Search Results (174)

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

This study identifies the optimal location for an offshore energy island to supply sustainable power to desalination plants along the Red Sea coast. As demand for clean energy in water production grows, integrating renewables into desalination systems becomes increasingly essential. A decision-making framework was developed to assess site feasibility based on renewable energy potential (solar, wind, and wave), marine traffic, site suitability, planned developments, and proximity to desalination facilities. Data was sourced from platforms such as Windguru and RETScreen, and spatial analysis was conducted using Inverse Distance Weighting (IDW) and Multi-Criteria Decision Analysis (MCDA). Results indicate that the central Red Sea region offers the most favorable conditions, combining high renewable resource availability with existing infrastructure. The estimated regional desalination energy demand of 2.1 million kW can be met using available renewable sources. Integrating these sources is expected to reduce local CO₂ emissions by up to 43.17% and global desalination-related emissions by 9.5%. Spatial constraints for offshore installations were also identified, with land-based solar energy proposed as a complementary solution. The study underscores the need for further research into wave energy potential in the Red Sea, due to limited real-time data and the absence of a dedicated wave energy atlas. Full article

The present study aimed to estimate the contribution of the mining and mineral processing steps of lithium concentrate production in Brazil to the Global Warming Potential (GWP100) using an LCA perspective. No previous national study was identified that quantified national GHG emissions in mining and beneficiation operations for lithium ores. This study is considered original and aims to contribute to filling this gap. The functional unit was 1 ton of lithium carbonate equivalent (LCE) in the mineral concentrate. The contribution to GWP100 was estimated at 1220 kg of CO_2eq per ton of LCE, of which 262 kg originated from foreground processes. In the background processes, the largest contribution was 456 kg of CO_2eq from emissions in the production of ammonium nitrate, used in the manufacture of mining explosives. An analysis of substituting electricity sources in the product system showed a reduction of 22.7% and 14.7% in the estimated global warming impact when using wind or solar power, respectively. Full article

The deployment of utility-scale hybrid wind–solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from the available data, addressing key challenges such as (1) the spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) the cost dependencies of the different components and system characteristics. When conducting such analyses at the regional or national scale, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, identifying the lack of national-scale product cost models for HPPs as the main gap and establishing a replicable methodology for hybrid plant mapping. A simplified example is shown using this methodology for a country-level analysis. Full article

Water status is a key determinant of physiological performance and vineyard productivity. However, its assessment through field measurements is time-consuming and labour-intensive. Remote sensing offers a fast and reliable alternative to traditional in situ methods for the monitoring of the water status in vineyards. This study aimed to assess the potential of high-resolution multispectral imagery acquired by UAVs to estimate the vine water status. The research was conducted over two growing seasons (2021 and 2022) in a commercial Merlot vineyard in Yepes (Toledo, Central Spain), under five irrigation regimes designed to generate a range of water statuses. UAV flights were performed at two times of day (09:00 and 12:00 solar time), coinciding with in-field measurements of physiological parameters. Stem water potential (SWP), chlorophyll content, and photosynthesis data were collected. The SWP consistently showed the strongest and most stable associations with vegetation indices (VIs) and the red spectral band at 12:00. A simple linear regression model using the NDVI explained up to 58% of the SWP variability regardless of the time of day or year. Multiple linear regression models incorporating the red and NIR bands yielded even higher predictive power (R² = 0.62). Stronger correlations were observed at 12:00, especially when combining data from both years, highlighting the importance of midday measurements in capturing water stress effects. These findings demonstrate the potential of UAV-based multispectral imagery as a non-destructive and scalable tool for the monitoring of the vine water status under field conditions. Full article

Solar-induced chlorophyll fluorescence (SIF) is a powerful tool for the estimation of gross primary productivity (GPP), but the relationship between SIF and GPP under drought stress remains incompletely understood. Elucidating the response of the relationship between SIF and GPP to drought stress is essential in order to enhance the precision of GPP estimation in forests. In this study, we obtained SIF in the red (SIF₆₈₇) and far-red (SIF₇₆₀) bands and GPP data from tower flux observations in a Chinese cork oak plantation to explore the response of the diurnal GPP-SIF relationship to drought stress. The plant water stress index (PWSI) was used to quantify drought stress. The results show that drought reduced SIF and GPP, but GPP was more sensitive to drought stress than SIF. The diurnal non-linear relationship of GPP-SIF (R²) decreased with the increase in drought stress, but a significant non-linear correlation remained for GPP-SIF (R²_GPP-SIF₇₆₀ = 0.30, R²_GPP-SIF₆₈₇ = 0.23) under severe drought stress (PWSI_bin: 0.8–0.9). Physiological coupling strengthened the GPP-SIF relationship under drought, while canopy structure effects were negligible. Random forest and path analyses revealed that VPD was the key factor reducing the GPP-SIF correlation during drought. Incorporating VPD into the GPP-SIF relationship improved the GPP estimation accuracy by over 48% under severe drought stress. The red SIF allowed for more accurate GPP estimations than the far-red SIF under drought conditions. Our results offer important perspectives on the GPP-SIF relationship under drought conditions, potentially helping to improve GPP model predictions in the face of climate change. Full article

This study evaluates the feasibility of repurposing produced water—an abundant byproduct of hydrocarbon extraction—for green hydrogen production in Wyoming, USA. Analysis of geospatial distribution and production volumes reveals that there are over 1 billion barrels of produced water annually from key basins, with a general total of dissolved solids (TDS) ranging from 35,000 to 150,000 ppm, though Wyoming’s sources are often at the lower end of this spectrum. Optimal locations for hydrogen production hubs have been identified, particularly in high-yield areas like the Powder River Basin, where the top 2% of fields contribute over 80% of the state’s produced water. Detailed water-quality analysis indicates that virtually all of the examined sources exceed direct electrolyzer feed requirements (e.g., <2000 ppm TDS, <0.1 ppm Fe/Mn for target PEM systems), necessitating pre-treatment. A review of advanced treatment technologies highlights viable solutions, with estimated desalination and purification costs ranging from USD 0.11 to USD 1.01 per barrel, potentially constituting 2–6% of the levelized cost of hydrogen (LCOH). Furthermore, Wyoming’s substantial renewable-energy potential (3000–4000 GWh/year from wind and solar) could sustainably power electrolysis, theoretically yielding approximately 0.055–0.073 million metric tons (MMT) of green hydrogen annually (assuming 55 kWh/kg H₂), a volume constrained more by energy availability than water supply. A preliminary economic analysis underscores that, while water treatment (2–6% LCOH) and transportation (potentially > 10% LCOH) are notable, electricity pricing (50–70% LCOH) and electrolyzer CAPEX (20–40% LCOH) are dominant cost factors. While leveraging produced water could reduce freshwater consumption and enhance hydrogen production sustainability, further research is required to optimize treatment processes and assess economic viability under real-world conditions. This study emphasizes the need for integrated approaches combining water treatment, renewable energy, and policy incentives to advance a circular economy model for hydrogen production. Full article

Air pollution imposes a substantial economic burden globally, with estimated annual losses exceeding $8.1 trillion due to healthcare costs, lost productivity, infrastructure degradation, and agricultural damage. This review assesses the economic effectiveness of advanced air pollution control technologies within the broader context of sustainable energy transitions. Through comparative life-cycle cost-benefit analyses, we evaluate the financial viability, energy efficiency, and policy relevance of innovations such as carbon capture and storage (CCS), AI-driven emissions monitoring, and nanotechnology-enhanced filtration. Among the technologies assessed, CCS presents the most significant capital expenditure (up to $500 million per facility) but offers long-term returns through carbon credits and enhanced oil recovery, yielding up to $30–40 in economic benefits for every $1 invested. AI-based monitoring systems demonstrate strong economic efficiency by reducing energy consumption in industrial operations by up to 15% and improving regulatory compliance at a larger scale. Nanotechnology-enabled filters provide high pollutant capture efficiency and reduce operational resistance, yet face scalability and end-of-life challenges. Additionally, emerging technologies such as bioengineered filters offer promise for low-resource settings but require further economic validation. The integration of these technologies with renewable energy systems, such as hydrogen-powered pollution control units and solar-driven filtration, further amplifies their environmental and economic benefits. By aligning air pollution mitigation with climate and energy goals, this review highlights a pathway for policymakers and industries to achieve both economic resilience and environmental sustainability. The findings underscore that, while upfront costs may be high, strategic investments in advanced pollution control deliver substantial long-term returns across sectors. Full article

The development of the building sector to the use of renewable energy, more so in photovoltaic (PV) systems, is a great step toward enhanced environmental sustainability and improved energy efficiency. This study seeks to determine the economic, environmental, and operational effects of integrating a PV system into a Polish production plant for buildings. Case study methodology was followed with the help of actual operating histories and simulation modeling to present the estimates of carbon emission savings, cost savings, and power efficiency. Key findings illustrate that 31.8% of the business’s full-year supply of electricity is through the utilization of solar energy and that it saves as much as 10,366 kg CO₂ of emissions every year. The economic rationale of the system is provided in the form of a 3.6-year payback period against long-term savings of over EUR 128,000 in 26 years. This work also addresses the broader implications of energy storage and management systems on the basis of scalability and reproducibility of intervention at the building construction scale. This study provides evidence towards the requirement of informing decision-making by business managers and policy decisionmakers as a step towards the solution of issues of interest to the utilization of renewable energy at industrial levels towards world agenda harmonization for sustainability and business practice. Full article

Due to global changes, interest in solar energy is increasing day by day. The share of solar energy in energy production is constantly increasing, replacing limited resources such as oil and gas, due to the fact that its source is inexhaustible and free and it does not emit CO₂. The increasing prevalence of photovoltaic (PV) technology has brought about the problem of disposing of end-of-life panels in an environmentally friendly manner. In this study, a two-stage system model was developed to estimate Türkiye’s PV panel waste amount up to 2050. First, a new Artificial Neural Network (ANN) model was developed to estimate Türkiye’s total PV panel installed power in the coming years. The performance of the ANN model was compared with PV panel installed power estimation data obtained using multiple regression analysis. In the second stage, a mathematical model was created to estimate the amount of PV module waste. In the waste potential estimations for both methods, end-of-life and early failure scenarios due to various reasons were taken into account. As a result of the study, it was found that Türkiye’s total waste potential aligns with the future projection data published by the International Energy Agency (IEA) and the International Renewable Energy Agency (IRENA). Full article

Replacing approximately 7,000,000 internal combustion vehicles by 2030 with battery electric vehicles (BEVs) and promoting renewable energy sources are among the main strategies for decreasing greenhouse gas emissions and pollution in urban areas proposed in the EU FIT 55 program. Increasing the number of BEVs will lead to an increase in the electrical energy demand, which, according to the FIT 55 program, will be mainly supplied by the exploitation of renewable energies. In the present study, several possible scenarios were investigated for supplying the electrical energy necessary for the 7,000,000 BEVs within the goals imposed by FIT 55. To address this objective, four scenarios were proposed and analyzed for Italy, paying attention to the renewable energy share imposed by the EU on this country. The scenarios were photovoltaic-based; wind based; nuclear power-based; and thermal resource-based. The results show that if the EU FIT 55 goals are realized and 20% of the current number of internal combustion vehicles are replaced by BEV ones, there will be an energy imbalance at different times of the day. In the first scenario, if photovoltaic resources are used to the maximum extent to address the energy deficit, a 5.5-fold increase in the number of solar panels is required compared to 2023. In the second scenario, a 2.6-fold increase in the number of existing wind turbines is estimated to be required. In the third scenario, the supply of the energy deficit from nuclear resources with the production of 8.5 kWh in the daily energy cycle is examined. The use of the BESS to store excess energy at certain hours of the day and during energy shortage hours has been examined, indicating that on average, based on different scenarios, a system with a minimum capacity of 24 gigawatts and a maximum of about 130 gigawatts will be required. The fourth scenario is also possible based on the Fit for 55 targets and the use of thermal resources. An increase of 10 to 25 gigawatts is visible in each scenario during peak energy production hours. Also, a comparison of the scenarios shows that the energy storage during the surplus hours of scenario 1 is much greater than in the other scenarios. Full article

The aim of this study is to evaluate the environmental impact of solar energy by analyzing its emissions, resource consumption, and waste generation throughout its life cycle. As one of the most widely adopted energy sources, solar power offers substantial benefits in reducing greenhouse gas emissions; however, its broader environmental footprint requires careful examination. The production, operation, and disposal of solar panels contribute to pollution, water consumption, and hazardous waste accumulation, with an estimated 250,000 tons of solar waste reported in 2016 alone. Furthermore, solar power generation requires significant water resources, averaging 650 gallons per megawatt-hour of electricity. A key focus of this study is the emissions associated with solar technology, particularly during panel manufacturing and operation. Using HOMER Pro software, this research quantifies the emissions from Trina Solar photovoltaic (PV) panels (345 Wp), revealing an annual output of 49,259 kg of carbon dioxide, 214 kg of sulfur dioxide, and 104 kg of nitrogen dioxide. This Study obtained using HOMER Pro primarily account for operational emissions and do not include full lifecycle impacts such as raw material extraction, transportation, and disposal. These findings highlight the trade-offs between solar energy’s environmental advantages and its indirect ecological costs. While solar systems contribute to energy security and long-term economic savings, their environmental implications must be factored into energy planning and sustainability strategies. This study underscores the importance of developing greener manufacturing processes, improving recycling strategies, and optimizing solar farm operations to reduce emissions and resource depletion. By providing a comprehensive assessment of solar energy’s environmental impact, this research contributes valuable insights for policymakers, researchers, and industry leaders seeking to balance the benefits of solar power with sustainable environmental management. Full article

Variations in solar energy when it reaches the Earth impact the production of photovoltaic (PV) solar plants and, in turn, the dynamics of clean energy expansion. This incentivizes the objective of experimentally forecasting solar energy by parametric models, the results of which are then refined by machine learning methods (MLMs). To estimate solar energy, parametric models consider all atmospheric, climatic, geographic, and spatiotemporal factors that influence decreases in solar energy. In this study, data on ozone, evenly mixed gases, water vapor, aerosols, and solar radiation were gathered throughout the year in the mid-north area of Mozambique. The results show that the calculated solar energy was close to the theoretical solar energy under a clear sky. When paired with MLMs, the clear-sky index had a correlational order of 0.98, with most full-sun days having intermediate and clear-sky types. This suggests the potential of this area for PV use, with high correlation and regression coefficients in the range of 0.86 and 0.89 and a measurement error in the range of 0.25. We conclude that evenly mixed gases and the ozone layer have considerable influence on transmittance. However, the parametrically forecasted solar energy is close to the energy forecasted by the theoretical model. By adjusting the local characteristics, the model can be used in diverse contexts to increase PV plants’ electrical power output efficiency. Full article

Accurate solar spectral irradiance (SSI) input is key to modelling climate systems. Traditional SSI data used in the climate modelling community are based on solar model calculations joined by limited observations. Recent advances in spaceborne high-resolution solar spectrum observations, such as the National Administration for Space and Aeronautics (NASA)’s Total and Spectral Solar Irradiance Sensor (TSIS), have provided more accurate and reliable SSI alternatives. Here, we investigate the differences between the observed and the model-based SSIs, and how these affect the modelled downward surface shortwave radiation (DSSR) over different regions of China. Special interest is dedicated to the implications for solar power estimation from solar farms. We conduct idealized calculations using the RRTMG_SW radiative transfer model, with the traditional China Meteorological Administration standard solar spectrum (CMA_STD) and the observed TSIS-1 Hybrid Solar Reference Spectrum (TSIS-1_HSRS). Results show that the CMA_STD SSI yields 4.45 Wm⁻² less energy than the TSIS-1_HSRS, and systematically overestimate energy in the infrared bands and underestimate that in the visible bands. These discrepancies result in an annual regional mean DSSR underestimation of ~0.44 Wm⁻², with localized underestimation for a particular month exceeding 2 Wm⁻². The estimated solar power productions with the two SSIs differ by 0.25~0.32% and 0.36~0.52% of the total power production capacity for fixed-angle and solar tracking panels, respectively. These findings suggest that long-term and high-resolution spaceborne SSI observations are crucial to improve surface climate modelling, especially on local scales, and to service climate change mitigations. Full article

This study evaluates the efficiency of EU countries in utilizing their geographical potential for wind and solar energy production. A two-stage radial network data envelopment analysis (NDEA) is used to estimate the efficiency of the utilization of natural resources. The research is of a computational-empirical nature on the basis of publicly available data. The basic variables included in the model are: mean wind speed, Global Horizontal Irradiance, population, land area, wind energy capacity, solar PV capacity, wind energy generation, and solar power generation. The relationship between the environmental potential and the installed power capacity is evaluated in the first stage. In the second stage, the actual production from the installed capacity is analyzed. The efficiency trends over time are also investigated. This approach offers a comprehensive assessment by considering both the technical performance and environmental constraints. Considering all studied countries together, a slight increase in the relative efficiency of renewable energy potential utilization is observed—from 23.2% in 2018 to 28.7% in 2022. Germany and the Netherlands achieved 100% relative efficiency in 2022. The results reveal that the development of alternative energy sources and the efficiency of the installed power capacity utilization are not always in line with the local environmental conditions. The average efficiency of the analyzed countries from this perspective was 26.8% in 2018, with an improvement to 37.4% in 2022. The relative efficiency of the installed capacity utilization was high in both periods (76.3% and 74.9%, respectively). The impact of exogenous variables on performance (GDP and R&D expenditures) is discussed. Broader implications of the results for a responsible renewable energy policy in the EU demonstrate the need to combine overarching targets with a flexible governance system. That flexibility should allow for individual energy transition pathways, cooperative mechanisms, market integration, and targeted funding in order to account for the diversity of renewable resource utilization potentials among countries. Full article