Development of a Dynamic Energy-economic Assessment Model with Multi-regions and Multi-sectors for the Evaluation of the Carbon Emission Reduction Policies

1
International Energy Workshop 2005 July 5-7, 2005
Development of a Dynamic Energy-economic
Assessment Model with Multi-regions and
Multi-sectors for the Evaluation of the Carbon
Emission Reduction Policies
Takashi HOMMA Research Institute of
Innovative Technology for the Earth (RITE), Japan
Shunsuke MORI RITE,Tokyo University of
Science, Japan Keigo AKIMOTO RITE, Japan
Toshimasa TOMODA RITE, Japan
2
Contents

• Background and purpose
• Model structure
• Outline of a developed model, DEARS
• Energy flow module
• Economic flow module
• Simulation study
• Simulation study w./w.o. carbon emission control
  policy
• (Reference case, IPCC-S550 stabilization
  scenario)
• Summary and future research

3
Background

• Mid-term analysis of mitigation options for
  climate change is required.
• Climate policies should be evaluated
  incorporating economy energy, and technological
  issues.
• Consideration of structural changes in
  international industry and energy systems is
  important for the CO2 emission reduction policy.
• In particular, industrial structure changes have
  the critical influences on energy demands.
• However,
• Most of past long-term energy systems models with
  a time horizon up to the middle of 21st century
  have not considered with the industrial structure
  changes.
• Most of existing economic models considering the
  industrial structures cannot assess the energy
  technologies explicitly, and were limited to the
  short-term analysis.

4
Purpose and outline

• To provide world and regional strategies for
  climate change mitigation up to the middle of the
  21st century with consideration of changes in
  industrial structures and energy systems.

• Development of DEARS (Dynamic Energy-economic
  Analysis model with multi-Regions and
  multi-Sectors) for climate change policies,
  integrating a top-down multi-sectoral economic
  model based on GTAP and a bottom-up energy
  systems model based on DNE21 model.
• Simulation study through DEARS (comparison
  between Reference case and carbon emission
  stabilization scenario)

5
Outline of a developed model, DEARS

• Integration of a top-down economic module and a
  bottom-up energy systems module
• Economic module based on GTAP(ver.5) database
• Energy module based on energy model DNE21
• 18 region division of the world
• 18 sector division of non-energy industry
• 7 kinds of primary energy and 4 kinds of
  secondary energy with the consideration of CCS
  (Carbon Capture and Storage)
• Model time span Up to the middle of the 21st
  century
• Intertemporal nonlinear optimization model
  (maximization of discounted total consumption
  utilities)

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Divided 18 regions
XAP
FSU
CAN
WEP
EEP
JPN
USA
NAF
CHN
MCM
TME
IND
CAF
ASN
BRA
SAM
SAF
ANZ
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18 non-energy industry and 11 types of energy

• 18 non-energy industrial sectors

• 7 types of primary energy and 4 types of
  secondary energy

8
Model structure

• Integration of energy and non-energy sectors

9
Relations between input and output of DEARS
Constraint
DEARS
Model results
CO2 emission reduction scenario

• GDP
• Sectoral energy consumption
• Sectoral value added
• Sectoral intermediate inputs
• Final consumption

Model assumptions
Economic Module

• Population
• Rate of technical progress
• Input-output coefficient
• Account balance scenario

etc.

• Primary energy production
• Power generation
• CO2 sequestration
• Energy price
• Marginal emission reduction cost
• CO2 emission

etc.
Optimization

• Primary energy resource and supply cost
• Energy conversion efficiency
• Potential and cost of CCS

Energy systems Module
etc.
etc.
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Model structure (Energy module)
-Energy Conversion Processes and CCS-
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Data assumption - Potentials and costs of fossil
fuels-

• Assumed fossil fuel potential (WEC,USGS)

20000

Natural Gas
Coal
15000
Crude Oil
10000
Energy Resources(EJ)
5000
0
IND
JPN
ANZ
XAP
CAN
FSU
NAF
CAF
SAF
CHN
TME
USA
BRA
EEP
ASN
SAM
MCM
WEP

• Assumed fossil fuel cost (supply curve)

linearized cost function based on Rogner (1997)
gradient of the cost function
cumulative production
12
Data assumption - Potentials and costs of hydro
and wind power-

• Assumed potentials of hydro and wind power

• Assumed costs of hydro and wind power

Accumulated production
gradient of the cost function
13
Data assumption - Potentials and costs of biomass
energy-

• Power generation by biomass1

Biomass Energy Potential estimated by GLUE model
Assumed cost resource cost 0-20(/GJ) plant
cost 1790(/kWe) Power generation efficiency
23()
Reference 1Yamamoto et el.Bioenergy in
Energy Systems Evaluated by a Global Land Use and
Energy Optimization Model , Socio-economic
Research Center Rep, (2001)
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Data assumption - Potentials and costs of CCS-

• CCS Potential (only Aquifer)

The potential was estimated by RITE based on a
sedimentary basin map of USGS database.
Assumed Cost2cost of transport and geological
storage8.2(/tCO2)
fixed cost by power generation with CCS
1.75-2.17times

(relative to
that without CCS) Required electricity by the
use of CCS 2 7.4-9.4 in power generation
References 2Rubin,E.S. et el. Comparative
Assessment of Fossil Fuel Power Plants with CO2
Capture and Storage, GHGT-7, (2004)
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Model structure (contd.)

• Production function of non-energy sectors and
  macro-economy

Output
Output
(non
-
energy1)
(non
-
energy2)
Leontief
Leontief
(
s
0)
(
s
0)
non
-
energy
Capital
-
Labor
non
-
energy
Capital
-
Labor
Intermediate
-
Energy
Intermediate
-
Energy
Leontief
Leontief
(
s
0)
(
s
0)
Energy
Capital
-
Labor
Energy
Capital
-
Labor
non
-
eng 1
non
-
eng 1
non
-
eng 2
non
-
eng 2
ELE
Non
-
ELE
ELE
Non
-
ELE
SLD
OIL
GDT
SLD
OIL
GDT
Cobb
-
Douglas
(
s
1)
i, j sector t time r region
Capital
Labor
Electricity
Non
-
Electricity
Cobb
-
Douglas
(
s
1)
Energy
Energy
non
-
eng 1
non
-
eng 2
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Model structure (contd.)

• Capital stock and investment in non-energy sector

K Capital Stock, IInvestment
i, j sector t time r region
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Model structure (contd.)

• Objective function Maximization of total
  consumption utilities

Objective function
i, j sector t time r region
C consumption
sectoral consumption share (exogenous),
ddiscount factor (exogenous) ,Lpopulation(exogen
ous)
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Model structure (contd.)

• Economic flow module through input-output table

Value added for Secondary Energy S(capital and
labor costs of energy conversion technologies
)(others)
Value added for Primary Energy S(capital and
labor costs of primary energy extraction and
production costs)(others)
Value added for Non-energy f(K,L,E,N)-(secondary
energy input costs)
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Model structure (contd.)

• Input-output table and macro-production function

ALrate of technical progress(exogenous),
AKLscale-adjusted parameter (exogenous),
Kcapital stock, Lpopulation(exogenous) ,
Eelectricity, Nnon-electricity
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Simulation study

• Reference Case
• Without carbon emission control policy
• Constraint Case
• S550 Case IPCC-WGI stabilization profile for the
  world with emission trading

Note In this study, we do not consider the limit
of the amount of the natural and labor capital,
the explicit stock of the energy conversion
plant. The model assumption includes the perfect
competitive market and the perfect substitution
between domestic and international commodities
(Armington elasticityinfinite).

• Data Assumption

• Population SRES-B2 scenario
• Discount rate 5/Year
• Depreciation rate 5/Year
• time steps ?T10 year

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World carbon emission -Comparison between
Reference case and S550 case -
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World loss of value added
-Comparison between Reference case and Constraint
case-
World loss of value added in 2027
S550 Case

• World loss of value added in energy intensive
  sector is much larger than that of energy-less
  sector and service sector.

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World loss of value added
-Comparison between Reference case and Constraint
case-
World loss of sectoral value added in 2027
S550 Case
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-Comparison between Reference case and S550 case-
World power generation

• Reference case

• S550 Case

• In 2027, the share of power generation by
  non-fossil energy in total
• 56.6(Reference Case) -gt 58.5(S550 Case)

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Summary

• In order to assess CO2 emission reduction
  policies, we have developed DEARS (Dynamic
  Energy-economic Analysis model with multi-Regions
  and multi-Sectors ).
• The analysis results show regional structure
  changes in industry, which is consistent with the
  energy systems, under a carbon control emission
  policy .
• Simulation studies for the reference case and
  S550 Case , were conducted

• World loss of value added in energy intensive
  sector is much larger than that of energy-less
  sector and service sector.
• CCS technology plays an important role in CO2
  reduction for power generation under S550 Case.

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Future Research

• Updating of the energy and economic database.
• GTAP ver.5(benchmark year 1997) -gt GTAP
  ver.6(benchmark year 2001)
• More precise modeling for the industrial
  structure changes, e.g.,the estimation of the
  input-output coefficients for the future.
• (the current version model assumes that the
  input-output coefficients for the future are
  equal to those of the benchmark year.)