The ICES model (special focus on impacts assessment)
ISI-MIP Integration Activity
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PIK, 18-19 September 2013
Fabio Eboli
FEEM,CMCC
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Outline
• Introduction
• The ICES framework
• CGE modelling of impact assessment
• Main results (ClimateCost, EUAdaptStrat)
• Conclusion and proposals for discussion
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Economic Assessment: methodologies
Framing the issue …. different economic assessment perspectives
Bottom-Up => deep analysis on specific impact/region/sector (first-order or partial equilibrium or direct effects)
Top-Down => economy-wide analysis, beyond the initial shock => propagation throughout the rest of the economic system (higher-order or general equilibrium or indirect effects) => CGE models
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CGE modelling framework
• Main scope: assessing higher-order (general) effects on the whole economic system assuming localized shocks within it
• Applications: International trade, taxation, agricultural policy => recent development on environmental economics (impacts and policies assessment, mainly climate change and other transboundary issues)
• Main results: impacts on GDP, sectoral output and prices, international trade when considering market-driven (autonomous) adaptation of economic agents => scenario analysis
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The ICES model
GTAP (Hertel, 1997)
GTAP-E (Burniaux and Troung, 2002)
ICES (Eboli, Parrado and Roson, 2010)
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Social Accounting Matrix
• GTAP7 database (Narayanan and Walmsley, 2008) => now GTAP8 database available
Content: all economic - and energy - flows in Input-Output (SAM) matrix format
National statistics balancement + International Trade Baseyear: 2004 (GTAP8: 2007) Geographic coverage: world (113 countries/regions)
(GTAP8: 129) Sector coverage: the whole economic system (split in 57
sectors)
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The ICES model
AIM => Quantify the economic relevance of possible future
“changes” triggered by specific climate change impacts or policies
Main model features
Intertemporal Computable Equilibrium System Dynamic-recursive multi-region and multi-sector computable general
equilibrium model for the world economy Interaction between exogenous and endogenous dynamics Geographical detail: country/macroregion (bundles of countries) One-year time steps Micro-economic perspective (agents’ behaviour): economic agents
perfectly clever (households and firms utility and profit maximizers, respectively) but not looking forward (vs fully dynamic CGE models; vs optimization model, e.g. REMIND, WITCH)
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The ICES model
AIM => Quantify the economic relevance of possible future
“changes” triggered by specific climate change impacts or policies
Main model features
Markets’ behaviour: All markets (primary factors and commodities) work under perfect competition hypothesis (supply-demand always matching => prices’ adjustment)
Relative price matters (market-driven adaptation) Macro-economics: domestic and international markets fully
interconnected Interaction between sectors (I-O relationships) and regions (international trade)
Main outputs: changes in GDP, national and sectoral production, prices, import/exports
Policy-relevant variables: GHGs, AEZ, RES, Climate Policy set (BTA, AUCT vs GDFR, sectoral ETS, CO2 vs multi-GHGs)
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Climate Change Impact Assessment: overview
Project Impact CoverageGeographical Coverage
Socio-economic and Climate Scenarios
CIRCE Energy, Sea Level Rise, Tourism
Mediterranean area (country level)
A1B IPCC SRES scenario
ClimateCostAgriculture, Energy, Forestry, Health, River Floods, Sea Level Rise, Tourism,
World (macro-regional level)
A1B IPCC SRES scenario
ClimWatAdapt River Floods EU (country level) 4 SCENES FP7 project scenarios
EUAdaptStrat
Agriculture, Ecosytems, Energy, Fishery, Forestry, Health, River Floods, Sea Level Rise, Tourism,
EU (country level) 2012 Ageing Report(2°C and 4°C)
PESETACoastal systems, Human health, Agriculture, Tourism, River Floods
EU (5 macro-regions)
A2 (high emissions) & B2 (low emissions) IPCC climate scenario:•2020s only one climate scenario; •2080s four alternative climate futures: 2.5°C, 3.9°C, 4.1°C and 5.4°C
SESAME Marine Ecosystems/FisheryMediterranean and Black Sea region (country level)
3 scenarios: 1)Baseline (A1B IPCC SRES scenario); 2)Policy Targeted; 3)Deep Blue
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Climate Change Impacts: main sources
Impact ModelGeographical Scope
Reference
Supply-side
Agriculture ClimateCrop Global Iglesias et al., 2009; Iglesias et al., 2010
Ecosystems n.a. Global Manne et al., 2005; Warren 2006
Fishery n.a. Global Cheung et al., 2010
Forest net primary productivity
LPJmL Dynamic Global Vegetation Model
Global Boundeau et al., 2007; Tietjen et al., 2009
Health n.a. EuropeKovats et al., 2006; Kjellstrom et al., 2009, Kovats and Lloyd, 2011
River floods LISFLOOD EU27 Van der Knijff et al., 2009; Feyen, 2009
Sea-level riseDIVA (Dynamic Integrated Vulnerability Assessment)
Global Vafeidis et al., 2008
Demand-side
Residential energy demand
POLES Global Criqui, 2001; Criqui et al., 2009
Tourism flowsHTM (Hamburg Tourism Model)
Global Bigano et al., 2007
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Climate Change Impact Assessment: rationale
Climate ModelsClimate Models
Physical Impact Models (bottom-up)
Physical Impact Models (bottom-up)
Economic Impact Models (top-down)Economic Impact
Models (top-down)
Socio-economic Scenarios
Climate Change Drivers
Socio-economic Scenarios
Climate Change Drivers
GHG EmissionsGHG Emissions
Climate VariablesClimate Variables
Physical Impact AssessmentPhysical Impact Assessment
Macro-economic Impact AssessmentMacro-economic Impact Assessment
CGE Models
(ICES)
CGE Models
(ICES)
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Climate Change Impact Assessment: channels
Sea Level RiseSea Level Rise
Energy DemandEnergy Demand
TourismTourism
FisheryFishery
River FloodsRiver Floods
HealthHealth
EcosystemsEcosystems
Loss in land and physical capital stockLoss in land and physical capital stock
Change in residential demand of oil, gas, electricityChange in residential demand of oil, gas, electricity
Changes in households’ demand in the market services sector; Changes in regional incomeChanges in households’ demand in the market services sector; Changes in regional income
Agriculture : Loss in land stock Other sectors: Change in capital productivityPopulation affected: Change in labour productivity
Agriculture : Loss in land stock Other sectors: Change in capital productivityPopulation affected: Change in labour productivity
Changes in labour productivity (morbidity/mortality or job-on-performance)Changes in labour productivity (morbidity/mortality or job-on-performance)
Change in stock/productivity in the fishing sectorChange in stock/productivity in the fishing sector
Loss in physical capital stockLoss in physical capital stock
AgricultureAgriculture Change in land productivity/fertility (also water scarcity driven)Change in land productivity/fertility (also water scarcity driven)
ForestryForestry Change in stock/npp in the forest sectorChange in stock/npp in the forest sector
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Impact Assessment: ICES main results
Impact on GDP (% wrt «no climate change scenario»): A1b SRESImpact on GDP (% wrt «no climate change scenario»): A1b SRES
Source: ClimateCost projectSource: ClimateCost project
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Impact Assessment: ICES main results
Impact on GDP (% wrt «no climate change scenario»)
=> decomposition by impact for the World …
Impact on GDP (% wrt «no climate change scenario»)
=> decomposition by impact for the World …
… and for
Macro-Regions
… and for
Macro-Regions
Source: ClimateCost projectSource: ClimateCost project
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Impact Assessment: comparing with B-U
Who provides higher impacts between T-D and B-U?
=> CGE involve all economic sectors but market-driven adaptation
Who provides higher impacts between T-D and B-U?
=> CGE involve all economic sectors but market-driven adaptation
Sea Level RiseSea Level Rise
Source: ClimateCost projectSource: ClimateCost project
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Impact Assessment: snapshot on EU
Impact on GDP (% wrt «no climate change scenario») =>
different temperature increase
Impact on GDP (% wrt «no climate change scenario») =>
different temperature increase
Source: EUAdaptStrat projectSource: EUAdaptStrat project
-0.16%
(World: -0.7%)
-0.74%
(World: -1.8%)
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Impact Assessment: snapshot on EU
Impact on GDP (% wrt «no climate change scenario») =>
decomposition by impact for different temperature increase
Impact on GDP (% wrt «no climate change scenario») =>
decomposition by impact for different temperature increase
Source: EUAdaptStrat projectSource: EUAdaptStrat project
-8
-6
-4
-2
0
2
4
6
%
Agriculture Tourism Energy Sea Level Rise Ecosystems Floodings Human Health Fishery-8
-6
-4
-2
0
2
4
6
%
Agriculture Tourism Energy Sea Level Rise Ecosystems Floodings Human Health Fishery
2°C4°C
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Conclusions
• Economic assessment of climate change does not show – not surprisignly - a huge loss in terms of aggregate welfare/GDP
• Nevertheless, impact distribution across regions and countries quite relevant
• The poorer the more vulnerable (also in EU – Southern suffers more)
• Changes in crop productivity, tourism flows and ecosystem services predominant
• Currently, acceptable coverage in terms of impacts• Possible extension to damage function calibration in
optimization model (e.g. Witch in ClimateCost)
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Challenges/Current limitations
Extension of time horizon ….…. uncertainty cascade on impacts (functional forms
wrt temperature increase and other climatic drivers) …. as well as baseline/reference scenario (SSPs vs
RCPs, GDP and Pop, energy prices, energy mix => emissions, technical progress) => harmonization of climate drivers for impact models
Extreme and Catastrophic events (tipping points)
Adaptation (Cost-Benefit analysis => Investment vs avoided damages)
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Thanks a lot!
http://www.feem-web.it/ices/
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The ClimateCost research structure
ClimaticDrivers
Environm.lImpacts
Socio-Economicimpacts
CGE-ICESEconomicTop-Down
Assessment
Mitigation
WITCHOptimalPolicy
Assessment
Updated
reduced-form
damage
functions
WP1 WP2 WP6
WP7
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Damage Function in IAM-WITCH
The analysis performed through the CGE-ICES model is used to update the reduced-form damage function for the IAM-WITCH model - based on Nordhaus (2007)
Nordhaus still used to quantify impacts categories not covered by the ICES impact assessment (non-market impacts)
Damage function allows computing SCC (Social Cost of Carbon) (damages caused by one extra ton on carbon in atmosphere) => useful criterion to assist policymakers on climate policy
(emissions => radiative forcing => temperature => quadratic region-specific relationship to compute feedback on GDP)
(emissions => radiative forcing => temperature => quadratic region-specific relationship to compute feedback on GDP)
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Impacts for Damage Function
IMPACT MODELGeographical Scope
Reference
sea-level riseDIVA (Dynamic Integrated Vulnerability Assessment)
Global Vafeidis et al., 2008
tourism flowsHTM (Hamburg Tourism Model)
Global Bigano et al., 2007
crops’ productivity
ClimateCrop GlobalIglesias et al., 2009; Iglesias et al., 2010
residential energy demand
POLES Global Criqui, 2001; Criqui et al., 2009
forest net primary productivity
LPJmL Dynamic Global Vegetation Model
GlobalBoundeau et al., 2007; Tietjen et al., 2009
river floods LISFLOOD EU27Van der Knijff et al., 2009; Feyen, 2009
job performance
n.a. EuropeKjellstrom et al., 2009, Kovats and Lloyd (2011)
IMPACT MODELGeographical Scope
Reference
sea-level riseDIVA (Dynamic Integrated Vulnerability Assessment)
Global Vafeidis et al., 2008
tourism flowsHTM (Hamburg Tourism Model)
Global Bigano et al., 2007
crops’ productivity
ClimateCrop GlobalIglesias et al., 2009; Iglesias et al., 2010
residential energy demand
POLES Global Criqui, 2001; Criqui et al., 2009
forest net primary productivity
LPJmL Dynamic Global Vegetation Model
GlobalBoundeau et al., 2007; Tietjen et al., 2009
river floods LISFLOOD EU27Van der Knijff et al., 2009; Feyen, 2009
job performance n.a. EuropeKjellstrom et al., 2009, Kovats and Lloyd (2011)
human health
biodiversity
catastrophic
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Damage function calibration
-20.0
-15.0
-10.0
-5.0
0.0
5.0
0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Pe
rce
nta
ge
GD
P lo
ss
Degree Celsius above pre-industrial levels
USA
OLDEURO
NEWEURO
KOSAU
CAJAZ
TE
MENA
SSA
SASIA
CHINA
EASIA
LACA
Average values => sensitivity analysis performed mainly for agriculture in case of > 2 degrees temperature increase
wrt pre-industrial level
Average values => sensitivity analysis performed mainly for agriculture in case of > 2 degrees temperature increase
wrt pre-industrial level
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Mitigation Policy Costs
-20
-15
-10
-5
0
5
10
15
2005 2015 2025 2035 2045 2055 2065 2075 2085 2095
US$
20
05
Tri
llio
ns
Policy costs net of avoided damages
Policy costs gross of avoided damages
Climate change benefits (avoided damages)
Considering climate change avoided damages reduces mitigation costs (cumulative GWP) from 2.86% (1.97%) to 2.24% (1.66%) at
3% (5%) annual discount rate
Considering climate change avoided damages reduces mitigation costs (cumulative GWP) from 2.86% (1.97%) to 2.24% (1.66%) at
3% (5%) annual discount rate