Journal of Applied Input-Output Analysis, Vol. 15, 2009

An Examination of Estimation Methods Employed for Determining

C02 Emissions at the Prefectural Level in Japan1

by

Ryoji Hasegawa*

Abstract

This paper estimates CO2 emissions of all prefectures in Japan in 1995 and 2000 using aconsistent estimation technique; the RAS method is applied to reflect the regional character
istics. Our results are compared with the findings in different studies to examine the reliability of our estimation method. Thereafter, on the basis our results, we investigate thetrends and characteristics of CO2 emissions at the prefectural level. Our conclusions show

the importance of policymaking by local governments to reduce emissions in their own regions, especially from the residential sector.

Keywords: C02 emission, Modified RAS method, Prefectural data, Local government

1

Introduction

The Japanese government encourages municipalities to voluntarily seek global
warming solutions by enacting the Law Concerning the Promotion of the Measures to
Cope with Global Warming. As a result, many local governments in Japan have be
come increasingly concerned about global warming and have established programs forthe reduction of green house gases (GHG). Global warming is an important issue that
requires appropriate regional policies.
As a precondition for addressing global warming at the regional level, we need to

identify the emission structure of GHGs in each region. Focusing on prefectures as aregional district, prefectural offices in Japan estimate regional emissions basically ac
cording to the guidelines established by Ministry of the Environment. However, estimation methods and statistics used by prefectures are not always consistent, and some
prefectural offices do not go public with the details of estimation methods. Therefore,
emissions estimated by different prefectural offices are not perfectly comparable acrossResearcher, Institute for Global Change Adaptation Science (ICAS), Ibaraki University, 2-1-1,
Bunkyo, Mito, Ibaraki, 310-8512, Japan. E-mail: rhase@mx.ibaraki.ac.jp
This paper is the modified version of Hasegawa (2008), submitted to Business Journal of PAPAIOS.
I would like to thank Assoc. Prof. Shigemi Kagawa (Kyusyu University) for encouraging me to sub
mit to the JAIOA. I am also grateful to anonymous referees for their useful comments for revising.
However, any remaining errors in this paper are mine.

Journal of Applied Input-Output Analysis, Vol. 15, 2009prefectures.

Considering this situation, it is important that emission estimations be carried out

according to a consistent method in all prefectures and that they reflect regional characteristics. Kainou (2007) has constructed a database2 of energy consumption and C02
emissions at the prefectural level by dividing the energy balance table in Japan (Sogo
Enerugi Tokei), published by Agency for Natural Resources and Energy, into prefectures, using what the author described as the "input-output estimation method." Although this database provides emission data for all prefectures based on the same esti
mation method, it does not include emissions in transportation other than private auto
mobile and the energy conversion sector. Besides, it integrates some sectors in the energy balance table in Japan into one sector.
Matsuhashi et al. (2004) estimated C02 emissions from petrol-fueled vehicles at
the municipality level in Japan in 1999. Yonezawa and Matsuhashi (2009) estimated thesame type of emissions for all municipalities in 2005. They calculated emissions ineach municipality in terms of home bases or travel destinations, using the master dataof the origin-destination travel survey, and compared the results to other estimate basedon person-trip data, traffic volume, and fuel sales volume data to investigate regional
characteristics from estimation differences due to different methods and data sources.

The results from Matsuhashi et al. (2004) are used by Kudoh et al. (2004) to analyze

the relationship between the status of emissions from petrol-fueled passenger vehicles
and various regional characteristics in Japan. Kudoh et al. (2004) consider improvement of fuel consumption in motorcars for reducing C02 emissions. Although the databases constructed by Matsuhashi et al. (2004) and Yonezawa and Matsuhashi (2009)
identify emissions at the municipality level, smaller regions and reflect regional characteristics, the authors confine their analysis to emissions in automobile transport.
Focusing on the issues highlighted in the literature cited above, this paper estimates C02 emissions in 1995 and 2000 in all Japanese prefectures according to a consistent estimation method. To reflect on regional characteristics, we used the RAS
method, described in this paper as the "modified RAS method." In the next section, we

explain the estimation method for C02 emissions conducted in this study. In section 3,
we examine the reliability of our results by comparing them with those of the databases constructed by Kainou (2007) and Yonezawa and Matsuhashi (2009), and investi
gate the trends and characteristics of C02 emissions at the prefecture level by using our
results. Finally, we summarize our conclusions in section 4.

2 Estimation method for C02 emissions at prefecture level
2.1 The framework

This paper considers energy-related emissions and emissions from the use of lime-

2 It is available from the web site of Agency for Natural Resources and Energy in Japan (http://www.
enecho.meti.go.jp/info/statistics/regional-energy/index.htm)

An Examination ofEstimation Methods Employed for Determining CO2 Emissions at the Prefectural Level in Japan

3

Figure 1. The framework of the emission database
Energy item (k)Sector (j)(including households (33))

Region (i)stone for cement production as emission sources. These two emission sources accountfor around 95% of total emissions in Japan. All inputted fossil fuels are not always
combusted but partially used for materials in some industries such as electric power,iron and steel, chemical products, and so on. In calculating C02 emissions, it is important to identify actual combustion of fossil fuel among industries and regions. Therefore, this paper excludes fossil fuels used for materials from the calculation to the extent they can be identified from available statistics. In addition, this paper does not al
locate emissions from electricity production to each user but regards them as emissions
in the power companies themselves. It means, in using electricity itself, the emissions
are not directly counted. The emissions from electricity are counted in power compa
nies, according to their input of fossil fuel, but the emissions from self-power genera

tion are in the sectors doing it, because they input fossil fuel to generate electricity.
That is, this paper considers C02 emitted directly from sectors and regions.
Manufacturing industries have 95.6% of the total self-power generation in Japanaccording to the 2000 input-output table. Most of them use by-product such as blast
furnace gases (BFG) and coke oven gases (COG) for self-power generation. The data
concerning by-product are not always credible and counted in an input-output table,
but we can identify fossil fuel including by-product in manufacture from the Structural
Survey of Energy Consumption in Commerce, Mining, and Manufacturing (Sekiyu Tou
Shohi Kozo Tokei Hyo). This statistics provides detailed information on self-power gen
eration in each sector at the prefectural level. Other self-power generators such as
wholesale, retail, hospital, and railway do not use by-product or other fossil fuel which
are not counted in an input-output table, if any, it must be tiny volume.
In order to estimate prefectural emissions and construct the emission database at

the prefecture level, this paper classifies energy consumption in Japan in 1995 and2000 by regions (/), sectors (/'), and energy items (k) as in Figurel, and constructs
region-industry energy consumption matrices as in Figure 2 for each energy item. The
divisions of regions are by prefecture -there are 47 prefectures in Japan (see Appendix
1 for locations). The sectors are 33 in number -32 industrial3 and 1 household sector4.
In Figure 2, there are two kinds of summation: by row and by column, that is, regional total consumption and sectoral consumption at the national level -not to men-

4

Journal ofApplied Input-Output Analysis, Vol. 15, 2009

Figure 2. The region-industry energy consumption matrix'• • *• Sector (j)

Sum

n

o'
S3

"Knownsectors"j"Unknown sectors"ri
Sum"Partial data"

Sectoral consumption ofnational leveltion, the total consumption in Japan shown on the right down corner in Figure 2. These
two parameters, -the data on these are relatively credible in Japan- are used as control

totals in estimating the elements in the energy matrices5. In Figure 2, the elements are
divided into "known sectors" and "unknown sectors." These categories are determinedaccording to whether or not the data for all prefectures concerning energy consumption
are available from existing statistics. There exist several credible individual data forsome energy types in some sectors in a few prefectures even within "unknown sectors."
This paper refers to such data as "partial data," and uses them directly to reflect regional characteristics in "unknown sectors.""Known sectors" includes manufacture6 and electric power, gas supply, and steam
and hot water supply (18) in almost all energy types. In addition, agriculture, forestry,
and fisheries (1), transport (23), and households (33) are also included in "known sec
tors" in some energy types. The ratio of the number of "known sectors" to "unknown

sectors" is around 50: 50, and 19 "partial data" are used in this paper7. As a result,
36% of the total emissions in Japan need to be estimated without a direct data source.
This category is based on the large classification of the Japan 1-0 table. The 32 industries consist of(1) agriculture, forestry, and fisheries; (2) mining; (3) food; (4) textiles; (5) pulp, paper, and wooden
products; (6) chemical products; (7) petroleum refinery and coal; (8) ceramic, stone, and clay prod
ucts; (9) iron and steel; (10) non-ferrous metal; (11) metal products; (12) general machinery; (13)electrical devices; (14) transport equipments; (15) precision machinery; (16) miscellaneous manufac
turing products; (17) construction; (18) electric power, gas supply, and steam and hot water supply;(19) water supply and waste disposal services; (20) trade; (21) finance and insurance; (22) real estate; (23) transport; (24) communication and broadcasting; (25) public administration; (26) education
and research; (27) medical service, health, social security, and nursing service; (28) other public
service; (29) business service; (30) personal service; (31) office supplies; and (32) activities not else
where classified.

This paper regards households as the 33rd sector.

Regarding limestone for cement production, we distribute the emission at the national level among
prefectures according to prefecture-country ratio of the corresponding monetary output because of
the restriction of available data.

This paper defines "manufacture" as the sum of sectors from food (3) to miscellaneous manufactur
ing products (16).

An Examination ofEstimation Methods Employed for Determining CO2 Emissions atthe Prefectural Level in Japan

5

Figure 3. The region-industry energy consumption matrix in "unknown sectors'1
Sector 2

Sector 1

Sector 3Sumen

2/*v-0Region 1enRegion 2

e2\

en

en

Region 3

e-sx

en

en

Region 4

e4\

e*2

e*

2,eo

H,ea-0

2ieo

Sum

I

e

•

I

2;<?2jHje3jHjeq
2,2,*-0

Figure 4. The monetary output matrix corresponding to Figure 3
Sector 2

Sector 1

Region 1

X\\

1I _

o

!

Sector 3

Sum

Xl3

jLijXy—Xu-J

Region 2

Xi\

Xn

#23

2jjXy

Region 3

Xii

x&

x&

lljXy

Region 4

Xa\

Xa&t

x»

2/Xv

Sum

£jiXiiZj iXi2M'Xl2

2-jiXa2j|2Li^~^12

2.2 Estimating "unknown sectors" with the modified RAS method
We estimate the elements in "unknown sectors" except "partial data" by applying
the modified RAS method. The RAS method, one of the estimation methods for technical coefficients in input-output tables, is highly appreciated in many studies8, and isused to estimate elements in various matrices other than technical coefficients. For ex

ample, Ohira et al. (1998) apply the RAS method to construct industrial waste matrices
classified by industry and waste type.
The modified RAS method estimates the area of "unknown sectors" shown in Fig

ure 2 by approximating it to the corresponding monetary output matrices9. Figure 3 assumes the area of "unknown sectors" shown in Figure 2, and Figure 4 shows a monetary output matrix of which regions and sectors correspond to those in Figure 3. The
shaded parts in Figure 3 and Figure 4 indicate that data are available; the other areas
are estimated by the modified RAS method. Before conducting RAS calculation, we
deduct "partial data" (0) from the region-industry energy consumption matrix shownin Figure 3. Similarly, in the monetary output matrix, an approximated matrix in the
RAS calculation, we deduct the element corresponding to "partial data" (6) in Figure
3 as shown Figure 4.
See Hasegawa (2004, 2008) regarding the data sources of "partial data" and other data used for con
structing the energy consumption matrices in this paper.
For example, see Toh (1998).
Instead for monetary output, total consumption expenditure is used for households (33).

6

Journal ofApplied Input-Output Analysis, Vol. 15, 2009

The modified RAS method needs to assume coefficients, corresponding to ordinaltechnical coefficients in input-output tables. Instead of technical coefficients, this paper

uses the prefecture-to-Japan ratio of each sector for approximation. First, we obtainthese coefficients from Figure 3 and Figure 4 as follows.
.

e'J

x

X'J

UU — \rioc ij — Uij L-J i c//(1)o

Xij — Uij Z—X i Xjj(2)

Z-J i Xij

where ay is the prefecture-to-Japan ratio of energy consumption and ay is theprefecture-to-Japan ratio of monetary output.
Note that ay is unknown. Therefore, we introduce ay in Equation (2) into ay inEquation (1), which leads to e\}\etl^aZHiey

(3)
yi

yi

We assume s}l)= ^ ' („ and r/!)= -=p-^-, which leads to 4n.
Z-i / Cy

Z-ij €yoJ'W/W(4)

Similarly, we introduce all' in Equation (4) into al in Equation (1), which leads toef, s}2\ r?\ and afas follows.

42)=ar2,^

(3)'

a?=W

(4)'

vin.
^-*' g<>'Wheres,-(2) = ^——,
r}(2) = ^-iJe'J(2)

2^f

E;4

If ,s/fl) and r/n) approximate 1, that is, sjn)= r/n)= 1, after repeating these procedures ntimes, ay is calculated approximately as follows.
„e- r(«)r(«-l) . . . r(i)^^ e(Dc(2) . . . „<«)

t*jy— fifi'i

Uy tJjtjj

oj

The element ei2 in Figure 3, where "partial data" is included, is estimated to be
zero. The other estimated elements are consistent in that the summation of elements in

the rows and the columns are simultaneously equal to the regional total consumption
and sectoral consumption at the national level, respectively, with "partial data" excluded. Therefore, adding "partial data" (0) to the element en in Figure 3, we con
struct region-industry energy consumption matrices that not only reflect regional characteristics but also keep the two summations, by rows and columns, consistent. We
convert energy and limestone consumption data to C02 emissions using the emission

transform factors provided by the "3EID10."

An Examination ofEstimation Methods Employed for Determining CO2 Emissions atthe Prefectural Level inJapan

3

7

Results

3.1 Comparisons to different studies
Before considering our results we must examine their reliability by comparison
with different studies. First, we compare our findings with the database constructed by
Kainou (2007), which is most related to this study.
Table 1 shows C02 emissions in 2000 based on this paper as well as Kainou(2007), integrating into three manufacture, non-manufacture, and households (33) sec
tors, because the sectorwise classifications in both studies are different; besides, it isdifficult to compare detailed sectorwise results. In addition, Table 1 integrates prefectures into 10 regions (see Appendix 2 for locations). As aforementioned, Kainou (2007)
excludes the emissions in transportation and energy conversion sectors; therefore, ourresults in Table 1 also exclude these emissions. Moreover, Table 1 shows the emissions

from direct consumption of fossil fuels, because Kainou (2007) allocates emissions
from electricity generation among users while this study regards them as emissions of
power companies themselves.
Although the values of the non-manufacture sector in Table 1 based on this paper

and Kainou (2007) are relatively close to each other", both values are significantly dif
ferent in most sectors and regions. These differences are mainly due to the differencesin estimation methods, statistics used, definition of sectors, and energy types considered.

In particular, it is clear that the large differences in manufacture are attributable toself-power generation and emission sources considered. The energy balance table in Japan (Sogo Enerugi Tokei) published by Agency for Natural Resources and Energy,
which is used as the main data source in Kainou (2007), counts consumption of fossil
fuel used for self-power generation for their own consumption as consumption of elec
tricity. On the other hand, this paper considers this as fossil fuel consumption. Further-

1The "3EID" is short for "embodied energy and emission intensity data for Japan using input-output
tables", developed by Nansai, Keisuke and Yuichi Moriguchi. It is available from the website of Na
tional Institute for Environmental Studies in Japan. (http://www-cger.nies.go.jp/publication/D031/index.html)

The non-manufacture sector also has self-power generation. Its volume accounts for 3.15% of total
self-power generation in Japan (in monetary output) according to the 2000 input-output table, whichcalculates self-power generation based on several statistics including the energy balance table in Japan (Sogo Enerugi Tokei). This ratio is considerably inconsistent with the difference between these
two studies in Table 1 (37,010 vs. 36,803, i.e. 0.56%). On the other hand, the energy balance table
in Japan does not always identify the volume and composition of fossil fuel used for self-power gen
eration from credible statistics or its original survey, but depends on estimation or assumption tolarge extent as far as the author checked its explanatory report. Therefore, the figure in Kainou(2007) is not always absolute credible. However, this contradiction is not clearly explained and remaining issue in this paper.

8

Journal ofApplied Input-Output Analysis, Vol. 15, 2009

Table 1. C02 emissions11 in 2000 based on this paper and Kainou (2007)(lOOOt-C, %)Non~manufacture2)

ManufactureThis paper

Kainou(2007)

5,079

1,377

(4.1)

(1.6)

Tohoku

4,855
(3.9)

Kanto

This paper

Kainou(2007)

Household(33)3)This paper

Kainou(2007)

3,507
(9.5)

2,068
(5.6)

3,097
(6.5)

3,071
(8.3)

1,635
(1.9)

3,424

3,074
(8.4)

4,434

(9.3)

(9.4)

3,830
(10.4)

30,000(24.3)

31,942(37.0)

11,692(31.6)

12,257(33.3)

15,427(32.6)

10,807(29.3)

4,029
(3.3)

1,459

2,278
(6.2)

2,280

3,670
(7.7)

2,587
(7.0)

Tokai

15,448(12.5)

8,803(10.2)

3,361

3,907

(9.1)

(10.6)

5,398(11.4)

4,272(11.6)

Kinki

17,498(14.2)

10,172(11.8)

4,920(13.3)(14.2)

6,533(13.8)(12.8)

Chugoku

26,725(21.7)

18,695(21.7)

2,089
(5.6)

2,222
(6.0)

2,713

2,258

(5.7)

(6.1)

1,285
(3.5)

1,309
(2.8)

1,284

4,115
(11.2)

4,455
(9.4)

3,735
(10.1)

Hokkaido

Shinetsu,
Hokuriku(1.7)

Shikoku

4,558
(3.7)

1,689

(2.0)

1,233
(3.3)

Kyusyu14,821(12.0)

10,435(12.1)

4,029(10.9)

Okinawa

Japan

(6.2)

5,232

4,733

(3.5)

268

70

477

363

345

306

(0.2)

(0.1)

(1.3)

(1.0)

(0.7)

(0.8)

123,282(100)

86,277(100)

37,01036,803(100)

47,38236,883

(100)

(100)

(100)

Note 1) It indicates emissions from direct consumption of fossil fuels but excludes the emissions
in transportation and energy conversion sectors.
Note 2) It indicates all industries other than manufacture, transportation and energy conversion sec
tors. That is, it consists of agriculture, forestry, and fisheries (1), mining (2), construction(17), from water supply and waste disposal services (19) to real estate (22), and from
communication and broadcasting (24) to activities not elsewhere classified (32).
Note 3) It includes emissions from private automobiles.

Note 4) Figures in parentheses indicate the region-country ratio of emissions in each category (%).

more, regarding emission sources, opposite to this paper, Kainou (2007) does not con

sider important emission sources for manufacture, limestone and black liquor12.
It is also clear that the large differences in household (33) are caused by different
12 According to the 2000 input-output table, 28.6% of electricity consumed in manufacture is selfpower generation at the national level. Using this ratio (28.6%) and the data in Kainou (2007), theemissions from self-power generation in manufacture are estimated to be 14,412 (1000 t-C). Further
more, according to this paper, the emissions from limestone and black liquor in Japan is 4,881 and
13,680 (1000 t-C), respectively. Therefore, if we include the emissions from self-power generation,
limestone, and black liquor in the data in Kainou (2007), the emissions in manufacture in Kainou(2007) in Table 1 become 119,250 (=14,412+13,680+4,881+86,277).

AnExamination of Estimation Methods Employed for Determining CO2 Emissions at the Prefectural Level in Japan

9

Figure 5-a. CO2 emissions per capita from automobiles at prefecture level (part 1)(t-C per capita)

H
0.2
0.1

*°
H This paper

Figure 5-b. C02 emissions per capita from automobiles at prefecture level (part 2)(t-C per capita)
0.7
0.6
0.5

0.4
0.3

0.2
0.1

^

r*r4?\<
H This paper<?

•^• Yonezawa and Matsuhashi (2009)

definitions of the household. The household in input-output tables includes selfemployed, and the residual of other final demands is also regarded as the household.
On the other hand, the household in the energy balance table excludes the household

who do not have jobs and the self-employed household13.

13 According to the 3EID, the volume of emissions in household is 47,269 (1000 t-C), which is extremely close to our result (47,382). It is notable that the household in the 3EID is based on the defi
nition of final demand in input-output tables, because it is a database constructed for input-output
analysis.

10

Journal ofApplied Input-Output Analysis, Vol. 15, 2009

Figure 6. Ratios of the 10 regions' per capita emissions to the national average(National average, Japan = 100)H This paper• Yonezawa and Matsuhashi (2009)

Therefore, it would be expected that the volume of emissions in manufacture andhousehold (33) is much larger in this paper compared to Kainou (2007). However theratios of regional to Japanese emissions are relatively consistent between the two studies.

Next, we focus on the database constructed by Yonezawa and Mastuhashi (2009),
which estimates C02 emissions from all kinds of automobiles at the municipality levelin 1999 and 2005. We must compare the results of Yonezawa and Mastuhashi (2009)
with those of this study with regard to emissions from gasoline, light oil, and LPG intransport (23), and from gasoline and light oil in households (33). However emissions
in transport (23) according to this study include those from trains and ships; therefore,
it is difficult to compare the two studies rigidly. Moreover, it must be remembered thatYonezawa and Mastuhashi (2009) and this study investigated emissions of different
years-1999 and 2000, respectively.
Figure 5-a and Figure 5-b indicate C02 emissions per capita from automobiles atthe prefecture level (see Appendix 1 for locations). It is understandable that per capita
emission of Japan according to this paper (0.507t-C) is higher than the corresponding
figure in Yonezawa and Matsuhashi (2009) (0.434t-C), because emissions from trains
and ships, in addition to those from automobiles, are included in this paper. Although
the two studies show large differences for some prefectures such as Chiba (12), Tokyo(13), and Hiroshima (34), both of them capture the trend that urban areas such as To
kyo (13) and Osaka (27) have small emissions while the northern and inland areas
have large emissions. This trend reflects the fact that public transportation systems are
more developed in urban areas and more heating is demanded in cars in the northernand inland areas in winter.

Figure 6 indicates the ratio of regional per capita emissions to the national average(i.e., the figure if the per capita emission of Japan were 100), integrating prefectures
into 10 regions (see Appendix 2 for locations as aforementioned). We can also recog
nize in Figure 6 that the two studies capture relatively the same regional characteristics

An Examination of Estimation Methods Employed for Determining CO: Emissions at the Prefectural Level in Japan11

although they show large differences in Hokkaido, Tohoku, and Shinestu-Hokuriku. It
is notable that the sectors considered in Figure 5-a, Figure 5-b, and Figure 6 are "unknown sectors" in these three energy types in this study, in which emissions are esti
mated by the modified RAS method in the absence of a direct data source.
3.2 Consideration from our results

Table 2 shows total C02 emissions in 2000 in each prefecture (see Appendix 1 forlocations as aforementioned). For total emissions, the top prefecture is Chiba (12), fol

lowed by Aichi (23) and Kanagawa (14). Tokyo (13) and Osaka (27) are ranked fifth
and eighth, respectively. Focusing on these five prefectures, first, we investigate thesectoral emission structure in Table 3. Table 3 indicates the top 10 prefectures in 2000
in different sectors -manufacture, electric power, gas supply, and steam and hot watersupply (18), transport (23), and households (33), in addition to population.
The volume of emissions from manufacture and electric power, gas supply, andsteam and hot water supply (18) accounts for around 60% of the total in Japan, andChiba (12) is the top emitter in both of these sectors. In particular, our results indicatethat Chiba (12) has significantly large emissions in material heavy industries. Further-

Table 2. Total C02 emissions in 2000 in each prefecture(lOOOt-C)
Emission

Ranking

Emission

Ranking

2,475

40

2,664

36

26

Shiga (25)
Kyoto (26)

2,505

39

Osaka (27)14,9038

Miyagi (4)

5,910

20

Hyogo (28)17,620

6

Akita (5)

4,051

24

Nara (29)912

47

Hokkaido (1)Aomori (2)Iwate (3)19,5084

3,591

Yamagata (6)

2,528

38

Wakayama (30)

5,358

23

Fukushima (7)Ibaraki (8)11,618

13

1,177

45

13,222

11

Tottori (31)Shimane (32)

2,413

41

Tochigi (9)

3,044

30

Okayama (33)

13,24110

Gumma (10)

2,788

35

Hiroshima (34)

17,193

7

Saitama (11)Chiba (12)

6,673

19

Yamaguchi (35)

12,2981230,8811Tokushima (36)

5,456

22

37

Tokyo (13)Kanagawa (14)Niigata (15)18,491

5

Kagawa (37)2,660

20,288

3

5,504

21

8,213

16

Ehime (38)Kochi (39)

1,507

43

44

Toyama (16)

3,026

31

Ishikawa (17)Fukui (18)

3,430

27

Fukuoka (40)
Saga (41)

14,449
1,197

2,979

32

Nagasaki (42)

7,092

18

Yamanashi (19)

1,024

46

3,301

28

Nagano (20)

2,953

34

Kumamoto (43)
Oita (44)10,33814

Gifu (21)
Shizuoka (22)Aichi (23)

3,274

29

42

17

Miyazaki (45)
Kagoshima (46)

2,140

7,334
25,2882

Okinawa (47)

2,979
3,698

25

9370

15

Japan

Mie (24)362561933—

Table 3. The top 10 prefectures in each sector in 2000
(lOOOt-C, Thousand)Electric power, gas supply,
Ranking

Manufacture (37.7%)°

and steam and hot waterTransport (23) (15.4%)

Households (33) (13.1%)

Population 2)>
3

supply (18) (23.6%)
1

Chiba (12)14,584 Chiba (12)9,442 Tokyo (13)7,362 Tokyo (13)2

Hiroshima (34)

11,053 Fukushima (7)8,224 Hokkaido (1)3,618

Hokkaido (1)3,097Osaka (27)

8,805

3

Hyogo (28)

10,294 Aichi (23)7,940 Aichi (23)3,425Kanagawa (14)3,017Kanagawa (14)8,490

4,449 Tokyo (13)12,0644

Aichi (23)9,573

Kanagawa (14)5,928 Osaka (27)3,129 Osaka (27)2,897

Aichi (23)

7,043

5

Okayama (33)9,516 Nagasaki (42)5,120 Chiba (12)2,813

Aichi (23)2,708Saitama (11)

6,938

6

Kanagawa (14)7,213

3,810 Fukuoka (40)2,607 Chiba (12)2,523

Chiba (12)

5,926

7

Oita (44)7,037 Tokushima (36)3,751

Ibaraki (8)2,196 Saitama (11)2,348

Hokkaido (1)

5,683

8

Fukuoka (40)6,768 Mie (24)3,657 Hyogo (28)1,948 Fukuoka (40)

1,720 Hyogo (28)

5,551

9

Yamaguchi (35)6,624 Yamaguchi (35)

3,417 Kanagawa (14)1,918 Hyogo (28)1,552 Fukuoka (40)

5,016

10

Ibaraki (8)6,578 Hiroshima (34)2,920 Shizuoka (22)1,735

1,214 Shizuoka (22)3,767

Hokkaido (1)Shizuoka (22)Note 1) Figures in parentheses indicate the ratio of emissions in each sector to total emissions at the national level (%).
Note 2) Source: Population census of Japan.

An Examination ofEstimation Methods Employed for Determining CO2 Emissions at the Prefectural Level in Japan13

more, Chiba (12) is ranked fifth in transport (23) and sixth in households (33), respec

tively, the sectors accounting for around 30% of the total emissions in Japan. In otherwords, Chiba (12) is the top generator of total emissions since it is ranked high in all
sectors that have the largest volume of emissions in Japan. Aichi (23) and Kanagawa(14) also show similar trends.

On the other hand, although Tokyo (13) and Osaka (27) are ranked high in transport (23) and households (33), they are ranked below the top 10 in manufacture andelectric power, gas supply, and steam and hot water supply (18), where emissions ac
count for around 60% of the total in Japan. This is the reason they are relatively low
ranked as far as total emissions are concerned, compared to the population ranking.

Next, we focus on the change of emission patterns from 1995 to 2000. Table 4 indicates the change in total emissions in each prefecture with population and gross re-

Table 4. Changes in population, GRP, and C02 emissions in each prefecture(1995-2000)(%)

Popula- !

Emis
sion

PopulaEmistion0 | GRP2)

sion

tion0

GRP2)

Hokkaido (1)

6.2

-0.2!

3.7

Shiga (25)

-9.1

4.3

5.6

Aomori (2)

-2.9

-0.4!

6.5

Kyoto (26)

-13.3

0.6

3.2

Iwate (3)1.5

-0.2 j

11.4

Osaka (27)

-6.8

0.1

3.8

Miyagi (4)

7.2

6.3

Hyogo (28)

-1.1

2.8

-3.5

Akita (5)

4.6

1.6|
-2.0!j._

-13,5

0.8

6.1

i

4.0 Nara (29)

Yamagata (6)

1.1

-l.Oi

7.7

Wakayama (30)

-22.8

-1.0

3.1

Fukushima (7)

20.7

-0.3j

6.3

Tottori (31)

12.5

-0.3

4.5

Ibaraki (8)

-0.6

l.Oi

3.0

Shimane (32)

118.4

-1.3

8.1

Tochigi (9)

-5.8

l.Oi

6.4

Okayama (33)

2.7

0.0

-1.9

Gumma (10)

-1.7

l.lj

3.8

Hiroshima (34)

10.8

-0.1

3.3

Saitama (11)

-6.2

2.61

4.9

Yamaguchi (35)

2.6

-1.8!

3.5

Chiba (12)

4.8

2.2 i

0.8

Tokushima (36)

139.5

-1.0

4.7

Tokyo (13)

15.0

2.5 i

8.1

Kagawa (37)

-2.8

-0.4

0.9

Kanagawa (14)

-L9

3.0!

2.7

Ehime (38)

7.3

-0.9

1.8

Niigata (15)12.0-0.5 i

4.1

Kochi (39)

0.3

-0.3

4.7

Toyama (16)-17.4

-0.2 i2.9

Fukuoka (40)

-2.0

1.7

4.4

Ishikawa (17)

21.9

0.1!

6.0

Saga (41)

-11.8

-0.9

5.3

Fukui (18)

19.7

0.21

3.0

Nagasaki (42)

15.1

-1.8

1.0

Yamanashi (19)

-1.9

0.7i

6.9 Kumamoto (43)

3.9

0.0

8.6

7.0

-0.8

10.0

6.2

Nagano (20)

-7.0

l.Oj

11.3

Oita (44)Gifu (21)

-15.4

0.4;

6.4

Miyazaki (45)

-5.4

-0.5

2.5

0.8 i

5.6

Kagoshima (46)

-0.9

-0.4

8.3

Aichi (23)

-6.0

2.5!

7.2

Okinawa (47)

5.4

3.5

11.0

Mie (24)

20.3

0.9!

5.8

Japan

3.5

1.1

4.9

Shizuoka (22)Note 1) Source: Population census of Japan.
Note 2) Values are based on 1995 price. Source: Annual report on prefectural accounts.

14

Journal of Applied Input-Output Analysis, Vol. 15, 2009

gional products (GRP). The shaded parts indicate the decrease from the 1995 level.
Prefectures that have shown significant increase in emissions are Tokushima (36), Shi
mane (32), Ishikawa (17), Fukui (18), and Mie (24). Tokushima (36) and Shimane(32), in particular, show increases of more than two times, mainly from newly built
thermal power stations. On the other hand, Wakayama (30), Toyama (16), and Gifu
(21) decreased their emissions significantly. In any case, you may notice that thechange in total emissions at the prefecture level does not always correspond to that of
population and GRP.

Moreover, we investigate changes in emissions at the prefectural level, focusing onsome industries. Figure 7 compares prefectures on the basis of change in emission vol
ume and emission intensity. Emission intensity is defined in this paper as emissions per
output of 1 million yen in industries, and as emissions per consumption expendituresof 1 million yen in households (33). In Figure 7, the horizontal axis represents change
in emission volume, and the vertical axis change in emission intensities; each marker
indicates a prefecture. Therefore, the first quadrant in Figure 7 shows the increase inboth volume and intensity; the second, decrease in volume and increase in intensity;
the third, decrease in both; and the fourth, increase in volume and decrease in intensity.
First, we focus on (a) ceramic, stone, and clay products (8), (b) transport (23) and,(c) iron and steel (9). The change in ceramic, stone, and clay products (8) and transport(23) are relatively small. These two industries decrease emissions in most prefectures,

although transport (23) significantly increases emissions in some prefectures. Moreover,
we may notice that transport (23) in many prefectures decreases both the intensity andvolume of emissions. On the other hand, iron and steel (9) shows significant change inboth the volume and intensity. With regard to iron and steel (9), prefectures are mainlyclassified into two types: those increasing volume and intensity and those decreasing
both.

Next, we focus on (d) business and (e) residential. In Figure 7, (d) business includes trade (20), finance and insurance (21), real estate (22), communication andbroadcasting (24), public administration (25), education and research (26), medical
service, health, social security and nursing service (27), other public service (28), busi
ness service (29), and personal service (30). Needless to mention, (e) residential corre
sponds to households (33). The pattern of change in (d) business has a large variety
because many prefectures are placed on all quadrants in Figure 7. On the other hand,almost all prefectures increase both the volume and intensity as far as (e) residential isconcerned, implying the necessity of a reduction policy for the household emissions in
all municipalities.
Finally, we investigate the status of (f) electric power, gas supply, and steam andhot water supply (18). Around half of all prefectures in Japan increase the volume,
some of them by more than 100%, apparently due to newly built thermal power sta
tions. On the other hand, 33 prefectures, more than half of all prefectures, decrease theintensity. In fact, although power companies in Japan increase the ratio of coal input toall fossil fuels during this period, the number of prefectures where the coal ratio increases in power companies are only 14, meaning that the increase of the coal ratio isconfined to specific regions. In addition, many plants increase the consumption of NG
or LNG in this sector. Therefore, many prefectures fulfill the reduction of intensity inthis sector.

An Examination ofEstimation Methods Employed for Determining CO2 Emissions at the Prefectural Level inJapan

15

Figure 7. Ratio of change in emission volume and emission intensity1)(1995-2000)
Intensity

Intensity(%)

Volume(%)

Volume (%)
20-100 -80 -60 -40

60

80 100-100 -80 -60 -40(a) Ceramic, stone and clay products (8)(b) Transport (23)Intensity

Intensity
(%)
—o

-100
o

80

o

60

O
40-

20^

0

%
Volume (%)

w-100 -80 -6€>-40 -2F £(k 20 40 60 80 100

8%00-100 -80 -60

Volume
-0

o

o0<x>

o40
-60

-801
-100

(c) Iron and steel (9)(d) Business

Intensity

Intensity(%)

Volume (%)-100 -80 -60 -40 -20

0

20

40

60

80 100-20""

Volume

-40"

(%)

-60"

-801
-100

(e) Residential(f) Electric power, gas supply, and steam andhot water supply (18)Note 1) The 2000 price based on the 1990-1995-2000 linked input-output tables in Japan is used
to compare emission intensities.

16

Journal of Applied Input-Output Analysis, Vol. 15, 20094

Conclusions

This paper estimated C02 emissions at the prefecture level in Japan in 1995 and2000 by using the modified RAS method in the context of the increasing concern
about global warming by local governments in Japan and their need to identify theemission structures of GHGs in their own regions. Furthermore, we compared our em
pirical results with the databases constructed by Kainou (2009) and Yonezawa and Mat
suhashi (2009) in order to verify the reliability of our estimation method, and we investigated regional differences of C02 emissions among all prefectures from different
perspectives.

Although our results were not perfectly consistent with the findings in these twostudies because of the differences in estimation methods, statistics used, and energy
types considered, both our study as well as these two studies did capture common regional characteristics in several points. Both this paper and Yonezawa and Matsuhashi(2009), in particular, found small per capita emissions in the urban area and large
emissions per capita in the northern and inland areas in transport, while the corre
sponding sectors in this study are "unknown sectors," where emissions are estimated by
the modified RAS method in the absence of a direct data source. This implies our esti
mation method could reflect regional characteristics with high reliability.
Our results revealed that the magnitude and change of total emissions at the prefecture level do not merely depend on the size of the economy or population but are
influenced by various regional characteristics such as industrial structures, location ofindustry, consumption patterns, climate, and so on. We also investigated changes inemission volume and emission intensity by focusing on some industries, and it turned
out that almost all prefectures show increases in both emission volume and emission
intensity in the residential sector. This shows the importance of policymaking by local
governments to reduce C02 emissions from households in their own regions, because
they have relatively more discretionary powers in the residential sector and are betterinformed of the circumstances of their own regions.
There remain issues for further examination. First, we did not consider the influ

ence of movement across prefectural borderlines on the transportation sector, including
private automobiles. If movements across borders are frequent, the estimated emissions
would deviate from the reality to a great extent. Second, we must acknowledge that
there exist "partial data" other than those considered in this paper, because "partial
data" are used only when they turned out to be credible, originally constructed, andtheir constructed methods are clearly reported as far as the author investigated. Therefore, this paper did not assume theoretical and objective criteria to determine what"partial data" should or should not be used. Third, we considered C02 emitted directlyfrom sectors and regions as the emissions in a prefecture. In other words, we estimated"production-based" emissions except for households. On the other hand, we can also
consider "consumption-based" emissions as regional emissions -which mean C02 emit
ted both within and outside the region in order to satisfy demand in the region con
cerned. It is necessary to identify not only production-based but also consumption-

AnExamination of Estimation Methods Employed for Determining C02 Emissions at the Prefectural Level in Japan17

based emissions in order to establish effective policies for the reduction of GHGs espe
cially, at the municipality level. However, in order to estimate consumption-based emis
sions at the regional level, we need information on production-based emissions, that is,direct emissions in the supply-side regions to satisfy the demand in other regions. In
this sense, our results are used as a useful database of regional emissions, too, for the
analysis of consumption-based emissions at the regional level.
Input-output theory is highly established as a strong analysis tool in environmental
economics, and environmental analyses based on input-output theory are widely ap
plied at various spatial scales. However, the databases of C02 emissions or other envi
ronmental burdens used with input-output tables are not sufficiently developed at theregional level, compared to those at the national or international level. Therefore, anapproach such as this study is expected to expand the scope of input-output analysis inenvironment or regional studies.

18

Journal of Applied Input-Output Analysis, Vol. 15, 2009

Appendix 1. The locations of prefectures in Japan(1) Hokkaido(2) Aomori(3)Iwate(4) Miyagi(5)Akita(6) Yamagata(7) Fukushima(8) Ibaraki(9) Tochigi(10) Gumma(11) Saitama(12) Chiba(13) Tokyo(14) Kanagawa(15)Niigata(16) Toyama

47(17) Ishikawa(18) Fukui(19) Yamanashi(20) Nagano(21) Gifu(22) Shizuoka(23) Aichi(24) Mie(25) Shiga(26) Kyoto(27) Osaka(28) Hyogo(29)Nara(30) Wakayama(31)Tottori(32) Shimane(33)0kayama(34) Hiroshima(35) Yamaguchi(36) Tokushima(37) Kagawa(38)Ehime(39)Kochi(40)Fukuoka(41) Saga(42) Nagasaki(43)Kumamoto(44)0ita(45)Miyazaki(46) Kagoshima(47) Okinawa

AnExamination of Estimation Methods Employed for Determining C02 Emissions at the Prefectural Level in Japan

19

Appendix 2. The locations of the 10 regions in Japan*

Okinawa

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Journal of Applied Input-Output Analysis, Vol. 15, 2009

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