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23rd Australasian Conference on the Mechanics of Structures and Materials
2014
Climate adaptation engineering for extreme events:
is adaptation a workable solution to climate change?
M G. Stewart
University of Newcastle
X Wang
CSIRO
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Publication details
Stewart, MG, Wang, X 2014, 'Climate adaptation engineering for extreme events: is adaptation a workable solution to climate change?', in ST Smith (ed.), 23rd Australasian Conference on the Mechanics of Structures and Materials (ACMSM23), vol. II, Byron Bay, NSW, 9-12 December, Southern Cross University, Lismore, NSW, pp. 683-688. ISBN: 9780994152008.
23rd Australasian Conference on the Mechanics of Structures and Materials (ACMSM23) Byron Bay, Australia, 9-12 December 2014, S.T. Smith (Ed.)
This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit
CLIMATE ADAPTATION ENGINEERING FOR EXTREME EVENTS:
IS ADAPTATION A WORKABLE SOLUTION TO CLIMATE CHANGE?
M.G. Stewart*
Centre for Infrastructure Performance and Reliability The University of Newcastle, Australia
[email protected] (Corresponding Author) X. Wang
CSIRO, Melbourne, Australia. [email protected]
ABSTRACT
The paper will describe how risk-based approaches are well suited to optimising climate adaptation strategies related to the design and maintenance of infrastructure. Risk-based decision support is described to assess the risks and economic viability of climate adaptation measures. Stochastic methods are used to model infrastructure performance, effectiveness of adaptation strategies, exposure, and costs. The concepts will be illustrated with current research of risk-based assessment of climate adaptation strategies including designing new houses in Brisbane to extreme wind events. Increasing the design wind classifications in the AS4055-2012 for all new housing can lead to risk reductions of 75-80%, at a cost of 1-2% of house replacement value. Designing new housing to enhance wind classifications is likely to be a cost-effective adaptation strategy for Brisbane for non-foreshore locations irrespective of climate scenario. This anticipatory adaptation measure will help pave the way for more efficient and resilient infrastructure, and help 'future proof' infrastructure to a changing climate.
KEYWORDS
Climate change, risk, climate adaptation, cost benefit analysis, wind.
INTRODUCTION
A changing climate may result in more intense tropical cyclones and storms, more intense rain events and flooding, and other climate-related hazards. Moreover, increases in CO2 atmospheric concentrations, and changes in temperature and humidity, may reduce the durability of concrete, steel and timber structures. The effect on infrastructure, particularly housing, will be significant if the frequency and/or intensity of these natural hazards increase. Some posit that climate change may even be a threat to national security, but Stewart (2014a) suggests that climate change threats to U.S.
national security are modest and manageable.
Climate adaptation engineering involves estimating the risks, costs and benefits of climate adaptation strategies and assessing at what point in time climate adaptation becomes economically viable.
Climate adaptation measures aim to reduce the vulnerability or increase the resiliency of built infrastructure to a changing climate, this may include, for example, enhancement of design standards, retrofitting or strengthening of existing structures, utilisation of new materials, and changes to inspection and maintenance regimes. There is a need for sound system and probabilistic modelling that integrates the engineering performance of infrastructure with the latest developments in stochastic modelling, structural reliability, and decision theory.
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The paper will describe how risk-based approaches are well suited to optimising climate adaptation strategies related to the design, construction, operation and maintenance of built infrastructure. An important aspect is assessing when climate adaptation becomes economically viable, if adaptation can be deferred, and decision preferences for future costs and benefits (many of them intergenerational).
Stochastic methods are used to model infrastructure vulnerability, effectiveness of adaptation strategies, exposure, and costs. The concepts will be illustrated with a case study that considers climate change and cost-effectiveness of designing new houses in Brisbane to be less vulnerable to severe storms. To be sure, there are other case studies of assessing the efficiency and cost- effectiveness of climate adaptation strategies for built infrastructure; for example, floods and sea-level rise, cyclones and severe storms, and corrosion reinforced concrete. This and other research will help 'future proof' built infrastructure to a changing climate.
There are a number of issues and questions that need addressing. These include: cost neglect (who pays? when? who benefits?), probability neglect (how confident are we in changes to future climate?
changes in impact and loss?), risk aversion (is action needed now?), and risk acceptance (what risk from weather/climate is acceptable? is risk reduction worth the cost?). These issues are similar to other controversial and emotive issues such as terrorism and homeland security (Mueller and Stewart 2011a,b). Climate change studies often assume there is certainty about the future, and so suffer from probability neglect, and cost neglect by ignoring the large costs involved to mitigate CO2 emissions.
The Australian CSIRO funded Climate Adaptation Engineering for Extreme Events (CAEx) Cluster led by The University of Newcastle is using engineering technology to ensure durable, environmentally friendly and less vulnerable infrastructure. This form of anticipatory adaptation is focused on housing, commercial and industrial buildings, railway lines, power poles, bridges and culverts. Funding exceeds $3.5 million over three years, and includes partner researchers from James Cook University, The University of New South Wales, Swinburne University of Technology, and Curtin University. For more details see http://www.csiro.au/caex-cluster.
CLIMATE CHANGE IMPACT
The 2014 Intergovernmental Panel for Climate Change (IPCC) Fifth Assessment Report (AR5) concluded that the “Warming of the climate system is unequivocal” (IPCC 2014). What is less certain is the impact that rising temperatures will have on rainfall, wind patterns, sea-level rise, and other phenomena. The latest IPCC AR5 report describes the following changes to climate by 2100 (IPCC 2014):
Temperatures to increase from 1990 levels anywhere from 1-6 oC.
Sea-level rise of 20-80 cm.
More intense tropical cyclones and other severe wind events.
Enhanced monsoon precipitation.
The IPCC (2014) then suggests with a high or very high confidence level that these changes to climate will increase drought affected areas, hundreds of millions of people will be affected by coastal flooding, increases in risks of fire, pests, and disease outbreak, will have significant consequences on food and forestry production, and food insecurity, and so on. These impacts will not be sudden, but gradual in their appearance. The observed increase in weather-related losses in the United States and elsewhere is more a function of increased exposure with more people moving to vulnerable coastal locations than climate-change increases in wind speed or flood levels (IPCC 2012). This suggests that there will be time to adapt to a changing climate.
The 2006 review by economist Nicholas Stern (Stern 2007) predicts that if no action is taken against climate change, the mean loss of GDP would be 2.9% and 13.8% each year (“now and forever”) by 2100 and 2200, respectively. This is equivalent to worldwide losses of up to $10 trillion each year by 2200. Not surprisingly, some consider it to be highly pessimistic in its assumptions (Mendelsohn 2006). However, the Australian Garnaut Review predicted that unmitigated climate change would reduce Australian GDP by approximately 8% by 2100 (Garnaut 2008).
Fatality rates and economic losses are much higher in developing countries. IPCC (2012) estimates that weather and climate related losses in high-income countries is 0.1% of GDP, which increases to 0.3-1.0% of GDP for low and middle income countries. This is to be expected as vulnerability of infrastructure is lower in developed countries due to more rigorous design and construction standards.
For example, a magnitude 7.0 earthquake in Haiti in 2010 killed more than 230,000 people, mainly because of poor building construction, whereas a larger earthquake in densely populated Kobe, Japan, in 1995 killed around 6,000, and a magnitude 6.9 earthquake in 1989 in the San Francisco Bay area killed some 63 people.
These losses, however, do not reflect wealth creation, human capital, and new improved technologies.
Goklany (2008) states that these “often reduce the extent of the human health and environmental
‘bads’ associated with climate change more than temperature increases exacerbate them”. Clearly then, if people are wealthier in the future, their vulnerability to natural hazards will reduce.
CO2 MITIGATION VS ADAPTATION
The cost to mitigate CO2 emissions is considerable. Stern (2007) estimates that to stabilise CO2 levels to 550 ppm (by reducing total emissions to three quarters of today’s levels by 2050) would cost of -1.0% to 3.5% of GDP, with a central estimate of approximately 1%. The mean estimate would result in an annual global mitigation cost of approximately $700 billion.
Mitigating CO2 emissions, and investing in research and development (R&D) of new technologies for emission reduction and carbon sequestration is another option to ameliorate the effects of climate change. Yohe et al. (2009) found that a global investment of $18 billion per year in “R&D and mitigation” can halve “business as usual” CO2 emissions by 2100. Such action would reduce the impact of climate change by at least 60%. If the burden of this investment was to be shared by OECD countries in proportion to their GDP, then the United States contribution would be around $5 billion per year, equivalent to a tax of only $1 per ton CO2. As a comparison, the price on carbon in the EU is
$6 per ton of emitted CO2, and in Australia the price was initially set at $23 per ton of emitted CO2. Many of the dire predictions of food and energy insecurity, and mass migration can be ameliorated by funding climate adaptation measures in the developing world. Adaptation measures to reduce vulnerability of infrastructure, coastal zones, agriculture, forestry, fisheries, and human health to climate change hazards would include: flood control dikes and levees, dams, cyclone shelters, storm and flood resistant housing, improved communication infrastructure, resettlement of populations to lower risk zones, and improved health care. The World Bank (2010) estimated that the cost to the developing world of adapting to an approximately 2o C warmer world by 2050 is approximately $75 billion per year. Clearly, investing in targeted adaptation measures has the potential to dramatically reduce the impact of climate change.
Mitigation costs are be very high, and the benefits of reduced CO2 levels will take many decades to accrue. Even stabilising CO2 levels to 550 ppm will not eliminate climate change impacts, but it will lessen its severity. Modest and sustained investments in R&D, CO2 mitigation and adaptation will lessen the worst impacts of climate change. Hence, a mix of mitigation and adaptation is desirable to cope with a changing climate.
RISK-BASED DECISION SUPPORT Risk for a system exposed to a climate hazard is
E L
Pr C
Pr HC
Pr DH
Pr L D
L (1)where Pr(C) is the annual probability that a specific climate scenario will occur, Pr(H|C) is the annual probability of a climate hazard (wind, heat, etc.) conditional on the climate, Pr(D|H) is the probability
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of infrastructure damage or other undesired effect conditional on the hazard (also known as vulnerability or fragility) for the baseline case of no extra protection (i.e. ‘business as usual’), Pr(L|D) is the conditional probability of a loss (economic loss, loss of life, etc.) given occurrence of the damage (resilience), and L is the loss or consequence if full damage occurs. The summation sign in Eqn. (1) refers to the number of possible climate scenarios, hazards, damage levels and losses.
The risk after climate adaptation is
Eadapt
L
1 R
E L
B (2)where ΔR is the reduction in risk caused by climate adaptation (or other protective) measures, E(L) is the ‘business as usual’ risk given by Eqn. (1), and ΔB is the co-benefit of adaptation such as reduced losses to other hazards, increased energy efficiency of new materials, etc. Costs of adaptation, timing of adaptation, discount rates, future growth in infrastructure and spatial and time-dependent increase in climate hazards need to be included in any risk analysis.
The ‘benefit’ of an adaptation measure is the reduction in damages associated with the adaptation strategy, and the ‘cost’ is the cost of the adaptation strategy. The net benefit or net present value (NPV) is equal to benefit minus the cost. The decision problem is to maximise NPV:
NPV
E(L)R BCadapt (3)where Cadapt is the cost of adaptation measures including opportunity costs that reduces risk by ΔR.
Confidence bounds of NPV can then be calculated if input parameters are random variables. The probability that an adaptation measure is cost-effective denoted herein as Pr(NPV>0) may also be inferred.
The vulnerability, loss and adaptation costs are subject to considerable uncertainty due to lack of available data and models. For this reason, calculations of risks, costs and benefits will be imprecise.
Hence, a ‘break-even’ analysis may be useful where minimum risk reduction or maximum cost of adaptation necessary for adaptation to be cost-effective is selected such that there is 50% probability that benefits equal cost - i.e. mean(NPV)=0. In other words, if the actual cost of adaptation exceeds the predicted break-even value, then adaptation is not cost-effective. Decision-makers can then judge whether an adaptation strategy meets these break-even values
STRENGTHENING NEW HOUSES IN BRISBANE AGAINST EXTREME WIND
The Australian Standard “Wind Loads for Houses” AS4055-2012 is used to determine the appropriate wind classification for design of residential (domestic) housing. In this case, residential housing is designed to resist wind speeds with annual probability of exceedance of 1 in 500. The standard AS4055-2012 classifies design loads on houses into categories N1-N6 for non-cyclonic regions. Each increase in non-cyclonic wind classification (e.g. N2 to N3) raises the design wind speed that is equivalent to at least a 50% increase in design wind pressure. These wind classifications are then used by building codes to determine appropriate deemed-to-comply sizing and detailing requirements.
To reduce housing damage in the future, a feasible adaptation strategy may be one that increases design wind loads for new houses leading to long-term reduction of vulnerability (and damages) of houses (Stewart et al. 2014, Stewart 2014b). It is important to note that AS4055-2012 (and many other building standards) is based on limited experimental and field data, and expert judgement by committee members, and has not been subject to risk or cost-benefit analysis (Walker and Musulin 2012). Hence, existing design requirements may be sub-optimal even for the current climate.
The adaptation strategy considered herein is to design new houses by enhanced design codes, in this case, increasing the current Australian Standard AS4055-2012 wind classification by one category.
For example, for Brisbane this means that new construction would be designed for wind classification N3 rather than the current requirement of N2 for non-foreshore locations. This means that new construction has increased strengths of structural components and connections, leading to significantly reduced wind vulnerability of houses. The overall reduction in risk caused by the adaptation strategy is ΔR=75-80% for Brisbane. These enhanced building requirements are estimated to increase costs of new construction (Cadapt) by 1-2% of the value of a house, or approximately $2,500 to $5,000.
However, some suggest that costs may be higher.
The case study herein applies break-even analyses to compare the risks, costs and benefits of climate adaptation strategies for new housing in Brisbane. CSIRO (2007) suggest the average annual change in mean wind speed is projected to increase by 6% in Brisbane by 2070, with 10th and 90th percentiles of -2% and +19%, respectively, for the A1FI (high) emission scenario, and 10th and 90th percentiles of -1% to +10% for the B1 (medium) emission scenario. The existing wind field is modelled as a Gumbel distribution. Normal distributions are used to represent uncertainty of changes in wind speeds by 2070, and a linear increase in wind speed with time is assumed. Costs and benefits are calculated using Monte-Carlo event-based simulation methods for the 52 year period 2018 to 2070 as 2070. The stochastic variability of wind speed means that NPV is variable. The distribution of NPV is highly non-Gaussian which suggests that Monte-Carlo methods are well suited to this type of analysis.
Discount rate is 4% and time of adaptation is 2018. Note that in this scenario-based approach Pr(C)=100%. Losses include direct damage to house and contents, and indirect losses to society. For more details on hazard, vulnerability and loss modelling see Stewart (2014b).
Figure 1 shows that break-even adaptation costs for risk reduction of 10-100%. If risk reduction is a modest 60% and worst (A1FI) emission scenario, the break-even analysis shows that adaptation is cost-effective only if the adaptation cost is less than 1.2% and 1.5% of house replacement cost for foreshore and non-foreshore locations, respectively. The break-even adaptation cost reduces by 0.2- 0.4% for no change and B1 emission scenario. However, it is possible that actual adaptation costs can be this low, particularly since estimate adaptation costs may be as low as 1.1-1.4% for Brisbane.
Figure 1. Break-Even Adaptation Costs for Foreshore and Non-Foreshore Locations in Brisbane.
The cost of adaptation for Brisbane is likely to be 1.4% for foreshore locations (N3 to N4), and 1.1%
for non-foreshore locations (N2 to N3). If we adopt a cost of adaptation of 1.4% and 1.1% for these locations, and no change of climate then the break-even risk reduction must exceed 100% (not possible) and 62% for foreshore and non-foreshore locations, respectively, to be 50% certain that NPV>0 for all climate projections. A changing climate (A1FI) reduces these break-even risk reductions to 70% and 42%, for foreshore and non-foreshore locations, respectively. Stewart (2014b) shows risk reductions of 75-80% can be achieved for Brisbane based on vulnerability modelling, then it is likely that designing new housing to enhance wind classifications is a cost-effective adaptation strategy for Brisbane for non-foreshore locations irrespective of climate scenario. However, adaptation is not likely to be cost-effective for foreshore locations unless the A1FI emission scenario is expected.
ACMSM23 2014 688 CONCLUSIONS
A ‘break-even’ economic assessment is developed to assess the conditions under which a climate adaptation strategy is cost-effective. Residential construction is vulnerable to wind hazards, and a changing climate and higher wind speeds means that residential constructions are likely to receive more damage in the future if its design standard is maintained at the current level. The vulnerability of residential construction may be reduced by an adaptation strategy that increases design wind speeds specified by Australian Standard. Increasing the design wind classifications in the Australian Standard
“Wind Loads for Houses” AS4055-2012 for all new housing can lead to risk reductions of 75-80%, at a cost of 1-2% of house replacement value. It is likely that designing new housing to enhance wind classifications is a cost-effective adaptation strategy for Brisbane for non-foreshore locations irrespective of climate scenario.
ACKNOWLEDGMENTS
The author appreciates the financial support of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Flagship Cluster Fund through the project Climate Adaptation Engineering for Extreme Events in collaboration with the Sustainable Cities and Coasts Theme, the CSIRO Climate Adaptation Flagship.
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CSIRO (2007) Climate Change in Australia: Technical Report 2007, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.
Garnaut, R. (2008) The Garnaut Climate Change Review: Final Report, 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, February 5, 2008.
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Mendelsohn, R.O. (2006) “A Critique of the Stern Report”, Regulation Vol. 29, No. 4, pp 42-46.
Mueller, J. & Stewart, M.G. (2011a) Terror, Security, and Money: Balancing the Risks, Benefits, and Costs of Homeland Security, Oxford University Press, Oxford and New York.
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Yohe, G.W., Tol, R.S.J., Richels, R.G. and Blanford, G.F. (2009) Climate Change, in Global Crises, Global Solutions, Bjorn Lomborg, ed., Cambridge University Press.
