• Energy Research
• 2025-2025close
• NL close
• DE close
• US close
• RU close
• Aurora Universities Network close

Conflict framing-the emphasis on clashing political positions, or the reproach of one actor to another-is central in political coverage and politics. This chapter discusses its conceptualization and the subsequent consequences for citizens' political attitudes and behavior. Previous research on conflict framing has yielded mixed results, showcasing conflicts' potential to inform and engage citizens alongside fostering cynicism and polarization. In this chapter, we argue that, to understand these divergent findings, it is necessary to distinguish between different types of conflicts. To address the fragmented literature on conflict and negativity, we propose a typology of various conflict types. This typology is exemplified through a hypothetical conflict scenario centered around climate change. We evaluate the moral, political, and epistemic implications of distinct conflict frames and propose conditions under which conflicts can positively contribute to deliberative democracy. Our typology provides a starting point for further investigations into the effects of conflict frames.

Grid parity is considered the tipping point of economic competitiveness of PV systems. However, accurately determining when grid parity is achieved hinges on the reliability and precision of the methodologies and data employed. This paper systematically reviews existing methods for assessing PV grid parity, proposes a structured three-step framework for grid parity assessment, and identifies the potential enhancements for more accurate evaluation outcomes. The framework begins with the calculation of PV costs using the Levelized Cost of Electricity (LCOE) method, continues with predicting PV cost trends through learning curves, and is completed by benchmarking PV costs against electricity prices. Our findings reveal that most current PV cost calculations for grid parity primarily rely on the LCOE method, which can be enhanced by incorporating modifications for integration costs, revenues, PV performance metrics, regional-specific characteristics, and uncertainties. Moreover, learning curve models used to predict PV cost trends can be refined by tailoring learning rates and model formulations to reflect specific stages of technological development and regional differences. Additionally, the results suggest that electricity prices used in grid parity assessment can be adjusted to reflect the impact of policies and market dynamics. This comprehensive review provides a robust framework for assessing grid parity and serves as an essential reference for conducting more precise techno-economic feasibility assessment of PV systems.

Climate change is expected to alter the population dynamics of pioneer tree species and their planned use in sustainable forest management, but we have a limited understanding of how their demographic rates change in response to climate changes during ecological restoration. Based on 12 years of demographic data for a pioneer tree species (Pinus massoniana) censused in three plots that correspond to three stages of ecological restoration in southeastern China. We built integral projection models (IPMs) to assess vital rates (survival, growth, reproduction) and population growth in each plot, then evaluated demographic changes to simulated changes in seasonal mean temperature and precipitation in the current and previous census period. The plot representing the medium restoration stage had the highest population growth rate (λ = 0.983). Mean population survival probability increased with ecological restoration, and reproduction probability was significantly suppressed at the high restoration stage. Survival is always the most important vital rate for λ, and climate affects λ primarily via survival at each restoration stage. The current spring temperature was the most critical climate variable for λ in the low and medium restoration stages, and previous summer temperature was most critical in the high restoration stage. Simulated warming leads to a decrease in the stochastic population growth rate (λs) of P. massoniana in every stage. These findings suggest that during ecological restoration, P. massoniana responds to habitat change via modified demographic performance, thus altering its response to climate change. Despite diverse responses to climate change, the persistence of P. massoniana populations is facing a widespread threat of warming states at each restoration stages.

Periglacial alluvial fans are common in northwestern and central Europe and their pre-Holocene stratigraphic records typically date back to late Middle Pleniglacial and Late Pleniglacial (late MIS3 and 2). Preserved stratigraphic records that include an entire interglacial-glacial cycle have, so far, not been described and it is thus unknown how periglacial alluvial fans responded during a full cycle of interglacial-glacial climate changes. In this paper, we reconstruct the evolution of the Eerbeek periglacial alluvial fan in the Netherlands which was deposited during the late Saalian (MIS 6) to late Weichselian (MIS 2) period, including the entire last interglacial–glacial cycle (MIS 5-2). Our reconstruction is based on 48, up-to 45-m deep borehole and Cone Penetration Test (CPT) logs that allowed the construction of an 8-km long longitudinal and a 7-km long transverse cross section over the Eerbeek periglacial alluvial fan. Age control was provided by means of 17, previously published, Optically Stimulated Luminescence ages of two boreholes on the fan, and 14 14C ages from three boreholes and a nearby, now abandoned, quarry.Overlying a thick, late Saalian (MIS 6) alluvial fan record, is a 4- to 18-m thick alternation of distinct organic (mainly peat and humic clays), siliciclastic alluvial fan (coarse- and medium-grained sands), Rhine (coarse- and medium grained sands), and aeolian (mainly medium-grained sands) stratigraphic units. Organic levels indicate fan stability during the Eemian interglacial (MIS 5e), and Brørup (MIS 5c), Odderade–Ognon interstadial complex (MIS 5a), and Middle Pleniglacial (MIS 3) interstadials 14, 13, 12 and 11 as well as late MIS 2 interstadial 1a. Clastic sediments indicate alluvial fan activity during the Herning (MIS 5d), Rederstall (MIS 5b), Ognon stadial complex (late MIS 5a), Early Pleniglacial (MIS 4) and upper Middle Pleniglacial (upper MIS 3) stadials 13, 12 and 11. Sediments from the coldest and driest period of the Last Glacial (late MIS 3 and MIS 2) are absent and following a phase of aeolian activity, the fan was only reactivated at the MIS 2 to MIS 1 transition (stadial 1). We attribute the absence of fan activity during the coldest period of the last interglacial-glacial cycle to the eastward orientation of the fan making it less sensitive to permafrost melt.The colder MIS substages and stadials in which the Eerbeek fan was active coincided with the presence of permafrost and/or a seasonal, deeply frozen soil, and a relatively humid climate during which vegetation was largely absent. The presence of channels that dissect the underlying organic units suggests that the Eerbeek fan initially responded to the changes from interstadials to stadials by means of erosion. As climate cooled and permafrost/deep frost developed, the fan switched to alluvial aggradation. The consistent presence of coarsening-fining upward sequences suggests a relation with cycles of increased overland flow due to increasingly more frozen subsoil conditions. The fan stratigraphy therefore shows the direct coupling between warmer-colder MIS substages and interstadial-stadial climate cyclicity and alluvial fan response over the entire last interglacial-glacial cycle.

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.

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.