Apêndice 1 - Economic analysis of adaptation to climate change

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Apêndice 1 - Economic analysis of adaptation to climate change

  1. 1. Economic analysis of adaptation to climate change Gordon Hughes University of Edinburgh 16th December 2013
  2. 2. Economic concepts and data requirements 2
  3. 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. 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. 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. 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. 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. 8. Global precipitation – differences between GCMs NCAR 2100 MIROC 2100
  9. 9. China - Change in precipitation NCAR (Global Wet)
  10. 10. China - Change in precipitation CSIRO (Global Dry)
  11. 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. 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. 13. 120 Maximum 3-day rainfall (mm) 140 160 180 Maximum 3-day rainfall for Serbia 1980 2010 2030 2050 2070 2090 13
  14. 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. 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. 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. 17. Baseline projections: electricity consumption for Serbia 17
  18. 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. 19. Baseline projections: gross urban water use for Serbia 19
  20. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 32. The cost of adaptation for infrastructure 32
  33. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 50. Water resources management 2 Price/Cost C PCC P B PNoCC A CC NoCC QNoCC QCC Quantity 50
  51. 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. 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. 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. 54. Net present value of ex-ante adaptation in China 1 ($ billion at 2010 prices, average climate scenario) 54
  55. 55. Net present value of ex-ante adaptation in China 2 ($ billion at 5% discount rate, average climate scenario) 55
  56. 56. Net present value of ex-ante adaptation in China 3 ($ billion at 5% discount rate by climate scenario) 56
  57. 57. Net present value of ex-ante adaptation in China 4 ($ billion at 5% discount rate, average climate scenario) 57
  58. 58. Net present value of ex-ante adaptation in Mongolia ($ billion at 2010 prices) 58
  59. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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: 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.
  69. 69. Resources 2 Sheffield, J., Goteti, G. & Wood, E.F. (2006) ‘ Development of a 50year 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. 69
  70. 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. 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

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