A general equilibrium analysis of potential demand side management programs in the household sector in Thailand Govinda R. Timilsina The World Bank, Washington, DC, USA, andRam M. Shrestha Asian Institute of Technology, Pathumthani, ThailandAbstract Purpose – The purpose of this paper is to examine potential demand side management (DSM) programs in terms of their impacts to the overall economy in Thailand. Design/methodology/approach – A multi-sector computable general equilibrium (CGE) model ofThailand has been developed to accomplish the objectives of this study. The potential DSM program considered refers to replacement of less efficient electrical appliances with their efficient counterparts in the household sector in Thailand. Findings – The study finds that the economy-wide impacts of the DSM program (e.g., economic welfare, GDP, international trade) depend on three key factors: the project economics of the DSM option or the ratio of unit cost of electricity savings to price of electricity (CPR); the implementation strategy of the DSM option; and scale or size of the DSM option. This paper shows that the welfare impacts of the DSM programs would improve along with the project economics of the DSM programs. If the DSM program is implemented under the CDM, the welfare impacts would increase along with the price for certified emission reductions units. On the other hand, the welfare impacts would increase up to the optimal size or scale of the program, but would start to deteriorate if the size is increased further. Research limitations/implications – The wel far e fun cti on con sid ered in thi s pap er doe s not account for benefits of local air pollution reductions. The study provides crucial insights on designing DSM projects in Thailand to ensure that DSM programs are beneficial for the economy as a whole. Originality/value – Analyses of DSM options under the CDM using CGE models are not available in the literature. This is the first paper in this area. Keywords Deman d manag ement , Equili brium metho ds, Econ omic develo pment , Energy manag ement , Thailand Paper type Research paper 1. Introduction The energy crises of the 1970s and high energy prices accompanied with high inflation and interest rates led to energy conservation or demand side management (DSM) The current issue and full text archive of this journal is available at www.emeraldinsight.com/1750-6220.htm The authors sincerely thanks Subhes C. Bhattacharyya and Ian C. Porter and two anonymous referees for their valuable comments and suggestions. The views expressed in this paper are those of the authors only, and do not necessarily represent the World Bank and its affiliated organizations. IJESM 2,4 570 Received 6 February 2008 Accepted 26 June 2008 International Journal of Energy Sector Management Vol. 2 No. 4, 2008 pp. 570-593 q Emerald Group Publishing Limited 1750-6220 DOI 10.1108/17506220810919072
A general equilibrium analysisof potential demand side
management programs in thehousehold sector in Thailand
Govinda R. TimilsinaThe World Bank, Washington, DC, USA, and
Ram M. Shrestha Asian Institute of Technology, Pathumthani, Thailand
AbstractPurpose – The purpose of this paper is to examine potential demand side management (DSM)programs in terms of their impacts to the overall economy in Thailand.
Design/methodology/approach – A multi-sector computable general equilibrium (CGE) model of Thailand has been developed to accomplish the objectives of this study. The potential DSM programconsidered refers to replacement of less efficient electrical appliances with their efficient counterpartsin the household sector in Thailand.
Findings – The study finds that the economy-wide impacts of the DSM program (e.g., economicwelfare, GDP, international trade) depend on three key factors: the project economics of the DSMoption or the ratio of unit cost of electricity savings to price of electricity (CPR); the implementationstrategy of the DSM option; and scale or size of the DSM option. This paper shows that the welfareimpacts of the DSM programs would improve along with the project economics of the DSM programs.If the DSM program is implemented under the CDM, the welfare impacts would increase along with
the price for certified emission reductions units. On the other hand, the welfare impacts would increaseup to the optimal size or scale of the program, but would start to deteriorate if the size is increasedfurther.
Research limitations/implications – The welfare function considered in this paper does notaccount for benefits of local air pollution reductions. The study provides crucial insights on designingDSM projects in Thailand to ensure that DSM programs are beneficial for the economy as a whole.
Originality/value – Analyses of DSM options under the CDM using CGE models are not available inthe literature. This is the first paper in this area.
Keywords Demand management, Equilibrium methods, Economic development, Energy management,Thailand
Paper type Research paper
1. IntroductionThe energy crises of the 1970s and high energy prices accompanied with high inflationand interest rates led to energy conservation or demand side management (DSM)
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1750-6220.htm
The authors sincerely thanks Subhes C. Bhattacharyya and Ian C. Porter and two anonymousreferees for their valuable comments and suggestions. The views expressed in this paper arethose of the authors only, and do not necessarily represent the World Bank and its affiliatedorganizations.
IJESM2,4
570
Received 6 February 2008Accepted 26 June 2008
International Journal of Energy SectorManagementVol. 2 No. 4, 2008pp. 570-593q Emerald Group Publishing Limited1750-6220DOI 10.1108/17506220810919072
programs all over the world (Gellings, 2000). Electric utilities in the US and Europeancountries launched DSM programs in the eighties. In the United States, about US$23billion was invested for DSM programs between 1989 and 1999 (Laughran and Kulick,2004). Growing environmental concerns, particularly over the increasing emissions of
air pollutants from energy production and consumption activities, further encouragedDSM programs (Wirl, 2000). In Asia, DSM programs were started in the early nineties.In Thailand, the Electricity Generating Authority of Thailand (EGAT) implemented afive-year DSM program during 1993 – 1998, which resulted in reductions of 468 MWof peak demand, 2,194 GWh of electricity generation and 1.64 million tons of CO2
emission (EGAT, 2000).There exists a large potential for reducing energy consumption as well as
environmental emissions from various DSM programs, including energy efficientlighting, refrigeration and air-conditioning, and energy efficient motors and otherelectrical appliances in developing countries (DC) in Asia (ALGAS, 1999b; Shresthaet al., 1998a,b). According to ALGAS (1999b), implementation of DSM programs ineight Asian countries (i.e. China, Myanmar, Mongolia, Pakistan, Philippines, SouthKorea, Thailand, and Vietnam) could reduce 17.4 billion tons of CO2 emission during2000-2020 period with net economic benefits in addition to the climate change benefits.In Thailand alone, DSM programs have the potential to mitigate 142 million tons of CO2, during the 2000-2020 period with net economic benefits (ALGAS, 1999a). Despitethe large potential of GHG mitigation and other environmental and economic benefits,DSM programs are not being implemented in many DCs due to the lack of financialresources.
Using partial equilibrium analysis[1], existing studies, such as Shrestha et al.(1998a,b), Schipper and Meyers (1991), Hsueh and Grener (1993), find DSM activitieseconomically attractive[2]. On the other hand, studies, such as Dufournaud et al. (1994)and Rose and Lin (1995) argue that DSM options, which are economically attractive
from a partial equilibrium approach, may not necessarily be attractive if they areexamined using general equilibrium models[3]. A question may, however, arise: wouldall DSM options, no matter how economically attractive they are in a partialequilibrium setting, lead to negative welfare effects in a general equilibrium setting?Would DSM programs with highly attractive internal rate of returns (IRRs) be stillwelfare regressive if their economy wide impacts are considered? This question is acrucial one for countries which are implementing DSM programs (e.g. Thailand).Moreover, even if DSM programs are found welfare regressive from a generalequilibrium perspective; are there ways to offset these negative impacts? The cleandevelopment mechanism (CDM) of the Kyoto Protocol could be an instrument toresolve this issue because the CDM not only enhances the economic attractiveness of aDSM program, but also helps reduce financial barriers to DSM programs. By the end
of December 2007, 52 energy efficiency projects have already been registered by theExecutive Board of the CDM (CDMEB) and more than 250 similar projects are inpipeline (UNEP RISØ Centre, 2007; UNFCCC, 1998).
In this paper, we examine the welfare effects of a potential DSM program inThailand, under which existing less efficient electrical appliances in the householdsector are replaced with their efficient counterparts, by using a general equilibriummodel. We first assess the welfare impacts if the DSM program is implemented in theabsence of a CDM scheme. This will be followed by an analysis of the roles for CDM to
improve the welfare effects of the DSM program. We show that not all DSM options arewelfare regressive. It depends on three factors:
(1) the ratio of unit cost of electricity savings to price of electricity (CPR);
(2) price of certified emission reductions (CERs); and(3) rate of substitution of less efficient appliances with their efficient counterparts.
We find that the DSM program would result in positive welfare impacts as long asthe CPR is smaller than 0.4 (or IRR . 23 percent) even in the absence of CDM.Implementation of the DSM program under the CDM would result in positive welfareeffects when CPR is higher than 0.4 (or IRR , 23 percent) depending on the price of CERs.
The paper is organized as follows: Section 2 briefly presents the general equilibriummodel developed for the purpose of the study and the source of the data. Section 3discusses results from model simulations (i.e. the economic welfare and environmentalimpacts of the DSM program), followed by sensitivity analyses of key parameters.
Finally, the major conclusions of the paper are summarized.
2. A brief description of the cge modelA static general equilibrium model has been developed for the purpose of this study.In this section, we briefly present approaches to modeling various economic agents(e.g. producers, households, the government, and the foreign sector)[4].
2.1 The production sector The study considers 21 production sectors (Table I), of which seven produce energygoods and services, and the rest material goods and services. The production behaviorof each sector is represented through a four level nested structure (Figure 1(a),(b)). Ineach sector, gross output (XD) is a nested function of capital (K), labor (L), material (Gk ),fossil fuel (Gf ), and electricity (GEL ):
XD ¼ b ½u ðK; LÞ;w g ðG1; . . . ; GkÞ; n ðf ðG1; . . . ; Gf Þ; GELÞ; ð1Þ
where u is the constant elasticity of substitution (CES) composite of primary factors;f is the CES composite of the material aggregate ( g ) and the composite of the fuelaggregate ( f ) and electricity ( n ). f is the CES aggregate of fossil fuels and g is theCobb-Douglas aggregate of materials. The CES functional form of XD can be written asfollows:
XD ¼ l1=s ·
u
u ðs 21Þ=s þ l1=s
w
·w ðs 21Þ=s h is =ðs 21Þ
: ð2Þ
where l is the share parameter and s is the elasticity of substitution between u and f .Similar functional forms could be written for u , f , g , n and f . u and f are derived asfollows:
where xdp is the output price, and pu and pf are prices of u and f , respectively. xdp isderived from a cost function, which is dual to the production function in equation (2)and is given as:
xdp ¼ lu pð12s Þu þ lw p
ð12s Þw
h i1=ð12s Þ
: ð5Þ
All other demand variables, as indicated in Figure 1(a), are determined in a similarmanner to equations (3) and (4), while the price variables are determined in a similarmanner to equation (5).
One of the key features of the model is that it treats the electrical sector in a differentmanner than most existing studies. First, the electricity sector is divided into sevensub-sectors based on technologies used for electricity generation (Table I(b))[5]. Thisallows the substitution possibilities between various technologies used for electricitygeneration. Secondly, the nested CES structure used for the electricity sector differsfrom those used in the rest of the sectors to allow direct substitution between capitaland fuel in the electricity generation industries. It is very important to treat theelectricity sector with special attention in GE models for environmental policy analysisin countries where electricity generation based on fossil fuels is one of the main sourcesof GHG emissions. The gross output of the electricity industry g is given as:
XD g ¼ b ½v K g ; f ðG g ; 1; . . . ; G g ; f Þ; x L;mðg ðG g ;1; . . . ; G g ;kÞ; G g ; ELÞ; ð6Þ
where v is the composite of capital and the fuel aggregate used in electricity industry g and x is the composite of labor and the material-electricity composite ( m ). The demandand price variables in the case of the electricity industries are determined in a similarmanner to the other sectors discussed above. Electricity generated with different typesof technologies is aggregated as shown in Figure 2.
As can be seen from Figure 2, the total electricity output (XDEL ) can be expressed as:
where L is the CES composite of the outputs of the hydropower industry (XDHY ) andthe thermal power industry ( k ). k is the CES aggregate of the outputs of the steamturbine electricity industry ( y ), the combined cycle/gas turbine electricity industry ( V )and the internal combustion electricity industry (XDIC ). y is the CES aggregate of the outputs of the coal fired steam turbine electricity industry (XDSTC ), the oil firedsteam turbine electricity industry (XDSTO ), and the gas fired steam turbine electricityindustry (XDSTG ), whileV is the CES composite of the outputs of the oil fired combinedcycle/gas turbine electricity industry (XDCGO ) and the gas fired combined cycle/gasturbine electricity industry (XDCGG ).
The demand for electricity generated from various types of technologies as well asthe demand for primary factors, energy, and material inputs are derived as discussedin other industries above. For example, demand for and price of thermal electricity aregiven as follows:
where pk is the aggregate of unit costs of electricity generation from steam turbine ( py ),combined cycle/gas turbine ( pV ) and internal combustion technologies ( xdpIC ).s HYTH is the elasticity of substitution between electricity generated from hydro andthermal power plants, and s TH is the elasticity of substitution between electricity
generated from steam turbine, combined cycle/gas turbine and internal combustiontechnologies.
2.2 The household sector 2.2.1 Household demand . This study considers a representative household that followsa five-step hierarchical optimization process to maximize its utility as shown inFigure 3. At the left hand side of the bottom of the nested structure (i.e. Tier 5 inFigure 3), household consumption of electricity (CHEL ) and electrical appliances (CHDG )are combined through a Cobb-Douglas function to get electrical services for thehouseholds ( h )[6]. At the right hand side of the same tier, a fuel aggregate ( f ) isobtained through a CES aggregation of different fuels, such as coal, oil, gas andfuelwood. The aggregate energy service ( n ) in the household sector is derived througha CES combination of the electrical services and the aggregate fuel consumption ( f )(please see left part of Tier 4). In the right hand part of Tier 4, a material aggregate ( g )is derived from the Cobb-Douglas aggregation of different materials. Theenergy-material composite ( f ) is combined with leisure (LS) to give the presentconsumption ( z ) at the second tier of the nested structure. Finally, at the top most tier of the nested structure, households trade off between present consumption and savings(S) while maximizing their utility. The household utility function is expressed asfollows[7]:
U ¼ c ½z w ðn ðh ðCHEL; CHDGÞ; f ðCH1; . . . ; CHf ÞÞ; g ðCH1; . . . ; CHkÞÞ; LS ; S : ð10Þ
The CES functional form for U is given as follows[8]:
U ¼ a1=s H
z · z ðs H21Þ=s H þ ð12 az Þ
1=s H · S .ðs H21Þ=s Hh is H=ðs H21Þ
; ð11Þ
where az is the scaling factor and s H is the elasticity of substitution between presentconsumption and household savings. z and S are derived from the first order conditionof maximizing utility under budget constraint, I ¼ z ; · pz þ S · pS , as follows:
z ¼ az · I= ps H
z · Z ; ð12Þ
and:
S ¼ 12 az
À Á· I= ps
H
S · Z
ð13Þ
with:
Z ¼ az · p12s H
z þ ð12 az Þ · p12s H
S :
Figure 3.Nested structure for thehousehold sector to modelthe DSM option
where pz and pS are prices of present consumption and savings. I is the full income of households. In the same manner, other demand and price variables in the householdmodel are derived through the different levels of the nested structure shown inFigure 3.
2.2.2 Incorporation of the DSM into the model . The DSM program is incorporatedinto the model while modeling household behavior. This is because the DSM programconsidered here refers to electrical appliances in the household sector. It is also possibleto include electrical appliances in other sectors, such as manufacturing and servicesectors. This could be a further expansion of the study because the end-use demandfor electricity in the manufacturing sector differs significantly from the householdsector[9].
Modeling demand side options in a general equilibrium framework is oftenconstrained by data limitations. For example, an analysis of the substitution of incandescent lamps by compact fluorescent lamps would require detailedinformation on the industries producing these appliances (e.g. labor, capital, andmaterial inputs). In other words, we need an input-output table (I/O table) that treatsthe lamp industry as a separate sector. However, in the existing I/O tables of Thailand, information is available only at an aggregated electricalappliances/machinery industry level. Because of this limitation, we incorporate theDSM options in our model by assuming an aggregate end-use appliance instead of individual appliances.
This approach now looks more relevant as the CDM Executive Board has developed,in its 28th meeting, guidance on the registration of a program of activities as a singleCDM project activity (CDMEB, 2006). According to the guidance, a number of GHGmitigation activities, such as efficient lighting, refrigeration, air-conditioning, energyefficient electric motors, etc. can be packaged and registered as a single CDM project. Inother word, the DSM program such as the one considered in this study can now be
registered as a single CDM project.Efficient appliances have relatively higher capital costs than their inefficientcounterparts. On the other hand, efficient appliances use less electricity than theirinefficient counterparts, leading to savings in fuel costs. In the general equilibriummodeling context, this implies a substitution of electricity costs with the capitalcosts. The increased use of efficient electrical appliances reflects a situation wherehouseholds allocate higher expenditure on appliances (i.e. purchase efficientappliances) and less expenditure on electricity. It is assumed that the use of efficient electrical appliances provides at least the same level of end use energyservices (e.g. lighting) as before (i.e. prior to replacement of the inefficientappliances).
The incorporation of the DSM aspect in the CGE model can be described with the
help of Figure 3. As illustrated in the Figure (bottom tier in the left hand side of the nested structure), electricity (CHEL ) and electrical appliances (CHDG ) are combinedthrough a Cobb-Douglas function to get electrical services (e.g. heat, air-conditioning,and light), h , for the households. The household maximizes its utility from the use of the electrical services subject to the budget constraint:
In the case of the CES functional form, the utility function to maximize is expressed as:
Maxh ¼ a1=s · CHðs 21Þ=s EL þ ð12 aÞ1=s CH
ðs 21Þ=s DG
h is =ðs 21Þ
: ð16Þ
The constraint is the same as in equation (15). Here, a is the share of electricity in the totalexpenditure on electrical services (i.e. sum of expenditure on electricity and electricalappliances). CH f is the household consumption of other fuels (e.g. natural gas, fuel wood,and petroleum products); CHk is the household consumption of goods and services exceptelectrical appliances (e.g. food and beverage, health care, and education); gpEL, gp f , gpDG,and gpk are the prices of electricity, other fuels, electrical appliances and other goods andservices, respectively; indtEL, indtf , indtDG, and indtk are the corresponding indirect taxrates. DI is disposable income; it also includes revenue generated from exports of CERs.
As we mentioned earlier, an improvement in the end-use energy efficiency of electrical appliances implies that households derive at least the same level of electricalservices as before, using smaller amount of electricity. This aspect of energy efficiencyis incorporated into the model through the addition of the following constraint:
h ¼ h 0; ð17Þ
where h 0 is the electrical services that the household is deriving in the base case (i.e.before implementation of the DSM program). Equation (17) implies:
CHaEL · CHð12aÞ
DG ¼ CH0a
EL · CH0ð12aÞ
DG ; ð18Þ
where CH0EL and CH0
DG are household consumption of electricity and electricalappliances in the base case. By rearranging equation (18), we get:
CHDG
CH0DG
¼CH0
EL
CHEL
a=ð12aÞ
: ð19Þ
Let:
CHDG
CH0DG
¼ u ; ð20Þ
u is the policy variable here and exogenous to the model. It represents the rate of replacement of inefficient appliances with their efficient counterparts. In the base run, u has a value of 1. In policy simulation runs (counterfactual runs),u is assigned to differentvalues. For example, if u is equal to 1.25, it can be interpreted as households spending25 percent more to buy efficient appliances than in the base case. Increasing theconsumption of electrical appliances would cause a reduction of electricity consumption
for deriving the same level of electricity services. New electricity demand is thencalculated as:
CHEL ¼
CH0EL
u ð12aÞ=a : ð21Þ
The rate of replacement also represents the level of emission reductions; a low rate of replacement would generate small amounts of emission reduction and vice versa.
Our model is a single year static model, but the DSM program generates costs andbenefits for multiple years. In order to deal with this situation, we calculate annuityof the total investment over the economic life of the DSM program. The annuity iscompared with the annual electricity savings in the absence of CDM, and with thesum of annual electricity savings and annual CDM revenue when the DSM programis considered under the CDM. Alternatively, we start with an exogenous unit cost of energy savings, which is equal to the total costs of the DSM program divided byelectricity savings throughout the economic life of the program. We then divide the
unit cost of electricity savings by an average electricity price to get a ratio of unit costof energy savings to electricity price (CPR)[10]. If the CPR is smaller than 1, the DSMprogram will have net savings. The smaller the value of CPR, the higher will be IRR of the DSM program. Thus, the model fully accounts for the overall costs and benefits of the DSM program. The net savings of the DSM program (DSMSAV) is calculated asfollows:
DSMSAV ¼ ðCHEL 2 CH0 ELÞ · ð12 CPRÞ: ð22Þ
Comparing the unit cost of DSM options with price of electricity from the Nam Theun 2hydropower project, du Pont (2005) estimates that CPR of Thai DSM options equals 0.4.However, instead of considering a particular value of CPR, we simulate the welfare
impacts of the DSM program at different CPR values. 2.2.3 Household income. Total household income (THI) consists of capital income,labor income, and net transfer from the rest of the world. Capital income also includesdepreciation. Labor income consists of not only salary and wages but also socialsecurity benefits to households. Total household income is then expressed as:
where wr and kpi are the gross tax prices of labor and capital, respectively. NTRH isthe net transfer from the rest of the world to households and is expressed as a constantfraction of total exports. DSMSAV is net household savings due to the DSM program
and CDMREV is revenue generated from the sales of GHG mitigation as certifiedemissions reduction (CER) units (hereafter the “CDM revenue”) and equation 22 ismodified as:
The CDM revenue (CDMREV) is calculated as follows:
CDMREV ¼ adf £ cerp £ ðTPOL0CO22 TPOLCO2
Þ ð24Þ
where adf is the fraction of total CDM revenue that is required to cover administrativecosts and adaptation fees[11]. The price of CER is represented by “cerp”, and TPOL0
and TPOLCO 2 are emissions of carbon dioxide (measured in tons of carbon) in the baseand policy simulation cases, respectively.
Considering the wide range of transaction activities under the CDM project cycle(e.g. project validation, registration, and monitoring; and credit verification and
certification), 25 percent of the total CER revenue derived from the DSM program isallocated to cover transaction costs. Besides, we also carry out sensitivity analyseslater, at various levels of transaction costs (e.g. 10 percent, 20 percent, and 30 percent of the total CER revenue). The price of carbon credits is another key factor in determiningthe economic impacts of a CDM project activity. In 2006, CERs were traded in thesecondary markets at a price range between US$14/tCO2 and US$20/tCO2 (Capoor andAmbrosi, 2007). Instead of setting a price for CERs, our model simulates the DSMprogram for a price range from zero to US$50/tCO2.
Total income tax paid by the household (ITAX) is given by:
ITAX ¼i XðKi · kpi · t
K þ Li · w r · t LÞ ð25Þ
We assume here that households do not pay tax on income generated from net transferfrom rest of world to households and CDM revenue. Total household income (I)corresponds to disposable income and the imputed value of leisure, and is given as:
I ¼ THI2 ITAX þ LS·wr ð26Þ
2.3 The government sector Total government revenue (GI) consists of indirect taxes paid by firms, direct taxespaid by households, import duties, and net transfers from the rest of the world (NTRG).
GI is allocated to government expenditure (GCE) and government savings (SAVG):
where Gi and Mi are demand for the composite good and imported good, respectively,and gpi and mpi are the corresponding prices; indti and impti are indirect tax and
import duty rates, respectively. Government consumption of good i (CGi ) is keptconstant at the same level as in the base case[12].
2.4 The foreign sector 2.4.1 Import demand . Following Armington (1969), we assume domestically producedand imported goods to be imperfect substitutes. The total domestic demand for a goodor a service (G) is assumed to be a CES composite of domestically produced (GD ) andimported components (GM ). It can be expressed as:
where aDi and aMi are scaling factors of GDi and G
Mi , respectively, and s i
DM
is theelasticity of substitution between GD
i and GMi . The dual function of equation (22) is used
to derive gpi :
gpi ¼ aDi xdp
ð12s DMi
Þ
i þ aMi gpwi · E R · ð1 þ impti Þð12s DM
i Þ
h i1=ð12s DMi
Þ
; ð30Þ
where gpwi is the world price of good i , and ER is the exchange rate. Both of them areexogenous to the model.
2.4.2 Export demand . The model calculates export demand as follows:
EXi ¼ aEXi
gpwi · ER
xdpi
1i
; ð31Þ
where aiEX is the share of good i in total export demand and e i is the price elasticity
of exported good i with respect to the world price of the same good[13]. Similar to anumber of existing general equilibrium models such as Dervis et al. (1982) andBenjamin (1994) the nominal exchange rate is kept fixed; domestic prices fluctuateagainst the fixed foreign price level, which serves as the price nume raire in the model.
2.4.3 Current balance. The current balance (TBAL) refers to the difference betweentotal value outflow (e.g. imports of goods and services) from the country to the totalvalue inflow (e.g. exports and transfers from the rest of the world) to the country:
TBAL ¼ j
X M j · mp j 2 EX j · ep j
24 352 NTRH2 NTRG: ð32Þ
2.5 Investment demand The model assumes that the total current investment in an economy is equal to thetotal capital goods delivered to the economy in the previous year (Capros et al., 1997).Current investment in the sector i (INVi ) is given as follows:
INVi ¼ Ki ·kpi · ð1 þ t KÞ
invpi :ðir þ dprÞ
s i
· ð1 þ grÞ2 ð12 dprÞ
!; ð33Þ
where invpi is price of investment in sector i; “ir”, “dpr,” and “gr” are interest rate,depreciation rate and growth rate of sectoral production, respectively. Although therate of depreciation and production growth rates can vary across the sectors, the modelassumes them the same for all the sectors. Delivery of investment good i (INVDi ) isassumed to be a fixed share of total investment goods delivered to the economy:
where ai INV is the share of investment demanded by sector i in total investment
demand.
2.6 Market clearing
Total production of good i is the sum of the domestic consumption of domesticallyproduced good and exported good:
XDi ¼ GDi þ EXi : ð35Þ
Total domestic demand (G) consists of intermediate (ZA) and final demand(i.e. household consumption CH, government consumption CG, capital goods INVD,and inventory goods STK):
Gi ¼ ZAi þ CHi þ CGi þ INVDi þ STKi : ð36Þ
Inventory demand for good i (STKi ) is maintained as a fixed fraction of output fromsector i before and after the carbon tax.
It is assumed that the total time endowment (i.e. the active population) in theeconomy does not change due a policy change. This assumption implies that the totallabor supply to the economy depends on the wage rate and labor supply elasticity.Following the Walrasian approach it is assumed that the total labor supply (TLS) inthe economy is equal to the total demand of labor in the economy. This gives us thefollowing relationship:
TLS ¼TTE
j ¼
j
XL j; ð37Þ
where TTE is the total time endowment, j is the ratio of total hours to working hours,
either on a daily basis or a weekly basis. The model allows capital mobility across theproduction sectors. However, the total capital stock in the economy (TK) is assumed tobe unchanged as a result of a policy change.
2.7 Emission estimationEmissions of a pollutant p from sector n (POLn, p ) ( p ¼ CO2, SO2 and NO x ) can beestimated as follows:
POLn; p ¼f
XFFf ;ncf · ef f ; p; ð38Þ
where n represents 20 industrial sectors (except the electricity sector), the householdsector and the government sector; FF f ,n refers to use of fossil fuel f (in monetary unit) insector n; c f converts FF f to energy unit (e.g. Giga Joule, GJ) and can be expressed asGJ/Baht; and ef f , p is the emission factor of pollutant p for fuel f , expressed in kg of pollutant per GJ unit fuel consumption. Emissions from the electricity sub-sectors arealso calculated in a similar manner.
2.8 Data and ParametersThe main data needed for the study include a social accounting matrix (SAM)of Thailand for the year 1990 and elasticity parameters as implied by Figures 1-3.
The SAM was taken from Timilsina and Shrestha (2002), and elasticity values weretaken from Timilsina and Shrestha (2006).
3. Results from model simulations3.1 Impacts on economic welfareWelfare impacts of the DSM program are presented in Figure 4(a)-(c). In each figure,the curve designated by “No CDM” represents welfare effects of the DSM program inthe absence of the CDM, whereas the other curves represent the welfare effects of theDSM program under the CDM with varying CER prices. Figure 4(a) assumes that theunit cost of electricity savings is 40 percent of the price of electricity (i.e. CPR ¼ 0.4).Figure 4(b) and 4(c) assume CPR to be equal to 0.6 and 1.0, respectively.
The figures illustrate quantitatively how the welfare impacts of the DSM programchange with the following three factors:
(1) The rate of substitution of less efficient appliances with their more efficientcounterparts; this factor can be interpreted as the “scale” or the size of the
DSM program. The higher the rate of substitution means the bigger size or scaleof the DSM program.
(2) The ratio of unit cost of electricity savings to electricity price (i.e. CPR). Thisratio reflects the project economics or the attractiveness of the project (i.e. IRR)from project level cost-benefit analysis.
(3) The price of CERs. This represents the implementation strategies for the DSMprojects. The projects can be implemented in the absence of CDM (i.e. CER priceis zero) or they can be implemented under the CDM (i.e. positive values for theCER price).
For a given value of CPR and CER price, scaling up of the DSM program woulddecrease the welfare gains. For example, with CPR 0.4 and CER price US25/tCO2, theDSM program would cause a welfare gain of 0.03 percent when 10 percent of inefficientappliances are replaced with their efficient counterparts (Figure 4(a)). If the rate of replacement (i.e. scale or size of the program) is increased to 20 percent, the welfare
gain decreases to 0.02 percent. The DSM program, in fact, would cause welfare losses athigher rates of substitution, particularly when the size or the scale of the DSM programexceeds its optimal level. For example, in Figure 4(a), if the rate of substitutionincreases beyond 40 percent, the DSM program would cause a welfare loss even if theCER price is US$50/tCO2. This clearly suggests that as the scale of the DSM programexpands less room remains to improve energy efficiency further. The scaling up of the program would certainly increase the volume of CO2 abatement, but it decreaseswelfare gain or increases welfare loss. Again from Figure 4(a), a 10 percent substitutionof inefficient electrical appliances with their efficient counterparts results inapproximately 0.91 percent reduction of national CO2 emissions per year in theabsence of the CDM. If the rate of substitution is increased to 20 percent, the DSMprogram would reduce approximately 1.63 percent of national CO2 emissions per year.
This would, however, cause a welfare loss of 0.01 percent. Although scaling up of DSMprograms is a crucial issue in developing countries and the CDM is expected to help forthis purpose, expanding the size or scale of DSM options beyond their optimal limitwould not produce desired results when the economy wide impacts of the programs aretaken into consideration.
The second factor that influences the economy-wide or general equilibrium impactsof the DSM option is the project economics of the option. Our study shows that unlessthe ratio of unit cost of electricity savings to the price of electricity (i.e. CPR) issufficiently low (or the IRR of the DSM programs is sufficiently high, greater than 23percent), the DSM program might not lead to positive welfare impacts from a generalequilibrium perspective. Figure 4(a) illustrates that in the “NO CDM Case” a 10 percentreplacement of inefficient appliances with their efficient counterparts would not cause
any welfare loss when the value of CPR is 0.4 (i.e. IRR 23 percent). If the value of CPR isincreased to 0.6 (or IRR drops to 12 percent), the DSM program would cause a0.02 percent welfare loss (Figure 4(b)). This result implies that the project economics of the DSM (here measured as the ratio of unit cost of electricity savings to price of electricity) is highly critical to the economy wide effects of a DSM program. In otherword, a DSM option with lower CPR would be more attractive in a project leveleconomic analysis (or partial equilibrium approach). Such an option could also producea welfare gain from an economy-wide analysis (i.e. general equilibrium approach).
On the other hand, a DSM option with higher CPR would be less attractive in a projectlevel economic analysis and could produce a welfare loss to the economy.
The third factor influencing the welfare impacts of the DSM program is itsimplementation strategy. The study demonstrates how the welfare impacts differ
when it is implemented with or without the CDM, with varying CER price. We find thatthe welfare impacts of a DSM program improve if it is implemented under the CDM.For example, for 0.4 CPR and 20 percent rate of substitution, the welfare impact of theDSM program would be2 .036 percent in the absence of the CDM; it would increase to0.018 percent if the program is implemented under the CDM and CER price is 25/tCO2
(Figure 4(a)). This is because an implementation of the DSM program significantlyenhances its profitability. We find that the IRR of the DSM program increases from 13percent to 26 percent if CER price increases from zero to US$50/tCO2 when CPR is 0.6.At a lower value of CPR (0.4), the IRR increases from 24 percent to 41 percent whenCER price increases from zero to US$50/tCO2. Even if CPR equals 1 (i.e. net savingsfrom the DSM program is zero), a CER price of US40/tCO2 could cause positive welfareimpacts as long as the rate of substitution is below 10 percent. Thus, CDM could playan instrumental role in enhancing the attractiveness of DSM programs in Thailand asit helps improve the welfare impacts of the programs. Moreover, CDM would notonly improve economic attractiveness of a DSM program, it could also help reducedeliverable barriers to DSM programs (e.g. financial barriers). Dealing with deliverablebarriers is crucial in Thailand because only a limited number of DSM projectshave been implemented in the country despite being highly attractive economically(IRR . 25 percent) (du Pont, 2005).
The mechanism of welfare loss or gain through a DSM program is explained asfollows. The replacement of inefficient appliances with efficient appliances wouldcause an increased demand for electrical appliances services and reduce the demandfor electricity. To meet the increased demand for appliances, their production (i.e. gross
output) and imports would increase. The increase in the production of electricalappliances would mean an increase in the production and imports of those goodsused in the electrical appliances industry. On the other hand, as demand for electricitydecreases, electricity generation together with demand for fuels and materials usedfor electricity generation also decrease. Since, the increase in sectoral outputs of theelectrical appliance industry and of those industries supplying goods to the electricalappliance industry is higher than the reduction in sectoral outputs of the electricity andfuel sectors, there would be a net increase in total gross output of the economy. Theincrease in the total gross output is also accompanied by higher labor demand as wellas higher labor supply in equilibrium. An increase in labor supply implies a decrease inleisure as a household’s total time endowment is fixed. There would also be reductionsin factor prices in equilibrium. The reductions in factor prices and leisure would result
in the reduction in full income of households, which in turn causes a reduction inwelfare. For lower values of CPR and higher CER prices, the positive feedback impactsfrom fuel savings and CDM revenue would be greater than the negative feedbackimpacts of the DSM program, thereby resulting in positive welfare impacts.
3.2 Impacts on GDP, demands for goods and services and international tradeImpacts of the DSM program on GDP, gross output, intermediate demand, householddemand, imports and exports are presented in Figure 5. Under the “No CDM” case,
GDP, gross output, intermediate demand, household consumption, exports, andimports are found to increase due to the DSM no matter whether the value of CPR is0.4 or 0.6. The percentage increases in GDP, gross output, intermediate demand andexports would, however, be smaller at higher CER prices. The percentage change ingross output and intermediate demand would be even negative when CER price is
US$25/tCO2 and CPR is 0.4. This is because, at higher CER prices, capital and laborprices would also be higher than those at the lower CER prices. This implies thatproducers’ prices would be higher at higher CER prices, thereby resulting in lessexports as compared to that at lower CER prices. On the other hand, there is nosignificant effect of changes in CER prices on imports. As a result, there would be lessnet exports and lower GDP at higher CER prices as compared to those at lower CERprices. A similar explanation would also apply to changes in gross output. The impactson intermediate demand follow the impacts on gross output. On the contrary,
Figure 5.Impacts on GDP, demandsfor goods and services andinternational trade
household demand increases along with the CER price as higher CER price results inhigher CER revenue, which is transferred to households in a lump sum manner.
Under the CDM, a higher CER price would increase the welfare gain of the DSMprogram or reduce the welfare loss if the DSM causes welfare loss. However, the
reverse is found in the case of GDP. This might cause ambivalence in the selection of an appropriate indicator to evaluate the overall performance of the DSM program. Weargue that economic welfare would be a better indicator than GDP. This is because theformer also accounts for the value of leisure, the value of environmental quality and soon, whereas the latter does not. Moreover, the concept of economic welfare is widelyused as the principal indicator to measure the impacts of environmental policy optionsin most of the existing literature (Goulder et al., 1999; Parry et al., 1999; Bohringer andRutherford, 1997; Capros et al., 1997).
3.3 Environmental impactsTable II presents the impacts of the DSM program on emissions of CO2, SO2, and NO x.
As can be seen from the table, percentage reductions in emissions would increase withthe rate of replacement of inefficient electrical appliances with efficient ones. On theother hand, the price of CER and the ratio of unit cost of electricity savings to electricityprice do not have noticeable impacts on emission reductions.
One interesting finding is that the DSM program would reduce SO2 emissions at ahigher proportion than CO2 emissions. If the value of local air pollutants are alsoaccounted for, the welfare impacts of the DSM program would be higher than that wereport in this study.
Please note that although the percentage reductions in emissions vary across therate of substitution, they do not vary noticeably along with the CER prices and acrossvalues of CPR. This is because, a change in CER price changes the amount of CDMrevenue and also household income, as the CDM revenue is recycled to households.
However, it does not change fuel demand noticeably in the production sectors, whichare the main contributors of GHG emissions. Similarly, a change in CPR only changeshousehold income and welfare but does not change emissions from the productionsectors.
Combining the welfare and environmental impacts yields some interesting insights.The household sector DSM program considered here would result in positive welfareimpacts even in the absence of CDM while reducing national level CO2, SO2, and NO x
annual emissions by 0.91 percent 1.31 percent and 0.64 percent, respectively, as long asthe unit cost of electricity savings to electricity price is smaller than 0.4(i.e. IRR . 23 percent). At 0.4 CPR, a CDM scheme with a CER price greater thanUS$10/tCO2 would increase CO2, SO2, and NO x reductions to 1.6 percent, 2.4 percent,and 1.2 percent while maintaining the positive welfare effects; if the price of CER is
greater than US$25/tCO2, the corresponding emissions reductions would be 2.8 percent3.9 percent, and 2.0 percent. The CDM registration would be more instrumental whenthe DSM program is not economically attractive otherwise. If the DSM program has aCPR greater than 0.6 (IRR , 12 percent) and is implemented in the absence of theCDM, it would reduce economic welfare by 0.02 percent to achieve the same level of emission reductions when CPR was 0.4. The welfare loss would be offset if the DSMprogram is implemented under the CDM and CERs are sold at a price slightly greaterthan US$10/tCO2; welfare improvement would be 0.01 percent if CERs are
sold at US$25/tCO2. The CDM with CER price US$25/tCO2 would reduce CO2, SO2, andNO x emissions by 1.6 percent, 2.4 percent, and 1.2 percent without reducing welfareeven if the DSM program has CPR 0.6.
4. Sensitivity analysisThe sensitivity analyses focus on the elasticity of substitution parameters for thehousehold sector as the demand side CDM option considered here corresponds tothe household sector. Moreover, we also carry out the sensitivity analysis using analternative functional form (i.e. CES) to combine electricity and electrical appliances, toderive household electrical services. The sensitivity analyses on parameter valuesunder the demand side CDM option focus on the following elasticities of substitution inthe household sector:
(1) present consumption and savings ( s FCS );
(2) the composite of the aggregate fuel and the electrical service, and the aggregate
material goods ( s HDEM
) [14]; and(3) the fuel aggregate and electricity service ( s HDFEL ).
The changes in emission mitigation and welfare loss due to the changes in elasticity of substitutions are found to be very small.
In the main analysis, we represent the households’ trade off between electricity andelectrical appliances by using a Cobb-Douglas functional form (equation 14). We nowreplace the Cobb-Douglas functional form by a CES functional form (equation 16). Weconsider different values of elasticity of substitution between electricity and electricalappliances. These values are smaller than 1 (i.e. considered in the main analysis underthe Cobb-Douglas specification). As expected, for all levels of CER prices considered(i.e. from 0 to US$50/tCO2 ) and at each rate of substitution of inefficient electrical
appliances by their efficient counterparts (i.e. from 10 percent to 80 percent), thewelfare cost of the DSM program is found to increase as the value of the elasticity of substitution between electricity and electrical appliances is decreased. However, thechanges in welfare impacts as well emission reductions are not significant unlessthe values of elasticity of substitution are lowered by 50 percent.
5. ConclusionsWe examine, using a CGE model, the welfare effects of a potential DSM program thatreplaces inefficient electrical appliances with their efficient counterparts in thehousehold sector in Thailand. Our study shows empirically that the attractiveness of aDSM option from an economy-wide perspective depends upon three key factors: the
project economics of the DSM option, the implementation strategy (e.g. under CDM orin the absence of CDM) and the scale of the DSM program. Although the existingliterature, such as Dufournaud et al. (1994) and Rose and Lin (1995), argue that a DSMprogram would lead to negative welfare implications, our study finds that not allDSM programs are regressive in Thailand. The DSM options with sound projecteconomics (e.g. IRR . 23 percent) would also be attractive from a general equilibriumperspective even if they are implemented in the absence of the CDM. However,DSM programs which are less attractive from a partial equilibrium (or project level
cost-benefit analysis) approach would not be attractive from a general equilibriumapproach as they cause welfare loss in the latter case.
The implementation of DSM programs under the CDM would certainly helpimprove their welfare effects due to the revenue generated from the sales of GHG
reductions resulting from the DSM program. Economic attractiveness and the welfaregains would increase along with CER prices. In Thailand, DSM projects whichhave IRR around 12 percent and cause welfare losses from a general equilibriumperspective would result in positive welfare impacts if they are registered under theCDM and CERs are sold at US$10/tCO2 or higher.
Although the scaling up of DSM options is one of the crucial issues, and the CDM isexpected to help scale up DSM options, a huge scaling up would be counter productive.We found that the DSM program would increase welfare up to the optimal size or scaleof the program. If the size or scale of the program is increased further, its welfareimpacts start to deteriorate.
The welfare function considered in the study does not account for benefits of localair pollution reductions; if these benefits are included, welfare impacts would be higherthan that found in this study.
Notes
1. A partial equilibrium analysis accounts for only direct costs and benefits of a project or aprogram in consideration, it does not account the indirect costs and benefits that wouldincur due to linkages between various agents of the economy (e.g. industry, household,government, and international trade). Thus, it cannot estimate the economy-wide impacts of the project or program (e.g. impacts on economic welfare, GDP, and trade balance).
2. Some existing literature challenge the cost effectiveness of the DSM options pointing out thelimitations of the partial equilibrium or bottom up approach to analyze project economics of DSM options (e.g. Rivers and Jaccard, 2005; Golove and Eto, 1996; Jaffe and Stavins, 1994;
Sutherland, 1994; Wirl, 1994).3. A general equilibrium model accounts for all direct and indirect impacts of an activity to an
economy.
4. Not all equations of the model are presented here. Please Timilsina (2007) for more detaileddescriptions of the model.
5. Some studies such as Brown et al. (1999) also consider different technologies to generateelectricity while modeling the electricity sector in GTEM model.
6. A sensitivity analysis is also presented later (Section 5), considering an alternative functionalform (i.e. CES) to combine consumption of electricity and electrical appliances in thehousehold sector.
7. A similar approach has been used in a number of existing general equilibrium models(Jorgenson and Wilcoxen, 1993; Bohringer and Rutherford, 1997; Shoven and Whalley, 1992;
Ballard et al., 1985).8. The difference in household utilities between the base and counterfactual simulations is used
as the measure of the change in economic welfare in this study.
9. While most of the electricity demand in the manufacturing sector is for motive power(i.e. electrical motors), household demands for electricity is for lighting, air-conditioning andrefrigeration, etc.
10. The economics of a DSM project is highly sensitive to two factors: (i) cost of its implementation and (ii) price of electricity, which is used to estimate DSM benefits.
Hence instead of assuming fixed values either on DSM cost or electricity price (i.e. DSMbenefit), we used a ratio letting both DSM cost and electricity price to vary. Another benefitof choosing a ratio is that we do not need how the price is determined. Use of ratio helps thestudy to have generic results, otherwise the results are subjected to assumptions on DSM
cost and electricity price.11. Article 12.8 of the Kyoto Protocol states that a share of the proceeds from certified project
activities is used to cover administrative expenses as well as to assist developing countriesthat are particularly vulnerable to the adverse effects of climate change, to meet their costs of adaptation (UNFCCC, 1998).
12. Similar approach also adopted in most existing general equilibrium models (Xie, 1996;Zhang, 1997).
13. Similar approach is followed by a number of existing studies such as Dervis et al. (1982),Proost and Van Rogemorter (1992), and Naqvi (1999) to model export demand.
14. Electrical service here represents composite of electricity and electrical appliances, whileaggregate material represents the composite of all material goods used in the householdsexcept electrical appliances.
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Further reading
CDMEB (2007), Report of the 32nd Meeting of the Clean Development Mechanism Executive Board , available at: http://cdm.unfccc.int/EB/032/eb32rep.pdf
UNFCCC (2007), United Nations Framework Convention on Climate Change, available at: http://cdm.unfccc.int/Projects/registered.html
Corresponding authorGovinda R. Timilsina can be contacted at: [email protected]
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