Assessing sea level rise costs and adaptation benefits under uncertainty in Greece

Although sea-level rise (SLR) is not the only driver of coastal change, it is expected to radically alter the living conditions and prosperity of coastal communities in the decades to come. The economic assessment of sea level rise impacts and of coastal adaptation measures proves to be rather demanding due to the fact that these are complex phenomena, affected by both global conditions and local parameters. All these factors add to the uncertainty involved in climate change assessments, in general, and in SLR impacts in particular, creating significant ambiguity during the policy-making process. This paper wishes to contribute to existing literature by exploring modern management tools for assessing the economic impacts of SLR and the effectiveness of proactive coastal adaptation under uncertain conditions. To this end, first, the economic damages from long-term shoreline retreat and land inundation for scenarios of 0.5-m and 1-m SLR up to 2100 are estimated for the Greek coastal zone, both deterministically and probabilistically. The results indicate that probabilistic damages are lower than the deterministic ones by 6%, on average. However, the uncertainty results in a range of probable values, which are 45% lower and up to 40% higher than the deterministic estimates. Second, and most important, the application of real options valuation (ROV) in the economic assessment of adaptation measures is illustrated using four specific case sites. The findings show that ROV provides decision-makers with the flexibility to 'adjust' the so-called hard engineering structures to future SLR conditions by keeping all options (i.e. scale, deferral, acceleration or abandonment) open till further knowledge is gained. In this way, ROV creates an additional value that cannot be captured by 'conventional' valuation methods and, thus, it offers the potential to maximize long-term benefits for the society by making efficient use of fiscal resources for adaptation strategies.