This document discusses economic analysis and planning for infrastructure adaptation to climate change. It addresses key concepts such as: - Developing baseline projections for infrastructure needs without climate change, accounting for uncertainty. - Incorporating the effects of climate change on infrastructure through dose-response relationships and changes to investment and operating costs. - The goal of adaptation is often to maintain the level and quality of infrastructure services without climate change, which may require adjusting design standards and quantities. - Analyzing the costs of adaptation by considering changes to construction and operating costs due to climate factors. Infrastructure sectors covered include energy, water, transport, and urban development. - Planning adaptation under uncertainty using approaches like payoff matrices to

1. Economic analysis of
adaptation to climate change
Gordon Hughes
University of Edinburgh
16th December 2013

2. Economic concepts and data
requirements
2

3. 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 uncertainty
Historic climate patterns vs projections from GCMs
Economic development with/without climate change
Baseline scenarios for population, urbanisation & GDP per head
How 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

4. 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 households
Impacts on the poor?
Regional or sectoral differences
Is economic growth the best form of adaptation?
Income and resilience
Overlap between good development policies and adaptation
Who pays?
4

5. 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 flooding
For roads, models make use of pavement temperatures which
depend on latitude as well as daily maximum temperatures
For 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 & precipitation
Heavy reliance upon combining model projections with historical
weather data
5

6. 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, etc
Substantial 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 infrastructure
Often 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

7. 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 sensitivity
Option 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 adaptation
Monitoring: essential to track how climate is changing and to
implement a framework for updating plans for adaptation
7

8. Global precipitation – differences between
GCMs
NCAR 2100
MIROC 2100

9. China - Change in precipitation NCAR
(Global Wet)

10. China - Change in precipitation CSIRO
(Global Dry)

11. 60
Maximum monthly rainfall (mm)
70
80
90
100
110
Monthly rainfall for Serbia - wettest
month
1980
2010
2030
2050
2070
2090
11

12. 0
Minimum monthly rainfall (mm)
10
20
30
40
50
Monthly rainfall for Serbia - driest month
1980
2010
2030
2050
2070
2090
12

13. 120
Maximum 3-day rainfall (mm)
140
160
180
Maximum 3-day rainfall for Serbia
1980
2010
2030
2050
2070
2090
13

14. Baseline scenarios – why and how?
The role of baseline scenarios
Economic growth and development without climate change
How much difference will climate change make?
What is the relative uncertainty due to climate change?
Projections to 2030 / 2050 or beyond
Infrastructure: growth of 2% per year, 40 year asset life – 75% of
roads, buildings, etc in 2050 will be built after 2030
Health: trends in malaria or infant mortality with/without CC
Long term plans for water allocation by sector/type of use
Incorporating uncertainty about future growth
Statistical patterns of economic development
Or, 25 year strategies and development goals
14

15. Baseline scenarios – socio-economic
projections
Demography, urbanisation & GDP to 2050+
Population and age structure: by region if possible
Urbanisation and growth of major cities
Growth in GDP per head: perhaps by sector
Sources of information
UN population and urbanisation projections
Allowing for regional differences: Brazil, China
Total population may be less important than age structure
Where 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
15

16. 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 negative
Feasible to allow for quantity changes for water & power but large
uncertainty & disagreement about other infrastructure
And what about health? Clear linkages between climate & the
burden of disease
16

17. Baseline projections: electricity consumption for Serbia
17

18. 100
Electricity consumption (2010=100)
150
200
250
Climate uncertainty and electricity consumption for Serbia
2010
2015
2020
2025
2030
2035
2040
2045
2050
18

19. Baseline projections: gross urban water use for Serbia
19

20. Gross urban water demand (2010=100)
100
105
110
115
120
Climate uncertainty and gross urban water use for Serbia
2010
2015
2020
2025
2030
2035
2040
2045
2050
20

21. Strategic planning for adaptation under
uncertainty
Adaptation is unavoidable
Distinction between ex-ante (planned) adaptation and ex-post (at
the time or after the event) adaptation
Ex-ante adaptation may reduce costs by eliminating or lowering the
damage caused by climate change
But, there is the risk of spending too much money on the wrong
things if climate impacts are uncertain in extent or timing
So, how far ahead should you look – the planning horizon
Ex-post adaptation involves waiting to collect information and then
responding when the outcome is fairly certain
The downside is the cost of the damage if the outcome is bad plus
higher costs of modifying/replacing existing assets
Do you know what today’s climate is?
21

22. Planning for adaption under climate
uncertainty 1
One possible approach using pay-off matrices
Choose 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 years
Benefits [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 worthwhile
Carry out this analysis for each X
22

23. Payoff matrices for adaptation under
certainty
B(X,Y) = Benefits of adaptation
Outcome scenario
Plan
scenario
1
2
3
4
1
2
3
C(X,Y) = Costs of adaptation
Outcome scenario
4
5
7
8
10
Plan
scenario
1
2
3
4
1
2
3
4
3
5
8
12
B(X,Y) - C(X,Y) = Net benefits of adaptation
Outcome scenario
Plan
1
2
3
4
scenario
1
2
2
2
3
0
4
-2
23

24. 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 outcomes
Now 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 scenario
B(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
24

25. Planning for adaption under climate
uncertainty 3
The result is a payoff matrix under all combinations of plan and
outcome
If 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 adaptation
The best option depends on how risk averse you are. Y is
uncertain but the planning scenario X is the choice we make
Consider
(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
25

26. Payoff matrices for adaptation under
uncertainty
B(X,Y) = Benefits of adaptation
Outcome scenario
Plan
scenario
1
2
3
4
C(X,Y) = Costs of adaptation
Outcome scenario
1
2
3
5
5
5
5
4
7
7
7
1
6
8
8
Plan
scenario
1
2
3
4
4
1
2
5
10
1
2
3
4
3
5
8
12
3
5
8
12
3
5
8
12
3
5
8
12
B(X,Y) - C(X,Y) = Net benefits of adaptation
Outcome scenario
Plan
scenario
1
2
3
4
1
2
3
4
Mean
Min
2
0
-3
-7
1
2
-1
-5
-2
1
0
-4
-2
-3
-3
-2
-0.25
0.00
-1.75
-4.50
-2.00
-3.00
-3.00
-7.00
26

27. Decisions under climate uncertainty:
option values
Preserving choice is worth money
Almost all adaptation is a combination of ex-ante and ex-post
This is because delaying some decisions can preserve options that
allow planners and users to adapt more flexibly
Uncertainty about economic development as much as climate
But, 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 expansion
Need to monitor outcomes in order to respond quickly
Avoid investments that are inflexible and are long lived
Climate uncertainty may be less than economic uncertainty, so
small changes to flexibility justified for other reasons
A different approach to planning and decision-making
27

28. Analysis of extreme events: how much
protection?
Forget climate change for a while
Do 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 events
Cuba, Haiti and Bangladesh – the role of economic development
Investment in infrastructure vs governance and social networks
Changes in the trade-offs between costs and risks
28

29. Analysis of extreme events: climate
change
The effects of climate change: intensity & frequency
Shifting the distributions of extreme events
Tropical cyclones: sea surface temperatures, other factors
Flooding: cumulative vs transitional (run-off)
Droughts: period without rain or probability that rain <
evapotranspiration + run-off over extended period
Example: Flooding in China
Seasonal 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 events
Changes in water management and urbanization which may
alter/accelerate run-off: hydro-power vs flood management
29

30. Analysis of extreme events: return
periods
0.180
0.160
0.140
Probability
0.120
0.100
0.080
0.060
0.040
0.020
0.000
200
300
400
500
600
700
800
900
1000
Maximum m onthly rainfall (mm)
NoCC
CC
R10
30

31. Analysis of extreme events: forms of
adaptation
Increasing resilience and/or reducing vulnerability
Land use and urban planning: don’t put assets in harm’s way
But, attractions of flood plains and coastal zones
Building codes, storm water drainage systems, etc
Civil defense, evacuation plans, shelters, effective governance
Investment in infrastructure
Coastal 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?
31

32. The cost of adaptation for
infrastructure
32

33. Overview
What is covered along with infrastructure?
Energy, water, transport, health & education, urban &
housing
Key steps in the analysis
Projections without and with climate change
Dose-response relationships
Changes in investment costs
Changes in O&M costs
Special factors
Engineering vs economic approach to adaptation
How far can incentives reduce the costs of adaptation
Planning horizon and analysis of uncertainty
33

34. Infrastructure – coverage and goal of
adaptation
Focus on long-lived & collective assets
Significant percentage of total capital stock
Often poorly maintained which increases vulnerability
Key starting point:
Maintain the level and quality of infrastructure services that
would have been provided without climate change
This requires an adjustment in design standards to cope
with changes in temperature, rainfall, etc
In 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 heating
Issue: What do we do if a different climate might result in a
change in the demand for infrastructure services?
34

35. Infrastructure – planning and who pays?
Flood damage is a recurrent theme
Land use planning as a low cost form of adaptation
But, very difficult to implement and enforce, especially
in dense and rapidly growing cities
Are 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
plains
Avoid perverse incentives via, for example,
collective schemes for flood or storm insurance
35

36. Infrastructure projections – no climate
change
Baseline projections from either development plans or
econometric analysis
Allow for country special factors and/or planning biases
What are the margins of uncertainty for the projections?
Scenario approach or quantify statistical uncertainty
How would investment in infrastructure be affected by
alternative policies?
Consider effects of pricing water resources or road user charges
Role of urban development or decentralization strategies
Regional policies and links to infrastructure provision
Flexibility: updating projections at regular intervals
36

37. 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 effect
Water 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 roads
Urban infrastructure: essential to examine storm water
drainage
Social infrastructure, housing – probably too difficult
37

38. Breaking down the cost of adaptation
Delta-C cost – changes in the cost of building and
operating the baseline (NoCC) level of infrastructure
DtC
CC
t
t t
cI
CC
t
mt Kt
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
DtQ
(ct
CC
t
t
c)
CC
t
t
I
(mt
CC
t
mt )
CC
t
Kt
38

39. 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 performance
Evidence derived from building codes and technical
experience
Most dose-response relationships are step functions:
increased costs are linked to going over thresholds such as a
10 mm increase in maximum monthly precipitation
In 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

40. Dose-response relationships 2
Issues in implementation
Starting point: absolute thresholds with the historical climate
just below the threshold lead to large discontinuities unlikely
to reflect actual engineering practice
Dealing with relationships based on climate indicators not
generated by climate models – e.g. extreme wind speeds or
daily precipitation
Influences 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
treatment
Significant 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

41. Dose-response relationships – asset
types
Infrastructure types
Electricity generation
Capital costs
Temperature
Maximum
temperature
Precipitation
Operating & maintenance costs
Other
Temperature
Mean
temperature
Paved roads
Unpaved roads
Mean
temperature
Mean
temperature
Water treatment
Wastewater
treatment
Storm water drainage
Maximum
precipitation
Precipitation
Type of
impact
Other
CC+; OM+
Flooding maximum
precipitation
Flooding maximum
precipitation
Maximum
precipitation
Maximum
precipitation
Maximum
precipitation
OM+; EL-
OM+; ELOM+
OM+
CC+; OM+
Wood buildings
Temperature
& precipitation
- Scheffer
index
Temperature
& precipitation
- Scheffer
index
CC+; EL-
Brick/concrete
buildings
Relative
humidity MEWS index
Relative
humidity MEWS index
CC+; OM+
41

42. 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, etc
Analysis focuses on these components with additions for special
purpose equipment, which may not be sensitive to climate
Example: 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

43. Investment costs – water & wastewater
Coverage
Water abstraction & treatment, water supply & sewer
networks, wastewater treatment & disposal, urban storm
drainage
Hydro, 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 population
Distinguish 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 use
Weather/climate conditions and variations in load margins
43

44. Investment costs – buildings & urban
infrastructure
Structures are major element of all infrastructure
Primary cost drivers are:
External water resilience, internal temperature & moisture
control
Varies with building material: wood vs concrete or brick
Special indices focusing on rain & humidity - Scheffer & MEWS
Balance between initial capital cost and later upgrades is
especially important for internal climate control
Urban storm drainage may be a very large item
Costs calculated on a coverage and per capita basis
Provision of temporary storage as well as collection
Costs depend a lot on land use controls, but our estimates are
at the high end and could be reduced by SUDS approach
44

45. 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 costs
Special treatment for power generation, water &
wastewater treatment and flooding
Increase 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

46. 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 standard
Due 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 greater
Damage 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 flooding
Spread costs of upgrading over average remaining life of the
asset – i.e. 50% of economic life
46

47. O&M costs – process efficiency
Electricity generation
Cooling systems for thermal power plants have to be upgraded
when ambient temperature exceeds a threshold – typically 3540°C. Models allow for annualized cost of alternative systems
Combustion efficiency for gas plans tends to degrade if ambient
temperature > 30°C. Higher fuel consumption per MWh
Water & wastewater treatment
Treatment design & costs affected by ambient temperatures – both
cold and hot
Primary 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

48. 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) planning
Coastal 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 standards
Poor information and high discount rates favor standards
Incentives encourage more cost-effective approaches
48

49. 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 demand
Option 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 constrained
Under 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

50. Water resources management 2
Price/Cost
C
PCC
P
B
PNoCC
A
CC
NoCC
QNoCC
QCC
Quantity
50

51. 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 land
Large vested interests in water management, land use, etc
Climate 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 better
Alternative mechanisms for water management: e.g. negotiated
water transfers
51

52. 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 life
Associated changes in maintenance & operating costs
Ex-post adaptation:
After the event expenditures on maintenance, repairs &
upgrades to respond to actual changes in climate conditions
Costs include losses due to damage or decline in performance
Uncertainty and mal-adaptation
Ex-post adaptation responds to actual climate outcomes
Ex-ante adaption responds to forecasts and we may get these
wrong, thus spending too much or too little money
52

53. What are the key sectors and types of
asset?
Strong differences across countries and regions due
to geographical variation in impact of climate change
Vulnerability 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 adaptation
Roads: the costs are extremely variable, primarily driven by
pavement costs (temperature) and flood upgrades
Urban infrastructure: the costs of building/upgrading urban
drainage may be very large
Social infrastructure & housing: key issue is moisture &
ventilation with very large costs but only if critical thresholds
are exceeded
53

54. Net present value of ex-ante adaptation in China 1
($ billion at 2010 prices, average climate scenario)
54

55. Net present value of ex-ante adaptation in China 2
($ billion at 5% discount rate, average climate scenario)
55

56. Net present value of ex-ante adaptation in China 3
($ billion at 5% discount rate by climate scenario)
56

57. Net present value of ex-ante adaptation in China 4
($ billion at 5% discount rate, average climate scenario)
57

58. Net present value of ex-ante adaptation in Mongolia
($ billion at 2010 prices)
58

59. 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 expenditure
Aggregate $ figures are pretty meaningless on their own
What 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 exceptions
This share tends to fall over time and as countries get richer
In most countries cost of adaptation is < 0.25% of GDP
59

60. Cost of adaptation by climate scenario & region
($ billion per year, average 2011-50)
1.20
5.21
1.16
1.02
1.53
0.79
1.02
2.72
1.22
1.30
0.84
0.77
0.62
3.48
1.47
4.53
2.85
1.87
1.39
0.63
1.08
0.41
0.56
1.04
0.22
0.17
1.01
0.43
0.60
1.41
0.15
0.18
2.31
0.41
1.25
0.52
0.73
0.57
8.53
25.49
0.97
1.08
1.36
1.03
0.78
8.84
1.18
2.29
1.07
1.24
0.90
5.46
2.95
7.79
1.24
4.25
6.18
0.35
5.71
0.21
0.52
0.54
0.39
0.46
4.23
0.59
0.72
0.27
0.31
0.10
1.50
0.29
11.76
0.58
1.68
3.02
SW
China
0.92
4.27
0.83
0.75
1.04
1.94
0.97
3.76
0.83
1.05
0.95
0.85
0.75
1.67
0.79
1.70
1.27
1.43
1.04
0.6%
0.7%
0.6%
0.4%
0.9%
1.0%
0.6%
2.3%
1.8%
0.4%
0.5%
0.2%
0.1%
0.6%
1.0%
0.4%
1.7%
1.6%
1.7%
2.2%
3.8%
2.7%
2.7%
2.3%
2.5%
5.3%
4.5%
N China NE China E China SE China
BCCR_BCM20
CCCMA_CGM3
CNRM_CM3
CSIRO_MK30
CSIRO_MK35
GFDL_CM20
GFDL_CM21
GISS_ER
INM_CM30
IPSL_CM4
MIROC_32
MPI_ECHAM5
MRI_CGCM232A
NCAR_CCSM30
NCAR_PCM1
UKMO_HADCM3
UKMO_HADGEM1
Average over GCMs
Standard deviation
Average as % of baseline
(NoCC) expenditures
Median as % of baseline
(NoCC) expenditures
Max as % of baseline
(NoCC) expenditures
W China
China
Japan
Korea
Mongolia
1.02
2.43
1.15
0.80
0.98
0.61
0.68
0.86
0.83
1.52
1.29
0.62
0.70
1.21
1.33
1.23
1.11
1.08
0.44
12.65
44.19
4.73
4.73
6.49
4.98
4.08
21.42
5.08
7.48
5.83
3.94
3.25
15.63
7.24
28.26
7.57
11.03
10.97
14.24
13.23
12.09
6.84
7.43
14.27
3.80
5.95
5.18
7.27
12.86
4.27
3.64
7.23
3.16
18.11
22.17
9.51
5.58
1.87
5.38
1.41
1.21
2.09
1.41
0.73
1.41
2.21
3.50
2.80
0.92
1.25
2.63
3.18
3.49
1.68
2.19
1.20
0.08
0.13
0.10
0.06
0.10
0.10
0.09
0.05
0.12
0.17
0.56
0.09
0.05
0.23
0.27
0.11
0.19
0.15
0.12
2.2%
60
1.5%
8.5%

61. Balkans: cost of adaptation by country
($ million per year at 2005 prices for 2011-50, H = 20)
Average
over GCMs
Albania
Bulgaria
Bosnia
Greece
Croatia
Kosovo
Macedonia
Montenegro
Romania
Serbia
Slovenia
Standard
deviation
18
117
20
979
94
13
19
17
383
65
51
12
33
10
655
44
5
6
9
190
20
22
Average as Median as Max as % Average as
of baseline % of GDP
% of
% of
baseline
baseline
0.5%
0.8%
0.5%
3.6%
1.2%
0.7%
0.7%
1.7%
1.1%
0.6%
0.7%
0.4%
0.8%
0.4%
3.1%
1.2%
0.6%
0.7%
1.7%
0.9%
0.5%
0.7%
1.6%
1.1%
1.1%
8.5%
2.0%
1.2%
1.1%
3.3%
2.1%
1.0%
1.4%
0.04%
0.08%
0.04%
0.28%
0.09%
0.05%
0.05%
0.16%
0.09%
0.05%
0.07%
61

62. Balkans: cost of adaptation incl quantity changes
($ million per year at 2005 prices for 2011-50, H = 20)
Average
over GCMs
Albania
Bulgaria
Bosnia
Greece
Croatia
Kosovo
Macedonia
Montenegro
Romania
Serbia
Slovenia
Standard
deviation
Average as
% of
baseline
3
30
-46
907
74
-16
-6
1
179
-79
29
14
73
22
660
46
15
14
13
246
77
35
0.1%
0.2%
-1.2%
3.3%
0.9%
-0.8%
-0.2%
0.1%
0.5%
-0.7%
0.4%
Median as % Max as % of
of baseline
baseline
0.0%
0.1%
-1.1%
2.8%
1.1%
-0.9%
-0.1%
0.1%
0.6%
-0.5%
0.4%
1.2%
1.2%
0.2%
8.3%
1.8%
0.8%
0.8%
2.5%
2.0%
0.4%
1.3%
62

63. Balkans: cost of adaptation by sector
(% of baseline costs for 2011-50, H = 20)
Power &
phones
Water &
sewers
Roads
Other
transport
Health &
schools
Urban
Housing
Total
Albania Bulgaria Bosnia Greece Croatia Kosovo
0.8%
MKD
MNE
Romania Serbia Slovenia
0.7%
0.8%
0.8%
1.2%
0.9%
0.8%
0.9%
0.8%
0.8%
0.8%
0.3%
1.0%
0.4%
2.4%
0.3%
1.7%
0.7% 0.3% 0.4%
10.1% 10.5% 11.2%
0.4%
1.9%
0.3%
8.8%
0.4%
5.1%
0.4%
1.7%
0.4%
1.4%
0.3%
0.1%
0.2%
0.9%
0.2%
0.3%
0.0%
0.5%
0.3%
0.1%
0.3%
1.4%
1.5%
0.1%
0.5%
1.6%
1.5%
0.0%
0.8%
1.4%
1.4%
0.0%
0.5%
3.7%
3.1%
3.3%
3.6%
1.5%
1.4%
0.0%
1.2%
1.5%
1.5%
0.0%
0.7%
1.5%
1.4%
0.0%
0.7%
1.4%
1.5%
0.0%
1.7%
1.6%
1.5%
0.0%
1.1%
1.6%
1.5%
0.0%
0.6%
1.5%
1.5%
0.0%
0.7%
63

64. 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 far
Crucial issue is to think about the climate roughly today (say
2020) rather than the climate 40 years in the past
This 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 level
Uncertainty associated with need to generate statistical models
of rainfall intensity (daily precipitation)
64

65. Balkans: cost of adaption by planning horizon
($ million per year at 2005 prices for 2011-50)
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
65

66. Analysis of uncertainty
Unfortunately, there are no general rules of thumb
Need to do a full analysis and then look for patterns
Often, there is a set of climate scenarios which generate
very similar pay-offs for adaptation in one sector
But these sets vary from sector to sector, so that you may
need to consider 8-10 climate scenarios for a country
It 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 overplay the importance of adaptation
However, it is really important to understand current risks
fully
66

67. Choice of adaptation strategy under
uncertainty
2011-15
Albania
Ex-post adaptation (NoCC)
Ex-ante adaptation
Best
Average
Worst
Base expenditure
Saving as % of baseline
Best vs NoCC
Best vs Average
Bulgaria
Ex-post adaptation (NoCC)
Ex-ante adaptation
Best
Average
Worst
Base expenditure
Saving as % of baseline
Best vs NoCC
Best vs Average
2016-20
2021-25
2026-30
2031-35
2036-40
2041-45
2046-50
64
83
101
128
162
203
277
381
43
49
60
11,191
54
61
80
11,896
64
75
110
12,807
82
95
146
14,367
110
123
170
16,038
140
158
206
17,574
185
234
261
19,010
265
349
405
19,810
0.2%
0.1%
0.2%
0.1%
0.3%
0.1%
0.3%
0.1%
0.3%
0.1%
0.4%
0.1%
0.5%
0.3%
0.6%
0.4%
428
625
847
1,013
1,198
1,430
1,711
1,985
257
313
400
46,115
399
468
556
52,884
531
576
696
59,347
617
666
771
63,090
735
768
829
67,254
851
896
959
71,863
1,014
1,052
1,128
76,411
1,187
1,234
1,323
80,029
0.4%
0.1%
0.4%
0.1%
0.5%
0.1%
0.6%
0.1%
0.7%
0.0%
0.8%
0.1%
0.9%
0.0%
1.0%
0.1%
67

68. 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:
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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
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HR Wallingford (2007) Evaluating flood damages: guidance and
recommendations on principles and methods, Report T09-06-01,
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69. Resources 2
Sheffield, J., Goteti, G. & Wood, E.F. (2006) ‘ Development of a 50year high-resolution global dataset of meteorological forcings for land
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Mendelsohn, R., Emanuel, K. & Chonabayashi, S. (2011) ‘The impact of
climate change on global tropical storm damages’, Policy Research
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69

70. 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.
70

71. 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.
71