Path to ProsperityPublished by The Center for Prosperity        Volume  10,  Issue  6                                                                                                                                                                                                                                                                              September  27,  2012  The  Economic  Impact  of  Maine’s  Renewable    Portfolio  Standard    By  the  Maine  Heritage  Policy  Center  and    Beacon  Hill  Institute  for  Public  Policy  Research    The   state   of   Maine   is   a   pioneer   in   passing   Renewable   Portfolio   Standard   (RPS)   legislation.     First   implemented   in  1999,   the   law   required   that   30   percent   of   total   retail   electric   sales   in   the   state   come   from   renewable   sources.1   The  law   itself   did   not   actually   alter   the   state’s   mix   of   fuel   sources   used   for   electricity   production,   to   the   chagrin   of  proponents.  Maine  was  already  producing  large  quantities  of  energy  from  renewable  sources.  Maine’s  numerous  lakes   and   streams   enabled   the   production   of   economically   viable   hydroelectric   power,   and   its   forestry   industry  supplied  wood  waste  for  biomass  electricity  production.    In   June   2006,   then-­‐Governor   John   Baldacci   signed   legislation   to   counter   the   perception   that   the   RPS   law   lacked  environmental  benefits.  The  new  goal:  Increase  the  amount  of  new  renewable  energy  to  10  percent  by  2017,  with  annual   increases   of   one   percent   beginning   in   2008   until   the   goal   is   reached.2   Since   these   “Class   I   standards”  consider   only   small   generation   plants   reaching   service   after   September   2005,   the   law   will   affect   the   fuel   mix   of  Maine’s  power  industry.    The   Beacon   Hill   Institute   applied   its   STAMP®   (State   Tax   Analysis   Modeling   Program)   to   estimate   the   economic  effects  of  these  RPS  mandates.  The  U.S.  Energy  Information  Administration  (EIA),  a  division  of  the  Department  of  Energy,   provides   optimistic   estimates   of   renewable   electricity   costs   and   capacity   factors.   We   base   our   estimates   on  EIA  projections,  but  we  also  provide  three  estimates  of  the  cost  of  Maine’s  RPS  mandates  ─  low,  average  and  high  ─  using   different   cost   and   capacity   factor   estimates   for   electricity-­‐generating   technologies   from   the   academic  literature.  Our  major  findings  show:    •  •The  Maine  RPS  law  will  raise  the  cost  of  electricity  by  $145  million  for  the  state’s  consumers  in  2017,  within  a  low-­‐range  estimate  of  $120  million  and  a  high-­‐range  estimate  of  $175  million  Maine’s  electricity  prices  will  rise  by  8  percent  by  2017,  due  to  the  RPS  law.  The  increased  energy  prices  will  hurt  Maine’s  households  and  businesses  and,  in  turn,  inflict  significant  harm  on  the  state  economy.  In  2017,  the  RPS  will:    ••••Lower  employment  by  an  average  of  995  jobs,  within  a  range  of  820  jobs  and  1,165  jobs  Reduce  real  disposable  income  by  $85  million,  within  a  range  of  $70  million  and  $100  million  Decrease  investment  by  $11  million,  within  a  range  of  $9  million  and  $13  million  Increase  the  average  household  electricity  bill  by  $80  per  year;  commercial  businesses  by  an  average  of  $615  per  year;  and  industrial  businesses  by  an  average  of  $14,350  per  year.                                                Volume 10, Issue 5Path to ProsperitySeptember 27, 2012Introduction    Maine  has  two  different  sets  of  Renewable  Portfolio  Standard  (RPS)  laws.  The  first  went  into  effect  in  1999  and,  in  effect,  codified  the  existing  30  percent  of  retail  energy  derived  from  renewable  sources.  Maine’s  abundant  natural  resources  provided  ample  and  cost-­‐effective  resources  to  produce  renewable  electricity.3  Many  small  and  efficient  hydroelectric   plants   produced   low   cost   energy   at   the   same   time   electric   utilities   burned   wood   waste   and   other  biomass  byproducts.  The  30  percent  mandate  had  minor,  if  any,  effect  on  the  energy  market  in  Maine.    The  second,  more  recent  RPS  law,  commonly  referred  to  as  the  Class  I  standards,  does  not  mandate  a  share  of  total  production  for  renewables,  like  many  state  RPS  laws.  Instead,  the  law  mandates  that  from  2017  onward,  at  least  10  percent   of   total   retail   electricity   sales   must   be   generated   from   new   renewable   sources.4   The   law   requires   that  beginning   in   2008   at   least   one   percent   of   electricity   must   be   from   renewable   generation   plants   reaching   service  after  September  2005,  increasing  one  percent  each  year  until  2017.        Another  component  of  the  law  –  the  use  of  Generation  Information  Systems  certificates  (GIS)  –  could  help  defray  costs.  GISs  are  similar  to  Renewable  Energy  Credits  (REC),  which  account  for  production  of  renewable  energy  and  are  equivalent  to  one  kilowatt  hour  of  renewable  production.  RECs  are  tradable  commodities  that  are  certified  to  represent  a  unit  of  production  of  renewable  energy.  The  GISs  may  only  be  banked  for  one  year,  so  the  actual  cost  effect  will  be  minimal  in  subsequent  years  if  electric  utilities  fail  to  exceed  the  mandate  for  the  previous  year.    By   producing   more   renewable   energy   than   required   by   the   law,   energy   suppliers   could   bank   credits   to   reduce  future  requirements.  However,  the  Energy  Information  Administration  (EIA)  projections  made  prior  to  the  law  show  a  baseline  scenario  in  which  renewable  electricity  generations  will  fall  below  RPS  minimums.  Therefore,  it  is  unlikely  that  producers  will  supply  excess  renewable  energy  to  trigger  significant  banking.  All  renewable  energy  produced  will   go   toward   the   requirement   that   year,   not   banked   for   future   consumption.   For   this   reason,   we   assume   that   the  GIS  certificates  will  have  no  effect  on  overall  price  of  production.    Additionally,   the   law   implements   an   Alternative   Compliance   Payment   (ACP)   that   Utilities   can   pay   instead   of  producing  renewable  energy.  The  ACP  rate  grows  at  the  speed  of  inflation,  and  is  currently  set  at  $62.10  per  MWh.5  Historically  the  ACP  has  not  played  much  part  in  meeting  the  RPS  for  any  utilities.  The  amount  of  money  spent  on  ACPs   has   declined   from   $690,000   in   2008   to   $20,000,   or   0.3   percent   of   compliance   costs,   in   2010.6   To   calculate   the  true  cost  of  the  RPS  law,  we  assume  that  the  ACP  will  continue  to  play  an  insignificant  role.    Since  renewable  energy  generally  costs  more  than  conventional  energy,  many  have  voiced  concerns  about  higher  electric  rates.  A  wide  variety  of  cost  estimates  exists  for  renewable  electricity  sources.  The  EIA  provides  estimates  for  the  cost  of  conventional  and  renewable  electricity  generating  technologies.  However,  the  EIA’s  assumptions  are  optimistic  regarding  the  cost  and  capacity  of  renewable  electricity  generating  sources  to  produce  reliable  energy.    A  review  of  the  literature  shows  that  in  most  cases  the  EIA’s  projected  costs  can  be  found  at  the  low  end  of  the  range  of  estimates,  while  the  EIA’s  capacity  factor  for  wind  to  be  at  the  high  end  of  the  range.  The  EIA  does  not  take  into   account   the   actual   experience   of   existing   renewable   electricity   power   plants.   Therefore   we   provide   three  estimates   of   the   cost   of   Maine’s   RPS   mandate:   low,   average   and   high,   using   different   cost   and   capacity   factor  estimates  for  electricity-­‐generating  technologies  from  the  academic  literature.    Governments   enact   RPS   policies   because   most   sources   of   renewable   electricity   generation   are   less   efficient   and  thus   more   costly   than   conventional   sources   of   generation.   The   RPS   policy   forces   utilities   to   buy   electricity   from          Page  2    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012renewable   sources   and   thus   guarantees   a   market   for   them.   These   higher   costs   are   passed   on   to   electricity  consumers,  including  residential,  commercial  and  industrial  customers.    Increases  in  electricity  costs  are  known  to  have  a  profound  negative  effect  on  the  economy  –  not  unlike  taxes  –  as  prosperity  and  economic  growth  are  dependent  upon  access  to  reliable  and  affordable  energy.  Since  electricity  is  an  essential  commodity,  consumers  will  have  limited  opportunity  to  avoid  these  costs.  For  the  poorest  members  of  society,  these  energy  taxes  will  compete  directly  with  essential  purchases  in  the  household  budget,  such  as  food,  transportation  and  shelter.    The  Maine  Heritage  Policy  Center  and  The  Beacon  Hill  Institute  at  Suffolk  University  (BHI)  estimates  the  costs  of  this  RPS  law  and  its  impact  on  the  state’s  economy.  To  that  end,  BHI  applied  its  STAMP®   (State  Tax  Analysis  Modeling  Program)  to  estimate  the  economic  effects  of  the  state  RPS  mandate.7    Estimates  and  Results    We   estimate   of   the   effects   of   Maine’s   Class   I   RPS   mandate   using   low,   average   and   high   cost   scenarios   of   both  renewable  and  conventional  generation  technologies.  Each  estimate  represents  the  change  that  will  take  place  in  the   indicated   variable   against   the   counterfactual   assumption,   or   baseline,   that   the  Class   I   mandate   would   not   be   in  place.   The   Appendix   contains   details   of   our   methodology.   Table   1   displays   the   cost   estimates   and   economic   impact  of  the  current  RPS  mandate  in  2017,  compared  to  a  baseline.    Table  1:    The  Cost  of  the  RPS  Mandate  on  Maine  (2012  $)  Costs  Estimates  Total  Net  Cost  in  2017  ($  m)  Low  Average  High  Total  net  cost  2012-­‐2017  ($  m)  120    535  145  655  175  775  Electricity  Price  Increase  in  2020  (cents  per  kWh)  1.01  1.24  1.46  6.6  8.0  9.5        -­‐820  -­‐995  -­‐1,165  -­‐9  -­‐70  -­‐11  -­‐85  -­‐13  -­‐100  Percentage  Increase  (%)  Economic  Indicators  Total  Employment  (jobs)  Investment  ($  m)  Real  Disposable  Income  ($  m)    The  current  RPS  will  impose  costs  of  $145  million  in  2017,  within  a  range  of  $120  million  and  $175  million.  Over  the  entire  period  between  2012  and  2017,  the  RPS  will  cost  Maine  $655  million  within  a  range  of  $535  million  and  $775  million.  As  a  result,  the  RPS  mandate  would  increase  electricity  prices  by  1.24  cents  per  kilowatt  hour  (kWh)  or  by  8  percent,  within  a  range  of  1.01  cents  per  kWh,  or  by  6.6  percent,  and  1.46  cents  per  kWh,  or  by  9.5  percent.8    The  STAMP  model  simulation  indicates  that,  upon  full  implementation,  the  electricity  price  increases  due  to  the  RPS  law   will   negatively   affect   the   Maine   economy.   The   state’s   ratepayers   will   face   higher   electricity   prices   that   will  increase   their   costs,   which   will   in   turn   put   downward   pressure   on   household   and   business   income.   By   2017   the  Maine  economy  will  shed  995  jobs,  within  a  range  of  estimates  of  820  and  1,165  jobs.            Page  3    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012The  job  losses  and  price  increases  will  reduce  real  incomes  as  firms,  households  and  governments  spend  more  of  their   budgets   on   electricity   and   less   on   other   items,   such   as   home   goods   and   services.   In   2017,   real   disposable  income   will   fall   by   an   average   of   $85   million,   between   $70   million   and   $100   million   under   the   low   and   high   cost  scenarios   respectively.   Furthermore,   net   investment   will   fall   by   $11   million,   within   a   range   of   $9   million   and   $13  million.  Table  2  shows  how  the  RPS  mandate  affects  the  annual  electricity  bills  of  households  and  businesses  in  Maine.  In  2017,   the   RPS   will   cost   families   an   average   of   $85   per   year;   commercial   businesses   $615   per   year;   and   industrial  businesses   $14,350   per   year.   Between   2012   and   2017,   the   average   residential   consumer   can   expect   to   pay   $365  more  for  electricity,  while  a  commercial  ratepayer  would  pay  $2,715  more  and  the  typical  industrial  user  would  pay  $63,305  more.    Table  2:    Annual  Effects  of  RPS  on  Electricity  Ratepayers  (2012  $)      Cost  in  2017  Residential  Ratepayer  ($)  Commercial  Ratepayer  ($)  Industrial  Ratepayer  ($)  Total  over  period  (2012-­‐2017)  Residential  Ratepayer  ($)  Commercial  Ratepayer  ($)  Industrial  Ratepayer  ($)  Low  Medium      High      70  505  11,745    300  2,220  51,765  85  615  14,350    365  2,715  63,305      100  725  16,955    430  3,205  74,845    Emissions:  Life  Cycle  Analysis    One  could  justify  the  higher  electricity  costs  if  the  environmental  benefits  –  in  terms  of  reduced    greenhouse  gases  (GHG)  and  other  emissions  –  outweighed  the  costs.  In  the  previous  sections  we  calculated  and  displayed  the  costs  and  economic  effects  to  require  more  renewable  energy  in  the  state  of  Maine.  The  following  section  conducts  a  Life  Cycle  Analysis  (LCA)  of  renewable  energy  and  the  total  effect  that  the  state  Class  I  RPS  law  is  likely  to  have  on  Maine’s  emissions.    The  burning  of  fossil  fuels  to  generate  electricity  produces  emission  of  gases  as  waste,  such  as  carbon  dioxide  (CO2),  sulfur  oxides  (SOx)  and  nitrogen  oxides  (NOx).  These  gases  are  found  to  negatively  affect  human  respiratory  health  and  the  environment  (SOx  and  NOx)  or  said  to  contribute  to  global  warming  (NOx  andCO2).    Many  proponents  of  renewable  energy,  such  as  wind  power,  solar  power  and  municipal  solid  waste  (MSW)  justify  the  higher  electricity  prices,  and  the  negative  economic  effects  that  follow,  based  on  the  claim  that  these  sources  produce  no  emissions  (see  examples  below).  But  this  is  misleading.  The  fuel  that  powers  these  services  -­‐-­‐  such  as  the  sun  and  wind  –  create  no  emissions.  However,  the  process  of  construction,  operation  and  decommissioning  of  renewable   power   plants   does   create   emissions.   This   begs   the   question:   Is   this   renewable   energy   production   as  environmentally  friendly  as  some  proponents  claim?        “Harnessing   the   wind   is   one   of   the   cleanest,   most   sustainable   ways   to   generate   electricity.   Wind  power   produces   no   toxic   emissions   and   none   of   the   heat   trapping   emissions   that   contribute   to  global  warming.”9          Page  4    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012  “Wind  turbines  harness  air  currents  and  convert  them  to  emissions-­‐free  power.”10  ~Union  of  Concerned  Scientists    “As  far  as  pollution…Zip,  Zilch,  Nada…  etc.  Carbon  dioxide  pollution  isn’t  in  the  vocabulary  of  solar  energy.  No  emissions,  greenhouse  gases,  etc.”11  ~Let’s  Be  Grid  Free.    Solar  Energy  Facts    The  affirmative  argument  is  usually  based  on  the  environmental  effects  of  the  operational  phase  of  the  renewable  source   (that   will   produce   electricity   with   no   consumption   of   fossil   fuel   and   no   emissions)   excluding   the   whole  manufacturing   phase   (from   the   extraction   to   the   erection   of   the   turbine   or   solar   panel,   including   the   production  processes  and  all  the  transportation  needs)  and  the  decommission  phase.  LCA  provides  a  framework  to  provide  a  more  complete  answer  the  question.    LCA  is  a  “cradle-­‐to-­‐grave”  approach  for  assessing  industrial  systems.  LCA  begins  with  the  gathering  of  raw  materials  from   the   earth   to   create   the   product   and   ends   at   the   point   when   all   materials   are   returned   to   the   earth.   By  including  the  impacts  throughout  the  product  life  cycle,  LCA  provides  a  comprehensive  view  of  the  environmental  aspects   of   the   product   or   process   and   a   more   accurate   picture   of   the   true   environmental   trade-­‐offs   in   product   and  process  selection.  Table  3  displays  LCA  results  for  conventional  and  renewable  sources.      Table  3:  Emissions  by  Source  of  Electricity  Generation  (Grams/kWh)  Phase  Construction  and  Decommission  Production  and  Operation  Total  Emission  CO2  NOx  SOx  CO2  NOx  SOx  CO2  SOx  NOx  Coal  2.59    0.01    0.06    1,022.00    3.35    6.70    1,024.59    3.36    6.76    Gas  2.20    0.01    0.05    437.80    0.56    0.27    440.00    0.57    0.32    Wind  Nuclear  6.84    2.65    0.06    0.00    0.02    0.00    0.39    1.84    0.00    0.00    0.00    0.01    7.23    4.49    0.06    0.01    0.02    0.01    Solar  Biomass  31.14    0.61    0.12    0.00    0.14    0.00    0.27    58.60    0.02    5.34    0.00    2.40    31.42    59.21    0.14    5.34    0.14    2.40      Coal  and  gas  produce  significantly  more  emissions  of  all  three  gases  than  all  the  other  technologies.  Nuclear  and  wind  produce  the  least  emissions  of  the  nonconventional  types,  with  solar  and  biomass  significantly  higher  due  to  construction   and   decommission   for   solar   and   production   and   operations   for   biomass.   However,   the   construction  and  decommission  phases  of  wind  and  solar  produce  non  trivial  levels  of  emissions,  with  solar  several  factors  higher  than  the  others.  Nevertheless,  LCA  analysis  shows  that  wind,  nuclear,  solar  and  biomass  produce  significantly  less  emissions  than  coal  and  gas.                      However,   this   LCA   analysis   is   incomplete.   The   analysis   shows   that   wind   and   solar  technologies   derive   benefits   from  their   ability   to   produce   electricity   with   no   consumption   of   fossil   fuels   and   subsequent   pollution   without   adequately  addressing   the   intermittency   of   these   technologies.   These   intermittent   technologies   cannot   be   dispatched   at   will  and,  as  a  result,  require  reliable  back-­‐up  generation  running  –  idling  –  in  order  to  keep  the  voltage  of  the  electricity  grid   in   equilibrium.   For   example   if   the   wind   ceases,   or   blows   too   hard   (which   trips   a   shutdown   mechanism   in          Page  5    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012commercial   windmills),   another   power   source   must   be   ramped   up   (or   cycled)   instantaneously.   Therefore   new   wind  and  solar  generation  plants  do  not  replace  any  dispatchable  generation  sources.    This   cycling   of   coal   and   (to   a   much   lesser   extent)   gas   plants   causes   them   to   run   inefficiently   and   produce   more  emissions  than  if  the  intermittent  technologies  were  not  present.  As  a  result  –  according  to  a  recent  study  –  wind  power  could  actually  increase  pollution  and  greenhouse  gas  emissions  in  areas  that  generate  a  significant  portion  of  their  electricity  from  coal.12  The  current  LCA  literature  ignores  this  important  portion  of  the  analysis,  which  provides  a  distorted  assessment  of  wind  and  solar  power.    Nevertheless,  even  incorporating  renewable  sources  does,  in  and  of  themselves,  produce  much  less  emissions  than  conventional  sources  renewable  sources,  displacing  only  a  small  amount  of  emissions  from  conventional  sources.  Indeed  this  amount  is  multiplied,  due  to  lower  capacity  ratings  of  many  green  energy  sources  and  required  back-­‐up  generation.    To  better  judge  the  actual  total  benefit  derived  from  switching  from  the  current  energy  source  portfolio  to  one  that  involves   more   renewable   energy   –   as   the   RPS   dictates   in   Maine   –   BHI   compared   the   total   emissions   impact  according  to  our  projections  using  a  life  cycle  analysis  for  the  various  energy  sources.  Table  4  displays  the  results.      Table  4:  Change  in  Emissions  Due  to  the  Maine  RPS  Mandates    (‘000  metric  tons)  Emission  Gas  2017  Total  2012-­‐2017  No  Capacity  Factor  Differences      Carbon  Dioxide      -­‐487  -­‐2,174  Sulfur  Oxide    4  18  Nitrogen  Oxide    2  7  Capacity  Factor  Differences      Carbon  Dioxide      -­‐163  -­‐728  Sulfur  Oxide    5  20  Nitrogen  Oxide    2  9    The  results  are  somewhat  counterintuitive.  The  RPS  mandates  reduce  emissions  of  CO2  by  163,000  metric  tons  in  2017,  with  a  total  reduction  compared  to  baseline  of  728,000  tons  between  2012  and  2017.  If  no  back  up  capacity  was  required  due  to  the  intermittency  issues  of  renewables,  then  the  reduction  would  be  more  than  three  times  as  much.  Surprisingly,  SOx  and  NOx  emissions  show  a  slight  increase  compared  to  a  baseline  in  all  years.  The  reason  for  this   is   that   biomass   and   wood   waste   –   two   large   sources   of   renewable   energy   in   Maine  –   emit   large   amounts   of  these  two  types  of  particulate  matter.    Conclusion    Proponents  of  renewable  energy  in  Maine  were  disappointed  with  the  outcome  of  the  first  RPS  laws  in  Maine.  In  effect   it   made   legal   requirements   and   consequences   for   what   was   already   taking   place   in   Maine.   Where   it   was   cost  efficient,  renewable  energy  was  growing  in  Maine.  But  that  was  not  enough  for  renewable  energy  advocates.  In  this          Page  6    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012paper  we  reviewed  the  implications  of  a  new  RPS  law  that  began  in  2008.  This  version,  commonly  referred  to  as  Class  I  requirements,  required  that  10  percent  of  energy  come  from  new  renewable  sources  by  2017.    The  most  recent  Maine  Public  Utilities  Commission  review  of  the  RPS  states:      “Assuming   half   of   the   wind   generation   proposed   in   the   Interconnection   Queue   for   Maine   is  developed   over   time   (625   MW   installed   capacity)   at   a   total   investment   cost   of   more   than  $2,000/KW  at  that  and  that  35  percent  of  the  capital  costs  are  spent  in  Maine  this  could  result  in  approximately   $560   million   of   investment   in   Maine.   This   level   of   investment   will   result   in   a   roughly  ($1.14  billion)  increase  in  GSP  and  11,700  jobs  created  during  construction.”13    This   thinking   –   that   the   higher   the   cost   of   renewable   technologies   rise,   the   more   investment   and   jobs   the  technologies   create   –   is   dangerous.   For   example,   if   investment   cost   rose   to   $4,000   per   KW,   then   the   resulting  investment  would  rise  to  $1.12  billion  and  state  GSP  would  rise  by  some  derivative  of  $2.28  billion  and  job  creation  by   23,400.   But   what   would   that   increase   in   investment   cost   mean   for   the   price   of   wind   energy   that   Maine’s  households   and   business   are   mandated   to   purchase?   The   price   would   rise   and   hurt   the   state’s   electricity  consumers.  Moreover,  the  investment  spending  has  an  opportunity  cost  in  terms  of  the  industries  that  might  have  received  this  investment  in  the  absence  of  the  RPS  mandates.    Supporters  of  the  Maine  RPS  use  a  hidden  tax  approach,  with  the  quote  above  showing  they  fail  to  undertake  any  reasonable   cost-­‐benefit   analysis   backed   up   by   economic   reasoning.   The   Maine   RPS   puts   the   state’s   robust  competitiveness  at  risk.  While  the  RPS  may  generate  economic  benefits,  Maine  electricity  ratepayers  will  pay  higher  rates,   face   fewer   employment   opportunities,   and   watch   investment   flee   to   other   states   with   more   favorable  business  climates,  resulting  in  net  negative  effects  on  the  state.    Firms   with   high   electricity   usage   will   likely   move   their   production,   and   emissions,   out   of   Maine   to   locations   with  lower   electricity   prices.   Therefore,   the   Maine   policy   will   not   reduce   global   emissions,   but   rather   send   jobs   and  capital  investment  outside  the  state.    Appendix    Electricity  Generation  Costs    As   noted   above,   governments   enact   RPS   policies   because   most   sources   of   renewable   electricity   generation   are   less  efficient   and   thus   more   costly   than   conventional   sources   of   generation.   RPS   policies   force   utilities   to   buy   electricity  from  renewable  sources  and  thus  guarantee  a  market  for  the  renewable  sources.  These  higher  costs  are  passed  to  electricity  consumers,  including  residential,  commercial  and  industrial  customers.    The  EIA  estimates  the  Levelized  Energy  Cost  (LEC),  or  financial  breakeven  cost  per  MWh,  to  produce  new  electricity  in   its   Annual   Energy   Outlook.14   The   EIA   provides   LEC   estimates   for   conventional   and   renewable   electricity  technologies   (coal,   nuclear   geothermal,   landfill   gas,   solar   photovoltaic,   wind   and   biomass)   assuming   the   new  sources   enter   service   in   2016.   The   EIA   also   provides   LEC   estimates   for   conventional   coal,   combined   cycle   gas,  advanced  nuclear  and  onshore  wind  only,  assuming  the  sources  enter  service  in  2020  and  2035.    While   the   EIA   does   not   provide   LEC   for   hydroelectric,   solar   photovoltaic   and   biomass   for   2020   and   2035,   it   does  project  overnight  capital  costs  for  2015,  2025  and  2035.  We  can  estimate  the  LEC  for  these  technologies  and  years          Page  7    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012using  the  percent  change  in  capital  costs  to  inflate  the  2016  LECs.  In  its  Annual  Energy  Outlook,  the  EIA  incorporates  many   assumptions   about   the   future   price   of   capital,   materials,   fossil   fuels,   maintenance   and   capacity   factor   into  their   forecast.   Table   5   shows   the   EIA   projects   that   the   LEC   for   all   four   electricity   sources   (coal,   gas,   nuclear   and  wind)  will  fall  significantly  from  2016  to  2035.  The  fall  in  capital  costs  drives  the  drop  in  total  system  LEC  over  the  period.    Using  the  EIA  change  in  overnight  capital  costs  for  solar  and  biomass  produces  reductions  in  LECs  similar  to  wind  from   2016   to   2035.   The   biomass   LEC   drops   by   38.7   percent   and   solar   by   53.5   percent   over   the   period.   These  compare  to  much  more  modest  cost  reductions  of  5.2  percent  for  coal,  an  increase  of  14.2  percent  for  gas,  and  a  drop   of   22.1   percent   for   nuclear   over   the   same   period.   EIA   does   provide   overnight   capital   costs   for   renewable  technologies   under   a   “high   cost”   scenario.   However,   for   each   renewable   technology   the   EIA   “high   cost”   scenario  projects  capital  costs  to  drop  between  2015  and  2035.    Table   5   displays   capacity   factors   for   each   technology.   The   capacity   factors   measure   the   ratio   of   electrical   energy  produced   by   a   generating   unit   over   a   period   of   time   to   the   electrical   energy   that   could   have   been   produced   at   100  percent  operation  during  the  same  period.  In  this  case,  capacity  factor  measures  the  potential  productivity  of  the  generating   technology.   Solar,   wind   and   hydroelectricity   have   the   lowest   capacity   factors   due   to   the   intermittent  nature   of   their   power   sources.   EIA   projects   a   34.4   percent   capacity   factor   for   wind   power,   which,   as   we   will   see  below,  appears  to  be  at  the  high  end  of  any  range  of  estimates  for  the  nation.    Estimating  a  capacity  factor  for  wind  power  is  particularly  challenging.  Wind  is  not  only  intermittent  but  its  variation  is  unpredictable,  making  it  impossible  to  dispatch  to  the  grid  with  any  certainty.  This  unique  aspect  of  wind  power  argues  for  a  capacity  factor  rating  of  close  to  zero.  Nevertheless,  wind  capacity  factors  have  been  estimated  to  be  between  20  percent  and  40  percent.15  The  other  variables  that  affect  the  capacity  factor  of  wind  are  the  quality  and  consistency  of  the  wind  and  the  size  and  technology  of  the  wind  turbines  deployed.  As  the  U.S.  and  other  countries  add  more  wind  power  over  time,  presumably  the  wind  turbine  technology  will  improve,  but  the  new  locations  for  power  plants  will  likely  have  less  productive  wind  resources.    The   EIA   estimates   of   LEC   and   capacity   factors   paint   a   particularly   rosy   view   of   the   future   cost   of   renewable  electricity  generation,  particularly  wind.  Other  forecasters  and  the  experience  of  current  renewable  energy  projects  portray  a  less  sanguine  outlook.    Today   wind   and   biomass   are   the   largest   renewable   power   sources   and   are   the   most   likely   to   satisfy   future   RPS  mandates.   The   most   prominent   issues   that   will   affect   the   future   availability   and   cost   of   renewable   electricity  resources  are  diminishing  marginal  returns  and  competition  for  scarce  resources.  These  issues  will  affect  wind  and  biomass  in  different  ways  as  state  RPS  mandates  ratchet  up  over  the  next  decade.    Both  wind  and  biomass  resources  face  land  use  issues.  Conventional  energy  plants  can  be  built  within  a  space  of  several  acres,  but  a  wind  power  plant  with  the  same  nameplate  capacity  (not  actual  capacity)  would  require  many  square  miles  of  land.  According  to  one  study,  wind  power  would  require  7,579  miles  of  mountain  ridgeline  to  satisfy  current   state   RPS   mandates   and   a   20   percent   federal   mandate   by   2025.16   Mountain   ridgelines   produce   the   most  promising  locations  for  electric  wind  production  in  the  eastern  and  far  western  United  States.    After  taking  into  account  capacity  factors,  a  wind  power  plant  would  need  a  land  mass  of  20  by  25  kilometers  to  produce  the  same  energy  as  a  nuclear  power  plant  that  can  be  situated  on  500  square  meters.17                            Page  8    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012Table  5:  Levelized  Cost  of  Electricity  from  Conventional  and  Renewable  Sources    (2009  $)  Plant  Type  Advanced  Coal  -­‐  2016            2020            2035  Gas  -­‐  2016            2020            2035  Nuclear  -­‐2016            2020            2035  Wind  -­‐  2016            2020            2035  Solar  PV  -­‐  2016            2025            2035  Biomass  -­‐2016            2025            2035  Hydro  -­‐2016            2025            2035  Capacity  Factor  0.85        0.87        0.9        .344        0.217        0.83        0.514    Levelized  Capital  Costs  65.3  75.84  55.4  17.5  18.4  13.5  90.1  89.1  62.3  83.9  86.4  71.4  194.6        55.3        74.5    Fixed  O&M  3.9  7.9  7.9  1.9  1.89  1.89  11.1  11.1  11.1  9.6  9.5  9.9  12.1        13.7        3.8    Variable  O&M    (with  fuel)  24.3  25.1  25.4  45.6  46.7  59.0  11.7  12.3  14.3  0  0  0  0        42.3        6.3    Transmission  Investment  1.2  1.2  1.19  1.2  1.2  1.2  1  1  1  3.5  3.4  3.6  4        1.3        1.9    Total    Levelized  Cost  94.8  110.0  89.8  66.1  68.2  75.5  113.9  113.5  88.7  97.0  99.2  84.9  210.7  142.0  98.0  112.5  88.0  69.0  86.4  69.0  55.0                        The   need   for   large   areas   of   land   to   site   wind   power   plants   will   require   the   purchase   of   vast   areas   of   land   by   private  wind   developers,   and/or   allowing   wind   production   on   public   lands.   In   either   case   land   acquisition/rent   or   public  permitting   processes   will   likely   increase   costs   as   wind   power   plants   are   built.   Offshore   wind   is   vastly   more  expensive  than  onshore  wind  power  and  suffers  from  the  same  type  of  permitting  process  faced  by  onshore  wind  power   plants,   as   seen   in   the   10-­‐year   permitting   process   for   the   planned   Cape   Wind   project   off   the   coast   of  Massachusetts.    The  swift  expansion  of  wind  power  will  also  suffer  from  diminishing  marginal  returns  as  new  wind  capacity  will  be  located  in  areas  with  lower  and  less  consistent  wind  speeds.  As  a  result,  fewer  megawatt  hours  of  power  will  be  produced  from  newly  built  wind  projects.  Moreover  the  new  wind  capacity  will  be  developed  in  increasingly  remote  areas  that  will  require  larger  investments  in  transmission  and  distribution,  which  will  drive  costs  even  higher.    The  EIA  estimates  of  the  average  capacity  factor  used  for  onshore  wind  power  plants,  at  34.4  percent,  appears  to  be  at  the  higher  end  of  the  estimates  for  current  wind  projects.  This  figure  is  inconsistent  with  estimates  from  other  studies.18  According  to  the  EIA’s  own  reporting  from  137  current  wind  power  plants  in  2003,  the  average  capacity  factor  was  26.9  percent.19  In  addition,  a  recent  analysis  of  wind  capacity  factors  around  the  world  finds  an  actual          Page  9    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012average  capacity  factor  of  21  percent.20  Moreover,  other  estimates  find  capacity  factors  in  the  mid-­‐teens  and  as  low  as  13  percent.21    Biomass  is  a  more  promising  renewable  power  source.  Biomass  combines  low  incremental  costs  relative  to  other  renewable  technologies  and  reliability.  Biomass  is  not  intermittent  and  therefore  it  is  distributable  with  a  capacity  factor  that  is  competitive  with  conventional  energy  sources.  Moreover  biomass  plants  can  be  located  close  to  urban  areas  with  high  electricity  demand.    But  biomass  electricity  suffers  from  land  use  issues  even  more  so  than  wind.    The   expansion   of   biomass   power   plants   will   require   huge   additional   sources   of   fuel.   Wood   and   wood   waste  comprise   the   largest   source   of   biomass   energy   today.   Other   sources   of   biomass   include   food   crops,   grassy   and  woody   plants,   residues   from   agriculture   or   forestry,   oil-­‐rich   algae,   and   the   organic   component   of   municipal   and  industrial  wastes.22  Biomass  power  plants  will  compete  directly  with  other  sectors  (construction,  paper,  furniture)  of  the  economy  for  wood  and  food  products  and  arable  land.    One  study  estimates  that  66  million  acres  of  land  would  be  required  to  provide  enough  fuel  to  satisfy  the  current  state   RPS   mandates   and   a   20   percent   federal   RPS   in   2025.23   When   the   clearing   of   new   farm   and   forestlands   are  figured  into  the  GHG  production  of  biomass,  it  is  likely  that  biomass  increases  GHG  emissions.    The   competition   for   farm   and   forestry   resources   would   not   only   cause   biomass   fuel   prices   to   skyrocket,   but   also  cause   the   prices   of   domestically-­‐produced   food,   lumber,   furniture   and   other   products   to   rise.   The   recent  experience  of  ethanol  and  its  role  in  surging  corn  prices  can  be  casually  linked  to  the  recent  food  riots  in  Mexico,  and   also   to   the   struggle   facing   international   aid   organizations   that   address   hunger   in   places   such   as   the   Darfur  region   of   Sudan.   These   two   examples   serve   as   reminders   of   the   unintended   consequences   of   government  mandates   for   biofuels.   The   lesson   is   clear:   biofuels   compete   with   food   production   and   other   basic   products,   and  distort  the  market.    Calculation  of  the  Net  Cost  of  New  Renewable  Electricity    To   calculate   the   cost   of   renewable   energy   under   the   RPS,   BHI   used   data   from   the   EIA   to   determine   the   percent  increase  in  utility  costs  that  Maine  residents  and  businesses  would  experience.  This  calculated  percent  change  was  then  applied  to  calculated  elasticities,  as  described  in  the  STAMP  modeling  section.    In  our  cost  analysis  we  only  reviewed  the  costs  for  the  Class  I  standards.  Class  II  standards,  we  assumed,  would  have  little  or  no  cost  due  to  the  base  line  scenario  already  covering  the  requirements.  To  determine  that  cost  of  the  Class  I  standards,  we  used  EIA  projections  to  determine  the  total  retail  sales  into  the  future.  Since  the  Class  I  standards  require  new  renewable  energy,  we  assumed  that  these  are  generation  sources  that  would  not  have  been  created  in  a  baseline  scenario.  So  we  multiplied  the  requirement  percentage  by  the  baseline  scenario,  and  the  resulting  figure  was  the  amount  of  MWhs  that  the  state  needs  to  add  to  meet  the  RPS  requirements.  This  figure  also  represents  the  maximum  number  of  MWhs  of  electricity  from  conventional  sources  that  are  avoided,  or  not  generated,  through  the  RPS  mandate.  We  will  revisit  this  shortly.  Table  6,  as  follows,  contains  the  results.            Page  10    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012Table  6:  Projected  Electricity  Demand  and  RPS  Requirements  Year      Projected  Electricity  Demand  RPS  Requirement  MWhs  (000s)  MWhs  (000s)  2012  11,626                                581  2013  11,679                                700    2014  11,735                                821    2015  11,794                                944    2016  11,857                          1,067    2017  11,923                          1,192    Total              70,614                            5,306    To   estimate   the   cost   of   producing   the   additional   extra   renewable   energy   under   an   RPS   against   the   baseline,   we  used  estimates  of  the  LEC,  or  financial  breakeven  cost  per  MWh,  to  produce  the  electricity.24  However  as  outlined  in  the  “electricity  generation  cost”  section  above,  the  EIA  numbers  provide  a  rather  optimistic  picture  of  the  cost  and   generating   capacity   of   renewable   electricity,   particularly   for   wind   power.   A   literature   review   provided  alternative  LEC  estimates  that  were  generally  higher  and  capacity  factors  that  were  lower  for  renewable  generation  technologies  than  the  EIA  estimates.25  We  used  these  alternative  figures  to  calculate  our  “high”  LEC  estimates  and  the   EIA   figures   to   calculate   our   “low”   cost   estimates   and   the   average   of   the   two   to   calculate   our   “average”   cost  estimates.  Table  7  below  displays  the  LEC  and  capacity  factors  for  each  generation  technology.    We  used  the  2016  LEC  for  the  years  2010  through  2018  to  calculate  the  cost  of  the  new  renewable  electricity  and  avoided   conventional   electricity,   assuming   that   before   2016   LEC   underestimates   the   actual   costs   for   those   years  and   for   2017   and   2018,   the   2016   LEC   slightly   overestimates   the   actual   costs.   We   assumed   that   the   differences   will,  on   balance,   offset   each   other.   For   2019   and   2020   we   used   the   2020   LEC.   The   assumption   is   that   LEC   will   decline  over  time  due  to  technological  improvements  over  time.      We  used  the  EIA’s  reference  case  scenario  for  all  technologies.  Since  capital  costs  represent  the  large  component  of  the  cost  structure  for  most  technologies,  we  used  the  percentage  change  in  the  capital  costs  from  2015  to  2025  to  adjust  the  2016  LECs  to  2025.  For  the  technologies  that  the  EIA  does  not  forecast  LECs  in  2020,  we  used  the  average  of  the  2016  and  2025  LEC  calculations,  assuming  a  linear  change  over  the  period.    Once   we   computed   new   LECs   for   the   years   2020   and   2025   we   applied   these   figures   to   the   renewable   energy  estimates  for  the  remainder  of  the  period.    For  conventional  electricity  we  assumed  that  the  technologies  are  avoided  based  on  their  costs,  with  the  highest  cost   combustion   turbine   avoided   first.   For   coal   and   gas,   we   assumed   they   are   avoided   based   on   their   estimated  proportion  of  total  electric  sales  for  each  year.  Although  hydroelectric  and  nuclear  are  not  the  cheapest  technology,            Page  11    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012Table  7:  LEC  and  Capacity  Factors  for  Electricity  Generation  Technologies    Capacity      Factor  Total  Production  Cost  (cents/MWh)      (percent)  2016  2020  2025    Coal            Low  74.0            67.41                64.82                63.53      Average  79.5            81.11                87.43              81.72      High  85.0            94.80          110.03                99.91    Gas            Low  85.0            66.10                68.17              71.84    Average  86.0            70.98                70.71              72.54    High  87.0            75.86                73.25                73.25      Nuclear            Low  90.0            76.94                59.20                49.33      Average  90.0            95.42                86.36              75.22    High  90.0        113.90            113.52          101.12    Biomass            Low  68.0        112.50            100.07              87.63      Average  75.5        112.50            101.80              93.00    High  83.0        113.90          103.54                98.36      Wind            Low  34.4        97.00  99.22  92.04    Average  15.5  192.34  184.38  171.72    High  26.9        287.67          269.54          251.40      we   assumed   no   hydroelectric   or   nuclear   sources   are   displaced   since   most   were   built   decades   ago   and   offer  relatively  cheap  and  clean  electricity  today.    We  also  adjusted  the  avoided  cost  of  conventional  energy  to  account  for  the  lower  capacity  factor  of  wind  relative  to   conventional   energy   sources.   We   multiplied   the   cost   of   each   conventional   energy   source   by   the   difference  between   its   capacity   factor   and   the   capacity   factor   for   the   renewable   source   and   then   by   the   ratio   of   the   new  generation   of   the   renewable   source   to   the   total   new   generation   of   renewable   under   the   RES.   With   coal,   for  example,   we   multiplied   the   avoided   amount   generation   of   electricity   from   coal   (3.41   million   MWhs   in   2020)   by   the  LEC   of   coal   ($85.21   per   MWh)   and   then   by   the   difference   between   the   capacity   factor   of   coal   and   the   weighted  average   (using   MWs   as   weights)   capacity   factor   of   wind   (37.4   percent).     This   process   is   repeated   for   each  conventional  electricity  resource.    These  LECs  are  applied  to  the  amount  of  electricity  supplied  from  renewable  sources  under  the  RES,  because  this  figure   represents   the   amount   of   conventional   electricity   generation   capacity   that   presumably   will   not   be   needed  under  the  RES.  The  difference  between  the  cost  of  the  new  renewable  sources  and  the  costs  of  the  conventional  electricity  generation  Maine  represents  the  net  cost  of  the  RPS.  Tables  8,  9  and  10  on  the  following  pages  display  the  results  of  our  Average,  Low  and  High  Cost  calculations  for  the  RPS,  respectively.    We  converted  the  aggregate  cost  of  the  RPS  into  a  cost  per-­‐kWh  by  dividing  the  cost  by  the  estimated  total  number  of  kWh  sold  for  that  year.  For  example,  for  2017  under  the  average  cost  scenario  above,  we  divided  $147  million  into  11,923  million  kWhs  for  a  cost  of  1.24  cents  per  kWh.          Page  12    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012Table  8:  Average  Cost  Case  RPS  Mandate  from  2012  to  2017  Year        Gross  Cost  Less  Conventional  Total  (2012  $000s)  (2012  $000s)  (2012  $000s)  2012                  75,775                              3,957                    71,818    2013                  91,816                              4,933                    86,883    2014              107,612                              5,768                101,844    2015              122,954                              6,398                116,555    2016              139,273                              7,309                131,964    2017              155,614                              8,163                147,451      Total              693,044                          36,529                656,515    Table  9:  Low  Cost  Case  RPS  Mandate  from  2012  to  2017  Year      Gross  Cost  Less  Conventional  Total  (2012  $000s)  (2012  $000s)  (2012  $000s)  2012                  62,650                              3,781                    58,870    2013                  75,436                              4,708                    70,728    2014                  88,434                              5,500                    82,934    2015              101,693                              6,101                    95,592    2016              114,978                              6,969                108,009    2017              128,471                              7,782                120,689      Total              571,663                          34,841                  536,822    Table  10:  High  Cost  Case  of  a  RPS  Mandate  from  2012  to  2017  Year      Gross  Cost  Less  Conventional  Total  (2012  $000s)  (2012  $000s)  (2012  $000s)  2012                  88,899                              4,135                    84,765    2013              108,196                              5,160                103,036    2014              126,790                              6,036                120,753    2015              144,215                              6,697                137,518    2016              163,568                              7,650                155,918    2017              182,758                              8,545                174,213      Total              814,426                          38,222                776,204                Page  13    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012Ratepayer  Effects    To   calculate   the   effect   of   the   RPS   on   electricity   ratepayers   we   used   EIA   data   on   the   average   monthly   electricity  consumption   by   type   of   customer:   residential,  commercial   and   industrial.26   The   monthly   figures   were   multiplied   by  12  to  compute  an  annual  figure.  We  inflated  the  2010  figures  for  each  year  using  the  average  annual  increase  in  electricity  sales  over  the  entire  period.27    We  calculated  an  annual  per-­‐kWh  increase  in  electricity  cost  by  dividing  the  total  cost  increase  –  calculated  in  the  section  above  ─  by  the  total  electricity  sales  for  each  year.  We  multiplied  the  per-­‐kWh  increase  in  electricity  costs  by   the   annual   kWh   consumption   for   each   type   of   ratepayer   for   each   year.   For   example,   we   expect   the   average  residential  ratepayer  to  consume  6,691  kWhs  of  electricity  in  2017  and  we  expect  the  average  cost  scenario  to  raise  electricity   costs   by   1.24   cents   per   kWh   in   the   same   year.   Therefore   we   expect   residential   ratepayers   to   pay   an  additional  $83  in  2020.    Modeling  the  RPS  using  STAMP    We   simulated   these   changes   in   the   STAMP   model   as   a   percentage   price   increase   on   electricity   to   measure   the  dynamic   effects   on   the   state   economy.   The   model   provides   estimates   of   the   proposals’   impact   on   employment,  wages  and  income.  Each  estimate  represents  the  change  that  would  take  place  in  the  indicated  variable  against  a  “baseline”  assumption  of  the  value  that  variable  for  a  specified  year  in  the  absence  of  the  RPS  policy.    Because  the  RPS  requires  Maine  households  and  firms  to  use  more  expensive  “green”  power  than  they  otherwise  would  have  under  a  baseline  scenario,  the  cost  of  goods  and  services  will  increase  under  the  RES.  These  costs  would  typically  manifest  through  higher  utility  bills  for  all  sectors  of  the  economy.  For  this  reason  we  selected  the  sales  tax  as   the   most   fitting   way   to   assess   the   impact   of   the   RES.   Standard   economic   theory   shows   that   a   price   increase   of   a  good  or  service  leads  to  a  decrease  in  overall  consumption,  and  consequently  a  decrease  in  the  production  of  that  good   or   service.   As   producer   output   falls,   the   decrease   in   production   results   in   a   lower   demand   for   capital   and  labor.    BHI   utilized   its   STAMP   (State   Tax   Analysis   Modeling   Program)   model   to   identify   the   economic   effects   and   to  understand  how  they  operate  through  a  state’s  economy.  STAMP  is  a  five-­‐year  dynamic  CGE  (computable  general  equilibrium)  model  that  has  been  programmed  to  simulate  changes  in  taxes,  costs  (general  and  sector-­‐specific)  and  other   economic   inputs.   As   such,   it   provides   a   mathematical   description   of   the   economic   relationships   among  producers,   households,   governments   and   the   rest   of   the   world.   It   is   general   in   the   sense   that   it   takes   all   the  important   markets,   such   as   the   capital   and   labor   markets,   and   flows   into   account.   It   is   an   equilibrium   model  because   it   assumes   that   demand   equals   supply   in   every   market   (goods   and   services,   labor   and   capital).   This  equilibrium  is  achieved  by  allowing  prices  to  adjust   within   the   model.   It   is   computable   because   it   can   be   used   to  generate  numeric  solutions  to  concrete  policy  and  tax  changes.28    In  order  to  estimate  the  economic  effects  of  a  national  RPS  we  used  a  compilation  of  six  STAMP  models  to  garner  the   average   effects   across   various   state   economies:   New   York,   North   Carolina,   Washington,   Kansas,   Indiana   and  Pennsylvania.   These   models   represent   a   wide   variety   in   terms   of   geographic   dispersion   (northeast,   southeast,  midwest,   the   plains   and   west),   economic   structure   (industrial,   high-­‐tech,   service   and   agricultural),   and   electricity  sector  makeup.    First  we  computed  the  percentage  change  to  electricity  prices  as  a  result  of  three  different  possible  RPS  policies.          Page  14    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012We   used   data   from   the   EIA   from   the   state   electricity   profiles,   which   contains   historical   data   from   1990-­‐2008   for  retail   sales   by   sector   (residential,   commercial,   industrial,   and   transportation)   in   dollars   and   MWhs   and   average  prices   paid   by   each   sector.29       We   inflated   the   sales   data   (dollars   and   MWhs)   though   2020   using   the   historical  growth  rates  for  each  sector  for  each  year.  We  then  calculated  a  price  for  each  sector  by  dividing  the  dollar  value  of  the  retails  sales  by  kWhs.  Then  we  calculated  a  weighted  average  kWh  price  for  all  sectors  using  MWhs  of  electricity  sales   for   each   sector   as   weights.   To   calculate   the   percentage   electricity   price   increase   we   divided   our   estimated  price   increase   by   the   weighted   average   price   for   each   year.   For   example,   in   2017   for   our   average   cost   case   we  divided   our   average   price   of   15.36   cents   per   kWh   by   our   estimated   price   increase   of   1.24   cents   per   kWh   for   a   price  increase  of  8.2  percent.    Table  11:  Elasticities  for  the  Economic  Variables  Economic  Variable  Employment  Gross  wage  rates  Investment    Disposable  Income    Elasticity  -­‐0.022  -­‐0.063  -­‐0.018  -­‐0.022    Using   these   three   different   utility   price   increases   –   1   percent,   4.5   percent   and   5.25   percent   –   we   simulated   each   of  the   six   STAMP   models   to   determine   what   outcome   these   utility   price   increases   would   have   on   each   of   the   six  states’   economy.   We   then   averaged   the   percent   changes   together   to   determine   what   the   average   effect   of   the  three   utility   increases.   Table   11   displays   these   elasticities,   which   were   then   applied   to   the   calculated   percent  change  in  electricity  costs  for  the  state  of  Maine  discussed  above.    We  applied  the  elasticities  to  percentage  increase  in  electricity  price  and  then  applied  the  result  to  Maine  economic  variables  to  determine  the  effect  of  the  RPS.  These  variables  were  gathered  from  the  Bureau  of  Economic  Analysis  Regional  and  National  Economic  Accounts  as  well  as  the  Bureau  of  Labor  Statistics  Current  Employment  Statistics.30    Life  Cycle  Analysis    For   our   LCA   we   used   various   studies   to   determine   what   the   cradle   to   grave   emissions   per   MWh   was,   taking   into  account  construction,  decommission,  operation  and  maintenance.    For   coal   we   reviewed   three   different   system   types:   An   ‘average   system’   that   accounts   for   emissions   from   typical  coal   fired   generation   in   1995;   New   Source   Performance   Standards   based   on   requirements   put   into   effect   for   all  plants   built   after   1978;   and   Low   Emission   Boiler   Systems,   which   are   newer,   more   efficient   coal   plants.31   The   LCA  calculations  account  for  various  inputs  including,  but  not  limited  to,  mining,  transportation  of  minerals,  power  plant  operation  as  well  as  decommissions  and  disposal  of  a  plant.  Natural  gas  plants’  LCAs  were  based  on  the  LCA  for  Gas  Combined  Cycle  Power  Generation  plants,  a  type  of  plant  that  is  similar  to  the  majority  of  the  natural  gas  plants  in  the  United  States.32    The   LCA   for   wind   power   accounted   for   both   onshore   and   offshore   wind   power,   which   has   different   values   for  manufacturing,  dismantling,  operation  and  transportation  for  each  type.33  Solar  photovoltaic  estimates  were  wide  ranging,  but  a  Science  Direct  paper  supplied  an  in-­‐depth,  comprehensive  review.34  It  reviewed  three  different  types  of   crystalline   silicone   modules   as   well   as   a   CdTe   thin   film   version   and   induced   many   different   costs   such   as  emissions   from   building   the   module   and   frame   (for   the   crystalline   silicone   version)   as   well   as   operation   and          Page  15    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012maintenance  emissions.  For  biomass  and  wood  waste  LCA  we  used  a  report  that  looked  at  the  production  of  energy  using   wood   and   biomass   byproducts   to   produce   energy.35   Different   types   of   delivery   systems   (lorry,   train   and  barge)  for  the  fuel  were  identified,  as  well  as  construction,  operation  and  decommissioning.    With  total  emissions  per  MWh  calculated,  we  were  able  to  use  our  in-­‐house  model  to  calculate  the  total  emissions  that  would  be  added  to  and  removed  from  the  Maine  energy  system.  The  first  calculation  used  the  amount  of  renewable  energy  added  per  the  Class  I  RPS  law,  as  well  as  the  amount  of  conventional  power  that  would  be  removed,  after  accounting  for  capacity  factor  requirements  to  keep  a  constant  amount  of  energy  produced.  Each  MWh  added  was  multiplied  by  its  respective  LCA  emission,  and  then  we  subtracted  the  amount  of  conventional  time  LCA  emissions.  With  a  basic  conversion  from  grams  to  metric  tons,  we  had  calculated  the  results  seen  in  Table  5.  An  identical  calculation  was  done,  but  not  accounting  for  capacity  factors.                                                                          Page  16    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012Notes  and  Sources                                                                                                                          1  Maine  Revised  Statutes.    Title  35-­‐A  Part  3,  Chapter  32.    Internet,  available  at  http://www.mainelegislature.org/legis/statutes/35A/title35-­‐Asec3210.html.    2  CMR  64-­‐407-­‐331.    Internet,  available  at  http://www.maine.gov/sos/cec/rules/65/407/407c311.doc.    3  Maine  Revised  Statutes.    Title  35-­‐A  Part  3,  Chapter  32.    Internet,  available  at    http://www.mainelegislature.org/legis/statutes/35-­‐A/title35-­‐Asec3210.html.    4  CMR  64-­‐407-­‐331.    Internet,  available  at  http://www.maine.gov/sos/cec/rules/65/407/407c311.doc.    5  MPUC  RPS  Report  2011  –  Review  of  RPS  Requirements  and  Compliance  in  Maine.    Internet,  available  at  http://www.maine.gov/tools/whatsnew/attach.php?id=349454&an=1.    6  Ibid.  p16    7®    Detailed  information  about  the  STAMP model  can  at    http://www.beaconhill.org/STAMP_Web_Brochure/STAMP_HowSTAMPworks.html.    8  Based  on  a  projected  price  of  15.36  cents  per  kWh  for  2017  from  the  U.S.  Department  of  Energy,  Energy  Information  Agency,  Annual  Energy  Outlook  2011,  Table  8.  Retail  Sales,  Revenue,  and  average  Retail  Price  by  Sector,  1990  through  2010.  http://www.eia.gov/electricity/state/maine/.    Projections  into  the  future  based  historical  trends.    9  How  Wind  Energy  Works.    Union  of  Concerned  Scientists.    http://www.ucsusa.org/clean_energy/our-­‐energy-­‐choices/renewable-­‐energy/how-­‐wind-­‐energy-­‐works.html.      10  Our  Energy  Choices:  Renewable  Energy.    Union  of  Concerned  Scientists.    http://www.ucsusa.org/clean_energy/our-­‐energy-­‐choices/renewable-­‐energy/.    11  Solar  Energy  Facts.    Let’s  Be  Grid  Free.  http://www.letsbegridfree.com/solar-­‐energy-­‐facts/.    12  See  “How  Less  Became  More:  Wind,  Power  and  Unintended  Consequences  in  the  Colorado  Energy  Market,”  Bentek  Energy,  LLC.  (Evergreen  Colorado:  May,  2010).        13  MPCU  RPS  Report  2011  –  Review  of  RPS  Requirements  and  Compliance  in  Maine.    January  20,  2012.    Internet,  available  at  http://www.maine.gov/tools/whatsnew/attach.php?id=349454&an=1.    14  U.S.  Department  of  Energy,  Energy  Information  Agency,  2016  Levelized  Cost  of  New  Generation  Resources  from  the  Annual  Energy  Outlook  2011  (2008/$MWh),  http://www.eia.doe.gov/oiaf/aeo/electricity_generation.html,  (accessed  February,  2012).      15  Renewable  Energy  Research  Laboratory,  University  of  Massachusetts  at  Amherst,  “Wind  Power,  Capacity  Factor  and  Intermittency:  What  Happens  When  the  Wind  Doesn’t  Blow?”  Community  Wind  Power  Fact  Sheet  #2a,  http://www.ceere.org/rerl/about_wind/RERL_Fact_Sheet_2a_Capacity_Factor.pdf.                16  Tom  Hewson  and  Dave  Pressman,  “Renewable  Overload:  Waxman-­‐Markey  RPS  Creates  Land-­‐use  Dilemmas,”  Public  Utilities  Fortnightly  61  (August  1,  2009).      17  “Evidence  to  the  House  of  Lords  Economic  Affairs  Committee  Inquiry  into  ‘The  Economics  of  Renewable  Energy’,”  Memorandum  by  Dr.  Phillip  Bratby,  May  15,  2008.          Page  17    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012                                                                                                                                                                                                                                                                                                                                                                                                                                        18  Nicolas  Boccard,  “Capacity  Factors  for  Wind  Power:  Realized  Values  vs.  Estimates,”  Energy  Policy  37,  no.  7  (July  2009):  2680.              19  Cited  by  Tom  Hewson,  Energy  Venture  Analysis,  “Testimony  for  East  Haven  Windfarm,”  January  1,  2005,      http://www.windaction.org/documents/720  (accessed  December  2011).      20  Boccard.      21  See  “The  Capacity  Factor  of  Wind,  Lightbucket,”  http://lightbucket.wordpress.com/2008/03/13/the-­‐capacity-­‐factor-­‐of-­‐wind-­‐power/,  (accessed  December  2011)  and  National  Wind  Watch,  FAQ,  http://www.wind-­‐watch.org/faq-­‐output.php  (accessed  December  2011).        22  Biomass  Energy  Basics,  National  Renewable  Energy  Laboratory,  Biomass  Basics,  http://www.nrel.gov/learning/re_biomass.html    (accessed  December,  2010).          23  Hewson,  61.    24  U.S.  Department  of  Energy,  Energy  Information  Agency,  2016  Levelized  Cost  of  New  Generation  Resources  from  the  Annual  Energy  Outlook  2011  (2009/$MWh),  http://www.eia.doe.gov/oiaf/aeo/electricity_generation.html  (accessed  February  2012).      25  For  coal,  gas  and  nuclear  generation  we  used  the  production  cost  estimates  from  the  International  Energy  Agencies,  Energy  Technology  Analysis  Programs,  “Technology  Brief  E01:  Cola  Fired  Power,  E02:  Gas  Fired  Power,  E03:  Nuclear  Power  and  E05:  Biomass  for  Heat  and  Power,”  (April  2010  http://www.iea-­‐etsap.org/web/Supply.asp  (accessed  February  2012).    To  the  production  costs  we  added  transmission  costs  from  the  EIA  using  the  ratio  of  transmissions  costs  to  total  LEC  costs.  For  wind  power  we  used  the  IEA  estimate  for  levelized  capital  costs  and  variable  and  fixed  O  &  M  costs.  For  transmission  cost  we  used  the  estimated  costs  from  several  research  studies  that  ranged  from  a  low  of  $7.88  per  kWh  to  a  high  of  $146.77  per  kWh,  with  an  average  of  $60.32  per  MWh.  The  sources  are  as  follows:  Andrew  Mills,  Ryan  Wiser,  and  Kevin  Porter,  “The  Cost  of  Transmission  for  Wind  Energy:  A  Review  of  Transmission  Planning  Studies,”  Ernest  Orlando  Lawrence  Berkeley  National  Laboratory,  http://eetd.lbl.gov/EA/EMP  (accessed  December  2011);    Competitive  Renewable  Energy  Zones  (CREZ)  Transmission  Optimization  Study,  The  Electric  Reliability  Council  of  Texas,  April  2,  2008  http://www.ercot.com/news/presentations/2006/ATTCH_A_CREZ_Analysis_Report.pdf  (accessed  December  2010);    Sally  Maki  and  Ryan  Pletka,  Black  &  Veatch,  California’s  Transmission  Future,  August  25,  2010,  http://www.renewableenergyworld.com/rea/news/article/2010/08/californias-­‐transmission-­‐future  (accessed  December  2011).    26  U.S.  Department  of  Energy,  Energy  Information  Administration,  “Average  electricity  consumption  per  residence  in  ME  in  2008,”  (January  2010)    http://www.eia.gov/electricity/sales_revenue_price/index.cfm.    27  U.S.  Department  of  Energy,  Energy  Information  Agency,  Annual  Energy  Outlook  2011,  “Table  8:  Electricity  Supply,  Disposition,  Prices,  and  Emissions,”  http://www.eia.doe.gov/oiaf/aeo/aeoref_tab.html.    28  For  a  clear  introduction  to  CGE  tax  models,  see  John  B.  Shoven  and  John  Whalley,  “Applied  General-­‐Equilibrium  Models  of  Taxation  and  International  Trade:    An  Introduction  and  Survey,”  Journal  of  Economic  Literature  22  (September,  1984):  1008.    Shoven  and  Whalley  have  also  written  a  useful  book  on  the  practice  of  CGE  modeling  entitled  Applying  General  Equilibrium  (Cambridge:    Cambridge  University  Press,  1992).    29  U.S.  Department  of  Energy,  Energy  Information  Agency,  Maine  Electricity  Profile  2010,  Table  8:  Retail  Sales,  Revenue,  and  Average  Retail  Price  by  Sector,  1990  through  2008,  http://www.eia.doe.gov/cneaf/electricity/st_profiles/maine.html            Page  18    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012                                                                                                                                                                                                                                                                                                                                                                                                                                        30  See  the  following:  Bureau  of  Economic  Analysis,  “National  Economic  Accounts,”  http://www.bea.gov/national/;  Regional  Economic  Accounts,    http://www.bea.gov/regional/index.htm.  See  also  Bureau  of  Labor  Statistics,  “Current  Employment  Statistics,”  http://www.bls.gov/ces/.        31  Pamela  L  Spath,  Margaret  K  Mann,  Dawn  R  Kerr.  “Life  Cycle  Assessment  of  Coal-­‐fired  Power  Production.”  National  Renewable  Energy  Laboratory.  June  1999.    32  Pamela  L  Spath,  Margaret  M  Mann.  “Life  Cycle  Assessment  of  a  Natural  Gas  Combined-­‐Cycle  Power  Generation  System.”  National  Renewable  Energy  Laboratory.  September  2000.    33  “Life  Cycle  Assessment  of  Offshore  and  Onshore  Sited  Wind  Farms.”  ELSAM  Engineering  S/A.  October  2004.    34  V  M  Fethankis,  H  C  Kim.  “Photovoltaics:  Life  Cycle  Analysis.”  Science  Direct.  October  2009.    35  Christian  Bauer.  “Life  Cycle  Assessment  of  Fossil  and  Biomass  Power  Generation  Chains.”  Paul  Sherrer  Institute.  December  2008.                                                                              Page  19    Volume 10, Issue 5Path to ProsperitySeptember 27, 2012                                                                                                                                                                                                                                                                                                                                                                                                                                                                              J. Scott Moody is the Chief Executive Officer at The Maine Heritage Policy Center. He may be reached at jsmoody@mainepolicy.org.    Path to Prosperity is a series of publications by The Maine Heritage Policy Center which focus on Maine’s overspending and the resultingtax burden that threaten long-term, stable and sustainable prosperity. All information is from sources considered reliable, but may besubject to inaccuracies, omissions, and modifications.    The Maine Heritage Policy Center is a 501 (c) 3 nonprofit, nonpartisan research and educational organization based in Portland. The MaineHeritage Policy Center formulates and promotes free market, conservative public policies in the areas of economic growth, fiscal matters,health care, education, constitutional law and transparency – providing solutions that will benefit all the people of Maine. Contributions toMHPC are tax deductible to the extent allowed by law.    Editor and Director of Government and External Affairs Sam Adolphsen can be reached at sam@mainepolicy.org    © 2012 The Maine Heritage Policy Center. Material from this document may be copied and distributed with proper citation.      The Maine Heritage Policy Center  P.O. Box 7829, Portland, Maine 04112  Phone: 207.321.2550 Fax: 207.773.4385  www.MainePolicy.org - www.TheMaineWire.com  David G. Tuerck is Executive Director of the Beacon Hill Institute for Public Policy Research at Suffolk University, where he also servesas Chairman and Professor of Economics. He holds a Ph.D. in economics from the University of Virginia and has written extensively onissues of taxation and public economics.    Paul Bachman is Director of Research at BHI. He manages the institute's research projects, including the development and deployment ofthe STAMP model. Mr. Bachman has authored research papers on state and national tax policy and on state labor policy and produces theinstitute’s state revenue forecasts for the Massachusetts legislature. He holds a Master Science in International Economics from SuffolkUniversity.    Michael Head is a Research Economist at BHI. He holds a Master of Science in Economic Policy from Suffolk University.    The authors would like to thank Frank Conte, BHI Director of Communications, for his editorial assistance.    The Beacon Hill Institute at Suffolk University  8 Ashburton Place Boston, MA 02108  Tel: 617-573-8750, Fax: 617-994-4279Email: bhi@beaconhill.org, Web: www.beaconhill.org            Page  20