SystR will propose a groundbreaking approach to feed the science-policy nexus with innovative solutions to address systemic risks (interconnection between multiple sectors and stakeholders and resulting cascading climate impacts). This approach will accelerate transformative adaptation and avoid maladaptation by breaking silos, harmonising multiple scales of governance and tailoring solutions to local needs and strengths with Smart Specialisation Strategies. Overall, while pursuing close collaboration with the Climate Adaptation Mission, SystR will demonstrate systemic resilience in three contexts most representative of Europe: coastal in Guadeloupe-France, rural in Bystrica-Slovakia and urban in Rome-Italy, which will be twinned with replication sites of similar challenges, Galicia, Strasbourg and Egaleo, respectively.

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The HuT will employ innovative disaster risk reduction solutions, accounting for the potential variations induced by climate change. This will involve integrating and leveraging best practices and successful multi-disciplinary experiences that have been recently developed within various territorial contexts by leading European research groups, institutions, and stakeholders, to deal with extreme climate events. The project’s main ambition beyond the state of the art is to promote the “best set” of trans-disciplinary risk management tools and approaches that could be adopted and used extensively across Europe, in as many situations as possible. 

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The THRAC3E project is structured around nine interlinked Tasks designed to deliver a comprehensive, regionally disaggregated carbon cycle assessment framework. The scientific approach integrates novel Earth observation (EO) dataset development, process-based and data-driven modelling, benchmarking, and low-latency carbon monitoring across three key regions: Europe, Africa, and Southeast Asia.

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TiCCA4Danu proposes a novel and comprehensive transformative innovation framework to accelerate just climate change adaptation (CCA) at the level of cities and their surrounding administrative regions. In order to address the structural barriers for CCA implementation at the city-region level, TiCCA4Danu proposes a systems-level approach, which requires effective governance changes, introduction of directionality, and a different use of policy instruments favouring discovery and experimentation processes. TiCCA4Danu directly relates to the EU Cities- and Adaptation Mission frameworks and builds on the emerging literature discussing the opportunities and limitations to governing socio-technical change for addressing grand challenges, such as climate change, through novel transformative approaches. At the core of TiCCA4Danu’s methodological approach lies the concept of place-based “Transformative Innovation Policy” (TIP), which postulates a systems-level change perspective in innovation policy. TiCCA4Danu aims at applying the theoretical TIP concept to the city-region level by linking TIP to “Local Green Deals” (LGD), an established instrument for sustainable transformation at the city level, and by complementing the LGDs with a novel approach for transformative CCA at the level of regions. With TIP facilitating wider and inclusive societal transformation, TiCCA4Danu features stronger involvement of vulnerable groups as well as stronger private sector involvement. TiCCA4Danu focussing its activities on four “Anchor Cities” and their surrounding regions in the Danube Macro Region, all constituting different bio-geographical regions, i.e. Coastal, Mountain and Continental. By establishing a direct link to the EU Strategy for the Danube Region (EUSDR) and by working with the relevant Priority Area Working Groups, TiCCA4Danu will

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TRANSEATION is a project funded by HORIZON Europe aimed at demonstrating the effectiveness of marine and coastal hybrid blue-grey infrastructures and validate a new level of Ecosystem-based Management (EBM) combining Nature-based Solutions (NbS), and social implication digitalization to protect and restore marine ecosystem health and services.

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UNDERPIN is a project funded by HORIZON Europe Research and Innovation actions in support of the implementation of the Adaptation to Climate Change Mission (HORIZON-MISS-2022-CLIMA-01). UNDERPIN addresses critical gaps in monitoring, evaluation and learning (MEL) for climate adaptation across Europe. UNDERPIN is going to review existing climate adaptation and disaster risk reduction (DRR) indicators and integrate novel data sources, including Earth observation and citizen science. As well as, involving stakeholders in three pilot regions to cocreate and refine the framework, ensuring practical relevance and adaptability. A user-friendly dashboard with AI and machine learning tools will provide real-time analysis at the EU and regional level, helping decision-makers track progress, identify gaps, and make data-driven decisions. A citizen science and just climate resilience lens is used throughout the different activities of UNDERPIN. By aligning with EU and global reporting standards, such as the EU Adaptation Strategy and the UNFCCC, UNDERPIN is going to harmonize climate adaptation monitoring across governance levels, fostering a coordinated approach to resilience-building in Europe.

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Geopolitical and socio-economic uncertainties are putting the European and Global energy transitions at stake. These deep uncertainties affect the analytical assessments underpinning energy and climate policies. For example, the models used to inform energy planning rely on uncertain forecasts and assumptions for future energy demands, macroeconomic indicators, social acceptance, fuel prices, technology costs, and climate scenarios. Due to fundamental methodological, computational, and data challenges, this uncertainty is at best rarely considered in energy planning, which increases the risk of failing to meet our urgent climate targets. This makes accounting for uncertainty one of the major unsolved problems in energy planning. UNITES addresses these limitations to enable a new paradigm for long-term energy planning. In contrast to current approaches, which try to accurately predict the future, UNITES’ ambition is a systematic integration of uncertainty in energy-climate models.

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UPTAKE aims to facilitate the sustainable upscaling of carbon dioxide removal (CDR) methods by developing a set of robust strategies through technical, theoretical, and practical analysis accompanied by interactive dialogue within a CDR stakeholder forum. As a result, UPTAKE will develop a harmonised, comprehensive, inclusive, integrated, and transparent CDR knowledge inventory to evaluate a wide range of CDR technologies and methods, quantifying their national, European, and global costs, effectiveness, and removal potential as well as risks, constraints, and side-effects at different scales, and their prospects of technological progress. The UPTAKE approach will allow the assessment of geographical, sectoral, socioeconomic, demographic, and temporal trade-offs, co-benefits, and opportunities emerging from portfolios of different CDR methods. The enhanced socio-technical understanding of CDR methods will feed into an ensemble of state-of-the-art integrated assessment models (IAMs), which will help improve the integration of CDR methods given the EU policy objectives set for 2030, 2050, and beyond climate neutrality. UPTAKE will assess CDR governance and policy frameworks considering social acceptance, accountability, monitoring, and regulations for sustainable CDR rollout at scale. As a result, UPTAKE will generate an open and interactive CDR roadmap explorer to investigate strategies that are resilient to risks of failure and disruption, and minimise adverse impacts on society, economy, and the environment, aiming for a just, inclusive, and sustainable transition.

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The project will build the WeatherGenerator – the world’s best generative Foundation Model of the Earth system – that will serve as a new Digital Twin for Destination Earth. The WeatherGenerator will be based on representation learning and create a general and versatile tool that models the dynamics of the Earth system based on a large variety of Earth system data. The WeatherGenerator will be task-independent and will improve results for a wide range of machine learning applications when compared to task specific machine learning tools. It will also be more resilient for climate applications when the underlying data distributions are changing, and it will lead to a significant reduction in computational costs and faster turnaround times. To achieve this, the project will: (1) Collect and use the most important datasets of Earth system science including data from Digital Twins of Destination Earth, selected observations, analysis and reanalysis datasets, and output of conventional Earth system models. (2) Build the WeatherGenerator as a novel representation learning- based machine learning tool that exploits the full potential of Europe’s largest supercomputers. (3) Engage with the wider community via services and apply the WeatherGenerator for 22 selected applications that can be integrated into the Destination Earth framework. The applications include global and local predictions, local downscaling, data assimilation, model post-processing, and impact applications in the domains of renewable energy, water, health and food. The project consortium that will build the WeatherGenerator consists of experts in machine learning, supercomputing and Earth system sciences, and includes