Adaptation Decisions under Climate Change Uncertainty and Weather Extremes

Farms and landowners seem reluctant to invest in climate change adaptation despite socio-economic benefits. However, a dynamic economic model suggests that under uncertain future climate change, observed adaptation “reluctance” may be in fact optimal from the decision-makers’ perspective. This is because decision-makers consider the value of waiting to gather information about future climate and weather before committing to investment. To investi-gate how future climate change shapes adaptation behavior, this study presents an analytical framework that integrate scenario-based climate model projections into a dynamic economic model of the adaptation decision. Using adaptation decision of the Austrian farms as a case example, we use projections of temperature and precipitation under the Shared Socioeconomic Pathways (SSP) scenarios from Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The future projections are used to calibrate the climate change parameters within the economic model to analyze adaptation responses. We find that adaptation reluctance in-tensifies particularly in response to revenue downside shocks induced by weather extremes, and due to increased uncertainty in future climate change the likelihood of adaptation under severe climate change scenarios is not necessarily higher than under mild scenarios. We also find that adaptation subsidies can enhance the likelihood. These results suggest that with in-creasing frequency and intensity of extreme events in future, the expected loss without adap-tation will increase while the reluctance will also increase, and the adaptation investment tim-ing will appear even more delayed. Such delays may incentivize policymakers to provide public interventions such as subsidies to induce adaptation investment. However, they should carefully compare the value of adaptation reluctance and the social costs of the reluctance (e.g., negative externalities) to justify the interventions.