The Non-operating Solar Projects: Examining the Impact of
the Feed-in Tariff Amendment in Japan
Ling Chu
Kenji Takeuchi
February , 2021
Discussion Paper No.2105

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANThe Non-operating Solar Projects:
Examining the Impact of the Feed-in Tariﬀ Amendment in Japan
Ling Chu∗, Kenji Takeuchi**

Graduate School of Economics, Kobe University

Rokko, Kobe 657-8501, Japan

Abstract
The non-operating solar power projects refers to a large gap between the operating and approved
solar capacity in Japan. The country amended the feed-in tariﬀ (FIT) law in 2017 to address this issue.
This empirical study investigates the impact of the amended FIT policy on non-operating solar projects
using municipality-level panel data from 2014 to 2019. We ﬁnd that the amended policy improved therelationship between the approved capacity and operating capacity of solar power projects. The impacts
are heterogeneous across diﬀerent sizes of solar power projects: they are more substantial in largeand mega-scale solar power than in small-scale projects. To explore the determinants of non-operation,
we also apply a cross-sectional analysis to identify municipal characteristics related to non-operating
capacity. We ﬁnd that the coeﬃcient for the terrain slope is negative in the operating capacity modeland positive in the non-operating capacity model. As a steep land surface leads to higher construction
and maintenance costs, the results indicate that non-operating projects are located in municipalities withhigher construction costs.

Keywords: Solar power; Feed-in tariﬀ; Japan; Non-operation; Panel data analysis∗ E-mail: chulingkoharu@gmail.com** E-mail: takeuchi@econ.kobe-u.ac.jp

1

1 Introduction
Renewable power generation in Japan is expected to increase further because of its importance in reducing
GHG emissions and attaining energy self-suﬃciency after the Fukushima Daiichi accident in 2011. The
Japanese government revised the Basic Energy Plan in July 2018; the plan emphasizes the need for renewableenergy as a major power source (METI, 2018b). However, as of 2018, the share of renewable energy in electric
power generation in Japan was 16.9% (9.2% if hydroelectric power is excluded) (METI, 2020), which seems
low compared to that in major developed countries. Japan launched the feed-in tariﬀ (FIT) scheme in 2012 asa policy measure to promote the deployment of renewable energy. FIT has helped increase renewable power
generation, particularly in solar cells, although it encountered several challenges. The biggest challenge isthe non-operating solar power projects: a large gap between the operating and approved solar capacity. The
country amended the feed-in tariﬀ (FIT) law in 2017 to address this issue by revising requirements to obtainproject approval.
This study empirically investigates the impact of amended FIT policies on the operation of solar power.
We estimate the relationship between operating capacity and approved capacity before and after the amendment using municipality-level data of solar power capacity in Japan from 2014 to 2019. To investigate
the heterogeneous eﬀect on solar projects among diﬀerent scales, we distinguish the projects according totheir capacity to explore whether the policy impact diﬀers according to project size. In addition, the study
examines the characteristics of municipalities that locate non-operating solar projects.
Many studies have evaluated policy instruments to support renewable energy. Jenner et al. (2013) assess
the eﬀectiveness of FIT policies in promoting renewable energy in 26 European Union countries from 1992
to 2008. They create an indicator for policy strength that represents the return on investment provided by
FIT and ﬁnd that for a 10% increase in return on investment, countries will install 3.8% more solar capacityand 2.8% more onshore wind capacity. Polzin et al. (2015) investigate the inﬂuence of diﬀerent measureson subsequent investments into renewable energy capacity by institutional investors in OECD countries
from 2003 to 2011. They ﬁnd that FIT is more eﬀective than subsidies for less-mature technologies suchas solar. Renewable portfolio standards and emission trading systems seem to be more eﬀective for mature2

technologies such as wind. Böhringer et al. (2017) examines the impact of the German FIT on the innovationof renewable energy technologies from 1990 to 2014. The results imply a positive eﬀect of FIT on inducing
innovation. However, the inducement eﬀect of prior FITs with moderate subsidy rates is not signiﬁcantly
diﬀerent from that of later FITs with much higher prices. Muhammad-Sukki et al. (2014) investigate theimpact of Japanese FIT schemes on residential and non-residential solar power using ﬁnancial analysis. They
indicate that FIT rates generate a good proﬁt, a moderate annual return on investment, and an acceptablepayback period, suggesting that this would potentially attract more interest in installing solar photovoltaic(PV) systems.
This study also relates to previous studies that examine the factors aﬀecting the growth of solar PV
capacity. Zhang et al. (2011) use prefecture-level data from 1996 to 2006 to analyze the factors aﬀecting
the diﬀusion of residential solar PV systems in Japan. They ﬁnd that the regional government’s policies
help promote PV system adoption. Installation costs have a signiﬁcant negative eﬀect, whereas housing
investment and environmental awareness have positive impacts. Tanaka et al. (2017) examine the factors
determining purchasing decision time for residential solar PV using survey data in 2012 in Japan. The results
show that FIT accelerates the decision-making process while subsidy schemes do not contribute to reducing
the decision-making time, leading to the purchase of a PV system. FIT oﬀers long-term beneﬁts to PV
system users by allowing them to sell surplus electricity back to the grid, contrary to subsidies that provide
ﬁnancial assistance only initially at the time of investment. Conversely, information obtained from otherusers lengthens the decision-making process regarding purchasing a PV system, since consumers who sought
information or who communicated with existing users were more cautious in their purchasing decisions. Using
county-level data from 2005 to 2013 in the northeastern United States, Crago and Chernyakhovskiy (2017)
investigated the impact of policy incentives on the commercial solar power capacity. They ﬁnd that rebates,
sales exemptions, and renewable portfolio standards have statistically signiﬁcant and positive eﬀects. Factorsthat directly aﬀect ﬁnancial viability and returns on investment, such as solar insolation and installation
cost, have the most impact on capacity growth in the commercial solar PV market.
This study diﬀers from the existing literature by focusing on the non-operating capacity of solar power
projects. We examine whether the amended FIT policy alleviates the gap between the operating and ap-

3

proved solar capacity. Few studies have mentioned the existence of non-operating solar power. For instance,
Kuramochi (2015) reviews policy measures on energy and climate change implemented in Japan and points
out a large gap between the installed capacity and the approved capacity, particularly for non-residential
facilities. However, no quantitative analysis was conducted in this policy review. To the best of our knowledge, our study is the ﬁrst to empirically investigate the issue of non-operating solar projects by examining
the eﬀect of FIT amendments. Furthermore, in contrast to studies focusing on drivers of growth in solar PV
installed capacity (Zhang et al. (2011); Crago and Chernyakhovskiy (2017); Crago and Koegler (2018)), we
examine municipal characteristics that tend to locate non-operating facilities and explore the characteristics
before and after the amendment of the FIT scheme.
The remainder of this paper is organized as follows. Section 2 provides an overview of Japan’s FIT policy
and the issue of non-operating solar projects while Section 3 introduces the revisions made by the amended
FIT scheme. Section 4 presents the regression model and describes the dataset used in the analysis, while
Section 5 discusses the empirical results. Finally, Section 6 presents the conclusions.

2 Feed-in Tariﬀ Policy in Japan
To increase the share of renewable energy in the energy mix,1 Japan launched a national FIT scheme basedon the Act on Special Measures Concerning Procurement of Electricity from Renewable Energy Sources byElectricity Utilities in July 2012. Under FIT, renewable energy producers̶ solar PV, wind power, small hydro, geothermal, and biomass, are required to submit documents to obtain FIT approval2 from the Japanese
government. Electric utilities3 are obliged to purchase electricity generated from renewable energy sources
at a ﬁxed price (tariﬀ) for a speciﬁc period. Electricity generated from renewable energy sources shall betransmitted to the power grid of the electric utility and distributed to end-users. All electricity customers

1

Japan implemented a renewable portfolio standard program from 2003 to 2011, but its impact on renewable power development
was small.

2

FIT approval refers to the application of an existing or proposed renewable energy power facility conﬁrmed and certiﬁed by the
Ministry of Economy, Trade, and Industry.

3

Ten regional electric utility companies are Hokkaido, Tohoku, Tokyo, Hokuriku, Chubu, Kansai, Chugoku, Shikoku, Kyushu,
and Okinawa.

4then pay a surcharge for renewable energy proportional to their usage to cover the expense of purchasing
renewable power (METI, 2012).
Table 1 presents the purchase price under FIT from 2012 to 2016. The Japanese government adopted
a nationwide but source- and size-speciﬁc price scheme. A uniﬁed FIT scheme would reduce unfavorable
renewable capacity allocations, foster market competition, and reduce electricity costs, but would not beneﬁtsome high-cost renewables. In contrast, setting diﬀerentiable purchase prices would encourage investment
by ensuring proﬁt margins, but it also could risk increasing the economic burden on electricity consumers(Li et al., 2019). Hence, the Ministry of Economy, Trade, and Industry (METI) sets the purchase pricediﬀerentiated by the category of renewable energy sources and the size of the power generation facilities.
The FIT payments are also adjusted for new projects to address changes in electricity supply and generation
cost over time. The purchase prices for electricity generated by solar power have continued to fall year by
year while prices for other renewable sources remained stable.
The FIT scheme guarantees a ﬁxed price (tariﬀ) for a designated period, thereby enhancing certainty andstability for FIT-eligible renewable electricity producers. The approved capacity of renewable energy has
been growing rapidly (it was 88,773 MW by the end of December 2016) (Ito, 2015). However, the operating
capacity among them was only 33,659 MW, which indicates that more than 62% of solar projects were notin operation, regardless of the FIT approval. This problem of “non-operation” was even more serious in
non-residential solar power (≥10 kW) projects because most of the non-operating capacities fall under thiscategory.
The economic reason behind a large number of non-operating solar projects is that the purchase price isdetermined when the METI approves the facility. Therefore, earlier approval means a higher tariﬀ and higher
revenue from selling electricity. For instance, by receiving the FIT approval in 2012, the electricity producerof non-residential solar power can sell the electricity at 43.2 Japanese yen per kWh, which is 35% higher
than the purchase price for solar power approved in 2014. Meanwhile, the price of a typical 10-kW solar
PV system decreased from 430,000 in 2012 to 346,000 JPY/kW in 2014 (METI, 2018a), which means thatby delaying installation of solar PV, one can enjoy a lower equipment cost. Moreover, there was no explicit
regulation on the deadline for approved projects to connect to the electricity grid and start their operation.

5

Thus, the scheme provides solar power developers an incentive to obtain FIT approval as early as possible
and to delay the operation to maximize proﬁts. In 2014, METI investigated the status of installations of
non-residential facilities approved during 2012. A total of 3 GW capacity of the approved facilities had either
not secured land or ordered the purchase of a solar PV system or did not respond to the inquiry (Kuramochi,2015).
Many FIT-approved projects should have been generating electricity already. The existence of nonoperating projects means that a substantial amount of potential electricity from renewable resources is notavailable despite the rapid expansion of approved capacity, thus suggesting that the original FIT scheme is
not eﬀective in promoting renewable electricity. Furthermore, even if these non-operating projects mightstart to operate and generate electricity long after the FIT approval, they have the privilege to sell electricity
with higher FIT prices. The higher purchase price would then be transferred to future consumers on theirelectricity bills.

6

Table 1. Purchase price under Japan’s FIT scheme (JPY/kWh), 2012 - 2016

Energy source

Category

2012

2013

2014

2015

2016

Duration (year)

Solar PV

<10 kW

42

38

37

35

33

10

≥ 10 kW

43.2

37.8

32

27

24

20

<20 kW

59.4

57.75

55

55

55

20

≥ 20 kW

23.76

23.1

22

22

22

20

<15,000 kW

43.2

42

40

40

40

15

≥ 15,000 kW

28.08

27.3

26

26

26

15

<200 kW

36.72

35.7

34

34

34

20

200 kW∼1000 kw

31.32

30.45

29

29

29

20

1000 kW∼3000 kW

25.92

25.2

24

24

24

20

Manure biogas

42.12

40.95

39

39

39

20

Forest residues

34.56

33.6

32

32

32

20

Primary mill residues

25.92

25.2

24

24

24

20

General waste

18.36

17.85

17

17

17

20

Recycled wood

14.04

13.65

13

13

13

20

Wind

Geothermal

Hydro

Biomass

Source: METI (2012-2016), Dong and Shimada (2017)
Note: Duration means the period for purchasing electricity generated by each power source.

3 Amended FIT Policy in JapanTo address non-operating solar projects, the Japanese government has promulgated the amendment to
the FIT Act on June 2, 2016. The amendments came into force on April 1, 2017, and made three main
revisions to the existing rules. First, renewable power producers must conclude grid connection agreements
before the approval of FIT. Concerning existing FIT-approved projects, a deadline for connection agreements
is set to ensure the continued validity of the applicable purchase price and purchase period after April 2017.
Projects approved on or before June 30, 2016, are required to make a grid connection agreement by March
31, 2017, while projects approved between July 1, 2016, and March 31, 2017, are required to do so within 9months of the approval (METI, 2017). The projects lose eligibility of FIT if the deadline is missed. Second,
7

METI has replaced a facility certiﬁcation system for FIT applications with a business-plan-based certiﬁcation
system. Renewable energy developers must submit a detailed business plan, including the commencement
date for operation and the description of the interconnection arrangement to maintain a valid FIT approval.
Third, a bidding system for FIT approval is introduced to reduce the applicable purchase price to promotecompetition among developers. A reverse auction system is introduced for the FIT price of ≥ 2M W solar
projects. The bidding process is managed by a nonproﬁt organization designated by the METI. In summary,the amended FIT policy tightens the requirements for FIT approval and introduces competition to determine
FIT prices.
Figure 1 shows the total approved capacity and operating capacity of solar power during 2012-2019.
According to METI (2017), 456,000 approved projects with a total capacity of 27.66 GW were expected tolose their FIT approval validity after the amendment. A sharp decline is observed in the cumulative capacityof approved projects from 2016 to 2017. The previous FIT policy only required information concerning facility
location and the PV system speciﬁcations for approval. In contrast, the amended FIT introduced a stricter
requirement on the commencement of operation, such as grid connection agreements and information on
the commencement date in 2017, thus increasing the operating capacity. The ﬁgure shows the gap between
operating capacity and approved capacity decreased after 2017, suggesting that the amended FIT policy
mitigated the issue of non-operating projects.

8Capacity (MW)85,00070,00055,00040,00025,00010,000
2014

2015

2016
Operating

2017

2018

2019

Approved

Figure 1: Time trend of FIT approved capacity and operating capacity

4 Empirical Strategy and Data
4.1 Model Speciﬁcation
Our empirical analysis investigates the impact of the amended FIT policies on the operation of new solar
power facilities4 approved by FIT. We estimate the impact by examining the relationship between operating
capacity and approved capacity before and after the amendment. We hypothesize that the linear relationshipbetween operating and FIT approved capacity changed after the amendment.
Figure 2 illustrates this hypothesis. The 45-degree dashed-line implies that the operating capacity equals
the approved capacity. However, in reality, because neither deadline for commencing operation nor gridconnection agreements were required when approving new facilities under the previous FIT policy, theoperating capacity is much lower than the approved capacity. This is shown by the ﬂatter line in the ﬁgure.
The amended FIT aﬀects this relationship by imposing stricter requirements. Thus, the slope becomessteeper than before. This change in slope reﬂects the impact of the FIT amendment.

4

In this context, new solar power facilities refer to facilities deployed after the implementation of the FIT policy.

9

45
∘

Operation Capacity

t
en
m
d

P

F
r)
e
t
af
t(
os

IT

en
am

f

o
act
p
Im

nt
me
d
en
am
T
FI

F
r to
o
i
r
P

nt
dme
n
e
m
IT a

Approved Capacity
Figure 2: Hypothesis on the eﬀect of amended FIT

A linear ﬁxed eﬀects regression is adopted to estimate the eﬀect of the amended FIT. The general
speciﬁcation can be written as follows:
Yit = β1 Ait + β2 Ait × P ost + β3 Eit + λt + θi + ϵit
where Yit indicates the installed capacity of operating solar facilities with FIT approval in municipality i by
the end of year t. Ait is the FIT approved capacity of new solar power facilities in municipality i by theend of year t, which has not necessarily started its operation. The interaction term Ait × P ost denotes theapproved capacity after enforcement of the amended FIT. Time-varying and municipality-varying economic
conditions are captured by E. λt is the vector of the year dummy capturing the year ﬁxed eﬀects. It
controls for unobserved factors that change over time and are constant across municipalities. θi denotes themunicipality ﬁxed eﬀects estimator, which accounts for time-invariant municipality characteristics. ϵit is theerror term. The operating capacity in this study only refers to the capacity of active solar power facilities
approved by FIT. In other words, it should not be larger than the approved PV capacity. Thus, the constantterm is removed in the ordinary least squares (OLS) model.
10In addition, a cross-sectional model is used to explore the determinants of the non-operating capacity of solar power across municipalities. This model focuses on municipality heterogeneity by including
municipality-speciﬁc variables in regressions. We run cross-sectional regression in 2014 and 2019, respectively, to investigate changes over time. Regression using operating capacity as the dependent variable wasalso performed as a benchmark. The speciﬁcations of our estimation model are as follows:

Yi = α + β1 Mi + β2 Ei + β3 Li + β4 Di + β5 Pi + ϵi .
where Yi denotes the operating or non-operating capacity of solar power facilities that have received FIT
approval in municipality i by year 2014 or 2019. Explanatory variables include meteorological factors suchas solar radiation (Mi ), an economic condition (Ei ), land availability (Li ), electricity grid access (Di ), anda geographic factor (Pi ) for developing solar power projects. We use the terrain slope of the land as animportant geographic variable. Hilly areas with steep slopes tend to suﬀer from natural disasters, such as
landslides, more than ﬂat areas. It also leads to higher construction costs and risks of a project, leading toa higher number of non-operating projects. ϵi denotes the disturbance term.
To explore the heterogeneity of the eﬀect among solar power with diﬀerent sizes, we decompose solar
facilities according to their capacity. The solar power less than 10 kW capacity is deﬁned as small-scale(residential) solar PV facilities. When the capacity is larger than 10 kW and less than 2 MW, we deﬁne it
as large-scale commercial solar power. Projects larger than or equal to 2 MW capacity are sorted into thecategory of mega commercial solar.

4.2 Data
We construct a panel dataset of 1,741 municipalities from 2014 to 2019. A sub-dataset for 2014 and2019 is also used in our cross-sectional analysis. Table 2 and Table 3 present the descriptions and summary
statistics of the variables used in the empirical analysis.
Data on solar power capacity have been aggregated at the municipality level. The data were obtained
from the website of the Agency for Natural Resources and Energy (ANRE) of Japan.5 The agency collects
capacity information from the application documents submitted by renewable energy project developers for
5

The website for information disclosure of FIT in Japanese. Available at https://www.fit-portal.go.jp/PublicInfoSummary.

11

FIT approval. As shown in Table 3, the average FIT approved capacity is 42.8 MW, while the average
operating capacity is approximately 19.3 MW. This indicates that, on average, at the municipality-level,
only 45% of the solar power capacity approved by FIT is in operation. Figure 3 depicts scatter plots between
the operating and approved capacity of large-scale solar power. This graphically suggests that the correlation
between operating and approved capacity improved after 2017 in support of our hypothesis on the eﬀect of
the amended FIT.
Data on new housing construction are used as a proxy for economic conditions. They were collectedfrom the Survey on Construction Statistics of the Ministry of Land, Infrastructure, Transport, and Tourism(MLIT). Data on the area of arable land and abandoned farmland as a measure for land availability wereobtained from the Statistical Survey on Crops of the Ministry of Agriculture, Forestry and Fisheries (MAFF).
Data on solar insolation are from the New Energy and Industrial Technology Development Organization(NEDO). Insolation is a measure of solar radiation energy received on a given surface area at a given
time. It is expressed as the average irradiance in kilowatt-hours per square meter per day (kWh/m2 /day).
Electricity grid access is related to the construction cost of the grid connection and aﬀects solar power plants
operation. The distance from the municipal oﬃce to the nearest electricity grid is used as an index of accessto the electricity grid and measured using Geographic Information System (GIS) software.

12

Table 2. Description of variables

Variable

Unit

Description

Operating Capacity

kW

Total installed capacity of new solar power facilities
approved by FIT that have been in operation

Non-operating Capacity

kW

Capacity of solar power facilities approved by FIT
but have not started operation

Approved Capacity
House
Solar Insolation
Arable Land
Electricity Grid Access
Slope

kW
house
kWh/m2 /dayhectarekm
degreeFIT Approved capacity of new solar power facilities
Number of new construction starts of dwellings
Solar radiation energy received
Area of arable landDistance from the municipal oﬃce to the nearest electricity grid
Steepness of the land surface

13

Table 3. Summary statistics

Variable

Obs

Mean

Std. Dev.

Min

Max

Solar

10,446

19,31331,8790497,904

Small Solar

10,446

2,768

5,441

0

86,436Large Solar

10,446

13,95522,7010372,875

Mega Solar

10,446

2,591

10,656

0

185,980

Solar

1,741

13,96132,3260440,561

Small Solar

1,741

156

323

0

4,272

Large Solar

1,741

5,917

10,705090,864Mega Solar

1,741

32,98153,763

0504,376

Solar

10,44642,81072,912

0905,690

Small Solar

10,446

3,044

5,895

0

90,207

Large Solar

10,44625,40940,7070457,453

Mega Solar

10,446

14,35843,7840676,420House

10,446

430

1,590

0

38,199Solar Insolation

1,741

3.5

0.2

3

4.5

Arable Land

1,741

2,526

3,679

0

63,300Electricity Grid Access

1,741

1.406

2.904

0

49.393

Slope

1,741

0.359

0.2

0

0.773

Operating Capacity

Non-operating Capacity

Approved Capacity

Note: Solar represents total solar PV facilities.
Small solar means < 10kW solar power. Large solar means 10kW ∼ 2M W . Mega solar means ≥ 2M W .

14

Before year 2017 (2014~2016)

After year 2017 (2017~2019)

500,000

15

Operating capacity (kW)400,000

300,000

200,000

100,00000

100,000

200,000

300,000

400,000

500,000 0

100,000

200,000

300,000

Approved capacity (kW)Operating capacity of solar power with 10 kW~2 MW scale
Fitted values
45 degree line

Figure 3: Scatter plots of operating and approved capacity (large-scale solar power)

400,000

500,000

5 Results and Discussion
5.1 The eﬀect of FIT amendment
We estimate the eﬀect of amended FIT on the gap between the approved and operating capacity of solar
power using a linear regression model. We assign solar power to three categories: residential solar (< 10kW ),large commercial solar (≥ 10kW and < 2M W ), and mega solar(≥ 2M W ). The OLS model with time ﬁxed
eﬀects was used as a baseline regression.
Table 4 presents the main regression results on the eﬀect of the amended FIT. The interaction term
between approved capacity and post captures the eﬀect of the amended FIT on the gap between the approved and the operating capacity. In all the model speciﬁcations, coeﬃcients for the interaction term arestatistically signiﬁcant and positive. The results suggest that the amendment of the FIT resulted in an
increase in the operation of solar power projects. The estimated coeﬃcient in column 1 indicates that, after
the amended FIT, 1 kW of added approved capacity related to an additional 0.265 kW increase of operating
solar PV capacity. When controlling for the municipality ﬁxed eﬀects in column 2, the estimated coeﬃcientof interaction term decreases by 0.053.
The results from columns 3 to 8 demonstrate the heterogeneous eﬀects across diﬀerent sizes of solar power.
The most substantial impact is found in large-scale solar radiation. In column 3, before the amendment,
1 kW of approved capacity was related to 0.320 kW of operating capacity. After the amendment, it isassociated with 0.68 kW of operating capacity. The estimated coeﬃcients decrease slightly when including
the municipality ﬁxed eﬀects shown in column 4. In contrast, the change after the amendment is marginalfor small-scale solar. As shown in column 4, the correlation between approved and operational capacity is0.85, even before the amendment. The amendment added 0.104, a 10% increase in the relationship. The
results in column 7 show that the amended FIT has a positive impact on mega solar power. The amendmentof FIT reduced the gap between operation and approved capacity by an additional 0.129 kW with regard
to 1 kW approval. These results suggest that the amendment was eﬀective for commercial facilities rather
than residential facilities.

16

Table 4. The eﬀect of FIT amendment

Solar

Small Solar

Large Solar

Mega Solar

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

OLS

FE

OLS

FE

OLS

FE

OLS

FE

0.186∗∗∗

0.035

0.850∗∗∗

0.887∗∗∗

0.320∗∗∗ 0.148∗∗∗ 0.039∗∗∗ -0.038∗∗∗(0.019)(0.028)(0.004)(0.006)(0.018)(0.010)(0.012)

Approved Capacity × Post 0.265∗∗∗

0.212∗∗∗

0.104∗∗∗

0.092∗∗∗

0.360∗∗∗ 0.298∗∗∗ 0.129∗∗∗0.111∗∗∗(0.018)(0.017)(0.003)(0.003)(0.011)(0.013)(0.023)(0.022)3.300∗∗∗ -1.239∗∗∗-0.001-0.022∗∗∗ 0.972∗∗∗ -0.329∗∗

0.313∗∗-0.225∗(0.584)(0.345)(0.006)(0.006)(0.220)(0.162)(0.145)(0.134)

Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

10,446

10,446

10,446

10,446

10,446

10,446

10,446

10,446

Adjusted R2

0.770

0.614

0.999

0.998

0.920

0.769

0.286

0.216

Approved CapacityHouse(0.032)

Note: Constant terms are excluded in OLS regressions.
Robust standard errors in parentheses.
∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

5.2 Locations of non-operating projects
In this subsection, we apply cross-sectional regressions to explore the municipality characteristics thatlocate non-operating projects. Table 5 reports the estimation results using cross-sectional data in 2014. We
focus on large-scale solar projects because more than half (about 56%) of the non-operating capacity comesfrom such solar projects. The most contrasting results are found in the coeﬃcient for terrain slope: it is17negative in the operating capacity model and positive in the non-operating capacity model. Because a steep
land surface may lead to higher construction and maintenance costs, the results indicate that non-operating
projects are located in municipalities with higher construction costs.
Estimated coeﬃcients for other variables also suggest that the non-operating projects are located inmunicipalities with less attractive conditions for solar projects. Solar insolation has statistically signiﬁcantand positive eﬀects on operating and non-operating capacity, but the coeﬃcient is lower in the non-operating
capacity model. According to column 6, for every 1% increase in solar insolation, the non-operating capacityof large-scale projects is increased by 11%. This indicates that solar power project developers tend tochoose municipalities with rich solar resources as their plants

locations to obtain FIT approval, but their

response is lower than that of operating projects. The estimated coeﬃcients of electricity grid access on
non-operating capacity are negative and statistically signiﬁcant. The response of the non-operating capacity
to a 1% increase in distance to the grid is less sensitive than the operating capacity.
Table 6 presents regression results of the cross-sectional analysis in 2019. We found similar impacts ofsolar insolation, electricity grid access, and slope, while the diﬀerences between operating and non-operating
capacity models are smaller compared to the results in Table 5. Because the FIT amendments reduced unreasonable non-operating projects, such as projects without a mature business plan and careful considerationon the practical operation, we can expect both the operating and non-operating projects to be located insimilar municipalities after 2017.
To test the statistical diﬀerence of coeﬃcients between the operating and non-operating regressions, weapply the Chow test. This test examines the equality of the regression coeﬃcients between diﬀerent samples.
The null hypothesis is that the coeﬃcients of the variables in one model are equal to those in another model(Chow, 1960). Tables 7 and 8 present the results of the Chow test. In general, the diﬀerence in coeﬃcients
between the operating and non-operating capacity models is less statistically signiﬁcant in 2019 than in 2014.
This implies that operating and non-operating projects are both located in more similar municipalities in
2019. Speciﬁcally, the coeﬃcients for solar insolation were not statistically diﬀerent between the operating
and non-operating models in 2019. The non-operating solar locations were no more diﬀerent from those of
the operating solar projects, with regard to the richness of solar resources.

18

Table 5. Location of projects in 2014

Solar
(1)

Small Solar
(2)

(3)

(4)

Large Solar
(5)

(6)

Mega Solar
(7)

(8)

Operating Non-Operating Operating Non-Operating Operating Non-Operating Operating Non-Operating
Capacity

Capacity

Capacity

Capacity

Capacity

Capacity

Capacity

Capacity

11.546∗∗∗

10.278∗∗∗7.919∗∗∗

8.597∗∗∗

14.192∗∗∗

11.673∗∗∗2.310∗∗∗

10.885∗∗∗(0.952)(1.047)(0.751)(0.646)(1.058)(1.023)(0.574)(1.046)

0.367∗∗∗

0.315∗∗∗

0.399∗∗∗

0.384∗∗∗

0.361∗∗∗

0.311∗∗∗

0.115∗∗∗

0.314∗∗∗(0.012)(0.016)(0.010)(0.010)(0.014)(0.015)(0.018)(0.016)-0.0170.033∗∗-0.038-0.014-0.010

0.022

0.032

0.036

(0.023)(0.032)(0.016)(0.017)(0.029)(0.032)(0.024)(0.033)-0.271∗∗∗-0.164∗∗∗-0.221∗∗∗-0.170∗∗∗-0.275∗∗∗-0.163∗∗∗-0.020-0.198∗∗∗(0.047)(0.064)(0.037)(0.034)(0.052)(0.061)(0.043)(0.061)-0.151∗∗∗0.201∗∗∗-0.213∗∗∗-0.190∗∗∗-0.142∗∗∗0.136∗∗∗

0.083∗∗

0.114∗∗∗(0.032)(0.042)(0.026)(0.026)(0.037)(0.041)(0.035)(0.039)-7.900∗∗∗-5.184∗∗∗-4.986∗∗∗-7.539∗∗∗-11.676∗∗∗-7.306∗∗∗-2.905∗∗∗-5.725∗∗∗(1.212)(1.349)(0.945)(0.814)(1.368)(1.331)(0.706)(1.366)

Observations

1741

1741

1741

1741

1741

1741

1741

1741

Adjusted R2

0.447

0.226

0.564

0.577

0.402

0.256

0.041

0.251

Solar Insolation

House
19
Arable Land

Electricity Grid Access

Slope

Constant

Note: All variables are in log-form.
Robust standard errors in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Table 6. Location of projects in 2019

Solar
(1)

Small Solar
(2)

(3)

(4)

Large Solar(5)

Mega Solar

(6)

(7)

(8)

Operating Non-Operating Operating Non-Operating Operating Non-Operating Operating Non-Operating
Capacity

Capacity

Capacity

Capacity

Capacity

Capacity

Capacity

Capacity

8.852∗∗∗

9.771∗∗∗

6.762∗∗∗

9.995∗∗∗

10.963∗∗∗10.425∗∗∗1.821

9.678∗∗∗(0.934)(1.176)(0.763)(0.631)(1.028)(1.117)(1.283)(1.057)0.420∗∗∗

0.425∗∗∗

0.512∗∗∗

0.516∗∗∗

0.401∗∗∗

0.400∗∗∗

0.379∗∗∗

0.383∗∗∗(0.016)(0.023)(0.013)(0.013)(0.018)(0.022)(0.041)(0.018)

0.032

0.081

∗∗

-0.019-0.024

0.039

0.096

∗∗∗

∗∗

0.096

0.047

(0.023)(0.034)(0.016)(0.017)(0.028)(0.033)(0.049)(0.030)-0.242∗∗∗-0.208∗∗∗-0.243∗∗∗-0.135∗∗∗-0.245∗∗∗-0.194∗∗∗-0.023-0.246∗∗∗(0.049)(0.066)(0.036)(0.032)(0.053)(0.064)(0.083)(0.057)-0.0450.227∗∗∗-0.182∗∗∗-0.137∗∗∗-0.0450.147∗∗∗

0.558∗∗∗

0.046(0.033)(0.049)(0.026)(0.030)(0.036)(0.049)(0.063)(0.038)-3.316∗∗∗-6.080∗∗∗-2.704∗∗∗-10.011∗∗∗-6.413∗∗∗-7.512∗∗∗-1.081-4.244∗∗∗(1.197)(1.518)(0.962)(0.789)(1.335)(1.438)(1.624)(1.379)Observations

1741

1741

1741

1741

1741

1741

1741

1741

Adjusted R2

0.354

0.222

0.561

0.606

0.304

0.326

0.088

0.065

Solar Insolation

House
20
Arable Land

Electricity Grid Access

Slope

Constant

Note: All variables are in log-form.
Robust standard errors in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Table 7. Chow test of location analysis in 2014

Solar

Small Solar

Large Solar

Mega Solar

Solar Insolation

∗

∗∗

∗∗∗

∗∗∗

House

∗∗∗

∗∗∗

∗∗∗

∗∗∗

Arable Land

∗∗

∗∗

∗

Electricity Grid Access

∗∗∗

∗∗

∗∗∗

Slope

∗∗∗

∗

∗∗∗

∗∗

Note: The diﬀerence of coeﬃcients between operating and non-operating regressions are tested.
∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Table 8. Chow test of location analysis in 2019

Solar

Small Solar

Large Solar

Mega Solar∗∗∗

Solar Insolation

∗∗∗

House
Arable land

∗∗

∗∗∗

Electricity grid access
Slope

∗∗∗

∗∗∗

∗∗∗

∗∗

∗∗∗

Note: The diﬀerence of coeﬃcients between operating and non-operating regressions are tested.

∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

21

∗∗∗

6 Conclusion and Policy Implications
This study has examined the impact of amended FIT policies on the gap between operating capacity andapproved capacity of solar power. Our empirical results suggest that amendments to the FIT scheme reduced
the gap and thus mitigated the problem of non-operating solar power projects. However, the impacts areheterogeneous across diﬀerent facility sizes. In large and mega scales, the eﬀects of the amended FIT are
more substantial than in small ones. Furthermore, we apply cross-sectional analyses to explore the municipal
characteristics that locate non-operating capacity. Regression results indicate that, in general, municipalities
with steeper land have a more non-operating solar capacity. Using a chow test, we conﬁrmed that operating
and non-operating solar projects are located in municipalities with similar characteristics.
Our ﬁndings have several policy implications. First, there is a trade-oﬀ between attracting investment
in renewable power and controlling the ﬁnancial burden of FIT. Under the 2012 Japanese FIT scheme,
generous procurement prices are provided to reduce the risks and uncertainty for the project developer.
The high procurement price attracts substantial capital investment in solar power generation, while there
is no explicit regulation on the deadline for approved projects to start operation. Project developers havean incentive to obtain FIT approval as soon as possible, even if the project is immature and even without
careful consideration of the practical operation. Similarly, the decreasing price of the solar panel may
tempt some project developers to maximize proﬁts by intentionally delaying the installation. This leads
to a large discrepancy between the approved and operating capacity of solar power projects. Second, theresults of this study shed light on how the revisions of existing policies based on actual market situations
could eﬀectively support renewable energy. The previous FIT scheme oﬀered a policy loophole for solar
power project developers to undermine the program s eﬀectiveness. The amendments in 2017 prescribe thedeadline for the starting operation and the requirement on grid connection contracts with relevant utility
companies when determining the FIT rates. In addition, a reverse auction system is introduced for the
FIT price of solar projects that has a capacity larger than 2 MW. It encourages competition among project
developers and signals the solar power market the commitment of the government to reduce the cost of the
FIT scheme. These amendments are eﬀective in addressing the issue of non-operating solar power projects.

22

The obtained results highlight the importance of a well-designed FIT scheme in supporting the sustainable
development of renewable energy. In this regard, our ﬁnding is consistent with Xia et al. (2020), which
explores wind-power curtailment issues under the Chinese FIT policy.
This study has some limitations. The data used in this study cannot distinguish how many non-operating
projects are delayed intentionally or how many are yet to operate within a reasonable period. In practice,for instance, it takes an average of 1∼1.5 years for solar power from FIT approval to start their operation(Li et al., 2019). Therefore, this study does not exactly capture the extent to which the amended FIT
reduces the intentional delay of solar PV installation. In addition, in the location analysis, poor electricity
grid access is measured by the distance from the municipal oﬃce to the nearest electricity grid. If detailed
data such as voltage information6 on the grid are available, the distance can represent the electricity grid
access more accurately.

6

The voltage information on the grid can distinguish whether the grid line is, for instance, low voltage (≤ AC600V , or ≤ DC750V )or high voltage (> AC600V and ≤ DC70, 000V or > DC750V and ≤ DC70, 000V ) and help identify the availability of gridconnections for solar size.

23

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