Introduction of waste
management in AIM/CGE

Toshihiko Masui
National Institute for Environmental Studies
APEIS Training Workshop
NIES, 8 December 2004

How to introduce waste in AIM/CGE¾ Waste generation– by activity: agriculture, manufacture,
household– by waste type: scrap metal, waste paper, ...
¾ Waste treatment– input for treatment (capital, labor, energy, ..)– output after treatment (recycling, reuse, ...)
¾ Recycle demand– by activity: steel production sector, power
sector, ...
– by recycling goods: paper, biomass, ...
AIM, NIESAdditional inputs for waste analysis
produced commodity
recyclepollution
management

pollution

CO2
energyintermediate

production
sectorCO2

capitallabormarketenv. capitallaborenergyintermediate

importexportenvironmental
industry/
investment
CO2householdenergyfinal demand government

Structure of AIM/Material
AIM, NIES

abroadProduction of AIM/Material

commodity 1 … commodity m
σ=0
σ：elasticity of substituteactivity
σ=0

waste generation

aggregatedintermediate input

energyvalue added

input for pollution
management

σ=0

σ=0

σ=1

σ=0
pollution 1 … pollution p

input 1 … input nCO2CO2σ=0virginreusedmaterial material

fuel 1

capital

labor

σ=∞
…

fuel eelectricity

selfmanagement

contract
treatment

Selection of waste treatment

Reused material
share is defined as
the diffusion of stock.

σ=0
intermediate
input

energyenv.
capitalinputs for waste treatment
AIM, NIES

emissionslabor

Demand structure in AIM/Material
householdenv. load

consumption
σ=0
fin. cons.
σ=0

σ=0

(energy: derived demand)

SEW

MWM
waste
generation

non-energy
σ=1

energyσ=0(energy share is decidedfrom technology)

AGR ... GOV
AIM, NIES

OGL ... ELE

Classification of solid wastes

ashanimal and plants wastes
sludgewaste rubberslush, waste oilmetal trash, scrap metal
waste acidwaste glass
waste alkalislag
waste plastics
construction and demolition waste
waste paper
dust, sootwaste wood
animal excrement
waste fiber and textile animal carcass•Yellow cells represent both industrial waste and municipal waste
classification.
•White cells represent industrial waste classification.
AIM, NIES

Progress in structure :(2) Reproduction of detailed waste flow

recycleself treatment
18 industrial
wastesint. man.
industrial
waste man.
sectorproduction

fin. disp.

dir. reusedir. disp.

8 municipal
wasteshouseholdAIM, NIES

8 municipal
wastesint. man.
municipal
waste man.
sectorrecyclefin.
disp.
recycledir. reusedir. disp.

fin.
disp.

Sample 1
Progress in structure: (1) Waste from stock and reuse

PurposeDirect reuse of waste is effective
environmental policy or not?
– It seems to be effective to solve waste
issues.
– But, it seems to delay energy efficiency
improvement.
¾ In order to answer this question, following
module is developed and integrated withAIM/Materialproduction/consumption (AIM/Material)Î stock (durable goods)
Î waste (Weibull distribution)
Î reuse / treatment¾AIM, NIESSample 1
Progress in structure: (1) Waste from stock and reuse

Model
Low energy efficiency
RepairProduction

Reuse

Stock in t-1

Reuse

Waste (stock)
Durable goods
Household

Stock in t

Othertreatment

Municipal waste (flow)
Stock in t+1
Waste management service
RecycleFinal disposal
AIM/Material

AIM, NIES

Waste generation modelSample 1
Progress in structure: (1) Waste from stock and reuse

Waste generation model
Total disposal rate:⎧⎪ ⎛ t ⎞α ⎫⎪F (t ) = 1 − ⎨− ⎜ ⎟ ⎬⎩⎪ ⎝ β ⎠ ⎭⎪total disposal rate: Weibull distribution
total disposal rate: Weibull distribution
total disposal rate: Weibull distribution
100%
total disposal rate: Weibull distribution
100%
100%
100%
50%
50%
50%
50%
0%
0%
t t+1 t+2 .... t+x-1 t+x
0%t+1 t+2 t+3 .... t+x t+x+1
equipment i consumed in t=t x=durable years
0% t t+1 t+2 .... t+x-1 t+x
equipment i consumed in t=t+1 x=durable years
t+1 t+2 t+3 .... t+x t+x+1
equipment j consumed in t=t
x=durable years
equipment j consumed in t=t+1 x=durable years
AIM, NIESSample 1
Progress in structure: (1) Waste from stock and reuse

Scenarios¾ Reference case
¾ Reuse promotion scenarios– promotion of reuse in household
small scale expansion of reuse– expansion of reuse in government
large scale expansion of reuse– expansion of rental service– reduction of repair costAIM, NIESSample 1
Progress in structure: (1) Waste from stock and reuse

Simulation results
20

bil yenat 1995 price15
105
smalllargerental
repair

0-5-10-15

2000

1998

: GDP change

02001

2004

-2000

2007
ton-201995

Municipal waste
disposal change

-4000

small
large
rentalrepair-6000-8000-10000-12000
1995

AIM, NIES

1998

2001

2004

2007

Sample 1
Progress in structure: (1) Waste from stock and reuse

MtC

Simulation results

160
140
120
100
80
60
40
20
0-20-40

small
large
rentalrepair1995 1997

1999 2001

2003 2005
2007 2009

CO2 emission changeAIM, NIESSample 1
Progress in structure: (1) Waste from stock and reuse

Messages from simulation
Expansion of reuse will make quantity ofmunicipal final disposal decrease.
¾ CO2 will increase in small expansion of reuse,because reuse delay energy efficiency
improvement.
¾ On the other hand, CO2 will decrease in large
amount of reuse, because economic structure
itself will shift from manufacture to service
industry such as repair.
¾ Please contact Ms. Miyashita (MHIR) for moredetail!
¾AIM, NIESSample 2

Direct link of top-down and bottom-up¾ Elasticity of substitution in AIM/Material= 0 or ∞Í to keep material balance
ex. produced pulp and waste pulp Î 0
produced energy and by-product energy Î ∞ÎNeed scenarios on structural changeÎFor support scenarios, bottom-up model to represent
the technology change has been constructed.

AIM, NIESSample 2

Overview of bottom-up model
Simple linear model (Sewage sludge treatment);
Minimizing Total Cost =initial cost + running cost + final disposal costs.t.
D≦Σi Xi: service demand of sewage sludge
Xi≦Ai･li: treated sewage sludge
Rj=Xi･rij=rdj : Recycle demand
ΣiDi=Xi･di : final disposal
i: technologies,
X: treated sludge, A: capacity of sludge treatment,
R: recycled products, D: other residuall, r, rd, d: parameters

AIM, NIESSample 2

Linkage of 2 models- sewage sludge treatment -AIM/Material
In 1995

Sewage sludge generation
Price of final disposal
Recycle material demand

Bottom-up
ModelIn 1995

Productivity of sewage sludge treatment
Generation of final disposal
AIM/Material
In 1996

AIM, NIES

Bottom-up
ModelIn 1996

Sample 2

Results of 2 models- Marginal cost of pollutants -40000

C O 2 em issions (yen/tC )30000

finaldisposalof solid w astes(yen/ton)

20000
10000
01995

AIM, NIES2000

Y ear 20052010

Sample 2

Results of 2 models- technology change 100%

100%

80%

80%
60%

60%

Introduction of new technologies40%Ô

2 0 0 %0 %40%

1 9 29 05 0 20 0 20 05 1 020%

20%0%19950%
1995

2000

2005

2010

Year
No constraints casenew othersconv. drying
new melt furnace
conv. incineration
AIM, NIES

conv. others
new compost
conv. melt furnace

2000

2005
Year
With constraints case2010

new drying
conv. compost
new incineration

Sample 2

Results of 2 models- Economic impact of constraints & technologies ¾In 2010
– Environmental constraints on CO2 emissions andfinal disposal of solid waste will bring 1.8 trillion yenof GDP loss.
In case of no constraints, GDP in 2010 will be 638 trillion yen.

– By introducing new technologies (systems) in
sewage sludge management, 10 billion yen of GDP
will be recovered.
– Both increase of recycle material demand andintroduction of new technologies will make GDPloss will be mitigated by 200 billion yen.

AIM, NIESSample 3Soft link of top-down & bottom-up
Necessary dataset¾ Consistent dataset: relationship between

input and output
Investment of new technology➜Technology improvement in stock in the nextyear➜Modification of production function

AIM, NIESSample 3

Procedure for assessing carbon tax
in Japan¾Reference scenario is baseline– Autonomous energy efficiency improvement will achieve
without additional cost.
– From enduse model, energy demand per output in stockis calculated every year.
– Technology improvement in new investment isestimated from stock average. ➜Scenario.xls¾Policy scenario– From enduse model, energy demand per output in stock
and additional cost compared to reference case tointroduce new technologies are calculated every year.
– Convert of technology efficiency in each sector fromstock average to new investment

AIM, NIESSample 3

Inputs data from AIM/Endusereference
Agriculture

MiningConstruction

2000
coal
oil
town gas
electricity
new energy
coaloil
town gas
electricity
new energy
coaloil
town gas
electricity
new energy

010,4480334007330
167
00
3,909
0960

2001
0
10,3120330
00724016500
policy3,914 case
Agriculture
0960

MiningConstruction

AIM, NIES

20092010

2011

2012

009,2229,085
002952910000
652
643
0
0
149
147
0
0
0
0
3,961
3,967
0 coal
0
oil
97
97
town
gas 0
0
electricity
new energy
coaloil
town gas
electricity
new energy
coaloil
town gas
electricity
new energy

0
8,949
0
286
0
0
634
0
145
0
0
2000
3,972
00
10,448
97
00
334
0
0
733
0
167
0
0
3,909
0
96
0

0
8,813
0
282
0
0
625
0
143
0
0
2001
3,978
00
10,312
97
00
330
00
724
0
165
00
3,914
0
96
0

2009
0
9,222
0
283
00
646
0
142
0
0
3,961
0
92
0

2010
0
9,085
0275
00635
0
13900
3,967
0910

2011
0
8,9490268006250
13500
3,972
0890

2012
0
8,813
0260
00614013100
3,9780880

Sample 3

Inputs data from AIM/Enduse

sectorprocess

technology
waste plastic injection

total subsidy in 1stcommitment period(100M¥)

annual subsidy(100M¥/year)

1,174

235

steel

production

high efficient kiln cooler

92

cementproductionwaste power system

224

coal boiler

00

agricult
ureboilerconversion
controloil boiler

00

gas boiler

00

Additional cost to introduce options➜ It is regarded that this type of investment only contributes to improvethe energy efficiency, not enhancement of production capital.
➜ In the process of capital accumulation, this additional investment
is excluded.
AIM, NIES