Introduction of waste Introduction of waste management in AIM/CGEmanagement in AIM/CGE

Toshihiko MasuiToshihiko Masui

National Institute for Environmental StudiesNational Institute for Environmental StudiesAPEIS Training WorkshopAPEIS Training Workshop

NIES, 8 December 2004NIES, 8 December 2004

AIM, NIES

How to introduce waste in AIM/CGEHow to introduce waste in AIM/CGE

Waste generation– by activity: agriculture, manufacture,

household– by waste type: scrap metal, waste paper, ...

Waste treatment– input for treatment (capital, labor, energy, ..)– output after treatment (recycling, reuse, ...)

Recycle demand– by activity: steel production sector, power

sector, ... – by recycling goods: paper, biomass, ...

AIM, NIES

Additional inputs for waste analysisAdditional inputs for waste analysis

productionsector

household

capitallabor

government

abroad

market

importexport

produced commodity

energyintermediate

CO2

energyfinal demand

CO2

pollutionpollution

management

energyintermediate

CO2

env. capitallabor

environmentalindustry/

investment

recycle

Structure of AIM/Material

AIM, NIES

Production of AIM/MaterialProduction of AIM/Material

commodity m

activity

input 1

energy

capital labor pollution 1

value added

input n

aggregatedintermediate input

…

input for pollutionmanagement

pollution p…

commodity 1 …

σ=0 σ=1 σ=0

σ=0

σ=0

electricityfuel 1 fuel e…

σ=0

virginmaterial

reusedmaterial

σ=0

σ：elasticity of substitute

env.capital

laborenergyintermediateinput

σ=0

selfmanagement

contracttreatment

emissions

σ=∞

Reused material share is defined as the diffusion of stock.

CO2 CO2

inputs for waste treatment

Selection of waste treatment

waste generation

AIM, NIES

Demand structure in AIM/MaterialDemand structure in AIM/Material

household

fin. cons.

non-energy energy

GOVAGR ELEOGL... ...

σ=0(energy: derived demand)

σ=1 σ=0(energy share is decided

from technology)

env. load

MWMSEW

σ=0

consumptionσ=0

wastegeneration

AIM, NIES

Classification of solid wastesClassification of solid wastes

ash animal and plants wastessludge waste rubberslush, waste oil metal trash, scrap metalwaste acid waste glasswaste alkali slagwaste plastics construction and demolition wastewaste paper dust, sootwaste wood animal excrementwaste fiber and textile animal carcass

•Yellow cells represent both industrial waste and municipal wasteclassification.

•White cells represent industrial waste classification.

AIM, NIES

recycle

production

18 industrialwastes

8 municipalwastes

household 8 municipalwastes

industrialwaste man.

sector

int. man.

dir. reuse

dir. disp.

self treatmentfin. disp.

municipalwaste man.

sector

int. man.

dir. reuse

dir. disp.

recycle

fin.disp.

recycle

fin.disp.

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

AIM, NIES

Progress in structure: (1) Waste from stock and reuseProgress in structure: (1) Waste from stock and reusePurposePurpose

Direct 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.In order to answer this question, following module is developed and integrated with AIM/Material

production/consumption (AIM/Material)stock (durable goods)waste (Weibull distribution)reuse / treatment

Sample 1Sample 1

AIM, NIES

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

ModelModel

AIM/Material Waste generation model

Production

Household

Reuse Stock in t-1

Stock in t

Waste (stock)

Reuse

Stock in t+1

Othertreatment

Waste management service

Municipal waste (flow)

Recycle Final disposal

Durable goods

RepairLow energy efficiency

Sample 1Sample 1

AIM, NIES

equipment i consumed in t=tt t+1 t+2 .... t+x-1 t+x

total disposal rate: Weibull distribution

x=durable years

100%

50%

0%

Total disposal rate:

( ) 1 tF tα

β

⎧ ⎫⎛ ⎞⎪ ⎪= − −⎨ ⎬⎜ ⎟⎝ ⎠⎪ ⎪⎩ ⎭

equipment i consumed in t=t+1t+1 t+2 t+3 .... t+x t+x+1

total disposal rate: Weibull distribution

x=durable years

100%

50%

0%

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

Waste generation modelWaste generation model

equipment j consumed in t=tt t+1 t+2 .... t+x-1 t+x

total disposal rate: Weibull distribution

x=durable years

100%

50%

0%

equipment j consumed in t=t+1t+1 t+2 t+3 .... t+x t+x+1

total disposal rate: Weibull distribution

x=durable years

100%

50%

0%

Sample 1Sample 1

AIM, NIES

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

ScenariosScenarios

Reference caseReuse 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 cost

Sample 1Sample 1

AIM, NIES

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

Simulation resultsSimulation results

19951998

20012004

2007

-20

-15

-10

-5

0

5

10

15

20

bil

yen

at

1995 p

rice

small

large

rental

repair

Sample 1Sample 1

: GDP change: GDP change

1995 1998 20012004

2007

-12000

-10000

-8000

-6000

-4000

-2000

0

2000

ton small

large

rental

repair

Municipal wasteMunicipal wastedisposal changedisposal change

AIM, NIES

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

Simulation resultsSimulation results

1995 1997 1999 2001 2003 2005 2007 2009

-40-20

020

40

60

80

100

120

140

160

MtC small

large

rental

repair

Sample 1Sample 1

CO2 emission changeCO2 emission change

AIM, NIES

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

Messages from simulationMessages from simulation

Expansion of reuse will make quantity of municipal final disposal decrease. CO2 will increase in small expansion of reuse, because reuse delay energy efficiency improvement. 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. Please contact Ms. Miyashita (MHIR) for more detail!

Sample 1Sample 1

AIM, NIES

Direct link of topDirect link of top--down and bottomdown and bottom--upup

Elasticity of substitution in AIM/Material= 0 or ∞

to keep material balance

ex. produced pulp and waste pulp 0

produced energy and by-product energy ∞

Need scenarios on structural change

For support scenarios, bottom-up model to represent the technology change has been constructed.

Sample 2Sample 2

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Overview of bottomOverview of bottom--up modelup model

Simple linear model (Sewage sludge treatment);

Minimizing Total Cost =initial cost + running cost + final disposal cost

s.t. D≦Σi Xi : service demand of sewage sludgeXi≦Ai･li : treated sewage sludgeRj=Xi･rij=rdj : Recycle demandΣiDi=Xi･di : final disposal

i: technologies, X: treated sludge, A: capacity of sludge treatment, R: recycled products, D: other residuall, r, rd, d: parameters

Sample 2Sample 2

AIM, NIES

Linkage of 2 modelsLinkage of 2 models-- sewage sludge treatment sewage sludge treatment --

AIM/Material

In 1995

Bottom-upModel

In 1995

Sewage sludge generationPrice of final disposalRecycle material demand

AIM/Material

In 1996

Productivity of sewage sludge treatmentGeneration of final disposal

Bottom-upModel

In 1996

Sample 2Sample 2

AIM, NIES

Results of 2 modelsResults of 2 models-- Marginal cost of pollutants Marginal cost of pollutants --

0

10000

20000

30000

40000

1995 2000 2005 2010

C O 2 em issions (yen/tC )

final disposal of solid w astes

(yen/ton)

Year

Sample 2Sample 2

AIM, NIES

Results of 2 modelsResults of 2 models-- technology change technology change --

0 %

2 0 0 %

1 9 9 52 0 0 02 0 0 52 0 1 0

new others conv. others new dryingconv. drying new compost conv. compostnew melt furnace conv. melt furnace new incinerationconv. incineration

0%

20%

40%

60%

80%

100%

1995 2000 2005 2010Year

With constraints case

0%

20%

40%

60%

80%

100%

1995 2000 2005 2010Year

No constraints case

Introduction of new technologies

Sample 2Sample 2

AIM, NIES

Results of 2 modelsResults of 2 models-- Economic impact of constraints & technologies Economic impact of constraints & technologies --

In 2010– Environmental constraints on CO2 emissions and

final disposal of solid waste will bring 1.8 trillion yen of 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 and introduction of new technologies will make GDP loss will be mitigated by 200 billion yen.

Sample 2Sample 2

AIM, NIES

Soft link of topSoft link of top--down & bottomdown & bottom--upupNecessary datasetNecessary dataset

Sample 3Sample 3

Consistent dataset: relationship between input and output

Investment of new technology➜Technology improvement in stock in the next

year➜Modification of production function

AIM, NIES

Procedure for assessing carbon tax Procedure for assessing carbon tax in Japanin Japan

Sample 3Sample 3

Reference scenario is baseline– Autonomous energy efficiency improvement will achieve

without additional cost.– From enduse model, energy demand per output in stock

is calculated every year. – Technology improvement in new investment is

estimated from stock average. ➜Scenario.xls

Policy scenario– From enduse model, energy demand per output in stock

and additional cost compared to reference case to introduce new technologies are calculated every year.

– Convert of technology efficiency in each sector from stock average to new investment

AIM, NIES

Inputs data from AIM/Inputs data from AIM/EnduseEndusereference 2000 2001 2009 2010 2011 2012

Agriculture coal 0 0 0 0 0 0oil 10,448 10,312 9,222 9,085 8,949 8,813

town gas 0 0 0 0 0 0electricity 334 330 295 291 286 282new energy 0 0 0 0 0 0

Mining coal 0 0 0 0 0 0oil 733 724 652 643 634 625

town gas 0 0 0 0 0 0electricity 167 165 149 147 145 143new energy 0 0 0 0 0 0

Construction coal 0 0 0 0 0 0oil 3,909 3,914 3,961 3,967 3,972 3,978

town gas 0 0 0 0 0 0electricity 96 96 97 97 97 97new energy 0 0 0 0 0 0

000000new energy

888991929696electricity

000000town gas

3,9783,9723,9673,9613,9143,909oil

000000coalConstruction

000000new energy

131135139142165167electricity

000000town gas

614625635646724733oil

000000coalMining

000000new energy

260268275283330334electricity

000000town gas

8,8138,9499,0859,22210,31210,448oil

000000coalAgriculture

201220112010200920012000policy case

Sample 3Sample 3

AIM, NIES

Inputs data from AIM/Inputs data from AIM/EnduseEnduse

sector process technology

total subsidy in 1st commitment period

(100M¥)annual subsidy

(100M¥/year)

cement production

waste plastic injection 1,174 235

steel production high efficient kiln cooler 9 2

waste power system 22 4

coal boiler 0 0

oil boiler 0 0

gas boiler 0 0agriculture

boiler conversion control

Additional cost to introduce options➜ It is regarded that this type of investment only contributes to improve

the energy efficiency, not enhancement of production capital. ➜ In the process of capital accumulation, this additional investment

is excluded.

Sample 3Sample 3