The emergence of blockchain technologies has created the possibility of transforming business processes in the form of immutable agreements called smart contracts. Smart contracts suffer from a major limitation; they cannot authenticate the trustworthiness of real-world data sources, creating the need for intermediaries called oracles. Oracles are trusted entities that connect on-chain systems with off-chain data, allowing smart contracts to operate on real-world inputs in a trustworthy manner. A popular oracle protocol is a crowdsourced oracle, where unrelated individuals attest to facts through voting mechanisms in smart contracts. Crowdsourced oracles have unique challenges: the trustworthiness and correctness of outcomes cannot be explicitly verified. These problems are aggravated by inherent vulnerabilities to attacks, such as Sybil attacks. To address this weakness, this paper proposes a reputation-based mechanism, where oracles are given a reputation value depending on the implied correctness of their actions over time. This reputation score is used to eliminate malicious agents from the participant pool. Additionally, two reputation-based voting mechanisms are proposed. The effectiveness of the proposed mechanism is evaluated using an agent-based simulation of a crowdsourced oracle platform, where a pool of oracles performs evaluate Boolean queries.

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Nature-based solutions (NbS) are a cornerstone of climate change adaptation worldwide. Yet, evidence on their economic benefits is scarce, especially since the provided environmental amenities usually spatially correlate with climate-induced hazards, effects of which NbS aim to curb. This lack of empirical evidence creates obscurity regarding social acceptability of NS, hindering their uptake and upscaling. We apply hedonic price models to estimate homeowners' willingness-to-pay for NbS (like flood safety, environmental benefits), while controlling for spatio-temporal changes in capitalized flood risk discounts due to the 1993-1995 floods in the Limburg Province, the Netherlands. We reveal a pre-flood effect of 5.6% (discounting on average -€12,753 for flood-prone properties), which rises to 10.9% (−€24,691 on average) immediately after the floods. However, the effect is only transitory. The flood discount of home values price discount diminishes over time and eventually vanishes in 9–12 years, which coincides with the implementation of the largest and oldest NbS intervention in the Netherlands. Our analysis shows that NbS amenities provide a 15% (€33,687 on average) premium to nearby residential property prices. This evidence of the evolving flood risk discount and the stable NbS premium for individual homeowners could support the economic feasibility and wide acceptability of NbS for climate change adaptations.@en

Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic processes. Therefore, there is a need to explore the impacts of different dimensions of the recovery, including individual behaviour and their interactions with socio-economic institutions. In this study, we develop an agent-based model to simulate and explore the PDR process in urban areas of Tacloban, the Philippines devastated by Typhoon Haiyan in 2013. Formal and informal (slum) sector households are differentiated in the model to explore their resilience and different recovery patterns. Machine learning-derived land use maps are extracted from remote sensing images for pre- and post-disaster and are used to provide information on physical recovery. We use the empirical model to evaluate two realistic policy scenarios: the construction of relocation sites after a disaster and the investments in improving employment options. We find that the speed of the recovery of the slum dwellers is higher than formal sector households due to the quick reconstruction of slums and the availability of low-income jobs in the first months after the disaster. Finally, the results reveal that the households' commuting distance to their workplaces is one of the critical factors in households’ decision to relocate after a disaster.@en