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Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative Renewable Energy Sources for Electricity Generation in U.S.

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An equilibrium economic model for policy evaluation related to electricity generation in U.S has been developed; the model takes into account the non-renewable and renewable energy sources, demand and supply factors and environmental constraints. The non-renewable energy sources include three types of fossil fuels: coal, natural gas and petroleum, and renewable energy sources include nuclear, hydraulic, wind, solar photovoltaic, biomass wood, biomass waste and geothermal. Energy demand sectors include households, industrial manufacturing and non-manufacturing commercial enterprises. Energy supply takes into account the electricity delivered to the consumer by the utility companies at a certain price which maybe different for retail and wholesale customers. Environmental risks primarily take into account the CO2 generation from fossil fuels. The model takes into account the employment in various sectors and labor supply and demand. Detailed electricity supply and demand data, electricity cost data, employment data in various sectors and CO2 generation data are collected for a period of nineteen years from 1990 to 2009 in U.S. The model is employed for policy analysis experiments if a switch is made in sources of electricity generation, namely from fossil fuels to renewable energy sources. As an example, we consider a switch of 10% of electricity generation from coal to 5% from wind, 3% from solar photovoltaic, 1% from biomass wood and 1% from biomass waste. The model is also applied to a switch from 10% coal to 10% from clean coal technologies. It should be noted that the cost of electricity generation from different sources is different and is taken into account. The consequences of this switch on supply and demand, employment, wages, and emissions are obtained from the economic model under three scenarios: (1) energy prices are fully regulated, (2) energy prices are fully adjusted with electricity supply fixed, and (3) energy prices and electricity supply both are fully adjusted.
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International Journal of Energy Economics and Policy
Vol. 1, No. 4, 2011, pp.78-94
ISSN: 2146-4553
www.econjournals.com
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite
of Alternative Renewable Energy Sources for Electricity Generation in U.S.
Ramesh Agarwal
Washington University in St. Louis, USA.
Email: rka@wustl.edu
Lee Chusak
Washington University in St. Louis, USA.
Email: Lee.Chusak@gmail.com
Zheming Zhang
Washington University in St. Louis, USA.
Email: zheming.zhang@wustl.edu
ABSTRACT: An equilibrium economic model for policy evaluation related to electricity generation
in U.S has been developed; the model takes into account the non-renewable and renewable energy
sources, demand and supply factors and environmental constraints. The non-renewable energy sources
include three types of fossil fuels: coal, natural gas and petroleum, and renewable energy sources
include nuclear, hydraulic, wind, solar photovoltaic, biomass wood, biomass waste and geothermal.
Energy demand sectors include households, industrial manufacturing and non-manufacturing
commercial enterprises. Energy supply takes into account the electricity delivered to the consumer by
the utility companies at a certain price which maybe different for retail and wholesale customers.
Environmental risks primarily take into account the CO2 generation from fossil fuels. The model takes
into account the employment in various sectors and labor supply and demand. Detailed electricity
supply and demand data, electricity cost data, employment data in various sectors and CO2 generation
data are collected for a period of nineteen years from 1990 to 2009 in U.S. The model is employed for
policy analysis experiments if a switch is made in sources of electricity generation, namely from fossil
fuels to renewable energy sources. As an example, we consider a switch of 10% of electricity
generation from coal to 5% from wind, 3% from solar photovoltaic, 1% from biomass wood and 1%
from biomass waste. The model is also applied to a switch from 10% coal to 10% from clean coal
technologies. It should be noted that the cost of electricity generation from different sources is
different and is taken into account. The consequences of this switch on supply and demand,
employment, wages, and emissions are obtained from the economic model under three scenarios: (1)
energy prices are fully regulated, (2) energy prices are fully adjusted with electricity supply fixed, and
(3) energy prices and electricity supply both are fully adjusted.
Keywords: carbon dioxide sequestration and mitigation, renewable energy, electricity generation,
economics
JEL Classifications: C54, C68, Q42, Q48
NOMENCLATURE
a household asset
c consumption
C aggregate household consumption demand
D CO2 emissions
E total electricity demand
EC commercial electricity demand
EF industrial electricity demand
eH household electricity demand
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative
Renewable Energy Sources for Electricity Generation in U.S.
79
EH aggregate household electricity demand
E(s) electricity generated from sources s
K capital input
M(s) material inputs for source s
N total labor demand
NC commercial sector labor
NE electricity sector labor
NE(s) electricity sector labor for sources s
NF industrial labor
p price of electricity
q relative price of an investment in units of the consumption good
r real interest rate
s source of electricity, i.e., coal, nuclear, etc.
VH household value function
FF industrial value function
w wage
x consumption good
X aggregate household goods consumption demand
Y output
z investment
Z total investment
βH household depreciation factor
βF industrial depreciation factor
δ capital depreciation rate
γ(s) unit pollution generation from source s
η Cobb-Douglass parameter
µ(s) unit cost of electricity from a given sources s
ν unit cost of other inputs (energy sources)
θ source labor requirement parameter
σ constant growth rate for commercial electricity demand
ζ employee-energy mix parameter
1. INTRODUCTION
Modeling of CO2 emissions and the economic factors related to the switch from fossil fuels to
renewable energy sources for electricity generation has become very important with the recent trends
of moving toward a more economically and environmentally sustainable society. The Brundland
definition of sustainable development, 'the development that meets the needs of the present without
compromising the ability of future generations to meet their own needs' is considered key to
sustainability (Brudtland, 1987). The effects of global warming and its impact on climate change of
the planet are making it apparent that the path humanity has taken so far, that is burning of excessive
amounts of fossil fuels for meeting the energy needs, is not sustainable. It is therefore important to
create economic models that can be used by the policy makers to make informed decisions which can
lead to a sustainable path to meet the energy requirements in an economically and environmentally
acceptable manner.
The United States generates most of the electricity from coal based power plants. The other
power generation sources include: nuclear, hydroelectric, natural gas, biomass waste, biomass wood,
geothermal, solar photovoltaic, solar thermal and wind. In 2006, coal (49.3%), nuclear (19.5%),
hydroelectric (7.2%) and natural gas (20.0%) constituted the major sources for electric power
generation compared to biomass waste (0.4%), biomass wood (1.0%), solar photovoltaic and solar
thermal (0.01%), wind (0.6%) and geothermal (0.4%). During the past 15 years, wind power has
become cheaper and competitive with fossil fuel based electricity generation, and therefore is
increasingly deployed in the U.S. and around the world. Photovoltaic power generation is still very
limited because at present it is not very efficient and is very expensive compared to other sources of
electricity generation. Recently, there has been considerable emphasis by the Department of Energy
(DOE) and electric utility companies on research in "Clean Coal Technologies". In particular carbon
International Journal of Energy Economics and Policy, Vol. 1, No. 4, 2011, pp.78-94
80
capture and sequestration (CCS) is being considered as a viable technology that may make it possible
the continued use of fossil fuels with CO2 emissions being captured and then sequestered in geological
formations. However, the CCS technology is yet to be tested for a medium to large scale power
generation facility. It appears unlikely that carbon capture and sequestration (CCS) will be wide
spread among power generation facilities within the next 15 years. It is therefore necessary to explore
both the alternative renewable energy sources along with CCS for power generation and assess the
relative economic viability of the two approaches in the near term horizon of twenty years.
In this paper, we consider the economics of electricity generation in the U.S. for two switches
from conventional fossil fuel based energy sources. The first switch is made from non-renewable
fossil fuel based energy sources to renewable energy sources. The second switch is made from non-
renewable fossil fuel based energy sources (coal) to clean coal technologies (in particular CCS). For
this purpose we develop an energy economic model, which is an optimization based equilibrium
model where the economy is modeled in a top-down manner and the electricity generation sector is
modeled using the bottom-up approach. Other significant energy economic models discussed in the
literature are the MRN-NEEM model (CRA International, 2008) and the National Energy Model
(Nakata, 2004). The MRN-NEEM model is a combination of the MRN (Multi-Region National)
model which is a top-down general equilibrium model and the NEEM (North American Electricity and
Environmental Model) model which is a bottom up model of the electricity generation sector. The
MRN-NEEM model has been applied to the United States. The National Energy Model is a dynamic
model that tracks the primary energy sources and how they are consumed by households and industry;
this model has only been applied to Japan.
The motivation behind the development of an energy economic model for electricity
generation in the U.S. has been to create a model that would forecast the effects on the United States
economy of policy changes in the usage of energy sources from fossil fuels to renewables in order to
achieve the target goals of greenhouse gas (GHG) emissions in the next 25 to 50 years. With a
worldwide emphasis on sustainability, there is a great interest in switching electricity generation
sources from predominantly coal based to more eco-friendly renewable sources. The goal then is to
create a model that can determine the economically best mix of energy generation sources to achieve
the environmental constraints on CO2 emissions in 2025 and 2050. The model should also determine
the impact of policy changes on electricity price, its supply and demand, and on employment. At
present, there are very few models that address this goal in a comprehensive manner.
There are mainly four types of approaches currently employed in the majority of energy-
economic models: top-down, bottom-up, optimization and equilibrium, and dynamic. The top-down
and bottom-up models can be used together to create a more detailed model. The salient features of the
models are briefly described below.
Top-Down/Bottom-Up Models
According to Nakata, "The top-down label comes from the way modelers apply
macroeconomic theory and econometric techniques to historical data on consumption, prices, incomes,
and factor costs to model the final demand for goods and services, and the supply from main sectors
(energy sector, transportation, agriculture, and industry)" (Nakata, 2004). All of the agents in the
model respond to changes in prices and allow for multiple regions to be linked by trade (CRA
International, 2008). Bottom-Up models model a given sector in detail, in the present case – electricity
generation. These models use detailed costs for current and future technologies to model the effects of
policy on the electricity generation sector (CRA International, 2008). They, "capture technology in the
engineering sense: a given technique related to energy consumption or supply, with a given technical
performance and cost" (Nakata, 2004).
Optimization Based Models
Optimization based models are based on the concept of maximizing utility and minimizing the
cost. The optimization takes place at a given point in time and is considered to be in steady state. The
optimization based models employ either the top-down or bottom-up approach to modeling. The
optimization equations used in this paper, for the most part, follow the format of the Bellman equation:
0
0 0 0 1
( ) max ( , ) ( )
a
V x F x a V x
(1)
where V is the value function (Bellman, 1957). The value function is "the best possible value of the
objective, written as a function of the state [variable]" (Bellman, 1957). The Bellman equation (1)
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative
Renewable Energy Sources for Electricity Generation in U.S.
81
gives the value function at a given time period as the maximum of some objective function (F) plus
the value function of the next time period with a discounting factor β. This recursive format of the
Bellman equation allows for the calculation of the value function at normalized time t = 1 if the value
function and the objective function (F) are known at normalized time t = 0. The first-order conditions
are the partial derivatives of the Bellman equation with respect to the variables over which the
optimization is being preformed (not the state variables).
 
 
0
0 0 0 1
0
( ) max ( , ) ( )
a
V x F x a V x
a
 
(2)
In this model, the states x0 and x1 are recursively defined as:
1 0
x G x
(3)
where G is a specified function. The Benveniste-Scheinkman condition, also known as the envelope
condition, allows the calculation of the derivative of the value function with respect to the state
variable (Bergin, 1998; Boileau, 2002):
 
)(),(max)( 1000
0
0xVaxFxV
xa
(4)
Using the first-order necessary conditions and the Benveniste-Scheinkman condition, the
value function can be calculated.
The present model, developed in this paper, basically falls under this category; however it is
only concerned with the steady state results. A bottom-up approach was applied to the electricity
generation sector so that the effect of switching from one energy source to another could be analyzed;
a top-down approach was also used to determine the economy wide effects of the policy changes.
Dynamic Models
Dynamic models are an extension of the optimization based models. They operate in a manner
similar to the optimization models except that the optimization takes place on a time interval and does
not assume the steady state. Dynamic models are based on the same mathematical background as
described in the previous section. They "can also be termed partial equilibrium models. These
technology-oriented models minimize the total costs of the [system], including all end-use sectors,
over a 40-50 year horizon and thus compute a partial equilibrium for the [markets]" (CRA
International, 2008). Unlike the present model developed in this paper, the dynamic model results into
a time series that can provide information as to how the current decisions affect the future outcomes.
2. PRESENT MODEL: OPTIMIZATION BASED GENERAL EQUILIBRIUM MODEL
Operative Sectors of the Economy
We consider a model economy with a continuum of households of mass N and three operative
sectors: the industrial manufacturing sector, the commercial sector and the electricity generation sector.
We omit the insignificant transportation sector because of relatively insignificant consumption of
electricity compared to residential, manufacturing and commercial sectors. The government sector is
also omitted because its behavior is different from the other sectors. The households provide the firms
with labor and investment while the firms provide the households with goods, services and wages. The
households pay the government taxes and the government grants the households subsidies. Firms can
provide each other with goods and services. The optimum level of production by a firm is the point at
which profit is maximized.
Household
Each household owns one unit of labor, whose consumption is produced by the consumption
good (x) and electricity (eH):
( , )
H
c h x e
(5)
Set the consumption good x as the numeraire and denote the unit price of electricity as p. The
optimization problem is given by:
International Journal of Energy Economics and Policy, Vol. 1, No. 4, 2011, pp.78-94
82
1
,
1
( ) max ( ) ( )
. . (1 )
( , )
H
t t
H H H
t t t
c e
H
t t t t t t t
H
t t t
V a U c V a
s t a r a w x p e
c h x e
 
 
(6)
where a denotes household asset, w the wage, r the real interest rate and βH the subjective discount
factor facing each household. The total population of households (N) is assumed to be fully employed
in the three (industrial manufacturing, commercial and electricity generation) sectors of the model
economy. Aggregate household demands are then defined by:
t t t
C N c
(7)
t t t
X N x
(8)
H H
t t t
E N e
(9)
Industrial Sector
There is a mass of producers normalized to one. Each producer hires labor (NF), in conjunction with
capital input (K) and electricity (EF), to manufacture goods Y:
( , , )
F F
Y f K N E
(10)
The output Y is used for consumption and capital investment:
Y X qZ
 
(11)
where q denotes the relative price of investment in units of the consumption good. Let capital
depreciate at rate δ. The optimization problem is given by:
1
, ,
1
( ) max ( )
. . (1 )
( , , )
F F
t t t
F F F F F
t t t t t t t t t
N E Z
t t t
F F
t t t t
V K Y q Z w N p E V K
s t K Z K
Y f K N E
 
 
(12)
where βF the subjective discount factor facing each producer.
The Commercial Sector
This is a sector with measuring difficulties. This sector includes not only commercial firms, but
educational institutions and other nonprofit organizations. Its inputs and outputs are hard to measure.
For simplicity, the commercial sector is modeled in a stylized manner with its demand for electricity
given by:
1
(1 )
C C
t t
E E
  (13)
where σ > 0 is assumed an exogenous constant. Under a Leontief production function specification,
the demand for labor is given by:
C C
t t
N E
(14)
where ζ > 0 is the employee-energy mix parameter.
Aggregate Electricity Demand and Electricity Generation
Total electricity demand is therefore given by:
, ,
i
i H F C
E E
(15)
Electricity can be generated via various sources s = 1 (coal), s = 2 (nuclear), s = 3 (hydro), s = 4
(petroleum), s = 5 (natural gas), s = 6 (biomass wood), s = 7 (biomass waste), s = 8 (geothermal), s = 9
(solar thermal and photovoltaic), s = 10 (wind) and s = 11 (clean coal). The generation function can be
specified as follows:
( ) ( ( ), ( ), )
E
E s m N s M s s
(16)
depending on labor (NE) and other inputs (M). Total electricity generated from all sources is:
( )
s
E E s
(17)
while the labor demand by all sources of electricity generation is:
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative
Renewable Energy Sources for Electricity Generation in U.S.
83
( )
E E
s
N N s
(18)
We assume fixed unit labor requirements θ across all sources:
( ) ( )
E
N s E s
(19)
Thus, we have:
( )
( )
E E
E s
N s N
E
(20)
and can rewrite (16) as:
1
( ) min ( ), ( ( ))
E
E s N s g M s
 
 
 
(21)
where g(M(s)) = m(θE (s), M(s), s).
Denote the unit cost of other inputs as v. Utility firms using source s face the following optimization
problem:
min ( ) ( )
1
. . ( ) min ( ), ( ( ))
E
E
wN s vM s
s t E s N s g M s
 
 
 
(22)
Total cost incurred in electricity generation is:
( ) ( )
E
s
wN s vM s
 
 
(23)
Let µ(s) denote the unit cost of electricity generation under source s. We can compute:
( ) ( ) ( ) ( )
E
vM s s E s wN s
(24)
Since we can measure M(1), ν can be backed out as well as M(2), M(3), M(4), M(5), M(6), M(7), M(8),
M(9), M(10) and M(11).
Denote unit pollution generation of source s as γ(s). Total pollution generation in electricity generation
is:
( ) ( )
s
s E s
(25)
Aggregate Labor Market
Total labor demand is:
, ,
i
i F C E
N N
(26)
In equilibrium, labor supply equals labor demand.
Optimization and Equilibrium
Household's optimization can be rewritten as:
,
( ) max ( ( , )) (( 1) )
H
t t
H H H H H
t t t t t t t t t
x e
V a U h x e V r a w x p e
  (27)
The first-order necessary conditions are given by:
1
t
H H
c x a
U h V
(28)
1
Ht
H H
c a t
e
U h V p
 
(29)
implying
H
e
x
h
p
h
(30)
where the time subscript is suppressed whenever it would not cause any confusion. The Benveniste-
Scheinkman condition is given by:
1
( 1)
t t
H H H
a a t
V V r
 
(31)
Manufacturer's optimization problem can be rewritten as:
, ,
( ) max ( , , ) ( (1 ) )
F F
t t t
F F F F F F F
t t t t t t t t t t t t
N E Z
V K f K N E q Z w N p E V Z K
 
  (32)
International Journal of Energy Economics and Policy, Vol. 1, No. 4, 2011, pp.78-94
84
The first-order conditions are derived below:
F
t
t
N
f w
(33)
F
t
t
E
f p
(34)
1t
F F
K t
V q
(35)
The Benveniste-Scheinkman condition is given by:
1
(1 )
t t t
F F F
K K K
V f V
 
 
(36)
which can be combined with (35) to yield:
1
(1 )
t t t
F F F
K K K
V f V
 
 
(37)
Under fixed labor requirements (19), utility firm's optimization leads to:
( ( ))
M
v
g M s
w
(38)
1
( ) ( ) ( ( ))
E
E s N s g M s
  (39)
Steady-State Equilibrium
In steady-state equilibrium, all variables are constant. As a consequence, (31) implies:
1
1
H
r
  (40)
whereas (6), (12) and (37) yield the following steady-state relationships:
1
1
H
H
x pe w a
 
 
 
 
(41)
Z K
(42)
11
KF
f q
 
 
 
 
(43)
Model Calibration
For the purpose of calibration analysis, we impose the following functional forms:
ln( )
U c
(44)
1
( , ) ( )
H H
h x e x e
 
(45)
 
 
 
1/
1
( , , ) 1
F F F F
f K N E A K N E
 
 
 
 
 
 
 
 
 
(46)
( ( )) ( )
g M s BM s
(47)
The model is calibrated based on the following steady-state relationships. In this paper, we use
the 1990-2006 average values of X , Z, NF, NC, NE, EH, EF, EC, E(s), µ(s), M(1), w, and p as their
steady-state values, where all values are in million dollars at 2000 constant prices. The calibration is
conducted for each year on which the model is run. There are a few adjustments needed to fit the
model. First, the total employment in our model economy is computed using (26):
6
22, 424, 294 76, 203,146 62,139 99.249 10
F C E
N N N N    (48)
Since total employment of the U.S. is 123.035×106, we must scale down all the aggregates by a factor
of 99.249/123.035 = 0.8067, yielding:
5,019,207 , 759, 482 , 912,595 , 814,054
, 797,165
H F C
X Z E E E (49)
Now the employee-energy mix parameter in the commercial sector can be derived using
equation (14) as ζ = 95.5927. Second, aggregate electricity demand and supply are not identical in the
data. We thus adjust E(s) so that the values in (15) and (17) are consistent. That is, call the raw data of
electricity generation as ES(s), define ES = ∑sES(s) and set E = EH + EF + EC. We must adjust
electricity generation by the factor E to have: E(s) = (E/ES)∙ES(s). Accordingly, we obtain:
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative
Renewable Energy Sources for Electricity Generation in U.S.
85
   
1 1,298, 463, 2 496, 095 3 204,693, 5 393,439
6 26, 208, 7 12,878, 8 10,581, 9 354, 10 5,181
E E E E
E E E E E
 
   (50)
Third, material inputs of various forms of electricity generation are very different. To
circumvent the problem, we choose to normalize the material inputs to generate E(1) as unity, that is,
M(1) = 1. We can then use the cost data (million dollars per million megawatt-hours):
   
1 0.030509, 2 0.022675, 3 0.009513, 4 0.0599
74, 5 0.049816
6 0.072496, 7 0.039934, 8 0.08, 9 0.348, 10 0.052359
µ µ µ µ µ
µ µ µ µ µ

  (51)
in conjunction with (24) to compute:
     
 
29,270, 2 0.249303, 3 0.010817, 4 0.135025
5 0.562525, 6 0.057777, 7 0.01065, 8 0.02603
9
9 0.004115229, 10 0.007857166
 
 
 
v M M M
M M M M
M M
(52)
The total electricity cost is then computed as follows:
( ) ( ) ( ) ( )
E
s s
TC wN s vM s s E s
 
 
 
 
(53)
Next, we can use (8) and (9) to yield x = 0.050572 and eH = 0.009195031. The average real
interest rate is set at a commonly selected rate 5%, faced by all agents. Thus, (40) implies βH = βF =
1/(1+r) = 1/1.05. The capital depreciation rate usually falls in the range between 5% and 10%, which
we set at 7.5%. The annual wage rate and the relative price of energy are given by w = 0.03236 and p
= 0.06936, respectively. Then, from (41) and (42), we can compute:
0.37694
H
x pe w
a
r
 
(54)
10,126,430
Z
K
  (55)
The Cobb-Douglass utility function simplifies (30) to:
1
H
x
p
e
(56)
which gives the calibrated parameter value
0.98755
H
x
x pe
 
The nested CES production function implies that (33), (34) and (43) can be rewritten as:
(1 )
FF
N
Y
f w
N
 
(57)
(1 )
FF
E
Y
f p
E
 
(58)
( )
K
Y
f r q
K
 
  (59)
where
1
1
( )
( ) (1 )( )
F
F F
K N
K N E
 
 
 
 
 
   
 
. The last equation above can be combined with (11) to
derive:
1
X
Y
K
r

(60)
and
Y
q
r Z

(61)
which can be further substituted into the marginal product of labor and marginal product of energy
expressions to solve jointly α and ρ as functions of φ. From the household side, we learn that the
energy demand share is 1 − η = 0.012454. It is reasonable to set the energy demand share by
manufacturers twice as much 1 φ = 0.02491, or φ = 0.975092. We can then calibrate α = 0.935881
International Journal of Energy Economics and Policy, Vol. 1, No. 4, 2011, pp.78-94
86
and ρ = 0.635049. Thus, manufactured output and the unit cost of capital investment are computed as:
Y = 11,374,760 and q = 8.36827. These values together with the production function enable us to pin
down the scaling parameter.
 
1/
1
1.102033
( ) (1 )( )
F F
Y
A
K N E
 
 
 
 
 
(62)
Finally, we manipulate (20), (38) and (39), using the specific functional form, to calibrate:
24615.0
E
NE
(63)
82979.2
)(
)( sE
E
wN
svM
E
(64)
 
463,298,1
)1(
)1(
M
E
B (65)
Given the CO2 production of 2,229.756 million metric tons essentially from sources 1, 4 and 5,
we can obtain an emission conversion ratio (per million megawatts of electricity generated) at γ(fossil
fuels) = 2,229.756/1,767,895 = 0.00126125, with γ(2) = γ(3) =γ(6) = γ(7) = γ(8) = γ(9) = γ(10) =
γ(11) = 0, due to the fact that the majority of carbon emissions are coming from the combustion of
fossil fuels. This completes the sample calibration procedure in the steady-state equilibrium.
3. POLICY ANALYSIS
In this section, we perform the policy analysis. In order to do this, we need to derive a few
more useful steady-state equilibrium relationships. From (41) and (56), we can write household's
goods consumption demand and electricity demand as:
)( rawx
(66)
 
raw
p
eH
1
1 (67)
From (14), (18), and (26), manufacturing firm's labor demand is given by:
EENN CF
(68)
Substituting this into the production function, (57) and (58) enable us to express Y, w and p all as
functions of (K, EF). Using (8), (10), (42) and (66), we can write household's asset as:
w
N
qKY
r
a
1 (69)
which is a function of (K, EF) as well, as are x and eH, based on the demand relationships derived
above. Aggregating each household's electricity demand with use of (69) and equating it with
electricity supply, we obtain:
1
H C F
Y qK
E E E E
p
 
 
  (70)
This together with (59) enables us to solve jointly (K, EF). The solution can then be
substituted into other functions to derive Y, w, p, a, x, eH, and EH. We are now ready for policy
experiments.
We perform two experiments. In the first, we consider switching 10% of electricity generation
from coal to 5% wind, 3% solar thermal and photovoltaic, 1% biomass waste and 1% biomass wood
by 2030. In the second, we consider switching 10% electricity generation from coal to clean coal
(using CCS) by 2030. Figures I-III respectively show the energy generation mix in 2030 for the
business as usual (BAU) scenario and for proposed scenarios with 10% switch from coal to
renewables, and 10% switch from coal to clean coal with CCS.
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative
Renewable Energy Sources for Electricity Generation in U.S.
87
Figure I: Energy generation mix for 2030 in business as usual (BAU) case
2030 BAU
Coal
Petroleum
Natural Gas
Nuclear
Hydroelectric (Conventional)
Wind
Solar Thermal and Photovoltaic
Biomass - Wood
Geothermal
Biomass - Waste
Clean Coal
Figure II: Energy generation mix for 2030 for 10% switch from coal to renewables
2030 Renewable
Coal
Petroleum
Natural Gas
Nuclear
Hydroelectric (Conventional)
Wind
Solar Thermal and Photovoltaic
Biomass - Wood
Geothermal
Biomass - Waste
Clean Coal
International Journal of Energy Economics and Policy, Vol. 1, No. 4, 2011, pp.78-94
88
Figure III: Energy generation mix for 2030 for 10% switch from coal to clean coal with CCS
2030 CC
Coal
Petroleum
Natural Gas
Nuclear
Hydroelectric (Conventional)
Wind
Solar Thermal and Photovoltaic
Biomass - Wood
Geothermal
Biomass - Waste
Clean Coal
Utilizing a series of curve fits to the data from 1990-2009, the projected business as usual energy
generation mix from 2010 to 2030 is obtained as shown in Figure IV.
Figure IV: Projected electricity generation mix from 2010 to 2030 for business as usual scenario
Electricity Gene ration Mix - Business As Usual
0
1000000
2000000
3000000
4000000
5000000
6000000
2010 2015 2020 2025 2030
Year
Elec tric ity G en era ted [1000 M W*h r]
Clean Coal
Biomass - Waste
Geothermal
Biomass - Wood
Solar Thermal and Photovoltaic
Wind
Hydroelectric (Conventional)
Nuclear
Natural Gas
Petroleum
Coal
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative
Renewable Energy Sources for Electricity Generation in U.S.
89
The first policy scenario, that is switching 10% coal to 5% wind, 3% solar, 1% biomass waste
and 1% biomass wood was applied to the business as usual energy generation mix in Figure IV to
yield the renewable energy generation mix shown in Figure V.
Figure V: Projected electricity generation mix from 2010 to 2030 for policy scenario 1, that is switching
10% coal to renewables (5% wind, 3% solar, 1% biomass waste and 1% biomass wood)
Electricity Generation Mix - Rene wa ble
0
1000000
2000000
3000000
4000000
5000000
6000000
2010 2015 2020 2025 2030
Year
Electric ity G enerat ed [1000 MW *hr]
Clean Coal
Biomass - Waste
Geothermal
Biomass - Wood
Solar Thermal and Photovoltaic
Wind
Hydroelectric (Conventional)
Nuclear
Natural Gas
Petroleum
Coal
The second policy scenario, that is switching 10% coal to clean coal technologies using CCS,
was applied to the business as usual energy generation mix in Figure IV to yield the energy generation
mix with clean coal as shown in Figure VI.
For the three scenarios: (1) BAU, (2) 10% switch from coal to renewables, and (3) 10% switch
from coal to clean coal using CCS, the policy implications are examined under the following
conditions: (a) both the energy supply and price are regulated, (b) energy price is fully adjusted with
electricity supply fixed, and (c) both the energy price and electricity supply are fully adjusted. The
results of policy simulations using our economic model for the three scenarios under the three types of
price and supply conditions are summarized in Tables 1, 2 and 3 respectively.
International Journal of Energy Economics and Policy, Vol. 1, No. 4, 2011, pp.78-94
90
Figure VI: Projected electricity generation mix from 2010 to 2030 for policy scenario 2,
that is switching 10% coal to clean coal using CCS
Electricity Gene ration Mix - Clean Coal
0
1000000
2000000
3000000
4000000
5000000
6000000
2010 2015 2020 2025 2030
Year
El e c t ri cit y G en er a ted [ 10 0 0 M W *h r]
Clean Coal
Biomass - Waste
Geothermal
Biomass - Wood
Solar Thermal and Photovoltaic
Wind
Hydroelectric (Conventional)
Nuclear
Natural Gas
Petroleum
Coal
Table 1. Policy simulation results for the three scenarios under the condition – both the energy supply and
price are regulated. All values in the table are percentage change from the business as usual case
Energy Supply and Price
Regulated
2015 - Renewables
2015 - Clean Coal
2020 - Renewables
2020 - Clean Coal
2025 - Renewables
2025 - Clean Coal
2030 - Renewables
2030 - Clean Coal
K - Capital Input 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
EF-Industrial Electricity Demand 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Y - Output 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
p - Price 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
w - Wage 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a - Household Asset 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
x - Consumption 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
eH -Household Electricity Demand 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
D - Emission -8.1% -8.1% -8.1% -8.1% -8.1% -8.1% -8.1% -8.1%
TCV - Total Cost of Generation 8.8% 3.0% 7.7% 2.6% 6.9% 2.2% 6.3% 2.0%
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative
Renewable Energy Sources for Electricity Generation in U.S.
91
Table 2. Policy simulation results for the three scenarios under the condition – the energy supply is
regulated and the energy price is adjusted. All values in the table are percentage change from the business
as usual case
Energy Supply Regulated and
Energy Price Adjusted
2015 - Renewables
2015 - Clean Coal
2020 - Renewables
2020 - Clean Coal
2025 - Renewables
2025 - Clean Coal
2030 - Renewables
2030 - Clean Coal
K - Capital Input -0.2% -0.2% -0.6% -0.2% -0.6% -0.2% -0.5% -0.2%
EF-Industrial Electricity Demand
-
21.0
%
-8.0% -18.9% -6.9% -17.3% -6.0% -16.1% -5.4%
E - Total Electricity Demand -8.5% -2.3% -7.4% -2.7% -6.5% -2.2% -5.9% -2.0%
Y - Output -0.7% -0.3% -0.6% -0.2% -0.6% -0.2% -0.6% -0.2%
p - Price 8.8% 3.0% 7.7% 2.6% 6.9% 2.2% 6.3% 2.0%
w - Wage -0.7% -0.2% -0.6% -0.2% -0.6% -0.2% -0.5% -0.2%
a - Household Asset -0.9% -0.3% -0.8% -0.3% -0.7% -0.2% -0.7% -0.2%
x - Consumption -0.8% -0.3% -0.7% -0.2% -0.6% -0.2% -0.6% -0.2%
eH -Household Electricity Demand -8.8% -3.2% -7.8% -2.7% -7.1% -2.4% -6.5% -2.1%
D - Emission -8.1% -8.1% -8.1% -8.1% -8.1% -8.1% -8.1% -8.1%
TCV - Total Cost of Generation 8.8% 3.0% 7.7% 2.6% 6.9% 2.2% 6.3% 2.0%
Table 3. Policy simulation results for the three scenarios under the condition – both the energy supply and
price are fully adjusted. All values in the table are percentage change from the business as usual case
Energy Supply and
Price Fully
Adjusted
2015 - Renewables
2015 - Clean Coal
2020 - Renewables
2020 - Clean Coal
2025 - Renewables
2025 - Clean Coal
2030 - Renewables
2030 - Clean Coal
D - Emission -16.0% -11.1% -14.9% -10.6% -14.2% -10.2% -13.6% -9.9%
TCV - Total Cost
of Generation -0.5% -0.3% -0.2% -0.2% -0.1% 0.0% 0.1% 0.0%
Layoff [ people ] 3.8E+04 1.4E+04 2.9E+04 1.1E+04 2.3E+04 7.8E+03 1.7E+04 5.7E+03
w - Wage -0.7% -0.3% -0.6% -0.2% -0.6% -0.2% -0.5% -0.2%
x - Consumption -0.8% -0.3% -0.7% -0.3% -0.7% -0.2% -0.6% -0.2%
eH - Household
Electricity
Demand
-8.8% -3.2% -7.8% -2.7% -7.1% -2.4% -6.5% -2.1%
The results in Tables 1, 2 and 3 under three types of regulatory conditions are summarized below for
years 2015, 2020, 2025 and 2030.
Condition 1: Energy Price Fully Regulated
When energy prices are fully regulated, the source switch under scenario 2 (renewables)
causes total electricity generation cost to go up by 8.8% in 2015 and by 6.3% in 2030 and emissions to
decrease by 8.1% without changing any other endogenous variables. However, the source switch
under scenario 3 (clean coal) causes total electricity generation cost to go up only by 3.0% in 2015 and
by 2% in 2030 and emissions to decrease by 8.1%. This type of regulatory environment is undesirable
at this time because the government would have to pay for the increase in total cost of electricity
generation. If at some future time fossil fuel based electricity became equal priced or more expensive
than renewables or clean coal then the government would either not lose money or make a profit.
International Journal of Energy Economics and Policy, Vol. 1, No. 4, 2011, pp.78-94
92
Condition 2: Energy Price Fully Adjusted with Electricity Supply Fixed
Under this condition, electricity supply and the level of employment remains fixed. When
energy prices are fully adjusted, the source switch described above under scenarios 2 and 3 will raise
the energy price by the same amount as under condition 1 and reduce the emissions by the same
amount. However, higher energy price lowers demand: under scenario 2 (renewables) the household
demand lowers by 8.8% in 2015 and by 6.5% in 2030, the industrial demand lowers by 21% in 2015
and by 16.1% in 2030 and total demand by 8.5% in 2015 and by 5.9% in 2030. The capital input
decreases by 0.2% in 2015 and by 0.5% in 2030. The wages reduce by 0.7% in 2015 and by 0.5% in
2030. The output is lowered by 0.7% in 2015 and by 0.6% in 2030. As a consequence, the household
assets are lowered by 0.9% in 2015 and by 0.7% in 2030 and the household consumption decreases by
0.8% in 2015 and by 0.6% in 2030. Under scenario 3 (clean coal) the household demand lowers by
3.2% in 2015 and by 2.1% in 2030, the industrial demand lowers by 8% in 2015 and by 5.4% in 2030
and total demand by 2.3% in 2015 and by 2% in 2030. The capital input decreases by 0.2% in 2015
and by 0.2% in 2030. The wages reduce by 0.2% in 2015 and by 0.2% in 2030.The output is lowered
by 0.3% in 2015 and by 0.2% in 2030. As a consequence, the household assets are lowered by 0.3% in
2015 and by 0.2% in 2030 and the household consumption decreases by 0.3% in 2015 and by 0.2% in
2030. Additionally, fixed electricity supply implies emissions decrease by exactly 10% of the
emissions from coal. This represents an overall reduction of 8.1% in CO2 emissions.
Condition 3: Energy Price and Electricity Supply Both Fully Adjusted
Under this condition, the source switch under both scenarios 2 and 3 will raise the energy
price and lower the electricity demand in the same manner as under condition 2. However, in contrast
with condition 2, electricity supply is now fully adjusted to meet the demand, which causes a layoff of
workers. Under scenario 2, it will result in a layoff of 38,000 workers in 2015 and of 17,000 workers
in 2030. So the expected market wages reduce by 0.7% in 2015 and by 0.5% in 2030 and goods
consumption decreases by 0.8% in 2015 and by 0.6% in 2030. Because electricity supply is now fully
adjusted downward, the total electricity generation cost goes down by 0.5% in 2015 and by 0.1% in
2030 and emissions decrease by 16% in 2015 and by 13.6% in 2030. Under scenario 3, it will result in
layoff of 14,000 workers in 2015 and of 5,700 workers in 2030. So the expected market wages reduce
by 0.3% in 2015 and by 0.2% in 2030 and goods consumption decreases by 0.3% in 2015 and by 0.2%
in 2030. Because electricity supply is now fully adjusted downward, the total electricity generation
cost goes down by 0.3% in 2015 and by 0.0% in 2030 and emissions decrease by 11.1% in 2015 and
by 9.9% in 2030.
The above analysis shows that scenario 3 (switch to clean coal) is a better policy option than
the scenario 2 (switch to renewable). Furthermore, policy condition 3 yields the largest CO2 reduction
for the given energy generation mix. Allowing the free market to adjust price and supply will lead to
the largest decreases in CO2 emissions for a given energy generation mix for the near future (while
fossil fuel based electricity generation is cheaper than renewable sources and clean coal with CCS).
4. CONCLUSIONS
1. An economic model for electricity generation in the U.S has been created that runs policy
simulations for 2010-2030.
2. The model predicts that utilizing clean coal technologies such as CCS will affect the economy
less than utilizing the renewable energy sources. This is due to clean coal technologies being
cheaper than renewable energy technologies based on the current estimates.
3. While fossil fuel based electricity generation is cheaper than renewables based electricity
generation, government regulation will be necessary to achieve any sort of CO2 emissions
reduction. Clean coal technologies could be used to bridge the gap until renewables based
electricity becomes less expensive than fossil fuel based electricity.
FUTURE WORK
1. The model should be applied to the major emerging economies of India and China. The
agriculture sector is important in these countries. The agriculture sector can be modeled in a
manner similar to the commercial sector in the present model.
Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative
Renewable Energy Sources for Electricity Generation in U.S.
93
2. The non-fixed labor requirements should be added to the model. It is likely that the older and
more developed power generation methods will become increasingly more automated and
therefore less labor intensive compared to the power plants employing newer less-traditional
renewable power generation sources; thus, the value of θ is likely to be larger for the newer
technologies than the older established technologies.
3. The provision for carbon tax should be included in the model. Carbon tax is a way to
encourage the electricity generation companies to reduce the carbon emissions by either
switching to alternative renewable energy generation sources or by developing the CO2
capture and sequestration (CCS) technologies.
4. The current model does not take into account the cost associated with switching from one
energy source to another. A cost function should be included which can model this cost.
5. The current model is a steady state model. It should be extended to conduct the dynamic
analysis using the tools of dynamic programming. This will allow for the ongoing growth of
households and firms over time; it will also capture shifts in supply and demand factors over
time.
DATA COLLECTION SOURCES
1. Employment for each state by sector for 2001-2006. Sectors: Agriculture, forestry, fishing and
hunting, Mining; Utilities, Construction, Manufacturing, Transportation and Warehousing
(excluding Postal Service), Government and Other as well as the total employment; Source:
Bureau of Labor Statistics.
2. US electricity retail sales by sector in thousand megawatt hours for each state for 1990-2006.
Sectors: Residential, Commercial, Industrial and Other, as well as total sales. Source: Energy
Information Administration (EIA), 2008.
3. US energy generation data for 1980-2009 by source. Electricity generation sources: coal,
petroleum, natural gas, other gases, total fossil fuels, nuclear, hydro (conventional), biomass
wood, biomass waste, geothermal, solar/PV, wind, total renewables, other; as well as total for
all sources. Source: EIA, Annual Energy Review, 2008.
4. Total coal usage in power generation for 1990-2006 in thousands of tons of coal. Source: EIA,
2008.
5. US CO2 emissions from the electric power industry for each state by source for 2003-2006.
Sources: coal, petroleum, natural gas, geothermal and other renewables as well as the total.
Source EIA, 2008.
6. US average electricity retail price in cents per kilowatt hour for 1998-2006. Source: EIA, 2007.
7. US electricity generation costs in cents per kilowatt hour.
8. A full data set is available for 2006. Additional years of data are available for some of the
sources so that a curve fit could be made to fill in the gaps in the data for other years. Sources:
coal, natural gas, nuclear, petroleum, wind, residential photovoltaics, commercial
photovoltaics, industrial photovoltaics, solar thermal, geothermal, hydroelectric small and
hydroelectric large. Sources: Nuclear Energy Institute, U. S. Electricity Production Costs and
Components (1995-2008); Energy Information Administration, Annual Energy Review 2008;
Table 8.2a Electricity Net Generation: Total (All Sectors), Selected Years, 1949-2008; World
Energy Assessment; Overview: 2004 Update. Solarbuzz.com, Solar Electricity Price Index
verses US Electricity tariff Price Index; Facts About Hydropower, Wisconsin Valley
Improvement Company.
9. US electricity generation for each state by source in megawatt hours for 1990-2006. Sources:
coal, petroleum, natural gas, other gases, nuclear, hydroelectric, other renewables, pumped
storage and other as well as the total. Source EIA, 2008.
10. US CO2 emissions from energy consumption for each sector from 1980 to 2005. Sectors:
residential, commercial, transportation, electric power. Source: EIA, 2008.
11. US electricity demand from 1980 to 2006 for each sector.
12. Sectors: residential, commercial, industrial, transportation as well as total. Source: EIA, 2008.
13. State level CO2 emissions from fossil fuel combustion for electricity generation from 1990 to
2004 in million metric tons of CO2. Source: EIA, 2008.
International Journal of Energy Economics and Policy, Vol. 1, No. 4, 2011, pp.78-94
94
14. Cost of living statistics (consumer price index) for the Northeast Urban, Midwest Urban,
South Urban, and West Urban, as well as US total for 1985-2006. Source: Bureau of
Economic Analysis, 2008.
15. State level population data for 1970-2007. Source: US Census Bureau, 2008.
16. State level average number of people per household for 2007. Source: Bureau of Labor
Statistics, 2008.
17. US Gross State Product (GSP) for each state for each industry in non-chained dollars for
1997-2006. Industries: agriculture, forestry, fishing and hunting, mining, utilities, construction,
manufacturing, transportation and warehousing (excluding postal service), government and
other; as well as the total. Source: Bureau of Economic Analysis, 2008.
18. State level motor-vehicle registration for 2003-2005. Sectors: automobiles, motorcycles,
busses and trucks. Source: Bureau of Transportation Statistics, 2008.
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Cgdynpro.PDF. Accessed September 15, 2011
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The current century—an era of environmental awareness—requires energy resources to satisfy the world's future energy demands. We can use current energy use scenarios to help us to understand how energy systems could change. Such scenarios are not an exercise in prophecy; rather they are designed to challenge our thinking in order to make better decisions today. The conventional modeling approach tends to extrapolate changes in energy consumption from historical trends; however, technology innovation sometimes causes drastic reforms in energy systems in the industrial, commercial, residential and transportation sectors. The economic aspects are another key issue to be considered in order to understand future changes in energy systems. The quantity of the energy supply is set to meet the price of the energy demand of end users. This occurs on the condition that the price of the energy supply equates with the price on the demand side under the market mechanism. This paper reviews the various issues associated with the energy-economic model and its application to national energy policies, renewable energy systems, and the global environment.
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US electricity demand from 1980 to 2006 for each sector. 12. Sectors: residential, commercial, industrial, transportation as well as total. Source: EIA, 2008.