análise do indicador de consumo sustentável de nansai et al. (2007)

8/18/2019 Análise do indicador de consumo sustentável de Nansai et al. (2007)

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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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a*$ Into!u"#o&

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different objective function of minimizing nn environment burden” &


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• Ade$ua%?se H %e!odologia e s+o avaliveis&

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• ;+o a(resen!ados no (resen!e de indica!ivo&

1$,% 0onstu"#o !o mo!elo !e an&lise .om ,ase em 2 Go3in

*$ A/alia"#o !a 4uali!a!e .ient)'i.a

*$a% En4ua!amento te5i.o

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recurso, mas formal ser" o acordo #alianças, joint$ventures, fus%es e a&uisiç%es'. (m dos fatores$chave


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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&

• Consu%o e (rodu)+o sus!en!veis&

• Macroecono%ia clssica& Kei da o*er!a e da (rocura&

' ar!igo co%e)a (or revelar $ue a $ues!+o dos (adres de (rodu)+o e consu%o ao nível gloal se%(rees!ive na %anga (ara a sua discuss+o, sendo u%a (reocu(a)+o (ara a sociedade con!e%(orLnea (or isso, aAgenda 21 no seu ca(í!ulo 4 *az %en)+o da $ues!+o de (rodu)+o e consu%o sus!en!veis& Co% ase no(argra*o seguin!e Dtoday, it is important that +e eamine ideal patterns of apanese household consumptionso that apan can maintain a sustainable balance bet+een economic and environmental needs” , veri*ica?se $ueo ar!igo iden!i*ica a *inalidade da (es$uisa& F%ora a:a a *unda%en!a)+o do (role%a, (or-%, o ar!igo n+o*az re*er6ncia a ou!ros es!udos si%ilares sore es!a %a!-ria&

*$,% 6eto!ologia$

,1$ Estatégi.as meto!ol5gi.as$• 3o es!udo - a(resen!ado o %-!odo usado (ara en!rada e saída dos dados (ara a sua anlise /%odelo

input$output de (rogra%a)+o linear&

• Fs!udo de ase (o(ulacional, (or-% n+o - aco%(anado (elo !a%ano da a%os!ra&

• 3+o se *az %en)+o ao res(aldo -!ico /$ues!es -!icas e ne% *az re*er6ncia do seu acau!ela%en!o&

• 3+o *az alus+o aos ins!ru%en!os usados (ara a recola dos dados e ne% se $uer aorda a suavalida)+o&

• Fs!udo $uan!i!a!ivo&

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• 's dados s+o a(resen!ados de *or%a se$uencial&

•

A(oia?se das !aelas dos es!udos an!eriores, (or ee%(lo, a !aela 1&• 3a *ig&1 (eca?se (or !razer duas si!ua)es /8as!e e 3' co% re(resen!a)+o de arras da %es%a cor?

isso di*icul!a a sua dis!in)+o

• 3+o re*erencia)+o das grandes !aelas de en!rada e saída&

,+$ Limita"7es !o méto!o utili8a!o$

• so de valores sus!i!u!os eGou es!i%ados

• 3+o (er%i!e u%a in!er(re!a)+o clara dos resul!ados

• 3+o usa %-dias e desvio (adr+o (ara *acili!ar a anlise dos resul!ados

•


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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&

A anlise input$output - u%a *erra%en!a $uan!i!a!iva de al!o nível %acroecon.%ico& Consis!e no uso de!aelas de insu%o?(rodu!o e e%isses (or sec!or (ara calcular os i%(ac!os a%ien!ais& ;cal!egger /199Ocri!ica $ue es!e %-!odo n+o !e% a du(la (ers(ec!iva /de ci%a (ara aio e de aio (ara ci%a $ue serianecessria (ara u%a %edi)+o co%(le!a da sus!en!ailidade&

'u!ras crí!icas H anlise input$output es!+o relacionadas co% as enor%es necessidades de dados de oa$ualidade, e% co%o os (ressu(os!os si%(li*icadores necessrios no !ra!a%en!o de dados, $ue deve% sere(lici!ados /Palovii!a, A&, 2004& P&e& as res!ri)es QQ, KQQ e


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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

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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.


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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.


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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.)


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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


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


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


commodity and direct NO x

impact-based emission per unit production of

commodity.

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análise do indicador de consumo sustentável de nansai et al. (2007)

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