ALBERT

Description: Public support for stringent climate policies is currently weak. We develop a model to study the dynamics of public support for climate policies. It comprises three interconnected modules: one calculates policy impacts; a second translates these into policy support mediated by social influence; and a third represents the regulator adapting policy stringency depending on public support. The model combines general-equilibrium and agent-based elements and is empirically grounded in a household survey, which allows quantifying policy support as a function of effectiveness, personal wellbeing and distributional effects. We apply our approach to compare two policy instruments, namely carbon taxation and performance standards, and identify intertemporal trajectories that meet the climate target and count on sufficient public support. Our results highlight the importance of social influence, opinion stability and income inequality for public support of climate policies. Our model predicts that carbon taxation consistently generates more public support than standards. Finally, we show that under moderate social influence and income inequality, an increasing carbon tax trajectory combined with progressive revenue redistribution receives the highest average public support over time.

Description: Pluvial floods are increasingly threatening urban environments worldwide due to human-induced climate change. High-resolution, state-of-the-art pluvial flood models are urgently needed to inform climate change adaptation and disaster risk reduction measures but are generally not empirically tested because of the rarity of local high-intensity precipitation events and the lack of monitoring capabilities. Volunteered Geographic Information (VGI) collected by professionals, non-professionals and citizens and made available on the internet can be used to monitor the dynamic extent of a pluvial flood during and after an extreme rain event but is sometimes considered to be unreliable. In this paper, we explore the general utility of VGI to evaluate the performance of pluvial flood models and gain new insights to improve these models. As background for our research, we use the capital city of Budapest, which recently suffered three heavy rainfall events in just five years (2015, 2017 and 2020). For each pluvial flood event, we collected photographic evidence from different online media sources and estimated the associated water depths at various locations in the city from the image context. These were compared with the results of a 2D pluvial flood model that has been shown to provide comparable results to other state-of-the-art inundation models and is easily transferred to other urban areas due to its reliance on open data sources. We introduce a general methodology for comparing VGI with model data by probing different spatial resolutions. Our findings highlight untapped potential and fundamental challenges in using VGI for model evaluation. It is proposed that VGI may become an essential tool and improve the confidence in model-based risk assessments for climate change adaptation and disaster risk reduction.

Description: The recent droughts and unprecedented floods in Central Europe reveal our vulnerability to extreme weather. Besides climate change as a driver of more frequent and intensifying extreme events, demographic change and socio-economic development exacerbate severe impacts. International frameworks for disaster risk reduction and climate change adaptation acknowledge the critical need for integrating risk governance, communication and operational mechanisms for coping with extreme climate events throughout the Disaster Risk Management cycle. DIRECTED aspires to foster disaster-resilient European societies by expanding our capabilities to communicate, utilise and exchange data, information and knowledge between different actors. The project strives to boost the integration, accessibility and interoperability of models, facilitating knowledge sharing and improving dialogue and cooperation on all levels of the Disaster Risk Management cycle. Four regional and municipal Real World Labs in the Capital Region of Denmark, the Danube Region, the Emilia Romagna Region, Italy and the Rhine-Erft District, Germany, are central to the bottom-up, value-driven co-development approach. The Real World Labs ensure the continuous and active involvement of key stakeholders in the development process. and the addressing of topical multi-hazard risk management and climate change adaptation problems. A DATA-FABRIC, an innovative, federated, cloud platform, will enable the secure and flexible discovery and sharing of structured and unstructured data. Through an interdisciplinary approach that brings together natural and social scientists, data experts, local stakeholders as well as first and second responders DIRECTED builds lasting real-world partnerships and leverages synergies for Disaster Risk Reduction and Climate Impact Adaptation efforts in Europe.