Carlo Jaeger

Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy of traffic prediction under extreme weather, but their robustness still has much room for improvement. As the frequency of extreme weather events increases due to climate change, accurately predicting spatiotemporal patterns of urban road traffic is crucial for a resilient transportation system. The compounding effects of the hazards, environments, and urban road network determine the spatiotemporal distribution of urban road traffic during an extreme weather event. In this paper, a novel Knowledge-driven Attribute-Augmented Attention Spatiotemporal Graph Convolutional Network (KA3STGCN) framework is proposed to predict urban road traffic under compound hazards. We design a disaster-knowledge attribute-augmented unit to enhance the model’s ability to perceive real-time hazard intensity and road vulnerability. The attribute-augmented unit includes the dynamic hazard attributes and static environment attributes besides the road traffic information. In addition, we improve feature extraction by combining Graph Convolutional Network, Gated Recurrent Unit, and the attention mechanism. A real-world dataset in Shenzhen City, China, was employed to validate the proposed framework. The findings show that the prediction accuracy of traffic speed can be significantly increased by 12.16%~31.67% with disaster information supplemented, and the framework performs robustly on different road vulnerabilities and hazard intensities. The framework can be migrated to other regions and disaster scenarios in order to strengthen city resilience.

Balancing climate change mitigation with biodiversity conservation remains a pivotal challenge for sustainable forest management in Europe. This study investigates the nexus between carbon sequestration and biodiversity across European forest regions from 1990 to 2020, integrating data on forest biomass, carbon stock, and biodiversity indicators using statistical modeling in R. Results show a steady increase in forest area and carbon stocks, particularly in Central-East and Central-West Europe, driven by afforestation and adaptive forest policies. However, biodiversity responses vary by region and the intensity of management. Forests under low-intensity or semi-natural management, particularly mixed-species stands, exhibit positive correlations between carbon stock and biodiversity metrics, such as species richness and the Shannon index. In contrast, intensively managed monocultures—such as Eucalyptus plantations in Southwestern Europe—exhibit rapid carbon gains but reduced ecological resilience, characterized by low evenness and structural diversity. Generalized additive models (GAMs) reveal non-linear, species- and region-specific dynamics in the carbon-biodiversity relationship, emphasizing the importance of ecological context. These findings highlight critical trade-offs and synergies in forest management, calling for integrated policies that consider forest structure, species composition, and long-term ecosystem resilience. The study recommends regionally differentiated strategies, stronger biodiversity monitoring, and enhanced policy coherence to align with the EU Green Deal and Forest Strategy 2030, advancing Europe’s path toward climate-smart, biodiversity-rich forestry.

Global warming is exacerbating flood hazards, making the robustness of flood risk management a critical issue. Without considering future scenarios, flood risk analysis built only on historical knowledge may not adequately address the coming challenges posed by climate change. A comprehensive risk analysis framework based on both historical inundations and future projections to tackle uncertainty is still lacking. In this view, a scenario-based, data-driven risk analysis framework that for the first time integrates recent historical floods and future risk trends is here presented, consisting of flood inundation-prone and high-risk zones. Considering the Poyang Lake Eco-Economic Zone (PLEEZ) in China as the study area, we reproduced historical inundation scenarios of major flood events by using Sentinel-1 imagery from 2015 to 2021, and used them to build the risk baseline model. The results show that 11.7% of the PLEEZ is currently exposed to the high-risk zone. In the SSP2-RCP4.5 scenario, the risk would gradually decrease after peaking around 2040 (with a 19.3% increase in high-risk areas), while under the traditional fossil fuel-dominated development pathway (SSP5-RCP8.5), the risk peak would occur with a higher intensity about a decade earlier. The attribution analysis results reveal that the intensification of heavy rainfall is the dominant driver of future risk increase and that the exploitation of unused land such as wetlands induces a significant increase in risk. Finally, a hierarchical panel of recommended management measures was developed. We hope that our risk analysis framework inspires newfound risk awareness and provides the basis for more effective flood risk management in river basins.

Strategies of community‐based disaster risk reduction have been advocated for more than 2 decades. However, we still lack in‐depth quantitative assessments of the effectiveness of such strategies. Our research is based on a national experiment in this domain: the “Comprehensive Disaster Reduction Demonstration Community” project, a governmental program running in China since 2007. Information on more than 11,000 demonstration communities was collected. Combined with the local disaster information and socioeconomic conditions, the spatiotemporal characteristics of these communities over 12 years and their differences in performance by region and income group were analyzed. We performed an attribution analysis for disaster risk reduction effectiveness. This is the first time a series of quantitative evaluation methods have been applied to verify the effectiveness of a large‐scale community‐based disaster risk reduction project, both from the perspective of demonstrative effects and loss reduction benefits. Here, we find that the project is obviously effective from these two perspectives, and the disaster loss reduction effectiveness illustrates clear regional differences, where the regional economic level and hazard severity act as important drivers. Significant differences of urban‐rural and income call for matching fortification measures, and the dynamic management of demonstration community size is required, since the loss reduction benefit converges when the penetration rate of the demonstration community reaches approximately 4% in a province. These and further results provide diverse implications for community‐based disaster risk reduction policies and practices.

Klaus Hasselmann has earned the 2021 Nobel Prize in physics for his breakthroughs in analysing the climate system as a complex physical system. Since decades, as a leading climate scientist he is aware of the need for creative cooperation between climate scientists and researchers from other fields, especially economics. To facilitate such cooperation, he has designed a productive research program for economic analysis in view of climate change. Without blurring the differences between economics and physics, the Hasselmann program stresses the complexities of today’s economy. This includes the importance of heterogeneous actors and different time scales, of making major uncertainties explicit and bringing researchers and practitioners in close interaction. The program has triggered decades of collaborative research, especially in the network of the Global Climate Forum, that he has founded for this purpose. Research inspired by Hasselmann’s innovative ideas has led to a farewell to outdated economic approaches: single-equilibrium models, a single constant discount rate, framing the climate challenge as a kind of prisoner’s dilemma and framing it as a problem of scarcity requiring sacrifices from the majority of today’s population. Instead of presenting the climate problem as the ultimate apocalyptic narrative, he sees it as a challenge to be mastered. To meet this challenge requires careful research in order to identify underutilisation of human, technical and social capacities that offer the keys to a climate friendly world economy. Climate neutrality may then be achieved by activating these capacities through investment-oriented climate strategies, designed and implemented by different actors both in industrialised and developing countries. The difficulties to bring global greenhouse gas emissions down to net zero are enormous; the Hasselmann program holds promise of significant advances in this endeavour.

We would like to extend on the article by Alley et al

The European Green Deal aims at climate neutrality for Europe by 2050, implying a significant acceleration of emission reductions. To gain the necessary support, it needs to reduce regional and social inequalities in Europe. We present objectives in terms of jobs, growth and price stability to complement the emission reduction targets and sketch a proof-of-concept investment profile for reaching these goals. Substantial additional annual public investments, of about 1.8% of pre-COVID-19 GDP, are proposed for the next decade. Their allocation includes retrofitting the European building stock, consciously fostering a renewal of the European innovation system as well as complementary measures in the fields of education and health. The scenario outlined in this article is meant as an input to the urgently needed discussion on how the European Green Deal can shift the EU economy to a new development path that realises a carbon-neutral Europe by 2050 while strengthening European cohesion.

The global financial crisis of 2008 has shown that the present financial system involves global systemic risks. The dimension of these risks is hard to grasp with the conceptual tools that have been developed to tackle conventional risks like fire or car accidents. While modern societies know quite well how to deal with conventional risks, we have not yet been equally successful at dealing with global systemic risks. For managing this kind of risks, one needs to understand critical features of specific global systems where many human agents interact in ever changing complex networks. Here we apply two specific dimensions of complexity theory for dealing with global systemic risk in an integrated fashion: normal accidents and extended evolution. Both of them have successfully been applied to the analysis of systemic risks. As a paradigmatic example of global systemic risks, we focus on the global financial crisis that began in 2008, and suggest that the future evolution of the financial system could either see a further increase in complexity, or a reversal to a less complex system. We explore and contrast the implications of normal accident theory and extended evolution perspectives and suggest a four‐point research strategy informed by complexity theory for better understanding global systemic risks in financial systems.