Transcript David Raitzer and Francesco Bosello_Southeast Asia and Climate

Southeast Asia and the Economics
of Global Climate Stabilization
David Raitzer, Francesco Bosello, Massimo Tavoni, Carlo Orecchia, Giacomo
Marangoni, and Jindra Samson
Preface
• National support, collaboration and many
inputs have made this work possible
– Contributions of advice and guidance from experts
in focal ministries
– Analytical contributions from national experts
– Modeling mostly by team at CMCC including
Francesco Bosello, Massimo Tavoni, Carlo Orecchia,
and Giacomo Marangoni
Structure of presentation
• Background, objectives and methodology
• Policy scenario explanation
• Results on business as usual
• Results on global climate policy scenarios
– Regional economic
– Energy
– National comparison
• Key policy messages
Background
• The region also has had the
fastest growth in CO2
emissions in the world
• Focus on Indonesia,
Malaysia, Philippines,
Thailand and Viet Nam,
which account for 90% of
Southeast Asia’s greenhouse
gas emissions in 2010
• Context of Paris Agreement
and need to ramp up national
contributions over time
275
Percent increase in CO2 emissions (1990-2010)
• Southeast Asia is among the
world’s most vulnerable
regions to climate change
225
175
125
75
25
-25
Increases in Total Annual Carbon Dioxide
Emissions in World Regions between 1990
and 2010
Source: authors’ aggregations from WDI
(2014)
Climate change matters to Southeast Asia
• One of the most climate vulnerable regions of the globe
– Increased risk of river flooding, coastal inundation, and sea level
rise
– Increased water stress
– Increased risk from intense cyclones and storms
– Agricultural production and productivity declines
– Increased risk of heat-related mortality and water- and vectorborne diseases
– Losses of labor productivity
– Higher resource demands
– Coral reef extinction and coastal ecosystem collapse
– Loss of terrestrial forests and biodiversity
• Economy-wide risks of loss of up to 6.7% of GDP by 2100
(ADB, 2009)
Southeast Asia matters to climate change
• Regional greenhouse gas (GHG) emissions have been rapidly rising at nearly 5%
annually from 1990-2010.
• Deforestation and land use account for a majority of recent emissions from the
DA5, although most of these emissions come from Indonesia.
• Deforestation is disproportionately on peat soils. Nationally, Indonesia and
Malaysia’s, peat soils contain 500% of the carbon in above-ground forest biomass.
• Forest cover is stable or increasing outside of Indonesia and Malaysia.
2010 Emissions
1374.80
108.27
163.70
49.74
48.69
27.02
71.06
70.93
47.68
33.26
1705.94
289.22
Share
(%)
55.0%
9.3%
Electricity/heat (MtCO2)
150.16
105.25
34.33
97.30
42.37
429.41
13.9%
6.9%
Manufacturing/construction
(MtCO2)
Other fuel combustion
(MtCO2e)
Fugitive emissions (MtCO2e)
105.72
30.59
12.31
65.46
44.51
258.59
8.3%
5.4%
65.57
11.29
10.83
25.83
23.04
136.56
4.4%
3.1%
47.85
21.32
1.03
8.13
12.55
90.88
2.9%
1.8%
Others (MtCO₂e)
Total GHG emissions
(MtCO₂e)
Per capita GHG emissions
(tCO₂e)
Annual total GHG emissions
growth 1990–2010 (%)
78.24
1928.02
50.69
425.32
21.33
152.02
32.64
355.77
37.44
237.82
220.34
3098.95
7.1%
3.7%
8.01
15.04
1.63
5.36
2.74
6.01
3.2
NA - neg
1990
emissions
2.0
3.7
4.8
4.6
Land use (MtCO₂e)
All transportation (MtCO₂e)
Indonesia
Malaysia
Philippines
Thailand
Viet Nam
Total
GHG = greenhouse gas, MtCO2e = million tons carbon dioxide equivalent, NA = not applicable, neg =negative
Source Climate Data Explorer (World Resources Institute, 2015).
CAGR 1990–
2010 (%)
4.4%
5.2%
• Lower rates of energy &
carbon intensity
improvements than
other world regions
• With rapid expansion of
fossil fuel use,
Southeast Asia is on a
carbon intensive
development trajectory
Energy intensity improvement (average annual %
1990-2012)
Southeast Asia matters to climate change, continued
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
-0.5%
-1.0%
Average Annual Change in Energy Intensity,
(GDP/toe)
GDP = gross domestic product, toe = tons of oil equivalent.
Source: World Development Indicators (World Bank 2015).
1990–2012
Objectives
• The objectives of this study are to
i.
ascertain what the longer-term economic effects are on
the DA5 for different levels of global climate ambition
(including the benefits of mitigation and co-benefits of
climate action);
ii. examine mitigation responses in the economic systems of
the countries; and
iii. determine how mitigation costs can be best contained
while meeting global climate goals.
• Analyzes scenarios that allow mitigation to occur where it is
most cost effective under transparent assumptions, considering
global interactions
– Possible Paris Agreement future evolution, not current structure
• Focus is on “how” not “who first”
Modeling framework
Two models applied: 1) the Intertemporal Computable Equilibrium System
(ICES) model and 2) the World Induced Technical Change Hybrid (WITCH)
model.
ICES is a multi-country, multi-sector, top-down, recursive-dynamic,
computable general equilibrium (CGE) model for the world economic system
(based on GTAP database)
WITCH is a multi country, multi technology, economic-energy-environment
“hard-linked” dynamic optimization model for the world economic system
Both models are global, have a global carbon market, and are linked to the
IIASA cluster model to reflect land use and Reducing Emissions from
Deforestation and forest Degradation (REDD)
Both reflect peat emissions and abatement as part of REDD for
Indonesia
The ICES model main features
ICES Intertemporal Computable Equilibrium System
- Recursive-dynamic CGE model, running 2004 (2010)-2050.
- Multicountry (13 + 5 DA5) and multisector (18) model with
international trade
- Multi gas emissions are depicted: CO2, N2O, CH4, Fluorinated
- Biofuel production
- Renewable energy production (solar, wind, hydro)
- Nuclear production
- Land supply is specified per agro-ecological zoning
The ICES model
Sectors/industries
Nested production function
Output
Output
Representative Firm - cost minimizing
V.A. + Energy
TOP Level
1° Level
Leontief
Natural
Resources
Land
Other Inputs
CES
 VAE
D
Domestic
Capital
+
Energy
Labour
CES
KE
2° Level
Foreign
M
Region 1
Capital
Region n
Region ...
Energy
CES
=1
3° Level
CES
=0.5
Non Nuclear
4° Level
Electric
Non Electric
CES
=2
Nuclear
Non Intermittent
CES
=0.5
Other fuel
5° Level
Coal
Hydro
CES
=1
6° Level
Oilgas
Non Oilgas
CES
=1
7° Level
Crude Oil
CES
=0.5
Gas
Intermittent
CES
=0.5
Petroleum Prod
Biofuel
CES
=2
Other Ely
Solar
Wind
Rice
Oth_Crops
Veg_Fruits
Livestock
Timber
Coal
Oil
Gas
Oil_Pcts
Biofuels
Nuclear
Solar
Wind
Hydro
OthEly
Heavy_ind
Light_ind
Services
The WITCH model: main features
WITCH: world Induced Technical Change Hybrid model
Economy: Ramsey-type optimal growth (inter-temporal)
Energy: Energy sector detail (technology portfolio)
The macro-economic structure of the model is hard-linked to cemission and
temperature) and to a detailed energy sector, which makes WITCH a hybrid model.
– Scale: global, with the world divided in 14 regions
– Economy: top-down intertemporal optimal growth model, dynamic, perfect
foresight
– Energy: bottom-up description of technological options:
• Electric and Non Electric energy use
• Six fuel types specified (oil, gas, coal, uranium, traditional and advanced
biofuels)
• More than 10 technologies for electricity generation
– Endogenous technical change – Learning-By-Doing and Learning-ByResearching
– Strategic: non cooperative interactions between region with externalities
(environmental, price of exhaustible resources, technological spillovers, and
trade of emission permits)
12
Model complementary roles
ICES
WITCH
Strengths
Represents countries, sectors
Reflects substitution effects,
trade interactions, rebound
effects
Endogenous technological progress
Reflects research, learning by doing
Reflects strategic country group
interactions
Includes advanced energy
technologies
Weaknesses
Technical change is exogenous
Tied to 2010 economic structure
Limited energy portfolio, with no
advanced energy technologies
Does not represent individual
countries
Does not represent economic sectors
Does not represent non-energy
substitution or rebound effects
Trade other than in energy omitted
Most
appropriate
for
Short to mid term (e.g. up to 25
years)
Longer term (25 to 100 years)
Policy cost bias Upwards
Downwards
WITCH and ICES are jointly applied
Assessment of co-benefits
• Health
– Uses model results on reductions in consumption of coal, gas and
oil in low carbon scenarios
– Adds reductions in forest/peat fire emissions of PM2.5, drawing on
land use results and estimates of Marlier et al. (2014)
• Emissions factors, intake fractions and breathing rates convert
reduced combustion into reduced SO2, NOx and PM2.5 equivalent
concentration changes (from Parry et al., 2014)
• Pollution attributable Loss of Life Years (2010 Global Burden of
Disease) and remote sensing pollution estimates used to calculate
effects of concentration change
• Averted loss of life years values valued as proportion of USEPA
Value of Statistical Life adjusted for GDP
• Transport congestion and accidents
– Uses model results on oil to approximate changes in transport
diesel and gasoline, which are applied in an updated model of
Parry et al (2014) to calculate associated changes in vehicle-km
driven, costs of congestion and costs of accidents.
Limitations
• Only energy and deforestation related abatement
possibilities are reflected
• The models reflect market responses to carbon
price signals
– Nonmarket (e.g. government infrastructure) responses
are partial or omitted
– Behavior is assumed as rational
• WITCH results are not available for the individual
countries of Malaysia, Philippines, Thailand and
Viet Nam
• Co-benefit coverage is very partial
Policy scenarios assessed
Representative concentration pathways
Name
RCP2.6
RCP4.5
RCP6.0
RCP8.5
Radiative
forcing
in
2100
(watts/square
meter)
2.6
4.5
6.0
8.5
Source: Rogelj et al, 2012
2100 Carbon
dioxide
equivalent
(ppm)
Approximate
2100
temperature
increase (°C)
Most similar
SRES
scenario
490
650
850
1370
1.5
2.4
3.0
4.9
None
B1
B2
A1F1
Regional report scenarios (contraction and
convergence framework)
Fragmented
ICES-WITCH
Joint Scenario
Matrix
Full REDD
potential
Business
as Usual Low
(BAU) Copenhagen
pledges in
2020,
extrapolation
thereafter
Moderate Ambition High Ambition
International
International
Climate Agreement Climate Agreement
~RCP 4.5: Low
Copenhagen pledges
in 2020 and longterm GHG
concentration at 650
ppm CO2eq
~RCP 2.6: High
Copenhagen pledges
in 2020 and longterm GHG
concentration at 500
ppm CO2eq
2
3 (650 Full REDD)
(Fragmented)
6 (500 Full REDD)
Higher REDD
cost (+150%)
4 (650 Low REDD)
7 (500 Low REDD)
No REDD
5 (650 No REDD)
8 (500 No REDD)
1 (BAU)
Likely < 2°C
Likely < 3°C
Scenario details
Country Decarbonization Targets for 2020
Country
Target Description
Indonesia
26% reduction of emissions relative to BAU 23.4% CO2e emissions decrease
by 2020
Up to 40% CO2e emissions reduction per 19.8% CO2e emissions increase
unit of GDP relative to 2005
Malaysia
Interpretation (relative to 2010)
Philippines 10% energy savings from all sectors, 2009– 5.7% CO2e emissions decrease
2030
Thailand
8% reduction of energy intensity by 2015 18.0% CO2e emissions increase
and 25% by 2030 compared with 2005
Viet Nam
Total energy savings of 3%–5% by 2010 15.0% CO2e emissions increase
compared with 2006 and by 5%–8% in
2012–2015 versus total energy demand
forecast in Power Development Plan 7
Contraction and convergence framework
for 2020-2050
• Proposed in 1989 by the Global Commons Institute
• Idea of transition from existing emissions to equal per
capita emissions globally
• Most transparent equitable allocation framework
• Widely accepted
• Implemented here as a carbon cap, with a global carbon
market and national allowances allocated to reach equal
per capita emissions by 2050
• Intertemporally optimized emissions cap according to
WITCH
Business as usual results
Business as usual is a fossil fuel dependent future
Indonesia
Indonesia GHG emission by GAS
Rest of Southeast Asia
South East Asia GHG emission by GAS
Primary Energy Consumption under Business as Usual Scenario
Structural transformation and energy drive emissions
Mt CO2E = million tons carbon dioxide equivalent.
Greenhouse Gas Emissions by
Country under the Business as
Usual Scenario
.
The cost of “Business as Usual” is high
-8
-10
Indonesia Market losses
SEA Market losses
Indonesia Market losses + labour productivity
losses
Indonesia Market losses + labour productivity
losses + non market losses
SEA Market losses + labour productivity losses
2100
2090
2080
Year
-12
2070
2100
2090
2080
2070
2060
2040
2030
2020
2010
2050
Year
-10
-6
2060
-8
-4
2050
-6
-2
2040
-4
rest of Southeast Asia
2030
-2
0
2020
Indonesia
2010
0
GDP loss (% deviation from BAU)
GDP loss (% deviation from BAU)
• Reduced form climate change damage function for RCP8.5 (similar to BAU) based
on
– Market impacts – sector (agriculture, land, tourism, energy) shocks from
literature applied in ICES
– Labor – estimates derived from Kjellstrom et al., (2015)
– Nonmarket losses from ADB (2009)
SEA Market losses + labour productivity losses +
non market losses
Climate policy scenario results
GHG emissions need to strongly fall under
contraction and convergence
Greenhouse Gas Emissions Pathways for the World, Indonesia, and the Rest of Southeast Asia
Moderate global carbon prices are needed to
trigger sufficient abatement
70
Carbon price (2005$/tCO2eq, discounted at 5%)
600
Undiscounted
Carbon price (2005$/tCO2eq)
500
400
300
200
100
0
2025
2030
2035
2040
Year
ICES 650
ICES 650_noredd
ICES 500_lowredd
2045
2050
Discounted
60
50
40
30
20
10
0
2025
2030
2035
2040
2045
2050
Year
WITCH 650
WITCH 650_noredd
WITCH 500_lowredd
ICES 650 low REDD
ICES 500
ICES 500_noredd
International Carbon Prices Modeled without Discounting and Discounted at 5%
WITCH 650 low REDD
WITCH 500
WITCH 500_noredd
Carbon trade is substantial
REDD
No REDD
A global carbon market benefits the region
Present value of 500ppm policy cost (% of BAU,
2005$ discounted at 5%)
Average DA5 carbon price ($2005/ton CO2eq,
discounted at 5%)
40
35
30
25
20
15
10
5
0
No trade
Trade
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
Trade
2.0%
1.5%
No trade
1.0%
0.5%
0.0%
GDP
Welfare
Average Discounted Carbon Price and Policy Costs under the 500 ppm Full REDD Scenario Using ICES
model for the DA5 with and without Global Carbon Trade
Land use, energy efficiency and low carbon
energy drive abatement
2500
Indonesia: Full REDD
2000
Other SEA: Full REDD
Emissions reduction (MtCO2-eq)
Emissions reduction (MtCO2-eq)
1800
1600
1400
1200
1000
800
600
400
2000
1500
1000
500
200
0
0
ICES WITCH ICES WITCH ICES WITCH ICES WITCH
2020
2030
650 Overall ec. activity
650 Energy mix (ex. CCS)
650 Non-CO2
500 Overall ec. activity
500 Energy mix (ex. CCS)
500 Non-CO2
2040
2050
650 Energy efficiency
650 CCS
650 Land Use
500 Energy efficiency
500 CCS
500 Land Use
ICES WITCH ICES WITCH ICES WITCH ICES WITCH
2020
2030
2040
2050
GDP effects (no co-benefits)
REDD
REDD
NO REDD
No REDD
The key role of low carbon technologies
Indonesia
Southeast Asia
Share (%)
Share (%)
Energy mix
Share (%)
% share of total
energy volumes
in 2050
Share (%)
Electricity mix
Delayed mitigation becomes more expensive later
WITCH estimates of Policy Costs of Early and Delayed Action in the World and Southeast Asia (including Indonesia)
Co-benefits are substantial
500 ppm
Vehicular
accident
reduction,
$12.02
Transport
congestion
reduction,
$2.51
Transport
congestion
reduction,
$35.69
Co-benefits (% of GDP)
3.5%
3.0%
2.5%
Avoided
loss of life
years,
$61.95
3.0%
Indonesia
500 ICES
500 WITCH
650 ICES
650 WITCH
2.5%
2.0%
1.5%
1.0%
Avoided
loss of life
years,
$24.35
Note: all are
scenarios with REDD
Co-benefits (% of GDP)
4.0%
Vehicular
accident
reduction,
$1.34
650 ppm
2.0%
rest of Southeast Asia
500 ICES
500 WITCH
650 ICES
650 WITCH
1.5%
1.0%
0.5%
0.5%
0.0%
0.0%
Year
Year
Initial costs lead to long term returns, when avoided
climate damage and co-benefits are considered
8
Year
Year
Note: all scenarios are with REDD
2100
2090
2080
2070
2060
2050
2045
2100
2090
2080
2070
2060
2050
2045
2040
-6
2035
-6
2030
-4
2025
-4
2020
-2
2015
-2
2040
0
2035
0
2
2030
2
4
2025
4
6
2015
6
2010
WITCH Total benefits 650 stab.
WITCH Total benefits 500 stab.
ICES Total benefits 650 stab.
ICES Total benefits 500 stab.
10
Net policy effect (% of GDP)
8
rest of Southeast Asia
2020
WITCH Total benefits 650 stab.
WITCH Total benefits 500 stab.
ICES Total benefits 650 stab.
ICES Total benefits 500 stab.
10
Net policy ceffect (% of GDP )
12
Indonesia
2010
12
• Net benefits/ net
costs (5% discount
rate) are:
– 5:1 for WITCH
500ppm
– 11:1 for WITCH
650ppm
6000
5000
4000
3000
WITCH Total benefits 650
stab.
WITCH Total benefits 500
stab.
ICES Total benefits 650
stab.
ICES Total benefits 500
stab.
2000
1000
0
-1000
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2100
• Economic returns in
levels are high
because GDP is
growing over time
Net annual economic effect of global climate
agreement (billions of 2005 US$)
Economic payoff (in levels) to mitigation
for the region is substantial
Year
Policy scenarios results: energy transition
(WITCH only)
Increase in energy efficiency (%)
Energy efficiency improvement is accelerated
under stabilization scenarios
BAU SEA
200
650 SEA
500 SEA
150
BAU Indonesia
500 Indonesia
100
650 Indonesia
50
0
2010
2015
2020
2025
2030
Year
2035
2040
2045
2050
BAU
Electricity
Energy
650 ppm
Electricity
Energy
500 ppm
Electricity
Energy
Low carbon power investment matches fossil fuel divestment
20
15
60
Indonesia
Annual power generation
investment (2005$, billions)
Annual power generation
investment (2005$, billions)
25
bau
650
500
10
5
Year
0
2010
2020
2030
50
40
rest of Southeast Asia
bau
650
500
30
20
10
Year
0
2040
2050
2010
Investments in Energy Supply (top) and Power Generation (bottom)
Note: Scenarios include REDD
2020
2030
2040
2050
R&D investment in energy efficiency happens
earlier under climate stabilization
Note: It is a fully manageable share of regional GDP (0.05% Indonesia, 0.03% SEA in
2050 under the 500 ppm stabilization)
Policy scenarios results: national
comparison (ICES only)
Cumulative emissions reduction (% relative to
BAU)
Cumulative emissions reduction by country and scenario
60%
50%
40%
30%
frag
20%
500
10%
650
0%
Share of 2010-2050 cumulative emissions
Net Present Value of 2025-2050 Carbon Trade
(billions of discounted 2005$)
Cumulative emissions trade comparison
80
60
40
20
0
-20
-40
500
-60
650
-80
-100
-120
Notes: Discounted at 5%
Cumulative 2010-2050 cost to sector (% of
discounted $ value added lost)
-5%
-10%
Agriculture
Industry
Notes: Excludes co-benefits, includes REDD
Discounted at 5%
Services
Viet Nam
Thailand
Philippines
Malaysia
Indonesia
Viet Nam
Thailand
Philippines
Malaysia
Indonesia
Viet Nam
Thailand
Philippines
Malaysia
Indonesia
Cumulative effects by sector
25%
20%
15%
10%
500
5%
650
0%
Co-benefits as a Percentage of Cumulative GDP
Co-benefits (% of cumulative 2010-2050
GDP, discounted at 5%)
2.00%
1.50%
500 Transport accidents
500 Transport congestion
1.00%
500 Pollution mortality
650 Transport accidents
0.50%
650 Transport congestion
650 Pollution mortality
0.00%
-0.50%
Note: Includes REDD
Discounted at 5%
Cumulative policy cost (% of 2010-2050
discounted GDP)
Cumulative policy cost comparison
7.0%
6.0%
5.0%
4.0%
3.0%
500
2.0%
650
1.0%
0.0%
Notes: Includes co-benefits, but not avoided climate damage
Discounted at 5%
Unit GDP cost of 2010-2050 emissions
reduction (discounted 2005$/t)
Cumulative unit GDP cost of emissions reduction
comparison
40.00
35.00
30.00
25.00
20.00
15.00
500
10.00
650
5.00
0.00
-5.00
Notes: Includes co-benefits, but not avoided climate damage
Discounted at 5%
Key policy messages
The COP 21 context
Among the positive outcomes of Paris: the “emission coverage”
(>95%) and the “ambition” (well below the 2°C)
However current (I)NDCs estimated to lead to 3.1°C (on average)
by the end of the century.
INDCs compared to 1990 emissions
Source: CPO (2016)
Comparison of INDCs and ICES stabilization scenarios
Intended Nationally Determined Contribution
Country 2030
2030 Conditional
Unconditional Emissions
Emission
Reduction
Reduction
Indonesia
Malaysia
29% from
BAU
35%
reduction in 45% reduction in
intensity
intensity from
(tCO2e/GDP) 2005
from 2005
Philippines …
Thailand
Viet Nam
41% from BAU
20% from
BAU
70% from BAU
25% from BAU
8% from BAU 25% from BAU
Coverage
Energy (including transport),
industrial processes and
product use, agriculture, land
use, land use change and
forestry, waste
2030
Business As
Usual (BAU)
Emissions
(MtCO2e)
2882
2030 Emissions
from ICES Model
(MtCO2e)
Implied Un- Implied BAU 650 500
conditional Condition
ppm ppm
2030 Target al 2030
(MtCO2e) Target
(MtCO2e)
2046
1700 2396 1769 1270
…
657*
556* 545 455 315
…
…
… 255 221 172
440
413 566 502 388
724
590 427 373 239
Energy, industrial processes,
waste, agriculture, land use,
land use change and forestry
Energy, transport, waste,
forestry and industry sectors
Economy-wide (inclusion of
land use, land use change and
forestry not yet decided)
550
787
Energy, agriculture, land use,
(excluding
land use change and forestry,
industrial
waste
processes)
More ambitious mitigation will benefit
Southeast Asia
• Although countries in the region have many important
low carbon targets, unconditional 2030 INDCs from the
region are only slightly below the ICES/WITCH regional
BAU
• Conditional 2030 INDCs from the region are similar to
650 contraction and convergence
• Modeling results here indicate high returns to 500ppm
contraction and convergence for the region, so more
ambitious mitigation is economically justified
• More mitigation ambition with a global carbon market
sooner, rather than later, is in economic interest of the
region
Energy efficiency is the largest potential source
of abatement and can4.5%be accelerated
• Elimination of remaining fossil
fuel subsidies can help set right
economic incentives for efficiency
• Recent expenditure on subsidies
has been more than discounted
2010-2050 cost of 500ppm
scenarios as share of GDP
4.0%
3.5%
Cost of policy (% of GDP)
• Although countries in the region
have important efficiency
measures in place, these can be
accelerated through expanded
performance standards and green
transport infrastructure.
500ppm stabilization 2010-2050
2010 fossil fuel subsidies
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
Comparison of NPV of climate stabilization cost and
2010 fossil fuel subsidies as shares of GDP.
Source of subsidy estimate: IEA, 2013
REDD is critical to mitigation costs in the short
and medium term
• REDD is the lowest economic cost major mitigation
opportunity, if policies and institutions are
appropriate
• There are important steps towards progress in the
region, including REDD preparedness, as well as
reforms to concession issuance and forest
management
• However, these measures can still be expanded to
eliminate remaining “rent” from forest clearance by
concessionaires, reform forestry institutions, and
eliminate tenure conflicts, especially in Indonesia
Low carbon and advanced energy technologies
are essential to long term decarbonization
• Ambitious renewable energy targets very important, but not
enough for the long term stabilization scenarios
• Technological advancement in the energy sector is critical to long
term policy costs
– Without Carbon Capture and Storage (CCS) and advanced
biofuels 2050 costs are doubled
• Preparatory steps can begin now
– Scaling up R&D (current intensity low in region)
– Piloting CCS in gas sector
– Establishing legal and regulatory frameworks for CCS
– Piloting advanced biofuels, which are already being
commercialized in other regions
– Establishing supply chains for advanced biofuels from wastes
Thank you!
• For more details, please
see the full report at
http://www.adb.org
• Available today