Children in low- and middle-income countries are among those most at risk to the health impacts of climate change, not least through undernutrition, which has serious long-lasting consequences for individuals and society and may undermine decades of global health gains. Attribution science can drive urgent societal action. However, it is currently limited in scope, focusing mostly on heat and adult populations in high-income settings, largely because of the lack of accessible tools, methods, data, and interdisciplinary expertise. To fill this research gap, this project will derive multiple climate attribution datasets, advance process-based crop models, and apply econometric, epidemiological and health impact assessment methods on underused data sources in order to quantify the already occurring impacts of climate change on child health. These will be integrated into an interactive digital open source tool (MILK). MILK will be co-designed in a series of workshops with the interdisciplinary project team and scientists and stakeholders from West Africa, Central/Eastern Africa, and South Asia. The attribution results will be set into the context of mitigation and adaptation options and complemented with an intergenerational justice perspective.The involvement of policy makers will be ensured throughout the project to advance the policy integration of the generated evidence.

Nutrition security, health and environmental systems are closely interlinked and need to be jointly managed. This requires long-term monitoring and observations systems, modelling and assessment tools, as well as integrated technological and institutional solutions. NUTRI-SCOPE will link global-scale, long-term scenarios on the food system under socio-economic and environmental change with a well-selected sample of empirical sub-national assessments of nutritional impacts in children under five. Bridging global and sub-national scales will be facilitated by specific national socio-economic assessments. Within the existing INDEPTH network of long-term health and demographic surveillance systems we will determine the causal relationships between environmental changes, food availability, nutritional status and health indicators. The derived impact functions will be used in national and global-scale models to generate future scenarios on nutrition and public health under socio-economic and environmental changes. With this modelling framework, integrated technological and institutional solutions for improving nutrition security and health will be assessed, contributing to improved policy decisions. The initial focus will be on six countries in Africa and Asia, including ground-truthing of modelling results. The long-term goal is to establish an integrated, multi-scale monitoring and assessment framework at the interface between the global food system, public health, nutrition, and environmental change.

The What Works Climate Solutions Summit is a transdisciplinary, inclusive, high-level conference to promote and catalyze synthetic evidence on climate solutions for upcoming climate change assessments – particularly the IPCC’s 7th Assessment Report ─ as well as other forms of scientific policy advice. It brings together leading experts on climate solutions, key institutions curating scientific policy advice on climate change such as science assessment bodies (IPCC, UNEP Emissions Gap etc.) and science academies, evidence synthesis communities (Campbell Collaboration, Cochrane, Collaboration for Environmental Evidence, Evidence Synthesis International, Evidence-based Research Network) as well as policymakers, research funders and other users of evidence to make progress towards major goals: 1. Initiate an ambitious work program on climate solutions and advance evidence synthesis methods for evidence-based policy 2. Building evidence synthesis capacity across the climate community: 3. Establishing a dialogue and forming new collaborations for evidence-based climate solution 4. Mainstream the topic of health in the wider discourse on climate solutions in science, policy and practice

Countries are not on track to meet the 2030 Agenda for Sustainable Development that comprises 17 Sustainable Development Goals (SDGs) and 169 targets to be achieved by 2030. Although SDGs aim to shift the world onto a sustainable and resilient path, countries are not yet able to make transformative changes for long-term sustainability that requires building social prosperity and foundations within planetary boundaries. Failing to achieve SDGs will negatively affect billions of people and worsen environmental conditions and socio-economic problems. Therefore, BeyondSDG aims to understand the necessary conditions for long-term sustainability, including achieving SDGs, based on the following specific objectives: i) identify critical targets for prioritising SDGs; ii) investigate the effects of (under)achieving SDGs on long-term sustainability beyond 2030; and iii) identify sustainability targets for the post-2030 development agenda. For this, BeyondSDG will apply a threefold scientific approach that combines statistical analysis of empirical and modelled data, qualitative analysis of literature, and knowledge co-creation with stakeholders, including sectoral experts and policymakers, based on systems thinking. This combination of three approaches is complementary and essential to deal with the complex topic of long-term sustainability. Consequently, BeyondSDG will lead to a breakthrough in interdisciplinary research by combining approaches from sustainability science, earth system modelling, and environment and resource management. Mainly, it will bring the following ground-breaking findings: critical targets where adequate actions can lead to progress across most SDGs, negative impacts of underachieving SDGs on people, the planet, and prosperity, required extra efforts for long-term sustainability besides achieving SDGs, and sustainability targets for the post-2030 Agenda based on lessons learned from SDGs, state-of-the-art science, and stakeholder partnerships.

The proposed research aims to establish groundbreaking new methods for the numerical analysis of dynamical systems by using tools from the field of machine learning. The intersection of the fields of machine learning and computational dynamics is largely unexplored, and this proposal aims at the first systematic development of a unified theory, with a view to applying the ideas to problems in the commercial and energy sectors. Recent results by the applicant in set approximation for control systems demonstrate the power of this approach, the results of which significantly improve on the current state-of-the-art methods for set approximation. This approach is based on a functional analytic framework frequently exploited in modern machine learning methods: the reproducing kernel Hilbert space (RKHS). Algorithms are designed to seek functions in the RKHS that characterise important dynamical properties of the system. This highly interdisciplinary research programme will develop a powerful and unified approach to create new algorithms that can either use input data generated from the evolution equations (if they are available) or measured data obtained directly from applications. The host institution PIK is a transdisciplinary host institution focused on climate modeling and sustainability. The tools developed during the course of the fellowship will be applied to the problem of basin stability and synchronisation of power grid networks. This proposal also includes two secondment phases to be spent at the non-academic partner organisation Ambrosys GmbH (AMB). There, the applicant will apply the research results to problems in image rendering in movies and turbulent flow across aerofoils, which are commercial applications already studied at AMB. The applicant will benefit from training in climate modeling and complex systems at PIK, and industrial training during the secondment phases.