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1
TRABALHO DO GRUPO Nansai 1
Xosé Manuel Carreira Rodríguez, nº 1400957, Emmanuel Pereso Aliceu Jovo, nº 1402550, Agostinho Aler!o"uea, nº 15010#2 e Sheila Joa$ui% Co%e, nº 1500957&
Into!u"#o$
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de an"lise ) a capacidade estrat)gica do grupo organizacional, ou seja, a sua capacidade pol*tica em
negociar e estruturar relaç%es de poder I /auer, M& A& K&, 2015&
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4
+$ 0on.lus7es$
A avalia)+o da sus!en!ailidade da (rodu)+o e o consu%o %edian!e u% Nnico indicador aseado naanlise input$output *oi inves!igado e% vrios ar!igos (or 3ansai e! al& /2007& 3ou!ro ar!igo coe!Lneo dos
%es%os inves!igadores :a(oneses, (or ee%(lo, o consu%o de elec!ricidade *oi escolido co%o %edida daeco?velocidade, co% ase na analogia co% a no)+o de velocidade na *ísica&
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5
Re'e2n.ias ,i,liog&'i.as(
1& Agenda 21 /1992& ni!ed 3a!ions Con*erence on Fnviron%en! n@ ;e%inrio@ Anlise crí!ica de ar!igo cien!í*ico& niversidadeederal de ois, rasil&!!(s@GG((gnu!&*anu!&u*g&rGu(G240GoGanaliseSar!igoScien!i*icoS2&(d*
E& auer, M& A& K& /2015& eorias A%ien!ais& niversidade ederal do Rio rande do ;ul& rasil&!!(s@GG888&(asseidire!o&co%Gar$uivoG2#O97E0G!eorias?a%ien!ais
4& Caeiro, ;&, Ra%os, & &, =uising, ;C;PGK, no (relo&
O&
=er!8ic, F& /2010& Assessing !e environ%en!al i%(ac!s o* consu%(!ion and (roduc!ion@ (riori!T(roduc!s and %a!erials& 3FPGFar!(rin!&!!(@GG888&une(&orgGresource(anelGPor!alsG24102GP>@ Uri!ing a "uali!T Manuscri(!@ ar!icle s!ruc!ure !!(@GG(laTer&vi%eo&co%GvideoG#4054OE0 ? ídeo >>>@ Uri!ing a "uali!T Manuscri(!@ language, !ecnical issues, su%ission, revision and res(onses!o !e revie8ers !!(@GG(laTer&vi%eo&co%GvideoG#475OE41 ? ídeo >@ Uri!ing a "uali!T Manuscri(!@ Acce(!ing re:ec!ion, e!ical, (eer revie8, e!c$!!(@GG(laTer&vi%eo&co%GvideoG#4#7O#9E
#& 3ansai &, aga8a, ;& Moriguci, V& /2007& Pro(osal o* a si%(le indica!or *or sus!ainaleconsu%(!ion@ classi*Ting goods and services in!o !ree !T(es *ocusing on !eir o(!i%al consu%(!ion
levels&
Journal o* Cleaner Produc!ion, 15 /10, #79?##5&9& 3ansai, &, aga8a, ;&, ;u, ;&, >naa, R&, Moriguci, V& /2007& ;i%(le indica!or !o iden!i*T !e
environ%en!al soundness o* gro8! o* consu%(!ion and !ecnologT@DFco?veloci!T o* consu%(!ionI&Fnviron%en!al ;cience ecnologT, 41/4, 14O5?1472&
10& Palovii!a, A& /2004& Ma!ri sus!ainaili!T@ a((lTing in(u!?ou!(u! analTsis !o environ%en!al andecono%ic sus!ainaili!T indica!ors@ case@ innis ores! ;ec!or& niversi!T o* JTvWsXTlW&!!(s@GG:T&:Tu&*iGds(aceGi!s!rea%GandleG12E45O7#9G1E19EG951E919#97&(d*
11& ;cal!egger, ;& /199O& Ki*e CTcle Assess%en! /KCAY"uo vadisZ& ;(ringer ;cience usiness Media&!!(s@GGooXs&google&esGooXsZid[r\3O'Aa#dcC
12& odorov, &, Marinova, n!erna!ional E1& Au!u%n 2000, 101?120&!!(@GG888&i*a&edu&rG(ro*essoresGar%andoGFng5E1Gnid]20>GM>]20Ko8ell]20>ndica!orsus!enale(roduc!ion&(d*
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Proposal of a simple indicator for sustainable consumption: classifyinggoods and services into three types focusing on their optimal
consumption levels
Keisuke Nansai a,*, Shigemi Kagawa b, Yuichi Moriguchi a
a Research Center for Material Cycles and Waste Management, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba,
Ibaraki 305-8506, Japanb Graduate School of Information Sciences, Tohoku University, Aoba, Aoba-ku, Sendai 980-8579, Japan
Received 31 March 2005; accepted 27 February 2006
Available online 18 May 2006
Abstract
We calculated optimal consumption patterns of Japanese households using a linear programming model, taking into account the different
environmental burdens to be minimized. Ninety-four industrial sectors and 94 commodities were defined in the model. In terms of environmental
burdens to be minimized, this study considered energy consumption, CO2 emission, waste, and NO x emission. According to the direction
(increase or decrease) of adjusted final demand for a commodity in the household, commodities were classified into three types: (1) a commodity
for which optimal demand should be decreased in all cases of reducing various environmental burdens; (2) a commodity whose optimal demand
should be increased in all cases; and (3) a commodity whose optimal demand depends on the type of environmental burden. Among 63
commodities whose final demand was assumed to be adjustable, 47 were categorized as commodity type 1, nine were categorized as commodity
type 2, and seven belonged to commodity type 3. This work also characterized each type of commodity from the viewpoint of economic and
environmental properties. 2006 Elsevier Ltd. All rights reserved.
Keywords: Indicator; Consumption pattern; Multiple-environmental burdens; Household consumption; Linear programming model
1. Introduction
In recent years, ideal patterns of consumption have been
discussed under the concept of ‘‘sustainable consumption’’.
This concept has been incorporated into international policies
[1]. In 1992, for example, Chapter 4 of Agenda 21 referred tosustainable consumption and production, and the United Na-
tions has compiled guidelines for consumer protection that
provide governments with a comprehensive framework for set-
ting policy for more sustainable consumption and production.
At the World Summit on Sustainable Development held in
Johannesburg in 2002, the agenda called for the development
of a 10-year framework of programs to promote the shift
toward sustainable consumption and production patterns.
In Japan, approximately 48% of the total domestic CO2emissions in 1995 originated from household consumption.
Fuels directly consumed for car and house heating accounted
for 25% of CO2 emissions, electric power use and mains gasuse accounted for about 17%, and the rest of the emissions
were attributed to production and provision of goods and ser-
vices consumed by households. However, we must consider
that household consumption is an important driving force be-
hind the Japanese economy. About 46% of gross domestic pro-
duction (GDP) is induced by the expenditure of household
consumption [2].
Today, it is important that we examine ideal patterns of
Japanese household consumption so that Japan can maintain
a sustainable balance between economic and environmental* Corresponding author. Tel.: þ81 29 850 2889; fax: þ81 29 850 2917.
E-mail address: [email protected] (K. Nansai).
0959-6526/$ - see front matter 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jclepro.2006.02.009
Journal of Cleaner Production 15 (2007) 879e885www.elsevier.com/locate/jclepro
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-mailto:[email protected]:[email protected]://www.elsevier.com/locate/jcleprohttp://www.elsevier.com/locate/jclepromailto:[email protected]://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-
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needs. To accomplishthis goal, it is interestingto evaluatewhich
commodities’ consumption should be reduced for environmental
gainsand which commodities’ consumptionshould be increased
for economic gains. When investigating environmental effects,
we shouldconsider various types of environmental burdens to be
mitigated.
This study compiled the linear programming model based onan inputeoutput system using an economic inputeoutput table.
The model demonstrated multiple optimal states (increase or de-
crease) of household consumption for each commodity, by setting
different objective function of minimizing an environmental bur-
den. According to the type of change in thedemand, we classified
commodities into three types: (1) a commodity for which optimal
demand should be decreased in all cases of reducing various en-
vironmental burdens; (2) a commodity whose optimal demand
should be increased in all cases; and (3) a commodity whose op-
timal demand depends on the type of environmental burden. We
also identified commodity types, while considering both their
economic and environmental properties.
We used energy consumption, CO2 emission (global warm-ing), waste emission, and NO
x (air pollutant) emission as
environmental burdens in our model, because reliable environ-
mental data on these parameters were available.
2. Materials and methods
2.1. The linear programming model
2.1.1. Input eoutput system
This study employed a linear programming model based on
the von Neumann inputeoutput system or SNA inputeoutput
system [3]. This inpute
output system identifies the primaryand secondary products of an industrial sector; it permits joint
production, so that a single production activity can have more
than one output, and relaxes the assumption of a fixed ratio of
inputs to output. Table 1 is the make-use table used in our
model. The letters used in the table are explained below.
2.1.2. Objective function
Household consumption directly and indirectly causes envi-
ronmental burdens. Direct environmental burdens result from,
for example, the use of fuels for driving and cooking and the
emission of CO2 and air pollutants from burning these fuels.
Indirect environmental burdens of household consumption en-
compass the energy or fuels consumed in, for example, car
production and provision of services, which are also accompa-
nied by CO2 and pollutant emissions.
Representing the total demand of each industrial sector by
vector g, the final demands of each commodity, h and f satisfy:
hþ f ¼ ðCBÞg; ð1Þ
where C is the product mix matrix, with coefficients showing
the amount of commodity supplied by unit total output of in-
dustry, and B is the input matrix, with coefficients representingthe amount of commodity required for the unit total output of
industry. Matrices U and V represent the absorption matrix in-
dicating the values of purchase of commodities by industries
and the make matrix showing the values of commodities pro-
duced by industries (Table 1) and are related to matrices B and
C according to the following equations:
U ¼ B b g; ð2Þ
and
VT ¼ C b g; ð3Þ
where b g denotes a diagonal matrix with the vector g, whose
element number is the same as the number of commodity
and VT is the transposed matrix of V.
We express the direct environmental burden factor of each
commodity by vector ec, which represents the amount of direct
environmental burden accompanied by the unit commodity
consumption. The total direct environmental burden caused
by commodity consumption can be calculated as follows:
direct ¼ ecðhþ f Þ ¼ ecðCBÞg: ð4Þ
The indirect environmental burden factor of each commodity,
or the environmental burden from industrial production activ-ity, is calculated as:
indirect ¼ eig; ð5Þ
where ei is a vector with environmental burden per unit output
of industrial sector as its element.
The objective of this model is to minimize the sum of direct
and indirect environmental burdens:
directþ indirect ¼ ecðCBÞgþ eig
¼ fecðCBÞ þ eigg/min: ð6Þ
The model calculates the optimal state of vector g. Here-after, we describe it as vector g*.
2.1.3. Constraints
2.1.3.1. Commodity supplyedemand balance. We assumed
an adjustable range for the final demand for household
consumption. Representing the upper range of the household
consumption as vector hU and the lower range as vector hL, the
commodity supplyedemand balance should meet these criteria:
hU þ f CgBg hL þ f : ð7Þ
That is, the difference between the total supply Cg and the
intermediate demand Bg is more than the sum of hL
and the
Table 1
Intraregional make-use table of primary and secondary products
Commodity Industry Household
consumption
Other
final
demands
Total
demand
Commodity X U h f q
Industry V g
Imports M
Value added V
Total supply q
T
g
T
880 K. Nansai et al. / Journal of Cleaner Production 15 (2007) 879e885
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final demand by others (government, capital investment and
exports), f , and is less than the sum of hU and f .
2.1.3.2. Capital constraint . Eq. (8) accounts for a limitation of
capital stock availability, or capital constraint:
kg K ; ð8Þ
where k is capital utilization vector showing the sectoral
capital requirement per unit total output of industry and
K ** is the total capital stock. It was difficult to estimate
actual stock accumulated in the past and capital utilization
rate by industry, so we used surrogate values: the present
annual total capital depreciation was used for K **, and cap-
ital depreciation rate by industry was used as the capital
requirement.
2.1.3.3. Labor constraint . The number of workers was in-
cluded in the constraints:
lg L
;
ð9Þ
Table 2
Sector numbers and names of commodity and industrial sectors
Sector
number
Sector name Final
demandaSector
number
Sector name Final
demanda
1 Coal mining and lignite e 48 Metal products for construction, architecture e
2 Crude petroleum and natural gas no 49 Other metal products e
3 Petroleum refinery products e 50 General industrial machinery e
4 Coal products e
51 Special industrial machinery e
5 Electricity e 52 Other general machines e
6 Gas supply, steam and hot water supply e 53 Office machines and machinery for service industry e
7 Agriculture un 54 Household electric appliance e
8 Livestock-raising and sericulture un 55 Electrical and communications equipment e
9 Agricultural services un 56 Heavy electrical equipment no
10 Forestry e 57 Other electrical equipment e
11 Fisheries and culture un 58 Motor vehicles e
12 Metal ores no 59 Ships and its repair e
13 Non-ferrous metal ores e 60 Other transport equipment and its repair e
14 Slaughtering and meat processing un 61 Scientific instruments e
15 Livestock-raising foods un 62 Miscellaneous manufacturing products e
16 Seafood un 63 Construction no
17 Grain milling and flour un 64 Repair of construction no
18 Preserved agricultural foodstuffs un 65 Civil construction no
19 Sugar and other foods un 66 Water supply un
20 Beverages un 67 Waste disposal services e
21 Feeds and organic fertilizers un 68 Wholesale trade and retail trade e
22 Tobacco e 69 Financial service and insurance e
23 Fabricated textile products e 70 Real estate rental service e
24 Wearing apparel and other textile products e 71 House rental e
25 Timber and wooden products e 72 Railway transport un
26 Furniture and fixtures e 73 Road transport un
27 Pulp and paper e 74 Ocean transport and coastal transport un
28 Processed paper products e 75 Air transport un
29 Printing and publishing e 76 Storage facility service e
30 Chemical fertilizer e 77 Services relating to transport un
31 Industrial inorganic chemicals e 78 Telecommunication e
32 Industrial organic chemicals e 79 Broadcasting e
33 Resins no 80 Education e
34 Chemical fibers no 81 Research no
35 Final chemical products e 82 Medical services, health and hygiene un
36 Plastic products e 83 Other public services e
37 Rubber products e 84 Advertising services e
38 Leather, leather products and fur skins e 85 Information services e
39 Glass and glass products e 86 Goods rental and leasing e
40 Cement and cement products e 87 Repair of motor vehicles and machinery e
41 Pottery, china and earthenware e 88 Other business services e
42 Miscellaneous ceramic, stone and clay products e 89 Amusement and recreation services e
43 Pig iron and crude steel no 90 Eating and drinking places e
44 Steel no 91 Hotels and other places of accommodation e
45 Steel products e 92 Other personal services e
46 Non-ferrous metals e 93 Activities not elsewhere classified un
47 Non-ferrous metal products e 94 Administration e
a
un, Unadjustable final demand commodity; no, commodity with no final demand by households.
881 K. Nansai et al. / Journal of Cleaner Production 15 (2007) 879e885
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where l is the labor coefficient vector, which represents the
number of workers needed for a unit total output of industry,
and L** is the current total number of workers.
2.1.3.4. GDP constraint . To maintain the current economic
scale, GDP constraint must meet the criterion
vg GDP; ð10Þ
where v is the value-added coefficient vector, which indicates
the value added by the unit total output of each industry.
GDP** is the present value of GDP.
2.2. Data compilations
2.2.1. Economic data
To obtain matrix V in Eq. (3), we employed the 1995
Japanese make-use table of primary and secondary products
(Table 1; [2]). Because matrix U (Eq. (2)) couldn’t be obtained
from the 1995 make-use table, we estimated matrix B by using
matrix A, and then determined matrix U by Eq. (2):
X ¼ A b g; ð11Þ
and
B ¼ AC; ð12Þ
where matrix X is the commodity-by-commodity flow matrix
showing the values of purchases of commodities by commod-
ities (Table 1). The industry and commodity areas together
contained 94 sectors (Table 2).
Considering the likelihood of change in consumption, the
adjustable range of final demand by household consumption
was assumed to be 10% of the present value of final demand.We also assumed that final demands for some commodities
are unadjustable, however, because they are fundamental
commodities required for daily life, such as foods and
medical care. (These unadjustable commodities are noted inTable 2.)
For the other final demand vector, f , we used the current
value in the make-use table. The total capital stock, K **,
and the capital depreciation rate, k, were derived from the
make-use table. Labor coefficients, l, were calculated by divid-
ing the total output of industry into the sectoral total labor pro-
vided in the labor table, one of the supplementary tables in the
Japanese inputeoutput table [2]. Total labor, L**, was also
provided in the labor table. Value-added coefficients of vector
v were estimated from the make-use table; for the constant
GDP** we used the 1995 value [2].
2.2.2. Environmental data
This model requires us to input environmental burden fac-
tors for each commodity and industry, ec and ei. We calculated
Table 3
Economic and environmental changes from the present state in Japan by each
minimization of environmentalburdens(Maximum adjustable rangeof the house-
hold consumption for each commodity is assumed as 10% of the present state)
State items Type of environmental burden to be minimized
(%change from the present state)
Energy CO2 Waste NO x
Economy
GDP 0.00 0.00 0.00 0.00
Labor 0.43 0.43 0.37 0.43Capital 0.00 0.00 0.00 0.00
Environment
Energy 2.37 2.37 1.35 2.37CO2 1.99 1.99 1.08 1.99Waste 1.27 1.27 1.34 1.27NO
x 1.10 1.10 0.98 1.10
-40
-30
-20
-10
0
10
20
30
40
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91
Energy CO2
Waste NOx
Commodity sector number
C h a n g e i n f i n a l d e m a n
d f o r c o m m o d i t y
Fig. 1. Optimal changes in Japanese household consumption for commodities by each minimization of four environmental burdens: Commodities whose bar chart
extend only to the minus side can be identified as type 1 commodity, commodities whose bar chart extend only to the plus side can be identified as the type 2, and
others are the type 3. Here, the maximum adjustable range of household consumption for each commodity is assumed as 10% of the present level.
882 K. Nansai et al. / Journal of Cleaner Production 15 (2007) 879e885
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ec and ei for energy consumption, CO2 emission, waste emis-
sion, and NO x
emission. For energy consumption and CO2emission, environmental burden factors or energy intensity
and CO2 emission factors for industrial sectors were estimated
by converting environmental burden factors for commodity
sectors [4]. In terms of waste, emission factors for industrial
sectors, ei, were obtained from Kagawa et al. [5], and those
for commodities were originally determined by using the total
quantity of municipal waste and its composition [6]. For emis-sion factors of NO x
, we used impact-based emission factors
expressed as the product of the emission amount and the num-
ber of its receptors per unit output [7].
3. Results and discussion
3.1. Comparison of economic and environmental values
We implemented the model by the type of environmental
burden to be minimized in Eq. (6) and obtained four types of
vector g*. Then, based on the vector g*, we calculated four dif-
ferent optimized states for economic and environmental items,
and determined changes of those items, which would occur be-tween the present state and the optimized one (Table 3).
In terms of economic values, for all environmental targets
GDP and capital did not change, but total labor demand
-40
-30
-20
-10
0
10
20
30
40
0.23 0.30 0.34 0.36 0.39 0.40 0.44 0.48 0.51 0.61 0.68 0.72 0.83
Value-added factor of commodity
0.0
0.2
0.4
0.6
0.8
1.0
C u m u l a t i v e f r e q u e n c y o f c o m m o d i t y t y p e 1 ( - )Energy CO2 Waste
NOx
CFD
C h a n g e i n f i n a l d e m a n d f o r c o m m o d i t y
Fig. 2. The relationship between value added factor of commodity and changes in household consumption by each minimization of four environmental burdens.
(The maximum adjustable range of household consumption for each commodity is assumed as 10% of the present level.)
-40
-30
-20
-10
0
10
20
30
40
0.003 0.015 0.025 0.029 0.036 0.043 0.049 0.054 0.059 0.069 0.082 0.109 0.131
Labor factor of commodity
0.0
0.2
0.4
0.6
0.8
1.0
C u m u l a t i v e f r e q u e n c y o f
c o m m o d i t y t y p e 1 ( - )
Energy CO2Waste
NOxCFD
C h a n g e i n f i n a l d e m a n d
f o r c o m m o d i t y
Fig. 3. The relationship between labor factor of commodity and changes in household consumption by each minimization of four environmental burdens. (The
maximum adjustable range of household consumption for each commodity is assumed as 10% of the present level.)
883 K. Nansai et al. / Journal of Cleaner Production 15 (2007) 879e885
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decreased, indicating that unemployment may increase by
0.37e0.43% in an optimized system. Every environmental
item in all minimized cases had a negative value. Thus, opti-
mally minimizing an environmental burden with regard to
household consumption does not increase other environmental
burdens. In other words, a consumption pattern that is appro-
priate to the minimization of one environmental burden con-tributes to the reduction of all other environmental burdens.
Quantitatively, in terms of environmental values, energy con-
sumption can be expected to decrease by 1.35e2.37%, CO2emission by 1.08e1.99%, waste emission by 1.27e1.34%,
and NO x
emission by 0.98e1.10%. Compared with other tar-
get environmental burdens, minimization of waste emission
shows less of an ability to decrease energy consumption and
CO2 emission, owing to differences in the mechanisms of
emission process between waste and other environmental
burdens.
3.2. Classifying commodities into three types
We compared four different optimal pattern vectors, h*, of
household consumption for each commodity, which were re-
spectively converted from each vector g* by Eq. (13).
h ¼ ðCBÞg f ð13Þ
Fig. 1 shows the optimized status of final demand from house-
hold for each commodity by respective minimizations of envi-
ronmental burdens. The x -axis contains the commodity sector
number, and the y-axis represents accumulated values of respec-
tive changes in the optimized consumption (h*) from the current
level (h). Commodities can be classified into three types: (1)
a commodity for which optimal demand should be decreased
in all cases of reducing various environmental burdens; (2)
a commodity whose optimal demand should be increased in
all cases; and (3) a commodity whose optimal demand depends
on the type of environmental burden. Among 63 commodities,
47 commodities for which final demand was relaxed are classi-
fied as commodity type 1, nine are classified as commodity type
2, and seven are classified as commodity type 3. The type 3
commodities, whose optimal demand depends on the type of en-
vironmental burden, were petroleum refinery products, forestry,tobacco, telecommunication, broadcasting, goods rental and
leasing, and administration. But unfortunately, it is difficult to
determine the truly optimal demand state of these commodities
without comprehensive environmental assessment methods for
proper weighting in the model. Accordingly, we focused on
type 1 and 2 commodities here, which contribute to all environ-
mental reduction and economic sustainability factors. In an op-
timized household consumption system, households should
refrain from consuming type 1 commodities as a means to re-
duce environmental burdens and could shift the surplus money
raised by the refrainment to consumption of type 2 commodities.
In this study, our model considers only 94 commodity sectors,
however, and does not allow the classification of all commodi-
ties existing in Japan. Therefore, we attempted to characterize
commodities by their environmental and economic properties
and discover ways of distinguish the type (1 or 2) a commodity
can be classified into.
To identify the characteristics of type 1 commodities, we
looked into the relationships between the demand change and
primary properties of each commodity. Fig. 2 illustrates the
0
0.2
0.4
0.6
0.8
1
0.1 1 10 100 1000
Direct energy consumption per unit production (GJ/MY)
T h e f r e q u e n c i e s o f b e i n g t h e t y
p e 1 o r
t y p e 2 c o m m o d i t y ( - )
Type 1
Type 2
Fig. 4. Relationship between the probability of being classified as a type 1 or 2
commodity and direct energy consumption per unit production of commodity.
0
0.2
0.4
0.6
0.8
1
1 10 100 1000 10000
Direct CO2 emission per unit production (kg-C/MY)
T h e f r e q u e n c i e s o
f b e i n g t h e t y p e 1 o r
t y p e 2 c o
m m o d i t y ( - )
Type 1
Type 2
Fig. 5. Relationship between the probability of being classified as a type 1 or 2
commodity and direct CO2 emission per unit production of commodity.
0
0.2
0.4
0.6
0.8
1
0.001 0.01 0.1 1 10 100 1000 10000 100000
Direct waste emission per unit production (kg/MY)
T h e f r e q u e n c i e s o f b e i n g t h e t y p e 1 o r
t y p e 2 c o m m o d i t y ( - )
Type 1
Type 2
Fig. 6. Relationship between the probability of being classified as a type 1 or 2
commodity and direct waste emission per unit production of commodity.
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relationship between the optimized states of demand for com-
modities and their value-added factors (million yen [MY]/
MY). In general, type 1 commodities had small value-added fac-
tors: 80% of type 1 commodities can be identified by a value-
added factor of 0.50, as illustrated by the cumulative frequency
in Fig. 2. Most of the type 2 commodities have a value-added
factor above 0.62, whereas type 3 commodities have a value-
added factor above 0.53, making it difficult to distinguish these
two commodity types by their value-added factors.
It is also hard to confirm the specific characteristics of type
1 commodities from the relationship between demand fluctua-
tion and the labor factor (Fig. 3). Compared with value-added
factors, type 2 commodities have a wide dispersion within the
range of labor factors. Thus, it would not be effective to char-
acterize commodity type on the basis of labor factors.
Recently, for instance, from product environmental reports
and case studies of life cycle assessment, it is getting to be rela-
tively easy for us to know an environmental performance value
for a commodity. Figs. 4e7 illustrate the relationship between
the direct environmental burdens imposed by unit production
(MY) of a commodity, which are elements of eiCT, and the fre-
quency of the commodity being considered type 1 or 2. By
grouping the commodities by their direct environmental burden
per unit production, wecalculated the ratios of type 1 and 2 com-
modities to the total commodities in the same range of direct en-vironmental burden per unit production. The frequency of being
classified as a type 1 commodity increased sharply above cer-
tain values of direct environment burden per unit production.
For instance, to correctly identify a type 1 commodity with
a frequency of more than 80%, we should focus on commodi-
ties whose direct environmental burden per unit production is
more than 10 GJ/MY of energy, more than 100 kg-C/MY of
CO2, more than 100 kg/MY of waste emission, or more than
100 Mt person/MY of NO x
impact-based emission.
4. Conclusions
We calculated optimal patterns of household consumption
using a linear programming model, taking into account differ-
ent environmental burdens to be minimized. According to the
direction (increase or decrease) of optimal final demand for
a commodity, the commodity was classified into one of threetypes: (1) a commodity for which optimal demand should be
decreased in all cases of reducing various environmental bur-
dens; (2) a commodity whose optimal demand should be in-
creased in all cases; and (3) a commodity whose optimal
demand depends on the type of environmental burden. Among
63 commodities whose final demand was assumed to be ad-
justable, 47 were categorized as type 1 commodities, nine
were type 2 commodities, and seven were type 3 commodities.
Additionally, this work characterizes each type of commodity
from the viewpoint of its economic and environmental proper-
ties. Its result can be applied to identify the commodity types
of various commodities in our daily life. The classification of
commodities into three types can be useful for shifting Japan’spresent household consumption pattern toward a sustainable
pattern and for finding a contradiction in our consumption be-
haviors between promotion of waste management and preven-
tion of global warming.
This paper emphasized on description of the concept of
commodity classifications as a simple indicator for our
consumptions, hence the number of considered environmental
burdens and sector classifications were very limited. Our future
work should set more detailed sector classifications in the
model, include other environmental burdens, especially water
pollutants and chemical emissions, and examine the dynamic
stability of commodity types e
namely, determining whethercommodity types change over the years.
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0
0.2
0.4
0.6
0.8
1
1 10 100 1000 10000
Direct NO x
impact-based emission per unit production
(Mt x person/MY)
T h e f r e q u e n c i e s o f b e i n g t h e t y p e 1 o r t y p e 2
c o m m o
d i t y ( - )
Type 1
Type 2
Fig. 7. Relationship between the probability of being classified as a type 1 or 2
commodity and direct NO x
impact-based emission per unit production of
commodity.
885 K. Nansai et al. / Journal of Cleaner Production 15 (2007) 879e885
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