linquiti vonortas adaptation & ros

8/11/2019 Linquiti Vonortas Adaptation & ROs

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Real Option Analysis as a Tool for

Valuing Investments in Adaptation to Climate Change

Peter Linquiti and Nicholas VonortasThe Center for International Science and Technology Policy

George Washington University

1. Overview

The challenge of adapting to climate change is a daunting one, particularly for developing

countries. Climate-driven impacts will likely be substantial while investments in adaptation will

be limited by resource constraints and competing demands from other development priorities. In

this analysis, we explore whether a real option paradigm that explicitly recognizes uncertainty

and maintains future flexibility can provide an investment strategy that developing countries

would find beneficial. We use a Monte Carlo model to test the strategy for two coastal cities in

developing countries and find that, under certain circumstances, a real option strategy has the

potential to reduce the costs of adapting to climate change.

2. Introduction

2.1.Global Context

Even if global greenhouse gas (GHG) emissions are reduced in the near future, continued

warming of the climate now appears unavoidable, leading the Intergovernmental Panel on

Climate Change (IPCC) to conclude that at least some adaptation to climate change will be

necessary (IPCC, 2007a). Particularly in low-income countries, decisions about adaptation will

be made in the face of scarce resources and competing social and economic development

priorities (IPCC, 2007d). The difficulty of such decisions will be exacerbated by the high stakes

involved. The World Bank estimates that adaptation to 2!C of warming could cost developing

countries between $70 billion to $100 billion per year by 2050 and that such costs are likely to be

very unevenly distributed across regions (2010). At the same time, the consequences of climate

change will fall heavily on developing countries, not only because of limited resources, but also

because so many are located in low-latitudes, where climate impacts are expected to be more

severe (Mendelsohn, Dinar, & Williams, 2006).

Decisions about how best to adapt to climate change will be made in an environment of

substantial uncertainty. In its latest report, the IPCC noted at least 18 key uncertainties in our


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understanding of the causes and effects of climate change (IPCC, 2007c). Global emissions of

greenhouse gases in the coming decades, for example, will be driven by difficult-to-forecast

forces such as the extent of development in countries around the world, the state of the global

macroeconomy, choices among existing and new technologies by consumers and firms, and

policy measures implemented either multilaterally or within specific countries. Similarly, while

scientists are becoming more adept at characterizing the relationship between greenhouse gas

emissions and climate impacts, significant uncertainty remains about the nature, magnitude, and

regional distribution of a broad range of impacts including sea level rise, extreme weather events,

water scarcity, eco-system impacts, and changes in agricultural productivity, disease patterns,

and human migration. Finally, as the impacts of climate change manifest themselves,

individuals, firms, communities, civil society, and governments will make hard-to-predict

choices about how to adapt to changes in the planets climate. Made autonomously or as

purposive policy, such choices will reflect available technical options, resources, capabilities,

and competing priorities and likely be driven by a mix of economic, political, and institutional

considerations. In short, when it comes to adaptation to climate change, uncertainty is

significant and pervasive across multiple dimensions.

Current uncertainties notwithstanding, however, our collective scientific understanding of

global climate change and its potential impacts has improved markedly in recent years (National

Research Council, 2010). The IPCC began its series of extensive peer-reviewed assessments of

the relevant scientific literature in 1990. The latest assessment, released in 2007, contains

several instances in which IPCC observes that important uncertainties are being reduced over

time (IPCC, 2007b). For example, the Panel now describes as unequivocal the conclusion that

the climate is warming, whereas back in 1990, natural variability could not be ruled out, forcing

the Panel to conclude that the unequivocal detection of the enhanced greenhouse effect from

observations is not likely for a decade or more (IPCC, 1990). Similarly, the Panel now

characterizes the relationship between greenhouse gas emissions and observed global

temperature increases as very likely, while in 2001, the relationship was characterized simply

as likely. Other areas where scientific progress has been made, relative to the 2001

assessment, include:


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large amounts of new and more comprehensive data, more sophisticated analysesof data, improvements in understanding of [climate] processes and their simulationin models, and more extensive exploration of uncertainty ranges (IPCC, 2007b, p. 2).

Given the global resources being devoted to climate change research, it seems reasonable to

assume that progress in reducing scientific and other uncertainties will continue. The IPCC, for

example, recently launched its fifth global assessment of climate change intended to synthesize

the latest scientific literature. It will be completed by 2014 (IPCC, 2010). As a result, our

understanding of important scientific phenomena and of the impacts of localized climate change

is likely to improve. Even if, going forward, scientific research yielded no new insights (a

seemingly unlikely scenario), the simple passage of time would allow for additional direct

observation and measurement of the impacts of climate change.

Todays policymakers thus face a dilemma. Should they make irreversible investments in

adapting topotentialclimate change? Or should they take the risk of delaying action until

uncertainty has been reduced and actualclimate impacts are being felt? Perhaps there is a

middle course in situations where investments made today create the opportunity, though not the

obligation, to take a future action to adapt to climate change. To resolve this dilemma,

policymakers require a framework for systematically considering the costs and benefits of these

three possibilities.

In our view, the ideal framework for making adaptation decisions in developing countrieswould comprise several elements. It would explicitly incorporate uncertainty about the future

conditions that will ultimately determine the value of todays adaptation investments. It would

also recognize that such uncertainties are likely to diminish over time, thanks to both

improvements in forecasting techniques and the passage of time over which actual climate

impacts can be observed. The decision-making paradigm ought to recognize that many

investments in adapting to climate change are not now-or-never investments, but rather that the

flexibility often exists to expand, contract, or otherwise modify such investments. In addition,

the framework should recognize that adaptation investments are rarely all-or-nothing

investments, but instead are choices along continua of costs, risks, and benefits. Finally, the

framework ought to recognize that delayed investment in adaptation may create additional risk of

climate-driven damages now or in the future. We concur with Weyant when he notes that:


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Climate change is a long-run problem that will provide us with many opportunities tolearn and to revise our strategy over many decades. Thus, it is best conceived of as aproblem requiring sequential decision-making under uncertainty rather thanrequiring a large, one-shot, bet-the-planet decision (Weyant, 2008, p. 88).

2.2.

Characterizing Investments in Adaptation as Real Options

This paper explores whether a real options paradigm can provide a useful framework for

making policy decisions in such situations. In this section, we begin with a brief explication of

the key concepts underlying real option analysis, move to a quick review of the literature in this

field, and then conclude by framing the research questions that motivate this analysis.

2.2.1. Conceptual Explication

Real options are similar to financial options in that both give the option holder the right, but

not the obligation, to take a future action if doing so is advantageous based on future conditions.

For a financial option, the opportunity for action typically involves a time-limited right to buy or

sell a financial asset such as a share of stock or a commodity contract for a specified price. By

contrast, the term real options refers to cases where the underlying asset is a real asset such as

land, natural resources, a business opportunity, valuable information, or enhanced protection

against hazards. Real options exist when future outcomes are uncertain, the uncertainty is likely

to be reduced over time, the flexibility exists to take action in the future as the uncertainty is

resolved, and the action can reduce costs or increase benefits when it is taken (Triantis, 2003).Real option analysis has been applied in myriad contexts including, among others, corporate

research and development, oil and gas exploration projects, mergers and acquisitions, real estate

development projects, and public sector research and development (Shockley, 2007), (Triantis,

2001), (Vonortas & Desai, 2007).

If a real option exists but is not properly valued, the traditional decision criterion of

maximizing net present value may yield a suboptimal choice (Copeland & Antikarov, 2003),

(Shockley, 2007), (Triantis, 2003). In the typical cost-benefit analysis, uncertainty is capturedthrough the use of expected values which reflect the mean values of the stochastic distributions

that describe the relevant uncertainties. The effect of this approach is to attach a now-or-never

quality to the investment choices; this quality is appropriate if indeed no flexibility exists to

adjust todays decision in future time periods. If, on the other hand, decision-makers can adjust


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Figure 5

Simulation of Do-Nothing Baseline and Inflexible and Predict & Respond Strategies

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in investment costs of $7.5 billion. In the case of Dhaka, however, the Inflexible Strategy is

marginally more costly than the Do-Nothing baseline (i.e., $12.0 billion versus $11.1 billion),

but the mix of costs is radically different between the two scenarios. The Inflexible Strategy

entails a much large investment in coastal protection, and in turn yields much lower inundation

damages and population impacts, than does the Do-Nothing Baseline.

The two real option strategies display strikingly different results. In both cities, the Sense &

Respond Strategy has higher aggregate costs than any other approach, including doing nothing.

It also entails the highest level of investment in coastal defense, but fails to deliver a sufficient

reduction in damages to offset the costs of such investments. Largely because the Sense &

Respond Strategy is reactive in nature, investments in protection tend to be made after property

damage and population impacts have already been experienced. In Dar-es-Salaam, for example,

the Sense & Respond Strategy is about $4 billion more costly that the next most expensive

strategy. In Dhaka, it is over $8 billion more costly.

By contrast, the second real option strategy the Predict & Respond Strategy delivers the

best result in Dhaka. While it does not have the best performance on any one parameter

investment cost, economic damages, or population impacts its aggregate performance outranks

all other scenarios. The result is somewhat different in Dar-es-Salaam, where the Predict &

Respond Strategy is essentially tied with the Inflexible Strategy as the second-best performing

scenario. In Dar-es-Salaam, all of the strategies are dominated by the Do-Nothing baseline,

owing to the balance of vulnerable assets and populations relative to the costs of protection.

That notwithstanding, if policymakers in Dar-es-Salaam choose to take action, then the Predict &

Respond Strategy offers results comparable to the Inflexible Strategy, and outperforms the Sense

& Respond Strategy.

It is also instructive to compute the value of flexibility by comparing the Inflexible Strategy

to the better-performing of the two real option strategies (i.e., the Predict & Respond Strategy).

For Dar-es-Salaam, given the model inputs, there is no value to flexibility both strategies

generate the same cost of $7.9 billion. In the case of Dhaka, however, there is substantial value

to flexibility, with the real option strategy costing $3.4 billion less ($8.6 billion versus $12.0

billion) than the Inflexible Strategy a savings of about 28%.


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In sum, depending on local circumstance, the Do-Nothing baseline may provide the optimal

result. In all cases, the second real option strategy (Predict & Respond) outperforms the first real

option strategy (Sense & Respond). It is also comparable to, or outperforms, the Inflexible

Strategy, which makes significant up-front investments in coastal defense, but does not always

yield reductions in economic damages and population impacts sufficient to offset those

investments. In addition, depending on local conditions, a flexible real option strategy may yield

substantial savings relative to an inflexible approach.

4.2. Results of Sensitivity Analyses

In order to better understand the potential value of flexible strategies for adapting to sea level

rise, we developed two alternative scenarios for model inputs, each of which was applied to the

simulation of inundation events in the two cities. The first scenario focused on changes in

conditions on the land-side of the coastal defense. For this scenario, asset values are assumed to

be both more variable and faster growing, with a mean of 5% and a standard deviation of 4% (as

opposed to, respectively, 4% and 2% in the base case). Population growth was assumed to be

more variable, with the two bounds of the triangular probability distribution extended by an

amount equal to half the initial minimum rate (which was 1.91% for Dar-es-Salaam and 0.76%

for Dhaka). The value of a statistical life, and by extension the value of a displaced person, was

increased by 50%. Finally, the cost of constructing and maintaining coastal defenses was

decreased by one-third, yielding a unit cost of $3,745 and an O&M cost of 2.67% of invested

capital. Taken together, this set of changes to the simulation inputs is meant to represent a

situation where the value of protection is both increased and made more variable while the cost

of that protection is lower.

The second alternative scenario focuses on the prospect of more significant climate change

than simulated in the base case. The mean global sea level rise is assumed to be 5mm/year rather

than 3mm/year, and the standard deviation is assumed to be 4mm/year, rather than 2mm/year. In

addition, the extreme value distribution for sea surges was adjusted to increase the probability of

more extreme sea levels. In particular, the scale factor (!) was increased by a factor of five, from

0.08 to 0.40 for Dar-es-Salaam and from 0.225 to 1.125 for Dhaka. The effect of this change on,

for example, Dar-es-Salaam is to raise the height of the 100-year event from 3.1m to 4.6m while

leaving the height of the 1-year event unchanged and the mean surge height about 0.2m higher.


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The height of the 100-year event in Dhaka is increased from 4.7m to 8.9m. Again, the height of

the 1-year event is unchanged; the mean surge height is about 0.5m higher.

In conducting the sensitivity analyses, no changes beyond those described above were made

to the model inputs. Each of the sensitivity scenarios was applied separately; that is, we did notconsider a case where both the land-side changes and climate changes were considered

simultaneously. Table 6 presents the highlights of the sensitivity analyses.

When it comes to the sensitivity analysis of land-side economic factors, we observe that costs

drop in both cities under all strategies, although not for the Do-Nothing baseline. In the Do-

Nothing baseline, there is no new investment in coastal defenses, so unsurprisingly the assumed

reduction in unit construction cost has no impact; instead, the increase in asset values and

populations means that inundation has a higher cost which, in turn, causes the aggregate cost of

the Do-Nothing baseline to rise. In all other scenarios, however, total costs decrease despite the

increase in vulnerability, suggesting that the unit protection costs are an important driver of the

analytic results. In turn, successful research into methods for constructing lower cost, yet still

effective, coastal defenses would be valuable.

When it comes to the sensitivity analysis of greater climate change, results for the two cities

differ markedly. Total costs do increase in both cities under all scenarios, an unsurprising result

owing to the increase in the height and frequency of storm surges, but the effect in Dhaka is

more pronounced than in Dar-es-Salaam. Under the Inflexible Strategy, for example, costs

increase by about 80% (from $7.9 to $14.2 billion) in Dar-es-Salaam, but in Dhaka, costs go up

by about 338% (from $12.0 to $52.6 billion). While cost increases are negligible for two of the

four scenarios in Dar-es-Salaam under greater climate change, costs in Dhaka rise by a factor of

about four in all scenarios, suggesting that vulnerability to higher seas is much higher in Dhaka.

Finally, the sensitivity analyses demonstrate the connection between climate change and the

value of flexibility. In Dar-es-Salaam, for example, we observe no value to flexibility in the base

case or in the sensitivity case where the land-side parameters are varied. In the face of more

significant and variable sea level rise, however, the value of flexibility jumps markedly, to $4.6

billion. The effect is even more pronounced in Dhaka, where the value of flexibility jumps from

$3.4 billion in the base case to over $15 billion in the sensitivity analysis where the climate

changes more significantly.


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Table 6

Sensitivity Analyses:

Total Cost by Scenario5

Scenario

Dar-es-Salaam Dhaka

Base Case

Increased

Vulnerability

& Lower

Protection

Cost

Greater

Climate

Change

Base Case

Increased

Vulnerability

& Lower

Protection

Cost

Greater

Climate

Change

Do-Nothing

Baseline$1.4 $1.8 $1.5 $11.1 $14.9 $43.5

Inflexible Strategy $7.9 $4.2 $14.2 $12.0 $6.4 $52.6

Real Option: Sense

& Respond Strategy$11.8 $7.3 $11.8 $20.4 $16.3 $85.5

Real Option:

Predict & Respond

Strategy

$7.9 $4.3 $9.5 $8.6 $5.9 $37.5

Value of Flexibility6 $0.0 $0.0 $4.6 $3.4 $0.6 $15.1

5Results are mean values from a simulation of 10,000 iterations. All costs are 2009 US$ millions and represent the present value of costs incurred over a 100

years, computed at a 3 percent discount rate. Population impacts are monetized as described in the text.

6Value of flexibility is defined as the maximum of $0.0 and the difference between the cost of the Inflexible Strategy and the cost of the Real Option: Predict &Respond Strategy.


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4.3. Limitations of Our Analysis

Before moving to a discussion of our analytic results, it is important to quickly take stock of

some of the key limitations to our approach, virtually all of which originate in the simplifying

assumptions made in order to render the analysis tractable. Some of these assumptions relate tothe characteristics of the coastal defense where we assume that construction can be completed

within a single year, that land is readily available for the widening of the base of the defense

when its height is increased, and that the only pathway to inundation is from the over-topping of

the defense rather than its outright failure. When it comes to development patterns, we did not

simulate a behavioral link between policy decisions about coastal defense and the location

decisions of firms and residents. Presumably, a stronger coastal defense system would increase

the propensity to locate in lower elevations while a policy decision to minimize or defer

protection might induce some firms or residents to locate outside vulnerable areas. Finally, we

modeled climate change in a rather simple fashion by, for example, assuming a constant century-

long trend in the annual change in global sea levels (albeit with variability around that central

tendency), by assuming that local sea level change (i.e., either subsidence or uplift) is constant,

by assuming that the parameters underlying the extreme value distribution for characterizing

storm surges are constant, and by assuming no abrupt change in global sea levels as might be

caused, for example, by a sudden collapse of the West Antarctic Ice Shelf.

5.

Discussion

5.1. Implications for Local Planning Decisions

Our analysis has several interesting implications for local planning initiatives to defend a

coastal city from rising sea levels. First, real option strategies that provide flexibility have the

potential to materially reduce aggregate costs. This cost advantage originates from two factors.

The first is that, in contrast to an inflexible strategy where the protection height is selected once

at the outset of the policy process, the flexible strategy holds out the chance that in some cases,

sea level rise may not be as significant as initially thought, or that the size of the vulnerable

assets and populations may be lower. In such cases, then, less robust defenses are needed and

investment costs can be lower than with an inflexible strategy. The second source of a flexible

strategys cost advantage comes from the opportunity to defer investment. If planners are able to


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postpone construction costs by even say, twenty years, then the present value cost drops by about

45 percent (at a discount rate of 3 percent).

In Dhaka, the value of flexibility is convincingly demonstrated in both the base case and the

sensitivity case for greater climate change where the Predict & Respond real option cuts costsrelative to the Inflexible Strategy by, respectively, $3.4 and $15.1 billion. Given Bangladeshs

2009 GDP of $208.6 billion (PPP), these savings are material (World Bank Databank, 2011).

Flexibility has less immediate value in the case of Dar-es-Salaam where the Predict & Respond

Strategy yields savings over the Inflexible Strategy only in the case of greater climate change

than simulated in the base case. In that instance, however, the value of flexibility is $4.6 billion

a not insignificant share of Tanzanias PPP-adjusted GDP of $57.9 billion (World Bank

Databank, 2011).

These results are consistent with traditional methods of valuing financial options in which

two of the important drivers of option value are the price of the underlying asset and the

volatility of that price. Increases in either parameter lead to higher option valuations (Hull,

2009). When it comes to sea level rise, the underlying real asset is the protection afforded by the

coastal defenses. As sea levels rise more quickly and large surges become more frequent as

seen in the sensitivity case where the mean annual rise is increased from 3mm to 5mm and the

Gumbel extreme value distribution is scaled up the value of protection increases, making the

real option more valuable. In addition, just like a stock option that becomes more valuable when

the price of the underlying stock becomes more volatile, the value of the real option for

protecting against sea level rise becomes more valuable when the standard deviation of the trend

in global sea level rise is doubled from 2mm/year to 4mm/year. The value of flexibility (i.e., of

the Predict & Respond Strategy relative to the Inflexible Strategy) is highest for both cities in the

sensitivity case with greater climate change.

Another implication of our analysis is that flexibility sometimes comes with a high price. As

clearly demonstrated by the Sense & Respond strategy, if a city postpones investments in coastal

defense until rising seas clearly threaten its existing defenses, it may end up waiting too long to

protect itself and may incur devastating inundations as a result. This risk therefore must be

balanced against the potential cost savings of delaying investments in protective measures.


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This finding underscores an important point: not all real option strategies are inherently

superior to inflexible strategies. The real option strategy must be structured so that the resolution

of uncertainty occurs with sufficient time to allow the option holder to make a well-reasoned

decision about whether to exercise the option. For example, a financial investor holding a call

option on a stock learns about the value of the underlying asset by observing the stock price on

the date of option expiration. He or she can then determine whether exercising the option would

be profitable. In contrast, if a local government holding a real option to increase the height of its

coastal defense learns about the value of the protection only in the aftermath of a catastrophic

flood, then the value of the flexibility must be decremented by the value of the losses incurred.

Finally, we observe that the qualityof the information that is being obtained over time the

source of a real options potential to create value has an important bearing on the options

value. The superior performance of the Predict & Respond strategy for example is due, in large

measure, to the increasingly accurate prediction of sea levels over the 100-year analysis period.

In year 61, for example, local planners have 60 prior years of observation to better understand

the trajectory of global sea level rise. This information, however, never enters the decision

calculus under the Inflexible strategy.

Predictions of sea level rise do not, of course, come only from local planners. Indeed, the

scientific community has a vitally important role to play here. While projections of total sea

level rise over the next century are important to decisions about global climate policy, they are

only relevant to local planning decisions if an inflexible, single-investment, strategy is being

considered with the goal of constructing a coastal defense sufficient for a centurys worth of sea

level rise. If, on the other hand, local planners aspire to implement a cost-minimizing real option

strategy, then their information needs are markedly different from those of global policymakers.

The time period for which they need projections of sea level rise would be much shorter

perhaps only 20 to 40 years. Projections over longer time frames would not be needed, since

optional increases in sea level defenses can be used by planners in later years to addresssubsequent sea level rise should it actually occur.

Another important piece of information that would enhance the local planning effort is the

rate of local sea level rise. Subsidence, caused either by human activity such as ground-water

withdrawal or by natural process, or increases in land elevation, caused by tectonic uplift or


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sediment deposition, can have a significant on the local consequences of global sea level rise.

The former would exacerbate global sea level rise while the latter would mitigate it. In

conducting the background research for this study, we discovered that reliable data on local sea

level rise in developing countries appear to be sparse.

Especially important for local planning efforts is not just the mean, or expected value, of

potential sea level rise in the next few decades, but also estimates of the nature and width of the

dispersion around the mean. Perhaps the largest challenge in using a real option approach to

constructing coastal defenses is the possibility that such defenses will be over-topped (because

they have been built to a lower height than under an inflexible strategy), leading to inundation.

With information on variability, planners can assess the risk of over-topping prior exercising the

option to raise the coastal defense and balance such risks against potential savings in protection

costs. If information is only available on the central tendency, and not the dispersion, of

expected sea level rise, then this balancing cannot be done.

5.2. Implications for Policy Makers

Beyond the relative economic merits of inflexible and real option strategies for adapting to

climate change, there also exist at least two pragmatic considerations that are relevant to the

choice between the two strategies. The first relates to the in-country institutional capability to

manage a real option strategy and the second involves the practical realities of international

development assistance for adaptation to climate change.

5.2.1. Institutional Capability to Manage a Real Option

The process of a managing a real option strategy is likely to be much more complex than

implementing an inflexible strategy. While ongoing maintenance of the coastal defense would

be needed in either case to prevent deterioration of its protective capability, the inflexible

strategy has a once and forget it character that is lacking for the real option strategy. With the

real option strategy, changes in local sea conditions and in the scientific predictions of future sealevel rise must be monitored on an ongoing basis. Changes in the value of economic assets and

vulnerable populations on the land-side of the defenses must also be regularly assessed in order

to re-calibrate the value of protection. In turn, sequential decisions must be made about whether

changing circumstances warrant a fortification of the coastal defense. If a decision to improve

the defense is taken, another series of activities must be launched: project design, bid


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6. Conclusion

6.1. Next Steps

The foregoing analysis suggests several possible avenues for future research. As mentioned

previously, addressing the subject of risk aversion, and whether and how it should be handled in

analyzing adaption to climate change is one area for further investigation. In addition,

addressing the limitations of the simulation model that were described in Section 4.3 above

would make for a more robust set of results, as would applying the model to other locations. We

also anticipate using the analytic framework to better understand the key scientific, economic,

and policy information that is needed to improve the quality of sequential decision making about

adaptation to rising sea levels. Doing so would, in turn, allow us to characterize the economic

value of research to improve the quality of that information. Finally, an understanding of the

degree to which findings drawn from the field of sea level rise can be generalized to adaptation

to other types of climate impacts would be valuable.

6.2. Summary

Developing countries face large potential costs for adapting to climate change and must make

investment decisions in the context of limited resources and competing development priorities.

Accordingly, investment strategies that minimize such costs are particularly valuable. This

analysis demonstrates that, at least with respect to sea level rise, framing and valuing adaptiondecisions as real options has the potential to materially reduce costs to developing countries.

This finding is not a universal one because, in some cases, the local relationship among sea

levels, coastal features, protection measures, and vulnerable assets and population means that

there is little value to be gained with a flexible approach. That said, in some locations and under

conditions of high uncertainty, the flexibility to postpone even some adaptation investments until

more information is obtained and uncertainty is reduced, can create substantial cost savings. The

analysis also points to certain types of information as particularly valuable for decision makers,

such as predictions of sea level rise over two or three decades rather than a century or more, an

estimate not just of the central tendency of future sea level rise but also about the potential

variability of that estimate, and the rates of local sea level rise for specific locations. Finally, we

observe that despite the potential economic value of real option strategies, several policy and

institutional issues must be addressed before such potential value can be realized.


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!"#$% '()*+

Arrow, K., & Lind, R. (1970). Uncertainty and the Evaluation of Public Investment Decisions. TheAmerican Economic Review, 60(3), 364-378.

Boardman, A., Greenberg, D., Vining, A., & Weimer, D. (2006). Cost Benefit Analysis: Concepts andPractices, 3e.Upper Saddle River, NJ: Pearson Prentice Hall.

Bureau of Meteorology. (2011).ARI and AEP Explained. Retrieved March 27, 2011, from Government ofAustralia: www.bom.gov.au/hydro/has/ari_aep.shtml

Copeland, T., & Antikarov, V. (2003).Real Options: A Practitioner's Guide.New York: Cengage.

Dasgupta, S., Laplante, B., Murray, S., & Wheeler, D. (2009). Sea-Level Rise and Storm Surges: AComparative Analysis of Impacts in Developing Countries.Environment and Energy Team, DevelopmentResearch Group. Washington, DC: The World Bank.

Dobbes, L. (2010).Notes on Applying 'Real Options' to Climate Change Adaptation Measures, withExamples from Vietnam.Crawford School of Ecconomics and Government, Centre for ClimateEconomics & Policy. Canberra: Australian National University.

H.M. Treasury. (2009).Accounting for the Effects of Climate Change: Supplementary Green BookGuidance.

Hertzler, G. (2007). Adapting to Climate Change and Managing Climate Risks by Using Real Options.Australian Journal of Agricultural Research, 58, 985-992.

Hillen, M., Jonkman, S., Kanning, W., Kok, M., Geldenhuys, M., & Stive, M. (2010). Coastal DefenceCost Estimates.Delft University of Technology.

Hull, J. (2009). Options, Futures, and Other Derivativesq(7th ed.). Upper Saddle River, NJ: PerasonEducation.

IPCC. (1990). Policymakers Summary prepared by Working Group 1. In J. Houghton, G. Jenkins, & J.Ephraums, Climate Change: The IPCC Scientific Assessment.Cambridge, UK: Cambridge UniversityPress.

IPCC. (2007a). Summary for Policy Makers. In M. Parry, O. Canaiani, J. Palutkof, P. v. Linden, & C.Hanson, Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group IIto the Fourth Assessment Report.Cambridge, UK: Cambridge University Press.

IPCC. (2007b). Summary for Policy Makers. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis,K. Avery, et al., Climate Change 2007: The Physical Science Basis. Contribution of Working Group I tothe Fourth Assessment Report.Cambridge, UK: Cambridge University Press.

IPCC. (2007c). Synthesis Report. In R. Pachauri, & A. Reisinger, Climate Change 2007: Contribution ofWorking Groups I, II, and III to the Fourth Assessment Report.Geneva, Switzerland: IntergovernmentalPanel on Climate Change.

IPCC. (2007d). Technical Summary. In M. Parry, O. Canaiani, J. Palutkof, P. v. Linden, & C. Hanson,

Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to theFourth Assessment Report.Cambridge, UK: Cambridge University Press.

IPCC. (2010).Fifth Assessment Report. Retrieved February 1, 2011, from IPCC - Activities:http://www.ipcc.ch/activities/activities.htm

Kebede, A., & Nicholls, R. (2011).Population and Assets Exposed to Coastal Flooding in Dar-es-Salaam (Tanzania).University of Southampton. Global Climate Adaptation Partnership.


38/39

!"# %&' (&%% )*"+, - ./* 012314 567# 8"91 :?

Kotz, S., & Nadarajah, S. (2000).Extreme Value Distributions: Theory and Applications.London:Imperial College Press.

Lowe, J., & Gregory, J. (2010). A Sea of Uncertainty.Nature Reports Climate Change, 4, 42-43.

Mendelsohn, R., Dinar, A., & Williams, L. (2006). The Distributional Impact of Climate Change on Richand Poor Countries.Environment and Development Economics, 11, 159-178.

Miller, T. R. (2000). Variations between Countries in Values of Statistical Life.Journal of TransportEconomics and Policy, 34(2), 169-188.

Milliman, J., & Haq, B. (1996). Sea-Level Rise and Coastal Subsidence: Casues, Consequences, andStrategies.Boston: Klewer Academic Publishers.

National Research Council. (2010).Advancing the Science of Climate Change.Washington, DC: TheNational Academies Press.

Ng, W.-S., & Mendelsohn, R. (2005). The Impact of Sea Level Rise on Singapore.Environment andDevelopment Economics, 10, 201-215.

Nicholls, R., Hanson, S., Herweijer, C., Patmore, N., Hallegatte, S., Corfee-Morlot, J., et al. (2008).

Ranking Port Cities with High Exposure and Vulnerability to Climate Extremes: Exposure Estimates.OECD Environment Working Papers. OECD Publishing.

Population Division of the Dept of Economic and Social Affairs of the United Nations Secretariat. (2008).World Population Prospects. Retrieved April 28, 2011, from The 2008 Revision Population Database:esa.un.org/unpp/p2k0data.asp

Rahmstorf, S. (2010). A New View on Sea Level Rise.Nature Reports Climate Change, 4, 44-45.

Scandizzo, P. (2010).Adapting to Climate Changes: How to Evaluate Strategies and Projects.Saarbrucken, Germany: VDM Verlag.

Shockley, R. (2007).An Applied Course in Real Options Valuation.Mason, OH: Thomson HigherEducation.

Subsidiary Body for Scientific and Technological Advice. (2010).Report on the Technical Workshop onCosts and Benefits of Adaptation Options.Cancun, Mexico: United Nations Framework Convention onClimate Change.

Triantis, A. (2003). Real Options. In D. Logue, & J. Seward,Handbook of Modern Finance(pp. D1-D32). New York: Research Institute of America.

Triantis, A. (2001). Real Options: State of the Practice.Journal of Applied Corporate Finance, 14(2), 8-24.

U.S. Council of Economic Advisors. (2011).Economic Report of the President.Table B-7.

Valfedis, A., Boot, G., Cox, J., Maatens, R., McFadden, L., Nicholls, R., et al. (n.d.).Diva DatabaseDocumentation. Retrieved May 2, 2011, from www.civil.soton.ac.uk/diva

Vonortas, N. S., & Desai, C. A. (2007, 12). A "Real options" framework to assess public researchinvestments. 34(10), pp. 699-708.

Weyant, J. (2008). A Critique of the Stern Review's Mitigation Cost Analyses and IntegrateedAssessment.Review of Environmental Economics and Policy, 2(1), 77-93.

Woodward, M., Gouldby, B., Kapelan, Z., Khu, S., & Townend, I. (2010). Incorporating Real Optionsinto Flood Risk Management Decision Makingn. 14th Annual International Real Options Conference.Rome.


39/39

World Bank Databank. (2011). Retrieved May 3, 2011, from databank.worldbank.org

World Bank. (2010).Economics of Adaptation to Climate Change: Synthesis Report.Washington, DC.

World Bank. (2009).Mainstreaming Adaptation to Climate Change in Agriculture and NaturalResources Management Projects, Guidance Note 7, Annex 12.Environment Department.

Yohe, G., Neumann, J., & Marshall, P. (1999). The Economic Damage Induced by Sea Level Rise in theUnited States. In R. Mendelsohn, & J. Neumann, The Impact of Climate Change on the United StatesEconomy(pp. 178-208). Cambridge, UK: Cambridge University Press.

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