Economic decision analysis is an important tool for developing cost-efficient adaptation pathways in sectors that involve costly adaptation options, such as flood risk management. Standard economic approaches, however, do not consider learning about future changes in climate variables even though a large literature on adaptive planning emphasises the key role of learning over time, because uncertainties about climate change are substantial. An emerging, diverse and fragmented set of economic adaptive decision making approaches, coming under labels such as real-option analysis or optimal control, have started to address this challenge by including the economic valuation of learning in the economic appraisal of adaptation options through making use of so-called climate learning scenarios. We synthesise this literature and classify the climate learning scenarios applied with respect to which climate variable is learned about, which learning sources are employed, how the learning is modelled, which climate data is used for calibrating learning scenarios, which goodness of fit information is provided and how deep uncertainty is handled. Our results show that publications consider learning through observations or do not explicitly state the source of learning. Most authors generate climate learning scenarios through stochastic processes or Bayesian approaches and use climate model output from the IPCC or the UK Met Office to calibrate the learning scenarios. The reviewed literature rarely provides information on the goodness of fit of learning scenarios to the underlying climate data. We conclude that most of the methods used to generate climate learning scenarios are not well-grounded in climate science and are inadequate to represent climate uncertainty. One avenue to improve climate learning scenarios would be to combine a Bayesian approach with emulators that mimic climate model runs based on observations from future moments in time.

Völz V., Hinkel J., Climate learning scenarios for adaptation decision analyses: Review and classification, 2023, Climate Risk Management, https://doi.org/10.1016/j.crm.2023.100512.

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Climate change-induced sea level rise (SLR) is projected to be substantial, triggering human adaptation responses, including increasing protection and out-migration from coastlines. Yet, in macroeconomic assessments of SLR the latter option has been given little attention. We fill this gap by providing a global analysis of the macroeconomic effects of adaptation to SLR, including coastal migration, focusing on the higher end of SLR projections until 2050. We find that when adapting simultaneously via protection and coastal migration, macroeconomic costs can be lower than with protection alone. For some developing regions coastal migration is even less costly (in GDP) than protection. Additionally, we find that future macroeconomic costs are dominated by accumulated macroeconomic effects over time, rather than by future direct damages, implying the need for immediate adaptation. Finally, we demonstrate the importance of including autonomous adaptation in the reference scenario of economic assessment studies to avoid overestimation of adaptation benefits.

Bachner, G., Lincke, D. & Hinkel, J., 2022. The macroeconomic effects of adapting to high-end sea-level rise via protection and migration. Nat Commun 13, 5705. https://doi.org/10.1038/s41467-022-33043-z

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Relocating communities out of increasingly risk-prone areas is effective for adapting to climate change. Relocations are particularly relevant for small island regions, where sea-level-rise-induced retreat from the coast will be inevitable for some communities. However, relocations are contested because communities are generally reluctant to move, and decision-makers face high political risks. As a consequence, relocations mostly occur after extreme events. In such situations, existing rules can be undermined by politics and power, driving relocation policy and resulting in varying relocation outcomes. However, these political and policy dimensions of post-disaster relocations have received little attention. Here, we study the politics and power dynamics of two post-tsunami relocations in the Maldives. Using process tracing, we find that vested interests, rather than adaptation considerations, explain varying relocation outcomes. Our findings highlight the complex power structures inherent in post-disaster relocations, which explain why similar events and drivers did not produce similar outcomes.

Gussmann, G. and Hinkel, J., 2021. Vested interests, rather than adaptation considerations, explain varying post-tsunami relocation outcomes in Laamu atoll, Maldives. One Earth. https://doi.org/10.1016/j.oneear.2021.09.004

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This study provides a literature-based comparative assessment of uncertainties and biases in global to world-regional scale assessments of current and future coastal flood risks, considering mean and extreme sea-level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea-level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio-economic development (factors 2.3–5.8), digital elevation data (factors 1.2–3.8), ice sheet models (factor 1.6–3.8) and greenhouse gas emissions (factors 1.6–2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth-damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea-levels, storm surges, tides and waves. Advancing the state-of-the-art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth-damage functions would be best reduced through open community-based efforts, in which many scholars work together in collecting and validating these data.

Hinkel, J., L. Feyen, M. Hemer, G. Le Cozannet, D. Lincke, M. Marcos, L. Mentaschi et al. Uncertainty and bias in global to regional scale assessments of current and future coastal flood risk. Earth’s Future: e2020EF001882. (2021) https://doi.org/10.1029/2020EF001882

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