Learning Scenarios: A New Method for the Economic Appraisal of Adaptation Decisions

There is wide consensus that many adaptation decisions are multi-stage decisions shaped by future learning through observations, as the growing literature of adaptation pathways shows [1]. However, most methods for economic appraisal of adaptation decisions (e.g. cost-benefit analysis) do not consider future learning through observations. Methods that do consider future learning through observations (e.g. real-option analysis [2] and optimal control studies [3]) require specific projections of critical variables (e.g. sea level rise or precipitation), which we will call learning scenarios [4]. Learning scenarios are a generalisation of static projections (used by cost-benefit analysis) in that they do not only provide trajectories of values of future critical variables as seen from today, but also as seen from future moments in time (e.g., sea level trajectories from 2050 onwards assuming that, e.g., 40 cm of sea level rise has been experienced until 2050). Such learning scenarios are not publicly available, instead, scientists independently generate them based on static projections. The consideration of learning scenarios in adaptation decision-making results in adaptive adaptation strategies, which plan future adaptation decisions conditional on actual future observations. The crucial benefit of such methods, in contrast to traditional cost-benefit analysis, is that they quantify the value of future learning in combination with flexible adaptation options and thereby justify whether implementing flexible adaptation options today are worth the extra costs, or if waiting for further knowledge is beneficial [5]. This can lead to reduced adaptation costs compared to traditional methods that ignore future learning through observations. This contribution i) presents a novel method to generate learning scenarios; ii) applies this method to generate learning scenarios for sea level rise based on AR6 and iii) applies this learning scenario within a case study in Lübeck at the Baltic sea to evaluate adaptation and flood damage costs.

 

References

[1] Werners, S., Wise, R., Butler, J., Totin, E. and Vincent, K. (2021). Adaptation pathways: A review of approaches and a learning framework. Environmental Science & Policy, Volume 116, Pages 266-275. https://doi.org/10.1016/j.envsci.2020.11.003.

[2] Ginbo, T., Corato, L.D. & Hoffmann, R. (2020). Investing in climate change adaptation and mitigation: A methodological review of real-options studies. Ambio, 50(1):229–241.

[3] Herman, J.D., Quinn, J.D., Steinschneider, S., Giuliani, M., Fletcher, S. (2020). Climate adaptation as a control problem: Review and perspectives on dynamic water resources planning under uncertainty. Water Resources Research, 56. doi:10.1029/2019wr025502.

[4] Hinkel, J., Church, J.A., Gregory, J.M., Lambert, E., Cozannet, G.L., Lowe, J., McInnes, K.L., Nicholls, R.J., Pol, T.D., Wal, R. (2019). Meeting user needs for sea level rise information: A decision analysis perspective. Earth's Future 7, 320–337. doi:10.1029/2018ef001071.

[5] Kind, J. M., Baayen, J. H., & Botzen, W. J. W. (2018). Benefits and limitations of real options analysis for the practice of river flood risk management. Water Resources Research, 54 (4), 3018–3036. doi:10.1002/2017wr022402.