APPENDIX 3A. UTILITY REBATE PROGRAMS FOR CENTRAL AIR 

CONDITIONERS AND HEAT PUMPS 

TABLE OF CONTENTS 

3A.1 

3A.2 

REBATE PROGRAMS FOR RESIDENTIAL CENTRAL AIR 

CONDITIONERS ................................................................................................... 3A-1 

REBATE PROGRAMS FOR RESIDENTIAL HEAT PUMPS .......................... 3A-12 

LIST OF TABLES 

Table 3A.1.1 Rebate Programs for Central Air Conditioners ........................................... 3A-2
 

Table 3A.2.1 Rebate Programs for Residential Heat Pumps .......................................... 3A-13
 

3A-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

APPENDIX 3A. UTILITY REBATE PROGRAMS FOR CENTRAL AIR 

CONDITIONERS AND HEAT PUMPS
 

3A.1 REBATE PROGRAMS FOR RESIDENTIAL CENTRAL AIR 

CONDITIONERS 

Table 3A.1.1 provides a listing of rebate programs that DOE identified for residential 

central air conditioners. Included in Table 3A.1.1 are the following: (1) the utility or agency; (2) 

the state for which the rebate program applies; (3) the rebate amount; (4) the qualifying SEER of 

the equipment; (5) where applicable, the qualifying EER of the equipment; and (6) the website 

for additional information on the program. 

3A-1
 

EERE-2008-BT-STD-0006 COMMENT 54
Table 3A.1.1 Rebate Programs for Central Air Conditioners 

Utility or Agency State Amount SEER EER Website APS AZ $250 14 11.5 http://www.aps.com/main/green/choice/choice_3.html?source=a

APS AZ $400 16 12.25 http://www.aps.com/main/green/choice/choice_3.html?source=a

Tucson Electric Power AZ $200 14 http://www.tep.com/Green/EHC/rebates.asp Tucson Electric Power AZ $250 15 http://www.tep.com/Green/EHC/rebates.asp UniSource Energy Services AZ $200 14 http://uesaz.com/Green/EHC/rebates.asp UniSource Energy Services AZ $250 15 http://uesaz.com/Green/EHC/rebates.asp Anaheim Public Utilities CA $100/ton 14.5 12 http://www.anaheim.net/utilities/adv_svc_prog/nrg_star/flyer.pdAnaheim Public Utilities CA $100/ton 14 11 http://www.anaheim.net/utilities/adv_svc_prog/nrg_star/flyer.pd

Burbank Water and Power CA Burbank: $100/ton Non- Burbank: $80/ton 14-14.9 http://www.burbankwaterandpower.com/download/Home-RewarBurbank Water and Power CA Burbank: $120/ton Non- Burbank: $100/ton 15-15.9 http://www.burbankwaterandpower.com/download/Home-RewarBurbank Water and Power CA Burbank: $140/ton Non- Burbank: $120/ton 16 http://www.burbankwaterandpower.com/download/Home-RewarCity of Palo Alto Utilities CA $200 14 http://www.cpau.com/civica/filebank/blobdload.asp?BlobID=43City of Palo Alto Utilities CA $300 15 http://www.cpau.com/civica/filebank/blobdload.asp?BlobID=43Glendale Water and Power CA Glendale: $125/ton Non- Glendale: $100/ton 14 http://www.glendalewaterandpower.com/save_money/residentiaImperial Irrigation District Energy CA $120/ton 14 http://www.iid.com/Media/instruct_residential09_ENG_FIN022

Imperial Irrigation District Energy CA $145/ton 15 http://www.iid.com/Media/instruct_residential09_ENG_FIN022

Imperial Irrigation District Energy CA $85/ton 10.2 http://www.iid.com/Media/instruct_residential09_ENG_FIN022

Los Angeles Department of Water and Power CA $100 14 http://www.ladwp.com/ladwp/cms/ladwp000478.jsp 

Modesto Irrigation District CA $250 14.5 12 http://www.mid.org/rebates/pkts/homeCooling.pdf 

Modesto Irrigation District CA $250 14 11 http://www.mid.org/rebates/pkts/homeCooling.pdf 

Pasadena Water and Power CA Pasadena: $110/ton Non- Pasadena: $100/ton 14 http://www.cityofpasadena.net/waterandpower/CoolHome/defauPasadena Water and Power CA Pasadena: $120/ton Non- Pasadena: $110/ton 15 http://www.cityofpasadena.net/waterandpower/CoolHome/defauPasadena Water and Power CA Pasadena: $130/ton Non- Pasadena: $120/ton 16 http://www.cityofpasadena.net/waterandpower/CoolHome/defauPasadena Water and Power CA Pasadena: $140/ton Non- Pasadena: $130/ton 17 http://www.cityofpasadena.net/waterandpower/CoolHome/defauPasadena Water and Power CA Pasadena: $150/ton Non- Pasadena: $140/ton 18 http://www.cityofpasadena.net/waterandpower/CoolHome/defau

Redding Electric Utility CA $750 14 12-12.5 http://www.reupower.com/energysvc/documents/Jan09HVAC.pd

Redding Electric Utility CA $1,200 14 12.6 http://www.reupower.com/energysvc/documents/Jan09HVAC.pd

Roseville Electric CA $550 14 12 http://www.roseville.ca.us/electric/home/rebates/ac.asp Roseville Electric CA $550 15 12.5 http://www.roseville.ca.us/electric/home/rebates/ac.asp Roseville Electric CA $750 14 11 http://www.roseville.ca.us/electric/home/rebates/ac.asp 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

Roseville Electric CA $750 14 12 http://www.roseville.ca.us/electric/home/rebates/ac.asp Sacramento Municipal Utility District CA $400 14.5 12 http://www.smud.org/en/rebates/Documents/airheatpump-contr-sSacramento Municipal Utility District CA $400 14 11 http://www.smud.org/en/rebates/Documents/airheatpump-contr-sSacramento Municipal Utility District CA $500 15 12.5 http://www.smud.org/en/rebates/Documents/airheatpump-contr-sSacramento Municipal Utility District CA $500 14 12 http://www.smud.org/en/rebates/Documents/airheatpump-contr-sSacramento Municipal Utility District CA $600 16 13 http://www.smud.org/en/rebates/Documents/airheatpump-contr-sMerced Irrigation District CA $250 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $350 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $325 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $450 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $360 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $510 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $410 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $600 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $490 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $685 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $550 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $800 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $250 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $350 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $325 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $450 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $360 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $510 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $410 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $600 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $490 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $685 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $550 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Merced Irrigation District CA $800 14 http://www.mercedid.org/_images/709_mid_rebate_res_iaircond

Azusa Light and Water CA $50/ton http://www.ci.azusa.ca.us/DocumentView.asp?DID=507 

Ukiah Public Utilities Dept. CA $75/ton 14 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-

Ukiah Public Utilities Dept. CA $100/ton 15 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-

Ukiah Public Utilities Department CA $125/ton 16 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-Ukiah Public Utilities Department CA $175/ton 17 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-Ukiah Public Utilities Department CA $200/ton 18 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

Groton Utilities CN $200/ton 15 12 http://www.grotonutilities.com/files/conservation_forms/comm_Groton Utilities CN $300/ton 15.5 12.3 http://www.grotonutilities.com/files/conservation_forms/comm_

Connecticut Light and Power CT $500 14 12 http://www.cl-p.com/Home/SaveEnergy/Rebates/ACRebate.aspx 

United Illuminating Company CT $500 14.5 12 http://www.uinet.com/uinet/connect/UINet/Top+Navigator/Your

ng+and+Cooling/Energy+Star+Rebates/ 

City of Tallahassee Utilities FL $100 14 12 http://www.talgov.com/you/energy/pdf/es_hvac_existing.pdf, http://www.talgov.com/you/energy/pdf/es_hvac_new.pdf 

City of Tallahassee Utilities FL $350 15 12.5 http://www.talgov.com/you/energy/pdf/es_hvac_existing.pdf, http://www.talgov.com/you/energy/pdf/es_hvac_new.pdf Florida Power and Light FL $140 14 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $265 15 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $405 16 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $515 17 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $585 18 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $670 19 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $755 20 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $165 14 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $320 15 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $500 16 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $640 17 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $740 18 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $840 19 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $950 20 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $210 14 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $375 15 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $585 16 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $770 17 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $880 18 http://www.fpl.com/residential/savings/energy_advisor/ac_buyin

Florida Power and Light FL $1,005 19 http://www.fpl.com/residential/savings/energy_advisor/ac_buyin

Florida Power and Light FL $1,130 20 http://www.fpl.com/residential/savings/energy_advisor/ac_buyin

Florida Power and Light FL $265 14 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $475 15 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $685 16 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $895 17 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,035 18 http://www.fpl.com/residential/savings/energy_advisor/ac_buyin

Florida Power and Light FL $1,175 19 http://www.fpl.com/residential/savings/energy_advisor/ac_buyin

Florida Power and Light FL $1,330 20 http://www.fpl.com/residential/savings/energy_advisor/ac_buyin

Florida Power and Light FL $305 14 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $545 15 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $780 16 http://www.fpl.com/residential/savings/energy_advisor/ac_buyin

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

Florida Power and Light FL $1,020 17 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,175 18 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,340 19 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,510 20 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $375 14 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $640 15 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $895 16 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,145 17 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,330 18 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,510 19 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,705 20 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $430 14 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $725 15 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,005 16 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,285 17 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,495 18 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,705 19 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinFlorida Power and Light FL $1,930 20 http://www.fpl.com/residential/savings/energy_advisor/ac_buyinGainesville Regional Utilities FL $300 15 http://www.gru.com/Pdf/YourHome/Rebates/rebateForm.pdf 

Gainesville Regional Utilities FL $550 Super SEER http://www.gru.com/Pdf/YourHome/Rebates/rebateForm.pdf 

Idaho Power ID $50 13 http://www.idahopower.com/EnergyEfficiency/Residential/Progr

Idaho Power ID $150 14 11.99 http://www.idahopower.com/EnergyEfficiency/Residential/ProgrIdaho Power ID $250 15 12.5 http://www.idahopower.com/EnergyEfficiency/Residential/Progr

Rocky Mountain Power ID $250 15 12.5 http://www.homeenergysavings.net/idaho/docs/IDCACForm09.pRocky Mountain Power ID $50 13 http://www.homeenergysavings.net/idaho/docs/IDCACForm09.p

Rocky Mountain Power ID $50 13 http://www.homeenergysavings.net/idaho/docs/IDCACForm09.p

MidAmerican Energy Company IA $200 14 11.5 http://www.midamericanenergy.com/pdf/il_res_equip.pdf 

MidAmerican Energy Company IA $300 15 11.5 http://www.midamericanenergy.com/pdf/il_res_equip.pdf 

MidAmerican Energy Company IA $400 16 11.5 http://www.midamericanenergy.com/pdf/il_res_equip.pdf 

Duke Energy IN $200 14 http://www.duke-energy.com/indiana/savings/smart-saver.asp 

Indianapolis Power and Light IN $50 14 http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=0

9803007c00 

Indianapolis Power and Light IN $75 15 http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=0

9803007c00 

Indianapolis Power and Light IN $100 16 http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=0

9803007c00 

WIN Energy REMC IN $200 14 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $200 15 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $250 16 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $300 17 http://www.winenergyremc.com/rebates.html 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

Alliant Energy IA $100 14 http://alliantenergy.com/UtilityServices/ForYourHome/RewardsAlliant Energy IA $200 15 http://alliantenergy.com/UtilityServices/ForYourHome/RewardsAlliant Energy IA $200 http://alliantenergy.com/UtilityServices/ForYourHome/Rewards

Alliant Energy IA $400 http://alliantenergy.com/UtilityServices/ForYourHome/Rewards

City of Ames Electric Services IA $200 14 http://www.cityofames.org/SmartEnergy/Documents/AC%20Reb

City of Ames Electric Services IA $300 15 http://www.cityofames.org/SmartEnergy/Documents/AC%20Reb

City of Ames Electric Services IA $400 16 http://www.cityofames.org/SmartEnergy/Documents/AC%20Reb

Cedar Falls Utilities IA $200 14 http://www.cfu.net/pdf/rebates_08.pdf Cedar Falls Utilities IA $300 15 http://www.cfu.net/pdf/rebates_08.pdf Cedar Falls Utilities IA $400 16 http://www.cfu.net/pdf/rebates_08.pdf Indianola Municipal Utilities IA $100 14 http://www.i-m-u.com/Web%20Forms/2008%20ac%20rebate%2Indianola Municipal Utilities IA $150 15 http://www.i-m-u.com/Web%20Forms/2008%20ac%20rebate%2Indianola Municipal Utilities IA $200 16 http://www.i-m-u.com/Web%20Forms/2008%20ac%20rebate%2Indianola Municipal Utilities IA $250 17 http://www.i-m-u.com/Web%20Forms/2008%20ac%20rebate%2Linn County Rural Electric Cooperative Association IA $100 14.5 http://www.linncountyrec.com/cgi-script/csarticles/articles/0000

Linn County Rural Electric Cooperative Association IA $150 15.5 http://www.linncountyrec.com/cgi-script/csarticles/articles/0000

Linn County Rural Electric Cooperative Association IA $200 16.5 http://www.linncountyrec.com/cgi-script/csarticles/articles/0000

MidAmerican Energy IA $200 14 http://www.midamericanenergy.com/pdf/ee_resident_rebates.pdf 

MidAmerican Energy IA $300 15 http://www.midamericanenergy.com/pdf/ee_resident_rebates.pdf 

MidAmerican Energy IA $400 16 http://www.midamericanenergy.com/pdf/ee_resident_rebates.pdf 

Muscatine Power and Water IA $150 14 http://www.mpw.org/pdf/2009ResidentialRebateForm.pdf 

Waverly Light and Power IA $200 14 http://wlp.waverlyia.com/appliance_programs.asp 

Kansas City Board of Public Utilities KS $25/ton 15 http://www.bpu.com/documents/ResidentialHPRebateScheduleeMassachusetts Municipal Wholesale Electric Company MA $100 14 

http://www.munihelps.org/MuniHELPS%20Mailin%20rebate%20forms%202009/MailinformAshburnham.pdf, http://www.munihelps.org/MuniHELPS%20Mail-in%20rebate%

Massachusetts Municipal Wholesale Electric Company MA $325 14 http://www.munihelps.org/MuniHELPS%20Mail-in%20rebate%

Massachusetts Municipal Wholesale Electric Company MA $475 15 http://www.munihelps.org/MuniHELPS%20Mail-in%20rebate%

Massachusetts Municipal Wholesale Electric Company MA $325 13 http://www.marbleheadelectric.com/rebate.htm 

Massachusetts Municipal Wholesale Electric Company MA $475 14 http://www.marbleheadelectric.com/rebate.htm 

Belmont Municipal Light Department MA $100 14 http://www.town.belmont.ma.us/Public_Documents/BelmontMA

0Program%20&%20Form%20Brochure.pdf 

Chicopee Electric Light MA $100 14 http://www.celd.com/index.php?page=energy-star-appliance-reb

Mansfield Municipal Electric MA $100 E* http://www.mansfieldelectric.com/consumerforms/Appliance-Re

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

Department NStar MA $500 14.5 12 http://www.mycoolsmart.com/html/rebate-info.html 

NStar MA $600 15 12.5 http://www.mycoolsmart.com/html/rebate-info.html Reading Municipal Light Department MA $100 14 http://www.rmld.com/from_priscilla/energy_star/es_appl_rebate

Alliant Energy-Interstate Power & Light MN $75 14 http://www.alliantenergy.com/UtilityServices/ForYourHome/Re

ms/014684 

Alliant Energy-Interstate Power & Light MN $150 15 http://www.alliantenergy.com/UtilityServices/ForYourHome/Re

ms/014684 

Alliant Energy-Interstate Power & Light MN $225 16 http://www.alliantenergy.com/UtilityServices/ForYourHome/Re

ms/014684 

Alliant Energy-Interstate Power & Light MN $225 http://www.alliantenergy.com/UtilityServices/ForYourHome/Re

ms/014684 

Anoka Municipal Utility MN $250 14 http://www.ci.anoka.mn.us/index.asp?Type=B_BASIC&SEC={0

EB42DA396059} 

Anoka Municipal Utility MN $300 15 http://www.ci.anoka.mn.us/index.asp?Type=B_BASIC&SEC={0

EB42DA396059} 

Anoka Municipal Utility MN $350 16 http://www.ci.anoka.mn.us/index.asp?Type=B_BASIC&SEC={0

EB42DA396059} 

Austin Utilities MN $200 14 http://www.austinutilities.com/pages/residential_conserve_incen

Blooming Prairie Public Utilities MN $200 http://www.smmpa.org/Upload/2009%20RES%20summary%20

North St Paul Electric Utility MN $350 14 http://www.ci.north-saint-paul.mn.us/index.asp?Type=B_BASIC

59C3FB4192FF} 

Connexus Energy MN $30 13 http://www.connexusenergy.com/2009%20ASHP%20Rebate.pd

Connexus Energy MN $180 14 http://www.connexusenergy.com/2009%20ASHP%20Rebate.pdConnexus Energy MN $280 15 http://www.connexusenergy.com/2009%20ASHP%20Rebate.pdConnexus Energy MN $330 16 http://www.connexusenergy.com/2009%20ASHP%20Rebate.pdBuffalo Municipal Utilities MN $350 14 http://www.ci.buffalo.mn.us/Utilities/Electric/EnergyRebate.htm 

Crow Wing Power MN $30 - $330 http://www.cwpower.com/airconditioning.shtml 

Dakota Electric Association MN $30 13 https://www.dakotaelectric.com/residential/programs/rebates/res

Dakota Electric Association MN $180 14 https://www.dakotaelectric.com/residential/programs/rebates/res

Dakota Electric Association MN $280 15 https://www.dakotaelectric.com/residential/programs/rebates/res

Dakota Electric Association MN $330 16 https://www.dakotaelectric.com/residential/programs/rebates/res

East Central Energy MN $30 13 http://www.eastcentralenergy.com/rebates.aspx 

East Central Energy MN $180 14 http://www.eastcentralenergy.com/rebates.aspx 

East Central Energy MN $280 15 http://www.eastcentralenergy.com/rebates.aspx 

East Central Energy MN $330 16 http://www.eastcentralenergy.com/rebates.aspx 

Elk River Municipal Utilities MN $180 14 http://www.elkriverutilities.com/pdfs/PDF%20energy%20star_E

Elk River Municipal Utilities MN $280 15 http://www.elkriverutilities.com/pdfs/PDF%20energy%20star_EElk River Municipal Utilities MN $330 16 http://www.elkriverutilities.com/pdfs/PDF%20energy%20star_EFairmont Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

Grand Marais PUC MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20Lake City Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20rebate%20sheLake Country Power MN $30 13 http://www.lakecountrypower.coop/userfiles/2009%20Rates%20

Lake Country Power MN $180 14 http://www.lakecountrypower.coop/userfiles/2009%20Rates%20

Lake Country Power MN $280 15 http://www.lakecountrypower.coop/userfiles/2009%20Rates%20

Lake Country Power MN $330 16 http://www.lakecountrypower.coop/userfiles/2009%20Rates%20

Litchfield Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20rebate%20she

Marshall Municipal Utilities MN $100/ton 14 http://www.marshallutilities.com/residential/heatingandcooling.p

Mora Municipal Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20

New Prague Utilities Commission MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20

New Ulm Public Utilities MN $200 14 http://www.ci.new-ulm.mn.us/vertical/Sites/%7B1E9156B6-EE8

37AF3590FA0E%7D/uploads/%7B71F357A8-67B1-47CD-BF4

New Ulm Public Utilities MN $275 15 http://www.ci.new-ulm.mn.us/vertical/Sites/%7B1E9156B6-EE8

37AF3590FA0E%7D/uploads/%7B71F357A8-67B1-47CD-BF4

New Ulm Public Utilities MN $350 16 http://www.ci.new-ulm.mn.us/vertical/Sites/%7B1E9156B6-EE8

37AF3590FA0E%7D/uploads/%7B71F357A8-67B1-47CD-BF4

North Branch Municipal Water & Light MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20

Owatonna Public Utilities MN $200 14 http://www.owatonnautilities.com/documents/ResidentialRebate

Preston Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20

Princeton PUC MN $200 14 http://www.smmpa.org/Upload/Res%20Rebate%20Summary%2

Redwood Falls Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20

Rochester Public Utilities MN $200 14 http://www.rpu.org/pdfs/2009resrebateapp.pdf 

Saint Peter Municipal Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20

Shakopee Public Utilities MN $250 14 http://www.shakopeeutilities.com/2007%20Customer%20Advan

Spring Valley Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20

Waseca Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20Wells Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20summary%20Fairmont Public Utilities MN $100 15 http://www.wildriceelectric.com/images/Minnkota%20Res%20E

Willmar Municipal Utilities MN $200 http://wmu.willmar.mn.us/energy_services.htm Xcel Energy MN $180 14 http://www.xcelenergy.com/Residential/Programs_Resources/He

Rebate.aspx 

Xcel Energy MN $280 15 http://www.xcelenergy.com/Residential/Programs_Resources/He

Rebate.aspx 

Xcel Energy MN $330 16 http://www.xcelenergy.com/Residential/Programs_Resources/He

Rebate.aspx 

City Utilities of Springfield, Missouri MO $100/ton http://www.cityutilities.net/conserve/forms/HVAC-AirCondReb

Columbia Water & Light MO $100 14 http://www.gocolumbiamo.com/WaterandLight/Documents/acre

Columbia Water & Light MO $200 15 http://www.gocolumbiamo.com/WaterandLight/Documents/acre

Columbia Water & Light MO $300 16 http://www.gocolumbiamo.com/WaterandLight/Documents/acre

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

Columbia Water & Light MO $400 17 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $500 18 http://www.gocolumbiamo.com/WaterandLight/Documents/acre

Columbia Water & Light MO $500 19 http://www.gocolumbiamo.com/WaterandLight/Documents/acre

Columbia Water & Light MO $600 20 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $100 14 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $300 15 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $500 16 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $600 17 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $700 18 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $800 19 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $900 20 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $200 14 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $400 15 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $600 16 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $800 17 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $1,000 18 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $1,100 19 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $1,200 20 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $300 14 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $600 15 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $800 16 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $1,000 17 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $1,200 18 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $1,400 19 http://www.gocolumbiamo.com/WaterandLight/Documents/acreColumbia Water & Light MO $1,600 20 http://www.gocolumbiamo.com/WaterandLight/Documents/acreEmpire District Electric Company MO $400 15 https://www.empiredistrict.com/Dochandler.ashx?id=3553 

Empire District Electric Company MO $450 16 https://www.empiredistrict.com/Dochandler.ashx?id=3553 Empire District Electric Company MO $500 17 https://www.empiredistrict.com/Dochandler.ashx?id=3553 Empire District Electric Company MO 14 https://www.empiredistrict.com/Dochandler.ashx?id=2612 

Independence Power & Light MO $137 15 http://www.ci.independence.mo.us/pl/pdf/rebate_ac.pdf 

Independence Power & Light MO $207 15 http://www.ci.independence.mo.us/pl/pdf/rebate_ac.pdf 

Independence Power & Light MO $260 15 http://www.ci.independence.mo.us/pl/pdf/rebate_ac.pdf 

Kansas City Power & Light MO $650 14 http://www.kcpl.com/coolhomes/customers.htm 

Kansas City Power & Light MO $650 14 http://www.kcpl.com/coolhomes/customers.htm 

Kansas City Power & Light MO $850 16 http://www.kcpl.com/coolhomes/customers.htm 

New Jersey Board of Public Utilities NJ $100 14 12 http://www.njcleanenergy.com/residential/programs/cooladvanta

New Jersey Board of Public Utilities NJ $150 15 12.5 http://www.njcleanenergy.com/residential/programs/cooladvanta

Progress Energy Carolinas NC $300 15 http://www.progress-energy.com/custservice/carres/efficiency/pr

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

Progress Energy Carolinas NC $300 15 http://www.progress-energy.com/custservice/carres/efficiency/pr

Boulder City NV $70/ton 14 http://www.bcnv.org/Conservation/Rebates.asp 

Boulder City NV $125/ton 15 http://www.bcnv.org/Conservation/Rebates.asp 

Long Island Power Authority NY $250 14 12 http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdf 

Long Island Power Authority NY $400 15 12.5 http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdf 

Long Island Power Authority NY $600 16 13 http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdf 

Duke Energy OH $200 14 http://www.duke-energy.com/ohio/savings/smart-saver.asp Duke Energy OH $300 14 http://www.duke-energy.com/ohio/savings/smart-saver.asp Duncan Power OK $150/ton 17 http://www.duncanpower.com/airconditionrebate.htm 

Duncan Power OK $175/ton 18 http://www.duncanpower.com/airconditionrebate.htm 

Duncan Power OK $200/ton 19 http://www.duncanpower.com/airconditionrebate.htm 

Duncan Power OK $225/ton 20 http://www.duncanpower.com/airconditionrebate.htm 

Duncan Power OK $250/ton 21 http://www.duncanpower.com/airconditionrebate.htm 

Duncan Power OK $275/ton 22 http://www.duncanpower.com/airconditionrebate.htm 

Duncan Power OK $300/ton 23 http://www.duncanpower.com/airconditionrebate.htm 

Austin Energy TX $180 14 11.5 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

Family%20Properties/rebateLevels.htm 

Austin Energy TX $480 15 12.5 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

Family%20Properties/rebateLevels.htm 

Austin Energy TX $120 14 11.5 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

Family%20Properties/rebateLevels.htm 

Austin Energy TX $220 15 12.5 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

Family%20Properties/rebateLevels.htm 

Austin Energy TX $180 14 11.5 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

ateLevels.htm 

Austin Energy TX $300 14 12 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

ateLevels.htm 

Austin Energy TX $480 15 12.5 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

ateLevels.htm 

Austin Energy TX $600 16 13 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

ateLevels.htm 

Austin Energy TX $300 14 11 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

ateLevels.htm 

Austin Energy TX $480 14 12 http://www.austinenergy.com/Energy%20Efficiency/Programs/R

ateLevels.htm 

College Station Utilities TX $200 14 http://www.cstx.gov/docs/energy_back_ii_application_form.pdf 

College Station Utilities TX $400 15 http://www.cstx.gov/docs/energy_back_ii_application_form.pdf 

College Station Utilities TX $500 16 http://www.cstx.gov/docs/energy_back_ii_application_form.pdf 

College Station Utilities TX $600 18 http://www.cstx.gov/docs/energy_back_ii_application_form.pdf 

CPS Energy TX $110/ton 15 http://www.cpsenergy.com/files/2009_AC_Central_Application.CPS Energy TX $125/ton 16 http://www.cpsenergy.com/files/2009_AC_Central_Application.CPS Energy TX $140/ton 17 http://www.cpsenergy.com/files/2009_AC_Central_Application.

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER EER Website 

CPS Energy TX $160/ton 18 http://www.cpsenergy.com/files/2009_AC_Central_Application.Denton Municipal Electric TX $600 16 http://dmepower.com/userfiles/file/GreenSense%20Rebate%20M

Denton Municipal Electric TX $600 16 http://dmepower.com/userfiles/file/GreenSense%20Rebate%20M

Garland Power and Light TX $400 15 http://www.garlandpower-light.org/pdfs/2008- 2009%20Central%20Air%20Conditioning%20%20Heat%20Pum

Garland Power and Light TX $500 16 http://www.garlandpower-light.org/pdfs/2008- 2009%20Central%20Air%20Conditioning%20%20Heat%20Pum

Garland Power and Light TX $600 17 http://www.garlandpower-light.org/pdfs/2008- 2009%20Central%20Air%20Conditioning%20%20Heat%20Pum

Magic Valley Electric Cooperative TX $200 13 http://www.magicvalley.coop/Electric-Service/Residential/Rebat

Magic Valley Electric Cooperative TX $225 14 http://www.magicvalley.coop/Electric-Service/Residential/Rebat

Magic Valley Electric Cooperative TX $250 15 http://www.magicvalley.coop/Electric-Service/Residential/Rebat

Pedernales Electric Cooperative TX $225 14 http://www.pec.coop/Documents/HVACRebate.pdf Efficiency Vermont VT $100 14 12 http://www.efficiencyvermont.org/stella/filelib/2009_HVACformPacific Power WA $250 15 12.5 http://www.homeenergysavingspp.net/washington/docs/WAHVA

Barron Electric Cooperative WI $60/ton 14 http://www.barronelectric.com/services/centralair.html 

Eau Claire Energy Cooperative WI $60/ton 14 http://www.ecec.com/programs/incentives/index.cfm Focus on Energy WI $100 15 http://www.focusonenergy.com/files/Document_Management_S

gcoolingcashback_rewardform.pdf 

Riverland Energy Cooperative WI $60/ton 14 http://riverlandenergy.com/RIVERLAND%20HOMEPAGE/prog

Pierce Pepin Cooperative Services WI $60/ton 14 http://www.piercepepin.com/nrgsense/docs/ES_RebateForm.pdf 

Warren Heating and A/C WI $100 14 http://www.warrenheating.com/rebates-promotions.html 

Warren Heating and A/C WI $150 15 http://www.warrenheating.com/rebates-promotions.html Warren Heating and A/C WI $200 16 http://www.warrenheating.com/rebates-promotions.html 

EERE-2008-BT-STD-0006 COMMENT 54
3A.2 REBATE PROGRAMS FOR RESIDENTIAL HEAT PUMPS 

Table 3A.1.2 provides a listing of rebate programs that DOE identified for residential 

heat pumps. Included in Table 3A.1.1 are the following: (1) the utility or agency; (2) the state 

for which the rebate program applies; (3) the rebate amount; (4) the qualifying SEER of the 

equipment; (5) where applicable, the qualifying EER of the equipment; and (6) the website for 

additional information on the program.. 

3A-12
 

EERE-2008-BT-STD-0006 COMMENT 54
Table 3A.2.1 Rebate Programs for Residential Heat Pumps 

Utility or Agency State Amount SEER HSPF EER Website Central Alabama Electrical Cooperative AL $400 http://www.caec.coop/cms/content/view/42/ Central Alabama Electrical Cooperative AL $500 http://www.caec.coop/cms/content/view/42/ Central Alabama Electrical Cooperative AL $600 http://www.caec.coop/cms/content/view/42/ Central Alabama Electrical Cooperative AL $700 http://www.caec.coop/cms/content/view/42/ Central Alabama Electrical Cooperative AL $800 http://www.caec.coop/cms/content/view/42/ Central Alabama Electrical Cooperative AL $900 http://www.caec.coop/cms/content/view/42/ Central Alabama Electrical Cooperative AL $1,000 http://www.caec.coop/cms/content/view/42/ Central Alabama Electrical Cooperative AL $1,200 http://www.caec.coop/cms/content/view/42/ Sand Mountain Electric Cooperative AL http://www.smec.coop/ 

South Alabama Electric Cooperative AL $200 http://www.southaec.com/content.cfm?id=2020#rebatAPS Energy AZ $250 14 11.5 http://www.aps.com/main/green/choice/choice_3.html

APS Energy AZ $400 16 12.3 http://www.aps.com/main/green/choice/choice_3.html

Sulphur Springs Valley Electric Cooperative AZ $500 14 http://www.ssvec.org/documents/Heat_Pump_Rebate_

Sulphur Springs Valley Electric Cooperative AZ $200 14 http://www.ssvec.org/documents/Heat_Pump_Rebate_

Tucson Electric Power AZ $200 14 http://www.tep.com/Docs/EHC/EHCRebateForm.pdf Tucson Electric Power AZ $250 15 http://www.tep.com/Docs/EHC/EHCRebateForm.pdf UniSource Energy Services AZ $200 14 http://uesaz.com/Green/EHC/rebates.asp 

UniSource Energy Services AZ $250 15 http://uesaz.com/Green/EHC/rebates.asp 

Imperial Irrigation District Energy CA $135/ton 14 7.8 http://www.iid.com/Media/instruct_residential09_ENG

Imperial Irrigation District Energy CA $165/ton 15 7.8 http://www.iid.com/Media/instruct_residential09_ENGImperial Irrigation District Energy CA $85/ton 10.2 http://www.iid.com/Media/instruct_residential09_ENG

Los Angeles Department of Water and Power CA $100 14 http://www.ladwp.com/ladwp/cms/ladwp004363.pdf 

Modesto Irrigation District CA $250 14.5 8.5 12 http://www.mid.org/rebates/pkts/homeCooling.pdf 

Modesto Irrigation District CA $250 14 8 11 http://www.mid.org/rebates/pkts/homeCooling.pdf 

Pasadena Water and Power CA Pasadena: $115/ton Non- Pasadena: $100/ton 13 8 http://www.cityofpasadena.net/waterandpower/pdf/Ap

Pasadena Water and Power CA Pasadena: $115/ton Non- Pasadena: $100/ton 12 7.6 http://www.cityofpasadena.net/waterandpower/pdf/Ap

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Pasadena Water and Power CA Pasadena: $140/ton Non- Pasadena: $125/ton 14 8.5 12 http://www.cityofpasadena.net/waterandpower/pdf/Ap

Pasadena Water and Power CA Pasadena: $140/ton Non- Pasadena: $125/ton 13 7.9 11 http://www.cityofpasadena.net/waterandpower/pdf/ApPasadena Water and Power CA Pasadena: $160/ton Non- Pasadena: $150/ton 15 8.5 12.5 http://www.cityofpasadena.net/waterandpower/pdf/Ap

Pasadena Water and Power CA Pasadena: $160/ton Non- Pasadena: $150/ton 14 7.9 12 http://www.cityofpasadena.net/waterandpower/pdf/Ap

Redding Electric Utility CA $1,350 14 12 http://www.reupower.com/energysvc/documents/Jan0

Redding Electric Utility CA $1,600 14 12.6 http://www.reupower.com/energysvc/documents/Jan0

Riverside Public Utilities CA 14 http://www.riversideca.gov/utilities/pdf/rebates/Cool%

Roseville Electric CA $200 8.5 http://www.roseville.ca.us/electric/home/rebates/ac.as

Roseville Electric CA $200 8.2 http://www.roseville.ca.us/electric/home/rebates/ac.as

Sacramento Municipal Utility District CA $400 14.5 8.5 12 http://www.smud.org/en/rebates/Documents/airheatpu

Sacramento Municipal Utility District CA $400 14 8 11 http://www.smud.org/en/rebates/Documents/airheatpu

Sacramento Municipal Utility District CA $500 15 8.5 12.5 http://www.smud.org/en/rebates/Documents/airheatpuSacramento Municipal Utility District CA $500 14 8 12 http://www.smud.org/en/rebates/Documents/airheatpu

Ukiah Public Utilities Department CA $125/ton 14 8.6 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_aUkiah Public Utilities Department CA $150/ton 15 8.8 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_aUkiah Public Utilities Department CA $175/ton 16 8.4 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_aUkiah Public Utilities Department CA $200/ton 17 8.6 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_aUkiah Public Utilities Department CA $225/ton 18 9.2 http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_aMountain View Electric Association, Inc. and Tri-State Generation and Transmission Association Inc. 

CO $125 - $150 14.5 8.2 12 http://www.mvea.coop/documents/A-012%20fact%20

Poudre Valley Rural Electric Association CO 12 7.4 http://www.pvrea.com/members/rebates/heat_pumps.h

United Power CO $400 http://www.unitedpower.com/rebate.aspx#heatpump 

United Power CO $400 http://www.unitedpower.com/rebate.aspx#heatpump Connecticut Light and Power CT $500 14 8.2 12 http://www.cl-p.com/Home/SaveEnergy/Rebates/ACRConnecticut Energy Efficiency Fund CT $500 14.5 8.2 12.5 http://www.uinet.com/uinet/resources/file/ebeaee01b5

ate%20Form%20Rev%2003.09.pdf 

Groton Utilities CT $200/ton 15 9 http://www.grotonutilities.com/files/conservation_formGroton Utilities CT $300/ton 15.5 9 http://www.grotonutilities.com/files/conservation_formGroton Utilities CT $200/ton 14 8 http://www.grotonutilities.com/files/conservation_formGroton Utilities CT $300/ton 15.5 9 http://www.grotonutilities.com/files/conservation_formGroton Utilities CT $200/ton 11.5 http://www.grotonutilities.com/files/conservation_form

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Groton Utilities CT $200/ton 10 http://www.grotonutilities.com/files/conservation_form

United Illuminating Company CT $500 14.5 8.2 12 http://www.uinet.com/uinet/connect/UINet/Top+Navi

vices/Heating+and+Cooling/Energy+Star+Rebates/ 

City of Tallahassee Utilities FL $100 14 8.2 12 http://www.talgov.com/you/energy/pdf/es_hvac_exist

http://www.talgov.com/you/energy/pdf/es_hvac_new.

City of Tallahassee Utilities FL $350 15 8.2 12.5 http://www.talgov.com/you/energy/pdf/es_hvac_exist

http://www.talgov.com/you/energy/pdf/es_hvac_new.

Clay Electric Cooperative FL $100 14 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $125 15 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $125 16 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $150 17 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $150 18 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $125 14 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $150 15 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $175 16 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $200 17 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $200 18 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $150 14 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $175 15 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $200 16 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $225 17 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $250 18 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $175 14 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $225 15 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $250 16 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $275 17 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $300 18 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $225 14 http://clayelectric.com/documents/energysmartguideli

Clay Electric Cooperative FL $250 15 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $300 16 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $325 17 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $350 18 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $250 14 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $300 15 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $325 16 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $350 17 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $400 18 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $325 14 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $375 15 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $425 16 http://clayelectric.com/documents/energysmartguideli

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Clay Electric Cooperative FL $450 17 http://clayelectric.com/documents/energysmartguideliClay Electric Cooperative FL $500 18 http://clayelectric.com/documents/energysmartguideliFlorida Power and Light FL $140 14 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $265 15 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $405 16 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $515 17 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $585 18 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $670 19 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $755 20 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $165 14 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $320 15 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $500 16 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $640 17 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $740 18 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $840 19 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $950 20 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $210 14 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $375 15 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $585 16 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $770 17 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $880 18 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $1,005 19 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,130 20 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $265 14 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $475 15 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $685 16 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $895 17 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $1,035 18 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $1,175 19 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $1,330 20 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $305 14 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $545 15 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $780 16 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,020 17 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,175 18 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,340 19 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $1,510 20 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $375 14 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $640 15 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $895 16 http://www.fpl.com/residential/savings/energy_adviso

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Florida Power and Light FL $1,145 17 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,330 18 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,510 19 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,705 20 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $430 14 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $725 15 http://www.fpl.com/residential/savings/energy_advisoFlorida Power and Light FL $1,005 16 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,285 17 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,495 18 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,705 19 http://www.fpl.com/residential/savings/energy_adviso

Florida Power and Light FL $1,930 20 http://www.fpl.com/residential/savings/energy_adviso

Orlando Utilities Commission FL $100 14 http://www.ouc.com/home/rebates.htm#2 

Orlando Utilities Commission FL $200 15 http://www.ouc.com/home/rebates.htm#2 

Orlando Utilities Commission FL $300 16 http://www.ouc.com/home/rebates.htm#2 

Progress Energy Florida FL $100 14 7.8 http://progress-energy.com/custservice/flares/energyh

Progress Energy Florida FL $150 15 8 http://progress-energy.com/custservice/flares/energyh

Progress Energy Florida FL $250 14 7.8 http://progress-energy.com/custservice/flares/energyh

Progress Energy Florida FL $350 15 8 http://progress-energy.com/custservice/flares/energyh

Progress Energy Florida FL $50 14 http://progress-energy.com/custservice/flares/energyh

Tampa Electric FL $275 https://secure32.tecoenergy.com/tampaelectricsecure/r

/ 

Tampa Electric FL $125 https://secure32.tecoenergy.com/tampaelectricsecure/r

/ 

Central Georgia Electric Member Corporation GA $200 13 http://www.cgemc.com/rebates.aspx 

Diverse Power GA $300 http://www.diversepower.com/rebates_incentives.php Diverse Power GA $500 http://www.diversepower.com/rebates_incentives.php Diverse Power GA $100 http://www.diversepower.com/rebates_incentives.php Diverse Power GA $300 http://www.diversepower.com/rebates_incentives.php 

Habersham Electric Membership Corporation GA $150 http://www.habershamemc.com/rebates.aspx 

Habersham Electric Membership Corporation GA $200 http://www.habershamemc.com/rebates.aspx 

Walton Electric Membership Corporation GA $200 13 10 9.5 http://www.waltonemc.com/residential/index.php?pag

City of Ames Electric Services IA (((Actual SEER) – 14) x $100) + $200 14 http://www.cityofames.org/SmartEnergy/Documents/A

09%20Claim%20Form.pdf 

Avista Utilities ID $400 8.5 https://www.avistautilities.com/savings/rebates/Pages/

x 

Avista Utilities ID $400 13 7.7 https://www.avistautilities.com/savings/rebates/Pages/

x 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Avista Utilities ID $1,000 https://www.avistautilities.com/savings/rebates/Pages/

x 

Avista Utilities ID $1,500 https://www.avistautilities.com/savings/rebates/Pages/

x 

Idaho Power ID $200 8.2 http://www.idahopower.com/EnergyEfficiency/Reside

ives.cfm 

Idaho Power ID $250 8.5 http://www.idahopower.com/EnergyEfficiency/Reside

ives.cfm 

Idaho Power ID $300 8.2 http://www.idahopower.com/EnergyEfficiency/Reside

ives.cfm 

Idaho Power ID $400 8.5 http://www.idahopower.com/EnergyEfficiency/Reside

ives.cfm 

Kootenai Electric Cooperative ID $300 14 8.5 http://www.kec.com/rebates.php Kootenai Electric Cooperative ID $965 14 8.5 http://www.kec.com/rebates.php Kootenai Electric Cooperative ID $1,690 14 8.5 http://www.kec.com/rebates.php Kootenai Electric Cooperative ID $1,500 http://www.kec.com/rebates.php City Water Light and Power IL $600 7 http://www.cwlp.com/Energy_services/ESO_servicesCity Water Light and Power IL $525 8 http://www.cwlp.com/Energy_services/ESO_servicesCity Water Light and Power IL $470 9 http://www.cwlp.com/Energy_services/ESO_servicesCity Water Light and Power IL $900 7 http://www.cwlp.com/Energy_services/ESO_servicesCity Water Light and Power IL $790 8 http://www.cwlp.com/Energy_services/ESO_servicesCity Water Light and Power IL $700 9 http://www.cwlp.com/Energy_services/ESO_servicesCity Water Light and Power IL $1,200 7 http://www.cwlp.com/Energy_services/ESO_services

City Water Light and Power IL $1,050 8 http://www.cwlp.com/Energy_services/ESO_services

City Water Light and Power IL $930 9 http://www.cwlp.com/Energy_services/ESO_servicesMidAmerican Energy Company -Illinois IL $100 14 <8.5 http://www.midamericanenergy.com/pdf/ia_res_equip

MidAmerican Energy Company -Illinois IL $200 14 >/=8.5 http://www.midamericanenergy.com/pdf/ia_res_equip

MidAmerican Energy Company -Illinois IL $200 15 <8.5 http://www.midamericanenergy.com/pdf/ia_res_equip

MidAmerican Energy Company -Illinois IL $400 15 >/=8.5 http://www.midamericanenergy.com/pdf/ia_res_equip

Bartholomew County Rural Electric Membership Cooperative IN $300 14 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $300 15 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $350 16 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $400 17 http://www.bcremc.com/airpump.html 

Bartholomew County Rural IN $800 14 http://www.bcremc.com/airpump.html 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Electric Membership Cooperative Bartholomew County Rural Electric Membership Cooperative IN $800 15 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $900 16 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $1,000 17 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $300 14 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $300 15 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $350 16 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $400 17 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $200 14 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $200 15 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $250 16 http://www.bcremc.com/airpump.html 

Bartholomew County Rural Electric Membership Cooperative IN $300 17 http://www.bcremc.com/airpump.html 

Clark County REMC IN $100 14 http://www.theremc.com/rebates.html#anchor3 Clark County REMC IN $100 15 http://www.theremc.com/rebates.html#anchor3 Clark County REMC IN $150 16 http://www.theremc.com/rebates.html#anchor3 Clark County REMC IN $200 17 http://www.theremc.com/rebates.html#anchor3 Clark County REMC IN $600 14 http://www.theremc.com/rebates.html#anchor3 Clark County REMC IN $600 15 http://www.theremc.com/rebates.html#anchor3 Clark County REMC IN $700 16 http://www.theremc.com/rebates.html#anchor3 Clark County REMC IN $800 17 http://www.theremc.com/rebates.html#anchor3 Dubois REC IN $200 14 http://www.duboisrec.com/rebates.html 

Dubois REC IN $200 15 http://www.duboisrec.com/rebates.html 

Dubois REC IN $250 16 http://www.duboisrec.com/rebates.html 

Dubois REC IN $300 17 http://www.duboisrec.com/rebates.html 

Dubois REC IN $300 14 http://www.duboisrec.com/rebates.html 

Dubois REC IN $300 15 http://www.duboisrec.com/rebates.html 

Dubois REC IN $350 16 http://www.duboisrec.com/rebates.html 

Dubois REC IN $400 17 http://www.duboisrec.com/rebates.html 

Dubois REC IN $800 14 http://www.duboisrec.com/rebates.html 

Dubois REC IN $800 15 http://www.duboisrec.com/rebates.html 

Dubois REC IN $900 16 http://www.duboisrec.com/rebates.html 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Dubois REC IN $1,000 17 http://www.duboisrec.com/rebates.html 

Duke Energy IN $200 14 http://www.duke-energy.com/indiana/savings/smart-s

Harrison REMC IN $200 14 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $200 15 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $250 16 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $300 17 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $200 14 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $200 15 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $250 16 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $300 17 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $700 14 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $700 15 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $800 16 http://www.harrisonremc.com/documents/2009_Reba

Harrison REMC IN $900 17 http://www.harrisonremc.com/documents/2009_Reba

Indianapolis Power and Light IN $100 14 http://www.iplpower.com/ipl/index?page=IPLGeneral

701096a4e9803007c00 

Indianapolis Power and Light IN $125 15 http://www.iplpower.com/ipl/index?page=IPLGeneral

701096a4e9803007c00 

Indianapolis Power and Light IN $150 16 http://www.iplpower.com/ipl/index?page=IPLGeneral

701096a4e9803007c00 

Jackson County REMC IN $225 14 http://www.jacksonremc.com/rebates.htm Jackson County REMC IN $275 15 http://www.jacksonremc.com/rebates.htm Jackson County REMC IN $325 16 http://www.jacksonremc.com/rebates.htm Jackson County REMC IN $375 17 http://www.jacksonremc.com/rebates.htm Jackson County REMC IN $325 14 http://www.jacksonremc.com/rebates.htm Jackson County REMC IN $375 15 http://www.jacksonremc.com/rebates.htm Jackson County REMC IN $425 16 http://www.jacksonremc.com/rebates.htm Jackson County REMC IN $475 17 http://www.jacksonremc.com/rebates.htm Kosciusko REMC IN $150 http://www.kremc.com/page1.asp?pi=25&pc=services 

Miami-Cass REMC IN $500 13 http://www.mcremc.coop/default1.asp?pi=49 

Miami-Cass REMC IN $100 13 http://www.mcremc.coop/default1.asp?pi=49 Northeastern REMC IN $250 http://www.nremc.com/ps_airsource_heatpump.shtml Orange County REMC IN $200 14 http://www.orangecountyremc.org/rebates.html 

Orange County REMC IN $200 15 http://www.orangecountyremc.org/rebates.html 

Orange County REMC IN $250 16 http://www.orangecountyremc.org/rebates.html 

Orange County REMC IN $300 17 http://www.orangecountyremc.org/rebates.html 

Orange County REMC IN $500 14 http://www.orangecountyremc.org/rebates.html 

Orange County REMC IN $500 15 http://www.orangecountyremc.org/rebates.html 

Orange County REMC IN $650 16 http://www.orangecountyremc.org/rebates.html 

Orange County REMC IN $800 17 http://www.orangecountyremc.org/rebates.html 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Paulding-Putnam Electric Cooperative IN $300 http://www.ppec.coop/rebate/Air.html 

Rush Shelby Energy IN $300 14 http://www.rse.coop/heatpump.html 

Rush Shelby Energy IN $300 15 http://www.rse.coop/heatpump.html 

Rush Shelby Energy IN $350 16 http://www.rse.coop/heatpump.html 

Rush Shelby Energy IN $400 17 http://www.rse.coop/heatpump.html 

Rush Shelby Energy IN $800 14 http://www.rse.coop/heatpump.html 

Rush Shelby Energy IN $800 15 http://www.rse.coop/heatpump.html 

Rush Shelby Energy IN $900 16 http://www.rse.coop/heatpump.html 

Rush Shelby Energy IN $1,000 17 http://www.rse.coop/heatpump.html 

South Central Indiana REMC IN $300 15 http://www.sciremc.com/incentiveprograms.aspx 

South Central Indiana REMC IN $350 16 http://www.sciremc.com/incentiveprograms.aspx 

South Central Indiana REMC IN $400 17 http://www.sciremc.com/incentiveprograms.aspx 

Southern Indiana Rural Electric Coop IN $200 14 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $200 15 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $250 16 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $300 17 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $300 14 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $300 15 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $350 16 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $400 17 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $800 14 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $800 15 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $900 16 http://www.sirec.com/Forms/2009RebateTable_Form_

Southern Indiana Rural Electric Coop IN $1,000 17 http://www.sirec.com/Forms/2009RebateTable_Form_

Tipmont REMC IN $70/ton 13 http://www.tipmont.org/Services/rebates.htm 

Tipmont REMC IN $85/ton 14 http://www.tipmont.org/Services/rebates.htm 

Tipmont REMC IN $100/ton 15 http://www.tipmont.org/Services/rebates.htm 

Utilities District of Western Indiana REMC IN $300 14 http://www.udwiremc.com/inquiryhome_udi.nsf/page

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Utilities District of Western Indiana REMC IN $300 15 http://www.udwiremc.com/inquiryhome_udi.nsf/page

Utilities District of Western Indiana REMC IN $350 16 http://www.udwiremc.com/inquiryhome_udi.nsf/page

Utilities District of Western Indiana REMC IN $400 17 http://www.udwiremc.com/inquiryhome_udi.nsf/page

Wabash Country REMC IN $500 http://www.wabashremc.com/customer-service/rebateWabash Country REMC IN $200 http://www.wabashremc.com/customer-service/rebateWIN Energy REMC IN $300 14 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $300 15 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $350 16 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $400 17 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $800 14 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $800 15 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $900 16 http://www.winenergyremc.com/rebates.html 

WIN Energy REMC IN $1,000 17 http://www.winenergyremc.com/rebates.html 

Midland Power Cooperative IA $150/ton 13 7.7 http://www.midlandpower.com/aspx/General/ClientPa

Midland Power Cooperative IA $150/ton + $250 bonus 14 8.2 http://www.midlandpower.com/aspx/General/ClientPa

Guthrie County REC IA $150/ton 13 7.7 http://www.guthrie-rec.coop/cgi-script/csArticles/artic

Guthrie County REC IA $150/ton + $100 bonus 14 8.2 http://www.guthrie-rec.coop/cgi-script/csArticles/artic

Alliant Energy IA $100 14 http://alliantenergy.com/UtilityServices/ForYourHom

ms/014675 

Alliant Energy IA $200 15 http://alliantenergy.com/UtilityServices/ForYourHom

ms/014675 

Alliant Energy IA $200 < 8.5 http://alliantenergy.com/UtilityServices/ForYourHom

ms/014675 

Alliant Energy IA $400 > 8.5 http://alliantenergy.com/UtilityServices/ForYourHom

ms/014675 

Cedar Falls Utilities IA $300 15 8 http://www.cfu.net/heatpump_rebates.php?loc=top 

Indianola Municipal Utilities IA $200 14 http://www.i-m-u.com/Web%20Forms/2008%20heat%

Indianola Municipal Utilities IA $300 15 http://www.i-m-u.com/Web%20Forms/2008%20heat%

Indianola Municipal Utilities IA $400 16 http://www.i-m-u.com/Web%20Forms/2008%20heat%

Indianola Municipal Utilities IA $500 17 http://www.i-m-u.com/Web%20Forms/2008%20heat%

Linn County IA $225/ton 14.5 8.2 http://www.linncountyrec.com/cgi-script/csarticles/artMidAmerican Energy Company IA $100 14 <8.5 http://www.midamericanenergy.com/pdf/ia_res_equipMidAmerican Energy Company IA $200 14 >/=8.5 http://www.midamericanenergy.com/pdf/ia_res_equip

MidAmerican Energy Company IA $200 15 <8.5 http://www.midamericanenergy.com/pdf/ia_res_equipMidAmerican Energy Company IA $400 15 >/=8.5 http://www.midamericanenergy.com/pdf/ia_res_equip

MidAmerican Energy Company IA $100/home http://www.midamericanenergy.com/pdf/ia_res_equip

Waverly Light and Power IA $450 14 http://wlp.waverlyia.com/appliance_programs.asp Kansas City Board of Public KS http://www.bpu.com/documents/ResidentialHPRebate

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Utilities Jackson Energy Cooperative KY $100 http://www.jacksonenergy.com/rebatePrograms.aspx 

Kenergy KY $700 13 http://www.kenergycorp.com/incentive.aspx 

Kenergy KY $800 14 http://www.kenergycorp.com/incentive.aspx 

Kenergy KY $900 15 http://www.kenergycorp.com/incentive.aspx 

Kenergy KY $90/ton http://www.kenergycorp.com/incentive.aspx Nolin RECC KY $500 14 8 http://www.nolinrecc.com/home-rebateprograms.asp 

Nolin RECC KY $100 14 8 http://www.nolinrecc.com/home-rebateprograms.asp 

Owen Electric Cooperative, Inc. KY $100 14 8 http://www.owenelectric.com/pdfs/forms/Residential%

Pennyrile Rural Electric Cooperative Corporation KY $100 13 http://www.precc.com/precc/res_existing_home.htm 

Pennyrile Rural Electric Cooperative Corporation KY $50 13 http://www.precc.com/precc/res_existing_home.htm Pennyrile Rural Electric Cooperative Corporation KY $200 http://www.precc.com/precc/res_existing_home.htm Pennyrile Rural Electric Cooperative Corporation KY $200 13 http://www.precc.com/precc/res_new_home.htm Pennyrile Rural Electric Cooperative Corporation KY $400 http://www.precc.com/precc/res_new_home.htm Salt River Electric KY $200 http://www.srelectric.com/products/gaselectric.htm 

Salt River Electric KY $200 13 http://www.srelectric.com/products/electricheatpump.

South Kentucky Rural Electric Cooperative Corporation KY $300 http://www.skrecc.com/ee_programs.htm 

Taylor County RECC KY $200 13 8 http://www.tcrecc.com/rebates.php 

Munihelps - Marblehead rebate program 

MA $325 13 8 http://www.marbleheadelectric.com/rebate.htm 

Munihelps - Marblehead rebate program 

MA $525 13 8 http://www.marbleheadelectric.com/rebate.htm 

Munihelps - Marblehead rebate program 

MA $475 14 8.5 http://www.marbleheadelectric.com/rebate.htm 

Munihelps - Marblehead rebate program 

MA $675 14 8.5 http://www.marbleheadelectric.com/rebate.htm 

Austin Public Utilities MN $200 14 8.2 http://www.austinutilities.com/files/pdf/Res_Reb_AppAustin Public Utilities MN $200 14 8 http://www.austinutilities.com/files/pdf/Res_Reb_AppSouthern Minnesota Municipal Power Agency MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

Connexus Energy MN $30 13 http://www.connexusenergy.com/ashprebate.htm 

Connexus Energy MN $180 14 http://www.connexusenergy.com/ashprebate.htm Connexus Energy MN $280 15 http://www.connexusenergy.com/ashprebate.htm Connexus Energy MN $330 16 http://www.connexusenergy.com/ashprebate.htm Crow Wing Power MN $200 http://www.cwpower.com/heatingcoolingoptions.shtmDakota Electric Service MN $300 https://www.dakotaelectric.com/residential/programs/

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

bates 

Dakota Electric Service MN $30 13 https://www.dakotaelectric.com/residential/programs/

bates 

Dakota Electric Service MN $180 14 https://www.dakotaelectric.com/residential/programs/

bates 

Dakota Electric Service MN $280 15 https://www.dakotaelectric.com/residential/programs/

bates 

Dakota Electric Service MN $330 16 https://www.dakotaelectric.com/residential/programs/

bates 

East Central Energy MN $30 13 http://www.eastcentralenergy.com/rebates.aspx 

East Central Energy MN $180 14 http://www.eastcentralenergy.com/rebates.aspx 

East Central Energy MN $280 15 http://www.eastcentralenergy.com/rebates.aspx 

East Central Energy MN $330 16 http://www.eastcentralenergy.com/rebates.aspx 

Elk River Municipal Utilities MN $150/ton 14 http://www.elkriverutilities.com/cipstar.php 

Fairmont Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

Grand Marais PUC MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

Lake City Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20reb

Litchfield Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20reb

Marshall Municipal Utilities MN $150/ton 14 http://www.marshallutilities.com/rebates/Central_AC_Minnesota Power MN $500 16 9 http://www.mnpower.com/powerofone/one_home/hva

Minnesota Power MN $325 http://www.mnpower.com/powerofone/one_home/hva

Minnesota Valley Electric Cooperative MN $500 13 http://www.mvec.net/save/rebates.asp#energystarashp Mora Municipal Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

New Prague Utilities Commission MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

North Branch Municipal Water & Light MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sumOtter Tail Power Company MN $20/1000 heating BTU 14.5 8.2 12 http://www.otpco.com/SaveEnergyMoney/heatPumpROtter Tail Power Company MN $20/1000 heating BTU 14 8 11 http://www.otpco.com/SaveEnergyMoney/heatPumpR

Owatonna Public Utilities MN $200 14 8.2 http://www.owatonnautilities.com/documents/Residen

Owatonna Public Utilities MN $200 14 8 http://www.owatonnautilities.com/documents/Residen

Princeton PUC MN $200 14 http://www.smmpa.org/Upload/Res%20Rebate%20SuRedwood Falls Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

Rochester Public Utilities MN $200 14 8.2 http://www.rpu.org/pdfs/2009resrebateapp.pdf Rochester Public Utilities MN $200 14 8 http://www.rpu.org/pdfs/2009resrebateapp.pdf Shakopee Public Utilities MN $200 14 http://www.shakopeeutilities.com/resrebates.htm 

Saint Peter Municipal Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

Spring Valley Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

Waseca Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

Wells Public Utilities MN $200 14 http://www.smmpa.org/Upload/2009%20RES%20sum

Wild Rice Electric MN $200 15 http://www.wildriceelectric.com/images/Rebate%20A

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Wright-Hennepin Cooperative Electric Association MN $30 13 http://www.whe.org/WH_Electric/Rebates/index.html Wright-Hennepin Cooperative Electric Association MN $180 14 http://www.whe.org/WH_Electric/Rebates/index.html Wright-Hennepin Cooperative Electric Association MN $280 15 http://www.whe.org/WH_Electric/Rebates/index.html Wright-Hennepin Cooperative Electric Association MN $330 16 http://www.whe.org/WH_Electric/Rebates/index.html Xcel Energy MN $180 14 http://www.xcelenergy.com/Residential/Programs_Re

HeatingandCoolingRebates.aspx 

Xcel Energy MN $280 15 http://www.xcelenergy.com/Residential/Programs_Re

HeatingandCoolingRebates.aspx 

Xcel Energy MN $330 16 http://www.xcelenergy.com/Residential/Programs_Re

HeatingandCoolingRebates.aspx 

Coast Electric Power Association MS $300 13 http://www.coastepa.com/documents/CArebateincentiPearl River Valley Electric Power Association MS $150 13 http://www.prvepa.com/comfadv.html Pearl River Valley Electric Power Association MS $150 15 http://www.prvepa.com/comfadv.html Pearl River Valley Electric Power Association MS $200 http://www.prvepa.com/comfadv.html 

City Utilities of Springfield Missouri MO $100/ton 14 http://www.cityutilities.net/conserve/rebates.htm#AirS

Columbia Water & Light MO $100 14 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $200 15 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $300 16 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $400 17 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $500 18 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $500 19 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $600 20 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $100 14 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $300 15 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $500 16 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $600 17 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $700 18 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $800 19 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $900 20 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $200 14 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $400 15 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $600 16 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $800 17 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $1,000 18 http://www.gocolumbiamo.com/WaterandLight/Docu

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Columbia Water & Light MO $1,100 19 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $1,200 20 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $300 14 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $600 15 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $800 16 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $1,000 17 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $1,200 18 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $1,400 19 http://www.gocolumbiamo.com/WaterandLight/Docu

Columbia Water & Light MO $1,600 20 http://www.gocolumbiamo.com/WaterandLight/Docu

Co-Mo Electric Cooperative MO $200/ton 14 http://www.co-mo.coop/Website%20Rebate%20Appl

Co-Mo Electric Cooperative MO $150/ton http://www.co-mo.coop/Website%20Rebate%20ApplEmpire District Electric MO $400 15 https://www.empiredistrict.com/Dochandler.ashx?id=

Empire District Electric MO $450 16 https://www.empiredistrict.com/Dochandler.ashx?id=

Empire District Electric MO $500 17 https://www.empiredistrict.com/Dochandler.ashx?id=

Independence Power & Light MO $255 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $327 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $399 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $472 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $544 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $598 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $298 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $383 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $468 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $553 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $638 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Independence Power & Light MO $698 14 7 http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_

Intercounty Electric Cooperative MO $150/ton 14 http://www.ieca.coop/PROGRAMS/REBATES/tabid/Kansas City Power and Light MO $650 14 http://www.kcpl.com/coolhomes/customers.htm 

Kansas City Power and Light MO $650 15 http://www.kcpl.com/coolhomes/customers.htm 

Kansas City Power and Light MO $850 16 http://www.kcpl.com/coolhomes/customers.htm 

Kirkwood Electric MO $300 9.5 http://www.ci.kirkwood.mo.us/electric/rebateprogramOzark Border Electric Cooperative MO $150/ton 10 http://www.ozarkborder.org/rebates.html 

Platte-Clay Electric Cooperative MO $150/ton 14.5 http://www.pcec.coop/Energy-Products/Heat-Pump-S

Black Hills Power MT $150/ton 13 http://www.blackhillspower.com/rebprog.htm 

Flathead Electric MT $1,000 14 8.5 http://www.flatheadelectric.com/energy/ASHPumpInf

Nebraska Public Power District NE $200 14 8.2 http://www.nppd.com/Energy_Efficiency/EnergyWiseNebraska Public Power District NE $250 15 8.2 http://www.nppd.com/Energy_Efficiency/EnergyWiseNebraska Public Power District NE $300 16 8.2 http://www.nppd.com/Energy_Efficiency/EnergyWiseOmaha Public Power District NE $200 13 http://www.oppd.com/ResidentialCustomers/FAQs/22Omaha Public Power District NE $200 14 http://www.oppd.com/ResidentialCustomers/FAQs/22

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Omaha Public Power District NE $250 15 http://www.oppd.com/ResidentialCustomers/FAQs/22

Omaha Public Power District NE $300 16 http://www.oppd.com/ResidentialCustomers/FAQs/22

Omaha Public Power District NE $50 http://www.oppd.com/ResidentialCustomers/FAQs/22New Hampshire Electric Co-Op NH $2,000 16 http://www.nhec.com/residential_residentialheatpumpNew Hampshire Electric Co-Op NH $2,000 16 http://www.nhec.com/residential_residentialheatpumpNew Hampshire Electric Co-Op NH $1,000 16 http://www.nhec.com/residential_residentialheatpumpCOOLAdvantage NJ $100 14.5 8.5 12 http://www.njcleanenergy.com/files/file/Residential%

CContractorLetter.pdf 

COOLAdvantage NJ $150 15 8.5 12.5 http://www.njcleanenergy.com/files/file/Residential%

CContractorLetter.pdf 

Kit Carson Electric Cooperative NM $924 to $3009 http://www.kitcarson.com/dialup/products.html 

City of New Bern Electric Department NC $400 14 http://www.newbern-nc.org/Elec/ad_energy_rbt.php City of Statesville Electric Utility Department NC $400 10 http://www.ci.statesville.nc.us/index.php/content/?cgi

bin=0&session_id=c9d782b865c54ab96ef46c4b69020

Piedmont Electric Membership Corporation NC $50/ton 13 

Progress Energy Carolinas' Home Advantage Program NC $300 15 http://www.progress-energy.com/custservice/carres/efBoulder City NV $70/ton 14 http://www.bcnv.org/Conservation/Rebates.asp Boulder City NV $125/ton 15 http://www.bcnv.org/Conservation/Rebates.asp 

Long Island Power Authority NY $250 14.5 8.2 12 http://www.lipower.org/pdfs/cei/rebate-coolhomes-am

Long Island Power Authority NY $400 15 8.5 12.5 http://www.lipower.org/pdfs/cei/rebate-coolhomes-amLong Island Power Authority NY $600 16 8.5 13 http://www.lipower.org/pdfs/cei/rebate-coolhomes-am

Butler Rural Electric Cooperative, Inc. OH $600 http://www.butlerrural.coop/index.php?module=pagem

E_id=50&53161e6ea2cc6f24e637a1819ab85c3c=420

Consolidated Electric Cooperative OH $600 http://www.consolidatedelectric.coop/pdf/rebateDualFDuke Energy OH $200 14 http://www.duke-energy.com/ohio/savings/smart-save

Duke Energy OH $300 14 http://www.duke-energy.com/ohio/savings/smart-save

Paulding-Putnam Electric Cooperative OH $300 http://www.ppec.coop/rebate/Air.html Paulding-Putnam Electric Cooperative OH $50 http://www.ppec.coop/rebate/Air.html The Energy Cooperative OH $599 http://www.theenergycoop.com/documents/TEC_Dua

Edmond Electric OK $250/ton 15 http://edmondok.com/docs/utility_customer_services/

Indian Electric OK $150/ton 14, 15 http://www.iecok.com/pdf/february_lamp_2009.pdf 

City of Ashland Conservation District OR $500 14 8.5 http://www.ashland.or.us/Page.asp?NavID=1366 

Central Electric Cooperative OR $800 http://www.cec-co.com/custserv/energy_info/brochureCentral Electric Cooperative OR $400 http://www.cec-co.com/custserv/energy_info/brochureCentral Electric Cooperative OR $600 http://www.cec-co.com/custserv/energy_info/brochureCentral Electric Cooperative OR $200 http://www.cec-co.com/custserv/energy_info/brochure

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Columbia River PUD OR $1,300 14 8.5 http://www.crpud.net/residential/efficiency/pdf/HeatP

Columbia River PUD OR $750 13 7.7 http://www.crpud.net/residential/efficiency/pdf/HeatP

Columbia River PUD OR $400 13 7.7 http://www.crpud.net/residential/efficiency/pdf/HeatP

Columbia River PUD OR $300 13 7.7 http://www.crpud.net/residential/efficiency/pdf/HeatP

Consumers Power, Inc. OR $400 http://www.cpi.coop/rebates/heatpump.php 

Consumers Power, Inc. OR $500 http://www.cpi.coop/rebates/heatpump.php 

Consumers Power, Inc. OR $300 http://www.cpi.coop/rebates/heatpump.php 

Consumers Power, Inc. OR $300 http://www.cpi.coop/rebates/heatpump.php 

Consumers Power, Inc. OR $750 http://www.cpi.coop/rebates/heatpump.php 

Consumers Power, Inc. OR $850 http://www.cpi.coop/rebates/heatpump.php 

Energy Trust of Oregon OR $300 14 8.2 11.5 http://www.energytrust.org/residential/existinghomes/

Energy Trust of Oregon OR $400 8.5 12 http://www.energytrust.org/residential/existinghomes/

Energy Trust of Oregon OR $150 14 8.2 11.5 http://www.energytrust.org/residential/existinghomes/

Energy Trust of Oregon OR $200 8.5 12 http://www.energytrust.org/residential/existinghomes/

Emerald People's Utility District OR $1,500 http://www.epud.org/documents/DuctlessHeatPumpPi

Eugene Water and Electric Board OR $1,000 http://www.eweb.org/content.aspx/20f5adf5-842f-4c2

Forest Grove Light & Power OR $935 14 8.5 http://www.forestgrove-or.gov/images/stories/servicesForest Grove Light & Power OR $480 14 8.5 http://www.forestgrove-or.gov/images/stories/servicesForest Grove Light & Power OR $1,500 http://www.forestgrove-or.gov/city-services/light-&-p

program.html 

Idaho Power OR $200 8.2 http://www.idahopower.com/EnergyEfficiency/Reside

ives.cfm 

Idaho Power OR $250 8.5 http://www.idahopower.com/EnergyEfficiency/Reside

ives.cfm 

Idaho Power OR $300 8.2 http://www.idahopower.com/EnergyEfficiency/Reside

ives.cfm 

Idaho Power OR $400 8.5 http://www.idahopower.com/EnergyEfficiency/Reside

ives.cfm 

Lane Electric Cooperative OR $1,000 14 8.5 http://www.laneelectric.com/heatpump.htm 

Midstate Electric OR $1,400 14 8.5 http://www.midstateelectric.coop/ProductsAndService

heatpumps/Default.aspx#HP%20Rebates 

Midstate Electric OR $1,400 14 9.5 http://www.midstateelectric.coop/ProductsAndService

heatpumps/Default.aspx#HP%20Rebates 

Oregon Trail Electric Cooperative OR $1,500 http://www.otec.coop/member_services/energy_conse

Oregon Trail Electric Cooperative OR $500 http://www.otec.coop/member_services/energy_conse

Portland General Electric OR $200 13 7.7 http://www.portlandgeneral.com/home/energy_saving

ult.aspx 

Salem Electric OR $750 8.5 http://www.salemelectric.com/PDFs/heatpumpbrochuSalem Electric OR $500 8.5 http://www.salemelectric.com/PDFs/heatpumpbrochuTillamook County PUD OR $500 13 8.5 http://www.tpud.org/nrg_heatpump.html Tillamook County PUD OR $2,000 14 8.5 http://www.tpud.org/nrg_heatpump.html 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Keystone HELP PA $250 E* > E* http://www.keystonehelp.com/info/documents/KeystoKeystone HELP PA $500 E* > E* http://www.keystonehelp.com/info/documents/KeystoBlack Hills Power SD $150/ton 13 http://www.blackhillspower.com/rebprog.htm 

Middle Tennessee Electric Membership Corporation TN $100 13 7.7 http://www.mtemc.com/pages.cfm/name/r09_heatinga

Upper Cumberland Electric Membership Corporation TN $150 http://www.ucemc.com/services.htm#rebates 

Winchester Utilities TN $150 12 http://www.winchesterutilities.com/Services_files/Ene

.PDF 

Winchester Utilities TN $150 11 http://www.winchesterutilities.com/Services_files/Ene

.PDF 

Austin Energy TX $240 14 11.5 http://www.austinenergy.com/Energy%20Efficiency/P

Family%20Properties/rebateLevels.htm 

Austin Energy TX $540 15 12.5 http://www.austinenergy.com/Energy%20Efficiency/P

Family%20Properties/rebateLevels.htm 

Austin Energy TX $240 14 11.5 http://www.austinenergy.com/Energy%20Efficiency/P

itioner/rebateLevels.htm 

Austin Energy TX $360 14 12 http://www.austinenergy.com/Energy%20Efficiency/P

itioner/rebateLevels.htm 

Austin Energy TX $540 15 12.5 http://www.austinenergy.com/Energy%20Efficiency/P

itioner/rebateLevels.htm 

Austin Energy TX $600 16 13 http://www.austinenergy.com/Energy%20Efficiency/P

itioner/rebateLevels.htm 

Austin Energy TX $360 14 11 http://www.austinenergy.com/Energy%20Efficiency/P

itioner/rebateLevels.htm 

Austin Energy TX $540 > 14 12 http://www.austinenergy.com/Energy%20Efficiency/P

itioner/rebateLevels.htm 

College Station Utilities TX $200 14 http://www.cstx.gov/docs/energy_back_ii_application

College Station Utilities TX $400 15 http://www.cstx.gov/docs/energy_back_ii_application

College Station Utilities TX $500 16 http://www.cstx.gov/docs/energy_back_ii_application

College Station Utilities TX $600 18 http://www.cstx.gov/docs/energy_back_ii_application

Denton Municipal Electric TX $700 16 http://dmepower.com/userfiles/file/GreenSense%20ReDenton Municipal Electric TX $700 16 http://dmepower.com/userfiles/file/GreenSense%20ReFarmers Electric Cooperative TX $150 14 http://www.fecelectric.com/fecinternet2005/PDF/Reb

Garland Power and Light TX $500 15 

http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20He

08.pdf 

Garland Power and Light TX $600 16 

http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20He08.pdf 

Garland Power and Light TX $700 17 

http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20He08.pdf 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Guadalupe Valley Electric Cooperative TX $500 15 http://www.gvec.org/safety_con/GVEC%20HOME%

ND%20APPLICATIONS.pdf 

Guadalupe Valley Electric Cooperative TX $600 16 http://www.gvec.org/safety_con/GVEC%20HOME%

ND%20APPLICATIONS.pdf 

Guadalupe Valley Electric Cooperative TX $700 17 http://www.gvec.org/safety_con/GVEC%20HOME%

ND%20APPLICATIONS.pdf 

Magic Valley Electric Cooperative TX $250 13 http://www.magicvalley.coop/Electric-Service/Reside

Magic Valley Electric Cooperative TX $300 14 http://www.magicvalley.coop/Electric-Service/Reside

Magic Valley Electric Cooperative TX $350 15 http://www.magicvalley.coop/Electric-Service/Reside

Pedernales Electric Cooperative TX $350 14 http://www.pec.coop/Documents/HVACRebate.pdf 

Pedernales Electric Cooperative TX $1,000 14 http://www.pec.coop/Documents/HVACRebate.pdf 

United Cooperative Services TX $50/ton 15 http://www.united-cs.com/rebates.htm United Cooperative Services TX $100/ton 16 http://www.united-cs.com/rebates.htm United Cooperative Services TX $150/ton 17 http://www.united-cs.com/rebates.htm United Cooperative Services TX $25/ton http://www.united-cs.com/rebates.htm United Cooperative Services TX $200 http://www.united-cs.com/rebates.htm 

Avista Utilities WA $400 8.5 https://www.avistautilities.com/savings/rebates/Pages/

x 

Avista Utilities WA $400 13 7.7 https://www.avistautilities.com/savings/rebates/Pages/

x 

Avista Utilities WA $1,000 https://www.avistautilities.com/savings/rebates/Pages/

x 

Avista Utilities WA $1,500 https://www.avistautilities.com/savings/rebates/Pages/

x 

Clallam County PUD WA $470-$1500 14 8.5 http://www.clallampud.net/conservation/res_HighEffi

Clallam County PUD WA $470-$915 14 8.5 http://www.clallampud.net/conservation/res_HighEffi

Clark Public Utilities WA $1,500 http://www.clarkpublicutilities.com/yourhome/heating

P 

Clark Public Utilities WA $750 14 8.5 http://www.clarkpublicutilities.com/yourhome/heating

Cowlitz County PUD WA $900 14 8.5 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf 

Cowlitz County PUD WA $900 13.5 8.6 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf Cowlitz County PUD WA $900 13 8.7 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf Cowlitz County PUD WA $450 14 8.5 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf 

Cowlitz County PUD WA $450 13.5 8.6 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf Cowlitz County PUD WA $450 13 8.7 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf Cowlitz County PUD WA $1,800 14 8.5 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf Cowlitz County PUD WA $1,800 13.5 8.6 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf Cowlitz County PUD WA $1,800 13 8.7 http://www.cowlitzpud.org/pdf/H2AdvSum.pdf Franklin County PUD WA $400 14 8.5 http://www.franklinpud.com/html/res_rebates.html Franklin County PUD WA $800 14 8.5 http://www.franklinpud.com/html/res_rebates.html Franklin County PUD WA $1,000 http://www.franklinpud.com/html/res_rebates.html 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Franklin County PUD WA $850 http://www.franklinpud.com/html/res_rebates.html 

Franklin County PUD WA $1,500 http://www.franklinpud.com/html/res_rebates.html 

Grays Harbor PUD WA $1,500 https://www.ghpud.org/index.php?option=com_docm

Grays Harbor PUD WA $1,000 https://www.ghpud.org/index.php?option=com_docm

Grays Harbor PUD WA $1,500 https://www.ghpud.org/index.php?option=com_docm

Inland Power & Light Company WA $1,400 14 8.5 http://www.inlandpower.com/customer_carec.php?id=

Inland Power & Light Company WA $650 14 8.5 http://www.inlandpower.com/customer_carec.php?id=Inland Power & Light Company WA $650 14 8.5 http://www.inlandpower.com/customer_carec.php?id=Inland Power & Light Company WA $1,500 14 8.5 http://www.inlandpower.com/customer_carec.php?id=

Mason County PUD 3 WA $915 14 8.5 http://www.masonpud3.org/powerSupply/applianceRe

Modern Electric Water Company WA $250 http://www.mewco.com/PDFs/Rebate%20Flyer.pdf Orcas Power and Light Cooperative WA $750 14 8.5 http://www.opalco.com/ 

Port Angeles Public Works and Utilities WA $470 14 8.5 https://www.cityofpa.us/PDFs/Conservation/2007/HP

Port Angeles Public Works and Utilities WA $915 14 8.5 https://www.cityofpa.us/PDFs/Conservation/2007/HP

Port Angeles Public Works and Utilities WA $300 13 8 https://www.cityofpa.us/PDFs/Conservation/2007/HP

Port Angeles Public Works and Utilities WA $850 13 8 https://www.cityofpa.us/PDFs/Conservation/2007/HP

Puget Sound Energy WA $1,200 http://www.pse.com/solutions/foryourhome/pages/reb

Puget Sound Energy WA $200 14 8.5 http://www.pse.com/solutions/foryourhome/pages/reb

Puget Sound Energy WA $350 14 9 http://www.pse.com/solutions/foryourhome/pages/reb

Snohomish County PUD No 1 WA 50% of job cost up to $450 http://www.snopud.com/energy/home/econpgms.ashx

Snohomish County PUD No 1 WA $1,200 http://www.snopud.com/energy/home/econpgms.ashx

Tacoma Power WA $400 14 8.5 http://www.mytpu.org/customer-service/your/rebates-

Vera Irrigation District #15 WA $400 http://www.verawaterandpower.com/Energy_Rebates

Cedarburg Light & Water WI $100 15 http://www.focusonenergy.com/files/Document_Mana

cientheatingcoolingcashback_rewardform.pdf 

Cedarburg Light & Water WI $100 15 http://www.focusonenergy.com/files/Document_Mana

cientheatingcoolingcashback_rewardform.pdf 

Eau Claire Energy Cooperative WI $120/ton 14 http://www.ecec.com/programs/incentives/index.cfm Focus on Energy WI $100 15 http://www.focusonenergy.com/files/Document_Mana

cientheatingcoolingcashback_rewardform.pdf 

Focus on Energy WI $100 15 http://www.focusonenergy.com/files/Document_Mana

cientheatingcoolingcashback_rewardform.pdf 

Riverland Energy Cooperative WI $60/ton 14 http://riverlandenergy.com/RIVERLAND%20HOME

Pierce Pepin Cooperative Services WI $120/ton 14 http://www.piercepepin.com/nrgsense/rebate_heatpum

Black Hills Power WY $150/ton 13 http://www.blackhillspower.com/rebprog.htm 

Carbon Power and Light WY $125/ton http://www.carbonpower.com/rebates.htm 

EERE-2008-BT-STD-0006 COMMENT 54
Utility or Agency State Amount SEER HSPF EER Website 

Carbon Power and Light WY $150/ton http://www.carbonpower.com/rebates.htm 

Rocky Mountain Power WY $250 8.2 http://www.homeenergysavings.net/wyoming/heatpum

Rocky Mountain Power WY $350 8.2 http://www.homeenergysavings.net/wyoming/heatpum

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 6A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS
 

TABLE OF CONTENTS
 

6A.1 INTRODUCTION ........................................................................................................... 6A-1
 

6A.2 DETAILED WHOLESALER COST DATA .................................................................. 6A-1
 

6A.3 DETAILED MECHANICAL CONTRACTOR DATA.................................................. 6A-2
 

6A.4 DETAILED GENERAL CONTRACTOR COST DATA............................................... 6A-3
 

6A.5 ESTIMATION OF WHOLESALER AND CONTRACTOR MARK-UP STANDARD 

DEVIATIONS .............................................................................................................. 6A-4
 

LIST OF TABLES 

Table 6A.2.1 Disaggregated Costs and Expenses for Wholesalers ..................................... 6A-1 

Table 6A.3.1 Mechanical Contractor Expenses and Markups Used To Scale the 

Incremental Markups ..................................................................................... 6A-3
 

Table 6A.4.1 General Contractor Expenses and Markups................................................... 6A-4
 

Table 6A.5.1 Wholesaler Incremental Markup Standard Deviation.................................... 6A-5
 

Table 6A.5.2 Mechanical Contractor Baseline Mark-ups by State, 2002 ........................... 6A-5
 

Table 6A.5.3 Residential General Contractor Baseline Mark-ups by State, 2002 .............. 6A-7
 

Table 6A.5.4 Commercial General Contractor Baseline Mark-ups by State, 2002............. 6A-8
 

6A-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

APPENDIX 6A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS 

6A.1 INTRODUCTION 

This appendix provides further details on information presented in Chapter 6, markups 

for equipment price determination. 

6A.2 DETAILED WHOLESALER COST DATA 

Based on data provided in the 2008 Profit Report (2007 Data) of the Heating Airconditioning & Refrigeration Distributors International (HARDI), Table 6.4.1 of Chapter 6, 

section 6.4, Estimation of Wholesale Mark-ups shows wholesaler revenues and costs in 

aggregated form. Table 6A.2.1 below provides the complete breakdown of costs and expenses as 

provided in the 2008 Profit Report (2007 Data). The column labeled “Scaling” in Table 6A.2.1 

indicates which expenses the U.S. Department of Energy (DOE) assumed to scale with only the 

baseline markup and which it scaled with both the baseline and incremental markups. As 

described in Chapter 6, section 6.4, only those expenses that scale with baseline and incremental 

costs are marked-up when there is an incremental change in equipment costs. 

Table 6A.2.1 Disaggregated Costs and Expenses for Wholesalers 

Item 

Percent of Revenue 

% Scaling 

Cost of Goods Sold 73.0 

Gross Margin 27.0 

Payroll Expenses 15.6 Baseline 

Executive Salaries & Bonuses 1.9 

Branch Manager Salaries and Commissions 1.3 

Sales Executive Salaries & Commissions 0.5 

Outside Sales Salaries & Commissions 2.3 

Inside/Counter Sales/Wages 2.6 

Purchasing Salaries/Wages 0.4 

Credit Salaries/Wages 0.2 

IT Salaries/Wages 0.2 

Warehouse Salaries/Wages 1.7 

Delivery Salaries/Wages 0.7 

All Other Salaries/Wages & Bonuses 1.2 

Payroll Taxes 1.1 

Group Insurance 1.1 

Benefit Plans 0.4 

Occupancy Expenses 3.1 Baseline 

Utilities: Heat, Light, Power, Water 0.4 

Telephone 0.3 

Building Repairs & Maintenance 0.2 

Rent or Ownership in Real Estate 2.2 

6A-1 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

Table 6A.2.1 (contd) 

Item 

Percent of Revenue 

% Scaling 

Other Operating Expenses 5.2 Baseline & Incremental 

Advertising & Promotion 0.5 

Insurance (business liability & casualty) 0.2 

Depreciation 0.4 

Vehicle Expenses 1.1 

Personal Property Taxes/Licenses 0.1 

Bad Debt Losses 0.3 

All Other Operating Expenses 2.6 

Total Operating Expenses 23.9 

Operating Profit 3.1 Baseline & Incremental 

Other Income 0.5 

Interest Expense 0.9 

Other Non-operating Expenses 0.0 

Profit Before Taxes 2.7 

Source: Heating, Air-conditioning & Refrigeration Distributors International. 2008. 2008 Profit Report (2007 Data).
 

Note: The wholesaler costs and expenses are percentage values as opposed to the per-dollar of sales revenue values shown in Table 6.4.1.
 

6A.3 DETAILED MECHANICAL CONTRACTOR DATA 

Based on data in the 2005 edition of Financial Analysis for the HVACR Contracting 

Industry, published by the Air Conditioning Contractors of America (ACCA), Tables 6.6.1, 

6.6.2, and 6.6.3 of Chapter 6, section 6.6, Estimation of Mechanical Contractor Markups provide 

mechanical contractor revenues and costs in aggregated form by ‘Cost of Goods Sold’ and 

‘Gross Margin.’ The Financial Analysis report did not provide a more disaggregate tabulation of 

these costs and expenses. As in section 6A.2, the gross margin category was assumed to scale 

only with the baseline markup. 

The more disaggregated breakdown of costs used to scale the incremental markup are 

shown in Table 6A.3.1 by both dollar value and percentage terms from the 2002 Census of 

Business. As the ACCA data were used to calculate the baseline markup, only the categories in 

the ‘Scaling’ column that scaled with both the baseline and incremental markups are marked. 

6A-2 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

Table 6A.3.1 Mechanical Contractor Expenses and Markups Used To Scale the 

Incremental Markups 

Item 

Dollar Value 

$ 

Percentage 

% Scaling 

Total Cost of Equipment Sales 77,482,735 66.01 

Cost of materials, components, and supplies 37,813,136 32.21 

Payroll, construction workers 25,619,932 21.83 

Cost of construction work subcontracted out to others 12,462,622 10.62 

Cost of selected power, fuels, and lubricants 1,587,045 1.35 

Gross Margin 39,898,412 33.99 

Payroll Expenses 19,648,990 16.74 

Fringe benefits, all employees 9,326,660 7.95 

Payroll, other employees 10,322,330 8.79 

Occupancy Expenses 

Rental cost for machinery, equipment, and buildings + 

Purchased communication services 

2,926,539 2.49 

Other Operating Expenses 2,969,716 2.53 Baseline & 

Depreciation charges during year Incremental 1,666,352 1.42 

Cost of repairs to buildings and other structures + Cost of 

repairs to machinery and equipment 

1,303,364 1.11 

Net Profit Before Income Taxes 14,353,167 12.23 Baseline & 

Incremental 

Source: U.S. Census Bureau. 2002. Plumbing, Heating, and Air-Conditioning Contractors: 2002. Report EC02-231-238220, Table 4. Detailed
 

Statistics for Establishments: 2002. 

Note: Mechanical contractor costs and expenses are first presented as total dollar values and than converted to percentage values. This is in
 

contrast to the per dollar of sales revenue values shown in Table 6.5.4. 

6A.4 DETAILED GENERAL CONTRACTOR COST DATA 

Based on U.S. Department of Census data, Table 6.5.1 of Chapter 6, section 6.5, General 

Contractor Expenses and Markups shows general contractor revenues and costs in aggregated 

form. Table 6A.4.1 below shows the complete breakdown of costs and expenses provided by the 

U.S. Department of Census. The column labeled “Scaling” in Table 6A.4.1 indicates which 

expenses DOE assumed to scale with only the baseline markup and which scaled with both the 

baseline and incremental markups. As described in Chapter 6, section 6.6, only those expenses 

that scale with baseline and incremental costs are marked-up when there is an incremental 

change in equipment costs. 

The fact that the mechanical contractor relative standard deviation is 3.7 percent of the 

baseline markup implies that the standard deviation for the replacement and new markets, 

relative to their respective baseline markups, is also 3.7 percent. 

DOE also assumed that wholesaler, general contractor, and mechanical contractor 

incremental markups have the same relative standard deviation as wholesaler, general contractor, 

and mechanical contractor baseline markups. It used this assumption to calculate the standard 

deviation of wholesaler, general contractor, and mechanical contractor incremental markups. 

6A-3 

EERE-2008-BT-STD-0006 COMMENT 54
 

Table 6A.4.1 General Contractor Expenses and Markups 

Item 

Dollar Value 

$ 

Percentage 

% Scaling 

Total Cost of Equipment Sales 187,494,802 77.54 Baseline 

Cost of materials, components, and supplies 36,592,170 15.13 

Payroll, construction workers 16,516,884 6.83 

Cost of construction work subcontracted out to others 132,835,992 54.94 

Cost of selected power, fuels, and lubricants 1,549,756 0.64 

Gross Margin 54,310,758 22.46 

Payroll Expenses 19,517,206 8.07 Baseline 

Fringe benefits, all employees 6,823,998 2.82 

Payroll, other employees 12,693,208 5.25 

Occupancy Expenses 

Rental cost for machinery, equipment, and buildings + 

Purchased communication services 

3,737,333 1.55 Baseline 

Other Operating Expenses 2,747,096 1.14 Baseline & 

Depreciation charges during year 1,318,065 0.55 Incremental 

Cost of repairs to buildings and other structures + Cost of 

repairs to machinery and equipment 

1,429,031 0.59 

Net Profit Before Income Taxes 28,309,123 11.71 Baseline & 

Incremental 

Source: U.S. U.S. Census Bureau. 2002. Commercial and Institutional Building Construction: 2002. Report EC02-231-236220, 

Table 4. Detailed Statistics for Establishments: 2002. 

Note: General contractor costs and expenses are first presented as total dollar values and than converted to percentage values.
 

This is in contrast to the per dollar of sales revenue values shown in Table 6.6.1. 

6A.5 ESTIMATION OF WHOLESALER AND CONTRACTOR MARK-UP 

STANDARD DEVIATIONS 

DOE used U.S. Census survey data to estimate CAC/CHP equipment wholesaler, general 

contractor, and mechanical contractor markup distributions. In the case of wholesalers, survey 

data were available only in summary form at the national level (Table 6A.5.1). In the case of 

mechanical and general contractors, the survey data include state-level heating, ventilating, and 

air-conditioning (HVAC) total value of work, number of firms, cost of goods sold, cost of 

subcontract work, cost of materials, and construction payroll as shown in Table 6A.5.2, 

Table 6A.5.3, and Table 6A.5.4. 

DOE estimated the size of the baseline markup distribution from national statistics and 

the standard deviation of across-state markups. DOE defined the relative standard deviation as 

the standard deviation as a percent of the baseline markup. The wholesale markup standard 

deviation is 5.7 percent, derived from the average markup and published relative markup of 

7.7 percent, as shown in Table 6A.5.1. The mechanical contractor markup standard deviation is 

5.6 percent and the relative markup is 3.7 percent, derived from the average of state-level 

markups and their standard deviation in Table 6A.5.2. The residential general contractor markup 

standard deviation is 9.3 percent and the relative standard deviation is 6.8 percent, derived from 

the average of state-level markups and their standard deviation in Table 6A.5.3. The commercial 

general contractor markup standard deviation is 9.5 percent and the relative standard deviation is 

7.5 percent, derived from the average of state-level markups and their standard deviation in 

Table 6A.5.4. 

6A-4 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

Table 6A.5.1 Wholesaler Incremental Markup Standard Deviation 

Number of Firms Sales 

($000) 

Cost of Goods Sold 

($000) 

Gross Margin 

($000) 

5180 17,715,524 

Cv for 4237 = 1.6% 

12,789,493 4,627,533 

Cv for 4237 = 7.7% 

Since Cv = σ / μ, σ = Cv / μ = 0.077 / 1.362 = 0.057 

Where: Cv = Coefficient of variation 

σ = Standard deviation 

μ = mean value 

Sources: U.S. Census Bureau. 2005. Sector 42: Wholesale Trade: Subject Series - Misc Subjects: Gross Margin and its Components for Merchant 

Wholesalers for the United States: 2002 @ http://factfinder.census.gov/servlet/IBQTable?_bm=y&-geo_id=&-_skip=0&-

ds_name=EC0242SXSB06&-_lang=en 

U.S. Census Bureau. 2005. Annual Report for Wholesale Trade: 2002-2003. Report BW(MSBO)/04-A. 

http://www.census.gov/svsd/www/msbo2003.pdf 

U.S. Bureau of Census. 2005. Table 4. Estimated Coefficients of Variation of Annual Sales, End-of-Year Inventories, Purchases, Gross Margins, 

and Gross Margin/Sales Ratios for Merchant Wholesalers, excluding Manufacturers' Sales Branches and Offices (MSBOs) @ 

http://www.census.gov/svsd/www/atstable4.txt 

Notes: The data are at the national level.
 

NAICS 4237 applies to Hrdwr, plumb & heat equip & supp merchant wholesalers and 42373 applies to Warm air heating & AC equip merchant
 

wholesalers.
 

Cv = coefficient of variation.
 

Table 6A.5.2 Mechanical Contractor Baseline Mark-ups by State, 2002 

State 

Number 

of Firms 

Value of 

Construction 

($000) 

Cost of 

Subcontract 

Work 

($000) 

Cost of 

Materials 

($000) 

Construction 

Payroll 

($000) 

Baseline 

Markup 

Relative 

Markup 

Alabama 1,425 1,490,919 151,240 547,075 308,967 1.480 0.981 

Alaska 229 384,009 68,230 110,136 73,456 1.525 1.010 

Arizona 1,510 1,958,430 141,885 776,733 420,945 1.462 0.969 

Arkansas 1,045 799,157 65,491 305,198 154,099 1.523 1.009 

California 7,272 12,346,970 1,172,183 4,018,408 2,701,227 1.565 1.037 

Colorado 2,015 2,354,183 235,012 821,432 547,419 1.468 0.973 

Connecticut 1,321 1,580,361 156,607 505,340 320,528 1.609 1.066 

Delaware 306 460,330 42,001 150,096 112,360 1.512 1.002 

District of 

Columbia 

22 117,971 D 29,263 21,379 1.490 0.987 

Florida 5,069 6,255,428 616,097 2,270,555 1,263,030 1.507 0.999 

Georgia 2,534 3,436,673 380,819 1,226,368 653,972 1.520 1.007 

Hawaii 280 358,921 36,053 129,870 65,305 1.552 1.029 

Idaho 594 546,349 47,466 211,341 133,160 1.394 0.924 

Illinois 3,848 6,112,025 605,868 1,770,254 1,504,166 1.575 1.044 

Indiana 1,867 2,964,787 418,904 869,816 753,388 1.452 0.962 

Iowa 1,066 1,189,684 133,934 428,842 265,529 1.436 0.952 

Kansas 966 1,225,986 155,011 431,331 264,876 1.440 0.954 

Kentucky 1,219 1,357,063 121,960 469,639 307,205 1.510 1.000 

Louisiana 1,469 1,439,726 174,283 497,169 291,819 1.495 0.990 

Maine 458 393,322 36,149 138,587 76,034 1.568 1.039 

Maryland 2,024 3,902,613 583,237 1,251,986 797,428 1.482 0.982 

Massachusetts 2,520 3,531,662 503,155 1,038,151 713,798 1.566 1.038 

Michigan 3,051 4,334,221 492,957 1,338,229 957,980 1.554 1.030 

Minnesota 1,635 2,860,054 280,556 958,849 599,935 1.555 1.030 

Mississippi 655 754,652 70,985 283,993 132,824 1.547 1.025 

6A-5 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

Table 6A.5.2 (contd) 

State 

Number 

of Firms 

Value of 

Construction 

($000) 

Cost of 

Subcontract 

Work 

($000) 

Cost of 

Materials 

($000) 

Construction 

Payroll 

($000) 

Baseline 

Markup 

Relative 

Markup 

Missouri 1,816 2,538,706 276,621 849,770 581,090 1.487 0.985 

Montana 432 356,269 44,987 135,009 68,332 1.435 0.951 

Nebraska 683 819,918 90,582 321,124 177,052 1.393 0.923 

Nevada 498 1,093,329 78,259 350,851 236,089 1.644 1.089 

New Hampshire 531 535,046 52,209 179,259 123,215 1.509 1.000 

New Jersey 3,551 4,210,275 466,668 1,230,617 949,465 1.591 1.054 

New Mexico 599 543,713 51,116 191,757 104,423 1.566 1.037 

New York 5,750 7,561,952 949,038 2,335,220 1,595,864 1.550 1.027 

North Carolina 2,978 3,231,394 339,388 1,127,518 684,958 1.502 0.995 

North Dakota 272 291,850 34,971 105,405 66,002 1.414 0.937 

Ohio 3,514 4,611,590 502,061 1,470,907 1,037,116 1.532 1.015 

Oklahoma 1,158 906,621 77,207 341,921 185,111 1.500 0.994 

Oregon 1,031 1,440,390 132,523 506,559 332,135 1.483 0.983 

Pennsylvania 3,653 5,929,629 639,074 1,766,004 1,193,465 1.648 1.092 

Rhode Island 397 490,872 60,217 171,994 99,426 1.480 0.981 

South Carolina 1,472 1,395,359 111,394 518,386 327,238 1.458 0.966 

South Dakota 337 333,758 22,754 141,297 58,708 1.498 0.993 

Tennessee 1,370 2,292,875 201,748 846,426 459,475 1.521 1.008 

Texas 5,653 8,061,607 807,501 2,889,320 1,746,739 1.481 0.981 

Utah 892 1,010,317 113,775 353,958 202,682 1.507 0.999 

Vermont 282 208,056 19,987 77,251 43,158 1.482 0.982 

Virginia 2,547 3,036,417 254,085 1,074,309 700,273 1.497 0.992 

Washington 1,602 2,344,257 222,282 773,211 543,668 1.523 1.009 

West Virginia 416 296,407 19,109 99,089 71,401 1.563 1.036 

Wisconsin 1,839 2,578,014 193,399 941,806 617,852 1.471 0.974 

Wyoming 262 166,318 D 59,507 35,307 1.451 0.962 

Average Baseline Markup 

Standard Deviation 

Relative Standard Deviation 

1.509 1.000 

0.056 

0.037 

Sources: U.S. Bureau of the Census. American Factfinder. 2002 Economic Census. Sector 23: Construction: Geographic Area Series: General 

Statistics for Establishments by State: 2002. Accessed on March 25, 2009 at http://factfinder.census.gov/servlet/IBQTable?_bm=y&-

fds_name=EC0200A1&-_industry=23822&-NAICS2002sector=8699097&-ib_type=NAICS2002&-_lang=en&-geo_id=&-

NAICS2002=23822&-ds_name=EC0223A1&-NAICS2002subsector=8701562 and 

Bureau of the Census. American Factfinder. 2002 Economic Census. Sector 23: Construction: Industry Series: General Statistics for 

Establishments by State: 2002. Accessed March 25, 2009 at http://factfinder.census.gov/servlet/IBQTable?_bm=y&-fds_name=EC0200A1&-

_industry=238220&-NAICS2002sector=8699097&-ib_type=NAICS2002&-_lang=en&-geo_id=&-NAICS2002=238220&-

ds_name=EC0223I03&-NAICS2002subsector=8701562 

Notes: The Census Bureau withheld data for some states. 

Markups may vary across states for several reasons, including differences in firm size.
 

6A-6 

EERE-2008-BT-STD-0006 COMMENT 54
Table 6A.5.3 Residential General Contractor Baseline Mark-ups by State, 2002 

State 

Number of 

Firms, 

Residential 

Value of 

Residential 

Construction 

($000) 

Cost of 

Subcontract 

Work 

($000) 

Cost of 

Materials 

($000) 

Construction 

Payroll 

($000) 

Baseline 

Markup 

Relative 

Markup 

Alabama 2,156 2,352,156 879,887 913,466 121,743 1.228 0.896 

Alaska 593 555,693 180,602 182,651 45,102 1.361 0.993 

Arizona 2,927 8,944,590 4,077,531 1,520,550 340,851 1.506 1.099 

Arkansas 1,102 965,784 330,411 399,722 64,603 1.215 0.887 

California 17,492 35,599,566 14,223,394 6,517,163 2,185,454 1.553 1.133 

Colorado 4,294 8,969,090 3,654,221 2,207,474 368,475 1.440 1.051 

Connecticut 2,033 2,376,761 836,239 795,422 175,075 1.315 0.960 

Delaware 668 954,727 324,103 258,374 55,222 1.497 1.093 

District of 

Columbia 

134 D D D 24,484 1.479 1.080 

Florida 8,816 25,237,559 9,614,388 6,421,377 904,292 1.490 1.087 

Georgia 4,811 9,930,327 3,671,913 3,454,040 374,997 1.324 0.966 

Hawaii 566 1,450,660 686,288 267,088 102,698 1.374 1.003 

Idaho 1,348 219,332 573,558 537,916 73,854 1.399 1.021 

Illinois 7,651 11,426,833 4,762,202 2,800,886 681,895 1.386 1.011 

Indiana 3,831 5,017,140 1,806,047 1,840,614 267,379 1.282 0.936 

Iowa 2,059 1,233,036 417,991 440,248 99,750 1.287 0.939 

Kansas 1,844 1,844,443 699,013 615,232 106,010 1.299 0.948 

Kentucky 1,809 2,785,820 1,025,346 964,139 117,856 1.322 0.965 

Louisiana 1,547 1,316,948 475,260 428,061 92,753 1.322 0.965 

Maine 1,344 833,920 278,251 269,031 81,018 1.327 0.969 

Maryland 3,998 6,532,241 2,343,107 1,725,383 423,633 1.454 1.061 

Massachusetts 3,529 4,338,533 1,630,030 1,230,025 313,075 1.367 0.998 

Michigan 7,134 7,210,856 2,488,963 2,332,357 501,934 1.355 0.989 

Minnesota 4,190 6,581,446 2,664,325 1,815,880 315,586 1.372 1.002 

Mississippi 892 D D D 39,835 1.251 0.913 

Missouri 3,880 4,060,799 1,414,323 1,244,589 298,295 1.373 1.002 

Montana 1,217 857,782 323,303 283,334 69,987 1.268 0.925 

Nebraska 1,228 1,233,763 523,773 337,636 76,779 1.315 0.960 

Nevada 1,050 4,671,330 1,890,254 689,856 204,723 1.677 1.224 

New Hampshire 978 1,014,569 347,603 285,545 82,571 1.418 1.035 

New Jersey 5,833 7,773,252 2,556,482 2,284,090 502,713 1.455 1.062 

New Mexico 1,495 1,410,525 524,152 389,342 85,732 1.412 1.030 

New York 10,007 9,608,316 3,878,184 2,639,624 761,915 1.320 0.963 

North Carolina 6,576 9,462,181 3,339,029 3,114,139 443,960 1.372 1.001 

North Dakota 406 248,629 82,158 88,810 22,954 1.282 0.936 

Ohio 6,183 7,955,878 3,054,947 2,348,619 440,867 1.361 0.993 

Oklahoma 1,502 1,454,942 451,357 593,842 77,547 1.296 0.946 

Oregon 3,484 2,981,961 1,116,078 854,412 178,699 1.387 1.013 

Pennsylvania 6,828 8,045,755 2,744,933 2,665,008 509,054 1.359 0.992 

Rhode Island 805 643,949 235,284 245,728 46,251 1.221 0.891 

South Carolina 2,615 3,490,308 1,222,905 1,263,978 178,744 1.309 0.956 

South Dakota 750 497,463 D 200,146 33,480 1.295 0.945 

Tennessee 2,219 3,173,248 985,320 1,039,114 252,650 1.394 1.017 

Texas 6,735 21,459,933 7,977,898 6,337,942 791,977 1.420 1.037 

6A-7 

EERE-2008-BT-STD-0006 COMMENT 54
 

Table 6A.5.3 (contd) 

State 

Number of 

Firms, 

Residential 

Value of 

Residential 

Construction 

($000) 

Cost of 

Subcontract 

Work 

($000) 

Cost of 

Materials 

($000) 

Construction 

Payroll 

($000) 

Baseline 

Markup 

Relative 

Markup 

Utah 1,768 2,251,069 898,273 677,157 114,364 1.332 0.972 

Vermont 888 599,311 184,049 166,305 52,650 1.487 1.085 

Virginia 5,424 9,467,694 2,996,475 2,786,061 509,630 1.505 1.098 

Washington 7,005 6,513,741 2,204,286 1,814,321 426,371 1.465 1.069 

West Virginia 1,473 563,776 146,451 188,680 59,677 1.428 1.042 

Wisconsin 4,003 4,087,572 1,513,293 1,396,311 312,214 1.269 0.926 

Wyoming 532 371,662 133,759 113,250 49,138 1.255 0.916 

Average Baseline Markup 1.370 1.000 

Standard Deviation 0.093 

Relative Standard Deviation 0.068 

Sources: U.S. Bureau of the Census, American Factfinder. 2002 Economic Census. Sector 23: Construction: Geographic Area Series: General 

Statistics for Establishments by State: 2002. accessed 3-25-2009 at http://factfinder.census.gov/servlet/IBQTable?_bm=y&-

fds_name=EC0200A1&-_industry=23611&-NAICS2002sector=8699097&-ib_type=NAICS2002&-_lang=en&-geo_id=&-

NAICS2002=23611&-ds_name=EC0223A1&-NAICS2002subsector=8701533 and 

U.S. Bureau of the Census. American Factfinder. 2002 Economic Census. Sector 23: Construction: Industry Series: General Statistics for 

Establishments by State: 2002. Accessed March 25, 2009 at http://factfinder.census.gov/servlet/IBQTable?_bm=y&-fds_name=EC0200A1&-

_industry=236115&-_industry=236116&-_industry=236117&-_industry=236118&-_industry=236220&-NAICS2002sector=8699097&-

ib_type=NAICS2002&-_lang=en&-geo_id=&-NAICS2002=236115|236116|236117|236118|236220&-ds_name=EC0223I03&-

NAICS2002subsector=8701533 

Notes: The Census Bureau withheld data for some states. 

Markups may vary across states for several reasons, including differences in firm size
 

Table 6A.5.4 Commercial General Contractor Baseline Mark-ups by State, 2002 

State 

Number of 

Firms, 

Commercial 

Value of 

Commercial 

Construction 

($000) 

Cost of 

Subcontract 

Work 

($000) 

Cost of 

Materials 

($000) 

Construction 

Payroll 

($000) 

Baseline 

Markup 

Relative 

Markup 

Alabama 633 4,087,391 2,224,073 648,488 257,100 1.306 1.026 

Alaska 179 1,013,830 513,186 232,958 102,651 1.194 0.938 

Arizona 647 4,793,249 2,824,778 554,056 238,438 1.325 1.041 

Arkansas 339 1,764,890 891,847 361,034 121,985 1.284 1.008 

California 4,179 27,905,058 16,477,915 3,569,406 1,859,722 1.274 1.000 

Colorado 929 6,466,717 3,835,598 1,022,903 351,770 1.241 0.975 

Connecticut 365 2,958,160 1,697,294 440,540 248,136 1.240 0.974 

Delaware 122 633,739 325,254 98,580 85,416 1.244 0.977 

District of 

Columbia 

47 641,796 425,080 32,556 38,424 1.294 1.016 

Florida 1,900 11,708,047 6,593,086 1,727,883 556,589 1.319 1.036 

Georgia 1,335 10,267,854 5,885,254 1,879,470 551,798 1.235 0.969 

Hawaii 169 1,483,121 739,526 250,037 134,938 1.319 1.036 

Idaho 290 909,164 516,919 142,880 67,147 1.251 0.982 

Illinois 1,493 10,232,559 5,296,152 1,432,918 772,060 1.364 1.071 

Indiana 665 4,732,683 2,541,628 755,937 363,268 1.293 1.015 

Iowa 512 2,124,574 1,142,078 490,254 188,518 1.167 0.916 

Kansas 341 1,947,757 1,026,208 410,497 135,895 1.239 0.973 

Kentucky 527 2,199,782 1,182,207 383,228 184,386 1.257 0.987 

6A-8 

EERE-2008-BT-STD-0006 COMMENT 54
 

Table 6A.5.4 (contd) 

State 

Number of 

Firms, 

Commercial 

Value of 

Commercial 

Construction 

($000) 

Cost of 

Subcontract 

Work

 ($000) 

Cost of 

Materials 

($000) 

Construction 

Payroll 

($000) 

Baseline 

Markup 

Relative 

Markup 

Louisiana 513 2,909,041 1,410,150 703,189 247,488 1.232 0.968 

Maine 185 584,457 330,848 91,490 39,088 1.267 0.995 

Maryland 872 6,675,194 3,829,059 766,415 422,357 1.330 1.045 

Massachusetts 786 8,075,207 5,233,003 1,114,320 430,337 1.191 0.936 

Michigan 1,237 6,501,437 3,372,219 1,009,192 436,199 1.350 1.060 

Minnesota 748 6,161,105 3,771,957 1,030,060 428,143 1.178 0.925 

Mississippi 297 2,017,236 925,348 438,336 216,914 1.276 1.002 

Missouri 834 7,193,405 4,625,554 1,062,202 519,994 1.159 0.910 

Montana 139 456,228 216,656 100,544 46,776 1.253 0.984 

Nebraska 285 1,361,395 737,300 257,308 106,558 1.236 0.971 

Nevada 350 2,488,460 1,352,898 500,324 185,380 1.221 0.959 

New Hampshire 153 1,123,795 709,159 168,953 75,195 1.179 0.926 

New Jersey 1,191 7,134,900 3,823,338 959,859 549,231 1.338 1.051 

New Mexico 265 1,111,735 585,306 189,063 100,363 1.271 0.998 

New York 2,147 16,092,933 7,732,253 2,214,809 1,103,423 1.456 1.144 

North Carolina 1,053 6,123,409 3,212,008 1,143,321 387,044 1.291 1.014 

North Dakota 164 346,507 151,554 94,777 39,360 1.213 0.952 

Ohio 1,354 6,956,588 3,743,681 1,226,315 600,282 1.249 0.981 

Oklahoma 440 2,242,093 1,261,310 438,198 124,791 1.229 0.965 

Oregon 614 2,998,924 1,762,863 577,844 338,286 1.119 0.879 

Pennsylvania 1,419 10,114,293 5,579,230 1,612,363 697,391 1.282 1.007 

Rhode Island 138 1,205,052 534,577 80,447 62,293 1.779 1.397 

South Carolina 525 2,298,379 1,049,437 521,385 197,114 1.300 1.021 

South Dakota 117 540,082 258,352 140,454 51,216 1.200 0.942 

Tennessee 763 5,003,780 2,657,587 745,164 325,486 1.342 1.054 

Texas 2,890 17,578,086 8,870,626 2,817,907 961,374 1.390 1.091 

Utah 432 2,742,884 1,428,086 589,679 162,060 1.258 0.988 

Vermont 100 376,473 199,521 56,407 41,172 1.267 0.995 

Virginia 851 6,287,509 3,781,530 928,059 402,428 1.230 0.966 

Washington 929 5,086,570 2,947,223 836,605 449,325 1.202 0.944 

West Virginia 200 419,620 155,537 110,001 58,024 1.297 1.018 

Wisconsin 595 4,867,473 2,460,336 1,030,059 479,144 1.226 0.963 

Wyoming 133 291,926 129,865 69,637 26,669 1.291 1.014 

Average Baseline Markup 

Standard Deviation 

Relative Standard Deviation 

1.273 1.000 

0.095 

0.075 

Sources: U.S. Bureau of the Census, American Factfinder. 2002 Economic Census. Sector 23: Construction: Geographic Area Series: General 

Statistics for Establishments by State: 2002. accessed 3-25-2009 at http://factfinder.census.gov/servlet/IBQTable?_bm=y&-

fds_name=EC0200A1&-_industry=23611&-NAICS2002sector=8699097&-ib_type=NAICS2002&-_lang=en&-geo_id=&-

NAICS2002=23611&-ds_name=EC0223A1&-NAICS2002subsector=8701533
 

Notes: The Census Bureau withheld data for some states. 

Markups may vary across states for several reasons, including differences in firm size
 

6A-9 

EERE-2008-BT-STD-0006 COMMENT 54
DOE assumed that new construction and replacement market mechanical contractor 

markups have the same relative standard deviation as that developed from the baseline markups 

for all contractors. The fact that the mechanical contractor relative standard deviation is 

3.7 percent of the baseline markup implies that the standard deviation for the replacement and 

new markets, relative to their respective baseline markups, is also 3.7 percent. 

DOE also assumed that wholesaler, general contractor, and mechanical contractor 

incremental markups have the same relative standard deviation as wholesaler, general contractor, 

and mechanical contractor baseline markups. It used this assumption to calculate the standard 

deviation of wholesaler, general contractor, and mechanical contractor incremental markups. 

The relative standard deviations for mechanical and general contractors are not used 

directly to estimate markups as for wholesale trade. The markups in construction vary 

systematically relative to their mean by state, so the state relative markups are applied 

individually to average markups by class of construction in each state. 

6A-10 

EERE-2008-BT-STD-0006 COMMENT 54
 

APPENDIX 7A. INTERNAL LOADS SCHEDULES AND PART-LOAD 

PERFORMANCE CURVES 

LIST OF TABLES 

Table 7A.1 Occupancy, Lighting and Plug Loads Schedules..................................... 7A-1 

Table 7A.2 CAC and CHP Performance Curves Used in Small Office Modeling..... 7A-2 

7A-i 

EERE-2008-BT-STD-0006 COMMENT 54
EERE-2008-BT-STD-0006 COMMENT 54
7A-1
 

APPENDIX 7A. INTERNAL LOADS SCHEDULES AND PART-LOAD PERFORMANCE C

Table 7A.1. Occupancy, Lighting and Plug Loads Schedules 

Schedule Day of 

Week 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 

Lighting (1.0 W/sf) 

Weekday 0.18 0.18 0.18 0.18 0.18 0.23 0.23 0.42 0.90 0.90 0.90 0.90 0.80 0.90 0.90 0.90 0.90 0.61 0.42 

Sat 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 

Sun, Hol, Other 

0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 

Plug Equipment (0.63 W/sf) 

Weekday 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.50 1.00 1.00 1.00 1.00 0.94 1.00 1.00 1.00 1.00 0.50 0.20 

Sat 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 

Sun, Hol, Other 

0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 

Occupancy Weekday 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.21 1.00 1.00 1.00 1.00 0.53 1.00 1.00 1.00 1.00 0.32 0.11 

Sat 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Sun, Hol, Other 

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Heating Setpoint 

Weekday 60.0 60.0 60.0 60.0 60.0 60.0 65.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 

Sat 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 

Sun, Hol, Other 

60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 

Cooling Setpoint 

Weekday, 85.0 85.0 85.0 85.0 85.0 85.0 80.0 75.0 75.0 75.0 75.0 75.0 75.0 75.0 75.0 75.0 75.0 75.0 75.0 

Sat 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 

Sun, Hol, Other 

85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 85.0 

Fan Operation and Ventilation 

Weekday, 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 

Sat 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Sun, Hol, Other 

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Infiltration Weekday, 1.00 1.00 1.00 1.00 1.00 1.00 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 

Sat, 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Sun, Hol, Other 

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Hot Water Weekday, 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.55 0.64 0.64 0.82 1.00 0.91 0.55 0.55 0.73 0.37 0.37 

Sat, 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Sun, Hol, Other 

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

EERE-2008-BT-STD-0006 COMMENT 54
Table 7A.2. CAC and CHP Performance Curves Used in Small Office Modeling 

Type Performance Curve 

Equation Curve Coefficients 

a b c d 

CAC Total Cooling Capacity Function of Temperature Curve 

EDBWBfEDBe

COOL EDBdWBcWBbaFTCAP

××+×+

×+×+×+=−−

2 

2 

1.186036 -0.06799 0.003167 -0.00902 

Total Cooling Capacity Function of Flow Fraction Curve 

FF 2 COOL cFFbaFFCAP ×+×+=−−

0.63751 0.409767 -0.12055 NA 

Energy Input Ratio Function of Temperature Curve 

EDBWBfEDBeEDBd

COOL WBcWBbaFTEIR

××+×+×+

×+×+=−−

2 

2 

-0.43891 0.145182 -0.00394 -0.02064 

Energy Input Ratio Function of Flow Fraction Curve 

FF 2 COOL cFFbaFFEIR ×+×+=−−

1.54819 -0.49832 0.204972 NA 

Cooling Part Load Fraction Correlation Curve 

PLR2 COOL cPLRbaPLREIR ×+×+=−−

0.771 0.229 0.0 NA 

PTHP Heating Capacity Function of Temperature Curve 

DB2 HEAT cDBbaFTCAP ×+×+=−−

0.691549 0.031304 0.000501 NA 

Heating Capacity Function of Flow Fraction Curve 

DB2 HEAT cDBbaFFCAP ×+×+=−−

1.014278 -0.16365 0.149368 NA 

Heating Energy Input Ratio Function of Temperature Curve 

2 3 HEAT ×+×+×+=−− DBcDBcDBbaFTEIR

0.829204 0.000697 0.000779 -0.00320 

Heating Energy Input Ratio Function of Flow Fraction Curve 

FF 2 HEAT cFFbaFFEIR ×+×+=−−

1.40347 -0.51017 0.106692 NA 

Heating Part Load Fraction Correlation Curve 

PLR2 HEAT cPLRbaFPLREIR ×+×+=−−

0.771 0.229 0.0 NA 

7A-2
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

Where: 

COOL − CAP − FT = Total Cooling Capacity Function of Temperature, 

COOL −CAP − FF = Total Cooling Capacity Function of Flow Fraction Curve, 

COOL − EIR − FT = Energy Input Ratio Function of Temperature, 

COOL − EIR − FF = Energy Input Ratio Function of Flow Fraction, 

COOL − EIR − FPLR = Total Cooling Part Load Fraction Correlation Curve Name, 

HEAT − CAP − FT = Heating Capacity Function of Temperature Curve Name, 

HEAT −CAP − FF = Heating Capacity Function of Flow Fraction Curve Name, 

HEAT − EIR − FT = Heating Energy Input Ratio Function of Temperature Curve Name, 

HEAT − EIR − FF = Heating Energy Input Ratio Function of Flow Fraction Curve 

Name, 

HEAT − EIR − FPLR = Heating Part Load Fraction Correlation Curve Name, 

WB = entering coil wet-bulb temperature (° F), 

DB = outside dry-bulb temperature (° F), 

EDB = entering coil dry-bulb temperature (° F), and 

PLR = Part Load Ratio (cooling load/steady-state capacity). 

Normalized performance curves for the CAC and CHP condensing units were developed 

based on manufacturers catalog data for Carrier CAC (50ES036) for cooling performance and 

Carrier CHP (50VT036) equipment for heating performance.1,2 Equations are in formats 

accepted for use in the EnergyPlus simulation tool input files (*. idf) files for each simulation 

run. Part load fraction correlation curves were based on commercial performance data used in the 

DOE Benchmark model for cooling part-load performance degradation. Nomenclature used for 

left hand side of each performance curve equation based on DOE2.1E descriptors for these 

parameters and is not normally found in the EnergyPlus documentation. 

1

 Carrier performance data for CAC. (Last accessed April 2009.) 

http://www.docs.hvacpartners.com/idc/groups/public/documents/techlit/50es-02pd.pdf

2

 Carrier performance data for CAC. (Last accessed April 2009.) 

http://www.docs.hvacpartners.com/idc/groups/public/documents/techlit/50vt-01pd.pdf 

7A-3 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

APPENDIX 7B. TYPICAL METEOROLOGICAL YEAR (TMY2) ASSIGNMENTS 

TABLE OF CONTENTS 

7B.1 Introduction ...................................................................................................................... 7B-1
 

7B.1.1 Purpose and Intent................................................................................................ 7B-1
 

7B.1.2 Issues .................................................................................................................... 7B-1
 

7B.2 Approach.......................................................................................................................... 7B-1
 

7B.2.1 Additional Data Sets ............................................................................................ 7B-1
 

7B.2.2 Mapping Approach .............................................................................................. 7B-4
 

7B.2.3 Calculating State-by-State Weighting Factors..................................................... 7B-7
 

LIST OF FIGURES 

Figure 7B.2.1. Locations of TMY2 Stations Superimposed on Heating Degree-Day Contours. 7B-2 

Figure 7B.2.2. Locations of 4775 NOAA Stations................................................................... 7B-3 

Figure 7B.2.3. Locations of Populated Places (PPL) ................................................................ 7B-3 

Figure 7B.2.4. PPL→NOAA Mappings for Iowa Showing a Predominance of Nearest-Location 

Mappings ............................................................................................................ 7B-5 

Figure 7B.2.5. PPL→NOAA Mappings for Washington Showing Some Distant Mappings in 

Mountainous Regions......................................................................................... 7B-5 

Figure 7B.2.6. NOAA→TMY2 Mappings ............................................................................... 7B-7 

 7B-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

APPENDIX 7B. TYPICAL METEOROLOGICAL YEAR (TMY2) ASSIGNMENTS 

7B.1 Introduction 

7B.1.1 Purpose and Intent 

This appendix describes the methodology by which weighting factors were developed to 

scale the influence of each TMY2 weather station on each state’s results. The intent is to ensure 

that the cost-effectiveness calculations properly reflect the geographic distribution of buildings in 

which the HVAC systems under analysis will be used. State-level aggregate results need to be 

most heavily influenced by the climates having the most buildings. 

7B.1.2 Issues 

TMY2 stations, of which there are 239 in the United States, are distributed throughout the 

country so as to give good coverage of the climatic variation in the United States. Unfortunately 

for this study, the TMY2 stations are not distributed so as to match the distribution of buildings. 

It is therefore necessary to identify a reasonable mapping between the climate-based (TMY2) 

simulation results and the geographical distribution of buildings in each state. Although TMY2 

data give good climatic coverage, they do not give sufficient geographical coverage. 

Detailed data on the geographical/climatic distribution of buildings were not readily 

available for this task. However, data on the geographical distribution of persons (population 

data) can serve as a reasonable surrogate for buildings data, and are available from a number of 

sources. The problem facing this study was to match up population data, which are available in 

great geographical detail but not matched to climate indicators, with TMY2 data, which 

characterize climate quite well but are not connected to any population indicators. 

7B.2 Approach 

Figure 7B.2.1 shows the locations of the 239 TMY2 stations superimposed on a contour 

map showing heating degree-days (HDD). It is clear that the geographic distribution of the 

TMY2 stations is too sparse to capture many of the climate variations that occur over relatively 

short distances, especially in mountainous regions. It is therefore not feasible to develop 

weighting factors based solely on the TMY2 stations within a state. 

7B.2.1 Additional Data Sets 

To develop weighting factors, we identified two data sets (in addition to the TMY2 data 

set) that provide a connection between detailed population distribution and TMY2 stations. 

These are: 

•	 NOAA Climate Stations – The National Climatic Data Center's (NCDC)'s “CLIM81” 

database contains summary statistics from a large number of climate stations in the United 

States. We used the 1961–1990 period of record (POR), which corresponds to the POR used 

to define the TMY2 weather tapes, for which 4775 climate stations are represented (see 

http://lwf.ncdc.noaa.gov/oa/documentlibrary/clim81supp3/clim81.html). Each NOAA station 

is characterized by its location (latitude/longitude), annual heating and cooling degree-days, 

elevation, and various other metrics. 

 7B-1 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

Figure 7B.2.1. Locations of TMY2 Stations Superimposed on Heating Degree-Day Contours 

•	 USGS Populated Places – The United States Geological Survey (USGS), in its Geographic 

Names Information System, maintains a detailed database of cities, towns, and other 

important features. (For the latest version of this data set, see 

http://geonames.usgs.gov/stategaz/00README.html. Our analysis used a version of this data 

set from the early 1990s, making it contemporaneous with the TMY2 and NOAA data sets.) 

Of interest to this task are the PPL features known as “populated places,” which are generally 

cities and towns, but also include large housing subdivisions, trailer parks, and other places 

where people may live. For many of these populated places, the USGS has a population 

estimate. The populated places (PPL) data give excellent geographical coverage. The version 

of the data set used here has over 22,000 entries that include a population estimate out of 

more than 164,000 total.1 Each PPL location is characterized by its location 

(latitude/longitude), elevation, population, and various other metrics. 

The NOAA data are important because they contain climate summary information that 

can be mapped to the TMY2 stations, greatly increasing our geographical coverage. 

Figure 7B.2.2 shows the locations of the 4775 NOAA stations. The PPL database is used to 

make the final link between the climate information in the mapped NOAA/TMY2 stations and 

the geographical distribution of population. Figure 7B.2.3 shows the PPL locations. 

1

 Locations without USGS population estimates tend to be those with very little population. We have kept these 

smaller sites in our mapping analysis by assigning each a population estimate of one. This gives them negligible 

influence against the more populous locations but allows the mapping procedure to work even in very sparsely 

populated regions of some states. 

 7B-2 

EERE-2008-BT-STD-0006 COMMENT 54
Figure 7B.2.2. Locations of 4775 NOAA Stations 

Figure 7B.2.3. Locations of Populated Places (PPL) 

 7B-3 

EERE-2008-BT-STD-0006 COMMENT 54
 

7B.2.2 Mapping Approach 

Our approach to generating TMY2 weighting factors involves three major steps: 

1.	 Map each PPL location in the United States to a best-representative NOAA station. This 

gives each location some summary climate metrics (chiefly, heating and cooling degreedays) that facilitate further mapping. 

2.	 Map each NOAA station to a best-representative TMY2 station. This completes the link 

between the geographic population estimates (PPL data) and the TMY2 stations. 

3.	 For each state, compute the fraction of the total PPL population that “points” (via its 

PPL→NOAA→TMY2 mappings) to each TMY2 station. 

These are described in order below. 

B.2.2.1Mapping Populated Places Locations to NOAA Stations 

Mapping each of the 164,000+ PPL locations to a best-representative NOAA station is a 

mostly straightforward process. Because there is no climate information in the PPL database, the 

only metrics available to associate each PPL location with a NOAA station are location 

(latitude/longitude) and elevation (although elevation is not known for all PPL locations). The 

mapping algorithm is as follows. 

1. For each PPL location, identify the nearest NOAA station. Distances between 

PPL/NOAA pairs are calculated using the latitudes and longitudes of the two locations 

and simple spherical geometry. If the elevation of the nearest NOAA station is within 300 

feet of the PPL location or if the elevation of the PPL location is unknown, then the 

nearest NOAA station is the final mapping.

 2. If the nearest NOAA station differs in elevation from the PPL location by more than 

300 feet: 

a)	 Identify the 20 closest NOAA stations to the PPL location. 

b) Choose, from among the 20, the NOAA station that is nearest in elevation to the PPL 

location. 

This algorithm is imperfect in many situations, but was designed by trial and error to give 

reasonable mappings in a large majority of cases. In locations with relatively flat terrain and 

fairly dense population, the algorithm almost always maps to the closest NOAA station. 

Figure 7B.2.4 shows the mappings in the state of Iowa as an example. Each plotted point on the 

graphic is one NOAA station; the “hairs” are drawn outward to the various PPL locations 

mapped to that station. In mountainous terrain or in locations with large distances between PPL 

locations (e.g., Alaska), the second part of the algorithm does a reasonable job of identifying a 

representative NOAA station, even if that station is some distance from the PPL location. 

Figure 7B.2.5 shows the mappings for Washington as an example. Note that many of the PPL 

locations (the ends of the hairs) are mapped to distant NOAA stations. 

 7B-4 

EERE-2008-BT-STD-0006 COMMENT 54
Figure 7B.2.4. PPL→NOAA Mappings for Iowa Showing a Predominance of NearestLocation Mappings 

Figure 7B.2.5. PPL→NOAA Mappings for Washington Showing Some Distant Mappings 

in Mountainous Regions 

 7B-5 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

B.2.2.2Mapping NOAA Stations to TMY2 Stations 

Having mapped each PPL location to a best-representative NOAA station, the next step is 

to map each NOAA station to a best representative TMY2 station. Because the NOAA and 

TMY2 data sets provide less dense geographical coverage and because both data sets contain 

climate information, this mapping process relies less on spatial proximity and more on similarity 

of climate. Proximity remains important, though. In finding a best TMY2 mapping for a NOAA 

station, we hope for similarity in a number of climate variables: temperature (HDD and CDD), 

solar and wind characteristics, rainfall and humidity characteristics, snowfall, etc. The NOAA 

data set supports direct comparison only of heating and cooling degree-days, but we rely on 

spatial proximity to ensure similarity in the other variables. 

The NOAA-to-TMY2 mapping algorithm is as follows. 

1.	 For each NOAA station, identify the nearest TMY2 station. If the elevation of the nearest 

TMY2 station is within 300 feet of the NOAA station, then the nearest TMY2 station is 

the final mapping. 

2.	 If the nearest TMY2 station differs in elevation from the NOAA station by more than 

300 feet, then select instead the TMY2 station that has the minimum “combined distance” 

from the NOAA station. The combined distance is defined as the sum of the literal 

distance (miles) between the two stations and an “equivalent latitude miles” value that 

accounts for known differences in heating and cooling degree-days and elevation. This 

latter metric requires some explanation (see below). 

The equivalent latitude miles metric was developed as a means to characterize 

temperature (HDD and CDD) and elevation in the same units as literal distance (i.e., miles). By 

casting HDD, CDD, and elevation effects into units of miles, we are able to simply sum the 

literal distance between two locations with the equivalent HDD/CDD/elevation distance to give 

the two values equal weight in assigning a best-representative TMY2 station to each NOAA 

station. 

The equivalent latitude miles values are based on several related observations. First, 

differences in both heating and cooling degree-days correlate with differences in latitude. 

Second, those same degree-day differences also correlate with differences in elevation. 

Combining these observations, we discovered that differences in north-south distances 

(i.e., latitude miles) can be characterized in terms of differences in HDD, CDD, and elevation. 

That is, as one moves northward (increasing latitude) and upward (increasing elevation), HDD 

tends to increase and CDD tends to decrease. A regression analysis of these correlations allows 

us to cast HDD, CDD, and elevation differences into units of distance (miles). A linear 

regression on NOAA/TMY2 station pairs within 300 miles of each other gives the following 

result. 

dequiv = I +α × ΔHDD + β ×CDD + γ × ΔElev	 Eq. 7B.1 

Where: 

dequiv = equivalent latitude distance (miles), 

ΔHDD= difference in heating degree-days (base-65 °F), 

ΔCDD = difference in cooling degree-days (base-65 °F), 

 7B-6 

EERE-2008-BT-STD-0006 COMMENT 54
ΔElev = difference in elevation (feet),
 

I = -6.8938,
 

α = 0.1061,
 

β = -0.0149, and
 

γ = -0.0718.
 

The best representative TMY2 station for each NOAA station was selected as the one 

with the minimum sum of actual distance and equivalent latitude distance. Figure 7B.2.6 shows 

the final NOAA→TMY2 mappings for the continental United States. Each plotted point is a 

TMY2 station, from which lines are drawn outward to the NOAA stations mapped to it. 

Figure 7B.2.6. NOAA→TMY2 Mappings 

7B.2.3 Calculating State-by-State Weighting Factors 

Weighting factors were developed for each U.S. state that express the fraction of that 

state’s population that is represented by each TMY2 station. These weighting factors are based 

directly on the PPL→NOAA→TMY2 mappings described above. The weighting factor for a 

TMY2 station is defined as the summed population of all PPL locations in the state that point to 

that TMY2 station divided by the summed population of all PPL locations in the state. Thus, the 

sum of all the TMY2 weighting factors for a state is 1.0. 

 7B-7 

EERE-2008-BT-STD-0006 COMMENT 54
 

APPENDIX 7C. PROPOSED REGIONS FOR CAC REGIONAL STANDARDS AND
 

MAPPING OF RECS OBSERVATIONS 

TABLE OF CONTENTS 

7C.1 Introduction ...................................................................................................................... 7C-1
 

7C.1.1 Cooling Load Hours and their use to establish Hot Regions................................7C-1 

7C.2 Cooling Degree Days and Relative Humidity and their use to verify Hot-Humid 

and Hot-Dry Regions ....................................................................................................... 7C-3 

7C.3 Mapping of RECS observations to Regions .................................................................... 7C-4 

7C.4 Use of Population Weights and TMY-Based Temperature Bin Data to Develop 

Cooling Load Profiles for SEERHD Metric ...................................................................... 7C-8
 

7C.4.1 State by State Analysis..........................................................................................7C-8
 

7C.5 Conclusion ..................................................................................................................... 7C-22

 LIST OF TABLES 

Table 7C.1. TMY2 Weights for Arizona ...................................................................................7C-9
 

Table 7C.2. TMY2 Weights for California................................................................................7C-9
 

Table 7C.3. TMY2 Weights for New Mexico .........................................................................7C-10
 

Table 7C.4. TMY2 Weights for Nevada..................................................................................7C-10
 

Table 7C.5. Distribution of Fractional Hours within Cooling Season Temperature 

Bins—Arizona Climates ....................................................................................7C-11 

Table 7C.6. Distribution of Fractional Hours within Cooling Season Temperature 

Bins— California Climates ...............................................................................7C-11 

Table 7C.7. Distribution of Fractional Hours within Cooling Season Temperature 

Bins— New Mexico Climates ...........................................................................7C-12 

Table 7C.8. Distribution of Fractional Hours within Cooling Season Temperature 

Bins— Nevada Climates ...................................................................................7C-12 

Table 7C.9. Design Load Conditions for TMY3 locations ......................................................7C-13 

Table 7C.10. Distribution of Cooling Load across Cooling Season Temperature 

Bins—Arizona Climates ....................................................................................7C-14 

Table 7C.11. Distribution of Cooling Load across Cooling Season Temperature 

Bins—California Climates ................................................................................7C-15 

Table 7C.12. Distribution of Cooling Load across Cooling Season Temperature 

Bins—New Mexico Climates ............................................................................7C-15 

Table 7C.13. Distribution of Cooling Load across Cooling Season Temperature 

Bins—New Nevada Climates ............................................................................7C-16
 

Table 7C.14. Distribution of Cooling Load across Temperature Bins, by State .....................7C-16
 

LIST OF FIGURES 

Figure 7C.1.1. Distribution of Cooling Load Hours throughout the US....................................7C-2 

Figure 7C.2.1. Scatter Plot of the Average Relative Humidity (RH) During Cooling 

Hours vs. CDD by Region ...................................................................................7C-3
 

7C-i 

EERE-2008-BT-STD-0006 COMMENT 54
Figure 7C.3.1. U.S. Census Divisions with RECS Defined Hot-Humid and Hot-Dry 

Regions—Over Allocated States Indicated with Blue-Dashed Areas.................7C-5 

Figure 7C.4.1. Calculated Cooling Load Profiles for Candidate Hot-Dry States ....................7C-18 

Figure 7C.4.2. Cooling-Load-Weighted Average Outdoor Air Temperatures for 

Selected TMY Locations and Average Values for Four Candidate 

Hot-Dry States...................................................................................................7C-19 

Figure 7C.4.3. Comparison of Temperature Bin Data-CAC Test Procedure and 

Proposed SEERHD ............................................................................................7C-21 

Figure 7C.4.4. Comparison of Cooling Load Profile-Current SEER Test Procedure 

and Proposed SEERHD.......................................................................................7C-22
 

7C-ii 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

APPENDIX C. PROPOSED REGIONS FOR CAC REGIONAL STANDARDS AND 

MAPPING OF RECS OBSERVATIONS 

7C.1 Introduction 

Under EPACT, regional efficiency standards may be defined for up to two regions 

consisting of contiguous groups of states. For cooling equipment performance, the most 

important regional variations in climate are the annual number of operating hours, and the 

relative humidity during the hours when cooling is used. Currently DOE is considering using two 

variants of the SEER rating to establish performance standards: the current definition and a 

modified definition termed “SEER hot-dry” (SEERHD). SEER is established based on test data 

measured at various operating conditions, which are weighted and summed into a single 

numerical rating using a temperature profile that is representative of an average U.S. cooling 

climate. The SEERHD rating can be developed using the same test data, using a different set of 

weightings that represent a hot, dry climate. 

A national standard may be established using SEER. To account for higher operating 

hours in the south, a higher SEER level may be considered for this region. The SEERHD rating is 

expected to be appropriate for the arid west. Whether these regional standards are appropriate 

will be determined by the LCC and other analyses. However, to carry out these analyses requires 

a definition of the regions within which the different standard levels may apply. In this memo, a 

method is proposed for establishing these regions. Region definitions are based on three metrics: 

cooling load hours (CLH) as defined by the DOE CAC test procedure, annual cooling degree 

days (CDD), defined as usual using a base temperature of 65 degrees Fahrenheit (°F), and the 

average relative humidity (RH) during those hours in which the temperature is greater than 65 

°F. 

The two proposed regions are defined as follows: 

•	 Region 1 is defined as the hot-humid area with high CLH, high CDD, and high RH; in 

this region a higher SEER level may apply. 

•	 Region 2 is defined as the hot-dry area with high CLH, high CDD, and low RH; in this 

region the SEERHD standard may apply. 

The remainder of the continental U.S. is termed Region 0 and has low CLH and low CDD, with 

a range on RH. 

7C.1.1 Cooling Load Hours and their use to establish Hot Regions 

Cooling load hours (CLH) are the operational hours of an air conditioner to meet the 

cooling load in a building. The current DOE test procedure includes a CLH map of the U.S. 

(Figure 1). The CLH map was determined based on research conducted in the 1970s and, in 

simple terms, was developed by simulating the performance of a prototypical residence 

throughout the US.1,2 

7C-1 

EERE-2008-BT-STD-0006 COMMENT 54
Figure 7C.1.1. Distribution of Cooling Load Hours throughout the US 

CLHs can be used to estimate hot regions of the US, irrespective of whether the regions are 

humid or dry. As established by the DOE test procedure, 1000 CLHs are estimated to be the 

U.S. average. As depicted in Figure 1, 1000 CLHs roughly separates the U.S. into two equal 

regions (by area)—a Northern region and Southern region. Because 1000 CLHs define average 

climatic conditions for air-conditioning, it is reasonable to assume that CLHs above 1000 

generally represent a hotter than average region. The area below the 1200 CLH line in Figure 

7C.1.1 can reasonably be assumed to represent a hot region of the U.S. Because the 1200 CLH 

line cuts through several States, any State with an appreciable fraction of the population in areas 

with CLHs of 1200 and greater are considered to lie in the hot region. Based on general 

knowledge of the climatic conditions in this hot region, the area can be reasonably divided into 

the following hot-humid and hot-dry regions: 

•	 Region 1 (Hot-Humid): Alabama, Arkansas, Florida, Georgia, North Carolina, 

Louisiana, Mississippi, Oklahoma, South Carolina, Tennessee, and Texas. 

•	 Region 2 (Hot-Dry): Arizona, California, Nevada, and New Mexico. 

As noted above, the States identified as being in the hot-humid region correspond to Region 

1, and the States identified as being in the hot-dry region correspond to Region 2. Note that in 

the four states identified for Region 2, a large fraction of the geographic area of the states falls in 

regions of the country north of the 1000 CLH line in Figure 1. However, the majority of the 

population in each of these states lies below the 1000 CLH line. The remaining States, also 

referred to as Rest-of-Country, have been termed Region 0 for purposes of discussion. The 

following section analyzes CDD and RH data for each State to verify whether the proposed 

regional breakdowns based on CLH data alone are reasonable. 

7C-2 

EERE-2008-BT-STD-0006 COMMENT 54
 

7C.2	 Cooling Degree Days and Relative Humidity and their use to verify Hot-Humid and 

Hot-Dry Regions 

The National Climatic Data Center (NCDC) provides typical meteorological data (TMY) 

data, which the most recent version is TMY3. The TMY3 data set can be used to calculate the 

CDD and cooling RH by State. TMY data consists of measured values that typify the weather at 

a given location over a long period of time. The TMY3 data set has been developed at NREL 

from data for the period 1976-2005.3

 Annual CDD and the average RH for hours for which the 

temperature is greater than 65 °F are calculated for each station in the data set. 

Figure 2 shows a scatter plot of the calculated values of CDD and RH for the TMY3 stations, 

sorted into the three regions defined above in Section 2. Region 1 (hot-humid) is dark blue, 

Region 2 (hot-dry) is purple, and Region 0 (remainder of US) is yellow. Hawaii is called out 

separately in light blue. 

region 0 region 1 region 2 Hawaii 

90 

) 

80

Hours

70

ling 

60

(Coo

50

idity 

40

Hum

30 

20

Average Relative 

10 

0 

0 1000 2000 3000 4000 5000 6000 

CDD base 65 

Figure 7C.2.1. Scatter Plot of the Average Relative Humidity (RH) During Cooling Hours 

vs. CDD by Region 

Figure 7C.1.1 demonstrates that, in general, Regions 1 and 0 reflect the assumed trends in 

climatic variables, i.e., high CDD and high RH for Region 1 and low CDD for Region 0. 

Specifically, Region 1 (hot-humid) generally consists of stations with CDD > 2000 and RH > 

60% and Region 0 (remainder of the US) generally consists of stations with CDD < 1500. 

As shown in Figure 7C.1.1, several stations for Region 2 (hot-dry) exhibit the assumed 

climatic trend of high CDD and low RH, i.e., CDD > 2000 and RH < 40%. But there are two 

7C-3 

EERE-2008-BT-STD-0006 COMMENT 54
other groupings within Region 2 which do not exhibit the assumed trend: one grouping has low 

CDD and high RH and a second grouping has low CDD and low RH. This outcome is a result of 

the climatic diversity within Region 2. The Region 2 grouping with high RH generally consists 

of stations on the coast of California with marine climates while the grouping with low RH 

generally consists of stations in the northern or mountainous areas of California, Nevada, and 

New Mexico. There is clearly significant variation in climates in Region 2, but because EISA 

2007 requires that regions for which higher regional standards are set be comprised of 

contiguous States; areas of Region 2 without a characteristic hot-dry climate would be included. 

7C.3 Mapping of RECS observations to Regions 

As proposed in the Framework Document, the Residential Energy Consumption Survey 

(RECS)4 is to serve as the basis for conducting the LCC analysis. In order to conduct the 

analysis for Region 1 (hot-humid) and Region 2 (hot-dry), RECS must be able to specify the 

geographic location of each household record so that it can be properly assigned to one of the 

three regions. 

RECS specifies the geographic location of each household record down to the Census 

Division and Large State level. Figure 7C.3.1 shows the nine Census Divisions. The four large 

States are California, Florida, New York, and Texas. Additionally, because RECS specifies the 

CDD and Heating Degree Days (HDD) of each household record, the observations in the 

Mountain Census Division can be readily split into northern, cold states (Idaho, Montana, 

Wyoming) and southern warmer states (Arizona, Colorado, Nevada, New Mexico, Utah). 

Therefore, the RECS observations can be most readily be allocated the Census Divisions and 

Large States to the following three geographic areas: 

• RECS Hot-Humid: South Atlantic Census Division (Florida, Delaware, Georgia, 

Maryland, North Carolina, South Carolina, Virginia, West Virginia), East South Central 

Census Division (Alabama, Kentucky, Mississippi, Tennessee), and West South Central 

Census Division (Arkansas, Louisiana, Oklahoma, Texas). 

•	 RECS Hot-Dry: California, Southern Mountain Census Division (Arizona, Colorado, 

Nevada, New Mexico, Utah). 

•	 RECS Remainder of U.S.: New England Census Division, Middle Atlantic Census 

Division, East North Central Census Division, West North Central Census Division, 

Northern Mountain Census Division (Idaho, Montana, Wyoming), Pacific Census 

Division minus California. 

The RECS geographic areas above are similar the proposed climate regions specified using 

CLH, CDD, and RH data. But RECS does over allocate a number of States based on its limited 

geographic data (as indicated in Figure 7C.3.1 by the three areas bordered with blue dashed 

lines). The RECS hot-humid area defined by the three census divisions allocates five more 

States than it should—Delaware, Kentucky, Maryland, Virginia, and West Virginia. The RECS 

Hot-Dry geographic designation above allocates two more States than it should—Colorado and 

Utah. As a result of this over allocation, it was necessary to develop for excluding these overallocated States. 

7C-4 

EERE-2008-BT-STD-0006 COMMENT 54
 

Hot-Dry Region 

Hot-Humid Region 

Figure 7C.3.1. U.S. Census Divisions with RECS Defined Hot-Humid and Hot-Dry 

Regions—Over Allocated States Indicated with Blue-Dashed Areas 

Analysis of energy consumption of the CAC and CHP equipment in the proposed climate 

regions for which separate efficiency standards could be applicable required that the RECS 

records located in the following census divisions first be partitioned as described below: 

•	 South Atlantic: RECS records for the South Atlantic census division are separated 

in two sets i.e., one set for the northern states of the census division viz. Virginia, 

West Virginia, Maryland, District of Columbia and Delaware, and the second set 

for the southern states of the division viz. North Carolina, South Carolina and 

Georgia. 

7C-5 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

•	 Mountain: RECS records for the Mountain census division are separated in two 

sets i.e., one set for the southern states of the division viz., Nevada, Arizona and 

New Mexico and a second set comprising the remaining states. 

•	 East South Central: RECS records for Kentucky, the state at the northern 

extremity of the East South Central census division was separated out from other 

states of the division. 

Once these partitions were identified, an approach for mapping specific RECS records to 

the states of the census division partition was developed. Attempts to identify the city or 

county location of a given RECS household by matching the reported CDD and HDD with 

the specific year CDD and HDD data obtained from historical meteorological records for 

specific locations (e.g. urban areas) are problematic as the CDD and the HDD figures 

reported in RECS are masked by addition of small randomization components (noise). An 

approaching using RECS household weight data in conjunction with available population 

data and climate statistics was developed. This approach is outlined as follows (using a 

South Atlantic Census Division example): 

•	 For the reference census divisions, the state wise population-weighted CDD and HDD 

data for the year 2005 was obtained from National Climatic Data Center (NCDC). This 

was used in conjunction with the census data on state wise population to compute the 

weighted average CDD and the HDD for the partitioned regions of the census divisions 

outlined above. The weighted average CDD and the HDD values obtained are then used 

as target metrics which are compared with the similar weighted average values of any 

disaggregated trial set of RECS observations. 

•	 The base weights assigned to each RECS record represents the number of housing units 

in the total stock of residential housing units. The RECS sampling plan is based on 

multistage area-probability cluster-sample design in which the sample housing units are 

geographically clustered to reduce survey expenses5

. This particular sampling design 

offers the opportunity of identifying large urban clusters as all the sampled housing units 

in a sampling cluster have identical sample weights. Consequently in a given census 

division, the records with identical sampling weights are grouped together and it is 

inferred that they are from a single geographical area e.g., a specific city or a county. The 

reported CDD and HDD figures for the grouped RECS records are examined to confirm 

whether these values for the grouped records indeed vary within narrow ranges. 

•	 For comparison and matching of the climate characteristic of RECS households with that 

of a known geographical area, direct comparison of the CDD and HDD values was 

inconvenient due to the masking of the reported values. Consequently the HDD and the 

CDD values were instead represented by a polar coordinate form. For a given RECS 

record, the CDD was considered as the value of the abscissa and HDD as the value of 

ordinate corresponding to a point defining the climate characteristic in the Cartesian 

plane. In this approach, the polar values of this coordinate i.e., the polar radius and the 

polar angle defines the climate characteristic of the RECS record. This polar 

characterization was found more convenient for matching of the climate characteristic of 

the record clusters with that of known geographical areas. 

7C-6 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

•	 The standard deviations and the average values of the polar radius and the polar angle of 

the climate characteristic points of the RECS records in a cluster were computed. It was 

observed that, in most cases, the standard deviation of a cluster of observation in a known 

census division and with the same sample weights were within 5% of their mean value 

for the cluster. The CDD and HDD data reported in RECS for a given household relates 

to the values recorded in the nearest NOAA weather station, but with a small perturbation 

for masking. For some such clusters, particularly with fewer records, it was noticed that 

the standard deviations are higher than 5%. It was concluded that these are primarily 

from counties where there are multiple NOAA weather stations and which have 

significant divergent climate characteristic, for example due to altitude variations. 

•	 The mean values of the polar distances and the polar angles of the RECS record trial 

clusters were subsequently compared with the NOAA data for urban areas in the census 

division and an approximate urban cluster is matched. For example in the South Atlantic 

census division, the largest record cluster is a group with 17 records having identical 

sampling weights of 29248.47633 with a mean climate characteristic polar distance of 

3637 degree days and a polar angle of 59°. Since Atlanta in Georgia is the largest 

metropolitan area in this census division, identical climate metrics i.e., the polar distance 

and the polar angle for the 2005 CDD and HDD for Atlanta were computed and the 

values obtained for the polar distance was found to be 3356 and the polar angle value was 

57° which matches approximately with the similar parameters for these 17 RECS records. 

Similarly a cluster of 12 RECS records each having an identical sampling weight of 

31433.23266 were identified with climate characteristic of Charlotte, NC. More records 

for these multi-record clusters with hotter climate characteristic were identified and 

added to the subset. Some single records with ‘hotter’ climate characteristic were also 

added to this subset and a tally of 263 RECS records was obtained. These were then 

identified as likely in the target zone of the ‘hot and humid’ region of the South Atlantic 

Census division comprising the three states of Georgia, North Carolina, and South 

Carolina. 

•	 For the partitioning of the set of RECS records for the South Atlantic census division 

(excluding Texas) into a subset corresponding to target ‘hot and humid’ southern part 

comprising of the three states of Georgia and Carolina and the remainder region, three 

key metrics were computed for this region using the population and 2005 climate data for 

all the states of the South Atlantic census division. These were compared with the 

identical metrics for the subset of RECS records identified as above. These three metrics 

were the population ratio (defined as the ratio of the target ‘hot-humid’ states to that of 

the entire census division) and the ratios of the population weighted CDD and HDD for 

the target ‘hot-humid’ states to that of the entire census division. From the data, the 

population ratio for the target ‘hot and humid’ region was established at 55% of the South 

Atlantic census division. The target ratios for the population weighted HDD and the CDD 

were found to be 82% and 117% of the respective averages for the entire census division. 

•	 Out of the 483 RECS records assigned to this census division excluding Texas, 263 

identified RECS records formed the subset for which the climate characteristics 

approximately matched with the same for the counties and large urban centers of the 

7C-7 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

target ‘hot and humid’ region. The population weighted HDD ratio for these subset of 

records was 79% and the corresponding CDD metric was 120%. The population ratio, 

computed using the RECS sampling weights assigned to these records was found at 55%. 

It was concluded that the key characteristics for these subset of RECS records identified 

were well matched with that for the target region. Consequently these records were 

assigned to the target ‘hot and humid’ zone. 

RECS records for the colder state of Kentucky from the East South Central Census Division 

were isolated in similar fashion. A subset of 84 RECS records from a total of 344 RECS records 

in that census division were obtained for which the key metrics described above approximately 

matched with the climate and the population ratio parameters for Kentucky. 

Similarly, a subset of 165 RECS records out of the 344 assigned to the Mountain census 

division were identified to belong to the hotter states of Arizona and Nevada and these records 

were included in the Hot-Dry region. 

Finally, two RECS records in the Pacific Census Division were identified with Hawaii based 

on the reported CDD data and high electricity costs. These records were grouped into Region 1. 

7C.4	 Use of Population Weights and TMY-Based Temperature Bin Data to Develop 

Cooling Load Profiles for SEERHD Metric 

The candidate hot-dry region consisted of Arizona, California, New Mexico, and Nevada, all 

with significant fractions of the geographic area, the population, or both, in areas with greater 

than 1200 CLH. Because of the need to establish regions based on whole states however, DOE 

undertook to examine how a SEERHD rating could be developed based on available temperature 

bin data for climates within each state and using population based-weighting of those specific 

climates. 

7C.4.1 State by State Analysis 

Through previous work, PNNL had developed population-based weightings for TMY2 

weather locations. A detailed discussion of this mapping process can be found in Appendix 7-B. 

For the purpose of this analysis, DOE only relied on the mapping as it applied to the four 

candidate hot-dry states. Table 7C.1 through Table 7C.4 shows the resulting population based 

weights for each of 237 TMY2 locations found within these four states, in order of weight. For 

simplicity within the analysis, DOE examined data for the TMY2 locations whose weight 

comprised ~98% of the total population weighting within a given state. These locations are 

shown in Bold. Note that in some instances, due to local climate mapping, the best 

representative TMY2 climate for a given population falls outside of the state boundary. 

7C-8 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

Table 7C.1. TMY2 Weights for Arizona 

Arizona WT. CUM. WT 

PHOENIX SKY HARBOR INTL AP 

TUCSON INTERNATIONAL AP 

PRESCOTT MUNICIPAL AP 

SAN DIEGO LINDBERGH MUNI AP 

FLAGSTAFF PULLIAM ARPT 

70.4%

18.5%

4.6%

2.4%

2.1%

 70.4% 

88.9% 

93.5% 

95.9% 

97.9% 

LAS VEGAS MCCARRAN INTL AP 

ALBUQUERQUE INTL ARPT 

CEDAR CITY MUNICIPAL AP 

GRAND JUNCTION WALKER FIELD 

DAGGETT BARSTOW-DAGGETT AP 

EL PASO INTERNATIONAL AP 

1.5% 

0.4% 

0.1% 

0.0% 

0.0% 

0.0% 

99.4% 

99.9% 

100.0% 

100.0% 

100.0% 

100.0% 

Table 7C.2. TMY2 Weights for California 

California WT. CUM. WT 

LOS ANGELES MUNICIPAL ARPT 

LONG BEACH DAUGHERTY FLD 

SAN FRANCISCO INTL AP 

SACRAMENTO EXECUTIVE ARPT 

SAN DIEGO LINDBERGH MUNI AP 

DAGGETT BARSTOW-DAGGETT AP 

FRESNO AIR TERMINAL 

BAKERSFIELD MEADOWS FIELD 

24.0%

21.5%

17.0%

14.0%

9.9%

5.6%

4.0%

1.6%

 24.0% 

45.5% 

62.5% 

76.5% 

86.5% 

92.0% 

96.0% 

97.7% 

SANTA MARIA PUBLIC ARPT 1.2% 98.9% 

ARCATA AIRPORT 0.7% 99.6% 

RENO CANNON INTL AP 0.2% 99.8% 

MEDFORD ROGUE VALLEY INTL AP 0.1% 99.9% 

LAS VEGAS MCCARRAN INTL AP 0.1% 100.0% 

TONOPAH AIRPORT 0.0% 100.0% 

WINNEMUCCA MUNICIPAL ARPT 0.0% 100.0% 

PHOENIX SKY HARBOR INTL AP 0.0% 100.0% 

PRESCOTT MUNICIPAL AP 0.0% 100.0% 

7C-9 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

Table 7C.3. TMY2 Weights for New Mexico 

New Mexico WT. CUM. WT 

ALBUQUERQUE INTL ARPT 

EL PASO INTERNATIONAL AP 

MIDLAND REGIONAL AIR TERMINAL 

TUCUMCARI FAA AP 

GRAND JUNCTION WALKER FIELD 

61.2%

13.5%

11.6%

6.3%

5.5%

 61.2% 

74.7% 

86.3% 

92.7% 

98.2% 

PUEBLO MEMORIAL AP 0.8% 98.9% 

ALAMOSA SAN LUIS VALLEY RGNL A 0.5% 99.5% 

TUCSON INTERNATIONAL AP 0.3% 99.7% 

AMARILLO INTERNATIONAL AP 0.1% 99.8% 

LUBBOCK WEST TEXAS AIR TERMINA 0.1% 99.9% 

COLORADO SPRNGS MUNI 0.1% 100.0% 

PRESCOTT MUNICIPAL AP 0.0% 100.0% 

FLAGSTAFF PULLIAM ARPT 0.0% 100.0% 

PHOENIX SKY HARBOR INTL AP 0.0% 100.0% 

Table 7C.4. TMY2 Weights for Nevada 

Nevada WT. CUM. WT 

LAS VEGAS MCCARRAN INTL AP 

RENO CANNON INTL AP 

ELKO MUNICIPAL ARPT 

WINNEMUCCA MUNICIPAL ARPT 

61.7%

32.8%

2.7%

1.3%

 61.7% 

94.4% 

97.1% 

98.4% 

ELY YELLAND FIELD 

TONOPAH AIRPORT 

SALT LAKE CITY MUNICIPAL ARPT 

CEDAR CITY MUNICIPAL AP 

BURNS MUNICIPAL ARPT 

DAGGETT BARSTOW-DAGGETT AP 

0.7% 

0.5% 

0.3% 

0.1% 

0.0% 

0.0% 

99.1% 

99.6% 

99.9% 

100.0% 

100.0% 

100.0% 

DOE developed temperature bin data for each of the locations identified in bold in Table 

7C.1 through 7C.4. While the mapping of weights had been previously done using TMY2 

weather locations, DOE opted to use the more recent TMY3 weather data for each site for 

calculation of typical bin data as TMY3 data was available for each location identified above. 

Five degree temperature bins were defined as are used in the current CAC test procedure for the 

development of the load profile and the total number of hours in the TMY3 file belonging to 

each temperature bin was accumulated. For example, the 65-69 oF temperature bin would be 

allocated all the hours with an outside temperature greater than or equal to 64.5 oF, but less than 

69.5 oF. This binning method appears to be most similar to what would have been used for the 

development of the current central air conditioning test procedure temperature bins6

. A 

representative bin temperature is defined for each bin as the midway point in the bin. So, for the 

65-69oF bin, the representative temperature is 67 oF, again in accordance with the current test 

procedure. Because of the wider range of temperature in several of the hot and dry climates, 

additional high temperature bins beyond the 8 bins provided in the CAC test procedure were 

used. 

7C-10 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

The CAC test procedure is structured to consider all hours with outdoor air temperature 

above 65oF as hours with a defined cooling load. Table 16 of the CAC test procedure provides 

the fraction of the total temperature bin hours above 65oF as determined for the average U.S. 

Climates used for the SEER rating. Similarly, for each of the TMY3 locations, DOE calculated 

the fraction of total temperature bin hours above 65oF. Table 7C.5 through 7C.8 shows this data 

for each of the 20 TMY3 data files used in the analysis. 

Table 7C.5. Distribution of Fractional Hours within Cooling Season Temperature Bins— 

Arizona Climates 

Temperature Bin DOE PT.430 

PHOENIX 

SKY 

HARBOR 

INTL AP 

TUCSON 

INTERNATIO 

NAL AP 

PRESCOTT 

MUNICIPAL 

AP 

SAN DIEGO 

LINDBERGH 

MUNI AP 

FLAGSTAFF 

PULLIAM 

ARPT 

65 – 69 0.214 0.123 0.175 0.260 0.597 0.339 

70 – 74 0.231 0.100 0.147 0.200 0.269 0.239 

75 – 79 0.216 0.116 0.174 0.199 0.107 0.246 

80 – 84 0.161 0.177 0.175 0.181 0.026 0.153 

85 – 89 0.104 0.161 0.116 0.117 0.000 0.024 

90 – 94 0.052 0.109 0.084 0.041 0.000 0.000 

95 – 99 0.018 0.116 0.082 0.003 0.000 0.000 

100 - 104 0.004 0.071 0.041 0.000 0.000 0.000 

105 - 109 0 0.024 0.005 0.000 0.000 0.000 

110 - 114 0 0.002 0.000 0.000 0.000 0.000 

115 - 119 0 0.000 0.000 0.000 0.000 0.000 

120 - 124 0 0.000 0.000 0.000 0.000 0.000 

125 - 129 0 0.000 0.000 0.000 0.000 0.000 

Table 7C.6. Distribution of Fractional Hours within Cooling Season Temperature Bins— 

California Climates 

Temperature 

Bin 

DOE 

PT.430 

LOS 

ANGELE 

S 

MUNICI 

PAL 

ARPT 

LONG 

BEACH 

DAUGH 

ERTY 

FLD 

SAN 

FRANCI 

SCO 

INTL AP 

SACRA 

MENTO 

EXECUT 

IVE 

ARPT 

SAN 

DIEGO 

LINDBE 

RGH 

MUNI 

AP 

DAGGE 

TT 

BARSTO 

WDAGGE 

TT AP 

FRESNO 

AIR 

TERMIN 

AL 

BAKERS 

FIELD 

MEADO 

WS 

FIELD 

65 - 69 0.214 0.689 0.498 0.663 0.248 0.597 0.173 0.231 0.218 

70 - 74 0.231 0.243 0.250 0.203 0.162 0.269 0.142 0.157 0.168 

75 - 79 0.216 0.057 0.156 0.083 0.169 0.107 0.163 0.167 0.186 

80 - 84 0.161 0.010 0.076 0.045 0.182 0.026 0.162 0.160 0.168 

85 - 89 0.104 0.001 0.016 0.005 0.115 0.000 0.123 0.101 0.104 

90 - 94 0.052 0.000 0.003 0.001 0.064 0.000 0.074 0.076 0.068 

95 - 99 0.018 0.000 0.001 0.000 0.040 0.000 0.073 0.069 0.059 

100 - 104 0.004 0.000 0.000 0.000 0.016 0.000 0.052 0.035 0.025 

105 - 109 0 0.000 0.000 0.000 0.003 0.000 0.033 0.003 0.004 

110 - 114 0 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 

115 - 119 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 

120 - 124 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 

125 - 129 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 

7C-11 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

Table 7C.7. Distribution of Fractional Hours within Cooling Season Temperature Bins— 

New Mexico Climates 

Temperature Bin DOE PT.430 

ALBUQUER 

QUE INTL 

ARPT 

EL PASO 

INTERNATIO 

NAL AP 

MIDLAND 

REGIONAL 

AIR 

TERMINAL 

TUCUMCARI 

FAA AP 

GRAND 

JUNCTION 

WALKER 

FIELD 

65 – 69 0.214 0.288 0.199 0.224 0.288 0.260 

70 – 74 0.231 0.189 0.183 0.198 0.182 0.179 

75 – 79 0.216 0.196 0.198 0.184 0.187 0.174 

80 – 84 0.161 0.162 0.191 0.176 0.162 0.177 

85 – 89 0.104 0.100 0.133 0.102 0.094 0.113 

90 – 94 0.052 0.047 0.061 0.071 0.051 0.062 

95 – 99 0.018 0.015 0.030 0.037 0.031 0.035 

100 - 104 0.004 0.004 0.005 0.007 0.004 0.000 

105 - 109 0 0.000 0.000 0.000 0.000 0.000 

110 - 114 0 0.000 0.000 0.000 0.000 0.000 

115 - 119 0 0.000 0.000 0.000 0.000 0.000 

120 - 124 0 0.000 0.000 0.000 0.000 0.000 

125 - 129 0 0.000 0.000 0.000 0.000 0.000 

Table 7C.8. Distribution of Fractional Hours within Cooling Season Temperature Bins— 

Nevada Climates 

Temperature Bin DOE PT.430 

LAS VEGAS 

MCCARRAN 

INTL AP 

RENO CANNON 

INTL AP 

ELKO 

MUNICIPAL 

ARPT 

WINNEMUCCA 

MUNICIPAL 

ARPT 

65 – 69 0.214 0.153 0.230 0.250 0.215 

70 – 74 0.231 0.116 0.172 0.172 0.174 

75 – 79 0.216 0.139 0.191 0.183 0.174 

80 - 84 0.161 0.174 0.196 0.193 0.182 

85 - 89 0.104 0.133 0.122 0.132 0.129 

90 - 94 0.052 0.096 0.060 0.055 0.075 

95 - 99 0.018 0.098 0.028 0.017 0.042 

100 - 104 0.004 0.069 0.000 0.000 0.009 

105 - 109 0 0.020 0.000 0.000 0.000 

110 - 114 0 0.001 0.000 0.000 0.000 

115 - 119 0 0.000 0.000 0.000 0.000 

120 - 124 0 0.000 0.000 0.000 0.000 

125 - 129 0 0.000 0.000 0.000 0.000 

In addition to the distribution-of-fractional bin hours data from the TMY3 files, DOE 

define a peak sizing temperature. As shown in equation 4.1-2 of the CAC test procedure, the 

building load in any bin is calculated as 

k =2 (Tj − 65) Qc (95) BL(Tj ) = • Eq. 7C- 1 95 − 65 1.1 

7C-12 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

Where 

BL is the building load

 Tj is the representative temperature for bin j

 Qc (95) is the equipment cooling capacity at 95 oF 

Implicitly, the building load varies linearly with outdoor air temperature from 65oF to 95oF 

where it is essentially equal to the equipment capacity (at the 95oF condition) divided by 1.1. We 

presume that the best interpretation of this equation is that the equipment is sized for the 95oF 

condition, with a presumed oversizing factor of 10%. Using equation 7C-1, the building load 

does not reach the equipment capacity until the representative temperature T equals 98oF. 

Equation 1 is used to establish a building load profile based on the representative bin temperature 

Tj and the fractional bin hour ratio for bin j. 

To establish realistic building load profiles for hot and dry climates where the peak design 

temperatures may be significantly higher, the 95oF temperature in equation 1 was replaced with 

the ASHRAE 1% cooling dry bulb temperature design condition for each TMY3 location7. 

Those design conditions are shown in Table 7C.9. 

Table 7C.9. Design Load Conditions for TMY3 locations 

TMY 3 Location 

1% DB Design 

Temp 

PHOENIX SKY HARBOR INTL AP 108.1 

TUCSON INTERNATIONAL AP 102.9 

PRESCOTT MUNICIPAL AP 91.5 

SAN DIEGO LINDBERGH MUNI AP 81.5 

FLAGSTAFF PULLIAM ARPT 83.2 

LOS ANGELES MUNICIPAL ARPT 80.4 

LONG BEACH DAUGHERTY FLD 87.7 

SAN FRANCISCO INTL AP 78.0 

SACRAMENTO EXECUTIVE ARPT 97.8 

SAN DIEGO LINDBERGH MUNI AP 81.5 

DAGGETT BARSTOW-DAGGETT AP 106.2 

FRESNO AIR TERMINAL 101.0 

BAKERSFIELD MEADOWS FIELD 101.0 

ALBUQUERQUE INTL ARPT 93.5 

EL PASO INTERNATIONAL AP 98.6 

MIDLAND REGIONAL AIR TERMINAL 97.8 

TUCUMCARI FAA AP 95.7 

GRAND JUNCTION WALKER FIELD 94.4 

LAS VEGAS MCCARRAN INTL AP 106.2 

RENO CANNON INTL AP 92.5 

ELKO MUNICIPAL ARPT 92.3 

WINNEMUCCA MUNICIPAL ARPT 94.9 

7C-13 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

Using the 1% design condition to replace the 95oF sizing temperature in equation 1, and 

using the number of hours in each bin and the building load calculated for each bin, DOE 

developed cooling load profiles for each TMY location. In addition, by weighting the 

representative bin temperatures by the relative load in each bin, DOE generated a cooling-loadweighted average outdoor air temperature (CLWT) for each TMY location. This temperature 

represents the most representative temperature for the climate in terms of calculating the cooling 

load served by the equipment, and thus the most representative temperature that might be used 

for calculation of an annualized efficiency (assuming cooling efficiency is generally linear with 

outdoor air temperature) for that climate. The resulting cooling load profiles and the coolingload-weighted average outdoor air temperature are shown in Table 7C.10 through Table 7C.13 

along with the CAC test procedure load profile. It is noted that the cooling load weighted 

average outdoor temperature from the current SEER test procedure is 82oF. The performance (in 

terms of cooling output and power) at 82oF is currently used in an optional rating methodology 

for establishing SEER in the current test procedure. 

Using the population based weighting factors for each TMY location; DOE calculated 

population-averaged load profiles for each state. Note, because the sites for a given state only 

represented approximately 98% of the state population, the weighting factors were all 

normalized to 100% prior to weighting across climates. The resulting average load profiles by 

state are shown in Table 7C.14. 

Table 7C.10. Distribution of Cooling Load across Cooling Season Temperature Bins— 

Arizona Climates 

Temperature Bin (°F) DOE PT.430 

PHOENIX 

SKY 

HARBOR 

INTL AP 

TUCSON 

INTERNATIO 

NAL AP 

PRESCOTT 

MUNICIPAL 

AP 

SAN DIEGO 

LINDBERGH 

MUNI AP 

FLAGSTAFF 

PULLIAM 

ARPT 

65 – 69 0.036 0.013 0.022 0.047 0.248 0.080 

70 – 74 0.137 0.036 0.066 0.125 0.391 0.198 

75 – 79 0.220 0.072 0.134 0.214 0.267 0.350 

80 – 84 0.232 0.156 0.190 0.276 0.093 0.309 

85 – 89 0.194 0.184 0.163 0.230 0.001 0.063 

90 – 94 0.119 0.153 0.145 0.100 0.000 0.000 

95 – 99 0.049 0.192 0.168 0.009 0.000 0.000 

100 – 104 0.013 0.137 0.098 0.000 0.000 0.000 

105 – 109 0.000 0.052 0.014 0.000 0.000 0.000 

110 – 114 0.000 0.004 0.000 0.000 0.000 0.000 

115 – 119 0.000 0.000 0.000 0.000 0.000 0.000 

120 – 124 0.000 0.000 0.000 0.000 0.000 0.000 

CLWT (°F) 82.1 90.6 87.4 81.3 73.0 77.4 

7C-14 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

Table 7C.11. Distribution of Cooling Load across Cooling Season Temperature Bins— 

California Climates 

Temperature 

Bin (°F) 

DOE 

PT.430 

LOS 

ANGELE 

S 

MUNICI 

PAL 

ARPT 

LONG 

BEACH 

DAUGH 

ERTY 

FLD 

SAN 

FRANCI 

SCO 

INTL AP 

SACRA 

MENTO 

EXECUT 

IVE 

ARPT 

SAN 

DIEGO 

LINDBE 

RGH 

MUNI 

AP 

DAGGE 

TT 

BARSTO 

WDAGGE 

TT AP 

FRESNO 

AIR 

TERMIN 

AL 

BAKERS 

FIELD 

MEADO 

WS 

FIELD 

65 – 69 0.036 0.348 0.157 0.285 0.038 0.248 0.021 0.033 0.032 

70 – 74 0.137 0.430 0.275 0.306 0.087 0.391 0.060 0.077 0.085 

75 - 79 0.220 0.172 0.295 0.215 0.156 0.267 0.118 0.141 0.162 

80 - 84 0.232 0.045 0.203 0.163 0.238 0.093 0.166 0.192 0.207 

85 - 89 0.194 0.004 0.055 0.024 0.195 0.001 0.163 0.157 0.165 

90 - 94 0.119 0.000 0.013 0.006 0.134 0.000 0.121 0.145 0.134 

95 - 99 0.049 0.000 0.003 0.000 0.098 0.000 0.141 0.155 0.138 

100 - 104 0.013 0.000 0.000 0.000 0.045 0.000 0.117 0.091 0.067 

105 - 109 0.000 0.000 0.000 0.000 0.009 0.000 0.083 0.010 0.011 

110 - 114 0.000 0.000 0.000 0.000 0.000 0.000 0.011 0.000 0.000 

115 - 119 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 

120 - 124 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 

CLWT (°F) 82.1 71.6 75.9 73.8 84.7 73.0 89.4 86.7 85.7 

Table 7C.12. Distribution of Cooling Load across Cooling Season Temperature Bins—New 

Mexico Climates 

Temperature Bin (°F) DOE PT.430 

ALBUQUER 

QUE INTL 

ARPT 

EL PASO 

INTERNATIO 

NAL AP 

MIDLAND 

REGIONAL 

AIR 

TERMINAL 

TUCUMCARI 

FAA AP 

GRAND 

JUNCTION 

WALKER 

FIELD 

65 – 69 0.036 0.052 0.030 0.035 0.050 0.043 

70 – 74 0.137 0.119 0.098 0.109 0.111 0.103 

75 – 79 0.220 0.212 0.183 0.174 0.195 0.172 

80 – 84 0.232 0.249 0.250 0.236 0.240 0.248 

85 – 89 0.194 0.198 0.225 0.177 0.181 0.204 

90 – 94 0.119 0.114 0.127 0.152 0.121 0.138 

95 – 99 0.049 0.044 0.073 0.094 0.088 0.091 

100 - 104 0.013 0.012 0.014 0.021 0.013 0.000 

105 - 109 0.000 0.000 0.000 0.000 0.000 0.000 

110 - 114 0.000 0.000 0.000 0.000 0.000 0.000 

115 - 119 0.000 0.000 0.000 0.000 0.000 0.000 

120 - 124 0.000 0.000 0.000 0.000 0.000 0.000 

CLWT (°F) 82.1 82.0 83.4 83.8 82.8 83.2 

7C-15 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 7C.13. Distribution of Cooling Load across Cooling Season Temperature Bins—New 

Nevada Climates 

Temperature Bin (°F) DOE PT.430 

LAS VEGAS 

MCCARRAN 

INTL AP 

RENO 

CANNON INTL 

AP 

ELKO 

MUNICIPAL 

ARPT 

WINNEMUCCA 

MUNICIPAL 

ARPT 

65 – 69 0.036 0.017 0.037 0.041 0.032 

70 – 74 0.137 0.046 0.096 0.100 0.091 

75 – 79 0.220 0.094 0.184 0.181 0.156 

80 – 84 0.232 0.166 0.266 0.271 0.231 

85 – 89 0.194 0.163 0.215 0.240 0.213 

90 – 94 0.119 0.145 0.130 0.123 0.150 

95 – 99 0.049 0.176 0.071 0.044 0.100 

100 - 104 0.013 0.144 0.001 0.000 0.026 

105 - 109 0.000 0.048 0.000 0.000 0.000 

110 - 114 0.000 0.002 0.000 0.000 0.000 

115 - 119 0.000 0.000 0.000 0.000 0.000 

120 - 124 0.000 0.000 0.000 0.000 0.000 

CLWT (°F) 82.1 89.9 83.0 82.6 84.4 

Table 7C.14. Distribution of Cooling Load across Temperature Bins, by State 

Temperature Bin (°F) DOE PT.430 Arizona California New Mexico Nevada 

65 – 69 0.036 0.023 0.203 0.046 0.025 

70 – 74 0.137 0.058 0.280 0.114 0.065 

75 – 79 0.220 0.101 0.209 0.200 0.127 

80 – 84 0.232 0.170 0.149 0.247 0.203 

85 – 89 0.194 0.175 0.064 0.198 0.183 

90 – 94 0.119 0.142 0.038 0.122 0.139 

95 – 99 0.049 0.170 0.031 0.059 0.136 

100 - 104 0.013 0.117 0.018 0.013 0.091 

105 - 109 0.000 0.040 0.007 0.000 0.030 

110 - 114 0.000 0.003 0.001 0.000 0.001 

115 – 119 0.000 0.000 0.000 0.000 0.000 

120 – 124 0.000 0.000 0.000 0.000 0.000 

CLWT (°F) 82.1 88.8 76.8 82.5 87.3 

Based on this process, only two states clearly showed average load profiles that were 

significantly hotter, in terms of CLWT than that used in the DOE test procedure. During the 

course of review of this analysis, it was noted that for California, which showed an average 

cooling load-weighted outdoor temperature of 77oF, the wide variation in climates very close to 

the coast compared with climates only miles inland made it difficult to ensure characterization of 

the climates experienced by the population with the TMY sites that are near the coast (Los 

Angeles Municipal Airport, Long Beach Daugherty Field, San Francisco Intl. Airport, San Diego 

Lindbergh Municipal Airport). This is largely a question of the mapping and weighting process 

for the TMY locations in that state. To examine this, DOE calculated the CDD65 for the eight 

7C-16 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

California TMY locations and applied the weighting factors developed for those TMY locations 

directly to those CDD65 to see how close this calculated value was to other estimates of the 

average CDD65 for California. The weighting of the TMY3 data resulted in a state-wide CDD65 

of 763. To calculate a more representative average CDD, Census Population Data available by 

county was used to weight county-average CDD values developed directly from the PPL and 

NOAA sites without the mapping to TMY. This result and two other estimates are as follows: 

•	 Based on using the county CDD65 values directly and using county population data from 

the 2000 census data, the average CDD65 value calculated for California was 1134. 

•	 Based on using the county CDD65 values and using the 1990 to 2000 census data for 

population growth by county (a possible surrogate for new construction only), the 

average CDD65 value for California was 1217. 

•	 Based on using CDD65 data for each of the 16 climate zones identified by the California 

Energy Commission8 in conjunction with and 2000 census data for housing units by zip 

code within the state (mapped using CEC data for each zip code9

), the average CDD65 

value for California was 1065. 

The results from examining the calculations based on the county CDD65 values indicated 

that a value of CDD65 near 1100 was probably most reasonable for the state based on 2000 

census data for population. With that in mind, DOE did a sensitivity analysis by adjusting the 

TMY weights for the California locations to better reflect an average CDD65 value of ~1100. 

This was achieved by multiplying the calculated TMY weights for the four coastal TMY sites by 

an adjustment factor of 0.635 and the four inland TMY sites by an adjustment factor of 2.05. 

These factors were chosen so that they would correctly result in a combined total weight for the 

state identical to that calculated originally and preserve the relative weight between the four 

states. Using these revised weights, the weighted-average CDD65 from the TMY sites for 

California was 1100. These revised weights were also then used to recalculate the average 

cooling load profile for the state, as shown in Table 7C.15 and shown graphically in Figure 

7C.4.1. The revised CLWT for California was 80.2°F, still the lowest of the four candidate hotdry states and below that of the DOE test procedure load profile. These temperatures are shown 

graphically in Figure 7C.4.2, along with similar figures calculated for other locations in the U.S 

using the same methodology. In addition, Table 7C.15 shows the fraction of the cooling load 

calculated for binned temperatures above 85°F. 

7C-17 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 7C.15. Distribution of Cooling Load within Temperature Bins by State –Revised 

Weights 

Temperature Bin 

(oF) DOE PT.430 Arizona California New Mexico Nevada 

65 - 69 0.036 0.023 0.141 0.046 0.025 

70 - 74 0.137 0.058 0.207 0.114 0.065 

75 - 79 0.220 0.101 0.186 0.200 0.127 

80 - 84 0.232 0.170 0.172 0.247 0.203 

85 - 89 0.194 0.175 0.106 0.198 0.183 

90 - 94 0.119 0.142 0.073 0.122 0.139 

95 - 99 0.049 0.170 0.064 0.059 0.136 

100 - 104 0.013 0.117 0.037 0.013 0.091 

105 - 109 0.000 0.040 0.014 0.000 0.030 

110 - 114 0.000 0.003 0.001 0.000 0.001 

115 – 119 0.000 0.000 0.000 0.000 0.000 

120 – 124 0.000 0.000 0.000 0.000 0.000 

CLWT (°F) 82.1 88.8 80.2 82.5 87.3 

Fraction of cooling 

load above 85 oF 0.375 0.648 0.294 0.393 0.581 

Weighted Average Load Profile (Hot Dry States)
 

(Based on Population Wts for Selected TMY Sites and TMY3 Data)
 

Fraction of Cooling Load

(Calculated using Bin Temp Data and CFR 430 Method)

0.400 

0.350 

0.300 

0.250 

0.200 

0.150 

0.100 

0.050 

0.000 

65-70 70-75 75-80 80-85 85-90 90-95 95-100 100-105 105-110 110-115 115-120 120-125 125-130 

Temperature Bin 

10CFR Pt. 430 

California 

New Mexico 

Nevada 

Arizona 

Figure 7C.4.1. Calculated Cooling Load Profiles for Candidate Hot-Dry States 

7C-18 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

10CFR430 

Phoenix 

Tucson 

Prescott 

San Diego 

Flagstaff

Los Angeles

Long

Beach

San Francisco

Sacramento 

San Diego 

Daggett 

Fresno 

Bakersfield

Albuquerque 

El Paso 

Midland 

Tucumcari 

Grand

Junction 

Las Vegas 

Reno 

Elko

Winnemucca 

Chicago 

Yuma 

Orlando 

Atlanta 

Baltimore

Kansas City 

Cooling-Load-Weighted Average Outdoor Air Temperature (Tj) 

110 

105 

100 

95 

90 

85 

80 

75 

70 

65 

60 

55 

50 

45 

82.1 

90.6 

87.4 

81.3 

73.0 

77.4 

71.6 

75.9 

73.8 

84.7 

73.0 

89.4 

86.7 85.7 

82.0 83.4 83.8 82.8 83.2 

89.9 

83.0 82.6 

84.4 

79.1 

90.6 

80.0 80.6 80.0 81.0 

ASHRAE 1% Design 

Condition 

120 

100 

80 

60 

40 

20 

0 

Temperature (F) 

Location 

Figure 7C.4.2. Cooling-Load-Weighted Average Outdoor Air Temperatures for Selected 

TMY Locations and Average Values for Four Candidate Hot-Dry States 

Using population based weights two of these candidate states, Arizona and Nevada, stand out 

as having CLWT and fraction of cooling hours greater than 85°F that are distinctly higher than 

that of the DOE test procedure bins. While other western states, including New Mexico and 

California may indeed be considered “dry” as compared to the country as a whole, they do not 

appear necessarily hotter, when considering the state as a whole, than the climate characterized 

by the test procedure. DOE used the relative populations of the four states (Arizona-6.5 million 

people, California- 36.5 million people, Nevada-2.6 million people, New Mexico- 2.0 million 

people) to derive a weighted cooling load profile and design day rating that would be 

characteristic of that experienced by customers in these state. This design day temperature was 

94.3oF. As this was very close to the test procedures current value of 95oF, DOE did not propose 

a modification of the design day value for the Hot-Dry region. DOE then back calculated a 

cooling bin distribution for the region that, when combined with the design day rating, would 

generate the region-average cooling load profile. These profiles are shown in Table 7C-16 and 

Figure 7C.4.3 and Figure 7C.4.4. 

7C-19 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

Table 7C.16. Weighted Cooling Bin Data and Corresponding Load Profile for Proposed for 

Hot-Dry Rating 

Temperature Bin 

(oF) 

Fraction of Hours in 

Cooling Bin (Bin > 65oF) 

Resulting Cooling Load 

Profile 

DOE 

PT.430 SEERHD 

DOE 

PT.430 SEERHD 

65 - 69 0.214 0.477 0.036 0.115 

70 - 74 0.231 0.208 0.137 0.175 

75 - 79 0.216 0.119 0.220 0.172 

80 - 84 0.161 0.086 0.232 0.176 

85 - 89 0.104 0.047 0.194 0.124 

90 - 94 0.052 0.027 0.119 0.088 

95 - 99 0.018 0.021 0.049 0.082 

100 - 104 0.004 0.011 0.013 0.050 

105 - 109 0.000 0.004 0.000 0.018 

110 - 114 0.000 0.000 0.000 0.001 

115 – 119 0.000 0.000 0.000 0.000 

120 – 124 0.000 0.000 0.000 0.000 

Design Temp 

(oF) 95 95 94.3 

CLWTemp (oF) 82.1 82.1 81.9 

Fraction of 

cooling load 

above 85oF 

- - 0.375 0.362 

7C-20 

EERE-2008-BT-STD-0006 COMMENT 54
0.00 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

65 - 69 70 - 74 75 - 79 80 - 84 85 - 89 90 - 94 95 - 99 100 - 104 105 - 109 110 - 114 

Fraction of Cooling Hours in Bin Profile 

Outdoor Temperature Bin (oF) 

DOE PT.430

SEERHD

Figure 7C.4.3. Comparison of Temperature Bin Data-CAC Test Procedure and Proposed 

SEERHD 

7C-21 

EERE-2008-BT-STD-0006 COMMENT 54
0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

65 - 69 70 - 74 75 - 79 80 - 84 85 - 89 90 - 94 95 - 99 100 - 104 105 - 109 110 - 114 

Fraction of Cooling Load 

Outdoor Temperature Bin (oF) 

DOE PT.430

SEERHD

Figure 7C.4.4. Comparison of Cooling Load Profile-Current SEER Test Procedure and 

Proposed SEERHD 

7C.5 Conclusion 

The analysis presented provides a methodology for defining two additional regions, and 

Hot-Dry and a Hot-Humid region proposed for use in setting regional air conditioning standards 

in the U.S. for the CAC rulemaking. In addition, the analysis presents a methodology for 

establishing bin temperature data for use in a new SEERHD rating for the proposed four-state 

Hot-Dry Region, with emphasis on preserving a weighted-average cooling load profile for the 

region assuming that cooling equipment is sized to meet the design day conditions for each 

climate. The resulting temperature distribution of bin hours needed to obtain this cooling load 

profile is derived and compared with that of the current test procedure, with the result that a 

greater fraction of bin hours exist at lower temperature bins in the hot dry region, but also at the 

very high temperature bins above 95°F. Since the population-weighted average of the design day 

temperature data for all locations in the Hot-Dry region was very close to 95°F, this analysis did 

not propose changing the cooling load line described by equation 4.1-2 of the current test 

method. The significantly higher cooling load calculated at the high temperature bins and the 

higher bin hours in those bins results in greater emphasis on cooling performance at high 

temperatures (above 95°F) and at low temperatures (below 75°F compared to the current test 

method. 

7C-22 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

1. L.W. Nelson and J.R. Tobias. “Energy Savings in Residential Buildings,” 1974. 

ASHRAE Journal. pp. 34–45. February. 

2. R.R. McConnell, J.R. Tobias, and L.W. Nelson. “Reducing Energy Consumption During 

the Cooling Season,” 1976. ASHRAE Journal. pp. 61–65. June. 

3. S. Wilcox and W. Marion, Users Manual for TMY3 Data Sets, NREL/TP-581-43156, 

May 2008. 

4. U.S. Department of Energy-Energy Information Administration. Residential Energy 

Consumption Survey, 2005 Public Use Data Files, 2005. Washington, DC. 

http://www.eia.doe.gov/emeu/recs/recspubuse05/pubuse05.html (Accessed Apr, 2009) 

5. U.S. Department of Energy-Energy Information Administration. Residential Energy 

Consumption Survey, 2001 Survey Methods. Washington, DC. 

http://www.eia.doe.gov/emeu/recs/recs2001/append_a.html (Accessed Apr, 2009) 

6. 10CFR430 Appendix M, Subpart B. Uniform Test Method for Measuring the Energy 

Consumption of Central Air Conditioners and Heat Pumps 

7. ASHRAE. 2005 Handbook of Fundamentals, American Society of Heating, 

Refrigerating, and Air Conditioning Engineers. Atlanta, GA 

8. PGE. Guide to California Climate Zones. Available at 

http://www.pge.com/mybusiness/edusafety/training/pec/toolbox/arch/climate/index.shtml 

(Accessed Feb, 2009) 

9. CEC. Climate Zones by Zip Code List. MS Excel File available at 

http://www.energy.ca.gov/maps/building_climate_zones.html (Accessed Feb, 2009) 

7C-23 

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 8A. USER INSTRUCTIONS FOR LIFE CYCLE COST SPREADSHEET 

TABLE OF CONTENTS
 

8A.1 Introduction...................................................................................................................... 8A-1
 

8A.2 User Instructions for LLC Spreadsheet............................................................................ 8A-1
 

LIST OF TABLES 

Table 8A.2.1 Description of Tabs in the Life-Cycle Cost Spreadsheet............................... 8A-1
 

8A-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

APPENDIX 8A. USER INSTRUCTIONS FOR LIFE CYCLE COST SPREADSHEET 

8A.1 Introduction 

Appendix E contains instructions to examine and reproduce the detailed results of the 

life-cycle cost (LCC) and payback period (PBP) analyses for beverage vending machine (BVM) 

equipment. This analysis is performed using a Microsoft Excel spreadsheet available on the U.S. 

Department of Energy’s website at: 

http://www1.eere.energy.gov/buildings/appliance_standards/residential/central_ac_hp.html. 

8A.2 User Instructions for LLC Spreadsheet 

The spreadsheet is called “CAC_LCC_PreNOPR_03-18-10_yyy_topost.xls” where yyy 

indicates whether the spreadsheet addresses Coil-Only systems or Coil and Blower systems. It 

allows the user to perform LCC analyses of any of seven equipment classes CAC and CHP 

equipment, of which four are active—Split System A/C, Split System HP, Single-Package A/C 

and Single Package HP. To fully execute in sensitivity mode the spreadsheet requires only 

Microsoft Excel software. To fully execute in uncertainty mode, the model also requires 

commercially-available Crystal Ball®. 

The spreadsheet posted on the DOE website represents the latest version of the applicable 

model and has been developed and tested with Microsoft Excel 2003. The LCC spreadsheet or 

workbook consists of the following tabs as described in Table 8A.2.1. 

Table 8A.2.1 Description of Tabs in the Life-Cycle Cost Spreadsheet 

Notes Notes on changes to this model since the advance notice of proposed rulemaking 

(ANOPR) 

Instructions Instructions for use of the model 

Revision Notes Provides a history of changes that have been made to the model 

Split AC Replacement 

Scenarios 

Split-System air conditioners have system components that can be replaced 

separately. Most depend on a furnace fan to circulate air. The need to replace 

the A/C condenser (outside unit) can be combined with a decision to replace the 

furnace. This sheet specifies assumed scenarios for furnace replacement. For 

Coil-Only system replacements, the air handling unit (furnace fan) is not 

included. 

Split AC Costs This sheet provides data and calculations for purchase and installation of a 

system for various efficiency levels (SEER levels). The cost curve for a coilonly system shows higher costs to meet a given SEER than if both coil and 

blower are considered. 

Furnace This sheet shows the expected life and percent of furnaces reaching retirement, 

based on age of the furnace. 

Replacement Costs This sheet assigns equipment price and installation costs based on whether or not 

early costs of furnace replacement are considered in the cost of air conditioner 

replacement. These costs are not considered in the cost of coil-only 

replacements. 

Equip Price, Repair, Mntc This sheet estimates equipment price, transportation costs, installation costs, and 

repair and maintenance costs based on equipment class and SEER level. 

Names 5-06-2009 This sheet describes the purpose of named ranges in the model 

CB Statistical Results .TWhen the model is run in Crystal Ball this sheet captures regional summary 

statistics (e.g., mean median, 5th,25th, 75th, and 95th percentiles) on key model 

outputs (installed costs, annual energy use and operating costs, LCC and 

8A-1 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

payback period) from the distributions of model results. 

Table 8A.2.1 (contd) Description of Tabs in the Life-Cylce Cost Spreadsheet 

Summary Contains the input selections and a summary table of regional energy use, 

elements of regional installed cost, regional annual operating costs, and regional 

results for LCC and payback period. 

Labels This is used as a go-between for user inputs and the rest of the tabs—do not 

modify this sheet. 

National Variables This sheet provides values for input data that only varies at the national level— 

wholesale and contractor markups, purchaser discount rates. It also determines 

from probability distributions whether any given sale of AC equipment is new or 

replacement and whether it is being installed in a residential or commercial 

building. 

Commercial Int. Processing This sheet provides regional calculations for year 2016 projected annual energy 

use, heating and cooling bills based on data concerning the equipment class, 

location and projected efficiency of individual commercial installations of AC 

and HP equipment. 

Commercial Sim Results This sheet queries the database of individual commercial equipment locations for 

an equipment type and provides information on current cooling and heating 

energy consumption and annual cooling and heating bill (based on 8760 hours 

with price variation by time of use, where relevant) by efficiency level (SEER 

level) 

Residential Int. Processing This sheet provides regional calculations for year 2016 projected annual energy 

use, heating and cooling bills based on data concerning the equipment class, 

location and projected efficiency of individual residential installations of AC and 

HP equipment. 

Input Data on kWh Use, Price This sheet queries the database of individual residential equipment locations for 

an equipment type and provides information on current annual cooling and 

heating energy consumption and average and marginal cost for cooling and 

heating by efficiency level (SEER level) by region for individual observations. 

Costs Workup Uses equipment class and locations, national and regional markup factors, sales 

tax rates and average manufacturer sales prices (including delivery charges) by 

efficiency level to calculate delivered equipment prices by efficiency level by 

region for individual observations. 

Base Install Costs Contains national average data on components of installation costs for each 

equipment class. 

Calculated Regional Indices Contains factors for calculating regionalized values for wholesale and contractor 

markups, sales taxes, and installation costs for individual observations. 

First Cost Region Vals. Provides projected values for new and replacement installation costs, annual 

repair costs, and annual maintenance costs for individual observations by 

efficiency level. 

State-Level Variables and 

Wts. 

Provides data and likelihood of wholesale markup index, contractor markup 

indices, sate and local sales tax index, and installation cost index for individual 

states. Used to calculate markups and other indices for individual observations 

in the Calculated Regional Indices tab. 

Chart Res Prices Chart of real price indices for future escalation of electricity prices in the 

residential sector. 

Chart Commercial Prices Chart of real price indices for future escalation of electricity prices in the 

commercial sector. 

Energy Price Escalation Translates scenario projections of future energy prices from the Annual Energy 

Outlook into average real residential and commercial electricity price indices. 

Discount Rate Contains data from which an average discount rate and a distribution of discount 

rates are determined for residential and commercial consumers. 

Summary Cost of Capital Contains the commercial firm financial data and calculations used to estimate a 

weighted average cost of capital and discount rate for commercial consumers. 

Lifetime Contains the calculations for the survival function for central air conditioning 

8A-2 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

and heat pump units (lifetime in years). 

Basic instructions for operating the LCC spreadsheets are as follows: 

To execute the spreadsheets fully you will need Microsoft Excel and Crystal Ball, 

Version 7.0 or later. The spreadsheet can be run in sensitivity mode using only Excel. 

1.	 Once you have downloaded the LCC file from the Web, first open Crystal Ball and then 

open the file inside of Crystal Ball using Excel. At the bottom, click on the tab for sheet 

Summary. 

2.	 Use Excel’s commands at the top View/Zoom to change the size of the display to make it 

fit your monitor. 

3.	 The user interacts with the spreadsheet by clicking choices or entering data using the
 

graphical interface that comes with the spreadsheet. Choices can be selected from the 

various inputs listed under "User Options" heading.
 

Under the "User Options" heading select choices from the selection buttons and boxes for 

the following: (1) Uncertainty option (2) electricity price projection, (3) start year, 

(4) equipment class (and capacity), (5) installation option (currently limited to only new 

units). (6) level of any cost-reducing measures such as Federal tax credits. 

4.	 To change inputs listed under “User Options” either select the input you wish to change 

by either clicking on the appropriate button or selecting the appropriate input from the 

input box. 

5.	 Crystal Ball results may be saved by using the options in Create Report and Extract Data 

functions on the Control Box window. 

To run a Crystal Ball simulation, do the following: 

To produce Monte Carlo results using Crystal Ball, you need simply select Run from the 

Run menu (on the menu bar). To make basic changes in the run sequence, including 

altering the number of trials, select Run Preferences from the Run menu. After each 

simulation run, the user needs to select Reset (also from the Run menu) before Run can 

be selected again. Once Crystal Ball has completed its run sequence it will produce a 

series of distributions. Using the menu bars on the distribution results it is possible to 

obtain further statistical information. The time taken to complete a run sequence can be 

reduced by minimizing the Crystal Ball window in Excel. Ten thousand runs is the 

default number of runs for this analysis. 

A step-by-step summary of the procedure, for running a distribution analysis, is outlined 

below: 

1) Find the Crystal Ball toolbar (at top of screen) 

2) Click on RUN 

3) Select Preference and choose from the following choices: 

8A-3 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

a) Monte Carlo 

b) Latin Hypercube (recommended)
 

c) Initial seed choices and whether you want it to be constant between runs
 

d) Select number of Monte Carlo Trials (we suggest 10,000). 

4) To run the simulation, follow the following sequence (on the Crystal Ball toolbar) 

RUN 

RESET 

RUN 

5)	 Now wait until the program informs you that the simulation is completed. 

Depending on the speed of your computer, this takes only a few minutes 

(typically, about 90 minutes on a Pentium 5 machine for 10,000 runs). 

Crystal Ball Results 

1)	 After the simulation has finished, to see the distribution charts generated, click on 

the Windows tab bar that is labeled Crystal Ball. 

2)	 Several of the variables of interest, including life-cycle cost, the life-cycle cost 

savings, and the payback periods are defined as Forecast cells. The frequency 

charts display the results of the simulations, or trials, performed by Crystal Ball. 

Click on any chart to bring it into view. The charts show the low and high 

endpoints of the forecasts. The View selection on the Crystal Ball toolbar can be 

used to specify whether you want cumulative or frequency plots shown. 

3) To calculate the probability that LCC savings will occur, either type 0 in the box 

by the right arrow, or move the arrow key with the cursor to 0 on the scale. The 

value in the Certainty box shows the likelihood that the LCC savings will occur. 

To calculate the certainty of payback period being below a certain number of 

years, choose that value as the high endpoint. 

4) To generate a printout report, select Create Report from the Run menu. The 

toolbar choice of Forecast Windows allows you to select the charts and statistics 

you are interested in. For further information on Crystal Ball outputs, please refer 

to Understanding the Forecast Chart in the Crystal Ball manual. Crystal Ball 

results also may be saved to a separate Excel spreadsheet by using the Extract 

Data function under Analyze on the Control Panel window. 

8A-4 

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 8B. TECHNICAL ASPECTS OF THE TARIFF-BASED APPROACH 

TABLE OF CONTENTS
 

8B.1 REVIEW OF DATASETS AND CALCULATION METHODS...........................8B-1
 

8B.2 THE TAP DATABASE AND CALCULATION TOOLS......................................8B-2
 

LIST OF TABLES 

Table 8B.1.1 List of data sources that could be used to estimate electricity prices ..........8B-1
 

Table 8B.1.2 Residential average prices estimated from EIA Form 861 ..........................8B-2
 

LIST OF FIGURES 

Figure 8B.2.1 Comparison of average prices for summer (July) calculated using TAP 

and EEI data.................................................................................................8B-4 

Figure 8B.2.2 Comparison of average prices for winter (January) calculated using 

TAP and EEI data ........................................................................................8B-4
 

 8B-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

APPENDIX 8B. TECHNICAL ASPECTS OF THE TARIFF-BASED APPROACH 

8B.1 REVIEW OF DATASETS AND CALCULATION METHODS 

The methodology used to calculate electricity prices is dependent on the data set. Table 

8B.1.1 lists the data that was available as this analysis was being completed, and the type of 

methodology that goes with the data. The table includes information on the resolution of the 

data both geographically and in terms of the ability to distinguish customer type, and the degree 

of complexity and accuracy of the calculation methods. 

Table 8B.1.1 List of data sources that could be used to estimate electricity prices 

Method Data Source/Year 

Resolution 

Complexity Accuracy 

Customer 

Type Geographic 

Average price EIA 861 2006a Low High Low Low 

Price by consumption 

block EEI 2007 Low Medium Low Low 

Average or marginal price 

by household/building RECS/CBECS 2001 High Low Low Low 

Tariff-based prices using 

existing data (2004) LBNL-TAP 2004 High Medium Low Medium 

Tariff-based prices using 

updated data (2008) LBNL-TAP 2008 High Medium Medium High 

If EIA 861 or EEI data are used, only average prices can be estimatedb. Residential 

average prices are likely to be slightly above residential marginal prices due to the relatively 

large fixed charges on residential bills. However, many areas use ascending block rates, and in 

these cases the marginal price may be higher than the average price. Commercial average prices 

are typically lower than commercial marginal prices. Due to the complexity and diversity of 

commercial rate structures, it can be very difficult to relate an average electricity price to the 

actual bill savings that may occur under an efficiency scenario in which both the electricity 

consumption and demand are reduced. This is especially true for peak-coincident end uses. The 

only method that allows the direct estimation of marginal prices is the tariff-based approach. 

Both the RECS and CBECS datasets provide monthly billing data (consumption and 

expenditures, and demand for commercial buildings) for some survey years (1992 and 1995 for 

CBECS, 1997 and 2001 for RECS). While these data can in principle be used to estimate prices, 

the age of the expenditure data and the size of recent price increases make this approach 

problematic. In particular, in many areas tariff structures (not just rates) may have changed 

a

 EIA Form 861 data for 2007 became available in May 2009, too late for use in this analysis. 

b

 The average price is defined as the ratio of expenditure to energy use. 

 8B-1
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

significantly in the last 5-10 years. The EEI dataset covers only investor-owned utilities and does 

not include publicly-owned companies. In some regions, a significant fraction of consumers are 

served by publicly-owned utilities; EIA data show that on average residential prices tend to be 

lower, and commercial prices somewhat higher, for publicly-owned vs. investor-owned utilities. 

It is therefore important to include these utilities in the analysis. 

Table 8B.1.2 illustrates some of these points using EIA form 861 data for the residential 

sector.1 In each of the years 2004, 2005 and 2006, the consumer-weighted average revenues 

divided by sales is calculated over all utilities in a given region, for different market sectors. 

These numbers are an estimate of the average price paid by a customer in the given sector. The 

table shows the average price estimated in this way for 2006 (in nominal dollars) for publiclyand privately- owned utilities separately. The growth rate is calculated from the price ratios 

between year 2004 and 2005, 2005 and 2006, and the compound increase between 2004 and 

2006. The table illustrates the variability in growth rates by region. Commercial prices show 

similar characteristics. 

Table 8B.1.2 Residential average prices estimated from EIA Form 861 

Residential Average Price ¢/kWh (2006) Increase 2004 to 2006 

Code Area All Private Public All 

1 New England 16.0 16.3 13.1 33.3% 

2 Mid-Atlantic 14.1 13.9 16.6 12.4% 

3 ENC 9.3 9.3 9.1 10.2% 

4 WNC 8.3 8.3 8.3 7.6% 

5 South Atlantic 9.8 9.7 10.0 17.2% 

6 ESC 8.2 8.2 8.1 14.3% 

7 WSC 11.5 12.4 9.6 26.9% 

8 Mountain 9.0 9.1 8.9 8.4% 

9 Pacific 12.6 13.8 9.2 15.3% 

HI & AK 20.6 22.8 17.2 25.5% 

8B.2 THE TAP DATABASE AND CALCULATION TOOLS 

The core of the current TAP database is a statistically representative sample of 

approximately 90 electric utilities that reflect industry diversity in geographic region, ownership 

type, and company size.2

 The finest level of regional variation is currently a set of seventeen 

regions, constructed from the intersection of census divisions with major climate zones.3,4 The 

region definitions resolve three sources of variability: 1) For datasets such as CBECS and RECS, 

general demographic variability is represented at the census division level. To facilitate the use 

of these data in the tariff-based electricity price analysis, the TAP regions are defined as subsectors of census divisions. (2) Weather variability plays an important role in determining load 

characteristics, which in turn can influence prices, as utilities will try to encourage or discourage 

certain behaviors to make their system operations more efficient. Therefore, the census divisions 

are subdivided according to how they overlap with the nine climate regions defined by the 

 8B-2
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

National Climatic Data Center. (3) Since the onset of deregulation, different areas of the country 

can have very different electricity market structures, which can affect prices and billing 

practices. To capture this variability, states or regions with independent markets have been 

broken out into separate regions. In practice this means that California, New York, Texas and 

Florida are defined as separate regions. 

Within each region, the number of utilities chosen was determined by the relative 

population living in that area, and the proportion of customers served by privately- vs. publiclyowned companies. With the current database, variability in prices is well represented at the level 

of these seventeen regions. For the current analysis, regional variation is actually somewhat 

coarser, consisting of nine census divisions plus four large states as is used in RECS. 

Electricity tariffs are typically classed as residential or non-residential. Non-residential 

may be further subdivided into general service, and special-use tariffs such as street-lighting, 

agricultural, etc. For the general service category, some utilities explicitly distinguish between 

commercial and industrial, but most do not. There are usually several general-service tariffs for 

different customer sizes, where size is defined here by the value of the annual peak load, and in 

each size class there is generally a default tariff. TAP currently contains the default residential 

and commercial tariffs for each customer size and market. Given the importance of residential 

tariffs for the current analysis, these data were updated in 2008. TAP also includes any optional 

time-of-use (TOU) residential tariffs that offered by the utilities in the sample. On the 

commercial side, TOU tariffs are always included when they are the default tariff. Tariffs have 

been collected for both primary and secondary delivery voltage. As commercial users of 

residential-style equipment are a small portion of the total customer base, it was not considered 

necessary to update the commercial data for this analysis. Hence, the commercial tariffs used 

here were collected in 2004. The resulting prices are rescaled to reflect recent price increases. 

Tariff data are collected using a series of spreadsheet forms which automatically populate 

the set of normalized tables that comprise the TAP database. Most of the tariff features (various 

types of block rates, TOU rates, seasonal rates, demand charges, etc.) are captured without 

approximation (a complete discussion of the level of detail in the data can be found in published 

work5,3). Given a standardized data format, it is relatively straightforward to develop bill 

calculation tools. These tools take as input the monthly electricity use data, consisting of 

consumption and demand, by TOU period if necessary. The bill calculation then produces the 

monthly bill. 

To validate the tariff data and bill calculation tools, we perform a series of diagnostic 

tests which make use of the electricity price information described above, particularly the EEI 

data. We present a detailed discussion here of the validation for the residential data; the 

commercial data has been validated in a similar manner through previous work.5,3 EEI provides a 

“typical” bill at three monthly consumption levels (500 kWh, 750 kWh and 1000 kWh for 

residential). These are reported by utility for summer and winter separately. It is straightforward 

to use TAP to calculate bills at each of these consumption levels, for the summer and winter 

seasonal rates as appropriate. The corresponding average prices can then be directly compared to 

the EEI data. The results are presented graphically in Figures 8B.2.1 and 8B.2.2, for bills 

 8B-3
 

EERE-2008-BT-STD-0006 COMMENT 54
calculated with 750 kWh of consumption. In these plots, the horizontal axis shows the state and 

data for all the utilities within a given state are used in the determination of the values plotted 

along the vertical axis. The figures show the median average price calculated from the EEI data, 

and the range (high, low and median) of average prices for all the tariffs in TAP. All prices are 

given in cents per kWh. Note the TAP data include publicly-owned utilities, which introduces 

more variability than is in the EEI dataset. 

Summer Avg Prices for 750kwh 

0 

5 

10 

15 

20 

25 

30 

AK 

AL 

AR 

AZ 

CA 

CO 

CT 

DC 

DE 

FL 

GA 

HI 

IA 

ID 

IL 

IN 

KS 

KY 

LA 

MA 

MD 

ME 

MI 

MN 

MO 

MS 

MT 

NC 

ND 

NE 

NH 

NJ 

NM 

NV 

NY 

OH 

OK 

OR 

PA 

RI 

SC 

SD 

TN 

TX 

UT 

VA 

VT 

WA 

WI 

WV 

WY 

0 

5 

10 

15 

20 

25 

30 

E E I 2008 S S um H i S u m L o S u mMe d 

Figure 8B.2.1 Comparison of average prices for summer (July) calculated using TAP and EEI data 

Winter Avg Prices for 750kwh 

0 

5 

10 

15 

20 

25 

30 

AK 

AL 

AR 

AZ 

CA 

CO 

CT 

DC 

DE 

FL 

GA 

HI 

IA 

ID 

IL 

IN 

KS 

KY 

LA 

MA 

MD 

ME 

MI 

MN 

MO 

MS 

MT 

NC 

ND 

NE 

NH 

NJ 

NM 

NV 

NY 

OH 

OK 

OR 

PA 

RI 

SC 

SD 

TN 

TX 

UT 

VA 

VT 

WA 

WI 

WV 

WY 

0 

5 

10 

15 

20 

25 

30 

E E I 200 8W W i n H i W i n Lo Wi n M e d 

Figure 8B.2.2 Comparison of average prices for winter (January) calculated using TAP and EEI data 

 8B-4
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

REFERENCES 

1	 Regulatory Research Associates. Average Regulated Retail Price of Electricity, 2007 & 

Comparative Historical Data. 2008. Jersey City NJ. 

2	 U.S. Department of Energy -- Office of Energy Efficiency and Renewable Energy. 

Commercial Unitary Air Conditioners and Heat Pumps Technical Support Document. 

Including: Appendix M: Sample Utilities. July, 2004. Washington, D.C. 

http://www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/cuac_tsd_app_ 

m.pdf 

3	 Coughlin, K., C. Bolduc, R. Van Buskirk, G. Rosenquist & J. E. McMahon. Tariff-based 

Analysis of Commercial Building Electricity Prices. 2008. Lawrence Berkeley National 

Laboratory: Berkeley, CA. Report No. LBNL-55551. 

4	 U.S. Department of Energy -- Office of Energy Efficiency and Renewable Energy. 

Commercial Unitary Air Conditioners and Heat Pumps Technical Support Document. 

Including: Life-Cycle Cost and Pay-back Period Analysis. July, 2004. Washington, D.C. 

http://www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/cuac_tsd_chp_ 

8.pdf 

5	 Coughlin, Katie, Richard White, Chris Bolduc, Diane Fisher & Greg Rosenquist. The Tariff 

Analysis Project: A database and analysis platform for electricity tariffs. 2006. Lawrence 

Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-55680. For data updates 

see the website <tariffs.lbl.gov>. 

 8B-5
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

APPENDIX 8C. COMPARISON OF MARGINAL PRICES FOR TOU AND DEFAULT
 

TARIFFS
 

TABLE OF CONTENTS
 

8C.1 INTRODUCTION ...................................................................................................8C-1
 

8C.2 COMPARISON OF TOU AND DEFAULT TARIFF MARGINAL PRICES .......8C-1
 

LIST OF TABLES 

Table 8C.2.1 Whole building energy consumption for July (CD is census division) .......8C-2
 

LIST OF FIGURES 

Figure 8C.2.2 Comparison of the effective marginal price for each account. ....................8C-3 

Figure 8C.2.3 Effective marginal prices for all accounts, organized by census 

division .........................................................................................................8C-4
 

8C-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

APPENDIX 8C. COMPARISON OF MARGINAL PRICES FOR TOU AND DEFAULT
 

TARIFFS 

8C.1 INTRODUCTION 

There is a concern that the electricity prices used in the life-cycle cost (LCC) and 

payback period (PBP) analysis do not reflect the real cost of serving system-peak coincident 

loads such as air-conditioning. There have been suggestions that basing electricity costs on timeof-use (TOU) tariffs would provide a more realistic picture of the real value of reducing peakcoincident electricity use. To investigate this issue, the Department conducted an analysis to 

compare prices derived from default (i.e. non-TOU) tariffs and TOU tariffs, described in this 

Appendix. The tariff data were collected as part of the Tariff Analysis Project (TAP) database 

update (see appendix 8-B). 

8C.2 COMPARISON OF TOU AND DEFAULT TARIFF MARGINAL PRICES 

Many utilities offer optional TOU tariffs, but for the moment there are no utilities which 

have mandatory TOU tariffs for residential customers. The Federal Energy Regulatory 

Commission (FERC) 2008 staff report on demand response provides a figure (Figure III-15) that 

indicates approximately 1.2 million residential customers are on TOU tariffs.1

 Based on the total 

residential consumer count from the Energy Information Administration (EIA) Form 861 

(2007),2 this represents less than 1 percent of all consumers. The penetration of TOU rates is thus 

still very low. For this reason, the Department does not include TOU tariffs in the sample used to 

develop electricity prices for the LCC. 

Optional TOU tariff are included in the TAP database for all utilities that offer them. 

Currently, 48 of the 102 utilities in the sample offer TOU rates. These optional TOU tariffs are 

used here to determine whether the difference in electricity prices for TOU vs. non-TOU tariffs 

is significant for this rule-making. This determination can only be made for current rate 

structures; it may be repeated in the future as rate structures continue to evolve. 

Each utility defines the hours associated with TOU periods (on-peak, off-peak and 

shoulder) differently. To correctly calculate electricity bills for TOU tariffs therefore requires 

hourly whole-building electricity consumption data. For this analysis, these data were obtained 

from EnergyPlus building simulations.3

 The simulations use a prototype residential single-family 

detached home, with a central air conditioning (CAC) system and gas furnace, sited in twentytwo different weather locations. Two sets of simulations were performed, with CAC efficiencies 

of 13 SEER and 16 SEER. Both assume a single-speed air conditioner. 

As in the LCC, a set of customer accounts are defined by pairing each residential building 

with all utilities in its region. For the analysis presented here, regions are defined as census 

divisions. For each building-utility pair, monthly utility bills are calculated using both the default 

8C-1
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

tariff and the TOU tariff (utilities that do not offer TOU tariffs are not included in this analysis). 

For each account, the electricity cost savings are defined as the difference between the bill in the 

13 SEER case and the bill in the 16 SEER case. The energy savings are defined similarly. The 

effective marginal electricity price is defined as the ratio of the bill savings to the energy savings. 

The output of this analysis is summarized in Table 8-C.2.1 and Figures 8-C.2.1 and 8-

C.2.2. Seasonal rates can be represented by looking at data for January and July. For the month 

of January the energy savings between 13 and 16 SEER are essentially zero, so only data for July 

are presented. Table 8-C.2.1 shows, for each location, the monthly energy use in July in the 13 

and 16 SEER cases; the energy use is for the whole building, but the difference between the two 

is due to CAC only. The energy savings are also given as a percentage of the 13 SEER whole 

building load. 

Table 8C.2.1 Whole building energy consumption for July (CD is census division) 

City State CD kWh SEER 13 kWh SEER 16 kWh Savings 

Boston MA 1 1177 1064 9.6% 

New York NY 2 1432 1282 10.5% 

Chicago IL 3 1427 1279 10.4% 

Kansas City MO 4 986 933 5.3% 

Minneapolis MN 4 950 913 4.0% 

Atlanta GA 5 1340 1184 11.6% 

Miami FL 5 1234 1095 11.3% 

Sterling VA 5 1202 1084 9.8% 

Dallas TX 7 1460 1283 12.1% 

El Paso TX 7 1200 1086 9.5% 

New Orleans LA 7 1364 1210 11.3% 

San Antonio TX 7 1292 1156 10.6% 

Albuquerque NM 8 1207 1078 10.7% 

Denver CO 8 953 893 6.3% 

Las Vegas NV 8 1721 1468 14.7% 

Phoenix AZ 8 2254 1872 16.9% 

Tucson AZ 8 1605 1381 14.0% 

Fresno CA 9 1529 1336 12.6% 

Los Angeles CA 9 892 853 4.4% 

Sacramento CA 9 1156 1053 8.9% 

San Francisco CA 9 771 761 1.3% 

Seattle WA 9 805 770 4.4% 

Figures 8-C.2.1 and 8-C.2.2 summarize the output of the price calculations. Figure 8-

C.2.1 is a scatter plot of the effective marginal price for the TOU tariff vs. the effective marginal 

price for the default tariff. Each point represents one account, i.e. one building-utility pair. A 

diagonal line is included in the plot for reference. The data show that there is no coherent pattern 

of higher prices for TOU tariffs; in fact there are cases where the effective price is substantially 

lower than for the default tariff. This pattern is confirmed in Figure 8-C.2.2, which shows the 

8C-2 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

default and TOU prices separately. The two sets of prices have been organized by census 

division for this plot. Figure 8-C.2.2 shows that the spread of TOU and default prices is 

comparable, with the exception of census division 4 (West North Central) where the TOU July 

price is substantially higher than the default price. 

These data show that, currently, there is no significant difference on average between 

TOU and default tariffs for the electricity costs used in the LCC. This may be due to several 

factors. For current tariffs, the commodity price (cents per kWh) in different TOU periods is 

often comparable to the default tariff price, and if the on-peak prices are higher the off-peak or 

shoulder prices are typically lower. The distribution of energy savings across the different TOU 

periods also has a strong impact on the effective marginal price. For residential cooling there 

may be significant appliance use outside the utility’s peak hours (which tend to be week-day 

afternoons), which will moderate the impact of higher on-peak prices. 

0 

5 

10 

15 

20 

25 

30 

35 

40 

0 10 20 30 40 

Default price cents/kWh 

TOU price cents/kWh 

July y=x 

Figure 8C.2.2 Comparison of the effective marginal price for each account. 

8C-3
 

EERE-2008-BT-STD-0006 COMMENT 54
0 

5 

10 

15 

20 

25 

30 

35 

40 

1 2 3 4 5 6 7 8 9 10 

Census Division 

Price cents/kWh 

July default July TOU 

Figure 8C.2.3 Effective marginal prices for all accounts, organized by census 

division 

8C-4
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

REFERENCES 

1	 FERC. Assessment of Demand Response and Advanced Metering. Staff Report, Federal 

Energy Regulatory Commission, December 2008. Available at: 

http://www.ferc.gov/legal/staff-reports/12-08-demand-response.pdf 

2	 EIA. Energy Information Agency Annual Electric Power Industry Database Form 861, 

2007. Available at: http://www.eia.doe.gov/cneaf/electricity/page/eia861.html. 

3	 U. S. Department of Energy, EnergyPlus Energy Simulation Software. Available at: 

http://apps1.eere.energy.gov/buildings/energyplus/ 

8C-5
 

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 8D. DEVELOPMENT OF MONTHLY ALLOCATION FACTORS FOR 

RESIDENTIAL BASELINE ENERGY USE 

TABLE OF CONTENTS
 

8D.1 INTRODUCTION .................................................................................................. 8D-1
 

8D.2 DEVELOPMENT OF ALLOCATION FACTORS ............................................... 8D-1
 

LIST OF TABLES 

Table 8D.2.1	 Fraction of RECS 2001 records with monthly billing data by census 

division........................................................................................................ 8D-1 

Table 8D.2.2	 Monthly allocation factors by census division (CD), large state (LS), 

space heating fuel type (EH) and water heating fuel type (EWH) for 

households with central air conditioners..................................................... 8D-3 

Table 8D.2.3	 Monthly allocation factors by census division (CD), large state (LS), 

space heating fuel type (EH) and water heating fuel type (EWH) for 

households with heat pumps ....................................................................... 8D-4 

8D-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

APPENDIX 8D. DEVELOPMENT OF MONTHLY ALLOCATION FACTORS FOR 

RESIDENTIAL BASELINE ENERGY USE 

8D.1 INTRODUCTION 

Utility bills for residential consumers frequently include block rates and/or seasonal rates 

for electricity use. For tariffs with block rates, the unit price per kilowatt-hour (kWh) varies 

according to how much electricity the consumer uses during the billing period. For tariffs with 

seasonal rates, these unit prices differ in summer vs. winter months, with the assignment of 

months to a particular season varying by utility. To correctly calculate marginal prices for this 

type of tariff, an estimation of a residential household’s monthly baseline electricity consumption 

is required. This appendix describes the methodology used to make this estimation. 

8D.2 DEVELOPMENT OF ALLOCATION FACTORS 

The approach uses the monthly household billing data available for the RECS 2001 

survey year to develop monthly allocation factors that define the fraction of annual energy use 

that occurs in each month. The RECS 2001 survey included the collection of monthly electricity 

consumption and expenditure data for a large fraction of the total household sample. For this 

analysis, a minimum of eight bills for a calendar year, with a minimum of three for each season, 

were required for a record to be included in the data set. This results in monthly billing data for 

approximately 70-75% of all households by weight, as shown in Table 8-D.2.1. 

Table 8D.2.1	 Fraction of RECS 2001 records with monthly billing data by census 

division 

Census Division 

All Records Records with Bills 

N N % by Weight 

1 396 279 71% 

2 691 482 69% 

3 681 522 76% 

4 366 269 72% 

5 626 469 75% 

6 409 289 70% 

7 454 349 75% 

8 407 310 76% 

9 792 566 70% 

Nation 4822 3535 73% 

The billing periods for each RECS household are approximately one month long, but 

with varying start and end dates. The data were interpolated onto calendar months by (1) 

calculating the average daily electricity use during each billing period and (2) defining a total 

8D-1 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

electricity use for the calendar month as the sum of the daily values. In cases where a household 

is missing one or more bills, the daily values for the missing periods were estimated as an 

average of the daily values for the preceding and following periods. This procedure leads to a set 

of twelve monthly electricity consumption values for 3535 of the RECS 2001 records. The 

percent of annual energy use that occurs in each month, which we call the monthly allocation 

factor, is then easily calculated for each household. 

From this set, those RECS households having central air conditioners or heat pumps, and 

with non-zero annual cooling energy use, were chosen to represent the consumer populations 

impacted by the current rulemaking. For these consumers, the monthly variability in electricity 

consumption depends primarily on three factors: geographic region (which incorporates both 

climate and building codes), the type of fuel used for space heating, and the fuel used for water 

heating. Accordingly, the records were grouped according to four categorical variables: the two 

geographic variables census division CD and large state LS, a flag (1/0) for electric vs. other 

space heating (EH in the tables), and a flag (1/0) for electric vs. other water heating (EWH in the 

tables). For each category, weighted-average monthly allocation factor were calculated as a 

weighted sum over all households in that category, using the RECS “NWEIGHT” variable as the 

weight. In some regions a category did not contain enough records to calculate a weighted 

average. In these cases, the number of categories was reduced by dropping the water-heating flag 

and, if necessary, the space-heating flag. The weighted averages were then calculated for the 

broader category. For such cases, the values in the table are identical across the sub-categories 

defined by EWH and EH (for example in census division 1 for central air conditioners). The 

complete set of monthly allocation factors are presented in Tables 8-D.2.2 (for central air 

conditioners) and 8-D.2.3 (for heat pumps). 

8D-2
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8D.2.2 Monthly allocation factors by census division (CD), large state (LS), space 

heating fuel type (EH) and water heating fuel type (EWH) for households 

with central air conditioners 

CD LS EH EWH Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

1 0 0 0 0.088 0.074 0.079 0.073 0.075 0.089 0.098 0.098 0.079 0.076 0.080 0.092 

1 0 0 1 0.088 0.074 0.079 0.073 0.075 0.089 0.098 0.098 0.079 0.076 0.080 0.092 

1 0 1 0 0.088 0.074 0.079 0.073 0.075 0.089 0.098 0.098 0.079 0.076 0.080 0.092 

1 0 1 1 0.088 0.074 0.079 0.073 0.075 0.089 0.098 0.098 0.079 0.076 0.080 0.092 

2 0 0 0 0.078 0.067 0.071 0.066 0.071 0.099 0.121 0.122 0.085 0.070 0.070 0.081 

2 0 0 1 0.087 0.074 0.079 0.072 0.075 0.090 0.096 0.111 0.080 0.073 0.076 0.088 

2 0 1 0 0.133 0.103 0.101 0.070 0.053 0.055 0.060 0.068 0.058 0.070 0.095 0.134 

2 0 1 1 0.133 0.103 0.101 0.070 0.053 0.055 0.060 0.068 0.058 0.070 0.095 0.134 

2 1 0 0 0.088 0.074 0.076 0.064 0.073 0.090 0.108 0.118 0.080 0.072 0.074 0.084 

2 1 0 1 0.087 0.079 0.079 0.064 0.072 0.088 0.110 0.112 0.078 0.076 0.075 0.081 

2 1 1 0 0.070 0.069 0.091 0.084 0.093 0.087 0.108 0.106 0.082 0.085 0.061 0.065 

2 1 1 1 0.070 0.069 0.091 0.084 0.093 0.087 0.108 0.106 0.082 0.085 0.061 0.065 

3 0 0 0 0.083 0.070 0.075 0.067 0.070 0.092 0.126 0.113 0.077 0.069 0.072 0.087 

3 0 0 1 0.079 0.069 0.071 0.069 0.077 0.098 0.123 0.107 0.077 0.074 0.074 0.082 

3 0 1 0 0.128 0.103 0.101 0.072 0.050 0.058 0.072 0.074 0.070 0.075 0.080 0.117 

3 0 1 1 0.128 0.103 0.101 0.072 0.050 0.058 0.072 0.074 0.070 0.075 0.080 0.117 

4 0 0 0 0.079 0.066 0.067 0.065 0.072 0.101 0.136 0.118 0.077 0.065 0.071 0.084 

4 0 0 1 0.086 0.077 0.076 0.066 0.069 0.093 0.125 0.102 0.068 0.070 0.079 0.090 

4 0 1 0 0.052 0.049 0.055 0.066 0.090 0.139 0.170 0.114 0.077 0.067 0.064 0.057 

4 0 1 1 0.129 0.112 0.097 0.057 0.050 0.061 0.067 0.060 0.052 0.066 0.110 0.141 

5 0 0 0 0.068 0.056 0.060 0.063 0.081 0.115 0.140 0.135 0.086 0.063 0.061 0.071 

5 0 0 1 0.078 0.066 0.072 0.068 0.082 0.103 0.119 0.117 0.084 0.067 0.066 0.079 

5 0 1 0 0.080 0.065 0.068 0.064 0.070 0.120 0.131 0.133 0.073 0.056 0.057 0.083 

5 0 1 1 0.132 0.100 0.089 0.057 0.057 0.069 0.080 0.074 0.060 0.068 0.084 0.132 

5 4 0 0 0.059 0.048 0.055 0.063 0.099 0.115 0.125 0.141 0.101 0.074 0.059 0.063 

5 4 0 1 0.062 0.063 0.070 0.080 0.092 0.100 0.103 0.114 0.096 0.087 0.072 0.061 

5 4 1 0 0.081 0.065 0.062 0.064 0.087 0.097 0.106 0.113 0.098 0.084 0.065 0.078 

5 4 1 1 0.085 0.065 0.069 0.070 0.087 0.097 0.100 0.107 0.089 0.075 0.070 0.088 

6 0 0 0 0.066 0.058 0.062 0.068 0.086 0.109 0.140 0.131 0.091 0.063 0.061 0.067 

6 0 0 1 0.080 0.070 0.075 0.070 0.081 0.096 0.119 0.110 0.082 0.068 0.067 0.083 

6 0 1 0 0.056 0.047 0.057 0.071 0.096 0.125 0.139 0.128 0.099 0.062 0.059 0.062 

6 0 1 1 0.121 0.086 0.085 0.062 0.066 0.074 0.087 0.083 0.065 0.068 0.083 0.121 

7 0 0 0 0.061 0.053 0.057 0.064 0.089 0.117 0.148 0.138 0.092 0.060 0.057 0.064 

7 0 0 1 0.064 0.056 0.061 0.071 0.102 0.107 0.138 0.132 0.088 0.065 0.057 0.060 

7 0 1 0 0.074 0.059 0.059 0.064 0.073 0.118 0.154 0.129 0.079 0.059 0.054 0.078 

7 0 1 1 0.108 0.083 0.081 0.062 0.073 0.082 0.099 0.097 0.071 0.064 0.073 0.107 

7 3 0 0 0.058 0.048 0.053 0.062 0.089 0.119 0.156 0.141 0.094 0.064 0.055 0.060 

7 3 0 1 0.065 0.062 0.067 0.063 0.083 0.114 0.139 0.129 0.086 0.063 0.059 0.070 

7 3 1 0 0.084 0.074 0.069 0.063 0.084 0.103 0.127 0.124 0.080 0.056 0.053 0.082 

7 3 1 1 0.102 0.082 0.079 0.060 0.073 0.088 0.107 0.101 0.074 0.061 0.071 0.103 

8 0 0 0 0.081 0.068 0.070 0.066 0.080 0.096 0.112 0.114 0.084 0.073 0.074 0.085 

8 0 0 1 0.083 0.072 0.077 0.075 0.084 0.088 0.104 0.102 0.081 0.078 0.074 0.083 

8 0 1 0 0.066 0.055 0.065 0.065 0.097 0.104 0.116 0.110 0.096 0.084 0.067 0.077 

8 0 1 1 0.100 0.085 0.071 0.062 0.075 0.080 0.095 0.095 0.081 0.074 0.084 0.098 

9 0 0 0 0.092 0.075 0.086 0.075 0.075 0.081 0.089 0.086 0.076 0.082 0.085 0.096 

9 0 0 1 0.092 0.075 0.086 0.075 0.075 0.081 0.089 0.086 0.076 0.082 0.085 0.096 

9 0 1 0 0.092 0.075 0.086 0.075 0.075 0.081 0.089 0.086 0.076 0.082 0.085 0.096 

9 0 1 1 0.092 0.075 0.086 0.075 0.075 0.081 0.089 0.086 0.076 0.082 0.085 0.096 

9 2 0 0 0.082 0.069 0.070 0.068 0.077 0.094 0.101 0.106 0.089 0.079 0.078 0.088 

9 2 0 1 0.100 0.093 0.088 0.075 0.070 0.078 0.075 0.084 0.072 0.070 0.094 0.101 

9 2 1 0 0.098 0.088 0.082 0.075 0.076 0.082 0.082 0.087 0.081 0.069 0.086 0.096 

9 2 1 1 0.097 0.088 0.083 0.072 0.078 0.083 0.087 0.087 0.076 0.072 0.084 0.094 

8D-3
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8D.2.3 Monthly allocation factors by census division (CD), large state (LS), space 

heating fuel type (EH) and water heating fuel type (EWH) for households 

with heat pumps 

CD LS EH EWH Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

1 0 0 0 0.086 0.057 0.063 0.052 0.065 0.091 0.146 0.134 0.080 0.063 0.072 0.091 

1 0 0 1 0.086 0.057 0.063 0.052 0.065 0.091 0.146 0.134 0.080 0.063 0.072 0.091 

1 0 1 0 0.086 0.057 0.063 0.052 0.065 0.091 0.146 0.134 0.080 0.063 0.072 0.091 

1 0 1 1 0.086 0.057 0.063 0.052 0.065 0.091 0.146 0.134 0.080 0.063 0.072 0.091 

2 0 0 0 0.125 0.098 0.099 0.072 0.058 0.066 0.077 0.075 0.056 0.066 0.086 0.122 

2 0 0 1 0.125 0.098 0.099 0.072 0.058 0.066 0.077 0.075 0.056 0.066 0.086 0.122 

2 0 1 0 0.125 0.098 0.099 0.072 0.058 0.066 0.077 0.075 0.056 0.066 0.086 0.122 

2 0 1 1 0.125 0.098 0.099 0.072 0.058 0.066 0.077 0.075 0.056 0.066 0.086 0.122 

2 1 0 0 0.081 0.055 0.065 0.057 0.061 0.085 0.119 0.110 0.070 0.095 0.095 0.107 

2 1 0 1 0.081 0.055 0.065 0.057 0.061 0.085 0.119 0.110 0.070 0.095 0.095 0.107 

2 1 1 0 0.086 0.075 0.085 0.102 0.071 0.097 0.100 0.076 0.070 0.056 0.086 0.096 

2 1 1 1 0.086 0.075 0.085 0.102 0.071 0.097 0.100 0.076 0.070 0.056 0.086 0.096 

3 0 0 0 0.078 0.070 0.072 0.061 0.069 0.094 0.132 0.113 0.084 0.070 0.075 0.084 

3 0 0 1 0.078 0.070 0.072 0.061 0.069 0.094 0.132 0.113 0.084 0.070 0.075 0.084 

3 0 1 0 0.124 0.096 0.086 0.060 0.055 0.071 0.086 0.089 0.066 0.068 0.080 0.119 

3 0 1 1 0.124 0.096 0.086 0.060 0.055 0.071 0.086 0.089 0.066 0.068 0.080 0.119 

4 0 0 0 0.080 0.063 0.061 0.059 0.099 0.110 0.134 0.100 0.071 0.068 0.081 0.074 

4 0 0 1 0.080 0.063 0.061 0.059 0.099 0.110 0.134 0.100 0.071 0.068 0.081 0.074 

4 0 1 0 0.116 0.094 0.088 0.056 0.059 0.066 0.082 0.087 0.066 0.073 0.093 0.119 

4 0 1 1 0.116 0.094 0.088 0.056 0.059 0.066 0.082 0.087 0.066 0.073 0.093 0.119 

5 0 0 0 0.077 0.059 0.061 0.068 0.080 0.116 0.123 0.132 0.085 0.061 0.063 0.076 

5 0 0 1 0.092 0.080 0.085 0.075 0.075 0.097 0.095 0.097 0.076 0.065 0.076 0.090 

5 0 1 0 0.080 0.062 0.062 0.062 0.076 0.096 0.125 0.134 0.086 0.069 0.066 0.084 

5 0 1 1 0.121 0.089 0.086 0.065 0.065 0.075 0.085 0.086 0.065 0.063 0.084 0.116 

5 4 0 0 0.087 0.065 0.069 0.067 0.087 0.096 0.101 0.108 0.087 0.072 0.066 0.096 

5 4 0 1 0.087 0.065 0.069 0.067 0.087 0.096 0.101 0.108 0.087 0.072 0.066 0.096 

5 4 1 0 0.087 0.065 0.069 0.067 0.087 0.096 0.101 0.108 0.087 0.072 0.066 0.096 

5 4 1 1 0.087 0.065 0.069 0.067 0.087 0.096 0.101 0.108 0.087 0.072 0.066 0.096 

6 0 0 0 0.054 0.068 0.097 0.089 0.114 0.122 0.112 0.105 0.073 0.059 0.052 0.055 

6 0 0 1 0.069 0.058 0.060 0.072 0.089 0.111 0.134 0.130 0.089 0.061 0.058 0.071 

6 0 1 0 0.075 0.053 0.072 0.063 0.083 0.106 0.123 0.115 0.087 0.074 0.068 0.082 

6 0 1 1 0.109 0.084 0.083 0.064 0.067 0.080 0.094 0.095 0.069 0.065 0.077 0.112 

7 0 0 0 0.062 0.064 0.068 0.069 0.084 0.105 0.136 0.127 0.089 0.064 0.067 0.067 

7 0 0 1 0.067 0.054 0.060 0.063 0.088 0.112 0.143 0.139 0.082 0.062 0.058 0.073 

7 0 1 0 0.082 0.066 0.080 0.070 0.081 0.101 0.132 0.125 0.081 0.052 0.049 0.081 

7 0 1 1 0.101 0.079 0.075 0.071 0.085 0.089 0.102 0.101 0.075 0.064 0.065 0.092 

7 3 0 0 0.097 0.077 0.076 0.065 0.081 0.096 0.115 0.103 0.076 0.059 0.063 0.092 

7 3 0 1 0.097 0.077 0.076 0.065 0.081 0.096 0.115 0.103 0.076 0.059 0.063 0.092 

7 3 1 0 0.097 0.077 0.076 0.065 0.081 0.096 0.115 0.103 0.076 0.059 0.063 0.092 

7 3 1 1 0.097 0.077 0.076 0.065 0.081 0.096 0.115 0.103 0.076 0.059 0.063 0.092 

8 0 0 0 0.060 0.050 0.053 0.060 0.096 0.113 0.132 0.130 0.112 0.074 0.060 0.061 

8 0 0 1 0.060 0.051 0.060 0.057 0.073 0.097 0.153 0.152 0.104 0.067 0.059 0.066 

8 0 1 0 0.086 0.069 0.055 0.048 0.094 0.115 0.124 0.117 0.086 0.057 0.064 0.085 

8 0 1 1 0.098 0.080 0.071 0.062 0.078 0.088 0.099 0.103 0.088 0.068 0.072 0.092 

9 0 0 0 0.091 0.082 0.082 0.078 0.081 0.073 0.083 0.085 0.074 0.084 0.085 0.102 

9 0 0 1 0.120 0.112 0.078 0.064 0.066 0.072 0.071 0.065 0.072 0.083 0.085 0.115 

9 0 1 0 0.129 0.111 0.088 0.081 0.063 0.051 0.046 0.046 0.052 0.094 0.108 0.130 

9 0 1 1 0.123 0.109 0.099 0.078 0.060 0.053 0.051 0.051 0.050 0.092 0.109 0.124 

9 2 0 0 0.103 0.076 0.084 0.077 0.075 0.068 0.066 0.072 0.074 0.077 0.113 0.115 

9 2 0 1 0.102 0.075 0.071 0.062 0.057 0.059 0.078 0.097 0.082 0.090 0.113 0.115 

9 2 1 0 0.105 0.090 0.081 0.073 0.073 0.073 0.080 0.090 0.080 0.076 0.083 0.096 

9 2 1 1 0.093 0.077 0.073 0.064 0.069 0.085 0.101 0.100 0.084 0.082 0.080 0.093 

8D-4
 

EERE-2008-BT-STD-0006 COMMENT 54
 8D-5
 

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 8E. HOUSEHOLD DISCOUNT RATE DISTRIBUTIONS 
 

TABLE OF CONTENTS
 

8E.1 INTRODUCTION ...................................................................................................8E-1 

8E.2 DISTRIBUTION OF MORTGAGE INTEREST RATES ......................................8E-1 

8E.3 DISTRIBUTION OF RATES FOR TYPES OF DEBT AND EQUITY USED 

TO FINANCE REPLACEMENT EQUIPMENT....................................................8E-2 

8E.4 DISTRIBUTION OF RATES FOR TYPES OF EQUITY USED TO 

FINANCE REPLACEMENT EQUIPMENT..........................................................8E-5
 

LIST OF FIGURES 

Figure 8E.2.1 Distribution of New Home Mortgage Interest Rates ...................................8E-1
 

Figure 8E.3.1 Distribution of Home Equity Loan Interest Rates .......................................8E-2
 

Figure 8E.3.2 Distribution of Credit Card Interest Rates ...................................................8E-3
 

Figure 8E.3.3 Distribution of Installment Loan Interest Rates...........................................8E-3
 

Figure 8E.3.4 Distribution of Other Residence Loan Interest Rates ..................................8E-4
 

Figure 8E.3.5 Distribution of Other Lines of Credit Loan Interest Rates ..........................8E-4
 

Figure 8E.4.1 Distribution of Annual Rate of Return on CD’s ..........................................8E-5
 

Figure 8E.4.2 Distribution of Annual Rate of Return on Savings Bonds...........................8E-6
 

Figure 8E.4.3 Distribution of Annual Rate of Return on Corporate AAA Bonds..............8E-6
 

Figure 8E.4.4 Distribution of Annual Rate of Savings Accounts.......................................8E-7
 

Figure 8E.4.5 Distribution of Annual Rate of Return on S&P 500....................................8E-7
 

Figure 8E.4.6 Distribution of Annual Rate of Return on Mutual Funds ............................8E-8
 

8E-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

APPENDIX 8E. HOUSEHOLD DISCOUNT RATE DISTRIBUTIONS 

8E.1 INTRODUCTION 

DOE derived discount rates for the LCC analysis using data on interest or return rates for 

various types of debt and equity. To account for variation among households in rates for each of 

the types, DOE sampled a rate for each household from a distribution of rates for each debt and 

equity type. This appendix describes the distributions used. 

8E.2 DISTRIBUTION OF MORTGAGE INTEREST RATES 

Figure 8E.2.1 shows the distribution of real interest rates for new home mortgages. 

The data source DOE used for mortgage interest rates is the Federal Reserve Board’s Survey of 

Consumer Finances (SCF) in 1989, 1992, 1995, 1998, 2001, 2004 and 2007.1 Using the 

appropriate SCF data for each year, DOE adjusted the nominal mortgage interest rate for each 

relevant household in the SCF for mortgage tax deduction and inflation. In cases where the 

effective interest rate is equal to or below the inflation rate (resulting in a negative real interest 

rate), DOE set the real effective interest rate to zero. 

0%

5%

10%

15%

20%

25%

30%

35%

0.5%

1.6%

2.5%

3.5%

4.4%

5.4%

6.4%

7.4%

8.5%

9.5%

10.5%

11.6%

12.4%

13.5%

14.4%

16.7%

22.9%

Real Interest Rate 

Probability 

Figure 8E.2.1 Distribution of New Home Mortgage Interest Rates 

8E-1
 

EERE-2008-BT-STD-0006 COMMENT 54
8E.3	 DISTRIBUTION OF RATES FOR TYPES OF DEBT AND EQUITY USED 

TO FINANCE REPLACEMENT EQUIPMENT 

Figure 8E.3.1 through Figure 8E.3.5 show the distribution of real interest rates for 

different types of debt used to finance replacement equipment. The data source for the interest 

rates for home equity loans, credit cards, installment loans, other residence loans, and other lines 

of credit is the Federal Reserve Board’s SCF in 1989, 1992, 1995, 1998, 2001, 2004, and 2007.1 

DOE adjusted the nominal rates to real rates using the annual inflation rate in each year. For 

home equity loans, DOE calculated effective interest rates in a similar manner as for mortgage 

rates, since interest on such loans is tax deductible. 

0%

5%

10%

15%

20%

25%

30%

35%

0.4%

1.5%

2.6%

3.5%

4.5%

5.4%

6.4%

7.5%

8.5%

9.5%

10.6%

11.8%

12.7%

13.7%

14.7%

16.7%

17.5%

42.5%

Real Interest Rate 

Probability 

Figure 8E.3.1 Distribution of Home Equity Loan Interest Rates 

8E-2
 

EERE-2008-BT-STD-0006 COMMENT 54
0%

2%

4%

6%

8%

10%

12%

0.0%

1.4%

2.5%

3.5%

4.5%

5.6%

6.7%

7.5%

8.7%

9.6%

10.6%

11.5%

12.6%

13.6%

14.7%

15.6%

16.4%

17.6%

18.5%

19.4%

20.5%

21.4%

22.5%

23.6%

24.6%

25.5%

26.3%

27.3%

Real Interest Rate 

Probability 

Figure 8E.3.2 Distribution of Credit Card Interest Rates 

0%

5%

10%

15%

20%

25%

30%

35%

0.0%

2.4%

4.5%

6.7%

8.6%

10.6%

12.5%

14.7%

16.5%

18.5%

20.5%

22.3%

24.6%

26.6%

28.5%

34.1%

43.6%

47.2%

Real Interest Rate 

Probability 

Figure 8E.3.3 Distribution of Installment Loan Interest Rates 

8E-3
 

EERE-2008-BT-STD-0006 COMMENT 54
0%

5%

10%

15%

20%

0.2%

1.5%

2.5%

3.5%

4.5%

5.5%

6.5%

7.5%

8.6%

9.4%

10.6%

11.6%

12.6%

14.7%

15.7%

16.6%

17.9%

Real Interest Rate 

Probability 

Figure 8E.3.4 Distribution of Other Residence Loan Interest Rates 

0%

2%

4%

6%

8%

10%

12%

14%

16%

0.1%

1.4%

2.5%

3.5%

4.5%

5.5%

6.7%

7.6%

8.7%

9.5%

10.5%

11.6%

12.5%

13.5%

14.7%

15.6%

16.5%

17.3%

18.5%

19.1%

20.2%

21.5%

22.1%

23.5%

Real Interest Rate 

Probability 

Figure 8E.3.5 Distribution of Other Lines of Credit Loan Interest 

Rates 

8E-4
 

EERE-2008-BT-STD-0006 COMMENT 54
8E.4	 DISTRIBUTION OF RATES FOR TYPES OF EQUITY USED TO 

FINANCE REPLACEMENT EQUIPMENT 

Figure 8E.4.1 through Figure 8E.4.6 show the distribution of real interest rates for 

different types of equity used to finance replacement equipment. Data for equity classes are not 

available from the Federal Reserve Board’s SCF, so the Department derived data for these 

classes from national-level historical data. The interest rates associated with certificates of 

deposit (CDs),2 savings bonds,3 and bonds (AAA corporate bonds)4 are from Federal Reserve 

Board time-series data covering 1977–2007. DOE assumed rates on checking accounts to be 

zero. Rates on savings and money market accounts are from Cost of Savings Index data covering 

1984–2007.5

 The rates for stocks are the annual returns on the Standard and Poor’s (S&P) 500 in 

the 1977–2007 period.6 The mutual fund rates are a weighted average of the stock rates (twothirds weight) and the bond rates (one-third weight) in each year of the 1977–2007 period. DOE 

adjusted the nominal rates to real rates using the annual inflation rate in each year. 

-2%

0%

2%

4%

6%

8%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8E.4.1 Distribution of Annual Rate of Return on CD’s 

8E-5
 

EERE-2008-BT-STD-0006 COMMENT 54
-2%

0%

2%

4%

6%

8%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8E.4.2 Distribution of Annual Rate of Return on Savings 

Bonds 

-2%

0%

2%

4%

6%

8%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8E.4.3 Distribution of Annual Rate of Return on Corporate 

AAA Bonds 

8E-6
 

EERE-2008-BT-STD-0006 COMMENT 54
-2%

0%

2%

4%

6%

8%

1984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008

Year 

Annual Rate of Return 

8E-7 

Figure 8E.4.4 Distribution of Annual Rate of Savings Accounts 

-30%

-20%

-10%

0%

10%

20%

30%

40%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8E.4.5 Distribution of Annual Rate of Return on S&P 500 

EERE-2008-BT-STD-0006 COMMENT 54
-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8E.4.6 Distribution of Annual Rate of Return on Mutual 

Funds 

8E-8
 

EERE-2008-BT-STD-0006 COMMENT 54
 

REFERENCES 

1. The Federal Reserve Board, Survey of Consumer Finances 1989, 1992, 1995, 1998, 2001, 

2004, 2007. <http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html> 

2. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: 

Annual, Description: Average rate on 6-month negotiable certificates of deposit 

(secondary market), quoted on an investment basis, 2008. 

<http://www.federalreserve.gov/releases/H15/data.htm> 

3. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: 

Monthly, Description: Bond buyer go 20-bond municipal bond index, 2008. 

<http://www.federalreserve.gov/releases/H15/data.htm> 

4. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: Corporate bonds/Moody's Seasoned AAA, Frequency: 

Annual, Description: Moody's yield on seasoned corporate bonds - all industries, AAA, 

2008. <http://www.federalreserve.gov/releases/H15/data.htm> 

5. Wachovia, Cost of Savings Index (COSI) Index History, 2008. (Last accessed March 10, 

2008.) <http://mortgage-x.com/general/indexes/default.asp> 

6. Damodaran Online, Damodaran Online:The Data Page (Historical Returns on Stocks, 

Bonds, and Bills - United States), 2008. Stern School of Business, New York University. 

(Last accessed March 10, 2008.) <http://pages.stern.nyu.edu/~adamodar/> 

8E-9
 

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 8F. DETAILED LIFE-CYCLE COST AND PAYBACK PERIOD 

RESULTS
 

TABLE OF CONTENTS 

8F.1 Split-System Air Conditioners.......................................................................................... 8F-1
 

8F.1.1 Split-System Air Conditioner LCC Savings Results ............................................ 8F-1
 

8F.1.2 Split-system Air Conditioner Payback Period Results ......................................... 8F-4
 

8F.2 Split-System Heat Pumps ................................................................................................. 8F-7
 

8F.2.1 Split-System Heat Pump LCC Savings Results.................................................... 8F-7
 

8F.2.2 Split-System Heat Pump Payback Period........................................................... 8F-10
 

8F.3 Single-Package Air Conditioners .................................................................................... 8F-12
 

8F.3.1 Single-Package Air Conditioner LCC Savings Results...................................... 8F-12
 

8F.3.2 Single-Package Air Conditioner Payback Period Results .................................. 8F-13
 

8F.4 Single-Package Heat Pumps ........................................................................................... 8F-14
 

8F.4.1 Single-Package Heat Pump LCC Savings Results.............................................. 8F-14
 

8F.4.2 Single-Package Heat Pump Payback Period Results.......................................... 8F-16
 

LIST OF TABLES 

Table 8F.1. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Coil-Only): National ..................................................................... 8F-1 

Table 8F.2. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Coil-Only): Hot-Humid Region ................................................... 8F-1 

Table 8F.3. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Coil-Only): Hot-Dry Region ........................................................ 8F-1 

Table 8F.4. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Coil-Only): Rest of Country Region ............................................ 8F-2 

Table 8F.5. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Blower-Coil): National ................................................................. 8F-2 

Table 8F.6. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Blower-Coil): Hot-Humid Region................................................ 8F-3 

Table 8F.7. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Blower-Coil): Hot-Dry Region .................................................... 8F-3 

Table 8F.8. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Blower-Coil): Rest of Country Region ........................................ 8F-4 

Table 8F.9. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Coil-Only): National ..................................................................... 8F-4 

Table 8F.10. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Coil-Only): Hot-Humid Region ................................................... 8F-5 

Table 8F.11. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Coil-Only): Hot-Dry Region ........................................................ 8F-5
 

8F-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

Table 8F.12. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Coil-Only): Rest of Country Region ............................................ 8F-5 

Table 8F.13. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Blower-Coil): National ................................................................. 8F-6 

Table 8F.14. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Blower-Coil): Hot-Humid Region................................................ 8F-6 

Table 8F.15. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Blower-Coil): Hot-Dry Region .................................................... 8F-7 

Table 8F.16. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Blower-Coil): Rest of Country Region ........................................ 8F-7 

Table 8F.17. Regional Distributions of LCC Savings for Split-System Heat 

Pumps: National .................................................................................................. 8F-8 

Table 8F.18. Regional Distributions of LCC Savings for Split-System Heat 

Pumps: Hot-Humid Region ................................................................................. 8F-8 

Table 8F.19. Regional Distributions of LCC Savings for Split-System Heat 

Pumps: Hot-Dry Region...................................................................................... 8F-9 

Table 8F.20. Regional Distributions of LCC Savings for Split-System Heat 

Pumps: Rest of Country Region.......................................................................... 8F-9 

Table 8F.21. Regional Distributions of Payback Period for Split-System Heat 

Pumps: National ................................................................................................ 8F-10 

Table 8F.22. Regional Distributions of Payback Period for Split-System Heat 

Pumps: Hot-Humid Region ............................................................................... 8F-10 

Table 8F.23. Regional Distributions of Payback Period for Split-System Heat 

Pumps: Hot-Dry Region................................................................................... 8F-11 

Table 8F.24. Regional Distributions of Payback Period for Split-System Heat 

Pumps:: Rest of Country Region....................................................................... 8F-11 

Table 8F.25. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: National ...................................................................................... 8F-12 

Table 8F.26. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Hot-Humid Region ..................................................................... 8F-12 

Table 8F.27. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Hot-Dry Region .......................................................................... 8F-12 

Table 8F.28. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Rest of Country Region .............................................................. 8F-13 

Table 8F.29. Regional Distributions of Payback Period for Single-Package Air 

Conditioners: National ...................................................................................... 8F-13 

Table 8F.30. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Hot-Humid Region ..................................................................... 8F-13 

Table 8F.31. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Hot-Dry Region .......................................................................... 8F-14 

Table 8F.32. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Rest of Country Region .............................................................. 8F-14 

Table 8F.33. Regional Distributions of LCC Savings for Single-Package Heat 

Pumps: National ................................................................................................ 8F-14 

Table 8F.34. Regional Distributions of LCC Savings for Single-Package Heat 

Pumps: Hot-Humid Region ............................................................................... 8F-15
 

8F-ii 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

Table 8F.35. Regional Distributions of LCC Savings for Single-Package Heat 

Pumps: Hot-Dry Region................................................................................... 8F-15 

Table 8F.36. Regional Distributions of LCC Savings for Single-Package Heat 

Pumps: Rest of Country Region....................................................................... 8F-15 

Table 8F.37. Regional Distributions of Payback Period for Single-Package Heat 

Pumps: National ................................................................................................ 8F-16 

Table 8F.38. Regional Distributions of Payback Period for Single-Package Heat 

Pumps: Hot-Humid Region ............................................................................... 8F-16 

Table 8F.39. Regional Distributions of Payback Period for Single-Package Heat 

Pumps: Hot-Dry Region................................................................................... 8F-16 

Table 8F.40. Regional Distributions of Payback Period for Single-Package Heat 

Pumps: Rest of Country Region....................................................................... 8F-17
 

8F-iii 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDIX F. DETAILED LIFE-CYCLE COST AND PAYBACK PERIOD 

RESULTS 

8F.1 Split-System Air Conditioners 

8F.1.1 Split-System Air Conditioner LCC Savings Results 

Table 8F.1. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Coil-Only): National 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 4 (80) 0 0 0 63 

3 14 (1) (173) (87) (27) 0 274 

4 14.5 (29) (304) (190) (87) 0 451 

5 15 (83) (472) (311) (173) 0 585 

6 15.5 (180) (684) (469) (301) 0 652 

7 16 (341) (978) (688) (487) (146) 646 

8 16.5 (565) (1,332) (957) (724) (358) 593 

Table 8F.2. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Coil-Only): Hot-Humid Region 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 11 (49) 0 0 0 109 

3 14 36 (112) (47) 0 57 325 

4 14.5 39 (209) (109) 0 80 533 

5 15 19 (331) (192) (44) 82 699 

6 15.5 (39) (491) (310) (129) 49 809 

7 16 (154) (708) (479) (272) 0 839 

8 16.5 (324) (969) (696) (465) (130) 820 

Table 8F.3. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Coil-Only): Hot-Dry Region 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 8 (93) 0 0 0 103 

3 14 20 (197) (98) (8) 20 408 

4 14.5 4 (346) (208) (72) 15 675 

5 15 (40) (528) (342) (163) 0 885 

6 15.5 (134) (767) (517) (302) 0 1,031 

7 16 (300) (1,086) (757) (506) (29) 1,074 

8 16.5 (537) (1,483) (1,058) (772) (279) 1,057 

8F-1 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

Table 8F.4. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Coil-Only): Rest of Country Region
 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 (15) (98) 0 0 0 0 

3 14 (75) (206) (116) (78) 0 3 

4 14.5 (157) (358) (239) (172) (62) 0 

5 15 (265) (541) (380) (286) (151) 0 

6 15.5 (414) (778) (562) (438) (288) 0 

7 16 (624) (1,105) (806) (647) (474) 0 

8 16.5 (902) (1,500) (1,101) (905) (711) (252) 

Table 8F.5. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Blower-Coil): National 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 2 (88) 0 0 0 0 

3 14 0 (178) (88) (24) (88) 0 

4 14.5 (15) (290) (177) (72) (177) 0 

5 15 (5) (371) (232) (86) (232) 19 

6 15.5 (5) (459) (294) (120) (294) 39 

7 16 (14) (555) (361) (161) (361) 46 

8 16.5 (36) (656) (436) (216) (436) 46 

9 17 (68) (764) (518) (281) (518) 29 

10 18 (159) (1,003) (701) (428) (701) 0 

11 19 (285) (1,265) (906) (605) (906) (87) 

12 20 (443) (1,563) (1,136) (801) (1,136) (217) 

13 21 (634) (1,886) (1,389) (1,023) (1,389) (390) 

14 22 (859) (2,253) (1,667) (1,267) (1,667) (587) 

8F-2 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.6. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Blower-Coil): Hot-Humid Region
 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 11 (54) 0 0 0 108 

3 14 38 (113) (45) 0 53 337 

4 14.5 53 (196) (95) 0 87 563 

5 15 90 (254) (120) 0 151 800 

6 15.5 118 (318) (151) 0 206 1,023 

7 16 137 (388) (191) 0 249 1,223 

8 16.5 142 (460) (241) (31) 277 1,395 

9 17 137 (539) (298) (61) 294 1,544 

10 18 100 (715) (433) (146) 294 1,791 

11 19 28 (912) (587) (258) 262 1,967 

12 20 (77) (1,130) (768) (396) 189 2,072 

13 21 (214) (1,372) (970) (562) 80 2,129 

14 22 (384) (1,641) (1,193) (758) (54) 2,144 

Table 8F.7. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Blower-Coil): Hot-Dry Region 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7 (99) 0 0 0 103 

3 14 24 (200) (96) (6) 21 422 

4 14.5 27 (321) (192) (50) 33 703 

5 15 56 (414) (252) (68) 88 1,001 

6 15.5 74 (514) (316) (97) 130 1,280 

7 16 81 (622) (387) (136) 158 1,533 

8 16.5 73 (736) (474) (193) 181 1,743 

9 17 54 (858) (567) (263) 190 1,939 

10 18 (19) (1,122) (771) (425) 167 2,238 

11 19 (133) (1,417) (1,001) (613) 97 2,472 

12 20 (287) (1,741) (1,259) (827) 0 2,626 

13 21 (480) (2,103) (1,541) (1,065) (145) 2,702 

14 22 (713) (2,502) (1,848) (1,335) (339) 2,710 

8F-3 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.8. Regional Distributions of LCC Savings for Split-System Air 

Conditioners (Blower-Coil): Rest of Country Region 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 (16) (102) 0 0 0 0 

3 14 (73) (207) (112) (76) 0 10 

4 14.5 (140) (333) (221) (157) (39) 7 

5 15 (183) (424) (292) (208) (65) 39 

6 15.5 (234) (526) (369) (265) (101) 62 

7 16 (291) (632) (450) (328) (150) 72 

8 16.5 (361) (745) (537) (399) (210) 58 

9 17 (437) (868) (630) (477) (276) 32 

10 18 (611) (1,129) (831) (651) (432) 0 

11 19 (810) (1,421) (1,057) (846) (609) (90) 

12 20 (1,036) (1,746) (1,308) (1,064) (810) (240) 

13 21 (1,288) (2,100) (1,585) (1,308) (1,032) (421) 

14 22 (1,568) (2,492) (1,890) (1,575) (1,276) (641) 

8F.1.2 Split-system Air Conditioner Payback Period Results 

Table 8F.9. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Coil-Only): National 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 48.6 2.5 7.3 27.8 100.0 100.0 

3 14 53.5 3.0 9.0 41.9 100.0 100.0 

4 14.5 57.0 3.4 11.0 59.4 100.0 100.0 

5 15 61.0 4.0 13.6 100.0 100.0 100.0 

6 15.5 66.0 4.7 17.9 100.0 100.0 100.0 

7 16 71.6 5.8 25.8 100.0 100.0 100.0 

8 16.5 76.8 7.2 41.3 100.0 100.0 100.0 

8F-4 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.10. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Coil-Only): Hot-Humid Region 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 27.3 2.6 5.3 9.9 28.7 100.0 

3 14 30.2 2.9 6.0 11.6 38.6 100.0 

4 14.5 33.8 3.3 7.1 14.3 54.0 100.0 

5 15 38.2 3.8 8.3 17.5 84.4 100.0 

6 15.5 44.0 4.5 10.2 23.1 100.0 100.0 

7 16 51.6 5.4 13.2 34.5 100.0 100.0 

8 16.5 59.7 6.5 17.2 63.5 100.0 100.0 

Table 8F.11. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Coil-Only): Hot-Dry Region 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 47.6 2.2 5.7 22.3 100.0 100.0 

3 14 50.1 2.5 6.7 28.4 100.0 100.0 

4 14.5 53.5 2.9 8.1 42.6 100.0 100.0 

5 15 57.2 3.4 9.7 64.3 100.0 100.0 

6 15.5 61.8 4.0 12.3 100.0 100.0 100.0 

7 16 67.2 4.9 16.8 100.0 100.0 100.0 

8 16.5 72.0 5.9 24.6 100.0 100.0 100.0 

Table 8F.12. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Coil-Only): Rest of Country Region 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 75.9 9.9 38.1 100.0 100.0 100.0 

3 14 80.8 11.7 65.9 100.0 100.0 100.0 

4 14.5 84.1 14.5 100.0 100.0 100.0 100.0 

5 15 87.3 17.9 100.0 100.0 100.0 100.0 

6 15.5 90.8 23.7 100.0 100.0 100.0 100.0 

7 16 94.1 36.9 100.0 100.0 100.0 100.0 

8 16.5 96.5 69.1 100.0 100.0 100.0 100.0 

8F-5 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.13. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Blower-Coil): National 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 48.8 2.9 8.0 27.4 8.0 100.0 

3 14 50.8 3.2 9.1 33.3 9.1 100.0 

4 14.5 52.1 3.4 10.0 36.2 10.0 100.0 

5 15 49.4 3.2 9.3 29.9 9.3 100.0 

6 15.5 49.0 3.2 9.3 29.0 9.3 100.0 

7 16 49.4 3.3 9.5 29.6 9.5 100.0 

8 16.5 51.5 3.4 10.1 34.8 10.1 100.0 

9 17 53.3 3.6 10.7 39.9 10.7 100.0 

10 18 56.9 3.9 12.1 56.4 12.1 100.0 

11 19 60.2 4.3 13.9 84.1 13.9 100.0 

12 20 63.3 4.7 16.1 100.0 16.1 100.0 

13 21 66.3 5.2 18.9 100.0 18.9 100.0 

14 22 69.1 5.8 22.4 100.0 22.4 100.0 

Table 8F.14. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Blower-Coil): Hot-Humid Region
 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 26.3 2.8 5.6 10.4 27.4 100.0 

3 14 27.9 3.1 6.3 11.7 30.7 100.0 

4 14.5 29.0 3.3 6.9 12.8 34.4 100.0 

5 15 26.7 3.1 6.4 11.7 28.7 100.0 

6 15.5 26.3 3.1 6.4 11.5 27.9 100.0 

7 16 26.8 3.2 6.6 11.7 28.7 100.0 

8 16.5 28.6 3.3 6.9 12.6 32.7 100.0 

9 17 30.2 3.4 7.2 13.5 36.8 100.0 

10 18 33.7 3.7 8.0 15.5 48.5 100.0 

11 19 37.3 4.1 8.9 17.9 67.7 100.0 

12 20 41.0 4.5 10.0 20.9 100.0 100.0 

13 21 44.7 5.0 11.3 24.7 100.0 100.0 

14 22 48.4 5.5 12.8 29.7 100.0 100.0 

8F-6 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.15. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Blower-Coil): Hot-Dry Region 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 46.6 2.5 6.2 21.5 100.0 100.0 

3 14 48.0 2.7 6.8 24.3 100.0 100.0 

4 14.5 49.0 2.9 7.5 28.2 100.0 100.0 

5 15 46.3 2.8 7.0 24.2 100.0 100.0 

6 15.5 45.6 2.8 7.0 23.3 100.0 100.0 

7 16 45.8 2.8 7.2 23.8 100.0 100.0 

8 16.5 48.0 3.0 7.4 26.9 100.0 100.0 

9 17 49.7 3.1 7.8 30.1 100.0 100.0 

10 18 53.0 3.3 8.7 38.6 100.0 100.0 

11 19 55.9 3.7 9.9 51.9 100.0 100.0 

12 20 58.6 4.0 11.2 76.4 100.0 100.0 

13 21 61.3 4.5 12.9 100.0 100.0 100.0 

14 22 63.9 4.9 14.9 100.0 100.0 100.0 

Table 8F.16. Regional Distributions of Payback Period for Split-System Air 

Conditioners (Blower-Coil): Rest of Country Region 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 75.3 10.4 37.8 100.0 100.0 100.0 

3 14 77.8 11.7 45.7 100.0 100.0 100.0 

4 14.5 79.2 13.0 51.2 100.0 100.0 100.0 

5 15 76.5 11.9 40.7 100.0 100.0 100.0 

6 15.5 76.1 11.6 39.1 100.0 100.0 100.0 

7 16 76.6 11.8 40.2 100.0 100.0 100.0 

8 16.5 78.4 13.0 47.9 100.0 100.0 100.0 

9 17 80.2 13.9 57.2 100.0 100.0 100.0 

10 18 83.5 15.9 89.6 100.0 100.0 100.0 

11 19 86.5 18.4 100.0 100.0 100.0 100.0 

12 20 89.0 21.7 100.0 100.0 100.0 100.0 

13 21 91.2 26.3 100.0 100.0 100.0 100.0 

14 22 93.1 32.3 100.0 100.0 100.0 100.0 

8F.2 Split-System Heat Pumps 

8F.2.1 Split-System Heat Pump LCC Savings Results 

8F-7 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.17. Regional Distributions of LCC Savings for Split-System Heat Pumps: 

National 

Efficiency 

Level SEER HSPF 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 8 (13) 0 0 0 59 

3 14 8 40 (115) (29) 0 45 349 

4 14.5 8.15 95 (172) (43) 0 135 649 

5 15 8.3 150 (231) (63) 0 227 934 

6 15.5 8.45 194 (300) (101) 1 306 1,195 

7 16 8.6 200 (399) (171) 0 345 1,398 

8 17 9.1 262 (572) (271) 0 490 1,904 

9 18 9.6 275 (767) (403) (34) 567 2,301 

10 19 10.1 241 (990) (564) (128) 588 2,616 

11 19.5 10.3 196 (1,118) (662) (194) 572 2,726 

Table 8F.18. Regional Distributions of LCC Savings for Split-System Heat Pumps: 

Hot-Humid Region 

Efficiency 

Level SEER HSPF 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 11 0 0 0 0 78 

3 14 8 51 (92) (9) 0 66 359 

4 14.5 8.15 113 (136) (15) 0 168 650 

5 15 8.3 176 (180) (28) 28 270 930 

6 15.5 8.45 228 (234) (52) 50 359 1,179 

7 16 8.6 245 (314) (108) 37 410 1,402 

8 17 9.1 312 (458) (193) 53 556 1,896 

9 18 9.6 333 (626) (305) 30 648 2,278 

10 19 10.1 311 (813) (443) (14) 683 2,576 

11 19.5 10.3 276 (920) (529) (72) 677 2,682 

8F-8 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.19. Regional Distributions of LCC Savings for Split-System Heat Pumps: 

Hot-Dry Region 

Efficiency 

Level SEER HSPF 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 17 0 0 0 0 121 

3 14 8 81 (124) 0 0 106 490 

4 14.5 8.15 174 (181) 0 9 249 901 

5 15 8.3 269 (236) (9) 69 391 1,295 

6 15.5 8.45 350 (304) (37) 110 511 1,656 

7 16 8.6 388 (403) (98) 107 590 1,962 

8 17 9.1 509 (606) (191) 151 799 2,677 

9 18 9.6 570 (837) (311) 142 927 3,286 

10 19 10.1 574 (1,099) (467) 83 1,010 3,746 

11 19.5 10.3 544 (1,240) (564) 33 1,011 3,912 

Table 8F.20. Regional Distributions of LCC Savings for Split-System Heat Pumps: 

Rest of Country Region 

Efficiency 

Level SEER HSPF 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 0 (43) 0 0 0 4 

3 14 8 (10) (123) (61) 0 0 142 

4 14.5 8.15 (1) (183) (91) 0 18 293 

5 15 8.3 8 (246) (124) (2) 54 431 

6 15.5 8.45 3 (320) (172) (31) 73 554 

7 16 8.6 (36) (427) (253) (90) 54 631 

8 17 9.1 (44) (599) (367) (140) 110 919 

9 18 9.6 (94) (798) (511) (223) 117 1,127 

10 19 10.1 (185) (1,021) (680) (343) 81 1,253 

11 19.5 10.3 (259) (1,151) (783) (429) 27 1,265 

8F-9 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

8F.2.2 Split-System Heat Pump Payback Period 

Table 8F.21. Regional Distributions of Payback Period for Split-System Heat 

Pumps: National 

Efficiency 

Level SEER HSPF 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 22.1 2.7 4.5 8.0 16.9 100.0 

3 14 8 24.2 3.0 5.0 9.0 21.0 100.0 

4 14.5 8.15 20.1 2.7 4.5 7.8 17.0 100.0 

5 15 8.3 18.4 2.6 4.3 7.6 16.1 100.0 

6 15.5 8.45 18.6 2.7 4.5 7.9 16.8 100.0 

7 16 8.6 21.1 3.0 5.1 9.1 20.1 100.0 

8 17 9.1 22.0 3.2 5.3 9.3 19.9 100.0 

9 18 9.6 23.8 3.4 5.8 10.1 21.8 100.0 

10 19 10.1 26.0 3.8 6.3 11.2 25.0 100.0 

11 19.5 10.3 27.5 4.0 6.7 12.0 28.2 100.0 

Table 8F.22. Regional Distributions of Payback Period for Split-System Heat 

Pumps: Hot-Humid Region 

Efficiency 

Level SEER HSPF 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 16.1 2.6 4.1 6.4 12.3 100.0 

3 14 8 17.8 3.0 4.6 7.4 14.4 100.0 

4 14.5 8.15 13.5 2.7 4.1 6.5 12.1 56.2 

5 15 8.3 11.8 2.6 4.0 6.3 11.6 40.2 

6 15.5 8.45 11.9 2.7 4.2 6.6 12.2 39.3 

7 16 8.6 13.8 3.0 4.7 7.5 14.3 49.4 

8 17 9.1 15.5 3.2 5.0 7.8 14.9 68.4 

9 18 9.6 17.6 3.5 5.4 8.5 16.3 100.0 

10 19 10.1 19.8 3.8 6.0 9.4 18.5 100.0 

11 19.5 10.3 21.2 4.0 6.3 10.0 20.1 100.0 

8F-10 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.23. Regional Distributions of Payback Period for Split-System Heat 

Pumps: Hot-Dry Region 

Efficiency 

Level SEER HSPF 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 16.7 2.0 3.8 5.2 9.5 100.0 

3 14 8 18.2 2.3 4.3 6.2 11.3 100.0 

4 14.5 8.15 15.8 2.1 3.9 5.5 9.8 100.0 

5 15 8.3 14.0 2.0 3.7 5.4 9.4 100.0 

6 15.5 8.45 13.8 2.1 3.9 5.6 9.9 94.8 

7 16 8.6 15.4 2.3 4.4 6.4 11.7 100.0 

8 17 9.1 17.4 2.3 4.7 6.7 12.3 100.0 

9 18 9.6 18.9 2.5 5.1 7.4 13.7 100.0 

10 19 10.1 20.6 2.7 5.6 8.3 15.8 100.0 

11 19.5 10.3 21.7 2.8 5.9 8.8 17.3 100.0 

Table 8F.24. Regional Distributions of Payback Period for Split-System Heat 

Pumps:: Rest of Country Region 

Efficiency 

Level SEER HSPF 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 28.7 4.2 7.5 12.0 31.1 100.0 

3 14 8 34.5 5.0 9.0 15.2 51.1 100.0 

4 14.5 8.15 29.1 4.4 7.8 12.7 33.2 100.0 

5 15 8.3 27.1 4.3 7.5 12.0 29.1 100.0 

6 15.5 8.45 27.8 4.4 7.8 12.6 30.3 100.0 

7 16 8.6 31.7 5.0 8.9 15.0 39.8 100.0 

8 17 9.1 30.9 4.9 8.7 13.9 37.0 100.0 

9 18 9.6 32.5 5.2 9.2 14.7 41.6 100.0 

10 19 10.1 35.1 5.6 10.0 16.3 50.7 100.0 

11 19.5 10.3 37.2 6.0 10.7 17.8 62.0 100.0 

8F-11 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

8F.3 Single-Package Air Conditioners 

8F.3.1 Single-Package Air Conditioner LCC Savings Results 

Table 8F.25. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: National 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 (68) (224) (159) (97) 0 122 

3 14 (95) (364) (255) (145) (17) 331 

4 14.5 (195) (586) (415) (287) (107) 448 

5 15 (266) (765) (545) (393) (162) 585 

6 15.5 (391) (1,008) (723) (542) (273) 648 

7 16 (564) (1,300) (948) (731) (428) 637 

8 16.6 (842) (1,731) (1,288) (1,023) (680) 549 

Table 8F.26. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Hot-Humid Region 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 (29) (162) (110) (31) 0 179 

3 14 (17) (260) (165) (61) 43 414 

4 14.5 (71) (420) (284) (152) 18 575 

5 15 (101) (547) (378) (215) 25 745 

6 15.5 (180) (721) (515) (322) (25) 848 

7 16 (303) (936) (688) (467) (122) 888 

8 16.6 (517) (1,264) (958) (703) (304) 868 

Table 8F.27. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Hot-Dry Region 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 (57) (243) (173) (84) 0 219 

3 14 (64) (396) (278) (134) 0 533 

4 14.5 (155) (637) (452) (291) (25) 727 

5 15 (212) (833) (593) (404) (74) 946 

6 15.5 (332) (1,093) (788) (568) (172) 1,076 

7 16 (507) (1,417) (1,035) (776) (324) 1,124 

8 16.6 (799) (1,898) (1,405) (1,100) (572) 1,078 

8F-12 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.28. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Rest of Country Region 

Efficiency 

Level SEER 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 (123) (247) (182) (145) 0 0 

3 14 (214) (405) (296) (232) (121) 0 

4 14.5 (375) (649) (480) (385) (274) (36) 

5 15 (504) (849) (629) (510) (386) (133) 

6 15.5 (683) (1,119) (828) (682) (541) (265) 

7 16 (907) (1,443) (1,078) (896) (731) (436) 

8 16.6 (1,243) (1,923) (1,443) (1,217) (1,016) (692) 

8F.3.2 Single-Package Air Conditioner Payback Period Results 

Table 8F.29. Regional Distributions of Payback Period for Single-Package Air 

Conditioners: National 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 70.6 5.9 24.1 100.0 100.0 100.0 

3 14 64.3 4.6 16.6 100.0 100.0 100.0 

4 14.5 68.6 5.5 21.6 100.0 100.0 100.0 

5 15 68.9 5.7 22.3 100.0 100.0 100.0 

6 15.5 71.5 6.3 26.7 100.0 100.0 100.0 

7 16 74.6 7.1 34.5 100.0 100.0 100.0 

8 16.6 78.6 8.4 52.5 100.0 100.0 100.0 

Table 8F.30. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Hot-Humid Region 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 49.2 5.3 12.3 30.0 100.0 100.0 

3 14 41.7 4.3 9.4 20.4 100.0 100.0 

4 14.5 46.6 5.0 11.4 26.5 100.0 100.0 

5 15 46.8 5.1 11.7 27.1 100.0 100.0 

6 15.5 50.1 5.6 13.1 32.6 100.0 100.0 

7 16 54.6 6.3 15.3 42.2 100.0 100.0 

8 16.6 61.1 7.4 19.2 69.5 100.0 100.0 

8F-13 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.31. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Hot-Dry Region 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 65.0 4.7 14.8 100.0 100.0 100.0 

3 14 59.0 3.9 10.9 85.9 100.0 100.0 

4 14.5 63.1 4.6 13.5 100.0 100.0 100.0 

5 15 63.4 4.6 13.9 100.0 100.0 100.0 

6 15.5 65.9 5.1 16.1 100.0 100.0 100.0 

7 16 68.8 5.7 19.6 100.0 100.0 100.0 

8 16.6 72.6 6.8 26.2 100.0 100.0 100.0 

Table 8F.32. Regional Distributions of LCC Savings for Single-Package Air 

Conditioners: Rest of Country Region 

Efficiency 

Level SEER 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 93.7 33.5 100.0 100.0 100.0 100.0 

3 14 89.6 21.2 100.0 100.0 100.0 100.0 

4 14.5 92.6 29.4 100.0 100.0 100.0 100.0 

5 15 92.7 30.7 100.0 100.0 100.0 100.0 

6 15.5 94.0 37.9 100.0 100.0 100.0 100.0 

7 16 95.5 50.4 100.0 100.0 100.0 100.0 

8 16.6 97.2 91.9 100.0 100.0 100.0 100.0 

8F.4 Single-Package Heat Pumps 

8F.4.1 Single-Package Heat Pump LCC Savings Results 

Table 8F.33. Regional Distributions of LCC Savings for Single-Package Heat 

Pumps: National 

Efficiency 

Level SEER HSPF 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 27 (83) 0 0 17 228 

3 14 8 31 (199) (86) 0 76 423 

4 14.5 8 (65) (395) (256) (127) 12 492 

5 15 8 (146) (574) (403) (234) 0 583 

6 15.5 8.5 (169) (732) (507) (285) 13 761 

7 16 9 (253) (949) (669) (400) (25) 867 

8 16.4 9 (455) (1,237) (904) (612) (203) 778 

8F-14 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8F.34. Regional Distributions of LCC Savings for Single-Package Heat 

Pumps: Hot-Humid Region 

Efficiency 

Level SEER HSPF 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 34 (64) 0 0 40 242 

3 14 8 54 (160) (54) 0 108 433 

4 14.5 8 (10) (319) (188) (66) 78 524 

5 15 8 (62) (457) (305) (138) 68 631 

6 15.5 8.5 (88) (600) (406) (189) 103 789 

7 16 9 (168) (788) (554) (293) 78 894 

8 16.4 9 (336) (1,019) (758) (474) (67) 824 

Table 8F.35. Regional Distributions of LCC Savings for Single-Package Heat 

Pumps: Hot-Dry Region 

Efficiency 

Level SEER HSPF 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 54 (87) 0 0 69 318 

3 14 8 96 (210) (44) 0 164 607 

4 14.5 8 30 (417) (195) (34) 144 735 

5 15 8 (21) (596) (328) (116) 141 877 

6 15.5 8.5 (21) (780) (449) (167) 196 1,221 

7 16 9 (91) (1,019) (634) (289) 170 1,467 

8 16.4 9 (291) (1,319) (873) (501) 2 1,401 

Table 8F.36. Regional Distributions of LCC Savings for Single-Package Heat 

Pumps: Rest of Country Region 

Efficiency 

Level SEER HSPF 

LCC Savings (2008$) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 (3) (86) (22) 0 0 90 

3 14 8 (44) (208) (113) (46) 0 146 

4 14.5 8 (199) (418) (301) (217) (124) 42 

5 15 8 (340) (611) (465) (366) (243) 0 

6 15.5 8.5 (379) (769) (572) (421) (233) 133 

7 16 9 (480) (990) (734) (534) (289) 228 

8 16.4 9 (720) (1,279) (983) (772) (524) 14 

8F-15 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

8F.4.2 Single-Package Heat Pump Payback Period Results 

Table 8F.37. Regional Distributions of Payback Period for Single-Package Heat 

Pumps: National 

Efficiency 

Level SEER HSPF 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 21.5 2.6 4.3 7.5 17.0 100.0 

3 14 8 27.6 3.3 5.7 10.5 28.9 100.0 

4 14.5 8 42.2 4.5 8.9 20.2 100.0 100.0 

5 15 8 46.5 5.0 9.9 24.9 100.0 100.0 

6 15.5 8.5 39.8 5.2 9.6 19.9 91.8 100.0 

7 16 9 39.8 5.7 10.3 20.5 82.4 100.0 

8 16.4 9 47.0 6.7 12.6 28.1 100.0 100.0 

Table 8F.38. Regional Distributions of Payback Period for Single-Package Heat 

Pumps: Hot-Humid Region 

Efficiency 

Level SEER HSPF 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 15.5 2.5 3.9 6.1 11.5 100.0 

3 14 8 19.9 3.2 5.0 8.2 16.9 100.0 

4 14.5 8 30.3 4.4 7.4 13.4 36.9 100.0 

5 15 8 33.3 4.8 8.2 15.6 46.5 100.0 

6 15.5 8.5 30.5 5.1 8.3 14.3 36.5 100.0 

7 16 9 32.0 5.6 9.0 15.4 39.8 100.0 

8 16.4 9 38.0 6.5 10.8 19.4 61.7 100.0 

Table 8F.39. Regional Distributions of Payback Period for Single-Package Heat 

Pumps: Hot-Dry Region 

Efficiency 

Level SEER HSPF 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 16.3 1.9 3.6 5.2 9.2 100.0 

3 14 8 19.8 2.5 4.7 7.1 13.2 100.0 

4 14.5 8 27.9 3.8 6.9 11.4 25.8 100.0 

5 15 8 30.2 4.3 7.7 13.0 31.9 100.0 

6 15.5 8.5 29.2 3.9 7.9 12.6 29.8 100.0 

7 16 9 31.0 3.9 8.8 13.8 34.7 100.0 

8 16.4 9 36.0 4.6 10.5 17.3 53.2 100.0 

8F-16 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

Table 8F.40. Regional Distributions of Payback Period for Single-Package Heat 

Pumps: Rest of Country Region 

Efficiency 

Level SEER HSPF 

Payback Period (Years) 

Mean 5th 

Percentile 

25th 

Percentile 

Median 

(50th 

Percentile) 

75th 

Percentile 

95th 

Percentile 

2 13.5 7.85 30.0 4.3 7.5 12.2 33.9 100.0 

3 14 8 39.0 5.6 10.5 18.5 86.3 100.0 

4 14.5 8 64.1 9.4 22.6 87.1 100.0 100.0 

5 15 8 72.3 11.6 32.6 100.0 100.0 100.0 

6 15.5 8.5 56.1 8.9 19.0 43.8 100.0 100.0 

7 16 9 53.0 8.8 17.8 36.4 100.0 100.0 

8 16.4 9 63.6 11.2 25.2 70.0 100.0 100.0 

8F-17 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR 

APPLIANCES
 

TABLE OF CONTENTS 

9A.1 INTRODUCTION ........................................................................................................ 9A-1
 

9A.2 LITERATURE REVIEW ............................................................................................. 9A-1
 

9A.2.1 Price ...................................................................................................... 9A-1 

9A.2.2 Income................................................................................................... 9A-1 

9A.2.3 Appliance Efficiency and Discount Rates ............................................ 9A-2 

9A.3	 VARIABLES DESCRIBING THE MARKET FOR CENTRAL AIR CONDITIONERS 

AND HEAT PUMPS .................................................................................................... 9A-3 

9A.3.1 Physical Household/Appliance Variables............................................. 9A-3 

9A.3.2 Economic variables ............................................................................... 9A-3 

9A.4	 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS ................................................................................................................ 9A-5
 

9A.4.1 Variable Definition ............................................................................... 9A-5
 

9A.4.2 Model Specification.............................................................................. 9A-6
 

9A.4.3 Model Discussion.................................................................................. 9A-6
 

9A.4.4 Analysis Results.................................................................................... 9A-7
 

9A.4.5 Analysis Critique .................................................................................. 9A-7
 

9A.5 LONG RUN IMPACTS ................................................................................................ 9A-9
 

9A.6 SUMMARY ................................................................................................................ 9A-10
 

LIST OF TABLES 

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on 

Automobile and Appliance Sales................................................................ 9A-2
 

Table 9A.3.1 Physical Household/Appliance Variables................................................... 9A-3
 

Table 9A.3.2 Economic Variables .................................................................................... 9A-4
 

Table 9A.4.1 Individual Appliance Model Results .......................................................... 9A-7
 

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a
 

Purchase Price Change................................................................................ 9A-9 

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change.............................................................................. 9A-10 

LIST OF FIGURES 

Figure 9A.3.1 Trends in CAC Shipments, Housing Starts and Replacements .................. 9A-4
 

Figure 9A.4.1 Trends in CAC Residual Shipments and Relative Price ............................. 9A-8
 

Figure 9A.4.2 CAC Shipments vs Relative Price .............................................................. 9A-8
 

9A-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR 

APPLIANCES 

9A.1 INTRODUCTION 

This appendix summarizes DOE’s study of the price elasticity of demand for central air 

conditioners and heat pumps (CAC). This appendix begins with a review of the existing 

economics literature describing the impact of economic variables on the sale of durable goods in 

section 9-A.2. In section 9-A.3, the market for CAC and changes in it over the past 20 years is 

described. In section 9-A.4, DOE summarizes the results of its analysis and presents estimates of 

the price elasticity of demand. In section 9-A.5, DOE developes an ‘effective’ purchase price 

elasticity. DOE’s interpretation of these results is presented in section 9-A.6. 

9A.2 LITERATURE REVIEW 

There are relatively few studies measuring the impact of price, income and efficiency on 

the sale of appliances like central air conditioners and heat pumps. In this section DOE provides 

a short review of this literature which suggests the likely importance of these variables. 

9A.2.1 Price 

The goal of many of the studies covered in this review is to measure the impact of price 

on sales in a dynamic market. One study of the automobile market prior to 1970 finds the price 

elasticity of demand to decline over time. The author explains this as the result of buyers 

delaying purchases after a price increase but eventually making the purchase (Table 9-A.2.1).1 A 

contrasting study of household white goods also prior to 1970, finds the elasticity of demand to 

increase over time as more price-conscious buyers enter the market.2

 A recent analysis of 

refrigerator market survey data finds that consumer purchase probability decreases with an 

increase in survey asking price.3

 Estimates of the price elasticity of demand for different brands 

of the same product tend to be relatively high. A review of 41 studies of the impact of price on 

market share found the average price elasticity to be -1.75.4

 Estimates of the price elasticity of 

demand for appliances and other consumer durables are much lower. The price elasticity of 

demand reported in several studies averaged -0.33 in studies of the appliance market and -0.47 

in studies of the automobile and appliance markets. 

9A.2.2 Income 

Higher income households are more likely to own household appliances.5 The impact of 

income on appliance shipments is explored in two econometric studies of the automobile and 

appliance markets.1,2 The average income elasticity of demand in our literature review is 0.50 for 

appliances and over 1.0. for automobiles (Table 9.A.2.1). 

9A-1
 

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9A.2.3	 Appliance Efficiency and Discount Rates 

Many studies estimate the impact of appliance efficiency on consumer appliance 

purchases. Typically, this impact is summarized by the implicit discount rate, i.e., the rate 

consumers use to compare future appliance operating cost savings against an appliance purchase 

price premium. One early and much cited study concludes that consumers use a 20 percent 

implicit discount rate when purchasing room air conditioners (Table 9-A.2.1).6

 A survey of 

several studies of different appliances suggests that the consumer implicit discount rate has a 

broad range and averages about 37 percent.7 

Additionally, a previous study by DOE on the price elasticity of demand for refrigerators, 

dishwashers, and clothes washers estimates that the relative price elasticity of demand for home 

appliances is -0.34.8 

Table 9A.2.1	 Estimates of the Impact of Price, Income and Efficiency on Automobile 

and Appliance Sales 

Durable Good 

Price 

Elasticity 

Income 

Elasticity 

Brand 

Price 

Elasticity 

Implicit 

Discount 

Rate Model 

Data 

Years 

Time 

Period 

Automobiles1

 -1.07 3.08 - - Linear Regression, stock 

adjustment - Short run 

Automobiles1

 -0.36 1.02 - - Linear Regression, stock 

adjustment - Long run 

Clothes Dryers2

 -0.14 0.26 - - Cobb-Douglas, diffusion 1947-1961 Mixed 

Room Air 

Conditioners2 -0.378

 0.45 - - Cobb-Douglas, diffusion 1946-1962 Mixed 

Dishwashers2

 -0.42 0.79 - - Cobb-Douglas, diffusion 1947-1968 Mixed 

Refrigerators3

 -0.37 - - 39% Logit probability, survey 

data 1997 Short run 

Various4

 - - -1.769 - Multiplicative regression - Mixed 

Room Air 

Conditioners5 - - -1.72 - Non-linear diffusion 1949-1961 Short run 

Clothes Dryers5 - - -1.32 - Non-linear diffusion 1963-1970 Short run 

Room Air 

Conditioners6 - - - 20% Qualitative choice, survey 

data - -

Household 

Appliances7 - - - 37%10 Assorted - -

Sources:	 1

 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997; 4

 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985. 

Notes:	 8

 Logit probability results are not directly comparable to other elasticity estimates in this table. 

9

 Average brand price elasticity across 41 studies.

10 Averaged across several household appliance studies referenced in this work. 

9A-2
 

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9A.3	 VARIABLES DESCRIBING THE MARKET FOR CENTRAL AIR 

CONDITIONERS AND HEAT PUMPS 

In this section DOE evaluates variables that are expected to account for central air 

conditioner and heat pump shipments, including physical household/appliance variables, and 

economic variables. 

9A.3.1	 Physical Household/Appliance Variables 

Several variables influence the sale of central air conditioners and heat pumps. The most 

important for explaining these sales trends are the annual number of new houses constructed 

(housing starts) and the number of appliances reaching the end of their operating life 

(replacements). Housing starts influence sales because new homes increasingly include central 

air conditioners or heat pumps, with a new housing saturation of 90% nationally, and nearly 

100% in some regions. Replacements are correlated with sales because new appliances are 

typically purchased when old ones wear out. 

9A.3.2	 Economic variables 

Appliance price, appliance operating cost and household income may also influence 

appliance shipments. Low prices and costs encourage purchases and a rise in income increases 

householder ability to pay for appliances. In principle, changes in these economic variables can 

help explain changes in the number of shipments. 

During the 1983-2005 study period, annual shipments of central air conditioners grew by 

238 percent (Table 9-A.3.1). This rising shipments trend is explained in part by housing starts, 

which increased 21 percent and by replacements, which rose by 212 percent over the period 

(Table 9-A.3.1). 

Table 9A.3.1 Physical Household/Appliance Variables 

1983 2005 Change 

Shipments1

 (millions) 1.75 5.92 238% 

Housing Starts2

 (millions) 1.70 2.07 21% 

Replacements3

 (millions) 0.90 2.79 212% 1

Shipments: Number of units sold. Sources: AHRI.
 2

Housing Starts: Annual number of new homes constructed. Source: U.S. Census.
 3

Replacements: Estimated from past shipments with Weibull distribution, Weibull scale: 20.288, Weibull shape: 

1.885, Weibull delay: 1 year, resulting in mean lifetime: 19.01 years and median lifetime: 17.70 years.
 

It is apparent that housing starts and replacements do not account for all shipments of 

central air conditioners. This is shown by comparing the trend in total shipments against the 

trend in shipments to housing starts, and replacements between 1983 and 2005 (Figure 9-A.3.1). 

This residual shipments trend, illustrated by the un-shaded portion of the Figure, rises over time 

and exhibits significant volatility. 

9A-3
 

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1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

1991 

1992 

1993 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

6,000,000 

Year 

Figure 9A.3.1 Trends in CAC Shipments, Housing Starts and Replacements 

Economic variables, including price, cost and income, may explain part of the trend in 

residual CAC shipments. Over the period, appliance prices decreased 40 percent to 50 percent, 

operating costs fell between 33 and 72 percent, and median household income rose 16 percent 

(Table 9-A.3.2). However, a lack of data makes it difficult to determine how much of the trend 

is explained by these economic variables and how much by other variables including changes in 

consumer preferences and regional saturation levels. 

Table 9A.3.2 Economic Variables 

0 

1,000,000 

2,000,000 

3,000,000 

4,000,000 

5,000,000 

Total Central Air Conditioner Shipments 

Total Shipments 

Replacements 

Ship to Housing Starts 

1983 2005 Change 

Price1

 (1999$) 3319 2567 -23% 

Operating Cost2 (1999$) 1031 607 -41% 

Household Income3

 (1999$) 33623 40110 19% 1

Price: Estimated from average price in 1999, weighted by inflation adjusted Producer Price Index. Source:
 

Burearu of Labor Statistics.
 2

Operating Cost: Annual electricity price times electricity consumption., 37% implicit discount rate Source:
 

AHAM Fact Book.
 3

Income: Mean Household income. Source: U.S. Census.
 

9A-4
 

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9A.4	 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS 

Limited data is available for estimating the impact of economic variables on the demand 

for appliances. Shipments data is available only between 1983-2005 and there is no industry 

price data, The producer price index is used as a proxy for the trend in the average price of 

central air conditioners. 

The limited data suggests that regression analysis can at best evaluate broad data trends. 

This section begins by describing broad trends apparent in the economic and physical household 

data sets and then specifies a simple regression model to measure these trends, making 

assumptions to minimize the number of explanatory variables. Finally, results are presented of 

the regression analysis. 

9A.4.1	 Variable Definition 

As noted above, housing starts and appliance replacements (physical household demand), 

explain much of the increase in CAC shipments over the 1983-2005 period. Before specifying a 

regression model, DOE defines two new variables—residual shipments and relative price.

 Residual shipments are defined as the difference between shipments and physical 

household demand, and estimated by the following equation: 

Residual Shipments = Total Shipments – Shipments to Starts – Replacements 

where: 

Shipments to Starts = New Housing Starts x New Housing CAC Saturation. 

Much of the CAC market is unlikely to be price sensitive. For example, CAC in new 

housing is likely to be very price insensitive. In recent years, CAC has become a standard 

feature of new houses—most (90%) new homes in the U.S. have CAC and in rapidly growing 

regions in the South and Midwest, virtually all new homes have CAC. The cost of CAC 

represents a small fraction of the typical house price, and most analysts believe that house buyer 

purchase behavior is independent of CAC cost. CAC replacement sales may be somewhat more 

price sensitive, but only slightly so. The industry literature suggests that appliance owners grow 

accustomed to CAC over time and come to regard it as a necessity. In other words, appliance 

owners may at most delay replacing old air conditioners after a rise in price, but in the long run 

old units are almost always replaced. 

Household income rose during the study period, making it easier for households to 

purchase appliances. Assuming that a rise in income has a similar impact on shipments as a 

decline in price, the impact of income is incorporated by defining relative price, represented by 

the following equation: 

PP + PVOC RP = Income 

9A-5
 

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where: 

RP = Relative price, 

PP = Appliance purchase price, and 

PVOC = Present value of operating costa 

Income = Median household income. 

9A.4.2 Model Specification 

The limited price data suggests using a simple regression model to estimate the impact of 

economic variables on shipments, using few explanatory variables. The following equation 

chosen for this analysis includes one physical household variable (starts plus replacements) and 

one relative price variable (the sum of purchase price plus operating cost, divided by income). 

Shipments = a + b × RP + c ×[Starts + Re placements]

where: 

Shipments = Total CAC shipments, 

RP = Relative price, 

Starts = Shipments to housing starts, and 

Replacements = Number of CAC at the end of their operating life. 

The natural logs are taken of all variables so that the estimated coefficients for each 

variable in the model may be interpreted as the percent change in shipments associated with the 

percent change in the variable. Thus, the coefficient b in this model is interpreted as the relative 

price elasticity of demand for the three appliances. 

9A.4.3 Model Discussion 

DOE specified a regression model similar to the model used to estimate relative price 

elasticity of demand for the home appliances rulemaking.9

 One assumption used to specify this 

model is that changes in economic variables over the study period—income, price, and operating 

cost—are responsible for observed growth in residual shipments and that other variables, 

including changing consumer preferences and changes in the quality of CAC—have no 

additional or independent impact on shipments.b

 This assumption seems unlikely but without 

more data, the impact of this assumption on the price elasticity of demand cannot be measured. 

An additional, perhaps even more important assumption is that the explanatory variables are 

a

 Estimated using a 37% implicit discount rate. 

b

 DOE considered additional variables including the impact of GDP and stock market indices as measures of wealth 

(instead of income), annual cooling degree days, and electricity prices as additional variables, but none of these 

variables was found to be significantly correlated with the trend in residual shipments. 

9A-6
 

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accurately measured. This assumption is also incorrect. Recall for example, that the CAC 

producer price index is used in the regression as a proxy for CAC pricesc 

9A.4.4 Analysis Results 

In regression analysis of this model, the elasticity of relative price (coefficient b) is 

estimated to be -1.26 for the residual shipments market for CAC. 

Table 9A.4.1 Individual Appliance Model Results 

Variable Coefficient t-stat 

Intercept 4.77 2.07 

Relative Price -1.26 -3.3 

Starts + Replacements 0.49 2.29 

R2

 0.93 

Observations 24 

9A.4.5 Analysis Critique 

As reported above, the regression model analysis suggests that the relative price elasticity 

of demand for residual shipments of CAC will be -1.26. For several reasons, DOE rejects this 

estimate of relative price elasticity. There is a high degree of multicolinearity between relative 

price and the “starts + replacements” variable in the regression. Thus, it is difficult to determine 

the statistical significance of the relative price coefficient. The coefficient associated with other 

explanatory variable in the regression (starts + replacements), appears to be biased downward. 

The coefficient for this variable is estimated to be 0.49 but is expected to be close to 1. That is 

because each CAC installed in new housing or purchased as a replacement requires an additional 

unit to be shipped. Since this coefficient is significantly less than 1, it suggests that the relative 

price coefficient is picking up some of the influence on shipments that should be attributed to 

housing starts and replacements. DOE concludes that the regression model is overestimating the 

magnitude of the relative price elasticity of demand for CAC. 

This conclusion is supported by a graphical comparison of residual shipments and 

relative price. During the time period of 1983-2005, residual shipments of CAC varied 

significantly, with a small peak around 1990, a much larger peak around 1999, and a spike in 

2004 (Figure 9-A.4.1). Over the same time period, the relative price of CAC decreased steadily 

(Figure 9-A.4.2). This suggests that the relationship between relative price and residual 

shipments within our data set is tenuous at best. For example, at the end of the study period, the 

same relative price (roughly 0.08) is associated with residual shipments ranging from about .4 

million to almost 2.0 million. 

Three additional assumptions used to specify this model deserve comment. The relative price variable is 

specified in the model, assuming that (1) the correct implicit discount rate is used to combine appliance price and 

operating cost and that (2) rising income has the same impact on shipments as falling total price. The “starts + 

replacements” variable is specified, assuming (3) that starts and replacements have similar impacts on shipments. 

9A-7
 

c 

EERE-2008-BT-STD-0006 COMMENT 54
0 

200,000 

400,000 

600,000 

800,000 

1,000,000 

1,200,000 

1,400,000 

1,600,000 

1,800,000 

2,000,000 

Residual Shipments 

0.000 

0.020 

0.040 

0.060 

0.080 

0.100 

0.120 

0.140 

Relative Price ($CAC Cost / $Income) 

Residual 

Shipments 

Relative Price 

1983 1988 1993 1998 2003
 

Year
 

Figure 9A.4.1 Trends in CAC Residual Shipments and Relative Price 

0.14 

0.12 

0.1 

0 250000 500000 750000 1000000 1250000 1500000 1750000 2000000
 

Residual Shipments
 

Relative Price 0.08 

0.06 

0.04 

0.02 

0 

Figure 9A.4.2 CAC Shipments vs Relative Price 

9A-8 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

For reasons described above, DOE relies on the results of previous analysis of other 

appliances to estimate the relative price elasticity of CAC demand. In previous analysis of those 

appliances, DOE was able to obtain a better set of data, including more accurate appliance price 

data. In that previous analysis, DOE estimated the relative price elasticity of demand of 

refrigerators, dishwashers and clothes washers to be -0.34.9

 DOE acknowledges that there may 

be differences between the market for central air conditioners and heat pumps, and the market of 

other household appliances, but given the problems associated with the above CAC regression 

analysis, and the lack of other CAC price elasticity studies, DOE believes that the relative price 

elasticity of demand for refrigerators, dish washers, and clothes washers is an acceptable proxy 

for the relative price elasticity of demand for CAC. 

9A.5 LONG RUN IMPACTS 

As noted above in Table 9-A.2.1, the literature review provides price elasticities over 

short and long time periods, also referred to as short run and long run price elasticities. As noted 

in the first two rows of Table 9-A.2.1, one source (Hymans) shows that the price elasticity of 

demand is significantly different over the short run and long run for automobiles.1 Because 

DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time 

period, consideration must be given as to how the relative price elasticity is affected once a new 

standard takes effect. 

DOE considers the relative price elasticity of -0.34 determined above in section 9-A.4 to 

be a short run elasticity. DOE was unable to identify sources specific to household durable 

goods, such as appliances, to indicate how short run and long run price elasticities differ. 

Therefore, to estimate how the relative price elasticity changes over time, DOE relied on the 

Hymans study pertaining to automobiles. Based on the Hymans study, Table 9-A.5.1 shows how 

the automobile price elasticity of demand changes in the years following a purchase price 

change. With increasing years after the price change, the price elasticity becomes more inelastic 

until it reaches a terminal value around the tenth year after the price change. 

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a Purchase 

Price Change 

Years Following Price Change 

1 2 3 5 10 20 

Price Elasticity of Demand -1.20 -0.93 -0.75 -0.55 -0.42 -0.40 

Relative Change in 

Elasticity to 1st year 

1.00 0.78 0.63 0.46 0.35 0.33 

Source: Hymans, 1971. 

Based on the relative change in the automobile price elasticity of demand shown in Table 

9-A.5.1, DOE adopted the time series of relative price elasticities developed for home appliances 

and applies them to CAC. Table 9-A.5.2 presents the time series. 

9A-9
 

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Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change 

Years Following Price Change 

1 2 3 5 10 20 

Relative Change in 

Elasticity to 1st year 

1.00 0.78 0.63 0.46 0.35 0.33 

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11 

9A.6 SUMMARY 

This appendix describes the results of a literature review and regression analysis of the 

impact of price and other variables on CAC shipments. In the literature, DOE finds only a few 

studies of appliance markets that are relevant to this analysis, and no studies using time series 

price and shipments data after 1980. The information that can be summarized from the literature 

suggests that the demand for appliances is price inelastic. Other information in the literature 

suggests that appliances are a normal good, such that rising incomes increase the demand for 

appliances. Finally, the literature suggests that consumers use relatively high implicit discount 

rates, when comparing appliance prices and appliance operating costs. 

The available price and operating cost data are too limited to perform a complex analysis 

of dynamic changes in the CAC market. In this analysis, DOE uses data available for CAC to 

evaluate broad market trends and to perform simple regression analysis. These data indicate that 

there has been an unsteady rise in CAC shipments and a constant decline in CAC price and 

operating cost over the period. Household income has also risen during this time. To simplify 

the analysis, DOE combined the available economic information into one variable, termed 

relative price, and used this variable in a regression analysis. 

Due to a lack of CAC price and other data, DOE doubts the accuracy of relative price 

elasticity estimates obtained using that data. Instead DOE relies on a previous regression 

analysis of the relative price elasticity of demand for other appliances, including refrigerators, 

dish washers, and clothes washers. That analysis suggests that the relative price elasticity of 

appliances is -0.34. Although this elasticity is consistent with other estimates in the literature, 

DOE stresses that the measure is based on a small data set, using very simple statistical analysis. 

More important, the measure is based on an assumption that economic variables, including price, 

income and operating costs, explain most of the trend in appliances per household in the United 

States since 1980. 

9A-10
 

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REFERENCES 

1	 S. Hymans. Consumer Durable Spending: Explanation and Prediction, Brookings Papers on 

Economic Activity, 1971. Vol. 1971, No. 1, pp. 234-239. 

2	 P. Golder and G. Tellis. Beyond Diffusion: An Affordability Model of the Growth of New 

Consumer Durables, Journal of Forecasting, 1998. 

3	 D. Revelt and K. Train. Mixed Logit with Repeated Choices: Household's Choice of 

Appliance Efficiency Level, Review of Economics and Statistics, 1997. 

4	 G. Tellis. The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric 

Models of Sales, Journal of Marketing Research, 1988. 

5	 U.S. Department of Energy-Energy Information Agency. The Effect of Income on 

Appliances in U.S. Households, 2002. Washington, DC. 

<http://www.eia.doe.gov/emeu/recs/appliances/appliances.html> 

6	 J. Hausman. Individual discount rates and the purchase and utilization of energy using 

durables, The Bell Journal of Economics, 1979. 

7	 K. Train. Discount rates in consumer' energy related decisions: a review of the literature, 

Energy, 1985. 

8	 Larry Dale and K. Sydny Fujita. An Analysis of the Price Elasticity of Demand for 

Household Appliances, February 2008. Lawrence Berkeley National Laboratory: Berkeley, 

CA. LBNL-326E. 

9	 U.S. DOE. Technical Support Document: Energy Efficiency Program for Consumer 

Products and Commercial and Industrial Equipment: Residential Dishwashers, 

Dehumidifiers, and Cooking Products, and Commercial Clothes Washers. March 2009. 

http://www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products_fi 

nal_rule_tsd.html 

9A-11
 

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 10A. USER INSTRUCTIONS FOR SHIPMENTS AND NIA 

SPREADSHEETS
 

TABLE OF CONTENTS
 

10A.1 INTRODUCTION ................................................................................................ 10A-1
 

10A.2 INDIVIDUAL WORKSHEETS ........................................................................... 10A-1
 

10A.3 BASIC INSTRUCTIONS..................................................................................... 10A-5
 

10A-i 

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APPENDIX 10A. USER INSTRUCTIONS FOR SHIPMENTS AND NIA 

SPREADSHEETS
 

10A.1 INTRODUCTION 

The results obtained for the shipments analysis and the national impact analysis (NIA) 

can be examined and reproduced using the Microsoft Excel spreadsheet available on the U.S. 

Department of Energy Building Technologies website 

at: http://www1.eere.energy.gov/buildings/appliance_standards/residential/central_ac_hp.html. 

There is one NIA spreadsheet covering the four primary product classes: split system 

central air conditioners (CAC), single package CACs, split system heat pumps (HP), single 

package HPs. The spreadsheet is posted on the DOE website represents the latest version and 

has been tested with Microsoft Excel 2003. 

To execute the spreadsheet requires Microsoft Excel 2003 or a later version. The NIA 

spreadsheet performs calculations to forecast the change in national energy use and net present 

value due to an energy conservation standard. The energy use and associated costs for a given 

standard are determined first by calculating the shipments and then calculating the energy use 

and costs for all equipment shipped under that standard. The differences between the standards 

and base cases can then be compared and the overall energy savings and present values 

determined. 

10A.2 INDIVIDUAL WORKSHEETS 

The NIA spreadsheet or workbook consists of the following worksheets: 

User Inputs Contains user input selections for the following: (1) CAC and HP 

market by sector, i.e., percent of annual shipments to residential 

and commercial sectors; (2) CAC and HP market by product type, 

i.e., percent of annual shipments as split systems and single 

package systems; (3) rebound effect, i.e., percent of energy savings 

lost associated with more efficient equipment due to increased 

usage; (4) real discount rates for (a) discounting the future dollar 

value of costs and savings due to standards and (b) future energy 

savings; (5) year in which to discount future costs and savings; (6) 

relative price elasticity (selected from pull-down menu); and (7) 

economic growth scenario based on Annual Energy Outlook 

(AEO) forecast (selected from pull-down menu). 

Summary	 Contains a summary of NIA results for each of the four primary 

product classes. Note that NIA results are presented separately for 

10A-1 

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Energy and Cost Data 

Default Distr. 

Efficiency Dist. 

Fuel Prices 

split system CAC coil only units and split system CAC blower coil 

units. The summary table in the upper left corner of the worksheet 

presents the following for each product class for a specified 

candidate standard level (CSL): (1) national energy savings; (2) 

national energy savings discounted to a specified discount rate; (3) 

discounted national incremental equipment (total installed) costs; 

(4) discounted national operating cost savings; and (5) national net 

present value (NPV). The NIA results are presented by region 

(hot-dry, hot-humid, rest of U.S., and entire U.S.) and sector 

(residential and commercial). 

The user can select a CSL by ‘clicking’ on the ‘Select CSL’ button 

in the top right corner of the summary of NIA results table. 

Below the NIA summary table are two pull-down menus for 

selecting the ‘region’ and ‘sector’. Data in the Base Case and 

Standard Case worksheets (explained below) are specific to the 

selected combination of region and sector. Choosing different 

combinations of region and sector does not affect the NIA results 

displayed in the NIA summary table. 

Data and figures to the right of the NIA summary table display 

residential equipment stock saturation data by year, region, and 

product class. 

Contains the per-unit annual energy use and cost data from the lifecycle cost and payback period analysis. All per-unit annual energy 

use and cost data are provided by product class. Within each 

product class, the data are provided by CSL, region, and sector. 

Included as part of the cost data are: (1) total installed costs; (2) 

repair costs; and (3) maintenance costs. 

For each product class, contains the efficiency distributions in the 

year that standards become effective (2016) for the base case and 

each standards case. 

For each product class, contains the shipment-weighted efficiency 

trends, i.e., the forecasted efficiency over time, for the base case 

and each standards case. 

Contains projected average electricity prices for the three AEO 

economic scenarios. Projected prices are provided by region and 

the nation as a whole. 

10A-2 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

Base Case SAC-CO 

Base Case SAC-BC 

Base Case PAC 

Base Case SHP 

For the split system CAC coil only (SAC-CO) product class, 

contains the calculations for determining the shipments, energy 

consumption, and operating costs for the base case. The sheet starts 

with the stock accounting of the equipment and uses the survival 

function to calculate the surviving stock. It then performs 

calculations of replacements, and shipments going into new 

housing. The sheet calculates replacement units, shipments going 

into new units, and shipments going to first time owners (existing 

households that do not already own the product), and aggregates 

them into total shipments. 

For the split system CAC blower coil (SAC-BC) product class, 

contains the calculations for determining the shipments, energy 

consumption, and operating costs for the base case. The sheet starts 

with the stock accounting of the equipment and uses the survival 

function to calculate the surviving stock. It then performs 

calculations of replacements, and shipments going into new 

housing. The sheet calculates replacement units, shipments going 

into new units, and shipments going to first time owners (existing 

households that do not already own the product), and aggregates 

them into total shipments. 

For the single package CAC (PAC) product class, contains the 

calculations for determining the shipments, energy consumption, 

and operating costs for the base case. The sheet starts with the 

stock accounting of the equipment and uses the survival function to 

calculate the surviving stock. It then performs calculations of 

replacements, and shipments going into new housing. The sheet 

calculates replacement units, shipments going into new units, and 

shipments going to first time owners (existing households that do 

not already own the product), and aggregates them into total 

shipments. 

For the split system HP (SHP) product class, contains the 

calculations for determining the shipments, energy consumption, 

and operating costs for the base case. The sheet starts with the 

stock accounting of the equipment and uses the survival function to 

calculate the surviving stock. It then performs calculations of 

replacements, and shipments going into new housing. The sheet 

calculates replacement units, shipments going into new units, and 

shipments going to first time owners (existing households that do 

not already own the product), and aggregates them into total 

shipments. 

10A-3 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

Base Case PHP 

Standards Case SAC-CO 

Standards Case SAC-BC 

Standards Case PAC 

Standards Case SHP 

Standards Case SHP 

For the single package HP (PHP) product class, contains the 

calculations for determining the shipments, energy consumption, 

and operating costs for the base case. The sheet starts with the 

stock accounting of the equipment and uses the survival function to 

calculate the surviving stock. It then performs calculations of 

replacements, and shipments going into new housing. The sheet 

calculates replacement units, shipments going into new units, and 

shipments going to first time owners (existing households that do 

not already own the product), and aggregates them into total 

shipments. 

For the SAC-CO product class, contains stock accounting of the 

equipment that calculates annual shipments estimates, energy 

savings, and operating cost savings for the standards case. The 

energy and cost savings in a single year are the difference between 

the base case energy use and costs and the standard case energy 

use and costs for that year. 

For the SAC-BC product class, contains stock accounting of the 

equipment that calculates annual shipments estimates, energy 

savings, and operating cost savings for the standards case. The 

energy and cost savings in a single year are the difference between 

the base case energy use and costs and the standard case energy 

use and costs for that year. 

For the PAC product class, contains stock accounting of the 

equipment that calculates annual shipments estimates, energy 

savings, and operating cost savings for the standards case. The 

energy and cost savings in a single year are the difference between 

the base case energy use and costs and the standard case energy 

use and costs for that year. 

For the SHP product class, contains stock accounting of the 

equipment that calculates annual shipments estimates, energy 

savings, and operating cost savings for the standards case. The 

energy and cost savings in a single year are the difference between 

the base case energy use and costs and the standard case energy 

use and costs for that year. 

For the PHP product class, contains stock accounting of the 

equipment that calculates annual shipments estimates, energy 

savings, and operating cost savings for the standards case. The 

energy and cost savings in a single year are the difference between 

the base case energy use and costs and the standard case energy 

use and costs for that year. 

10A-4 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

Historical Shipments	 Contains data for annual historical shipments of CAC and HP by 

product class. Data are presented to disaggregate the historical 

shipments into region. 

AEO Residential Housing	 Contains the projected new housing construction starts and total 

housing stock for the three economic scenarios (Reference, Low 

Growth, and High Growth) by region. 

AEO Commercial Floor Space	 Contains the projected new commercial construction floor space 

starts and total commercial floor space stock for the three 

economic scenarios (Reference, Low Growth, and High Growth) 

by region. 

New Saturation Resid	 Contains the historical and projected saturation of CAC and HP 

equipment in new residential housing by region. 

New Saturation Comm Contains the historical and projected saturation of CAC and HP 

equipment in new commercial floor space by region. 

Heat Rates	 Contains the marginal site to source conversion factors that are 

used in the source energy savings calculations. 

Lifetime	 Contains the probability of survival of CAC and HP equipment at a 

given age and the average lifetime of a unit. 

10A.3 BASIC INSTRUCTIONS 

Basic instructions for operating the NIA spreadsheets are as follows: 

1.	 Once the NIA spreadsheets have been downloaded from the Web, open the file using 

Excel. At the bottom, click on the tab for the worksheet ‘User Inputs’. 

2.	 Use Excel’s View/Zoom commands at the top menu bar to change the size of the display 

to make it fit your monitor. The user can change the model parameters listed in the white 

box labelled “User Inputs”. 

3.	 Once the parameters have been set, click on the tab for the worksheet ‘Summary’. 

4.	 Use Excel’s View/Zoom commands at the top menu bar to change the size of the display 

to make the summary table in the upper left portion of the worksheet fit your monitor. 

5.	 Click the “Select CSL’ button in the upper right corner of the summary table to choose 

which candidate standard level to analyze. 

10A-5 

EERE-2008-BT-STD-0006 COMMENT 54
6. The results are automatically updated and are reported in the summary table for each 

product class. 

10A-6 

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 12A: PRELIMINARY MANUFACTURER INTERVIEW GUIDES
 

TABLE OF CONTENTS 

12A.1 INTRODUCTION .......................................................................................... 12A-1 

12A.2 RESIDENTIAL CENTRAL AIR CONDITIONERS AND HEAT PUMPS 

PRELIMINARY MANUFACTURER INTERVIEW GUIDE ...................... 12A-1 

12A.3 RESIDENTIAL CENTRAL AIR CONDITIONERS AND HEAT PUMPS 

PRELIMINARY ENGINEERING INTERVIEW GUIDE ............................ 12A-3 

12A-i 

EERE-2008-BT-STD-0006 COMMENT 54
 

12A.1 INTRODUCTION 

This appendix presents samples of the interview guides used during the 

preliminary manufacturer interviews. The interview guides detail the questions the 

Department of Energy (DOE) used as the basis for the discussions with manufactures of 

central air conditioners and heat pumps. The results of the manufacturer interviews were 

aggregated and incorporated into the engineering analysis (see chapter 5 of the 

preliminary technical support document (TSD)) and the preliminary manufacturer impact 

analysis (see chapter 12 of the preliminary TSD). 

12A.2 RESIDENTIAL CENTRAL AIR CONDITIONERS AND HEAT PUMPS 

PRELIMINARY MANUFACTURER INTERVIEW GUIDE 

The following section consists of the general questions regarding key issues and 

industry information that DOE asked while interviewing central air conditioner and heat 

pump manufacturers. 

1 KEY ISSUES 

1.1	 What are the key issues for your company regarding DOE’s residential central air 

conditioners and heat pumps rulemaking? 

1.2	 Are there any patent, technology, or other issues that you are aware of that would 

prevent you or your competitors from implementing higher-efficiency designs? 

2 MARKET SHARES AND INDUSTRY CONSOLIDATION 

2.1	 What is your company’s approximate market share in each product class? 

• Split-system air conditioners 

• Split-system heat pumps 

• Single-package air conditioners 

• Single-package heat pumps 

• Small-duct high-velocity (SDHV) systems 

• Space-constrained products – air conditioners 

• Space-constrained products – heat pumps 

2.2	 Would you expect your market share to change once standards become effective? 

2.3	 Does your outlook change with higher standard levels? 

2.4	 How would you expect shipments to change for the industry as a whole as a 

function of standards and why? 

12A-1
 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

2.5	 Would an additional standard specific to certain climate regions cause you to exit 

those markets? 

2.6	 Looking at price/cost effects only, how would you expect shipments to change for a 

5%, 10%, 25% or 50% manufacturer price/cost increase? 

2.7	 Please provide estimations of your full production cost percentages for each 

equipment class below. 

Average Manufacturing Production Cost Breakdown 

Percentage Cost Breakdown by Central AC Type 

(average for finished DOE sample units) 

Splitsystem 

AC 

Splitsystem 

HP 

Singlepackage 

AC 

Singlepackage 

HP 

Spaceconstrained 

AC 

Spaceconstrained 

HP 

SDHV 

Systems 

Materials 

Labor 

Depreciation 

Overhead 

2.8	 In the absence of new standards, do you expect any industry consolidation? 

2.9	 How would new standards affect your ability to compete? 

2.10	 Could new standards disproportionately advance or harm the competitive positions 

of some firms? 

2.11	 Could new standards result in disproportionate economic or performance penalties 

for particular consumer/user subgroups? 

2.12	 Beyond price and energy efficiency, could new standards result in products that 

will be more or less desirable to consumers due to changes in product functionality, 

utility, or other features? 

3 CUMULATIVE REGULATORY BURDEN 

3.1	 Are there recent or impending regulations on your specific product or other 

products that impose a cumulative burden on the industry? 

3.2	 If so, what is the total expected impact of those other regulations? 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

12A.3 RESIDENTIAL CENTRAL AIR CONDITIONERS AND HEAT PUMPS 

PRELIMINARY ENGINEERING INTERVIEW GUIDE 

The following section consists of the questions regarding the product details and 

the engineering analysis that DOE asked while interviewing central air conditioner and 

heat pump manufacturers. 

Product Classes for Central Air Conditioners and Heat Pumps under Consideration 

for the Rulemaking 

Central Air Conditioner and Heat Pump Type Characteristics 

Split-system Air Conditioner Cooling capacity no greater than 65,000 Btu/h with 

condenser housed in a separate unit from 

Split-system Heat Pump evaporator. 

Single-package Air Conditioner Cooling capacity no greater than 65,000 Btu/h with 

condenser and evaporator contained in single unit. Single-package Heat Pump 

Space-constrained Air Conditioner Footprint restricted with rated cooling capacity no 

greater than 30,000 Btu/h Space-constrained Heat Pump 

Small –duct-high-velocity (SDHV) Systems 

1.2 inches of external static pressure at certified air 

volume rate (220-350 cfm/ton) using high velocity 

room outlets greater than 1,000fpm with under 6 in2 

free area 

1 BASELINE PRODUCTS AND DESIGN OPTIONS 

1.1	 Which design features impacting energy use are generally incorporated into 

“baseline” residential central air-conditioners and heat pumps in each product 

class? 

1.2	 Which design features impacting energy use are incorporated into products to reach 

higher efficiencies? 

1.3	 How do these design options vary by product class and capacity? 

1.4	 At what SEER level have you found it to be more cost effective to use a multispeed blower motor as opposed to a single-speed blower motor? Variable speed? 

1.5	 What factors limit the use of increased coil area to reach higher efficiencies (e.g., 

aesthetic appeal, cost, air quality, etc.)? What efficiency increases can be obtained 

from increasing coil area before these factors come into play? Does this change 

with the use of different blower motors or compressors? How does this increase in 

coil area affect EER? 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

1.6	 What factors determine whether or not to incorporate high pressure and/or low 

pressure switches into your units? 

1.7	 Do you specifically manufacture any products for use in specific climates? Do you 

recommend different system combinations for different climates? 

1.8	 What modifications to a current baseline product would be necessary to design a 

product specifically for use in a hot-dry climate? A hot-humid climate? 

1.9	 How would these design modifications affect the SEER rating? HSPF? Do you 

expect that this would cause the rating to increase or decrease and why? 

1.10	 What are the costs associated with these individual design options? 

1.11	 What hardware features, equipment parameters (e.g., fan speed setting), and/or 

installation options are available to have a system that is well suited for a mixed 

climate and also performs above average in a hot-dry climate and/or a hot-humid 

climate? Please describe how the setup and operation in a mixed climate would 

differ from the hot-dry or hot-humid climate. 

1.12	 How would you recommend that an installer size your systems for use in a hot-dry 

climate? Do you suggest a de-rating of the certified cooling capacity and/or the 

certified SEER? 

1.13	 How do you recommend that the installer size and install your systems for use in a 

hot-humid climate? Do you recommend using a different fan speed setting/air 

volume rate or using a smaller capacity indoor coil for split systems (as compared 

to the mixed climate installation)? 

1.14	 What percentage of your air-conditioners and heat pumps include a compressor 

crankcase heater? What is a range for the power draw of such heaters (2 ton versus 

5 ton) and what is the logic for its on/off control? 

1.15	 Under what conditions or efficiency levels will you include a crankcase heater as a 

standard accessory as opposed to an optional accessory? 

1.16	 Are installation costs a function of efficiency? Maintenance costs? Repair costs? 

If yes, would you please characterize this relationship by providing incremental 

installation, maintenance, and/or repair cost data? 

1.17	 What degree of system efficiency degradation is associated with situations where 

someone will install a new split condensing unit, but retains an existing indoor 

unit? 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2 COMPONENT PARTS AND MATERIAL COSTS 

2.1	 For purchased parts, price quotations were gathered from major suppliers at 

different production volumes. For parts produced in-house, manufacturing 

processes were reconstructed for each part using internal expertise. Please 

comment on the following estimates of purchased part costs and estimated material 

costs. 

R410a Compressor Cost Estimates, as of 1/2009 

Capacity, EER, Type of Compressor, Voltage/Phase Model # example 

Price 

($/ea) 

Manufacturer 

Comments 

20.3kBTU, 9.10EER, R410A, 1-Speed Scroll, 230/1 ZP20K3EPFV $160.42 

20.0kBTU, 9.80EER, R410A, 1-Speed Scroll, 230/1 ZP20K5EPFV $175.18 

29.0kBTU, 10.10EER, R410A, 1-Speed Scroll, 230/1 ZP29K5EPFV $188.63 

31.3kBTU, 10.10EER, R410A, 1-Speed Scroll, 230/1 ZP31K5EPFV $190.21 

31.3kBTU, 10.00EER, R410A, 1-Speed Scroll, 230/1 ZP32K3EPFV $181.78 

44.3kBTU, 10.30EER, R410A, 1-Speed Scroll, 230/1 ZP44K3EPFV $205.88 

44.0kBTU, 10.40EER, R410A, 1-Speed Scroll, 230/1 ZP44K5EPFV $223.75 

50.5kBTU, 10.20EER, R410A, 1-Speed Scroll, 230/1 ZP50K3EPFV $216.17 

51.0kBTU, 10.50EER, R410A, 1-Speed Scroll, 230/1 ZP51K5EPFV $246.69 

30.9kBTU, 10.10EER, R410A, 2-Speed Scroll, 230/1 ZPS30K4EPFV $218.74 

36.8kBTU, 10.45EER, R410A, 2-Speed Scroll, 230/1 ZPS35K4EPFV $230.00 

52.0kBTU, 10.40EER, R410A, 2-Speed Scroll, 230/1 ZPS51K4EPFV $283.69 

34.5kBTU, 10.20EER, R410A, VC Scroll, 230/1 ZPD34K5EPFV $363.55 

51.5kBTU, 10.50EER, R410A, VC Scroll, 230/1 ZPD51K5EPFV $456.37 

3 ton, 3Phase DC Rotary, 420cc/Rev 5JD420XAC22 $412.55 

3ton Variable Speed Drive System (IGBT) for 3Phase DC Compressor $92.48 

Misc. Sealed System Component Price Estimates, as of 1/2009 

Description 

Price 

($/ea) 

Manufacturer 

Comments 

Outdoor Unit Refrigerant Shutoff Valve, 0.375”OD $6.69 

Outdoor Unit Refrigerant Shutoff Valve, 0.750”OD $9.01 

Outdoor Unit Refrigerant Shutoff Valve, 0.875”OD $11.53 

Outdoor Unit Refrigerant Shutoff Valve, 1.125”OD $20.12 

Piston Kit, (i.e. no distribution header) $5.95 

Bare TXV (i.e. no distribution header), 2 ton $20.39 

Bare TXV (i.e. no distribution header), 3 ton $22.64 

Bare TXV (i.e. no distribution header), 4 ton $24.90 

Bare TXV (i.e. no distribution header), 5 ton $27.16 

Bare EEV, up to 2 tons R410a (ex: SEI210S) $40.66 

Bare EEV, up to 3.5 tons R410a (ex: SEI3510S) $42.02 

Bare EEV, up to 5 tons R410a (ex: SEI0510S) $44.73 

Refrigerant Distributor, 1x 5/8” to 2 or 3 outlets (0.25”), w/o capillaries $6.86 

Refrigerant Distributor, 1x 5/8” to 4 to 6 outlets (0.25”), w/o capillaries $7.34 

Refrigerant Distributor, 1x 7/8” to 7 to 8 outlets (0.25”), w/o capillaries $8.79 

Liquid Line Splitter, Stamped, 1-to-2, 0.375” $2.72 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Liquid Line Splitter, Stamped, 1-to-3, 0.375” $4.02 

Accumulator for Heat Pump, up to 2.5tons R410a $12.56 

Accumulator for Heat Pump, up to 3.5tons R410a $15.14 

Accumulator for Heat Pump, up to 4.5tons R410a $19.60 

Accumulator for Heat Pump, up to 9tons R410a $25.87 

Inline Filter Dryer, 1-way, 1”OD x 4.75” long $4.02 

Filter Dryer, 1-way, 3”OD x 4.6” long (ex: Sporlan C163a) $7.33 

Filter Dryer, 2-way, 3”OD x 4.6” long (ex: Sporlan HPC 163Shh) $14.98 

Reversing Valve, up to 2.5t R410a, (ex: Ranco V2 series) $33.56 

Reversing Valve, up to 3.5t R410a, (ex: Ranco V3 series) $44.54 

Reversing Valve, up to 6.7t R410a, (ex: Ranco V6 series) $50.74 

Outdoor Fan Motor Cost Estimates, as of 1/2009 

Power Rating, Motor Type, Weight 

Price 

($/ea) 

Manufacturer 

Comments 

0.08hp, 835RPM, 220VAC, PSC, 8.4lb $22.32 

0.16hp, 835RPM, 220VAC, PSC, 12.2lb $26.00 

0.16hp, 835RPM, 220VAC, PSC, 10.4lb $22.14 

0.20hp, 830RPM, 220VAC, PSC, 12.2lb $41.12 

0.25hp, 830RPM, 220VAC, PSC, 11.8lb $32.00 

Indoor Fan Motor Cost Estimates, as of 1/2009 

Power Rating, # Poles, # Speeds, Motor Type, Weight 

Price 

($/ea) 

Manufacturer 

Comments 

0.33hp, 835/925/1075RPM, 220V, PSC, $35.00 

0.50hp, 1075/900RPM, 220V, PSC, $38.62 

0.50hp, ECM, 220V, 13.8lb (ex. GE) $76.63 

0.75hp, ECM, 220V, 18lb (ex. GE) $80.00 

1.00hp, ECM, 220V, 20.6lb (ex. GE) $92.17 

Fan Blade Cost Estimates, as of 1/2009 

Description 

Price 

($/ea) 

Manufacturer Comments 

Indoor Blower Wheel, GCRS, 11.875" OD, 9.5" High $7.44 

Indoor Blower Wheel, GCRS, 10.5" OD, 9.5" High $6.90 

Indoor Blower Wheel, GCRS, 11.875" OD, 8" High $7.05 

Indoor Blower Wheel, GCRS, 10.5" OD, 10.75" High $7.46 

Indoor Blower Wheel, GCRS, 10.5" OD, 8" High $5.91 

Indoor Blower Wheel, GCRS, 11.875" OD, 10.875" High $8.36 

Outdoor Fan Blade, GCRS, 22" OD, 3 Blade, Rect Paddle $2.39 

Outdoor Fan Blade, Al, 18" OD, 4 Blade, Sicle Paddle $4.00 

Outdoor Fan Blade, Al, 24" OD, 3 Blade, Rect. Paddle $4.56 

Outdoor Fan Blade, Al, 22" OD, 3 Blade, Rect. Paddle $4.12 

Outdoor Fan Blade, Al, 26" OD, 3 Blade, Sicle Paddle $5.40 

Outdoor Fan Blade, Al, 26" OD, 3 Blade, Rect. Paddle $6.30 

Outdoor Fan Blade, Al, 25" OD, 2 Blade, Sicle Paddle $3.78 

Misc. Electrical Part Cost Estimates, as of 1/2009 

Description 

Price 

($/ea) 

Manufacturer Comments 

40VA Transformer (bare, conductors are extra) $3.03 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

60VA Transformer (bare, conductors are extra) $3.74 

80VA Transformer (bare, conductors are extra) $5.02 

30A, 250VAC, Dual Pole Contactor $3.50 

Starter Capacitor: 10 MFD, 370VAC $1.49 

Starter Capacitor: 40/5 MFD 370V $3.27 

Starter Capacitor: 40/7.5 MFD, 370VAC $3.44 

Starter Capacitor: 45/5 MFD, 370VAC $3.63 

Starter Capacitor: 45/7.5 MFD, 370VAC $3.79 

Starter Capacitor: 80/5 MFD, 370VAC $9.47 

Starter Capacitor: 88-106 MFD, 330VAC $9.27 

Refrigerant Low Pressure Switch $3.57 

Refrigerant High Pressure Switch $3.57 

Refrigerant Pressure Transducer $30.10 

2.2 The following chart shows our assumptions for raw material prices for common 

metals found in air conditioning units. Please assume delivered price at shipping dock of 

your facility, i.e. inclusive of all processing, shipping, etc. costs. 

Metal Raw Material Costs 

Metals Five Year Cost Avg. 

($/lb 2004-2009) 

Cost ($/lb) 

As of 1/2009 

Manufacturer Feedback 

Cold Rolled Steel (CRS) $0.41 $0.37 

Aluminized CRS $0.55 $0.49 

Galvanized CRS $0.54 $0.48 

Pre-Painted CRS $0.70 $0.62 

Textured CRS $0.61 $0.55 

Pre-Painted Textured CRS $0.86 $0.77 

Fin Aluminum $1.23 $1.43 

Cu Tube - Plain for Refrigeration $2.45 $1.95 

Cu Tube - Rifled for Refrigeration $2.65 $2.15 

2.3 For plastics, as a starting point we assume that most parts are made in-house, 

including drain pans, logos, etc. in a variety of injection-molding machines. Please 

indicate what parts are typically made in-house vs. those that are made by outside 

vendors. 

In-House Parts: Sourced Parts: 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

2.4 Below is an abridged table containing our cost assumptions for plastic resin prices. 

These prices are in rail-car quantities, fully-delivered to your shipping dock. Please 

indicate how the following prices compare to your raw material costs. 

Plastics Raw Material Costs 

Resin Five Year Cost Avg. 

($/lb 2004-2009) 

Cost ($/lb) 

As of 1/2009 

Manufacturer Feedback 

ABS $0.79 $0.75 

ABS w/GF $1.31 $1.25 

Nylon-6 $0.84 $0.80 

Nylon-6 w/GF $1.28 $1.22 

Polypropylene (PP) $0.72 $0.68 

PP w/GF $0.89 $0.84 

Polystyrene (PS) $0.63 $0.60 

Hi-Impact PS $1.30 $1.23 

A sourced part is assumed to cost approximately twice the raw material cost of a part 

made in house. For your sourced parts, please indicate how close that assumption comes 

to actual parts costs (i.e. compare on a $/lb basis to the above raw material costs). 

2.5 Below are some “other” raw materials that we would like to get feedback on. For the 

cardboard and paper, please assume fully converted prices (i.e. printed, folded, etc.) 

delivered at your shipping dock. The fiberglass is either foil-faced or plain appliance 

grade. Lastly, there are costs for refrigerant and compressor oils. 

Other Raw Material Costs 

Material Description Five Year Cost Avg. 

($/lb 2004-2009) 

Cost ($/lb) 

As of 1/2009 

Manufacturer Feedback 

Plain Cardboard for Shipping $0.46 $0.54 

2-Color Cardboard for Shipping $0.55 $0.64 

Paper $0.57 $0.65 

Wood for Shipping $0.25 $0.25 

Foil Faced Fiberglass $0.88 $0.93 

Dense Fiberglass $0.89 $0.95 

R-22 $1.52 $1.80 

R-410a $2.30 $2.72 

Compressor Oil 

3 FACTORY PARAMETER ASSUMPTIONS 

3.1	 Information gathered from analysis of common industry practices were used to 

formulate factory parameters for manufacturers. Please comment on the following 

factory parameter assumptions. 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Central Air Conditioner and Heat Pump Factory Parameter Assumptions 

Parameter Estimate Manufacturer 

Feedback 

Actual Annual Production Volume (units/year) 1,250,000 

Work Days Per Year (days) 250 

Assembly Shifts Per Day (shifts) 2 

Fabrication Shifts Per Day (shifts) 2.5 

Fabrication Labor Wages ($/hr) 16 

Assembly Labor Wages ($/hr) 16 

Assembly Worker Hours Per Year 3,600 

Fabrication Worker Hours Per Year 4,500 

Length of Shift (hrs) 8 

Units Per Day 6,000 

Average Equipment Installation Cost (% of purchase price) 10% 

Fringe Benefits Ratio 

Indirect to Direct Labor Ratio 33% 

Average Scrap Recovery Value 30% 

Building Cost ($/ft2

) 100 

Worker Downtime 15% 

Building Life (in years) 30 

Burdened Assembly Labor Wage ($/hr) 24 

Burdened Fabrication Labor Wage ($/hr) 24 

Supervisor Span (workers/supervisor) 

Supervisor Wage Premium (over fabrication and assembly 

wage) 

4 OFF-MODE POWER CONSUMPTION 

4.1	 How does your company currently define standby mode and off mode for your 

products? 

4.2	 Do you have any suggestions for how DOE could best incorporate off-mode power 

consumption into this rulemaking? 

4.3	 What percentage of a product’s total energy use can be attributed to power 

consumption during times when the compressor is not actually operating? 

4.4	 What components contribute to off-mode power consumption (e.g., heat pump 

reversing valve, crankcase heater, 24 volt transformer that powers the wallmounted thermostat, etc.)? How does the parasitic power consumption vary 

depending on whether the thermostat is set to “cool”, “heat”, or “off”? Are there 

any differences in this power consumption between split air-conditioners matched 

with furnaces and split heat pumps matched with air-handlers? 

4.5	 For your heat pumps, is the reversing valve energized only when the thermostat is 

set to “cool”? 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 

 

 

4.6	 Do you have any insight as to the extent that your customers use continuous fan 

mode. Do any of your products operate the indoor fan at a different/lower speed 

when operating in a continuous fan mode when the compressor is off? 

5 TEST PROCEDURE 

5.1	 Do you have any problems conducting the tests and following the SEER and HSPF 

calculations in the DOE test procedure (ARI Standard 210/240)? 

5.2	 Are there any energy conserving features of your products that are not adequately 

captured within the DOE test procedure (ARI Standard 210/240)? 

5.3	 Would your company have any comments on using a bin method for calculating the 

SEER of single-speed units as opposed to using the SEER short-cut method? 

5.4	 Does your company sell any equipment having a variable-speed compressor? If so, 

does the allowed minimum compressor speed and/or the allowed maximum 

compressor speed change depending upon the outdoor temperature or some other 

operating parameter (e.g., at 15oF the compressor’s maximum allowed speed is 

higher or lower than the allowed maximum speed at 50oF)? 

5.5	 What change in total capacity and EER (or power consumption) do you see 

between the B and C Tests for your products? 

5.6	 Within the DOE test procedure, do you have any comments on handing the 

minimum external static pressure requirements differently for ducted mini-splits 

and ducted multi-splits as compared to conventional centrally ducted systems? For 

example, specify a new set of fixed external static pressures that are lower than the 

current minimums of 0.10 to 0.20 inches of water for ducted mini-splits or ducted 

multi-splits. 

5.7	 Do you have laboratory test data that you would be willing to share with DOE 

related to: 

•	 how capacity and power consumption change as a function of the compressor 

modulation rate and the number of indoor units that are turned on for residential 

multi-split systems; 

•	 fan curves for indoor blowers that use multi-speed PSC motors; 

•	 fan curves for indoor blowers that use variable-speed ICM motors? 

•	 the effect that lowering the air volume rate by 10 percent from its rated value 

has on total capacity and power consumption (EER) for the DOE A and B 

Tests? 

5.8	 During the data collection period of a DOE Frost Accumulation Test, what is a 

typical interval between defrost cycles (e.g., 2 hours) for your heat pump models 

that have a demand defrost feature? What is a representative maximum interval 

between defrost cycles for such systems during the test? 

EERE-2008-BT-STD-0006 COMMENT 54
5.9	 For your in-house lab testing, do you use the same or different sensors to measure 

the change in temperature across the indoor coil for the DOE C and D Tests? What 

type of sensors do you use (e.g., in-duct thermopile or thermocouple grids, 

remotely located RTDs, etc.)? 

EERE-2008-BT-STD-0006 COMMENT 54
APPENDIX 15A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866 

TABLE OF CONTENTS 

15A.1 EXECUTIVE SUMMARY ........................................................................................ 15A-1 

15A.2 MONETIZING CARBON DIOXIDE EMISSIONS .................................................. 15A-2 

15A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES ................................................................................................................ 15A-4
 

15A.4 APPROACH AND KEY ASSUMPTIONS ............................................................... 15A-4
 

15A.4.1 Integrated Assessment Models ................................................................ 15A-5
 

15A.4.2 Global versus Domestic Measures of SCC ........................................... 15A-11
 

15A.4.3 Valuing Non-CO2 Emissions ................................................................. 15A-12
 

15A.4.4 Equilibrium Climate Sensitivity............................................................ 15A-13
 

15A.4.5 Socio-Economic and Emissions Trajectories ........................................ 15A-15
 

15A.4.6 Discount Rate ........................................................................................ 15A-18
 

15A.5 REVISED SCC ESTIMATES .................................................................................. 15A-24 

15A.6 LIMITATIONS OF ANALYSIS.............................................................................. 15A-29 

15A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS ............................................................................................................ 15A-31
 

15A.7.1 Extrapolation of Climate Damages to High Levels of Warming .......... 15A-31
 

15A.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional Interactions .. 15A-33
 

15A.7.3 Imperfect Substitutability of Environmental Amenities ....................... 15A-33
 

15A.8 CONCLUSION ......................................................................................................... 15A-33
 

15A.9 APPENDIX ............................................................................................................... 15A-41
 

15A.9.1 Other (non-CO2) Gases ......................................................................... 15A-42
 

15A.9.2 Extrapolating Emissions Projections to 2300 ........................................ 15A-44
 

15A-i 

EERE-2008-BT-STD-0006 COMMENT 54
LIST OF TABLES 

Table 15A.1.1 Social Cost of CO2, 2010–2050 (2007$) .................................................... 15A-2 

Table 15A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distribution 15A-14 

Table 15A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 

Reference Scenarios ................................................................................. 15A-17 

Table 15A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic 

Trajectory, and Discount Rate for 2010 (2007$) ..................................... 15A-27 

Table 15A.5.2 Social Cost of CO2, 2010–2050 (2007$) .................................................. 15A-28 

Table 15A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050............................................................................ 15A-29 

Table 15A.7.1 Probabilities of Various Tipping Points from Expert Elicitation .............. 15A-32 

Table 15A.9.1 Annual SCC Values: 2010–2050 (2007$) ................................................ 15A-41 

Table 15A.9.2 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) .... 15A-49 

Table 15A.9.3 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) ....... 15A-49 

Table 15A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) .......................................................................................................... 15A-50
 

Table 15A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates ............... 15A-51
 

15A-ii 

EERE-2008-BT-STD-0006 COMMENT 54
LIST OF FIGURES 

Figure 15A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due 

to an Increase in Annual Global Temperature in the DICE, FUND, 

and PAGE Models................................................................................. 15A-10 

Figure 15A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE................................................................................. 15A-10 

Figure 15A.4.3 Estimates of the Probability Density Function for Equilibrium 

Climate Sensitivity ................................................................................ 15A-15 

Figure 15A.5.1 Level of Global GDP Across EMF Scenarios....................................... 15A-28 

Figure 15A.9.1 Sulphur Dioxide Emission Scenarios .................................................... 15A-44 

Figure 15A.9.2 Global Population, 2000–2300 (Post-2100 Extrapolations Assume the 

Population Growth Rate Changes Linearly to Reach a Zero Growth 

Rate by 2200) ........................................................................................ 15A-46 

Figure 15A.9.3 World GDP, 2000-2300 (Post-2100 Extrapolations Assume GDP Per 

Capita Growth Declines Linearly, Reaching Zero in 2300) .................. 15A-46 

Figure 15A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post-2100 

Extrapolations Assume Growth Rate of CO2 Intensity (CO2/GDP) 

Over 2090–2100 is Maintained Through 2300) .................................... 15A-47 

Figure 15A.9.5 Global Net Land Use CO2 Emissions, 2000–2300 (Post-2100 

Extrapolations Assume Emissions Decline Linearly, Reaching Zero in 

2200) ...................................................................................................... 15A-47 

Figure 15A.9.6 Global Non-CO2 Radiative Forcing, 2000–2300 (Post-2100 

Extrapolations Assume Constant Non-CO2 Radiative Forcing after 

2100) ...................................................................................................... 15A-48 

Figure 15A.9.7 Global CO2 Intensity (Fossil & Industrial CO2 Emissions/GDP), 

2000–2300 (Post-2100 Extrapolations Assume Eecline in CO2/GDP 

Growth Rate Over 2090–2100 is Maintained Through 2300) .............. 15A-48 

Figure 15A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

Discount Rate* ...................................................................................... 15A-50
 

15A-iii 

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APPENDIX 15A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT
 

ANALYSIS UNDER EXECUTIVE ORDER 12866 

Interagency Working Group on Social Cost of Carbon, United States Government
 

With participation by
 

Council of Economic Advisers 

Council on Environmental Quality
 

Department of Agriculture
 

Department of Commerce 

Department of Energy
 

Department of Transportation 

Environmental Protection Agency
 

National Economic Council 

Office of Energy and Climate Change 

Office of Management and Budget
 

Office of Science and Technology Policy
 

Department of the Treasury
 

15A.1 EXECUTIVE SUMMARY 

Under Executive Order (E.O.) 12866, agencies are required, to the extent permitted by 

law, “to assess both the costs and the benefits of the intended regulation and, recognizing that 

some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a 

reasoned determination that the benefits of the intended regulation justify its costs.” The purpose 

of the “social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate 

the social benefits of reducing carbon dioxide (CO2) emissions into cost-benefit analyses of 

regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The 

estimates are presented with an acknowledgement of the many uncertainties involved and with a 

clear understanding that they should be updated over time to reflect increasing knowledge of the 

science and economics of climate impacts. 

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. It is intended to include but is not limited to 

changes in net agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services due to climate change. 

This document presents a summary of the interagency process that developed these SCC 

estimates. Technical experts from numerous agencies met on a regular basis to consider public 

comments, explore the technical literature in relevant fields, and discuss key model inputs and 

assumptions. The main objective of this process was to develop a range of SCC values using a 

defensible set of input assumptions grounded in the existing scientific and economic literatures. 

In this way, key uncertainties and model differences transparently and consistently inform the 

range of SCC estimates used in the rulemaking process. 

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The interagency group selected four SCC values for use in regulatory analyses (Table 

15A.1.1). Three values are based on the average SCC from three integrated assessment models, 

at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile 

SCC estimate across all three models at a 3 percent discount rate, is included to represent higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

Table 15A.1.1 Social Cost of CO2, 2010–2050 (2007$) 

Year Discount Rate 

% 

5 3 2.5 3 

Avg Avg Avg 95th 

2010 4.7 21.4 35.1 64.9 

2015 5.7 23.8 38.4 72.8 

2020 6.8 26.3 41.7 80.7 

2025 8.2 29.6 45.9 90.4 

2030 9.7 32.8 50.0 100.0 

2035 11.2 36.0 54.2 109.7 

2040 12.7 39.2 58.4 119.3 

2045 14.2 42.1 61.7 127.8 

2050 15.7 44.9 65.0 136.2 

15A.2 MONETIZING CARBON DIOXIDE EMISSIONS 

The “social cost of carbon” (SCC) is an estimate of the monetized damages associated 

with an incremental increase in carbon emissions in a given year. It is intended to include but is 

not limited to changes in net agricultural productivity, human health, property damages from 

increased flood risk, and the value of ecosystem services. We report estimates of the social cost 

of carbon in dollars per metric ton of CO2 throughout this document.a 

When attempting to assess the incremental economic impacts of CO2 emissions, the 

analyst faces a number of serious challenges. A recent report from the National Academies of 

Science (NRC 2009) points out that any assessment will suffer from uncertainty, speculation, 

and lack of information about (1) future emissions of greenhouse gases; (2) the effects of past 

and future emissions on the climate system; (3) the impact of changes in climate on the physical 

and biological environment; and (4) the translation of these environmental impacts into 

economic damages. As a result, any effort to quantify and monetize the harms associated with 

climate change will raise serious questions of science, economics, and ethics and should be 

viewed as provisional. 

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing CO2 emissions. Under E.O. 12866, agencies 

are required, to the extent permitted by law, “to assess both the costs and the benefits of the 

intended regulation and, recognizing that some costs and benefits are difficult to quantify, 

propose or adopt a regulation only upon a reasoned determination that the benefits of the 

intended regulation justify its costs.” The purpose of the SCC estimates presented here is to make 

a

 In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67). 

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it possible for agencies to incorporate the social benefits from reducing CO2 emissions into costbenefit analyses of regulatory actions that have small or “marginal” impacts on cumulative 

global emissions. Most Federal regulatory actions can be expected to have marginal impacts on 

global emissions. 

For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be estimated by multiplying the change in emissions in that year by the SCC 

value appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

CO2 emissions. For policies that have a large (non-marginal) impact on global cumulative 

emissions, there is a separate question of whether the SCC is an appropriate tool for calculating 

the benefits of reduced emissions; we do not attempt to answer that question here. 

An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was to 

develop a range of SCC values using a defensible set of input assumptions that are grounded in 

the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process. 

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $4.7, $21.4, $35.1, and $64.9 (2007$). The first three estimates are 

based on the average SCC across models and socio-economic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. See 

Appendix A for the full range of annual SCC estimates from 2010 to 2050. 

It is important to emphasize that the interagency process is committed to updating these 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC 

values within 2 years or at such time as substantially updated models become available, and to 

continue to support research in this area. In the meantime, we will continue to explore the issues 

raised in this document and consider public comments as part of the ongoing interagency 

process. 

15A-3 

EERE-2008-BT-STD-0006 COMMENT 54
15A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES 

To date, economic analyses for Federal regulations have used a wide range of values to 

estimate the benefits associated with reducing CO2 emissions. In the final model year 2011 

CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of $2 

per ton of CO2 and a “global” SCC value of $33 per ton of CO2 for 2007 emission reductions 

(2007$), increasing both values at 2.4 percent per year. It also included a sensitivity analysis at 

$80 per ton of CO2. A domestic SCC value is meant to reflect the value of damages in the United 

States resulting from a unit change in CO2 emissions, while a global SCC value is meant to 

reflect the value of damages worldwide. 

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO2 

(in 2006 dollars) for 2011 emission reductions (with a range of $0-$14 for sensitivity analysis), 

also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a 

domestic SCC range of $0 to $20 per ton CO2 for 2007 emission reductions (2007$). In addition, 

EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified what it 

described as “very preliminary” SCC estimates subject to revision. EPA’s global mean values 

were $68 and $40 per ton CO2 for discount rates of approximately 2 percent and 3 percent, 

respectively (2006$ for 2007 emissions). 

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing CO2 emissions. To ensure consistency in how 

benefits are evaluated across agencies, the Administration sought to develop a transparent and 

defensible method, specifically designed for the rulemaking process, to quantify avoided climate 

change damages from reduced CO2 emissions. The interagency group did not undertake any 

original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. 

The outcome of the preliminary assessment by the interagency group was a set of five 

interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per 

ton of CO2. The $33 and $5 values represented model-weighted means of the published estimates 

produced from the most recently available versions of three integrated assessment models— 

DICE, PAGE, and FUND—at approximately 3 and 5 percent discount rates. The $55 and $10 

values were derived by adjusting the published estimates for uncertainty in the discount rate 

(using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, 

respectively. The $19 value was chosen as a central value between the $5 and $33 per ton 

estimates. All of these values were assumed to increase at 3 percent annually to represent growth 

in incremental damages over time as the magnitude of climate change increases. 

These interim values represent the first sustained interagency effort within the U.S. 

government to develop an SCC for use in regulatory analysis. The results of this preliminary 

effort were presented in several proposed and final rules and were offered for public comment in 

connection with proposed rules, including the joint EPA-DOT fuel economy and CO2 tailpipe 

emission proposed rules. 

15A.4 APPROACH AND KEY ASSUMPTIONS 

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Since the release of the interim values, interagency group has reconvened on a regular 

basis to generate improved SCC estimates. Specifically, the group has considered public 

comments and further explored the technical literature in relevant fields. This section details the 

several choices and assumptions that underlie the resulting estimates of the SCC. 

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Academy of Science (2009) points 

out that there is tension between the goal of producing quantified estimates of the economic 

damages from an incremental ton of carbon and the limits of existing efforts to model these 

effects. Throughout this document, we highlight a number of concerns and problems that should 

be addressed by the research community, including research programs housed in many of the 

agencies participating in the interagency process to estimate the SCC. 

The U.S. Government will periodically review and reconsider estimates of the SCC used 

for cost-benefit analyses to reflect increasing knowledge of the science and economics of climate 

impacts, as well as improvements in modeling. In this context, statements recognizing the 

limitations of the analysis and calling for further research take on exceptional significance. The 

interagency group offers the new SCC values with all due humility about the uncertainties 

embedded in them and with a sincere promise to continue work to improve them. 

15A.4.1 Integrated Assessment Models 

We rely on three integrated assessment models (IAMs) commonly used to estimate the 

SCC: the FUND, DICE, and PAGE models.b

 These models are frequently cited in the peerreviewed literature and used in the IPCC assessment. Each model is given equal weight in the 

SCC values developed through this process, bearing in mind their different limitations (discussed 

below). 

These models are useful because they combine climate processes, economic growth, and 

feedbacks between the climate and the global economy into a single modeling framework. At the 

same time, they gain this advantage at the expense of a more detailed representation of the 

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

science in their modeling frameworks but do not link physical impacts to economic damages. 

There is currently a limited amount of research linking climate impacts to economic damages, 

which makes this exercise even more difficult. Underlying the three IAMs selected for this 

exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ 

b

 The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of 

energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy 

Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decisionmakers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate 

Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, 

originally to study international capital transfers in climate policy. is now widely used to study climate impacts (e.g., 

Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009). 

15A-5 

EERE-2008-BT-STD-0006 COMMENT 54
 

best attempts to synthesize the available scientific and economic research characterizing these 

relationships. 

The three IAMs translate emissions into changes in atmospheric greenhouse 

concentrations, atmospheric concentrations into changes in temperature, and changes in 

temperature into economic damages. The emissions projections used in the models are based on 

specified socio-economic (GDP and population) pathways. These emissions are translated into 

concentrations using the carbon cycle built into each model, and concentrations are translated 

into warming based on each model’s simplified representation of the climate and a key 

parameter, climate sensitivity. Each model uses a different approach to translate warming into 

damages. Finally, transforming the stream of economic damages over time into a single value 

requires judgments about how to discount them. 

Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. In PAGE, for example, the consumption-equivalent damages 

in each period are calculated as a fraction of GDP, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. In FUND, damages in each 

period also depend on the rate of temperature change from the prior period. In DICE, 

temperature affects both consumption and investment. We describe each model in greater detail 

here. In a later section, we discuss key gaps in how the models account for various scientific and 

economic processes (e.g., the probability of catastrophe, and the ability to adapt to climate 

change and the physical changes it causes). 

The parameters and assumptions embedded in the three models vary widely. A key 

objective of the interagency process was to enable a consistent exploration of the three models 

while respecting the different approaches to quantifying damages taken by the key modelers in 

the field. An extensive review of the literature was conducted to select three sets of input 

parameters for these models: climate sensitivity, socio-economic and emissions trajectories, and 

discount rates. A probability distribution for climate sensitivity was specified as an input into all 

three models. In addition, the interagency group used a range of scenarios for the socio-economic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. In DICE, these 

parameters are handled deterministically and represented by fixed constants; in PAGE, most 

parameters are represented by probability distributions. FUND was also run in a mode in which 

parameters were treated probabilistically. 

The sensitivity of the results to other aspects of the models (e.g., the carbon cycle or 

damage function) is also important to explore in the context of future revisions to the SCC but 

has not been incorporated into these estimates. Areas for future research are highlighted at the 

end of this document. 

15A.4.1.1 The DICE Model 

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric CO2 concentrations). Emission reductions are treated as 

analogous to investment in “natural capital.” By investing in natural capital today through 

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reductions in emissions—implying reduced consumption—harmful effects of climate change can 

be avoided and future consumption thereby increased. 

For purposes of estimating the SCC, CO2 emissions are a function of global GDP and the 

carbon intensity of economic output, with the latter declining over time due to technological 

progress. The DICE damage function links global average temperature to the overall impact on 

the world economy. It varies quadratically with temperature change to capture the more rapid 

increase in damages expected to occur under more extreme climate change, and is calibrated to 

include the effects of warming on the production of market and nonmarket goods and services. It 

incorporates impacts on agriculture, coastal areas (due to sea level rise), “other vulnerable 

market sectors” (based primarily on changes in energy use), human health (based on climaterelated diseases, such as malaria and dengue fever, and pollution), non-market amenities (based 

on outdoor recreation), and human settlements and ecosystems. The DICE damage function also 

includes the expected value of damages associated with low probability, high impact 

“catastrophic” climate change. This last component is calibrated based on a survey of experts 

(Nordhaus 1994). The expected value of these impacts is then added to the other market and nonmarket impacts mentioned above. 

No structural components of the DICE model represent adaptation explicitly, though it is 

included implicitly through the choice of studies used to calibrate the aggregate damage function. 

For example, its agricultural impact estimates assume that farmers can adjust land use decisions 

in response to changing climate conditions, and its health impact estimates assume 

improvements in healthcare over time. In addition, the small impacts on forestry, water systems, 

construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these 

sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level 

rise are incorporated into damage estimates, but their magnitude is not clearly reported. 

Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective 

adaptation, and largely ignores adaptation costs.” 

Note that the damage function in DICE has a somewhat different meaning from the 

damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because 

damages in a given year reduce investment in that year, damages propagate forward in time and 

reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.c 

15A.4.1.2 The PAGE Model 

c

 Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is endogenous. 

Specifically, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the 

optimizing representative agent in the model. We made two modifications to DICE to make it consistent with EMF 

GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we re-calibrated the exogenous 

path of total factor productivity so that DICE would produce GDP projections in the absence of warming that 

exactly matched the EMF scenarios. 

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PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 

2 as in DICE). 

PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing 

a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are 

all modeled probabilistically. 

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2 °C for developed countries and 0 °C for developing 

countries for economic impacts, and 0 °C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2 °C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006). 

15A.4.1.3 The FUND Model 

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

impact sectors, the fraction of output lost or gained due to climate change depends not only on 

the absolute temperature change but also on the rate of temperature change and level of regional 

income.d In the forestry and agricultural sectors, economic damages also depend on CO2 

concentrations. 

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns:” for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

d

 In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning 

demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in 

those regions at low to moderate levels of warming (Warren et al. 2006). 

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they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.” 

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen 

in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as 

energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero). 

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change. 

15A.4.1.4 Damage Functions 

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-average 

surface temperature) on the consumption-equivalent value of both market and non-market goods 

(represented as a fraction of global GDP). We recognize that these representations are 

incomplete and highly uncertain. Given the paucity of data linking the physical impacts to 

economic damages, we were not able to identify a better way to translate changes in climate into 

net economic damages, short of launching our own research program. 

The damage functions for the three IAMs are presented in Figure 15A.4.1 and Figure 15A.4.2, 

using the modeler’s default scenarios and mean input assumptions. There are significant 

differences between the three models both at lower (Figure 15A.4.1) and higher (Figure 15A.4.2) 

increases in global-average temperature. 

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Figure 15A.4.1	 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due to an 

Increase in Annual Global Temperature in the DICE, FUND, and PAGE 

Modelse 

Figure 15A.4.2	 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE 

e

 The x-axis represents increases in annual, rather than equilibrium, temperature, while the y-axis represents the 

annual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity, socioeconomic, and emissions parameters will produce a different realization of damages for each IAM. The damage 

functions represented in Figure 15A.4.1 and Figure 15A.4.2 are the outcome of default assumptions. For instance, 

under alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater 

than 3 °C. 

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The lack of agreement among the models at lower temperature increases is underscored 

by the fact that the damages from FUND are well below the 5th percentile estimated by PAGE, 

while the damages estimated by DICE are roughly equal to the 95th percentile estimated by 

PAGE. This is significant because at higher discount rates we expect that a greater proportion of 

the SCC value is due to damages in years with lower temperature increases. For example, when 

the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to 

damages that occur in years when the temperature is less than or equal to 3 °C. This increases to 

approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively. 

These differences underscore the need for a thorough review of damage functions—in 

particular, how the models incorporate adaptation, technological change, and catastrophic 

damages. Gaps in the literature make modifying these aspects of the models challenging, which 

highlights the need for additional research. As knowledge improves, the Federal government is 

committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages. 

15A.4.2 Global versus Domestic Measures of SCC 

Because of the distinctive nature of the climate change problem, we center our current 

attention on a global measure of SCC. This approach is the same as that taken for the interim 

values, but it otherwise represents a departure from past practices, which tended to put greater 

emphasis on a domestic measure of SCC (limited to impacts of climate change experienced 

within U.S. borders). As a matter of law, consideration of both global and domestic values is 

generally permissible; the relevant statutory provisions are usually ambiguous and allow 

selection of either measure.f 

15A.4.2.1 Global SCC 

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. 

f

 It is true that Federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give 

extraterritorial effect to federal law and hence does not intrude on such interests. 

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When quantifying the damages associated with a change in emissions, a number of 

analysts (e.g., Anthoff et al. 2009a) employ “equity weighting” to aggregate changes in 

consumption across regions. This weighting takes into account the relative reductions in wealth 

in different regions of the world. A per-capita loss of $500 in GDP, for instance, is weighted 

more heavily in a country with a per-capita GDP of $2,000 than in one with a per-capita GDP of 

$40,000. The main argument for this approach is that a loss of $500 in a poor country causes a 

greater reduction in utility or welfare than does the same loss in a wealthy nation. 

Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group 

concluded that this approach would not be appropriate for estimating a SCC value used in 

domestic regulatory analysis.g For this reason, the group concluded that using the global (rather 

than domestic) value, without equity weighting, is the appropriate approach. 

15A.4.2.2 Domestic SCC 

As an empirical matter, the development of a domestic SCC is greatly complicated by the 

relatively few region- or country-specific estimates of the SCC in the literature. One potential 

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio 

of domestic to global benefits of emission reductions varies with key parameter assumptions. For 

example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7–10 percent of the 

global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP 

lost due to climate change is assumed to be similar across countries, the domestic benefit would 

be proportional to the U.S. share of global GDP, which is currently about 23 percent.h 

On the basis of this evidence, the interagency workgroup determined that a range of 

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic 

effects. Reported domestic values should use this range. It is recognized that these values are 

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach. 

15A.4.3 Valuing Non-CO2 Emissions

 While CO2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. 

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 

hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these 

gases is commonly discussed in terms of their 100-year global warming potential (GWP). GWP 

measures the ability of different gases to trap heat in the atmosphere (i.e., radiative forcing per 

unit of mass) over a particular timeframe relative to CO2. However, because these gases differ in 

g

 It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development 

of the appropriate “equity weight” is challenging. Emissions reductions also impose costs, and hence a full account 

would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor 

nation than on a wealthy one. Even if equity weighting—for both the costs and benefits of emissions reductions—is 

appropriate when considering the utility or welfare effects of international action, the interagency group concluded 

that it should not be used in developing an SCC for use in regulatory policy at this time. 

h

 Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report. 

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both radiative forcing and atmospheric lifetimes, their relative damages are not constant over 

time. For example, because methane has a short lifetime, its impacts occur primarily in the near 

term and thus are not discounted as heavily as those caused by longer-lived gases. Impacts other 

than temperature change also vary across gases in ways that are not captured by GWP. For 

instance, CO2 emissions, unlike methane and other greenhouse gases, contribute to ocean 

acidification. Likewise, damages from methane emissions are not offset by the positive effect of 

CO2 fertilization. Thus, transforming gases into CO2-equivalents using GWP, and then 

multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the 

social costs of non-CO2 gases. 

In light of these limitations, and the significant contributions of non-CO2 emissions to 

climate change, further research is required to link non-CO2 emissions to economic impacts. 

Such work would feed into efforts to develop a monetized value of reductions in non-CO2 

greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the 

interagency group hopes to develop methods to value these other greenhouse gases. The goal is 

to develop these estimates by the time we issue revised SCC estimates for CO2 emissions. 

15A.4.4 Equilibrium Climate Sensitivity 

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.i It is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric CO2 concentration relative to pre-industrial levels 

(or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties 

in this important parameter have received substantial attention in the peer-reviewed literature. 

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC): 

Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, or 

‘equilibrium climate sensitivity,’ is likely to lie in the range 2 °C to 4.5 °C, with a most likely 

value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C.j 

For fundamental physical reasons as well as data limitations, values substantially higher than 

4.5 °C still cannot be excluded, but agreement with observations and proxy data is generally 

worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et al. 2007, p. 

799) 

After consulting with several lead authors of this chapter of the IPCC report, the 

interagency workgroup selected four candidate probability distributions and calibrated them to 

i

 The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas 

concentrations over the short to medium term (up to 100–200 years), but it does not include long-term feedback 

effects due to possible large-scale changes in ice sheets or the biosphere, which occur on a time scale of many 

hundreds to thousands of years (e.g., Hansen et al. 2007).

j

 This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of 

“likely” as greater than 66 percent probability (Le Treut et al. 2007). “Very likely” indicates a greater than 90 

percent probability. 

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be consistent with the above statement: Roe and Baker (2007), log-normal, gamma, and Weibull. 

(Table 15A.4.1) gives summary statistics for the four calibrated distributions. 

Table 15A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distribution 

Rank Roe & Baker Log-Normal Gamma Weibull 

Pr(ECS < 1.5 °C) 0.013 0.050 0.070 0.102 

Pr(2 °C < ECS < 4.5 °C) 0.667 0.667 0.667 0.667 

5th Percentile 1.72 1.49 1.37 1.13 

10th Percentile 1.91 1.74 1.65 1.48 

Mode 2.34 2.52 2.65 2.90 

Median (50th percentile) 3.00 3.00 3.00 3.00 

Mean 3.50 3.28 3.19 3.07 

90th Percentile 5.86 5.14 4.93 4.69 

95th Percentile 7.14 5.97 5.59 5.17 

Each distribution was calibrated by applying three constraints from the IPCC: 

(1) a median equal to 3 °C, to reflect the judgment of “a most likely value of about 3 °C;”k 

(2) two-thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C; 

and 

(3) zero probability that it is less than 0 °C or greater than 10 °C (Hegerl et al. 2006, p. 721). 

We selected the calibrated Roe and Baker distribution from the four candidates for two 

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a 

theoretical understanding of the response of the climate system to increased greenhouse gas 

concentrations (Roe and Baker 2007; Roe 2008). In contrast, the other three distributions are 

mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general 

shape. The Roe and Baker distribution results from three assumptions about climate response: 

(1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback 

factors are proportional to the change in surface temperature; and (3) uncertainties in feedback 

factors are normally distributed. There is widespread agreement on the first point and the second 

and third points are common assumptions. 

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that 

“values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no 

quantitative judgment, the 95th percentile of the calibrated Roe & Baker distribution (7.1 °C) is 

much closer to the mean and the median (7.2 °C) of the 95th percentiles of 21 previous studies 

summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and 

k

 Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage 

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC 

report is not specific on this point. For the distributions we considered, the median was between the mode and the 

mean. For the Roe and Baker distribution, setting the median equal to 3 °C, rather than the mode or mean, gave a 

95th percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and 

mode equal to 3 °C produced 95th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end 

of the range in the literature. Finally, the median is closer to 3 °C than is the mode for the truncated distributions 

selected by the IPCC (Hegerl et al. 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is 

most consistent with a Roe and Baker distribution with the median set equal to 3 °C. 

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median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl et al. 2006) 

than are the 95th percentiles of the three other calibrated distributions (5.2–6.0 °C). 

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very 

likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the 

probability of equilibrium climate sensitivity being greater than 1.5 °C is almost 99 percent, is 

not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent 

probability,” it reflects a greater degree of certainty about very low values of ECS than was 

expressed by the IPCC. 

To show how the calibrated Roe and Baker distribution compares to different estimates 

of the probability distribution function of equilibrium climate sensitivity in the empirical 

literature, Figure 15A.4.3 overlays it on Figure 9.20 from the IPCC Fourth Assessment Report. 

These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal bars 

show the respective 5 percent to 95 percent ranges; dots indicate the median estimate.l 

Calibrated

Roe & Baker

Figure 15A.4.3	 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity 

15A.4.5 Socio-Economic and Emissions Trajectories 

Another key issue considered by the interagency group is how to select the set of socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socio-economic 

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

l

 The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed 

line, anthropogenic forcings only), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. 

(2002), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on 

multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown 

are the 5–95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 

2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties. 

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tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, CO2 emissions, and non-CO2 radiative forcing. A wide variety of 

scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, 

CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed 

to select scenarios that span most of the plausible ranges of outcomes for these variables. 

To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22, which uses ten well-recognized models to evaluate 

substantial, coordinated global action to meet specific stabilization targets. A key advantage of 

relying on these data is that GDP, population, and emission trajectories are internally consistent 

for each model and scenario evaluated. The EMF-22 modeling effort also is preferable to the 

IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 of 4 of the SRES 

scenarios are now extreme outliers in one or more variables. Although the EMF-22 scenarios 

have not undergone the same level of scrutiny as the SRES scenarios, they are recent, peerreviewed, published, and publicly available. 

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socio-economic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (). Four of these 

represent potential business-as-usual (BAU) growth in population, wealth, and emissions and are 

associated with CO2 (only) concentrations ranging from 612 to 889 ppm in 2100. One represents 

an emissions pathway that achieves stabilization at 550 ppm CO2e (i.e., CO2-only concentrations 

of 425–484 ppm or a radiative forcing of 3.7 W/m2

) in 2100, a lower-than-BAU trajectory.m Out 

of the 10 models included in the EMF-22 exercise, we selected the trajectories used by 

MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. For the BAU 

pathways, we used the GDP, population, and emission trajectories from each of these four 

models. For the 550 ppm CO2e scenario, we averaged the GDP, population, and emission 

trajectories implied by these same four models. 

We explore how sensitive the SCC is to various assumptions about how the future will 

evolve without prejudging what is likely to occur. The interagency group considered formally 

assigning probability weights to different states of the world, but this proved challenging to do in 

an analytically rigorous way given the dearth of information on the likelihood of a full range of 

future socio-economic pathways. 

m Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, 

though it could also result from technological advances. It was chosen because it represents the most stringent case 

analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario. 

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Table 15A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios 

Reference Fossil and Industrial CO2 Emissions 

GtCO2/yr 

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100 

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1 

MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9 

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7 

MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5 

550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8 

Reference GDP 

market exchange rates in trillion 2005$n 

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100 

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6 

MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0 

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9 

MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5 

550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9 

Global Population 

billions 

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100 

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1 

MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7 

MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4 

MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7 

550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1 

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy 

Outlook projected that global CO2 emissions will grow to 30.8, 35.6, and 40.4 gigatons in 2010, 

2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 and $93.9 trillion 

(2005$ using market exchange rates) in 2010, 2020, and 2030, respectively. These projections 

are consistent with one or more EMF-22 scenarios. Likewise, the United Nations’ 2008 

Population Prospect projects population will grow from 6.1 billion people in 2000 to 9.1 billion 

people in 2050, which is close to the population trajectories for the IMAGE, MiniCAM, and 

MERGE models. 

n

 While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use 

purchasing power parity (PPP), which takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that 

differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). 

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross-sectional PPP 

measures for relative incomes and outputs and national accounts price and quantity indexes for time-series 

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that 

exchange-rate-conversion issues are as important as uncertainties about population, technological change, or the 

many geophysical uncertainties. 

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In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO2 

emissions out to 2100. These assumptions also are used in the three models while retaining the 

default radiative forcings due to other factors (e.g., aerosols and other gases). See the Appendix 

for greater detail. 

15A.4.6 Discount Rate 

The choice of a discount rate, especially over long periods of time, raises highly 

contested and exceedingly difficult questions of science, economics, philosophy, and law. 

Although it is well understood that the discount rate has a large influence on the current value of 

future damages, there is no consensus about what rates to use in this context. Because CO2 

emissions are long-lived, subsequent damages occur over many years. In calculating the SCC, 

we first estimate the future damages to agriculture, human health, and other market and nonmarket sectors from an additional unit of CO2 emitted in a particular year in terms of reduced 

consumption (or consumption equivalents) due to the impacts of elevated temperatures, as 

represented in each of the three IAMs. Then we discount the stream of future damages to its 

present value in the year when the additional unit of emissions was released using the selected 

discount rate, which is intended to reflect society’s marginal rate of substitution between 

consumption in different time periods. 

For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. 

As Circular A-4 acknowledges, however, the choice of discount rate for intergenerational 

problems raises distinctive problems and presents considerable challenges. After reviewing those 

challenges, Circular A-4 states, “If your rule will have important intergenerational benefits or 

costs you might consider a further sensitivity analysis using a lower but positive discount rate in 

addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here.

 Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on observations of people’s 

actual choices—e.g., savings versus consumption decisions over time, and allocations of savings 

among more and less risky investments. Advocates of this approach generally call for inferring 

the discount rate from market rates of return “because of a lack of justification for choosing a 

social welfare function that is any different than what decision makers [individuals] actually use” 

(Arrow et al. 1996). 

One theoretical foundation for the cost-benefit analyses in which the social cost of carbon 

will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

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generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital. 

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view. 

Other concerns also motivate making adjustments to descriptive discount rates. In 

particular, it has been noted that the preferences of future generations with regard to 

consumption versus environmental amenities may not be the same as those today, making the 

current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount rate should be below market rates to correct for 

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above; Arrow et al. 1996, Weitzman 1999). 

Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that underlies 

the prescriptive approach assumes a representative agent with perfect foresight and no credit 

constraints. This is an artificial rendering of the real world that misses many of the frictions that 

characterize individuals’ lives and indeed the available descriptive evidence supports this. For 

instance, many individuals smooth consumption by borrowing with credit cards that have 

relatively high rates. Some are unable to access traditional credit markets and rely on payday 

lending operations or other high cost forms of smoothing consumption. Whether one puts greater 

weight on the prescriptive or descriptive approach, the high interest rates that credit-constrained 

individuals accept suggest that some account should be given to the discount rates revealed by 

their behavior. 

We draw on both approaches but rely primarily on the descriptive approach to inform the 

choice of discount rate. With recognition of its limitations, we find this approach to be the most 

defensible and transparent given its consistency with the standard contemporary theoretical 

foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance. 

The logic of this framework also suggests that market rates should be used for discounting future 

consumption-equivalent damages. Regardless of the theoretical approach used to derive the 

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of 

adequately capturing consumption trade-offs over many decades or even centuries. While relying 

primarily on the descriptive approach in selecting specific discount rates, the interagency group 

has been keenly aware of the deeply normative dimensions of both the debate over discounting 

in the intergenerational context and the consequences of selecting one discount rate over another. 

15A.4.6.1 Historically Observed Interest Rates 

In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

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discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital. 

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services— 

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 

The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 

marginal change in carbon emissions (Lind 1990, Arrow et al., 1996, Arrow 2000). The 

consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC. 

Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 

discount rate typically assumes a representative agent with perfect foresight and no credit 

constraints. The risk-free rate is appropriate for discounting certain future benefits or costs, but 

the benefits calculated by IAMs are uncertain. To use the risk-free rate to discount uncertain 

benefits, these benefits first must be transformed into “certainty equivalents,” i.e., the maximum 

certain amount that we would exchange for the uncertain amount. However, the calculation of 

the certainty-equivalent requires first estimating the correlation between the benefits of the 

policy and baseline consumption. 

If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the appropriate discount rate generally does not equal the risk-free rate. 

If the benefits of the policy tend to be high in those states of the world in which consumption is 

low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice 

versa). Since many (though not necessarily all) of the important impacts of climate change will 

flow through market sectors such as agriculture and energy, and since willingness to pay for 

environmental protections typically increases with income, we might expect a positive (though 

not necessarily perfect) correlation between the net benefits from climate policies and market 

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless 

rate. Alternatively, a negative correlation between the returns to climate policies and market 

returns would imply that a discount rate below the riskless rate is appropriate. 

This discussion suggests that both the post-tax riskless and risky rates can be used to 

capture individuals’ consumption-equivalent interest rate. As a measure of the post-tax riskless 

rate, we calculate the average real return from Treasury notes over the longest time period 

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal 

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rate from tax years 2003 through 2006 is around 27 percent).o This calculation produces a real 

interest rate of about 2.7 percent, which is roughly consistent with Circular A-4’s 

recommendation to use 3 percent to represent the consumption rate of interest.p

 A measure of the 

post-tax risky rate for investments whose returns are positively correlated with overall equity 

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes yields a real rate of roughly 5 percent.q 

15A.4.6.2 The Ramsey Equation 

Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).r

 These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η·g.s In the simplest version of the Ramsey model, 

with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey 

discount rate,” ρ + η·g, will be equal to the rate of return to capital, i.e., the market interest rate. 

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

•	 η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.t 

o

 The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 

percent for 30-year Treasury securities. 

p

 The positive approach reflects how individuals make allocation choices across time, but it is important to keep in 

mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon. 

q

 Cambell et al. (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950–2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20–40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006). 

r

 The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an increase 

in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. The 

parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than consumption 

today, so diminishing marginal utility of consumption implies that the same monetary damage will cause a smaller 

reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar 

increase in income is equally valuable regardless of level of income; if η = 1, then a one percent increase in income 

is equally valuable no matter the level of income; and if η > 1, then a one percent increase in income is less valuable 

to wealthier individuals. 

s

 In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

t

 Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of 

values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman 

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on 

labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a 

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and 

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting 

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. 

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Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η

equal to 1 suggests savings rates that do not conform to observed behavior. 

•	 ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates 

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued that to use any value other than ρ = 0 would unjustly discriminate against future 

generations (e.g., Arrow et al. 1996, Stern 2006). However, even in an inter-generational 

setting, it may make sense to use a small positive pure rate of time preference because of 

the small probability of unforeseen cataclysmic events (Stern 2006). 

•	 g. A commonly accepted approximation is around 2 percent per year. For the socioeconomic scenarios used for this exercise, the EMF models assume that g is about 1.5–2 

percent to 2100. 

Some economists and non-economists have argued for constant discount rates below 2 

percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to 

one generation over another. The choice of η has also been posed as an ethical choice linked to 

the value of an additional dollar in poorer countries compared to wealthier ones. Stern (2006) 

applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 percent 

per year, which yields an annual discount rate of 1.4 percent. In the context of permanent income 

savings behavior, however, Stern’s assumptions suggest that individuals would save 93 percent 

of their income.u 

Recently, Stern (2008) revisited the values used in Stern (2006), stating that there is a 

case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate greater 2 percent. 

We conclude that arguments made under the prescriptive approach can be used to justify 

discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most 

appropriate value for η, we find it difficult to justify rates at the lower end of this range under the 

Ramsey framework. 

15A.4.6.3 Accounting for Uncertainty in the Discount Rate 

While the consumption rate of interest is an important driver of the benefits estimate, it is 

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate 

Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year 

using data on mortality rates. Anthoff et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the 

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 

1.07. 

u

 Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied
 

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same
 

time, adding exogenous technical change—all else equal—would increase g as well.)
 

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sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and 

Panipoulou et al. (2004) confirm empirically that discount rate uncertainty can have a large 

effect on net present values. A main result from these studies is that if there is a persistent 

element to the uncertainty in the discount rate (e.g., the rate follows a random walk), then it will 

result in an effective (or certainty-equivalent) discount rate that declines over time. 

Consequently, lower discount rates tend to dominate over the very long term (Weitzman 1998, 

1999, 2001; Newell and Pizer 2003; Panipoulou et al. (2004); Gollier 2008; Summers and 

Zeckhauser 2008; and Gollier and Weitzman 2009). 

The proper way to model discount rate uncertainty remains an active area of research. 

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

forecast future discount rates. Their model incorporates some of the basic features of how 

interest rates move over time, and its parameters are estimated based on historical observations 

of long-term rates. Subsequent work on this topic, most notably Panipoulou et al. (2004), uses 

more general models of interest rate dynamics to allow for better forecasts. Specifically, the 

volatility of interest rates depends on whether rates are currently low or high and variation in the 

level of persistence over time. 

While Newell and Pizer (2003) and Panipoulou et al. (2004) attempt formally to model 

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over 

time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach 

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further 

out in time.v

 A key question that has emerged with regard to both of these approaches is the 

trade-off between potential time inconsistency and giving greater weight to far future outcomes 

(see the EPA Science Advisory Board’s recent comments on this topic as part of its review of 

their Guidelines for Economic Analysis).w 

15A.4.6.4 The Discount Rates Selected for Estimating SCC 

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches. 

The central value (3 percent) is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

v

 For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31–75; 2.5 

percent for years 76–125; 2 percent for years 126–200; 1.5 percent for years 201–300; and 1 percent after 300 years. 

As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time. 

w Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that 

Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a 

low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value 

calculation and result in an infinite willingness-to-pay for mitigation today. Newbold and Daigneault, (2009) and 

Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, 

utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in 

the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required. 

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mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 3 

percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent is 

included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods. 

The low value (2.5 percent) is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meanreverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.x Without giving preference to a 

particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless 

rate would be justified if climate investments are negatively correlated with the overall market 

rate of return. Use of this lower value also responds to certain judgments using the prescriptive 

or normative approach and to ethical objections that have been raised about rates of 3 percent or 

higher. 

15A.5 REVISED SCC ESTIMATES 

Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group: 

•	 A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds. 

•	 Five sets of GDP, population and carbon emissions trajectories based on EMF-22. 

•	 Constant annual discount rates of 2.5, 3, and 5 percent. 

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and 

FUND incorporate uncertainty in other model parameters, the final output from each model run 

is a distribution over the SCC in year t. 

For each of the IAMS, the basic computational steps for calculating the SCC in a particular year t 

are: 

1.	 Input the path of emissions, GDP, and population from the selected EMF-22 scenarios, 

and the extrapolations based on these scenarios for post-2100 years. 

2.	 Calculate the temperature effects and (consumption-equivalent) damages in each year 

resulting from the baseline path of emissions. 

a.	 In PAGE, the consumption-equivalent damages in each period are calculated as a 

fraction of the EMF GDP forecast, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. 

x

 Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003). 

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b.	 In FUND, damages in each period depend on both the level and the rate of 

temperature change in that period. 

c.	 In DICE, temperature affects both consumption and investment, so we first adjust the 

EMF GDP paths as follows: Using the Cobb-Douglas production function with the 

DICE2007 parameters, we extract the path of exogenous technical change implied by 

the EMF GDP and population paths, then we recalculate the baseline GDP path 

taking into account climate damages resulting from the baseline emissions path. 

3.	 Add an additional unit of carbon emissions in year t. (The exact unit varies by model.) 

4.	 Recalculate the temperature effects and damages expected in all years beyond t resulting 

from this adjusted path of emissions, as in step 2. 

5.	 Subtract the damages computed in step 2 from those in step 4 in each year. (DICE is run 

in 10 year time steps, FUND in annual time steps, while the time steps in PAGE vary.) 

6.	 Discount the resulting path of marginal damages back to the year of emissions using the 

agreed upon fixed discount rates. 

7.	 Calculate the SCC as the net present value of the discounted path of damages computed 

in step 6, divided by the unit of carbon emissions used to shock the models in step 3. 

8.	 Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of CO2 

(2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 in PAGE). 

The steps above were repeated in each model for multiple future years to cover the time 

horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three 

IAMs, climate damages are calculated as lost consumption in each future year. 

It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND. 

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 

population, and greenhouse gas emission trajectories after 2100, the last year for which these 

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Appendix.) 

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 

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To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no integrated assessment model or socioeconomic scenario is 

given greater weight than another. Because the literature shows that the SCC is quite sensitive to 

assumptions about the discount rate, and because no consensus exists on the appropriate rate to 

use in an intergenerational context, we present SCCs based on the average values across models 

and socioeconomic scenarios for each discount rate. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socio-economic and emissions scenarios 

at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount 

rate. (The full set of distributions by model and scenario combination is included in the 

Appendix.) As noted above, the 3 percent discount rate is the central value, and so the central 

value that emerges is the average SCC across models at the 3 percent discount rate. For purposes 

of capturing the uncertainties involved in regulatory impact analysis, we emphasize the 

importance and value of considering the full range. 

As previously discussed, low probability, high impact events are incorporated into the 

SCC values through explicit consideration of their effects in two of the three models as well as 

the use of a probability density function for equilibrium climate sensitivity. Treating climate 

sensitivity probabilistically results in more high temperature outcomes, which in turn lead to 

higher projections of damages. Although FUND does not include catastrophic damages (in 

contrast to the other two models), its probabilistic treatment of the equilibrium climate sensitivity 

parameter will directly affect the non-catastrophic damages that are a function of the rate of 

temperature change. 

In Table 15A.5.1, we begin by presenting SCC estimates for 2010 by model, scenario, 

and discount rate to illustrate the variability in the SCC across each of these input parameters. As 

expected, higher discount rates consistently result in lower SCC values, while lower discount 

rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there 

are differences in the SCC estimated across the three main models. For these estimates, FUND 

produces the lowest estimates, while PAGE generally produces the highest estimates. 

These results are not surprising when compared to the estimates in the literature for the 

latest versions of each model. For example, adjusting the values from the literature that were 

used to develop interim SCC values to 2007 dollars for the year 2010 (assuming, as we did for 

the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or 

near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There 

are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using 

similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on 

Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE 

(based on Hope 2006, 2008) close to $8 per ton for a 4 percent discount rate. Note that these 

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comparisons are only approximate since the literature generally relies on Ramsey discounting, 

while we have assumed constant discount rates.y 

Table 15A.5.1	 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic 

Trajectory, and Discount Rate for 2010 (2007$) 

Discount rate: 5% 3% 2.5% 3% 

Model Scenario Avg Avg Avg 95th 

DICE IMAGE 10.8 35.8 54.2 70.8 

MERGE 7.5 22.0 31.6 42.1 

Message 9.8 29.8 43.5 58.6 

MiniCAM 8.6 28.8 44.4 57.9 

550 Average 8.2 24.9 37.4 50.8 

PAGE IMAGE 8.3 39.5 65.5 142.4 

MERGE 5.2 22.3 34.6 82.4 

Message 7.2 30.3 49.2 115.6 

MiniCAM 6.4 31.8 54.7 115.4 

550 Average 5.5 25.4 42.9 104.7 

FUND IMAGE -1.3 8.2 19.3 39.7 

MERGE -0.3 8.0 14.8 41.3 

Message -1.9 3.6 8.8 32.1 

MiniCAM -0.6 10.2 22.2 42.6 

550 Average -2.7 -0.2 3.0 19.4 

The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models. 

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Further, in FUND increases in income over time decrease 

vulnerability to climate change (a form of adaptation), whereas this does not occur in DICE and 

PAGE. These structural differences among the models make FUND more sensitive to the path of 

emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 15A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE 

Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the 

rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result 

that is to be expected given its less direct relationship between its damage function and GDP. 

y

 Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) 

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 

0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC 

estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g., Anthoff et al. 2009). The path of per-capita 

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate 

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term. 

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450
 

400
 

350
 

Global 300

GDP

(trillions 250
 

of U.S.
 

dollars) 200
 

150

100

50

0
 

2000 2010 2020 2030 2050 2100
 

IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing

Figure 15A.5.1 Level of Global GDP Across EMF Scenarios 

Table 15A.5.2 shows the four selected SCC values in 5-year increments from 2010 to 

2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per model run) from all scenarios and models for a given discount rate. Values 

for the years in between are calculated using a simple linear interpolation. 

Table 15A.5.2 Social Cost of CO2, 2010–2050 (2007$)

 Discount Rate 5% 3% 2.5% 3% 

Year Avg Avg Avg 95th 

2010 4.7 21.4 35.1 64.9 

2015 5.7 23.8 38.4 72.8 

2020 6.8 26.3 41.7 80.7 

2025 8.2 29.6 45.9 90.4 

2030 9.7 32.8 50.0 100.0 

2035 11.2 36.0 54.2 109.7 

2040 12.7 39.2 58.4 119.3 

2045 14.2 42.1 61.7 127.8 

2050 15.7 44.9 65.0 136.2 

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC 

directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as 

was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are 

internally consistent with other modeling assumptions. Table 15A.5.3 illustrates how the growth 

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 

2010 and 2050 is reported in the Appendix. 

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Table 15A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050 

Average Annual 

Growth Rate 

5% 3% 2.5% 3.0% 

Year Range Avg Avg Avg 95th 

2010–2020 3.6 2.1 1.7 2.2 

2020–2030 3.7 2.2 1.8 2.2 

2030–2040 2.7 1.8 1.6 1.8 

2040–2050 2.1 1.4 1.1 1.3 

While the SCC estimate grows over time, the future monetized value of emissions 

reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must 

be discounted to the present to determine its total net present value for use in regulatory analysis. 

Damages from future emissions should be discounted at the same rate as that used to calculate 

the SCC estimates themselves to ensure internal consistency—i.e., future damages from climate 

change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. For example, climate damages in 2020 that are calculated using a 

SCC based on a 5 percent discount rate also should be discounted back to the analysis year using 

a 5 percent discount rate.z 

15A.6 LIMITATIONS OF ANALYSIS 

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

several areas in particular need of additional exploration and research. These caveats and 

additional observations in the following section are necessary to consider when interpreting and 

applying the SCC estimates. 

Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical, 

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. It is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

z

 However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates. 

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Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. 

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman’s results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios.” 

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high-impact 

low-probability risks, using a right-skewed gamma distribution for climate sensitivity as well as 

an uncertain damage coefficient, but in most cases found only a modest risk premium. Given this 

difference in opinion, further research in this area is needed before its practical significance can 

be fully understood and a reasonable approach developed to account for such risks in regulatory 

analysis. (The next section discusses the scientific evidence on catastrophic impacts in greater 

detail.) 

Uncertainty in extrapolation of damages to high temperatures. The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios. 

Incomplete treatment of adaptation and technological change. Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation. 

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning, so much so that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009). 

Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.aa For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change. 

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs under or 

overstate the likely damages. 

aa However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher). 

15A-30 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

 

 Risk aversion. A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to 

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability but lower-impact damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion. 

Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al. (2009) explored the sensitivity of 

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude 

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue. 

15A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS 

As noted above, the damage functions underlying the three IAMs used to estimate the 

SCC may not capture the economic effects of all possible adverse consequences of climate 

change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, 

the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping 

point” behavior in Earth systems; (2) inter-sectoral and inter-regional interactions, including 

global security impacts of high-end warming; and (3) limited near-term substitutability between 

damage to natural systems and increased consumption. 

It is the hope of the interagency group that over time researchers and modelers will work 

to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. In the meantime, we 

discuss some of the available evidence. 

15A.7.1 Extrapolation of Climate Damages to High Levels of Warming 

The damage functions in the models are calibrated at moderate levels of warming and 

should therefore be viewed cautiously when extrapolated to the high temperatures found in the 

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upper end of the distribution. Recent science suggests that there are a number of potential 

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with 

potentially severe social and economic consequences (e.g., Lenton et al. 2008, Kriegler et al. 

2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of 

the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West 

Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, 

strengthening of El Niño-Southern Oscillation, and the release of methane from melting 

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al. 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 15A.7.1. Ranges of probability are averaged across core experts on each 

topic. 

Table 15A.7.1 Probabilities of Various Tipping Points from Expert Elicitation 

Possible Tipping Points Duration before 

effect is fully realized 

Additional Warming by 2100 

% 

years 0.5–1.5 C 1.5–3.0 C 3–5 C 

Reorganization of Atlantic Meridional 

Overturning Circulation 

about 100 0–18 6–39 18–67 

Greenland Ice Sheet Collapse at least 300 8–39 33–73 67–96 

West Antarctic Ice Sheet Collapse at least 300 5–41 10–63 33–88 

Dieback of Amazon rainforest about 50 2–46 14–84 41–94 

Strengthening of El Niño-Southern 

Oscillation 

about 100 1–13 6–32 19–49 

Dieback of Boreal Forests about 50 13–43 20–81 34–91 

Shift in Indian Summer Monsoon about 1 not formally assessed 

Release of Methane from Melting 

Permafrost 

less than 100 not formally assessed 

As previously mentioned, FUND does not include potentially catastrophic effects. DICE 

assumes a small probability of catastrophic damages that increases with increased warming, but 

the damages from these risks are incorporated as expected values (i.e., ignoring potential risk 

aversion). PAGE models catastrophic impacts in a probabilistic framework, so the high-end 

output from PAGE potentially offers the best insight into the SCC if the world were to 

experience catastrophic climate change. For instance, at the 95th percentile and a 3 percent 

discount rate, the SCC estimated by PAGE across the five socio-economic and emission 

trajectories of $113 per ton of CO2 is almost double the value estimated by DICE, $58 per ton in 

2010. We cannot evaluate how well the three models account for catastrophic or noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the 

distribution to the assumptions made about catastrophic impacts. 

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it 

deterministically (i.e., by adding the expected value of the damage from a catastrophe to the 

aggregate damage function). In part, this results in different probabilities being assigned to a 

catastrophic event across the two models. For instance, PAGE places a probability near zero on a 

catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16–36 percent of 

15A-32 

EERE-2008-BT-STD-0006 COMMENT 54
 

crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2–4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact. 

15A.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional Interactions 

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 

region of the world on another region are not included in some of the models (FUND includes 

the effects of migration from sea level rise). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al. 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3–6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al. 2007; 

Campbell et al. 2007). 

15A.7.3 Imperfect Substitutability of Environmental Amenities 

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400–900 miles in the range of plants (Wing et 

al. 2005), and dwarfing of both land mammals (Gingerich 2006) and soil fauna (Smith et al. 

2009). 

The three IAMs used here assume that it is possible to compensate for the economic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (Levy et 

al. 2005). For instance, as water supplies become scarcer or ecosystems become more fragile and 

less bio-diverse, the services they provide may become increasingly more costly to replace. 

Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 

15A.8 CONCLUSION 

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The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $4.7, $21.4, $35.1, and $64.9 (2007$). The first three estimates are 

based on the average SCC across models and socio-economic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. 

We noted a number of limitations to this analysis, including the incomplete way in which 

the integrated assessment models capture catastrophic and non-catastrophic impacts, their 

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

damages to high temperatures, and assumptions regarding risk aversion. The limited amount of 

research linking climate impacts to economic damages makes this modeling exercise even more 

difficult. It is the hope of the interagency group that over time researchers and modelers will 

work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. 

15A-34 

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15A.9 APPENDIX 

This Appendix provides additional technical information about the non-CO2 emission 

projections used in the modeling and the method for extrapolating emissions forecasts through 

2300, and shows the full distribution of 2010 SCC estimates by model and scenario combination. 

Annual SCC values for the next 40 years are provided in Table 15A.9.1. 

Table 15A.9.1 Annual SCC Values: 2010–2050 (2007$) 

Discount Rate 5% 3% 2.5% 3% 

Year Avg Avg Avg 95th 

2010 4.7 21.4 35.1 64.9 

2011 4.9 21.9 35.7 66.5 

2012 5.1 22.4 36.4 68.1 

2013 5.3 22.8 37.0 69.6 

2014 5.5 23.3 37.7 71.2 

2015 5.7 23.8 38.4 72.8 

2016 5.9 24.3 39.0 74.4 

2017 6.1 24.8 39.7 76.0 

2018 6.3 25.3 40.4 77.5 

2019 6.5 25.8 41.0 79.1 

2020 6.8 26.3 41.7 80.7 

2021 7.1 27.0 42.5 82.6 

2022 7.4 27.6 43.4 84.6 

2023 7.7 28.3 44.2 86.5 

2024 7.9 28.9 45.0 88.4 

2025 8.2 29.6 45.9 90.4 

2026 8.5 30.2 46.7 92.3 

2027 8.8 30.9 47.5 94.2 

2028 9.1 31.5 48.4 96.2 

2029 9.4 32.1 49.2 98.1 

2030 9.7 32.8 50.0 100.0 

2031 10.0 33.4 50.9 102.0 

2032 10.3 34.1 51.7 103.9 

2033 10.6 34.7 52.5 105.8 

2034 10.9 35.4 53.4 107.8 

2035 11.2 36.0 54.2 109.7 

2036 11.5 36.7 55.0 111.6 

2037 11.8 37.3 55.9 113.6 

2038 12.1 37.9 56.7 115.5 

2039 12.4 38.6 57.5 117.4 

2040 12.7 39.2 58.4 119.3 

2041 13.0 39.8 59.0 121.0 

2042 13.3 40.4 59.7 122.7 

2043 13.6 40.9 60.4 124.4 

2044 13.9 41.5 61.0 126.1 

2045 14.2 42.1 61.7 127.8 

2046 14.5 42.6 62.4 129.4 

2047 14.8 43.2 63.0 131.1 

2048 15.1 43.8 63.7 132.8 

2049 15.4 44.4 64.4 134.5 

2050 15.7 44.9 65.0 136.2 

15A-41 

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15A.9.1 Other (non-CO2) Gases 

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane (CH4), nitrous oxide (N2O), fluorinated gases, and net land use CO2 

emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s 

default radiative forcings (RF) due to other factors (e.g., aerosols and other gases). Specifically, 

to obtain the RF associated with the non-CO2 EMF emissions only, we calculated the RF 

associated with the EMF atmospheric CO2 concentrations and subtracted them from the EMF 

total RF.bb This approach respects the EMF scenarios as much as possible and at the same time 

takes account of those components not included in the EMF projections. Since each model treats 

non-CO2 gases differently (e.g., DICE lumps all other gases into one composite exogenous 

input), this approach was applied slightly differently in each of the models. 

FUND: Rather than relying on RF for these gases, the actual emissions from each 

scenario were used in FUND. The model default trajectories for CH4, N2O, SF6, and the CO2 

emissions from land were replaced with the EMF values. 

 PAGE: PAGE models CO2, CH4, sulfur hexafluoride (SF6), and aerosols and contains an 

“excess forcing” vector that includes the RF for everything else. To include the EMF values, we 

removed the default CH4 and SF6 factors,cc decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N2O, and fluorinated 

gases, as well as the model default values for aerosols and other factors. Net land use CO2 

emissions were added to the fossil and industrial CO2 emissions pathway.

 DICE: DICE presents the greatest challenge because all forcing due to factors other than 

industrial CO2 emissions is embedded in an exogenous non-CO2 RF vector. To decompose this 

exogenous forcing path into EMF non-CO2 gases and other gases, we relied on the references in 

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment 

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report 

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing 

from all non-CO2 sources is -0.06 W/m2 in 2005, as reported in AR4, and increases linearly to 

0.3 W/m2 in 2105, based on GISS projections, and then stays constant after that time. 

According to AR4, the RF in 2005 from CH4, N20, and halocarbons (approximately 

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m2 and RF 

from total aerosols was -1.2 W/m2. Thus, the -.06 W/m2 non-CO2 forcing in DICE can be 

decomposed into: 0.98 W/m2 due to the EMF non-CO2 gases, -1.2 W/m2 due to aerosols, and the 

remainder, 0.16 W/m2, due to other residual forcing. 

bb Note EMF did not provide CO2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed 

the fossil, industrial and land CO2 emissions into MAGICC (considered a “neutral arbiter” model, which is tuned to 

emulate the major global climate models) and the resulting CO2 concentrations were used. Note also that MERGE 

assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE Optimistic 

reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use 

emissions from the other three models and divide by 4). 

cc Both the model default CH4 emissions and the initial atmospheric CH4 is set to zero to avoid double counting the 

effect of past CH4 emissions. 

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EERE-2008-BT-STD-0006 COMMENT 54
 

For subsequent years, we calculated the DICE default RF from aerosols and other nonCO2 gases based on the following two assumptions: 

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR 

and then stays constant thereafter, and 

(2) With respect to RF from non-CO2 gases not included in the EMF-22 scenarios, the share 

of non-aerosol RF matches the share implicit in the AR4 summary statistics cited above and 

remains constant over time. 

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000, 

which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black 

carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. Since the 

SRES marker scenarios were not updated for the AR4, the TAR provides the most recent IPCC 

projection of aerosol forcing. We rely on the A1B projection from the TAR because it provides 

one of the lower aerosol forecasts among the SRES marker scenarios and is more consistent with 

the AR4 discussion of the post-SRES literature on aerosols: 

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor 

emissions, including sulphur dioxide, black carbon and organic carbon, has improved in the 

post-SRES scenarios. Generally, these emissions are projected to be lower than reported in 

SRES. {WGIII 3.2, TS.3, SPM}.dd 

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent 

with the recent literature on these emissions. For example, Figure 15A.9.1 shows that the sulfur 

dioxide emissions peak over the short-term of some SRES scenarios above the upper bound 

estimates of the more recent scenarios.ee Recent scenarios project sulfur emissions to peak earlier 

and at lower levels compared to the SRES in part because of new information about present and 

planned sulfur legislation in some developing countries, such as India and China.ff The lower 

bound projections of the recent literature have also shifted downward slightly compared to the 

SRES scenario (IPCC 2007). 

With these assumptions, the DICE aerosol forcing changes from -1.2 in 2005 to -0.792 in 

2105 W/m2

; forcing due to other non-CO2 gases not included in the EMF scenarios declines from 

0.160 to 0.153 W/m2. 

dd AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf
 ee See Smith, S.J., R. Andres, E. Conception, and J. Lurz. 2004. “Historical sulfur dioxide emissions, 1850-2000:
 

methods and results.” Joint Global Research Institute, College Park, 14 pp.

ff See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda. 2002. “Changing
 

trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate.” Environmental 

Science and Technology 36(22):4707- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.-Q. Zhang, D. Xu, M. 

Jacobson, and J. Hansen. 2001. “Recent reductions in China’s greenhouse gas emissions.” Science 294(5548):1835­

1837.
 

15A-43 

EERE-2008-BT-STD-0006 COMMENT 54
 

 

. 

Figure 15A.9.1 Sulphur Dioxide Emission Scenarios 

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines show the median, 

5th and 95th percentile of the frequency distribution for the full ensemble of 40 SRES scenarios. The blue 

area (and the thin dashed lines in blue) illustrates individual scenarios and the range of Smith et al. (2004). 

Dotted lines indicate the minimum and maximum of SO2 emissions scenarios developed pre-SRES. 

Source: IPCC (2007), AR4 WGIII 3.2, http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-ens3-2­

2-4.html. 

Although other approaches to decomposing the DICE exogenous forcing vector are 

possible, initial sensitivity analysis suggests that the differences among reasonable alternative 

approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to 

assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values 

(for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 

2100 increases average 2010 SCC values by 6–7 percent (or $0.50–$3), depending on the 

discount rate. These differences increase slightly for SCC values in later years but are still well 

within 10 percent of each other as far out as 2050. 

Finally, as in PAGE, the EMF net land use CO2 emissions are added to the fossil and 

industrial CO2 emissions pathway. 

15A.9.2 Extrapolating Emissions Projections to 2300 

To run each model through 2300 requires assumptions about GDP, population, 

greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which 

these projections are available from the EMF-22 models. These inputs were extrapolated from 

2100 to 2300 as follows: 

1. Population growth rate declines linearly, reaching zero in 2200. 

2. GDP/per capita growth rate declines linearly, reaching zero in 2300. 

3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate over 

2090-2100 is maintained from 2100 through 2300. 

4. Net land use CO2 emissions decline linearly, reaching zero in 2200. 

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EERE-2008-BT-STD-0006 COMMENT 54
 

 

5. Non-CO2 radiative forcing remains constant after 2100. 

Long run stabilization of GDP per capita was viewed as a more realistic simplifying 

assumption than a linear or exponential extrapolation of the pre-2100 economic growth rate of 

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the 

degradation of environmental sinks available for assimilating pollution from economic 

production activities may eventually overtake the rate of technological progress. Thus, the 

overall rate of economic growth may slow over the very long run. The interagency group also 

considered allowing an exponential decline in the growth rate of GDP per capita. However, since 

this would require an additional assumption about how close to zero the growth rate would get 

by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by 

2300. 

The population growth rate is also assumed to decline linearly, reaching zero by 2200. 

This assumption is reasonably consistent with the United Nations long run population forecast, 

which estimates global population to be fairly stable after 2150 in the medium scenario (UN 

2004).gg The resulting range of EMF population trajectories (Figure 15A.9.2) also encompass the 

UN medium scenario forecasts through 2300 – global population of 8.5 billion by 2200, and 9 

billion by 2300. 

Maintaining the decline in the 2090–2100 carbon intensity growth rate (i.e., CO2 per 

dollar of GDP) through 2300 assumes that technological improvements and innovations in the 

areas of energy efficiency and other carbon reducing technologies (possibly including currently 

unavailable methods) will continue to proceed at roughly the same pace that is projected to occur 

towards the end of the forecast period for each EMF scenario. This assumption implies that total 

cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range 

of the total potential global carbon stock estimated in the literature. 

Net land use CO2 emissions are expected to stabilize in the long run, so in the absence of 

any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori 

reasons for assuming a long run increase or decline in non-CO2 radiative forcing, it is assumed to 

remain at the 2100 levels for each EMF scenario through 2300. 

Figure 15A.9.2 through Figure 15A.9.7 show the paths of global population, GDP, fossil 

and industrial CO2 emissions, net land CO2 emissions, non-CO2 radiative forcing, and CO2 

intensity (fossil and industrial CO2 emissions/GDP) resulting from these assumptions. 

gg United Nations. 2004. World Population to 2300. 

http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf. 

15A-45 

EERE-2008-BT-STD-0006 COMMENT 54
 

5.5

6.5

7.5

8.5

9.5

10.5

11.5

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Global Population [billion]

Year

Figure 15A.9.2 Global Population, 2000–2300 (Post-2100 Extrapolations Assume the 

Population Growth Rate Changes Linearly to Reach a Zero Growth Rate 

by 2200) 

Note: In the fifth scenario, 2000–2100 population is equal to the average of the population under the 550 

ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]

Figure 15A.9.3	 World GDP, 2000-2300 (Post-2100 Extrapolations Assume GDP Per 

Capita Growth Declines Linearly, Reaching Zero in 2300) 

Note: In the fifth scenario, 2000–2100 GDP is equal to the average of the GDP under the 550 ppm CO2e, 

full-participation, not-to-exceed scenarios considered by each of the four models. 

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EERE-2008-BT-STD-0006 COMMENT 54
 

0

50

100

150

200

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil&IndustrialCO2 Emissions[GtCO2/decade]

Year

Figure 15A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post-2100 

Extrapolations Assume Growth Rate of CO2 Intensity (CO2/GDP) Over 

2090–2100 is Maintained Through 2300) 

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 

ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

‐3.00

‐2.00

‐1.00

0.00

1.00

2.00

3.00

4.00

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

Extrapolations

Netland CO2 emissions[GtCO2/yr]

Year

z 

Figure 15A.9.5 Global Net Land Use CO2 Emissions, 2000–2300 (Post-2100 

Extrapolations Assume Emissions Decline Linearly, Reaching Zero in 

2200)hh 

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 

ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

hh MERGE assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE 

Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land 

use emissions from the other three models and divide by 4). 

15A-47 

EERE-2008-BT-STD-0006 COMMENT 54
15A-48 

Figure 15A.9.6 Global Non-CO2 Radiative Forcing, 2000–2300 (Post-2100 Extrapolations 

Assume Constant Non-CO2 Radiative Forcing after 2100) 

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 

ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

Figure 15A.9.7 Global CO2 Intensity (Fossil & Industrial CO2 Emissions/GDP), 2000– 

2300 (Post-2100 Extrapolations Assume Eecline in CO2/GDP Growth 

Rate Over 2090–2100 is Maintained Through 2300) 

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 

ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

0.00

0.75

1.50

2.25

3.00

3.75

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

nonCO2 RF [W/m^2]

Year

0

0

0

0

0

1

1

1

1

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil

&IndustrialCO2 intensity [GtCO2/trillion $]

Year

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Table 15A.9.2 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th 

Scenario PAGE 

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8 

MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3 

Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4 

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3 

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9 

Scenario DICE 

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0 

MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3 

Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0 

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3 

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8 

Scenario FUND 

IMAGE -33.1 -18.9 -13.3 -5.5 4.1 19.3 18.7 43.5 67.1 150.7 

MERGE optimistic -33.1 -14.8 -10 -3 5.9 14.8 20.4 43.9 65.4 132.9 

Message -32.5 -19.8 -14.6 -7.2 1.5 8.8 13.8 33.7 52.3 119.2 

MiniCAM base -31.0 -15.9 -10.7 -3.4 6 22.2 21 46.4 70.4 152.9 

5th scenario -32.2 -21.6 -16.7 -9.7 -2.3 3 6.7 20.5 34.2 96.8 

Table 15A.9.3 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th 

Scenario PAGE 

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4 

MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0 

Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0 

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0 

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5 

Scenario DICE 

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1 

MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8 

Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4 

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8 

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6 

Scenario FUND 

IMAGE -25.2 -15.3 -11.2 -5.6 0.9 8.2 10.4 25.4 39.7 90.3 

MERGE optimistic -24.0 -12.4 -8.7 -3.6 2.6 8 12.2 27 41.3 85.3 

Message -25.3 -16.2 -12.2 -6.8 -0.5 3.6 7.7 20.1 32.1 72.5 

MiniCAM base -23.1 -12.9 -9.3 -4 2.4 10.2 12.2 27.7 42.6 93.0 

5th scenario -24.1 -16.6 -13.2 -8.3 -3 -0.2 2.9 11.2 19.4 53.6 

15A-49 

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Table 15A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th 

Scenario PAGE 

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2 

MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4 

Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8 

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6 

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7 

Scenario DICE 

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1 

MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4 

Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8 

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9 

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0 

Scenario FUND 

IMAGE -11.7 -8.4 -6.9 -4.6 -2.2 -1.3 0.7 4.1 7.4 17.4 

MERGE optimistic -10.6 -7.1 -5.6 -3.6 -1.3 -0.3 1.6 5.4 9.1 19.0 

Message -12.2 -8.9 -7.3 -4.9 -2.5 -1.9 0.3 3.5 6.5 15.6 

MiniCAM base -10.4 -7.2 -5.8 -3.8 -1.5 -0.6 1.3 4.8 8.2 18.0 

5th scenario -10.9 -8.3 -7 -5 -2.9 -2.7 -0.8 1.4 3.2 9.2 

0

2000

4000

6000

8000

10000

12000

14000

16000

‐25‐12

1

14274053667992

105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%

Figure 15A.9.8	 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

Discount Rate* 

* The distribution of SCC values ranges from -$5,192 to $66,116, but the X-axis has been truncated at 

approximately the 1st and 99th percentiles to better show the data. 

15A-50 

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Table 15A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates 

Discount 

Rate 

5% 3% 2.5% 

Scenario Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis 

DICE 9.0 13.1 0.8 0.2 28.3 209.8 1.1 0.9 42.2 534.9 1.2 1.1 

PAGE 6.5 136.0 6.3 72.4 29.8 3,383.7 8.6 151.0 49.3 9,546.0 8.7 143.8 

FUND -1.3 70.1 28.2 1,479.0 6.0 16,382.5 128.0 18,976.5 13.6 150,732.6 149.0 23,558.3 

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