Economic Impacts of Exemplary Climate Change and Adaptation Effects Under Different Socio-Economic Developments in Germany

Three different socioeconomic developments are implemented at the national level in the macro-econometric model PANTA RHEI. While one scenario is oriented towards sustainability, a second one continues current trends. The third scenario is characterized by more dynamic socio-economic development. This paper extends the impact chain methodology. Exemplary climate impacts and adaptation measures from literature are quantified for the transport, energy and health sectors based on literature analyses and entered into the model. These are the interruption of a road due to flooding, cooling water scarcity for power plants due to drought and higher health expenditures. Their quantifications are linked to the three different socio-economic developments. The findings indicate that the economic costs of climate change may vary significantly across different socioeconomic scenarios. Climate impacts generate socio-economic costs that can be reduced by adaptation measures. In the sustainability scenario, the negative economic impacts of climate change are significantly lower than in the other scenarios, especially in the energy and transport sectors. The effects are higher in the case of dynamic socio-economic development. In contrast, socio-economic development plays only a minor role for the effects in the health sector, which are mainly driven by demographic trends. The adaptation measures considered have positive macroeconomic effects, mainly due to lower damages, but also because of the additional investment activity. The model calculations quantify some impacts of climate change under different socioeconomic scenarios and assess the effects of climate risks and adaptation on critical infrastructure.   Received: 1 September 2023 | Revised: 19 December 2023 | Accepted: 5 January 2024   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.   Data Availability Statement Data is available on request from the authors

Global Land Use Impacts of Bioeconomy: An Econometric Input–Output Approach

Many countries have set ambiguous targets for the development of a bioeconomy that not only ensures sufficient production of high-quality foods but also contributes to decarbonization, green jobs and reducing import dependency through biofuels and advanced biomaterials. However, feeding a growing and increasingly affluent world population and providing additional biomass for a future bioeconomy all within planetary boundaries constitute an enormous challenge for achieving the Sustainable Development Goals (SDG). Global economic models mapping the complex network of global supply such as multiregional input–output (MRIO) or computable general equilibrium (CGE) models have been the workhorses to monitor the past as well as possible future impacts of the bioeconomy. These approaches, however, have often been criticized for their relatively low amount of detail on agriculture and energy, or for their lack of an empirical base for the specification of agents’ economic behavior. In this paper, we address these issues and present a hybrid macro-econometric model that combines a comprehensive mapping of the world economy with highly detailed submodules of agriculture and the energy sector in physical units based on FAO and IEA data. We showcase the model in a case study on the future global impacts of the EU’s bioeconomy transformation and find small positive economic impacts at the cost of a considerable increase in land use mostly outside of Europe

Global Land Use Impacts of Bioeconomy: An Econometric Input–Output Approach

Many countries have set ambiguous targets for the development of a bioeconomy that not only ensures sufficient production of high-quality foods but also contributes to decarbonization, green jobs and reducing import dependency through biofuels and advanced biomaterials. However, feeding a growing and increasingly affluent world population and providing additional biomass for a future bioeconomy all within planetary boundaries constitute an enormous challenge for achieving the Sustainable Development Goals (SDG). Global economic models mapping the complex network of global supply such as multiregional input–output (MRIO) or computable general equilibrium (CGE) models have been the workhorses to monitor the past as well as possible future impacts of the bioeconomy. These approaches, however, have often been criticized for their relatively low amount of detail on agriculture and energy, or for their lack of an empirical base for the specification of agents’ economic behavior. In this paper, we address these issues and present a hybrid macro-econometric model that combines a comprehensive mapping of the world economy with highly detailed submodules of agriculture and the energy sector in physical units based on FAO and IEA data. We showcase the model in a case study on the future global impacts of the EU’s bioeconomy transformation and find small positive economic impacts at the cost of a considerable increase in land use mostly outside of Europe

Towards improved adaptation action in Europe: Policy recommendations from the UNCHAIN project

Policymakers can make adaptation "smarter" by using the Impact Chain approach to analyse both conventional local climate risks and lesser well-known transboundary climate risks. • Policymakers should embrace uncertainties by applying a reflect-then-act rather than the predict-then-act approach; by including socioeconomic scenarios for a range of possible developments, from likely to unanticipated; and by ensuring transparency in communications. Below we summarize our policy-relevant insights and recommendations for the European Commission and EU member states

Urban Rural SOLUTIONS : Innovationen im regionalen Daseinsvorsorgemanagement durch Unterstützung von interkommunalen Kooperationen Endbericht

Die zu erwartenden demografischen und raumstrukturellen Veränderungen der kommenden Jahre sowie die Anforderung, Angebote der Daseinsvorsorge für die Bevölkerung einer Region zugänglich, bezahlbar und gleichzeitig für die Kommunen finanzierbar zu halten, werfen Fragen zur langfristigen Sicherung der Daseinsvorsorge auf. Ein regionales Daseinsvorsorgemanagement kann zur Sicherung der Daseinsvorsorge beitragen, ist jedoch nicht einfach umzusetzen: den verantwortlichen Behörden fehlt es häufig an Zeit, Personen, Ressourcen und geeigneten Strukturen; erforderliche Methoden oder Informationen müssen aufwendig erschlossen werden. Die vom Bundesministerium für Bildung und Forschung (BMBF) geförderte Innovationsgruppe UrbanRural SOLUTIONS hat Werkzeuge entwickelt, die ein regionales Daseinsvorsorgemanagement sowie die transdisziplinäre und interkommunale Zusammenarbeit künftig erleichtern sollen. Diese werden im vorliegenden Band vorgestellt: Der als »Daseinsvorsorgeatlas Niedersachsen« bereits als Pilotprojekt implementierte digitale Daseinsvorsorgeatlas zielt auf einen leichten Zugang zu digital aufbereiteten räumlichen Informationen. Der »Dialogprozess Regionale Daseinsvorsorge« erleichtert die Gestaltung kooperativer, grenz- und disziplinübergreifender Innovationsprozesse.Deutschland, Bundesministerium für Bildung und Forschung (BMBF

Research advancements for impact chain based climate risk and vulnerability assessments

International audienceAs the climate crisis continues to worsen, there is an increasing demand for scientific evidence from Climate Risk and Vulnerability Assessments (CRVA). We present 12 methodological advancements to the Impact Chain-based CRVA (IC-based CRVA) framework, which combines participatory and data-driven approaches to identify and measure climate risks in complex socio-ecological systems. The advancements improve the framework along five axes, including the existing workflow, stakeholder engagement, uncertainty management, socio-economic scenario modeling, and transboundary climate risk examination. Eleven case studies were conducted and evaluated to produce these advancements. Our paper addresses two key research questions: (a) How can the IC-based CRVA framework be methodologically advanced to produce more accurate and insightful results? and (b) How effectively can the framework be applied in research and policy domains that it was not initially designed for? We propose methodological advancements to capture dynamics between risk factors, to resolve contradictory worldviews, and to maintain consistency between Impact Chains across policy scales. We suggest using scenario-planning techniques and integrating uncertainties via Probability Density Functions and Reverse Geometric Aggregation. Our research examines the applicability of IC-based CRVAs to address transboundary climate risks and integrating macro-economic models to reflect possible future socio-economic exposure. Our findings demonstrate that the modular structure of IC-based CRVA allows for the integration of various methodological advancements, and further advancements are possible to better assess complex climate risks and improve adaptation decision-making