinate system. This approach ensures consistency between CMAQ and the 

meteorological modeling system. The generalized coordinate system determines the 

necessary grid and coordinate transformations, and it can accommodate various vertical 

coordinates and map projections.

The CMAQ modeling system simulates various chemical and physical processes that are 

thought to be important for understanding atmospheric trace gas transformations and 

distributions. The CMAQ modeling system contains three types of modeling 

components: a meteorological modeling system for the description of atmospheric states 

and motions, emission models for man-made and natural emissions that are injected into 

the atmosphere, and a chemistry-transport modeling system for simulation of the 

chemical transformation and fate. 

CMAQ ambient emissions outputs are then routed to BenMAP to assess the public health 

impacts of various policy approaches to reducing criteria pollutants. BenMAP is a 

Windows-based computer program that uses a Geographic Information System (GIS)-

based to estimate the health impacts and economic benefits occurring when populations 

experience changes in air quality. BenMAP is used to estimate the health impacts from 

air quality changes. Some of the purposes for which BenMAP is used include the 

following:

 Generation of population/community level ambient pollution exposure maps;
- 21 -

 Comparison of benefits across multiple regulatory programs;

 Estimation of health impacts associated with exposure to existing air pollution 

concentrations;

 Estimation of health benefits of alternative ambient air quality standards;

 Performance of sensitivity analyses of health or valuation functions, or of other 

inputs; and

 Hypothetical, or ―what-if,‖ type analyses.

REMI (Regional Economic Models, Inc.) is used to evaluate the economic impacts of 

policies and emission reduction goals. The REMI model incorporates aspects of four 

major modeling approaches: Input-Output, General Equilibrium, Econometric, and 

Economic Geography. Each of these methodologies has distinct advantages as well as 

limitations when used alone. The REMI integrated modeling approach builds on the 

strengths of each of these approaches.

The REMI model at its core, has the inter-industry relationships found in Input-Output 

models. As a result, the industry structure of a particular region is captured within the 

model, as well as transactions between industries. Changes that affect industry sectors 

that are highly interconnected to the rest of the economy will often have a greater 

economic impact than those for industries that are not closely linked to the regional 

economy.

General Equilibrium is reached when supply and demand are balanced. This tends to 

occur in the long run, as prices, production, consumption, imports, exports, and other 

changes occur to stabilize the economic system. For example, if real wages in a region 

rise relative to the U.S., this will tend to attract economic migrants to the region until 

relative real wage rates equalize. The general equilibrium properties are necessary to 

evaluate changes such as tax policies that may have an effect on regional prices and

competitiveness. 

REMI is sometimes called an ―Econometric model,‖ as the underlying equations and 

responses are estimated using advanced statistical techniques. The estimates are used to 

quantify the structural relationships in the model. The speed of economic responses is 

also estimated, since different adjustment periods will result in different policy 

recommendations and even different economic outcomes.

The New Economic Geography features represent the spatial dimension of the economy. 

Transportation costs and accessibility are important economic determinants of 

interregional trade and the productivity benefits that occur due to industry clustering and 

labor market access. Firms benefit having access to a large, specialized labor pool and 

from having access to specialized intermediate inputs from supplying firms. The 

productivity and competitiveness benefits of labor and industry concentrations are called 

agglomeration economies, and are modeled in the economic geography equations.

APPLYING THE MPAF IN MARYLAND
- 22 -

Maryland began to multi-pollutant planning with the adoption of the Healthy Air Act in 

2006. The Act requires affected coal-fired electricity generating units (EGUs) to reduce 

emissions of NOx, SO2 and mercury by the imposition of caps. This Act also addressed 

greenhouse gases by requiring the State to participate in the Regional Greenhouse Gas 

Initiative (RGGI). RGGI is a market-based regulatory program designed to cap 

greenhouse gas emissions from fossil-fueled fired EGUs. RGGI is a cooperative effort 

among 9 northeast states that will reduce carbon dioxide (CO2) emissions from the power 

sector 10 percent by 2018.

The early successes from the Healthy Air Act and RGGI led Maryland into a multi-year 

process of exploring the potentials for multi-pollutant planning. The Maryland 

Department of the Environment (MDE) worked with NESCAUM to develop a 

conceptual framework and produced a draft report and reference case in 2009. 

Subsequent work with the Maryland Energy Administration (MEA) and Public Services 

Commission (PSC) refined the reference case to include better descriptions of 

Maryland’s energy services and demands.

Two concerns motivated the multi-pollutant work: the need to comply with the National 

Ambient Air Quality Standards and the need to reduce greenhouse gas emissions. 

Maryland has regulated most sources of criteria pollutants and has reduced ozone and 

particle precursors dramatically. The 0.75 ozone standard is producing new challenges as 

we look for ways to further reduce emissions and incorporate these reductions in a State 

Implementation Plan due in 2015. In addition to the criteria pollutants, Maryland is also 

obligated by state law to reduce emissions of greenhouse gas emissions 25 percent from a 

2006 base by 2020. It makes sense to identify the policies and technologies that yield the 

greatest co-benefits. In the context of preparing the 2012 Climate Plan, MDE decided to 

explore the utility of the MPAF for climate planning and to identify potential co-benefits 

that may be considered as part of a weight of evidence document for the 2015 Ozone 

State Implementation Plan.

Visioning

The visioning process included representatives from MDE, MEA, PSC and the Maryland 

Department of Transportation (MDOT). Together, these agencies detailed the emissions 

reduction goals and the policies that would enable the State to achieve the goal of 

reducing greenhouse gas emissions 25 percent by 2020.

Major policies like Maryland’s Clean Cars Program, RGGI, EmPower Maryland and the 

Renewable Portfolio Standard were selected for analysis. Implementation technologies 

were identified and include low carbon fuels, increased wind and solar electricity 

generation and increased use of hybrid-electric and battery-electric vehicles.

Through an iterative series of meetings and draft reports, the process resulted in the 

development of a reference case and evaluation scenario, referred to as the ―GGRA 

Case‖.4Elements of the GGRA Case include:

 RGGI relative to a 2011 baseline,

 
- 23 -

 Renewable portfolio standard as defined by least-cost optimization,

 Light-duty vehicle GHG standards,

 EV technology deployment consistent with low-range of regional clean fuel 

standards,

 Vehicle miles traveled reductions consistent with transit and ―smart growth‖ plans 

in GGRA, and

 Conservative EmPower Maryland program.

PROCESSING AND ANALYSIS

NESCAUM ran the MARKAL and BenMAP models and contracted with the University 

of Maryland to run CMAQ and Towson University Regional Economic Studies Institute 

(RESI) to run REMI. The complexity of the models and the time and expense needed to 

contract with experienced modelers is an important concern for agencies considering this 

approach. The following illustrations depict select output and results from the models.

MARKAL-modeled Power Sector Emissions Under the GGRA Case Through 2020

MARKAL-Modeled Transportation Sector Emissions Under the GGRA Case 

Through 2020
- 24 -

CMAQ Output - Difference Between Average 24-hour Mean PM2.5 Calculated for the GGRA 

and Reference Cases Maryland 

CMAQ Output – Difference Between Average Maximum Daily 8-Hour Average Ozone 

Calculated for Control and Reference Cases - Maryland
- 25 -

BenMAP Output – Health Impact Incidence and Valuation, Change from Base to GGRA Case, by 

State for Ozone 
- 26 -

Incidence Valuation (millions $)

Mortality 

(All Cause)

Morbidity

Mortality Morbidity

State

(Abbrev.)

Acute Respiratory SymptomsEmergency Room Visits, RespiratoryHospital Admissions, RespiratorySchool Loss Days

CT - 52 - - 15 - 35 0.2 - 0.3 0.0

DC - 260 - 0 - 1 76 - 181 1.0 - 1.4 0.0

DE - 643 - 1 - 3 201 - 479 2.5 - 3.5 0.1

MA - 12 - - 3 - 8 0.1 0.0

M D 3 - 5 6,853 3 - 6 3 - 20 2,107 - 5,020 24.9 - 35.1 0.6 - 0.7

ME - (84) - - (53) – (22) (0.6) – (0.4) 0.0

NH - 3 - - 1 - 3 0.0 0.0

NJ 1 1,806 1 - 2 1 - 6 542 - 1,292 7.0 - 9.9 0.2

NY 2 3,731 3 - 6 2 - 10 1,095 - 2,613 12.2 - 17.2 0.3 - 0.4

PA 2 - 3 2,939 1 - 3 2 - 13 873 - 2,083 13.8 - 19.4 0.3

RI - - - - 2 - 5 0.0 0.0

VA 1 2,151 1 - 2 2 - 9 676 - 1,613 6.7 - 9.4 0.2 - 0.3

VT - (16) - - (10) – (4) (0.1) 0.0

REMI Findings – Trend Over Time

• Over the short-term (5-10 years during technology transition), we see large benefits due 

to increased spending and investment in new technologies;

• Subsequent loss of fuel sector jobs/wages could lead to negative trend in output if the 

Maryland economy is not ―re-tooled‖ to fit with new opportunities (e.g. clean tech 

sectors); and 

• Complementary incentive or subsidy programs could be considered with GGRA 

ADDENDUM 3: Report on Emission Reduction Efforts of RGGI States and 

Recommendations for Guidelines under Section 111(d) of the Clean Air Act
- 27 -

Report on Emission Reduction Efforts of the States Participating in the 

Regional Greenhouse Gas Initiative and Recommendations for Guidelines 

under Section 111(d) of the Clean Air Act 

Introduction 

The states participating in the Regional Greenhouse Gas Initiative (RGGI) have successfully achieved 

substantial reductions in greenhouse gas (GHG) emissions from the power sector in a cost-effective 

manner, while promoting economic growth and vitality. The experience of the RGGI states provides 

a particularly relevant demonstration of the effectiveness of a multi-faceted suite of programs in 

reducing GHG emissions from the power sector. It also illustrates the potential for the power sector 

to reduce emissions by substantially more than 17% from 2005 levels, which will help the United 

States to achieve the targeted economy-wide reductions of 17% by 2020. 

Experience of the RGGI States in Reducing Emissions

1 

The states involved in RGGI are demonstrating that environmental protection can go hand-inhand with economic development and job creation. In operation since 2009, RGGI is the first capand-invest program in the United States – it caps GHG emissions from the power sector and 

reduces those emissions over time. The states participating in RGGI are investing the proceeds 

generated from auctioning emission allowances to further reduce emissions, lower the cost of 

compliance, and develop the clean energy economy in the region. 

The RGGI cap-and-invest program is just one of the tools the RGGI states utilize to reduce emissions. 

The RGGI states are promoting renewable energy through some of the nation’s most aggressive 

renewable portfolio standard programs and supporting investments in energy efficiency that have 

reduced the amount of electricity consumed and lowered bills paid by electricity consumers. The 

RGGI states are also implementing various regulatory programs directed at pollutants other than 

GHGs that, along with RGGI, are fostering the transition from high-emitting coal and oil to 

renewable energy and lower-emitting natural gas as a fuel for generating electricity. 

1 This section responds to many of the questions posed by EP! under heading number 1 (“What is state and 

stakeholder experience with programs that reduce CO2 emissions in the electric power sector?”) 
- 28 -

In this context, the RGGI cap-and-invest program plays three integral roles in achieving emission 

reductions. The declining cap and corresponding change in the cost of allowances provides a 

market signal that supports fuel switching, on-site efficiency improvements, the retirement of 

high-emitting plants, the construction of new more efficient plants, and other measures that 

reduce emissions. The auction mechanism provides a source of funding for complementary 

energy efficiency and renewable energy investments that further reduce emissions. The 

enforceable emissions cap ensures that the combined effect of the RGGI program and the suite of 

supporting policies is to actually reduce emissions to below the cap level. 

The experience in the RGGI states shows the magnitude of emission reductions possible from the 

power sector: a projected 50% decline in tons of carbon dioxide (CO2) emissions and a fossil fuelfired generation fleet that is projected to achieve emission rates on par with the recently proposed 

new source performance standard for new electric generating units. Between 2005 and 2012, CO2 

emissions from the power sector in the nine participating RGGI states dropped more than 40%, 

from 162.5 million tons in 2005

2 

to 92 million tons in 2012. The RGGI states are locking in this 

reduction by reducing the regional cap to 91 million tons in 2014, and reducing it an additional 2.5% 

each year thereafter to 78 million tons in 2020. In 2020, the RGGI emissions cap will ensure that 

regional emissions are 50% below 2005 emission levels (See Figure 1). 

Some of this reduction is attributable to the successful energy efficiency programs 

implemented by each of the RGGI participating states. For example, New York’s energy 

efficiency programs have reduced electricity use in New York by a cumulative total of 6.5% in 2012. 

As a result, CO2 emissions associated with New York’s electricity use are estimated to be 

2.68 million tons lower in 2012 than they would have been otherwise. In the four years since it 

began in 2009, Maryland’s EmPOWER program has reduced electricity consumption by 3.25%, 

reducing CO2 emissions by 1.17 million tons. Massachusetts projects that its investment in energy 

efficiency will accelerate the reduction in electricity demand to approximately 2.5% each year from 

2013-15. From 2005 through 2015, these energy efficiency investments will 

reduce Massachusetts’ electricity demand by 17.1%, for a total annual reduction of 3 million tons of 

CO2 in 2015. Similarly, Connecticut’s energy efficiency programs have reduced electric consumption 

by over 10% since 2001, resulting in a total reduction of over 2 million tons of CO2 emissions. 

2 http://rggi.org/historical_emissions; https://rggicoats.org/eats/rggi/index.cfm?fuseaction=search.rggi_summary_report_input&clearfuseattribs=true 
- 29 -

Figure 1: New RGGI Cap and Projected CO2 Emissions Without Cap Reduction 

Much of the reduction in power sector emissions is attributable to better utilization of a cleaner 

power system, resulting in a substantially reduced system-wide emission rate. Between 2005 and 

2010, the overall CO2 emission rate of the fossil fuel-fired power sector in the RGGI states declined 

from 1,694 lbs/MWh to 1,393 lbs/MWh (1026 lbs/MWh to 841 lbs/MWh, including zero emission 

sources).

3 

By 2020, modeling of the new RGGI cap indicates that the fossil fleet emission rate will 

decline further to 1,028 lbs/MWh (568 lbs/MWh for all sources).

4 

Thus, in the 15 years between 

2005 and 2020, the RGGI states will have achieved a 39% reduction in the emission rate from fossil 

fuel-fired power plants and a 45% reduction in the emission rate of the entire power sector. 

3 From data used to produce: http://rggi.org/docs/Documents/Elec_monitoring_report_2011_13_06_27.pdf 

4 http://rggi.org/design/program_review 
- 30 -

Figure 2: RGGI Region Electricity Generation Carbon Intensity Rates 

This reduction in the emission intensity of electricity generation in the RGGI states is due in part to 

the ramping up of renewable energy sources, pursuant to state renewable portfolio standards that 

provide for steep increases in the percentage of renewable energy sold in each state, as the table 

below illustrates: 

Table 1: RGGI State Renewable Portfolio Standards or Goals 

State Connecticut 

Target Renewable 

Portfolio Standard or 

Goal 27% Target Year 

2020 

Delaware 25% 2025 

Maine 40% 2017 

Maryland 20% 2022 

Massachusetts 15% 2020 

New Hampshire 24.8% 2025 

New York 30% 2015 

Rhode Island 16% 2019 

Vermont 20% 2020 
- 31 -

As the foregoing demonstrates, the RGGI states’ experience can be an effective model for state 

programs under section 111(d): 

 It is extremely cost-effective. RGGI enables compliance through market 

 mechanisms that seek out the least expensive emission reductions across the 

 region.

5 

 It provides economic benefits. According to an independent analysis, the RGGI 

states’ investment of auction proceeds from just the first three years of the 

program (2009-2011) is creating thousands of jobs, reducing energy bills by over 

$1 billion and adding a net of $1.6 billion to the economies in the RGGI states.

6 

It aligns with the regional nature of the electricity grid. The nation’s regional 

electricity grids allow electricity to flow from the cheapest, most efficient producer to 

meet consumer demand, wherever located. As a result, generation and emissions within 

a region may not always trend in unison, such that emission increases in some locations 

due to market fundamentals may be offset by emission decreases elsewhere. The RGGI 

cap ensures that emissions decrease across the region, even as it allows increases in 

some locations in order to reap the benefits of more efficient sources in those locations. 

It provides a simple, transparent, verifiable compliance system. It can be difficult to 

document and verify the emission reductions attributable to programs that support 

renewable energy and energy efficiency. Under RGGI, the emissions are limited by the 

allowances that are distributed, providing certainty that the projected emission reductions 

will be achieved, including reductions attributable to energy efficiency and renewable 

energy. 

The RGGI market-based model for achieving emission reductions is a well-established system of 

emission reduction. It is based on the models for reducing the pollutants that cause acid rain and 

ozone that are embodied in Title IV of the Clean Air Act and in the nitrogen oxide 

5 This is consistent with recent analysis of the Organisation for Economic Co-operation and Development (OECD) 

that concludes that carbon markets are a highly efficient mechanism to mitigate carbon emissions. See OECD, 

Climate and Carbon, Aligning Prices and Policies, OECD Environment, Policy Paper, October 2013. 6 The Economic 

Impacts of the Regional Greenhouse Gas Initiative on Ten Northeast and Mid-Atlantic States, Review of the Use of 

RGGI Auction Proceeds from the First Three-Year Compliance Period. The Analysis Group, November 15, 2011. 

http://www.analysisgroup.com/uploadedFiles/Publishing/Articles/Economic_Impact_RGGI_Report.pdf 
- 32 -

trading program established by EPA in 1995 and 2003. But RGGI improved on those models by 

auctioning allowances and using the proceeds from those auctions to support complementary 

efforts to further reduce emissions and decrease compliance costs, such as investment in 

renewable energy and energy efficiency. This innovation has reduced the cost of complying with 

the cap and provided net economic benefits to the economies of the participating states. 

Implications of RGGI for Development of EPA Guidelines under Section 111(d)

7 

EPA should recognize that the RGGI model is an effective system of emission reduction for GHG 

emissions from the power sector that combines various policy tools with an enforceable cap. Under 

the RGGI regional cap, the RGGI states will achieve a 50% reduction in CO2 emissions from the 

power sector from 2005 levels by 2020. This reduction in emissions is projected to be realized in 

part through a 45% reduction in emission rates across the electricity system in the participating 

states, while the rest of the reductions come from complementary policies that reduce demand. 

Relying on an emission budget trading system, the RGGI states are ensuring that this level of 

reduction will in fact be achieved. The specific lessons of the RGGI experience include the following: 

1. A system of emission reduction that is focused on the electricity system as a whole 

achieves the greatest emission reductions. 

The RGGI states implement a suite of programs to pursue the best opportunities for emission 

reductions from the power sector. Programs within the system of emission reduction adopted by 

each RGGI state, such as energy efficiency goals and renewable energy standards, do not require 

emission reductions at any specific plant but focus on system-wide emission reductions. The price 

signal provided by the cost of RGGI allowances raises the relative cost of higher-emitting plants, 

leading to increased generation at lower-emitting, more efficient plants, even as overall systemwide emissions have declined substantially. A system-based approach is not only best-suited to 

realize the emission reduction potential of cleaner energy supplies and energy efficiency, it fits 

precisely within section 111(d)’s mandate to EPA to develop guidelines for states to implement the 

“best system of emission reduction.” 

7 This section responds to EP!’s questions under heading number 2 (“How should EP! set the performance 

standard for state plans?”) 
- 33 -

2. The RGGI states are demonstrating the feasibility of reducing emissions by 50%. 

Since 2005, CO2 emissions from the power sector have declined more than 40% across the RGGI 

region, as energy efficiency programs have contributed to reduced demand and generation has 

shifted from coal and oil to gas and renewable power. Some states, like New York, achieved this 

level of reduction even though the energy system was already relatively clean in 2005, with nearly 

half of electricity provided by carbon-free sources. Even greater reductions should be achievable in 

states that rely more heavily on coal because of the low-cost alternatives that remain available. By 

reducing the cap to approximately 50% below 2005 levels by 2020, the RGGI states are ensuring that 

this transition to a lower-emitting power sector will continue. The RGGI states are achieving this 

reduction while continuing to grow the regional economy by more than 7% since 2005.

8 

Figure 3: RGGI CO2 Emissions and Economic Output (2005-2012) 

8 Bureau of Economic Analysis (BEA), Gross Domestic Product by State (chained 2005$); 

http://www.bea.gov/regional/index.htm 
- 34 -

As mentioned above, the reinvestment of auction proceeds is contributing to this economic growth 

and analyses prepared for the RGGI states predict that over $8 billion and more than 125,000 jobyears will be added to the RGGI states’ economies as a result of the cap reduction through 2040.

9 

3. An emissions cap is a reliable system for monitoring and verifying compliance. 

For states that rely on a suite of policies to reduce emissions, like the RGGI states, an emissions 

cap is a simple but rigorous method of ensuring and verifying that the policies have achieved the 

emission reductions targeted. Significantly, even though the required emission reductions are 

achieved on a regional basis, the point of compliance is with the source. Because sources cannot 

emit more than the number of allowances they hold at the relevant compliance deadline, the 

RGGI system ensures compliance. Verification is simple and routine: at the end of each compliance 

period, the amount of allowances in each source’s compliance account must be adequate to cover 

that source’s emissions. The measurement of CO2 emissions at sources covered by the cap is easily 

accomplished utilizing existing emissions monitoring equipment and protocols already in place at 

these sources, and covered sources report CO2 emissions in accordance with 40 CFR Part 75. If a 

source does not have adequate allowances to cover its emissions, enforcement can be taken 

directly against that source. Because of the simple and straightforward nature of determining 

whether the cap is met, budget trading programs obviate the need for EPA or states to conduct a 

complex analysis to determine whether a state meets its compliance requirements, as described 

below. 

4. Regional systems of emission reduction best reflect the regional nature of the electrical 

grid.

10 

A program that corresponds with the borders of an electricity grid is potentially more efficient than 

programs that are constrained by state borders. If EPA only allows for compliance on a state-bystate basis, without regard to the scope of the electricity system, it may create inefficiencies and 

unnecessary complications for EPA, states, and regulated sources. A regional program like RGGI 

helps to ensure that the most cost-effective emission reductions occur across the region. For 

example, since the program was commenced, generation has shifted from coal-fired plants within 

the six state New England region covered by ISO New 

9 http://rggi.org/design/program_review 10 This subsection responds to questions about how EPA should 

account for the regional nature of the electricity grid. 
- 35 -

England to natural gas and renewable sources located elsewhere in that region. Indeed, emissions

in Rhode Island actually increased because it is home to some of the more efficient natural gasfired power plants in the region that had excess capacity. If Rhode Island’s generation had been 

constrained by a Rhode Island-specific cap, one or more of the coal-fired plants that closed 

elsewhere in New England may have had to remain open to meet demand, thereby increasing 

emissions and costs to consumers. 

Even if a program that encompasses an entire regional program is not feasible, a multi-state 

regional program like RGGI provides greater efficiency by allowing for the most cost-effective 

emission reductions among the states participating in the program. 

Recommended Principles for EPA Guidelines

11 

The RGGI states offer the following recommendations for EP!’s development of guidelines 

for state programs that would deliver the emission reductions needed as cost-effectively 

and equitably as possible. 

1. EPA’s Guidelines should achieve meaningful nationwide emission reductions. 

In structuring its guidelines, EPA should take account of the emission reductions that are being 

achieved from the electricity system nationwide through a variety of programs, including RGGI and 

California’s similar program, investments in energy efficiency, renewable energy programs, and 

switching to lower-carbon fuels, and also consider the potential for contributions from available 

technologies that are not yet widely deployed in the United States, such as offshore wind and 

carbon capture and sequestration technology. EPA should recognize that the best system of 

emission reduction considers the electricity system as a whole, and utilizes all the opportunities for 

reducing emissions from this system. 

Conceptually, the methods of reducing emissions from the fossil fuel-fired electricity system can be 

grouped into two categories. The first category consists of systems of emission reduction that 

reduce the amount of electricity needed from fossil fuel-fired power plants, such as energy 

efficiency programs that reduce the demand for electricity, demand-side 

11 This section responds generally to EP!’s questions under heading numbers 2 (“How should EP! set the 

performance standard for state plans?”) and 3 (“What requirements should state plans meet, and what flexibility 

should be provided to states in developing their plans?”). 
- 36 -

management, and investments in renewable energy that displace fossil fuel-generated electricity. 

Second, emissions can be reduced by lowering the carbon intensity of the electricity generated by 

fossil fuel-fired power plants. This is done through shifting generation from high-emitting plants to 

new or under-utilized lower-emitting plants, and using the latest technology to reduce emissions at 

existing plants. 

12 

Combined, these two categories, or wedges, of emission reductions can be substantial. The RGGI 

states’ 40% emission reduction is due to a suite of actions that address both wedges, including the 

RGGI mechanism, investments in energy efficiency and other demand-side programs, support for 

renewable energy, and regulatory programs directed at criteria air pollutants and air toxics that 

have reduced the amount of electricity generated by higher-emitting plants. These programs have 

combined with market forces that have supported a major shift in electricity generation from coalfired to natural gas-fired plants to transform the regional electricity system in the past eight years. 

By investing in energy efficiency and renewable energy and shifting generation to more efficient 

plants, other states and regions should be able to approach the level of performance that the RGGI 

states are already demonstrating. EPA should evaluate whether and when this level of performance 

can be achieved throughout the United States using the various tools at the disposal of the states. 

While it may take longer for some regions of the nation to achieve comparable levels of 

performance, EPA should structure the emission guidelines to require that states make significant 

progress in the next decade toward achieving the reductions and performance level demonstrated 

by RGGI to be readily achievable by the best systems of emission reduction. 

12 Currently available options for reducing carbon dioxide emissions through measures implemented “on-site” at 

existing fossil fuel-fired power plants have the potential to reduce emissions from individual power plants by 20% 

or more, especially if used in combination. In addition to improving the efficiency or “heat rate” of the plant, these 

options include, but are not limited to, co-firing or re-powering with lower-carbon fuels such as sustainable 

biomass and natural gas; utilizing renewable energy sources such as solar power to provide supplemental steam 

heating; implementing combined heat and power (CHP) systems at plants near industrial facilities or district 

heating systems; and carbon capture technology. 
- 37 -

Figure 4: Illustrative Example of Factors Driving CO2 Emission Reductions 

2. EPA should provide equitable treatment to early movers. 

Many states, including the RGGI states, have already made substantial progress in reducing 

emissions from their power sector. EPA should structure the guidelines in a way that recognizes 

this progress and provides equitable treatment to those states. EPA should avoid any approach 

that imposes inequitable or disproportionate burdens on early mover states and fails to recognize 

their substantial progress. For example, requiring an equivalent percentage reduction for state A, 

which has already achieved most cost-effective reductions, and state B, which has taken little 

action and finds many inexpensive emission reduction opportunities still available, would 

effectively disadvantage state A for having taken early action. 

One approach that EPA should consider is setting a single emission intensity target (e.g., a systemwide average of 1100 lb/MWh) that would apply to each state, individually or as part of a region. 

That approach would require all states to reduce emissions but it would be equitable to those 

states that have already made progress toward meeting the emission 
- 38 -

intensity target. EPA could consider providing more time to states that have more work to do to 

meet the target. 

3. EPA should allow states to use a mass-based system of compliance. 

A mass-based approach has a number of advantages, including simplicity and its ability to 

accommodate many emission reduction strategies, including energy efficiency and renewable 

power, and add-on controls should they become technically and economically viable. An emission 

rate target, in contrast, does not easily provide credit for energy efficiency investments that reduce 

energy demand without reducing the emission rate of the units operating. Thus, requiring the 

regulated fossil fuel-fired power plants to meet a specific emission rate, or achieve a set reduction

in their emission rate, does not credit investments in energy efficiency. 

Therefore, EPA should allow states to utilize a mass-based system of compliance, applied to the 

energy system as a whole. Indeed, if EPA does not establish mass-based targets in its guidelines, it 

should provide the states with clear direction in developing mass-based emission budgets based 

on emission rates designated by EPA. That direction could include designation of factors (e.g., rate 

of economic growth) and consistent data sources that would allow for conversion of an emission 

rate target into an emission budget. 

4. EPA should allow states to demonstrate compliance on a regional basis. 

EPA should allow and encourage compliance on a regional basis, while providing 

individual states the opportunity to determine how to achieve compliance with each state’s 

emission budget within its state implementation plan. Under a mass-based regional system of 

compliance like RGGI, states would pool their individual state emission budgets and comply with 

those emission budgets on a regional basis, while still allowing for enforcement by states against 

their own sources that do not have sufficient allowances. As long as the overall regional emissions 

cap complies with the guidelines, it should be immaterial to EPA how the participating states elect 

to apportion the regional emissions cap among the states. Although a particular state’s actual 

emissions could theoretically exceed its individual state emission budget in a particular year, this 

should not affect EP!’s willingness to accept a regional program as a pathway for compliance. As 

long as the regional program demonstrates that emissions from sources within the region will 

collectively meet EP!’s emission guideline, it can still serve as the basis for each state’s 

implementation plan. 
- 39 -

A regional program has the benefit of addressing some of the interstate issues raised by EPA in its 

questions. For example, under a state-by-state approach, if an energy efficiency policy in State A 

leads to a reduction in emissions in neighboring State B, State A cannot necessarily take direct 

credit for those emission reductions outside its borders in its section 111(d) implementation plan. 

Likewise, because State B would have no basis for enforcing State !’s energy efficiency program, 

State B cannot necessarily include State !’s efficiency policy in its plan. For any state that is part of a 

multistate electricity grid, it may be challenging to make a rigorous demonstration that investments 

in energy efficiency or renewable energy result in any quantifiable level of emission reductions 

within the state. On the other hand, a regional program that encompasses both the state that 

invests in efficiency and the state in which emissions decline as a result would avoid these 

complications. In a regional budget trading program, emission reductions anywhere in the region 

reduce the overall demand for emission allowances, as regulated sources require fewer allowances 

for compliance. As a result, the cost of allowances, or the cost of complying with that regional 

emissions cap, is reduced. 

Thus, allowing regional compliance can avoid market distortions that would result in less than 

optimal policy decisions. For example, a state that is not participating in a regional program might 

choose not to invest in energy efficiency or renewable energy if it would not be able to fully credit 

the benefits of doing so in its section 111(d) compliance plan. Instead, it might choose to make less 

than optimal investments in fuel-switching or plant-specific improvements in order to ensure that 

the emissions of its power plants are reduced. The result would be less than optimal allocation of 

limited resources and less reduction of emissions for a given level of effort. EPA should avoid that 

inefficient outcome by supporting (but not requiring) the development of regional compliance plans. 

5. EPA should permit states to demonstrate compliance on a multi-year basis. 

Emissions across an electricity system can vary between years depending on factors outside the 

ability of plant operators to influence, including weather, economic conditions, and unexpected 

shutdowns. EPA can require a more substantial level of cost-effective reductions if it allows states 

to average emissions over a multi-year period and enables states to bank, or carry-over, early 

reductions. Unlike other pollutants that may have short-term impacts, the environmental harm 

caused by CO2 and other GHG pollutants have much longer periods of impact. Therefore, allowing 

compliance on a multi-year basis would not reduce the environmental benefits of the program. 
- 40 -

The RGGI program uses a three-year compliance approach. The RGGI states’ experience is that this 

approach has the benefit of allowing sources to take advantage of multi-year compliance strategies. 

By allowing sources three years, the regulated units have flexibility to address variations in 

emissions, unexpected shutdowns, or uneconomic dispatch orders, without impacting the 

enforceability or environmental effectiveness of the program’s requirements. 

6. EPA’s should provide clear guidelines for a rigorous demonstration of equivalency of state 

programs.

13 

EPA should provide clear direction to the states regarding demonstrating equivalency of state 

programs. EP!’s guidelines should identify the tools that states can use to demonstrate that state 

emission reduction programs will achieve equal or greater reductions in pollution than the base 

standards set by EPA. For a mass-based budget trading program like RGGI, that process is 

straightforward. As long as EPA provides a mechanism that enables states to potentially have an 

annual mass-based emissions budget under section 111(d), then determining whether a regional 

budget trading program like RGGI is equivalent to EP!’s emission guideline will be a simple matter. 

In particular, the participating states will have to demonstrate that the annual regional emissions 

cap under the regional program achieves emission reductions equal to or greater than those 

allowed by EP!’s guidelines. 

To evaluate programs that are not mass-based, EPA should build on current program evaluation 

guidance such as the “Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and 

Programs into State and Tribal Implementation Plans” or the “State and Local Energy Efficiency 

Action Network. 2012. Energy Efficiency Program Impact Evaluation Guide.” These guides describe 

the terminology, structures, and approaches used for evaluating energy and demand savings as 

well as avoided emissions and other non-energy benefits resulting from energy efficiency programs 

that are implemented by local governments, states, utilities, private companies, and nonprofits. 

These guides provide context, planning guidance, and discussion of issues that determine the most 

appropriate evaluation objectives and best practices approaches for different efficiency portfolios. 

By using standard evaluation terminology and structures and best practices approaches, 

evaluations can support the adoption, continuation, and expansion of effective efficiency actions 

for consistent inclusion in 

State Plans. 

13 This section responds to EP!’s questions under heading number 3 (“What requirements should state plans 

meet, and what flexibility should be provided to states in developing their plans?”). 
- 41 -

7. EPA should ensure that state plans are enforceable. 

EPA should require state plans to demonstrate that the requirements are legally and practically 

enforceable. Under a budget trading program like RGGI, enforceability, measurement, and 

verification are already incorporated into the program in a straightforward manner. Based on 

consistent regulations adopted in each RGGI state, sources subject to RGGI are required to obtain 

and hold a sufficient amount of allowances by the relevant compliance deadline to cover emissions 

over the relevant compliance period. Under the existing terms of RGGI states’ respective 

implementing regulations, this regulatory requirement is generally incorporated as a condition of 

each source’s operating permit. Thus, RGGI is enforceable directly against individual sources by the 

state where the sources are located, and the failure of a source to hold sufficient allowances 

constitutes violations of the state’s program and of the source’s permit. Under an approved section 

111(d) plan, this obligation of each individual source to comply with RGGI would become a federally 

enforceable condition of an individual source’s Title V permit. !t the end of the compliance period, 

the “true-up” process, in which states deduct allowances to cover sources’ emissions, provides 

verification that the emission reductions included as part of the participating states’ section 111(d) 

plans are actually achieved. 

State plans that rely on a suite of strategies including energy efficiency, renewable energy, and 

changes in dispatch should be encouraged, as long as a mechanism is available to ensure that the 

promised emission reductions are achieved. If the emission reductions anticipated from those 

strategies are encompassed within a federally enforceable emission budget program, the various 

strategies themselves would not have to be federally enforceable. 

Conclusion 

The states participating in RGGI have demonstrated that significant emission reductions are 

feasible through a suite of clean energy activities, complemented by an enforceable emissions cap. 

EPA should consider this record of success in developing guidelines for state plans that require and 

empower states to achieve meaningful reductions through a comprehensive package of activities, 

including market-based emission budget programs like RGGI. 
- 42 -

ADDENDUM 4: EPA Modeling of Benefits from Regional EE/RE Efforts

The intention of this addendum is to present links to information specific to modeling 

results for EE/RE efforts in Maryland and the region. Only a brief summary of the 

modeling conditions and the resulting benefits are presented here; full details on the 

criteria used in setting up the modeling runs and the results are available at the respective 

links.

In March 2012, the EPA released a draft of the projected benefits from existing state and 

regional EE/RE programs. With guidance from NESCAUM and EPA, additional state 

and regional scenarios will be selected by MDE for further modeling, and, in 

combination with weight of evidence (WOE), a range of projected impacts, or reductions 

in energy demand and consumption, from these scenarios will be developed. These 

reductions, ranging from the most conservative (low impact) to the least conservative 

(high impact) will be converted to reductions in NOx and SO2 emissions from EGUs. 

This range of projected reductions will be evaluated using WOE to show RFP towards 

attainment in the Baltimore ozone nonattainment SIP.

The MDE intends to use existing results from IPM runs, with initial scenario 

identification, selection and application to be run beginning in the First Quarter 2013. As 

the results of the scenarios are received, a more focused identification and selection 

process will be used in identifying the state or regional programs for additional testing.

The following webpage, which was updated on 9/14/2012, includes an abundance of 

information on the development of EE/RE policies. Some of the text from the web page 

has been copied here:

Projected Impacts of Existing State Energy Efficiency and Renewable Energy Policies

http://epa.gov/statelocalclimate/state/statepolicies.html

This page presents an EPA analysis of projected energy savings and demand impacts of 

existing State energy efficiency and renewable energy (EE/RE) policies. EPA anticipates 

that its methods and projected energy impacts may be useful to states preparing State 

Implementation Plan (SIP) submittals to meet the National Ambient Air Quality 

Standards (NAAQS) for ozone and particulate matter. States can also use this information 

to assess the energy impact estimates in their air quality management plans and 

greenhouse gas mitigation plans.

This analysis covers 29 States with EE/RE policies that are adopted in state law and 

codified in rule or utility regulators’ order, but that are not reflected in the Energy 

Information Administration's Annual Energy Outlook (AEO) 2010 electricity demand 

projections. Impact estimates are provided for:

 Energy-efficiency policies that reduce electricity demand in key end-use sectors 

by encouraging the use of more energy efficient equipment, technologies and 

practices. 
- 43 -

 Renewable Portfolio Standard (RPS) policies where States have increased 

requirements for renewable energy generation beyond what is documented in 

AEO 2010. 

The energy (MWh) and demand (MW) impacts presented here do not reflect comments 

received from states and other stakeholders in the summer of 2011. EPA plans to release 

that information in the coming months.

Four additional links within this webpage provide details on:

1. The background and methodology of the project, 

2. State-by-state summary pages,

3. State policy characterization and annual energy savings, and

4. Peak energy savings.

1. Background and Draft Methodology

http://epa.gov/statelocalclimate/documents/pdf/background_and_draft_methodology.pdf

 Provides a project overview and describes objectives for the analysis. This 

document also includes a detailed description of the draft methodology, including 

policy definitions, data sources, sample equations, and reference tables.

A copy of the first page of the Background document is copied here:

 
- 44 -

Within the document are additional details referring to IPM (see page 17):

State-level peak savings were estimated as the hourly load impact of energy efficiency 

programs during the state’s peak hour. In the absence of state-specific information on the 

timing of the peak, the peak hour for each state was assumed to be the same as the peak 

hour for the Integrated Planning Model (IPM) region in which it largely sits (based on 

population) in EPA’s Base Case.

―Model region‖ refers to the geographic regions defined for the ―EPA Base Case using 

IPM® v.4.10,‖ a projection of electricity sector activity that takes into account only those 

Federal and state air emission laws and regulations whose provisions were either in effect 

or enacted and clearly delineated at the time the base case was finalized in August 2010. 

The peak hour is taken from load shapes used in EPA’s Base Case using IPM®, which 

are compiled by aggregating EIA-714 data to the model region level.

US Environmental Protection Agency (EPA) (2010). Documentation for EPA Base 

Case v.4.10 Using the Integrated Planning Model. August 2010. Available online at 

http://www.epa.gov/airmarkt/progsregs/epa-ipm/docs/v410/Chapter1.pdf

2. State by State Summary Pages

http://epa.gov/statelocalclimate/documents/pdf/State-by-StateSummaryPages.pdf

Provides a one-page snapshot of each state's energy savings results (GWh) on an annual 

and cumulative basis for the years, 2010, 2012, 2015, 2020. This file also includes 

estimates of future renewable energy sales for states that adopted or revised their RPS 

beyond what is assumed in AEO 2010. 

A single page summary table for each of the 29 states is presented, with a brief 

description of the conditions and assumptions used. The summary for the State of 

Maryland has been copied here. All results are still draft.
- 45 -

In working with the EPA and NESCAUM on this pilot project to investigate the 

application of potential EE/RE programs to reduce energy demand, single state scenarios 

and regional scenarios will be run. State summaries for only 29 states were available. Of 

these 29 states, 17 are located to the east of the Mississippi River: Connecticut, Delaware, 

Florida, Illinois, Indiana, Maine, Maryland, Massachusetts, Michigan, New Hampshire, 

New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont and Wisconsin. 

State summaries for an additional 9 states east of the Mississippi River were not 

available: West Virginia, Virginia, North Carolina, South Carolina, Georgia, Alabama, 

Mississippi, Tennessee and Kentucky.

In summary, savings from energy efficiency for the 17 states east of the Mississippi River 

for which summaries were available is provided here; additional details are available at 

the web page. http://epa.gov/statelocalclimate/documents/pdf/State-byStateSummaryPages.pdf
- 46 -

 Estimated Future Impact of EE/RE Policies for Select States

STATE 2012 2015 2020

Connecticut Annual 493 500 511

Cumulative 1,505 2,996 5,528

Delaware Annual 9 13 18

Cumulative 27 65 149

Florida Annual 968 1,042 0

Cumulative 2,604 5,700 9,402

Illinois Annual 966 952 952

Cumulative 2,757 5,611 10,369

Indiana Annual 581 1,082 0

Cumulative 1,248 3,996 9,852

Maine Annual 180 302 318

Cumulative 336 1,141 2,696

Maryland Annual 949 958 0

Cumulative 2,840 5,705 5,705

Massachusetts Annual 1,324 1,333 1,348

Cumulative 3,278 7,268 13,980

Michigan Annual 989 988 989

Cumulative 2,222 5,187 10,130

New Hampshire Annual 94 97 97

Cumulative 276 564 1,047

New Jersey Annual 917 919 920

Cumulative 2,403 5,158 9,752

New York Annual 1,281 1,031 0

Cumulative 4,072 7,655 7,655

Ohio Annual 1,188 1,490 2,959

Cumulative 3,050 7,367 17,766

Pennsylvania Annual 1,386 0 0

Cumulative 2,778 4,174 4,174

Rhode Island Annual 166 228 230

Cumulative 430 1,081 2,227

Vermont Annual 175 177 181

Cumulative 521 1,050 1,947

Wisconsin Annual 769 1,064 1,073

Cumulative 1,404 4,444 9,792

Total 12,435 12,176 9,596

31,751 69,162 122,171

Note: all results expressed in gigawatt hour (GWh)

The following sections:

3. State Policy Characterizations and Annual Energy Savings and Generation Estimates, 

and 

4. Peak Energy Savings Summaries, 
- 47 -

were used in the preparation of the State-by-State Summary Pages (discussed above). 

3. State Policy Characterizations and Annual Energy Savings and Generation 

Estimates

http://epa.gov/statelocalclimate/documents/excel/state_policy_characterizations.xls

Provides a detailed description of each state's energy efficiency policies, as well as 

energy savings estimates on an annual and cumulative basis (GWh) for the years 2010-

2020. This file also describes RPS policy details and provides annual and cumulative 

renewable energy sales (in GWh) for states that revised their RPS policies beyond what is 

assumed in AEO 2010.

A copy of the main page of the work sheet, which describes the multiple inputs, has been 

copied here. Each colored ―block‖ refers to a separate worksheet in the workbook, which 

when appropriate, contains state specific information.

4. Peak Energy Savings Summaries
- 48 -

http://epa.gov/statelocalclimate/documents/excel/LoadImpactShapesandPeakSavingsQua

ntification.xls

Provides estimates of annual peak savings (in GW) for 2010, 2012, 2015 and 2020, as 

well as hourly demand impacts for each year in states with relevant energy efficiency 

policies. 

The peak energy savings for the 29 states are calculated and presented separately, and 

then summarized on the main page of the workbook.

 
- 49 -

IPM Modeling

The MDE intends to use existing results from IPM runs, with initial scenario 

identification, selection and application to be run beginning in the First Quarter 2013. 

EPA’s IPM Base Case v.4.10

September 1, 2010 – EPA announced a Federal Register Notice of Data Availability 

(NODA) supporting the Proposed Transport Rule.

Updated 9/8/2010

http://www.epa.gov/airmarkt/progsregs/epa-ipm/BaseCasev410.html

This webpage has a link to a very useful document, which provides details on Base Case 

v.4.10. The table of contents for this document is copied here. 

Of particular interest in this document are:

 General information on IPM, and what data entry 

 Existing environmental regulations for SO2, NOx and CO2 

 State specific environmental regulations (MD: Healthy Air Act)

 New Source Review Settlements (MD: Morgantown and Chalk Point)

 Renewable Portfolio Standards in EPA Base Case v.4.10 (Appendix 3-6)

 Trading and Banking Rules in EPA Base Case v.4.10

 Proposed Transport Rule results using EPA’s IPM Base Case v.4.10

IPM Analyses of the Cross-State Air Pollution Rule (CSAPR) 

http://www.epa.gov/airmarkt/progsregs/epaipm/docs/v410/Guide_to_IPMv410_Input_and_Output_Files.pdf

Analysis of the Final Cross-State Air Pollution Rule

On July 6, 2011, the EPA Administrator signed a Notice of Final Rulemaking for the 

Cross-State Air Pollution Rule (CSAPR). EPA analyzed the impact of the final CSAPR 

on the U.S. electric power sector using version 4.10_FTransport of the Integrated 

Planning Model (IPM). From this page you can download documentation for IPM 
- 50 -

v4.10_FTransport, the NEEDS database of electric generation unit records used in the 

modeling, and the IPM run results files.

The information on this webpage reflects changes to the IPM Base Case v.4.10 after 

receiving comments from stakeholders.

ADDENDUM 5: Maryland’s Efforts Based On OTC Energy Efficiency Status 

Report (December 19, 2012) 

Purpose: Implement a voluntary energy efficiency program involving public and private 

sectors that is projected to generate in initial startup and implementation phase, 

approximately 1.2 tons/day NOx reductions by 2015 in Maryland. Increased reductions 

are expected in later stages of implementation.

Maryland will apply the recommendations of the OTC status report ―Promoting Deep 

Energy Retrofits of Large Commercial Buildings To Reduce Nitrogen Oxide Emissions‖ 

and develop along with other member states and EPA, a low cost program to reduce 

energy consumption at commercial buildings. The status report lists several low-cost 

policy strategies that jurisdictions in the OTR could pursue to promote profitable NOx 

reductions. Commercial building deep energy retrofit projects have recently achieved 

profitable energy reductions of 38 percent to 70 percent, with profitability demonstrated by 

simple payback periods as low as three years. High profitability often begins with planning a 

retrofit at a time when the heating/ventilation /air-conditioning, or HVAC, system will be 

replaced. Then, replacing windows with highly insulating windows and implementing other 

energy efficiency measures allows the purchase of a smaller HVAC system, at lower capital 

and operating costs. These profitable deep energy retrofit projects achieve year-round energy 

reductions, including reductions in air conditioning demand during the ozone-season. 

Data Collection and Reporting

Maryland will work with OTC, EPA and stakeholders on an efficient and automated form 

of collecting data on energy use by large commercial buildings and making it publicly 

available. Experience gained from New York City’s approach, requiring owners of large 

commercial buildings to measure and report their energy use will be applied in Maryland. 

New York City requires owners of large non-residential and residential buildings to upload 

data into an Internet-based database tool developed by the U.S. Environmental Protection 

Agency called Energy Star Portfolio Manager that is used to track and assess energy and 

water use relative to similar buildings. Maryland will attempt to reduce the time lag between 

data collection and reporting. It is expected that energy service companies will use the data to 

market their services and offer energy-saving retrofits.

Maryland will be working with stakeholder to verify that the data is submitted by 

building owners and operators. 

Financing
- 51 -

Credit assistance plays a significant role in implementing energy efficiency programs. 

Leading initiatives and approaches will be reviewed for potential application in 

Maryland. Data collection and reporting effort coupled with property assessed clean 

energy financing and program services to municipal and commercial property owners 

throughout the state would be a significant help. These measures will ensure that energy 

efficiency and renewable energy projects help property owners in reducing energy 

consumption and save costs. 

Benchmarking 

Economical approach to benchmarking involves the utilization of Portfolio Manager. 

Maryland will apply the Portfolio Manager for benchmarking purposes. The U.S. EPA’s 

online energy benchmarking system, is a tool that enables building owners to track 

energy use in their buildings and compare a building’s energy performance against 

similar buildings. Portfolio Manager is used by building owners nationally as a tool to 

track and evaluate energy and water consumption, develop energy management goals 

over time, and identify strategic opportunities for cost savings. The U.S. Green Building 

Council references Portfolio Manager as the measurement tool to verify energy 

performance under the Leadership in Energy and Environmental Design (LEED) for 

Existing Buildings, Operations and Maintenance standard. 

Portfolio Manager energy performance is reported as a score on a scale of one (1) to one 

hundred (100) relative to similar buildings nationwide, or as an Energy Use Intensity 

(EUI) result when the data on similar buildings is not sufficient to allow for a 

comparative statistical classification. Portfolio Manager is capable of accounting for the 

impact of local weather variations, as well as for changes in key physical and operating 

characteristics of building type. From data for on-site fuel combustion, purchased 

electricity, and heating and cooling data, Portfolio Manager can calculate building 

greenhouse gas emissions such as carbon dioxide, methane, and nitrous oxide. Portfolio 

Manager also tracks energy and water use trends as compared with the costs as a versatile 

tool.

Implementation of Recommendations

1. Monitor and implement leading regional approaches and programs in the region.

2. Review the data that is scheduled for release regarding the energy footprint of 

buildings and compare to the next report of 2013 and develop cost effective program 

based on the results. 

3. Develop the method to calculate NOx emissions reduced based on electricity 

consumption using the tool within EPA's forthcoming Energy Efficiency and Renewable 

Energy in SIPs Manual. 

4. Maryland will develop examples of NOx reductions and electricity consumption to 

help make a more solid connection between energy efficiency programs and ozone SIP. 
- 52 -

5. The energy generation and utilization will be more closely and directly linked in the 

ozone transport and high energy demand day efforts in view of the compelling economics 

of reducing NOx emissions through energy efficiency programs. 

6. Energy efficiency will be used more widely based on the example of boiler MACT 

program with the additional benefit of potential SIP credits. 

7. Maryland will work in cooperation with other regional organizations such as 

NESCAUM, MARAMA, member states, EPA and technology developers on EE and SIP 

credit. 

ADDENDUM 6: Maryland’s Efforts to Quantify GHG (and other) Emission 

Reduction Benefits from EE/RE Programs 

Recent Maryland Energy Sector Analysis

Maryland Energy Trends

This is an analysis of the effectiveness of current energy policies such as the RPS and 

EmPOWER to see how much GHG emissions they would reduce. It also looks at general 

fuel switching from coal to natural gas that has happened in the PJM marketplace 

independent of specific policies. The current policies are analyzed in the Table 

1. GGRA was the original analysis. SAIC was done by a consulting group for MDE last 

year. MEA is Maryland Energy Administration analysis, and CCAN is the Chesapeake 

Climate Action Network, an environmental stakeholder.

The analysis of the impact of changing certain policies is in Table 2. BLQ and WDS are 

black liquor and wood waste, respectively. These are currently qualifying technologies in 

RPS, and this enhancement tries to measure what would happen if you remove them from 

the RPS and replace them with cleaner wind power. The Enhanced policy assumes the 

removal of these technologies from the RPS, but also increases the RPS from 18% to 

25% and increases EmPOWER (energy efficiency) from 15% to 20%. The BAU 

adjustment changes the assumptions in the GGRA business as usual forecast. Finally, the 

CCAN proposal is CCAN’s enhanced policy recommendation.
- 53 -

Table 1.Reduction Potential From Current Policies

Reduction Potential - Current Policies GGRA SAIC MEA CCAN

BAU Forecast Changes

Original 2020 BAU 58.79 58.79 58.79 58.79

Updated 2020 BAU 54.42 54.42 54.42 54.42

Original 2006 Baseline 42.18 42.18 42.18 42.18

Updated 2006 Baseline 42.74 42.74 42.74 42.74

Forecast Impact (2020 delta - 25% of 2006 delta) 4.51 4.51 4.51 4.51

Program Reductions

RGGI 17.71 8.33 0.00 0.00

EmPower 7.27 3.65 11.15 7.30

RPS 6.78 3.40 7.36 5.50

Fuel Switching 0.00 0.00 6.84 3.70

Imported Power 2.75 1.53 0.00 0.00

GHG New Source 4.84 2.31 0.00 0.00

Other 0.16 0.14 0.00 0.00

Total Independent Reductions 39.51 0.00 25.35 16.50

Combined Scenario Reductions 30.97 19.36 20.07 16.10

Net GGRA Reduction Anticipated from Energy 30.97 30.97 30.97 30.97

Forecast Impact Reductions 4.51 4.51 4.51 4.51

Combined Scenario Reductions 30.97 19.36 20.07 16.10

Gap in Reductions -4.51 7.10 6.40 10.36

Actual 2006 Emissions 42.18 42.18 42.18 42.18

Actual 2011 Emissions 37.80 37.80 37.80 38.80

Forecasted 2020 Emissions 23.45 35.06 34.36 38.32

Forecasted Reduction % 2006-2020 44.4% 16.9% 18.5% 9.1%

25% Reduction
- 54 -

Table 2. Reduction Potential From Enhanced Policies

Reduction Potential - Enhanced Policies

Exclude 

BLQ/WDS

Enhanced 

Policies

BAU 

Adjustment

CCAN 

Proposal

BAU Forecast Changes

Original 2020 BAU 58.79 58.79 58.79 58.79

Updated 2020 BAU 54.42 54.42 53.87 54.42

Original 2006 Baseline 42.18 42.18 42.18 42.18

Updated 2006 Baseline 42.74 42.74 42.74 42.74

Forecast Impact (2020 delta - 25% of 2006 delta) 4.51 4.51 5.07 4.51

Program Reductions

RGGI 0.00 0.00 0.00 0.00

EmPower 11.15 14.33 14.33 21.10

RPS 8.88 12.69 12.55 7.60

Fuel Switching 6.84 6.84 7.06 3.70

Imported Power 0.00 0.00 0.00 0.00

GHG New Source 0.00 0.00 0.00 0.00

Other 0.00 0.00 0.00 0.00

Total Independent Reductions 26.87 33.86 33.94 32.40

Combined Scenario Reductions 21.44 25.97 26.00 30.40

Net GGRA Reduction Anticipated from Energy 30.97 30.97 30.97 30.97

Forecast Impact Reductions 4.51 4.51 5.07 4.51

Combined Scenario Reductions 21.44 25.97 26.00 30.40

Gap in Reductions 5.02 0.49 -0.10 -3.94

Actual 2006 Emissions 42.18 42.18 42.18 42.18

Actual 2011 Emissions 37.80 37.80 37.80 37.80

Forecasted 2020 Emissions 32.98 28.45 27.86 24.02

Forecasted Reduction % 2006-2020 21.8% 32.5% 33.9% 43.0%
- 55 -

Notes: 

1. All the difference between the original and updated BAU is applied to the energy sector gap

2. Current Policy assumes 15% EmPower target is hit by 2020 and that PJM energy is 30% 

NG by 2020

3. Exclude BLQ/WDS removes those technologies from RPS, but otherwise leaves % RPS 

and EmPower unchanged

4. Enhanced Policy assuems 20% EmPower by 2020, 25% RPS by 2020, and no black 

liquor or wood waste allowed

5. BAU Adjustment is based on Enhanced Policy and reverts coal to its historic average 

and petroleum to 2006 baseline

ADDENDUM 7: Overview of 2015 Ozone SIP-related Multi-Pollutant Analysis Work 

For Maryland’s ozone SIP, due in 2015, MDE has already begun work to develop its 

weight-of-evidence demonstration. Starting in May 2013, MDE engaged NESCAUM, 

Towson University’s Regional Economic Studies Institute, and University of 

Maryland/College Park to assist in a multi-pollutant analytical exercise--with updated 

assumptions from the Greenhouse Gas Reduction Act Plan of 2012--in an effort to: (1) 

integrate energy efficiency into Maryland SIP planning; and (2) explore how reductions 

from energy and energy efficiency programs could be credited for SIP planning purposes. 

This effort will take approximately 18 months, consistent with other SIP planning and 

analytical exercises. 

For this effort, NESCAUM will update the NE-MARKAL model and reference case, 

update Greenhouse Gas Reduction Act Plan policy scenarios, build policy scenarios to 

incorporate ozone strategies being considered for SIP purposes and that are appropriate 

for NE-MARKAL modeling, and conduct NE-MARKAL analyses. Outputs of the NEMARKAL modeling will be pre-processed and provided to Towson University’s 

Regional Economic Studies Institute so that economic analysis can be conducted using 

the REMI model. NE-MARKAL outputs will also be pre-processed and provided to 

University of Maryland/College Park for air quality modeling, using the CMAQ model, 

can be conducted for two policy scenarios. The CMAQ outputs will be used by 

NESCAUM as inputs into the BenMAP tool to conduct health assessments. The results 

from these analytical efforts will be used to develop a multi-pollutant narrative for use in 

the ozone SIP as well as for a progress report on Greenhouse Gas Reduction Act Plan of 

2012 (also due in 2015). 

Multi-Pollutant Analysis Work Completed

 Analytical Plan

 An initial analytical plan and timeline for the multi-pollutant analysis has been 

developed. This entailed consultation and coordination with multiple 
- 56 -

Maryland agencies and other project partners. The plan outlines anticipated 

tasks and analytical approaches for the energy, air quality, economic, and 

health benefits analyses. For example, for the NE-MARKAL energy analysis, 

the plan outlines steps necessary to update and recalibrate the NE-MARKAL 

model, revisit and update or refine the characterization of specific policies 

from the last phase of assessment, and identify the base analytical runs needed 

for Ozone SIP planning purposes; for the air quality portion of the analysis, 

the plan currently identifies the CMAQ base year, the number of metascenarios to be run, the temporal bounds of the CMAQ model simulations, 

and emissions processing approach. The plan serves to delineate the overall 

project schedule, and will be updated throughout the project as MDE’s 

analytical needs become more defined.

NE-MARKAL Model Updates

The NE-MARKAL base year was updated from 2002 to 2005, and the model 

timeframe was extended from 2029 to 2053. This effort required 

extrapolating time-dependent data inputs in each sector of the model to 

conform to the new modeling timeframe. Examples of time-dependent data 

that were extrapolated in this effort included: (1) energy demands; (2) fuel 

prices; (3) fuel share constraints; and (4) technology investment costs. 

Specific updates were also made within the energy supply, power plant, 

commercial and residential, and transportation sectors of the model. 

 Reference Case Updates and Assessment of Initial Calibration

The preliminary reference case, projected to 2053, was updated, incorporating 

the core model updates. The modeled results were compared with observed 

historical trends through 2011, and with baseline forecasts through 2053 made 

by the U.S. Department of Energy’s Energy Information Administration in its 

Annual Energy Outlook. Model performance and areas of inquiry for 

completing the calibration were assessed.

 Start Developing Policy Scenarios

For purposes of this analysis, MDE is assessing the ozone co-benefits of a 

subset of Greenhouse Gas Reduction Act Plan policies. MDE ascertained the 

policies of greatest interest and then worked with NESCAUM to assess which 

policies were most appropriate to model in NE-MARKAL. Partner agencies 

are involved in determining how the policies should be defined and how best 

to characterize them for modeling. Twelve to 16 policies are currently being 

considered for the analysis.

Upcoming Multi-Pollutant Analysis Tasks and Targeted Dates for Completion

Note that targeted Dates for Completion are in parentheses.
- 57 -

 Complete Development of Policy Scenarios and Meta-scenarios (3/3/14)

 Conduct NE-MARKAL Policy Analysis Modeling (3/3/14)

 Review Economic Analytical Approach and Format NE-MARKAL Outputs 

for Economic Analysis (3/15/14)

 Conduct Emissions Processing for the Air Quality Analysis (4/1/14)

 Conduct REMI Economic Analysis (5/1/14)

 Conduct SMOKE Modeling (6/1/14)

 Conduct CMAQ Modeling for Reference and Future Cases (9/1/14)

 Assess the Potential Need for Additional Refined NE-MARKAL Modeling 

(ongoing, through 9/1/14)

 Conduct the Health Benefits Analysis (11/1/14)

 Draft the Multi-pollutant Narrative and Discuss Possible Future Analyses 

(12/1/14)

 Finalize the Multi-pollutant Report for Inclusion in the Ozone SIP (12/31/14)

Activities to Support SIP Crediting for Energy Efficiency and Renewable Energy 

Programs

The multi-pollutant analytical work being conducted from May 2013 through December 

2014 will culminate in a multi-pollutant narrative and analytical report for inclusion in 

Maryland’s ozone SIP. That report will describe and highlight the modeled results of 

implementing a suite of energy programs, i.e., the technology shifts with associated costs 

and/or savings that lead to changes in emissions, air quality, public health outcomes, and 

macro-economic indicators. It will also describe the key assumptions and caveats, 

limitations of and opportunities resulting from this integrated analytical approach, as well 

as recommended analyses that could inform future work. While this work requires 

considerable effort, it is anticipated that its results will be useful not only for ozone SIP 

development purposes, but for climate and other programmatic uses.

All of the tools that MDE is using for this analysis, including MARKAL, are tools that 

U.S. EPA has used for analysis in various contexts. Discussions to date with EPA 

indicate that the use of NE-MARKAL for characterizing energy programs within a SIP 

context will be accepted. 
- 58 -

Attachment 

OTC Energy Efficiency Status Report : Promoting Deep Energy Retrofits of Large 

Commercial Buildings To Reduce Nitrogen Oxide Emissions In the Ozone 

Transport Region

Ozone Transport Commission Energy Efficiency Workgroup

Status Report 08-15-12

Overview: In June 2011, the Ozone Transport Commission (OTC) members charged the 

OTC with evaluating the potential for energy efficiency strategies to reduce ozone levels 

in the Ozone Transport Region (OTR), and recommending an appropriate strategy or 

strategies. In September 2011, the OTC’s Stationary and Area Sources Committee 

launched the Energy Efficiency Workgroup to fulfill the OTC’s charge. The workgroup 

decided to initially focus on the ozone reduction potential from profitable ―deep energy 

retrofits‖ of commercial buildings. 

Purpose of this report: This report: 1) estimates the magnitude of NOx emission 

reductions possible in the OTR through profitable deep energy retrofits of large 

commercial buildings; and 2) lists several low-cost policy strategies that jurisdictions in 

the OTR could pursue to promote these profitable NOx reductions (including strategies 

that some jurisdictions are already pursuing).

Partly because OTC works with state air quality agencies, and energy efficiency 

strategies are typically pursued by state and local energy agencies, the strategies listed 

here are not subjected to an in depth analysis. Rather, they are presented as options which 

air divisions may discuss with their respective states’ energy divisions, for further 

evaluation and possible implementation. 

NOx Reduction Potential from Profitable Deep Energy Retrofits

Potential for profitable NOx and ozone reductions from commercial building energy 

efficiency: Commercial building deep energy retrofit projects have recently achieved 

profitable energy reductions of 38 percent to 70 percent, with profitability demonstrated 

by simple payback periods as low as three years.5 High profitability often begins with 

planning a retrofit at a time when the heating/ventilation/air-conditioning, or HVAC, 

system will be replaced. Then, replacing windows with highly insulating windows and 

implementing other energy efficiency measures allows the purchase of a smaller HVAC 

system, at lower capital and operating costs. 

These profitable deep energy retrofit projects achieve year-round energy reductions, 

including reductions in air conditioning demand during the ozone-season. Reducing air 

conditioning demand reduces electricity demand, thus reducing electric generating unit 

emissions of nitrogen oxides (NOx) - an ozone precursor. 

 

5

http://retrofitdepot.org/TrueStories (a website of the Rocky Mountain Institute).
- 59 -

Potential magnitude of NOx reductions from deep energy retrofits of large commercial 

buildings in the OTR: The spreadsheet analysis presented at the end of this paper in 

Attachment 1 shows a potential annual reduction of 36,000 tons of NOx emissions from 

deep energy retrofits of large commercial buildings in the OTR. The spreadsheet analysis 

is designed to be self-explanatory, with data and assumptions presented in the top portion 

of the analysis (along with data sources), and estimated NOx reductions in the bottom 

portion. The Excel version of the spreadsheet, which shows the formulas used in the 

bottom portion of this analysis, is available from the OTC upon request.

Policy Options to Promote NOx Reductions from Profitable Deep Energy Retrofits

To date the OTC Energy Efficiency Workgroup has become aware of the following lowcost strategies to promote NOx reductions from profitable deep energy retrofits:

Collecting data on energy use by large commercial buildings and making it publicly 

available: 

New York City has taken this approach, requiring owners of large commercial buildings 

to measure and report their energy use. 

New York City requires owners of large non-residential and residential buildings to 

―upload data into an Internet-based database tool developed by the U.S. Environmental 

Protection Agency [called Energy Star Portfolio Manager] that is used to track and assess 

energy and water use relative to similar buildings‖. New York City will make the data 

publicly available after a time lag—for example, on September 1, 2012 for nonresidential private buildings.

6

 David Bragdon, head of New York City’s Office of LongTerm Planning and Sustainability, expects that energy service companies will use the 

data to market their services and offer energy-saving retrofits.7 

New York City also requires owners of large buildings to conduct energy audits and 

―submit energy efficiency reports to the Department of Buildings that include both an 

energy audit report and a retro-commissioning report.‖8

Although cities are probably better suited than states for verifying that building owners 

submit energy use data (because each city maintains databases of properties in the city, 

for property tax and other purposes), state energy offices could assist cities in their states 

to develop the capability to collect and make available building-level energy data.

Credit assistance: Offering credit assistance could be a low-cost option for a city or 

state, depending on how the credit assistance is structured—including, for example, 

whether the city or state can borrow at a lower interest rate than it can lend for such 

projects. The New York City Energy Efficiency Corporation offers credit assistance for 

 

6

http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=NY16R&re=1&ee=1

7

―Energy Efficiency: Plenty of data, many confused landlords,‖ CNNMoney online, November 

21, 2011

8 DSIREUSA web page for New York City, cited above
- 60 -

retrofit projects.9 On June 15, 2012, Daniel P. Malloy, the Governor of Connecticut, 

signed into law a revised property-assessed clean energy statute (C-PACE) allowing the 

State’s newly formed ―Green Bank‖, the Clean Energy Finance and Investment Authority 

(CEFIA) to offer properly assessed clean energy financing and program services to 

municipal and commercial property owners throughout the State of Connecticut. 

Connecticut’s approach (C-PACE) is exciting because CEFIA will play a central role in 

developing statewide program guidelines that municipalities will agree to follow when 

joining. CEFIA is also empowered by the legislation to provide financing for projects. 

Program measures to ensure that energy efficiency and renewable energy projects help 

property owners and local governments achieve their goals of saving costs, safeguarding 

the environment, and creating jobs.6 Currently, 28 states, plus DC authorize PACE. 

PACE-enabling legislation has been adopted by 27 states (Hawaii had existing authority). 

A map of States with PACE programs can be downloaded from:

http://www.dsireusa.org/solar/solarpolicyguide/?id=26

Property-assessed clean energy (PACE) financing is one option whereby the city or state 

lends the property owner funds for the retrofit project, and the property owner pays back 

the loan through an incremental charge on the property tax bill. If the building is sold, 

the incremental charges must be paid by the building’s new owner, until the loan is paid 

off. Typically the city requires due diligence, including an energy audit, before 

approving the PACE loan. There has been a controversy over which debt obligation 

takes seniority in the event of foreclosure —the original mortgage on the property, or the 

PACE debt. For buildings that have no mortgage, however - as is the case for many 

commercial buildings - this would not be an issue.

Building ratings: State and local governments across the country are adopting policies to 

reduce energy use in commercial buildings through both required policy measures and 

voluntary campaigns. For example, the Massachusetts Department of Energy Resources 

is launching a building energy labeling program, which will be akin to the miles-pergallon ratings for cars. The program is designed to ―provide clear and actionable energy 

information about a building's potential energy performance, increase the value of good 

energy performance in the marketplace, and lead ultimately to greater uptake of 

efficiency investments.‖

7

 More information on State and Local policy development in 

the U.S. can be found at: http://www.imt.org/performance-policy/us-policies, and a 

detailed list of policies and incentive programs leveraging Portfolio Manager, EPA’s 

ENERGY STAR measurement tracking tool can be found at:

http://www.energy 

star.gov/ia/business/government/State_Local_Govts_Leveraging_ES.pdf

Efforts to adopt policies to reduce energy use in commercial buildings are taking place in 

other countries as well. For example, the Australian Government has graded buildings 

on their energy efficiency, with grades of A, B, or C. The Government will only rent 

space in buildings graded ―A.‖ One observer has noted that this grading scheme 

 

9

―Energy Efficiency: Plenty of data, many confused landlords,‖ cited above

6

http://www.ctcleanenergy.com/Home/tabid/36/Default.aspx
- 61 -

influenced both commercial tenants, many of whom sought office space with a high 

grade, and property owners, many of whom sought to improve their grade by improving 

their building’s energy efficiency.8

Recommendations

1. OTC's energy efficiency workgroup recommends a commitment to monitor the 

implementation of New York City's Green Buildings and Energy Efficiency 

program and other similar leading programs. 

2. Specifically, commitment is needed to review the data that is scheduled for this 

Fall regarding the energy footprint of buildings that report. Since this will be the 

first report, a comparison can only be made with the next report of 2013. 

3. This year’s report will help develop the method to calculate NOx emissions 

reduced based on electricity consumption using the tool within EPA's 

forthcoming Energy Efficiency and Renewable Energy in SIPs Manual. This is 

expected to be a significant number based on the estimates of the workgroup. 

4. Examples of NOx reductions and electricity consumption need to be created to 

help make a more solid connection between energy efficiency programs and 

ozone SIP.

5. The energy generation and utilization should be more closely and directly linked 

in the ozone transport and high energy demand day efforts in view of the 

compelling economics of reducing NOx emissions through energy efficiency 

programs.

6. Energy efficiency could be used more widely based on the example of boiler 

MACT program with the additional benefit of potential SIP credits.

7. OTC provides a large forum for energy efficiency initiatives and therefore 

significant NOx reductions. The pace of developments is such that the workgroup 

firmly believes that best outcomes can come about in cooperation with other 

regional organizations such as NESCAUM, MARAMA, member states, EPA and 

technology developers and independent efforts as well.

7 http://www.mass.gov/eea/energy-utilities-clean-tech/energy-efficiency/ee-for/businessinstitutions/energy-labeling-for-commercial-buildings.html

8 David Cote, Chairman and CEO of Honeywell, speaking at the Center for American 

Progess’s Rountable Discussion on Energy Efficiency Leadership, “Unlocking 

Investment in Smart and Higg-Performance Buildings,” November 17, 2011

Attachment 1
- 62 -

 Spreadsheet analysis estimating potential magnitude of NOx reductions from deep 

energy retrofits of large commercial buildings in the OTR

Attachment 2

 Number of Office Buildings in the OTR (Source: CoStar Group, Ms. Kristen Joy)

Attachment 3

 Number of Energy Efficient Buildings in the OTR

Attachment 1 - Estimating Magnitude of NOx Reductions from Deep Energy 

Retrofits in the Ozone Transport Region

Table 1 - Data and Assumptions

Data Element Value Units Source

Commercial, governmental 

and institutional building 

space in Mid-Atlantic and 

New England states in 2003 12,900,000,000 square feet 

http://www.eia.gov/emeu/cbec

s/cbecs2003/officereport/office

1.html

Percentage of U.S. 

commercial, governmental 

and institutional building 

space in large buildings (over 

50,000 square feet) in 2003 48%

http://www.eia.gov/emeu/cbec

s/cbecs2003/officereport/office

1.html

Percentage of Mid-Atlantic 

and New England 

commercial, governmental 

and institutional building 

space in large buildings (over 

50,000 square feet) in 2003 48%

Assumed to be the same 

percentage as for the U.S. as a 

whole

Median U.S. electricity usage 

in commercial, governmental 

and institutional building 

space in 2003 11.5

kilowatthours per 

square foot

http://www.eia.gov/emeu/cbec

s/cbecs2003/officereport/office

2.html

Average Mid-Atlantic and 

New England electricity 

usage in office buildings in 

2003 11.5

kilowatthours per 

square foot

Assumed to be the same as the 

*median* for the U.S. as a 

whole

Average percentage reduction 

in energy use in profitable 

"deep energy retrofits" 42%

http://retrofitdepot.org (a 

website of the Rocky 

Mountain Institute)--average 

of the 2 profitable projects: 

Empire State Building and a 

retail franchise chain
- 63 -

Note: Average emissions per kWh across the OTC states, using state-level data from "Source Energy 

and Emission Factors for Energy Use in Buildings," National Renewable Energy Laboratory, 2007, 

pp. 27-28 (http://www.nrel.gov/docs/fy07osti/38617.pdf)

Attachment 2 - Number of Office Buildings in OTR

Source: CoStar Group, Ms. Kristen Joy

 

Average percentage reduction 

in electricity consumption 

from profitable "deep energy 

retrofits" in the Ozone 

Transport Region 42%

Assumed to be the same as the 

average reduction in annual 

*energy* use for the deep 

energy retrofit projects cited 

above

Table 2 - Estimates of Potential Energy Reductions & NOx Reductions in OTR

(based on data and assumptions above)

Data Element Value Units

Commercial, governmental and institutional space in large buildings 

(over 50,000 square feet) in the Ozone Transport Region (MidAtlantic and New England states) 

6,211,111,111 square feet

Electricity usage in large OTR in commercial, governmental and 

institutional space large buildings

 

71,427,777,778 

kilowatthours per 

year

Potential reduction in electricity usage from deep energy retrofits of 

office space in large commercial, governmental and institutional 

space buildings in the Ozone Transport Region

 

29,642,527,778 

kilowatthours per 

year

Potential reduction in NOx emissions from deep energy retrofits of 

commercial, governmental and institutional space in large buildings 

in the OTR

 

35,980 tons per year

Average NOx emissions in the Ozone Transport Region from 

electricity generation/consumption 2.43E-03

pounds per 

kilowatt-hour

State

Number of 

Buildings

Number of Office 

Buildings

DC 10,767 2,368

DE 7,803 1,697

MA 61,595 11,913

MD 51,497 10,684

ME 15,720 2,761

NH 14,178 2,666

NJ 93,617 18,630

PA 105,809 23,571

RI 14,063 2,437

VA 70,580 14,055

VT 2,397 506
- 64 -

Attachment 3 - Number of Energy Efficient Buildings in OTR

State Number of Energy Star Certified 

Buildings

CT 65

DC 154

DE 31

MA 200

MD 114

ME 15

NH 66

NJ 120

NY 327

PA 248

RI 36

VA 274

VT 10
Three Case Studies That Apply and Evaluate EPA’s Roadmap to Incorporate 

Energy Efficiency and Renewable Energy in State Implementation Plans Page D-1 

Appendix D: EE/RE in SIPs Policy Issues, 

Memorandum to EPA from NESCAUM on Behalf 

of the Case Study States, March 27, 2013 
MEMORANDUM 

TO: Julie Rosenberg, U.S. EPA/OAP/CPPD 

FROM: Leah Weiss, NESCAUM 

DATE: March 27, 2013 

RE: Issues Arising from Case Studies Applying and Evaluating EPA’s Roadmap 

to Incorporate Energy Efficiency/Renewable Energy in State Implementation 

Plans 

NESCAUM submits this memorandum on behalf of the states that are developing case studies 

using EPA’s Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and 

Programs into State and Tribal Implementation Plans (Roadmap). As detailed in our project 

progress memorandum of January 9, 2013, Massachusetts, New York, and Maryland are 

assessing how they might include energy efficiency and/or renewable energy (EE/RE) programs 

in State Implementation Plans (SIPs) using one of the three new pathways presented in the 

Roadmap.1

 NESCAUM has been leading the project since October, 2012, with additional 

expertise provided by the Regulatory Assistance Project. 

Incorporating EE/RE into SIPs is a high priority for EPA and states. The purpose of this 

memorandum is to formally articulate concerns and issues that have arisen as the participating 

states begin using the Roadmap, as well as some recommended solutions. The states have 

devoted -- and will continue to devote -- significant resources to “road-testing” the Roadmap. 

They seek assurance that EPA will work to expeditiously resolve the issues raised in this 

memorandum. This would provide a degree of certainty for states as they develop SIPs, as well 

as regional consistency as EPA Regional Offices begin evaluating EE/RE-related SIP submittals. 

It could also foster greater interest for states not currently considering including EE/RE programs 

in their SIPs. 

We are pleased that dialogue with EPA on each of these issues has begun. We understand that 

EPA is also working to identify its own list of potential policy issues and develop responses. We 

appreciate EPA’s coordinated approach for this project, and look forward to continuing 

discussions in the coming months with the Office of Atmospheric Programs, the Office of Air 

Quality Planning and Standards, and the Regional Offices to facilitate in-depth exploration and 

resolution of these issues. 

1. Clarify EPA’s expectations regarding the location of emissions reductions associated 

with EE/RE programs. 

There is concern that EPA may be looking for greater specificity about the location of emissions 

reductions for EE programs than it does for mobile or area source control measures. We 

 

1

The fourth pathway, innovative and emerging measures, was the basis for EPA’s guidance on EE in SIPS, issued 

in 2004. 
2 

understand that this may be an appropriate analytical starting point in some cases, given 

historical reliance on electric sector modeling that specifies individual plant emissions. However, 

it is clearly not appropriate in all cases. The states recommend that EPA examine and adopt an 

approach for addressing the location of EE/RE program emissions reductions that is similar to 

how area and mobile source programs are treated within SIPs. 

 

EPA does not expect states to specify the exact geographic location of emissions reductions for 

area or mobile source control measures in SIPs, yet states are responsible for achieving those 

reductions. The states involved in the Roadmap project intend to demonstrate that analyses using 

techniques that generalize the location of emissions reductions, similar to those that have been 

used for mobile source modeling and area source program assessments, are a viable approach for 

EE/RE programs. This approach balances the need for accountability and flexibility. States are 

well aware that emissions reductions from EE/RE must be real, permanent, and enforceable, and 

must occur in locations that contribute to air quality improvements consistent with demonstrating 

attainment of the NAAQS. 

Attributing energy savings and emissions reductions to specific EGUs, as the Roadmap appears 

to require, is not achievable with any degree of certainty aside from a comprehensive 

retrospective analysis using an electricity dispatch model. Attributing energy savings and 

emissions reductions to specific locations or EGUs is even more challenging when considering a 

portfolio of EE/RE programs implemented across a locality, state, or region. There is also 

concern that a location-based approach could preclude states and EPA from obtaining the 

significant benefits of regional EE/RE programs being implemented or considered in some areas. 

2. Evaluate and then provide guidance on acceptable applications of MARKAL and other 

energy models for assessing the benefits of EE/RE programs. 

With the advent of the Roadmap, EPA will need to consider the appropriateness of various 

analytical tools to assess energy programs within the SIP context. The states would like EPA to 

begin reviewing the Market Allocation (MARKAL) energy model now in order to decide how it 

may be used as an analytical tool for assessing the benefits of EE/RE programs within the SIP 

context. 

Some states have been working with NE-MARKAL, the northeast version of MARKAL, to 

quantify potential avoided emissions associated with a suite of EE programs in proof-of-concept 

exercises, and they have used the results within a broad weight-of-evidence context. For the 

Roadmap project, the New York State Department of Conservation (NYSDEC) is generating 

results for statewide EE programs using a calibration of the NE-MARKAL model from a study 

completed in 2011. NYSDEC does not currently have an ozone attainment SIP requirement, and 

is thus creating hypothetical SIP documentation for this project. Maryland Department of 

Environment (MDE) is using NE-MARKAL to quantify potential avoided emissions from a suite 

of state EE/RE programs. MDE has NE-MARKAL runs from a 2012 analysis that will be 
3 

available for examination by EPA. It also plans to update the model and generate additional runs 

for its 2015 Ozone SIP. 

The states and EPA regional offices will need guidance on appropriate uses of MARKAL and 

other analytical tools (e.g., for screening, assessing, and/or quantifying, EE/RE programs). With 

ozone SIPs due in 2015, it would be helpful for states to have this guidance in the near future. In 

addition, EPA should explore how it could build upon the current capabilities of MARKAL and 

similar analytical tools to make them more accessible and/or tailored for representing and/or 

quantifying state and regional EE/RE programs for SIP purposes. 

3. Evaluate an expanded weight-of evidence approach. 

The states would like to explore with EPA whether employing an expanded weight-of-evidence 

approach could allow for SIP crediting under certain conditions, and whether this approach raises 

any policy concerns within the Agency. 

For the Roadmap project, MDE is using an expanded weight-of-evidence approach for assessing 

and presenting the benefits of EE/RE programs in its SIP. This approach is based on the Ozone 

Transport Commission’s expanded weight-of-evidence approach, outlined in a June 17, 2011 

letter to EPA.2 It builds considerably upon the Roadmap’s weight-of-evidence pathway by using 

traditional air quality modeling coupled with less traditional assessment tools. It also takes a 

multi-pollutant approach that assesses trade-offs across sectors. The Roadmap specifically states 

that the weight-of-evidence pathway does not offer SIP credit, but an expanded approach to 

weight-of-evidence may achieve supportable, quantifiable results. The states request that EPA 

assess options for and provide guidance on an expanded approach. 

4. Clarify the purposes and limitations of EPA’s Power Plant Emissions Calculator and 

Hourly Marginal Emissions Tools and the level of specificity required for SIP purposes. 

The states would like EPA to clearly state in the Roadmap and accompanying materials that the 

Power Plant Emissions Calculator (P-PEC) and the Hourly Marginal Emissions Tool are for 

planning or screening purposes and not for quantifying programs within a SIP. 

The tools seem to imply a level of precision about the location and timing of emissions 

reductions (i.e., at the EGU and hourly levels) that is not achievable. Moreover, the assumption 

built into P-PEC that that emissions reductions will always occur within the Emissions & 

Generation Resource Integrated Database (eGRID) subregion where the EE/RE program is 

located is problematic, particularly for states that import much of their electricity. In addition, the 

tools require updates and inputs that might not be possible for states to provide. 

 

2

Letter to Chet Wayland and Scott Mathias, EPA/OAQPS, from the Ozone Transport Commission. June 17, 2011. 

See: http://www.otcair.org/upload/Documents/Correspondence/OTC%20Expanded%20Weight-ofEvidence%20Letter%20and%20Recommendation.pdf
4 

For the P-PEC, the documentation states that, without modification by states, the tool is best used 

for retrospective analysis of EE and solar programs. States would need to make substantial 

updates to use the tool for forecasting emissions reductions of EE and solar programs. States 

would need to update the tool’s eGRID emission factors (which were last updated in 2012 to 

include 2009 emissions data), capacity factors, and list of currently operating power plants. Such 

an effort may make the tool inaccessible to states with limited resources, access to data, and 

capacity for making such adjustments. We recommend that EPA explore how updates could be 

made without the burden falling solely on states. 

For the Hourly Marginal Emissions Tool, it appears that states must have an understanding of 

how an EE/RE program is anticipated to affect hourly energy demand. This level of detail would 

be difficult for many states to achieve with limited resources and capacity, especially when 

considering portfolios of EE/RE programs that would reduce load at different times of day and in 

different seasons. We recommend that EPA provide a clearinghouse or reference manual of EE 

programs and their corresponding estimated impacts on hourly load so that all states could use 

this tool. 

We are concerned that the level of specificity suggested by the Hourly Marginal Emissions Tool 

could mean that EPA is expecting from states hourly information about energy savings and 

emissions reductions for SIP quantification purposes. Such a level of specificity exceeds the 

requirements for other programs, such as mobile or area source control measures in the SIP 

context. We hope this is not the case, and would like clarification on this. We also recommend 

that EPA build an application within the tool that provides data at a more SIP-appropriate (e.g., 

seasonal) level. 

5. Provide states with information on the magnitude of EE/RE needed to achieve certain 

levels of reductions. 

The states urge EPA to educate states about the magnitude of EE/RE needed to achieve 

meaningful emissions reductions. This would include the environmental and economic benefits 

of a portfolio approach to EE (i.e., a suite of programs) and technical information on energy 

savings associated with sample EE measures. This would also include the benefits of thermal 

efficiency programs that target reductions of on-site natural gas and oil use. Accounting for the 

energy savings and emissions benefits associated with a suite of programs that reduce electricity 

and heating fuels can be complex, and thus guidance on accounting for a portfolio approach that 

includes acceptable methods would be helpful in fostering their inclusion in SIPs, as appropriate.