Adaptation decision-scientists increasingly use real-option analysis to consider the value of learning about future climate variable development in adaptation decisions. Toward this end learning scenarios are needed, which are scenarios that provide information on future variable values seen not only from today (as static scenarios), but also seen from future moments in time. Decision-scientists generally develop learning scenarios themselves, mostly through time-independent (stationary) or highly simplified methods. The climate learning scenarios thus attained generally only poorly represent the uncertainties of state-of-the-art climate science and thus may lead to biased decisions. This paper first motivates the need for learning scenarios by providing a simple example to illustrate characteristics and benefits of learning scenarios. Next, we analyze how well learning scenarios represent climate uncertainties in the context of sea level rise and present a novel method called direct fit to generate climate learning scenarios that outperforms existing methods. This is illustrated by quantifying the difference of the sea level rise learning scenarios created with both methods to the original underlying scenario. The direct fit method is based on pointwise probability distributions, for example, boxplots, and hence can be applied to static scenarios as well as ensemble trajectories. Furthermore, the direct fit method offers a much simpler process for generating learning scenarios from static or “ordinary” climate scenarios.

Völz, V., & Hinkel, J. (2023). Sea level rise learning scenarios for adaptive decision-making based on IPCC AR6. Earth’s Future, 11, e2023EF003662. https://doi.org/10.1029/2023EF003662

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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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