### 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

### 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 qualifyi ng EER of the equipment; and (6) the website

for additional information on the program.

3A-1Table 3A.1.1Rebate Programs for Central Air ConditionersUtility or AgencyStateAmountSEEREERWebsiteAPSAZ$2501411.5http://www.aps.com/main/green/choice/choice_3.html?source=acjumpAPSAZ$4001612.25http://www.aps.com/main/green/choice/choice_3.html?source=acjumpTucson Electric PowerAZ$20014http://www.tep.com/Green/EHC/rebates.aspTucson Electric PowerAZ$25015http://www.tep.com/Green/EHC/rebates.aspUniSource Energy ServicesAZ$20014http://uesaz.com/Green/EHC/rebates.aspUniSource Energy ServicesAZ$25015http://uesaz.com/Green/EHC/rebates.aspAnaheim Public UtilitiesCA$100/ton14.512http://www.anaheim.net/utilities/adv_svc_prog/nrg_star/flyer.pdfAnaheim Public UtilitiesCA$100/ton1411http://www.anaheim.net/utilities/adv_svc_prog/nrg_star/flyer.pdfBurbank Water and PowerCABurbank: $100/ton Non-Burbank: $80/ton14-14.9http://www.burbankwaterandpower.com/download/Home-Rewards-Application-FY-0708-final-web3.pdfBurbank Water and PowerCABurbank: $120/ton Non-Burbank: $100/ton15-15.9http://www.burbankwaterandpower.com/download/Home-Rewards-Application-FY-0708-final-web3.pdfBurbank Water and PowerCABurbank: $140/ton Non-Burbank: $120/ton16http://www.burbankwaterandpower.com/download/Home-Rewards-Application-FY-0708-final-web3.pdfCity of Palo Alto UtilitiesCA$20014http://www.cpau.com/civica/filebank/blobdload.asp?BlobID=4366City of Palo Alto UtilitiesCA$30015http://www.cpau.com/civica/filebank/blobdload.asp?BlobID=4366Glendale Water and PowerCAGlendale: $125/ton Non-Glendale: $100/ton14http://www.glendalewaterandpower.com/save_money/residential/sh_energy_saving_rebates.aspxImperial Irrigation District EnergyCA$120/ton14http://www.iid.com/Media/instruct_residential09_ENG_FIN02262009.pdfImperial Irrigation District EnergyCA$145/ton15http://www.iid.com/Media/instruct_residential09_ENG_FIN02262009.pdfImperial Irrigation District EnergyCA$85/ton10.2http://www.iid.com/Media/instruct_residential09_ENG_FIN02262009.pdfLos Angeles Department of Waterand PowerCA$10014http://www.ladwp.com/ladwp/cms/ladwp000478.jspModesto Irrigation DistrictCA$25014.512http://www.mid.org/rebates/pkts/homeCooling.pdfModesto Irrigation DistrictCA$2501411http://www.mid.org/rebates/pkts/homeCooling.pdfPasadena Water and PowerCAPasadena: $110/ton Non-Pasadena: $100/ton14http://www.cityofpasadena.net/waterandpower/CoolHome/default.aspPasadena Water and PowerCAPasadena: $120/ton Non-Pasadena: $110/ton15http://www.cityofpasadena.net/waterandpower/CoolHome/default.aspPasadena Water and PowerCAPasadena: $130/ton Non-Pasadena: $120/ton16http://www.cityofpasadena.net/waterandpower/CoolHome/default.aspPasadena Water and PowerCAPasadena: $140/ton Non-Pasadena: $130/ton17http://www.cityofpasadena.net/waterandpower/CoolHome/default.aspPasadena Water and PowerCAPasadena: $150/ton Non-Pasadena: $140/ton18http://www.cityofpasadena.net/waterandpower/CoolHome/default.aspRedding Electric UtilityCA$7501412-12.5http://www.reupower.com/energysvc/documents/Jan09HVAC.pdfRedding Electric UtilityCA$1,2001412.6http://www.reupower.com/energysvc/documents/Jan09HVAC.pdfRoseville ElectricCA$5501412http://www.roseville.ca.us/electric/home/rebates/ac.aspRoseville ElectricCA$5501512.5http://www.roseville.ca.us/electric/home/rebates/ac.aspRoseville ElectricCA$7501411http://www.roseville.ca.us/electric/home/rebates/ac.asp3A-2Utility or AgencyStateAmountSEEREERWebsiteRoseville ElectricCA$7501412http://www.roseville.ca.us/electric/home/rebates/ac.aspSacramento Municipal UtilityDistrictCA$40014.512http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfSacramento Municipal UtilityDistrictCA$4001411http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfSacramento Municipal UtilityDistrictCA$5001512.5http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfSacramento Municipal UtilityDistrictCA$5001412http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfSacramento Municipal UtilityDistrictCA$6001613http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfMerced Irrigation DistrictCA$25014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$35014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$32514http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$45014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$36014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$51014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$41014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$60014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$49014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$68514http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$55014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$80014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$25014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$35014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$32514http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$45014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$36014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$51014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$41014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$60014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$49014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$68514http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$55014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfMerced Irrigation DistrictCA$80014http://www.mercedid.org/_images/709_mid_rebate_res_iairconditioning.pdfAzusa Light and WaterCA$50/tonhttp://www.ci.azusa.ca.us/DocumentView.asp?DID=507Ukiah Public Utilities Dept.CA$75/ton14http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfUkiah Public Utilities Dept.CA$100/ton15http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfUkiah Public Utilities DepartmentCA$125/ton16http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfUkiah Public Utilities DepartmentCA$175/ton17http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfUkiah Public Utilities DepartmentCA$200/ton18http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdf3A-3Utility or AgencyStateAmountSEEREERWebsiteGroton UtilitiesCN$200/ton1512http://www.grotonutilities.com/files/conservation_forms/comm_air_rebate.pdfGroton UtilitiesCN$300/ton15.512.3http://www.grotonutilities.com/files/conservation_forms/comm_air_rebate.pdfConnecticut Light and PowerCT$5001412http://www.cl-p.com/Home/SaveEnergy/Rebates/ACRebate.aspxUnited Illuminating CompanyCT$50014.512http://www.uinet.com/uinet/connect/UINet/Top+Navigator/Your+Home/UI+Products+%26+Services/Heating+and+Cooling/Energy+Star+Rebates/City of Tallahassee UtilitiesFL$1001412http://www.talgov.com/you/energy/pdf/es_hvac_existing.pdf,http://www.talgov.com/you/energy/pdf/es_hvac_new.pdfCity of Tallahassee UtilitiesFL$3501512.5http://www.talgov.com/you/energy/pdf/es_hvac_existing.pdf,http://www.talgov.com/you/energy/pdf/es_hvac_new.pdfFlorida Power and LightFL$14014http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$26515http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$40516http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$51517http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$58518http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$67019http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$75520http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$16514http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$32015http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$50016http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$64017http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$74018http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$84019http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$95020http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$21014http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$37515http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$58516http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$77017http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$88018http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,00519http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,13020http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$26514http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$47515http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$68516http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$89517http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,03518http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,17519http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,33020http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$30514http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$54515http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$78016http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtml3A-4Utility or AgencyStateAmountSEEREERWebsiteFlorida Power and LightFL$1,02017http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,17518http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,34019http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,51020http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$37514http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$64015http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$89516http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,14517http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,33018http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,51019http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,70520http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$43014http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$72515http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,00516http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,28517http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,49518http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,70519http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,93020http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlGainesville Regional UtilitiesFL$30015http://www.gru.com/Pdf/YourHome/Rebates/rebateForm.pdfGainesville Regional UtilitiesFL$550SuperSEERhttp://www.gru.com/Pdf/YourHome/Rebates/rebateForm.pdfIdaho PowerID$5013http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmIdaho PowerID$1501411.99http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmIdaho PowerID$2501512.5http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmRocky Mountain PowerID$2501512.5http://www.homeenergysavings.net/idaho/docs/IDCACForm09.pdfRocky Mountain PowerID$5013http://www.homeenergysavings.net/idaho/docs/IDCACForm09.pdfRocky Mountain PowerID$5013http://www.homeenergysavings.net/idaho/docs/IDCACForm09.pdfMidAmerican Energy CompanyIA$2001411.5http://www.midamericanenergy.com/pdf/il_res_equip.pdfMidAmerican Energy CompanyIA$3001511.5http://www.midamericanenergy.com/pdf/il_res_equip.pdfMidAmerican Energy CompanyIA$4001611.5http://www.midamericanenergy.com/pdf/il_res_equip.pdfDuke EnergyIN$20014http://www.duke-energy.com/indiana/savings/smart-saver.aspIndianapolis Power and LightIN$5014http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=01050400&DocID=0205016c986701096a4e9803007c00Indianapolis Power and LightIN$7515http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=01050400&DocID=0205016c986701096a4e9803007c00Indianapolis Power and LightIN$10016http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=01050400&DocID=0205016c986701096a4e9803007c00WIN Energy REMCIN$20014http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$20015http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$25016http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$30017http://www.winenergyremc.com/rebates.html3A-5Utility or AgencyStateAmountSEEREERWebsiteAlliant EnergyIA$10014http://alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/IowaIncentivePrograms/014675Alliant EnergyIA$20015http://alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/IowaIncentivePrograms/014675Alliant EnergyIA$200http://alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/IowaIncentivePrograms/014675Alliant EnergyIA$400http://alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/IowaIncentivePrograms/014675City of Ames Electric ServicesIA$20014http://www.cityofames.org/SmartEnergy/Documents/AC%20Rebate%2008-09%20Claim%20Form.pdfCity of Ames Electric ServicesIA$30015http://www.cityofames.org/SmartEnergy/Documents/AC%20Rebate%2008-09%20Claim%20Form.pdfCity of Ames Electric ServicesIA$40016http://www.cityofames.org/SmartEnergy/Documents/AC%20Rebate%2008-09%20Claim%20Form.pdfCedar Falls UtilitiesIA$20014http://www.cfu.net/pdf/rebates_08.pdfCedar Falls UtilitiesIA$30015http://www.cfu.net/pdf/rebates_08.pdfCedar Falls UtilitiesIA$40016http://www.cfu.net/pdf/rebates_08.pdfIndianola Municipal UtilitiesIA$10014http://www.i-m-u.com/Web%20Forms/2008%20ac%20rebate%20form%20web.pdfIndianola Municipal UtilitiesIA$15015http://www.i-m-u.com/Web%20Forms/2008%20ac%20rebate%20form%20web.pdfIndianola Municipal UtilitiesIA$20016http://www.i-m-u.com/Web%20Forms/2008%20ac%20rebate%20form%20web.pdfIndianola Municipal UtilitiesIA$25017http://www.i-m-u.com/Web%20Forms/2008%20ac%20rebate%20form%20web.pdfLinn County Rural ElectricCooperative AssociationIA$10014.5http://www.linncountyrec.com/cgi-script/csarticles/articles/000004/000425.htmLinn County Rural ElectricCooperative AssociationIA$15015.5http://www.linncountyrec.com/cgi-script/csarticles/articles/000004/000425.htmLinn County Rural ElectricCooperative AssociationIA$20016.5http://www.linncountyrec.com/cgi-script/csarticles/articles/000004/000425.htmMidAmerican EnergyIA$20014http://www.midamericanenergy.com/pdf/ee_resident_rebates.pdfMidAmerican EnergyIA$30015http://www.midamericanenergy.com/pdf/ee_resident_rebates.pdfMidAmerican EnergyIA$40016http://www.midamericanenergy.com/pdf/ee_resident_rebates.pdfMuscatine Power and WaterIA$15014http://www.mpw.org/pdf/2009ResidentialRebateForm.pdfWaverly Light and PowerIA$20014http://wlp.waverlyia.com/appliance_programs.aspKansas City Board of PublicUtilitiesKS$25/ton15http://www.bpu.com/documents/ResidentialHPRebateScheduleeffJan2007withIncentives.pdfMassachusetts MunicipalWholesale Electric CompanyMA$10014http://www.munihelps.org/MuniHELPS%20Mailin%20rebate%20forms%202009/MailinformAshburnham.pdf,http://www.munihelps.org/MuniHELPS%20Mail-in%20rebate%20forms%202009/MailinformChicopee.pdfMassachusetts MunicipalWholesale Electric CompanyMA$32514http://www.munihelps.org/MuniHELPS%20Mail-in%20rebate%20forms%202009/MailinformIpswich.pdfMassachusetts MunicipalWholesale Electric CompanyMA$47515http://www.munihelps.org/MuniHELPS%20Mail-in%20rebate%20forms%202009/MailinformIpswich.pdfMassachusetts MunicipalWholesale Electric CompanyMA$32513http://www.marbleheadelectric.com/rebate.htmMassachusetts MunicipalWholesale Electric CompanyMA$47514http://www.marbleheadelectric.com/rebate.htmBelmont Municipal LightDepartmentMA$10014http://www.town.belmont.ma.us/Public_Documents/BelmontMA_Light/2009%20Appliance%20Rebate%20Program%20&%20Form%20Brochure.pdfChicopee Electric LightMA$10014http://www.celd.com/index.php?page=energy-star-appliance-rebatesMansfield Municipal ElectricMA$100E*http://www.mansfieldelectric.com/consumerforms/Appliance-Rebate-App.pdf3A-6Utility or AgencyStateAmountSEEREERWebsiteDepartmentNStarMA$50014.512http://www.mycoolsmart.com/html/rebate-info.htmlNStarMA$6001512.5http://www.mycoolsmart.com/html/rebate-info.htmlReading Municipal LightDepartmentMA$10014http://www.rmld.com/from_priscilla/energy_star/es_appl_rebate_0308.pdfAlliant Energy-Interstate Power &LightMN$7514http://www.alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/MinnesotaIncentivePrograms/014684Alliant Energy-Interstate Power &LightMN$15015http://www.alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/MinnesotaIncentivePrograms/014684Alliant Energy-Interstate Power &LightMN$22516http://www.alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/MinnesotaIncentivePrograms/014684Alliant Energy-Interstate Power &LightMN$225http://www.alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/MinnesotaIncentivePrograms/014684Anoka Municipal UtilityMN$25014http://www.ci.anoka.mn.us/index.asp?Type=B_BASIC&SEC={03DDAD7B-EC66-4214-97D8-EB42DA396059}Anoka Municipal UtilityMN$30015http://www.ci.anoka.mn.us/index.asp?Type=B_BASIC&SEC={03DDAD7B-EC66-4214-97D8-EB42DA396059}Anoka Municipal UtilityMN$35016http://www.ci.anoka.mn.us/index.asp?Type=B_BASIC&SEC={03DDAD7B-EC66-4214-97D8-EB42DA396059}Austin UtilitiesMN$20014http://www.austinutilities.com/pages/residential_conserve_incentives.aspBlooming Prairie Public UtilitiesMN$200http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20BP.pdfNorth St Paul Electric UtilityMN$35014http://www.ci.north-saint-paul.mn.us/index.asp?Type=B_BASIC&SEC={F5C1E5FE-1ADD-49AF-ACD8-59C3FB4192FF}Connexus EnergyMN$3013http://www.connexusenergy.com/2009%20ASHP%20Rebate.pdfConnexus EnergyMN$18014http://www.connexusenergy.com/2009%20ASHP%20Rebate.pdfConnexus EnergyMN$28015http://www.connexusenergy.com/2009%20ASHP%20Rebate.pdfConnexus EnergyMN$33016http://www.connexusenergy.com/2009%20ASHP%20Rebate.pdfBuffalo Municipal UtilitiesMN$35014http://www.ci.buffalo.mn.us/Utilities/Electric/EnergyRebate.htmCrow Wing PowerMN$30 - $330http://www.cwpower.com/airconditioning.shtmlDakota Electric AssociationMN$3013https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_rebatesDakota Electric AssociationMN$18014https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_rebatesDakota Electric AssociationMN$28015https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_rebatesDakota Electric AssociationMN$33016https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_rebatesEast Central EnergyMN$3013http://www.eastcentralenergy.com/rebates.aspxEast Central EnergyMN$18014http://www.eastcentralenergy.com/rebates.aspxEast Central EnergyMN$28015http://www.eastcentralenergy.com/rebates.aspxEast Central EnergyMN$33016http://www.eastcentralenergy.com/rebates.aspxElk River Municipal UtilitiesMN$18014http://www.elkriverutilities.com/pdfs/PDF%20energy%20star_Energy%20Star%20brochure.pdfElk River Municipal UtilitiesMN$28015http://www.elkriverutilities.com/pdfs/PDF%20energy%20star_Energy%20Star%20brochure.pdfElk River Municipal UtilitiesMN$33016http://www.elkriverutilities.com/pdfs/PDF%20energy%20star_Energy%20Star%20brochure.pdfFairmont Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20FM.pdf3A-7Utility or AgencyStateAmountSEEREERWebsiteGrand Marais PUCMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20GM.pdfLake City UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20rebate%20sheet%20LC.pdfLake Country PowerMN$3013http://www.lakecountrypower.coop/userfiles/2009%20Rates%20&%20Rebates(3).pdfLake Country PowerMN$18014http://www.lakecountrypower.coop/userfiles/2009%20Rates%20&%20Rebates(3).pdfLake Country PowerMN$28015http://www.lakecountrypower.coop/userfiles/2009%20Rates%20&%20Rebates(3).pdfLake Country PowerMN$33016http://www.lakecountrypower.coop/userfiles/2009%20Rates%20&%20Rebates(3).pdfLitchfield Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20rebate%20sheet%20LF.pdfMarshall Municipal UtilitiesMN$100/ton14http://www.marshallutilities.com/residential/heatingandcooling.phpMora Municipal UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20MORA.pdfNew Prague Utilities CommissionMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20NP.pdfNew Ulm Public UtilitiesMN$20014http://www.ci.new-ulm.mn.us/vertical/Sites/%7B1E9156B6-EE87-4ADE-9BB6-37AF3590FA0E%7D/uploads/%7B71F357A8-67B1-47CD-BF41-54E2C0EC0917%7D.PDFNew Ulm Public UtilitiesMN$27515http://www.ci.new-ulm.mn.us/vertical/Sites/%7B1E9156B6-EE87-4ADE-9BB6-37AF3590FA0E%7D/uploads/%7B71F357A8-67B1-47CD-BF41-54E2C0EC0917%7D.PDFNew Ulm Public UtilitiesMN$35016http://www.ci.new-ulm.mn.us/vertical/Sites/%7B1E9156B6-EE87-4ADE-9BB6-37AF3590FA0E%7D/uploads/%7B71F357A8-67B1-47CD-BF41-54E2C0EC0917%7D.PDFNorth Branch Municipal Water &LightMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20NB.pdfOwatonna Public UtilitiesMN$20014http://www.owatonnautilities.com/documents/ResidentialRebateAppl09_000.pdfPreston Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20PRES.pdfPrinceton PUCMN$20014http://www.smmpa.org/Upload/Res%20Rebate%20Summary%20Sheet%20PRIN.pdfRedwood Falls Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20RF.pdfRochester Public UtilitiesMN$20014http://www.rpu.org/pdfs/2009resrebateapp.pdfSaint Peter Municipal UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20SP.pdfShakopee Public UtilitiesMN$25014http://www.shakopeeutilities.com/2007%20Customer%20Advantage%20Programs%20Brochure.pdfSpring Valley Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20SV.pdfWaseca UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20WAS.pdfWells Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20WELLS.pdfFairmont Public UtilitiesMN$10015http://www.wildriceelectric.com/images/Minnkota%20Res%20EE%20Incentive%20Form%20040109.pdfWillmar Municipal UtilitiesMN$200http://wmu.willmar.mn.us/energy_services.htmXcel EnergyMN$18014http://www.xcelenergy.com/Residential/Programs_Resources/HeatingandCoolingRebates/Pages/CentralACRebate.aspxXcel EnergyMN$28015http://www.xcelenergy.com/Residential/Programs_Resources/HeatingandCoolingRebates/Pages/CentralACRebate.aspxXcel EnergyMN$33016http://www.xcelenergy.com/Residential/Programs_Resources/HeatingandCoolingRebates/Pages/CentralACRebate.aspxCity Utilities of Springfield,MissouriMO$100/tonhttp://www.cityutilities.net/conserve/forms/HVAC-AirCondRebate.pdfColumbia Water & LightMO$10014http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$20015http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$30016http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdf3A-8Utility or AgencyStateAmountSEEREERWebsiteColumbia Water & LightMO$40017http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$50018http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$50019http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$60020http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$10014http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$30015http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$50016http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$60017http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$70018http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$80019http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$90020http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$20014http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$40015http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$60016http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$80017http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,00018http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,10019http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,20020http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$30014http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$60015http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$80016http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,00017http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,20018http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,40019http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,60020http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfEmpire District Electric CompanyMO$40015https://www.empiredistrict.com/Dochandler.ashx?id=3553Empire District Electric CompanyMO$45016https://www.empiredistrict.com/Dochandler.ashx?id=3553Empire District Electric CompanyMO$50017https://www.empiredistrict.com/Dochandler.ashx?id=3553Empire District Electric CompanyMO14https://www.empiredistrict.com/Dochandler.ashx?id=2612Independence Power & LightMO$13715http://www.ci.independence.mo.us/pl/pdf/rebate_ac.pdfIndependence Power & LightMO$20715http://www.ci.independence.mo.us/pl/pdf/rebate_ac.pdfIndependence Power & LightMO$26015http://www.ci.independence.mo.us/pl/pdf/rebate_ac.pdfKansas City Power & LightMO$65014http://www.kcpl.com/coolhomes/customers.htmKansas City Power & LightMO$65014http://www.kcpl.com/coolhomes/customers.htmKansas City Power & LightMO$85016http://www.kcpl.com/coolhomes/customers.htmNew Jersey Board of PublicUtilitiesNJ$1001412http://www.njcleanenergy.com/residential/programs/cooladvantage/cooladvantage-programNew Jersey Board of PublicUtilitiesNJ$1501512.5http://www.njcleanenergy.com/residential/programs/cooladvantage/cooladvantage-programProgress Energy CarolinasNC$30015http://www.progress-energy.com/custservice/carres/efficiency/programs/habuilder/3A-9Utility or AgencyStateAmountSEEREERWebsiteProgress Energy CarolinasNC$30015http://www.progress-energy.com/custservice/carres/efficiency/programs/habuilder/Boulder CityNV$70/ton14http://www.bcnv.org/Conservation/Rebates.aspBoulder CityNV$125/ton15http://www.bcnv.org/Conservation/Rebates.aspLong Island Power AuthorityNY$2501412http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdfLong Island Power AuthorityNY$4001512.5http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdfLong Island Power AuthorityNY$6001613http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdfDuke EnergyOH$20014http://www.duke-energy.com/ohio/savings/smart-saver.aspDuke EnergyOH$30014http://www.duke-energy.com/ohio/savings/smart-saver.aspDuncan PowerOK$150/ton17http://www.duncanpower.com/airconditionrebate.htmDuncan PowerOK$175/ton18http://www.duncanpower.com/airconditionrebate.htmDuncan PowerOK$200/ton19http://www.duncanpower.com/airconditionrebate.htmDuncan PowerOK$225/ton20http://www.duncanpower.com/airconditionrebate.htmDuncan PowerOK$250/ton21http://www.duncanpower.com/airconditionrebate.htmDuncan PowerOK$275/ton22http://www.duncanpower.com/airconditionrebate.htmDuncan PowerOK$300/ton23http://www.duncanpower.com/airconditionrebate.htmAustin EnergyTX$1801411.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Commercial/Multi-Family%20Properties/rebateLevels.htmAustin EnergyTX$4801512.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Commercial/Multi-Family%20Properties/rebateLevels.htmAustin EnergyTX$1201411.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Commercial/Multi-Family%20Properties/rebateLevels.htmAustin EnergyTX$2201512.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Commercial/Multi-Family%20Properties/rebateLevels.htmAustin EnergyTX$1801411.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$3001412http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$4801512.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$6001613http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$3001411http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$4801412http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmCollege Station UtilitiesTX$20014http://www.cstx.gov/docs/energy_back_ii_application_form.pdfCollege Station UtilitiesTX$40015http://www.cstx.gov/docs/energy_back_ii_application_form.pdfCollege Station UtilitiesTX$50016http://www.cstx.gov/docs/energy_back_ii_application_form.pdfCollege Station UtilitiesTX$60018http://www.cstx.gov/docs/energy_back_ii_application_form.pdfCPS EnergyTX$110/ton15http://www.cpsenergy.com/files/2009_AC_Central_Application.docCPS EnergyTX$125/ton16http://www.cpsenergy.com/files/2009_AC_Central_Application.docCPS EnergyTX$140/ton17http://www.cpsenergy.com/files/2009_AC_Central_Application.doc3A-10Utility or AgencyStateAmountSEEREERWebsiteCPS EnergyTX$160/ton18http://www.cpsenergy.com/files/2009_AC_Central_Application.docDenton Municipal ElectricTX$60016http://dmepower.com/userfiles/file/GreenSense%20Rebate%20Manuals.pdfDenton Municipal ElectricTX$60016http://dmepower.com/userfiles/file/GreenSense%20Rebate%20Manuals.pdfGarland Power and LightTX$40015http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20Heat%20Pump%20Application%209-26-08.pdfGarland Power and LightTX$50016http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20Heat%20Pump%20Application%209-26-08.pdfGarland Power and LightTX$60017http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20Heat%20Pump%20Application%209-26-08.pdfMagic Valley Electric CooperativeTX$20013http://www.magicvalley.coop/Electric-Service/Residential/RebatesMagic Valley Electric CooperativeTX$22514http://www.magicvalley.coop/Electric-Service/Residential/RebatesMagic Valley Electric CooperativeTX$25015http://www.magicvalley.coop/Electric-Service/Residential/RebatesPedernales Electric CooperativeTX$22514http://www.pec.coop/Documents/HVACRebate.pdfEfficiency VermontVT$1001412http://www.efficiencyvermont.org/stella/filelib/2009_HVACform_FINAL.pdfPacific PowerWA$2501512.5http://www.homeenergysavingspp.net/washington/docs/WAHVACCAC09.pdfBarron Electric CooperativeWI$60/ton14http://www.barronelectric.com/services/centralair.htmlEau Claire Energy CooperativeWI$60/ton14http://www.ecec.com/programs/incentives/index.cfmFocus on EnergyWI$10015http://www.focusonenergy.com/files/Document_Management_System/Residential_Programs/efficientheatingcoolingcashback_rewardform.pdfRiverland Energy CooperativeWI$60/ton14http://riverlandenergy.com/RIVERLAND%20HOMEPAGE/programs_rebates/rebates2009.pdfPierce Pepin Cooperative ServicesWI$60/ton14http://www.piercepepin.com/nrgsense/docs/ES_RebateForm.pdfWarren Heating and A/CWI$10014http://www.warrenheating.com/rebates-promotions.htmlWarren Heating and A/CWI$15015http://www.warrenheating.com/rebates-promotions.htmlWarren Heating and A/CWI$20016http://www.warrenheating.com/rebates-promotions.html3A-11

### 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 foll owing: (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-12Table 3A.2.1Rebate Programs for Residential Heat PumpsUtility or AgencyStateAmountSEERHSPFEERWebsiteCentral Alabama ElectricalCooperativeAL$400http://www.caec.coop/cms/content/view/42/Central Alabama ElectricalCooperativeAL$500http://www.caec.coop/cms/content/view/42/Central Alabama ElectricalCooperativeAL$600http://www.caec.coop/cms/content/view/42/Central Alabama ElectricalCooperativeAL$700http://www.caec.coop/cms/content/view/42/Central Alabama ElectricalCooperativeAL$800http://www.caec.coop/cms/content/view/42/Central Alabama ElectricalCooperativeAL$900http://www.caec.coop/cms/content/view/42/Central Alabama ElectricalCooperativeAL$1,000http://www.caec.coop/cms/content/view/42/Central Alabama ElectricalCooperativeAL$1,200http://www.caec.coop/cms/content/view/42/Sand Mountain ElectricCooperativeALhttp://www.smec.coop/South Alabama ElectricCooperativeAL$200http://www.southaec.com/content.cfm?id=2020#rebatesAPS EnergyAZ$2501411.5http://www.aps.com/main/green/choice/choice_3.html?source=acjumpAPS EnergyAZ$4001612.3http://www.aps.com/main/green/choice/choice_3.html?source=acjumpSulphur Springs Valley ElectricCooperativeAZ$50014http://www.ssvec.org/documents/Heat_Pump_Rebate_Form_2008.pdfSulphur Springs Valley ElectricCooperativeAZ$20014http://www.ssvec.org/documents/Heat_Pump_Rebate_Form_2008.pdfTucson Electric PowerAZ$20014http://www.tep.com/Docs/EHC/EHCRebateForm.pdfTucson Electric PowerAZ$25015http://www.tep.com/Docs/EHC/EHCRebateForm.pdfUniSource Energy ServicesAZ$20014http://uesaz.com/Green/EHC/rebates.aspUniSource Energy ServicesAZ$25015http://uesaz.com/Green/EHC/rebates.aspImperial Irrigation District EnergyCA$135/ton147.8http://www.iid.com/Media/instruct_residential09_ENG_FIN02262009.pdfImperial Irrigation District EnergyCA$165/ton157.8http://www.iid.com/Media/instruct_residential09_ENG_FIN02262009.pdfImperial Irrigation District EnergyCA$85/ton10.2http://www.iid.com/Media/instruct_residential09_ENG_FIN02262009.pdfLos Angeles Department of Waterand PowerCA$10014http://www.ladwp.com/ladwp/cms/ladwp004363.pdfModesto Irrigation DistrictCA$25014.58.512http://www.mid.org/rebates/pkts/homeCooling.pdfModesto Irrigation DistrictCA$25014811http://www.mid.org/rebates/pkts/homeCooling.pdfPasadena Water and PowerCAPasadena: $115/ton Non-Pasadena: $100/ton138http://www.cityofpasadena.net/waterandpower/pdf/Application-%20All%20Electric%202006.pdfPasadena Water and PowerCAPasadena: $115/ton Non-Pasadena: $100/ton127.6http://www.cityofpasadena.net/waterandpower/pdf/Application-%20All%20Electric%202006.pdf3A-13Utility or AgencyStateAmountSEERHSPFEERWebsitePasadena Water and PowerCAPasadena: $140/ton Non-Pasadena: $125/ton148.512http://www.cityofpasadena.net/waterandpower/pdf/Application-%20All%20Electric%202006.pdfPasadena Water and PowerCAPasadena: $140/ton Non-Pasadena: $125/ton137.911http://www.cityofpasadena.net/waterandpower/pdf/Application-%20All%20Electric%202006.pdfPasadena Water and PowerCAPasadena: $160/ton Non-Pasadena: $150/ton158.512.5http://www.cityofpasadena.net/waterandpower/pdf/Application-%20All%20Electric%202006.pdfPasadena Water and PowerCAPasadena: $160/ton Non-Pasadena: $150/ton147.912http://www.cityofpasadena.net/waterandpower/pdf/Application-%20All%20Electric%202006.pdfRedding Electric UtilityCA$1,3501412http://www.reupower.com/energysvc/documents/Jan09HVAC.pdfRedding Electric UtilityCA$1,6001412.6http://www.reupower.com/energysvc/documents/Jan09HVAC.pdfRiverside Public UtilitiesCA14http://www.riversideca.gov/utilities/pdf/rebates/Cool%20Cash_31908.pdfRoseville ElectricCA$2008.5http://www.roseville.ca.us/electric/home/rebates/ac.aspRoseville ElectricCA$2008.2http://www.roseville.ca.us/electric/home/rebates/ac.aspSacramento Municipal UtilityDistrictCA$40014.58.512http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfSacramento Municipal UtilityDistrictCA$40014811http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfSacramento Municipal UtilityDistrictCA$500158.512.5http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfSacramento Municipal UtilityDistrictCA$50014812http://www.smud.org/en/rebates/Documents/airheatpump-contr-summary.pdfUkiah Public Utilities DepartmentCA$125/ton148.6http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfUkiah Public Utilities DepartmentCA$150/ton158.8http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfUkiah Public Utilities DepartmentCA$175/ton168.4http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfUkiah Public Utilities DepartmentCA$200/ton178.6http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfUkiah Public Utilities DepartmentCA$225/ton189.2http://www.cityofukiah.com/pdf/utilities/Ukiah_AC_and_HP_5-16-07.pdfMountain View ElectricAssociation, Inc. and Tri-StateGeneration and TransmissionAssociation Inc.CO$125-$15014.58.212http://www.mvea.coop/documents/A-012%20fact%20sheet%20only%20EEC%20credits.pdfPoudre Valley Rural ElectricAssociationCO127.4http://www.pvrea.com/members/rebates/heat_pumps.htmUnited PowerCO$400http://www.unitedpower.com/rebate.aspx#heatpumpUnited PowerCO$400http://www.unitedpower.com/rebate.aspx#heatpumpConnecticut Light and PowerCT$500148.212http://www.cl-p.com/Home/SaveEnergy/Rebates/ACRebate.aspxConnecticut Energy EfficiencyFundCT$50014.58.212.5http://www.uinet.com/uinet/resources/file/ebeaee01b52f9f0/Final%20C0036%20HVAC%20Rebate%20Form%20Rev%2003.09.pdfGroton UtilitiesCT$200/ton159http://www.grotonutilities.com/files/conservation_forms/comm_air_rebate.pdfGroton UtilitiesCT$300/ton15.59http://www.grotonutilities.com/files/conservation_forms/comm_air_rebate.pdfGroton 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CooperativeFL$20017http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$20018http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$15014http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$17515http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$20016http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$22517http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$25018http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$17514http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$22515http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$25016http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$27517http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$30018http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$22514http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$25015http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$30016http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric CooperativeFL$32517http://clayelectric.com/documents/energysmartguidelinesandapplicationoctober2007.pdfClay Electric 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LightFL$54515http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$78016http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,02017http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,17518http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,34019http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$1,51020http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$37514http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and LightFL$64015http://www.fpl.com/residential/savings/energy_advisor/ac_buying_guide/ac_evaluate.shtmlFlorida Power and 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Energy FloridaFL$350158http://progress-energy.com/custservice/flares/energyhome/heatpump.aspProgress Energy FloridaFL$5014http://progress-energy.com/custservice/flares/energyhome/heatpump.aspTampa ElectricFL$275https://secure32.tecoenergy.com/tampaelectricsecure/residential/saveenergy/heatingcoolingrebate/Tampa ElectricFL$125https://secure32.tecoenergy.com/tampaelectricsecure/residential/saveenergy/heatingcoolingrebate/Central Georgia Electric MemberCorporationGA$20013http://www.cgemc.com/rebates.aspxDiverse PowerGA$300http://www.diversepower.com/rebates_incentives.phpDiverse PowerGA$500http://www.diversepower.com/rebates_incentives.phpDiverse PowerGA$100http://www.diversepower.com/rebates_incentives.phpDiverse PowerGA$300http://www.diversepower.com/rebates_incentives.phpHabersham Electric MembershipCorporationGA$150http://www.habershamemc.com/rebates.aspxHabersham Electric MembershipCorporationGA$200http://www.habershamemc.com/rebates.aspxWalton Electric MembershipCorporationGA$20013109.5http://www.waltonemc.com/residential/index.php?page=rebatesCity of Ames Electric ServicesIA(((Actual SEER) – 14) x$100) + $20014http://www.cityofames.org/SmartEnergy/Documents/AC%20Rebate%2008-09%20Claim%20Form.pdfAvista UtilitiesID$4008.5https://www.avistautilities.com/savings/rebates/Pages/WashingtonandIdahoCustomerRebates.aspxAvista UtilitiesID$400137.7https://www.avistautilities.com/savings/rebates/Pages/WashingtonandIdahoCustomerRebates.aspx3A-17Utility or AgencyStateAmountSEERHSPFEERWebsiteAvista UtilitiesID$1,000https://www.avistautilities.com/savings/rebates/Pages/WashingtonandIdahoCustomerRebates.aspxAvista UtilitiesID$1,500https://www.avistautilities.com/savings/rebates/Pages/WashingtonandIdahoCustomerRebates.aspxIdaho PowerID$2008.2http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmIdaho PowerID$2508.5http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmIdaho PowerID$3008.2http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmIdaho PowerID$4008.5http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmKootenai Electric CooperativeID$300148.5http://www.kec.com/rebates.phpKootenai Electric CooperativeID$965148.5http://www.kec.com/rebates.phpKootenai Electric CooperativeID$1,690148.5http://www.kec.com/rebates.phpKootenai Electric CooperativeID$1,500http://www.kec.com/rebates.phpCity Water Light and PowerIL$6007http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfCity Water Light and PowerIL$5258http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfCity Water Light and PowerIL$4709http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfCity Water Light and PowerIL$9007http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfCity Water Light and PowerIL$7908http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfCity Water Light and PowerIL$7009http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfCity Water Light and PowerIL$1,2007http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfCity Water Light and PowerIL$1,0508http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfCity Water Light and PowerIL$9309http://www.cwlp.com/Energy_services/ESO_services_programs/HPApp2007AirSource1.pdfMidAmerican Energy Company-IllinoisIL$10014<8.5http://www.midamericanenergy.com/pdf/ia_res_equip.pdfMidAmerican Energy Company-IllinoisIL$20014>/=8.5http://www.midamericanenergy.com/pdf/ia_res_equip.pdfMidAmerican Energy Company-IllinoisIL$20015<8.5http://www.midamericanenergy.com/pdf/ia_res_equip.pdfMidAmerican Energy 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REMCIN$90017http://www.harrisonremc.com/documents/2009_RebateForm.pdfIndianapolis Power and LightIN$10014http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=01050400&DocID=0205016c986701096a4e9803007c00Indianapolis Power and LightIN$12515http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=01050400&DocID=0205016c986701096a4e9803007c00Indianapolis Power and LightIN$15016http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=01050400&DocID=0205016c986701096a4e9803007c00Jackson County REMCIN$22514http://www.jacksonremc.com/rebates.htmJackson County REMCIN$27515http://www.jacksonremc.com/rebates.htmJackson County REMCIN$32516http://www.jacksonremc.com/rebates.htmJackson County REMCIN$37517http://www.jacksonremc.com/rebates.htmJackson County REMCIN$32514http://www.jacksonremc.com/rebates.htmJackson County REMCIN$37515http://www.jacksonremc.com/rebates.htmJackson County REMCIN$42516http://www.jacksonremc.com/rebates.htmJackson County REMCIN$47517http://www.jacksonremc.com/rebates.htmKosciusko REMCIN$150http://www.kremc.com/page1.asp?pi=25&pc=servicesMiami-Cass REMCIN$50013http://www.mcremc.coop/default1.asp?pi=49Miami-Cass REMCIN$10013http://www.mcremc.coop/default1.asp?pi=49Northeastern REMCIN$250http://www.nremc.com/ps_airsource_heatpump.shtmlOrange County REMCIN$20014http://www.orangecountyremc.org/rebates.htmlOrange County REMCIN$20015http://www.orangecountyremc.org/rebates.htmlOrange County REMCIN$25016http://www.orangecountyremc.org/rebates.htmlOrange County REMCIN$30017http://www.orangecountyremc.org/rebates.htmlOrange County REMCIN$50014http://www.orangecountyremc.org/rebates.htmlOrange County REMCIN$50015http://www.orangecountyremc.org/rebates.htmlOrange County REMCIN$65016http://www.orangecountyremc.org/rebates.htmlOrange County REMCIN$80017http://www.orangecountyremc.org/rebates.html3A-20Utility or AgencyStateAmountSEERHSPFEERWebsitePaulding-Putnam ElectricCooperativeIN$300http://www.ppec.coop/rebate/Air.htmlRush Shelby EnergyIN$30014http://www.rse.coop/heatpump.htmlRush Shelby EnergyIN$30015http://www.rse.coop/heatpump.htmlRush Shelby EnergyIN$35016http://www.rse.coop/heatpump.htmlRush Shelby EnergyIN$40017http://www.rse.coop/heatpump.htmlRush Shelby EnergyIN$80014http://www.rse.coop/heatpump.htmlRush Shelby EnergyIN$80015http://www.rse.coop/heatpump.htmlRush Shelby EnergyIN$90016http://www.rse.coop/heatpump.htmlRush Shelby EnergyIN$1,00017http://www.rse.coop/heatpump.htmlSouth Central Indiana REMCIN$30015http://www.sciremc.com/incentiveprograms.aspxSouth Central Indiana REMCIN$35016http://www.sciremc.com/incentiveprograms.aspxSouth Central Indiana REMCIN$40017http://www.sciremc.com/incentiveprograms.aspxSouthern Indiana Rural ElectricCoopIN$20014http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$20015http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$25016http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$30017http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$30014http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$30015http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$35016http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$40017http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$80014http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$80015http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$90016http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfSouthern Indiana Rural ElectricCoopIN$1,00017http://www.sirec.com/Forms/2009RebateTable_Form_Southern_flds.pdfTipmont REMCIN$70/ton13http://www.tipmont.org/Services/rebates.htmTipmont REMCIN$85/ton14http://www.tipmont.org/Services/rebates.htmTipmont REMCIN$100/ton15http://www.tipmont.org/Services/rebates.htmUtilities District of WesternIndiana REMCIN$30014http://www.udwiremc.com/inquiryhome_udi.nsf/pages/incentive-programs3A-21Utility or AgencyStateAmountSEERHSPFEERWebsiteUtilities District of WesternIndiana REMCIN$30015http://www.udwiremc.com/inquiryhome_udi.nsf/pages/incentive-programsUtilities District of WesternIndiana REMCIN$35016http://www.udwiremc.com/inquiryhome_udi.nsf/pages/incentive-programsUtilities District of WesternIndiana REMCIN$40017http://www.udwiremc.com/inquiryhome_udi.nsf/pages/incentive-programsWabash Country REMCIN$500http://www.wabashremc.com/customer-service/rebatesprograms.php#heatpumpsWabash Country REMCIN$200http://www.wabashremc.com/customer-service/rebatesprograms.php#heatpumpsWIN Energy REMCIN$30014http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$30015http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$35016http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$40017http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$80014http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$80015http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$90016http://www.winenergyremc.com/rebates.htmlWIN Energy REMCIN$1,00017http://www.winenergyremc.com/rebates.htmlMidland Power CooperativeIA$150/ton137.7http://www.midlandpower.com/aspx/General/ClientPage.aspx?PageID=268&n=164&b=4Midland Power CooperativeIA$150/ton + $250 bonus148.2http://www.midlandpower.com/aspx/General/ClientPage.aspx?PageID=268&n=164&b=4Guthrie County RECIA$150/ton137.7http://www.guthrie-rec.coop/cgi-script/csArticles/articles/000000/000049.htmGuthrie County RECIA$150/ton + $100 bonus148.2http://www.guthrie-rec.coop/cgi-script/csArticles/articles/000000/000049.htmAlliant EnergyIA$10014http://alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/IowaIncentivePrograms/014675Alliant EnergyIA$20015http://alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/IowaIncentivePrograms/014675Alliant EnergyIA$200< 8.5http://alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/IowaIncentivePrograms/014675Alliant EnergyIA$400>8.5http://alliantenergy.com/UtilityServices/ForYourHome/RewardsIncentives/IowaIncentivePrograms/014675Cedar Falls UtilitiesIA$300158http://www.cfu.net/heatpump_rebates.php?loc=topIndianola Municipal UtilitiesIA$20014http://www.i-m-u.com/Web%20Forms/2008%20heat%20pump%20application%20web.pdfIndianola Municipal UtilitiesIA$30015http://www.i-m-u.com/Web%20Forms/2008%20heat%20pump%20application%20web.pdfIndianola Municipal UtilitiesIA$40016http://www.i-m-u.com/Web%20Forms/2008%20heat%20pump%20application%20web.pdfIndianola Municipal UtilitiesIA$50017http://www.i-m-u.com/Web%20Forms/2008%20heat%20pump%20application%20web.pdfLinn CountyIA$225/ton14.58.2http://www.linncountyrec.com/cgi-script/csarticles/articles/000003/000333.htmMidAmerican Energy CompanyIA$10014<8.5http://www.midamericanenergy.com/pdf/ia_res_equip.pdfMidAmerican Energy CompanyIA$20014>/=8.5http://www.midamericanenergy.com/pdf/ia_res_equip.pdfMidAmerican Energy CompanyIA$20015<8.5http://www.midamericanenergy.com/pdf/ia_res_equip.pdfMidAmerican Energy CompanyIA$40015>/=8.5http://www.midamericanenergy.com/pdf/ia_res_equip.pdfMidAmerican Energy CompanyIA$100/homehttp://www.midamericanenergy.com/pdf/ia_res_equip.pdfWaverly Light and PowerIA$45014http://wlp.waverlyia.com/appliance_programs.aspKansas City Board of PublicKShttp://www.bpu.com/documents/ResidentialHPRebateScheduleeffJan2007withIncentives.pdf3A-22Utility or AgencyStateAmountSEERHSPFEERWebsiteUtilitiesJackson Energy CooperativeKY$100http://www.jacksonenergy.com/rebatePrograms.aspxKenergyKY$70013http://www.kenergycorp.com/incentive.aspxKenergyKY$80014http://www.kenergycorp.com/incentive.aspxKenergyKY$90015http://www.kenergycorp.com/incentive.aspxKenergyKY$90/tonhttp://www.kenergycorp.com/incentive.aspxNolin RECCKY$500148http://www.nolinrecc.com/home-rebateprograms.aspNolin RECCKY$100148http://www.nolinrecc.com/home-rebateprograms.aspOwen Electric Cooperative, Inc.KY$100148http://www.owenelectric.com/pdfs/forms/Residential%20Incentives.pdfPennyrile Rural ElectricCooperative CorporationKY$10013http://www.precc.com/precc/res_existing_home.htmPennyrile Rural ElectricCooperative CorporationKY$5013http://www.precc.com/precc/res_existing_home.htmPennyrile Rural ElectricCooperative CorporationKY$200http://www.precc.com/precc/res_existing_home.htmPennyrile Rural ElectricCooperative CorporationKY$20013http://www.precc.com/precc/res_new_home.htmPennyrile Rural ElectricCooperative CorporationKY$400http://www.precc.com/precc/res_new_home.htmSalt River ElectricKY$200http://www.srelectric.com/products/gaselectric.htmSalt River ElectricKY$20013http://www.srelectric.com/products/electricheatpump.htmSouth Kentucky Rural ElectricCooperative CorporationKY$300http://www.skrecc.com/ee_programs.htmTaylor County RECCKY$200138http://www.tcrecc.com/rebates.phpMunihelps - Marblehead rebateprogramMA$325138http://www.marbleheadelectric.com/rebate.htmMunihelps - Marblehead rebateprogramMA$525138http://www.marbleheadelectric.com/rebate.htmMunihelps - Marblehead rebateprogramMA$475148.5http://www.marbleheadelectric.com/rebate.htmMunihelps - Marblehead rebateprogramMA$675148.5http://www.marbleheadelectric.com/rebate.htmAustin Public UtilitiesMN$200148.2http://www.austinutilities.com/files/pdf/Res_Reb_App_09_5.pdfAustin Public UtilitiesMN$200148http://www.austinutilities.com/files/pdf/Res_Reb_App_09_5.pdfSouthern Minnesota MunicipalPower AgencyMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20BP.pdfConnexus EnergyMN$3013http://www.connexusenergy.com/ashprebate.htmConnexus EnergyMN$18014http://www.connexusenergy.com/ashprebate.htmConnexus EnergyMN$28015http://www.connexusenergy.com/ashprebate.htmConnexus EnergyMN$33016http://www.connexusenergy.com/ashprebate.htmCrow Wing PowerMN$200http://www.cwpower.com/heatingcoolingoptions.shtmlDakota Electric ServiceMN$300https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_re3A-23Utility or AgencyStateAmountSEERHSPFEERWebsitebatesDakota Electric ServiceMN$3013https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_rebatesDakota Electric ServiceMN$18014https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_rebatesDakota Electric ServiceMN$28015https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_rebatesDakota Electric ServiceMN$33016https://www.dakotaelectric.com/residential/programs/rebates/residential_heating_and_cooling_rebatesEast Central EnergyMN$3013http://www.eastcentralenergy.com/rebates.aspxEast Central EnergyMN$18014http://www.eastcentralenergy.com/rebates.aspxEast Central EnergyMN$28015http://www.eastcentralenergy.com/rebates.aspxEast Central EnergyMN$33016http://www.eastcentralenergy.com/rebates.aspxElk River Municipal UtilitiesMN$150/ton14http://www.elkriverutilities.com/cipstar.phpFairmont Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20FM.pdfGrand Marais PUCMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20GM.pdfLake City UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20rebate%20sheet%20LC.pdfLitchfield Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20rebate%20sheet%20LF.pdfMarshall Municipal UtilitiesMN$150/ton14http://www.marshallutilities.com/rebates/Central_AC_and_heat_pump.pdfMinnesota PowerMN$500169http://www.mnpower.com/powerofone/one_home/hvac/specials/ASHPs/miniductless.htmMinnesota PowerMN$325http://www.mnpower.com/powerofone/one_home/hvac/specials/combo/index.htmMinnesota Valley ElectricCooperativeMN$50013http://www.mvec.net/save/rebates.asp#energystarashpMora Municipal UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20MORA.pdfNew Prague Utilities CommissionMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20NP.pdfNorth Branch Municipal Water &LightMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20NB.pdfOtter Tail Power CompanyMN$20/1000 heating BTU14.58.212http://www.otpco.com/SaveEnergyMoney/heatPumpRebatesCIP.aspOtter Tail Power CompanyMN$20/1000 heating BTU14811http://www.otpco.com/SaveEnergyMoney/heatPumpRebatesCIP.aspOwatonna Public UtilitiesMN$200148.2http://www.owatonnautilities.com/documents/ResidentialRebateAppl09_000.pdfOwatonna Public UtilitiesMN$200148http://www.owatonnautilities.com/documents/ResidentialRebateAppl09_000.pdfPrinceton PUCMN$20014http://www.smmpa.org/Upload/Res%20Rebate%20Summary%20Sheet%20PRIN.pdfRedwood Falls Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20RF.pdfRochester Public UtilitiesMN$200148.2http://www.rpu.org/pdfs/2009resrebateapp.pdfRochester Public UtilitiesMN$200148http://www.rpu.org/pdfs/2009resrebateapp.pdfShakopee Public UtilitiesMN$20014http://www.shakopeeutilities.com/resrebates.htmSaint Peter Municipal UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20SP.pdfSpring Valley Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20SV.pdfWaseca UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20WAS.pdfWells Public UtilitiesMN$20014http://www.smmpa.org/Upload/2009%20RES%20summary%20sheet%20WELLS.pdfWild Rice ElectricMN$20015http://www.wildriceelectric.com/images/Rebate%20Agreement%20Form.pdf3A-24Utility or AgencyStateAmountSEERHSPFEERWebsiteWright-Hennepin CooperativeElectric AssociationMN$3013http://www.whe.org/WH_Electric/Rebates/index.htmlWright-Hennepin CooperativeElectric AssociationMN$18014http://www.whe.org/WH_Electric/Rebates/index.htmlWright-Hennepin CooperativeElectric AssociationMN$28015http://www.whe.org/WH_Electric/Rebates/index.htmlWright-Hennepin CooperativeElectric AssociationMN$33016http://www.whe.org/WH_Electric/Rebates/index.htmlXcel EnergyMN$18014http://www.xcelenergy.com/Residential/Programs_Resources/HeatingandCoolingRebates/Pages/HeatingandCoolingRebates.aspxXcel EnergyMN$28015http://www.xcelenergy.com/Residential/Programs_Resources/HeatingandCoolingRebates/Pages/HeatingandCoolingRebates.aspxXcel EnergyMN$33016http://www.xcelenergy.com/Residential/Programs_Resources/HeatingandCoolingRebates/Pages/HeatingandCoolingRebates.aspxCoast Electric Power AssociationMS$30013http://www.coastepa.com/documents/CArebateincentivesflyerRevisednov1208_000.pdfPearl River Valley Electric PowerAssociationMS$15013http://www.prvepa.com/comfadv.htmlPearl River Valley Electric PowerAssociationMS$15015http://www.prvepa.com/comfadv.htmlPearl River Valley Electric PowerAssociationMS$200http://www.prvepa.com/comfadv.htmlCity Utilities of SpringfieldMissouriMO$100/ton14http://www.cityutilities.net/conserve/rebates.htm#AirSourceColumbia Water & LightMO$10014http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$20015http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$30016http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$40017http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$50018http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$50019http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$60020http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$10014http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$30015http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$50016http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$60017http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$70018http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$80019http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$90020http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$20014http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$40015http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$60016http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$80017http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,00018http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdf3A-25Utility or AgencyStateAmountSEERHSPFEERWebsiteColumbia Water & LightMO$1,10019http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,20020http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$30014http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$60015http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$80016http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,00017http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,20018http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,40019http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfColumbia Water & LightMO$1,60020http://www.gocolumbiamo.com/WaterandLight/Documents/acrebateapplication.pdfCo-Mo Electric CooperativeMO$200/ton14http://www.co-mo.coop/Website%20Rebate%20Application-HeatPump.pdfCo-Mo Electric CooperativeMO$150/tonhttp://www.co-mo.coop/Website%20Rebate%20Application-HeatPump.pdfEmpire District ElectricMO$40015https://www.empiredistrict.com/Dochandler.ashx?id=3553Empire District ElectricMO$45016https://www.empiredistrict.com/Dochandler.ashx?id=3553Empire District ElectricMO$50017https://www.empiredistrict.com/Dochandler.ashx?id=3553Independence Power & LightMO$255147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$327147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$399147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$472147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$544147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$598147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$298147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$383147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$468147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$553147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$638147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIndependence Power & LightMO$698147http://www.ci.independence.mo.us/pl/pdf/rebate_hpf_hpe.pdfIntercounty Electric CooperativeMO$150/ton14http://www.ieca.coop/PROGRAMS/REBATES/tabid/116/Default.aspxKansas City Power and LightMO$65014http://www.kcpl.com/coolhomes/customers.htmKansas City Power and LightMO$65015http://www.kcpl.com/coolhomes/customers.htmKansas City Power and LightMO$85016http://www.kcpl.com/coolhomes/customers.htmKirkwood ElectricMO$3009.5http://www.ci.kirkwood.mo.us/electric/rebateprogram.htmOzark Border Electric CooperativeMO$150/ton10http://www.ozarkborder.org/rebates.htmlPlatte-Clay Electric CooperativeMO$150/ton14.5http://www.pcec.coop/Energy-Products/Heat-Pump-Systems/Take-Control-Dual-Fuelf.pdfBlack Hills PowerMT$150/ton13http://www.blackhillspower.com/rebprog.htmFlathead ElectricMT$1,000148.5http://www.flatheadelectric.com/energy/ASHPumpInfo.pdfNebraska Public Power DistrictNE$200148.2http://www.nppd.com/Energy_Efficiency/EnergyWise/heatpump_guidelines.pdfNebraska Public Power DistrictNE$250158.2http://www.nppd.com/Energy_Efficiency/EnergyWise/heatpump_guidelines.pdfNebraska Public Power DistrictNE$300168.2http://www.nppd.com/Energy_Efficiency/EnergyWise/heatpump_guidelines.pdfOmaha Public Power DistrictNE$20013http://www.oppd.com/ResidentialCustomers/FAQs/22_001511Omaha Public Power DistrictNE$20014http://www.oppd.com/ResidentialCustomers/FAQs/22_0015113A-26Utility or AgencyStateAmountSEERHSPFEERWebsiteOmaha Public Power DistrictNE$25015http://www.oppd.com/ResidentialCustomers/FAQs/22_001511Omaha Public Power DistrictNE$30016http://www.oppd.com/ResidentialCustomers/FAQs/22_001511Omaha Public Power DistrictNE$50http://www.oppd.com/ResidentialCustomers/FAQs/22_001511New Hampshire Electric Co-OpNH$2,00016http://www.nhec.com/residential_residentialheatpumps.phpNew Hampshire Electric Co-OpNH$2,00016http://www.nhec.com/residential_residentialheatpumps.phpNew Hampshire Electric Co-OpNH$1,00016http://www.nhec.com/residential_residentialheatpumps.phpCOOLAdvantageNJ$10014.58.512http://www.njcleanenergy.com/files/file/Residential%20Programs/WARMAdvantage/2009HVACContractorLetter.pdfCOOLAdvantageNJ$150158.512.5http://www.njcleanenergy.com/files/file/Residential%20Programs/WARMAdvantage/2009HVACContractorLetter.pdfKit Carson Electric CooperativeNM$924 to $3009http://www.kitcarson.com/dialup/products.htmlCity of New Bern ElectricDepartmentNC$40014http://www.newbern-nc.org/Elec/ad_energy_rbt.phpCity of Statesville Electric UtilityDepartmentNC$40010http://www.ci.statesville.nc.us/index.php/content/?cgi-bin=0&session_id=c9d782b865c54ab96ef46c4b69020e34&id=161Piedmont Electric MembershipCorporationNC$50/ton13Progress Energy Carolinas' HomeAdvantage ProgramNC$30015http://www.progress-energy.com/custservice/carres/efficiency/programs/habuilder/Boulder CityNV$70/ton14http://www.bcnv.org/Conservation/Rebates.aspBoulder CityNV$125/ton15http://www.bcnv.org/Conservation/Rebates.aspLong Island Power AuthorityNY$25014.58.212http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdfLong Island Power AuthorityNY$400158.512.5http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdfLong Island Power AuthorityNY$600168.513http://www.lipower.org/pdfs/cei/rebate-coolhomes-amt.pdfButler Rural Electric Cooperative,Inc.OH$600http://www.butlerrural.coop/index.php?module=pagemaster&PAGE_user_op=view_page&PAGE_id=50&53161e6ea2cc6f24e637a1819ab85c3c=4209d004603c2492d70a9b02484ed735Consolidated Electric CooperativeOH$600http://www.consolidatedelectric.coop/pdf/rebateDualFuel_Geothermal.pdfDuke EnergyOH$20014http://www.duke-energy.com/ohio/savings/smart-saver.aspDuke EnergyOH$30014http://www.duke-energy.com/ohio/savings/smart-saver.aspPaulding-Putnam ElectricCooperativeOH$300http://www.ppec.coop/rebate/Air.htmlPaulding-Putnam ElectricCooperativeOH$50http://www.ppec.coop/rebate/Air.htmlThe Energy CooperativeOH$599http://www.theenergycoop.com/documents/TEC_DualFuelMBRAgreementFORM.pdfEdmond ElectricOK$250/ton15http://edmondok.com/docs/utility_customer_services/heat_pump_rebate_brochure.pdfIndian ElectricOK$150/ton14, 15http://www.iecok.com/pdf/february_lamp_2009.pdfCity of Ashland ConservationDistrictOR$500148.5http://www.ashland.or.us/Page.asp?NavID=1366Central Electric CooperativeOR$800http://www.cec-co.com/custserv/energy_info/brochures/heat_pump_app.pdfCentral Electric CooperativeOR$400http://www.cec-co.com/custserv/energy_info/brochures/heat_pump_app.pdfCentral Electric CooperativeOR$600http://www.cec-co.com/custserv/energy_info/brochures/heat_pump_app.pdfCentral Electric CooperativeOR$200http://www.cec-co.com/custserv/energy_info/brochures/heat_pump_app.pdf3A-27Utility or AgencyStateAmountSEERHSPFEERWebsiteColumbia River PUDOR$1,300148.5http://www.crpud.net/residential/efficiency/pdf/HeatPumpInfoSheetWeb.pdfColumbia River PUDOR$750137.7http://www.crpud.net/residential/efficiency/pdf/HeatPumpInfoSheetWeb.pdfColumbia River PUDOR$400137.7http://www.crpud.net/residential/efficiency/pdf/HeatPumpInfoSheetWeb.pdfColumbia River PUDOR$300137.7http://www.crpud.net/residential/efficiency/pdf/HeatPumpInfoSheetWeb.pdfConsumers Power, Inc.OR$400http://www.cpi.coop/rebates/heatpump.phpConsumers Power, Inc.OR$500http://www.cpi.coop/rebates/heatpump.phpConsumers Power, Inc.OR$300http://www.cpi.coop/rebates/heatpump.phpConsumers Power, Inc.OR$300http://www.cpi.coop/rebates/heatpump.phpConsumers Power, Inc.OR$750http://www.cpi.coop/rebates/heatpump.phpConsumers Power, Inc.OR$850http://www.cpi.coop/rebates/heatpump.phpEnergy Trust of OregonOR$300148.211.5http://www.energytrust.org/residential/existinghomes/heatpump.html#1Energy Trust of OregonOR$4008.512http://www.energytrust.org/residential/existinghomes/heatpump.html#1Energy Trust of OregonOR$150148.211.5http://www.energytrust.org/residential/existinghomes/heatpump.html#1Energy Trust of OregonOR$2008.512http://www.energytrust.org/residential/existinghomes/heatpump.html#1Emerald People's Utility DistrictOR$1,500http://www.epud.org/documents/DuctlessHeatPumpPilotProject.pdfEugene Water and Electric BoardOR$1,000http://www.eweb.org/content.aspx/20f5adf5-842f-4c29-ad19-f1250c41c10aForest Grove Light & PowerOR$935148.5http://www.forestgrove-or.gov/images/stories/services/lightandpower/pdf/fgheatpump2009.pdfForest Grove Light & PowerOR$480148.5http://www.forestgrove-or.gov/images/stories/services/lightandpower/pdf/fgheatpump2009.pdfForest Grove Light & PowerOR$1,500http://www.forestgrove-or.gov/city-services/light-&-power-residential-ductless-heat-pump-program.htmlIdaho PowerOR$2008.2http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmIdaho PowerOR$2508.5http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmIdaho PowerOR$3008.2http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmIdaho PowerOR$4008.5http://www.idahopower.com/EnergyEfficiency/Residential/Programs/HeatingCooling/availIncentives.cfmLane Electric CooperativeOR$1,000148.5http://www.laneelectric.com/heatpump.htmMidstate ElectricOR$1,400148.5http://www.midstateelectric.coop/ProductsAndServices/PSResidential/ConservationPrograms/resheatpumps/Default.aspx#HP%20RebatesMidstate ElectricOR$1,400149.5http://www.midstateelectric.coop/ProductsAndServices/PSResidential/ConservationPrograms/resheatpumps/Default.aspx#HP%20RebatesOregon Trail Electric CooperativeOR$1,500http://www.otec.coop/member_services/energy_conservation.htmOregon Trail Electric CooperativeOR$500http://www.otec.coop/member_services/energy_conservation.htmPortland General ElectricOR$200137.7http://www.portlandgeneral.com/home/energy_savings/heat_cool/heat_pumps/money_back/default.aspxSalem ElectricOR$7508.5http://www.salemelectric.com/PDFs/heatpumpbrochure.pdfSalem ElectricOR$5008.5http://www.salemelectric.com/PDFs/heatpumpbrochure.pdfTillamook County PUDOR$500138.5http://www.tpud.org/nrg_heatpump.htmlTillamook County PUDOR$2,000148.5http://www.tpud.org/nrg_heatpump.html3A-28Utility or AgencyStateAmountSEERHSPFEERWebsiteKeystone HELPPA$250E*> E*http://www.keystonehelp.com/info/documents/KeystoneHELPGuidelines03-23-09FINAL.pdfKeystone HELPPA$500E*> E*http://www.keystonehelp.com/info/documents/KeystoneHELPGuidelines03-23-09FINAL.pdfBlack Hills PowerSD$150/ton13http://www.blackhillspower.com/rebprog.htmMiddle Tennessee ElectricMembership CorporationTN$100137.7http://www.mtemc.com/pages.cfm/name/r09_heatingacUpper Cumberland ElectricMembership CorporationTN$150http://www.ucemc.com/services.htm#rebatesWinchester UtilitiesTN$15012http://www.winchesterutilities.com/Services_files/Energy%20Right%20Program_files/image006.PDFWinchester UtilitiesTN$15011http://www.winchesterutilities.com/Services_files/Energy%20Right%20Program_files/image006.PDFAustin EnergyTX$2401411.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Commercial/Multi-Family%20Properties/rebateLevels.htmAustin EnergyTX$5401512.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Commercial/Multi-Family%20Properties/rebateLevels.htmAustin EnergyTX$2401411.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$3601412http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$5401512.5http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$6001613http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$3601411http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmAustin EnergyTX$540> 1412http://www.austinenergy.com/Energy%20Efficiency/Programs/Rebates/Residential/Air%20Conditioner/rebateLevels.htmCollege Station UtilitiesTX$20014http://www.cstx.gov/docs/energy_back_ii_application_form.pdfCollege Station UtilitiesTX$40015http://www.cstx.gov/docs/energy_back_ii_application_form.pdfCollege Station UtilitiesTX$50016http://www.cstx.gov/docs/energy_back_ii_application_form.pdfCollege Station UtilitiesTX$60018http://www.cstx.gov/docs/energy_back_ii_application_form.pdfDenton Municipal ElectricTX$70016http://dmepower.com/userfiles/file/GreenSense%20Rebate%20Manuals.pdfDenton Municipal ElectricTX$70016http://dmepower.com/userfiles/file/GreenSense%20Rebate%20Manuals.pdfFarmers Electric CooperativeTX$15014http://www.fecelectric.com/fecinternet2005/PDF/Rebate%20-%20Heat%20Pump.pdfGarland Power and LightTX$50015http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20Heat%20Pump%20Application%209-26-08.pdfGarland Power and LightTX$60016http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20Heat%20Pump%20Application%209-26-08.pdfGarland Power and LightTX$70017http://www.garlandpower-light.org/pdfs/2008-2009%20Central%20Air%20Conditioning%20%20Heat%20Pump%20Application%209-26-08.pdf3A-29Utility or AgencyStateAmountSEERHSPFEERWebsiteGuadalupe Valley ElectricCooperativeTX$50015http://www.gvec.org/safety_con/GVEC%20HOME%20IMPROVEMENT%20REBATES%20AND%20APPLICATIONS.pdfGuadalupe Valley ElectricCooperativeTX$60016http://www.gvec.org/safety_con/GVEC%20HOME%20IMPROVEMENT%20REBATES%20AND%20APPLICATIONS.pdfGuadalupe Valley ElectricCooperativeTX$70017http://www.gvec.org/safety_con/GVEC%20HOME%20IMPROVEMENT%20REBATES%20AND%20APPLICATIONS.pdfMagic Valley Electric CooperativeTX$25013http://www.magicvalley.coop/Electric-Service/Residential/RebatesMagic Valley Electric CooperativeTX$30014http://www.magicvalley.coop/Electric-Service/Residential/RebatesMagic Valley Electric CooperativeTX$35015http://www.magicvalley.coop/Electric-Service/Residential/RebatesPedernales Electric CooperativeTX$35014http://www.pec.coop/Documents/HVACRebate.pdfPedernales Electric CooperativeTX$1,00014http://www.pec.coop/Documents/HVACRebate.pdfUnited Cooperative ServicesTX$50/ton15http://www.united-cs.com/rebates.htmUnited Cooperative ServicesTX$100/ton16http://www.united-cs.com/rebates.htmUnited Cooperative ServicesTX$150/ton17http://www.united-cs.com/rebates.htmUnited Cooperative ServicesTX$25/tonhttp://www.united-cs.com/rebates.htmUnited Cooperative ServicesTX$200http://www.united-cs.com/rebates.htmAvista UtilitiesWA$4008.5https://www.avistautilities.com/savings/rebates/Pages/WashingtonandIdahoCustomerRebates.aspxAvista UtilitiesWA$400137.7https://www.avistautilities.com/savings/rebates/Pages/WashingtonandIdahoCustomerRebates.aspxAvista UtilitiesWA$1,000https://www.avistautilities.com/savings/rebates/Pages/WashingtonandIdahoCustomerRebates.aspxAvista UtilitiesWA$1,500https://www.avistautilities.com/savings/rebates/Pages/WashingtonandIdahoCustomerRebates.aspxClallam County PUDWA$470-$1500148.5http://www.clallampud.net/conservation/res_HighEfficient_Heating.aspClallam County PUDWA$470-$915148.5http://www.clallampud.net/conservation/res_HighEfficient_Heating.aspClark Public UtilitiesWA$1,500http://www.clarkpublicutilities.com/yourhome/heatingCooling/heatingCoolingSystems/ductlessHPClark Public UtilitiesWA$750148.5http://www.clarkpublicutilities.com/yourhome/heatingCooling/archives/heatPumpRebate.pdfCowlitz County PUDWA$900148.5http://www.cowlitzpud.org/pdf/H2AdvSum.pdfCowlitz County PUDWA$90013.58.6http://www.cowlitzpud.org/pdf/H2AdvSum.pdfCowlitz County PUDWA$900138.7http://www.cowlitzpud.org/pdf/H2AdvSum.pdfCowlitz County PUDWA$450148.5http://www.cowlitzpud.org/pdf/H2AdvSum.pdfCowlitz County PUDWA$45013.58.6http://www.cowlitzpud.org/pdf/H2AdvSum.pdfCowlitz County PUDWA$450138.7http://www.cowlitzpud.org/pdf/H2AdvSum.pdfCowlitz County PUDWA$1,800148.5http://www.cowlitzpud.org/pdf/H2AdvSum.pdfCowlitz County PUDWA$1,80013.58.6http://www.cowlitzpud.org/pdf/H2AdvSum.pdfCowlitz County PUDWA$1,800138.7http://www.cowlitzpud.org/pdf/H2AdvSum.pdfFranklin County PUDWA$400148.5http://www.franklinpud.com/html/res_rebates.htmlFranklin County PUDWA$800148.5http://www.franklinpud.com/html/res_rebates.htmlFranklin County PUDWA$1,000http://www.franklinpud.com/html/res_rebates.html3A-30Utility or AgencyStateAmountSEERHSPFEERWebsiteFranklin County PUDWA$850http://www.franklinpud.com/html/res_rebates.htmlFranklin County PUDWA$1,500http://www.franklinpud.com/html/res_rebates.htmlGrays Harbor PUDWA$1,500https://www.ghpud.org/index.php?option=com_docman&task=doc_download&gid=191Grays Harbor PUDWA$1,000https://www.ghpud.org/index.php?option=com_docman&task=doc_download&gid=193Grays Harbor PUDWA$1,500https://www.ghpud.org/index.php?option=com_docman&task=doc_download&gid=193Inland Power & Light CompanyWA$1,400148.5http://www.inlandpower.com/customer_carec.php?id=188Inland Power & Light CompanyWA$650148.5http://www.inlandpower.com/customer_carec.php?id=188Inland Power & Light CompanyWA$650148.5http://www.inlandpower.com/customer_carec.php?id=188Inland Power & Light CompanyWA$1,500148.5http://www.inlandpower.com/customer_carec.php?id=188Mason County PUD 3WA$915148.5http://www.masonpud3.org/powerSupply/applianceRebates.aspxModern Electric Water CompanyWA$250http://www.mewco.com/PDFs/Rebate%20Flyer.pdfOrcas Power and LightCooperativeWA$750148.5http://www.opalco.com/Port Angeles Public Works andUtilitiesWA$470148.5https://www.cityofpa.us/PDFs/Conservation/2007/HPUMPREBATEAPP.pdfPort Angeles Public Works andUtilitiesWA$915148.5https://www.cityofpa.us/PDFs/Conservation/2007/HPUMPREBATEAPP.pdfPort Angeles Public Works andUtilitiesWA$300138https://www.cityofpa.us/PDFs/Conservation/2007/HPUMPREBATEAPP.pdfPort Angeles Public Works andUtilitiesWA$850138https://www.cityofpa.us/PDFs/Conservation/2007/HPUMPREBATEAPP.pdfPuget Sound EnergyWA$1,200http://www.pse.com/solutions/foryourhome/pages/rebatesOnHeating.aspx?tab=2&chapter=1Puget Sound EnergyWA$200148.5http://www.pse.com/solutions/foryourhome/pages/rebatesOnHeating.aspx?tab=3&chapter=1Puget Sound EnergyWA$350149http://www.pse.com/solutions/foryourhome/pages/rebatesOnHeating.aspx?tab=3&chapter=1Snohomish County PUD No 1WA50% of job cost up to $450http://www.snopud.com/energy/home/econpgms.ashx?p=1885#Snohomish County PUD No 1WA$1,200http://www.snopud.com/energy/home/econpgms.ashx?p=4086Tacoma PowerWA$400148.5http://www.mytpu.org/customer-service/your/rebates-promotions/heating-cooling.htmVera Irrigation District #15WA$400http://www.verawaterandpower.com/Energy_Rebates.htmCedarburg Light & WaterWI$10015http://www.focusonenergy.com/files/Document_Management_System/Residential_Programs/efficientheatingcoolingcashback_rewardform.pdfCedarburg Light & WaterWI$10015http://www.focusonenergy.com/files/Document_Management_System/Residential_Programs/efficientheatingcoolingcashback_rewardform.pdfEau Claire Energy CooperativeWI$120/ton14http://www.ecec.com/programs/incentives/index.cfmFocus on EnergyWI$10015http://www.focusonenergy.com/files/Document_Management_System/Residential_Programs/efficientheatingcoolingcashback_rewardform.pdfFocus on EnergyWI$10015http://www.focusonenergy.com/files/Document_Management_System/Residential_Programs/efficientheatingcoolingcashback_rewardform.pdfRiverland Energy CooperativeWI$60/ton14http://riverlandenergy.com/RIVERLAND%20HOMEPAGE/programs_rebates/dualfuel.htmPierce Pepin Cooperative ServicesWI$120/ton14http://www.piercepepin.com/nrgsense/rebate_heatpump.htmBlack Hills PowerWY$150/ton13http://www.blackhillspower.com/rebprog.htmCarbon Power and LightWY$125/tonhttp://www.carbonpower.com/rebates.htm3A-31Utility or AgencyStateAmountSEERHSPFEERWebsiteCarbon Power and LightWY$150/tonhttp://www.carbonpower.com/rebates.htmRocky Mountain PowerWY$2508.2http://www.homeenergysavings.net/wyoming/heatpumps.htmlRocky Mountain PowerWY$3508.2http://www.homeenergysavings.net/wyoming/heatpumps.html3A-32

### 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

DEVIATIO NS .............................................................................................................. 6A-4

LIST OF TABLES

Table 6A.2.1 Disaggregated Costs and Expe nses 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 Expe nses and Markups................................................... 6A-4

Table 6A.5.1 Wholesaler Incremental Mar kup Standard Deviation.................................... 6A-5

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

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

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

6A-i

### APPENDIX 6A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS

6A.1 INTRODUCTION

This appendix provides furthe r details on information pres ented 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 Intern ational (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 bot h the baseline and incremental markups. As

described in Chapter 6, section 6.4, only those expe nses 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

### 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 Tab le 6.4.1.



### 6A.3 DETAILED MECHANICAL CONTRACTOR DATA

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

Industry, published by the Air Conditioning Contr actors 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 a ggregated 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, th e 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 a nd percentage terms from the 2002 Census of

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

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

6A-2

### Table 6A.3.1 Mechanical Contractor Expens es 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 &

Incremental Depreciation charges during year 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. D etailed

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 revenu es 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 mech anical 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 baselin e markups, is also 3.7 percent.

DOE also assumed that wholesaler, genera l contractor, and mechanical contractor

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

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

deviation of wholesaler, general contractor, a nd mechanical contractor incremental markups.

6A-3

### Table 6A.4.1 General Contract or 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 &

Incremental Depreciation charges during year 1,318,065 0.55

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 Ins titutional 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 contr actor markup distributions. In the case of wholesalers, survey

data were available only in summary form at th e 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, numbe r 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 mar kup 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 av erage markup and publishe d relative markup of

7.7 percent, as shown in Table 6A.5.1. The mechani cal contractor markup st andard deviation is

5.6 percent and the relative markup is 3.7 percen t, 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 deviati on is 6.8 percent, derived from

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

general contractor markup standard deviation is 9.5 percent and th e 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

### 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 me rchant

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

### 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 f or

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

### Table 6A.5.3 Residential General Contract or 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

### 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 Statist ics 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 Contract or 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

### 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

### DOE assumed that new construction and repl acement market mechanical contractor

markups have the same relative standard deviation as that de veloped 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 devi ation for the replacement and

new markets, relative to their respectiv e baseline markups, is also 3.7 percent.

DOE also assumed that wholesaler, genera l contractor, and mechanical contractor

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

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

deviation of wholesaler, general contractor, a nd mechanical contractor incremental markups.

The relative standard deviations for mechanical and general contractors are not used

directly to estimate markups as for wholesal e trade. The markups in construction vary

systematically relative to their mean by stat e, so the state relative markups are applied

individually to average markups by cl ass of construction in each state.

6A-10

### 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 Us ed in Small Office Modeling..... 7A-2

7A-i

### 7A-1APPENDIX 7A. INTERNAL LOADS SCHEDULES AND PART-LOAD PERFORMANCE CURVESTable 7A.1. Occupancy, Lighting and Plug Loads SchedulesScheduleDay ofWeek123456789101112131415161718192021222324Lighting(1.0 W/sf)Weekday0.180.180.180.180.180.230.230.420.900.900.900.900.800.900.900.900.900.610.420.420.320.320.230.18Sat0.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.18Sun, Hol,Other0.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.180.18PlugEquipment(0.63W/sf)Weekday0.200.200.200.200.200.200.200.501.001.001.001.000.941.001.001.001.000.500.200.200.200.200.200.20Sat0.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.20Sun, Hol,Other0.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.200.20OccupancyWeekday0.000.000.000.000.000.000.110.211.001.001.001.000.531.001.001.001.000.320.110.110.110.110.050.00Sat0.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00Sun, Hol,Other0.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00HeatingSetpointWeekday60.060.060.060.060.060.065.070.070.070.070.070.070.070.070.070.070.070.070.060.060.060.060.060.0Sat60.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.0Sun, Hol,Other60.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.060.0CoolingSetpointWeekday,85.085.085.085.085.085.080.075.075.075.075.075.075.075.075.075.075.075.075.085.085.085.085.085.0Sat85.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.0Sun, Hol,Other85.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.085.0FanOperationandVentilationWeekday,0.00.00.00.00.00.01.01.01.01.01.01.01.01.01.01.01.01.01.00.00.00.00.00.0Sat0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0Sun, Hol,Other0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0InfiltrationWeekday,1.001.001.001.001.001.000.250.250.250.250.250.250.250.250.250.250.250.250.251.001.001.001.001.00Sat,1.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.00Sun, Hol,Other1.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.00Hot WaterWeekday,0.000.000.000.000.000.000.000.270.550.640.640.821.000.910.550.550.730.370.370.180.270.090.000.00Sat,0.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00Sun, Hol,Other0.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00Table 7A.2. CAC and CHP Performance Curves Used in Small Office ModelingTypePerformanceCurveEquation CurveCoefficientsabcdefCACTotal Cooling Capacity Function of Temperature CurveEDBWBfEDBeEDBdWBcWBbaFTCAPCOOL××+×+×+×+×+=−−221.186036-0.067990.003167-0.009023.91E-05-0.00064Total Cooling Capacity Function of Flow Fraction CurveFF2cFFbaFFCAPCOOL×+×+=−−0.637510.409767-0.12055NANANAEnergy Input Ratio Function of Temperature CurveEDBWBfEDBeEDBdWBcWBbaFTEIRCOOL××+×+×+×+×+=−−22-0.438910.145182-0.00394-0.020640.001119-0.00078Energy Input Ratio Function of Flow Fraction CurveFF2cFFbaFFEIRCOOL×+×+=−−1.54819-0.498320.204972NANANACooling Part Load Fraction Correlation CurvePLR2cPLRbaPLREIRCOOL×+×+=−−0.7710.2290.0NANANAPTHPHeating Capacity Function of Temperature CurveDB2cDBbaFTCAPHEAT×+×+=−−0.6915490.0313040.000501NANANAHeating Capacity Function of Flow Fraction CurveDB2cDBbaFFCAPHEAT×+×+=−−1.014278-0.163650.149368NANANAHeating Energy Input Ratio Function of Temperature Curve32DBcDBcDBbaFTEIRHEAT×+×+×+=−−0.8292040.0006970.000779-0.003200.001721-0.00181Heating Energy Input Ratio Function of Flow Fraction CurveFF2cFFbaFFEIRHEAT×+×+=−−1.40347-0.510170.106692NANANAHeating Part Load Fraction Correlation CurvePLR2cPLRbaFPLREIRHEAT×+×+=−−0.7710.2290.0NANANA



### 7A-2

### 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,

H

EA T

− E IR

− FT 

### = Heating Energy Input Ratio Function of Temperature Curve Name,

H

EA T

− E IR

− F F 

### = Heating Energy Input Ratio Function of Flow Fraction Curve

Name,

H

EA T

− E IR

− F PLR 

### = 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 CA C and CHP condensing un its 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 t ool 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 pe rformance 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

### 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-Sta te Weighting Factors..................................................... 7B-7

LIST OF FIGURES

Figure 7B.2.1. Locations of TMY2 Stations Superi mposed on Heating Degree-Day Contours. 7B-2

Figure 7B.2.2. Locations of 4775 NOAA Stations................................................................... 7B-3

Figure 7B.2.3. Locations of P opulated 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

### 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 re flect 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 dist ributed throughout the

country so as to give good coverage of the c limatic variation in the Un ited States. Unfortunately

for this study, the TMY2 stations are not distributed so as to ma tch 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 bu ildings data, and are available from a number of

sources. The problem facing this study was to match up population data, wh ich 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 s hows the locations of the 239 TMY2 stations superimposed on a contour

map showing heating degree-days (HDD). It is clear that the geographi c 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 dist ribution 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 whic h 4775 climate stations are represented (see

http://lwf.ncdc.noaa.gov/oa/doc umentlibrary/clim81supp3/clim81.html ). Each NOAA station

is characterized by its location (latitude/longit ude), annual heating and cooling degree-days,

elevation, and various other metrics.

7B-1

### 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 contemporaneo us 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 park s, 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 entr ies that include a popul ation 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 c ontain 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 NO AA stations. The PPL database is used to

make the final link between th e 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

### Figure 7B.2.2. Locations of 4775 NOAA Stations

Figure 7B.2.3. Locations of Populated Places (PPL)

7B-3

### 7B.2.2 Mapping Approach

Our approach to generating TMY2 weigh ting 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 me trics (chiefly, heating and cooling degreedays) that facilitate further mapping.

2. Map each NOAA station to a best-representativ e 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 popula tion that “points” (via its

PPL → NOAA → TMY2 mappings) to each TMY2 station.

These are described in order below.

B.2.2.1 Mapping 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 elevat ion 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 latit udes and longitudes of the two locations

and simple spherical geometry. If the eleva tion of the nearest NOAA station is within 300

feet of the PPL location or if the eleva tion of the PPL location is unknown, then the

nearest NOAA station is the final mapping.

2. If the nearest NOAA station differs in elev ation 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 Io wa 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 mountai nous terrain or in locations w ith 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 st ation 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

### 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

### B.2.2.2 Mapping NOAA Stations to TMY2 Stations

Having mapped each PPL location to a best-r epresentative NOAA station, the next step is

to map each NOAA station to a best repres entative TMY2 station. Because the NOAA and

TMY2 data sets provide less de nse geographical coverage and be cause both data sets contain

climate information, this mapping process relies less on spatial pr oximity and more on similarity

of climate. Proximity rema ins important, though. In finding a best TMY2 mapping for a NOAA

station, we hope for similarity in a number of climate variables: temp erature (HDD and CDD),

solar and wind characteristics, rainfall and humidity characteristics, snowfall, etc. The NOAA

data set supports direct comparison only of hea ting 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 el evation from the NOAA station by more than

300 feet, then select instead the TMY2 sta tion 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 term s 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 re gression analysis of these correlations allows

us to cast HDD, CDD, and elevation differenc es into units of distance (miles). A linear

regression on NOAA/TMY2 station pairs within 300 miles of each other gives the following

result.

d

equiv 

### = I + α × Δ HDD + β × CDD + γ × Δ Elev 

### Eq. 7B.1

Where:

d

equiv 

### = equivalent latitude distance (miles),

Δ HDD = difference in heating degree-days (base-65 ° F)

, Δ CDD = difference in cooling degree-days (base-65 ° F)

,

7B-6

### Δ Elev = 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 e quivalent 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 out ward to the NOAA stations mapped to it.

Figure 7B.2.6. NOAA → TMY2 Mappings

7B.2.3 Calculating State-by-Sta te 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

### 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-Base d Temperature Bin Data to Develop

Cooling Load Profiles for SEER HD 

### 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 Mexi co .........................................................................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 Conditi ons 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 ac ross Temperature Bins, by State .....................7C-16

LIST OF FIGURES

Figure 7C.1.1. Dist ribution of Cooling Load Ho urs 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

### 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. Calculat ed Cooling Load Profiles for Ca ndidate Hot-Dry States ....................7C-18

Figure 7C.4.2. Cooling-Load -Weighted Average Outdoor Air Temperatures for

Selected TMY Locations and Averag e 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 SEER HD 

### .......................................................................................7C-22

7C-ii

### APPENDIX C. PROPOSED REGIONS FOR CAC REGIONAL STANDARDS AND

MAPPING OF RECS OBSERVATIONS

7C.1 Introduction

Under EPACT, regional effi ciency standards may be defined for up to two regions

consisting of contiguous groups of states. Fo r cooling equipment performance, the most

important regional variations in climate are the annual numbe r 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 perf ormance standards: the current definition and a

modified definition term ed “SEER hot-dry” (SEER HD 

### ). SEER is establishe d based on test data

measured at various operating conditions, whic h are weighted and summed into a single

numerical rating using a temperature profile that is representative of an average U.S. cooling

climate. The SEER HD 

### 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 ma y be considered for this region. The SEER HD 

### 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 differe nt 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 temp erature 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 SEER HD 

### 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 base d on research conducted in the 1970s and, in

simple terms, was developed by simulating th e performance of a prototypical residence

throughout the US. 1,2

7C-1

### 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 te st procedure, 1000 CLHs are estimated to be the

U.S. average. As depicted in Figure 1, 1000 CL Hs 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 reas onably 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, Sout h Carolina, Tennessee, and Texas.

• Region 2 (Hot-Dry) : Arizona, California, Ne vada, and New Mexico.

As noted above, the States identified as bein g 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 frac tion 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 th e 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

### 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. Th e 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)

80Hours

70ling

60(Coo

50idity

40Hum

30

20Average Relative

10

0

0 1000 2000 3000 4000 5000 6000

CDD base 65



### Figure 7C.2.1. Scatter Plot of the Average Rela tive 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 cons ists 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 a nd low RH, i.e., CDD > 2000 and RH < 40%. But there are two

7C-3

### 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 ma rine 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 varia tion 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 ch aracteristic hot-dry clima te would be included.

7C.3 Mapping of RECS observations to Regions

As proposed in the Framework Document, th e Residential Energy Consumption Survey

(RECS) 4 

### is to serve as the basis for conducting th e 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 show s the nine Census Divisions. The four large

States are California, Florida, New York, and Te xas. 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 th e 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 Divisi on, Middle Atlantic Census

Division, East North Central Census Divi sion, West North Central Census Division,

Northern Mountain Census Division ( Idaho, Montana, Wyoming ), Pacific Census

Division minus California

.

The RECS geographic areas above are similar th e 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 th e three census divisions allocates five more

States than it should—Delaware, Kentucky, Mary land, 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 de velop for excluding these overallocated States.

7C-4Hot-Dr y Re g ion

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 CA C and CHP equipment in the proposed climate

regions for which separate efficiency standard s 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 Sout h Atlantic census division are separated

in two sets i.e., one set for the northern st ates of the census division viz. Virginia,

West Virginia, Maryland, District of Co lumbia and Delaware , and the second set

for the southern states of the division viz. North Carolina, South Carolina and

Georgia.

7C-5

### • Mountain: RECS records for the Mountai n 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 cen sus 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 fr om 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 da ta 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 st ate wise population-we ighted CDD and HDD

data for the year 2005 was obtained from Nati onal Climatic Da ta 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 a nd 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 r ecord represents the number of housing units

in the total stock of reside ntial 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 expenses 5 

### . This particular sampling design

offers the opportunity of identifying large ur ban 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 comp arison of the CDD and HDD values was

inconvenient due to the masking of the repo rted 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 th e climate characteris tic in the Cartesian

plane. In this approach, the polar values of this coordinate i.e., th e 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

### • 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 de viation 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 repor ted 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 c limate 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, identi cal 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 f ound 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 c limate characteristic of Ch arlotte, 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 RE CS records was obtained. These were then

identified as likely in the target zone of th e ‘hot and humid’ region of the South Atlantic

Census division comprising th e 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 censu s 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 fo r the population weighted HDD and the CDD

were found to be 82% and 117% of the respectiv e 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

### target ‘hot and humid’ region. The population weighted HDD ratio for these subset of

records was 79% and the corresponding C DD 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 describe d 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 Cens us Division were identif ied with Hawaii based

on the reported CDD data and high electricity co sts. 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 SEER HD 

### Metric

The candidate hot-dry region consisted of Arizona, California, New Me xico, 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 SEER HD 

### rating could be developed ba sed on available temperature

bin data for climates within each state and using population base d-weighting of those specific

climates.

7C.4.1 State by State Analysis

Through previous work, PNNL had develope d population-based weightings for TMY2

weather locations. A detailed disc ussion of this mapping process can be found in Appendix 7-B.

For the purpose of this analysis, DOE only relie d on the mapping as it applied to the four

candidate hot-dry states. Table 7C.1 throug h 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 popul ation 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 popul ation falls outside of the state boundary.

7C-8

### 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

### 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 lo cation 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 bins 6 

### . 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 bi ns provided in the CAC test procedure were

used.

7C-10

### The CAC test procedure is structured to consider all hours with outdoor air temperature

above 65oF as hours with a define d cooling load. Table 16 of th e CAC test procedure provides

the fraction of the total temperature bin hours ab ove 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 temperatur e 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

### 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-fractiona l 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 

# ( T

j 

### − 65 

# ) Q

c 

### (95)

BL ( T

j 

### ) = • 

### Eq. 7C- 1

95

− 65 1.1

7C-12

### Where

BL is the building load

T

j 

### is the representative temperature for bin j

Q

c 

### (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 cap acity (at the 95oF condition) divided by 1.1. We

presume that the best interpretation of this equa tion 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 repres entative bin temperature

T

j 

### 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 95o F temperature in equation 1 was replaced with

the ASHRAE 1% cooling dry bulb temperatur e design condition for each TMY3 location 7



### . Those design conditions are shown in Table 7C.9.

Table 7C.9. Design Load Condi tions 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

### 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 TM Y 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 (CLW T) 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 temperatur e 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 cu rrently used in an optional rating methodology

for establishing SEER in the current test procedure.

Using the population based weighting fact ors 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 stat e population, the weighting factors were all

normalized to 100% prior to weighting across clim ates. 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

### 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 ac ross 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

### Table 7C.13. Distribution of Cooling Load ac ross 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 clear ly 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 Daughert y 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

### California TMY locations and app lied the weighting factors deve loped 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 aver age 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 dire ctly and using county population data from

the 2000 census data, the average CDD65 va lue 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 Commission 8 

### in conjunction with and 2000 census data for housing units by zip

code within the state (mapped using CEC data for each zip code 9 

### ), 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 probabl y 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 be tter 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 inla nd 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 we ighted-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, s till the lowest of the four candidate hotdry states and below that of the DOE test proce dure load profile. These temperatures are shown

graphically in Figure 7C.4.2, along with s imilar figures calculated for other locations in the U.S

using the same methodology. In addition, Tabl e 7C.15 shows the fraction of the cooling load

calculated for binned temperatures above 85°F.

7C-17

### Table 7C.15. Distribution of Cooling Load with in 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-181 0 C F R 4 3 0

P h o e n i x

T

u c so n

P r e s co t t

S a n D i e g o

F l a g s t a f f

L o s A n g e l e s

L o n g B e a c h

S a n F r a n c i s co

S a cr a m e n t o

S a n D i e g o

D

a g g e t t

F

r e s n o

B a ke r sf i e l d

A l b u q u e r q u e

E

l P a s o

M i d l a n d

T u cu m c a r i

G r a n d Ju n c t i o n

L a s V e g a s

R e n o

E l k o

W i n n e m u c c a

C h i ca g o

Y

u m a

O r l a n d o

A

t l a n t a

B a l t i m o r e

K a n sa s C i t y 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

0Temperature (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 ca ndidate 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, in cluding New Mexico and

California may indeed be considered “dry” as co mpared 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 pro cedures current value of 95oF, DOE did not propose

a modification of the design day value for th e 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

### 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 SEER HD DOE

PT.430 SEER HD

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-200.00 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 - 114Fraction 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-210.00 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 - 114Fraction 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 SEER HD



### 7C.5 Conclusion

The analysis presented provides a methodolog y 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 SEER HD 

### rating for the proposed four-state

Hot-Dry Region, with emphasis on preserving a we ighted-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 w ith that of the current test procedure, with the result that a

greater fraction of bin hours exist at lower temper ature bins in the hot dry region, but also at the

very high temperature bins above 95°F. Since th e 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 desc ribed by equation 4.1-2 of the current test

method. The significantly higher cooling load cal culated at the high temperature bins and the

higher bin hours in those bins results in gr eater emphasis on cooling performance at high

temperatures (above 95°F) and at low temperatur es (below 75°F compared to the current test

method.

7C-22

### 1. L.W. Nelson and J.R. Tobias. “Energy Savings in Residentia l Buildings,” 1974.

ASHRAE Journal . pp. 34–45. February.

2. R.R. McConnell, J.R. Tobias, and L.W. Ne lson. “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-E nergy 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-E nergy Information Administration. Residential Energy

Consumption Survey, 2001 Survey Methods . Washington, DC.

http://www.eia.doe.gov/emeu/r ecs/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

### APPENDIX 8A. USER INSTRUCTIONS FOR LIFE CYCLE COST SPREADSHEET

TABLE OF CONTENTS

8A.1 Introduction................................................................................................................... ... 8A-1

8A.2 User Instructions fo r LLC Spread sheet............................................................................ 8A-1

LIST OF TABLES

Table 8A.2.1 Description of Tabs in the Life-Cycle Cost Spreadsheet............................... 8A-1

8A-i

### APPENDIX 8A. USER INSTRUCTIONS FOR LIFE CYCLE COST SPREADSHEET

8A.1 Introduction

Appendix E contains instructions to examin e 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 Ener gy’s website at:

http://www1.eere.energy.gov/buildings/applian ce_standards/residen tial/central_ac_hp.html

.

8A.2 User Instructions for LLC Spreadsheet

The spreadsheet is called “CAC_LCC_Pre NOPR_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 equipmen t 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 repres ents 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 cons idered in the cost of coil-only

replacements.

Equip Price, Repair, Mntc This sheet estimates equipment price, tran sportation 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, 5 th ,25 th , 75 th , and 95 th percentiles) on key model

outputs (installed costs, annual energy use and operating costs, LCC and

8A-1payback 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 an d 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-2and 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) el ectricity 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 a ppropriate 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 R un Preferences from the Run menu. After each

simulation run, the user needs to select Re set (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 info rmation. 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

### 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 s ee 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 sel ection 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 savi ngs 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 inform ation 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

### 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 es timated 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

### APPENDIX 8B. TECHNICAL ASPECTS OF THE TARIFF-BASED APPROACH

8B.1 REVIEW OF DATASETS AND CALCULATION METHODS

The methodology used to calcula te electricity prices is depe ndent 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 2006 a 

### 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 estimated b 

### . Residential

average prices are likely to be slightly above re sidential 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 esp ecially true for peak-coincident end uses. The

only method that allows the direct estimation of ma rginal 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 increase s 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

### 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 vari ation 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 wi th the nine climate regions defined by the

8B-2

### National Climatic Data Center. (3) Since the onset of dere gulation, 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 ofcustomers 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 an alysis, regional variation is actually somewhat

coarser, consisting of nine census divisions pl us four large states as is used in RECS.

Electricity tariffs are typically classed as residential or non-reside ntial. Non-residential

may be further subdivided into general service, an d 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 si ze 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 we re 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 wh en 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 custom er 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 TA P 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

work 5,3 

### ). Given a standardized data format, it is relatively straightforward to develop bill

calculation tools. These tools take as input th e monthly electricity use data, consisting of

consumption and demand, by TOU period if n ecessary. 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 in formation 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 aver age prices can then be directly compared to

the EEI data. The results are presented gra phically in Figures 8B.2.1 and 8B.2.2, for bills

8B-3

### 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

A K AL A R AZ C A C O CT D C DE F L GA HI IA I D IL IN KS K Y LA MA MD ME MI MN MO MS MT N C N D NE N H NJ N M N V N Y OH OK OR PA R I SC S D TN TX UT VA V T W A WI W V 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 (J uly) calculated using TAP and EEI data

Winter Avg Prices for 750kwh

0 5 10 15 20 25 30

A K AL AR AZ CA CO C T DC D E FL G A H I

I

A ID I L I

N K

S K Y L

A MA MD M E MI MN MO M S M T NC N D NE NH NJ N M N V NY OH OK O R PA R I S

C S D TN TX U T V

A 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

### 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 Cond itioners 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 Cond itioners 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 Bo lduc, 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

### 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 fo r 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

### 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 realis tic picture of the real value of reducing peakcoincident electricity use. To investigate this issue, the Depa rtment 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 fo r residential customers. The Federal Energy Regulatory

Commission (FERC) 2008 staff report on demand res ponse provides a figure (Figure III-15) that

indicates approximately 1.2 million reside ntial 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 co nsumers. 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 o ffer TOU rates. These optional TOU tariffs are

used here to determine whether the difference in electricity prices fo r 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 simu lations 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 ar e 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

### 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 S EER are essentially zero, so only data for July

are presented. Table 8-C.2.1 shows, for each locat ion, 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 fo r 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 out put 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 represen ts one account, i.e. one building-utility pair. A

diagonal line is included in the pl ot 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

### 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 pric e (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 lowe r. 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 m oderate the impact of higher on-peak prices.

0 5 10 15 20 25 30 35 40

0 10 20 30 40

Default price cents/kWhTOU price cents/kWh 

### July y=x



### Figure 8C.2.2 Comparison of the effective marginal price for each account.

8C-3

### 0 5 10 15 20 25 30 35 40

1 2 3 4 5 6 7 8 9 10

Census DivisionPrice cents/kWh 

### July default July TOU



### Figure 8C.2.3 Effective marginal prices for all accounts, organized by census

division

8C-4

### REFERENCES

1 

### FERC . Assessment of Demand Response and Adv anced Metering. Staff Report, Federal

Energy Regulatory Commission , December 2008. Available at:

http://www.ferc.gov/legal/staff-repo rts/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

### 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

### 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 rate s, 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 summe r 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 hous ehold’s monthly baseline electricity consumption

is required. This appendix de scribes the methodology used to make this estimation.

8D.2 DEVELOPMENT OF ALLOCATION FACTORS

The approach uses the monthly househol d billing data available for the RECS 2001

survey year to develop monthly allocation factor s 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 fr action 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 a ll 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 we re interpolated onto calendar months by (1)

calculating the average daily electricity use dur ing each billing period and (2) defining a total

8D-1

### electricity use for the calendar m onth 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 ac cording 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 categor y, using the RECS “NWEIGHT” variable as the

weight. In some regions a category did not c ontain 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 we ighted 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 alloca tion factors are presented in Tables 8-D.2.2 (for central air

conditioners) and 8-D.2.3 (for heat pumps).

8D-2

### Table 8D.2.2 Monthly allocation factors by census di vision (CD), large state (LS), space

heating fuel type (EH) and water heat ing 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

### Table 8D.2.3 Monthly allocation factors by census di vision (CD), large state (LS), space

heating fuel type (EH) and water heat ing 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

### 8D-5

### 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 Residen ce Loan Interest Rates ..................................8E-4

Figure 8E.3.5 Distribution of Other Lines of Cr edit 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

### 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 fo r 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 desc ribes 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%

1 2.

4 %

1 3.

5 % 14.4% 16.7% 22.9%

Real Interest RateProbability



### Figure 8E.2.1 Distribution of New Home Mortgage Interest Rates

8E-1

### 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 us ing 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%

1 3.

7 % 14.7% 16.7%

1 7.

5 % 42.5%

Real Interest RateProbability



### Figure 8E.3.1 Distribution of Home Equity Loan Interest Rates

8E-20% 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%

1 0.

6 % 11.5% 12.6%

1 3.

6 %

1 4.

7 % 15.6% 16.4%

1 7.

6 % 18.5% 19.4% 20.5% 21.4% 22.5% 23.6%

2 4.

6 % 25.5% 26.3%

2 7.

3 %

Real Interest RateProbability



### 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%

1 2.

5 %

1 4.

7 % 16.5% 18.5% 20.5%

2 2.

3 % 24.6% 26.6% 28.5%

3 4.

1 % 43.6% 47.2%

Real Interest RateProbability



### Figure 8E.3.3 Distribution of Installment Loan Interest Rates

8E-30% 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%

1 2.

6 %

1 4.

7 % 15.7% 16.6% 17.9%

Real Interest RateProbability



### 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%

1 8.

5 %

19.1% 20.2%

2 1. 5 %

22.1% 23.5%

Real Interest RateProbability



### Figure 8E.3.5 Distribution of Other Lines of Credit Loan Interest

Rates

8E-4

### 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 ar e from Cost of Savings Index data covering

1984–2007. 5 

### The rates for stocks are the annual return s 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 (two

thirds 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%

197

7 1979 1981 198 3 1985 1987

1 989 199 1 1993

1 995 199 7 1999

2 001 200 3 2005

2 007

YearAnnual Rate of Return



### Figure 8E.4.1 Distribution of Annual Rate of Return on CD’s

8E-5-2% 0% 2% 4% 6% 8%

197

7 1979 1981 198 3 1985 1987

1 989 199 1 1993

1 995 199 7 1999

2 001 200 3 2005

2 007

YearAnnual Rate of Return



### Figure 8E.4.2 Distribution of Annual Rate of Return on Savings

Bonds

-2% 0% 2% 4% 6% 8%

197

7 1979 1981 198 3 1985 1987

1 989 199 1 1993

1 995 199 7 1999

2 001 200 3 2005

2 007

YearAnnual Rate of Return



### Figure 8E.4.3 Distribution of Annual Rate of Return on Corporate

AAA Bonds

8E-6-2% 0% 2% 4% 6% 8%1984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008

YearAnnual 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 197 9 1981 1983

1 985 1987 1989

1 991 199 3 1995

1 997 199 9 2001

2 003 200 5 2007

YearAnnual Rate of Return



### Figure 8E.4.5 Distribution of Annual Rate of Return on S&P 500-15 % -10 % -5 % 0% 5% 10 % 15 % 20 % 25 %

1 977

1 97

9

1 981

1 983 1985

1 987

1 989 1991

1 99

3

1 995 1997

1 99

9

2 001 2003

2 00

5

2 007

YearAnnual Rate of Return



### Figure 8E.4.6 Distribution of Annual Rate of Return on Mutual

Funds

8E-8

### 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 Releas e, 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 Releas e, Selected Interest Rates,

Historical Data, Instrument: State and lo cal 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 Releas e, Selected Interest Rates,

Historical Data, Instrument: Corporat e bonds/Moody's Seasoned AAA, Frequency:

Annual, Description: Moody's yield on seas oned corporate bonds - all industries, AAA,

2008. < http://www.federalreserve.g ov/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

### 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

### 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

### 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

### 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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### 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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### 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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.8. Regional Distributions of LCC Savings for Split-System Air

Conditioners (Blower-Coil): Re st of Country Region

Efficiency

Level SEER LCC Savings (2008$)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 Condition er Payback Period Results

Table 8F.9. Regional Distributions of Payback Period fo r Split-System Air

Conditioners (Coil- Only): National

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.10. Regional Distributions of Payback Period fo r Split-System Air

Conditioners (Coil-Only): Hot-Humid Region

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 fo r Split-System Air

Conditioners (Coil-Only): Hot-Dry Region

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 fo r Split-System Air

Conditioners (Coil-Only): Re st of Country Region

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.13. Regional Distributions of Payback Period fo r Split-System Air

Conditioners (Blower-Coil): National

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 fo r Split-System Air

Conditioners (Blower-Coil): Hot-Humid Region

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.15. Regional Distributions of Payback Period fo r Split-System Air

Conditioners (Blower-Coil): Hot-Dry Region

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 fo r Split-System Air

Conditioners (Blower-Coil): Re st of Country Region

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.17. Regional Distributions of L CC Savings for Split-System Heat Pumps:

National

Efficiency

Level SEER HSPF LCC Savings (2008$)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 L CC Savings for Split-System Heat Pumps:

Hot-Humid Region

Efficiency

Level SEER HSPF LCC Savings (2008$)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.19. Regional Distributions of L CC Savings for Split-System Heat Pumps:

Hot-Dry Region

Efficiency

Level SEER HSPF LCC Savings (2008$)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 L CC Savings for Split-System Heat Pumps:

Rest of Country Region

Efficiency

Level SEER HSPF LCC Savings (2008$)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### 8F.2.2 Split-System Heat Pu mp Payback Period

Table 8F.21. Regional Distributions of Payback Period for Split-System Heat

Pumps: National

Efficiency

Level SEER HSPF Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.23. Regional Distributions of Payback Period for Split-System Heat

Pumps: Hot-Dry Region

Efficiency

Level SEER HSPF Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### 8F.3 Single-Package Air Conditioners

8F.3.1 Single-Package Air Conditi oner LCC Savings Results

Table 8F.25. Regional Distributions of LCC Savings for Single-Package Air

Conditioners: National

Efficiency

Level SEER LCC Savings (2008$)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.28. Regional Distributions of LCC Savings for Single-Package Air

Conditioners: Rest of Country Region

Efficiency

Level SEER LCC Savings (2008$)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 Condition er Payback Period Results

Table 8F.29. Regional Distributions of P ayback Period for Single-Package Air

Conditioners: National

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.31. Regional Distributions of LCC Savings for Single-Package Air

Conditioners: Hot-Dry Region

Efficiency

Level SEER Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 Pu mp 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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.34. Regional Distributions of LCC Savings for Single-Package Heat

Pumps: Hot-Humid Region

Efficiency

Level SEER HSPF LCC Savings (2008$)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### 8F.4.2 Single-Package Heat Pump Payback Period Results

Table 8F.37. Regional Distributions of P ayback Period for Single-Package Heat

Pumps: National

Efficiency

Level SEER HSPF Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 P ayback Period for Single-Package Heat

Pumps: Hot-Humid Region

Efficiency

Level SEER HSPF Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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 P ayback Period for Single-Package Heat

Pumps: Hot-Dry Region

Efficiency

Level SEER HSPF Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### Table 8F.40. Regional Distributions of P ayback Period for Single-Package Heat

Pumps: Rest of Country Region

Efficiency

Level SEER HSPF Payback Period (Years)

Mean 5 th

Percentile 25 th

Percentile Median

(50 th

Percentile) 75th

Percentile 95 th

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

### 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 App liance Sales................................................................ 9A-2

Table 9A.3.1 Physical Household/Appliance Variables................................................... 9A-3

Table 9A.3.2 Economic Vari ables .................................................................................... 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, Housin g Starts and Replacements .................. 9A-4

Figure 9A.4.1 Trends in CAC Resi dual Shipments and Re lative Price ............................. 9A-8

Figure 9A.4.2 CAC Shipments vs Re lative Price .............................................................. 9A-8

9A-i

### APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR

APPLIANCES

9A.1 INTRODUCTION

This appendix summarizes DOE’s study of th e price elasticity of demand for central air

conditioners and heat pumps (CAC). This appe ndix begins with a review of the existing

economics literature describing the impact of econo mic 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 re sults 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 th is 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

### 9A.2.3 Appliance Efficiency and Discount Rates

Many studies estimate the impact of appliance efficiency on consumer appliance

purchases. Typically, this impact is summarize d 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 st udy concludes that consumers use a 20 percent

implicit discount rate when purchasing r oom air conditioners (Table 9-A.2.1). 6 

### A survey of

several studies of different appliances suggests that the consumer imp licit 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, In come and Efficiency on Automobile

and Appliance Sales

Durable Good Price

Elasticity Income

Elasticity Brand

Price

Elasticity Implicit

Discount

Rate Model Data

Years Time

Period

Automobiles 1 -1.07 3.08

- - Linear Regression, stock

adjustment - Short run

Automobiles 1 -0.36 1.02

- - Linear Regression, stock

adjustment - Long run

Clothes Dryers 2 -0.14 0.26

- - Cobb-Douglas, diffusion 1947-1961 Mixed

Room Air

Conditioners 2 -0.37 8 0.45

- - Cobb-Douglas, diffusion 1946-1962 Mixed

Dishwashers 2 -0.42 0.79

- - Cobb-Douglas, diffusion 1947-1968 Mixed

Refrigerators 3 -0.37

- - 39% Logit probability, survey

data 1997 Short run

Various 4

- - -1.76 9

- Multiplicative regression

- Mixed

Room Air

Conditioners 5

- - -1.72

- Non-linear diffusion 1949-1961 Short run

Clothes Dryers 5

- - -1.32

- Non-linear diffusion 1963-1970 Short run

Room Air

Conditioners 6 - - - 20% Qualitative choice, survey

data - -

Household

Appliances 7 - - - 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 applia nce studies referenced in this work.



### 9A-2

### 9A.3 VARIABLES DESCRIBING THE MARKET FOR CENTRAL AIR

CONDITIONERS AND HEAT PUMPS

In this section DOE evaluates variables th at 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 cent ral air conditioners and heat pumps. The most

important for explaining these sales trends ar e the annual number of ne w houses constructed

(housing starts) and the number of applian ces 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 hous ing saturation of 90% na tionally, 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 prin ciple, 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 shipment s trend is explained in part by housing starts,

which increased 21 percent and by replacemen ts, which rose by 212 percent over the period

(Table 9-A.3.1).

Table 9A.3.1 Physical Household/Ap pliance Variables

1983 2005 Change

Shipments 1 (millions) 1.75 5.92 238%

Housing Starts 2 (millions) 1.70 2.07 21%

Replacements 3 (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 re placements 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 st arts, and replacements between 1983 and 2005 (Figure 9-A.3.1).

This residual shipments trend, illu strated by the un-shaded portion of the Figure, rises over time

and exhibits significant volatility.

9A-31 9 8 3 1 9 8 4 1 9 8 5 1 9 8 6 1 9 8 7 1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 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 th e 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 make s 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 5,000,000Total Central Air Conditioner Shipments Total Shipments

Replacements

Ship to Housing Starts

1983 2005 Change

Price 1 (1999$) 3319 2567 -23%

Operating Cost 2 (1999$) 1031 607 -41%

Household Income 3 (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 electri city consumption., 37% implicit discount rate Source:

AHAM Fact Book.

3 Income: Mean Household income. Source: U.S. Census.



### 9A-4

### 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 da ta 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 describi ng broad trends apparent in the economic and physical household

data sets and then specifies a simple regr ession 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 shipme nts over the 1983-2005 period. Before specifying a

regression model, DOE defines two new variab les—residual shipments and relative price.

Residual shipments are defined as the di fference 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 pr ice, 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 conditione rs 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

= I ncome



### 9A-5

### where:

RP = Relative price,

PP = Appliance purchase price, and

PVOC = Present value of operating cost a



### 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 econom ic variables over the study peri od—income, price, and operating

cost—are responsible for observed growth in re sidual shipments and that other variables,

including changing consumer preferences and changes in the qua lity 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 th e 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 electri city prices as additional variables, but none of these

variables was found to be significantly correlated with the trend in residual shipments.



### 9A-6

### 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 prices c



### 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

R 2 0.93

Observations 24



### 9A.4.5 Analysis Critique



### As reported above, the regression m odel 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 pu rchased as a replacemen t requires an additional

unit to be shipped. Since this coefficient is si gnificantly less than 1, it suggests that the relative

price coefficient is picking up some of the infl uence 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 shipme nts 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 re lationship between relativ e 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 implic it 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 c0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000 2,000,000Residual Shipments

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140Relative 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 ShipmentsRelative Price

0.08

0.06

0.04

0.02

0



### Figure 9A.4.2 CAC Shipments vs Relative Price



### 9A-8

### 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 ac curate 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 ai r 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 sour ce (Hymans) shows that the price elasticity of

demand is significantly different over th e short run and long run for automobiles. 1 

### Because

DOE’s forecasts of shipments and national imp acts 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 determ ined 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 Elastici ty 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 1 st 

### 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

### 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 1 st 

### 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, su ch 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 econom ic 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 ba sed 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

### 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 w ith 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

### 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

### 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 Bu ilding Technologies website

at: http://www1.eere.energy.gov/buildings/applian ce_standards/residential/central_ac_hp.html

.

There is one NIA spreadsheet covering th e 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 st andard. The differences between the standards

and base cases can then be compared and th e 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 a nd 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 fo r (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 ba sed 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 re sults are presented separately for

10A-1

### 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 increm ental equipment (total installed) costs;

(4) discounted national operating co st 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 ener gy 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 provide d 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

### 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 (P AC) 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

### 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 (P HP) 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 annua l 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, c ontains stock accounting of the

equipment that calculates annua l 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 annua l 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 annua l 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 annua l 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

### Historical Shipments Contains data for annual histor ical 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 housin g 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 comme rcial 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 pr ojected saturation of CAC and HP

equipment in new residential housing by region.

New Saturation Comm Contains the historical and proj ected 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 me nu 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 me nu bar to change the size of the display

to make the summary table in the upper left por tion of the worksheet fit your monitor.

5. Click the “Select CSL’ button in the upper ri ght corner of the summary table to choose

which candidate standard level to analyze.

10A-5

### 6. The results are automatically updated and are reported in the summary table for each

product class.

10A-6

### 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

### 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 ba sis for the discussions with manufactures of

central air conditioners and heat pumps. The re sults 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 ge neral 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 cha nge for the industry as a whole as a

function of standards and why?

12A-1

### 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 w ould 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 dispr oportionately advance or ha rm the competitive positions

of some firms?

2.11 Could new standards result in disproporti onate economic or performance penalties

for particular consumer/user subgroups?

2.12 Beyond price and energy efficiency, coul d 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 regulati ons 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?

### 12A.3 RESIDENTIAL CENTRAL AIR CONDITIONERS AND HEAT PUMPS

PRELIMINARY ENGINEERING INTERVIEW GUIDE

The following section consists of the que stions 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

evaporator. Split-system Heat Pump

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 in 2

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?

### 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-d ry climate? A hot -humid climate?

1.9 How would these design modifications affe ct 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 th is relationship by providing incremental

installation, mainte nance, and/or re pair 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?

### 2 COMPONENT PARTS AND MATERIAL COSTS

2.1 For purchased parts, price quotations we re gathered from major suppliers at

different production volumes. For part s 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, Stampe d, 1-to-2, 0.375” $2.72Liquid Line Splitter, Stampe d, 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) $3.0360VA 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 assumpti ons for raw material prices for common

metals found in air conditioning units. Please a ssume 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 vari ety of injection-molding machines. Please

indicate what parts ar e typically made in-house vs. t hose that are made by outside

vendors.

In-House Parts: Sourced Parts:

### 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 approximate ly twice the raw material cost of a part

made in house. For your sourced parts, pl ease 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 manufact urers. Please comment on the following

factory parameter assumptions.

### 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 ($/ft 2 ) 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 en ergy 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 consump tion between split air-c onditioners matched

with furnaces and split heat pumps matched with air-handlers?

4.5 For your heat pumps, is the reversing valv e energized only when the thermostat is

set to “cool”?

### 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 pr ocedure (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 th e 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 min i-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 ch ange as a function of the compressor

modulation rate and the numb er 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 th at use variable-speed ICM motors?

• the effect that lowering the air volum e 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?

### 5.9 For your in-house lab testing, do you use th e 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-duc t thermopile or thermocouple grids,

remotely located RTDs, etc.)?

### APPENDIX 15A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXE CUTIVE 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-CO 2 

### Emissions ................................................................. 15A-12

15A.4.4 Equilibrium Climate Sensitivity............................................................ 15A-13

15A.4.5 Socio-Economic and Emissi ons 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 a nd 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-CO 2 

### ) Gases ......................................................................... 15A-42

15A.9.2 Extrapolating Emissions Pr ojections to 2300 ........................................ 15A-44

15A-i

### LIST OF TABLES

Table 15A.1.1 Social Cost of CO 2 

### , 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 Pr ojections from Select EMF-22

Reference Scen arios ................................................................................. 15A-17

Table 15A.5.1 Disaggregated Social Cost of CO 2 

### Values by Model, Socio-Economic

Trajectory, and Discount Ra te for 2010 (2007$) ..................................... 15A-27

Table 15A.5.2 Social Cost of CO 2 

### , 2010–2050 (2007$) .................................................. 15A-28

Table 15A.5.3 Changes in the Average Annual Gr owth Rates of SCC Estimates

between 2010 and 2050............................................................................ 15A-29

Table 15A.7.1 Probabilities of Various Tipping Poin ts 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 Per cent Discount Rate (2007$/ton CO 2 

### ) .... 15A-49

Table 15A.9.3 Global SCC Estimates at 3 Per cent Discount Rate (2007$/ton CO 2 

### ) ....... 15A-49

Table 15A.9.4 2010 Global SCC Estimates at 5 Pe rcent Discount Rate (2007$/ton

CO 2 

### ) .......................................................................................................... 15A-50

Table 15A.9.5 Additional Summary St atistics of 2010 Global SCC Estimates ............... 15A-51

15A-ii

### LIST OF FIGURES

Figure 15A.4.1 Annual Consumption Loss as a Frac tion 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 Ex trapolations Assume the

Population Growth Rate Changes Li nearly to Reach a Zero Growth

Rate by 2200) ........................................................................................ 15A-46

Figure 15A.9.3 World GDP, 2000-2300 (Post-2100 Extra polations Assume GDP Per

Capita Growth Declines Linearly , Reaching Zero in 2300) .................. 15A-46

Figure 15A.9.4 Global Fossil and Industrial CO 2 

### Emissions, 2000-2300 (Post-2100

Extrapolations Assume Growth Rate of CO 2 

### Intensity (CO 2 

### /GDP)

Over 2090–2100 is Maintained Through 2300) .................................... 15A-47

Figure 15A.9.5 Global Net Land Use CO 2 

### Emissions, 2000–2300 (Post-2100

Extrapolations Assume Emissions D ecline Linearly, Reaching Zero in

2200) ...................................................................................................... 15A-47

Figure 15A.9.6 Global Non-CO 2 

### Radiative Forcing, 2000–2300 (Post-2100

Extrapolations Assu me Constant Non-CO 2 

### Radiative Forcing after

2100) ...................................................................................................... 15A-48

Figure 15A.9.7 Global CO 2 

### Intensity (Fossil & Industrial CO 2 

### Emissions/GDP),

2000–2300 (Post-2100 Extrapolations Assume Eecline in CO 2 

### /GDP

Growth Rate Over 2090–2100 is Ma intained Through 2300) .............. 15A-48

Figure 15A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 

### ), by

Discount Rate* ...................................................................................... 15A-50

15A-iii

### APPENDIX 15A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXE CUTIVE ORDER 12866

Interagency Working Gr oup 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 a nd the benefits of the intended regulation and, recognizing that

some costs and benefits are difficult to qua ntify, propose or adopt a regulation only upon a

reasoned determination that the be nefits of the intended regulation justify its costs.” The purpose

of the “social cost of carbon” (S CC) estimates presented here is to allow agencies to incorporate

the social benefits of reducing carbon dioxide (CO 2 

### ) emissions into cost-benefit analyses of

regulatory actions that have small, or “mar ginal,” 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 ov er 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. Itis 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 in teragency process that developed these SCC

estimates. Technical experts from numerous agenci es met on a regular basis to consider public

comments, explore the technical literature in re levant 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 assump tions grounded in the existing scie ntific and econo mic literatures.

In this way, key uncertainties and model differenc es transparently and consistently inform the

range of SCC estimates used in the rulemaking process.

15A-1

### The interagency group selected four SCC valu es 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 95 th 

### percentile

SCC estimate across all thr ee models at a 3 percent discount rate , is included to represent higherthan-expected impacts from temperature change furt her out in the tails of the SCC distribution.

Table 15A.1.1 Social Cost of CO 2 

### , 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 se rvices. We report estimates of the social cost

of carbon in dollars per metric ton of CO 2 

### throughout this document. a



### When attempting to assess the incremental economic impacts of CO 2 

### emissions, the

analyst faces a number of serious challenges. A recent report from the National Academies of

Science (NRC 2009) points out that any assessm ent will suffer from uncertainty, speculation,

and lack of information about (1) future emissi ons 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 tran slation 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, economic s, and ethics and should be

viewed as provisional.

Despite the serious limits of both quantifica tion and monetization, SCC estimates can be

useful in estimating the social benefits of reducing CO 2 

### 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 th e 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 CO 2 emissions. Alternatively, one

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of

CO 2 and the mass of carbon is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12

= 3.67).

15A-2

### it possible for agencies to incorporate the social benefits from reducing CO 2 

### emissions into costbenefit analyses of regulatory actions that have small or “marginal” impacts on cumulative

global emissions. Most Federal re gulatory 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 ba seline emissions path, an approximation that is

reasonable for policies that have effects on emissions that are small relative to cumulative global

CO 2 

### emissions. For policies that have a large (non-marginal) impact on global cumulative

emissions, there is a separate que stion 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 ba sis to consider public comments, explore

the technical literature in releva nt 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 De partments 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, O ffice of Energy and Climate

Change, and Office of Science and Technology Polic y. 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 estima tes 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 soci o-economic and emissions scenarios at the 5, 3,

and 2.5 percent discount rates, resp ectively. The fourth value is included to represent the higherthan-expected impacts from temperature change furt her out in the tails of the SCC distribution.

For this purpose, we use the SCC value for the 95 th 

### 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 CO 2 

### in 2015 and $26 per ton of CO 2 

### in 2020. See

Appendix A for the full range of annual SCC estimates from 2010 to 2050.

It is important to emphasize that the in teragency process is committed to updating these

estimates as the science and economic understanding of clima te 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 substa ntially updated models beco me available, and to

continue to support research in this area. In th e meantime, we will continue to explore the issues

raised in this document and consider public co mments as part of the ongoing interagency

process.

15A-3

### 15A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY

ANALYSES

To date, economic analyses for Federal regula tions have used a wide range of values to

estimate the benefits associated with reducing CO 2 

### emissions. In the final model year 2011

CAFE rule, the Department of Transportation (DOT) used bot h a “domestic” SCC value of $2

per ton of CO 2 

### and a “global” SCC va lue of $33 per ton of CO 2 

### for 2007 emission reductions

(2007$), increasing both values at 2.4 percent per y ear. It also included a sensitivity analysis at

$80 per ton of CO 2 

### . A domestic SCC value is meant to reflect the value of damages in the United

States resulting from a unit change in CO 2 

### 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 CO 2 

### (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 CO 2 

### 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 revi sion. EPA’s global mean values

were $68 and $40 per ton CO 2 

### for discount rates of approxima tely 2 percent and 3 percent,

respectively (2006$ for 2007 emissions).

In 2009, an interagency process was initiate d to offer a preliminary assessment of how

best to quantify the benefits from reducing CO 2 

### 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 CO 2 

### 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 comprehe nsive 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 CO 2. 

### 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 appr oximately 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 fi nal rules and were offere d for public comment in

connection with proposed rules, including the joint EPA-DOT fuel economy and CO 2 

### tailpipe

emission proposed rules.

15A.4 APPROACH AND KEY ASSUMPTIONS

15A-4

### Since the release of the interim values, interagency group has reconvened on a regular

basis to generate improved SCC estimates. Specifically, th e group has considered public

comments and further explored the technical literature in relevant fields. This section details the

several choices and assumpti ons that underlie the resultin g 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 interage ncy 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 a bout the uncertainties

embedded in them and with a sincere pro mise to continue work to improve them.

15A.4.1 Integrated Assess ment 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 de tailed 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

### best attempts to synthesize the available scientific and economic research characterizing these

relationships.

The three IAMs translate emissions in to changes in atmospheric greenhouse

concentrations, atmospheric conc entrations 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) pa thways. These emissions are translated into

concentrations using the carbon cycle built into ea ch 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 approa ch to model how changes in emissions result

in changes in economic damages. In PAGE, for example, the consumpti on-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 temperat ure in each region. In FUND, damages in each

period also depend on the rate of temperatur e change from the prior period. In DICE,

temperature affects both consumption and investme nt. 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 assump tions 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 quant ifying 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: c limate sensitivity, soci o-economic and emissions trajectories, and

discount rates. A probability distri bution for climate sensitivity was sp ecified as an input into all

three models. In addition, the interagency group us ed a range of scenarios for the socio-economic

parameters and a range of values for the disc ount rate. All other mode l features were left

unchanged, relying on the model developers’ best estimates and judgments. In DICE, these

parameters are handled deterministically and re presented by fixed constants; in PAGE, most

parameters are represented by probability distri butions. 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 m odel based on a global pr oduction function with

an extra stock variable (atmospheric CO 2 

### concentrations). Emission reductions are treated as

analogous to investment in “natural capital.” By investing in natural capital today through

15A-6

### reductions in emissions—implying reduced consum ption—harmful effects of climate change can

be avoided and future consum ption thereby increased.

For purposes of estimating the SCC, CO 2 

### emissions are a function of global GDP and the

carbon intensity of economic output, with the latte r declining over time due to technological

progress. The DICE damage f unction links global average temp erature 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 mala ria and dengue fever, and pollu tion), 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 impact s is then added to the other market and nonmarket impacts mentioned above.

No structural components of the DICE mode l represent adaptation explicitly, though it is

included implicitly through the choice of studies us ed 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 imp act estimates assume

improvements in healthcare over time. In addition, the small impacts on forestry, water systems,

construction, fisheries, and out door recreation imply optimistic a nd costless adaptation in these

sectors (Nordhaus and Boyer, 2000; Warren et al ., 2006). Costs of resettleme nt due to sea level

rise are incorporated into damage estimates, but their magnitude is not clearly reported.

Mastrandrea’s (2009) review c oncludes that “in general, DI CE 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. Becaus e 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.

15A-7

### PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into

economic, non-economic, and catastrophic categories a nd calculates these impacts separately for

eight geographic regions. Damages in each regi on are expressed as a fraction of output, where

the fraction lost depends on the temperature ch ange in each region. Damages are expressed as

power functions of temperature change. The expone nts 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 catast rophic 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, th e rate at which the probability of experiencing

a discontinuity increases above the threshold, a nd 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 fo r developed countries and 0 °C for developing

countries for economic impacts, and 0 °C fo r all regions for non-economic impacts), but

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the

developed countries can ultimate ly 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: agricu lture, 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 he alth (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 sect ors, economic damages also depend on CO 2



### concentrations.

Tol (2009) discusses impacts not included in FU ND, 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 ec onomic development and political violence. With

regard to potentially catastrophic events, he not es, “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).

15A-8

### 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 popula tions 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 CO 2 

### fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by a llowing damages to be smaller if climate

change happens more slowly. The combined effect of CO 2 

### 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 cons umption-equivalent value of bot h market and non-market goods

(represented as a fraction of global GDP). We recognize that these representations are

incomplete and highly uncertai n. 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, s hort of launching our ow n 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 me an 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.

15A-9

### Figure 15A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due to an

Increase in Annual Global Temperatu re in the DICE, FUND, and PAGE

Models e



### 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 differe nt 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.

15A-10

### 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 5 th 

### percentile estimated by PAGE,

while the damages estimated by DICE are roughly equal to the 95 th 

### percentile estimated by

PAGE. This is significant because at higher disc ount 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 perc ent 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 pe rcent 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 adap tation, 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 Domest ic 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, cons ideration of both global and domestic values is

generally permissible; the relevant statutor y 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 fro m 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 cause d by GHG emissions. Second, climate change

presents a problem that the Unite d States alone cannot solve. Even if the United States were to

reduce its greenhouse gas emissions to zero, th at 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 Stat es 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 intera gency 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.

15A-11

### 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 take s 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 we ighting, 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 estima tes 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 countri es, the domestic benefit would

be proportional to the U.S. share of globa l 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 ra nge. 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-CO 2 

### Emissions

While CO 2 

### 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 thei r 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 CO 2 

### . 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 re ductions 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

.

15A-12

### 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 t hose caused by longer-lived gases. Impacts other

than temperature change also vary across gases in ways that are not captured by GWP. For

instance, CO 2 

### emissions, unlike methane and other greenhouse gases, contribute to ocean

acidification. Likewise, damages from methane emissi ons are not offset by the positive effect of

CO 2 

### fertilization. Thus, tran sforming gases into CO 2 

### -equivalents using GWP, and then

multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the

social costs of non-CO 2 

### gases.

In light of these limitations, and th e significant contributions of non-CO 2 

### emissions to

climate change, further research is required to link non-CO 2 

### emissions to economic impacts.

Such work would feed into efforts to deve lop a monetized value of reductions in non-CO 2 

### greenhouse gas emissions. As part of ongoing wo rk to further improve the SCC estimates, the

interagency group hopes to devel op methods to value these othe r greenhouse gases. The goal is

to develop these estimates by the time we issue revised SCC estimates for CO 2 

### 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 CO 2 

### concentration relative to pre-industrial levels

(or stabilization at a concentration of approx imately 550 parts per million (ppm)). Uncertainties

in this important parameter have received substantial attention in the peer-reviewed literature.

The most authoritative statement about equ ilibrium climate sensitivity appears in the

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of seve ral independent lines of evidence...including

observed climate change and th e strength of known feedbacks simulated in [g lobal climate

models], we conclude that the global mean equilibrium warming for doubling CO 2 

### , 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. Equilibri um climate sensitivit y 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 prox y data is generally

worse for those high values than for valu es 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 pr obability 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.

15A-13

### be consistent with the above statement: Roe a nd 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

5 t h Percentile 1.72 1.49 1.37 1.13

10 t h Percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90

Median (50 t h percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90 t h Percentile 5.86 5.14 4.93 4.69

95 t h Percentile 7.14 5.97 5.59 5.17



### Each distribution was calibrated by appl ying three constraints from the IPCC:

(1) a median equal to 3 °C, to reflect the judg ment of “a most likely value of about 3 °C;” k



### (2) two-thirds probability that the equilibrium climate sensitiv ity 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 th e climate system to increased greenhouse gas

concentrations (Roe and Baker 2007; Roe 2008). In contrast, the other th ree distributions are

mathematical functions that are arbitrarily chos en based on simplicity, c onvenience, and general

shape. The Roe and Baker distribution results fro m 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 distribu tion better reflects the IPCC judgment that

“values substantially higher than 4.5°C still ca nnot be excluded.” Although the IPCC made no

quantitative judgment, the 95 th 

### percentile of the calibrated Roe & Baker distribution (7.1 °C) is

much closer to the mean and the median (7.2 °C) of the 95 th 

### 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

95 th percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and

mode equal to 3 °C produced 95 th 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.

15A-14

### median (7.9 °C) of the nine truncated di stributions examined by the IPCC (Hegerl et al . 2006)

than are the 95 th 

### percentiles of the three other ca librated 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 certain ty 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 overlay s it on Figure 9.20 from the IPCC Fourth Assessment Report.

These functions are scaled to integrate to uni ty 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 interage ncy 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.

15A-15

### tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid

climate disruptions. For this r eason, we consider how to model several input pa rameters in

tandem: GDP, population, CO 2 

### emissions, and non-CO 2 

### 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 scenario s 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 us es 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 leve l 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) grow th in population, wealth, and emissions and are

associated with CO 2 

### (only) concentrations ranging from 612 to 889 ppm in 2100. One represents

an emissions pathway that achieves stabilization at 550 ppm CO 2 

### e ( i.e. , CO 2 

### -only concentrations

of 425–484 ppm or a radiative forcing of 3.7 W/m 2 

### ) in 2100, a lower-than-BAU trajectory. m 

### Out

of the 10 models included in the EMF-22 exerci se, 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 emi ssion trajectories from each of these four

models. For the 550 ppm CO 2 

### e scenario, we averaged th e GDP, population, and emission

trajectories implied by these same four models.

We explore how sensitive the SCC is to va rious assumptions about how the future will

evolve without prejudging what is likely to o ccur. 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.

15A-16

### Table 15A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference

Scenarios

Reference Fossil and Industrial CO 2 Emissions

GtCO 2 /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 CO 2 

### 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 populati on 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 assu me 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 signif icant 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 appr oach 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.

15A-17

### In addition to fossil and industrial CO 2 

### emissions, each EMF scenario provides

projections of methane, nitrous oxide, fl uorinated greenhouse gases, and net land use CO 2 

### 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 th e discount rate has a large influence on the current value of

future damages, there is no consensus about wh at rates to use in this context. Because CO 2 

### emissions are long-lived, subsequent damages occu r over many years. In calculating the SCC,

we first estimate the future damages to agri culture, human health, and other market and nonmarket sectors from an additional unit of CO 2 

### emitted in a particular year in terms of reduced

consumption (or consumption equi valents) 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 marg inal 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 c hoice 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 benef its using discount rates of 3 a nd 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) pe rspective based on obser vations 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 “b ecause of a lack of justification for choosing a

social welfare function that is any different than what decision makers [i ndividuals] actually use”

(Arrow et al . 1996).

One theoretical foundation for the cost-benefit an alyses 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 co mpensation is difficult to define in the

intergenerational context. On the other ha nd, societies provide compensation to future

15A-18

### generations through investments in human capita l and the resulting incr ease 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 gene rations, and many agree with this view.

Other concerns also motivate making adju stments to descriptive discount rates. In

particular, it has been noted that the preferen ces 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 ra te should be below market rates to correct for

market distortions and uncertaintie s or inefficiencies in intergenerational transfers of wealth,

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a

potentially controversial assu mption, as noted above; Arrow et al . 1996, Weitzman 1999).

Further, a legitimate concern about both desc riptive and prescriptive approaches is that

they tend to obscure important heterogeneity in the population. Th e utility function that underlies

the prescriptive approach assumes a representative agent with perfect foresight and no credit

constraints. This is an artificial rendering of th e real world that misses ma ny of the frictions that

characterize individuals’ lives a nd 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 reco gnition of its limitations, we find th is approach to be the most

defensible and transparent give n its consistency with the sta ndard contemporary theoretical

foundations of benefit-cost analys is and with the approach required by OMB’s existing guidance.

The logic of this framework also suggests that ma rket rates should be used for discounting future

consumption-equivalent damages. Regardless of the theoretic al 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 selec ting 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 consequen ces of selecting one disc ount 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 appr opriate choice for the social

15A-19

### discount rate. In the real world risk, taxes, and other market imperfections drive a wedge

between the risk-free rate of return on capital a nd the consumption rate of interest. Thus, the

literature recognizes two conceptual discount conc epts—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 inst ance, via higher prices for goods and services—

a lower discount rate of 3 percen t is appropriate to reflect how private individuals trade-off

current and future consumption.

The interagency group examined the econo mics literature and c oncluded that the

consumption rate of interest is the correct concep t 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 th e three integrated assessment models used for

estimating the SCC.

Individuals use a variety of savings instrume nts that vary with ri sk level, time horizon,

and tax characteristics. The standard analytic framework used to develop intuition about the

discount rate typically assumes a representativ e 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 th e 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 a ppropriate 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-e quivalent 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

15A-20

### rate from tax years 2003 through 2006 is around 27 percent). o 

### This calculation produces a real

interest rate of about 2.7 pe rcent, which is roughly cons istent with Circular A-4’s

recommendation to use 3 percent to repres ent 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 perc ent) 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 pr escriptive 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 ra nging 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.

15A-21

### 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 neut rality. 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-econo mists have argued for cons tant 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 dolla r in poorer countries compared to wealthier ones. Stern (2006)

applies this perspectiv e 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 us ed 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 pr escriptive approach can be used to justify

discount rates between roughly 1.4 and 3.1 percen t. 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.)

15A-22

### sensitivity. Weitzman (1998, 2001) showed theo retically 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-equivale nt) discount rate that declines over time.

Consequently, lower discount rates tend to do minate 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 uncer tainty remains an active area of research.

Newell and Pizer (2003) employ a model of how long-term interest rate s change over time to

forecast future discount rates. Their model inco rporates 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 Pize r (2003) and Panipoulou et al. (2004) attempt fo rmally to model

uncertainty in the discount rate, others argue fo r a declining scale of di scount rates applied over

time ( e.g. , Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach

uses a higher discount rate initia lly, but applies a graduated scal e of lower discount rates further

out in time. v 

### A key question that has emerged with rega rd 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 cert ainty-equivalent consta nt 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 consis tent with estimates pr ovided 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.

15A-23

### 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 riskle ss 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 in corporate the concern that interest rates are

highly uncertain over time. It represents the av erage certainty-equivalent rate using the meanreverting and random walk approaches from Newe ll and Pizer (2003) startin g 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 re sponds to certain judgments using the prescriptive

or normative approach and to ethical objections that have been raised abou t 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 clima te sensitivity parameter bounded between 0

and 10 with a median of 3 °C and a cumula tive 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 rate s of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilis tically, and because PAGE and

FUND incorporate uncertainty in other model pa rameters, 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 popula tion from the selected EMF-22 scenarios,

and the extrapolations based on th ese 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).

15A-24

### b. In FUND, damages in each period depe nd on both the level and the rate of

temperature change in that period.

c. In DICE, temperature affects both consumpti on and investment, so we first adjust the

EMF GDP paths as follows: Using the C obb-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 re sulting 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 st eps, while the time steps in PAGE vary.)

6. Discount the resulting path of marginal damage s 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 emissi ons 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 CO 2 

### (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO 2 

### in PAGE).

The steps above were repeated in each mode l for multiple future years to cover the time

horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three

IAMs, climate damages are cal culated 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 unde r 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 mo re detailed discussion of these assumptions is

included in the Appendix.)

This exercise produces 45 separate distribu tions of the SCC for a given year, the product

of 3 models, 3 discount rates, a nd 5 socioeconomic scenarios. This is clearly too many separate

distributions for consideration in a regulatory impact analysis.

15A-25

### To produce a range of plausible estimates that still reflects the uncertainty in the

estimation exercise, the distributi ons 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 di stributions are then used to de fine 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 ra te, 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 valu es for use in regulatory analyses. Three

values are based on the average SCC across mode ls 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 95 th 

### 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 re gulatory 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 illust rate the variability in the SCC across each of these input parameters. As

expected, higher discount rates consistently result in lower S CC 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 va lues to 2007 dollars for the year 2010 (assuming, as we did for

the interim process, that SCC grows at 3 per cent per year), FUND yields SCC estimates at or

near zero for a 5 percent discount rate and ar ound $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 genera te 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 fo r a 4 percent discount rate. Note that these

15A-26

### comparisons are only approximate since the litera ture generally relies on Ramsey discounting,

while we have assumed constant discount rates. y



### Table 15A.5.1 Disaggregated Social Cost of CO 2 

### 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, wh ereas 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.

15A-27450

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 va lues 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 r un) 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 CO 2 

### , 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 e missions 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 a ssumptions. 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.

15A-28

### 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 de termine 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, clima te 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 evolvi ng scientific, economic,

and ethical understandings. During the course of our modeling, it became a pparent that there are

several areas in partic ular need of additional explorati on and research. These caveats and

additional observations in the following section ar e 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 hetero geneous. In addition, the exact magnitude of these

impacts is uncertain because of the inherent co mplexity 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 assi gn 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 in to these models understandably lags behind the

most recent research. Our ability to quantif y 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 rema in non-monetized. (Ocean acidifi cation is one example of a

potentially large damage from CO 2 

### 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 CO 2 emissions will be

discounted at rates that differ from those used to develop the SCC estimates.

15A-29

### 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 ev ent far in the future dominate the effect of

the discount rate in a present value calculation an d 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 Daig neault (2009) confirmed the potential for

large catastrophe risk premiums but also show ed 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 cas es 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 devel oped to account for such risks in regulatory

analysis. (The next section discusses the scie ntific 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 extrapolat ed 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 extrem e 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 adapta tion in FUND, including widespread reliance on

air conditioning, so much so that the largest si ngle benefit category in FUND is the reduced

electricity costs from not havi ng 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 unde r the assumption that farmers will change land

use practices in response to climate change (M astrandrea 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 poten tial 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 what ever their next best use would have been in the absence

of climate change (so productivity/GDP would have been still higher).

15A-30

### Risk aversion . A key question unanswered during this interagency process is what to

assume about relative risk aversi on 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 95 th 

### 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 fo r regulatory policy. Even if individuals are not

risk-averse for such scenarios, it is possible th at 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] shoul d in general assume ‘risk neutrality’ in [their]

analysis.”

Nordhaus (2008) points to the need to expl ore 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 assumpti ons 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 underl ying 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 underestimate s of the SCC (Mastrandr ea 2009). In particular,

the models’ functional forms may not adequately capture: (1) pot entially 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 a nd increased consumption.

It is the hope of the interagency group that over time researchers a nd 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 Damage s 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 extra polated to the high temperatures found in the

15A-31

### upper end of the distribution. Recent science su ggests that there are a number of potential

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with

potentially severe social a nd economic consequences ( e.g. , Lenton et al. 2008, Kriegler et al.

2009). These tipping points include the disrupti on 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 Ov erturning Circulation,

strengthening of El Niño-Southern Oscilla tion, and the release of methane from melting

permafrost. Many of these tippi ng points are estimated to have thresholds between about 3 °C

and 5 °C (Lenton et al. 2008). Probabilities of se veral of these tipping points were assessed

through expert elicitatio n 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 in clude potentially catastrophic effects. DICE

assumes a small probability of ca tastrophic damages that increase s 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 in sight into the SCC if the world were to

experience catastrophic climate change. For instance, at the 95 th 

### percentile and a 3 percent

discount rate, the SCC estimated by PAGE ac ross the five socio- economic and emission

trajectories of $113 per ton of CO 2 

### 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 inst ance, PAGE places a probability near zero on a

catastrophe at 2.5 °C warming, while DICE assu mes 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

### 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 IA Ms. 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 environmen tal change with high economic impact.

15A.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional Interactions

The damage functions do not fully incorpor ate 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 incl uded in some of the models (FUND includes

the effects of migration from sea level rise). Th ese 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 scen arios, 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 syst ems. 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 increa sed consumption of non-climate

goods, a common assumption in many economic m odels. In the context of climate change,

however, it is possible that the damages to na tural 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 s carcer 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

15A-33

### 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 soci o-economic and emissions scenarios at the 5, 3,

and 2.5 percent discount rates, resp ectively. The fourth value is included to represent the higherthan-expected impacts from temperature change furt her out in the tails of the SCC distribution.

For this purpose, we use the SCC value for the 95 th 

### 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 CO 2 

### in 2015 and $26 per ton of CO 2 

### in 2020.

We noted a number of limitations to this analysis, including the incomplete way in which

the integrated assessment models capture cat astrophic 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 impact s 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-40

### 15A.9 APPENDIX

This Appendix provides additional technical info rmation about the non-CO 2 

### emission

projections used in the modeling and the method for extrapolating emissions forecasts through

2300, and shows the full distribution of 2010 SCC es timates by model and sc enario 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

### 15A.9.1 Other (non-CO 2 

### ) Gases

In addition to fossil and industrial CO 2 

### emissions, each EMF scenario provides

projections of methane (CH 4 

### ), nitrous oxide (N 2 

### O), fluorinated gases, and net land use CO 2 

### emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s

default radiative forcings (R F) due to other factors ( e.g. , aerosols and other gases). Specifically,

to obtain the RF associated with the non-CO 2 

### EMF emissions only, we calculated the RF

associated with the EMF atmospheric CO 2 

### 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-CO 2 

### 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 CH 4 

### , N 2 

### O, SF 6 

### , and the CO 2 

### emissions from land were replaced with the EMF values.

PAGE : PAGE models CO 2 

### , CH 4 

### , sulfur hexafluoride (SF 6 

### ), and aerosols and contains an

“excess forcing” vector that includes the RF for everything else. To include the EMF values, we

removed the default CH 4 

### and SF 6 

### factors, cc 

### decomposed the excess forcing vector, and

constructed a new excess forcing vector that includes the EMF RF for CH 4 

### , N 2 

### O, and fluorinated

gases, as well as the model default values fo r aerosols and other factors. Net land use CO 2



### emissions were added to the fossil and industrial CO 2 

### emissions pathway.

DICE : DICE presents the greatest challenge because all forcing due to factors other than

industrial CO 2 

### emissions is embedded in an exogenous non-CO 2 

### RF vector. To decompose this

exogenous forcing path into EMF non-CO 2 

### gases and other gases, we relied on the references in

DICE2007 to the Intergovernmental Panel on C limate Change’s (IPCC) Fourth Assessment

Report (AR4) and the discussion of aerosol fo recasts in the IPCC’s Third Assessment Report

(TAR) and in AR4, as explained below. In DICE 2007, Nordhaus assumes that exogenous forcing

from all non-CO 2 

### sources is -0.06 W/m 2 

### in 2005, as reported in AR4, and increases linearly to

0.3 W/m 2 

### in 2105, based on GISS projections, and th en stays constant after that time.

According to AR4, the RF in 2005 from CH 4 

### , N 2 

### 0, and halocarbons (approximately

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m 2 

### and RF

from total aerosols was -1.2 W/m 2 

### . Thus, the -.06 W/m 2 

### non-CO 2 

### forcing in DICE can be

decomposed into: 0.98 W/m 2 

### due to the EMF non-CO 2 

### gases, -1.2 W/m 2 

### due to aerosols, and the

remainder, 0.16 W/m 2 

### , due to other residual forcing.

bb Note EMF did not provide CO 2 concentrations for the IMAGE reference sc enario. Thus, for this scenario, we fed

the fossil, industrial and land CO 2 emissions into MAGICC (considered a “neutral arbiter” model, which is tuned to

emulate the major global climate models) and the resulting CO 2 concentrations were used. Note also that MERGE

assumes a neutral biosphere so net land CO 2 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 CH 4 emissions and the initial atmospheric CH 4 is set to zero to avoid double counting the

effect of past CH 4 emissions.

15A-42

### For subsequent years, we calculated the DICE default RF from aerosols and other nonCO 2 

### 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-CO 2 

### gases not included in the EMF-22 scenarios, the share

of non-aerosol RF matches the share implicit in the AR4 summary sta tistics 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 to tal RF from aerosols (including sulfates, black

carbon, and organic carbon) in 2100 vs. 2000 under th e A1B SRES emissions scenario. Since the

SRES marker scenarios were not updated for th e 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 re presentation 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 pa rt 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/m 2 

### ; forcing due to other non-CO 2 

### gases not included in the EMF scenarios declines from

0.160 to 0.153 W/m 2



### .

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

### .

Figure 15A.9.1 Sulphur Dioxide Emission Scenarios

Notes: Thick colored lines depict the four SRES mark er scenarios and black dashed lines show the median,

5 th and 95 th 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 SO 2 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 decomposin g 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 throug h 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 late r 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 CO 2 

### emissions are added to the fossil and

industrial CO 2 

### emissions pathway.

15A.9.2 Extrapolating Emissions Projections to 2300

To run each model through 2300 requi res assumptions about GDP, population,

greenhouse gas emissions, and radi ative forcing trajectories afte r 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 (CO 2 

### /GDP) growth rate over

2090-2100 is maintained from 2100 through 2300.

4. Net land use CO 2 

### emissions decline linearly, reaching zero in 2200.

15A-44

### 5. Non-CO 2 

### 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 extrapol ation 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 ava ilable 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 c onsistent 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 traject ories (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. , CO 2 

### per

dollar of GDP) through 2300 assumes that tec hnological improvements and innovations in the

areas of energy efficiency and other carbon re ducing 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 CO 2 

### 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-CO 2 

### 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 CO 2 

### emissions, net land CO 2 

### emissions, non-CO 2 

### radiative forcing, and CO 2 

### intensity (fossil and industrial CO 2 

### 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-455.5 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

ExtrapolationsGlobalPopulation[billion]

Year



### Figure 15A.9.2 Global Population, 2000–2300 (Post-2100 Extrapolations Assume the

Population Growth Rate Changes Linear ly 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 CO 2 e, full-participation, not-to-e xceed 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

YearWorldGDP[$trillion]



### Figure 15A.9.3 World GDP, 2000-2300 (Post-2100 Extrapolations Assume GDP Per

Capita Growth Declines Line arly, Reaching Zero in 2300)

Note: In the fifth scenario, 2000–2100 GDP is equal to the average of the GDP under the 550 ppm CO 2 e,

full-participation, not-to-exceed scenarios co nsidered by each of the four models.

15A-460 50 100 150 200

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil&IndustrialCO2Emissions[GtCO2/decade]

Year



### Figure 15A.9.4 Global Fossil and Industrial CO 2 

### Emissions, 2000-2300 (Post-2100

Extrapolations Assume Growth Rate of CO 2 

### Intensity (CO 2 

### /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 scenario s 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

ExtrapolationsNetlandCO2emissions[GtCO2/yr]

Year z



### Figure 15A.9.5 Global Net Land Use CO 2 

### Emissions, 2000–2300 (Post-2100

Extrapolations Assume Emissions D ecline 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 scenar ios considered by each of the four models.

hh MERGE assumes a neutral biosphere so net land CO 2 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-4715A-48 

### Figure 15A.9.6 Global Non-CO 2 

### Radiative Forcing, 2000–2300 (Post-2100 Extrapolations

Assume Constant Non-CO 2 

### 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 scenario s considered by each of the four models.



### Figure 15A.9.7 Global CO 2 

### Intensity (Fossil & Industrial CO 2 

### Emissions/GDP), 2000–

2300 (Post-2100 Extrapolations Assume Eecline in CO 2 

### /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 CO 2 e, full-participation, not-to-e xceed 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

ExtrapolationsnonCO2RF[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

ExtrapolationsFossil&IndustrialCO2intensity[GtCO2/trillion$]

Year

### Table 15A.9.2 Global SCC Estimates at 2.5 Per cent Discount Rate (2007$/ton CO 2 

### )

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 Per cent Discount Rate (2007$/ton CO 2 

### )

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

### Table 15A.9.4 2010 Global SCC Estimates at 5 Pe rcent Discount Rate (2007$/ton CO 2 

### )

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 16000‐25‐12114274053667992105118131144157170183196209222235248261274

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 CO 2 

### ), 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 1 st and 99 th percentiles to better show the data.

15A-50

### 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

15A-51