Effects of Consumer Subsidies for Renewable Energy on Industry
Growth and Welfare: Japanese Solar Energy ∗
Satoshi Myojo†
Hiroshi Ohashi‡
January 2011
Abstract
This paper examines the effectiveness of consumer subsidies to encourage the installment of solar panels in Japan. Such subsidies can be justified on the ground that the
prices to consumers of the conventional energy alternative do not reflect their full social
costs. The paper investigates two types of subsidies: buy-back rebates and feed-in tariffs. Simulations, based on structural demand and supply estimates, indicate that the
subsidies can have either beneficial or detrimental effects on social welfare. The paper
concludes that the impacts of the subsidies critically rely on the structure of production
cost and the magnitude of external costs arising from greenhouse emissions.
Keywords: Subsidies; Photovoltaic; Externalities; Social welfare; Learning-by-Doing; Oligopoly
JEL classification:
1
Introduction
Global warming has become one of the most pressing issues in our modern society. Most of the recent
temperature increase attributes to the concentrations of greenhouse gases, resulting from human
activity such as the burning of fossil fuels and deforestation. Renewable energy sources, including
solar, wind and hydraulic power, offer clean alternatives to the conventional energy sources, and
produce little greenhouse gas without risk of depletion. Led by China and the U.S., many nations
are spending significant amounts of public money on programs that promote the use of renewable
energy.1 However, compared to the size of the governmental supports and emphasis on renewable
∗
Preliminary and incomplete
Faculty of Economics, Kobe University
‡
Faculty of Economics, the University of Tokyo
1
According to World Bank (2010), China needs an additional investment of $64 billion annually over the next two
†
decades to implement an “energy smart” growth strategy. In the American Recovery and Reinvestment Act of 2009
expanded funding of new allocations to $2.4 billion on renewable energy (EU, 2010).
1
energy, empirical research that evaluates in quantitative terms the economic benefits and costs of
such governmental subsidies on renewable energy is modest. This paper uses the data pertaining to
solar photovoltaic (hereafter PV) in Japan, and investigates the effects of governmental subsidies
on the diffusion of the technology and on the eventual economic welfare of the policy.
With the scarcity of natural conventional energy resources and the need to address global
environmental problems, such as reducing greenhouse gases emissions, Japan has spared substantial
resources on research and development of renewable resources since the 1970s. Their particular
attention has been paid on small-scale roof-top solar PV, primarily intended for residential use.2
To encourage the adoption of PV system, Japanese government has introduced consumer subsidies
since the 1990s. From the viewpoint of economics, such subsidies can be justified in that they
correct for market failure arising from environmental externality. However, there is a severe lack
of empirical research that identifies the existence of and measure the magnitude of the impacts of
consumer subsidies on environmental externalities and resulting economic welfare. Such empirical
research would aid in evaluating the validity of such subsidies policies on renewable energy sources.
In particular, this paper focuses on Japan’s Residential PV Dissemination (hereafter RPVD)
Program initiated in 1994. The federal subsidy has been provided for households to cover half of
its installment costs for roof-top PV systems. Upon the enactment of the program, Japan took the
lead in the world production of solar panels: Japanese production volume increased more than five
fold, with the average annual growth rate of 40 percent through to the mid 2000s (See Figure 1).
The situation was changed, however, when the RPVD Program ended in 2007: the Japanese market
declined to 210MW, overtaken by the markets of China, Taiwan and the EU. It is interesting to note
that the period of subsidy provision coincides with a period of remarkable growth in the Japanese
PV system market: indeed, EC (2010: 14) describes: “[T]he end of the Residential PV System
Dissemination Programme was considered the main reason for the decrease of new installations
[in Japan].” However, it is not clear as to the extent to which the RPVD Program promoted the
diffusion of PV systems, and the sign and magnitude of which it had an impact on social welfare.
Since there seems no obvious way to perform controlled experiements regarding the federal
subsidy program, we instead conduct counterfactual simulations in the following two steps. First,
use observed data along with an economic model to recover estimated parameters of underlying
economic primitves that are invariant to policy environment. In our application, we estimate
parameters of households demand for PV system, and derive firm marginal costs of production.
The second step involves using the model to simulate change in equilibrium outcomes resulting from
change in the provision of the subsidy. Using the simulation method, we evaluate the effects of the
2
Developing residential PV system depends partly on the quality of semiconductor, and it is speculated that
Japanese government has a long standing interest in promoting the industry.
2
consumer subsidy program on the diffusion of PV systems. Furthermore, our structural estimation
method allows us to perform the benefit-and-cost analysis of the program with an explicit account
of the environmental externalities attributed to the carbon emissions.
Our simulation result, based on estimates obtained from the data in the study period from 1997
to 2005, demonstrates that the RPVD Program doubles the PV installation by boosting additional
540MW, leading to the reduction in the carbon emissions by approximately 4 million ton. This
amount, however, accounts for a mere third of one percent in terms of the annual emissions in
Japan. Whether the program improved social welfare depends upon external costs in the reduction
of carbon emissions. As the reduction in the emissions become costly, the Program would be more
likely to be justified in the economic welfare point of view. The paper indeed finds that, in order
for the Program to have improved the welfare, the economic value of carbon dioxide should have
been well above the transaction price at Japan’s voluntary emissions trading scheme.
In 2008 when the RPVD Program was supended, the former prime minister, Yasuo Fukuda,
pledged to increase the cumulative PV installed capacity by 10 times, from the 2005 level, to be 14
GW by 2020. With this goal in place, the Ministry of Economy, International Trade and Industry
(hereafter METI) introduced a federal feed-in tariff (hereafter FIT) program in 2009, which allows
for the purchase of excess electricity from PV systems at a higher rate, and requires to share the
cost burden across all electricity customers. Based on the obtained demand and cost estimates,
this paper performs another simulation exercises to assess FIT scheme, to provide subsidies for any
households who generate solar power to the grid.
Using the structural estimates of demand and supply, we predict the future paths of equilibrium
prices and outputs of PV systems up until 2020. We examine in total nine scenarios in the respective
trajectories of production cost and the amount of FITs. The paper concludes that the impacts of
the subsidies critically rely on the structure of production cost and the magnitude of external costs
arising from greenhouse emissions.
The rest of the paper is organized as follows. Section 2 provides an overview of the Japanese
PV market, along with a discussion of the government policies in place in the market. The section
finds that providing a subsidy to cover a portion of household’s installation cost was a seemingly
effective policy instrument of the period. Section 3 outlines a model, and introduces an estimation
framework on both demand and supply sides of the PV industry. Section 4 presents estimation
results and their interpretations, and also offers several specification tests to check the sensitivity
of the results. It finds only a small learning-by-doing effect, which may reflect the nature of PV
production process. Section 5 analyzes the results of the simulation exercises used to measure the
effect of the subsidy policy on the evolution of the industry. It demonstrates that, while the subsidy
provided by the government until 2005 accounted for an average half of the output increase, it is
3
not uniquely justified in the welfare viewpoint. The section also examines the effect of Japanese
FIT, introduced in September of 2009, on the penetration of PV and its economic welfare. The
paper concludes that the impact of the subsidy policies critically depends on the future production
cost and the economic value of carbon dioxide. Section 6 contains the study’s conclusions. Data
appendix follow.
2
Overview of Solar PV in Japan
Photovoltaics (PV) is a method of generating electrical power by converting solar radiation into
direct current electricity using semiconductors that exhibit the PV effect. Partly with efforts to
decrease the dependence on imported oil and to stimulate the growth of its semiconductor industry,
Japan has been seeking to expand solar power since the late 1990s. As shown in Figure 1, the
country is a leading manufacturer of solar panels and is in the third in the world in total solar
power installed, behind Germany and Spain.
PV power generation employs solar panels comprising a number of cells. Various materials have
been used for the cells, including silicons of monocrystalline, polycrystalline and amorphous. To
focus on PV of relatively homogeneous characteristics, we limit the study period from 1997 to 2005,
the period in which a majority of PV panels used polycrystalline silicon in Japan with negligible
imports. Nine companies controlled nearly all of the Japanese market during the study period:
Sanyo Electric, Kaneka, Kyocera, Mitsubishi Electric, Sharp, Air Water, Cannon, Panasonic and
Mitsubishi Heavy Electric. Because imports were negligible, our analysis focuses on these nine
firms.
Japanese PV production used wafer-based crystalline silicon technology. A major advantage of
this technology was that complete production lines could be bought, installed and activated within
a short time frame. This aspect of the production technology ensured the ease of market entry,
contributing to overcapacity of the market.
The Japanese government is seeking to expand solar power by enacting subsidies and recently
a feed-in tariff scheme. The most important federal programs initiated was the Residential PV
System Dissemination (RPVD) Program. Between the fiscal year 1994 and 2005, it funded for total
installations of over 930MW, comprised of over 250,000 residential PV systems, and successfully
reduced the buy-down rebate, which coverd 50% of the installation cost, as long as the rebate is
under the upper limit. As is shown in Figure 2, the ceiling of the rebate decreased from JPY
900,000 per kW in 1994 to mere 20,000 in 2005, indicating the decline in PV prices.
In the subsidy provisional period from 1997 to 2005, Japanese production volume increased
more than five fold, with the average annual growth rate of 42 percent. The situation was changed,
however, when the RPVD Program ended in the fiscal year of 2006. Japanese production stagnated
4
in 2007, and only recovered slightly in the following year, when the program was reinstated (see
Figure 2). In the meantime, China and the Europe accerelated their production, placing Japan
the third of the world PV production.3 It is interesting to note in Figures 1 and 2 that the period
of subsidy provision coincides with the period of remarkable growth in the Japanese PV market.
In the following sections, we discuss how the RPVD program affected the equilibrium price and
output of the PV market by use of a structural estimation method.4
Under the leadership initiated by the former prime minister Fukuda, Japanese government aims
to increase the cumulative PV installed capacity by 10 times, from the 2005 level, to be 14 GW
by 2020, and by 40 times to be 53 GW by 2030. The former Prime Minister also pledged to
install PV systems on 70% of newly built homes by 2020. To align with the Fukuda Vision, federal
agencies such as the Ministry of Economy, Trade and Industry (hereafter METI) have re-launched
the RPVD Program for the residential market from 2009. The METI also enacted a feed-in tarriff
(FIT) on November 2009 that requires utilities to purchase excess solar power to the grid by homes
and businesses and pay twice the standard electricity rate for that power. As of 2009, FIT policies
have been enacted in more than 60 jurisdictions around the world. The paper performs simulation
exercises to assess the effect of Japanese FIT scheme.
3
A Model of the Japanese Solar PV Market
This section describes a model used to explain the Japanese solar PV market in the period from
1997 to 2005. We begin with the model of solar PV production technology. Availability of firmlevel factor input data is limited; therefore, we build a cost function incorporating three important
elements of the the solar PV production process: learning by doing, physical capital and input
material. The model does not allow for knowledge spillovers; the limited availability of firm-level
observations prevents us from testing the existence of knowledge spillovers across firms nor within
a firm.
Learning by doing is inherently difficult to measure because it is unobervable. Following the
treatment in the literature, I use a cumulative output level, z, as a proxy for the firm’s learing level.
Each company accumulates its experience only by producing steel. The transition of experience
for firm i is thus described by zi,t = δzi,t−1 + qi,t−1 , in which qi,t−1 is firm i’s PV output at year
t − 1. The cumulative output is calculated starting from 1997, when the firm level output data
were made available. We model firm i’s marginal cost, mci,t as the following Cobb-Douglas form:
3
4
Note that the data compiled by GTM Research do not show the outputs of China and Taiwan separetely.
This paper ignores the non-residential PV’s, because they were not covered by the subsidy program, and accounted
for a mere fraction of the overall PV market in Japan.
5
ln (mci,t ) = ψ ln (ki,t ) + λ ln (zi,t ) + γ ln (mt ) + η i,t
(1)
This functional form is useful in that the marginal cost has the common learing curve assumption, the constant elasticity version, with an additive error term, η. Capital stock for firm i at year
t is denoted by ki,t . It is important to control for input prices in the estimation of a learning rate.
Otherwise the estimated learning rate would be biased upward with decreasing input prices, even
withtou any learning actually taking place. The primary input for PV production is multi-crystal
silicon, represented by mt . All firms are assumed to face the same input price. A set of Greek
letters, Θc ≡ {ψ, λ, γ}, is the supply parameters to be estimated.
Other than three factors described in Eq.(1), important influences on the unit cost include R&D
activity and technological innovation. Such supply shocks are captured by the term, η i,t . I allow
this term to have a firm-specific dummy variable, ν i , and a time trend, $t , in the estimation:
η i,t = ν i + $t + εi,t , where εi,t is an error term. This treatment deals with time-invariant efficiency
difference among firms, and industry-wide trend of supply shocks.
Since it is difficult to find accurate cost data to directly analyze Eq. (1), we estimate the pricecost margins by building a competition model and thereby obtain the cost parameters. In particular,
we construct a PV maker’s profit maximization problem and solve the first-order condition. We
establish the following supply-side model. Suppose that firm i competes and chooses its output
at time t in the domestic market j for solar PV’s. In the estimation, we use a regional data,
which include 47 prefectures in the country. In each period t, firms face the demand at region j,
¡
¢
Qj,t ocj,t , xj,t , ξ j,t ; Θd , in which ocj,t is the cost of owning a PV system incurred by a household
who reside in region j at time t. As we discuss shortly below, since the owner of the PV system can
sell electricity generated from the panel to a grid at a specified price, ocj,t is usually lower than the
PV system price. The respective vectors of xj,t and ξ j,t are demand characteristics and the error
for region j at time t. The demand characteristics at region j contain several variables, as detailed
in Section 4.1. We assume that xj,t is observed by the econometrician, while ξ j,t is unobserved.
Nevertheless, since both the demand shifters are observed by firms, we correct for this potential
endogeneity in the next section. The set of demand parameters to be estimated is Θd .
The household’s cost for installing a PV, ocj,t , consists of three elements: a PV system price
(pB
j,t ), a buy-back rebate offered by the government (Gt ) and the sum of discounted profits arising
from transacting electricity generated from the PV (mj,t ). We assume that the PV system has
durability of T years, in which T is determined exogenously and uniformly across all the systems.
Once the PV system is installed, it generates electricity in the annual kWh of Ej,t at the typical
household who resides in region j in year t. The household can sell to a grid excess PV generated
electricity, SEj,t kWh, at the price, pSE
j,t , specified by the government. Thus the household saves
6
BE
the electricity bill of pBE
j,t · (Ej,t − SEj,t ), in which pj,t is the unit electricity price. The household
discount future profits according to a common discount factor, δ, with a common information set.
The discount factor is set equal to 0.95. The ownership cost of the PV system is described as
follows:
´
³
− Gt − mj,t
ocj,t = r pSt + pothers
t
where
¡
¢ 1 − δT
BE
mj,t = pSE
j,t · SEj,t + pj,t · (Ej,t − SEj,t ) ·
1−δ
The domestic demand at time t is thus defined as the sum of demand for all regions: Qt ≡
¡
¢
d
j Qj,t . The inverse demand function is derived as pt = P Qt , Gt, xt , ξ t ; Θ , in which j-th elements
P
of xt and ξ t are xj,t and ξ j,t , respectively. We discuss demand estimation in Section 4.1.
We now model the behavior of the firm that sells PV cells and modules. Note that virtually all
PV’s sold in Japan were domestically manufactured. We treat the amount of export as exogenously
given, because exported steel is reasonably assumed to be competitively supplied in the world
market. Thus, we restrict our attention to the domestic market in the following analysis. In each
period, firm i observes the shocks ξ t and η i,t , and simultaneously chooses the output quantity qi,t
to maximize the following per-period payoff:
in which Qt ≡
P
i Qi,t .
³ ³
´
¡
¢´
pt Qt ; Gt, xt , ξ t , Θd − mci,t ki,t , η i,t ; Θc · qi,t .
(2)
The first-order condition derived from firm i’s static profit maximization
under Cournot competition takes the familiar form of the Lerner index, namely,
pt − mci,t
1 qi,t
·
=
,
pt
|εt | Qt
(3)
where εt is the elasticity of demand with respect to price. During the study period, the planned
capacity increases were way above the market growth, leading to the low utilization rate (EU,
2005). We thus do not consider the possibility of capacity constraint in Eq.(3). Using the demand
estimates obtained in Section 4 and the data, we can derive mci,t from the first-order condition in
Eq. (3). We now report the estimation results in the next section.
4
Empirical Results
This section applies the estimation models described in the previous section to the annual frequency
data set for the period from 1997 to 2005. We first discuss the estimation of the demand, followed
7
by marginal cost functions. The summary statistics pertaining to important variables used in the
estimation appear in Table 1, and the data sources are presented in the Data Appendix. Section
4 uses the estimates reported in this section to assess the economic consequence of the subsidy
policies.
4.0.1
Demand Estimates
We follow the literature on the homogeneous good demand model and estimate the demand function
of solar PV, Qj,t (pt ; wt , ξ t , Θ). Demand estimation typically involves a functional-form assumption.
The shape of the demand function determines the demand elasticity with respect to price, and thus,
influcences the marginal cost estimate. For example, under our assumption of Cournot competition
with a constant marginal cost, a linear-demand specification imposes the LHS of Eq. () proportional
to firm i’s output quantity, while a log-linear specification restricts the LHS proportional to firm i’s
market share. We are therefore interested in comparing the implied marginal cost estimates from
a variety of commonly used functional forms. Following Genesove and Mullin (1998), we estimate
three different demand functions of the linear, quadratic and log-log forms:
L
L
L
L
Qj,t = αL
0 + α1 xj,t + α2 (pt − Gt ) + vj + ξ j,t
Linear
Log-linear
Log-log
Q
Q
Q
L
ln (Qj,t ) = αQ
0 + α1 xj,t + α2 ln (pt − Gt ) + vj + ξ j,t
(4)
LL
LL
LL
LL
ln (Qj,t ) = αLL
0 + α1 xj,t + α2 (pt − Gt ) + vj + ξ j,t
A
d
where αA
B belongs to Θ , in which A = {L, LL, Q} and B = {0, 1, 2, 3}, and ξ t is the demand error
for each specifications. Note that αA
1 is a vector of the coefficients on both demand characteristics
and a time trend. We also include the region-specific component, vjA , to deal with differences
associated with geographical locations that do not change over time. The potential endogenous
variables in Eq. (4) are Qj,t and pt . To correct for this possible endogeneity problem, we employ
two-stage least squared (2SLS) estimation. Regarding the instrument, we use the price of the major
factor input used in the production of solar PV, namely polycrystalline silicon. Polysilicon was a
key component for integrated circuit, embedded in solar PV in our study period.5
Table 2 presents two broad columns of demand estimates. Each column contains the three
demand specifications presented in Eq.(4). The upper portion of the table contains the estimates
of the demand coefficients. The first column is based on the ordinary least squred (OLS) method,
and the second is based on the 2SLS method.
The estimated coefficients shows that the variables of new housing starts, sunshine in hours
and the number of households have positive influences on the PV diffusion in region j. The two
5
While the upgraded metallurgical-grade silicon is now considered as a cost-effective alternative to polysilicon, it
was not available in the study period.
8
variables, the average electricity consumption per household and the share of PV generation with
respect to the eletricity consumption per year, have negative coefficients in the linear form. This
may be due to that the statistical correlation is present between the two variables: the correlation
coefficient is -0.59. The coefficient of the household income are neither statistically nor economically
significantly different from zero.
The implied demand elasticity with respect to price is calculated for each specification on the
basis of the obtained demand estimates. The elasticity is fairly elastic: its values range from 0.99
to 10.9. Comparison between the demand elasticities obtained from the OLS and 2SLS methods
points to the successful elimination of the endogeneity from the positive correlation between the
ownership cost and demand shock: The mean value of the implied demand elasticity obtained from
2SLS estimates is more than three times lower than those obtained from the OLS estimates. In
the reminder of this paper, we use the log-linear form as the base specification of PV demand,
because it achieves the highest log-likelihood concentrated with respect to a variance parameter.
The use of the log-log demand estimates make no qualitative change to the main empirical results
discussed in the subsequent sections. The linear form has a poor match to the data and generates
a considerably elastic demand estimates, leading to overstating qualitative effects of government
subsidy policies.
4.0.2
Cost Estimates
Using the demand estimates obtained in Table 2 and the first-order condition in Eq.(3), we calculate
the marginal cost of PV module production and estimate Eq.(1). Three estimation results are
presented in Table 3. While the first specification assumes that the marginal cost error, η i,t ,
follows i.i.d and apply the OLS estimation method to the data, the other two specifications allow
for other types of error structures and use the feasible generalized least squared (FGLS) method.
Specification (3-A) includes the firm-specific component, ν i , along with a yearly trend, $t , already
introduced in Section 3. Specifications (3-B) and (3-C) allow for first-order autocorrelation, namely
AR(1), in εi,t , and (3-C) further incorporates additional heteroskedasticity in the error. The table
shows the autocorrelation coefficient, which is common across all the firms, is estimated at the value
of 0.39, and the Breusch-Pagan test did not reject the presence of heteroskedasticity. Nevertheless,
all specifications yield similar estimates. The capital stock variable are neither statistically nor
economically significantly different from zero. This result is consistent with the discussion made in
Section 2: the trade press was concerned about supply glut during the study period. It also makes
sense that the price of the primary input, polycrystalline silicon, has an significant effect on the
marginal cost of production. The elasticities are estimated in the range from 0.17 to 0.21.
While the average values in the learning-parameter estimate are of similar magnitude across the
9
specifications, only (3-C) yields a precise estimate. The learning rate is 0.8%, considerably lower
than values found in the literature. Note that the learning rate is the magnitude of the cost drop
with doubling the experience. It is calculated as 1 − 2λ . Ghemawat (1985), for example, reviewed
97 academic studies from the learning-curve literature. He finds that the learning rates for the vast
majority of products, namely 79 out of 97 examined, fall in the range of 11 − 21 %. The small
learning-by-doing effects found in Table 3 have an important bearing on the policy effects discussed
in the next section.
5
Measuring the Impacts of Consumer Subsidy Policies
This section comprises two subsections and assesses the economic consequences of the consumer
subsidy policies. We evaluate two independent consumer subsidy policies: the RPVD Program and
FIT scheme. The RPVD Program was initiated in 1994 and continues up to now, with the two-year
suspension in 2006 and 2007. The FIT scheme has been implemented in the mid 2009. On the
basis of the model and the estimates reported in the previous section, we perform a retrospective
and ex-post evaluation on the former policy, and a prospective and ex-ante one on the latter. To
be specific, Section 5.1 evaluates the welfare trade off associated the RPVD Program during our
study period from 1997 to 2005 by comparing the counterfactural situation in the absence of the
Program. The paper finds that the Program doubled the penetration of the PV system despite of
the small learning effect found in Section 4.2.
The section also assesses the effectiveness of the FIT scheme endorsed in Japan in 2009. Under
various future scenarios regarding the marginal cost and the tariff rates, Section 5.2 performs
simulation exercises to predict the welfare consequence of this policy into the year of 2020. The
analyses made in this section illustrate that the impacts of the consumer subsidies, either a buyback rebate on the PV system or a transaction subsidy on the PV generated electricity, critically
rely on the structure of production cost and the magnitude of external costs arising from greenhouse
gas emissions.
5.1
5.1.1
Impacts of the RPVD Program
Impact on outputs
This subsection intends to measure the impacts of the RPVD Program on industry growth and
social welfare by asking what would have happened to the PV market had there been no provision
of such governmental support. I conduct the following experiment in determining a firm’s output
level, leaving long-run strategies, such as the level of production capacity, constant. I assume no
subsidy to household’s PV purchase in the period from 1997 to 2005 (the subsidy was suspended in
10
2006) to calculate new equilibrium firm outputs for each year. This assumption should not change
the nature of a firm’s cost function estimated in Section 4, because the subsidy appeared exogenous
to the promotion of the diffusion of PVs, as discussed in Section 2.
How we obtain the counter-factual values is worth explaining. Using the estimates obtained
from the log-linear form shown in Table 2, we compute the current output level using Eqs.(4) and
(3) for 1997. We accumulate the calculated current outputs to the pools of experience, zi,t , and
then use the result of the computation in the next period. We repeat the same process for each
year until the end of the study period.
Figure 2 shows the effect of the subsidy on the industry output level by year. The dotted line
indicates the industry outputs under the subsidy provision, and the dushed line represents those
outputs without the subsidy. The figure indicates that, throughout the study period, the subsidy
provision appeared to promote the diffusion: indeed, 350MW of PV’s were installed in total in the
period from 1997 to 2005, boosting 32 percent of sales increase over the nine years.
5.1.2
Impact on Social Welfare
This section computes the impacts on social welfare of the RPVD Program in a partial equilibrium framework. Social welfare is assumed to consist of four elements: consumer and producer
surpluses, subsidies and externality arising from carbon dioxide emissions. The first two elements
are calculated on the basis of the model and obtained estiamtes of the demand and marginal cost
functions introduced in the previous section. The amount of subsidies is obtained by multiplication
of the subsidy rate and the sales of PV system, both presented in Figure 1. Provision of consumer
subsidies to renewable energy in principle can be justified when it corrects for market failure arising
from environmental externality. In the context of this paper, the RPVD Program, along with the
FIT scheme discussed in Section 5.2, would ameriorate the externality accrued by carbon dioxide
emissions. We first discuss the extent to which the carbon dioxide emissions would be reduced by
the introduction of PV, and then turn to the measurement of such externality in the monetary
unit.
Provided that the electricity demand is constant, the diffusion of PV’s replaces conventional
electric supply sources. According to NEDO (2009), the residential roof-top PV under our study
has CO2 emission intensity of 58.6 g per kWh with the product duration of twenty years. Note that
the emission intensity is positive, because it releases the emissions at the stages of production and
scrapage. Comparing with the average emission intensity of 445.6 g per kWh for the conventional
electric supplies, the diffusion of PV would save 387 g per kWh, or 8.2 million tonnage in total for
the period from 1997 to 2005. This amount saved by the PV would not be considered as large; as
of 2006, the annual CO2 emission is 1.3 billion tonnage. Thus, the RPVD Program would have cut
11
down 34.1 % of carbon dioxide emissions, as it promoted 350MW of PV systems found in Section
5.1.1.
This paper uses three estimates on economic values in metigating carbon dioxide emissions. The
first estimate is to incorporate non-market impacts, such as effects on ecosystems or human health,
in a monetary metrics. Tol (2005) reviews 28 published studies that contain a total of 103 estimates,
including a wide array of sensitivity analysis. It combines them to form a probability density
function on the marginal damage costs of carbon dioxide emissions. The probability distribution
is found to be strongly right-skewed with the average value of 3044 JPY per tonnage of CO2 , the
value of which we use as an estimate.
The second estimate is derived from the marginal cost of mitigating one tonnage of carbon
dioxide emissions. The Ministry of the Environment reports in 2008 that the average abatement
cost of reducing carbon emissions by 70 percent from 1990 level by the year of 2020 ranges from 6818
to 10882 JPY per tonnage of CO2 . The last estimate is obtained from the emissions trading system
initiated by the MOE in 2005. During the period from 2005 to 2007, the total of 31 companies
voluntarily participated in the emissions transactions to trade the quota of mere 82,000 tonnage of
carbon emissions. the average unit price was 1212 JPY.
To sum, we use in the analysis below the three monetary estimates of carbon emissions per
tonnage of CO2 in order of the value: 10882 JPY (the upper limit of the abatement cost), 3044
JPY (the average of the marginal damage cost) and 1212 JPY (the average price at the emissions
trading). The economic value of reducing carbon emissions are calculated in the range from 10.3
billion to 93.0 billion JPY.
Table 3 lists the four elements, for each of which we calculate the difference between the value
in the presence of policy and the one without it. The table finds that the economic value of carbon
emissions critically depends on whether the RPVD Program was beneficial to social welfare. In
order to justify the Program in the economics perspective, the magnitude of externality arising
from the carbon emissions plays a critical role.
5.2
Impact of the FIT Scheme
This section assesses the feed-in tariff scheme introduced in 2009, in which owners of grid connected
PV systems are paid a premium rate for surplus electricity generated. As discussed in Section 2,
this nationwide scheme aims to accomplish a goal pledged by the former prime minister, Yasuo
Fukuda, that the cumulative PV installed capacity be increased by 10 times, from the 2005 level,
to be 14 GW by 2020. While this FIT scheme would create an additional incentive for the purchase
of PV system by reducing the ownership cost, ocj,t , it is not totally clear as to whether or not the
incentive provided by the current scheme is suffice for the cumulative capacity to reach 14GW by
12
2020. While the FIT scheme, albeit a variety of forms, has been adopted by as many as 60 countries
across the world, to the best of our knowlege, this is the first paper to quantitatively assess the
effectiveness of the FIT scheme on industry growth and social welfare.
The initial rate in 2010 paid to owners of systems was 48 JPY per surplus kWh produced,
double the current rate of 24 JPY. The premium rate was calculated in order to enable customers
who have purchased grid connected systems to recover their initial outlay over 10 years. This rate
will be reviewed every year, so as to be able to reflect the reduction in the PV system price. The
government anticipates that the cost of PV system would decline by half in the next five years, and
thus the premium rate would drop to 24 JPY in 2015. On the contrary, if the system cost along
with the system price, would not decline, the premium rate should stay the same in order to fully
cover the initial outlay on the PV system.
In the evaluation of Japanese FIT scheme in the period from 2010 to 2020, we consider three
scenarios, (a) - (c), for premium rates, and three scenarios, (A) - (C), for PV system costs, as
follows:
(a) The premium rate were set at 24 JPY from 2010 to 2020.
(b) The premium rate were set at 48 JPY from 2010 to 2020.
(c) The premium rate were set at 48 JPY in 2010, and would decrease to the level of
24 JPY in 2015 and afterwards.
(A) The marginal cost of PV system would stay at the same level as that in 2007 in the
period from 2010 to 2020.
(B) The marginal cost of PV system would decline at the same rate as in the study
period (namely 3.6 % per year).
(C) The marginal cost of PV system would decline to a half of that in 2008, and stay
at the level afterwards.
Combining these respective three scenarios, we have in total nine future scenarios. Note that
for a particular PV system, the same premium rate is applied for 10 years, and the amount of the
rate differs depending on the year when the system is purchased.
Figure 3 shows the diffusion paths of PV systems under nine scenarios. Note that scenario (xY )
combines the scenario for premium rates, x = {a, b, c} and that for PV system costs, Y = {A, B, C}.
The figure shows that, for any scenario of marginal cost, the diffusion accerelates as we move from
scenarios (a) to (c). The highest PV penetration level is achieved under scenario (bC) to reach at
61MW in 2020, by far beyond the government goal of 14MW. On the other hand, scenario (aA)
shows the lowest penetration of mere 6.5MW in 2020. Figure 3 also witnesses that the production
cost decline plays a crucial role in the diffusion of PV systems.
13
Finally we also compute the welfare consequence of Japanese FIT scheme. We follow the
same procedure described in the previous subsection, and calculate the economic value of carbon
emissions, along with consumer and producer surpluses and the amounts of subsidy embedded in
the premium rates. Table 4 summarizes the results. The table concludes that the impacts of the
subsidies critically rely on the structure of production cost and the magnitude of external costs
arising from greenhouse emissions.
6
Conclusion
This paper provided an empirical framework to assess the role of consumer subsidies in the adoption
of solar energy source. In principle, consumer subsidies would be justified if they correct for market
failure arising from greenhouse gas emissions. Using the simulation method, we evaluate the effects
of the program on the industry growth and social welfare with regards to the diffusion of solar
energy.
Our simulation result, based on estimates obtained from the data in the study period from 1997
to 2005, demonstrates that the RPVD Program doubles the PV installation by boosting additional
540MW, resulting in the reduction in the emission of carbon dioxide by approximately 4 million
ton. This amount, however, accounts for a mere third of one percent in terms of annual emissions
of carbon dioxide in Japan. Whether the program improved social welfare depends upon external
costs of the emissions.
Furthermore, this paper performed another simulation exercises to assess the feed-in-tarriffs
(hereafter FIT). Using the structural estimates of demand and supply sides, we predict the future
paths of equilibrium prices and outputs of solar PVs up until 2020. We examine in total nine
scenarios in the respective trajectories of production cost and the amount of FITs. The paper
concluded that the impacts of the subsidies critically rely on the structure of production cost and
the magnitude of external costs arising from greenhouse emissions.
References
[1] European Commission, 2010, PV Status Report 2010, JRC Scientific and Technical Reports.
[2] Genesove, D., and W. P. Mulllin, 1998, “Testing Static Oligopoly Models: Conduct and Cost
in the Sugar Industry, 1890-1914,” Rand Journal of Economics, 29(2): 355-77.
[3] GTM Research, 2006-2010, PV News.
[4] NEDO, 2009, “Research Report on Life-cycle Evaluation of PV,” No. 20090000000073, (in
Japanese)
14
[5] Tol, R.S.J., 2005, “The Marginal Damage Costs of Carbon Dioxide Emissions: An Assessment
of the Uncertainties,” Energy Policy, 33: 2064-74.
[6] World Bank, 2010, Winds of Change – East Asia’s Sustainable Energy Future
15
TABLE 1
Summary Statistics: 1997 -2007
Shipment of Residential PV
kW per firm
Growth rate in Sales
Production Capacity
kW per firm
Growth rate in Sales
PV system Price (JPY/kWh)
in constant 1997 price
Number of Firms
HHI
Mean
Min
Max
Std Error
19366.0
27.2
3247.7
-20.8
37391.7
105.0
11688.0
36.2
57721.8
38.1
50.9
5833.3
-0.7
42.8
132414.3
65.3
67.4
50535.8
21.2
9.2
6.6
3180.2
6.0
2580.4
7.0
3589.0
0.5
298.2
Mean
Min
Max
Std Error
Prefecture-level Data
Number of Households
1034498 201174 6060432 1075242
Income per Household (10,000JPY/month)
56.1
35.5
80.9
6.8
Electricity Consumption per Household (MkW 5.66E-03 4.02E-03 9.32E-03 6.93E-04
Annual Sunshine in hours
1888.6
668.4
2401.9
230.3
TABLE 2
Demand Estimates
Constant
PV ownership cost (JPY)
Share of PV electricity w.r.t
electricity consumption
Number of Households
Household income
Number of Housing Starts
Electricity consumption (kWh)
Annual Sunshine in hours
Trend
R2
F-stat
Elasticity w.r.t ownership cost b
(1) Linear
-36.572
(2561.500)
-32.166 *
(17.315)
-783.990
(1660.100)
2.197E-04
(1.579E-04)
-1.151
(1.118)
0.146
(0.027)
-0.644
(0.236)
2.417
(0.412)
552.400
(38.617)
0.580
87.565
-2.430
***
**
***
***
***
OLS
(2) Log-Log
(3) Log-Linear
-27.759 ***
-30.803
***
(3.867)
(3.715)
-1.035
***
-0.021
***
(0.253)
(0.005)
2.026
***
2.012
***
(0.319)
(0.319)
0.380
(0.093)
0.029
(0.209)
0.426
(0.096)
1.661
(0.406)
1.988
(0.215)
0.973
(0.054)
0.817
283.274
-1.035
***
***
***
***
***
***
0.380
(0.093)
0.026
(0.209)
0.426
(0.097)
1.672
(0.406)
1.984
(0.215)
0.970
(0.054)
0.816
282.440
-0.991
***
***
***
***
***
***
2SLS
(4) Linear
(5) Log-Log
(6) Log-Linear
9027.500 **
-22.403 ***
-28.464
***
(3180.400)
(4.169)
(3.848)
-144.740 ***
-2.184
***
-0.048
***
(28.084)
(0.387)
(0.008)
-3583.500 *
1.788
***
1.779
***
(1808.900)
(0.330)
(0.332)
1.863E-04
(1.645E-04)
-0.211
(1.178)
0.148
(0.028)
-0.941
(0.253)
2.050
(0.434)
550.670
(40.192)
0.545
75.961
-10.933
***
***
***
***
***
0.436
(0.095)
0.109
(0.214)
0.378
(0.099)
1.624
(0.414)
1.800
(0.224)
0.901
(0.058)
0.809
269.748
-2.184
***
***
***
***
***
***
0.446
(0.096)
0.117
(0.215)
0.368
(0.100)
1.643
(0.417)
1.757
(0.227)
0.880
(0.059)
0.807
265.175
-2.292
***
***
***
***
***
***
Notes:
Starndard error of the estimates is shown inside parenthesis. Subscripts ***, ** and * indicate significance at the 99-, 95- and 90-percent confidence levels.
Elasticity w.r.t ownership cost is the average of annual elasticity of regional demand with respect to ownership cost in the period from 1997 to 2005.
TABLE 3
Cost Estimates
OLS
(1)
Capacity
Learning by Doing
Polysilicon Price
Trend
Firm-specific components
Air Water
Cannon
Kaneka
Kyosera
Matsushita
Mitsubishi Electric
Mitsubishi Heavy Electric
Sanyo Electric
Sharp
Heteroskedastic residuals
Breusch-Pagan tests
AR(1) coefficient of residuals
Durbin Watson test
F statistic
Wald χ2 statistic
-0.0148
(0.014)
-0.016
(0.014)
0.205 ***
(0.029)
-0.221 ***
(0.031)
4.188
4.206
4.208
4.176
4.181
4.205
4.195
4.207
4.095
No
4.950
0.727 **
209.950 ***
-
FGLS
(2)
-0.013
(0.012)
-0.011
(0.012)
0.191 ***
(0.026)
-0.238 ***
(0.028)
4.186
4.198
4.204
4.157
4.184
4.193
4.202
4.194
4.082
No
0.387
6.76E+05 ***
(3)
0.002
(0.006)
-0.011 **
(0.003)
0.171 ***
(0.012)
-0.255 ***
(0.010)
4.131
4.135
4.122
4.046
4.128
4.093
4.129
4.093
3.964
Yes
0.387
4.89E+06 ***
Notes:
The first three explanatory variables in the table are in the logarithm.
Standard error of the estimate is shown inside parenthesis. Subscripts ***, ** and * indic
at the 99-, 95- and 90-percent confidence levels.
Breusch-Pagan tests would not reject the presence of heteroskedasticity. The AR(1)
coefficients are estimated, which are assumed to be common across firms.
TABLE 4
Effects of Subsidy on PV Adoption and its Price. 1997-2005
PV Adoption
PV Price
70 300 W/o Subsidy
65 250 W/ Subsidy
60 MW
10,000JPY/kW
200 150 100 55 W/ Subsidy
50 W/o Subsidy
50 45 0 40 1997
1999
2001
2003
2005
1997
1999
2001
2003
2005
TABLE 5
Welfare Analysis on the RPVD Program
W/ Subsidy
W/o Subsidy
Difference
Consumer
Surplus
( A5 )
Producer
Surplus
( B5 )
2186.2
1450.4
735.8
693.2
464.4
228.8
Effects of CO2 Reduction
( C5 )
1212 JY a 3044 JY b 10822 JY c
103.6
69.6
34
260.2
174.9
85.3
930.1
625
305.1
Subsidy
Payment
( D5 )
1046
0
1046
Social Surplus
(A5)+(B5)+(C5)-(D5)
1212 JY a 3044 JY b 10822 JY c
1937
1984.4
-47.4
2093.6
2089.7
3.9
2763.5
2539.8
223.7
Notes:
The unit of measurement is 100 million JPY (in the 1997 price).
a. The average transaction price at Japan's voluntary emissions trading scheme in the period from 2005 to 2007.
b. The average damage cost of carbon emissions (93 USD/t-C with the 1997 exchange rate of 120JPY/USD), reported in Tol (2005).
c. Japan Scenario and Actions towards Low-Carbon Societies (2008)
TABLE 6
Welfare Analysis on the FIT Scheme
⊿Value of CO2 Reduction
1212JPY/t-C
3044JPY/t-C
10882JPY/t-C
⊿Social Welfare
3044JPY/t-C
10882JPY/t-C
⊿CS
⊿PS
⊿Subsidies
(2a)-(1a)
(2b)-(1a)
(2c)-(1a)
317.1
2166.7
2706.5
124.3
536.4
663.1
910.9
4946.2
6124.8
15.9
108.5
135.5
39.9
272.5
340.4
142.6
974.3
1217.0
-453.6
-2134.6
-2619.6
-429.6
-1970.5
-2414.7
-326.9
-1268.8
-1538.1
(3a)-(1a)
(3b)-(1a)
(3c)-(1a)
25.0
795.6
1082.8
9.8
182.2
248.9
101.4
1125.3
1603.3
1.3
39.8
54.2
3.1
100.1
136.2
11.3
357.8
486.9
-65.3
-107.6
-217.4
-63.4
-47.4
-135.4
-55.3
210.3
215.3
Note: Unit of measurement is in 10 billion JPY in constant 1997.
1212JPY/t-C
FIGURE 1
PV Production, 2001-2009
Major Countries
MW
6000
5000
China/Taiwan
4000
3000
Europe
2000
Japan
1000
US
0
2001
2002
2003
2004
2005
2006
2007
2008
2009
Source: PV NEWS
FIGURE 2
Subsidy and Price
120
40
100
30
80
25
60
20
15
40
10
20
5
0
0
97
98
99
00
01
02
03
04
05
06
07
Note: System price is after tax rate, and does not include
subsidy.
System Price （10,000JPY/kW）
JPY/kW）
Subsidy (10,000JPY/kW）
0,000JPY/kW）
35
FIGURE 3
Cumulative PV System Installed
Counterfactual Scenarios, 1996-2020
70000 (2c)
60000 53GW
50000 MW
40000 30000 (2b)
(3c)
28GW
(1c)
20000 (1b)
(3b)
10000 (2a)
(3a)
(1a
)
0 1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020