GDS Associates, Inc. • 1181 Elm Street • Suite 205 • Manchester, NH 03101 • www.gdsassociates.com

Marietta, GA • Austin, TX • Auburn, AL • Manchester, NH • Madison, WI • Indianapolis, IN • Augusta, ME 

Additional Opportunities for 

Energy Efficiency in New Hampshire 

Final Report – January 2009 

Prepared for the 

New Hampshire Public Utilities Commission 

Prepared and Submitted by: 

In partnership with 

RLW Analytics and Research Into Action 

With Telephone Survey Support Provided by 

RKM Research and Communication 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. Page i 

ACKNOWLEDGEMENTS 

This report was prepared for the New Hampshire Public Utilities Commission (NHPUC) 

by GDS Associates, Inc. with substantial data collection and analysis assistance 

provided by RLW Analytics, Research Into Action, and RKM Research and 

Communications (collectively, the GDS Team). The GDS Team would like to 

acknowledge the many helpful data sources and the technical input provided by the 

NHPUC, the New Hampshire electric and gas utility sponsors and the Office of the 

Consumer Advocate. GDS would like to give special recognition to Tom Frantz, Anne 

Ross and Commissioner Clifton Below all of the NHPUC, and Gil Gelineau and Tom 

Belair (PSNH), Derek Buchler (Northern Utilities), Lisa Glover (Unitil), David Jacobson 

(National Grid), Carol Woods (NHEC), Stephen Eckberg (NH OCA), and Tom Rooney 

and Eric Readdy of TRC Companies, Inc., all of whom were instrumental in providing 

insights and information and reviewing detailed plans, data collection instruments and 

inputs and results from analyses developed by the GDS Team including electric and gas 

load forecasts, energy efficiency measures, costs, energy savings, useful lives, 

saturation levels and remaining potential estimates. 

This report provides valuable and up-to-date electric and natural gas (and associated 

propane and oil) energy efficiency potential savings information for New Hampshire’s 

regulators and utility decision-makers. It will also be useful to electric and gas energy 

efficiency program designers and implementers and for others who may need a template 

for their own energy efficiency potential studies. This report includes a thorough and upto-date assessment of the impacts that energy efficiency measures and programs can 

have on electricity and gas, propane and oil use in New Hampshire. Clearly there 

remains a significant amount of cost effective energy savings potential to be tapped 

within the state. 

NOTICE 

This report was prepared by GDS Associates, Inc., in the course of performing work 

contracted for and sponsored by the New Hampshire Public Utilities Commission, with 

review participation by National Grid (electric and gas), the New Hampshire Electric 

Cooperative, Northern Utilities, Public Service Company of New Hampshire, and Unitil 

Energy Services (hereinafter the “Sponsors”). The opinions expressed in this report do 

not necessarily reflect those of the Sponsors or the State of New Hampshire, and 

reference to any specific product, service, process, or method does not constitute an 

implied or expressed recommendation or endorsement of it. Further, the Sponsors, the 

State of New Hampshire, and the contractor make no warranties or representations, 

expressed or implied, as to the fitness for particular purpose or merchantability of any 

product, apparatus, or service, or the usefulness, completeness, or accuracy of any 

processes, methods, or other information contained, described, disclosed, or referred to 

in this report. The Sponsors, the State of New Hampshire, and the contractor make no 

representation that the use of any product, apparatus, process, method, or other 

information will not infringe privately owned rights and will assume no liability for any 

loss, injury, or damage resulting from, or occurring in connection with, the use of 

information contained, described, disclosed, or referred to in this report. 

Scott M. Albert, Principal & Northeast Region Manager 

GDS Associates, Inc., January 2009 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. Page ii 

Table of Contents 

Page Number

Section 1: Executive Summary .................................................................................... 1

1.1 Study Scope .......................................................................................................................... 1

1.2 Results Overview .................................................................................................................. 5

1.3 Potentially Obtainable Scenario ........................................................................................ 17

1.4 Implementation Costs ........................................................................................................ 18

1.5 Market-Driven vs. Retrofit .................................................................................................. 18

1.6 Customer Participation and Barriers ................................................................................ 19

1.7. Past/Current Program Capture and Recommendations .................................................. 20

1.8 Structure of this Report ...................................................................................................... 22 

Section 2: Characterization of Customer Base, Electric and Natural Gas 

Usage, and Load Forecasts for the State of New Hampshire ............... 23

2.1 New Hampshire Geographic and Demographic Characteristics .................................... 23

2.2 New Hampshire Economic and Demographic Characteristics ....................................... 25

2.3 Forecasted Electricity and Natural Gas Sales in New Hampshire .................................. 28 

Section 3: Overall Project Implementation Approach ............................................. 34

3.1 Energy Using Equipment Saturations and Efficiency Penetrations Analysis ............... 34

3.2 Measures List Development .............................................................................................. 44

3.3 Customer Program Participation Rates and Barriers ...................................................... 47

3.4 Forecast Model of State Electricity and Natural Gas Consumption and Peak Demand 57

3.5 Estimates of 10-Year Technical, Maximum Achievable, Maximum Achievable Cost 

Effective Potentials and Potentially Obtainable Scenario ............................................... 58

3.6 Assessment of Past and Current Program Capture and Recommendations ................ 65 

Section 4: Residential Sector Energy Efficiency Potential ..................................... 66

4.1 Residential Sector Savings Methodology Overview ........................................................ 67

4.2 Residential Sector – Energy Efficiency Potential Results ............................................... 69 

Section 5: Commercial Sector Energy Efficiency Potential .................................... 87

5.1 Commercial Sector Savings Methodology Overview ...................................................... 88

5.3 Commercial Sector End-Use Breakdowns ....................................................................... 92

5.4 Commercial Sector – Energy Efficiency Potential Results ............................................. 92 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. Page iii 

Section 6: Industrial Sector Energy Efficiency Potential ...................................... 107

6.1 Industrial Sector Savings Methodology Overview ......................................................... 108

6.2 Industrial Sector Segmentation ....................................................................................... 110

6.3 Industrial Sector End-Use Breakdowns .......................................................................... 111

6.4 Industrial Sector – Energy Efficiency Potential Results ................................................ 113 

Section 7: Primary Data Collection Highlights ....................................................... 115

7.1 Data Summary and Analysis ............................................................................................ 116

7.2 Application of Survey Data .............................................................................................. 119

7.3 Summary and Recommendations ................................................................................... 125 

Section 8: Past Program Capture and Recommendations ................................... 127

8.1 Electric Utility Energy Efficiency Programs ................................................................... 128

8.2 Gas Efficiency Programs ................................................................................................. 132

8.3 Summary and Recommendations ................................................................................... 135

List of Appendices 

Appendix A – Residential Sector Telephone Survey 

Appendix B – Small Commercial/Industrial Sector Telephone Survey 

Appendix C – On-Site Data Collection Instrument for Larger Commercial and Industrial Sector 

Appendix D – PUC advance letter and GDS Team’s recruiting script 

Appendix E – Residential electric and non-electric measures, assumptions and sources 

Appendix F – Commercial electric and non-electric measures, assumptions and sources 

Appendix G – Industrial electric and non-electric measures, assumptions and sources 

Appendix H – Benefit/Cost Model Key Inputs and Assumptions 

Appendix I – Good/Better/Best Measures and Cost Scenarios 

Appendix J – Phone Surveys and Site Visit Demographics 

Appendix K – Residential Sector Base and Remaining Factors Derivations 

Appendix L – Commercial Sector Base and Remaining Factors Derivations 

Appendix M – Industrial Sector Factors Derivations 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. Page iv 

List of Tables 

Page Number

Table 1. Summary of Energy Savings Potentials by 2018 – Combined Electric and Non-Electric Measures 7

Table 2. Summary of Energy Savings Potentials by 2018 – Electric 8

Table 3. Summary of Energy Savings Potentials by 2018 – Non-Electric 9

Table 4. Summary of Energy Savings Potentials by 2018 – Natural Gas 10

Table 5. Additional Energy Efficiency Opportunities Potential by 2018 - Breakdown by Utility – Electric 16

Table 6. Additional Energy Efficiency Opportunities Potential by 2018 - Breakdown by Utility – Nat Gas 17

Table 7. Cumulative Annual Program Savings as Percent of 2008 Sales: 2002-2008 – Electric 20

Table 8. Cumulative Annual Program Savings as Percent of 2008 Sales: 2003-2008 – Natural Gas 20

Table 9. Total Customer Counts – Residential, Low Income NH Electric and Natural Gas Utilities 25

Table 10. Total Customer Counts – Small Non-Residential NH Electric and Gas Utilities 26

Table 11. Customer Count of Large C&I Population Summary (Number of Accounts) 27

Table 12. Electric Energy Consumption of Large C&I Population Summary (kWh - 2007) 27

Table 13. Residential Sector Building Types and Energy Using Equipment 35

Table 14. Commercial Sector Building Types and Energy Using Equipment 35

Table 15. Industrial Sector Business Types and Energy Using Equipment 36

Table 16. Sample Quotas - Residential NH Electric and Gas Utilities 38

Table 17. Sample Quotas – Small C/I NH Electric and Gas 38

Table 18. Disposition of Residential Survey 38

Table 19. Disposition of Small Commercial/Industrial Survey 39

Table 20. Large C&I Population Summary (Accounts) 40

Table 21. Large C&I Population Summary (kWh) 40

Table 22. Large C&I Accounts by Sponsor and by Commercial versus Industrial 41

Table 23. Large C&I Sample Design 42

Table 24. Measure End Uses and Number of Measures Per End Use – Residential 45

Table 25. Measure End Uses and Number of Measures Per End Use - Commercial 45

Table 26. Measure End Uses and Number of Measures Per End Use - Industrial 45

Table 27. Measure End Uses and Number of Measures Per End Use - Residential 46

Table 28. Measure End Uses and Number of Measures Per End Use - Commercial 46

Table 29. Measure End Uses and Number of Measures Per End Use - Industrial 46

Table 30. Attention Paid to Controlling Household Energy Costs 47

Table 31. Likelihood of Purchasing Energy Efficient Equipment 48

Table 32. Primary Reasons for Not Purchasing Efficient Equipment/Making Efficiency Improvements 48

Table 33. Awareness of Utility’s Energy Efficiency Programs 49

Table 34. Participation in Utility’s Energy Efficiency Programs 49

Table 35. Possible Reasons for Not Participating in an Energy Efficiency Program 50

Table 36. Respondent Characteristics Summary 52

Table 37. Awareness of Existing Energy Efficiency Programs and Incentives 53

Table 38. Participation in Utility’s Energy Efficiency Programs 54

Table 39. Attention Paid to Controlling Company Energy Costs – Small/Large Respondents Combined 55
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. Page v 

Table 40. Likelihood of Purchasing Energy Efficient Equipment 56

Table 41. Primary Reasons for Not Purchasing Equipment/Making Improvements – Small/Large 57

Table 42. Summary of Residential Electric Energy Efficiency Savings Potential 66

Table 43. Summary of Residential Non-Electric Energy Efficiency Savings Potential 66

Table 44. Residential Electric Energy Efficiency Savings Potential by Measure 71

Table 45. Residential Non-Electric Energy Efficiency Savings Potential by Measure 73

Table 46. Summary of Commercial Sector Electric Energy Efficiency Savings Potential 87

Table 47. Summary of Commercial Non-Electric Energy Efficiency Savings Potential 88

Table 48. Commercial Sector Segmentation by Industry Type - Electric 91

Table 49. Commercial Sector Segmentation by Industry Type – Non-Electric 91

Table 50. Commercial Sector End Use Breakdowns Allocation Table – Electric 92

Table 51. Commercial Sector End Use Breakdowns Allocation Table – Non-Electric 92

Table 52. Commercial Electric Savings Potential by Measure – Existing Buildings 95

Table 53. Commercial Electric Savings Potential by Measure – New Construction 97

Table 54. Commercial Non-Electric Savings Potential by Measure – Existing Construction 99

Table 55. Commercial Non-Electric Savings Potential by Measure – New Construction 100

Table 56. Summary of Industrial Sector Electric Energy Efficiency Savings Potential 107

Table 57. Summary of Industrial Sector Non-Electric Energy Efficiency Savings Potential Error! Bookmark not defined.

Table 58. Industrial Sector Segmentation by Industry Type - Electric 110

Table 59. Industrial Sector Segmentation by Industry Type – Non-Electric 110

Table 60. Industrial Sector End Use Breakdowns by Industry Type – Electric 112

Table 61. Industrial Sector End Use Breakdowns by Industry Type – Non-Electric 112

Table 62. Number of Commercial and Industrial Facilities Surveyed 117

Table 63. Small Commercial vs. Large Commercial Surveys by Building Type 117

Table 64. Small Industrial vs. Large Industrial Surveys by Building Type 118

Table 65. Industrial Electric End-Uses Informed by Primary Data 124

Table 66. Industrial Non-Electric End-Uses Informed by Primary Data 125

Table 67. Energy Efficiency Program Savings as Percent of 2008 Sales: 2002-2008 – Electric Utilities 128

Table 68. Energy Efficiency Program Savings as Percent of 2008 Sales: 2003-2008 – Nat Gas Utilities 128

Table 69. New Hampshire Electric Utility Core and Additional Programs 129

Table 70. Customer Served Through Utility HES and HEA Programs: 2005-2008 129

Table 71. Percent of Customers Aware of Utility Efficiency Programs 130

Table 72. Residential Measures Not Included in Current Programs 131

Table 73. Commercial/Industrial Measures Not Included in Current Programs 132

Table 74. Gas Efficiency Programs Evaluated 132

Table 75. Customer Served Through Natural Gas Utility Programs: 2005-2008 134

Table 76. Measures Not Included in Current Natural Gas Efficiency Programs 135
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. Page vi 

List of Figures 

Page Number

Figure 1. Residential Electric Energy Efficiency Maximum Achievable Cost Effective – by End Use 11

Figure 2. Residential Non-Electric Efficiency Maximum Achievable Cost Effective – by End Use 12

Figure 3. Max Achievable Cost Effective Electric Savings by End Use for Commercial Existing Buildings 13

Figure 4. Max Achievable Cost Effective Electric Savings by End Use for Commercial New Construction 13

Figure 5. Max Achievable Cost Effective Non-Electric Savings by End Use -Commercial Existing Buildings 14

Figure 6. Max Achievable Cost Effective Non-Electric Savings by End Use -Commercial New Construction 14

Figure 7. Max Achievable Cost Effective Electric Savings by End Use for NH Industrial Sector 15

Figure 8. Max Achievable Cost Effective Non-Electric Savings by End Use for NH Industrial Sector 15

Figure 9. New Hampshire Map 24

Figure 10. Forecasted Electric GWH Sales Total (2008 – 2018) - from Utility Data vs. ISO-NE Projections 29

Figure 11. Forecasted Electric GWH Sales By Sector (2008 – 2018) 29

Figure 12. Forecasted Electric Demand (MW) Total 2008 – 2018 - Utility Data vs. ISO-NE Projections 30

Figure 13. Forecasted Electric Demand (MW) By Sector 2008 – 2018 31

Figure 14. Forecasted Natural Gas MMBTu Sales Total (2008 – 2018) - Utility Projections vs. EIA Data 32

Figure 15. Forecasted Natural Gas MMBTu Sales By Sector (2008 – 2018) 32

Figure 16. Venn Diagram of the Stages of Energy Savings Potential 60

Figure 17. Residential Sector Savings Methodology – Bottom Up Approach 67

Figure 18. Residential Electric Savings Potential Results Comparison 75

Figure 19. Residential Non-Electric Savings Potential Results Comparison 75

Figure 20. Residential Max. Achievable Cost Effective Electric Savings Potential by End Use 76

Figure 21. Residential Max Achievable Cost Effective Non- Electric Savings Potential by End Use 77

Figure 22. Residential Electric Savings Potential by End Use (with kWh values) 78

Figure 23. Residential Non-Electric Savings Potential by End Use (with MMBTu values) 79

Figure 24. Residential Electric Energy Efficiency Supply (< $1.10/kWh) Curve for NH – Tech Potential 80

Figure 25. Residential Electric Energy Efficiency Supply (< $0.10/kWh) Curve for NH – Tech Potential 81

Figure 26. Residential Electric Energy Efficiency Supply (< $1.10/kWh) Curve for NH – Max Achievable 81

Figure 27. Residential Electric Energy Efficiency Supply (< $0.10/kWh) Curve for NH – Max Achievable 82

Figure 28. Residential Electric Energy Efficiency Supply (< $1.10/kWh) Curve for NH – M.A.C.E 82

Figure 29. Residential Electric Energy Efficiency Supply (< $0.10/kWh) Curve for NH – M.A.C.E. 83

Figure 30. Residential Non-Electric Energy Efficiency Supply (< $10/MMBTu) Curve for NH – Technical 83

Figure 31. Residential Non-Electric Energy Efficiency Supply (< $5/MMBTu) Curve for NH – Technical 84

Figure 32. Residential Non-Electric Efficiency Supply (< $10/MMBTu) Curve for NH – Max Achievable 84

Figure 33. Residential Non-Electric Efficiency Supply (< $5/MMBTu) Curve for NH – Max Achievable 85

Figure 34. Residential Non-Electric Energy Efficiency Supply (< $10/MMBTu) Curve for NH – M.A.C.E 85
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. Page vii 

Figure 35. Residential Non-Electric Energy Efficiency Supply (< $5/MMBTu) Curve for NH – M.A.C.E. 86

Figure 36. Commercial Sector Savings Methodology – Top Down Approach 88

Figure 37. Commercial Sector Segmentation by Industry Type - Electric 91

Figure 38. Commercial Sector Segmentation by Industry Type – Non-Electric 92

Figure 39. Commercial Electric Max. Achievable Cost Effective Savings By End Use – Existing 101

Figure 40. Commercial Electric Max. Achievable Cost Effective Savings By End Use – New 101

Figure 41. Commercial Max. Achievable Cost Effective Non-Electric Savings By End Use – Existing 102

Figure 42. Commercial Max. Achievable Cost Effective Non-Electric Savings By End Use – New 102

Figure 43. Commercial Electric Supply Curve: Existing Buildings – Technical Potential Scenario 103

Figure 44. Commercial Electric Supply Curve: Existing Buildings – Max. Maximum Achievable 103

Figure 45. Commercial Electric Supply Curve: Existing Buildings – Max. Achievable Cost Effective 103

Figure 46. Commercial Electric Supply Curve: New Buildings – Technical Potential Scenario 104

Figure 47. Commercial Electric Supply Curve: New Buildings – Max. Achievable 104

Figure 48. Commercial Electric Supply Curve: New Buildings – Max. Achievable Cost Effective 104

Figure 49. Commercial Non-Electric Supply Curve: Existing Buildings – Technical Potential 105

Figure 50. Commercial Non-Electric Supply Curve: Existing Buildings – Max. Achievable 105

Figure 51. Commercial Non-Electric Supply Curve: Existing Buildings – Max. Achievable Cost Effective 105

Figure 52. Commercial Non-Electric Supply Curve: New Buildings – Technical Potential 106

Figure 53. Commercial Non-Electric Supply Curve: New Buildings – Max. Achievable 106

Figure 54. Commercial Non-Electric Supply Curve: New Buildings – Max. Achievable Cost Effective 106

Figure 55. Industrial Sector Savings Methodology – Top Down Approach 108

Figure 56. Industrial Max. Achievable Cost Effective Electric Savings by End Use 113

Figure 57. Industrial Max. Achievable Cost Effective Non-Electric Savings by End Use 113

Figure 58. Industrial Max. Achievable Cost Effective Electric Savings by Building Type 114

Figure 59. Industrial Max. Achievable Cost Effective Non-Electric Savings by Building Type 114
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 1 

Section 1: Executive Summary 

This study presents results from an evaluation of additional opportunities for energy efficiency in 

New Hampshire. Estimates of technical potential, maximum achievable potential, and 

maximum achievable cost effective potential by the year 2018 (a 10-year period) are provided 

for electricity, natural gas and related propane and fuel oil savings at the state level and for each 

of the four New Hampshire retail electricity providers and two natural gas distribution 

companies. Results from a potentially obtainable savings scenario are also presented to 

estimate that portion of the cost effective potential that might be achievable after consideration 

of customer behavior. Finally, estimates are presented of the installed costs required to achieve 

resulting savings for each scenario (excluding costs for marketing, program design and 

administration) 

All results were developed using customized residential, commercial and industrial sector-level 

energy efficiency potential assessment models and New Hampshire Public Utilities Commission 

(NHPUC)-specified cost-effectiveness criteria1

 including the region’s most recent avoided 

energy cost projections.2

 To help inform these models, actual electric and gas utility customer 

information was collected through a combination of telephone surveys with residential and small 

commercial/industrial customers and site visits at larger commercial and industrial facilities. 

Work was conducted by GDS Associates, Inc. with important input and assistance provided by 

RLW Analytics, Research Into Action and RKM Research and Communications (the GDS 

Team). 

Technical potential studies need to be understood and viewed as a highly theoretical 

construct/tool – therefore, the data used for this report was based on the best data available at 

the time the models were run – when better data was identified, it was used where possible, but 

given the demands and limits of time for this project, it is possible that some sources were 

overlooked. 

1.1 Study Scope 

The objective of this study was to evaluate additional opportunities for energy efficiency in New 

Hampshire to provide insights for continued electric and gas utility program filings and 

implementation plans and to inform expanded planning for energy efficiency programs that may 

rise from New Hampshire’s participation in the Regional Greenhouse Gas Initiative and the 

recommendations of the NH Climate Change Policy Task Force. Following is a listing and a 

brief overview of the approach undertaken to complete each of the major tasks required for this 

study effort: 

 

1

 The NHPUC’s total resource cost effectiveness test (TRC) derives from the 7/6/99 report from the NH Energy 

Efficiency Working Group (pp. 14-18) in DR 96-150, available at: www.puc.nh.gov/Electric/96-150%20%20NH 

%20Energy%20Efficiency%20Working%20Group%20Final%20Report%20(1999).pdf, and was modified by 

Attachment C of the 2008 Core Energy Efficiency filing approved by Order No. 24,815 in DE 07-106 that provided 

that “[t]he use of the 15% adder to represent environmental and other benefits as recommended by the [NHEEWG] 

...was discontinued because the 2007 AESC avoided costs include market-based price proxies for power plant 

emission of NOx, SO2, Mercury and CO2.” 

2 Avoided Energy Supply Costs in New England: 2007 Final Report, August 10, 2007, prepared by Synapse Energy 

Economics, Inc., available at: www.synapse-energy.com/Downloads/SynapseReport.2007-08.AESC.AvoidedEnergy-Supply-Costs-2007.07-019.pdf. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 2 

Analyze current saturations of energy using equipment and penetrations of energy 

efficiency equipment and practices in each end-use category. This task was completed 

through analysis of a combination of primary and secondary data sources including carefully 

designed questions and a statistically valid sample of telephone surveys and site visits. 

Produce an up-to-date list of currently available and soon to be commercially available 

technologies which may play a part in future efficiency programs – This task was based 

initially on existing GDS databases of sector-specific electricity and natural gas end-use 

technologies and efficiency measures. It was extensively supplemented to include other 

technology areas of interest to the New Hampshire Public Utilities Commission, the New 

Hampshire Office of the Consumer Advocate, and the four electric and two natural gas utilities 

supporting this project. 

Estimate customer participation rates/levels by program, based on different 

payback/incentive levels and define/analyze significant barriers that customers face 

when investing in additional energy efficiency – This task was based on results from the 

GDS Team’s phone surveys and site visits. Where insufficient customer-specific data was 

available, these estimates were informed through the project sponsors’ and Team’s combined 

existing and extensive knowledge of not only NH’s current electric/gas utility programs, but also 

best practices and barriers associated with programs being implemented elsewhere in the 

region and throughout the country. 

Develop, by sector, a simplified end-use model of state electricity and natural gas 

consumption and peak demand – This task was completed using data provided directly by 

each participating New Hampshire electric and gas utility. Results were assessed against 

forecasts published through the New England Power Pool (NEPOOL) and ISO-New England, 

Inc. to ensure reasonableness. 

Estimate, state-wide and for each of the four New Hampshire retail electricity providers 

and two natural gas distribution companies, the technical, maximum achievable, 

maximum achievable cost effective potential, and a potentially obtainable scenario, for 

electricity, natural gas and related propane and fuel oil savings over the next 10-year 

period, and the budgets (where appropriate) required to achieve that potential – These 

activities were based on the GDS Team’s existing sector-level supply curve and potential 

analysis models, NHPUC cost-effectiveness criteria/methodologies and associated up-to-date 

assumptions3

 including the region’s current avoided energy cost projections, elements of which 

were already in hand. Wherever possible, these models were customized based on state utilityspecific data and the saturation and penetration survey results obtained through this project’s 

primary data collection (telephone survey and site visit) activities. All results were analyzed and 

compared for reasonableness against overall state consumption and consideration of past New 

Hampshire utility energy efficiency program participation. 

 

3

 The measure specific savings values used to develop the following estimates of technical potential vary 

considerably in the level of certainty. Some measures. such as commercial lighting. have a long history of 

implementation and have fairly well documented costs and savings while some measures which also show large 

potential, such as retro-commissioning, have had little large scale implementation to date and estimates of their 

savings and cost effectiveness are based on a limited number of real world installations. Other high potential 

measures, such as floating head pressure controls have tended to work well in the short term but are often overridden 

by on-site maintenance personnel who are not comfortable with running their systems at lower pressures. It is 

important for anyone using this study to set actual program budget and savings targets to further refine the less 

certain estimates before starting large scale implementation of such measures. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 3 

Evaluate extent to which past and current energy efficiency programs have achieved 

energy savings to date, provide sensitivity analysis of realized energy savings based on 

different resource levels (including absence of current SBC-funded model), and 

recommend modifications to program and measure offerings that would increase the 

likelihood of achieving identified potential – These activities were based on a combination of 

factual data comparisons, analysis of survey results associated with end-use customer sector 

barriers identification, the collective GDS Team’s experience with looking at programs from a 

logic-modeling perspective, and the GDS Team’s extensive knowledge of other local, regional 

and national programs and best practices.4

 Focus of these evaluations and sensitivities were at 

the statewide level (vs. utility-specific). 

More information on each of these items is presented in the methodologies and subsequent 

sections of this report. 

The definitions used in this study for energy efficiency potential estimates are as follows: 

• Technical potential is defined in this study as the complete and immediate penetration 

of all measures analyzed in applications where they were deemed technically feasible 

from an engineering perspective. For the residential sector, two technical potential 

scenarios were developed: a technical potential (best) scenario, where “best” options are 

assumed to be installed in situations where “good/better/best” options exist; and a 

technical potential (traditional) scenario, where “good/better/best” options are allocated 

for model installation across applicable populations. 

• Maximum Achievable potential is defined as the maximum penetration of an efficient 

measure that would be adopted absent consideration of cost or customer behavior. The 

term "achievable" refers to efficiency measure penetration, based on estimates of New 

Hampshire-specific building stock, energy using equipment saturations and realistic 

efficiency penetration levels that can be achieved by 2018 if all remaining standard 

efficiency equipment were to be replaced on burnout (at the end of its useful measure 

life) and where all new construction and major renovation activities in the state were 

done using energy efficient equipment and construction/installation practices. In certain 

circumstances, where early replacement of specific measures is becoming standard 

practice, maximum achievable potential includes the retrofit of measures before the end 

of their useful measure life (i.e., T8 lighting, thermostats, insulation and weatherization of 

existing homes). 

• Maximum Achievable Cost Effective (M.A.C.E.) potential is defined as the portion of 

the maximum achievable potential that is cost effective according to the economic 

criteria currently used to determine energy efficiency program cost-effectiveness (New 

Hampshire Public Utility Commission’s approved Total Resource Cost Test – NH TRC), 

before consideration of customer behavior. Application of the TRC test is based on the 

 

4

 Assessments based on a logic-modeling perspective recognize current program resources (dollars, staffing, etc.) 

and activities (measure installations, promotional rebates/incentives, marketing/outreach, education/training, etc.) 

and seek to identify their causal links to anticipated outputs (measures installed, in-program energy and capacity 

savings, # of customers served, market actors trained, etc.), short-, intermediate- and long-term outcomes (changes 

in awareness and behavior, market-wide/sustainable energy, economic and environmental benefits, etc.). In 

addition, logic models recognize the existence and potential impacts of external influences (price of energy, state of 

the local and regional economy, federal tax incentives, other non-program sponsored activities, etc.). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 4 

latest values for avoided cost (electric, natural gas and other fuels) and excludes 

environmental externalities not already captured with avoided cost values, consistent 

with current utility and PUC procedures. 

• Potentially Obtainable scenario is a new output developed for this study5

 and can be 

defined as an estimate of the potential for the realistic penetration over time of energy 

efficient measures that are cost effective according to the NH TRC, taking customer 

behavior into consideration (including consideration of priorities and price). To achieve 

this potential, a concerted, sustained campaign involving aggressive programs and 

market interventions would be required. As demonstrated later in this report, the State 

of New Hampshire and its electric and gas utilities would need to continue to undertake, 

and perhaps aggressively expand its efforts to achieve these levels of savings. 

LIMITATIONS TO THE SCOPE OF STUDY As with any assessment of energy efficiency 

potential, this study necessarily builds on a large number of assumptions, from average 

measure lives, savings and costs, to the discount rate for determining the net present value of 

future savings. The RFP for this study also called for a simplifying assumption that new 

buildings are constructed to meet minimum energy codes, even though that may not actually be 

the case. While, as noted above, the authors have sought to use the best available data, there 

are many assumptions where there may be reasonable alternative assumptions that would yield 

somewhat different results. For example, the “good, better and best” scenarios for housing 

weatherization and retrofit, while constructed to be reasonable illustrations, are not necessarily 

typical of many homes because of the wide diversity in size, age, type, and quality of 

construction, renovation and maintenance of existing homes. Furthermore, while the measures 

lists are extensive and represent most, if not all, commercially available, and some emerging, 

energy efficient measures, they are not exhaustive, particularly for peak electric demand 

reduction measures and potential fuel oil and propane savings, as further noted in footnote 30 of 

this report. Also, there was no attempt to place a dollar value on some difficult to quantify 

benefits that may result from some measures, such as increased comfort or reduced 

maintenance, which may in turn support some personal choices to implement particular 

measures that may otherwise not be cost-effective or only marginally so. 

Thus, the various potential estimates are specific to and limited by the detailed measures lists 

and assumptions described in this study. As new and improved energy efficiency products and 

strategies emerge and as regulatory, market, and behavioral barriers are reduced, the 

potentially obtainable estimate of energy efficiency might reasonably be expected to increase. 

In any case, we have provided here one well informed reasonable scenario of potentially 

obtainable increases in cost-effective energy efficiency for New Hampshire. Others are 

plausible. With this report we are providing the PUC with a complete copy of the spreadsheet 

model with all the measures and assumptions to facilitate further analysis by them, including 

revisions and updates to the assumptions and measures list. 

The main outputs of this study are summary data tables and figures identifying the potential for 

additional energy efficiency opportunities in New Hampshire over the ten-year period, 2009 

through 2018. Wherever possible, this study makes use of actual New Hampshire residential, 

 

5

 There has been a recent trend to temper estimates of cost-effective potential by taking into consideration 

behavioral, market, regulatory, financing and/or political barriers. A just released study by the Electric Power 

Research Institute used a similar concept that they called the “Realistically Achievable Potential (RAP).” See: 

Assessment of Achievable Potential from Energy Efficiency and Demand Response Programs in the U.S.: (2010–

2030), EPRI, Palo Alto, CA: 2009. 1016987, p. xiv. See also National Action Plan for Energy Efficiency (2007), 

Guide for Conducting Energy Efficiency Potential Studies, prepared by Philip Mosenthal and Jeffrey Loiter, Optimal 

Energy, Inc., www.epa.gov/eeactionplan, p. 2-4. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 5 

commercial and industrial customer data collected through phone surveys and site visits. Given 

the magnitude of efficiency measures included for consideration in this study, in cases where 

New Hampshire customer-specific information was not available, data on measure savings, 

costs and penetration rates were compiled through a combination of secondary research 

(including reviews of other previous relevant studies), utility-provided data, manufacturer 

specifications, and direct calculation through energy calculators and building simulation 

modeling. Collectively, these data sources provided an important and extensive foundation for 

estimates of electric energy, natural gas and related oil and propane savings potential by 

measure type, end-use and customer sector. 

1.2 Results Overview 

Energy-efficiency opportunities typically are physical, long-lasting changes to buildings and 

equipment that result in decreased energy use while maintaining the same or improved levels of 

energy service. This study shows that there is still significant savings potential in New 

Hampshire for cost effective electric and natural gas energy-efficiency measures and practices 

(and associated oil and propane savings). 

As shown in Table 1, the Technical potential savings (all sectors combined) for electric energy 

efficiency measures in New Hampshire is over 27 percent of projected 2018 kWh sales in the 

State, and similarly over 27 percent for non-electric (natural gas, oil and propane) efficiency 

measures. The Maximum Achievable Cost Effective potential (before consideration of customer 

behavior) is over 20 percent (nearly 2,700 gWh annually) of projected 2018 kWh sales (over 15 

percent summer peak demand reduction), and over 16 percent of projected 2018 non-electric 

sales (more than 15,440,000 MMBTu).6

 It is important to note, in the industrial sector, that the 

Maximum Achievable and Maximum Achievable Cost Effective potentials are the same. As 

explained in more detail in Section 6 of this report, this is because all end uses assessed in the 

industrial sector were screened as cost effective during the modeling process. The Potentially 

Obtainable scenario (including consideration of customer behavior) shows savings from electric 

and non-electric efficiency measures of approximately nearly 11 percent of 2018 kWh sales and 

approximately eight percent of projected 2018 non-electric (natural gas, oil and propane) sales. 

The Potentially Obtainable electric savings is equal to approximately 78 percent of the projected 

growth in electricity consumption over the next decade. 

Estimates of the associated potential reductions in CO2 emissions are also shown in Table 1, 

along with estimated costs that would be required to achieve these potentials. Depending on 

the scenario considered, these emission reductions and costs to achieve can be quite 

substantial (i.e., over three million tons at nearly seven billion dollars, based on the combined 

electric and non-electric Technical potential scenarios; or more than one million tons and nearly 

nine hundred million dollars based on the Potentially Obtainable scenarios).7

 In developing 

these estimates, savings opportunities from market driven (replace on burnout and new 

construction) and retrofit (early retirement) energy efficiency program strategies were 

considered, where applicable. 

The potential savings estimates, and costs to achieve those savings, are shown separately for 

electric, non-electric, and natural gas (a subset of non-electric) efficiency measures in Table 2, 

 

6

 Based on cost-effectiveness screening using the NH PUC- approved Total Resource Cost Test methodology as 

specified and described in Footnote 1, excluding environmental externalities not already captured within avoided 

cost values, consistent with current utility and NHPUC procedures. 7

 This is equivalent to removing over 509,000 cars from New Hampshire’s highways under the Technical Potential 

scenarios, or 178,000 cars under the Potentially Obtainable scenario. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 6 

Table 3, and Table 4 respectively. As shown in these tables, more electric savings can be 

obtained within the residential sector than in the commercial or industrial sectors. However, the 

cost to achieve that savings is substantially lower in the commercial and industrial sectors and 

highest in the residential sector. This implies that programs targeting the commercial and 

industrial sectors will yield the greatest electric energy savings per dollar spent, while 

substantial savings can also be obtained within the residential sector, but at nearly twice the 

cost per kWh saved. For instance, as shown in Table 2 under the commercial sector potentially 

obtainable scenario, 492 million kWh of annual savings is estimated by the year 2018 at an 

installed cost of just under $125 million (approximately 26 cents per kWh saved). In 

comparison, the residential sector yields approximately 698 million kWh of annual savings of 

estimated potential by the year 2018 at an installed cost of $383 million (55 cents/kWh saved). 

Similarly in the non-electric sectors, although there is more savings potential within the 

residential sector, the cost to achieve that savings is substantially greater than that required to 

save energy in the non-electric commercial and industrial sectors. For instance, per Table 3 

under the commercial sector potentially obtainable scenario, nearly 3.3 million MMBTu of 

annual non-electric energy savings is estimated by the year 2018 at an installed cost of just over 

$102 million ($31/MMBTu). In comparison, approximately 3.6 million MMBTu of annual savings 

potential is estimated in the residential sector by the year 2018 at an installed cost of over $200 

million. ($56/MMBTu). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 7 Table 1. Summary of Energy Savings Potentials by 2018 – Combined Electric and Non-Electric Measures 

Estimated Annual 

Electric Savings by 

2018 (MWh)

Savings in 

2018 as % of 

Sector 2018 

Electric 

Consumption

Estimated Annual 

Demand Savings 

by 2018 By Sector 

(MW)

Estimated 

Savings as % of 

Peak Sector 

Demand by 2018

Estimated Annual 

Non-Electric 

Savings by 2018 

(MMBtu)

Savings in 2018 as 

% of Sector 2018 

Non-Electric Fuel 

Consumption

Total Estimated Costs 

to Achieve 2018 

Annual Savings 

($2008 NPV)

Total Estima

CO2 Reducti

(tons)*

Technical Potential (Best Only) 1,770,861 31.7% 66.7 5.5% 16,918,392 50.0% $ 1,868,11

5,774,815,282 

Technical Potential (Traditional) 1,489,861 26.7% 56.1 4.7% 12,099,639 35.8% $ 1,422,16

4,426,572,142 

Max. Achievable Potential 1,217,145 21.8% 45.9 3.8% 7,463,743 22.1% $ 992,217 

2,421,842,542 

Max. Achievable Cost Effective 1,170,398 20.9% 44.1 3.7% 6,313,954 18.7% $ 893,638 

1,088,457,430 

Potentially Obtainable 698,069 12.5% 26.3 2.2% 3,633,554 10.7% $ 523,728 

583,533,793 

Technical Potential (Traditional) 1,598,032 29.8% 476.9 37.3% 11,981,017 26.4% $ 1,455,55

2,193,294,132 

Max. Achievable Potential 1,298,063 24.2% 385.9 30.2% 10,075,678 22.2% $ 1,206,40

1,887,366,888 

Max. Achievable Cost Effective 1,066,772 19.9% 317.1 24.8% 7,710,337 17.0% $ 951,512 

636,534,346 

Potentially Obtainable 492,023 9.2% 146.3 11.4% 3,252,204 7.2% $ 417,563 

227,057,997 

Technical Potential (Traditional) 515,486 24.5% 109.7 22.0% 1,755,089 11.2% $ 321,722 

153,382,708 

Max. Achievable Potential 442,671 21.1% 94.2 18.9% 1,415,809 9.0% $ 269,877 

130,703,312 

Max. Achievable Cost Effective 442,671 21.1% 94.2 18.9% 1,415,809 9.0% $ 269,877 

130,703,312 

Potentially Obtainable 213,810 10.2% 81.9 16.5% 683,836 4.4% $ 130,350 

63,129,699 

Technical Potential (Traditional) 3,603,379 27.6% 642.7 21.6% 25,835,745 27.2% $ 3,199,44

6,773,248,982 

Max. Achievable Potential 2,957,879 22.7% 525.9 17.6% 18,955,230 20.0% $ 2,468,50

4,439,912,741 

Max. Achievable Cost Effective 2,679,841 20.5% 455.3 15.3% 15,440,100 16.3% $ 2,115,02

1,855,695,087 

Potentially Obtainable 1,403,902 10.8% 254.5 8.5% 7,569,594 8.0% $ 1,071,64

873,721,489 

*The average vehicle in the United States produces around 12,100 lbs of carbon dioxide per year. This means that realizing the full Technical Potential calculated here would be t

carbon equivalent of taking over 509,000 cars off the road. Realizing the Potentially Obtainable figure would be the equivalent of removing 178,000 cars. 

RESIDENTIAL SECTOR

COMMERCIAL SECTOR

INDUSTRIAL SECTOR

ALL SECTORS COMBINED
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 8 Table 2. Summary of Energy Savings Potentials by 2018 – Electric88 For purposes of this study, a simplifying assumption was used to estimate peak demand savings. Percentage sector peak demand savi

savings over the summer coincident peak demand period only and are not broken out separately for summer and winder peak periods. 

Estimated Annual 

Sales by 2018 

(kWh)

Estimated Annual 

Savings by 2018 

(kWh)

Savings in 2018 

as % of Sector 

2018 Electric 

Consumption

Savings in 2018 

as % of Total 

2018 Electric 

Consumption

Estimated Annual

Sales by 2018 

(MW)

Estimated Annual 

Demand Savings 

by 2018 By Sector 

(MW)

Estimated 

Savings as % 

of Peak Sector 

Demand by 

2018

Estimated 

Savings as % 

of Total Peak 

Demand by 

2018

Estimate

Achieve 2

Sav

(10 Year C

($200

Technical Potential (Best Only) 1,770,860,535 31.7% 13.6% 66.7 5.5% 2.2% $2,554

Technical Potential (Traditional) 1,489,861,317 26.7% 11.4% 56.1 4.7% 1.9% $2,149

Max. Achievable Potential 1,217,144,947 21.8% 9.3% 45.9 3.8% 1.5% $1,214

Max. Achievable Cost Effective 1,170,397,964 20.9% 9.0% 44.1 3.7% 1.5% $632,

Potentially Obtainable 698,069,156 12.5% 5.4% 26.3 2.2% 0.9% $383,

Technical Potential (Traditional) 1,598,032,244 29.8% 12.2% 476.9 37.3% 16.0% $971,

Max. Achievable Potential 1,298,062,604 24.2% 9.9% 385.9 30.2% 12.9% $850,

Max. Achievable Cost Effective 1,066,771,952 19.9% 8.2% 317.1 24.8% 10.6% $311,

Potentially Obtainable 492,022,609 9.2% 3.8% 146.3 11.4% 4.9% $124,

Technical Potential (Traditional) 515,485,621 24.5% 4.0% 109.7 22.0% 3.7% $133,

Max. Achievable Potential 442,671,155 21.1% 3.4% 94.2 18.9% 3.2% $114,

Max. Achievable Cost Effective 442,671,155 21.1% 3.4% 94.2 18.9% 3.2% $114,

Potentially Obtainable 213,810,168 10.2% 1.6% 81.9 16.5% 2.7% $55,5

Technical Potential (Traditional) 3,603,379,183 27.6% 27.6% 642.7 21.6% 21.6% $3,254

Max. Achievable Potential 2,957,878,706 22.7% 22.7% 525.9 17.6% 17.6% $2,180

Max. Achievable Cost Effective 2,679,841,071 20.5% 20.5% 455.3 15.3% 15.3% $1,059

Potentially Obtainable 1,403,901,933 10.8% 10.8% 254.5 8.5% 8.5% $563,

0.322575231

Technical Potential (Traditional) 1,389,391

Max. Achievable Potential 1,140,499

Max. Achievable Cost Effective 1,033,293

Potentially Obtainable 541,317

Total NH 2018 Peak Demand 2982 MW

RESIDENTIAL SECTOR

INDUSTRIAL SECTOR

ALL SECTORS COMBINED

Total Estimated CO2 Reductions (tons)

5,589,807,380 1206

COMMERCIAL SECTOR

5,353,798,946

2,102,729,959

13,046,336,285

1279

498

2982
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 9 Table 3. Summary of Energy Savings Potentials by 2018 – Non-Electric

Estimated Annual Sales 

by 2018 (MMBtu)

Estimated Annual 

Savings by 2018 (MMBtu)

Savings in 2018 as % of 

Sector 2018 Non-Electric 

Fuel Consumption

Savings in 2018 as % of 

Total 2018 Non-Electric 

Fuel Consumption

Estimated Costs to Achieve 2018 

Annual Savings 

(10 Year Cumulative)

($2008 NPV)

W

Technical Potential (Best Only) 16,918,392 50.0% 17.8% 3,220,297,934 $ $ 

Technical Potential (Traditional) 12,099,639 35.8% 12.8% 2,277,404,262 $ $ 

Max. Achievable Potential 7,463,743 22.1% 7.9% 1,206,916,417 $ $ 

Max. Achievable Cost Effective 6,313,954 18.7% 6.7% 456,169,489 $ $ 

Potentially Obtainable 3,633,554 10.7% 3.8% 200,483,725 $ $ 

Technical Potential (Traditional) 11,981,017 26.4% 12.6% 1,222,077,201 $ $ 

Max. Achievable Potential 10,075,678 22.2% 10.6% 1,036,483,035 $ $ 

Max. Achievable Cost Effective 7,710,337 17.0% 8.1% 324,697,281 $ $ 

Potentially Obtainable 3,252,204 7.2% 3.4% 102,234,228 $ $ 

Technical Potential (Traditional) 1,755,089 11.2% 1.9% 19,467,779 $ $ 

Max. Achievable Potential 1,415,809 9.0% 1.5% 15,704,417 $ $ 

Max. Achievable Cost Effective 1,415,809 9.0% 1.5% 15,704,417 $ $ 

Potentially Obtainable 683,836 4.4% 0.7% 7,585,234 $ $ 

Technical Potential (Traditional) 25,835,745 27.2% 27.2% 3,518,949,242 $ 

$ 

Max. Achievable Potential 18,955,230 20.0% 20.0% 2,259,103,869 $ 

$ 

Max. Achievable Cost Effective 15,440,100 16.3% 16.3% 796,571,187 $ 

$ 

Potentially Obtainable 7,569,594 8.0% 8.0% 310,303,186 $ 

$ 

Technical Potential (Traditional) 1,679,847

Max. Achievable Potential 1,239,514

Max. Achievable Cost Effective 1,005,418

Potentially Obtainable 536,933

Total Estimated CO2 Reductions (tons)

33,838,195

RESIDENTIAL SECTOR

COMMERCIAL SECTOR

INDUSTRIAL SECTOR

94,841,92845,329,91515,673,818

ALL SECTORS COMBINED
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 10 Table 4. Summary of Energy Savings Potentials by 2018 – Natural Gas

Estimated Annual Sales 

by 2018 (MMBtu)

Estimated Annual 

Savings by 2018 (MMBtu)

Savings in 2018 as % of 

Sector 2018 Non-Electric 

Fuel Consumption

Savings in 2018 as % of 

Total 2018 Non-Electric 

Fuel Consumption

Estimated Cumulative Costs to 

Achieve 2018 Annual Savings 

(10 Year Cumulative)

($2008 NPV)

Total 

Annu

Ass

W/Comb

in 2018 

Technical Potential (Best Only) 5,250,770 64.1% 19.8% $1,122,335,585 $ 

Technical Potential (Traditional) 3,776,852 46.1% 14.2% $807,290,166 $ 

Max. Achievable Potential 2,262,674 27.6% 8.5% $426,300,163 $ 

Max. Achievable Cost Effective 1,807,030 22.1% 6.8% $117,928,736 $ 

Potentially Obtainable 1,057,239 12.9% 4.0% $54,192,333 $ 

Technical Potential (Traditional) 3,347,637 26.4% 12.6% $304,022,371 $ 

Max. Achievable Potential 2,815,263 22.2% 10.6% $261,039,375 $ 

Max. Achievable Cost Effective 2,154,359 17.0% 8.1% $88,161,415 $ 

Potentially Obtainable 908,704 7.2% 3.4% $27,607,959 $ 

Technical Potential (Traditional) 638,214 11.2% 2.4% $7,079,192 $ 

Max. Achievable Potential 514,840 9.0% 1.9% $5,710,697 $ 

Max. Achievable Cost Effective 514,840 9.0% 1.9% $5,710,697 $ 

Potentially Obtainable 248,667 4.4% 0.9% $2,758,267 $ 

Technical Potential (Traditional) 7,762,703 29.2% 29.2% $1,118,391,730 

$ 

Max. Achievable Potential 5,592,777 21.1% 21.1% $693,050,235 

$ 

Max. Achievable Cost Effective 4,476,228 16.9% 16.9% $211,800,848 

$ 

Potentially Obtainable 2,214,611 8.3% 8.3% $84,558,558 

$ 

Technical Potential (Traditional) 427,919

Max. Achievable Potential 308,302

Max. Achievable Cost Effective 246,752

Potentially Obtainable 133,064

Total Estimated CO2 Reductions (tons)

RESIDENTIAL SECTOR

8,189,374

COMMERCIAL SECTOR

12,665,712

INDUSTRIAL SECTOR

5,699,570

ALL SECTORS COMBINED

26,554,656
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 11 

As shown in Figure 1, in the residential sector, New Hampshire’s greatest areas for electric 

energy savings from the installation of cost-effective energy efficiency measures come from 

combined single family (SF) and multifamily lighting9

 (MF) (52% of the annual savings by the 

year 2018), electric appliances (16% by 2018, combined SF and MF), and space heating and 

cooling combined SF and MF (10% by 2018) followed by standby (phantom) power (9%) and 

water heating (9% – 5% SF and 4% MF) and new construction activities (4%). Figure 2 shows 

the greatest areas for non-electric savings come from space heating (oil-fueled) and water 

heating (all fuels), nearly 30% each when SF and MF potentials are combined, and 

weatherization packages (all fuels) in single family homes (16% SF and MF combined). The 

large potential for savings from oil-fueled space heating measures is not surprising since nearly 

60 percent of all homes in New Hampshire heat with oil. The greatest potential for natural gas 

savings in the residential sector comes from replacement of inefficient gas furnaces and boilers 

in multifamily and single family homes (nearly 9% and 6% respectively). 

Figure 1. Residential Electric Energy Efficiency Maximum Achievable Cost Effective – by End Use

 

9

 Lighting savings in the residential sector are largely being driven by savings from CFL bulbs and or CFL fixtures 

in single family and multi-family homes. It is very important to note, that these savings might be overstated for the 

post-2012 period for two main reasons. First, this study does not take into direct consideration future changes to 

energy codes as they relate to residential lighting applications, including improved federal efficiency standards for 

incandescent bulbs (the base technology from which current lighting savings are calculated) that are designated to 

become effective in 2012. This study was conducted based on the standards and energy savings differentials (e.g., 

between CFLs and incandescent bulbs) in existence as of 2009. Secondly, although this study includes emerging 

lighting technologies (i.e., LEDs), there is a high likelihood that as these new and emerging lighting technologies 

enter the market, the penetration of CFLs will be significant and new improved efficiency incandescent light bulbs 

will also be entering the market. Thus, the incremental savings going from a CFL to a new technology (such as 

LED or super high efficiency incandescent) will be dramatically lower than the current incremental savings going 

from standard incandescent to compact florescent (CFL). This consideration was addressed partially by the 

assumption that new technologies will always emerge, and savings will always be present as a result – however, it is 

true, that those savings, as stated previously, will be lower, and as a result, may be somewhat overstated during the 

second half of the study’s 2009 through 2012 forecast horizon. 

Lighting ‐ SF

34%

Lighting ‐ MF

Electric Appliances ‐ SF 18%

13%

Standby Power

9%

SpaceHeating and SpaceCooling ‐ SF

7%

Water

Heating ‐ SF

5%

Water Heating ‐ MF

4%

SF New Construction

4%

SpaceHeating and Space Cooling ‐ MF

3%

Electric Appliances ‐ MF

3% Pools

0%
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 12 

Figure 2. Residential Non-Electric Efficiency Maximum Achievable Cost Effective – by End Use

Savings within the commercial sector were assessed separately for existing buildings and for 

potential new construction. As shown in Figure 3 (existing buildings) and Figure 4 (new 

construction), New Hampshire’s greatest areas for electric savings from the installation of costeffective energy efficiency measures come from lighting and/or lighting controls – i.e., 39% by 

2018 from existing buildings, including retrofit of existing lighting systems; and 42% from new 

construction activities, mainly from lighting design. The next significant area for electric savings 

is from refrigeration – i.e., 19% by 2018 from existing buildings and 18% from new construction 

activities. HVAC systems and controls (in existing buildings) and building envelope 

improvement packages (in new construction) also provide substantial savings. 

HomeGas Heat (MF)

9%

HomeGas Heat

(SF)

6%

HomeOil Heat (SF)

25%

HomeOil Heat (MF)

3%

HomePropaneHeat (MF)

1%

Home

Propane

Heat(SF)

5%

New Construction (MF)

2%

New Construction (SF)

4%

Water Heating (MF)

11%

Water Heating (SF)

18%

Weatherization Packages(MF)

4%

Weatherization Packages(SF)

12%
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 13 

Figure 3. Max Achievable Cost Effective Electric Savings by End Use for Commercial Existing Buildings

Figure 4. Max Achievable Cost Effective Electric Savings by End Use for Commercial New Construction

Appliances, Computers, Office

Equipment

3%

Building Envelope

3%

Compressed Air

0%

HVAC Controls

13%

Lighting

29%

Non‐HVAC Motors

3%

Pools

1%

Refrigeration

19%

SpaceCooling ‐

Chillers

2%

SpaceCooling ‐ Unitary and Split

AC

4%

SpaceHeating

3%

Transformers

1% Ventilation

5%

Water Heating

3% Lighting Controls

10%

Appliances, Computers,

Office Equipment

3%

Building Envelope

11%

Compressed Air

0%

Cooking

0%

HVAC Controls

2%

Non‐HVAC Motors

3%

Pools

1%

Refrigeration

18%

Space Cooling ‐ Chillers

4%

Space Cooling ‐ Unitary and Split AC

4% Space Heating

3%

Transformers

1%

Ventilation

7%

Water Heating

3%

Lighting Design

42%
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 14 

New Hampshire’s greatest areas for non-electric energy savings in the commercial sector come 

from the installation of cost-effective space heating (44%), water heating and HVAC controls 

(17% each) and building envelope (13%) in existing buildings, as shown in Figure 5. Space 

heating measures also provide the greatest potential for non-electric savings in the commercial 

new construction area (44%) as shown in Figure 6, followed by building envelope and water 

heating (16%), and HVAC controls (15%). 

Figure 5. Max Achievable Cost Effective Non-Electric Savings by End Use - Commercial Existing Buildings

Figure 6. Max Achievable Cost Effective Non-Electric Savings by End Use - Commercial New Construction

Building Envelope

13%

Space Heating

44%

HVAC Controls

17%

Cooking

4%

Pools

4%

Ventilation

1%

Water Heating

17%

Building Envelope

16%

Space Heating

44%

HVAC Controls

15%

Cooking

4%

Pools

4%

Ventilation

1%

Water Heating

16%
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 15 

Within the Industrial sector, Figure 7 shows that the greatest areas for electric energy 

savings come from machine drives (40%), sensors and controls (16%), lighting (15%), 

process heating measures (13%), and facility HVAC (11%). As shown in Figure 8, the 

greatest areas for non-electric savings in the industrial sector come from process 

heating, conventional boiler use and facility HVAC measures (52%, 33% and 13% 

respectively).

Figure 7. Max Achievable Cost Effective Electric Savings by End Use for NH Industrial Sector 

Figure 8. Max Achievable Cost Effective Non-Electric Savings by End Use for NH Industrial Sector 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 16 

Table 5 and Table 6 present the estimated 2018 Technical, Maximum Achievable, Maximum 

Achievable Cost Effective potentials and results from the Potentially Obtainable scenario for 

each of the four New Hampshire retail electricity providers and two natural gas distribution 

companies. As can be seen from these tables, the greatest potential for electric savings exists 

within PSNH’s service territory (approximately 73% of the state’s projected kWh and MW 

savings), followed by Unitil (nearly 12%), National Grid (over 8%) and the NH Electric 

Cooperative (just under 7%). Seventy-seven percent of the natural gas savings potential exists 

within National Grid’s service territory, with the remaining 23% coming from Northern Utilities 

territory. It is important to note that a majority of the non-electric savings potential comes from 

measures installed in oil and propane-fueled homes and businesses. 

Table 5. Additional Energy Efficiency Opportunities Potential by 2018 - Breakdown by Utility – Electric

All Sectors

Estimated Annual 

Savings 

by 2018 (kWh)

Estimated Utility 

Max. Achievable 

Cost Effective 

Savings in 2018 as 

a Percent of Total 

Estimated Savings

Estimated 

Annual Sales by 

2018 (kWh)

Estimated Annual 

Demand Savings by

2018 (MW)

Estimated Utility Max. 

Achievable Cost 

Effective Demand 

Savings in 2018 as a 

Percent of Total 

Estimated Savings

Estimated 

Annual Sales by 

2018 (MW)

Technical Potential (Traditional) 2,641,281,301 466.2

Max. Achievable Potential 2,166,873,873 381.4

Max. Achievable Cost Effective 1,956,745,201 329.9

Potentially Obtainable 1,022,507,558 183.9

Technical Potential (Traditional) 240,590,220 43.3

Max. Achievable Potential 197,148,030 35.2

Max. Achievable Cost Effective 181,927,003 29.9

Potentially Obtainable 99,640,017 15.3

Technical Potential (Traditional) 410,156,541 75.7

Max. Achievable Potential 339,044,561 62.4

Max. Achievable Cost Effective 315,351,394 55.5

Potentially Obtainable 166,137,024 33.9

Technical Potential (Traditional) 311,351,120 57.6

Max. Achievable Potential 254,812,243 47.0

Max. Achievable Cost Effective 225,817,473 40.1

Potentially Obtainable 115,617,334 21.4

Technical Potential (Traditional) 3,603,379,183 642.7

Max. Achievable Potential 2,957,878,706 525.9

Max. Achievable Cost Effective 2,679,841,071 455.3

Potentially Obtainable 1,403,901,933 254.5

All Electric Utilities - Totals

100.0% 13,046,336,285

206

9,535,258,276

880,356,308

73.0% 72.5%

6.8% 6.6%

1,524,047,235

PSNH

NH Electric Co-op

Unitil

National Grid-Electric

2,139

2,982

12.2%

8.4% 231

11.8% 406

1,106,674,467 8.8%

100.0%
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 17 

Table 6. Additional Energy Efficiency Opportunities Potential by 2018 - Breakdown by Utility – Natural Gas

1.3 Potentially Obtainable Scenario 

In the Potentially Obtainable scenario developed for this report, all cost-effective energy 

efficiency measures were assessed in light of customer priorities and estimated pricing 

behaviors (i.e. sensitivity to payback). Concerning priorities, customers’ responses to questions 

included in this projects’ sector-specific telephone surveys and site visits were used to 

determine the percentage of customers that stated they were “extremely likely” to purchase 

energy efficient equipment (73% of residential customers, and 48% of commercial and industrial 

customers). Customer behaviors regarding pricing were estimated based on some simplifying 

assumptions that all “extremely likely to purchase” customers would potentially install energy 

efficient measures if the price were below a certain level (i.e., 7 cents levelized cost per /kWh 

saved) and half of those same customers would likely install cost-effective measures in cases 

where the costs were more than 7 cents/kWh saved (the model also built in functionality to 

eliminate those measures with extremely high levelized costs in order to avoid outliers from 

being considered in the Potentially Obtainable scenario). Embedded within this approach was 

the assumption that fifty percent of the associated energy efficiency measure cost would be 

provided to these customers through a measure rebate to achieve the desired customer 

purchase action (essentially reducing the customer’s out-of-pocket cost to 3.5 c/kWh in this 

example, or equivalent to approximately a 1 to 2 year payback on the customers’ portion of the 

energy efficiency measure investment). This rebate level assumption is based upon a previous 

review conducted by GDS of numerous energy efficiency studies, including a National Energy 

All Sectors Estimated Annual Savings 

by 2018 (MMBtu)

Estimated Utility Max. 

Achievable Cost Effective 

Savings in 2018 as a Percent 

of Total Estimated Savings

Estimated 

Annual Sales by 

2018 (MMBtu)

Technical Potential (Traditional) 5,294,129

Max. Achievable Potential 3,916,204

Max. Achievable Cost Effective 3,198,934

Potentially Obtainable 1,558,051

Technical Potential (Traditional) 1,589,633

Max. Achievable Potential 1,195,725

Max. Achievable Cost Effective 973,825

Potentially Obtainable 466,856

Technical Potential (Traditional) 6,883,763

Max. Achievable Potential 5,111,929

Max. Achievable Cost Effective 4,172,758

Potentially Obtainable 2,024,907

100.0% 26,283,248

6,193,361

76.7%

23.3%

National Grid - Natural Gas Savings Only

Northern Utilities - Natural Gas Savings Only

20,089,887

All Natural Gas Utilities Combined - Totals
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 18 

Efficiency Best Practices Study and was supplemented with data collected through the phone 

surveys and site visits conducted as part of this current project.10,11 

 1.4 Implementation Costs 

To achieve the Potentially Obtainable amount of energy efficiency savings by 2018, substantial 

efforts, including continued and expanded utility programmatic support will be required. Such 

programmatic support would include rebates to customers (including potential targeted midstream and upstream market actors), program marketing and outreach, administration, 

planning, and program evaluation activities. Although not included in this report’s “cost to 

achieve estimates,” all such costs would be required to ensure the delivery of quality and 

reliable energy efficiency products and services to New Hampshire’s consumers. As noted 

above, the projection for Potentially Obtainable electricity and non-electric energy savings 

assumes that customers receive rebates equivalent to fifty percent of measure incremental (or 

full) costs. This incentive level assumption will help to reduce customer out-of-pocket costs and 

will quicken the paybacks on measures installed to more actionable levels. The fifty percent 

incentive is based both upon customer provided input (via this project’s phone surveys and site 

visits data collection efforts), and from review of numerous energy efficiency studies including 

the National Energy Efficiency Best Practices Study. If customers had to receive 100% of 

measure incremental or full costs to achieve the Potentially Obtainable scenario’s savings 

levels, then program budgets would double. 

1.5 Market-Driven vs. Retrofit 

Energy efficiency potential in the existing stock of buildings can be captured over time through 

two principal processes: 

1. as equipment replacements are made normally in the market when a piece of equipment 

is at the end of its useful life (often referred to as “market-driven” or “replace-onburnout”); and, 

2. at any time in the life of the equipment or building (referred to as “retrofit”). 

Market-driven measures are generally characterized by incremental measure costs and savings 

(e.g., the incremental costs and savings of a high-efficiency versus a standard efficiency air 

conditioner); whereas retrofit measures are generally characterized by full costs and savings 

(e.g., the full costs and savings associated with retrofitting ceiling insulation into an existing 

attic). A specialized retrofit case is often referred to as “early replacement” or “early retirement”. 

This refers to a piece of equipment whose replacement is accelerated by several years, as 

compared to the market-driven assumption, for the purpose of capturing energy savings earlier 

than they would otherwise occur. 

 

10 See “National Energy Efficiency Best Practices Study, Volume NR5, Non-Residential Large Comprehensive 

Incentive Programs Best Practices Report”, prepared by Quantum Consulting for Pacific Gas and Electric Company, 

December 2004, page NR5-51. 

11 As part of this project, telephone surveys were conducted with 400 residential customers and 200 small 

commercial customers, and site visits were conducted with 100 larger commercial customers and 100 industrial 

customers. Questions were included in these surveys and site visits to assess customer interest in energy efficiency 

and the value of incentives to the customer decision-making process. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 19 

For this study, the GDS Team has examined the impacts of “early replacement” for a select 

group of measures (i.e., T-8 lighting, insulation and weatherization measures in existing 

buildings). For these measures, GDS assumed that customers would receive an incentive 

equivalent to 50% of the full cost of the energy efficiency measures at the time of retrofit.12 

1.6 Customer Participation and Barriers 

Based on results from the customer telephone surveys and on-site interviews, a number of 

insights regarding customer participation, preferences and barriers have been identified. 

Highlights are presented below. Please refer to Section 7 of this report for more detailed 

information. 

1.6.1 Residential Customer Program Participation and Barriers Summary

• Over 90% of the residential customers surveyed said they pay “some” or “substantial” 

attention to controlling energy costs 

• After being read a definition of energy efficiency and the fact that such measures 

typically cost more than less efficient models (often 20 to 30% more), 73% stated that 

they were “extremely likely” to purchase energy efficient equipment if it lowered their 

energy bill, increased comfort, or helped the environment. 

• Installation of energy efficiency features are commonly considered as part of remodeling 

projects (64% among recently remodeled homes, and 90% among homes with a future 

remodeling plan). 

• About half of the households surveyed are aware of their utility offering energy efficiency 

programs, and 30% have participated in them in some way. 

• Low income households were found to have a significantly higher participation rate (they 

are twice as likely to report participating in such programs). 

• Among participants, satisfaction with their utilities’ programs seems extremely high. 

• The two most frequently cited reasons for nonparticipation were: (1) there was no recent 

purchase of energy-using household items, and (2) unawareness of program resources. 

1.6.2 Commercial and Industrial Customer Program Participation and Barriers 

Summary

• Of the small and large commercial and industrial customers surveyed, 86% of 

respondents reported some or high level of attention to controlling energy costs. 

• 48% stated that they were “extremely likely” to purchase energy efficient equipment if it 

lowered their energy bill, increased comfort, or helped the environment. 

• Overall awareness of energy efficiency programs and incentives offered by utility 

providers was significantly higher in the large commercial/industrial respondents (86%) 

compared to the small commercial/industrial respondents (60%). 

• Past participation in utility provider offered programs was similarly higher in the large 

customer group who was aware of the programs offered (86%) compared to the small 

customer group aware of the programs offered (30%). 

• Of respondents who have participated in their utility’s energy efficiency programs, a 

significant majority of both small customers (94%) and large customers (98%) reported 

that they would participate in the programs again if given the opportunity. 

 

12 Tying incentives to the full installed cost of targeted measures in the case of early replacement (retrofits) is typical 

of the way that retrofit programs are currently being implemented here in New Hampshire and throughout the 

country. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 20 

• The single largest barrier to respondents investing in energy efficiency measures was 

concern about initial premium costs of equipment and insufficient payback (69%). 

• Respondents indicated that the two most important factors influencing decisions to 

invest in energy efficient equipment are: (1) expectations of lower monthly energy bills 

and (2) rebates or incentives for purchasing energy efficient equipment that would help 

offset some of the initial costs. 

• Other factors such as business image, environmental impact, occupant comfort, and 

sales person recommendation were less likely to influence decisions to invest in energy 

efficient equipment. 

1.7. Past/Current Program Capture and Recommendations 

To date, New Hampshire’s electric and gas utilities have been quite effective in achieving 

energy and capacity savings and energy efficiency measure penetration across the state. But, 

as shown in Table 7 and Table 8 below, there is much room for additional penetration. In total, 

from 2002 through 2008, the electric energy efficiency programs are saving an estimated 

cumulative total of nearly 560 million kWh per year of energy13. This represents a savings of 

five percent of the total forecast energy usage for New Hampshire in 2008. Similarly from 2003 

through 2008, the natural gas efficiency programs saved an estimated total of over 2.4 million 

therms per year14. This represents a savings of 1.1 percent of the total forecasted natural gas 

usage for New Hampshire in 2008. 

Table 7. Cumulative Annual Program Savings as Percent of 2008 Sales: 2002-2008 – Electric 

Sector

Total Annual 

Savings Since 2002

 (MWh)

Forecasted Sales 

2008 

(MWh)

Cummulative 

Annual Savings as a 

Percent of 2008 

Sector Sales

Cummulative 

Annual Savings as 

a Percent of 2008 

Total Sales

Residential 120,064 4,537,480 2.6% 1.1%

Commercial/Industrial 437,210 6,650,732 6.6% 3.9%

Total 557,274 11,188,212 5.0%

Table 8. Cumulative Annual Program Savings as Percent of 2008 Sales: 2003-2008 – Natural Gas 

Sector

Total Annual 

Savings Since 2003

 (decatherms)

Forecasted Sales 

2008 

(decatherms)

Cummulative 

Annual Savings as a 

Percent of 2008 

Sector Sales

Cummulative 

Annual Savings as 

a Percent of 2008 

Total Sales

Residential 95,387 8,435,900 1.1% 0.4%

Commercial/Industrial 150,248 14,267,000 1.1% 0.7%

Total 245,635 22,702,900 1.1%

It is important to note that the figures in the above two tables are conservative in several ways. 

First, the utility providers have been actively offering efficiency programs since well before 2002 

so the total amount of energy saved since the inception of efficiency programs is much higher. 

 

13 Estimate is based on reported lifetime savings from 2005-2008 available on NHPUC website, GDS estimates for 

program measure lives used to calculate annual savings, and extrapolated kWh savings estimates for 2002-2004. 

14 Estimate based on reported savings from 2003-2007 and GDS estimates for program measure lives 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 21 

Second, these figures consider only cumulative annual savings, not lifetime savings15. In reality, 

annual savings are realized every year over the assumed measure life of the programs. The 

data was reported in the above manner to provide an appropriate comparison to the forecast 

2008 usage. More details regarding this analysis are presented in Section 8 of this report. 

To increase the likelihood of achieving the additional energy efficiency savings potential 

highlighted in this study, the following findings/recommendations are suggested (see Section 8 

for more details): 

To date, the efficiency programs offered in New Hampshire by the state’s four largest electric 

utilities and two natural gas distribution companies have been successful and have saved a 

substantial amount of energy. Many of the programs have and are continuing to perform quite 

well in terms of cost per unit of energy saved and customer participation. Several other 

programs have shown positive trends becoming more cost effective on a yearly basis. 

For all programs, but most notably in the electric market, the cost per kWh saved in the 

commercial and industrial sectors has been better than in the residential market. This might 

explain why in general, commercial and industrial customers have indicated a higher awareness 

of the utilities’ efficiency programs available to them as well as an increased likelihood of 

program participation compared to residential customers. Given the scale of energy 

consumption in the commercial and industrial sectors, these customers continue to represent a 

substantial area for potential energy savings in the upcoming years. 

• Recommendation: Additional penetration can be achieved through increased outreach 

to small commercial/industrial customers and by expanding current program offerings to 

include other cost effective measures not currently included in the companies’ CORE 

and utility-specific programs. 

Residential customer participation in the state’s electric and natural gas energy efficiency 

programs has met or exceeded program expectations on a yearly basis. However, in the phone 

surveys more than half of respondents indicated that they were not aware of the programs 

offered by their utilities, or that they were even eligible. Of the customers who were aware of 

the programs, a high percentage participated and indicated they would participate in the future. 

• Recommendation: This data underscores the importance of increasing consumer 

education on the programs available to residential customers and of the associated 

benefits. 

One final finding from the study is that nearly all of the most cost effective energy efficiency 

measures are included in current programs in some manner. In several programs, however, the 

cost effective measures are targeted to a small percentage of consumers. The best example of 

this is the Home Energy Solutions program which targets consumers with 65% or greater 

electric heating. Customers with electric heat as their primary heating source represent 

approximately 4% of the total population based on the phone surveys.16 

 

15 Cumulative annual savings were calculated by determining the annualized savings in a given year and summing 

those annual savings for each of the program years reviewed. 

16 The 4% represents total number of customers with electric heat as their primary source for heating. A smaller 

percentage than 4% would qualify for participation in the Home Energy Solutions program, since 65% or more of 

their space heating needs to be met with electric heat. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 22 

• Recommendation: Expanding the number and types of products and services available 

through the existing residential energy efficiency programs, and promotion of those 

programs to include a larger number of potential participants may lead to increased 

overall energy savings. It is important to recognize that such expansion would require 

providing services to customers that heat with fuels other than electric or natural gas. 

Issues regarding who would pay for the provision of services to such customers would 

need to be addressed. 

1.8 Structure of this Report 

Section 2 of this report provides an overview of current and forecasted electric and natural gas 

energy usage in New Hampshire. Information on geographic, economic, demographic and 

energy usage characteristics of the State is also presented in Section 2. Section 3 of this report 

provides a detailed discussion of the research plan and methodologies used for the collection 

and analysis of all data in this report. Results from the participation, preferences and barriers 

questions asked as part of this project’s phone surveys and site visit interviews are also 

presented in Section 3. Sections 4, 5 and 6 provide detailed results from the electric and nonelectric energy efficiency potential analysis conducted for the residential, commercial and 

industrial sectors, respectively. Detailed results are presented in these sections regarding 

technical potential, maximum achievable potential, maximum achievable cost effective potential 

and the potentially obtainable scenario. Energy (kWh), capacity (kW), and associated therm 

(MMBTu) and environmental (tons of CO2) savings are presented along with additional 

description of the methodologies used, where applicable. 

This project included a major enhancement to a majority of the technical potential studies that 

have been conducted across the country in the past. Rather than relying on best available 

information from existing secondary sources to estimate current levels of energy using 

equipment saturations and penetration of energy efficiency measures, significant primary data 

collection efforts were undertaken to help inform and derive New Hampshire-specific values 

where possible within the time requirements and work scope specified for this project. As such, 

this effort was completed through a combination of primary and secondary data collection and 

analysis activities. Detailed findings and an assessment of the value resulting from this 

enhanced, New Hampshire-specific data collection effort is presented in Section 7 of this report. 

Section 8 assesses the amount of energy savings that past and current energy efficiency 

programs in the state have already captured. Recommendations for potential program 

modifications and measure offerings are also included in this section. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 23 

Section 2: Characterization of Customer Base, Electric 

and Natural Gas Usage, and Load Forecasts for the State of 

New Hampshire 

This section of the report provides electric and natural gas utility forecasts for energy usage in 

the State of New Hampshire based on data provided by the four electric and two natural gas 

utilities supporting this project. The utility-provided forecast information has been compared 

against the latest available ISO-NE forecasted data, where appropriate, to ensure 

reasonableness. In order to develop estimates of energy savings potential, it is important to 

understand how energy is used by households and businesses in New Hampshire. Therefore, 

this section also provides information on geographic, economic, demographic and energy usage 

characteristics of the State. 

2.1 New Hampshire Geographic and Demographic Characteristics 

New Hampshire is the third largest state in New England after Maine and Vermont by total land 

area (fourth largest by population after Massachusetts, Connecticut and Maine).17 The State is 

bordered by Canada, Vermont, Massachusetts, and Maine. The Connecticut River forms the 

western boundary with Vermont, while Maine forms a boundary for nearly its entire eastern 

border, until meeting the Atlantic Ocean near its southeastern border with Massachusetts. 

Manchester is the largest of New Hampshire’s 221 towns with an estimated population of 

109,497 in 2006 according the US Census data. 18 

New Hampshire ranks 41st in the country (by population), and at approximately 9,000 square 

miles, is the fourth smallest state by total area (68 miles at its widest point, and 190 miles long). 

New Hampshire is the second most forested state in the country, after Maine, in percentage of 

land covered by woods. Major regions of the state include the Great North Woods, the White 

Mountains, the Lakes Region, the Merrimack Valley, the Monadnock Region, the DartmouthLake Sunapee Region, and the Seacoast. 

The White Mountain National Forest covers approximately 1,171 square miles in the northcentral portion of the state (including 5.6% of which is in the neighboring state of Maine). Lake 

Winnipesaukee is the largest lake in New Hampshire, covering approximately 72 square miles 

in the east-central part of the state.19 The Seacoast area of New Hampshire has the smallest 

shoreline of any coastal state (just 18 miles long). Figure 9 provides a map of the state. 

 

17 http://quickfacts.census.gov/qfd/states/44000.html. New Hampshire’s population density of 137.8 persons per 

square mile is higher than the population density in Vermont (65.8) and Maine (41.3), but it is much lower than the 

other three New England states. For more detailed information, see http://www.answers.com/topic/list-of-u-s-statesby-population-density. 

18 2006 population estimate for Manchester, NH. http://www.nh.gov/nhes/elmi/htmlprofiles/manchester.html

19 http://en.wikipedia.org/wiki/New_Hampshire. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 24 

Figure 9. New Hampshire Map20

 

20 http://www.infoplease.com/atlas/state/newhampshire.html 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 25 

2.2 New Hampshire Economic and Demographic Characteristics 

New Hampshire is a rural state with a population of approximately 1,350,000 persons in 2008 

and 604,000 housing units.21 According to the Energy Information Administration, the state’s 

energy consumption per capita is among the lowest in the country. This is due, in part to the 

low demand for air conditioning and the fact that relatively few households use electricity as 

their primary energy source for home heating. Over half of the households in New Hampshire 

heat their homes with fuel oil in the winter.22

The New Hampshire Employment Security Economic and Labor Market Information Bureau 

prepares an annual Economic Analysis Report for the state.23 The Bureau’s 2008 Report noted 

that New Hampshire has been growing faster than any of the other New England states, 

although this growth is occurring as a decreasing rate. Gross Domestic Product was $57.3 

billion at the end of 2007, a 2.3% increase from 2006, but well below the 4.9% and 4.0% growth 

in 2005 and 2004 respectively. The seasonally adjusted unemployment rate for New 

Hampshire was 3.8% at the end of the first quarter 2008, and has been consistently below the 

country’s average rate over the past fifteen years. Total employment is projected to grow in the 

state by 13.9% from 2006 to 2016. Although real estate activity has declined 5.6% from April 

2007 to April 2008, this decline has been one of the smallest compared to the other New 

England states. Major areas for job growth are expected to include: healthcare, social services, 

computers and mathematics, and personal services. 

To get a sense of the population mix in the state, electric and natural gas utility customer 

information was provided and is summarized below. This information was used in sample plan 

development for telephone surveys and site visits that were conducted as part of this project. 

As shown in Table 9, the four investor-owned electric utilities analyzed for this report have a 

collective total of 612,636 residential and low income customers, with PSNH serving a majority 

of these customers (67% and 83% respectively). A majority of natural gas customers are 

served by National Grid (61%). 

Table 9. Total Customer Counts – Residential, Low Income NH Electric and Natural Gas Utilities24

Utility Residential Low Income Total 

PSNH 392,202 67% 22,118 83% 414,320 68% 

NH Electric Coop 64,164 11% 2,423 9% 66,587 11% 

Unitil 58,550 10% 2,083 8% 60,633 10% 

National Grid-Electric 70,986 12% 110 <0.5% 71,096 11% 

Subtotal Electric* 585,902 100% 26,624 100% 612,636 100% 

National Grid-Gas 33,882 61% 1,117 100% 34,999 61% 

Northern Utilities 21,988 39% 0 0% 21,988 39% 

Subtotal Natural Gas+ 58,870 100.0% 1,117 100.0% 56,987 100.0% 

 

21 Data obtained by GDS from “On-demand reports and maps from Business Analyst Online”, based on U.S Bureau 

of the Census, 2000 Census of Population and Housing, ESRI forecasts for 2008 and 2013. 

22 Primary data collection – results from this project’s residential telephone surveys 23 New Hampshire Economic Analysis Report 2008, New Hampshire Employment Security Economic and Labor 

Market Information Bureau 

24 Likely underestimates the number of low-income customers for each utility. As shown in this table, the estimated 

percentage of New Hampshire’s population within these combined utility service territories is 4.3% 

(26,624/612,636), excluding double counting from natural gas utility customers that are also electric utility 

customers. In comparison, according to the 2007 American Community Survey of the U.S. Census Bureau, the 

percentage of the state’s population at or below the poverty level is 7.1%. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 26 

* Excludes municipal electric utility customers 

+ Represents subset of electric customers 

The State’s commercial and industrial customer base, to accommodate data collection efforts 

required for this report, were separated into small (<100 kW peak demand or 300,000 

kWh/year) and larger customer groupings. As shown in Table 10, the majority of small nonresidential electric customers are located in PSNH’s service territory (74%). The number of 

small commercial/industrial natural gas customers are split fairly evenly at 53% Northern Utilities 

and 47% National Grid. Based on review of Standard Industrial Classification (SIC) Code data 

included in some of the utility-provided customer data files, it appears that approximately 40% of 

New Hampshire’s small non-residential electric and natural gas customers are in the Services 

sector (SIC codes 70-89). Between 11% and 12% of the state’s small commercial/industrial 

customers appear to be in the Retail Trades sector (SIC Codes 52-59). The Finance, Insurance 

and Real Estate sector (SIC Codes 60-67) make up the next largest small C/I customer focus at 

approximately 4%. Followed by Manufacturing and Transportation/Public Utilities (SIC Codes 

20-39 and 40-49 respectively). 

Table 10. Total Customer Counts – Small Non-Residential NH Electric and Gas Utilities 

Utility Count % Total 

PSNH 72,031 74% 

NH Electric Coop 9,845 10% 

Unitil 9,092 9% 

National Grid-Electric 6,627 7% 

Subtotal Electric* 97.595 100% 

National Grid-Gas 5,708 47% 

Northern Utilities 6,470 53% 

Subtotal Natural Gas+ 12,178 100.0% 

* Excludes municipal electric utility customers 

+ Represents subset of electric customers 

The overall number of estimated large commercial and industrial accounts, as shown in Table 

11 is 2,369. In summarizing data by SIC code in the top portion of the table, information 

provided by the utilities during the data acquisition/submission process was used. Not all 

utilities had complete customer SIC code information available for use, but based upon the SIC 

information received, manufacturing, services and retail trade were the three largest sectors 

observed. The bottom portion of the table allocates those SIC codes associated with 

Manufacturing as Industrial and the remainder as Commercial. As can be seen, just over 31% 

of those accounts classified in the data are industrial accounts. 

The information presented in Table 11, shows an estimate of New Hampshire’s large 

commercial and industrial (C&I) customer population based on a count of the number of utilityprovided customer accounts. For these larger accounts, it is helpful to view the customers 

based on their energy usage. As shown below in Table 12, the overall amount of electric 

consumption among the utilities’ larger commercial and industrial customers is estimated to be 

over 3,700 GWh. Although the industrial sector customers represent less than one third of all 

accounts classified in the utility data, these industrial sector customers represent nearly 43% of 

the consumption of all classified accounts. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 27 

Table 11. Customer Count of Large C&I Population Summary (Number of Accounts) 

SIC Code Grouping Gas Service Electric Only Total 

By SIC Code Grouping 

01-09: Agriculture, Forestry and Fishing 0 2 2 

10-14: Mining 0 3 3 

15-17: Construction 2 1 3 

20-39: Manufacturing 192 367 559 

40-49: Transportation and Public Utilities 26 93 119 

50-51: Wholesale Trade 9 21 30 

52-59: Retail Trade 134 240 374 

60-67: Finance, Insurance and Real Estate 46 97 143 

70-89: Services 137 352 489 

91-97: Public Administration 27 36 63 

99: Non Classified Establishments 3 6 9 

Not Provided 69 506 575 

Total 645 1,724 2,369 

By Commercial vs. Industrial 

Commercial 384 851 1,235 

Industrial 192 367 559 

Not Provided 69 506 575 

Total 645 1,724 2,369 

Table 12. Electric Energy Consumption of Large C&I Population Summary (kWh - 2007) 

SIC Code Grouping Gas Service Electric Only Total 

By SIC Code Grouping 

01-09: Agriculture, Forestry and Fishing 0 3,457,200 3,457,200 

10-14: Mining 0 1,345,640 1,345,640 

15-17: Construction 357,542 566,960 924,502 

20-39: Manufacturing 522,971,163 942,650,275 1,465,621,438 

40-49: Transportation and Public Utilities 50,356,974 184,107,025 234,463,999 

50-51: Wholesale Trade 9,624,800 27,927,516 37,552,316 

52-59: Retail Trade 184,430,793 395,511,692 579,942,485 

60-67: Finance, Insurance and Real Estate 62,338,610 225,973,672 288,312,282 

70-89: Services 165,927,326 567,668,427 733,595,753 

91-97: Public Administration 34,460,608 51,171,299 85,631,907 

99: Non Classified Establishments 2,094,145 3,322,085 5,416,230 

Not Provided 48,549,165 240,062,143 288,611,309 

Total (kWh) 1,081,111,126 2,643,763,935 3,724,875,061 

By Commercial vs. Industrial 

Commercial 509,590,798 1,461,051,516 1,970,642,314 

Industrial 522,971,163 942,650,275 1,465,621,438 

Not Provided 48,549,165 240,062,143 288,611,309 

Total (kWh) 1,081,111,126 2,643,763,935 3,724,875,061 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 28 

2.2.1 Survey Respondent Characteristics

The primary data collection efforts for this project included a combination of phone and site 

surveys of residential, commercial and industrial New Hampshire customers. The surveys were 

used to obtain a great deal of customer demographic information. The most relevant customer 

demographic information is summarized below; additional information obtained from the surveys 

is presented in Appendix J to this report. 

Of residential survey respondents, 94% were permanent as opposed to seasonal residents, and 

nearly 80% of respondents owned the property they were living in. Over 53% of homes were 

more than 28 years old. Over 72% of respondents had completed at least some college and 

nearly 18% have completed postgraduate studies. 

Among the small commercial and industrial respondents, 62% owned the property and 38% 

leased the space. 98.5% of respondents pay for electricity in the space. The mean square 

footage of the small commercial and industrial facilities surveyed was 11,747 square feet. 

Among the large commercial and industrial respondents, 73% owned the property and 27% 

leased the space. The mean square footage of the large commercial and industrial facilities 

surveyed was nearly 90,000 square feet. 

2.3 Forecasted Electricity and Natural Gas Sales in New Hampshire 

Based on sales information provided directly by this project’s four participating electric utilities 

and two participating natural gas distribution companies, total and customer sector-specific 

energy (GWH), demand (MW) and fuel (MMBTu) forecasts were compiled. Where applicable, 

these forecasts were compared against relevant ISO-NE and EIA data to assess 

reasonableness. As shown in Figure 10, electric energy sales projected by the four participating 

electric utilities in New Hampshire is projected to grow from approximately 11,200 GWH in 2008 

to over 13,000 GWH by the year 2018. This represents an annual rate of 1.3 percent. This 

represents nearly 93 percent of the total electric energy sales in the state, when compared with 

ISO-NE’s latest forecast and appears reasonable given that the utility forecasts do not include 

sales from a number of smaller municipal electric utilities that also serve customers in the state. 

Figure 11 shows how the utilities’ electric energy sales projections are broken down between 

residential, commercial and industrial customer sectors. The residential sector has the greatest 

sales, approximately 40 percent of total sales (4,537 GWH) in 2008, and is projected to grow 

slightly to 5,590 GWH by 2018 (representing a 1.7 percent annual growth rate). Commercial 

sector sales also currently make up approximately 40 percent (4,525 GWH) of the combined 

utilities’ total 2008 electric energy sales, and are projected to grow just slightly to 5,354 GWH by 

2018 (a 1.4 percent projected annual growth rate). The industrial sector currently represents 19 

percent of total 2008 sales (2,126 GWH) and is expected to stay fairly constant, dropping 

slightly to 2,103 GWH by the year 2018 (a 0.1 percent annual decline). This figure also shows 

approximately 42 GWH/year in projected street lighting sales (representing 0.3 percent of total 

projected sales in 2018). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 29 

Figure 10. Forecasted Electric GWH Sales Total (2008 – 2018) - from Utility Data vs. ISO-NE 

Projections

5,000

6,000

7,000

8,000

9,000

10,000

11,000

12,000

13,000

14,000

15,000

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Utility-Provided Forecasts

(Total)

ISO-NE Forecast (NH Base)

Figure 11. Forecasted Electric GWH Sales By Sector (2008 – 2018)

-

1,000

2,000

3,000

4,000

5,000

6,000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Residential

Commercial

Industrial

Other Energy
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 30 

Electric system peak load for the combined four participating electric utilities in New Hampshire, 

as shown in Figure 12, is projected to grow from approximately 2,400 MW in 2008 to nearly 

3,000 MW by the year 2018 (an annual rate of 1.8 percent). This represents nearly 95% of the 

state’s total forecasted electric demand, when compared with ISO-NE’s latest forecast, and 

appears reasonable given that the utility forecasts do not include peak load projections from a 

number of smaller municipal electric utilities that also serve customers in the state. Figure 13 

shows how the utilities’ electric peak load projections are broken down between residential, 

commercial and industrial customer sectors. The commercial sector has the greatest peak 

demand, approximately 43 percent (1,023 MW) in 2008, and is projected to grow slightly to 

1,279 MW by 2018 (representing a 1.8 percent annual growth rate). Residential sector demand 

currently makes up approximately 40 percent (962 MW) of the combined utilities’ total 2008 

peak, and is projected to grow to 1,206 MW by 2018 (also a 1.8 percent projected annual 

growth rate). The industrial sector currently represents just under 17 percent of total 2008 peak 

load (962 MW) and is expected to grow to 498 MW by the year 2018 (a 1.9 percent annual 

increase). This figure also shows approximately 3 MW per year in projected street lighting 

demand (constant for the period 2008 through 2018). 

Figure 12. Forecasted Electric Demand (MW) Total 2008 – 2018 - Utility Data vs. ISO-NE 

Projections

1,000

1,500

2,000

2,500

3,000

3,500

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Utility-Provided Forecasts

(Total)

ISO-NE Forecast (NH Base)
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 31 

Figure 13. Forecasted Electric Demand (MW) By Sector 2008 – 2018

-

200

400

600

800

1,000

1,200

1,400

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Residential

Commercial

Industrial

Other Peak

In addition to electric energy and peak demand, this study estimates the potential for additional 

natural gas energy efficiency and related propane and fuel oil savings opportunities. As such, 

Figure 14, shows that natural gas sales is projected to grow from 20,640 MMBTu in period 2008 

to 26,283 MMBTus by 2018 (an annual growth rate of 2 percent).25 This compares reasonably 

to the most recent data available from the US Department of Energy’s Energy Information 

Administration (EIA), where New Hampshire’s natural gas sales for 2007 was estimated to be 

21,722 MMBTu. Figure 15 shows how New Hampshire’s the natural gas utilities’ MMBTu sales 

projections are broken down between residential, commercial and industrial customer sectors. 

The commercial sector has the greatest sales approximately 44 percent (9,428 MMBTu) in 

2008, and is projected to grow to 12,666 MMBTu by 2018 (representing a 2.6 percent annual 

growth rate). Residential sector sales currently makes up approximately 36 percent (7,698 

MMBTu) of the combined utilities’ total 2008 natural gas sales, and is projected to grow to 8,189 

MMBTu by 2018 (a 0.6 percent projected annual growth rate). The industrial sector currently 

represents just over 19 percent of total 2008 sales (4,041 MMBTu) and is expected to grow to 

5,428 MMBTu by the year 2018 (a 2.6 percent annual increase). 

 

25 Based on participating New Hampshire Natural Gas distribution company-provided projections. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 32 

Figure 14. Forecasted Natural Gas MMBTu Sales Total (2008 – 2018) - Utility Projections vs. EIA 

Data

-

5,000

10,000

15,000

20,000

25,000

30,000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Utility-Provided Forecasts

(Total)

2007 EIA Estimate

Figure 15. Forecasted Natural Gas MMBTu Sales By Sector (2008 – 2018)

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Residential

Commercial

Industrial
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 33 

New Hampshire’s electric and natural gas utilities have been operating energy efficiency 

programs for a number of years. The above forecasts reflect the energy savings that have 

already resulted from these utilities’ previous efficiency program efforts. 

In New Hampshire, as with all states, the growth in the demand for electricity and natural gas 

will vary by region where some regions may see much higher growth rates. On a statewide 

basis, however, areas showing faster growth are offset by slower growth areas of the state to 

produce an overall projected growth rate of approximately only 1.3, 1.8 and 2.0 percent for 

electric energy, demand and natural gas sales respectively. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 34 

Section 3: Overall Project Implementation Approach 

This section of the report presents an overview of the approach and methodologies used by the 

GDS Team for completion of each of the following tasks: 

• Analyzing current saturations of energy using equipment and penetrations of energy 

efficiency equipment and practices in each end-use sector 

• Producing an up-to-date list of currently available and soon to be commercially available 

technologies which may play a part in future efficiency programs 

• Estimating customer participation rates/levels by program, based on different 

payback/incentive levels and define/analyze significant barriers that customers face 

when investing in additional energy efficiency 

• Developing, by sector, a simplified end-use model of state electricity and natural gas 

consumption and peak demand 

• Estimating, state-wide and for each of the four New Hampshire retail electricity providers 

and two natural gas distribution companies, the technical, maximum achievable, 

maximum achievable cost effective, and potentially obtainable scenario for electricity, 

natural gas, and related propane and fuel oil savings over the next 10 year period, and 

the budgets (where appropriate) required to achieve that potential 

• Evaluating extent to which past and current energy efficiency programs have achieved 

energy savings to date, provide sensitivity analysis of realized energy savings based on 

different resource levels (including absence of current SBC-funded model), and 

recommend modifications to program and measure offerings that would increase the 

likelihood of achieving identified potential 

3.1 Energy Using Equipment Saturations and Efficiency 

Penetrations Analysis 

This task represents a major enhancement to technical potential studies that have been 

conducted across the country in the past. Rather than relying on best available information from 

existing secondary sources to estimate current levels of energy using equipment saturations 

and penetration of energy efficiency measures, significant primary data collection efforts were 

undertaken to help inform and derive New Hampshire-specific values where possible within the 

time requirements and work scope specified for this project. As such, this effort was completed 

through a combination of primary and secondary data collection and analysis activities. 

Detailed results and an assessment of the value resulting from this enhanced, New Hampshirespecific data collection effort is presented in Section 7 of this report. Following is a discussion 

of the methodologies utilized to complete this task. 

First, a measure list was compiled, the approach for which is described in Section 3.2 below. 

The current saturation of each relevant type of energy using equipment and the penetration of 

associated energy efficiency equipment and practices was then determined. In this effort, it was 

important to recognize and quantify differences in end-use saturations and penetrations 

between the residential, commercial and industrial sectors, and building types within in each 

sector (see Table 13, Table 14 and Table 15). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 35 

Table 13. Residential Sector Building Types and Energy Using Equipment

Building Types/Considerations

Single Family 

Multi Family 

Low Income 

Existing Homes 

New Construction 

Energy Using Equipment/End-Use Measures

Appliances 

Water Heating 

Space Conditioning (heating/cooling) 

Lighting 

Building Envelope 

Other (pools, standby power) 

Table 14. Commercial Sector Building Types and Energy Using Equipment

Building Types/Considerations

Warehouse 

Retail 

Grocery 

Office 

Lodging 

Health 

Restaurant 

Education 

Other (assembly, etc.) 

Existing Buildings/New Construction 

Energy Using Equipment/End-Use Measures 

Appliances, Computers & Office Equipment 

Water Heating 

Space Heating 

Space Cooling – Chillers 

Space Cooling – Unitary & Split AC 

Ventilation 

HVAC Controls 

Non-HVAC Motors 

Building Envelope 

Lighting 

Lighting Controls 

Refrigeration 

Cooking 

Compressed Air 

Pools 

Other (transformers) 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 36 

Table 15. Industrial Sector Business Types and Energy Using Equipment

Business Types/Considerations

Apparel And Other Finished Products Made From Fabrics And Similar 

Materials 

Chemicals And Allied Products 

Electronic And Other Electrical Equipment And Components, Except 

Computer Equipment 

Fabricated Metal Products, Except Machinery And Transportation Equipment 

Food And Kindred Products 

Furniture And Fixtures 

Industrial And Commercial Machinery And Computer Equipment 

Leather And Leather Products 

Lumber And Wood Products, Except Furniture 

Measuring, Analyzing, And Controlling Instruments; Photographic, Medical, 

And Optical Goods; Watches And Clocks 

Miscellaneous Manufacturing Industries 

Paper And Allied Products 

Petroleum Refining And Related Industries 

Primary Metal Industries 

Printing, Publishing, And Allied Industries 

Rubber And Miscellaneous Plastics Products 

Stone, Clay, Glass, And Concrete Products 

Textile Mill Products 

Tobacco Products 

Transportation Equipment 

Energy Using Equipment/End-Use Measures 

Conventional Boiler Use 

CHP and /or Cogeneration Process 

Process Heating 

Process Cooling and Refrigeration 

Machine Drives 

Electro-Chemical Processes 

Other Process Use 

Facility HVAC 

Facility Lighting 

Other Facility Support 

Onsite Transportation 

Conventional Electric Generation 

Other Non-Process Use 

As noted above, a combination of primary and secondary data collection and analysis activities 

were conducted by the GDS Team to develop the New Hampshire sector and building-specific 

saturation and penetration rates used for this report. Primary data collection consisted of 

telephone surveys of a statistically valid sample of residential and small commercial/industrial 

customers (400 residential customers and 200 small commercial customers) and site visits for a 

sample of 100 larger commercial and 100 industrial customers. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 37 

3.1.1 Survey Instruments and Site Visit Data Collection Forms

The process of developing survey questions and site visit data collection forms was mostly 

dictated by the types of data required by the computer models being used by the GDS Team to 

estimate energy saving potential. For the questions that examined current saturations and 

penetrations of energy efficient equipment and practices, GDS first identified a list of currently 

and soon-to-be commercially available technologies that may play a part in future efficiency 

programs; then specific questions that address these technologies were developed. 

For the phone surveys, survey instruments from two existing studies served as references: the 

2004 California Statewide Residential Appliance Saturation Study (for the residential 

questionnaire), and the U.S. Department of Energy’s 2003 Commercial Buildings Energy 

Consumption Survey (for the small commercial/industrial questionnaires). For the site visit data 

collection forms, instruments based on somewhat relevant previous projects were used as a 

starting point. In addition, the survey instruments included questions to explore customers’ 

attitudes toward and perceptions of energy efficiency. These questions addressed factors that 

affect the adoption of energy efficiency measures, significant barriers customers may face when 

investing in energy efficiency measures, awareness of energy efficiency, program participation 

and satisfaction, and past purchase practices. A major challenge in this effort was to develop 

instruments that would provide useful information on a number of important energy end-use 

measures, without requiring respondents to spend too much time on the phone, or on site. 

Targeted durations of 15 minutes per phone survey and 2.5 hours per site visit were set. 

New Hampshire Public Utility Commission reviewed preliminary research instruments, both for 

the phone surveys and site visits, in several phone conferences, and discussed their priorities 

with the GDS Team. Based on these discussions, RIA finalized the phone survey instruments 

for the residential and the small commercial/industrial surveys which primarily asked questions 

in a closed-ended format, with a few opportunities for verbatim responses. GDS and RLW 

finalized the site visit data collection forms using identical questions from the phone surveys 

wherever practical and a tabular format for collection of end-use area and measure specific 

saturation and penetration data. Appendix A presents the Team’s Residential Sector Telephone 

Survey. Appendix B is the Small Commercial/Industrial Sector Telephone Survey. Appendix C 

provides a copy of the On-Site Data Collection Instrument for the Larger Commercial and 

Industrial Sector. 

3.1.2 Sampling

The sampling plans for residential and small commercial/industrial telephone surveys were 

developed based on records received from each of the electric and gas utilities. Records that 

represented duplicates due to multiple program participation were combined and participation 

codes were retained for programs. In residential accounts, all low-income customers were 

identified based on their rate code or income flag. Identified low-income customers represented 

5% of the customer accounts received. A similar approach was taken with the small 

commercial/industrial accounts, which were also screened to ensure that all electric accounts 

had less than 100kW demand or 300,000 kWh annual consumption. 

By definition, all records for gas utility customers are duplicates since all gas customers also are 

customers of one of the electric utilities and would be included in those records. Therefore, as 

an initial step, each gas customer record was matched by telephone number to one of the 

electric utilities. The next step was to remove records with no phone number. Table 16 and 

Table 17 display the final sample quotas for the residential and small commercial/industrial 

phone surveys. As shown in Table 16, quotas were included in the residential sample to ensure 

representation from both the non-low-income and low-income populations, and for electric and 

gas customers associated with each of the four major electric and two major natural gas utilities 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 38 

in the state. The residential sample is designed to achieve 5 percent precision at a 95 percent 

confidence level, with 10 percent precision and 90 percent confidence level for each of the 

utilities. 26 

Table 16. Sample Quotas - Residential NH Electric and Gas Utilities 

Utility 

Non Low Income Low Income 

Total Random 

Draw Electric Only w/Gas Service N Random Draw 

Electric 

Only 

w/Gas 

Service N 

PSNH 3,500 142 33 175 300 12 3 15 190 

NH Electric 

Coop 1,280 52 12 64 120 5 1 6 70 

Unitil 1,280 52 12 64 120 5 1 6 70 

Granite 

State 

Electric 

1,280 52 12 64 120 5 1 6 70 

Totals 7,340 298 69 367 660 27 6 33 400 

As shown in Table 17, specific quotas were also included with the small commercial/industrial 

sector to ensure representation from both electric and gas customers. This small 

commercial/industrial sample is designed to achieve 5 percent precision at the 85 percent 

confidence level, with 12 percent precision at the 85 percent confidence level for each of the 

utilities. 

Table 17. Sample Quotas – Small C/I NH Electric and Gas 

Utility Random 

Draw 

N 

Total 

Electric Only Gas Service 

PSNH 3,325 87 8 95 

NH Electric Coop 1,225 32 3 35 

Unitil 1,225 32 3 35 

Granite State Electric 1,225 32 3 35 

Totals 7,000 183 17 200 

In the end, 411 interviews with residential customers and 200 interviews with small 

commercial/industrial customers were completed. As shown in Table 18 and Table 19, over 

6,100 and 4,000 calls to residential and small commercial customers respectively had to be 

made to fill the 400/200 quotas targeted. More information and summary results from the phone 

survey efforts are presented later in this report. 

Table 18. Disposition of Residential Survey 

DISPOSITION TOTAL % TOTAL 

Complete 411 6.7% 

No answer 789 12.8% 

Answering machine 2,838 46.1% 

Busy 167 2.7% 

Bad number 476 7.7% 

Fax number 29 0.5% 

Call intercept 7 0.1% 

Appointment 451 7.3% 

First refusal 214 3.5% 

 

26. Estimates for subgroups within the residential sample, including the low-income estimates, are based on smaller 

sample sizes. Thus the margin of error for these estimates is higher 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 39 

DISPOSITION TOTAL % TOTAL 

Second refusal 549 8.9% 

Language barrier 24 0.4% 

No eligible respondent 24 0.4% 

Business – NPR 95 1.5% 

Never call 15 0.2% 

Quota full 0 0.0% 

Partial – Callback 39 0.6% 

Partial – Refusal 31 0.5% 

TOTAL DIALINGS 6,159 100% 

INCIDENCE (%) 95.46 

Table 19. Disposition of Small Commercial/Industrial Survey 

DISPOSITION TOTAL % TOTAL 

Complete 200 4.9% 

No answer 1,750 43.1% 

Answering machine 81 2.0% 

Busy 151 3.7% 

Bad number 135 3.3% 

Fax number 25 0.6% 

Updated contact 176 4.3% 

Appointment 815 20.1% 

First refusal 61 1.5% 

Second refusal 285 7.0% 

Language barrier 3 0.1% 

No eligible respondent 12 0.3% 

Private residence 194 4.8% 

Never call 3 0.1% 

Quota full 0 0.0% 

Partial – Callback 55 1.4% 

Partial – Refusal 25 0.6% 

Own but not occupy 10 0.3% 

Residential use 6 0.2% 

Made at corporate 76 1.9% 

TOTAL DIALINGS 4,063 100% 

INCIDENCE (%) 73.13 

A key element of the larger commercial and industrial on-site surveys was the systematic 

selection of sample points to visit. As originally proposed, 200 site visits were targeted for 

performance overall. A sample size of 68 provides an expected absolute precision of 10% for 

proportional results. This suggests that a sample size of 200 can be considered adequate for 

the consideration of targeting sub-groups of the sample such as commercial versus industrial or 

fuel types (gas). 

Table 20 below presents the number of accounts determined to be Large C&I after identifying27

them from the sponsor provided electric customer data. The overall number of estimated large 

commercial and industrial accounts is 2,369. In summarizing data by SIC code in the top 

portion of the table, information provided by the sponsors during the data acquisition/submission 

process was used. Not all sponsors had SIC code information available for use, but based 

upon the SIC information received, manufacturing, services and retail trade were the three 

largest sectors observed. The bottom portion of the table allocates those SIC codes associated 

with Manufacturing as Industrial and the remainder as Commercial. Just over 31 percent of 

those accounts classified in the data are industrial accounts. 

 

27 For PSNH, Large C&I were defined as their rate code GV or LV. For Unitil, they were defined as having a demand greater than 

100 kW based upon a provided Total Demand field or as having more than 300,000 kWh of annual consumption if demand was not 

available. Similarly for National Grid they were defined as customers having a demand greater than 100 kW based upon a provided 

Average Bill Demand kW field or as having more than 300,000 kWh/year if demand was not available. NHEC provided a list of 

Small C&I customers as queried to meet the study designated Small C&I definition of accounts with less than 100 kW of demand 

when available, otherwise less than 300,000 kWh/year. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 40 

To identify gas customers, the GDS Team gathered gas customer data from the sponsors. 

Using the address, company name, and phone numbers within the gas customer data, a total of 

645 customers within the large C&I electric customer dataset were identified as having gas 

service. There are sure to be more gas customers in the sample frame beyond those identified. 

In fact we received a total of 12,178 total (small and large) gas commercial or industrial 

customer records from National Grid and Northern Utilities from which the 645 Large 

Commercial and Industrial accounts were successfully mapped in to the dataset of identified 

large electric Commercial and Industrial customers. This identified group represents just over 

31 percent of the Large C&I population gathered from the electric sponsors. 

Table 20. Large C&I Population Summary (Accounts) 

SIC Code Grouping Gas Service Electric Only Total 

By SIC Code Grouping 

01-09: Agriculture, Forestry and Fishing 0 2 2 

10-14: Mining 0 3 3 

15-17: Construction 2 1 3 

20-39: Manufacturing 192 367 559 

40-49: Transportation and Public Utilities 26 93 119 

50-51: Wholesale Trade 9 21 30 

52-59: Retail Trade 134 240 374 

60-67: Finance, Insurance and Real Estate 46 97 143 

70-89: Services 137 352 489 

91-97: Public Administration 27 36 63 

99: Non Classified Establishments 3 6 9 

Not Provided 69 506 575 

Total 645 1,724 2,369 

By Commercial vs. Industrial 

Commercial 384 851 1,235 

Industrial 192 367 559 

Not Provided 69 506 575 

Total 645 1,724 2,369 

Table 21 below presents the electrical consumption (kWh) of the Large C&I customers, also by 

SIC Code and Commercial versus Industrial. The overall amount of electric consumption 

among the large Commercial and Industrial sample frame is estimated to be 3,725 GWh. 

Although the industrial accounts represent a third of the accounts classified in the sponsor data, 

they represent nearly 43 percent of the consumption of all classified accounts. 

Table 21. Large C&I Population Summary (kWh) 

SIC Code Grouping Gas Service Electric Only Total 

By SIC Code Grouping 

01-09: Agriculture, Forestry and Fishing 0 3,457,200 3,457,200 

10-14: Mining 0 1,345,640 1,345,640 

15-17: Construction 357,542 566,960 924,502 

20-39: Manufacturing 522,971,163 942,650,275 1,465,621,438 

40-49: Transportation and Public Utilities 50,356,974 184,107,025 234,463,999 

50-51: Wholesale Trade 9,624,800 27,927,516 37,552,316 

52-59: Retail Trade 184,430,793 395,511,692 579,942,485 

60-67: Finance, Insurance and Real Estate 62,338,610 225,973,672 288,312,282 

70-89: Services 165,927,326 567,668,427 733,595,753 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 41 

91-97: Public Administration 34,460,608 51,171,299 85,631,907 

99: Non Classified Establishments 2,094,145 3,322,085 5,416,230 

Not Provided 48,549,165 240,062,143 288,611,309 

Total 1,081,111,126 2,643,763,935 3,724,875,061 

By Commercial vs. Industrial 

Commercial 509,590,798 1,461,051,516 1,970,642,314 

Industrial 522,971,163 942,650,275 1,465,621,438 

Not Provided 48,549,165 240,062,143 288,611,309 

Total 1,081,111,126 2,643,763,935 3,724,875,061 

Based upon the exploration of the Large C&I data gathered, a sample approach that targeted 

large industrial and large commercial equally seemed reasonable – particularly given that the 

modeling and analysis of additional energy efficiency potential will be done discretely for each 

and that the consumption of industrial versus commercial accounts is moderately close to 50/50. 

The sample design requested in the RFP asked for adequate representation of each sponsor 

(i.e., the four electric utilities and the two natural gas distribution companies) in the final sample. 

Table 22 presents the number of accounts by utility for electric and gas as determined from 

aggregating the entire large commercial and industrial electric customer information and an 

effort to map in information from the gas utilities based upon information in common fields. 

Table 22. Large C&I Accounts by Sponsor and by Commercial versus Industrial 

Utility 

Commercial Industrial Unclassified Total 

N % N % N % N % 

Electric 

NHEC 15 1% 0 0 316 57% 331 14% 

PSNH 1,012 83% 492 88% 8 1% 1,512 65% 

Unitil 84 7% 

12 

2% 158 29% 254 11% 

GSE 112 9% 58 10% 71 13% 241 10% 

Total 1,223 100% 562 100% 553 100% 2,338 100% 

Gas 

National Grid 232 61% 142 74% 30 82% 404 63% 

Northern 149 39% 50 26% 42 18% 241 37% 

Total 381 100% 192 100% 72 100% 645 100% 

Due to an inability to fully categorize all of the sponsor information by the various sectors among 

electric customers, the GDS Team felt that the best approach to sampling for the large C&I site 

visits would be to target 100 commercial facilities and 100 industrial facilities with minimum 

sample quotas for each electric utility with an overall quota for gas customers. Such a sample 

would further seek to balance the need for targeting the number of large C&I customers from 

each sponsor to their approximate portion of the total (with a minimum quota size of 7) with the 

need to visit customers with gas use. This would be done iteratively as the recruitment process 

proceeds depending upon the actual incidence of gas customers among the recruited Large C&I 

sample frame (discussed later). 

Table 23 below provides the GDS Team’s proposed sample design in which we have allocated 

the targeted visits within the commercial and industrial categories similarly as the proportion of 

accounts by utility in each category are very similar (Table 22). The predicted gas column in 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 42 

Table 23 provides an estimate of the number of gas customers that would naturally fall into the 

sample for each sponsor given the identified gas customers in each sector – along with the gas 

utility they are likely to represent. An estimated 64 gas customers were anticipated to be 

recruited naturally in this regard, comprised of 30 in the large commercial sector and 34 in the 

industrial. Given the interest in gas measures as part of this study, the GDS Team believed that 

additional targeting of this group of customers was needed. Therefore, visiting 68 gas 

customers overall was suggested, which targets a 90 percent confidence with +/-10 percent 

relative precision for proportional results. Therefore, depending on the incidence of gas 

customers experienced in the recruitment process, an additional 4 gas sites may need to be 

explicitly targeted to achieve a total of 68. 

Table 23. Large C&I Sample Design 

Utility 

Large Commercial Large Industrial 

N Predicted Gas Subset N Predicted Gas Subset 

NHEC 7 0 7 0 

PSNH 76 16 NGRID and 9 Northern 75 21 NGRID and 7 Northern 

Unitil 8 2 NGRID and 1 Northern 7 5 NGRID 

GSE 9 2 (Northern) 11 2 (Northern) 

Total 100 30 100 34 

In the end, all the electric utility-specific quotas were met for both the commercial and industrial 

sectors. The predicted gas utility subsets were exceeded (23 Northern Utility completes vs. 

predicted 21, and 59 National Grid completes vs. predicted 44). 

Scheduling and fielding began on June 9th and all site visits were completed before August 9th, 

2008. Advance letters were sent by the PUC to 500 randomly selected customers within the 

quota areas targeted and a drawing for a $500 gift card was offered to increase likelihood of 

participation.28 Appendix D provides a copy of the PUC advance letter and the GDS Team’s 

recruiting script. Although the GDS Team was able to achieve a 40% response rate (200 

completes, out of a 500 customer sample frame), as discussed in more detail in the section 

below, the time required to recruit, schedule and conduct the site visits, and hard enter and 

analyze all resulting data greatly exceeded original estimates. 

3.1.3 Data Collection and Analysis

The telephone interviews were conducted from RKM Research and Communication’s call center 

using trained, professional survey managers and interviewers who utilized a computer assisted 

telephone interviewing (CATI) system. All staff were thoroughly trained as to the nature of the 

study, the importance of the information being collected, and management of the sample. 

Before the final data collection phase, RKM conducted a pretest with 20 residential and 22 

commercial/industrial completes to identify any problems the respondents or interviewers might 

have understanding questions, or with the survey length. Some modifications were made to 

questions based on the results of the pretest, but these were insignificant and the total number 

of pretests was included in the final dataset. 

Fielding of the phone surveys was conducted from June 17th through 26th, 2008, during the day, 

evening, and weekend hours to reach as many targets as possible. The average length of the 

 

28 NH Industries, Lebanon, NH was the winner of the drawing held on September 5th at the PUC offices. Instead of 

a $500 gift card, per their request, a charitable donation was made on their behalf to the United Way of the Upper 

Valley. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 43 

survey was 16 minutes for the residential survey and 17 minutes for the small 

commercial/industrial survey. To counteract non-response bias, up to six attempts per 

telephone number were made to complete the surveys. All soft refusals were put into a 

separate sample file and were assigned to different interviewers to call back. All were called 

back except those refusals received in the very last days of the study, as a result of the waiting 

period between when the initial refusal was received and when the callback was attempted. 

Detailed call disposition information was presented earlier in Table 18 and Table 19. The 

completed survey data was analyzed using SPSS statistical software and appropriate data entry 

accuracy verification and data cleaning procedures. 

The site visits were conducted by experienced RLW and GDS engineers and trained staff. 

Starting with development of the site visit data collection form, key staff from both RLW and 

GDS (including a number of those that would be conducting the actual site visits) were actively 

involved in the development of the form and the planning/scheduling approach for the site visits. 

All dedicated site visit staff (totaling more than 8 individuals) were thoroughly trained as to the 

nature of the study, the importance of the information being collected, and management of the 

sample. 

As part of the initial data collection phase, RLW and GDS staff pretested the site visit data 

collection form by jointly conducting visits during the first two weeks in the field to identify any 

problems the auditors might have understanding questions or with the visit length, collecting the 

required measure data, and ensuring consistency of interpretation and treatment of equipment 

and situations encountered in the field by multiple auditors. Some modifications were made to 

implementation approaches based on the results of the pretest, but these were insignificant and 

the total number of pretests was included in the final dataset. Open and regular communication 

between the multiple auditors was encouraged and conducted throughout the site visit fielding 

period to maximize consistency. 

Fielding of the site visits was conducted from June 9th through August 9th, 2008, during the 

workday hours to reach as many targets as possible. The average length per site visit was 3.25 

hours. Project sponsors were kept aware of weekly schedules and attended as observers on a 

number of the site visits. To counteract non-response bias, up to six attempts per potential 

respondent were made to recruit facilities for the site visits. After identifying the correct person 

or persons to speak with at the targeted facility, all soft refusals or referrals to other personnel 

within the office or corporate headquarters location, were noted in the sample file and called 

back. All were called back except those refusals received in the very last days of the study, as 

a result of the waiting period between when the initial refusal was received and when the 

callback was attempted. After preparing the random sample required to fill specified quotas, 

additional facility names were not added until a direct refusal was received or six attempts were 

made to recruit each facility on the quota list. 

The completed survey data for each site was recorded in paper files (22 pages per completed 

site visit) and was entered manually into an analyzable Excel spreadsheet file. Direct 

conversations between data entry personnel and field data collection staff were held when 

necessary to ensure proper interpretation of field notes. Data entry accuracy verification and 

data cleaning procedures were employed and analyses were conducted using pivot tables and 

targeted data mining where appropriate. 

3.1.4 Derivation of Saturation and Penetration Values and Weighting of Results

Results from the phone surveys and site visits were analyzed to derive values for saturation of 

energy using equipment and penetration of energy efficiency equipment and practices, where 

applicable, in each end-use sector. Results from these analyses are discussed in more detail in 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 44 

Section 7 of this report. For the residential sector and small commercial/industrial sector phone 

surveys, multiple cross tabulations were run to identify appropriate measure-specific responses 

based on heating source, single and multifamily housing types, building type and numerous 

other relevant variables. 

For the larger commercial and industrial facility results, values were derived using multiple pivot 

tables. In all cases where sufficient responses existed (N>30, or lower if deemed to be 

reasonably representative of the building type of interest), values from the site surveys were 

reported for the specific building type. Otherwise, values were averaged across and applied to 

all building types for a specific measure. This was done to ensure that statistical validity was 

maintained in the model and that results were not skewed by a small number of responses. 

Results from the small commercial and industrial phone surveys and large commercial and 

industrial site visits were combined using a weighted average. The weighting factors were 

developed using customer-specific energy sales information provided by the electric and gas 

utilities for their small (less than 100kW demand) and large customers. The weighting factors 

were based on the ratio of total electrical consumption in the small commercial and industrial 

sector compared to total electrical consumption in the large commercial and industrial sector. 

This ratio was applied to the results for the small and large customers to determine a weighted 

average for both the electric and non-electric models. 

Excellent New Hampshire-specific information was collected on saturations and penetrations 

(referred to in our models as base and remaining factors) for a number of residential, 

commercial and industrial energy using equipment through the phone surveys and site visits 

conducted as part of this project. Such real customer-specific values have typically not been 

collected as part of the numerous technical potential studies that have been completed to-date 

for others across the country. Given the extensive list of measures identified for assessment in 

this study (as discussed in more detail in the section below), it was not possible to develop 

survey and site visit instruments of sufficient depth and breadth to collect information from which 

to derive values for all measures of interest to the Commission, OCA and the project’s 

participating utilities. As such, in numerous cases, secondary sources for penetration and 

saturation data were identified, used and documented. Wherever possible, these secondary 

sources were verified for reasonableness, or modified based on results obtained through this 

project’s primary data collection activities. 

3.2 Measures List Development 

This task was initially proposed to be based mainly on the GDS Team’s existing information and 

databases of sector-specific electricity, gas and other fossil fuel end-use technologies and 

efficiency measures, and was to be supplemented as necessary to ensure inclusion of other 

technology areas of interest to the Commission, the OCA, and the four electric and two gas 

utilities supporting this project. Initial lists of electric and natural gas measures were compiled 

by GDS for the state’s residential, commercial and industrial customer sectors, and were shared 

with the project sponsors on April 3rd for review and comment. As shown in Table 24, Table 25, 

and Table 26, these initial sector-specific lists contained a total of 252 unique measures (79 

residential, 130 commercial, and 43 industrial). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 45 

Table 24. Measure End Uses and Number of Measures Per End Use – Residential 

Residential Sector 

Electric Measures Non-Electric Measures 

Appliances 9 2 Dryers 

Lighting 4 7 Building Envelope 

Space Heating, Cooling and Building 

Envelope 21 8 Space Heating, Cooling and Building 

Envelope 

Water Heating 9 13 Water Heating 

Standby Power 1 - 

Pools 1 - 

New Construction 1 - 

Low Income 3 - 

Total Measures in Sector 49 30 79 (total) 

Table 25. Measure End Uses and Number of Measures Per End Use - Commercial

Commercial Sector 

Electric Measures Non-Electric Measures 

Space Heating 3 17 Space Heating 

Water Heating 5 12 Water Heating 

Building Envelope 2 7 Building Envelope 

Space Cooling – Chillers 3 3 Pool heating 

Space Cooling – Packaged AC 8 1 Dryers 

Space Cooling – Maintenance 3 6 Cooking 

HVAC Controls 4 - 

Ventilation 11 - 

Motors 2 - 

Lighting 20 - 

Lighting Controls 7 - 

Refrigeration 12 - 

Compressed Air 2 - 

Monitor Power Management 1 - 

Transformers 1 - 

Total Measures in Sector 84 46 130 (total) 

Table 26. Measure End Uses and Number of Measures Per End Use - Industrial

Industrial Sector 

Electric Measures Non-Electric Measures 

Process Heating 1 2 Process Heating 

Process Cooling & Refrigeration 1 20 Space Heating 

Machine Drives 1 5 Water Heating 

Facility HVAC 1 7 Building Envelope 

Facility Lighting 1 - 

Other Facility Support 1 - 

Onsite Transportation 1 - 

Sensors & Controls 1 - 

Other End Uses 1 - 

Total Measures in Sector 9 34 43 (total) 

Following multiple meetings and discussions over the subsequent 5 month period, ending 

September 26th, 2008, these lists grew by nearly a factor of two to 471 individual measures as 

shown in Table 27, Table 28 and Table 29. A significant amount of time was also expended 

during this period identifying, reviewing and documenting secondary and other available data 

sources to develop reasonable assumptions regarding measure lives, installed incremental and 

full costs (where appropriate), and electric energy, demand, and MMBTu savings associated 

with each of the measures included on the final lists.29 Please refer to Appendix E for a 

comprehensive listing of all residential electric and non-electric measures and associated 

 

29 The GDS Team’s existing sector-specific technical potential calculation models were also modified substantially 

during this period to accommodate the large increase in the number of measures and expanded measure categories to 

be assessed. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 46 

assumptions and sources assessed in this report. Appendix F and Appendix G provide similar 

detailed information for the commercial and industrial sectors respectively. 30

Table 27. Measure End Uses and Number of Measures Per End Use - Residential

Residential Sector 

Combined Electric and Non-Electric Measures 

Appliances 17 

Lighting 13 

Space Heating and Cooling 59 

Building Envelope 75 

Water Heating 41 

Standby Power 3 

Pools 9 

New Construction Addressed in Building Envelope 

Low Income Measures 

Total Measures in Sector 217 total (up from 79) 

Table 28. Measure End Uses and Number of Measures Per End Use - Commercial

Commercial Sector 

Electric Measures Non-Electric Measures 

Appliances/Office Equipment 7 - 

Space Heating 3 30 Space Heating 

Water Heating 12 17 Water Heating 

Pools 7 5 Pools 

Building Envelope 5 13 Building Envelope 

Space Cooling – Chillers 7 2 Space Cooling – Chillers 

Space Cooling – Packaged AC 11 5 Process Heat 

Cooking 6 10 Cooking 

HVAC Controls 8 7 HVAC Controls 

Ventilation 17 6 Ventilation 

Motors 2 - 

Lighting 28 - 

Lighting Controls 12 - 

Refrigeration 18 - 

Compressed Air 2 - 

Transformers 1 - 

Total Measures in Sector 146 95 241 total (up from 130) 

Table 29. Measure End Uses and Number of Measures Per End Use - Industrial

Industrial Sector 

Electric Measures Non-Electric Measures 

Process Heating 1 1 Process Heating 

Process Cooling & Refrigeration 1 1 Conventional Boilers 

Machine Drives 1 - 

Facility HVAC 1 1 Facility HVAC 

Facility Lighting 1 - 

Other Facility Support 1 1 Other Facility Support 

Onsite Transportation 1 - 

Sensors & Controls 1 - 

Other End Uses 1 - 

Total Measures in Sector 9 4 13 total (down from 43) 

 

30 Although the measures lists are extensive, they are not exhaustive, particularly for potential fuel oil and propane 

savings. Some potential measures were identified that were not modeled due to data or other limitations. These 

include, but are not limited to air conditioning peak demand savings from off peak cooling with energy storage, 

more advanced windows than double pane with low-E, super high efficiency gas condensing hot water heaters used 

particularly in combo systems that provide both space and hot water heating, data center and certain information 

technology potential energy saving measures, and some emerging but not yet commercialized technologies. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 47 

3.3 Customer Program Participation Rates and Barriers 

Estimates of customer participation rates/levels by program and identification of barriers that 

customers face when investing in additional energy efficiency were developed based mainly on 

direct results from the GDS Team’s phone surveys and site visits. Specifically, for each 

customer sector, questions were asked to assess customers’ attitudes towards energy 

efficiency, past program participation and satisfaction, and barriers that might be preventing 

them from making future investments in energy efficiency. 

3.3.1 Residential Customer Attitudes

The residential survey included questions about respondents’ attitudes toward energy efficiency. 

More specifically, these questions attempted to explore respondents’ level of consideration of 

energy saving and to assess factors that affect the adoption of energy efficiency measures. 

First, the respondents were asked to rate the level of attention their household pays to 

controlling energy costs through general energy efficiency operational practices such as 

adjusting room temperatures, shutting computers and lights off, etc. Table 30 provides the 

result. In general, the level of attention paid to controlling energy cost seems high. About two 

thirds (63 percent) said they pay “substantial attention,” and 30 percent said they pay “some 

attention.” Only a small percentage of the respondents said they pay “very little” or “no attention” 

to these matters (6 percent). 

Table 30. Attention Paid to Controlling Household Energy Costs 

Frequency Valid Percent 

Substantial attention to these matters 256 63.4% 

Some attention 121 30.0% 

Very little attention 20 5.0% 

No attention 5 1.2% 

Don’t know 2 0.5% 

Total 404 100% 

Respondents rated their likelihood of purchasing energy efficient equipment instead of standard 

equipment given several conditions generally assumed to increase the attractiveness of 

adopting energy efficient equipment. Just before introducing this question, the term “energy 

efficient equipment” was defined by stating “I am referring to new equipment specifically 

designed to be more energy efficient than other new models. Energy efficient models typically 

cost more than other models, perhaps 20-30 percent more.” The order of these factors was 

randomized to avoid any response biases. The result is shown in Table 31. Overall, it seems 

the respondents found these factors appealing. In particular, a high percent of respondents (78 

percent) said they would be “extremely likely” to purchase energy efficient equipment if their 

monthly energy bill would be less. This was rated significantly higher than any other factor 

(p<.05). The next highest rated factors were increased comfort, increased home value, feeling 

pro-environment, and receiving a rebate (more than 70 percent of the respondents said they are 

“extremely likely” to choose energy efficiency equipment as a result of these factors). 31 In 

contrast, “sales persons’ recommendation” was rated significantly lower than any other factors 

(p<.05). Twenty-four percent reported they were “not at all likely” and 35 percent said they were 

“extremely likely” to purchase energy efficient equipment over standard items given this (sales 

person recommendation) condition. 

 

31 Respondents with lower educational achievement rated this factor significantly lower (p<.05). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 48 

Table 31. Likelihood of Purchasing Energy Efficient Equipment 

1=NOT AT 

ALL 

LIKELY 

2 3 4 5= 

EXTREMELY 

LIKELY 

TOTAL 

a... your monthly 

energy bill would be 

less 

N 12 1 22 50 306 391 

Row % 3% 0% 6% 13% 78% 100% 

b... it increased the 

level of comfort 

N 22 7 31 55 276 391 

Row % 6% 2% 8% 14% 71% 100% 

c... you felt you were 

helping to protect 

environment 

N 29 8 24 55 278 394 

Row % 7% 2% 6% 14% 71% 100% 

d... it increased the 

home value 

N 23 7 26 30 224 310 

Row % 7% 2% 8% 10% 72% 100% 

e... you received a 

rebate 

N 33 2 29 53 278 395 

Row % 8% 1% 7% 13% 70% 100% 

f... your sales 

person 

recommended it 

N 91 25 74 59 134 383 

Row % 24% 7% 19% 15% 35% 100% 

Note: “Don’t know” responses were treated as missing data. Frequency of “it increased the home value” (d) is 

shown only if the respondents were home owners. 

Table 32 shows factors respondents identified as barriers to investing in energy efficiency 

measures. The table provides a coded summary of the open-ended responses. Nearly three 

quarters of the responses dealt with uncertainty of payback and initial higher upfront costs (71 

percent). In a distant second place, 10 percent of respondents said that current equipment is 

meeting their needs; 5 percent said they are renters and not able to do home improvements. 

Six percent of the respondents were concerned about various aspects of energy efficient 

equipments such as quality, design, features, and safety. 

Table 32. Primary Reasons for Not Purchasing Efficient Equipment/Making Efficiency Improvements 

Frequency Valid Percent 

Cost / benefit, payback 189 71% 

Current equipment is satisfactory 27 10% 

Renters, not owners of property 14 5% 

Quality concern 10 4% 

Concerned about cosmetics, features 3 1% 

Concerned about safety 2 1% 

Other 20 8% 

Total 265 100% 

Note: “Don’t know,” “refusal,” and “no reason” responses were treated as missing data. 

3.3.2 Residential Customer Program Awareness and Participation

Finally, the surveyed households were asked about their awareness of and participation in their 

utilities’ energy efficiency programs. Table 33 shows the respondents’ awareness of their 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 49 

utilities’ energy efficiency programs. Overall, approximately 50 percent of the households know 

their utility offers energy efficiency programs. NH Electric Coop’s customers have the highest 

awareness level (61 percent) and Granite State Electric’s customers have the lowest awareness 

(42 percent) of their utilities’ efficiency programs.32 However, the differences in customers’ 

awareness among the four utilities were not statistically significant. 

Table 33. Awareness of Utility’s Energy Efficiency Programs 

GRANITE 

STATE 

ELECTRIC 

NH 

ELECTRIC 

COOP 

PSNH UNITIL TOTAL 

Yes N 28 41 89 35 193 

Column % 41.8% 61.2% 47.1% 51.5% 49.4% 

No N 39 26 100 33 198 

Column % 58.2% 38.8% 52.9% 48.5% 50.6% 

Total N 67 67 189 68 391 

Column % 100% 100% 100% 100% 100% 

Note: “Don’t know” responses were treated as missing data. 

Table 34 shows the respondents’ participation in their utilities’ energy efficiency programs, 

including participation by purchasing products promoted through these programs. Of the 

respondents who are aware of their utilities’ program, the overall participation rate was 31 

percent (15 percent of the sample population). There was no difference in the participation 

rates among the four utilities. One interesting finding was that the low income households 

reported significantly higher participation in efficiency programs.33 The low income group was 

about twice as likely to have participated in such programs (58 percent) as the non-low income 

group (29 percent). Though not shown in the table, the data indicate an extremely high rate of 

satisfaction among participants. Almost all participating respondents reported they would 

participate again in their utilities’ efficiency program if they have a future opportunity. 

Satisfaction and interest in repeat participation was equally high among both low and non-low 

income groups.

Table 34. Participation in Utility’s Energy Efficiency Programs 

GRANITE 

STATE 

ELECTRIC 

NH 

ELECTRIC 

COOP 

PSNH UNITIL TOTAL 

Yes N 9 14 28 8 59 

Column % 33.3% 34.1% 31.8% 23.5% 31.1% 

No N 18 27 60 26 131 

Column % 66.7% 65.9% 68.2% 76.5% 68.9% 

Total N 27 41 88 34 190 

Column % 100% 100% 100% 100% 100% 

Note: This question was asked only if the previous question (PS1) is “yes”. “Don’t know” responses were treated 

as missing. 

 

32 It is important to note that Granite State Electric customers differed significantly from those of the other utilities, 

having a larger proportion of lower income families and the demographic characteristics associated with that. 33 Low income was defined as 183% of Federal Poverty line (per utility low income program eligibility criteria). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 50 

For those who reported not participating in their utilities’ energy efficiency program, an additional 

question was asked about the reasons why they have not participated. Each possible reason 

was read by interviewers and the respondents were allowed to provide multiple reasons. If a 

respondent agreed that a certain reason contributed to their non participation, they were 

considered to have “endorsed” that particular reason. 

Table 35 summarizes the responses. The most frequently mentioned reason was they have not 

recently purchased items that use energy (44 percent). The next most frequent reasons for 

nonparticipation seem to relate to their lack of awareness of or knowledge about utility efficiency 

programs. Twenty-eight percent reported they “did not know they are eligible,” followed by “did 

not know how to find out about the program” (18 percent). It is possible that some respondents 

have not made recent purchases because they are unaware of programs that may alleviate their 

financial concerns about investing in energy efficient products. Several other reasons were 

mentioned with notably high frequencies. Those are: the sales person did not mention the 

program (13 percent), insufficient incentive (12 percent), bought unqualified equipment (11 

percent), and “was not worth the hassle” to participate in programs (10 percent). 

Table 35. Possible Reasons for Not Participating in an Energy Efficiency Program 

ENDORSED NOT ENDORSED TOTAL 

Haven’t recently purchased items N 59 75 134 

Row % 44% 56% 100% 

Didn’t know I was eligible N 37 97 134 

Row % 28% 72% 100% 

Don’t know how to find out more 

about program 

N 24 110 134 

Row % 18% 82% 100% 

Sales person didn’t talk about 

program 

N 18 116 134 

Row % 13% 87% 100% 

Incentives were not enough N 16 118 134 

Row % 12% 88% 100% 

Have purchased items but not 

energy efficient 

N 15 119 134 

Row % 11% 89% 100% 

Wasn’t worth the hassle N 13 121 134 

Row % 10% 90% 100% 

Renter, not owner (from “other: 

specify”) 

N 3 131 134 

Row % 2% 98% 100% 

No need (from “other: specify”) N 3 131 134 

Row % 2% 98% 100% 

Other N 11 123 134 

Row % 8% 92% 100% 

Note: Respondents were allowed to provide multiple answers to this question, and later all responses were 

coded. Thus, the N=134 represents the total number of valid responses. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 51 

3.3.3 Residential Customer Program Participation and Barriers Summary

Installation of energy efficiency features are commonly considered as part of remodeling 

projects (64 percent among recently remodeled homes, and 90 percent among homes with a 

future remodeling plan). About half of the households surveyed are aware of their utility offering 

energy efficiency programs, and 30 percent have participated in them in some way. Low 

income households were found to have a significantly higher participation rate—they are twice 

as likely to report participating in such programs (one likely reason for this higher participation 

could be the fact that these households qualify to receive rebates of 100%). Among 

participants, satisfaction with their utilities’ programs seems extremely high. The most 

frequently cited reasons for nonparticipation were there were no recent purchase of energyusing household items, and unawareness of program resources. It is possible the former 

reason may be triggered by the latter reason—that is, they have not made a recent purchase of 

efficient products because they are not informed of available programs. 

Awareness of the ENERGY STAR® logo also seems fairly high (82 percent), especially among 

non-low income households. Reducing the monthly energy bill, in particular, appears to be an 

important driving factor when making decisions of energy efficient product purchases. Other 

factors such as increased comfort, protection of the environment, increased home value, and 

receiving rebates are also highly appealing in making decisions on such purchases. The single 

biggest barrier for households in investing in energy efficient measures is their concern and 

uncertainty of payback and initial higher costs. 

3.3.4 Commercial and Industrial Customer Attitudes

This section summarizes commercial and industrial customer attitudes on energy efficiency 

practices and programs. The results are based upon phone surveys of small commercial and 

industrial customers in addition to site surveys and discussions with larger commercial and 

industrial customers. Large customers are defined as properties using over 300,000 kWh’s of 

energy per year. The surveys were utilized to obtain information on past purchases and 

practices, awareness of efficiency programs and equipment, and overall attitudes toward energy 

efficiency. 

3.3.5 Commercial and Industrial Customer Respondent Characteristics

The analyses began with an examination of characteristics of commercial and industrial 

respondents, followed by question-by-question analysis. Ownership characteristics of 

respondents and the primary business activities were recorded to determine the distribution of 

respondents among the four electric utility providers. A summary for both small commercial and 

industrial customers and large commercial and industrial customers is provided in Table 36 

below. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 52 

Table 36. Respondent Characteristics Summary Warehouse Retail Grocery Office 

Lodging Health Education Industrial Restaurant OtherTotal 

Small Commercial and Industrial Respondents

NATIONAL 

GRID 

N 0 6 3 10 0 5 2 2 2 5 35 

Column 

% .0% 26.1% 23.1% 17.9% .0% 35.7% 40.0% 8.7% 20.0% 10.9% 17.5% 

NH ELEC 

COOP 

N 1 2 2 10 5 0 0 3 1 11 35 

Column 

% 25.0% 8.7% 15.4% 17.9% 83.3% .0% .0% 13.0% 10.0% 23.9% 17.5% 

PSNH 

N 3 8 6 26 1 4 3 12 6 26 95 

Column 

% 75.0% 34.8% 46.2% 46.4% 16.7% 28.6% 60.0% 52.2% 60.0% 56.5% 47.5% 

UNITIL 

N 0 7 2 10 0 5 0 6 1 4 35 

Column 

% .0% 30.4% 15.4% 17.9% .0% 35.7% .0% 26.1% 10.0% 8.7% 17.5% 

Total 

N 4 23 13 56 6 14 5 23 10 46 200 

Column 

% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Large Commercial and Industrial Respondents

NATIONAL 

GRID 

N 0 0 0 3 1 2 0 10 0 2 18 

Column 

% .0% .0% .0% 14.3% 7.7% 16.7% .0% 11.1% .0% 9.1% 9.1% 

NH ELEC 

COOP 

N 1 2 0 0 3 0 1 2 2 1 12 

Column 

% 20.0% 28.6% .0% .0% 23.1% .0% 6.3% 2.2% 50.0% 4.5% 6.1% 

PSNH 

N 4 5 7 15 8 9 15 73 2 16 154 

Column 

% 80.0% 71.4% 100.0% 71.4% 61.5% 75.0% 93.8% 81.1% 50.0% 72.7% 78.2% 

UNITIL 

N 0 0 0 3 1 1 0 5 0 3 13 

Column 

% .0% .0% .0% 14.3% 7.7% 8.3% .0% 5.6% .0% 13.6% 6.6% 

Total 

N 5 7 7 21 13 12 16 90 4 22 197 

Column 

% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

3.3.6 Commercial and Industrial Customer Program Awareness and Participation

Respondents were polled to determine customer awareness and participation in the existing 

energy efficiency and incentive programs offered by the utility providers. Program awareness 

was significantly higher among the large commercial and industrial customers (86 percent) 

compared to small commercial and industrial customers (60 percent). Past participation in 

efficiency and incentive programs was also notably higher among large customers (85 percent) 

compared to small customers (30 percent). Differences in awareness and participation levels 

among utility providers were not statistically significant. Results of small and large customer 

surveys are summarized below in Table 37. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 53 

Table 37. Awareness of Existing Energy Efficiency Programs and Incentives Warehouse Retail Grocery Office Lodging Health Education 

Industrial Restaurant OtherTotal 

Small Commercial and Industrial Respondents

YES, 

AWARE 

N 4 13 7 33 4 7 4 14 2 27 115 

Column 

% 100.0% 56.5% 53.8% 62.3% 66.7% 53.8% 80.0% 63.6% 20.0% 61.4% 59.6% 

NO, NOT 

AWARE 

N 0 10 6 20 2 6 1 8 8 17 78 

Column 

% .0% 43.5% 46.2% 37.7% 33.3% 46.2% 20.0% 36.4% 80.0% 38.6% 40.4% 

TOTAL 

N 4 23 13 53 6 13 5 22 10 44 193 

Column 

% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Large Commercial and Industrial Respondents

YES, 

AWARE 

N 3 2 6 18 11 10 13 77 1 21 162 

Column 

% 60.0% 40.0% 85.7% 90.0% 84.6% 90.9% 92.9% 87.5% 25.0% 95.5% 85.7% 

NO, NOT 

AWARE 

N 2 3 1 2 2 1 1 11 3 1 27 

Column 

% 40.0% 60.0% 14.3% 10.0% 15.4% 9.1% 7.1% 12.5% 75.0% 4.5% 14.3% 

TOTAL 

N 5 5 7 20 13 11 14 88 4 22 189 

Column 

% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Respondents who indicated that they were aware of the existing efficiency and incentive 

programs offered by their utility providers were then asked whether they had participated in the 

programs. As summarized in Table 38 below, a substantial difference in participation levels was 

noted between small (30 percent) and large (86 percent) commercial and industrial 

respondents. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 54 

Table 38. Participation in Utility’s Energy Efficiency Programs Warehouse Retail Grocery Office Lodging 

Health Education Industrial Restaurant OtherTotal 

Small Commercial and Industrial Respondents

YES 

N 2 2 2 6 2 2 3 3 2 8 32 

Column 

% 50.0% 16.7% 28.6% 20.7% 66.7% 33.3% 75.0% 21.4% 100.0% 30.8% 29.9% 

NO 

N 2 10 5 23 1 4 1 11 0 18 75 

Column 

% 50.0% 83.3% 71.4% 79.3% 33.3% 66.7% 25.0% 78.6% .0% 69.2% 70.1% 

TOTAL 

N 4 12 7 29 3 6 4 14 2 26 107 

Column 

% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Large Commercial and Industrial Respondents 

YES 

N 3 1 2 14 8 8 13 66 1 12 128 

Column 

% 100.0% 100.0% 100.0% 82.4% 80.0% 88.9% 100.0% 88.0% 100.0% 66.7% 85.9% 

NO 

N 0 0 0 3 2 1 0 9 0 6 21 

Column 

% .0% .0% .0% 17.6% 20.0% 11.1% .0% 12.0% .0% 33.3% 14.1% 

TOTAL 

N 3 1 2 17 10 9 13 75 1 18 149 

Column 

% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Note: This question was only asked of respondents who were aware of their utilities’ energy efficiency programs 

Small commercial and industrial respondents who indicated that they had not participated in the 

energy efficiency programs were asked additional questions regarding their nonparticipation. 

The most frequently given response for nonparticipation among small customers was they “have 

not purchased energy-using equipment” (49 percent). The next three most frequently cited 

reasons seem to relate to their lack of awareness of the programs. Of respondents who have 

participated in their utility’s energy efficiency programs, a significant majority of both small 

customers (94 percent) and large customers (98 percent) reported that they would participate in 

the programs again if given the opportunity. 

3.3.7 Commercial and Industrial Customer Motivations and Barriers

To first assess customer attitudes towards energy efficiency, respondents were asked to qualify 

the amount of attention they spend on controlling energy costs through general efficiency 

practices such as adjusting room temperatures when not occupied and shutting off computers 

and lights at night. As shown in Table 39, 86 percent of respondents indicated that they pay at 

least “some attention” to controlling energy costs. No significant differences were observed 

among any groups or between small and large customers. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 55 

Table 39. Attention Paid to Controlling Company Energy Costs – Small/Large Respondents Combined 

Frequency Valid Percent 

Substantial attention to these matters 165 43% 

Some attention 163 43% 

Very little attention 37 10% 

No attention 13 3% 

Don’t know 4 1% 

Total 382 100% 

Respondents were then asked to rate the likelihood of purchasing energy efficient equipment 

instead of standard equipment given several conditions generally assumed to increase the 

attractiveness of adopting energy efficient equipment. Respondents were told to assume that 

the energy efficient cost between 20 and 30 percent more than standard models. Among all 

respondents, reduction of monthly energy bills and receiving a rebate were the reasons most 

likely to encourage the purchase of energy efficient equipment. Increasing occupant comfort, 

environmental protection and improving business image were less likely to motivate 

respondents to specify energy efficient equipment in both small and large customers. A 

complete summary of results across all building types is provided in Table 40 below. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 56 

Table 40. Likelihood of Purchasing Energy Efficient Equipment 

1=NOT AT 

ALL 

LIKELY 

2 3 4 5= 

EXTREMELY 

LIKELY 

TOTAL 

Small Commercial and Industrial Respondents 

a... your monthly 

energy bill would be 

less 

N 12 2 16 39 129 198 

Row % 6% 1% 8% 20% 65% 100% 

b... it increased 

occupant comfort 

N 19 5 38 40 97 199 

Row % 10% 3% 19% 20% 49% 100% 

c... you felt you were 

helping to protect the 

environment 

N 14 3 35 41 104 197 

Row % 7% 2% 18% 21% 53% 100% 

d... it improved 

business image or 

value 

N 21 4 29 49 94 197 

Row % 11% 2% 15% 25% 48% 100% 

e... you received a 

rebate 

N 17 2 20 31 128 198 

Row % 9% 1% 10% 16% 65% 100% 

f... your sales 

person 

recommended it 

N 23 11 44 41 75 194 

Row % 12% 6% 23% 21% 39% 100% 

Large Commercial and Industrial Respondents

a... your monthly 

energy bill would be 

less 

N 4 9 25 49 94 181 

Row % 2.2% 5.0% 13.8% 27.1% 51.9% 100.0% 

b... it increased 

occupant comfort 

N 8 30 45 47 50 180 

Row % 4.4% 16.7% 25.0% 26.1% 27.8% 100.0% 

c... you felt you were 

helping to protect the 

environment 

N 5 21 45 48 63 182 

Row % 2.7% 11.5% 24.7% 26.4% 34.6% 100.0% 

d... it improved 

business image or 

value 

N 8 24 29 48 69 178 

Row % 4.5% 13.5% 16.3% 27.0% 38.8% 100.0% 

e... you received a 

rebate 

N 2 7 21 49 102 181 

Row % 1.1% 3.9% 11.6% 27.1% 56.4% 100.0% 

f... your sales 

person 

recommended it 

N 14 19 50 54 42 179 

Row % 7.8% 10.6% 27.9% 30.2% 23.5% 100.0% 

Respondents were asked to identify the primary reasons why they would not purchase energy 

efficient equipment or make energy efficient improvements to the space. Table 41 provides a 

coded summary of the open-ended responses. By far the most frequent response was 

concerns over the cost of the equipment and the payback (69 percent). Other responses 

included satisfaction with current equipment (6 percent), purchasing decisions made at 

corporate level (5 percent), tenants unwilling to invest in capital improvements for spaces they 

do not own (4 percent), and no need to replace equipment that is currently in working order (3 

percent). Other reasons cited included the use of specialized equipment and the belief that 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 57 

energy efficient equipment would be not available for the specialized process (3 percent), and 

the quality and reliability of energy efficient equipment (3 percent). 

Table 41. Primary Reasons for Not Purchasing Equipment/Making Improvements – Small/Large 

Frequency Valid Percent 

Cost / benefit, payback 225 69% 

Current equipment is satisfactory/no need 20 6% 

Corporate decision 16 5% 

Renting, not owner of property 13 4% 

Replacing as needed 8 3% 

Not compatible with business needs 10 3% 

Quality 9 3% 

Not well informed 4 1% 

Other 21 6% 

Total 326 100% 

3.3.8 Commercial and Industrial Customer Program Participation and Barriers 

Summary

Of the small and large commercial and industrial customers surveyed, 86 percent of 

respondents reported some or high level of attention to controlling energy costs. Overall 

awareness of energy efficiency programs and incentives offered by utility providers was 

significantly higher in the large commercial/industrial respondents (86 percent) compared to the 

small commercial/industrial respondents (60 percent). Past participation in utility provider 

offered programs was similarly higher in the large customer group who was aware of the 

programs offered (86 percent) compared to the small customer group aware of the programs 

offered (30 percent). Of respondents who have participated in their utility’s energy efficiency 

programs, a significant majority of both small customers (94 percent) and large customers (98 

percent) reported that they would participate in the programs again if given the opportunity. 

The single largest barrier to respondents investing in energy efficiency measures was concern 

about initial premium costs of equipment and insufficient payback (69 percent). Respondents 

indicated that the two most important factors influencing decisions to invest in energy efficient 

equipment are expectations of lower monthly energy bills and rebates or incentives for 

purchasing energy efficient equipment that would help offset some of the initial costs. Other 

factors such as business image, environmental impact, occupant comfort, and sales person 

recommendation were less likely to influence decisions to invest in energy efficient equipment. 

3.4 Forecast Model of State Electricity and Natural Gas 

Consumption and Peak Demand 

Results from this task were presented in Section 2.3 above. As noted previously these forecast 

models were compiled by RLW for this project based on sales information provided to the GDS 

Team directly by the project’s four participating electric utilities and two participating natural gas 

distribution companies. Separate total and customer sector-specific energy (MWH), demand 

(MW) and fuel (MMBTu) forecasts were developed for the state as a whole and by utility service 

territory. Where applicable, these forecasts were compared against relevant ISO-NE and EIA 

data to assess reasonableness. Please refer to Figure 10, Figure 11, Figure 12, Figure 13, 

Figure 14, and Figure 15, presented in Section 2.3 of this report for a summary of these model 

forecast results. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 58 

As will be discussed in more detail in the additional energy efficiency potential modeling 

methodology section below, these customer-sector electric and gas forecast models served as 

critical inputs used to estimate the percent potential values for additional energy efficiency 

opportunities statewide. They were also used to develop energy efficiency potential 

percentages at the utility-specific territory level. 

3.5 Estimates of 10-Year Technical, Maximum Achievable, Maximum 

Achievable Cost Effective Potentials and Potentially Obtainable 

Scenario 

A main objective of this study was to estimate, state-wide and for each of the four New 

Hampshire retail electricity providers and two natural gas distribution companies, the technical, 

maximum achievable, maximum achievable cost effective potentials, and savings from a 

potentially obtainable scenario for electricity, natural gas, and related propane and fuel oil 

savings over the next 10 year period, and the budgets (where appropriate) required to achieve 

that potential. As described in more detail below, the activities undertaken to develop these 

estimates were based on the GDS Team’s existing sector-level models, DR 96-150 costeffectiveness criteria, and the region’s current avoided energy cost projections, as expanded to 

reflect the increased list of measures to be assessed and customized based on state utilityspecific data and the saturation and penetration survey results obtained through this project’s 

survey and site visit activities. All results have been analyzed and compared for 

reasonableness against overall state consumption and consideration of past participation. 

This section of the report presents an overview of the approach and methodology that was used 

to ultimately determine the various savings potentials additional energy efficiency opportunities 

in New Hampshire. 

3.5.1 Energy Efficiency Potential – Key Data Sources

Data required for performing the energy efficiency potential analysis elements of this study can 

be grouped into three major categories: 

• Measure-specific data including: energy savings (kWh, kW, MMBTu), measure costs 

(full/incremental), measure lives (full/effective and persistence), etc. 

• New Hampshire customer-specific historical, current and forecasted data including: number 

and types of customers (residential, low income, single/multi-family, commercial, industrial), 

customer sales by customer class and end use (space heating, space cooling, water 

heating, lighting, etc.), customer types (SIC/NAICS), average size (square footage of typical 

single, multi-family homes and commercial/industrial buildings), typical energy use intensity 

broken down by end use (lighting, cooling, water heating, process), saturation of electric 

water heating, central cooling, other energy efficiency measures and appliances (and 

associated appliance saturation trends), and peak load coincidence factors for major electric 

end-uses by sector. 

• New Hampshire statewide and utility-specific and other system-related data including: 

forecast of electric and natural gas avoided costs (generation, transmission, distribution), 

electric line losses, reserve margin planning assumption, general rate of inflation and 

appropriate discount rate, and information on environmental benefits that may occur per 

kWh or MMBTu saved from energy efficiency programs. Values and sources for these data 

are provided in Appendix H. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 59 

3.5.2 Energy Efficiency Potential Calculation Stages 

Three key calculations that have been undertaken to complete this assessment are described 

below. Following the descriptions, these three stages of potential energy savings calculation 

are shown graphically in a Venn diagram in Figure 16.34 A fourth stage, developed for this 

project, relates to calculation of the likely obtainable potential (a subset of the maximum 

achievable cost effective potential), and is described separately at the end of this section. 

Savings interactions for measures like lighting and lighting controls are taken into account at 

every potential stage listed below. 

The first stage in determining energy efficiency potential requires estimation of the technical 

potential for energy savings in New Hampshire. Technical potential is defined as the complete 

penetration of all measures analyzed in applications where they are deemed to be technically 

feasible from an engineering perspective. The technical potential for electric energy efficiency 

for this study was developed from estimates of the technical potential of individual energy 

efficiency measures applicable to each sector and for relevant end-uses within each sector 

(residential, commercial, industrial, energy efficient space heating, energy efficient water 

heating, etc.). For each energy efficiency measure, GDS calculated the electricity savings that 

could be captured if 100 percent of inefficient electric appliances and equipment were replaced 

instantaneously (where they are deemed to be technically feasible). Separate technical 

potentials were calculated for natural gas and related propane and oil saving measures, also by 

sector and end-use. 

The second stage relates to calculation of the maximum achievable energy efficiency potential. 

Maximum Achievable potential is defined as the maximum penetration of an efficient measure 

that would be adopted absent consideration of cost or customer behavior. The term 

"achievable" refers to efficiency measure penetration, based on estimates of New Hampshirespecific building stock, energy efficient equipment saturations and realistic penetration levels 

that can be achieved by 2018 if all remaining standard efficiency equipment were to be replaced 

on burnout (at the end of its useful measure life) and where all new construction and major 

renovation activities in the state were done using energy efficient equipment and 

construction/installation practices. Under this scenario, energy efficient measures with measure 

lives over ten (10) years would have their potential savings calculated based on the study life 

divided by measure life ((Study Life = 10) / Measure Life). 

In certain circumstances, where early replacement of specific measures is becoming standard 

practice, maximum achievable potential includes the retrofit of measures before the end of their 

useful measure life (i.e., T8 lighting, insulation and weatherization of existing homes). In such 

cases, the entire stock of measures to be retrofitted were modeled so that all were replaced 

over the ten year study period. 

Calculation of the Maximum Achievable Cost Effective (M.A.C.E) potential is the third stage. 

Maximum Achievable Cost Effective potential is defined as the potential for the realistic 

penetration of energy efficient measures that are cost effective according to the Total Resource 

Cost (TRC) Test, and would be adopted given aggressive funding levels, and was determined 

absent consideration of customer behavior. A concerted, sustained campaign involving highly 

aggressive programs and market interventions would be required to achieve this level of 

savings. 

 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 60 

To develop the maximum achievable cost effective potential, GDS retains only those electric 

and non-electric energy efficiency measures in the analysis that were found to be cost effective 

(according to the TRC) based on individual measure cost effective analyses conducted in this 

study. Energy efficiency measures that are not cost effective were excluded from the estimate 

of maximum achievable cost effective energy efficiency potential. 

Potentially Obtainable scenario is a new output developed for this study and can be defined 

as an estimate of the potential for the realistic penetration over time of energy efficient 

measures that are cost effective according to the NH TRC, and would be adopted after 

consideration of customer behavior and given aggressive funding levels, and by determining the 

level of market penetration that can be achieved with a concerted, sustained campaign involving 

highly aggressive programs and market interventions. As demonstrated later in this report, the 

State of New Hampshire and its electric and natural gas utilities would need to continue to 

undertake, and perhaps aggressively expand its efforts to achieve these levels of savings. 

Based on information collected through this project’s telephone surveys and site visits, a 

Potentially Obtainable scenario was developed for each customer sector by electric and nonelectric fuel types. 

Figure 16 below shows the four stages of electric energy savings potential (this Venn diagram 

figure is for illustrative purposes only and does not reflect actual data for New Hampshire). 

Figure 16. Venn Diagram of the Stages of Energy Savings Potential 

3.5.3 General Methodological Approach 

The GDS Team’s analytical approach began with a careful assessment of the existing 

saturation of energy using equipment and penetration of energy efficiency measures that has 

already been achieved in New Hampshire. As discussed earlier in this section, this was 

accomplished through a combination of primary data collection and identification, review and 

documentation of secondary data sources. For each energy efficiency measure, this analysis 

assessed how much energy efficiency has already been accomplished as well as the remaining 

potential for energy efficiency savings. For example, if 100 percent of the homes in New 

Hampshire had electric lighting, and 30 percent of light sockets were already using high 

efficiency compact fluorescent bulbs (CFLs), then the remaining potential for energy efficiency 

savings is the 70 percent of light sockets in the residential sector that are not already using high 

efficiency fluorescent bulbs. 

Technical 

Potential 

Maximum 

Achievable 

Potential Maximum Achievable Cost 

Effective Potential 

Potentially Obtainable 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 61 

The general methodology used for estimating the potential for energy efficiency in the 

residential, commercial and industrial sectors in New Hampshire included the following steps: 

1. Identification of energy efficiency measures to be included in the assessment. 

2. Identification of data sources for determining costs and savings for all electric and nonelectric energy efficiency measures. 

3. Determination of the characteristics of each energy efficiency measure including its 

incremental or total cost, electric energy consumption and savings, demand and MMBTu 

savings, current saturation, the percent of installations that are already energy efficient, 

and the useful life of the measure (with care taken to document the sources for each 

characteristic and to recognize potential difference in values by sector, building type 

and/or time of installation – i.e., new construction, existing buildings, replace on burnout, 

retrofit). In addition, the determination of any technical limitations or barriers that may be 

present when attempting to install an energy efficient measure is also considered. 

4. Calculation of cost-effectiveness screening metrics (e.g., the Total Resource Cost Test 

benefit cost ratio) and sorting of measures from least-cost to highest cost per kWh (or 

MMBTu) saved. Interactions between measures were not considered for determining 

measure specific benefit cost ratios. 

5. Collection and analysis (where data was available) of the baseline and forecasted 

characteristics of the electric and non-electric end use markets, including equipment 

saturation levels and consumption, by market segment and end use over the forecast 

period. 

6. Integration of measure characteristics and baseline data to produce estimates of 

cumulative costs and savings across all measures. 

7. Determination of the cumulative technical and maximum achievable potentials using 

supply curves, by sector (separately for electric and non-electric measures). 

8. Determination of the achievable cost effective potential for electric and non-electric 

energy savings over the forecast period. 

9. Estimation of the likely obtainable potential for electric and non-electric energy savings 

over the forecast period. 

A key element in this approach is the use of energy efficiency supply curves. The advantage of 

using an energy efficiency supply curve is that it provides a clear, easy-to-understand 

framework for summarizing a variety of complex information about energy efficiency 

technologies, their costs, and the potential for energy savings. Properly constructed, an energyefficiency supply curve avoids the double counting of energy savings across measures by 

accounting for interactions between measures. The supply curve also provides a simplified 

framework to compare the costs of energy efficiency measures with the costs of energy supply 

resources. 

The supply curve is typically built up across individual measures that are applied to specific 

base-case practices or technologies by market segment. Measures are sorted on a least-cost 

basis and total savings are calculated incrementally with respect to measures that precede 

them. Supply curves typically, but not always, end up reflecting diminishing returns, i.e., costs 

increase rapidly and savings decrease significantly at the end of the curve. There are a number 

of other advantages and limitations of energy-efficiency supply curves (see, for example, Rufo 

2003).35

 

35 Rufo, Michael, 2003. Attachment V – Developing Greenhouse Mitigation Supply Curves for In-State Sources, 

Climate Change Research Development and Demonstration Plan, prepared for the California Energy Commission, 

Public Interest Energy Research Program, P500-03-025FAV, April. http://www.energy.ca.gov/pier/reports/500-03-

025fs.html
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 62 

3.5.4 Energy Efficiency Potential Calculations - Core Equations 

This section describes the calculations used to estimate the energy efficiency potential in the 

residential, commercial, and industrial sectors. There is a core equation, shown below, used to 

estimate the technical potential for each individual efficiency measure and it is essentially the 

same for each sector. However, for the residential sector, the equation is applied using a 

“bottom-up” approach where the equation inputs are displayed in terms of the number of homes 

or the number of high efficiency units (e.g., compact fluorescent light bulbs, high efficiency air 

conditioning systems, programmable thermostats, etc.). For the commercial and industrial (C&I) 

sectors, a “top-down” approach was used for developing the technical potential estimates. In 

this case, the data is displayed in terms of energy rather than number of units or square feet of 

floor area.36 For the commercial and industrial sectors, GDS used New Hampshire-specific 

equipment saturation and end use data wherever such data was available. The core equations 

used by GDS are very similar to the equations used in prior energy efficiency potential studies. 

3.5.4.1 Core Equations for Estimating Technical Potential 

The core equation used to calculate the energy efficiency technical potential for each individual 

efficiency measure for the residential sector is shown below. Section 4 provides more details on 

how this core equation was applied within the residential sector’s bottom-up modeling approach. 

Technical 

Potential 

of Efficient 

Measure 

= 

Total 

Number of 

Residential 

Households 

X 

Base Case 

Equipment 

End Use 

Intensity 

(annual kWh 

use per 

home) 

X Base Case 

Factor X Remaining Factor X Convertible Factor X Savings Factor 

where: 

• Number of Households is the number of residential customers in the market segment. 

• Base-case equipment end use intensity is the energy used per customer per year by 

each base-case technology in each market segment. This is the consumption of the 

energy using equipment that the efficient technology replaces or affects. For example 

purposes only, if the efficient measure were a high efficiency light bulb (CFL), the base 

end use intensity would be the annual kWh use per bulb per socket associated with an 

incandescent light bulb that provides equivalent lumens to the CFL. 

• Base Case factor is the fraction of the end use energy that is applicable for the efficient 

technology in a given market segment. For example, for residential lighting, this would 

be the fraction of all residential electric customers that have electric lighting in their 

household. 

• Remaining factor is the fraction of applicable dwelling units or lighting sockets that have 

not yet been converted to the energy efficiency measure; that is, one minus the fraction 

of households that already have the energy-efficiency measure installed. 

 

36 It is important to note that square-foot based saturation assumptions cannot be applied to energy use values 

without taking into account differences in energy intensity (e.g., an area covered by a unit heater may represent two 

percent of floor space but a larger percent of space heating energy in the building because it is likely to be less 

efficient than the main heating plant). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 63 

• Convertible factor is the fraction of the applicable dwelling units that is technically 

feasible for conversion to the efficient technology from an engineering perspective (e.g., 

it may not be possible to install CFLs in all light sockets in a home because the CFLs 

may not fit in every socket in a home). 

• Savings factor is the percentage reduction in energy consumption resulting from 

application of the efficient technology. 

The core equation used to calculate the electric energy efficiency technical potential for each 

individual efficiency measure for the commercial and industrial sectors is shown below. More 

information is presented in Sections 5 and 6 regarding how this core equation was applied 

within the commercial and industrial sectors using the top-down modeling approach. 

Technical 

Potential of 

Efficient 

Measure 

= 

Total End 

Use kWh 

Sales by 

Building or 

Industry 

Type 

X Base Case 

Factor X Remaining 

Factor X Convertible 

Factor X Savings 

Factor 

where: 

• Total end use kWh or MMBTu sales (by segment) is the forecasted level of electric or 

natural gas sales for a given end-use (e.g., space heating) in a commercial or industrial 

market segment (e.g., office buildings).

• Base Case factor is the fraction of the end use energy that is applicable for the efficient 

technology in a given market segment. For example, for fluorescent lighting, this would 

be the fraction of all lighting kWh in a given market segment that is associated with 

fluorescent fixtures. 

• Remaining factor is the fraction of applicable kWh sales that are associated with 

equipment that has not yet been converted to the energy efficiency measure; that is, one 

minus the fraction of the market segment that already have the energy-efficiency 

measure installed. 

• Convertible factor is the fraction of the equipment or practice that is technically feasible 

for conversion to the efficient technology from an engineering perspective (e.g., it may 

not be possible to install VFDs on all motors in a given market segment). 

• Savings factor is the percentage reduction in energy consumption resulting from 

application of the efficient technology over the base technology. 

Technical electric and non-electric energy efficiency savings potential was calculated in two 

steps. In the first step, all measures are treated independently; that is, the savings of each 

measure are not reduced or otherwise adjusted for overlap between competing or synergistic 

measures. By treating measures independently, their relative economics are analyzed without 

making assumptions about the order or combinations in which they might be implemented in 

customer buildings. However, the total technical potential across measures cannot be 

estimated by summing the individual measure potentials directly because some savings would 

be double-counted. For example, the savings from a weatherization measure, such as low-e 

ENERGY STAR® windows, are partially dependent on other measures that affect the efficiency 

of the system being used to cool or heat the building, such as high-efficiency space heating 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 64 

equipment or high efficiency air conditioning systems; the more efficient the space heating 

equipment or electric air conditioner, the less energy can be saved from the installation of low-e 

ENERGY STAR windows. 

For the residential and commercial sectors, GDS addressed the new construction market as a 

separate market segment, with measures targeted specifically at the new construction market. 

In the residential new construction market segment, for example, detailed energy savings 

estimates for the ENERGY STAR Homes program were used as a basis for determining energy 

savings for this market segment in New Hampshire. For the commercial sector, in addition to 

end-use specific measures applicable to new construction projects, integrated design measures 

(e.g., for building shell, lighting design, etc.) were assessed. Within the new construction 

market segment for the commercial sector, an assumption was built into the model that 1⁄2 the 

commercial new construction sales were attributed to new construction projects, while the other 

half was directly attributable to growth of the existing commercial market segment. In the case 

of the industrial sector, the model functions very similarly but uses an all-inclusive factor which 

incorporates the four (4) factors discussed above into one multiplier to achieve the same 

approximate end result as the individual factor approach. 

3.5.5 Rates of Implementation for Energy Efficiency Measures 

For new construction, energy efficiency measures can be implemented when each new home or 

building is constructed, thus the rate of availability is a direct function of the rate of new 

construction. For existing buildings, determining the annual rate of availability of savings is 

more complex. Energy efficiency potential in the existing stock of buildings can be captured 

over time through two principal processes: 

1. as equipment replacements are made normally in the market when a piece of equipment 

is at the end of its useful life (we refer to this as the “market-driven” or “replace-onburnout” case); and, 

2. at any time in the life of the equipment or building (which we refer to as the “retrofit” 

case). 

Market-driven measures are generally characterized by incremental measure costs and savings 

(e.g., the incremental costs and savings of a high-efficiency versus a standard efficiency air 

conditioner); whereas retrofit measures are generally characterized by full costs and savings 

(e.g., the full costs and savings associated with retrofitting ceiling insulation into an existing 

attic). A specialized retrofit case is often referred to as “early replacement” or “early retirement”. 

This refers to a piece of equipment whose replacement is accelerated by several years, as 

compared to the market-driven assumption, for the purpose of capturing energy savings earlier 

than they would otherwise occur. 

For the market driven measures, existing equipment is assumed to be replaced with high 

efficiency equipment at the time a consumer is shopping for a new appliance or other energy 

using equipment, or if the consumer is in the process of building or remodeling. Using this 

assumption, equipment that needs to be replaced (replaced on burnout) in a given year is 

eligible to be upgraded to high efficiency equipment. For the retrofit measures, savings can 

theoretically be captured at any time; however, in practice it takes many years to retrofit an 

entire stock of buildings, even with the most aggressive of efficiency programs. 

As noted above, a special retrofit case is “early retirement” of energy equipment that is still 

functioning well, and replacing such equipment with high efficiency equipment. For this project, 

early retirements were considered only for a small number of measures (e.g., 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 65 

insulation/weatherization). For these early retirement energy efficiency measures, GDS 

assumed the same measure life for the measure that was replaced early as a time of 

replacement measure. In addition, savings were based on the whole measure life for retrofit / 

early replacement type measures. 

3.5.6 Benefit/Cost (Cost-Effectiveness) Modeling

To determine maximum achievable cost effective potential GDS has used its existing energy 

efficiency measure and program screening tool. The GDS screening tool is user friendly, well 

documented, and provides the following benefit/cost ratio calculations: Total Resource Cost 

Test, Utility Cost Test, Participant Test, Rate Impact Measure Test, and Societal Test. For this 

Report, only the Total Resource Cost Test was used for screening purposes (consistent with 

New Hampshire utility and Commission procedures). The annual discount rate assumed for this 

test to determine net present values (NPV) is 5.0%. 

The model is comprehensive and uses the following types of data as input: costs, useful lives 

and energy savings of energy efficiency, load management or demand response measures, 

load shape impacts of electric or natural gas energy efficiency measures, avoided costs of 

electricity for generation, transmission and distribution, avoided costs of natural gas and other 

fuels (propane, fuel oil, etc.), avoided water costs, projected or actual measure or program 

penetration assuming no program, projected or actual measure or program penetration with a 

program, participant costs, energy efficiency organization or utility costs (including rebates or 

financial incentives), non-energy benefits of measures or programs, electric line losses, discount 

rate, and inflation rate. 

As noted above, the model provides calculations of five benefit/cost ratios as well as year-byyear and cumulative energy savings, dollar costs and dollar benefits. The GDS screening tool 

provides the flexibility to vary assumptions in the analysis to reflect uncertainty, changing market 

circumstance, statutory change or other factors that influence assessment of reasonably 

available potential through the efficiency utility. The GDS measure and program screening tool 

allows for the incorporation of changes to reflect real world circumstances and a dynamic 

environment. The GDS tool exists in a single Microsoft Excel file, and includes several linked 

worksheets that present clearly documented inputs and outputs. More information on the model 

and key input assumptions being used for this report is included in Appendix H. 

3.6 Assessment of Past and Current Program Capture and 

Recommendations 

For this task, the GDS Team evaluated the penetration of energy efficiency savings (electric and 

natural gas) resulting from past and current utility-sponsored program activities. A review of the 

utilities’ annual Core New Hampshire Program Highlights reports formed the basis for this 

evaluation and results are presented from both a cumulative savings as a percent of sales and 

number of customers served as a percent of population basis. Recommendations for potential 

modifications to program and measure offerings that could increase the likelihood of achieving 

identified potentials are made and have been developed mainly through information on barriers 

collected directly from New Hampshire utility customers (through this project’s telephone 

surveys and site visits) and supplemented by the GDS Team’s experience with looking at 

programs from a logic-modeling perspective, and extensive knowledge of other local, regional 

and national programs and best practices.37 Results from these analysis and assessments are 

presented in Section 8 of this report. 

 

37 Assessments based on a logic-modeling perspective recognize current program resources (dollars, staffing, etc.) 

and activities (measure installations, promotional rebates/incentives, marketing/outreach, education/training, etc.) 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 66 

Section 4: Residential Sector Energy Efficiency Potential 

This section of the report presents the estimates of electric and non-electric technical (best), 

technical (traditional), maximum achievable, maximum achievable cost effective, and potentially 

obtainable energy efficiency potential for the existing and new construction market segments of 

the residential sector in New Hampshire. More information regarding how these potentials were 

derived is also presented. 

According to this analysis, there is still a large remaining potential for electric and non-electric 

energy efficiency savings in the residential sector. Table 42 and Table 43 below summarize the 

savings by potential type by the year 2018 for residential electric and non-electric measures 

respectively. The estimated total costs to achieve each level of savings by 2018 are also 

presented in these tables. In addition, Table 42 presents peak demand savings for each 

potential level of savings associated with the electric energy efficiency measures. 

Table 42. Summary of Residential Electric Energy Efficiency Savings Potential 

Estimated Cumulative 

Annual Savings by 2018 

(MWh)

Savings in 2018 as a Percent of 

Total 2018 Residential Sector 

Electric Energy Consumption

Estimated 

Summer MW*

Estimated Total Cost 

to Achieve 

(Cummulative)

Estimated Total Cost to 

Achieve 

(Annual)

Technical Potential (Best Only) 1,770,861 31.7% 66.7 $ 2,554,517,348 $ 255,451,735

Technical Potential (Good, Better, Best) 1,489,861 26.7% 56.1 $ 2,149,167,880 $ 214,916,788

Max Achievable Potential 1,217,145 21.8% 45.9 $ 1,214,926,125 $ 121,492,613

Max Achievable Cost Effective Potential 1,170,398 20.9% 44.1 $ 632,287,942 $ 63,228,794

Potentially Obtainable 698,069 12.5% 26.3 $ 383,050,068 $ 38,305,007

33% * Estimated Summer Load Factor 

Table 43. Summary of Residential Non-Electric Energy Efficiency Savings Potential 

Estimated Cumulative 

Annual Savings by 2018 

(MMBTU)

Savings in 2018 as a Percent of 

Total 2018 Residential Sector 

Other Fuels Energy Consumption

Estimated Total Cost to 

Achieve 

(Cummulative)

Estimated Total Cost to 

Achieve 

(Annual)

Technical Potential (Best Only) 16,918,392 50.4% $ 3,220,297,934 $ 322,029,793

Technical Potential (Good/Better/Best) 12,099,639 35.7% $ 2,277,404,262 $ 227,740,426

Max Achievable Potential 7,463,743 22.0% $ 1,206,916,417 $ 120,691,642

Max Achievable Cost Effective Potential 6,313,954 18.6% $ 456,169,489 $ 45,616,949

Potentially Obtainable 3,633,554 10.7% $ 200,483,725 $ 20,048,372

On the electric side, the maximum achievable cost effective potential in the residential sector is 

over 1.1 million MWh, approximately 21 percent of the New Hampshire residential sector sales 

forecast in 2018. With regard to non-electric end uses, the maximum achievable cost effective 

potential in the residential sector is more than 6.3 million MMBTu, just under 19 percent of New 

Hampshire’s residential sector fossil fuel (natural gas, oil and propane) sales forecast in 2018. 

The lists of measures that make up the savings for each of these levels are shown in Table 44 

and Table 45 in Section 4.2.1 below. 

 and seek to identify their causal links to anticipated outputs (measures installed, in-program energy and capacity 

savings, # of customers served, market actors trained, etc.), short-, intermediate- and long-term outcomes (changes 

in awareness and behavior, market-wide/sustainable energy, economic and environmental benefits, etc.). In 

addition, logic models recognize the existence and potential impacts of external influences (price of energy, state of 

the local and regional economy, federal tax incentives, other non-program sponsored activities, etc.). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 67 

4.1 Residential Sector Savings Methodology Overview 

The residential sector analysis was modeled using what is considered a “bottom-up approach”. 

This methodology, shown visually in Figure 17 below: 

Figure 17. Residential Sector Savings Methodology – Bottom Up Approach

As shown in this figure, the methodology started at the bottom based on the number of 

residential customers (splitting them into single-family and multi-family customers as well as 

existing vs. new construction). From that point, each home was then broken into a series of 

end-uses depending on whether the home fits the single-family or multi-family profile. An 

example of an end-use might be “Single-Family Water Heating”. From that point, a series of 

measures are identified that belong to that end-use. To keep with our example, we would then 

create a series of measures such as “Energy Star Clothes Washer”, “Energy Star Dishwasher”, 

“Pipe-Wrap”, etc – all these measures fit into that end use category of Single-Family Water 

Heating. 

The next step in our bottom up approach was to determine how many of the homes in the profile 

we are looking at (single-family or multi-family) have each of those measures within each of 

those end uses. This is one of the multiple applicability factors that were used to screen each 

measure to determine savings. The applicability factors include the base case factor, the 

remaining factor, the convertible factor, and the savings factor. The full formula to determine 

savings at the measure level is shown below. 

The goal of the formula is to determine how many households this measure applies to (base 

case factor), then of that group, how many already have the efficient version of the measure we 

are installing (remaining factor). From there, we looked to make sure there were not any 

technical reasons why the measure cannot be installed, and if so, made a correction 

(convertible factor) for that reason. The last factor which needed to be applied was the savings 

factor, which is the percentage savings achieved from installing the efficient measure over a 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 68 

standard measure. In cases where multiple measures could interact within the same end use, a 

“more than one choice” factor was also included in the model to avoid double counting of 

potential savings. In addition, the model ranks measures by levelized cost in order to make 

assumptions about which measures will be installed in what order. This ranking also takes into 

account measure interactions where applicable so that savings are not over-stated due to 

double-counting. For example, if you install insulation and air seal, and then install a 

programmable thermostat, the savings potential for the thermostat is reduced to account for the 

reality that insulation has already been improved thus reducing the potential for higher levels of 

savings. This type of scenario is done throughout the residential model among a variety of 

scenarios within the models. 

Another example to help illustrate the functionality of the model is single family homes with gas 

fired boilers. This measure would have space heating and space cooling measures installed in 

the following levelized cost order: programmable thermostats, energy efficient windows, and 

high efficiency boiler. The base use for space heating and space cooling would be adjusted 

based on savings from each measure. The full base use would have programmable thermostat 

savings applied. This new adjusted base use would then be used for the energy efficient 

window savings and finally the further adjusted base use would then be used for the high 

efficiency boiler savings. 

One other example is single family homes with electric water heaters. In this case, the measure 

includes more than one choice for dishwasher upgrades and efficient electric water heaters 

upgrades. The electric water heating measures are in the following levelized cost order: low 

flow shower showerhead/faucets, Energy Star Dishwasher, efficient water heater, beyond 

Energy Star dishwasher, pipe wrap, high efficiency water heater, water heater blanket, Energy 

Star clothes washer, heat pump water heater, whole-house tankless water heater, and solar 

water heating. In this case the adjusted domestic hot water base use for calculated savings is 

more complicated. Where a second measure for the same use would be installed the base 

usage would not be reduced by the earlier measure. The most direct path for base usage 

reduction is the following: initial base usage is used for the low flow showerhead/faucets; then 

the Energy Star dishwasher reduces the domestic hot water base use by the percentage of this 

measure’s electric savings that is associated with water heating; then the low flow and 

dishwasher reductions are used for the base usage for efficient water heater savings, then pipe 

wrap, then water heater blanket, then Energy Star Clothes Washer and finally solar water 

heating. In the case of high efficiency water heater- the base usage is decreased by low flow 

showerhead/faucets, Energy Star dishwasher water heating savings and pipe wrap before the 

high efficiency water heater savings are applied. 

This type of process was run on every measure within all measure end-use categories and for 

all customer groups (single-family, multi-family, new construction, existing-construction – and 

blends thereof). This process, while described here at a very high level, was run within the 

confines of a complicated model under various scenarios to determine the varying savings 

potential levels. 

In addition to the modeling technique described above, custom measures were included for the 

residential sector to achieve “Good, Better, Best” scenarios for weatherization (split further by 

each fuel type) and integrated building design (for each fuel type as well). All of these scenarios 

were reality-based through use of building simulation software to achieve targeted savings and 

cost levels for each distinct scenario level. This process required a mix of measures from lower 

cost and complexity to higher cost and complexity. The weatherization packages were 

designed to allow a degree of residential customers to follow a “good, better, or best” approach 

for insulating their existing home. For each of the weatherization approaches, a set of costs and 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 69 

savings assumptions were developed, as well as assumptions regarding current market 

penetration. 

For the new home stock, a good, better, best approach was also created along with 

assumptions regarding the percentage of new home customers who would be likely to follow 

each approach. The assumption made was that 80% of new homes would do one of the three 

(3) packages, while the remaining 20% would not participate in efficiency programs or install 

efficiency measures. More information regarding the specific measures and associated costs 

included within each good, better, best scenario is presented in Appendix I. 

The costs to achieve savings potential estimates within the residential sector are calculated on a 

measure by measure basis using the levelized cost ($/kWh in the electric model and $/MMBTu 

within the non-electric model) for each measure. These figures (levelized costs) represent the 

cost to save a unit of energy. These levelized costs are then taken and multiplied (again at the 

measure level) by the 2018 annual savings associated with the potential level being addressed 

(technical potential, maximum achievable, etc.). A net present value (NPV) formula is then used 

in conjunction with each measure’s measure life along with an overall discount rate to determine 

the $ cost per /first year kWh (or MMBTu) saved for each measure. The cost per first year 

savings figure is then multiplied by the savings potential estimate being evaluated in order to 

yield the cost to achieve the savings potential in the year 2018 at the measure level. Each 

measure is then summed up at each potential level to yield the total cost to achieve savings in 

the residential sector (within the potential level being analyzed) to represent the cost to achieve 

the potential savings level by the year 2018. This number can then be divided by the study 

length (10 years) in order to yield an estimate of annual spending needed to reach the potential 

level target in question. 

4.2 Residential Sector – Energy Efficiency Potential Results 

Eighty-seven (87) residential electric, and one-hundred-ten (110) residential non-electric energy 

efficiency measures or programs were included in the analysis for the residential sector. In 

order to develop the list of energy efficiency measures to be examined, GDS worked closely 

with project sponsors and reviewed recent measure life, savings and cost assumptions studies 

including a Measure Life Report prepared by GDS for the New England State Program Working 

Group in June 2007 and a GasNetworks measures assumptions update project completed by 

GDS during the summer 2008. In addition, GDS reviewed other related electric and non-electric 

residential energy efficiency measure-specific data sources and technical potential studies that 

have been conducted recently in the US. Focus was for comprehensiveness on the electric and 

natural gas measures, less so for fuel oil and propane. Even within electric and natural gas 

some measures were not analyzed due to a combination of measure-specific-limitations, and 

unavailability of reliable data (e.g., A/C peak demand savings from off peak cooling with thermal 

energy storage, more advanced windows than double pane with low-E, super high efficient gas 

hot water heaters/boilers and combo systems, air drying of laundry, etc.). 

The set of energy efficiency measures considered was pre-screened to include mainly those 

measures that are currently commercially available and cost effective (i.e., achieving a TRC 

benefit/cost ratios equal to or greater than 1.0 – although measures with TRC ratios between 

0.9 and 1.0 were also included). Thus, emerging technologies not currently in the marketplace 

that had benefit cost ratios below 0.9 were not included in the analysis. The portfolio of 

measures includes retrofit and replace on burnout programmatic approaches to achieve energy 

efficiency savings. 

4.2.1 Characteristics of Energy Efficiency Measures 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 70 

GDS collected data on the energy savings, incremental costs, useful lives and other key “per 

unit” characteristics of each of the residential electric and non-electric energy efficiency 

measures. Estimates of the size of the eligible market were also developed for each efficiency 

measure. For example, electric water heater efficiency measures are only applicable to those 

homes in New Hampshire that have electric water heaters. More information regarding 

measure-specific savings, cost and measure life assumptions can be found in Appendix E. 

For the residential new construction market segment, GDS calculated a forecast of the number 

of new homes estimated to be built each year based on NH new housing permits as reported by 

the US census bureau38 The sizes of various end-use market segments were informed based 

on project primary data collection efforts. This analysis is based on the most recent residential 

electric sales forecast for New Hampshire for the years 2009 to 2018.39 Energy-efficiency 

measures were analyzed for the most important electric and non-electric consuming end uses in 

the residential sector. 

Tables40 44 and 45 below list the residential sector electric and non-electric energy efficiency 

measures included in the technical (best), technical (traditional), maximum achievable, 

maximum achievable cost effective, and potentially obtainable potential analyses. 

 

38 The source of this economic/demographic forecast for NH is the US Census Bureau’s reporting of new building 

permits. http://www.census.gov/const/www/permitsindex.html 

39 This residential sector load forecast was provided to GDS by project sponsors.
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 71 Table 44. Residential Electric Energy Efficiency Savings Potential by Measure 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 72 Table 44. Residential Electric Energy Efficiency Savings Potential by Measure - Continued 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 73 Table 45. Residential Non-Electric Energy Efficiency Savings Potential by Measure 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 74 Table 45. Residential Non-Electric Energy Efficiency Savings Potential by Measure - Continued
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 75 

4.2.2 Residential Energy Efficiency Potential Comparisons and Savings By Measure Type

Figure 18 and Figure 19 display a graphical comparison of the varying degrees of potential 

results for both the electric and non-electric sector. 

Figure 18. Residential Electric Savings Potential Results Comparison 

Figure 19. Residential Non-Electric Savings Potential Results Comparison 

Figure 20 displays a graphical comparison of the varying electrical end-uses within the 

residential sector. As shown, lighting single-family and lighting multi-family make up the 

greatest savings potential focus areas (52% combined), followed by electric appliances at 16 

percent (SF and MF combined), space heating and cooling (10% combined SF and MF), 

standby (phantom-load) power and water heating at nine percent each (SM/MF combined). 

1,770,860,535

1,489,861,317

1,217,144,947

1,170,397,964

698,069,156

0

200,000,000

400,000,000

600,000,000

800,000,000

1,000,000,000

1,200,000,000

1,400,000,000

1,600,000,000

1,800,000,000

2,000,000,000

Technical Potential  (Best Only) Technical Potential (Good,

Better, Best)

Max Achievable Potential Max Achievable Cost Effective

Potential

PotentiallyObtainable

kWh

16,918,392

12,099,639

7,463,743

6,313,954

3,633,554

0

5,000,000

10,000,000

15,000,000

20,000,000

Technical Potential  (Best Only) Technical Potential

(Good/Better/Best)

Max Achievable Potential Max Achievable Cost Effective

Potential

Potentially Obtainable

MMBtu
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 76 

Figure 20. Residential Max. Achievable Cost Effective Electric Savings Potential by End Use 

Figure 21 displays a graphical comparison of the varying non-electric end-uses within the 

residential sector. As shown, single-family home oil heating measures represent the largest 

area of savings potential at 25%, followed by single-family water heating at 18%, and then 

single-family weatherization packages at 12%. The remainder is comprised mostly of multifamily water heating, gas-heating measures for single and multi-family, and home propane 

heating measures. 

Lighting ‐ SF

34%

Lighting ‐ MF

Electric Appliances ‐ SF 18%

13%

Standby Power

9%

SpaceHeating and Space Cooling ‐ SF

7%

Water

Heating ‐ SF

5%

Water Heating ‐ MF

4%

SF New Construction

4%

SpaceHeating and Space Cooling ‐ MF

3%

Electric Appliances ‐ MF

3%

Pools

0%
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 77 

Figure 21. Residential Max Achievable Cost Effective Non- Electric Savings Potential by End Use 

Figure 22 and Figure 23, displayed below, show a graphical comparison of the varying 

maximum achievable cost effective electric and non-electric savings by end use within the 

residential sector. While Figure 20 and Figure 21 show relative percent comparisons only, 

Figure 22 and Figure 23 show both relative and absolute (kWh and MMBTu) comparisons of the 

savings coming from each end use. 

HomeGas Heat (MF)

9%

HomeGas Heat

(SF)

6%

HomeOil Heat (SF)

25%

HomeOil Heat (MF)

3%

Home PropaneHeat (MF)

1%

Home

Propane

Heat(SF)

5%

New Construction (MF)

2%

New Construction (SF)

4%

Water Heating (MF)

11%

Water Heating (SF)

18%

Weatherization Packages(MF)

4%

Weatherization Packages(SF)

12%
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 78 Figure 22. Residential Electric Savings Potential by End Use (with kWh values) 

Lighting ‐ SF Lighting ‐ MF Electric Appliances ‐ SF Standby Power Space Heating and Space Cooling ‐ SF Water Heating ‐ SF Water Heating ‐ MF SF New Construction Space Heating and Space Cooling ‐ MF 

Max. Achievable Cost

Effective 399,588,082 210,479,584 151,300,008 107,603,656 84,066,189 53,160,336 45,937,312 42,689,666 36,320,508 

% of MACE 34.2% 18.0% 12.9% 9.2% 7.2% 4.5% 3.9% 3.6% 3.1% 

‐

50,000,000

100,000,000150,000,000200,000,000250,000,000300,000,000350,000,000400,000,000450,000,000

kWh Savings
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 79 Figure 23. Residential Non-Electric Savings Potential by End Use (with MMBTu values) 

Home Oil Heat

(SF)

Water Heating

(SF)

Weatherization

Packages(SF)

Water Heating

(MF)

Home Gas Heat

(MF)

Home Gas Heat

(SF)

Home Propane

Heat(SF)

New

Construction

(SF)

Weatherization

Packages(MF)

H

MACE 1,714,121 1,197,354 852,973 750,121 582,691 388,745 357,869 299,594 256,518 

% of MACE 25.19% 17.59% 12.53% 11.02% 8.56% 5.71% 5.26% 4.40% 3.77% 

‐

200,000400,000600,000800,000

1,000,0001,200,0001,400,0001,600,0001,800,000

MMBtu Savings
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 80 

4.2.3 Residential Energy Efficiency Measure Supply Curves

This report also presents results in the form of electric and non-electric energy efficiency supply 

curves. As noted previously, the advantage of using an energy efficiency supply curve is that it 

provides a clear, easy-to-understand framework for summarizing a variety of complex 

information about energy efficiency technologies, their costs, and the potential for energy 

savings. Properly constructed, an energy-efficiency supply curve avoids the double counting of 

energy savings across measures by accounting for interactions between measures. The supply 

curve also provides a simplified framework to compare the costs of energy efficiency measures 

with the costs of energy supply resources. 

The supply curves for residential electric energy efficiency savings are shown in Figure 24 

through Figure 29. Supply curves for residential non-electric energy efficiency savings are 

shown in Figure 30 through Figure 35. These supply curves were built up across individual 

measures and were sorted on a lowest to highest cost basis per unit of energy saved. As 

shown in these figures, nearly 12 percent of the projected 2018 residential sector kWh sales 

could be offset by installing electric efficiency measures at a levelized cost of less than two 

cents per/kWh (see Figure 29). Nearly eight percent of the projected maximum achievable cost 

effective savings potential from non-electric efficiency measures could be obtained at a 

levelized cost of less than three dollars per/MMBTu (see Figure 35). 

Figure 24. Residential Electric Energy Efficiency Supply (< $1.10/kWh) Curve for NH – Technical Potential 

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

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

Levelized $/kWh  ‐

Savings Technical Potential as a % of 2018 Forecasted Electric Use

Note: Only includes measures with a levelized $/kWh < $1.10
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 81 

Figure 25. Residential Electric Energy Efficiency Supply (< $0.10/kWh) Curve for NH – Technical Potential 

Figure 26. Residential Electric Energy Efficiency Supply (< $1.10/kWh) Curve for NH – Max Achievable 

$0.00

$0.01

$0.02

$0.03

$0.04

$0.05

$0.06

$0.07

$0.08

$0.09

$0.10

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%

Levelized $/kWh

Savings Technical Potential as a % of 2018 Forecasted Electric Use

Note: Only includes measures with a levelized $/kWh < $0.10.  

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

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

Levelized $/kWh  ‐

Savings Max Achievable Potential as a % of 2018 Forecasted Electric Use

Note: Only includes measures with a levelized $/kWh < $1.10
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 82 

Figure 27. Residential Electric Energy Efficiency Supply (< $0.10/kWh) Curve for NH – Max Achievable 

Figure 28. Residential Electric Energy Efficiency Supply (< $1.10/kWh) Curve for NH – M.A.C.E 

$0.00

$0.01

$0.02

$0.03

$0.04

$0.05

$0.06

$0.07

$0.08

$0.09

$0.10

0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

Levelized $/kWh

Savings Max Achievable Potential as a % of 2018 Forecasted Electric Use

Note: Only includes measures with a levelized $/kWh < $0.10.  

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

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

Levelized $/kWh  ‐

Savings Max Achievable Cost  Effective as a % of 2018 Forecasted Electric Use

Note: Only includes measures with a levelized $/kWh < $1.10
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 83 

Figure 29. Residential Electric Energy Efficiency Supply (< $0.10/kWh) Curve for NH – M.A.C.E. 

Figure 30. Residential Non-Electric Energy Efficiency Supply (< $10/MMBTu) Curve for NH – Technical 

$0.00

$0.01

$0.02

$0.03

$0.04

$0.05

$0.06

$0.07

$0.08

$0.09

$0.10

0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

Levelized $/kWh

Savings Max Achievable Cost Effective as a % of 2018 Forecasted Electric Use

Note: Only includes measures with a levelized $/kWh < $0.10.  

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

$10.00

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%

Levelized $/MMBtu

Savings Technical Potential as a % of 2018 Forecasted Non‐Electric Fuel Use

Note: Only includes measures with a levelized $/MMBTU < $10.00  
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 84 

Figure 31. Residential Non-Electric Energy Efficiency Supply (< $5/MMBTu) Curve for NH – Technical 

Figure 32. Residential Non-Electric Efficiency Supply (< $10/MMBTu) Curve for NH – Max Achievable 

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

0% 2% 4% 6% 8% 10% 12% 14% 16%

Levelized $/MMBTU

Savings Technical Potential as a % of 2018 Forecasted Non‐Electric Fuel Use

Note: Only includes measures with a levelized $/MMBTU < $5.00

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

$10.00

0% 2% 4% 6% 8% 10% 12% 14%

Levelized $/MMBtu

Savings Max Achievable Potential as a % of 2018 Forecasted Non‐Electric Fuel Use

Note: Only includes measures with a levelized $/MMBTU < $10.00  
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 85 

Figure 33. Residential Non-Electric Efficiency Supply (< $5/MMBTu) Curve for NH – Max Achievable 

Figure 34. Residential Non-Electric Energy Efficiency Supply (< $10/MMBTu) Curve for NH – M.A.C.E 

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

0% 2% 4% 6% 8% 10% 12%

Levelized $/MMBTU

Savings Max Achievable Potential as a % of 2018 Forecasted Non‐Electric Fuel Use

Note: Only includes measures with a levelized $/MMBTU < $5.00

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

$10.00

0% 2% 4% 6% 8% 10% 12% 14%

Levelized $/MMBtu

Savings Max Achievable Cost Achievable as a % of 2018 Forecasted Non‐Electric Fuel Use

Note: Only includes measures with a levelized $/MMBTU < $10.00  
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 86 

Figure 35. Residential Non-Electric Energy Efficiency Supply (< $5/MMBTu) Curve for NH – M.A.C.E. 

It is important to note that these levelized costs per unit of energy saved values exclude the 

costs for potential marketing, program design, administration and evaluation that would be 

required to encourage customer participation and to ultimately achieve any portion of this 

sectors savings potential. 

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

0% 2% 4% 6% 8% 10% 12%

Levelized $/MMBTU

Savings Max Achievable Cost Achievable as a % of 2018 Forecasted Non‐Electric Fuel Use

Note: Only includes measures with a levelized $/MMBTU < $5.00
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 87 

Section 5: Commercial Sector Energy Efficiency Potential 

This section of the report presents the estimates of electric and non-electric technical 

(traditional), maximum achievable, maximum achievable cost effective, and potentially 

obtainable energy efficiency potential for the existing and new construction market segments of 

the commercial sector in New Hampshire. More information regarding how these potentials 

were derived is also presented. 

According to this analysis, there is still a large remaining potential for electric and non-electric 

energy efficiency savings in the commercial sector. Table 46 and Table 47 below summarize 

the savings by potential type by the year 2018 for commercial electric and non-electric 

measures respectively (separate potentials are shown for new construction, existing buildings 

and combined within each table).40 The estimated total costs to achieve each level of savings 

by 2018 are also presented in these tables. In addition, Table 46 presents peak demand 

savings for each potential level of savings associated with the electric energy efficiency 

measures.41 

Table 46. Summary of Commercial Sector Electric Energy Efficiency Savings Potential 

 

40 The commercial sector sales forecast for the year 2018 was not available in terms of new and existing 

construction. As a result, in order to derive the split between new and existing construction, the growth of the sector 

over the ten (10) year study period was divided in half, and half was attributed to new construction sales, and the 

remaining half was attributed to growth in the existing sector. 

41 For purposes of this study, a simplifying assumption was used to estimate peak demand savings. Percentage 

sector peak demand savings are calculated to show savings over the summer coincident peak demand period only 

and are not broken out separately for summer and winter peak periods. 

Estimated Cumulative Annual 

Sales by 2018 (kWh)

Estimated Cumulative 

Annual Savings by 2018

(kWh)

Savings in 2018 as % of 

Total 2018 Electric 

Consumption

Estimated Cumulative 

Annual Demand Savings 

by 2018 By Sector (MW)

Estimated % of Peak 

Demand Savings by 2018

Estimated Costs to 

Achieve 2018 

Cummulative Annual 

Savings 

($ 2008 NPV)

Technical Potential 

(Traditional) 146,116,211 38.1% 54.0 1.8% $56,524,486

Max. Achievable Potential 99,371,416 25.9% 36.7 1.2% $44,385,181

Max. Achievable Cost 

Effective 81,088,647 21.1% 30.0 1.0% $22,010,481

Potentially Obtainable 37,713,403 9.8% 13.9 0.5% $8,926,584

Technical Potential 

(Traditional) 1,451,916,034 29.2% 422.9 14.2% $914,692,446

Max. Achievable Potential 1,198,691,188 24.1% 349.1 11.7% $806,498,673

Max. Achievable Cost 

Effective 985,683,305 19.8% 287.1 9.6% $289,826,583

Potentially Obtainable 454,309,206 9.1% 132.3 4.4% $115,897,185

Technical Potential 

(Traditional) 1,598,032,244 29.8% 476.9 16.0% $971,216,931

Max. Achievable Potential 1,298,062,604 24.2% 385.9 12.9% $850,883,854

Max. Achievable Cost 

Effective 1,066,771,952 19.9% 317.1 10.6% $311,837,064

Potentially Obtainable 492,022,609 9.2% 146.3 4.9% $124,823,769

COMMERCIAL SECTOR - NEW CONSTRUCTION

COMMERCIAL SECTOR - EXISTING BUILDINGS

383,672,438

4,970,126,508

5,353,798,946

COMMERCIAL SECTOR - TOTAL
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 88 

Table 47. Summary of Commercial Non-Electric Energy Efficiency Savings Potential 

Estimated Cumulative Annual 

Sales by 2018 (MMBtu)

Estimated Cumulative 

Annual Savings by 2018

(MMBtu)

Savings in 2018 as % of 

Total 2018 Gas 

Consumption

Estimated Costs to 

Achieve 2018 

Cummulative Annual 

Savings 

($ 2008 NPV)

Technical Potential 1,696,543 29% $174,415,757

Achievable Potential 1,143,559 20% $109,001,402

Achievable Cost Effective 

Potential 992,356 17% $58,593,673

Potentially Obtainable 401,855 7% $18,382,602

Technical Potential 10,284,474 26% $1,047,661,444

Achievable Potential 8,932,119 23% $927,481,632

Achievable Cost Effective 

Potential 6,717,981 17% $266,103,608

Potentially Obtainable 2,850,349 7% $83,851,626

Technical Potential 11,981,017 26% $1,222,077,201

Achievable Potential 10,075,678 22% $1,036,483,035

Achievable Cost Effective 

Potential 7,710,337 17% $324,697,281

Potentially Obtainable 3,252,204 7% $102,234,228

COMMERCIAL SECTOR - NEW CONSTRUCTION

5,793,062

39,536,853

COMMERCIAL SECTOR - EXISTING BUILDINGS

COMMERCIAL SECTOR - TOTAL

45,329,915

On the electric side, the combined existing and new buildings maximum achievable cost 

effective potential in the commercial sector in 2018 is nearly 1 million kWh, just under 20 

percent of the New Hampshire commercial sector sales forecast in 2018. With regard to nonelectric potential for new and existing buildings combined, the maximum achievable cost 

effective potential in the commercial sector is over 7.7 million MMBTu, or 17% of the New 

Hampshire commercial sector fossil fuel (natural gas, oil and propane) sales forecast in 2018. 

The lists of measures that make up the savings for each of these levels are shown in Table 52 

through Table 55 in Section 5.2.1 below. 

5.1 Commercial Sector Savings Methodology Overview 

The commercial sector analysis was modeled using what is considered a “top-down approach”. 

This methodology, shown visually in Figure 36 below: 

Figure 36. Commercial Sector Savings Methodology – Top Down Approach
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 89 

As shown in this figure, the methodology is started at the top with the total projected 2018 kWh 

sales for the commercial sector. Those sales are then split up by building type using SIC codes 

of actual customer data (provided to us by project sponsors). After the sales are distributed 

across the building types, they are broken down further to end-uses (e.g. lighting, space 

heating, appliances) within each of the building types. From the end-use level, the energy is 

then applied to each of the measures using applicability factors. The base case factor is applied 

first, to inform the model regarding how much of the sales in a particular end use was applicable 

to the specific measure in question. After identifying how much energy each measure uses 

within that end use (i.e., what degree of the end use sales is going to each measure), then 

models then look at the remaining factor. As discussed in Section 3, the remaining factor 

identifies what percentage of the building type in question already has the efficient measure. 

The remaining factor is then one minus that penetration – resulting in the percentage, by 

building type, of each measure that can still be installed in within the commercial sector. The 

model then considers the savings factor, which is defined is the percentage savings achievable 

from moving from a standard efficiency measure to a high efficiency measure. Finally, 

adjustments are made for any technical limitations that would prevent the measure from being 

installed in certain applications via the convertible factor (engineering adjustment). This 

scenario is repeated for every measure within every building type, for new and existing 

construction, and for electric measures, and non-electric measures. The formula that has just 

been explained to calculate savings at the measure level is displayed below graphically. 

Technical 

Potential of 

Efficient 

Measure 

= 

Total End 

Use kWh 

Sales by 

Building 

Type 

X Base Case 

Factor X Remaining Factor X Convertible Factor X Savings Factor 

Measure interactivity is also considered so as to prevent overstating (double-counting) of 

savings. To better illustrate this point, in the case of lighting, consider the upgrade of a T-12 

fixture to a T-8, and then the installation of an occupancy control. In such a case, the 

occupancy control is only able to save the amount of energy left after the upgrade has taken 

place. Through functionality included within the GDS supply curve model, measures are ranked 

by benefit cost ratio (highest to lowest) as a proxy to determine the order by which measures 

are installed. Through a combination of the proper classification of the base case factors, and 

the rankings in the supply curve model, the potential for double-counting is methodically 

eliminated. 

The supply curve model is designed in a manner that allows for each measure to have 

independent base, remaining, savings, and convertible factors for all of the nine (9) building 

types. In addition, every building type has its own energy consumption profile that defines how 

energy consumption within that building type is distributed among the end uses (e.g. lighting, 

water heating, appliances, etc) within the building type. This allows the model to run savings 

analyses on building specific energy consumption profiles and building specific energy savings 

profiles simultaneously in order to yield the most accurate and realistic savings potential 

estimates possible. In addition, individual models are run for commercial existing construction, 

and commercial new-construction for both electric and non-electric yielding a total of four unique 

(4) commercial supply curve models. The commercial electric models (existing and new 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 90 

construction) were based on kWh sales in the year 2018, while the commercial non-electric 

model used 2018 MMBTu sales for natural gas, oil, and propane combined.42 

The measures within each building type are organized and grouped by the end use energy 

consumption pools that they have the ability to potentially save energy from. Lighting measures 

for instance are all working off of the lighting energy sales in the year 2018 as a basis or starting 

point for the energy savings potential within a particular building type. The ordering of the 

lighting related measures within this grouping is determined by benefit cost ratio. Measures with 

higher benefit cost ratios are assumed to be installed first, and then ranked in descending order 

on down the line. As a measure is installed, the model reduces the remaining sales left to be 

saved for the next measure (the actual algorithm for how this happens within the model on a 

measure to measure basis is more complex than what is being described here). So if a lighting 

fixture is upgraded, and then a control is installed, the control has less potential energy to save 

since the light it is controlling has already been upgraded to a more efficient version. This 

process is repeated until all measures within each end use are exhausted in order to yield the 

savings potential at the measure level, end-use level, and the building-type level. 

The costs to achieve savings potential estimates within the commercial sector are calculated on 

a measure by measure basis using the levelized cost ($/kWh in the electric model and 

$/MMBTu within the non-electric model) for each measure. These levelized costs represent the 

cost to save a unit of energy. These levelized costs are then taken and multiplied (again at the 

measure level) by the 2018 annual savings associated with the potential level we are attempting 

to capture (technical potential, maximum achievable, etc.). A net present value (NPV) formula 

is then used in conjunction with each measure’s measure life and an overall discount rate to 

determine the $ cost per first year kWh (or MMBTu) saved for each measure. The cost per first 

year savings figure is then multiplied by the savings potential estimate being evaluated in order 

to yield the cost to achieve the savings potential being quoted in the year 2018 at the measure 

level. Each measure is then summed up at each potential level to yield the total cost to achieve 

savings in the commercial sector (within the potential level being analyzed) to represent the cost 

to achieve the potential savings level by the year 2018. This number can then be divided by the 

study length (10 years) in order to yield an estimate of annual spending needed to reach the 

potential level target in question. 

In addition, the model includes a number of measures that save energy across multiple end-use 

categories. Examples include retrocommissioning which can saves heating, cooling, and 

ventilation energy, and insulation which can provide both heating and cooling savings. As a 

result, these types of measure are placed within the model in a manner so that they can claim 

their proper savings within each one of the appropriate end-uses. A complexity occurs when 

attempting to properly estimate the cost for these measures. In order to avoid overstating the 

cost to install a measure like retrocommissioning, the cost needs to be divided across the 

different end-uses it affects. To simplify the modeling, it is assumed that the costs would be 

divided by the number of end-uses the measure effects. If the full cost for each end-use is 

applied, it would be inaccurate (i.e., for retrocommissioning, if the end-user is only paying for an 

engineer to walk-through the facility and assess and assist with implementation of identified 

savings opportunities in a single visit; the end-user will typically receive a single invoice for the 

combined retrocommissioning service, as opposed to multiple invoices being sent for 

implementation of each type of savings identified by the engineer). This approach is also used 

 

42 We were only provided actual sales forecasts through 2018 from the natural gas utilities. In order to determine the 

projected forecasts for oil and propane in MMBTu, we extrapolated based on the results of the commercial 

telephone survey (Question #16: What is the main energy source for heating?). The results of which yielded 

commercial customers in NH using natural gas to be 28%, oil at 46%, and bottled gas/propane at 26%. This allowed 

us to accurately estimate the year by year forecasts (particularly 2018) for all non-electric fuels combined. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 91 

in the model for many of the building envelope and HVAC controls measures, as they often 

affect more than one end-use when installed. This approach is due mainly to GDS’s technical 

potential model’s functionality. The description above, is provided to explain to readers how the 

model, within its existing framework, has been used to ensure that double counting of costs is 

avoided for these types of measures. 

Table 48 and Figure 37 illustrate the commercial sector electricity sales based segmentation. 

This segmentation is based on 2009 commercial sales data by SIC code as provided by project 

sponsors. 

Table 48. Commercial Sector Segmentation by Industry Type - Electric

Industry Type Commercial kWh Percent of kWh Sales* Business Categories

1 Warehouse 22,943,600 0.46% Wholesale Trade & Warehouse

2 Retail 1,305,235,571 26.26% Other Retail Trade

3 Grocery 843,032,754 16.96% Food/Grocery

4 Office 1,175,866,515 23.66% Business/Financial Services, Social Services, US Post Office, Government, Communications, Utilities, Transportation 

5 Lodging 280,529,174 5.64% Lodging

6 Health 437,017,809 8.79% Hospitals, Other Nursing & Care, Medical Offices & Other

7 Education 536,887,562 10.80% Elementary & Secondary Schools, Colleges & Education - Other

8 Restaurant 96,579,427 1.94% Restaurants, Eating & Drinking Establishments

9 Other 272,034,096 5.47% Agriculture, Forestries & Fishing, Mining & Construction, Water & Wastewater, Entertainment

Total 4,970,126,508 100.00%

* Based on NH Utilities 2008 - 2017 Forecast and allocations from actual Sales Data by SIC code categories

Figure 37. Commercial Sector Segmentation by Industry Type - Electric

Table 49 and Figure 38 illustrate the commercial sector non-electric sales based segmentation. 

This segmentation is based on 2009 commercial sales data by SIC code as provided by project 

sponsors. 

Table 49. Commercial Sector Segmentation by Industry Type – Non-Electric

Industry Type Percent of Non-Electric Sales* Business Categories

1 Warehouse 5.58% Wholesale Trade & Warehouse

2 Retail 10.94% Other Retail Trade

3 Grocery 1.82% Food/Grocery

4 Office 19.87% Business/Financial Services, Social Services, US Post Office, Government, Communications, Utilities, Transportation

5 Lodging 7.81% Lodging

6 Health 10.49% Hospitals, Other Nursing & Care, Medical Offices & Other

7 Restaurant 9.67% Elementary & Secondary Schools, Colleges & Education - Other

8 Education 11.38% Restaurants, Eating & Drinking Establishments

9 Other 22.44% Agriculture, Forestries & Fishing, Mining & Construction, Water & Wastewater, Entertainment

Total 100.00%

* Based on US DOE, Energy Information Administration (EIA), 2003 Commercial Buildings Energy Consumption Survey, Tables C23A and C25A 

Natural Gas Energy Efficiency Resource Development Potential in New York, Prepared for New York Energy Research and Development Authority, by OPTIMAL ENERGY, INC., AMERICAN 

COUNCIL FOR AN ENERGY-EFFICIENT ECONOMY, VERMONT ENERGY INVESTMENT CORPORATION, RESOURCE INSIGHT, INC., ENERGY AND ENVIRONMENTAL ANALYSIS, 

INC., October 31, 2006

Warehouse

Retail

Grocery

Office

Lodging

Health

Education

Restaurant Other

NHUtilities Commercial Sector

Sales By Business Segment
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 92 

Figure 38. Commercial Sector Segmentation by Industry Type – Non-Electric

5.3 Commercial Sector End-Use Breakdowns 

Table 50 and Table 51 illustrate the commercial sector energy sales based segmentation. The 

breakdown of commercial electricity use by end-use and industry type was developed based on 

data included in the 2003 New York Technical Potential Study while the breakdown for nonelectric was based on a similar New York Technical Potential Study43 conducted in 2006. This 

study divided New York into regions and the Albany region (Region F) was used as a 

reasonable representation of the commercial sector in New Hampshire. 

Table 50. Commercial Sector End Use Breakdowns Allocation Table – Electric

Table 51. Commercial Sector End Use Breakdowns Allocation Table – Non-Electric

5.4 Commercial Sector – Energy Efficiency Potential Results 

One-hundred-twenty-five (125) commercial electric, and sixty seven (67) commercial nonelectric energy efficiency measures were included in the analysis for the commercial sector. In 

order to develop the list of energy efficiency measures to be examined, GDS worked closely 

 

43 Natural Gas Energy Efficiency Resource Development Potential in New York, Prepared for New York Energy Research and 

Development Authority, by OPTIMAL ENERGY, INC., AMERICAN COUNCIL FOR AN ENERGY-EFFICIENT 

ECONOMY, VERMONT ENERGY INVESTMENT CORPORATION, RESOURCE INSIGHT, INC., ENERGY AND 

ENVIRONMENTAL ANALYSIS, INC., October 31, 2006

Warehouse

6%

Retail

11%

Grocery

2%

Office

20%

Lodging

8%

Health

10%

Restaurant

10%

Education

11%

Other

22%

NHUtilities Commercial Sector Non‐Electric Sales By Business

Segment

Non-Electric Warehouse Retail Grocery Office Lodging Health Restaurant Education Other

Space Heating 76% 62% 56% 72% 53% 45% 34% 60% 48%

Water Heating 16% 22% 25% 26% 34% 37% 27% 24% 29%

Cooking 3% 15% 17% 0% 9% 15% 37% 13% 20%

O ther 5% 1% 2% 2% 4% 4% 2% 3% 3%

Blank 0% 0% 0% 0% 0% 0% 0% 0% 0%

Warehouse Retail Grocery Office Lodging Health Restaurant Education Other TOTAL

Indoor Lighting 18% 25% 50% 38% 24% 28% 20% 43% 17% 29%

Outdoor Lighting 3% 2% 6% 4% 5% 2% 6% 4% 2% 4%

Cooling 2% 21% 18% 13% 13% 21% 10% 10% 8% 12%

Ventilation 10% 20% 10% 10% 18% 9% 7% 18% 6% 11%

Water Heating 1% 5% 5% 2% 8% 6% 16% 6% 4% 5%

Refrigeration 58% 9% 1% 1% 3% 3% 32% 2% 20% 13%

Space Heating 4% 12% 4% 13% 20% 9% 4% 9% 3% 8%

Office Equipment 2% 2% 2% 11% 3% 2% 1% 4% 2% 4%

Miscellaneous 3% 3% 3% 8% 5% 21% 3% 4% 38% 14%

TOTAL 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 93 

with project sponsors and reviewed recent measure life, savings and cost assumption studies 

including a Measure Life Report prepared by GDS for the New England State Program Working 

Group in June 2007 and a GasNetworks measures assumptions update project completed by 

GDS during the summer 2008. In addition, GDS reviewed other related electric and non-electric 

commercial sector energy efficiency measure-specific data sources and technical potential 

studies that have been conducted recently in the US. Focus was for comprehensiveness on the 

electric and natural gas measures, less so for fuel oil and propane. Even within electric and 

natural gas some measure limitations were required (e.g., Data Center/IT, etc.). 

The set of energy efficiency measures considered was pre-screened to mainly those measures 

that are currently commercially available (or were estimated to be cost effective within the ten 

year study period). Thus, emerging technologies not currently in the marketplace that had 

benefit cost ratios below 0.9 were not included in the analysis. The portfolio of measures 

includes retrofit and replace on burnout programmatic approaches to achieve energy efficiency 

savings. 

5.2.1 Characteristics of Energy Efficiency Measures

GDS collected data on the energy savings, incremental costs, useful lives and other key “per 

unit” characteristics of each of the commercial electric and non-electric energy efficiency 

measures, this data is available in Appendix F for the commercial sector. Estimates of the size 

of the eligible market were also developed for each efficiency measure. For example, electric T5 lighting efficiency measures are only applicable to those commercial building types in New 

Hampshire that have the potential to use that lighting technology in their building space. 

The commercial sector analysis was based on the most recent sales forecasts for New 

Hampshire for the years 2009 to 2018.44 For the commercial new construction market segment, 

GDS calculated a forecast of the new construction sales estimated to be built each year based 

on looking at the growth of the sector over the 10 year period, and making the assumption that 

half of that growth is from new construction, while the other half is coming from growth of 

existing buildings. This assumption was approved by the project sponsors, and has been used 

in previous technical potential projects around the US. The sizes of various end-use market 

segments were informed based on project primary data collection efforts. 

Energy-efficiency measures were analyzed for the most important electric and non-electric 

consuming end uses in the Commercial sector including: 

• Space heating 

• Water heating 

• Air conditioning 

• Lighting 

• Appliances 

• Pools 

• Cooking 

• Motors 

• Transformers 

• Ventilation 

 

44 This Commercial sector load forecast was provided to GDS by project sponsors. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 94 

Tables45 52 through 55 below list the commercial sector electric and non-electric energy 

efficiency measures included in the technical (traditional), maximum achievable, maximum 

achievable cost effective, and potentially obtainable potential analyses. The portfolio of 

measures includes retrofit, and replace on burnout programmatic approaches to achieve energy 

efficiency savings. More information regarding measure-specific savings, cost and measure life 

assumptions can be found in Appendix F. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 95 Table 52. Commercial Electric Savings Potential by Measure – Existing Buildings
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 96 Table 52. Commercial Electric Savings Potential by Measure – Existing Buildings (continued) 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 97 Table 53. Commercial Electric Savings Potential by Measure – New Construction
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 98 Table 53. Commercial Electric Savings Potential by Measure – New Construction (continued) 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 99 Table 54. Commercial Non-Electric Savings Potential by Measure – Existing Construction
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 100 Table 55. Commercial Non-Electric Savings Potential by Measure – New Construction
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 101 

The distribution of commercial sector electric and non-electric savings by end use is shown 

below in Figure 39 through Figure 42 for existing and new buildings. On the electric side, for the 

commercial sector in New Hampshire, the electric lighting end use still represents the largest 

savings potential in absolute terms for both energy and peak demand, despite the significant 

adoption of high-efficiency lighting since the 1990’s. Refrigeration represents the second 

largest end-use category for kWh savings and space heating and cooling makes up the third 

largest category for kWh savings. On the non-electric side, for the commercial sector in New 

Hampshire, the space-heating end use represents the largest savings potential (nearly 75%). 

Space heating is followed up by water heating, and the remainder is brought up by cooking, 

pools, and ventilation. 

Figure 39. Commercial Electric Max. Achievable Cost Effective Savings By End Use – Existing

Figure 40. Commercial Electric Max. Achievable Cost Effective Savings By End Use – New

Lighting Refrigeration HVAC

Controls

Lighting

Controls Ventilation

Space

Cooling ‐

Unitary and

Split AC

Space

Heating

Building

Envelope

Non‐HVAC

Motors

Water

Heating

Appliances,

Computers,

Office

Equipment

Space

Cooling ‐

Chillers

Transformers Pools Cooking Compressed

Air

Lighting

Design

Total 283,670,28 187,253,17 131,425,55 97,827,958 54,098,624 39,271,838 34,040,704 30,813,566 30,024,310 29,382,954 28,568,618 23,237,346 6,669,886 5,391,617 2,630,755 1,376,113  ‐

‐

50,000,000

100,000,000

150,000,000

200,000,000

250,000,000

300,000,000

kWh Savings

Lighting

Design

Refrigeratio

n

Building

Envelope Ventilation

Space

Cooling ‐

Chillers

Space

Cooling ‐

Unitary and

Split AC

Space

Heating

Non‐HVAC

Motors

Water

Heating

Appliances,

Computers,

Office

Equipment

HVAC

Controls

Transforme

rs Pools Cooking Compressed

Air

MACE kWh Savings 33,661,856 14,455,141 8,799,382 5,585,479 2,995,167 2,978,667 2,627,796 2,317,748 2,268,238 2,205,375 1,953,389 514,887 416,210 203,083 106,230

% of MACE 41.5% 17.8% 10.9% 6.9% 3.7% 3.7% 3.2% 2.9% 2.8% 2.7% 2.4% 0.6% 0.5% 0.3% 0.1%

‐

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

40,000,000

kWh Savings
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 102 

Figure 41. Commercial Max. Achievable Cost Effective Non-Electric Savings By End Use – Existing

Figure 42. Commercial Max. Achievable Cost Effective Non-Electric Savings By End Use – New

Space Heating HVAC Controls Water Heating Building

Envelope Pools Cooking Ventilation

MACE 2,945,089 1,137,607 1,114,772 911,895 263,209 256,502 88,907

% of MACE 43.8% 16.9% 16.6% 13.6% 3.9% 3.8% 1.3%

‐

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

MMBtu Savings

Space Heating Water Heating Building

Envelope HVAC Controls Pools Cooking Ventilation

MACE Savings 435,153 163,340 154,646 150,041 38,566 37,583 13,027

% of MACE 43.9% 16.5% 15.6% 15.1% 3.9% 3.8% 1.3%

‐

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

MMBtu Savings
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 103 

5.2.2 Commercial Energy Efficiency Measure Supply Curves

This report also presents results in the form of electric and non-electric energy efficiency supply 

curves. Figure 43 through Figure 48 present the electric existing and new construction supply 

curves under each scenario (technical potential, maximum achievable, maximum achievable 

cost effective). Figure 49 through Figure 54 present supply curves for the non-electric existing 

and new construction scenario (technical potential, maximum achievable, maximum achievable 

cost effective). As in the residential sector, these supply curves were built up across individual 

measures and were sorted on a lowest to highest cost basis per unit of energy saved. 

Figure 43. Commercial Electric Supply Curve: Existing Buildings – Technical Potential Scenario

Figure 44. Commercial Electric Supply Curve: Existing Buildings – Max. Maximum Achievable

Figure 45. Commercial Electric Supply Curve: Existing Buildings – Max. Achievable Cost Effective

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

$0.40

$0.45

$0.50

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/kWh Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH CommercialBuildings Technical Potential Supply Curve

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

$0.40

$0.45

$0.50

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/kWh Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NHCommercialBuildings ‐ Maximum Achievable Supply Curve

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

$0.40

$0.45

$0.50

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/kWh Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH CommercialBuildings ‐ Cost Effective Supply Curve
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 104 

Figure 46. Commercial Electric Supply Curve: New Buildings – Technical Potential Scenario

Figure 47. Commercial Electric Supply Curve: New Buildings – Max. Achievable

Figure 48. Commercial Electric Supply Curve: New Buildings – Max. Achievable Cost Effective

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

$0.40

$0.45

$0.50

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/kWh Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH CommercialBuildings ‐ Technical Potential Supply Curve

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

$0.40

$0.45

$0.50

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/kWh Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH CommercialBuildings ‐ Maximum Achievable Supply Curve

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

$0.40

$0.45

$0.50

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/kWh Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH CommercialBuildings ‐ Cost Effective Supply Curve
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 105 

Figure 49. Commercial Non-Electric Supply Curve: Existing Buildings – Technical Potential

Figure 50. Commercial Non-Electric Supply Curve: Existing Buildings – Max. Achievable

Figure 51. Commercial Non-Electric Supply Curve: Existing Buildings – Max. Achievable Cost Effective

$0.00 

$5.00 

$10.00 

$15.00 

$20.00 

$25.00 

Levelized $/MMBtu Saved

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH Non-Electric Commercial -Tech. Potential Supply Curve

$0.00 

$5.00 

$10.00 

$15.00 

$20.00 

$25.00 

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/MMBtu Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH Non-Electric Commercial - Max. Achievable Supply Curve

$0.00 

$5.00 

$10.00 

$15.00 

$20.00 

$25.00 

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/MMBtu Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH Non-Electric Commercial - Cost Effective Supply Curve 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 106 

Figure 52. Commercial Non-Electric Supply Curve: New Buildings – Technical Potential

Figure 53. Commercial Non-Electric Supply Curve: New Buildings – Max. Achievable

Figure 54. Commercial Non-Electric Supply Curve: New Buildings – Max. Achievable Cost Effective 

$0.00 

$5.00 

$10.00 

$15.00 

$20.00 

$25.00 

Levelized $/MMBtu Saved

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH Non-Electric - Commercial Tech. Potential Supply Curve

$0.00 

$5.00 

$10.00 

$15.00 

$20.00 

$25.00 

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/MMBtu Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH Non-Electric Commercial - Max. Achievable Supply Curve

$0.00 

$5.00 

$10.00 

$15.00 

$20.00 

$25.00 

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Levelized $/MMBtu Saved

Commercial Buildings Savings Potential as Percent of Commercial Sales

NH Non-Electric Commercial - Cost Effective Supply Curve 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 107 

Section 6: Industrial Sector Energy Efficiency Potential 

This section of the report presents the estimates of electric and non-electric technical 

(traditional), maximum achievable, maximum achievable cost effective, and potentially 

obtainable energy efficiency potential for the existing and new construction market segments of 

the industrial sector in New Hampshire. More information regarding how these potentials were 

derived is also presented. 

According to this analysis, there is still a large remaining potential for electric and non-electric 

energy efficiency savings in the industrial sector. Table 56 and Table 57 below summarize the 

savings by potential type by the year 2018. In addition, Table 56 presents peak demand 

savings for each potential level of savings associated with the electric energy efficiency 

measures.45 

On the electric side, the combined existing and new buildings maximum achievable cost 

effective potential in the industrial sector is over 440 million kWh, or 21 percent of the New 

Hampshire industrial sector sales forecast in 2018. With regard to non-electric potential, the 

maximum achievable cost effective potential in the industrial sector is 1.4 million MMBTu, or 9 

percent of projected New Hampshire industrial sector natural gas, oil and propane sales in 

2018. 

The results on both the electric and non-electric tables below display the Maximum Achievable 

being equal to the Maximum Achievable Cost Effective potential. This is due to the end-uses 

being screened in a combined manner, rather than at the measure level. While there is a high 

likelihood that some measures within each of the end-uses would screen as not cost-effective, 

given that this analysis was done at the end-use level, modeling limitations prevented 

consideration of such measure-specific results. 

Table 56. Summary of Industrial Sector Electric Energy Efficiency Savings Potential 

 

45 For purposes of this study, a simplifying assumption was used to estimate peak demand savings. Percentage 

sector peak demand savings are calculated to show savings over the summer coincident peak demand period only 

and are not broken out separately for summer and winder peak periods. 

Estimated Annual 

Sales by 2018 

(kWh)

Estimated Annual 

Savings by 2018 

(kWh)

Savings in 2018 

as % of Sector 

2018 Electric 

Consumption

Savings in 2018 

as % of Total 

2018 Electric 

Consumption

Estimated Annual

Sales by 2018 

(MW)

Estimated Annual 

Demand Savings 

by 2018 By Sector 

(MW)

Estimated 

Savings as % 

of Peak Sector 

Demand by 

2018

Estimated 

Savings as % 

of Total Peak 

Demand by 

2018

Estimated Costs to 

Achieve 2018 Annual 

Savings 

(10 Year Cumulative)

($2008 NPV)

Total Estimated 

Annual Benefits 

Associated 

W/Combined 

Savings in 2018 

($2008 NPV)

Simple Payback 

(NPV Total Costs / 

NPV Annual 

Savings)

Technical Potential (Traditional) 515,485,621 24.5% 4.0% 109.7 22.0% 3.7% $133,914,929 $ 2.9 46,000,232

Max. Achievable Potential 442,671,155 21.1% 3.4% 94.2 18.9% 3.2% $114,998,894 $ 2.9 39,502,510

Max. Achievable Cost Effective 442,671,155 21.1% 3.4% 94.2 18.9% 3.2% $114,998,894 $ 2.9 39,502,510

Potentially Obtainable 213,810,168 10.2% 1.6% 81.9 16.5% 2.7% $55,544,466 $ 2.9 19,079,712

 2,102,729,959 498

INDUSTRIAL SECTOR
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 108 

Table 57. Summary of Industrial Sector Non-Electric Energy Efficiency Savings Potential 

6.1 Industrial Sector Savings Methodology Overview 

The Industrial sector analysis was modeled using what is considered a “top-down approach”. 

This methodology, shown visually in Figure 55 below: 

Figure 55. Industrial Sector Savings Methodology – Top Down Approach

Similar to the commercial sector, this top-down methodology starts with the total projected 2018 

kWh sales for the industrial sector, and then splits those sales up by industry type using SIC 

codes of actual customer data (provided to GDS by project sponsors). After the sales are 

distributed across the industry types, they are broken down further to specific end-uses (e.g. 

lighting, space heating, appliances) within each of the building types. This was done using the 

2002 Mechanical Energy Consumption Survey data (MECS)46 for the New England Region. 

Given that the industrial sector equipment stock consists of highly specialized custom 

equipment, this sector was modeled at the end-use level as opposed to the detailed measure 

level. The end-uses being modeled in the Industrial sector include the following: 

• Conventional Boiler Use 

• CHP and/or Cogeneration Process 

• Process Heating 

• Process Cooling and Refrigeration 

• Machine Drive 

• Electro-Chemical Processes 

 

46 2002 Manufacturing Energy Consumption Survey (MECS) Data (http://www.eia.doe.gov/emeu/mecs/mecs2002/) 

Estimated Annual Sales 

by 2018 (MMBtu)

Estimated Annual 

Savings by 2018 (MMBtu)

Savings in 2018 as % of 

Sector 2018 Non-Electric 

Fuel Consumption

Savings in 2018 as % of 

Total 2018 Non-Electric 

Fuel Consumption

Estimated Costs to Achieve 2018 

Annual Savings 

(10 Year Cumulative)

($2008 NPV)

Total Estimated 

Annual Benefits 

Associated 

W/Combined Savings 

in 2018 ($2008 NPV)

Simple Payback 

(NPV Total Costs / 

NPV Annual Savings)

Technical Potential (Traditional) 1,755,089 11.2% 1.9% 19,467,779 $ $ 1.2 16,623,765

Max. Achievable Potential 1,415,809 9.0% 1.5% 15,704,417 $ $ 1.2 13,410,187

Max. Achievable Cost Effective 1,415,809 9.0% 1.5% 15,704,417 $ $ 1.2 13,410,187

Potentially Obtainable 683,836 4.4% 0.7% 7,585,234 $ $ 1.2 6,477,120

INDUSTRIAL SECTOR

15,673,818
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 109 

• Other Process Use 

• Facility HVAC 

• Facility Lighting 

• Other Facility Support 

• Onsite Transportation 

• Compressed Air 

• Sensors & Controls 

• End Use Not Reported 

Once sales is applied to each of the end-uses in both the electric models and the non-electric 

models, an all-inclusive applicability factor is applied to each end-use within each of the industry 

types to determine industry specific savings by end-use. This all inclusive applicability factor is 

applied to the end-use sales by industry type and takes into account the four (4) factors that 

have been used throughout this study (i.e., the base case factor, remaining factor, savings 

factor, and convertible factor). Detailed measure end-use specific factors and related 

information can be found within Appendix G of this report. 

The cost to achieve savings estimates within the industrial sector are calculated by multiplying 

the levelized cost per first year kWh or MMBTu savings within each measure category (e.g. 

machine drive, facility lighting, etc.) by the kWh or MMBTu savings in 2018 for the potential level 

being evaluated (e.g. technical potential, maximum achievable, etc.). The result of which is the 

cost to achieve the savings potential being quoted in the year 2018. This number can then be 

divided by the study length (10 years) in order to yield an estimate of annual spending needed 

to reach the potential level target in question. 

New Hampshire-specific industry types identified and used in the industrial models included the 

following: 

• Apparel And Other Finished Products Made From Fabrics And Similar Materials 

• Chemicals And Allied Products 

• Electronic And Other Electrical Equipment And Components, Except Computer 

Equipment 

• Fabricated Metal Products, Except Machinery And Transportation Equipment 

• Food And Kindred Products 

• Furniture And Fixtures 

• Industrial And Commercial Machinery And Computer Equipment 

• Leather And Leather Products 

• Lumber And Wood Products, Except Furniture 

• Measuring, Analyzing, And Controlling Instruments; Photographic, Medical, And Optical 

Goods; Watches And Clocks 

• Miscellaneous Manufacturing Industries 

• Paper And Allied Products 

• Petroleum Refining And Related Industries 

• Primary Metal Industries 

• Printing, Publishing, And Allied Industries 

• Rubber And Miscellaneous Plastics Products 

• Stone, Clay, Glass, And Concrete Products 

• Textile Mill Products 

• Tobacco Products 

• Transportation Equipment 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 110 

More information on the distribution of energy usage within each of these industrial building type 

categories is presented in the following section. 

6.2 Industrial Sector Segmentation 

Table 58 and Table 59 illustrate the industrial sector electricity and non-electric sales based 

segmentation. This segmentation is based on 2009 Industrial sales data by SIC code as 

provided by project sponsors. 

Table 58. Industrial Sector Segmentation by Industry Type - Electric

Table 59. Industrial Sector Segmentation by Industry Type – Non-Electric

Industry % Of Sales 2018 kWh Sales

ApparelAnd Other Finished P roducts Made From Fabrics And Similar Materials 0.23% 4,841,139

Chemicals And Allied Products 0.98% 20,537,715

Electronic And Other Electrical Equipment And Components, Except Compu ter Equipment 15.58% 327,660,088

Fabricated Metal Products,Except Machinery And Transportation Equipment 5.74% 120,602,372

Food And Kindred Products 8.88% 186,807,201

Furniture And Fixtures 0.56% 11,827,540

IndustrialAnd Commercial Machinery And Compu ter Equipment 10.22% 215,000,038

Leather And Leather Products 0.68% 14,208,070

Lumber And Wood Products, Excep t Furnitu re 4.03% 84,834,419

Measuring,Analyzing, And Controlling Instruments; Photographic, Medical, And OpticalGoods; Watches And Clocks 6.07% 127,539,458

Miscellaneous Manufacturing Indus tries 3.73% 78,459,698

Paper And Allied Products 6.66% 140,041,116

Petroleum RefiningAnd Related Industries 0.94% 19,849,417

Primary Metal Industries 9.93% 208,709,932

Printing, Publishing,And Allied Industries 3.14% 66,122,451

RubberAnd Miscellaneous Plastics Products 14.25% 299,645,250

Stone, Clay, Glass, And Concrete Products 3.98% 83,769,711

TextileMill P roducts 2.18% 45,780,294

Tobacco Products 0.04% 761,224

Transportation Equipment 2.17% 45,732,822

Totals: 100.00% 2,102,729,959

Industry % Of Sales 2018 Therms Sales

Apparel And Other Finished P roducts Made From Fabrics And Similar Materials 0.27% 153,399

Chemicals And Allied Products 0.95% 542,309

Electronic And Other Electrical Equipment And Components, Except Compu ter Equipment 2.73% 1,557,367

Fabricated Metal Products, Except Machinery And Transportation Equipment 5.59% 3,184,570

Food And Kindred Products 15.00% 8,550,090

Furnitu re And Fixtures 0.26% 149,910

Indu strial And Commercial Machinery And Compu ter Equipmen t 1.71% 973,233

Leather And Leather Products 0.79% 450,206

Lumber And Wood Products, Excep t Furnitu re 1.89% 1,075,247

Measuring,Analyzing, And Controlling Instruments; Photographic, Medical, And OpticalGoods; Watches And Clocks 8.18% 4,663,035

Miscellaneous Manufacturing Indus tries 1.45% 828,708

Paper And Allied Products 20.96% 11,946,935

Petroleum Refining And Related Industries 7.62% 4,342,829

Primary Metal Industries 19.34% 11,022,195

Printing, Publis hing,And Allied Industries 1.38% 785,699

Rubber And Miscellaneous Plastics Products 4.54% 2,589,478

Stone, Clay, Glass, And Concrete Products 3.88% 2,211,984

TextileMill P roducts 2.55% 1,450,624

Tobacco Products 0.06% 34,841

Transportation Equipment 0.85% 483,040

Totals: 100.00% 56,995,702
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 111 

6.3 Industrial Sector End-Use Breakdowns 

Table 60 and Table 61 illustrate the Industrial sector energy sales based segmentation across 

end uses. The breakdown of Industrial electricity use by end-use and industry type was 

developed based on the 2002 Mechanical Energy Consumption Survey (MECS47) data for the 

New England region. 

 

472002 Manufacturing Energy Consumption Survey (MECS) Data (http://www.eia.doe.gov/emeu/mecs/mecs2002/) 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 112 Table 60. Industrial Sector End Use Breakdowns by Industry Type – ElectricTable 61. Industrial Sector End Use Breakdowns by Industry Type – Non-Electric

2018 kWh Sales New Hampshire Specific Industry Conventional Boiler Use CHP and/or Cogeneration Process Process Heating Process Cooling and Refrigeration Machine Drive Electro-Chemical Processes Other Process Use Facility HVAC F

Li

4,841,139 Apparel And Other Finished Products Made From Fabrics And Similar Materials 0% 0% 4% 4% 36% 0% 0% 23% 

20,537,715 Chemicals And Allied Products 0% 0% 4% 9% 57% 14% 0% 6% 

327,660,088 Electronic And Other Electrical Equipment And Components, Except Computer Equipment 0% 0% 19% 4% 36% 3% 1% 17% 120,602,372 Fabricated Metal Products, Except Machinery And Transportation Equipment 0% 0% 23% 3% 44% 1% 0% 10% 186,807,201 Food And Kindred Products 0% 0% 3% 27% 48% 0% 0% 7% 

11,827,540 Furniture And Fixtures 1% 0% 6% 3% 53% 0% 0% 8% 

215,000,038 Industrial And Commercial Machinery And Computer Equipment 0% 0% 7% 3% 49% 1% 1% 18% 

14,208,070 Leather And Leather Products 0% 0% 3% 27% 38% 0% 0% 11% 84,834,419 Lumber And Wood Products, Except Furniture 0% 0% 5% 1% 72% 0% 0% 8% 

127,539,458 Measuring, Analyzing, And Controlling Instruments; Photographic, Medical, And Optical Goods; Watches And Clocks 0% 0% 12% 7% 50% 9% 0% 9% 

78,459,698 Miscellaneous Manufacturing Industries 0% 0% 9% 6% 36% 0% 0% 20% 

140,041,116 Paper And Allied Products 0% 1% 2% 2% 82% 1% 0% 4% 

19,849,417 Petroleum Refining And Related Industries 1% 0% 8% 4% 81% 0% 0% 3% 

208,709,932 Primary Metal Industries 0% 0% 29% 1% 30% 32% 0% 4% 

66,122,451 Printing, Publishing, And Allied Industries 0% 0% 2% 4% 49% 0% 0% 18% 

299,645,250 Rubber And Miscellaneous Plastics Products 0% 0% 14% 8% 52% 0% 1% 10% 

83,769,711 Stone, Clay, Glass, And Concrete Products 0% 0% 20% 4% 59% 0% 1% 6% 45,780,294 Textile Mill Products 0% 0% 10% 9% 58% 0% 0% 8% 

761,224 Tobacco Products 0% 0% 3% 14% 44% 0% 1% 27% 

45,732,822 Transportation Equipment 0% 0% 9% 4% 43% 1% 1% 19% 

2,102,729,959

% kWh Sales by Industry & End Use

Source: 2002 Manufacturing Energy Consumption Survey (MECS) Data (http://www.eia.doe.gov/emeu/mecs/mecs2002/)

2018 Therms 

Sales New Hampshire Specific Industry Conventional Boile r Use CHP and/or Cogenera tion Proces s Process He ating Process Cooling and Refrigeration Mac

153,399 Apparel And Other Finished Pr oducts Made From F abrics And Similar Materials 25% 0% 25% 0% 542,309 Chemicals And Allied Products 27% 28% 35% 2% 

1,557,367 Electronic And Other E lectric al Equipment And Components, Except Computer Equipment 12% 0% 53% 0% 3,184,570 Fabricated Metal Products, Ex cept Machinery And Transportation E quipment 4% 0% 62% 0% 8,550,090 Food And Kindr ed Products 41% 7% 38% 0% 

149,910 Furniture And Fixtures 4% 0% 42% 0% 973,233 Indus tr ial And Commercial Machinery And Computer Equipment 16% 1% 36% 0% 450,206 Leather And Leather Produc ts 75% 0% 0% 0% 

1,075,247 Lumber And W ood Products , Except Furniture 27% 0% 48% 0% 4,663,035 Meas uring, Analyz ing, And Controlling Instruments ; P hotographic, Medical, And Optical Goods; W atches And Cloc ks 23% 15% 47% 1% 

828,708 Miscellaneous Manufacturing Industries 29% 0% 26% 0% 

11,946,935 Paper And Allied Produc ts 29% 32% 26% 1% 

4,342,829 Petr oleum Refining And Related Industries 18% 15% 60% 0% 

11,022,195 Primary Metal Industries 6% 5% 79% 1% 

785,699 Printing, Publishing, And Allied Industries 13% 0% 40% 0% 

2,589,478 Rubber And Miscellaneous Plastic s Products 37% 2% 29% 1% 2,211,984 Stone, Clay , Glass, And Concrete Products 3% 1% 85% 0% 1,450,624 Textile Mill Products 21% 0% 39% 0% 

34,841 Tobacco Produc ts 50% 0% 25% 0% 

483,040 Tr ansportation Equipment 26% 2% 32% 1% 

56,995,702

% Gas S

Sourc e: 2002 Manufacturing Energy Cons umption Survey (MECS) Data ( http://www.eia.doe.gov/emeu/mecs/mec s2002/)
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 113 

6.4 Industrial Sector – Energy Efficiency Potential Results 

Fourteen (14) industrial specific end-uses were included in the analysis for the industrial sector. 

In order to develop the list of energy efficiency end-uses examined, GDS worked closely with 

project sponsors as well reviewed other related electric and non-electric energy efficiency 

technical potential studies that have been conducted recently in the US. 

Figure 56 and Figure 57 display a graphical comparison of the maximum achievable cost 

effective energy efficiency savings potential results by end use within the industrial sector (for 

electric and non-electric measures respectively). As shown in these figures, 40 percent of the 

electric savings comes from motors, followed by sensors and controls at 16 percent, facility 

lighting at 15 percent, and process heating at 13 percent. The remainder is made-up by 

compressed air and process cooling and refrigeration. With regard to savings from non-electric 

end-uses, process heating contributes the most at 52 percent of the savings, followed by 

conventional boiler use at 33 percent, facility HVAC at 13 percent, and the remaining 2 percent 

being classified as end-use not reported. Electric and non-electric savings allocations by 

building type are shown in Figure 58 and Figure 59 respectively. 

Figure 56. Industrial Max. Achievable Cost Effective Electric Savings by End Use 

Figure 57. Industrial Max. Achievable Cost Effective Non-Electric Savings by End Use 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 114 

Figure 58. Industrial Max. Achievable Cost Effective Electric Savings by Building Type 

Figure 59. Industrial Max. Achievable Cost Effective Non-Electric Savings by Building Type 

 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 115 

Section 7: Primary Data Collection Highlights

This project included a major enhancement to most of the technical potential studies that have 

been conducted across the country in the past. Rather than relying on best available 

information from existing secondary sources to estimate current levels of energy using 

equipment saturations and penetration of energy efficiency measures, significant primary data 

collection efforts were undertaken to help inform and derive New Hampshire-specific values 

where possible. The focus of this Section is to provide information on how the results from this 

project’s primary data collection efforts were used to help derive New Hampshire sector-specific 

estimates of energy using equipment saturations (base factors) and the penetration of energy 

efficiency measures (remaining factors). These factors were required inputs to the project’s 

energy efficiency potential assessment models. 

As highlighted below, a substantial amount of detailed information was gathered in the primary 

data collection efforts for this project. Although not all information collected was directly applied 

as model inputs, the data will serve as a valuable resource for future studies. The information 

obtained from the data collected includes the following: 

Ownership Characteristics: The telephone surveys and site visits collected information on 

whether the facilities were owned or leased, the building type, the approximate age and size of 

the buildings, the number of employees, and building schedules (i.e. hours of operation). 

Results are summarized in Appendix J.

 

Fuel Usage: Information was collected on the primary types of fuel usage (i.e. oil, natural gas, 

etc.) as well as the specific gas and electric utility providers for each facility. This information 

was useful in developing cross tabulations of other data to determine trends within groups of 

customer types. The surveys were also used to determine whether any facilities had on-site 

power generation and to identify the capacity and uses of such on-site generation. 

Efficiency Attitudes: Valuable information regarding customer attitudes towards energy 

efficiency and utility sponsored programs was collected, including primary motivations and 

barriers to participating in currently offered programs. Results were summarized in detail in 

Section 3.3 of this report. 

Heating and Cooling: The surveys collected data on the types of heating and cooling systems 

employed in each facility. The site visits, in particular, collected detailed information on the 

systems including but not limited to run times, heating and cooling capacity, motor horsepower 

and efficiency, humidity control, presence of outside air economizers, presence of variable air 

volume control, heat recovery, fuel used, and the approximate age and condition of the 

systems. 

Building Envelope: The site surveys gathered information on the general condition of building 

envelopes including wall types, insulation types, roof and floor construction, interior and exterior 

finish and color, and building fenestration (windows). 

Water Heating: Much of the survey information regarding water heating was directly applicable 

to the energy efficiency assessment potential models. The site surveys also collected detailed 

information on the types of water heating storage and distribution systems, areas served, 

capacities, insulation, process heating, and the relative age and condition of the systems. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 116 

Motors: Substantial information regarding motors was collected including the total number of 

units, service, types of control, drive type, run hours per week, and the approximate age and 

condition of the motors. 

Refrigeration Equipment: The surveys collected detailed information on the number and type of 

both commercial and non-commercial refrigeration equipment. Other valuable information 

collected included the amperage draw of commercial equipment, whether the equipment was 

ENERGY STAR, defrost control types, number, size and efficiency of compressors and 

condensers, and the relative condition of the systems in each facility. 

Compressed Air: The site surveys gathered information on all compressed air systems, 

including the type and application of each compressor, the control type, size (horsepower), total 

number of units, nominal efficiencies, drive types, average age, run hours per week, and 

manufacturer and model numbers. The site surveys were also used to determine whether the 

facilities had a leak reduction maintenance program and to assess the overall condition of the 

compressed air systems. 

Process Heating: The site surveys gathered information on all process heating systems, 

including the type of process, the products produced, the number of machines, rated heat 

inputs, whether waste heat recovery is utilized, primary fuel used, the average age of 

equipment, and the average run hours per week. The condition of process heating systems at 

each facility was also assessed. 

Cooking & Food Service Equipment: Where applicable, the site surveys gathered information 

on cooking and food service equipment. Information gathered included the total number of both 

electric and gas fueled units and the average fuel usage for each type of equipment. 

As discussed in more detail in Sections 7.1 and 7.2 below, where applicable, elements from this 

information were used to develop model-required base and remaining factors. 

7.1 Data Summary and Analysis 

The primary data collection efforts were summarized in Section 3.1 of this report, and included 

conducting 400 telephone surveys of residential customers, 200 telephone surveys of small 

commercial and industrial customers, and 200 site visits of large commercial and industrial 

customers. The methodologies utilized to create the survey instruments and sampling plans 

were outlined in detail in Sections 3.1.1 and 3.1.2, respectively. Data obtained from the surveys 

and site visits were coupled with secondary data collection and analysis where necessary, to 

develop New Hampshire-specific values for saturations and penetrations used in the GDS 

Team’s sector-specific energy efficiency potential calculation models (referred to in the models 

as base and remaining factors). These primary data collection efforts were also used to assess 

customer attitudes towards energy efficiency programs and practices, including awareness, 

motivations and barriers, results of which were summarized earlier in Section 3.3 of this report. 

Data collected from the residential and small commercial and industrial telephone surveys were 

analyzed by Research Into Action (RIA) using SPSS statistical software.48 During this process, 

GDS worked with RIA to identify the specific survey questions and develop the cross tabulations 

needed to derive base and remaining factors for use in the models. Data collected from the 

large commercial and industrial site visits were recorded in paper files and entered manually 

into an analyzable Excel spreadsheet file. The data was organized and sorted by relevant 

 

48 Further information regarding the SPSS program can be found at www.spss.com 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 117 

measure types, using pivot tables, to obtain information that helped inform the base and 

remaining values for specific efficiency measures needed for the models. 

It is important to note that sample sizes were designed at project outset to ensure statistical 

validity at the aggregate residential, commercial and industrial sector levels only. However, 

attempts were made to mine the data, where possible, to support determination of base and 

remaining factors for specific measures at the building-type level as described in more detail 

below. 

7.1.1 Residential Survey Data

Prior to the detailed evaluation of residential telephone survey data, responses were 

categorized as either single or multi-family homes. In total, 269 single family responses and 

135 multi-family responses were recorded. Of those 404 total responses, the data collection 

effort focused on the 253 single family and 127 multi-family homes that were classified as 

permanent residences as opposed to seasonal residences. Separate evaluation of the survey 

data was then performed for each type of home. Based on a total population of approximately 

600,000 housing units in New Hampshire, the margin of error for proportional results obtained 

from the single family surveys (253 total) was 6.2% with 95% confidence. The margin of error 

for proportional results obtained from the multi-family surveys (127 total) was 8.7% with 95% 

confidence. 

7.1.2 Commercial and Industrial Survey Data

Data from the 200 small commercial and industrial phone surveys and the 200 large commercial 

and industrial site visits were analyzed on several levels. The most basic level of evaluation 

was to separate the commercial properties data from the industrial properties data. The 

breakdown of commercial versus industrial facilities for each survey is shown in Table 62 below. 

Table 62. Number of Commercial and Industrial Facilities Surveyed

Small C/I Phone 

Surveys

Large C/I Site 

Surveys

Commercial 177 100

Industrial 23 100

Total 200 200

The small and large commercial properties were further categorized by model-defined building 

types, as shown below in Table 63.

Table 63. Small Commercial vs. Large Commercial Surveys by Building Type

Model Building 

Type

Small Commercial 

Phone Surveys

Large Commercial 

Site Surveys

Warehouse 4 5

Retail 23 7

Grocery 13 7

Office 56 22

Lodging 6 14

Health 14 12

Restaurant 10 4

Education 5 16

Other - unclassified 46 13

Total 177 100
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 118 

The GDS Team established a minimum sample size of 30 respondents as the threshold for 

collecting and reporting data at the building type-specific level for both small and large 

commercial buildings. This would ensure statistically valid results at a 15 percent margin of 

error and 90 percent confidence. As shown in the table above, based on this threshold, results 

from the small commercial phone surveys provided sufficient data to derive base and remaining 

factors for measures within both office (56) and other (46) building types. For the remaining 

building types, measure specific data was collected and reported based on the aggregate 

responses within the small commercial sector. Similarly in the large commercial sector, due to 

the limited number of responses within specific building types, a majority of the measurespecific base and remaining factors derived from the site visits were based on aggregate 

responses across the entire large commercial building stock. In some instances where a 

substantial amount of site visit data was available for a specific measure, base and remaining 

factors were determined at the building type level. An example is facility lighting, where a 

substantial volume of information was recorded during the site visits. It was the opinion of GDS 

that the data illuminated trends in lighting characteristics among the building types and 

warranted inclusion in the energy efficiency potential assessment models. 

Industrial properties were also categorized by building type. The large industrial properties were 

broken down into ten specific building types as listed in Table 64 below. The small49 industrial 

properties, where data was collected through a total of 23 phone surveys, were viewed as a 

single group (i.e., results were aggregated across all building types since the combined number 

of respondents was less than the 30 building type threshold required to ensure statistical 

validity). 

Table 64. Small Industrial vs. Large Industrial Surveys by Building Type 

Model Building Type

Small 

Industrial 

Phone 

Surveys

Large 

Industrial 

Site 

Surveys

Electronic and Other Electrical Equipment - 15

Fabricated Metals - 23

I & C Machinery and Computer Equipment - 1

Lumber and Wood Products - 14

Other Assembly / Light Manufacturing - 13

Other Medium/Heavy Equipment Manufacturing - 6

Paper and Allied Products - 2

Printing, Publishing and Allied Ind. - 7

Rubber and Miscellaneous Plastics Production - 9

Other – not classified - 10

Total 23 100

As described elsewhere in this report, the industrial supply model requires single savings factors 

for specific end uses (e.g., lighting, process heating, etc.) and recognizes that these factors can 

vary depending on the type of industry being assessed. Therefore, the information mined from 

the large industrial property site visits was analyzed to identify New Hampshire-specific 

equipment, system and process practices and trends that could be used to support adjustment 

of the original model assumptions which had initially been based only on secondary data 

sources from previous studies and prior experience. 

 

49 The 23 small industrial survey respondents consist of Seven (7) Industrial Metals Machining, Four (4) Industrial 

Parts Assembly, and Twelve (12) Industrial Other 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 119 

7.1.3 Weighting of Small and Large Commercial Survey Data

In both the commercial electric and the commercial gas energy efficiency potential analysis 

models, small (less than 100 kW or 300,000 kWh annual consumption) and large (greater than 

100 kW or 300,000 kWh annual consumption) commercial facilities have been treated as a 

single sector. Therefore, weighted averages were developed for the measure-specific base and 

remaining factors using results from both the small and large facilities data collection efforts. 

The method used for calculating these weighted averages was based on 2007 total kWh 

consumption data provided by the utilities for their customers classified as small 

commercial/industrial and large commercial/industrial. In total, the small commercial customers 

consumed 2,100,349,654 cumulative kWh of energy in 2007 and the large commercial 

customers consumed a total of 2,643,763,935 kWh of energy. Based on these values, small 

commercial customers consumed 44.3 percent of commercial energy usage in 2007 and the 

large commercial customers consumed 55.7 percent. These ratios were used, where 

applicable, to derive weighted average commercial sector and building specific end-use 

measure saturation (base factor) and energy efficiency equipment penetration (remaining factor) 

values for use in the commercial models. 

In several instances, survey data was available only from the small commercial facilities phone 

surveys, or from the large commercial facilities site visits, or from neither depending on the 

specific measure. If penetration and saturation values obtained from survey data were available 

for either the small commercial or large commercial facilities, but not both, an un-weighted 

survey value was utilized in the model. If values obtained from the surveys were not available 

for a specific measure, the original assumptions (based on existing secondary data) were 

utilized in the models and all applicable references that formed the basis for such assumptions 

were noted. 

7.2 Application of Survey Data 

As noted previously, a substantial amount of useful New Hampshire-specific information was 

collected on energy end use equipment saturations and energy efficiency measure penetrations 

for a number of residential, commercial, and industrial measures. In cases where such New 

Hampshire customer-specific information could not be collected from the phone surveys and 

site visits, the most prevalent barrier to obtaining that information tended to be the extremely 

specific nature of some measures and the time constraints existing for conducting the surveys 

and site visits. Wherever possible, when secondary sources were required to be used as the 

basis for base and remaining factors in the models, they were verified for reasonableness, or 

modified based on results obtained through the project’s primary data collection activities. 

The greatest percentage of model values that relied on primary survey data occurred in the 

residential sector, where nearly 70 percent of the model’s required base and remaining factors 

for efficiency measures came directly from survey information. In the commercial electric 

model, 36 percent of the measures’ base and remaining factors were also derived directly from 

this project’s primary data collection activities. In the commercial non-electric model, 24 percent 

of the measures were based on survey information. This large variation between the 

percentages of survey data applicable in the residential (70%) versus commercial sectors (24 to 

36%) is in part attributable to the complexity and specificity of the commercial measures 

compared to the residential measures. A more detailed analysis of the survey data application 

is provided in the following sections. 

7.2.1 Residential Sector

The measure end-use categories that were most informed by the residential sector telephone 

surveys were appliances and water heating. Overall, customers appeared knowledgeable and 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 120 

provided potentially useful information regarding the types of appliances they owned (i.e. 

refrigerators, ranges, water heaters, etc.) and whether the appliances were ENERGY STAR 

rated.50 The total number of responses used to derive base and remaining factors for use in the 

residential models in these end use categories was also very high since these end uses are 

quite common in across all residences. 

The measure end use categories of lighting and space conditioning were also fairly well 

informed by the surveys. Information on standard measures such as CFL bulbs, fixtures and 

programmable thermostats was readily available and was used to derive reliable base and 

remaining factors. Information on measures that were less well known to the general public, 

such as heat pumps, duct sealing, and photocell controlled outdoor lighting, proved to be less 

reliable (with little to no survey data responses) and were therefore supplemented in the model 

by secondary data sources. 

A complete list of the base and remaining factors derived through information collected in this 

project’s telephone surveys for each measure assessed within both single and multifamily 

homes is provided in Appendix K, along with links to specific survey questions and data 

sources. 

7.2.1.1 Residential Sector Example

Programmable thermostats provide a good example as to how the base and remaining factors 

were derived from this project’s telephone surveys. In the residential models, programmable 

thermostats are applied as energy efficiency measures within homes using electric heat, gas 

furnaces, gas boilers, and oil boilers. The measures are further categorized by systems for 

heating only and for systems with both heating and central air conditioning. In addition 

programmable thermostats are broken out by single family homes and multifamily homes and 

recognize that the savings associated with use of programmable thermostats will differ based on 

all of these factors. In the following example, the derivation of base and remaining factors for 

programmable thermostats is described within the context of single family homes without central 

air conditioning that heat with oil boilers. 

The first step in the analysis was to derive the end use saturation (base case factor) for each 

sub-category of the measure. The end use saturation is defined as the percentage of total 

single family homes that contain the end use or measure. For this example, the end use 

saturation is the percentage of single family homes that have oil-fueled boiler heating (heating 

only, no central air conditioning) and was derived from survey questions SH2, SH3 and SC1. 

Question SC1 was used to determine whether homes had central air conditioning. As illustrated 

in the summary table below, a total of 214 respondents did not have central air conditioning. 

Granite State 

Electric NH Elec CoOp PSNH Unitil Total

Count 5 3 25 6 39

% within UTILITY 17.9% 6.5% 18.5% 13.6% 15.4%

Count 23 43 110 38 214

% within UTILITY 82.1% 93.5% 81.5% 86.4% 84.6%

Count 28 46 135 44 253

% within UTILITY 100.0% 100.0% 100.0% 100.0% 100.0%

SC1: Do you have central air conditioning? * UTILITY Crosstabulation

UTILITY

SC1: Do you have central air conditioning? Yes

No

Total

Of the 214 single family respondents who did not have air conditioning, a summary table was 

developed that combined the responses to questions SH2 and SH3. SH2 was used to identify 

 

50 It is important to note however, that past studies have shown as many people incorrectly identify products as 

being ENERGY STAR as do those that do not think their products are ENERGY STAR when in fact they are. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 121 

the primary type of heating fuel utilized and Question SH3 was used to identify the primary 

types of heating systems. The responses to Question SH2 (fuel type) were used as the crosstab headings for the Question SH3 summary table shown below. A total of 36 responses are 

missing because they reported wood or electricity as the primary fuel used for heating and were 

excluded from this question. 

Natural gas 

purchased from

Bottled gas or 

propane: 

[CONFIRM: 

DELIVERED BY 

TRUCK?] Oil Kerosene Total

Count 12 12 48 1 73

% within SH2: What is the 

main fuel you use to heat this 

home?

46.2% 42.9% 39.3% 50.0% 41.0%

Count 4 9 28 0 41

% within SH2: What is the 

main fuel you use to heat this 

home?

15.4% 32.1% 23.0% .0% 23.0%

Count 3 2 34 0 39

% within SH2: What is the 

main fuel you use to heat this 

home?

11.5% 7.1% 27.9% .0% 21.9%

Count 12306

% within SH2: What is the 

main fuel you use to heat this 

home?

3.8% 7.1% 2.5% .0% 3.4%

Count 00404

% within SH2: What is the 

main fuel you use to heat this 

home?

.0% .0% 3.3% .0% 2.2%

Count 10113

% within SH2: What is the 

main fuel you use to heat this 

home?

3.8% .0% .8% 50.0% 1.7%

Count 21104

% within SH2: What is the 

main fuel you use to heat this 

home?

7.7% 3.6% .8% .0% 2.2%

Count 10304

% within SH2: What is the 

main fuel you use to heat this 

home?

3.8% .0% 2.5% .0% 2.2%

Count 22004

% within SH2: What is the 

main fuel you use to heat this 

home?

7.7% 7.1% .0% .0% 2.2%

Count 26 28 122 2 178

% within SH2: What is the 

main fuel you use to heat this 

home?

100.0% 100.0% 100.0% 100.0% 100.0%

Total

SH3: What type of heating 

system is your main source of 

heat?

Other (please specify)

DON'T KNOW

Boiler + Radiator + Steam 

Radiator + DK (i.e., other 

components unknown)

Could Identify Fuel, But Not 

Equipment

REFUSAL

SH2: What is the main fuel you use to heat this home?

Furnace: central forced air 

furnace (aka forced hot air)

Boiler + Radiator + Hot Water 

(aka forced hot water)

Boiler + Baseboard + Hot 

Water (forced hot water)-OR 

JUST BASEBOARD

SH3: What type of heating system is your main source of heat? * SH2: What is the main fuel you use to heat this home? Crosstabulation

The end use saturation of homes with oil boilers, heating only, was calculated by summing the 

number of oil using respondents with boilers (28 + 34 + 3 + 4 = 69) and dividing it by the total 

number single family respondents (253). The total number of single family homes was used in 

the calculation because the end use saturation is defined as the percentage of all single family 

homes with oil boilers and no central air conditioning. The percentage of oil customers with 

boilers for heating only (i.e. end use saturation) was then 69/253 = 27.3%. 

The next step was to derive the energy efficiency measure penetration for programmable 

thermostats. The penetration is defined as the fraction of the end use energy that is already 

energy efficient. For this example, the penetration is the percentage of homes that already have 

programmable thermostats and was derived from survey question SH14 which specifically 

asked whether the customers have a programmable thermostat. The cross tabulated 

responses, by primary fuel type (question SH2), are shown below for reference. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 122 

natural gas electricity propane oil kerosene wood Total

Count 26 0 26 77 1 14 144

% within Main 

heating fuel 76.50% 0.00% 68.40% 51.00% 50.00% 35.00% 53.50%

Count 8 4 12 73 1 26 124

% within Main 

heating fuel 23.50% 100.00% 31.60% 48.30% 50.00% 65.00% 46.10%

Count 0001001

% within Main 

heating fuel 0.00% 0.00% 0.00% 0.70% 0.00% 0.00% 0.40%

Count 34 4 38 151 2 40 269

% within Main 

heating fuel 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Main heating fuel

SH14: Do you have 

a programmable 

thermostat?

Yes

No

DON'T KNOW

Total

As illustrated in the table above, 51.3% (77/150) of customers indicated that they have 

programmable thermostats after discounting the “Don’t Know” response. Thus, the penetration 

for this measure is 51.3%. The remaining factor, defined as the percentage of homes in which 

this measure can be installed, is 1 minus the penetration, or 48.7% for this example. The 

resulting end use saturations (base case factors) and remaining factors were used in the 

residential model to calculate the potential for savings from this efficiency measure. Refer to 

Section 4.1 above for the actual equations used in the model to calculate potential savings. 

7.2.2 Commercial Sector

Valuable information was gathered from both the telephone surveys and site visits to help derive 

base and remaining factors use in the commercial sector electric and gas models. In the 

commercial electric model, 36% of the measure remaining factors were directly attributable to 

the survey data. In the commercial gas model, 24% of the measure remaining factors were 

directly attributable to the survey data. The measure end-use categories that were most 

informed by the surveys and site visits were lighting, refrigeration, appliances, compressed air, 

motors and water heating. The measure end-use categories that were least influenced by the 

primary data collection activities were space cooling (chillers), space cooling (unitary and split 

AC), and cooking. 

In the Lighting end use category for example, the base case factors were developed by first 

identifying sub-categories such as fluorescent tube lighting, screw-in incandescent/CFL lighting, 

high-bay lighting, exit signs and other specialty lighting. Then, the total number of fixtures 

reported in each sub-category from the site visits was tabulated and the relative percentages of 

each sub-category were calculated. Fixture counts were utilized to formulate the base case 

factors due to incomplete data on the wattage and run hours for all fixtures. These relative 

percentages, based on total fixture counts, were applied as base case factors for each building 

type to reflect the percentage of energy attributable to each sub-category. The site visits then 

provide useful information regarding the penetration of energy efficient lighting within each subcategory so remaining factors could be developed for each measure. 

Substantial data was also compiled for the commercial non-electric model. Measure end use 

categories that were the most well informed by the phone surveys and site visits included water 

heating, pools, HVAC controls, and cooking. Measure end use categories that were least 

informed by the surveys were ventilation, building envelope, and space heating. 

A complete list of base and remaining factors used in the commercial models is provided in 

Appendix L to this report. Factors that have been informed by data collected through the 

telephone and site visits conducted through primary data collection elements of this project have 

been highlighted for ease of reference. The survey instruments for the phone and site surveys 

are provided in Appendices B and C, respectively. Appendix L includes a summary sheet that 

identifies the questions used to derive the factors. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 123 

7.2.2.1 Commercial Sector Example

As with the residential sector, programmable thermostats provide a good example of how base 

and remaining factors were derived from survey data collected in the small commercial sector. 

To start, nearly all commercial customers were found to have thermostat-controlled heating 

systems as shown in the cross tabulation for Question 20 below. 90% of respondents indicated 

that they had control over the heating, while 10% did not have control, or did not know. 

Warehouse Retail Grocery Office Lodging Health Education Industrial Restaurant Other Total

Count 2 22 12 50 6 14 4 19 8 43 180

% within Building 

type recoded2 50.0% 95.7% 92.3% 89.3% 100.0% 100.0% 80.0% 82.6% 80.0% 93.5% 90.0%

Count 2 1 1 6 0 0 1 3 2 3 19

% within Building 

type recoded2 50.0% 4.3% 7.7% 10.7% .0% .0% 20.0% 13.0% 20.0% 6.5% 9.5%

Count 0 0 0 0 0 0 0 1 001

% within Building 

type recoded2 .0% .0% .0% .0% .0% .0% .0% 4.3% .0% .0% .5%

Count 4 23 13 56 6 14 5 23 10 46 200

% within Building 

type recoded2 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Q20: Do you have control over the temperature of the heating? * Building type_recoded2 Crosstabulation

Building type_recoded2

Q20: Do you have control 

over the temperature of the 

heating?

Yes

No

DON'T KNOW

Total

It was assumed that the percentage of respondents without control of the temperature of the 

heating is largely attributable to tenant or landlord circumstances, thus the base case factor for 

this end use was determined to be 100 percent. For the small commercial phone surveys, 

Question 21 specifically asked whether respondents had a programmable thermostat. The 

cross tabulated summary table is provided below for reference. 

Warehouse Retail Grocery Office Lodging Health Education Industrial Restaurant Other Total

Count 1 11 6 31 3 7 3 8 5 20 95

% within Building 

type recoded2 50.0% 50.0% 50.0% 62.0% 50.0% 50.0% 75.0% 42.1% 62.5% 46.5% 52.8%

Count 1 10 6 18 3 6 1 11 3 21 80

% within Building 

type recoded2 50.0% 45.5% 50.0% 36.0% 50.0% 42.9% 25.0% 57.9% 37.5% 48.8% 44.4%

Count 0 1 0 1 0 1 0 0 025

% within Building 

type recoded2 .0% 4.5% .0% 2.0% .0% 7.1% .0% .0% .0% 4.7% 2.8%

Count 2 22 12 50 6 14 4 19 8 43 180

% within Building 

type recoded2 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Q21: Do you have a programmable thermostat? * Building type_recoded2 Crosstabulation

Building type_recoded2

Q21: Do you have a 

programmable thermostat?

Yes

No

DON'T KNOW

Total

The energy efficient measure penetration, defined as the percentage that is already efficient, 

was calculated by dividing the total number of positive responses among non-industrial building 

types (95 total “yes” responses minus 8 “yes responses from the “industrial” building type = 87 

commercial “yes” responses) by the total number of non-industrial respondents, excluding “don’t 

know” responses (156). The average penetration for this measure was thus determined to be 

55.8%. The remaining factor was simply calculated as 1 minus the penetration, or 44.2% for 

this example. 

As can be seen from the cross tabulation table above, more than 30 total responses were 

received in both the Office and Other building type categories. Therefore, the remaining factors 

for programmable thermostats were derived at the building-specific level for each of these two 

building types. For example, the remaining factor for programmable thermostats in the Office

building type was 36.7% and 51.2% for the Other building type category. For all remaining 

commercial building types, the 44.2% remaining factor (calculated in aggregate across all 

building types) was applied to maintain statistical validity in the data with reasonable confidence. 

Programmable thermostats were not included in the survey for the large commercial surveys 

due to a larger prevalence of EMS systems as the primary means for temperature controls in 

these facilities and because larger facilities that use thermostatic control often have a large 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 124 

number of thermostats, or a mix of programmable and manual thermostats. This makes the 

quantification of programmable thermostats in larger commercial facilities very difficult. Since 

no large commercial data was available for programmable thermostats, the data from the small 

commercial surveys was applied without weighting. It is important to note that once the 

weightings were completed, the remaining factor for each measure was qualitatively assessed 

for reasonableness and adjusted if necessary. It was felt that applying the small commercial 

remaining factor overall was reasonable for this application based on industry experience. 

7.2.3 Industrial Sector

The industrial model varies from the residential and commercial models in that energy end-use 

areas are assessed from a top-down (end-use category) perspective vs. bottom-up (a measure 

specific assessment approach). Therefore, the base case, remaining and other factors that 

were considered independently (by measure) in the residential and commercial models, are 

combined into a single savings factor in the industrial model. Initial values for each industrial 

end-use category savings factor were based upon secondary data that was developed 

previously by the American Council for an Energy Efficient Economy (ACEEE)51 and used in 

other GDS technical potential studies in the region. These factors were then adjusted, where 

appropriate, based upon the New Hampshire industrial sector-specific survey results. 

Adjustments to the savings factor based upon this project’s primary data collection efforts are 

summarized in Table 65 and Table 66 below. 

Table 65. Industrial Electric End-Uses Informed by Primary Data

Industry End Use Area Adjustment 

Electronic and Other 

Electrical Equipment Process Heating 

The savings factor was raised 10% to 30% total because this industry type 

had the largest number of process heat machines and lower MECS data 

percentage than other industry types with fewer machines (19%) 

Electronic and Other 

Electrical Equipment 

Process Cooling and 

Refrigeration 

The savings factor was raised 5% to 10% total because this industry type had 

the largest number of both commercial and non-commercial refrigeration units 

and a low relative MECS percentage (4%) 

Electronic and Other 

Electrical Equipment Facility Lighting The savings factor was raised 9% due to the disproportional ratio of T12 to T8 

lighting in this industry type. 

Fabricated Metals Process Cooling and 

Refrigeration 

The savings factor was increased 2% to 7% total because this industry type 

had the second highest total of both commercial and non-commercial 

refrigeration units and a low relative MECS percentage (3%) 

Fabricated Metals Machine Drive 

The savings factor was increased 7% to 26% total because this industry type 

had the 2nd highest number of motors, and the most number of motors that 

run for 40 hours per week or more. The MECS percentage for this industry 

type (44%) was also at the low range for this measure category (36% - 82%) 

Measuring, Analyzing, and 

Controlling Instruments 

Facility Lighting The savings factor was increased 9% to 49% total due to a disproportionally 

high percentage of incandescent lighting fixtures in this industry type 

compared to other types 

Paper and Allied Products Machine Drive 

The savings factor was decreased 9% to 17% total due to the low number of 

machine drive processes compared to other industry types. The MECS data 

for this industry type was also high (82%) compared to other industry types 

Printing, Publishing and 

Allied Industries Process Heating 

The savings factor was reduced 10% to 20% total. This industry type had a 

50th percentile number of process heating applications so the savings factor 

was adjusted to represent the 50th percentile of factors for this end use area. 

The MECS data for this industry type was also the lowest of any industry type 

(2%) 

Rubber and Miscellaneous 

Plastics Products Machine Drive 

The savings factor was increased 6% to 23% total due to an average number 

of machine driven equipment, but a relatively high number of motors that run 

more than 40 hours per week. The MECS data for this industry type was also 

lower than average (52%) for this measure category 

Rubber and Miscellaneous 

Plastics Products Facility Lighting The savings factor was decreased 5% to 35% total due to a large percentage 

(97%) of fluorescent tube lighting already being T8 fixtures. Fluorescent 

 

51 Sources: Connecticut Efficiency Potential Analysis (GDS), EPRI Potential Study Webinar, Resource Assessment 

for Energy Trust of Oregon, California Industrial Energy Efficiency Potential (Ernest Orlando Lawrence, Berkeley 

National Laboratory), ACEEE Potential Studies (Vermont, Florida, Texas and Fan & Pump Analyses), Reading 

Industrial Tech Potential Analysis (GDS), Vermont Electric Energy Efficiency Potential Study (GDS). 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 125 

lighting comprised over 60% of all lighting in this industry type 

All Sensors and Controls 

The savings factors were increased uniformly 3% to 4% total to account for 

the low number of occupancy sensors currently in place, and the low number 

of reported energy management systems 

Table 66. Industrial Non-Electric End-Uses Informed by Primary Data 

Industry End Use Area Adjustment 

Electronic and Other 

Electrical Equipment 

Conventional Boiler 

Use 

The savings factor was raised 4% to 20% total because this industry type 

reported the largest number of conventional boilers and a low MECS 

percentage (12%) relative to other industry types 

Electronic and Other 

Electrical Equipment Facility HVAC The savings factor was decreased 2% to 11% total due to a high reported 

incidence of outside air economizers and EMS systems in this industry type 

Fabricated Metals Facility HVAC 

The savings factor was increased 4% to 17% total due to a lower than 

average number of outside air economizers, EMS systems, and heat recovery 

systems 

Lumber and Wood 

Products Facility HVAC 

The savings factor was increased 7% to 20% total due to the lowest reported 

number of outside air economizers, EMS systems, and heat recovery 

systems. This industry type also had a lower than average MECS percentage 

(14%) 

Petroleum Refining and 

Related Industries Process Heating 

The savings factor was decreased 2% to 11% total due to the lowest number 

of reported gas-fired process heating applications. This industry type also 

had a higher than average MECS percentage (60% in this measure category 

Petroleum Refining and 

Related Industries Facility HVAC 

The savings factor was increased 3% to 16% total due to a lower than 

average number of outside air economizers, EMS systems, and heat recovery 

systems. This industry type also had the lowest MECS percentage (2%) in 

this measure category 

The adjustments summarized above were made by compiling all relevant data for each end-use 

area by industry type and looking for trends in the data. As examples, industry types that were 

found to have disproportionate amounts of processes (i.e. process heating applications), or 

fixtures (i.e. T8 fluorescent tubes versus T12 fluorescent tubes) were adjusted to reflect the 

trends noted within that industry type. Trends noted from the surveys were also reviewed with 

respect to the MECS data. The MECS data was used to determine energy distribution among 

various processes in each facility. Using the example above, the industry type Electronics and 

Other Electrical Equipment had the highest number of process heating machines but a lower 

MECS percentage for process heating than other industry types. In this example, the savings 

factor for this industry type was increased to reflect the trend noted in the site surveys. In some 

instances, such as the end use area Sensors and Controls, a uniform adjustment was made to 

reflect what appeared to be increased potential for savings beyond the initial assumptions. 

A complete list of savings factors for all end use categories and industry types is provided in 

Appendix M. In the electric model, 10 of 180 (5.5%) of the factors were adjusted based on 

survey results. In the non-electric model, 6 of 80 (7.5%) were adjusted. Specific factors that 

have been amended based on survey data are highlighted for reference. 

7.3 Summary and Recommendations

The primary data collection effort for this project gathered an abundance of information relating 

to the saturation of electric and non-electric (natural gas, oil and propane) energy end uses and 

the penetration of energy efficiency measures across New Hampshire’s residential, commercial, 

and industrial sectors. These data were used, where applicable, to help derive base and 

remaining factors applied to the energy efficiency potential assessment models employed for 

this specific study and will serve as a valuable starting reference for New Hampshire-specific 

energy end use saturations and efficient equipment penetrations going forward. 

Beyond survey information used to derive critical model inputs, a wealth of additional customer 

and energy usage data was obtained through the telephone surveys and site visits conducted 

as part of this project. Considerable information regarding customer attitudes towards energy 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 126 

efficiency concepts and programs was acquired, as well as the main motivations and barriers 

considered by customers with respect to their participation in efficiency programs. Other data 

such as the distribution of heating fuel usage, facility information, and process information was 

also collected and will be a valuable resource for future studies. 

A number of lessons have been learned through this initial primary data collection effort which 

might help to increase the effectiveness and value of subsequent efforts. Following is a brief 

listing of the top three recommendations: 

1. Begin with the end in mind – Documenting and communicating a clear vision of the 

required results from the data collection effort and how those results will be used is a 

vital first step to ensuring success. This is especially important given the large number 

of project sponsors, consultant team data collection/evaluation staff, and other parties 

interested and involved in the process and outcomes from a project of this magnitude. 

For this current project, written work scopes were developed, discussed, refined and 

shared with all project participants. 

2. Set realistic expectations – It is important to set realistic expectations regarding the 

amount of measure-specific information that can be collected through a phone survey or 

site visit. Prioritizing measures and consolidating multiple measures within common 

end-use categories, up front, will help to maximize survey instrument effectiveness. For 

this project, multiple discussions and drafts of survey and site visit instruments were 

developed for this purpose. Results from this report identify measures within each 

sector that have the greatest potential for savings. Going forward, review of the base 

and remaining factors associated with these measures will identify clear areas where 

refined New Hampshire-specific information would be most valuable. 

3. Allot sufficient time for data collection and analysis – When large amounts of data are 

being collected, it is critical that sufficient time be made available, not only for the data 

collection phase of the project, but more importantly for data analysis. Time to enter, 

verify, clean and analyze data results is needed to ensure that the most value is mined 

out of the efforts expended. For this project, as discussed throughout this section of the 

report, substantial information from this primary data collection effort was used to help 

inform development of base and remaining factors and to identify customer behaviors 

and barriers. Going forward, additional review of the data collected through this project 

could yield further insights and value to the project sponsors. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 127 

Section 8: Past Program Capture and Recommendations 

This section summarizes results from evaluation of the penetration of energy efficiency savings 

(electric and natural gas) associated with past and current utility-sponsored program activities. 

A review of the utilities’ annual Core New Hampshire Program Highlights reports formed the 

basis for this evaluation, along with estimated savings for prior years not posted on the New 

Hampshire Public Utilities Commission website52. Results are presented from both a cumulative 

savings as a percent of sales perspective, and on a number of customers served as a percent 

of population basis. Recommendations for potential modifications to program and measure 

offerings that could increase the likelihood of achieving identified potentials are made and have 

been developed mainly through information on barriers collected directly from New Hampshire 

utility customers (through this project’s telephone surveys and site visits) and supplemented by 

the GDS Team’s experience with looking at programs from a logic-modeling perspective, and 

extensive knowledge of other local, regional and national programs and best practices.53

Energy efficiency programs offered in New Hampshire include both electric and gas efficiency 

measures and serve all customer sectors; residential (including low income), commercial and 

industrial. The electric efficiency programs are comprised of CORE programs offered jointly by 

the four electric utility providers, and additional efficiency programs offered by the individual 

utilities. The gas efficiency programs are offered through the individual utility providers. 

The CORE programs were formally launched in June 2002, although efficiency programs have 

been offered by the utilities for quite some time prior. As shown in Table 67, since the formal 

inception of the CORE programs in June 2002, an estimated total of 557,274 MWh (annual) 

have been saved.54 This savings value is based on the estimated annual savings for each year 

since 2002 added together. In other words, this savings value is calculated by adding the 

annual savings from the 2003 programs to the annual savings from the 2004 programs to the 

annual savings from the 2005 programs and so on. This value does not consider the fact that 

the annual savings from the programs are actually realized every year over the lifetime of the 

measures. The total annual savings were calculated in this manner to provide a useful 

comparison to the forecast sales in 2008. 

This total savings represents five percent of the total forecasted electric usage for New 

Hampshire in 2008. Nearly four percent of this savings has been achieved within the 

commercial industrial sector, with slightly more than one percent of the savings coming from the 

residential sector. 

 

52 http://www.puc.state.nh.us/Electric/coreenergyefficiencyprograms.htm 

53 Assessments based on a logic-modeling perspective recognize current program resources (dollars, staffing, etc.) 

and activities (measure installations, promotional rebates/incentives, marketing/outreach, education/training, etc.) 

and seek to identify their causal links to anticipated outputs (measures installed, in-program energy and capacity 

savings, # of customers served, market actors trained, etc.), short-, intermediate- and long-term outcomes (changes 

in awareness and behavior, market-wide/sustainable energy, economic and environmental benefits, etc.). In 

addition, logic models recognize the existence and potential impacts of external influences (price of energy, state of 

the local and regional economy, federal tax incentives, other non-program sponsored activities, etc.). 54 Estimate is based on reported lifetime savings from 2005-2008 available on NHPUC website, GDS estimates for 

program measure lives, and extrapolated kWh savings estimates for 2002-2004 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 128 

Table 67. Energy Efficiency Program Savings as Percent of 2008 Sales: 2002-2008 – Electric Utilities 

Sector

Total Annual 

Savings Since 2002

 (MWh)

Forecasted Sales 

2008 

(MWh)

Cummulative 

Annual Savings as a 

Percent of 2008 

Sector Sales

Cummulative 

Annual Savings as 

a Percent of 2008 

Total Sales

Residential 120,064 4,537,480 2.6% 1.1%

Commercial/Industrial 437,210 6,650,732 6.6% 3.9%

Total 557,274 11,188,212 5.0%

It is important to note that the above figure is conservative in several ways. First, the utility 

providers have been actively offering efficiency programs since well before 2002 so the total 

amount of energy saved since the inception of efficiency programs is much higher. Second, this 

figure considers only a single year of annual savings. In reality, annual savings are realized 

every year over the assumed measure life of the programs. The data was reported in the above 

manner to provide an appropriate comparison to the forecast 2008 usage. 

New Hampshire’s natural gas utilities offered energy efficiency programs from 1993 through 

1999, at which time the programs were suspended in light of gas industry restructuring and 

investigation of the electric industry’s development of energy efficiency programs. The natural 

gas utilities began offering the energy efficiency programs again on January 1, 2003, and since 

that time have saved an estimated total of nearly 250,000 decatherms (annual).55 This value 

was again calculated by adding the estimated annual savings for each year since 2003. This 

value does not consider the cumulative savings over the life of the measures installed in the 

programs each year (i.e. only one annual year of savings from 2003 programs). As shown in 

Table 68, this savings represents 1.1 percent of the total forecasted therm sales for New 

Hampshire in 2008. 

Table 68. Energy Efficiency Program Savings as Percent of 2008 Sales: 2003-2008 – Natural Gas Utilities 

Sector

Total Annual 

Savings Since 2003

 (decatherms)

Forecasted Sales 

2008 

(decatherms)

Cummulative 

Annual Savings as a 

Percent of 2008 

Sector Sales

Cummulative 

Annual Savings as 

a Percent of 2008 

Total Sales

Residential 95,387 8,435,900 1.1% 0.4%

Commercial/Industrial 150,248 14,267,000 1.1% 0.7%

Total 245,635 22,702,900 1.1%

Overall since 2003, a substantial amount of energy has been saved in both the residential and 

commercial/industrial sectors. The values presented above are also conservative as they do 

not reflect the efficiency efforts of the utility providers prior to 2003, nor do they consider the 

cumulative annual savings of the programs since 2003. The following sections discuss the 

programs in more detail, including customer participation, benefit cost ratios, and expansion 

potential. 

8.1 Electric Utility Energy Efficiency Programs 

The electric utility energy efficiency programs assessed in this section include the CORE 

programs and other utility-specific programs being offered by National Grid, the New Hampshire 

Electric Cooperative, Public Service Company of New Hampshire, and Unitil Energy Systems. 

Any programs being offered by New Hampshire’s municipal electric utilities or through 

conservation and educational programs that do not have reportable energy savings have not 

been included in this summary. Table 69 presents a listing of the programs and sponsoring 

 

55 Estimate based on reported savings from 2003-2007 and GDS estimates for program measure lives 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 129 

utilities included in this past and current electric utility energy efficiency program savings capture 

assessment. More detailed information on these programs can be found on the NHPUC 

website at http://www.puc.state.nh.us/Electric/coreenergyefficiencyprograms.htm. 

Table 69. New Hampshire Electric Utility Core and Additional Programs

Program Name Utility Sponsor 

ENERGY STAR Home Program CORE Program – NH Electric Providers 

Home Energy Solutions CORE Program – NH Electric Providers

Home Energy Assistance Program CORE Program – NH Electric Providers

ENERGY STAR Lighting Program CORE Program – NH Electric Providers

ENERGY STAR Appliance Program CORE Program – NH Electric Providers

Small Business Energy Solutions CORE Program – NH Electric Providers

Large Business Energy Solutions CORE Program – NH Electric Providers

New Equipment and Construction CORE Program – NH Electric Providers

NHEC High Efficiency Heat Pump Program New Hampshire Electric Cooperative (NHEC) 

PSNH ENERGY STAR Homes - geothermal Public Service Company of New Hampshire (PSNH) 

PSNH C&I RFP Pilot Program Public Service Company of New Hampshire (PSNH) 

8.1.1 Program Participation 

The number of customers participating in the CORE programs has remained relatively constant 

since 2005. Some programs, such as the Home Energy Solutions and the Home Energy 

Assistance programs have decreased slightly in participation since 2005. Participation in these 

programs is somewhat limited under current program design, as they serve limited markets (i.e., 

homes with electric heat and low income homes, respectively). Homes with electric heat as the 

main fuel source represent approximately 4 percent of all residences in the state.56 Low-income 

homes represent approximately 5.5 percent of all residences in New Hampshire. Since 2005, 

the total customer participation in the Home Energy Solutions and Home Energy Assistance 

programs is summarized in Table 70 below: 

Table 70. Customer Served Through Utility HES and HEA Programs: 2005-2008

 

Customers Served 

(2005-2008) 

Total 

Population Saturation 

Home Energy Solutions 5,087 20,849 24.3% 

Home Energy Assistance Program 4,143 28,668 14.5% 

Given that the CORE programs have been in effect since June 2002, the actual saturation of 

these two markets is likely greater than the totals shown above. It is important to note however, 

that there are over 16,000 households on the waiting list for the Home Energy Assistance 

programs that serves low-income households57 – therefore, there remains substantial demand 

for this program for the foreseeable future., 

 

56 Figure based on residential phone survey data for both single family and multi-family 

57 Based on recent testimony by PSNH in CORE docket hearings (DE 08-120). Also, the low-income subgroup of 

the CORE docket produced an estimate that there are about 87,000 low-income households in New Hampshire, 

almost one-fifth of the total housing stick, that still need energy efficiency services (Low Income Report entered as 

Appendix B to the CORE Settlement Agreement filed on December 11, 2008 in DE 08-120. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 130 

Participation in the ENERGY STAR lighting and appliance programs has remained mostly 

steady since 2005, with spikes in participation in the lighting program in 2007 and the highest 

participation rate for the appliance program in 2006. These two programs have consistently 

maintained a good return of lifetime kWh saved per unit of cost. Since these programs target 

individual appliances and fixtures, and not individual homes per se, it is difficult to assess the 

extent to which the available market for these programs has diminished since the inception of 

the programs. 

Participation in the ENERGY STAR Homes program has remained relatively stable since 2006 

likely due to an overall downturn in the real estate market. In 2007, 524 builders (i.e. 524 

homes) participated in New Hampshire’s ENERGY STAR Homes programs out of a total of 

3,772 single-family residential building permits58. This represents a saturation of 13.9 percent of 

ENERGY STAR homes among the residential new construction market in 2007. 

Since 2005, a total of 3,110 small businesses have participated in the Small Business Energy 

Solutions program and a total of 1,008 large businesses have participated in the Large C&I 

Retrofit Program. These participation rates cannot be directly correlated to program saturation 

rates because many of the large businesses may have participated more than once. It is clear 

from this data, however, that the saturation of the large business programs has been much 

greater than in the small business programs. 

In 2007, 194 builders or clients participated in the New Equipment and Construction program 

representing 24.6 percent of commercial/industrial building permits. 

With respect to the three utility specific programs reviewed59, total number of participants and 

total expenditures are far less than the CORE programs. 

8.1.2 Program Awareness

Customer attitudes towards energy efficiency practices and programs were obtained from 

sector-specific site visits and phone surveys and were summarized in detail in Section 3.3 of 

this report. It seems relevant to this discussion to reiterate the findings relative to customer 

awareness of existing energy efficiency programs offered by the utilities. From the site and 

phone surveys, the percentages of customers who reported being aware of the programs 

offered are summarized in Table 71. As shown in this table New Hampshire’s large commercial 

and industrial customers reported being most aware utility efficiency programs (over 85%). 

Residential customers were the least aware, at less than 50 percent. Nearly 60 percent of small 

commercial/industrial customers were aware of the utilities’ programs. 

Table 71. Percent of Customers Aware of Utility Efficiency Programs

 

% Aware of Efficiency 

Programs Offered 

Residential 49.4% 

Small Commercial/Industrial 59.6% 

Large Commercial/Industrial 85.9% 

Although less than half of the 400 residential customers surveyed were aware that their utility 

providers offered energy efficiency programs, over 90 percent of all residential survey 

 

58 http://www.bos.frb.org/ 

59 NHEC’s High Efficiency Heat Pump Program, PSNH’s Energy Star Homes Geothermal Program, and PSNH’s 

C&I RFP Pilot Program 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 131 

respondents indicated that they would incorporate energy efficient features in future 

renovations. This data indicates the potential for much greater participation in the residential 

marketplace with increased awareness. Similar opportunities exist with small 

commercial/industrial customers, although these customers are often the most hard to reach 

and to encourage to take action. 

8.1.3 Efficiency Measures Not Included in Current Programs

This New Hampshire Additional Energy Efficiency Opportunities Potential Assessment study 

identified an abundance of energy efficiency measures that were cost effective and represent 

potential energy savings for New Hampshire in the next 10 years. Many of the most cost 

effective measures such as lighting, programmable thermostats and ENERGY STAR appliances 

are already included in the current energy efficiency programs sponsored by the electric and 

natural gas utilities. The efficiency measures discussed in Table 72 below are not included in 

the current programs and may represent opportunities for program expansion upon further 

review. It is important to note that the table below is by no means comprehensive, it is intended 

to be illustrative of areas with potential for expansion. 

Table 72. Residential Measures Not Included in Current Programs

Efficiency Measure Measure End 

Use Comments 

Programmable 

thermostat 

Space heating 

and cooling 

For homes with Oil or Propane heating. Programmable thermostats are 

included in the Home Energy Solutions (HES) program for homes with 

electric heat and offered by the gas utilities for homes with gas heat. The 

Home Energy Assistance (HEA) offers them for low income customers 

Energy Efficient 

Windows 

Space heating 

and cooling 

For homes with Oil or Propane heating. The HEA low income program 

offers windows replacement for all fuels when cost-effective. The gas 

utilities offer a rebate for high efficiency window replacement for 

customers with gas heat. 

Duct Sealing Space heating 

and cooling 

For homes with Oil or Propane heating. Duct sealing is included in the 

Home Energy Solutions program for homes with electric heat and the 

Weatherization program for homes with gas heat. 

High efficiency heat 

pumps 

Space heating 

and cooling 

This measure is included in NHEC’s specific program but not in the CORE 

programs for existing residential homes 

Ground source heat 

pumps 

Space heating 

and cooling 

This measure is included in NHEC’s specific program but not in the CORE 

programs for existing residential homes. PSNH offers this measure for 

residential new construction 

Low flow shower 

heads/faucets Water heating 

For homes with Oil or Propane heating. Low flow shower heads and 

faucets are included in the Home Energy Solutions program for homes 

with electric heating (and also in the gas utility efficiency programs) 

Water Heating 

measures Water heating 

For homes with Oil or Propane heating. Low flow shower heads and 

faucets are included in the Home Energy Solutions program for homes 

with electric heating, HES for all fuels and in the gas Weatherization 

program. Water heater wraps are offered for older water heaters in the 

HES program. 

New water heater 

(efficient, tank less, 

heat pump) 

Water heating For all homes. Incentives are offered by the gas utilities 

ENERGY STAR 

Refrigerator Appliances 

For all homes, not currently included in ENERGY STAR Appliances 

program. Available in HEA & HES programs if qualification criterion are 

met. 

ENERGY STAR 

Freezer Appliances 

For all homes, not currently included in ENERGY STAR Appliances 

program. Available in HEA & HES programs if qualification criterion are 

met. 

ENERGY STAR 

Dehumidifier Appliances 

For all homes, not currently included in ENERGY STAR Appliances 

program. Available in HEA if qualification criterion are met but none have 

been done. 

ENERGY STAR 

Dishwasher Appliances 

For all homes, not currently included in ENERGY STAR Appliances 

program. Available in HEA if qualification criterion are met but none have 

been done. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 132 

Many of the efficiency measures identified above are efficiency measures that are cost effective 

and to date have been included only in programs serving homes with electric or natural gas 

heating. Homes with electric heat comprise approximately 4 percent of all residences based on 

the survey data whereas homes with natural gas, propane or oil heat comprise approximately 

84 percent of homes. The appliances noted consume a significant amount of electricity and are 

not currently included in the ENERGY STAR appliances program for existing residences. Table 

73 provides similar information regarding measures not currently included in the utilities’ 

commercial/industrial programs. 

Table 73. Commercial/Industrial Measures Not Included in Current Programs 

Efficiency Measure Measure End 

Use Comments 

Dishwasher, clothes 

washers, 

refrigeration, etc. 

Appliances 

Energy efficient appliances represents a potential for small and large 

businesses to conserve energy with small incremental costs, but is 

currently included only in the Small Business Energy Solutions Program 

Ground source heat 

pumps 

Space heating 

and cooling 

Ground source heat pumps represent both a significant initial cost as well 

as a significant payback in terms of energy savings 

General water 

heating measures Water heating 

Water heating measures such as low flow faucets and shower heads, 

efficient water heaters, water heater blankets, and similar water heating 

measures are only included in the Small Business Energy Solutions 

Program but represent a potential for energy savings in other facilities 

using electric hot water heating 

8.2 Gas Efficiency Programs 

Gas efficiency programs are offered independently in New Hampshire by the two gas utilities, 

National Grid and Northern Utilities. The gas efficiency programs were offered from 1993 

through 1999, at which time the programs were suspended in light of gas industry restructuring 

and investigation of the electric industry’s development of energy efficiency programs. The 

natural gas utilities began offering the energy efficiency programs again on January 1, 2003. 

The programs offered since 2003 are the programs evaluated in this analysis, and include the 

following (Table 74). 

Table 74. Gas Efficiency Programs Evaluated

Program Name Utility Sponsor 

Residential custom measures Northern Utilities (Gas) 

Residential Low Income custom measures Northern Utilities (Gas) 

residential high efficiency heating equipment Northern Utilities (Gas) 

High efficiency water heating Northern Utilities (Gas) 

ENERGY STAR homes Northern Utilities (Gas) 

ENERGY STAR Programmable thermostats Northern Utilities (Gas) 

ENERGY STAR Windows Northern Utilities (Gas) 

Weatherization Northern Utilities (Gas) 

Multifamily custom measures Northern Utilities (Gas) 

Small commercial and Industrial Custom Measures Northern Utilities (Gas) 

Med and large C/I custom measures Northern Utilities (Gas) 

Commercial high efficiency heating program Northern Utilities (Gas) 

Infrared heating program Northern Utilities (Gas) 

Commercial ENERGY STAR Thermostats Northern Utilities (Gas) 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 133 

Program Name Utility Sponsor 

Low Income National Grid (Gas) 

Residential High Efficiency heating National Grid (Gas) 

ENERGY STAR Windows National Grid (Gas) 

Residential Weatherization National Grid (Gas) 

ENERGY STAR Thermostats National Grid (Gas) 

Residential High Efficiency Water Heating National Grid (Gas) 

ENERGY STAR Homes National Grid (Gas) 

Commercial Energy Efficiency Program National Grid (Gas) 

Economic Redevelopment National Grid (Gas) 

Commercial High Efficiency Heating National Grid (Gas) 

Multifamily Housing National Grid (Gas) 

Programs that do not have reportable therm savings such as the online audit and educational 

programs have been excluded from this list. A complete list of efficiency programs offered by 

the New Hampshire natural gas utilities may be found on the NHPUC website. 60

8.2.1 Program Participation 

Program summary sheets reviewed for the gas efficiency programs did not report the total 

number of actual participants for each program on a yearly basis. Based on the design goals 

for each program, the total number of participants has increased each year since 2003 with the 

largest increase in targeted users occurring between 2005 and 2006. 

The design goal participation in several programs has increased steadily since 2003. Efficiency 

programs with notable increases in design goal participations since 2003 include the following: 

• ENERGY STAR Homes 

• ENERGY STAR Programmable Thermostats 

• ENERGY STAR Windows 

• Residential Weatherization 

• Commercial High Efficiency Heating 

GDS estimated the saturation of natural gas efficiency programs in the residential and 

commercial/industrial marketplace by calculating the cumulative number of design goal 

participants for each program since 2003 and comparing that number to the total number of 

potential users. The number of actual participants was not available from the documents posted 

to the NHPUC website. Potential users were determined by applying the percentage of natural 

gas users from the phone surveys, to the overall number of end use customers in each sector. 

For the purpose of estimating saturations, participation in programs offered by both utilities has 

been added together. The saturations presented in Table 75 below do not account for homes 

and facilities already equipped with the efficiency measure (i.e. homes already with 

programmable thermostats) and are intended only to reflect the percentage of the marketplace 

reached by the individual programs61. 

 

 

60 http://www.puc.state.nh.us/Gas-Steam/energyefficiencyprograms.htm 

61 It is also important to note that many of the programs are time of replacement, so the saturations may be 

misleading because the total population is actually the number of customers in need of a heating system replacement 

and that number is constantly changing. 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 134 

Table 75. Customer Served Through Natural Gas Utility Programs: 2005-2008 

  

Customers Served

(2005‐2008)

Total

Population62 Saturation

Residential Conservation Services 2,245 45,279 5.0%

Residential Custom Measures 580 45,279 1.3%

Residential Low Income Custom

Measures 621 2,402 25.9%

Residential High Efficiency Heating

Equipment 2,912 45,279 6.4%

ENERGY STAR Homes 307 15,08863 2.0%

ENERGY STAR Programmable

Thermostats 2,054 45,279 4.5%

Multi‐Family Custom Measures 56 84,989 0.1%

C&I Custom Measures 375 33,481 1.1%

Commercial High Efficiency Heating

Program   437 34,400 1.3%

Infrared Heating Program 28 34,440 0.1%

Commercial ENERGY STAR Thermostats 220 34,440 0.6%

Commercial Food Service Program 18 3,851 0.5%

The summary table above indicates that the gas efficiency programs since 2003 have 

penetrated the residential market to a greater extent than the commercial and industrial market. 

It appears that there is substantial opportunity for further penetration in all customer sectors. 

8.2.2 Program Awareness

Customer awareness of utility sponsored efficiency programs is summarized in Sections 3.3 and 

8.1.2 of this report. Of importance to this discussion of gas efficiency programs is the finding 

that less than half of residential customers are aware that programs are offered by their utility 

providers. More than 40 percent of small commercial and industrial customers are not aware of 

the programs offered by the utility providers. Increasing customer awareness will be an 

important barrier to overcome. 

8.2.3 Efficiency Measures Not Included in Current Programs

A significant majority of the efficiency measures identified in the technical potential study have 

already been incorporated in the programs offered by the natural gas utilities. Several 

measures that are cost effective and are not currently included in the efficiency programs 

offered by the utility providers are summarized in Table 76 below. It is important to note that the 

current program designs do not permit the utility providers to pay for programs for oil/propane 

measures because the programs are funded by electric and gas ratepayers. 

 

62 Total population estimates are based on the total number of available properties by program type, times the 

percentage of facilities reporting the usage of natural gas from the site and phone surveys 

63 Estimate is number of 2007 single family building permits (3,772) times four years 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 135 

Table 76. Measures Not Included in Current Natural Gas Efficiency Programs

Efficiency Measure Measure End 

Use Comments 

ENERGY STAR 

Dishwashers 

Appliances / 

Water heating In homes with natural gas water heating 

ENERGY STAR 

Clothes Washers 

Appliances / 

Water heating In homes with natural gas water heating 

Boiler Tune up Water Heating In homes with natural gas supplied boilers 

High efficiency 

cooking equipment Cooking 

High efficiency cooking equipment represents a potential for substantial 

savings. Northern Utilities and National Grid currently offer rebates for 

high-efficiency fryers and steamers. 

Both of New Hampshire’s natural gas utilities offer basic or prescriptive rebates for incorporating 

general energy efficiency products such as space heating equipment and water heaters. For 

small and large commercial and industrial customers, both utilities offer energy audit services 

and the potential for custom energy efficiency programs tailored to the specific facility. The 

audit and custom approach to efficiency programs in the commercial and industrial sector 

incorporate the potential for a wealth of energy efficiency measures to be incorporated at a 

specific facility. 

8.3 Summary and Recommendations 

To date, the efficiency programs offered in New Hampshire by the state’s four largest electric 

utilities and two natural gas distribution companies have been successful and have saved a 

substantial amount of energy. Many of the programs have and are continuing to perform quite 

well in terms of cost per unit of energy saved and customer participation. Several other 

programs have shown positive trends becoming more cost effective on a yearly basis. 

For all programs, but most notably in the electric market, the cost per kWh saved in the 

commercial and industrial sectors has been better than in the residential market. This might 

explain why in general, commercial and industrial customers have indicated a higher awareness 

of the utilities’ efficiency programs available to them as well as an increased likelihood of 

participation compared to residential customers. Given the scale of energy consumption in the 

commercial and industrial sectors, these customers continue to represent a substantial area for 

potential energy savings in the upcoming years. Additional penetration can be achieved 

through increased outreach to small commercial/industrial customers and by expanding current 

program offerings to include other cost effective measures not currently included in the 

companies’ CORE and utility-specific programs. 

Residential customer participation in the state’s electric and natural gas energy efficiency 

programs has met or exceeded program expectations on a yearly basis. However, in the phone 

surveys more than half of respondents indicated that they were not aware of the programs 

offered by their utilities, or that they were even eligible. Of the customers who were aware of 

the programs, a high percentage participated and indicated they would participate in the future. 

This data underscores the importance of increasing consumer education on the programs 

available to residential customers and of the associated benefits. 

One final finding from the study is that nearly all of the most cost effective energy efficiency 

measures are included in current programs in some manner. In several programs, however, the 

cost effective measures are targeted to a small percentage of consumers. The best example of 

this is the Home Energy Solutions program which targets consumers with 65 percent or greater 

electric heating. Customers with primarily electric heat represent approximately 4 percent of the 

total population based on the phone surveys. Customers with 65% or more electric heat likely 
Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

GDS Associates, Inc. 136 

represent a larger percentage of the total population but are nonetheless a small percentage of 

all customers. Adding more comprehensive programs and expanding the depth, breadth and 

promotion of the current programs to include a larger number of potential participants may lead 

to increased overall energy savings. It is important to recognize that such expansion would 

require providing services to customers that heat with fuels other than electric or natural gas. 

Issues regarding who would pay for the provision of services to such customers would need to 

be addressed. 
Appendices: Final Report: Additional Opportunities for Energy Efficiency in New Hampshire January, 2009 

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Appendix A 

 
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Appendix B 
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Appendix C 

 

 
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Appendix D 

 
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Appendix E 
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Appendix F 
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Appendix G 
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Appendix H 
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Appendix I 
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Appendix J 
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Appendix K 
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Appendix L 
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Appendix M