In an era of accelerating energy transition and growing demand for critical metals essential for clean technologies, the innovative integration of geothermal energy with critical metal extraction stands as a paradigm shift in sustainable resource utilization. This comprehensive review unravels the synergistic potential of coupling geothermal energy systems with critical metal extraction, thereby transforming a dual crisis of energy and resource scarcity into an opportunity for circular economy. Through rigorous analysis of existing geothermal technologies, and extraction methodologies, the study establishes a coherent framework that merges energy production with environmental stewardship. It scrutinizes current extraction techniques, and evaluates their compatibility with geothermal brine characteristics, proposing optimized pathways for maximum yield. Through detailed case studies and empirical data, the paper elucidates the economic and environmental advantages of this multifaceted approach, from reduced carbon footprint to enhanced energy efficiency and resource recovery. It concludes that combined heat and mineral production technology can open new, unexplored resources, increasing the supply of previously untapped resources, while the potential of geothermal energy for sustainable mineral extraction and energy production is in line with Sustainable Development Goal 7, which aims to ensure access to affordable, reliable, sustainable and modern energy for all.

This paper provides a summary of the current status of geothermal energy utilization in Hungary, with a brief reference to geothermal potential. Approximately 1000 thermal wells are in operation, producing from two principal aquifers: carbonate aquifers (predominantly Mesozoic) and Upper Miocene siliciclastic aquifers. The utilization of geothermal energy in Hungary commenced with balneological exploitation, with documented evidence dating back to the Roman era. Of particular note is the city of Budapest, which has the distinction of being home to 16 thermal spas, a fact that has led to its designation as the spa capital of Europe. In the Hungarian portion of the Pannonian Superbasin, 26 municipalities have implemented geothermal district heating systems. Furthermore, geothermal energy plays a significant role in the agricultural sector, with approximately 300 thermal wells currently in operation. Nevertheless, the generation of geothermal electricity is still in its infancy, with only one operational geothermal power plant. However, based on the exploration permits submitted, there are significant prospects for further development. The same is true for ground source heat pumps, where there is considerable potential for growth, particularly in comparison with our neighbouring country's utilization. Recent research directions are promising, ranging from the use of geothermal energy from abandoned hydrocarbon wells as deep borehole heat exchangers, to the potential for extracting critical materials from thermal water, to exploring the possibility of underground heat storage. The favourable geothermal conditions in Hungary offer the potential to achieve up to 15–20 times the current production of 6.5 PJ, assuming the application of existing technologies. This presents a significant opportunity for the transition to a more sustainable energy source, particularly in the context of heating.

Aqra Anticline is a double plunging anticline, oriented NW–SE with a steep southwestern limb and even overturned. Geomorphological features are interpreted using satellite images, as a result, it was found that the anticline shows clear geomorphological and structural features which indicate the lateral growth of the anticline. Among those features are water gaps, wind gaps, forked-shaped valleys, curved valleys, inclined valleys and dislocated and abandoned alluvial fans. Some of the vague interpreted features were checked and confirmed in the field.

This study introduces a robust methodology utilizing Multi-Criteria Decision Analysis (MCDA) combined with an Analytic Hierarchy Process (AHP) to repurpose abandoned hydrocarbon fields for energy storage, supporting the transition to renewable energy sources. We use a geostatistical approach integrated with Python scripting to analyze reservoir parameters—including porosity, permeability, thickness, lithology, temperature, heat capacity, and thermal conductivity—from a decommissioned hydrocarbon field in Southeast Hungary. Our workflow leverages stochastic simulation data to identify potential zones for energy storage, categorizing them into high-, moderate-, and low-suitability scenarios. This innovative approach provides rapid and precise analysis, enabling effective decision-making for energy storage implementation in depleted fields. The key finding is the development of a methodology that can quickly and accurately assess the feasibility of repurposing abandoned hydrocarbon reservoirs for underground thermal energy storage, offering a practical solution for sustainable energy transition.

Local Climate Zones (LCZs) have become a foundational framework for urban climate modeling, yet their use across model families has not been systematically evaluated. Crucially, the LCZ framework itself has served as a developmental basis, revealing the progression of urban canopy parameterizations (UCP) from early models to the diverse model families currently in use. This evolution is exemplified by systems like the Weather Research and Forecasting (WRF) model, where the application of LCZ has fundamentally shifted from an experimental add-on to a basic, built-in feature of its urban-modeling capabilities. This review synthesizes a decade of LCZ-based studies to clarify how LCZ improves surface representation, enhances comparability, and supports multiscale modeling workflows. It provides a comprehensive overview of peer-reviewed work up to the end of 2024, offering a baseline for understanding the field’s rapid recent growth. Using a structured evidence-mapping approach, we categorize applications into three maturity stages: testing and measurement, operational and planning-oriented applications, and expansions beyond urban climate to chemistry, hydrology, and Earth-system modeling. The assessment covers various iterations of mesoscale systems (WRF, SURFEX/TEB, COSMO), local-scale climatologies (MUKLIMO-3, UrbClim), microscale models (ENVI-met, CFD), and supporting tools including SUEWS, SOLWEIG, RayMan, VCWG, and CESM-CLMU. Results show clear divisions of labor: WRF and SURFEX/TEB anchor process-rich regional simulations; MUKLIMO-3 and UrbClim offer computationally efficient long-horizon or multi-city assessments; ENVI-met and CFD provide design-scale insight when parameterized with LCZ archetypes. Across all families, model skill is strongly constrained by LCZ data quality and by inconsistencies in LCZ to UCP translation. We conclude that advancing LCZ-based urban climate modeling will depend on improved LCZ products, standardized parameter libraries, and formalized cross-scale model couplings that allow existing tools to interoperate more reliably under growing urban-climate pressures.

This study explores a novel strategy to repurpose depleted clastic sediment hydrocarbon reservoirs in Hungary as High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems, incorporating machine learning to enhance system optimization. Hungary's extensive inventory of depleted fields, predominantly featuring clastic formations, presents significant potential for geothermal energy storage applications. Initially, detailed reservoir models were constructed by analyzing existing well logs and core data. Subsequent advanced numerical simulations of heat transport and groundwater flow were performed within the Bekesi Formation, concentrating on a dual-well configuration—one dedicated to hot fluid injection and extraction and the other to managing cold fluids. State-of-the-art simulation tools, including SGeMS, RockWorks, Python, MODFLOW, and GMS MT3DMS, were utilized to pinpoint optimal brine injection sites by evaluating critical parameters such as thermal conductivity, porosity, and permeability; additional core analyses filled essential gaps in thermal conductivity data. The study's central innovation lies in deploying a Random Forest algorithm to optimize thermal recovery efficiency. Data generated from comprehensive simulations across multiple wells were used to train the model, which then predicted and refined thermal performance for the remaining wells in the field. The outcomes are expected to yield precise identification of optimal injection locations, rigorous heat transport analyses, accurate estimates of storage capacities, and improved predictions of thermal recovery efficiency, thereby establishing a sustainable and data-driven methodology for converting depleted hydrocarbon reservoirs into effective thermal energy storage systems.