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Achieving a more sustainable, environmentally friendly future has long become an embraced goal of many societies worldwide where pro-environmental behavior is found at its core. Indeed, it is widely believed that changes in behavior are needed to promote a more sustainable future. However, engaging in and sustaining such behavior often comes at a price as it often costs more (e.g., organic products are more expensive than conventional products), requires more effort (like waste separation) or even new skills (like eco-driving). Important questions to be answered in this respect are: which factors determine whether people are willing to overcome such barriers to pro-environmental actions, and which factors promote pro-environmental (collective) actions?Various theories have been applied to explain actions to reduce environmental risks. The most popular theories in environmental psychology to examine factors influencing environmental actions are the Theory of Planned Behavior (Ajzen, 1991), the Norm

In this article, we propose an integrated direct and indirect flood risk model for small- and large-scale flood events, allowing for dynamic modeling of total economic losses from a flood event to a full economic recovery. A novel approach is taken that translates direct losses of both capital and labor into production losses using the Cobb-Douglas production function, aiming at improved consistency in loss accounting. The recovery of the economy is modeled using a hybrid input-output model and applied to the port region of Rotterdam, using six different flood events (1/10 up to 1/10,000). This procedure allows gaining a better insight regarding the consequences of both high- and low-probability floods. The results show that in terms of expected annual damage, direct losses remain more substantial relative to the indirect losses (approximately 50% larger), but for low-probability events the indirect losses outweigh the direct losses. Furthermore, we explored parameter uncertainty using a global sensitivity analysis, and varied critical assumptions in the modeling framework related to, among others, flood duration and labor recovery, using a scenario approach. Our findings have two important implications for disaster modelers and practitioners. First, high-probability events are qualitatively different from low-probability events in terms of the scale of damages and full recovery period. Second, there are substantial differences in parameter influence between high-probability and low-probability flood modeling. These findings suggest that a detailed approach is required when assessing the flood risk for a specific region.

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