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Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.
Currently, new business models created in the sharing economy differ considerably and they differ in the formation of trust as well. If and how trust can be created is shown by a comparison of two examples which diverge in their founding philosophy. The chosen example of community-based economy, Community Supported Agriculture (CSA), no longer trusts the capitalist system and therefore distances itself and creates its own environment including a new business model. It is implemented within rather small groups where trust is created by personal relations and face-to-face communication. On the contrary, the example of a platform economy, the accommodation-provider company Airbnb, shows trust in the system and pushes technological innovations through the use of platform applications. It promotes trust and confidence in the progress of technology. For the conceptual analysis, the distinction between personal trust and system trust defined by Niklas Luhmann is adopted. The analysis describes two different modes of trust formation and how they push distrust or improve trust. Grounded on these analyses, assumptions on the process of trust formation within varying models of the sharing economy are formulated as well as a hypothesis about possible developments is introduced for further research.