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Economic analysis of adaptation to climate change
Gordon HughesUniversity of Edinburgh
16th December 2013
Economic concepts and data requirements
2
Overview
What do we mean by adaptation?Adaptation and economic growth
Adapting to what?Temperature, precipitation, wind, etc: means or extremes?
Taking account of weather and climate uncertaintyHistoric climate patterns vs projections from GCMs
Economic development with/without climate changeBaseline scenarios for population, urbanisation & GDP per headHow will health, agriculture, etc evolve without climate change
How should we incorporate risk in strategic planning?Is it better to get the highest average return or to avoid the worst possible outcome?
3
Defining the goals of adaptation
What are the goals of adaptation?Maintaining income or welfare? At sector or asset level: maintaining level/quality of service
Who are most affected by climate change?Urban vs rural householdsImpacts on the poor?Regional or sectoral differences
Is economic growth the best form of adaptation?Income and resilienceOverlap between good development policies and adaptation
Who pays?
4
Adapting to what? GCMs and climate scenarios 1
Adaptation is often driven by very specific changes in weather outcomes
Changes in the amount, timing and intensity of precipitation may be critical : esp for agriculture and floodingFor roads, models make use of pavement temperatures which depend on latitude as well as daily maximum temperaturesFor buildings, indices rely upon changes in relative humidity for costs related to cooling & ventilation
Outputs from many GCMs are quite limited: changes in monthly averages for temperature & precipitationHeavy reliance upon combining model projections with historical weather data
5
Adapting to what? GCMs and climate scenarios 2
Daily data for 1° grid cells for 1948-2008 can be used to simulate alternative annual sequences in 2030/2050 or to estimate extreme value distributions for storms, etcSubstantial within-country or within-region variation in climate projections, so what must consider the right geographical unit for analysis
Answer depends on what is being studied: eg where possible use river basins for water modeling, but provinces or regions for roads or health or urban infrastructureOften data for sub-national analysis is a major constraint
Fairly heavy reliance on GIS methods, so good GIS databases must be collected or compiled
6
Adapting to what? GCMs and climate scenarios 3
Extent of variation in climate scenarios across climate scenarios: particularly large for monthly precipitation and related indicators (range between dry & wet seasons)
Option 1: UK approach is to use a central scenario with pdf of outcomes around this [tends to smooth over discontinuities]Option 2: Use 2-3 distinct scenarios (e.g. wet/moderate/dry) to identify main features of climate sensitivityOption 3: Give equal weight to full set of scenarios [technically difficult but can be use to identify patterns in impacts, etc]
Importance of monitoring climate impacts and adaptationMonitoring: essential to track how climate is changing and to implement a framework for updating plans for adaptation
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Global precipitation – differences between GCMs
NCAR 2100
MIROC 2100
China - Change in precipitation NCAR (Global Wet)
China - Change in precipitation CSIRO (Global Dry)
Monthly rainfall for Serbia - wettest month
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6070
8090
100
110
Max
imum
mon
thly
rain
fall
(mm
)
1980 2010 2030 2050 2070 2090
Monthly rainfall for Serbia - driest month
12
010
2030
4050
Min
imum
mon
thly
rain
fall
(mm
)
1980 2010 2030 2050 2070 2090
Maximum 3-day rainfall for Serbia
13
120
140
160
180
Max
imum
3-d
ay ra
infa
ll (m
m)
1980 2010 2030 2050 2070 2090
Baseline scenarios – why and how?
The role of baseline scenariosEconomic growth and development without climate changeHow much difference will climate change make?What is the relative uncertainty due to climate change?
Projections to 2030 / 2050 or beyondInfrastructure: growth of 2% per year, 40 year asset life – 75% of roads, buildings, etc in 2050 will be built after 2030Health: trends in malaria or infant mortality with/without CCLong term plans for water allocation by sector/type of use
Incorporating uncertainty about future growthStatistical patterns of economic developmentOr, 25 year strategies and development goals
14
Baseline scenarios – socio-economic projections
Demography, urbanisation & GDP to 2050+Population and age structure: by region if possibleUrbanisation and growth of major citiesGrowth in GDP per head: perhaps by sector
Sources of informationUN population and urbanisation projectionsAllowing for regional differences: Brazil, ChinaTotal population may be less important than age structureWhere and how is urban growth taking place?Long term projections of GDP growth – semi-official sources rarely extend beyond 2020 or 2030 – what then?Allowing for uncertainty and biases in projections
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The role of climate change in the projections
Climate change is likely to influence future demand for electricity (heating/cooling) and water
Should we incorporate such effects in the projections?Probably ok for electricity & water but what about roads?The reason the question matters is the difference between costing adaptation climate change (a) holding quantities constant vs (b) allowing quantities to change.Under (a) the costs of adaptation will almost always be positive, but under (b) they can easily be negativeFeasible to allow for quantity changes for water & power but large uncertainty & disagreement about other infrastructureAnd what about health? Clear linkages between climate & the burden of disease
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Baseline projections: electricity consumption for Serbia
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Climate uncertainty and electricity consumption for Serbia
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100
150
200
250
Ele
ctric
ity c
onsu
mpt
ion
(201
0=10
0)
2010 2015 2020 2025 2030 2035 2040 2045 2050
Baseline projections: gross urban water use for Serbia
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Climate uncertainty and gross urban water use for Serbia
20
100
105
110
115
120
Gro
ss u
rban
wat
er d
eman
d (2
010=
100)
2010 2015 2020 2025 2030 2035 2040 2045 2050
Strategic planning for adaptation under uncertainty
Adaptation is unavoidableDistinction between ex-ante (planned) adaptation and ex-post (at the time or after the event) adaptationEx-ante adaptation may reduce costs by eliminating or lowering the damage caused by climate changeBut, there is the risk of spending too much money on the wrong things if climate impacts are uncertain in extent or timingSo, how far ahead should you look – the planning horizonEx-post adaptation involves waiting to collect information and then responding when the outcome is fairly certainThe downside is the cost of the damage if the outcome is bad plus higher costs of modifying/replacing existing assetsDo you know what today’s climate is?
21
Planning for adaption under climate uncertainty 1
One possible approach using pay-off matricesChoose a planning horizon [40 years] and a discount rate [5%]Assume we know with certainty that the future climate scenario is X. The decision to invest in ex-ante adaptation can be evaluated as follows:
Costs [C(X,X)]: initial investment and future O&M costs required to climate-proof new assets against the impact of projected climate change under scenario X for the horizon of 40 yearsBenefits [B(X,X)]: savings by avoiding expenditures on ex-post adaptation: i.e. upgrading or replacing roads + the damage or loss of output associated with scenario X
If B(X,X) > C(X,X) then ex-ante adaptation is justified under certainty for scenario X, otherwise it is not worthwhileCarry out this analysis for each X
22
Payoff matrices for adaptation under certainty
23
Plan scenario 1 2 3 4 Plan
scenario 1 2 3 4
1 5 1 32 7 2 53 8 3 84 10 4 12
B(X,Y) - C(X,Y) = Net benefits of adaptation
Plan scenario 1 2 3 4
1 22 23 04 -2
Outcome scenarioOutcome scenario
Outcome scenario
B(X,Y) = Benefits of adaptation C(X,Y) = Costs of adaptation
Planning for adaption under climate uncertainty 2
This analysis gives us the diagonal elements of a pay-off matrix in which the rows represent planning scenarios and the columns are the actual outcomesNow consider the off-diagonal elements – combinations (X, Y) where X is the planning scenario, Y is the climate outcome.
C(X,Y)=C(X,X) – the ex-ante cost of adaptation depends solely on X, the planning scenarioB(X,Y) is more complicated because there are two cases:(a) if Y < X then we save the sum of ex-post adaptation costs under scenario Y, so B(X,Y)=B(Y,Y)(b) if Y > X then we must allow for the cost of some additional ex-post adaptation to cope with the climate impacts beyond those which were planned for so B(X,Y)=B(Y,Y)-E(X,Y) where E() represents the extra or unplanned costs of adaptation
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Planning for adaption under climate uncertainty 3
The result is a payoff matrix under all combinations of plan and outcomeIf all elements are negative then ex-ante adaptation is never worthwhile, but usually some will be negative, some positive: i.e. you can spend too much on adaptationThe best option depends on how risk averse you are. Y is uncertain but the planning scenario X is the choice we makeConsider
(a) The row averages – Mean(X). This is the expected net benefit from ex-ante adaptation for planning scenario X. It is the best choice if you are risk neutral – option 2 as illustrated(b) The row minima – Min(X). If you are extremely risk averse you would choose the X which has the least bad of the worst outcome –option 1 as illustrated
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Payoff matrices for adaptation under uncertainty
26
Plan scenario 1 2 3 4 Plan
scenario 1 2 3 4
1 5 4 1 1 1 3 3 3 32 5 7 6 2 2 5 5 5 53 5 7 8 5 3 8 8 8 84 5 7 8 10 4 12 12 12 12
Plan scenario 1 2 3 4 Mean Min
1 2 1 -2 -2 -0.25 -2.002 0 2 1 -3 0.00 -3.003 -3 -1 0 -3 -1.75 -3.004 -7 -5 -4 -2 -4.50 -7.00
Outcome scenarioB(X,Y) - C(X,Y) = Net benefits of adaptation
B(X,Y) = Benefits of adaptation C(X,Y) = Costs of adaptationOutcome scenario Outcome scenario
Decisions under climate uncertainty: option values
Preserving choice is worth moneyAlmost all adaptation is a combination of ex-ante and ex-postThis is because delaying some decisions can preserve options that allow planners and users to adapt more flexiblyUncertainty about economic development as much as climateBut, option values are worth more if the need for future modification is built into projects from the outset
Planning which protects road margins for future expansionNeed to monitor outcomes in order to respond quicklyAvoid investments that are inflexible and are long lived
Climate uncertainty may be less than economic uncertainty, so small changes to flexibility justified for other reasonsA different approach to planning and decision-making
27
Analysis of extreme events: how much protection?
Forget climate change for a whileDo we understand the distribution of extreme events today?If not, what information do we need to examine?Trends in mortality/loss of life due to extreme events – how is due to the population or value of assets at risk?Will economic development increase or reduce vulnerability?What is an efficient level of protection against extreme events today or in future
Planning for managing extreme eventsCuba, Haiti and Bangladesh – the role of economic development Investment in infrastructure vs governance and social networksChanges in the trade-offs between costs and risks
28
Analysis of extreme events: climate change
The effects of climate change: intensity & frequencyShifting the distributions of extreme eventsTropical cyclones: sea surface temperatures, other factorsFlooding: cumulative vs transitional (run-off)Droughts: period without rain or probability that rain < evapotranspiration + run-off over extended period
Example: Flooding in ChinaSeasonal flooding in Yangtze Basin: driver is variability in monthly (or even longer) precipitation in upper & middle basin Storm-related floods caused by cyclones or similar eventsChanges in water management and urbanization which may alter/accelerate run-off: hydro-power vs flood management
29
Analysis of extreme events: return periods
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0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
0.160
0.180
200 300 400 500 600 700 800 900 1000
Maximum monthly rainfall (mm)
Prob
abili
ty
NoCC CC R10
Analysis of extreme events: forms of adaptation
Increasing resilience and/or reducing vulnerabilityLand use and urban planning: don’t put assets in harm’s wayBut, attractions of flood plains and coastal zonesBuilding codes, storm water drainage systems, etcCivil defense, evacuation plans, shelters, effective governance
Investment in infrastructureCoastal and river flood defenses: always at risk unless they are built to very high standards (e.g. Netherlands)Diversion of flood waters: analogy to interruptible contracts for power, but what happens when the event occurs?How far ahead should we look and what levels of protection should be adopted?
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The cost of adaptation for infrastructure
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Overview
What is covered along with infrastructure?Energy, water, transport, health & education, urban & housing
Key steps in the analysisProjections without and with climate changeDose-response relationshipsChanges in investment costsChanges in O&M costsSpecial factors
Engineering vs economic approach to adaptationHow far can incentives reduce the costs of adaptation
Planning horizon and analysis of uncertainty
33
Infrastructure – coverage and goal of adaptation
Focus on long-lived & collective assetsSignificant percentage of total capital stockOften poorly maintained which increases vulnerability
Key starting point: Maintain the level and quality of infrastructure services that would have been provided without climate changeThis requires an adjustment in design standards to cope with changes in temperature, rainfall, etcIn addition, it may be necessary to adjust the quantity of infrastructure to provide the same level of service – e.g. higher flood defenses but less heatingIssue: What do we do if a different climate might result in a change in the demand for infrastructure services?
34
Infrastructure – planning and who pays?
Flood damage is a recurrent themeLand use planning as a low cost form of adaptationBut, very difficult to implement and enforce, especially in dense and rapidly growing citiesAre the costs of relocating infrastructure greater or less than coping with intermittent floods?What is or should be our discount rate
No 1 priority: think now about how to use (a) coastal zones, and (b) river margins and flood plainsAvoid perverse incentives via, for example, collective schemes for flood or storm insurance
35
Infrastructure projections – no climate change
Baseline projections from either development plans or econometric analysis
Allow for country special factors and/or planning biasesWhat are the margins of uncertainty for the projections?
Scenario approach or quantify statistical uncertaintyHow would investment in infrastructure be affected by alternative policies?
Consider effects of pricing water resources or road user chargesRole of urban development or decentralization strategies
Regional policies and links to infrastructure provisionFlexibility: updating projections at regular intervals
36
Infrastructure projections – allowing for climate change
How much do we know about interactions between climate and infrastructure requirements?
Short/medium term elasticities: how reliable?Long run econometrics/structural models: too long run?
Specific cases:Electricity demand: reasonable grounds for some effectWater use: clear impact but complex to model. Requires river basin models and analysis of sectoral demands.Roads: difficult, but may be worth considering balance between paved and unpaved roadsUrban infrastructure: essential to examine storm water drainageSocial infrastructure, housing – probably too difficult
37
Breaking down the cost of adaptation
Delta-C cost – changes in the cost of building and operating the baseline (NoCC) level of infrastructure
Allows for changes in the unit costs of constructing (c) and maintaining (m) a fixed stock of infrastructure in each period
Delta-Q costs – allows for changes in the quantity of infrastructure that is required as a consequence of climate change: e.g. more generating capacity or flood controls or less water treatment capacity
38
C CC CCt t t t t t tD c I m K
( ) ( )Q CC CC CC CCt t t t t t t t t t tD c c I m m K
Dose-response relationships 1
Key idea: by how much does the unit cost of a water treatment plant increase for each 1°C increase in mean temperate or 10 mm increase in precipitation
A combination of engineering & economics reflecting design standards required to achieve specific levels of performanceEvidence derived from building codes and technical experienceMost dose-response relationships are step functions: increased costs are linked to going over thresholds such as a 10 mm increase in maximum monthly precipitationIn some cases special indicators have been devised by engineers to understand specific problems: pavement temperature for road surfaces, MEWS index for moisture & ventilation, etc.
39
Dose-response relationships 2
Issues in implementationStarting point: absolute thresholds with the historical climate just below the threshold lead to large discontinuities unlikely to reflect actual engineering practiceDealing with relationships based on climate indicators not generated by climate models – e.g. extreme wind speeds or daily precipitationInfluences on O&M costs are often more complex than those for investment costs and require more special adjustments –e.g. cooling for power plants, operation of water/sewage treatmentSignificant problems can arise in interpolating climate projections for , say, 2040 with models that suggest, for example, precipitation = 120 in 2010, 130 in 2030 and 110 in 2050
40
Dose-response relationships – asset types
41
Infrastructure types Capital costs Operating & maintenance costsType of impact
Temperature Precipitation Other Temperature Precipitation Other
Electricity generationMaximum
temperatureMean
temperatureCC+; OM+
Paved roadsFlooding - maximum
precipitationOM+; EL-
Unpaved roadsFlooding - maximum
precipitationOM+; EL-
Water treatmentMean
temperatureMaximum
precipitationOM+
Wastewater treatment
Mean temperature
Maximum precipitation
OM+
Storm water drainageMaximum
precipitationMaximum
precipitationCC+; OM+
Wood buildings
Temperature & precipitation
- Scheffer index
Temperature & precipitation
- Scheffer index
CC+; EL-
Brick/concrete buildings
Relative humidity -
MEWS index
Relative humidity -
MEWS indexCC+; OM+
Investment costs – general approach
The capital cost of many types of infrastructure depend upon components of civil works and construction
Excavation, paved surfaces, concrete structures, large & small buildings, bridges, pipe or overhead networks, etcAnalysis focuses on these components with additions for special purpose equipment, which may not be sensitive to climateExample: building & maintaining a hospital or a school – the climate sensitive part is the structure and external elements but not the equipment, etc which may be up to 40% of the cost
Studies rely upon generic international unit costs (mostly from World Bank projects for Africa & Asia) adjusted by country construction cost indices – ref Chinowsky
42
Investment costs – water & wastewater
CoverageWater abstraction & treatment, water supply & sewer networks, wastewater treatment & disposal, urban storm drainageHydro, irrigation & flood defenses treated separately
Necessary to abstract from specific local issues so that costs based on generic “cost to serve” estimates per head of urban & rural populationDistinguish between gross and net water use
Gross = total abstraction; Net = gross – return flows.Treatment costs depend on gross use, but impact on water availability depends upon net useWeather/climate conditions and variations in load margins
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Investment costs – buildings & urban infrastructure
Structures are major element of all infrastructurePrimary cost drivers are:
External water resilience, internal temperature & moisture controlVaries with building material: wood vs concrete or brickSpecial indices focusing on rain & humidity - Scheffer & MEWS
Balance between initial capital cost and later upgrades is especially important for internal climate controlUrban storm drainage may be a very large item
Costs calculated on a coverage and per capita basisProvision of temporary storage as well as collectionCosts depend a lot on land use controls, but our estimates are at the high end and could be reduced by SUDS approach
44
O&M costs – general approach
For each asset type, base O&M costs excluding fuel are expressed as a % of capital costs and then adjusted by dose-response relationships
Base O&M costs increase with investment costsSpecial treatment for power generation, water & wastewater treatment and floodingIncrease in O&M costs limited by the option of early replacement of assets if this would reduce operating costs by sufficient amount
In practice it is rarely worthwhile accelerating the replacement of assets because of climate change alone
45
O&M costs – flood management
Similar treatment to that used for extreme events“Regular” maintenance costs should cover occasional repairs when floods exceed 1 in 10 or 20 year design standardDue to climate change the scale of the 1 in 10 year flood increases, so the average damage & cost of repairs when a flood exceeds the design standard is much greaterDamage caused is typically a power > 1 of the flood depth and, thus, of the precipitation indicator Assume that repairs take the form of upgrading or replacing existing assets so that they meet new design standards for future floodingSpread costs of upgrading over average remaining life of the asset – i.e. 50% of economic life
46
O&M costs – process efficiency
Electricity generationCooling systems for thermal power plants have to be upgraded when ambient temperature exceeds a threshold – typically 35-40°C. Models allow for annualized cost of alternative systemsCombustion efficiency for gas plans tends to degrade if ambient temperature > 30°C. Higher fuel consumption per MWh
Water & wastewater treatmentTreatment design & costs affected by ambient temperatures – both cold and hotPrimary effect via costs of power & chemicals to treat a given volume of water or wastewater In most systems heavier rainfall increases the inflow to WWTPs but dilutes the pollution concentration, increasing costs
47
Design standards vs incentives
Soft adaptation: consistent and important theme that the costs of adaptation can be (greatly) reduced by proper incentives plus effective (and early) planningCoastal zones and flood plains
Moral hazard: how do you persuade agents to take account of the costs of infrequent but large scale damage?Potentially perverse consequences of collective insurance, government emergency relief, etc: what conditions apply?Role and implementation of land use planning
Balance between incentives and design standardsPoor information and high discount rates favor standardsIncentives encourage more cost-effective approaches
48
Water resources management 1
Suppose climate change would increase demand for water – irrigation, industrial or industrial – under current arrangements and prices
Option 1: build more infrastructure to manage, transport & treat water to meet increased demandOption 2: manage use by pricing access to water resources
Under Option 1 the costs include construction and O&M costs plus opportunity cost of water in other uses
May be very expensive if water resources are constrainedUnder Option 2 the economic/social cost is the loss of social welfare due to pricing - area ABC in figure –while other costs are transfers
49
Water resources management 2
50
NoCC
CC
Price/Cost
QNoCC
QCC
Quantity
PNoCC
P
C
B
A
PCC
Water resources 3
Analysis shows that Option 2 is usually much less costly in economic terms – but what about the political costs?
Largest gains when key resources are not properly priced at the outset: typical case for water but also for landLarge vested interests in water management, land use, etcClimate change highlights endemic failures in resource management and policies
Economic development (baseline projections) will usually imply shift in water use from agriculture to urban
Adaptation to climate change should be integrated with the broader issue of managing water resources betterAlternative mechanisms for water management: e.g. negotiated water transfers
51
Recap: ex-ante vs ex-post adaptation
Ex-ante adaptation:Adjustments in design standards and, thus, capital costs to climate-proof infrastructure against projected changes in climate over all or part of its economic lifeAssociated changes in maintenance & operating costs
Ex-post adaptation:After the event expenditures on maintenance, repairs & upgrades to respond to actual changes in climate conditionsCosts include losses due to damage or decline in performance
Uncertainty and mal-adaptationEx-post adaptation responds to actual climate outcomesEx-ante adaption responds to forecasts and we may get these wrong, thus spending too much or too little money
52
What are the key sectors and types of asset?
Strong differences across countries and regions due to geographical variation in impact of climate changeVulnerability to seasonal patterns of rainfall tends to be the main source of variations in cost
Energy & telecoms networks + other transport (railways, ports & airports) face relatively small costs of adaptationRoads: the costs are extremely variable, primarily driven by pavement costs (temperature) and flood upgradesUrban infrastructure: the costs of building/upgrading urban drainage may be very largeSocial infrastructure & housing: key issue is moisture & ventilation with very large costs but only if critical thresholds are exceeded
53
Net present value of ex-ante adaptation in China 1($ billion at 2010 prices, average climate scenario)
54
Net present value of ex-ante adaptation in China 2($ billion at 5% discount rate, average climate scenario)
55
Net present value of ex-ante adaptation in China 3($ billion at 5% discount rate by climate scenario)
56
Net present value of ex-ante adaptation in China 4($ billion at 5% discount rate, average climate scenario)
57
Net present value of ex-ante adaptation in Mongolia($ billion at 2010 prices)
58
How large is large? Making comparisons
Many developing countries are growing rapidly (or hope to do so) so they may invest a lot in infrastructure
Adaptation may involve a marginal, rather than a large, change in expected levels and patterns of expenditureAggregate $ figures are pretty meaningless on their ownWhat are the costs of adaptation either as a % of the projected costs of infrastructure and/or as a % of GDP?
General patterns:Up to 2050 adaptation for infrastructure will increase projected spending by 1-2% with a few exceptionsThis share tends to fall over time and as countries get richerIn most countries cost of adaptation is < 0.25% of GDP
59
Cost of adaptation by climate scenario & region($ billion per year, average 2011-50)
60
N China NE China E China SE China SW China W China China Japan Korea Mongolia
BCCR_BCM20 1.20 0.63 8.53 0.35 0.92 1.02 12.65 14.24 1.87 0.08CCCMA_CGM3 5.21 1.08 25.49 5.71 4.27 2.43 44.19 13.23 5.38 0.13CNRM_CM3 1.16 0.41 0.97 0.21 0.83 1.15 4.73 12.09 1.41 0.10CSIRO_MK30 1.02 0.56 1.08 0.52 0.75 0.80 4.73 6.84 1.21 0.06CSIRO_MK35 1.53 1.04 1.36 0.54 1.04 0.98 6.49 7.43 2.09 0.10GFDL_CM20 0.79 0.22 1.03 0.39 1.94 0.61 4.98 14.27 1.41 0.10GFDL_CM21 1.02 0.17 0.78 0.46 0.97 0.68 4.08 3.80 0.73 0.09GISS_ER 2.72 1.01 8.84 4.23 3.76 0.86 21.42 5.95 1.41 0.05INM_CM30 1.22 0.43 1.18 0.59 0.83 0.83 5.08 5.18 2.21 0.12IPSL_CM4 1.30 0.60 2.29 0.72 1.05 1.52 7.48 7.27 3.50 0.17MIROC_32 0.84 1.41 1.07 0.27 0.95 1.29 5.83 12.86 2.80 0.56MPI_ECHAM5 0.77 0.15 1.24 0.31 0.85 0.62 3.94 4.27 0.92 0.09MRI_CGCM232A 0.62 0.18 0.90 0.10 0.75 0.70 3.25 3.64 1.25 0.05NCAR_CCSM30 3.48 2.31 5.46 1.50 1.67 1.21 15.63 7.23 2.63 0.23NCAR_PCM1 1.47 0.41 2.95 0.29 0.79 1.33 7.24 3.16 3.18 0.27UKMO_HADCM3 4.53 1.25 7.79 11.76 1.70 1.23 28.26 18.11 3.49 0.11UKMO_HADGEM1 2.85 0.52 1.24 0.58 1.27 1.11 7.57 22.17 1.68 0.19Average over GCMs 1.87 0.73 4.25 1.68 1.43 1.08 11.03 9.51 2.19 0.15Standard deviation 1.39 0.57 6.18 3.02 1.04 0.44 10.97 5.58 1.20 0.12Average as % of baseline (NoCC) expenditures 0.6% 0.7% 0.6% 0.4% 0.9% 1.0% 0.6% 2.3% 1.8% 2.2%Median as % of baseline (NoCC) expenditures 0.4% 0.5% 0.2% 0.1% 0.6% 1.0% 0.4% 1.7% 1.6% 1.5%Max as % of baseline (NoCC) expenditures 1.7% 2.2% 3.8% 2.7% 2.7% 2.3% 2.5% 5.3% 4.5% 8.5%
Balkans: cost of adaptation by country($ million per year at 2005 prices for 2011-50, H = 20)
61
Average over GCMs
Standard deviation
Average as % of
baseline
Median as % of
baseline
Max as % of baseline
Average as % of GDP
Albania 18 12 0.5% 0.4% 1.6% 0.04%Bulgaria 117 33 0.8% 0.8% 1.1% 0.08%Bosnia 20 10 0.5% 0.4% 1.1% 0.04%Greece 979 655 3.6% 3.1% 8.5% 0.28%Croatia 94 44 1.2% 1.2% 2.0% 0.09%Kosovo 13 5 0.7% 0.6% 1.2% 0.05%Macedonia 19 6 0.7% 0.7% 1.1% 0.05%Montenegro 17 9 1.7% 1.7% 3.3% 0.16%Romania 383 190 1.1% 0.9% 2.1% 0.09%Serbia 65 20 0.6% 0.5% 1.0% 0.05%Slovenia 51 22 0.7% 0.7% 1.4% 0.07%
Balkans: cost of adaptation incl quantity changes($ million per year at 2005 prices for 2011-50, H = 20)
62
Average over GCMs
Standard deviation
Average as % of
baseline
Median as % of baseline
Max as % of baseline
Albania 3 14 0.1% 0.0% 1.2%Bulgaria 30 73 0.2% 0.1% 1.2%Bosnia -46 22 -1.2% -1.1% 0.2%Greece 907 660 3.3% 2.8% 8.3%Croatia 74 46 0.9% 1.1% 1.8%Kosovo -16 15 -0.8% -0.9% 0.8%Macedonia -6 14 -0.2% -0.1% 0.8%Montenegro 1 13 0.1% 0.1% 2.5%Romania 179 246 0.5% 0.6% 2.0%Serbia -79 77 -0.7% -0.5% 0.4%Slovenia 29 35 0.4% 0.4% 1.3%
Balkans: cost of adaptation by sector(% of baseline costs for 2011-50, H = 20)
63
Albania Bulgaria Bosnia Greece Croatia Kosovo MKD MNE Romania Serbia SloveniaPower & phones 0.7% 0.8% 0.8% 1.2% 0.8% 0.9% 0.8% 0.9% 0.8% 0.8% 0.8%Water & sewers 0.3% 0.4% 0.3% 0.7% 0.3% 0.4% 0.4% 0.3% 0.4% 0.4% 0.4%Roads 1.0% 2.4% 1.7% 10.1% 10.5% 11.2% 1.9% 8.8% 5.1% 1.7% 1.4%Other transport 0.3% 0.1% 0.2% 0.9% 0.2% 0.3% 0.0% 0.5% 0.3% 0.1% 0.3%Health & schools 1.4% 1.6% 1.4% 3.7% 1.5% 1.5% 1.5% 1.4% 1.6% 1.6% 1.5%Urban 1.5% 1.5% 1.4% 3.1% 1.4% 1.5% 1.4% 1.5% 1.5% 1.5% 1.5%Housing 0.1% 0.0% 0.0% 3.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%Total 0.5% 0.8% 0.5% 3.6% 1.2% 0.7% 0.7% 1.7% 1.1% 0.6% 0.7%
Choosing a planning horizon
How far ahead should we look in setting building codes and design standards?
Case studies suggest the answer is not far ahead: e.g. 40 years is certainly too much and 20 years may also be too farCrucial issue is to think about the climate roughly today (say 2020) rather than the climate 40 years in the pastThis is especially important for extreme events
If or when the degree of uncertainty across climate scenarios is reduced, then case for looking further ahead
Key areas for better projections are total amount and seasonal variability in rainfall, especially at regional/grid levelUncertainty associated with need to generate statistical models of rainfall intensity (daily precipitation)
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Balkans: cost of adaption by planning horizon($ million per year at 2005 prices for 2011-50)
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Planning horizon
0 years 10 years 20 years 30 years 40 years
Albania 13 14 18 29 51
Bulgaria 102 108 117 128 146
Bosnia 17 18 20 23 25
Greece 522 693 979 1,329 1,660
Croatia 85 89 94 100 111
Kosovo 11 12 12 14 15
Macedonia 17 18 19 21 24
Montenegro 15 16 17 18 18
Romania 339 360 383 410 441
Serbia 56 60 66 74 91
Slovenia 43 47 52 58 66
Analysis of uncertainty
Unfortunately, there are no general rules of thumbNeed to do a full analysis and then look for patternsOften, there is a set of climate scenarios which generate very similar pay-offs for adaptation in one sectorBut these sets vary from sector to sector, so that you may need to consider 8-10 climate scenarios for a countryIt is worth identifying (perhaps excluding) extreme outliers
Usually some adaptation is cost-effective relative to no adaptation but the expected gains are not large
No adaptation may not be a foolish decision so don’t over-play the importance of adaptationHowever, it is really important to understand current risks fully
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Choice of adaptation strategy under uncertainty
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2011-15 2016-20 2021-25 2026-30 2031-35 2036-40 2041-45 2046-50
Albania Ex-post adaptation (NoCC) 64 83 101 128 162 203 277 381 Ex-ante adaptation
Best 43 54 64 82 110 140 185 265 Average 49 61 75 95 123 158 234 349 Worst 60 80 110 146 170 206 261 405
Base expenditure 11,191 11,896 12,807 14,367 16,038 17,574 19,010 19,810
Saving as % of baselineBest vs NoCC 0.2% 0.2% 0.3% 0.3% 0.3% 0.4% 0.5% 0.6%Best vs Average 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.3% 0.4%
Bulgaria Ex-post adaptation (NoCC) 428 625 847 1,013 1,198 1,430 1,711 1,985 Ex-ante adaptation
Best 257 399 531 617 735 851 1,014 1,187 Average 313 468 576 666 768 896 1,052 1,234 Worst 400 556 696 771 829 959 1,128 1,323
Base expenditure 46,115 52,884 59,347 63,090 67,254 71,863 76,411 80,029
Saving as % of baselineBest vs NoCC 0.4% 0.4% 0.5% 0.6% 0.7% 0.8% 0.9% 1.0%Best vs Average 0.1% 0.1% 0.1% 0.1% 0.0% 0.1% 0.0% 0.1%
Resources 1
World Bank (2009) The Costs to Developing Countries of Adapting to Climate Change: New Methods and Estimates, Washington, DC: The World Bank.World Bank (2010) Economics of Adaptation to Climate Change: Synthesis Report, Washington, DC: The World Bank.WRI (2008) Weathering the Storm: Options for Framing Adaptation and Development, Washington, DC: World Resources Institute. Larsen, P.H., Goldsmith, S., Smith, O., Wilson, M.L., Strzepek, K., Chinowsky, P. & Saylor, B. (2008) “Estimating future costs for Alaska public infrastructure at risk from climate change”, Global Environment Change, Vol 18, No 3, pp. 442-457.HR Wallingford (2007) Evaluating flood damages: guidance and recommendations on principles and methods, Report T09-06-01, FLOODsite project, Wallingford, UK: FLOODsite Consortium.
Resources 2
Sheffield, J., Goteti, G. & Wood, E.F. (2006) ‘ Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modelling’, Journal of Climate, Vol 19, pp. 3088-3111.Mendelsohn, R., Emanuel, K. & Chonabayashi, S. (2011) ‘The impact of climate change on global tropical storm damages’, Policy Research Working Paper No 5562, Washington, DC: The World Bank.Nordhaus, W. (2010) ‘The Economics of Hurricanes and Implications of Global Warming’, Climate Change Economics, Vol 1, pp. 1-24.Trigeorgis, L. (1996) Real Options: Managerial Flexibility and Strategy in Resource Allocation, Cambridge, Mass: MIT Press.World Bank (2006) Natural Disaster Hotspots: A Global Risk Analysis, Disaster Risk Management Series No 5, Washington, DC: The World Bank.
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Resources 3
Hughes, G.A, Chinowsky, P. & Strzepek, K. (2010a) ‘The Costs of Adapting to Climate Change for Infrastructure’, Economics of Adaptation to Climate Change Discussion Paper No 2, Washington, DC: The World Bank.Chinowsky, P.S., Price, J., Strzepek, K. & Neumann, J. (2010) ‘Estimating the cost of climate change adaptation for infrastructure’, Department of Civil, Environmental and Architectural Engineering, University of Colorado.Chinowsky, P.S., Hayles, C, Schweikert, A., Strzepek, N, Strzepek, K. & Schlosser, C.A. (2011) ‘Climate change: comparative impact on developing and developed countries’, Engineering Project Organization Journal, Vol 1, pp. 67-80.Canadian Standards Association (2006) The Role of Standards in Adapting Canada’s Infrastructure to the Impacts of Climate Change. Toronto: Canadian Standards Association.
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Resources 4
Cornick, S., A. Dalgliesh, N. Said, R. Djebbar, F. Tariku & K. Kumaran(2002) Report from Task 4 of the MEWS project, Research report 113, Institute for Research in Construction, Ottawa: National Research Council Canada.Morris, P.I. & Wang, J. (2011) ‘Scheffer index as preferred method to define decay risk zones for above ground wood in building codes’, International Wood Products Journal, Vol 2, pp. 67-70.
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