Climate learning scenarios for adaptation decision analyses: Review and classification

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,

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Decision-support for land reclamation location and design choices in the Maldives

Land reclamation in the Maldives is widespread. Current land reclamation practices, however, lack a systematic approach to anticipate sea-level rise and do not account for local flood risk differences to inform location and design choices. To address these limitations, this paper applies two decision-support tools: a hazard threshold analysis, and a cost-benefit analysis. Both tools produce site-specific estimates of land elevations or flood defence heights but do so for different goals. The hazard threshold analysis identifies hazard-based solutions that meet an acceptable flood probability for an intended lifespan without follow-up actions by reliability optimisation. The cost-benefit analysis identifies risk-based solutions using dynamic programming. We apply both tools to two land reclamation sites, a newly reclaimed airport island and a land extension of an inhabited island, in the Maldives. We find that total hazard-based heights for long-term planning horizons are highly uncertain, with local height differences of up to 1.9 m across sea-level rise scenarios by 2100. Risk-based Island elevations, in contrast, differ much less across scenarios, offering a practical advantage for decision-making. However, land reclamation choices on location, land elevation and investment in flood protection are not only driven by hazard-related aspects, such as reef characteristics, swell exposure, and sea-level rise, but also by estimates of exposed assets, reclamation, and flood protection costs. Taken together, the two decision-support tools are helpful for improving adaptation decisions and are also applicable in other small island regions.

Van der Pol T., Gussmann G., Hinkel J., Amores A., Marcos M., Rohmer J., Lambert E., Bisaro A., 2023, Decision-support for land reclamation location and design choices in the Maldives, Climate Risk Management,

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