With growing urban populations, methods of reducing the urban heat island effect have become increasingly important. Cool pavements altering the heat storage of materials used in pavements can lead to lower surface temperatures and reduce the thermal radiation emitted to the atmosphere. Cool pavement technologies utilize various strategies to reduce the temperature of new and existing pavements, including increased albedo, evaporative cooling, and reduced heat conduction. This process of negative radiation forces helps offset the impacts of increasing atmospheric temperatures. This paper presents an extensive analysis of the state of the art of cool pavements. The properties and principles of cool pavements are reviewed, including reflectivity, thermal emittance, heat transfer, thermal capacity, and permeability. The different types, research directions, and applications of reflective pavements are outlined and discussed. Maintenance and restoration technologies of cool pavements are reviewed, including permeable pavements. Results show that cool pavements have significant temperature reduction potential in the urban environment. This research is important for policy actions of the European Union, noting that European and international business stakeholders have recently expressed their interest in new ways of reducing energy consumption through technologically advanced pavements.

In these days of heightened environmental consciousness, many countries are shifting their focus towards renewable energy sources for both large-scale uses (such as power plants that generate electricity) and smaller-scale applications (e.g., building heating and cooling). In this light, it is not surprising that there is a growing interest in technologies that are reliant on non-conventional sources of power, such as geothermal energy. This study is making an effort to provide a comprehensive understanding of the possible advantages and multiple uses of geothermal energy systems, in the context of their technical and environmental evaluation through Life Cycle Assessment. A brief description of the analyzing methods and the tools used to study a particular system or application is presented. The geothermal technologies and the applications of specific systems are discussed in detail, providing their environmental advantages and their technical barriers as well. District and domestic heating systems cover a significant fraction of the geothermal energy potential. The majority of the discussed studies cover the electricity production as the most important application of geothermal energy. The overall conclusion of the current work is that geothermal energy is an extremely viable alternative that, combined with other renewable energy systems, may mitigate the negative effects of the existing energy mix worldwide.

The paper presents a design and operation analysis of an Integrated Collector Storage (ICS) solar water heater, which consists of an asymmetric Compound Parabolic Concentrating (CPC) reflector trough, while the water tank comprises two concentric cylinders. The annulus between these vessels is partially depressurized and contains a small amount of water in the bottom of the outer vessel which dominantly contributes to the heat transfer from the outer to the inner cylinder. A multi-criteria optimization algorithm is applied to re-evaluate the design specifications of the parabolic surface, thus modifying the design of the entire ICS system and predict the necessary number of units for achieving the highest possible effectiveness with minimized fabrication costs and environmental impacts. The environmental footprint of the device is assessed through Life Cycle Assessment (LCA). The produced thermal energy in conjunction with the environmental and economic results are evaluated as a function of different configuration parameters regarding the water storage conditions, the solar radiation and the total pressure inside the annulus. The ultimate aim of the evaluation process is to offer new perspectives on the design principles of environmentally friendly and cost-effective devices with improved thermal performance.

Accurate design and performance assessment of solar thermal domestic hot water systems coupled with a heat pump auxiliary typically requires transient simulation, as the system’s behavior depends on multiple interactions among collector characteristics, storage stratification, control logic, weather, and draw-off timing. Monthly methods such as the f-chart are useful for first-pass estimates, but they do not resolve stratification, thermostat operation, or demand timing, and they may become inaccurate for stratified thermostat-controlled systems. Direct comparisons of locally inspectable symbolic and black-box surrogate families for this system class remain limited. A 10,982-case development dataset was generated from minute-resolved annual MATLAB simulations, parameterized by collector area, optical efficiency, and first- and second-order loss coefficients. Three surrogate families were benchmarked under a unified protocol, random forest-assisted shape-constrained symbolic regression (SR), feed-forward artificial neural network (ANN) models, and Automatic Learning of Algebraic Models for Optimization (ALAMO), with the f-chart used as a monthly reference method. The targets were the 12 monthly solar fractions under the direct solar heat definition and the corresponding annual mean solar fraction, evaluated on the same independent 991-case test set. SR achieved the lowest average error (mean absolute percentage error, MAPE = 0.82%; root mean square error, RMSE = 0.006), followed by the ANN (MAPE = 2.07%, RMSE = 0.028) and ALAMO (MAPE = 3.67%, RMSE = 0.060), with Nash–Sutcliffe efficiency (NSE) values above 0.98 for all models. Evaluation times were 0.0026–0.124 s per target, compared with about 1000 s for one full-year simulation. These results define the study as a common protocol benchmark within the studied simulator-defined envelope. SR gives the strongest accuracy with local symbolic inspectability, the ANN remains the flexible retrainable option, and ALAMO provides compact algebraic evaluation with the shortest learned model runtime.