Download ALPS Project - Studies on Policies and Measures for Climate

October 13, 2014
ALPS Project  Studies on Policies and
Measures for Climate Change and Sustainable
Development toward Green Growth
Keigo Akimoto
Systems Analysis Group
Research Institute of Innovative Technology
for the Earth (RITE)
Background and Objective of ALPS project
ALPS: FY2007-2011; ALPSII: FY2012 The world is facing various challenges to be addressed, while global
warming is surely an important issue.
 It is really important to achieve such multiple objectives with well-
balanced priorities, in order to improve present and future
generations well-being.
 High economic growth is expected in developing countries, at least,
until the middle of 21st century.
 "Green growth" will be required for climate change measures to be
implemented continuously for the long time.
 Consistent analyses for climate change and other sustainable
development challenges are required to seek better future.
 This study aimed to present consistent and quantitative analyses for
climate change and sustainable development.
ALPS: ALternative Pathways toward Sustainable development and climate stabilization
2
Working Package of ALPSII
How to achieve “green growth” in the real world
[WP]
 Risk management of climate change, e.g., how to develop risk
management strategy of mitigation, and adaptation with
geoengineering in consideration; long-term climate goals
 Economy: better understanding of and exploring a possible
narrow path to green growth; gathering and analyses of related
data
 Technology: diffusion and development, proposals and analyses
of effects of integration of various technologies including cobenefit; e.g., smart grid, smart city, hydrogen systems
 Model development and analyses from perspectives of
technology, economy and sustainable development
3
Relationships among Models for Consistent
Scenario Analysis
Socio-economy
Assessment of
food access
Mid-term world
energy and
economic model:
DEARS (until 2050)
Energy
Assessment of energy
security (until 2050)
Assessment of food
security
GHGs excluding
energy-related CO2
Ultra-long-term energy and
macroeconomic model: DNE21
Assessment model for
GHGs excluding
energy-related CO2
Mid-term world energy and
mitigation measures
assessment model:
DNE21+ (until 2050)
Food, water resource, land use
Assessment models for food demand/supply ,
water resource and land use change
Assessment of
water stress
Assessment of population
living in poverty
Population, GDP
Climate change
Simplified climate change
model: MAGICC6
Grid-based estimation of
climate change: using results
from MIROC3.2
Estimation model for economic
damages from global warming
(developed by Nordhaus）
Impacts of global Assessment model for biodiversity
warming
（Impacts on terrestrial ecosystem and
ocean acidification）
Assessment model
for health impact
4
Energy Assessment Model: DNE21+
 Linear programming model (minimizing world energy system cost)
 Evaluation time period: 2000-2050
Representative time points: 2000, 2005, 2010, 2015, 2020, 2025, 2030, 2040, 2050
 World divided into 54 regions
Large area countries are further divided into 3-8 regions, and the world is divided
into 77 regions.
 Bottom-up modeling for technologies both in energy supply and demand




sides (200-300 specific technologies are modeled.)
Primary energy: coal, oil, natural gas, hydro&geothermal, wind,
photovoltaics, biomass and nuclear power
Electricity demand and supply are formulated for 4 time periods:
instantaneous peak, peak, intermediate and off-peak periods
Interregional trade: coal, crude oil, natural gas, syn. oil, ethanol,
hydrogen, electricity and CO2
Existing facility vintages are explicitly modeled.
- The model has detailed information in regions and technologies enough to analyze
sectoral approach.
- Consistent analyses among regions and sectors can be conducted.
5
Consistent Analyses for Climate
Change and Sustainable
Development
Assessed Major Indicator
Category
Economic and
poverty
Agriculture,
land-use, and
biodiversity
Water
7
Indicator
Income (GDP per capita)
People living in poverty (including impacts of climate change and mitigation
measures)
Food access (amount of food consumption per GDP) (including impacts of
climate change and mitigation measures)
Energy access (access to grid electricity; People relying on the traditional use of
biomass for cooking)
Agriculture land area (including impacts of climate change)
Food security (amount of food imports per GDP) (including impacts of climate
change and mitigation measures)
People living under water stress (including impacts of climate change)
Energy
Sustainable energy use (cumulative fossil fuel consumption)
Energy use efficiency (primary energy consumption per capita and per GDP)
Energy security (share of total primary energy consumption accounted for by oil
and gas imports with country risks)
Climate change
Economic impact of mitigation measures (marginal abatement cost (carbon
price) and GDP loss)
Global mean temperature change
Aggregated economic impact of climate change
ALPS CO2 Emission Scenarios
120
ALPS A-Baseline
CO2 emission (GtCO2eq/yr)
100
ALPS B-Baseline
80
ALPS A-CP6.0
ALPS A-CP4.5
ALPS A-Baseline
ALPS A-CP3.7
60
ALPS A-CP3.0
ALPS B-Baseline
40
RCP8.5
20
RCP6.0
RCP4.5
2150
2130
2110
2090
2070
2050
2030
2010
1990
0
-20
Note: CO2 emissions including those from industrial processes and LULUCF
RCP (Representative Concentration Pathway): IPCC new scenario
RCP3PD
8
CO2 Emission reductions by Sector and Technology
9
60
70
AI-CP4.5
6%
50
9%
9%
40
5%
13%
30
20
10
0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
CO2 emissions and reductions (GtCO2/yr)
AI-CP3.0
50
AI-CP3.7
50
12%
40
10%
14%
20
10
Power: renewables
13%
Power: efficiency improvement & fuel
switching among fossil fuels
Other energy conversion
9%
13%
20
13%
Power: nuclear power
Residential & commercial
Transportation
Industry
0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
6%
30
14%
12%
30
10
13%
6%
12%
Power: CCS
10%
40
60
0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
70
60
3%
CO2 emissions and reductions (GtCO2/yr)
CO2 emissions and reductions (GtCO2/yr)
70
Int. marine & aviation bunker
Industrial process
CO2 emission reductions from
LULUCF
CO2 emissions
Note 1: All numbers of the
emission reduction ratio are
represented by the rate in total
emission reductions in 2050 in
the case of CP3.0.
Note 2: The reduction effects
are represented as those
relative to the baseline
emissions. Some of the
sectors, e.g., transportation
sector, greatly reduce
emissions even in Baseline.
Impacts on Food Access Index by Factor in 2050
10
30%
Vulnerable
－Δ(GDP)/GDP : due to climate change damages
Change in food access index
(%, relative to the baseline)
25%
20%
－Δ(GDP)/GDP : due to climate mitigation costs
15%
Δ(Food consumptions)/(Food consumptions): due to change in food price induced by land‐use changes for bioenergy production and afforestation
Δ(Food consumptions)/(Food consumptions): due to change in food price induced by food productivity change
10%
5%
0%
Δ(Food consumptions/GDP)/(Food consumptions/GDP)
China
India
Midde‐East &
N.Africa
Subsahara
Africa
CP3.0
CP3.7
CP4.5
CP6.0
CP3.0
CP3.7
CP4.5
CP6.0
CP3.0
CP3.7
CP4.5
CP6.0
CP3.0
CP3.7
CP4.5
CP6.0
CP3.0
CP3.7
CP4.5
CP6.0
‐5%
Latin America
Deeper emission reduction will improve food access (decrease the food access index) due
to smaller climate change damages on aggregated economy and food productivities, but
will deteriorate food access (increase the index) due to decrease in GDP by mitigation
costs and food price increase induced by bioenergy production and afforestation.
Food Access Indicator
(Amounts of food consumption per GDP)
Vulnerabilities of food access will decrease in most countries and regions in
the long-term under any emission scenarios, because future incomes are
expected to increase.
Global warming impacts on food productions are relatively small compared
with the effects of income increase.
Global warming counter-measures of large scale forestation and bioenery use
slightly increase vulnerabilities of food access.
11
For Better Understanding and
Analyses of End-use Technologies
and their Diffusions
Technology Learning
13
Higher learning
rates are observed.
Source: IIASA report (2013)
Supply-side vs End-use and
Large-scale vs Small-scale Technologies
14
 Supply-side technologies:
[Role] supply energies
Energy supply cost is a determinant factor to the welfare for the same
kind of secondary energy regardless of the kind of original primary
energy.
 End-use technologies:
[Role] supply end-use products or services
Production or service cost compared with our welfare increase is an
important factor for preference changes induced by innovations: high
(e.g., cathode-ray TV -> LCD TV)
 Large-scale technologies: lower speed of diffusions
lifetime: long; technology learning: slow; technology innovation: slow
 Small-scale technologies: higher speed of diffusions
lifetime: short; technology learning: high; technology innovation: high
Assumed discount rate by sector (tentative)
Sector
Discount rate
Electricity generation
8-20%
Other energy conversion
15-25%
Industry (Energy intensive industry)
15-25%
Transport (Road)
30-45%
Residential and Commercial
30-55%
 Discount rates for different regions and different time points are
assumed to be within the ranges, depending on the region’s percapita GDP.
 Small passenger car (< 2,000cc) users are divided into two groups:
purchasers preferring environment conscious products and to
regular products. The purchasers preferring environment
conscious products adopts 10% discount rate.
15
Technology deployments of passenger cars to
developing countries (ALPS-3.0W/m2 Case) (tentative)
Vehicle in Use [Million vehicle]
900
16
OECD90
800
700
600
FCV
500
BEV
400
Plug‐In HEV
300
HEV
200
ICEV
Diffusion of HEV is started in OECD90.
According to cost reductions of HEV
and discount rate improvements in
other regions, HEV is adopted also in
other regions such as ASIA.
100
0
2010 2015 2020 2025 2030 2035 2040 2045 2050
140
120
100
80
60
40
20
2020
2030
2040
2050
450
400
350
300
250
200
150
100
50
0
2010
2020
2030
MAF
160
Vehicle in Use [Million vehicle]
Vehicle in Use [Million vehicle]
Vehicle in Use [Million vehicle]
160
0
2010
ASIA
500
2040
2050
180
Vehicle in Use [Million vehicle]
REF
180
140
120
100
80
60
40
20
0
2010
2020
2030
2040
2050
LAM
160
140
120
100
80
60
40
20
0
2010
2020
2030
2040
2050
Technology deployments of environment compatible
passenger cars within OECD90 (ALPS-3.0W/m2 Case)
(tentative)
450
Vehicle in Use [Million vehicle]
400
350
FCV (Other)
FCV (PECP)
300
BEV (Other)
250
BEV (PECP)
200
Plug‐In HEV (Other)
150
Plug‐In HEV (PECP)
HEV (Other)
100
HEV (PECP)
50
0
2010
2015
2020
2025
2030
2035
2040
2045
2050
PECP: Purchaser preferring
Environment Conscious
Products
 Purchasers preferring environment conscious products adopt new
technologies such as HEV at early stage of their diffusion.
17
International Symposium of ALPS Project
18
The international symposiums for better understandings for climate
change and sustainable development have been held in Tokyo every
years since 2002 (from PHOENIX project).
A few researchers from IIASA participated in all the symposiums.
Particularly Prof. Nakicenovic kindly participated in almost all the
symposiums.
FY2013 ALPS Symposium
(at Tokyo International Forum, on Feb. 4, 2014; about 240 attendees)
FY2014 ALPS Symposium will be held on February 27, 2015.
Prof. Nakicenovic will kindly give a lecture also this physical year!
Appendix I:
Overview of RITE
Basic Information about RITE
20
 Mission : R&D of industrial technologies that contribute to the
conservation of the global environment and the progress of the world
economy
 Established in July 1990 (Supported by MITI, local governments,
academic circles and industries)
 Location : Kansai Science City (Kyoto, Japan)
 Activities : Development of innovative environmental technologies for
CO2 mitigation
 Staffs : 175 (April 1, 2014)
 Annual budget : Approx. 2.7 billion JPY (27 million US$)
 President: Prof. Yoichi Kaya
 Director general: Prof. Kenji Yamaji
Research Staffs in Systems Analysis Group
Toshimasa Tomoda
Keigo Akimoto
Ayami Hayashi
Miyuki Nagashima
Takashi Homma
Fuminori Sano
Kenichi Wada
As of April 2014
Kohko Tokushige Bianka Shoai Tehrani
Junichiro Oda
Yosuke Arino
Appendix II:
Overview of DNE21+ model
Region divisions of DNE21+
23
Technology Descriptions in DNE21+ (1/2)
24
Fossil fuels
Coal
Oil (conventional, unconv.)
Gas (conventional, unconv.)
Energy conv.
processes
(oil refinery, coal
gasification, bioethanol, gas
reforming, water
electrolysis etc.)
Unit
production
cost
Industry
Iron & steel
Cement
Paper & pulp
Chemical (ethylene, propylene,
ammonia)
Aluminum
Cumulative production
Solid, liquid and gaseous fuels, and
electricity <Top-down modeling>
Renewable energies
Hydro power & geothermal
Wind power
Photovoltaics
Biomass
Electric
Power
generation
Unit
supply
cost
Transport
vehicle
Solid, liquid and gaseous fuels, and
electricity <Top-down modeling>
Residential & commercial
Annual production
Nuclear power
CCS
Refrigerator, TV, air conditioner
etc.
Solid, liquid and gaseous fuels, and
electricity <Top-down modeling>
Technology Descriptions in DNE21+ (2/2)
–An Example for High Energy Efficiency Process in Iron & Steel Sector–25
Blast furnace, sintering
furnace, BF, BOF,
casting, and hot rolling
Coal for
steel sector
Type III:
Current coke oven
24.1
GJ
23.8 GJ
Recycling of
waste plastics
and tires
0.25 GJ
Waste plastics
and tires
0.25 GJ
22.5 GJ
Type IV:
Next-generation
coke oven
Electricity (grid)
Power
generation
facility
Electricity
455 kWh
Type III and IV:
High-eff.
Intersection
(Sophisticated
steelmaking
process with many
energy saving
facilities including
CDQ, TRT, COG
and LDG
recovery)
(Larger scale
capacity plant)
91 kWh
111 kWh
Carbon capture
from BFG
Compressed
CO2
0.60 tCO2
0.98 GJ
Utility
4.1 GJ
Heavy
oil
Process gases recovery
8.6 GJ
Steel product derived
from BOF steel
1 ton of crude steel
equivalent for each type
BF: blast furnace, BOF: basic oxygen furnace, CDQ: Coke dry quenching,
TRT: top-pressure recovery turbine, COG: coke oven gas, LDG: oxygen furnace gas
Global Mean Temperature Rise
26
7
ALPS A-Baseline
Surface temperature
relative to pre-industrial (K)
6
ALPS A-CP6.0
5
ALPS A-CP4.5
ALPS A-CP3.7
4
ALPS A-CP3.0
3
RCP8.5
RCP6.0
2
RCP4.5
1
0
1990
RCP3PD
2010
2030
2050
2070
2090
2110
2130
2150
Note: Equilibrium climate sensitivity is assumed to be 3 C, which is a ”most likely value” in IPCC AR4.
The maximum global mean temperature change relative to the pre-industrial
level is about 2 C (1.94 C) for the ALPS CP3.0.
Agriculture Land Area
27
Required area for food productions to meet food demands
Required area for food productions
(Year 2000=100)
140
A-Reference(w.o. adaptations of changes
in varieties of crop and planting)
120
A-Baseline
100
A-CP6.0
80
A-CP4.5
60
A-CP3.7
40
A-CP3.0
20
B-Baseline
2100
2090
2080
2070
2060
2050
2040
2030
2020
2010
2000
0
The additional required area for crop productions will be about 20% in 2050
under Scenario A-Baseline. The area in the case of climate stabilization at a
low level will be smaller than that of the baseline. However, socioeconomic
conditions, such as population, will have larger effects on the required area.
Change in international food price (relative to the baseline)
Food Price Change by Factor in 2050
10%
Contribution by changes in land‐use for bioenergy production and afforestation
Contribution by changes in food productivity
International price
8%
6%
4%
2%
0%
‐2%
‐4%
CP6.0 CP4.5 CP3.7 CP3.0 CP6.0 CP4.5 CP3.7 CP3.0 CP6.0 CP4.5 CP3.7 CP3.0
Wheat
Rice
Maize
The productivity of wheat, rice and maize will increase under most of the
emission reduction scenarios compared with the productivity in the baseline,
and the food prices will decrease in lower emission scenarios. On the other
hand, large scale of bioenergy production and afforestation under emission
reduction scenarios will increase food prices.
28
Assessment of Energy Security
− For Different levels of concentration −
10,000
Vulnerable
Energy security index
2000
7,500
29
2050 A-Baseline
2050 A-CP4.5
5,000
2050 A-CP3.0
2,500
0
US
W. Europe
coil
ESI 
TPES
Share of imported oil in TPES
Japan
China
India and S. Asia
 r  S   TPES  r  S
2
i
c gas
i , oil
i
Political risks of region i
i
2
i , gas

i
Dependence on region i
ESI : energy security index, TPES: total primary energy supply
Note: index based on IEA, 2007
While the energy security index of Japan decreases (less vulnerable) for CP3.0,
that of China, India increases (more vulnerable) for deeper emission reductions
due to increase in imported gas shares.
Assumed discount rate and share of environmental
conscious purchasers in transport sector (tentative)
30
Discount rate in transport sector (road)
50
45
United Kingdom
40
France
Germany
35
Japan
China
30
India
Russia
25
20
2010
2020
2030
2040
2050
Share of purchaser of environment conscious products [%]
Discount rate [%]
United States
Share of purchaser preferring to
environmental conscious products
in small passenger car sales
70
60
United States
50
United Kingdom
France
40
Germany
30
Japan
China
20
India
Russia
10
0
2010
2020
2030
2040
2050