GEOBENE Benefit Assessment Now, Next and Emerging.
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Transcript of GEOBENE Benefit Assessment Now, Next and Emerging.
GEOBENE Benefit Assessment Now, Next and Emerging
www.geo-bene.eu © GEOBENE 20082
Objective of GEOBENE
… to develop methodologies (Ph1) and analytical tools (Ph2) to assess societal benefits of GEO and to perform
benefit assessments (Ph3).
www.geo-bene.eu © GEOBENE 20083
COSTS BENEFITS
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Most operationalarea: weather
2nd most obvious areas:Water &Agriculture:still mostlyin planning!!?( huge benefits …)
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CASE I: Value of Land cover uncertainty reduction
• Biofuels – Food Security – Water - GHG – Ecosystem Trade-off
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Integrated Data-Modeling System
benefits, costs performance indicators economic surpluses
HRUscale
DMUscale
daily weather records soil profiles topographical data land use and management (crop rotations, fertilization, irrigation, etc.)
bio-physicalimpacts
resource endowments economic data (e.g. prices, products, costs)
EconomicValuation
model
comparativestatic/dynamicbio-physicalimpact analysis
GLC-2000
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MODIS 2000
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IFPRI – percentage cropland
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Ramankutty/Foley – percentage cropland
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Scenario to compute value of land availability uncertaintyScenario to compute value of land availability uncertainty
GLOBIOM calculations2030 estimated food and wood demand
+Substitution of up to 10% of transport oil energy consumption
according to IPCC/GGI A2r baseline scenario 2030 in each of the 11 regions
by ethanol.
Variants
a) WITH additional land (explicit supply function)
b) WITOUT additional land
+ avoided deforestation
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AgLand Use ScenarioAgLand Use Scenario
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Water UseWater Use
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GHG BalanceGHG Balance
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Hot Spotting REDD
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Expenditures under Information Asymmetry
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Accuracy 10,000km
² (est.)
Analysis Costs (4.00€/km²)
MODIS
RapidEye
Landsat
• Biomass Estimation based on the integration of different ground truth models
Bio
me
Lo
w #
SP
lots
MODIS/Landsat: Low resolution No data availability guaranteed Cloud coverage unknown
80%
Explanation
Origin of Ground Truth Data Biome Low # of
sample plots LiDAR High # of sample plots
Biomass estimations
based on existing investigations/mo
dels
Low sampling density covering
all major ecosystems
LiDAR Scanning Flight campaign
covering representative
areas
High Sampling density covering
all forest formation and ecosystems
LiD
AR
High
# S
Plo
ts
over 1 Million ha(10,000km²)
50%
1,80€
Equal analysis costs
RapidEye: 5m pixel spacing Data availability guaranteed 4 Million km²/daily Cloud free > 95%
Data Costs/km²
Ground Truth Costs
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Crop PricesCrop Prices
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III. Illustrative applicationIII. Illustrative application
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Land availability uncertainty is a USD 350 billion
Gas bill Question in the scenario
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III. Illustrative applicationIII. Illustrative application
Further applications:
Ethanol production x Food security (Obesity)
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Undernourished childrenUndernourished children
Source: Keyzer 2006
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Human CostHuman Cost
Further applications:
Ethanol production x Food security (Obesity)
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CASE II: Value of Climate sensitivity uncertainty reduction
• Energy Investment Problem
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Observations Benefits Chain
Cloud ObservationNASA CloudSat
Global ClimateModels
Long-term Emission Reduction
TargetClimate Policy
CO2 “prices”Optimal
Technology Choice
Investments
Profit
Δ
Δ
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Meeting the 2°C objective
Source: Mainshausen, Hare 2004
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Net present value costs for atmospheric CO2 stabilization by the year 2100
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
300 350 400 450 500 550 600
MESSAGE
Green ~ BECCS is includedBlue ~ fossil CCS only Red ~ no CCS
Trillions of 2000 US$
Source: Azar et al. 2007
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Global Energy Portfolio
0
200
400
600
800
1000
1200
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Pri
ma
ry E
ne
rgy
(E
J)
TIMER
0
200
400
600
800
1000
1200
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Pri
mar
y E
ner
gy
(EJ)
GET
0
200
400
600
800
1000
1200
1400
1600
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Pri
mar
y E
ner
gy
(EJ)
MESSAGE
-5
-3
-1
1
3
5
7
9
11
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
CO
2 E
mis
sio
ns
(GtC
)
MESSAGE
GET
TIMER
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Real Options Model in the Power Generation Setup
add CCS module
sw
itch
ON
CC
S m
odul
e s
witc
h O
FF
CC
S m
odul
e
options to addCarbonCapturing andSequestrationmodule andswitch it on/off during the operation
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Purpose of the Real Options Approach
Maximizing of expected discounted profit facing uncertainty due to incomplete information on future prices and find:
1. Optimal time for investment in CCS module
2. Optimal control of the CCS module
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Results from the Real Options Model
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Prices Profit distributions
…
Electricity
CO2
Fuel
Real Options Model
…
Coal
Biomass
Wind
CVaR Portfolio Model
RobustInvestment
Strategy
Combined Real Options & Risk Management Framework
Robust Investment Strategy describes shares of different technologies in the investor’s portfolio according to risk aversion preferences
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Optimal Maximin Portfolios and 95%-CVaRs
1.62
1.64
1.66
1.68
1.7
1.72
1.74
1.76
Scenarios3, 1, 2
Scenarios1-3, 2-3, 1-2
Scenario1-2-3
CO2 Price Scenarios
0 20 40Time
CO
2 P
rice
Impact of Uncertainty Reduction on Returns
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Optimal Maximin Portfolios and 95%-CVaRs
1.6
1.62
1.64
1.66
1.68
1.7
1.72
1.74
1.76
Scenarios3, 1, 2
Scenarios1-3, 2-3, 1-2
Scenario1-2-3
CO2 Price Scenarios
0 20 40Time
CO
2 P
rice
Impact of Uncertainty Reduction on Returns
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Optimal Maximin Portfolios and 95%-CVaRs
1.6
1.62
1.64
1.66
1.68
1.7
1.72
1.74
1.76
Scenarios3, 1, 2
Scenarios1-3, 2-3, 1-2
Scenario1-2-3
CO2 Price Scenarios
0 20 40Time
CO
2 P
rice
Impact of Uncertainty Reduction on Returns
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CASE III: Banda Aceh Case Study• Determine value of information used by Space Charter• Assess value of information in disaster recovery – Banda
Aceh case study
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Relief Effort
• According to the RAN Database (Recovery Aceh Nias rand.brr.go.id), as of 10.01.08 a total of 490 agencies have commited 3.8 billion USD.
• Among this vast amount of support are various types of Earth Observation (EO) data (ie. orthophotos, satellite scenes and the creation of a group – SimCenter, to administer this data).
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Preliminary Efforts• Accomplished initial round of
interviews• Met with 20 different groups,
over 50 people• All organizations using to
some extent spatial data• Identified organizations with
specific examples for further analysis
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Org. Type Organisation Contact
Nat. Gov BRR, Pusdatin Mr. E. Darajat
Nat. Gov BRR, Bakosurtanal Mr. Darmawan
University UNSYIAH, GIS & RS Mr. M. Affan
University UNSYIAH, Vice Rector Mr. Dhalan
University UNSYIAH, TDMRC Mr. Dirhamsyah
Local Gov BPN Mr. G. Suprato
Local Gov AGDC Mr. S. Gan
NGO ABD - ETESP Mr. E. Van Der Zee
NGO Sea Defence Cons. Mr. J. Kraaij
UN UN ORC Mr. H. Busa
UN UNICEF Mr. B. Cahyanto
UN UNFAO Mr. Sugianto
NGO LOGICA Mr. D. Hurst
NGO GTZ-SLGSR Mr. M. Widodo
NGO ManGEONAD Mr. T. Rehman
NGO Leuser Int. Fnd. (YLI) Ms. D. R. Sari
NGO Flora Fauna Int. (FFI) Mr. Syaifuddin
NGO Sogreah Mr. B. Coiron
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Preliminary FindingsSeveral main issues repeating (ranked)…• Frequency of receiving data low• Access to data• Need for more/better trained staff• Need for higher resolution data in some
cases• Sharing data - Vendor restrictions• Downloading speed• A lot of uninformed decisions are made
=> inefficient allocation of 3.8 Billion $
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Conclusions
• Integrated Long-run benefits can be measured in the trillions - demand pull
• Is this pull matched by an adequate supply push?