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We and AI
(2021)
In recent years, nature-based solutions are receiving increasing attention in the field of disaster risk reduction and climate change adaptation as inclusive, no regret approaches. Ecosystem-based adaptation (EbA) can mitigate the impacts of climate change, build resilience and tackle environmental degradation thereby supporting the targets set by the 2030 Agenda, the Paris Agreement and the Sendai Framework. Despite these benefits, EbA is still rarely implemented in practice. To better understand the barriers to implementation, this research examines policy-makers' perceptions of EbA, using an extended version of Protection Motivation Theory as an analytical framework. Through semi-structured interviews with policy-makers at regional and provincial level in Central Vietnam, it was found that EbA is generally considered a promising response option, mainly due to its multiple ecosystem-service benefits. The demand for EbA measures was largely driven by the perceived consequences of natural hazards and climate change. Insufficient perceived response efficacy and time-lags in effectiveness for disaster risk reduction were identified as key impediments for implementation. Pilot projects and capacity building on EbA are important means to overcome these perceptual barriers. This paper contributes to bridging the knowledge-gap on political decision-making regarding EbA and can, thereby, promote its mainstreaming into policy plans.
Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.