Search Results (533)

Integrating renewable energy into agricultural systems has emerged as a critical strategy for reducing the sector’s greenhouse gas emissions. However, limited research has examined how farm-specific operational patterns influence the techno-economic performance of solar photovoltaic (PV) systems. This study presents a comprehensive techno-economic and environmental assessment of grid-connected solar PV systems for two types of dairy farm operations: spring-calving and winter-calving. Using detailed farm-specific energy consumption profiles and solar irradiance data, system performance was evaluated under Ireland’s policy framework, including the Targeted Agricultural Modernisation Scheme grant and the Clean-Export tariff. The spring-calving operation achieved superior economic performance (payback period: 3.25 years; levelised cost of electricity: EUR 0.091/kWh) compared to the winter-calving operation (3.83 years; EUR 0.099/kWh). This superior performance is due to better seasonal alignment between solar generation and electricity demand. Sensitivity analysis reveals solar irradiance, grid electricity cost, and grant funding as main economic viability influencing factors. Environmental analysis demonstrates CO₂ emission reductions of 77% for spring-calving and 61% for winter-calving operations. The findings demonstrate that solar PV systems are both economically viable and environmentally beneficial for dairy farms. These results provide actionable insights for farmers and policymakers seeking to promote clean energy adoption and emission reduction in agriculture. Full article

In the face of accelerating climate change and urbanization, sustainable mobility infrastructure plays a critical role in reducing greenhouse gas emissions. This article assesses the Sunglider concept—an elevated, solar-powered transport system—through the New European Bauhaus (NEB) Compass, which emphasizes sustainability, inclusion, and esthetic value. Designed by architect Peter Kuczia and collaborators, Sunglider combines photovoltaic energy generation with modular, parametrically designed wooden pylons to form a lightweight, climate-positive mobility solution. The study evaluates the system’s technological feasibility, environmental performance, and urban integration potential, drawing on existing design documentation and simulation-based estimates. While Sunglider demonstrates strong alignment with NEB principles, including zero-emission operation and material circularity, its implementation is challenged by high initial investment, political and planning complexities, and integration into dense urban environments. Mitigation strategies—such as adaptive routing, visual screening, and universal station access—are proposed to address concerns around privacy, esthetics, and accessibility. The article positions Sunglider as a scalable and replicable model for mid-sized European cities, capable of advancing inclusive, carbon-neutral mobility while enhancing the urban experience. It concludes with policy and research recommendations, highlighting the importance of embedding infrastructure innovation within broader ecological and cultural transitions. Full article

Residential buildings in Mediterranean regions remain major contributors to energy consumption and greenhouse gas emissions. Existing studies often assess renewable energy technologies or innovative building solutions in isolation, with limited attention to their combined performance across different residential typologies. This study evaluates the integrated impact of solar renewable energy systems and smart building technologies on the energy performance of residential buildings in Cyprus. A typology-based methodology is applied to three representative residential building types—detached, semi-detached, and apartment buildings—using dynamic energy simulation and scenario analysis. Results show that solar photovoltaic systems achieve higher standalone reductions than solar thermal systems, while smart building technologies significantly enhance operational efficiency and photovoltaic self-consumption. Integrated solar–smart scenarios achieve up to 58% reductions in primary energy demand and 55% reductions in CO₂ emissions, and 25–30 percentage-point increases in PV self-consumption, enabling detached and semi-detached houses to approach national nearly zero-energy building (nZEB) performance thresholds. The study provides climate-specific, quantitative evidence supporting integrated solar–smart strategies for Mediterranean residential buildings and offers actionable insights for policy-making, design, and sustainable residential development. Full article

Greenhouse gas emissions are a primary contributor to climate change and the observed rise in global temperatures. To reduce these emissions, renewable energy sources (RESs) must replace fossil fuels in power generation. Because of the mismatch between production and demand, the increase in RES is limited. To address this phenomenon, the addition of renewable energy generation should be accompanied by storage systems. In this paper, the island of Crete is examined for various renewable energy generations and storage capacities using the PowerWorld Simulator software. Four main scenarios are studied in which the installed renewable energy generation is increased to reach substation limits. For every scenario, different renewable energy generation mixes are considered between wind farms and photovoltaics. Furthermore, for all sub-scenarios, different storage capacities are considered, ranging from 1.6 GWh to 12.8 GWh. This study proves that storage systems are mandatory to increase renewable energy penetration. In certain scenarios, a battery energy storage system can further increase renewable energy penetration from 6.15% to 28.07%. Although the battery energy storage system enhanced renewable penetration, increasing transmission line capacities should also be considered regarding the scenario. Full article

With growing community awareness of greenhouse gas emissions and their environmental consequences, distributed rooftop photovoltaic (PV) systems have emerged as a sustainable energy alternative in residential settings. However, the high penetration of these systems without effective operational strategies poses significant challenges for local distribution grids. Specifically, the estimation of surplus energy production from these systems, closely linked to complex outdoor weather conditions and seasonal fluctuations, often lacks an accurate forecasting approach to effectively capture the temporal dynamics of system output during peak periods. In response, this study proposes a recurrent neural network (RNN)- based forecasting framework to predict rooftop PV surplus in the context of micro-residential communities over time horizons not exceeding 48 h. The framework includes standard RNN, long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) networks. In this context, the study employed estimated surplus energy datasets from six single-family detached houses, along with weather-related variables and seasonal patterns, to evaluate the framework’s effectiveness. Results demonstrated the significant effectiveness of all framework models in forecasting surplus energy across seasonal scenarios, with low MAPE values of up to 3.02% and 3.59% over 24-h and 48-h horizons, respectively. Simultaneously, BiLSTM models consistently demonstrated a higher capacity to capture surplus energy fluctuations during peak periods than their counterparts. Overall, the developed data-driven framework demonstrates potential to enable short-term smart energy scheduling in micro-residential communities, supporting electric vehicle charging from single-family detached houses through efficient rooftop PV systems. It also provides decision-making insights for evaluating renewable energy contributions in the residential sector. Full article

The EPBD 2024 recast sets the deadline for new Zero-Emission Building standards for all new publicly owned buildings to 2028 and to 2030 for all new buildings. In the scope of Life Cycle Assessment stages, all steps resulting in major emissions from buildings must be considered and presented. The research evaluates the life cycle greenhouse gas emissions of a single-family house, focusing on diverse construction types and the hourly method of the annual energy calculations for continental and coastal climate areas in Croatia under the upcoming standards. Embodied carbon of diverse construction types was compared mutually, and required steps to meet the operational zero-emission standards were analyzed. Embodied energy of a 137.0 m² family house built out of reinforced concrete results in up to 67 tons of CO₂eq emissions, while wood in cross-laminated timber structures absorbs more carbon than emitted for all other materials and construction processes—23 tons of CO₂eq. Regarding operational energy and accompanying emissions, in order to cost-effectively meet future ZEB standards in Croatia and offset the remaining operational emissions, photovoltaic systems of up to 2.5 kWp are required in continental areas and 1.6 kWp in coastal regions. Full article

The increasing impact of global warming is predominantly driven by the extensive use of fossil fuels, which release significant amounts of greenhouse gases into the atmosphere. This has led to a critical need for alternative, sustainable energy sources that can mitigate environmental impacts. Photovoltaic technology has emerged as a promising solution by harnessing renewable energy from the sun, providing a clean and inexhaustible power source. Perovskite solar cells (PSCs) are a class of hybrid organic–inorganic solar cells that have recently attracted significant scientific attention due to their low cost, relatively high efficiency, low–temperature processing routes, and longer carrier lifetimes. These characteristics make them a viable alternative to traditional fossil fuels, reducing the carbon footprint and contributing to the fight against global warming. In this study, the SCAPS–1D numerical simulator was used in the computational analysis of a PSC device with the configuration FTO/ETL/BaZr(S_0.6Se_0.4)₃/HTL/Ir. Different hole transport layer (HTL) and electron transport layer (ETL) material were proposed and tested. The HTL materials included copper (I) oxide (Cu₂O), 2,2′,7,7′–Tetrakis(N,N–di–p–methoxyphenylamine)9,9′–spirobifluorene (spiro–OMETAD), and poly(3–hexylthiophene) (P₃HT), while the ETLs included cadmium suphide (CdS), zinc oxide (ZnO), and [6,6]–phenyl–C₆₁–butyric acid methyl ester (PCBM). Finally, BaZr(S_0.6Se_0.4)₃ was proposed as an absorber, and a fluorine–doped tin oxide glass substrate (FTO) was proposed as an anode. The metal back contact used was iridium. Photovoltaic parameters such as short circuit density (I_sc), open circuit voltage (V_oc), fill factor (FF), and power conversion efficiency (PCE) were used to evaluate the performance of the device. The initial simulated primary device with the configuration FTO/CdS/BaZr(S_0.6Se_0.4)₃/spiro–OMETAD/Ir gave a PCE of 5.75%. Upon testing different HTL materials, the best HTL was found to be Cu₂O, and the PCE improved to 9.91%. Thereafter, different ETLs were also inserted and tested, and the best ETL was established to be ZnO, with a PCE of 10.10%. Ultimately an optimized device with a configuration of FTO/ZnO/BaZr(S_0.6Se_0.4)₃/Cu₂O/Ir was achieved. The other photovoltaic parameters for the optimized device were as follows: FF = 31.93%, J_sc = 14.51 mA cm⁻², and V_oc = 2.18 V. The results of this study will promote the use of environmentally benign BaZr(S_0.6Se_0.4)₃–based absorber materials in PSCs for improved performance and commercialization. Full article

The study presents a comprehensive assessment of greenhouse gas (GHG) emissions and the carbon footprint (CF) of high-percentage spirit production in a Polish distillery. The analysis followed the GHG Protocol and ISO 14067:2018 standards, covering direct and indirect emissions across three Scopes. Using life cycle assessment (LCA) with a gate-to-gate boundary, emissions were across key technological processes. Verified operational data for 2022–2024 included detailed records of energy and fuel consumption. Electricity use was identified as the dominant emission source, accounting for 70–93% of total GHG emissions, followed by natural gas and transport fuels. The integration of renewable energy sources, including biomass and photovoltaic installations, resulted in a significant decrease in GHG emissions. The average carbon footprint of spirit production declined from 1.02 kg CO_2eq/L in 2022 to 0.12–0.15 kg CO_2eq/L in 2023–2024, representing an over 85% reduction in emission intensity. Production increased, but the company implemented better practices, including the use of biomass and photovoltaics as energy sources, which translated into a reduction in its carbon footprint. Scenario analysis showed that implementing the replacement of conventional fuels with renewables could lower total GHG emissions by up to 35%. The results confirm that renewable energy implementation and energy-efficiency improvements are effective decarbonization strategies for the spirits industry, supporting compliance with European Green Deal objectives and the transition toward climate-neutral production. Full article

Renewable energy resources have become extremely important in the current context of air pollution and the production of significant amounts of greenhouse gas emissions that contribute to global warming. One of the most important renewable energy sources that has shown the highest growth in recent years is photovoltaic (PV) systems. Due to their significance, this research presents a review of the applications in which artificial computer vision can be used in photovoltaic systems. From the results presented in this review, it will be evident that artificial vision can be applied for several different purposes. The advantages of using this technique will also be highlighted. Additionally, a systematic literature review is presented on the research associated with this topic. Through this review, it will be evident that many advanced algorithms related to image acquisition equipment have been proposed to ensure high reliability and fast results. This review does not merely focus on a specific topic or algorithms associated with image processing applied to photovoltaic systems. Rather, this work presents a broad and comprehensive review detailing all viable applications and associated computer vision technologies that can be deployed within these systems. Besides that, the review will clearly specify which work one is based on public datasets. To allow future reproducibility or research, the links to all public datasets utilized in the works based on them are included. Full article

The research process was based on an analysis of an existing building equipped with a heat pump on which photovoltaic panels were installed; then, based on energy consumption, the investment profitability was evaluated. In this research, using the available data, the coefficient of self-consumption of energy from the PV installation, the potential index of the installation’s own needs coverage, and the index of energy use from photovoltaic modules were determined, which in practice is equated with the energy efficiency of the PV installation. The entire investment was subjected to simulation and field tests to determine the energy demand of a single-family building. The main aim of this work was to check whether a system equipped with a heat pump combined with a PV installation is an effective technical solution in the analysed climatic conditions in one of the countries of Central and Eastern Europe. In addition, both positive and negative aspects of renewable energy sources were analysed, including long-term financial savings, energy independence, and reductions in greenhouse gas emissions. It has been shown that the described solution is characterised by high initial costs depending on weather conditions. The installation presented would allow us to avoid 1891 kg/year of CO₂ emissions, which means that with this solution, we contribute to environmental protection activities. Full article

Remote islands face persistent challenges in achieving secure, sustainable and affordable energy supply due to their geographic isolation, fragile ecosystems and dependence on imported fossil fuels. Hybrid renewable energy systems (HRES)—typically combining photovoltaics (PV), wind turbines and battery energy storage systems (BESS)—have emerged as the dominant off-grid solution, demonstrating their potential to reduce fossil fuel dependence and greenhouse gas emissions. Yet, empirical case studies from Zanzibar, Thailand, Malaysia, the Galápagos, the Azores and Greece confirm that current systems remain transitional, relying on oversized storage and fossil backup during low-resource periods. Comparative analysis highlights both technical advances and persistent limitations, including seasonal variability, socio-economic barriers and governance gaps. Future directions for PV—wind-based (non-dispatchable) island microgrids point toward long-term hydrogen storage, artificial intelligence (AI)-driven predictive energy management and sector coupling—alongside participatory planning frameworks that enhance social acceptance and community ownership. By synthesizing technical, economic and social perspectives, this study provides a roadmap for advancing resilient, autonomous and socially embedded hybrid off-grid systems for remote islands. Full article

In the era of growing energy demand and the need to reduce greenhouse gas emissions, the development of renewable energy sources, including photovoltaic farms, is becoming a key element of a sustainable energy transition. In this context, the careful selection of cadastral plots on which farms can be built is crucial, as appropriate location influences the investment’s energy efficiency and minimizes environmental and planning risks. This article presents a proprietary methodology for identifying cadastral plots that are suitable for locating a photovoltaic farm. The presented methodology integrates the Fuzzy-AHP multi-criteria analysis method and the Fuzzy Membership fuzzy logic method, thereby reducing the subjectivity of expert assessments and improving the accuracy of estimating the values of factors considered in the research. A key element of the methodology is a detailed analysis of land and building register data, which results in the identification of specific plots with high investment potential. The multi-criteria analysis considered eight key factors related to climate, terrain, land cover, and cadastral data. Based on this, eight plots and 32 plot complexes were selected as the most suitable for the construction of PV farms. The most favorable locations were identified primarily in the eastern part of Częstochowa Poviat, as well as in the northern municipalities. The proposed methodology provides a ready-to-use, practical solution to the investment challenge of selecting specific cadastral plots for new solar investments. According to the reviewed literature, each of the 40 designated sites could support a photovoltaic farm of an estimated capacity of at least 1 MW. The obtained results provide significant input into the renewable energy investment planning process and emphasize that careful selection of plot locations is crucial for the investment’s success and the region’s sustainable energy transformation. Full article

Protected agriculture increasingly requires solutions that reduce energy consumption and environmental impacts while maintaining stable microclimatic conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) with solar technologies has emerged as a pathway toward autonomous and energy-efficient greenhouses and solar dryers. This study analyzes the scientific and technological evolution of this convergence using a mixed review approach bibliometric and systematic, following PRISMA 2020 guidelines. From Scopus records (2012–2025), 115 documents were screened and 79 met the inclusion criteria. Bibliometric results reveal accelerated growth since 2019, led by Engineering, Computer Science, and Energy, with China, India, Saudi Arabia, and the United Kingdom as dominant contributors. Thematic analysis identifies four major research fronts: (i) thermal modeling and energy efficiency, (ii) predictive control and microclimate automation, (iii) integration of photovoltaic–thermal (PV/T) systems and phase change materials (PCMs), and (iv) sustainability and agrivoltaics. Systematic evidence shows that AI, ML, and DL based models improve solar forecasting, microclimate regulation, and energy optimization; model predictive control (MPC), deep reinforcement learning (DRL), and energy management systems (EMS) enhance operational efficiency; and PV/T–PCM hybrids strengthen heat recovery and storage. Remaining gaps include long-term validation, metric standardization, and cross-context comparability. Overall, the field is advancing toward near-zero-energy greenhouses powered by Internet of Things (IoT), AI, and solar energy, enabling resilient, efficient, and decarbonized agro-energy systems. Full article

Remediating complex-contaminated soils demands the synergistic optimization of efficiency, cost-effectiveness, and carbon emission reduction. Currently, ultra-high-temperature thermal desorption technology is mature in terms of principle and laboratory-scale performance; however, ongoing efforts are focusing on achieving stable, efficient, controllable, and cost-optimized operation in large-scale engineering applications. To address this gap, this study aimed to (1) verify the energy efficiency and economic benefits of removing over 98% of target pollutants at a 7.5 × 10⁴ m³ contaminated site and (2) elucidate the mechanisms underlying parallel scale–technology dual-factor cost reduction and energy–carbon–cost optimization, thereby accumulating case experience and data support for large-scale engineering deployment. To achieve these objectives, a “thermal stability–chemical oxidizability” classification criterion was developed to guide a parallel remediation strategy, integrating ex situ ultra-high-temperature thermal desorption (1000 °C) with persulfate-based chemical oxidation. This strategy was implemented at a 7.5 × 10⁴ m³ large-scale site, delivering robust performance: the total petroleum hydrocarbon (TPH) and pentachlorophenol (PCP) removal efficiencies exceeded 99%, with a median removal rate of 98% for polycyclic aromatic hydrocarbons (PAHs). It also provided a critical operational example of a large-scale engineering application, demonstrating a daily treatment capacity of 987 m³, a unit remediation cost of 800 CNY·m⁻³, and energy consumption of 820 kWh·m⁻³, outperforming established benchmarks reported in the literature. A net reduction of 2.9 kilotonnes of CO₂ equivalent (kt CO₂e) in greenhouse gas emissions was achieved, which could be further enhanced with an additional 8.8 kt CO₂e by integrating a hybrid renewable energy system (70% photovoltaic–molten salt thermal storage + 30% green power). In summary, this study establishes a “high-temperature–parallel oxidation–low-carbon energy” framework for the rapid remediation of large-scale multi-contaminant sites, proposes a feasible pathway toward developing a soil carbon credit mechanism, and fills a critical gap between laboratory-scale success and large-scale engineering applications of ultra-high-temperature remediation technologies. Full article

This paper examines the sustainability of photovoltaic (PV) panel recycling through a case study in Greece. It traces the evolution of PVs and outlines the main construction characteristics, emphasizing that although PV systems reduce greenhouse gas emissions, they also generate substantial end-of-life (EoL) waste containing both valuable and potentially hazardous materials. The study estimates Greece’s annual PV waste generation and evaluates its environmental, social, and economic impacts. It focuses on advanced disassembly and recycling methods by PV types and calculates material-recovery rates. Using national installation data from 2009–2023, the analysis quantifies the potential mass of recoverable materials and assesses the sustainability of PV recycling in terms of environmental protection, public health, and economic feasibility. Results show high recovery rates: silicon (85%), aluminum (100%), silver (98–100%), glass (95%), copper (97%), and tin (32%). Although current recycling economics remain challenging, the environmental and health benefits are significant. This research contributes to the existing literature by providing the first detailed quantification of recoverable raw materials embedded in Greece’s PV stock and by highlighting the need for technological innovation and supportive policies to enable a circular and sustainable solar economy. Full article