Potential Assessment of Residential Solar Power System in Japanese Local Cities
Ritsumeikan Asia Pacific University
Ritsumeikan Asia Pacific University
Ritsumeikan University

LI Fan*QIAN Xuepeng
ZHOU Weisheng

1. Introduction
Solar energy, as one of the most promising and cleanest renewable energy resources, has been drawing
attention from both environmentalists and economists for several decades (Butti & Perlin, 1980). Almost all thescientists have reached an agreement that using solar panel system is one of the efficient and eco-friendly ways
to reduce the carbon dioxide emission and also to get relieved from the shortage of energy resources for the
future purposes. Japan, as a country which shares one of the largest photovoltaic cell productions, is ahead ofother countries in field of solar power generation. In 2009, the production of photovoltaic cells and modules inJapan grew by 19% from the previous year. Until 2004, it was the eighth year for Japan to become the largest
solar power production country in the world (NEDO). Nevertheless, disparities in development and utilizationof solar panel across the regions can be found when observing the penetration level of solar panel installationfrom city to city.
One of the most common explanations for the disparities of penetration level between regions is the diverse
climatic conditions, which in some area do not guarantee the minimum solar radiation to generate electricity
continuously. Although this obvious interpretation may help to understand the unsuccessful penetration of solar
panel utilization in particular cities, it is certainly not the only factor for the low penetration level.
In this paper, the problem is investigated from a comprehensive perspective, and the inter-correlations areanalyzed between one city’s penetration of solar panel with 22 factors, ranging from climatic aspect, social
aspect and economic aspect. By using statistic tools, we conclude the key factors which ensure one city’s highpenetration level of solar panel using. This particular research can give government the idea of the potential ofsolar market, suitable socioeconomic conditions for solar energy using and applications analysis, furthermore,
the strategies and incentive policies for promoting residential solar energy using.

2. Methodology and Data
2.1 Scope and Limitation
The scope of the present inquiry is limited in several respects. Firstly, the analysis is limited to the residential
solar panel system. The reason for confining the analysis to this particular sector is that the focus and the scaleof solar panel using from residential sector and industrial sector are different. Secondly, the target cities of thestudy are also limited to the Japanese local cities. According to The Fourth Comprehensive National
Development Plan, local city or rural central city is defined as city with population around 100,000. It isnecessary to restrict the sample cities based on the population size for the comparable the social and economic
conditions of similar level, since the existence of population differences can have pervasive influence on city’ssocial structure. The study collected randomly the sample cities with the population of 50,000 to 150,000.
Thirdly, the incentive policies of each city were also various, such as subsidies solar panel installation withcertain limits, which were regarded closely related to penetration level. The incentive policies were also
quantificated and analyzed, finally showed poor correlation to the penetration level and influenced theprecision of the overall analysis; therefore, this factor was removed from the data analysis.

2.2 Sampling and Data Analysis
Solar energy production efficiency is climatologically dependent, that’s to say, climate directly affects theperformance of solar energy processes, so 35 Japanese local cities, located at different longitudes and latitudes,
were randomly selected as the research samples.
All the data have been collected from three official homepages, which are: Japan Meteorological Agency,
Statistic Bureau of Japan and National Housing and Land Survey. After pilot data analysis, 22 factors wereextracted and divided into three major aspects, each with several subdivisions. These issues with theirrespective subdivisions are climatic condition (including solar duration, average temperature), social condition(including household structure and house condition), and economic condition (including household income andfinancial capability).
In the climatic category, average hours of solar duration and average temperature within recent 10 years werechosen as climatic parameters. Values for actual radiation refer to the summation of the irradiation on an object
throughout a day. Hence, solar duration was selected as an essential parameter to represent solar radiation aplace can obtain. Cell efficiency is related to materials, manufacturing process and operating temperature ofsolar cell. In general, efficiency decreased by about 0.5% for every 1 degree rise. Here, annual averagetemperature was included as another climatic factor.
Table 1: the Factors Analyzed in the ResearchClassification
Climatic condition

Householdstructure
SocialconditionHouse
condition

Other

Economic condition

Name
Average Solar duration
Average Temperature
1 ~ 2 people
Average Household
3 ~ 4 people
memberMore than 5 people
elderly people only
Household withelderly couple only
elderly people
unmarried elderly
people only
Own house
Exclusive residential owning house
Exclusive residential owning detached house
1981 ~ 2000
Construction period
2001 ~ 2008
2008
Population mobility (%)
less than 3,000,000 yen
3,000,000 ~ 6,000,000
yenHousehold Income
6,000,000 ~ 9,000,000
yenmore than 9,000,000
yenAnnual personal income
Annual household income
Financial capability index

Data typenumerical
numerical
ratio
ratio
ratio
ratio
ratio

Year/Period
2000~2010
2000~2010

ratio

2008

ratio
ratio
ratio
ratio
ratio
ratio
ratio
ratio

2009
2009
2009

2009
2009
2008

2009
2009

ratio
ratio

2009

ratio
numerical
numerical
numerical

2008
2009
2008

In the social category, 4 hypotheses were raised based on the household structure and house condition.
Hypothesis 1: Large number of household member has positive relation to penetration level.
Hypothesis 2: Aging society has negative relation to penetration level.
Hypothesis 3: The limitation of solar panel installation on renting house has negative impact on city’spenetration level.
Hypothesis 4: Frequent population mobility has negative relation to penetration level.
Regarding the four hypotheses, 13 social factors were selected which would affect the penetration level. (Seetable 1)In the economic category, it was assumed that personal income and household income would have strong
relation to penetration level. Even though the installation cost of solar panel decreased from average JPY 2,000,000 per kW in 1994 level to JPY 700,000 per kW in 2007 level (Agency for Natural Resources andEnergy), it is still considered as a “high cost, low return” project, especially for household use. For low-income
household, it will increase financial burden on family expenditure. Besides, Financial Capability Index (FCI)
was examined. FCI is used to indicate the financial strength of local public bodies. A higher figure for thefinancial capability indicator means that the local public body can be said to have a greater margin for revenue
sources.

3. Results
3.1 Bivariate Correlation Analysis Results
The term “penetration level” is used to indicate the popularity level of solar panel using in one city, and is
calculated as dividing number of home use solar panel installation (until 2009) by occupied dwellings number.
Firstly, a bivariate correlation was undertaken between each 22 factors with penetration level to find out thefundamental factor which can affect the overall penetration level in general. The results of correlation showedthat there was strong positive correlation between penetration level and average solar duration (r =0.557, p<0.01) and average temperature (r =0.364, p <0.05). Besides, we noticed that the relation between solar
radiation and temperature was strong as well (r =0.440, p <0.01). Therefore, we proved the statement that solar
radiation was the fundamental factor of the overall penetration level.
Table 2: Correlation between Average Solar Duration and Penetration Level
Average solar
Averageduration
temperature1.440**
Average solar.008
duration
35
35.557**.364*
Penetration level
.001
.032
35
35
**.. Correlation is significant at the .01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

3.2 Principle Component Analysis and Cluster Analysis Results
After finding out the general factor which affects penetration level of solar panel using, we conducted principal
component analysis and cluster analysis to find out the factor which attributed to the best performance of solar
panel by SPSS.
Using latest statistics from the official homepages, the PCA was made according to method of Orthogonal(VARIMAX) Rotation. In the result, the percentages of trace explained by each of the 6 components witheigenvalues greater than 1.0 (24.9%, 19.0%, 11.8%, 11.6%, 10.4% and 7.8% respectively) are shown as the lastrow of values of Table 2. The index for the overall solution showed that 85.5% of the total variance wasrepresented by the information contained in the factor matrix of the six-factor solution.

Factors loadings of 0.50 and higher is considered significant for interpretative purpose. Table 2 presents therotated component analysis factor matrix. By observing factors with loadings larger than 0.5, 22 factors weregrouped into 6 components. Similar factors fell into the same components. The analysis of factor loadings is as
the following.

(i)Component 1: (+) progressive city ---- (-) recessionary city

According to Table 3, component 1 explained 24.9% of total variances. In column 1, the factor with loadings
larger than 0.5, were household with elderly people only (-0.888), household with elderly couple only (-0.856),
household with elderly unmarried people only (-0.885), annual household Income less than 3,000,000 yen (0.774), annual household Income 3,000,000 ~ 6,000,000 yen (0.767), annual household Income 6,000,000 ~
9,000,000 yen (0.801) and annual household Income more than 9,000,000 yen (0.631). The factor loadings ofhousehold with elderly people and annual household Income less than 3,000,000 yen had negative signs. It
meant all these 7 factors varied together, but moved in the directions opposite to each other. Hence, the firstcomponent was labeled as progressive – recessionary component.

(ii)

Component 2: (+) permanent population ---- (-) floating population

According to Table 3, component 2 explained 19.0% of total variances. In column 2, the factors with loadings
larger than 0.5, were own house (0.846), exclusive residential owning house (0.927), exclusive residential
owning detached house (0.927) and population mobility (-0.769) with negative sign. They indicated one city’s
residential condition and population floating. Therefore, the component 2 was labeled as permanent – floating
component.

(iii)

Component 3: (+) new city ---- (-) old city

According to Table 3, component 3 explained 11.8% of total variances. In column 3, the factor with loadings
larger than 0.5, were construction period prior 1980 (-0.951), construction period 1981 ~ 2000 (0.728), andconstruction period 2001 ~ 2008 (0.787). The trend was that with the loadings increased, houses were more
newly constructed. Therefore, component 3 was labeled as new – old component.

(iv)

Component 4: (+) big family ---- (-) small family

According to Table 3, component 4 explained 11.6% of total variances. In column 4, the strongly influential
factors were household member 1 ~ 2 people (-0.897), household member 3 ~ 4 people (0.665) and household
member with more than 5 people (0.847). Hence, the component 4 can indicate one city’s major household
structure.

(v)

Component 5: (+) rich ---- (-) poor

According to Table 3, component 5 explained 10.4% of total variances. Here, three factors were extracted, they
were: financial capability index (0.833), annual household income (0.593) and annual personal income (0.764).
These three factors can respectively show the Affluence of city, household and personal economic condition.
Often, strong financial strength index indicated the abundance of local public institutions’ financial reserve.

(vi)Component 6: (+) affluent solar radiation with high temperature ---- lacking solar
radiation with low temperature

According to Table 3, component 6 explained 7.8% of total variances. Solar radiation and temperature would
make opposite impact on the efficiency of solar cell as we mentioned above, we combined these two factors inthis component. In addition, both of them had positive sign. It meant that both solar duration and temperature
would move towards the same direction when component 6 changed.

Table 3: Component Matrix of Principle Components Analysis

Household with elderly people only
Household with elderly couple only
Household with elderly unmarried people only
Annual household Income less than 3,000,000
yenAnnual household Income 3,000,000 ~
6,000,000 yen
Annual household Income 6,000,000 ~
9,000,000 yen
Annual household Income more than 9,000,000
yen
Own house
Exclusive residential owning house
Exclusive residential owning detached house
Population mobility
Construction period prior 1980
Construction period 1981 ~ 2000
Construction period 2001 ~ 2008
Household member 1 ~ 2 people
Household member 3 ~ 4 people
Household member more than 5 people
Financial Capability Index
Annual household income
Annual personal income
Average Solar duration
Average Temperature

1
-.888
-.856
-.885

2

Component
3
4

5

6

-.774
.767
.801
.631

Percentage of trace (85.5%)
24.9%
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 8 iterations.

.846
.927
.927
-.769
-.951
.728
.787
-.897
.665
.847
.833
.593
.764
.841
.756

19.0%

11.8%

11.6%

10.4%

7.8%

Based on the 6 components and penetration level, we conducted cluster analysis to find out the same features of
the city group which has the highest penetration level. Ward’s method was used during the analysis. According
to the result, the best performed group contained 7 cities. Iida (Nagano prefecture), Chino (Nagano prefecture),
Omura (Nagasaki prefecture), Kitakami (Iwate prefecture), Chikuma (Nagano prefecture), Sukagawa(Fukushima prefecture) and Kounosu (Saitama prefecture) had respectively No.1, No.2, No.3, No.4, No.6, No.7
and No.16 highest penetration level among all the sample cities. By investigating the factor scores of eachcomponent, we found that among 6 components above, only the factor scores of component 3 – new city/oldcity had same positive signs. In addition, Component 3 had both the highest median and average value. It
meant that all 7 cities shared same feature expressed by Component 3. Since all the scores of 7 cities in
Component 3 were larger than 0, and Component 3 explained the construction period in one city, we concluded
that there was a large proportion of living houses built during year 2001 to 2008 in these 7 cities, several built
during 1981 to 2000 and very seldom before 1980. The result showed that the cities with high penetration levelof solar panel using, shared a common trait that most of the dwellings were newly built in recent 10 years.

4. Conclusions and Discussions
The aim of the research was to explore the factors which play decisive role in high penetration level of solar
panel using in Japanese local cities. We concluded that climatic condition, especially solar duration value had
positive relation with solar panel using rate in national scale. Besides, we found that having a large proportionof living houses which newly constructed in recent 10 years was the common feature of the cities with highpenetration level. Furthermore, several important findings were discussed as follows.
Firstly, we proved that climatic condition was one of the key factors for the penetration level of solar panel
using; however, climatic conditions had no obvious influence on high penetration level of solar panel using in
the group highest penetration level. Hence, we concluded that adequate solar radiation was the fundamental
factor which ensured the solar panel system to run effectively, but it did not contribute to high penetration levelof solar panel using.
Secondly, according to the estimation from Agency for Natural Resources and Energy, in case of new tenement,
the initial installation fee for a 3.5kW solar system is about JPY 1,850,000. For an old tenement, the initialinstallation fee equals to JPY 2,250,000 which is more expensive than new tenement. By observing thetransition of the system cost for both new tenement and old tenement, we found that, the system cost for new
tenement in year 2007 was 571,000 yen/kW, which dropped 46.7% than the level of year 1997. However, thesystem cost for old tenement in year 2007 was 741,000 yen/kW, which only reduced 29.8% than the year 1997
(New Energy Foundation). Hence, for those cities which have newly constructed tenements more than oldtenement, they have obvious economic advantage of installing solar panel system with lower price. The big
trend of installing solar panel system on new tenement can be found all over the country, especially after thedevastating earthquake in northeastern Japan in 2011. With the merit of low cost to fuel and light charges, more
and more new tenements are sold with installed solar panel systems together.
Thirdly, when completing the installation of solar panel system on an older house, it requires much more carethan on a new house. Both building codes and construction practices will be taken into consideration.
According to the design and installation notebook from Japan Photovoltaic Energy Association, whether theroofs are strong enough to support the panels, whether the house’s foundation, structure and layout aresatisfactory for a solar panel system that will be mounted are very important points that new users should take
into account before embarking on a solar panel installation. It will be no problem for newly constructedbuilding to install solar panel system because those points have already been included in the building design.
However, it will be more complex for old building to mount a solar panel system. Therefore, we considered the
difficulty of installing solar panel system on old tenement as the second reason for explaining why highpenetration level cities shared the common feature of short construction period of living houses.
Finally, based on the result of cluster analysis, we found that, there were three cities in Nagano prefectures
included in the “best performed” group. In order to find out the specialty of Nagano prefecture, we searched theinformation of incentive policy for solar panel using provided by Nagano prefectural government. For year
2011, Nagano prefectural government provided a large quantity of subsidies include 1,000,000 yen for eachnew tenement solar panel users and 200,000 yen to 400,000 yen for old tenement solar panel users. For otherprefectures, the maximum subsidy up to 100,000 yen is often the case. The great incentive for new houseowners is another reason which contributes to high penetration level of these three cities in Nagano prefecture.
As mentioned before, incentives was not included in this analysis due to trial failures, however, we will enlarge
the sample scope, define new indicator and find the effect of incentive policies in the further study.