Track: A1. Investing in Climate Resilient Infrastructure

Background/Objectives

The growth of coastal populations world-wide is leading to increased exposure to coastal flooding, particularly for coastal infrastructure that facilitates economic growth in these regions, and these pressures are likely to require substantial future infrastructure investment. Traditional methods of investment appraisal (e.g., cost benefit analysis) have been criticized in the context of climate change adaptation. Economic assessment of adaptation options needs to explicitly incorporate the uncertainty of future climate conditions and should recognize that uncertainties may diminish over time because of improved understanding and learning. For several decades now, determining the accurate magnitude of future sea-level rise (SLR) has been a priority in global/national climate change assessments. During this pursuit there has been a transition from deterministic to stochastic estimates of sea-level change motivated by the desire to communicate the full uncertainties included in model estimations to potential end users. As an end user, it is therefore reasonable to ask what effect improved stochastic estimates of SLR might have on investment decisions on vulnerable coastal infrastructure. 

Approach/Activities

Using a relatively simple form of real options analysis (ROA) on a vulnerable piece of coastal rail infrastructure in the United Kingdom, and successive UK climate assessments, we estimate the values associated with utilising up-dated information on SLR.  ROA is an appraisal tool developed to incorporate concepts of flexibility and learning that relies on probabilistic data to characterize uncertainties. It is also a relatively resource-intensive decision support tool. Uniquely, this study conducts an ex post analysis using empirical data, and we can test whether, and to what extent, learning can result from the use of successive generations of real-life climate scenarios. Furthermore, we illustrate how non-probabilistic uncertainties can be handled through adapting the principles of ROA in coastal economic adaptation decisions. 

Results/Lessons Learned

The results from this study have both local and international implications. The capital cost of investment in infrastructure scale adaptation results in poor economic performance in terms of traditional economic evaluation and raises the question of how to value wider benefits of public infrastructures services. The value of learning identified from updated climate information can be compared to the capital cost of adaptation investment and may be used to illustrate the potential scale of the value of learning in coastal protection, and other adaptation contexts. The value identified in the study, ranging from 6% to 20% of the capital cost of investment, can also be utilized to decide whether – and to what extent – resources should be invested in scientific research.  There is an underlying assumption that new scientific understanding will result in reduced uncertainty in climate information, however, deeper understanding may result in deeper uncertainty.