Climate Impact Risks and Climate Adaptation Engineeringfor Built InfrastructureMark G. Stewart1 and Xiaoli Deng2

Abstract: A changing climate may increase the frequency or intensity of natural hazards, resulting in increased infrastructure damage. Thepaper will describe how risk-based approaches are well suited to optimising climate adaptation strategies related to the construction, design,operation, and maintenance of built infrastructure. Climate adaptation engineering involves estimating the risks, costs, and benefits of climateadaptation strategies and assessing at what point in time climate adaptation becomes economically viable. Stochastic methods are used tomodel infrastructure performance, risk reduction, and effectiveness of adaptation strategies, exposure, and costs. These concepts will beillustrated with recent research on risk-based life-cycle assessments of climate adaptation strategies for Australian housing subject to extremewind events. This will pave the way for more efficient and resilient infrastructure, and help future proof new and existing infrastructure to achanging climate. DOI: 10.1061/AJRUA6.0000809. © 2014 American Society of Civil Engineers.

Author keywords: Risk; Climate change; Cost-benefit analysis; Infrastructure; Climate adaptation.

Introduction

A changing climate may result in more intense tropical cyclonesand storms, more intense rain events and flooding, and otherclimate-related hazards. Moreover, increases in CO2 atmosphericconcentrations, as well as changes in temperature and humidity,may reduce the durability of concrete, steel, and timber structures(Bastidas-Arteaga et al. 2010, 2013; Wang et al. 2012; Stewart et al.2011b, 2012a; Nguyen et al. 2013; Peng and Stewart 2014; Wangand Wang 2012). The impact of climate change on infrastructureperformance is a temporal and spatial process, but most existingmodels of infrastructure performance are based on a stationary cli-mate. Hence, there is a need to quantify the costs and benefits ofadaptation strategies. Climate adaptation engineering involves es-timating the risks, costs, and benefits of climate adaptation strate-gies and assessing at what point in time climate adaptation becomeseconomically viable. Climate adaptation measures aim to reducethe vulnerability or increase the resiliency of built infrastructureto a changing climate; this may include, for example, enhancementof design standards, retrofitting or strengthening of existing struc-tures, utilization of new materials, and changes to inspection andmaintenance regimes. Engineers have a unique capability to modelinfrastructure vulnerability, and these skills will be essential tomodeling future climate impacts and measures to ameliorate theselosses.

The climate change literature places more emphasis on impactmodeling than climate adaptation engineering modeling. This is tobe expected when the current political and social environment isfocused on mitigating (reducing) CO2 emissions. Latest research

shows that business-as-usual CO2 emissions continue to track atthe high end of emission scenarios, with mean temperatureincreases of 4–5°C more likely by the year 2100 (Peters et al.2013). The impacts on people and infrastructure may be consider-able if there is no climate adaptation engineering to existing andnew infrastructure. Some posit that climate change may even bea threat to national security, but Stewart (2014a) suggests that cli-mate change threats to U.S. national security are modest and man-ageable. On the other hand, higher temperatures in higher-latituderegions such as Russia and Canada can be beneficial through higheragricultural yields, lower winter mortality, lower heating require-ments, and a potential boost to tourism (Stern 2007).

There is seldom mention of probabilities, or quantitative mea-sures of vulnerability, or the likelihood or extent of losses in riskand risk management reports on climate change and infrastructure.While useful for initial risk screening, intuitive and judgement-based risk assessments are of limited utility to complex decision-making since there are often a number of climate scenarios,adaptation options, limited funds, and doubts about the cost-effectiveness of adaptation options. In this case, the decision-makermay still be uncertain about the best course of action, and so a de-tailed risk analysis is required [e.g., AS 5334 (Standards Australia2013)]. For this reason, there is a need for sound system and prob-abilistic modeling that integrates the engineering performance ofinfrastructure with the latest developments in stochastic modeling,structural reliability, and decision theory.

The paper will describe how risk-based approaches are wellsuited to optimizing climate adaptation strategies related to the de-sign, construction, operation, and maintenance of built infrastruc-ture. An important aspect is assessing when climate adaptationbecomes economically viable, if adaptation can be deferred, anddecision preferences for future costs and benefits (many of themintergenerational). Stochastic methods are used to model infrastruc-ture vulnerability, effectiveness of adaptation strategies, exposure,and costs. The concepts will be illustrated with a case study thatconsiders climate change and cost-effectiveness of designingnew houses in Sydney, Australia to be less vulnerable to severestorms. To be sure, there are other case studies assessing theefficiency and cost-effectiveness of climate adaptation strategiesfor built infrastructure; for example, floods and sea-level rises

1Australian Professorial Fellow, Professor and Director, Centre forInfrastructure Performance and Reliability, Univ. of Newcastle, NSW 2308,Australia (corresponding author). E-mail: mark.stewart@newcastle.edu.au

2Senior Lecturer, School of Engineering, Univ. of Newcastle, NSW2308, Australia.

Note. This manuscript was submitted on April 8, 2014; approved onJuly 29, 2014; published online on August 27, 2014. Discussion periodopen until January 27, 2015; separate discussions must be submitted forindividual papers. This paper is part of the ASCE-ASME Journal of Riskand Uncertainty in Engineering Systems, Part A: Civil Engineering,© ASCE, 04014001(12)/$25.00.

© ASCE 04014001-1 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

(e.g., Hinkel et al. 2010; Hall et al. 2012; Botzen et al. 2013;Kundzewicz et al. 2013; Holden et al. 2013; Val et al. 2013), cyclo-nes and severe storms (Bjarnadottir et al. 2011a, b; Nishijima et al.2012; Li and Stewart 2011; Stewart et al. 2012b), and corrosion ofreinforced concrete (Stewart and Peng 2010; Bastidas-Arteaga andStewart 2013). This and other research will help future proof builtinfrastructure to a changing climate.

Key Issues

There are a number of issues and questions that need addressing.These include1. Cost neglect:

• Who pays?• When?• Who benefits?

2. Probability neglect—how confident are we about extremeevents in the current climate?• How confident are we in changes to future climate?• Changes in impact and loss?

3. Risk aversion:• Is action needed now, is there no time to lose?

4. Acceptable risk:• What risk from weather/climate is acceptable?• Is risk reduction worth the cost?

These are issues without easy answers, but the questions need tobe asked. They are also similar to other controversial and emotiveissues such as terrorism and homeland security (Mueller andStewart 2011a, b). All too often, climate change studies assumethere is certainty about the future, and so suffer from probabilityneglect, as well as cost neglect by ignoring the large costs involvedto mitigate CO2 emissions.

Climate Change Impact

The 2014 Intergovernmental Panel for Climate Change (IPCC) fifthassessment report (AR5) concluded that the “warming of the cli-mate system is unequivocal” (IPCC 2014). What is less certain isthe impact that rising temperatures will have on rainfall, wind pat-terns, sea-level rise, and other phenomena. The latest IPCC AR5report released in 2014 describes the following changes to climateby the year 2100 (IPCC 2014):1. Temperatures to increase from 1990 levels by anywhere from

1 to 6°C;2. Sea-level rise of 20–80 cm;3. More intense tropical cyclones and other severe wind events;4. Enhanced monsoon precipitation.The IPCC (2014) then suggests with a high or very high con-

fidence level that these changes to climate will increase drought-affected areas, with hundreds of millions of people to be affectedby coastal flooding and increases in the risk of fire, pests, and dis-ease outbreak. There will also be significant consequences for foodand forestry production and food insecurity, and so on. Theseimpacts will not be sudden, but gradual in their appearance. Theobserved increase in weather-related losses in the United Statesand elsewhere is more a function of increased exposure with morepeople moving to vulnerable coastal locations than climate-changeincreases in wind speed or flood levels (Crompton and McAneney2008; IPCC 2012). For instance, hurricane wind speeds are pre-dicted to increase at worst by 10% in 50 years due to climatechange, or a low 0.2% per year (Bjarnadottir et al. 2011a). Thissuggests that there will be time to adapt to a changing climate.

The 2006 review by economist Nicholas Stern (Stern 2007) pre-dicts that if no action is taken against climate change, the mean lossof GDP would be 2.9 and 13.8% each year (“now and forever”) byyears 2100 and 2200, respectively. This is equivalent to worldwidelosses of up to $10 trillion each year by 2200. Not surprisingly,some consider it to be highly pessimistic in its assumptions (Lomborg2006; Mendelsohn 2006). However, the Australian Garnaut Reviewpredicted that unmitigated climate change would reduce AustralianGDP by approximately 8% by 2100 (Garnaut 2008).

These losses, however, do not reflect wealth creation, humancapital, and new improved technologies. Goklany (2008) states thatthese “often reduce the extent of the human health and environmen-tal ‘bads’ associated with climate change more than temperatureincreases exacerbate them.” Fatality rates and economic losses re-lated to weather and climate are also 3–10 times higher in developingcountries (IPCC 2012). Clearly then, if people are wealthier in thefuture, their well-being will be higher, and “the argument that weshould shift resources from dealing with the real and urgent problemsconfronting present generations to solving potential problems oftomorrow’s wealthier and better positioned generations is unper-suasive at best and verging on immoral at worst.” (Goklany 2008).

CO2 Mitigation versus Adaptation

The cost to mitigate CO2 emissions is considerable. Stern (2007)estimates that to stabilize CO2 levels to 550 ppm (by reducing totalemissions to three-quarters of today’s levels by the year 2050)would cost−1.0 to 3.5% of GDP, with a central estimate of approx-imately 1%. The mean estimate would result in an annual mitiga-tion cost of approximately $720 billion.

Lomborg (2009) assembled a group of experts who found thatclimate change action ranked very low when compared with otherhazard and risk-reducing measures. In this case, the benefit-to-costratio for CO2 mitigation was only 0.9 (not cost-effective), butincreased to 2.9 for a mix of mitigation and adaptation strategies.Yohe et al. (2009) found that a global investment of $18 billionper year in “R&D and mitigation” can halve business-as-usualCO2 emissions by the year 2100. Such action would reduce theimpact of climate change by at least 60%.

Some of the more dire predictions of food and energy insecurity,and mass migration can be ameliorated by funding climate-adaptation measures in the developing world. Adaptation measuresto reduce vulnerability of infrastructure, coastal zones, agriculture,forestry, fisheries, and human health to climate change hazardswould include: flood control dikes and levees, dams, cyclone shel-ters, storm-resistant and flood-resistant housing, improved commu-nication infrastructure, resettlement of populations to lower riskzones, and improved health care. The World Bank (2010) estimatedthat the cost to the developing world of adapting to an approxi-mately 2°C warmer world by the year 2050 is approximately$75 billion per year. This represents less than 0.2% of globalGDP. Clearly, investing in targeted adaptation measures has thepotential to dramatically reduce the impact of climate change.

Mitigation costs may be high, and the benefits of reduced CO2

levels will take many decades to accrue. Modest and sustained in-vestments in R&D, CO2 mitigation and adaptation will lessen theworst impacts of climate change. Hence, a mix of mitigation andadaptation is desirable to cope with a changing climate.

Modelling Climate Hazards

Atmosphere-ocean general circulation models (AOGCMs) arecurrently the main tool for climate change studies (IPCC 2012).

© ASCE 04014001-2 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

However, they are computationally demanding, which limits theirspatial resolution. Downscaling is possible to grids of 25–50 km,but the resulting models are still too large to capture extreme eventssuch as tornadoes or extreme rainfall. Uncertainties associated withfuture emission scenarios are usually not quantified, and future cli-mate projections are produced separately for individual scenarios(Stewart et al. 2014). Not surprisingly, uncertainties in climate projec-tions are considerable. This makes impact and adaptation modelingeven more challenging. For more details, see Stewart et al. (2014).

A major climate hazard is coastal flooding induced by extremewater level events along low-lying, highly populated coastlines dueto presently and continuously rising sea levels. Statistical analysisbased on extreme value theory has been employed to estimate prob-abilities of extreme water levels and assess suitable design levels.Estimating the average recurrence interval (ARI) and the annualexceedance probability (AEP) needs sea-level measurements overa long period greater than 30 years, traditionally observed at tidegauges. Over the last five decades, several statistical analysis meth-ods for estimating the ARI and AEP have been developed (cf.Haigh et al. 2010). There is, however, no universally acceptedmethod available at transnational or even national scales. There-fore, the most applicable method has to be chosen based on differ-ent stretches of coastline and length of sea-level records.

Over regions where long periods of tide-gauge data are notavailable or the spatial distribution of tide-gauge sites is sparsealong coastline, modeling of extreme sea levels can be made fromsea level fields produced by ocean circulation models. A 2D depth-averaged barometric ocean circulation model is usually configuredover coastal oceans. The mode is driven by tidal and atmosphericforcing. The tidal component including the Earth geocentric tideand other nonlinear tidal constituents are simulated using existingglobal tidal modes and the harmonic analysis algorithm. The stormsurge component is simulated through the model driven by windstress and the atmospheric pressure at the sea level, for whichthe wind stress calculated from the wind velocity at 10 m abovethe sea surface and the sea level pressure can be taken from theclimate forecast system reanalysis fields. To achieve the fine struc-ture of the wind and pressure associated with tropical storms, thereanalysis field is enhanced by adding the idealized wind and pres-sure profiles during cyclones (e.g., Zhang and Sheng 2013). Thetotal sea levels due to the combination of tides and storm surgesuse Monte Carlo simulation methods.

In recent years, satellite data with high resolution and homo-geneous global coverage have already played an important rolein monitoring extreme sea levels, especially storm surges. How-ever, advanced satellite products such as wind speed, wind direc-tion from high resolution scatterometry, and sea state informationfrom coastal altimetry are not yet widely used in modeling extremesea levels. Since these satellite data have been collected for morethan 20 years, assimilating them into numerical models will havethe potential to improve estimation of extreme sea level. (e.g., Chenget al. 2012; Deng et al. 2011, 2013; Idris et al. 2014). Nonetheless,uncertainties are considerable, and remote sensing technologies areincreasingly relied upon for measuring and predicting sea-level riseand other phenomena. Wind speed projections also face distinctmeasuring and prediction challenges (e.g., Harper 2013).

Risk-Based Decision Support

Definition of Risk

Risk for a system exposed to a climate hazard is

EðLÞ ¼X

PrðCÞ PrðHjCÞPrðDjHÞ PrðLjDÞL ð1Þ

where PrðCÞ = annual probability that a specific climate scenariowill occur; PrðHjCÞ = annual probability of a climate hazard(wind, heat, etc.) conditional on the climate; PrðDjHÞ = probabilityof infrastructure damage or other undesired effects conditional onthe hazard (also known as vulnerability or fragility) for the baselinecase of no extra protection (i.e., business-as-usual); PrðLjDÞ =conditional probability of a loss (economic loss, loss of life,etc.) given occurrence of the damage; and L = loss or consequenceif full damage occurs. In some cases, ‘damage’may equate to ‘loss’and so PrðLjHÞ ¼ PrðDjHÞPrðLjDÞ. The summation sign inEq. (1) refers to the number of possible climate scenarios, hazards,damage levels, and losses.

If we modify Eq. (1) whereΔR is the reduction in risk caused byclimate adaptation measures, then expected loss after climate adap-tation is

EadaptðLÞ ¼X

ð1 −ΔRÞEðLÞ −ΔB ð2Þ

where ΔR = reduction in risk caused by climate adaptation (orother protective) measures; EðLÞ = business-as-usual risk givenby Eq. (1); andΔB = cobenefit of adaptation such as reduced lossesto other hazards, increased energy efficiency of new materials, etc.Costs of adaptation, timing of adaptation, discount rates, futuregrowth in infrastructure, and spatial and time-dependent increasesin climate hazards need to be included in any risk analysis. Fig. 1summarizes the major steps in developing risk-based decisionsupport for assessing the risks, costs, and benefits of climate-adaptation measures.

Future climate is projected by defining carbon emission sce-narios in relation to changes in population, economy, technol-ogy, energy, land use and agriculture—a total of four scenariofamilies, i.e., A1, A2, B1, and B2 are defined (IPCC 2000) andused in the IPCC’s Fourth Assessment Report in 2007 (IPCC2007). The A1 scenarios indicate very rapid economic growth,a global population that peaks in midcentury and declines there-after, and the rapid introduction of new and more efficient tech-nologies, as well as substantial reduction in regional differencesin per capita income. The B1 scenarios are of a world more in-tegrated and more ecologically friendly with reductions inmaterial intensity and the introduction of clean and resource ef-ficient technologies. The IPCC AR5 (2014) uses RepresentativeConcentration Pathways (RCPs) where RCP8.5, RCP6.0, andRCP4.5 are roughly equivalent to A1FI, A1B, and A1B toB1 CO2 emissions, respectively. For more details see Stewartet al. (2014)

The estimation of PrðCÞ may be based on expert opinion aboutthe likelihood of each emission scenario, and multiple AOGCMsmay be used to infer the probabilistic characterisation of PrðHjCÞfor future climate projections. Fig. 2 describes the projection ofCO2 concentrations based on the Model for Assessment of Green-house-Gas Induced Climate Change, known as MAGICC (Wigleyet al. 1996), specifically related to A1FI, A1B, and 550 ppm CO2

stabilization scenarios. The variability increases for projection oftemperatures (Fig. 3).

The stochastic modeling of infrastructure vulnerability (orfragility) is PrðDjHÞ and is the probability of damage conditionalon the occurrence of a specific hazard

PrðDjHÞ ¼ PrðRðXÞ −H < 0Þ ð3Þwhere RðXÞ = function for resistance or capacity; X = vector of allrelevant variables that affect resistance; and H = known hazardlevel. The performance functions can be expressed in terms ofstructural damage or other losses and are derived from engineer-ing models (e.g., Stewart and Melchers 1997). The reliability

© ASCE 04014001-3 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

modeling of structural systems is well developed for engineeredconstructions such as commercial buildings, bridges, towers, etc.where materials are uniform, and workmanship is subject to qualitycontrol measures. However, nonengineered infrastructures, particu-larly houses, are very complex systems comprising hundreds tothousands of components and connections between differing ma-terials. Poor detailing and workmanship issues contribute to mostdamage—so the engineering and stochastic models need to con-sider these variables—such as screw fasteners being spaced toofar apart, or some not connected to purlins and battens, etc. Insur-ance or building performance data may also be used to derive vul-nerability models. For example, Fig. 4 shows a vulnerability modelfor Australian houses subject to floods.

Exposure and loss data relates to direct and indirect loss or con-sequences due to location and extent of infrastructure damage, forexisting exposure and future projections. Most existing studies con-sider direct losses related to building damage and contents losses.Indirect losses caused by business interruption, clean-up, loss dur-ing reconstruction, extra demands on social services, and changesto demand and supply of intermediate consumption goods, post-disaster inflation, etc. can also be significant (e.g., NAS 1999;Hallegatte 2008; Walker 2011). Indirect losses were estimatedfor Hurricane Katrina as $42 billion or 39% of direct losses,and could have exceeded 100% of direct losses for a damagingevent twice as bad as Hurricane Katrina (Hallegatte 2008). AnAustralian assessment of direct and indirect costs shows indirect

Fig. 1. Flowchart of decision-support framework for assessing cost-effectiveness of adaptation measures

© ASCE 04014001-4 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

costs of 9–40% of direct losses for bushfires, cyclones, and floods(BTE 2001). Fig. 5 shows a typical loss function for wind vulner-ability, where indirect losses start to accumulate for damages thatexceed 20%, and total losses are twice the direct losses for a cata-strophic event where PrðDjHÞ ¼ 100%.

Risk reduction (ΔR) may result from reduced vulnerabilityPrðDjHÞ, PrðLjDÞ, or exposure (L). For instance, changes to plan-ning may reduce the number of new properties built in a flood plainwhich will reduce L, or more stringent design codes may reduce thevulnerability of new infrastructure. Systems and reliability model-ing are essential tools to quantify the level of risk reduction, and theextent of risk reduction due to adaptation measures will depend onthe hazard, the location, and the timing of adaptation.

The cobenefits of adaptation (ΔB) may include reduced embod-ied energy and reduced carbon footprint over the life cycle of thefacility. This might consider the initial embodied energy associ-ated with the dwelling including footings, structure and fit-outtogether with the recurrent embodied energy associated with refur-bishment over the life cycle and the operational energy neededto operate a building.

Cost-Effectiveness of Adaptation Strategies

Two criteria may be used to assess the cost-effectiveness of adap-tation strategies:1. Net present value (NPV); and2. Probability of cost-effectiveness or PrðNPV > 0Þ.The benefit of an adaptation measure is the reduction in dam-

ages associated with the adaptation strategy, and the ‘cost’ is thecost of the adaptation strategy. The net benefit or net present value(NPV) is equal to benefit minus the cost, which is also equivalent tothe present value or life-cycle cost of an adaptation strategy (sum ofdamage and adaptation costs) minus the business-as-usual or do-nothing present value. The decision problem is to maximize NPV

NPV ¼X

EðLÞΔRþΔB − Cadapt ð4Þ

where Cadapt = cost of adaptation measures including opportunitycosts that reduces risk by ΔR; ΔB = cobenefit; and EðLÞ =business-as-usual risk given by Eq. (1). Fig. 6 shows how adapta-tion costs increase with risk reduction, while benefits increase. Theoptimal adaptation occurs when NPV is a maximum, leading to anoptimal risk reduction. Other notations and formulae can be used toprovide optimal adaptation (e.g., Hall et al. 2012), but ultimatelythese also mostly rely on maximizing NPV.

Confidence bounds of NPV can then be calculated if inputparameters are random variables. The probability that an adaptationmeasure is cost-effective, denoted herein as PrðNPV > 0Þ, mayalso be inferred. The previously given equations can be generalized

300

400

500

600

700

800

900

1000

1100

2000 2020 2040 2060 2080 2100

lowmidhigh

CO

2 C

once

ntra

tion

(ppm

)

Year

A1FI

550 ppm

A1B

Year 2000 CO2 level

Fig. 2. Projected low, mid, and high estimates of CO2 concentrations

17

18

19

20

21

22

23

24

2000 2020 2040 2060 2080 2100

A1FIA1B550 ppmYear 2000

Tem

pera

ture

(°C

)

Year

Fig. 3. Projected median temperatures for the lowest and highest GCMpredictions for the A1FI, A1B, 550 ppm, and year 2000 emission sce-narios for Sydney (Australia)

0

10

20

30

40

50

60

70

80

90

100

-1 0 1 2 3 4 5 6

Vul

nera

bilit

y Pr

(D|H

) (%

)

Water Depth Above Floor Level (m)

Single Storey

Two Storey(lower floor partial use as garage)

Fig. 4. Flood vulnerability curves for residential construction inBrisbane (data from Mason et al. 2012)

0

100%

200%

0 10 20 30 40 50 60 70 80 90 100

Pr(L

|D)

Vulnerability Pr(D|H) (%)

Direct Loss

Direct + Indirect Loss

Fig. 5. Direct and indirect costs as function of wind vulnerability (datafrom Stewart et al. 2013)

© ASCE 04014001-5 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

for any time period, discounting of future costs and more detailedspatial and time-dependent cost and damage consequences.

The vulnerability, loss, and adaptation costs are subject to con-siderable uncertainty due to lack of available data and models. Forthis reason, calculations of risks, costs, and benefits will be impre-cise. Hence, a break-even analysis may be useful where minimumrisk reduction or maximum cost of adaptation necessary for adap-tation to be cost-effective is selected such that there is 50% prob-ability that benefits equal cost—i.e., meanðNPVÞ ¼ 0. In otherwords, if the actual cost of adaptation exceeds the predictedbreak-even value, then adaptation is not cost-effective. Decision-makers can then judge whether an adaptation strategy meets thesebreak-even values.

Governments and their regulatory agencies normally exhibitrisk-neutral attitudes in their decision-making, as described byEq. (4). This is confirmed by the U.S. Office of Managementand Budget (OMB) (OMB 1992), and also by many practitionersand researchers (e.g., Sunstein 2002; Faber and Stewart 2003;Ellingwood 2006). This entails using mean or average estimatesfor risk and cost-benefit calculations, and not worst-case or pessi-mistic estimates. Probability neglect is a form of risk aversion asdecision-makers are clearly averse to events of large magnitudeirrespective of the probability of it actually occurring. Utility theorycan be used if the decision maker wishes to explicitly factor riskaversion or proneness into the decision process (e.g., Stewartet al. 2011a).

Case Study: Strengthening New Houses in Sydneyagainst Extreme Wind

Severe storms have caused annual insured losses (1967–2005) ofnearly $300 million in the Australian states of Queensland, NewSouth Wales, and Victoria (BITRE 2008). These losses accountfor nearly 25% of all losses from natural disasters in Australia.Southern Australian contemporary housing generally comprisesa detached dwelling on a 600–800 m2 block, one or two storeyshigh, timber construction with brick veneer cladding, and a tiledor metal sheeting roof (Fig. 7). The wind vulnerability of this hous-ing type will not be too dissimilar to that for housing in the UnitedStates, Canada, and New Zealand.

The Australian Standard for wind loads [AS/NZS1170.2(Standards Australia 2011)] is the reference standard for designof all structures, including housing. The Australian Standard

Wind Loads for HousesAS4055 (Standards Australia 2012) is basedon AS/NZS1170.2 (Standards Australia 2011) and is used to deter-mine the appropriate wind classification for design of residential(domestic) housing. In this case, residential housing is designedto resist wind speeds with annual probability of exceedance of 1in 500. The standard AS4055 (Standards Australia 2012) classifiesdesign loads on houses into categories N1–N6 for noncyclonicregions. Each increase in noncyclonic wind classification (e.g., N1to N2) raises the design wind speed that is equivalent to at least a50% increase in designwind pressure. Thesewind classifications arethen used by building codes to determine appropriate deemed-to-comply sizing and detailing requirements for residential construction.

To reduce housing damage in the future, one option may be tostrengthen or retrofit existing construction. However, Stewart andWang (2011) found such strategies often failed to be cost-effective,and if cost efficient, then only marginally so. Other adaptation strat-egies may restrict construction of new housing in vulnerable(exposed) locations. A more feasible adaptation strategy may beone that increases design wind loads for new houses leading to long-term reduction of vulnerability (and damages) of houses (Stewartet al. 2013; Stewart 2014b). It is important to note that AS4055(Standards Australia 2012) (as well as many other Australianand international building standards) is based on limited experi-mental and field data and expert judgement by committee mem-bers, and has not been subject to risk or cost-benefit analyses(Walker and Musulin 2012). Hence, existing design requirementsmay be suboptimal even for the current climate.

The case study herein applies break-even (risk neutral) andrisk-averse analyses to compare the risks, costs, and benefits ofclimate adaptation strategies for new housing in the Australiancoastal city of Sydney. Sydney is the largest city in Australia witha population of nearly 5 million (about 20% of the total populationof Australia). Sydney is located in South-East Australia wherewind hazard is dominated by noncyclonic winds (thunderstormsand east-coast lows). For more details of this case study, seeStewart (2013).

0% 100%

$

Risk Reduction (%)

Adaptation Cost

BenefitMax. NPV

OptimalAdaptation

Fig. 6. Schematic of net present value (NPV) showing optimaladaptation

Fig. 7. Typical Australian truss roof and timber frame houses (imagesby Mark G. Stewart)

© ASCE 04014001-6 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

Wind Hazard Modelling

The annual probability of winds PrðHjCÞ is derived from the Gum-bel distribution to model the annual probability of exceedance ofnoncyclonic winds (winds not associated with tropical cyclonesthat occur in northern Australia) (Wang et al. 2013). Unless notedotherwise, wind speed is the gust wind speed at 10 m in terraincategory 2. Climate projections by the Australian CommonwealthScientific and Industrial Research Organisation (CSIRO) suggestthat for noncyclonic winds, the mean wind speed may increaseby up to 20% by the year 2070 along the east coast of Australia(CSIRO 2007). If the relationship between mean wind speedand peak gust wind speed is constant, then proportional increasesin gust wind and mean wind speeds are identical. Since there arestill many uncertainties to properly define the future trend ofextreme winds in Australia, three possible climate scenarios (C)are considered: (1) no change, (2) B1, and (3) A1FI emissionscenarios. The variability of current peak wind loads is significantwith COV of up to 50%.

CSIRO (2007) suggest the average annual change in mean windspeed is projected to decrease by 1% in Sydney by the year 2070,with 10 and 90% of −15 and þ12%, respectively, for the A1FI(high) emission scenario, and 10th and 90th percentiles of −8 toþ6% for the B1 (medium) emission scenario (Table 1). Projectedchanges in wind speeds for Brisbane are higher than for Sydney.Note that climate projections are relative to levels in the year 1990.Truncated normal distributions are used to represent uncertainty ofchanges in wind speeds where 10 and 90% bounds provided byCSIRO (2007) allow the standard deviation of the two truncatednormal distributions each with cumulative probabilities of 50%to be calculated, Fig. 8. A time-dependent linear change in windspeed for all emission scenarios is assumed.

Wind Vulnerability and Loss Function

Australian contemporary housing in South-East Australia is typicalof that shown in Fig. 7. The housing is weaker than those designedfor cyclonic (northern) regions of Australia where AustralianStandards dictate more stringent design requirements. A wind vul-nerability function expresses building damage or loss as a functionof wind speed. Vulnerability curves developed by GeoscienceAustralia and James Cook University (Wehner et al. 2010)representative of contemporary housing in Sydney are shown inFig. 9. These vulnerability curves are quite uncertain, but providea useful starting point.

The loss function includes direct and indirect losses, and isshown in Fig. 5. House replacement value is clearly variableand depends on the location, type, size, age, and conditionof the house. The average insured value of a house and itscontents is approximately 25% higher than house replace-ment value. The loss L is equal to insured value of the houseand its contents, normalised to L ¼ 1.25 of house replacementvalue.

Adaptation Strategy: Strengthen New Housing

The adaptation strategy considered herein is to design new housesby enhanced design codes, in this case, increasing the currentAustralian Standard AS4055 (Standards Australia 2012) wind clas-sification by one category (Table 2). For example, for Sydney thismeans that new construction would be designed for wind classifi-cation N2 rather than the current requirement of N1 for nonfore-shore locations. For example, this would mean that the number ofnails for a roof batten to roof truss connection should increase from

Fig. 8. Probability distribution of change in wind speeds to year 2070

0

10

20

30

40

50

60

70

80

90

100

20 30 40 50 60 70 80 90 100

Vul

nera

bilit

y Pr

(D|H

) (%

)

Peak Gust Wind Speed (m/s)

N1 N2 N4N3

Fig. 9. Wind vulnerability curves for Sydney, for contemporary resi-dential housing in South-East Australia

Table 2. Adaptation Measure Showing Proposed Increase in WindClassification

Location

Existing specifications(AS4055-2012)

Adaptation measure:proposed increase

in wind classifications

Windclassification

Designgust windspeed (m=s)

Windclassification

Designgust windspeed (m=s)

Sydney — — — —Foreshore N2 40 N3 50Nonforeshore N1 34 N2 40

Table 1. Change in Wind Speeds to Year 2070

Location

B1 emission scenario A1FI emission scenario

10th(%)

Mean(%)

90th(%)

10th(%)

Mean(%)

90th(%)

Brisbane −1 þ3 þ10 −2 þ6 þ19

Sydney −8 0 þ6 −15 −1 þ12

Melbourne −9 −1 þ6 −18 −1 þ12

© ASCE 04014001-7 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

one plain shank nail to two plain shank nails, or a single deformedshank nail should replace a single plain shank nail [AS 1684.4(Standards Australia 2010)]. This means that new constructionhas increased strengths of structural components and connections,leading houses to have significantly reduced wind vulnerability.These enhanced building requirements will result in additionalcosts of new construction (Cadapt) of 1–2% of the value of a house,or approximately $2,500 to $5,000 (AGO 2007).

Fig. 10 shows percentage reduction in vulnerability as a functionof wind speed for Sydney. It is evident that designing new houses toenhanced wind classification will reduce vulnerability often bymore than 50% for Sydney. The reduction in vulnerability reducesonly for very high wind speeds where damages asymptote to 100%resulting in reduced relative reductions in vulnerability. Vulnerabil-ity reduction is higher for nonforeshore locations due to lower windspeeds than foreshore locations. Similarly, vulnerability reductionwill decrease as climate change becomes more severe and windspeeds increase. The overall reduction in risk calculated as

percentage change in risk caused by the adaptation strategyis ΔR ¼ 50 − 65%.

The NPV for a single house built to enhanced standards at timetadapt for a given climate change scenario Cs (no change, B1, orA1FI) is

NPVðTjCsÞ ¼XT

t¼tadapt

ΔR × EðtÞð1þ rÞt−2018 −

Cadapt

ð1þ rÞtadapt−2018 ð5Þ

where NPV is expressed as percentage of replacement value of thehouse; Cadapt = cost of the adaptation strategy expressed as percent-age of house replacement value; EðtÞ = damage risk per houseassociated with current wind classification (business as usual risk);ΔR = reduction in risk associated with increasing the current windclassification by one category (e.g., from N2 to N3); and r =discount rate. Cobenefits are assumed as ΔB ¼ 0.

Results: Scenario-Based Analysis

The models described herein are the best available models, but asdescribed previously, have their limitations and uncertainties. Forthis reason, a break-even analysis is conducted.

Any proposal to change building regulation within the BuildingCode of Australia would take some time. Hence, we assume theearliest time of adaptation is by the year 2018. Results are calcu-lated using Monte-Carlo event-based simulation methods. Costsand benefits are calculated for the 52 year period 2018–2070 as2070 is the limit of projections of wind hazard provided by CSIRO(2007). Costs are in 2012 Australian dollars and the discount rate is4%. The stochastic variability of wind speed means that NPV isvariable. The probability distribution of NPV is highly nonGaussianand so Monte-Carlo methods are well suited to this type of analysis.In this scenario-based approach, PrðCsÞ ¼ 100%.

Figs. 11 and 12 show the maximum adaptation costCadapt for theadaptation measure (per new house) to be cost-effective for riskreductions of 10–100% for foreshore and nonforeshore locationsin Sydney. These figures show

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100 110 120

Red

uctio

n in

Vul

nera

bilit

y (%

)

Peak Gust Wind Speed (m/s)

N2 to N3

N1 to N2

Fig. 10. Reduction in vulnerability for new housing with increase ofwind classification, for Sydney

0%

(a) (b)

5%

10%

15%

20%

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

No change

B1

A1FI

Bre

ak-E

ven

Ada

ptat

ion

Cos

t

Risk Reduction R

0%

5%

10%

15%

20%

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

No change

B1

A1FI

Bre

ak-E

ven

Ada

ptat

ion

Cos

t

Risk Reduction R

Fig. 11. (a) Break-even adaptation costs for foreshore locations in Sydney; (b) break-even adaptation costs for nonforeshore locations in Sydney

© ASCE 04014001-8 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

1. The break-even cost of adaptation [i.e., maximum cost formeanðNPVÞ ¼ 0, risk neutral]; and

2. The maximum cost of adaptation to ensure that there is 90%surety that benefits exceed the cost (i.e., a risk-averse decisionmaker may prefer a small likelihood of a net loss).

Fig. 11 shows that if risk reduction is over 50% and there is nochange of climate, the break-even analysis shows that adaptation iscost-effective if the adaptation cost is less than 9.3 and 5.5% ofhouse replacement cost for foreshore and nonforeshore locations,respectively. The effect of a changing climate on break-even adap-tation costs is negligible, as this will increase the break-even adap-tation cost to 9.7 and 5.7% for foreshore and nonforeshorelocations, for the A1FI emission scenario. Hence, even if climateprojections are wrong, adaptation measures still satisfies a no re-grets or win-win policy (Susskind 2010).

On the other hand, the maximum cost of adaptation to ensurethat there is 90% surety that benefits exceed the cost will be lessthan the break-even costs (Fig. 12). In this case, the adaptation ispreferable if the risk reduction exceeds 50% and the adaptation costis less than 6.8 and 4.0% of house replacement cost for foreshoreand nonforeshore locations, respectively, and assuming no changein climate. As there is significant uncertainty associated with windhazard projections for B1 and A1FI emission scenarios, the vari-ability of NPV is higher for these scenarios. Hence, the maximumcost of adaptation to ensure that there is 90% surety that benefitsexceed the cost will be up to 3% lower than for no climate change.This suggests that even a risk averse decision-maker would adoptthe adaptation measure since the anticipated cost of adaptation isvery low (1–2%) and risk reduction exceeds 50%.

The cost of adaptation for Sydney is likely to be 1.1% forforeshore locations (N2 to N3), and less for nonforeshore locations(N1 to N2). If we adopt a cost of adaptation of 1.1 and 1.0% forthese locations, and no change of climate, then the break-even riskreduction must exceed 6 and 9% for foreshore and nonforeshorelocations, respectively, to be 50% certain that NPV > 0 for allclimate projections. The minimum risk reductions increase toensure 90% certainty that NPV > 0. For example, for the medium

(B1) emission scenario, minimum risk reduction must exceed 11and 17% for foreshore and nonforeshore locations, respectively.Given that the previous section shows that risk reductions of50–65% can be achieved for Sydney based on the vulnerabilitymodels described herein, then it is likely that designing new hous-ing to enhance wind classifications is a cost-effective adaptationstrategy for Sydney.

Clearly, more research is needed to improve the confidence ofwind hazard and vulnerability modeling. Nonetheless, preliminaryresults show that vulnerability reduced at a modest cost can lead toa cost-effective adaptation measure.

Discount Rates

There is some uncertainty about the level of discount rate, particu-larly for climate change economic assessments [e.g., Dasgupta(2008), for more details see Stewart (2013)]. A high discount ratereduces the cost-effectiveness of adaptation strategies because thebenefit of reduction in damages into the future is reduced, thusreducing NPVand lowering the break-even adaptation costs. None-theless, a 7 or 10% discount rate will still produce break-even adap-tation costs for Sydney of 3–6%, which are likely to be higher thanactual adaptation costs. Discount rates of 1.35 and 2.65% as usedby Garnaut (2008) result in higher break-even values (7–15%),which increase the likelihood of adaptation being cost-effective.These relatively low discount rates were selected so as to not under-estimate climate impacts on future generations. However, otherssuggest higher discount rates when assessing economic impactsof climate change (e.g., Nordhaus 2007).

Time of Adaptation

The effects of a changing climate tend to worsen into the future sobenefits of adaptation in the next decade or so are lower comparedto those later in the century. Hence, deferring time of adaptation isan option worth considering. There is also the benefit of reducedpresent (discounted) value of adaptation cost if this cost is deferred.

0%

(a) (b)

5%

10%

15%

20%

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

No change

B1

A1FI

Max

imum

Ada

ptat

ion

Cos

tfo

r Pr

(NPV

>0)

=90

%

Risk Reduction R

0%

5%

10%

15%

20%

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

No change

B1

A1FI

Max

imum

Ada

ptat

ion

Cos

tfo

r Pr

(NPV

>0)

=90

%

Risk Reduction R

Fig. 12. (a) Maximum adaptation costs to ensure PrðNPV > 0Þ ¼ 90% for foreshore locations in Sydney; (b) maximum adaptation costs to ensurePrðNPV > 0Þ ¼ 90% for nonforeshore locations in Sydney

© ASCE 04014001-9 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

Stewart et al. (2013) and Stewart (2014b) have shown that whileadaptation that is implemented as early as possible has the highestNPV, deferred adaptation of 5–20 years also yields a high NPVandhigh likelihood that adaptation is cost-effective.

Results: Likelihood of Climate Scenarios

The previous section described results for a scenario-based analysisby assuming that the probability of climate scenario PrðCsÞ is100%. However, there is unlikely to be such certainty about anyclimate scenario. The IPCC Special Report on Emissions Scenarios(SRES) states that probabilities or likelihood are not assigned toindividual SRES scenarios (IPCC 2000). One approach is to esti-mate the relative likelihood of the occurrence of each climate sce-nario, such as by expert opinion, Bayesian probability updatingwhen new data/models are available, etc. The overall NPV at timeT is calculated as

NPVðTÞ ¼XS

s¼1

PrðCsÞNPVðTjCsÞ ð6Þ

where S = total number of climate scenarios considered(S ¼ 3); Cs ¼ sth climate scenario; and NPVðTjCsÞ is givenby Eq. (5).

IPCC (2007) states that anthropogenic forcing is likely to havecontributed to changes in wind patterns. The term likely is associ-ated with a probability that exceeds 67%. It follows that thelikelihood of no change in wind patterns is 33%, leading toPrðno changeÞ ¼ 33%. If emissions scenarios B1 and A1FI areconsidered equally likely, then PrðB1Þ ¼ PrðA1FIÞ ¼ 33%.

For sake of illustration, the effect of climate scenario likelihoodis assessed for nonforeshore locations in Sydney and 50% riskreduction, as shown in Table 3. As expected, climate scenario like-lihood has little effect on the break-even adaptation cost as thismetric is less sensitive to the climate scenario. However, the maxi-mum adaptation cost to be 90% certain that adaptation is cost-effective is sensitive to the climate scenario. In this case, a weightedaverage of NPV given by Eq. (6) reduces variability of NPV,resulting in higher 10th percentiles of NPV and so higher maxi-mum adaptation costs. For example, if there is 100% certaintyof no change, the maximum adaptation cost is 4.0% for adapta-tion to be preferred, however, this falls to 3.5% if there is equallikelihood of all scenarios, and reduces further to 2.6% if PrðB1Þ ¼PrðA1FIÞ ¼ 50%.

Eq. (6) and Table 3 are clearly a simplification of what is a chal-lenging issue of degree of belief in climate scenarios and how itmight influence the cost-effectiveness of adaptation policy options.

Nonetheless, it illustrates how this may affect uncertainty and vari-ability of NPV, and its effect on decision-making.

Further Work

This paper highlights that a risk-based approach to optimizingadaptation requires the following information:1. Effect of climate scenarios on frequency and intensity of

hazards;2. Vulnerability of infrastructure to hazards;3. Loss functions;4. Risk reduction for adaptation measures; and5. Cost of adaptation measures.The break-even approach to economic assessment of the costs

and benefits of adaptation is used due to considerable modeling andparameter uncertainty of these variables.

More accurate predictions of hazard, vulnerability, risk reduc-tion, and economic assessment are challenging. A key challenge,at least for engineers, is the development of vulnerability modelsfor damage prediction. This will utilize reliability and probabilisticmodeling in time and space to model damage initiation and pro-gression. There is much work on predicting reliabilities for the ul-timate limit state where life-safety is the major criterion. However,modeling of damage and serviceability limit states is a less tractableproblem as this requires information about damage progression,load sharing, and redistribution of loads of failed components orconnections, and other spatial and time-dependent processes. Thereis also difficulty in modeling the vulnerability of housing and othernonengineered forms of construction where there is high variabilityof construction materials, processes, and structural systems, highvariability of connection capacities (e.g., nailed connections, roofsheeting fasteners), less code compliance, and other variabilitiesand uncertainties that are less evident in reinforced concrete, steeland other forms of engineered construction.

Conclusions

The performance of new and existing infrastructure will degrade ifsubject to more extreme climate-related hazards or accelerated cli-mate-change induced degradation of material properties. Climateadaptation engineering involves estimating the risks, costs, andbenefits of climate adaptation strategies and assessing at what pointin time climate adaptation becomes economically viable. Thispaper has described how risk-based approaches are well suitedto optimizing climate adaptation strategies for built infrastructure.The concepts were illustrated with a state-of-the-art application ofrisk-based assessment of climate adaptation strategies for Austral-ian housing subject to extreme wind events. It was found that windvulnerability of new housing in Sydney can be reduced by 50–65%at modest cost, and can be shown to be a cost-effective adaptationmeasure.

Acknowledgments

The authors appreciate the financial support of the Common-wealth Scientific and Industrial Research Organisation (CSIRO)Flagship Cluster Fund through the project Climate AdaptationEngineering for Extreme Events in collaboration with the Sustain-able Cities and Coasts Theme, the CSIRO Climate AdaptationFlagship.

Table 3. Effect of Climate Scenario Likelihood on Maximum Cost ofAdaptation, for 50% Risk Reduction and Nonforeshore Exposure inSydney

Probability ofclimate scenario

Maximum adaptation costs foradaptation to be preferred option

Pr (nochange) (%)

Pr(B1) (%)

Pr(A1FI) (%)

Mean(NPV > 0) (%)

PrðNPV > 0Þ¼ 90% (%)

100 0 0 5.5 4.00 100 0 5.5 3.00 0 100 5.7 1.933.3 33.3 33.3 5.6 3.50 50 50 5.6 2.6

© ASCE 04014001-10 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

References

Australian Greenhouse Office (AGO). (2007). “An assessment of the needto adapt buildings for the unavoidable consequences of climate change.”Final Rep., Commonwealth of Australia, Canberra, Australia.

Bastidas-Arteaga, E., Chateauneuf, A., Sánchez-Silva, M., Bressolette, P. H.,and Schoefs, F. (2010). “Influence of weather and global warming inchloride ingress into concrete: A stochastic approach.” Struct. Saf.,32(4), 238–249.

Bastidas-Arteaga, E., Schoefs, F., Stewart, M. G., and Wang, X. (2013).“Influence of global warming on durability of corroding RC structures:A probabilistic approach.” Eng. Struct., 51, 259–266.

Bastidas-Arteaga, E., and Stewart, M. G. (2013). “Probabilistic cost-benefitanalysis of climate change adaptation strategies for new RC structuresexposed to chloride ingress.” 11th Int. Conf. on Structural Safety andReliability, CRC Press, 1503–1510.

Bjarnadottir, S., Li, Y., and Stewart, M. G. (2011a). “A probabilistic-basedframework for impact and adaptation assessment of climate change onhurricane damage risks and costs.” Struct. Saf., 33(3), 173–185.

Bjarnadottir, S., Li, Y., and Stewart, M. G. (2011b). “Social vulnerabilityindex for coastal communities at risk to hurricane hazard and a changingclimate.” Nat. Hazards, 59(2), 1055–1075.

Botzen, W. J. W., Alerts, J. C. J. H., and van den Bergh, J. C. J. M. (2013).“Individual preferences for reducing flood risk to near zero throughelevation.” Mitigation Adaptation Strategies Global Change, 18(2),229–244.

Bureau of Infrastructure, Transport, and Regional Economics (BITRE).(2008). About Australia’s regions, Canberra, Australia.

Bureau of Transport Economics (BTE). (2001). “Economic costs of naturaldisasters in Australia.” Bureau of Transport Economics Rep. 103,Canberra, Australia.

Cheng, Y., Andersen, O. B., and Knudsen, P. (2012). “Integrating non-tidalsea level data from altimetry and tide gauges for coastal sea levelprediction.” Adv. Space Res., 50(8), 1099–1106.

Commonwealth Scientific and Industrial Research Organisation (CSIRO).(2007). Climate change in Australia: Technical Rep. 2007, Marine andAtmospheric Research Division, Canberra, Australia.

Crompton, R. P., and McAneney, J. K. (2008). “Normalised australianinsured losses from meteorological hazards: 1967–2006.” Environ.Sci. Policy, 11(5), 371–378.

Dasgupta, P. (2008). “Discounting climate change.” J. Risk Uncertainty,37(2–3), 141–169.

Deng, X., et al. (2011). “Satellite altimetry for geodetic, oceanographic, andclimate studies in the Australian region.” Coastal altimetry, Springer,Heidelberg, Germany, 473–508.

Deng, X., Andersen, O. B., Gharineiat, Z., and Stewart, M. G. (2013).“Observing and modelling the high sea level from satellite radar altim-etry during tropical cyclones.” Int. Association of Geodesy Symp.,Potsdam, Germany.

Ellingwood, B. R. (2006). “Mitigating risk from abnormal loads andprogressive collapse.” J. Perform. Constr. Facil., 10.1061/(ASCE)0887-3828(2006)20:4(315), 315–323.

Faber, M. H., and Stewart, M. G. (2003). “Risk assessment for civil engi-neering facilities: Critical overview and discussion.” Reliab. Eng. Syst.Saf., 80(2), 173–184.

Garnaut, R. (2008). “The Garnaut climate change review.” Final Rep.,Commonwealth of Australia, Cambridge University Press, Cambridge,U.K.

Goklany, I. M. (2008). “What to do about climate change.” Policy Analysis,No. 609, Cato Institute, Washington, DC.

Haigh, I. D., Nicholls, R., and Wells, N. (2010). “A comparison of the mainmethods for estimating probabilities of extreme still water levels.”Coastal Eng., 57(9), 838–849.

Hall, J. W., Brown, S., Nicholls, R. J., Pidgeon, N. F., and Watson, R. T.(2012). “Proportionate adaptation.”Nat. Clim. Change, 2(12), 833–834.

Hallegatte, S. (2008). “An adaptive regional input-output model and itsapplication to the assessment of the economic cost of Katrina.” RiskAnal., 28(3), 779–799.

Harper, B. A. (2013). “Best practice in tropical cyclone wind hazardmodelling: In search of data and emptying the skeleton cupboard.”

16th Australasian Wind Engineering Society Workshop, Brisbane,Australia, 1–10.

Hinkel, J., Nicholls, R. J., Vafeidis, A. T., Tol, R. S. J., and Avagianou, T.(2010). “Assessing risk of and adaptation to sea-level rise in theEuropean Union: An application of DIVA.” Mitigation AdaptationStrategies Global Change, 15(7), 703–719.

Holden, R., Val, D. V., Burkhard, R., and Nodwell, S. (2013). “A networkflow model for interdependent infrastructures at the local scale.” Saf.Sci., 53(3), 51–60.

Idris, N. H., Deng, X., and Andersen, O. B. (2014). “The importance ofcoastal altimetry retracking and detiding: A case study around the GreatBarrier Reef, Australia.” Int. J. Remote Sens., 35(5), 1729–1740.

Intergovernmental Panel on Climate Change (IPCC). (2000). “Emissionscenarios.” Special Rep. of the Intergovernmental Panel on ClimateChange, Cambridge University Press, Cambridge, U.K.

Intergovernmental Panel on Climate Change (IPCC). (2007). “Climatechange 2007: Synthesis report.” Contribution of Working Groups I,II and III to the Fourth Assessment Rep. on IntergovernmentalPanel on Climate Change, R. K. Pachauari and A. Reisinger, eds., Corewriting team, Geneva, Switzerland.

Intergovernmental Panel on Climate Change (IPCC). (2012). “Managingthe risks of extreme events and disasters to advance climate changeadaptation.” A Special Rep. of Working Groups I and II of the Intergov-ernmental Panel on Climate Change, C. B. Field, et al., eds.,Cambridge University Press, Cambridge, U.K.

Intergovernmental Panel on Climate Change (IPCC). (2014). “Climatechange 2014: Impacts, adaptation, and vulnerability.” Technical Sum-mary, Draft.

Kundzewicz, Z. W., et al. (2013). “Assessing river flood risk and adaptationin Europe—Review of projections for the future.” Mitigation Adapta-tion Strategies Global Change, 15(7), 641–656.

Li, Y., and Stewart, M. G. (2011). “Cyclone damage risks caused byenhanced greenhouse conditions and economic viability of strength-ened residential construction.” Nat. Hazards Rev., 10.1061/(ASCE)NH.1527-6996.0000024, 9–18.

Lomborg, B. (2006). “Stern review: The dodgy numbers behind the latestwarming scare.” Wall Street J., 2, in press.

Lomborg, B. (2009).Global crises, global solutions, Cambridge UniversityPress, Cambridge, U.K.

Mason, M., Phillips, E., Okada, T., and O’Brien, J. (2012). Analysis ofdamage to buildings following the 2010/2011 east Australian floods,NCCARF, Griffith Univ., Brisbane, Australia.

Mendelsohn, R. O. (2006). “A critique of the stern report.” Regulation,29(4), 42–46.

Mueller, J., and Stewart, M. G. (2011a). Terror, security, and money:Balancing the risks, benefits, and costs of homeland security, OxfordUniversity Press, Oxford, U.K.

Mueller, J., and Stewart, M. G. (2011b). “The price is not right: The U.S.spends too much money to fight terrorism.” Playboy, 58(10), 149–150.

National Academy of Sciences (NAS). (1999). The impact of naturaldisasters: A framework for loss estimation, Washington, DC.

Nguyen, M. N., Wang, X., and Leicester, R. H. (2013). “An assessmentof climate change effects on atmospheric corrosion rates of steelstructures.” Corros. Eng. Sci. Technol., 48(5), 359–369.

Nishijima, K., Maruyama, T., and Graf, M. (2012). “A preliminary impactassessment of typhoon wind risk of residential buildings in Japan underfuture climate change.” Hydrol. Res. Lett., 6(1), 23–28.

Nordhaus, W. D. (2007). “A review of the stern review on the economics ofclimate change.” J. Econ. Lit., 45(3), 686–702.

Office of Management, and Budget (OMB). (1992). “Guidelines anddiscount rates for benefit-cost analysis of federal programs (revised).”Circular No. A-94, Washington, DC.

Peng, L., and Stewart, M. G. (2014). “Climate change and corrosiondamage risks for reinforced concrete infrastructure in China.” Struct.Infrastruct. Eng.

Peters, G. P., et al. (2013). “The challenge to keep global warming below2°C.” Nat. Clim. Change, 3(1), 4–6.

Standards Australia. (2010). “Residential timber-framed construction—Part 4: Simplified—Non-cyclonic areas.” AS1684.4, Sydney, Australia.

© ASCE 04014001-11 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

Standards Australia. (2011). “Structural design actions—Part 2: Windactions.” AS/NZS 1170.2, Sydney, Australia.

Standards Australia. (2012). “Wind loads for houses.” AS4055, Sydney,Australia.

Standards Australia. (2013). “Climate change adaptation for settlements andinfrastructure—A risk based approach.” AS5334, Sydney, Australia.

Stern, N. (2007). The economics of climate change: The Stern review,Cambridge University Press, Cambridge, U.K.

Stewart, M. G. (2013). “Risk and economic analysis of residential housingclimate adaptation strategies for wind hazards in south-east Australia.”CAEx Rep. 1/2013, Climate Adaptation Engineering for Extreme EventsCluster, CSIRO Climate Adaptation Flagship, Canberra, Australia.

Stewart, M. G. (2014a). “Climate change and national security: Balancingthe costs and benefits.” Dangerous world? Threat perception and U.S.national security, C. Preble and J. Mueller, eds., Cato Institute,Washington, DC.

Stewart, M. G. (2014b). “Risk and economic viability of housing climateadaptation strategies for wind hazards in southeast Australia.” Mitiga-tion adaptation strategies global change, in press.

Stewart, M. G., Ellingwood, B. R., and Mueller, J. (2011a). “Homelandsecurity: A case study in risk aversion for public decision-making.”Int. J. Risk Assess. Manage., 15(5–6), 367–386.

Stewart, M. G., and Melchers, R. E. (1997). Probabilistic risk assessment ofengineering systems, Chapman and Hall, London.

Stewart, M. G., and Peng, J. (2010). “Life cycle cost assessment of climatechange adaptation measures to minimise carbonation-induced corrosionrisks.” Int. J. Eng. Uncertainty: Hazards, Assess. Mitigation, 2(1–2),35–46.

Stewart, M. G., Val, D., Bastidas-Arteaga, E., O’Connor, A., and Wang, X.(2014). Climate adaptation engineering and risk-based design andmanagement of infrastructure, maintenance and safety of aginginfrastructure, D. M. Frangopol, and Y. Tsompanakis, eds., Taylorand Francis, London.

Stewart, M. G., and Wang, X. (2011). Risk assessment of climate adapta-tion strategies for extreme wind events in Queensland, CSIRO ClimateAdaptation Flagship, Canberra, Australia.

Stewart, M. G., Wang, X., and Nguyen, M. (2011b). “Climate changeimpact and risks of concrete infrastructure deterioration.” Eng. Struct.,33(4), 1326–1337.

Stewart, M. G., Wang, X., and Nguyen, M. (2012a). “Climate changeadaptation for corrosion control of concrete infrastructure.” Struct.Saf., 35(3), 29–39.

Stewart, M. G., Wang, X., and Willgoose, G. R. (2012b). Indirect cost andbenefit assessment of climate adaptation strategies for extreme windevents in Queensland, CSIRO, Canberra, Australia.

Stewart, M. G., Wang, X., andWillgoose, G. R. (2013). “Direct and indirectcost and benefit assessment of climate adaptation strategies for housingfor extreme wind events in Queensland.” Nat. Hazards Rev., 10.1061/(ASCE)NH.1527-6996.0000136, 04014008.

Sunstein, C. R. (2002). The cost-benefit state: The future of regulatoryprotection, American Bar Association Publishing, Chicago.

Susskind, L. (2010). “Responding to the risks posed by climate change:Cities have no choice but to adapt.” Town Plann. Rev., 81(3), 217–235.

Val, D. V., Holden, R., and Nodwell, S. (2013). “Probabilistic assessment offailures of interdependent infrastructures due to weather relatedhazards.” Safety, reliability, risk and life-cycle performance of struc-tures and infrastructure, G. Deodatis, B. R. Ellingwood, and D. M.Frangopol, eds., Taylor and Francis, London, 1551–1557.

Walker, G. R. (2011). “Comparison of the impacts of cyclone tracy and thenewcastle earthquake on the Australian building and insurance indus-tries.” Aust. J. Struct. Eng., 11(3), 283–293.

Walker, G. R., and Musulin, R. (2012). “Utilising catastrophic risk mod-elling for cost benefit analysis of structural engineering code changes.”Australasian Structural Engineering Conf., Engineers Australia, 92–99.

Wang, C.-H., and Wang, X. (2012). “Vulnerability of timber in ground con-tact to fungal decay under climate change.” Clim. Change, 115(3–4),777–794.

Wang, C.-H., Wang, X., and Khoo, Y. B. (2013). “Extreme wind gusthazard in Australia and its sensitivity to climate change.” Nat. Hazards,67(2), 549–567.

Wang, X., Stewart, M. G., and Nguyen, M. (2012). “Impact of climatechange on corrosion and damage to concrete infrastructure inAustralia.” J. Clim. Change, 110(3–4), 941–957.

Wehner, M., Ginger, J., Holmes, J., Sandland, C., and Edwards, M. (2010).“Development of methods for assessing the vulnerability of Australianresidential building stock to severe wind.” IOP Conf. Ser. Earth Envi-ron. Sci., 11(1), 12–17.

Wigley, T. M. L., Richels, R., and Edmonds, J. A. (1996). “Economicand environmental choices in the stabilization of atmospheric CO2

concentrations.” Nature, 379(6562), 240–243.World Bank. (2010). “Economics of adaptation to climate change: Synthe-

sis report.” International Bank for Reconstruction and Development/World Bank, Washington, DC.

Yohe, G. W., Tol, R. S. J., Richels, R. G., and Blanford, G. F. (2009).“Climate change.” Global crises, global solutions, B. Lomborg, ed.,Cambridge University Press, Cambridge, U.K.

Zhang, H., and Sheng, J. (2013). “Estimation of extreme sea levels over theeastern continental shelf of North America.” J. Geophys. Res. Oceans,118(11), 6253–6273.

© ASCE 04014001-12 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 2015.1.

Dow

nloa

ded

from

asc

elib

rary

.org

by

182.

160.

103.

42 o

n 04

/12/

15. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.