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Novel fire regimes are emerging worldwide and pose substantial challenges to biodiversity conservation. Addressing these challenges and mitigating their impacts on biodiversity will require developing a wide range of fire management practices. In this paper, we leverage research across taxa, ecosystems and continents to highlight strategies for applying fire knowledge in biodiversity conservation. First, we define novel fire regimes and outline different fire management practices in contemporary landscapes from different parts of the world. Next, we synthesize recent research on fire use and biodiversity, and provide a decision-making framework for biodiversity conservation under novel fire regimes. We recommend that fire management strategies for preserving biodiversity should consider both social and ecological factors, iterative learning informed by effective monitoring, and developing and testing new management actions. An integrated approach to learning about fire and biodiversity will help to navigate the complexities of novel fire regimes and preserve biodiversity in a rapidly changing world. This article is part of the theme issue ‘Novel fire regimes under climate changes and human influences: impacts, ecosystem responses and feedbacks’.

ABSTRACTFreshwater ecosystems face significant threats, including pollution, habitat loss, invasive species, and climate change. To address these challenges, management strategies and restoration efforts have been broadly implemented. Across Europe, such efforts have resulted in overall improvements in freshwater biodiversity, but recovery has stalled or failed to occur in many localities, which may be partly caused by the limited dispersal capacity of many species. Here, we used a comprehensive dataset comprising 1327 time series of freshwater macroinvertebrate communities ranging from 1968 to 2021 across 23 European countries to investigate whether dispersal capacity changes with the ecological quality of riverine systems. Sites experiencing improvements in ecological quality exhibited a net gain in species and tended to have macroinvertebrate communities containing species with stronger dispersal capacity (e.g., active aquatic and aerial dispersers, species with frequent propensity to drift, and insects with larger wings). In contrast, sites experiencing degradation of ecological quality exhibited a net loss of species and a reduction in the proportion of strong dispersers. However, this response varied extensively among countries and local sites, with some improving sites exhibiting no parallel gains in macroinvertebrates with higher dispersal capacity. Dispersal capacity of the local species pool can affect the success of freshwater ecosystem restoration projects. Management strategies should focus on enhancing landscape connectivity to create accessible “source” areas and refugia for sensitive taxa, especially as climate change reshapes habitat suitability. Additionally, biodiversity initiatives must incorporate adaptive decision‐making approaches that account for the site‐specific responses of macroinvertebrate communities to changes in ecological quality.

This deliverable documents the establishment of the HEPHAESTUS Green Living Lab (HGLL) on Bornholm, Denmark. It outlines the conceptual foundations, co-creation processes, stakeholder engagement, implementation activities, and evaluation framework used to develop the Living Lab. Guided by New European Bauhaus values and Living Lab principles, the HGLL connects craft heritage with circular economy experimentation, positioning Bornholm as a “test island” for sustainable innovation in the craft sector.

The energy transition is crucial to unlocking the potential of the Paris Agreement and the global climate goals. To meet the projected demand for the transition, critical mineral extraction is expected to significantly increase in countries of the global South. The critical mineral mining boom has the potential to drive economic development, contributing to the achievement of the Sustainable Development Goals (SDGs) under the 2030 Agenda. However, considering historical tensions between extractive industries and development, critical mineral mining risks exacerbating socio-economic inequalities and poverty. Against this background, the paper investigates factors influencing the local socio-economic impact of critical mineral extraction. Using satellite data and mining data from the S&P database, the study examines the socio-economic effects of 94 critical mineral mines that opened in Africa between 2000 and 2020, focusing on mineral-specific attributes and contextual factors, as well as factors related to governance. Findings indicate that critical mineral extraction can have significant positive impacts on local socioeconomic activity, particularly in areas distant from existing infrastructure and urban centers. The results highlight the complex role of institutional quality in mediating the socio-economic impact of mines, and shift attention to the underlying factors that shape institutional performance to deliver local benefits.

The comprehensive change from known, classical energy production methods to the increased use of renewable energy requires new methods in the field of efficient application and use of renewable energy. The urban energy supply presents complex challenges in improving efficiency; therefore, the prediction of the dynamical availability of energy is required. Several approaches have been explored, including statistical models and machine learning using historical data and numerical weather prediction models using mathematical models of the atmosphere and weather conditions. Accurately forecasting renewable energy production involves analyzing factors such as related weather conditions, conversion systems, and their locations, which influence both energy availability and yield. This study focuses on the short-term forecasting of wind and photovoltaic (PV) energy using historical data and machine learning approaches, aiming for accurate 8 h predictions. The goal is to develop models capable of producing accurate short-term forecasts of energy production from both resources (solar and wind), suitable for later use in a model predictive control scheme where generation and demand, as well as storage, must be considered together. Methods include regression trees, support vector regression, and regression neural networks. The main idea in this work is to use past and future information in the model. Inputs for the PV model are past PV generation and future solar irradiance, while the wind model uses past wind generation and future wind speed data. The performance of the model is evaluated over the entire year. Two scenarios are tested: one with perfect future predictions of wind speed and solar irradiance, and another considered realistic situation where perfect future prediction is not possible, and uncertain prediction is accounted for by incorporating noise models. The results of the second scenario were further improved using the output filtering method. This study shows the advantages and disadvantages of different methods, as well as the accuracy that can be expected in principle. The results show that the regression neural network has the best performance in predicting PV and wind generation compared to other methods, with an RMSE of 0.1809 for PV and 5.3154 for wind, and a Pearson coefficient of 0.9455 for PV and 0.9632 for wind.

ABSTRACTBoreal forest regions, including East Siberia, have experienced elevated fire activity in recent years, leading to record‐breaking greenhouse gas emissions and severe air pollution. However, our understanding of the factors that eventually halt fire spread and thus limit fire growth remains incomplete, hindering our ability to model their dynamics and predict their impacts. We investigated the locations and timing of 2.2 million fire stops—defined as 300 m unburned pixels along fire perimeters—across the vast East Siberian taiga. Fire stops were retrieved from remote sensing data covering over 27,000 individual fires that collectively burned 80 Mha between 2012 and 2022. Several geospatial datasets, including hourly fire weather data and landscape variables, were used to identify the factors contributing to individual fire stops. Our analysis attributed 87% of all fire stops to a statistically significant (p < 0.01) change in one or more of these drivers, with fire‐weather drivers limiting fire growth over time and landscape drivers constraining it across space. We found clear regional and temporal variations in the importance of these drivers. For instance, landscape drivers—such as less flammable land cover and the presence of roads—were key constraints on fire growth in southeastern Siberia, where the landscape is more populated and fragmented. In contrast, fire weather was the primary constraint on fire growth in the remote northern taiga. Additionally, in central Yakutia, a major fire hotspot in recent years, fuel limitations from previous fires increasingly restricted fire spread. The methodology we present is adaptable to other biomes and can be applied globally, providing a framework for future attribution studies on global fire growth limitations. In northeast Siberia, we found that with increasing droughts and heatwaves, remote northern fires could potentially grow even larger in the future, with major implications for the global carbon cycle and climate.

The ongoing transformation of district heating systems (DHSs) aims to reduce emissions and increase renewable energy sources. The objective of this work is to integrate solar thermal (ST) and seasonal aquifer thermal energy storage (ATES) in various scenarios applied to a large DHS. Mixed-integer linear programming (MILP) is used to develop a comprehensive model that minimizes operating costs, including heat pumps (HPs), combined heat and power (CHP) units, electric heat boilers (EHBs), heat-only boilers (HOBs), short-term thermal energy storage (TES), and ATES. Different ATES scenarios are compared to a reference without seasonal TES (potential of 15.3 GWh of ST). An ATES system with an injection well temperature of about 55 °C has an overall efficiency of 49.8% (58.6% with additional HPs) and increases the integrable amount of ST by 178% (42.5 GWh). For the scenario with an injection well temperature of 20 °C and HPs, the efficiency is 86.6% and ST is increased by 276% (57.5 GWh). The HOB heat supply is reduced by 8.9% up to 36.6%. However, the integration of an ATES is not always economically or environmentally beneficial. There is a high dependency on the configurations, prices, or emissions allocated to electricity procurement. Further research is of interest to investigate the sensitivity of the correlations and to apply a multi-objective MILP optimization.