ZnO-NaNO3 nanocomposites for solar thermal energy storage systems.

High-temperature phase change materials (PCMs) with good energy storage density and thermal conductivity are needed to utilize solar thermal energy effectively to meet industrial thermal energy demands. Composite PCMs containing a material of higher thermal conductivity and an inorganic high-temperature PCM can be explored to meet these requirements. Accordingly, a high-temperature, composite inorganic PCM (ZnO-NaNO3) with enhanced thermophysical properties was prepared, and its energy storage potential was investigated experimentally. A maximum thermal conductivity enhancement of 22.7% was achieved at 200 °C for 2 wt% ZnO-NaNO3 nanocomposite. The increase in thermal conductivity at higher temperatures may be attributed to the formation of ordered sodium nitrate layers on the nanoparticle surfaces. The increase in surface area and surface energy due to the addition of ZnO nanoparticles increased the specific heat of the nanocomposite in both the solid and liquid phases (43.5% in the liquid phase for 2 wt% ZnO-NaNO3). Thus, the addition of ZnO nanoparticles to NaNO3 increased its energy storage capacity. The addition of ZnO nanoparticles to NaNO3 did not affect the onset, peak or endset temperature during melting and freezing. Moreover, 2 wt% ZnO-NaNO3 exhibited cyclic stability even after 500 cycles and thus has potential as an energy storage medium.

MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources.

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on [Formula: see text]spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with [Formula: see text]spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters [Formula: see text], [Formula: see text], and [Formula: see text]. These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

External Support for Solar-Powered Water Pumping Systems in Rural Areas: A Systematic Review.

The Sustainable Development Goals emphasize coordination and integration between sectors. Solar-powered submersible water pumping systems are versatile technology that help address community drinking water, irrigation, and electricity needs. Stakeholders external to the community, particularly solar photovoltaic experts, are vital in ensuring continued system services; however, there has been no comprehensive assessment of different solar-powered water pumping system support efforts. This review is the first to systematically evaluate external support for solar-powered systems from multiple regions and implementing organizations. We reviewed solar-powered water pumping system literature to identify implemented external support and factors that affect implementation. Publication databases, organization Web sites, and citations were searched. Seventy-four studies were included and evaluated using inductive coding and thematic synthesis. We derived a framework that organized support activities and factors into three nested levels of implementation: system, program, and sector. For support efforts implemented after 2010, most support providers worked at all levels. Each provider type worked at levels aligned with their knowledge and resources and complementary to other providers' work. Drivers of support specific to solar-powered water systems were the existence of solar photovoltaic markets and infrastructure, support providers experienced with solar photovoltaics, and government and community solar advocates. We grouped support factors that study authors associated with system functionality into four categories: location and quality of support, reliability of support arrangements, frequency and timeliness of support, and policy and regulatory environment. No study outlined support for multiple uses of the systems or end-of-lifecycle care of solar panels. Solar-powered water pumping systems provide multiple community services, and their management will be bolstered by support providers collaborating to optimally apply their skill sets and create support plans that comprehensively address system versatility.

The co-benefits of climate change mitigation strategies on cardiovascular health: a systematic review.

Background: Climate change is a significant threat to global human health and a leading cause of premature death. Global warming, leading to more extreme weather (in particular extreme heat events), and air pollution has been associated with increased cardiovascular disease (CVD) morbidity and mortality. According to the Global Burden of Disease Study 2019, 62% of the deaths attributable to climate change were from CVD. Climate change mitigation is a slow, steady process, and the concept of co-benefits has arisen to promote climate action. This systematic review examines how numerous mitigation strategies, such as plant-based diets, increasing green spaces, increasing active transport, using renewable energy sources, and smoking cessation, may have the co-benefit of reducing CVD. Methods: A mixed methods systematic review with narrative synthesis was conducted on four databases, according to the PRISMA guidelines. The articles retrieved (published between 2012 and 2022) had a mitigation strategy as the exposure, and CVD related morbidity or mortality reduction as an outcome. Findings: The review found that renewable energy has a stronger association with cardiovascular co-benefits compared to emission reduction targets. Multimodal transport is more beneficial for both the climate and cardiac health than zero emission vehicles. Diet modification, such as Mediterranean and plant-based-diets, is positively associated with CVD reduction. Proximity to green spaces and reducing urbanisation may also improve cardiac health. Interpretation: This systematic review demonstrates that implementing four mitigation strategies - increasing renewable energy use, active transport, green spaces, and plant-based diets; could lead to the co-benefit of reducing CVD morbidity and mortality. Furthermore, it illustrates the importance of plant-based diets and active transport to improve cardiovascular health. Funding: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Factors Affecting Renewable Energy for Sustainable Development: The Case of the Philippines.

This paper examines the nexus between carbon dioxide (CO2) emissions, electricity consumption, fossil fuels, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in the Philippines. This paper also explores the intricate relationships between carbon dioxide (CO2) emissions, electricity consumption, fossil fuel use, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in the Philippines. Utilizing time-series data from 1990 to 2022 and applying advanced econometric techniques such as vector error correction modeling (VECM) and Granger causality tests, the study reveals the significant impacts of economic growth and energy consumption on CO2 emissions. The findings highlight the crucial role of renewable energy in mitigating environmental degradation. Policy implications are discussed in the context of the Philippines' commitment to sustainable development and climate change mitigation, emphasizing the need for integrated policies that promote renewable energy and energy efficiency alongside economic growth. We use a comprehensive econometric analysis to understand these variables' dynamic interactions and causal relationships. The study employs time-series data from 1990 to 2022 and applies advanced econometric techniques, including vector error correction modeling (VECM) and Granger causality tests. The results highlight the significant impact of economic growth and energy consumption on CO2 emissions while also underscoring the critical role of renewable energy in mitigating environmental degradation. Policy implications are discussed considering the Philippines' commitment to sustainable development and climate change mitigation.

Research on the conversion of biowaste to MCCAs: a review of recent advances in the electrochemical synergistic anaerobic pathway.

Medium-chain carboxylic acids (MCCAs) show great promise as commercial chemicals due to their high energy density, significant product value, and wide range of applications. The production of MCCAs from waste biomass through coupling chain extension with anaerobic fermentation represents a new and innovative approach to biomass utilization. This review provides an overview of the principles of MCCAs production through coupled chain extension and anaerobic fermentation, as well as the extracellular electron transfer pathways and microbiological effects involved. Emphasis is placed on the mechanisms, limitations, and microbial interactions in MCCAs production, elucidating metabolic pathways, potential influencing factors, and the cooperative and competitive relationships among various microorganisms. Additionally, this paper delves into a novel technology for the bio-electrocatalytic generation of MCCAs, which promotes electron transfer through the use of different three-dimensional electrodes, various electrical stimulation methods, and hydrogen-assisted approaches. The insights and conclusions from previous studies, as well as the identification of existing challenges, will be valuable for the further development of high-product-selectivity strategies and environmentally friendly treatments.

Effect of geopolitical risk on energy consumption policy: New empirical evidence from BRICS.

This study investigates the impact of geopolitical risk (GPR) on energy consumption. For empirical analysis, we utilize the dataset of BRICS nations spanning 25 years from 1998 to 2022. We employ three econometric models, namely fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and autoregressive distributed lag (ARDL), to analyze the relationships between GPR and energy consumption. Our empirical findings reveal several significant insights. Firstly, we observe a substantial negative influence of GPR on both fossil fuel energy consumption (FEC) and total energy consumption (TEC). This suggests that higher levels of GPR are associated with reduced utilization of fossil fuels and overall energy consumption within the BRICS countries. Conversely, we identify a significant positive effect of GPR on renewable energy consumption (REC). This implies that, as GPR rises, there is a corresponding increase in the adoption and usage of renewable energy sources. Furthermore, our analysis uncovers the presence of asymmetric effects pertaining to other key determinants of energy consumption, including FDI inflow, economic growth, banking sector development, and inflation rate. This study offers fresh empirical evidence on the intricate interplay between GPR and energy consumption in BRICS nations, shedding light on the significant impacts of GPR and the nuanced effects of various economic factors. These findings have important implications for policymakers and stakeholders seeking to navigate energy policy decisions in a geopolitically dynamic world.

Evaluating challenges of circular economy and Internet of Things in renewable energy supply chain through a hybrid decision-making framework.

Due to industrial development, expansion of communities, and attention to sustainable development, sustainable energy supply has become a big challenge for communities. In this regard, the development and use of Renewable Energy (RE) are considered due to reducing the harmful environmental effects of fossil fuels. Improving the efficiency of the Renewable Energy Supply Chain (RESC) is important for using RE. To improve the performance and efficiency of RESC, it is necessary to use emerging technologies such as the Internet of Things (IoT) and its integration with the principles of the Circular Economy (CE). Therefore, this study proposes integrating IoT and CE for sustainable development and resource management in RESC. Also, this research provides a hybrid decision framework to assess the challenges of IoT and CE in the RESC of Iran. The CRiteria Importance Through Intercriteria Correlation (CRITIC) technique is used to specify the importance of the criteria. The Fuzzy Evaluation Based on Distance from Average Solution (FEDAS) technique ranks the challenges. The findings indicated that considering the cost of investment, the rate of return on investment, and the productivity rate were the most important sub-criteria with values of 0.149, 0.129, and 0.106 respectively. Then, the sensitivity of the results is examined and the validation of the findings is analyzed with decision-making methods. The results indicate the high priority of the challenge related to transparency in the implementation procedures of IoT and RE projects and information dissemination protocols, the development of guidelines for the integration of IoT in other systems in the information network, and the amount of investment and lack of access to financial resources. This study provided practical insights for RE development based on IoT and CE capabilities for energy planning.

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids.

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

Multi-objective evolutionary method for multi-area dynamic emission/economic dispatch considering energy storage and renewable energy units.

Reducing thermal unit operating costs and emissions is the goal of the multi-objective issue known as multi-area economic/emission dispatch (MAEED) in smart grids. Using renewable energy (RE) have significantly lowered greenhouse gas emissions and ensured the sustainability of the environment. With regard to constraints such as prohibited operating zones (POZs), valve point effect (VPE), transmission losses in the network, ramp restrictions, tie-line capacity, this study aims to minimize operating costs and emission objectives by solving the multi-area dynamic economic/emission dispatch (MADEED) problem in the presence of RE units and energy storage (ES) systems. The conventional economic dispatch (ED) optimization approach has the following shortcomings: It is only designed to solve the single-objective optimization problem with a cost objective, in addition, it also does not have high calculation accuracy and speed. Therefore, to address this multi-objective MADEED problem with non-linear constraints, this paper introduces hybrid particle swarm optimization (PSO)-whale optimization algorithms (WOA). The reason for combining two algorithms is to use the advantages of both algorithms in solving the desired optimization problem. The introduced method is tested in two separate scenarios on a test network of 10 generators. Using the suggested hybrid methodology in this study, the MADED and MADEED problems are resolved and contrasted with other evolutionary techniques, such as original WOA, and PSO methods. Examining the results of the proposed method shows the efficiency and better performance of the proposed method compared to other methods. Finally, the results obtained by simulations indicate that integrating the necessary system restrictions gives the system legitimacy and produces dependable output. With regard to the results obtained from the introduced approach, the value of the overall cost function has clearly decreased by about 3 % compared to other methods.

Enhancing environmental quality and economic growth through potential effects of energy efficiency and renewable energy in Asian economies.

This study examines the potential impacts of energy efficiency and renewable energy on economic growth proxies by gross domestic product and environmental quality proxies by carbon dioxide emissions across eight selected Asian countries from 2000 to 2020. This study contributes by calculating green total factor productivity and carbon total factor productivity based on the famous Solow's residual via employing a modified extensive growth accounting model that internalized ignored factors such as energy efficiency and renewable energy. The employed panel cointegration techniques confirm that all variables are co-integrated with carbon dioxide emissions and economic growth. The pooled mean group/autoregressive distributed lag model analysis results indicate that energy efficiency is positively associated with both environmental quality and economic growth. Renewable energy hurts economic growth but has a positive effect on environmental quality which suggests the necessity of implementing an effective strategy for renewable energy alongside energy efficiency measures to enhance economic growth and environmental quality in the selected Asian countries. The findings from the fully modified ordinary least squares estimator are consistent with the environmental quality model. The average growth rate of green total factor productivity is positive despite negative contributions from energy efficiency and renewable energy. Similarly, the average growth rate of carbon total factor productivity is negative despite positive contributions from labor and capital. This discrepancy may be attributed to the beneficial effects of labor and capital as input productivity-driven. Embracing renewable energy sources can take significant steps toward improving environmental quality for future generations. Focusing on green technologies that enhance energy efficiency can substantially promote environmental quality and stimulate sustainable economic growth through innovation and climate change integration to achieve Sustainable Development Goals.

Effects of wind turbine dimensions on the collision risk of raptors: A simulation approach based on flight height distributions.

Wind energy development is a key component of climate change mitigation. However, birds collide with wind turbines, and this additional mortality may negatively impact populations. Collision risk could be reduced by informed selection of turbine dimensions, but the effects of turbine dimensions are still unknown for many species. As analyses of mortality data have several limitations, we applied a simulation approach based on flight height distributions of six European raptor species. To obtain accurate flight height data, we used high-frequency GPS tracking (GPS tags deployed on 275 individuals). The effects of ground clearance and rotor diameter of wind turbines on collision risk were studied using the Band collision risk model. Five species had a unimodal flight height distribution, with a mode below 25 m above ground level, while Short-toed Eagle showed a more uniform distribution with a weak mode between 120 and 260 m. The proportion of positions within 32-200 m ranged from 11 % in Marsh Harrier to 54 % in Red Kite. With increasing ground clearance (from 20 to 100 m), collision risk decreased in the species with low mode (-56 to -66 %), but increased in Short-toed Eagle (+38 %). With increasing rotor diameter (from 50 to 160 m) at fixed ground clearance, the collision risk per turbine increased in all species (+151 to +558 %), while the collision risk per MW decreased in the species with low mode (-50 % to -57 %). These results underpin that wind turbine dimensions can have substantial effects on the collision risk of raptors. As the effects varied between species, wind energy planning should consider the composition of the local bird community to optimise wind turbine dimensions. For species with a low mode of flight height, the collision risk for a given total power capacity could be reduced by increasing ground clearance, and using fewer turbines with larger diameter.

Interpretation of possible biogas production capacity by investigating the effects of anaerobic digester tank geometry and angular velocity on flow characteristics.

Mixing performance in reactors producing biogas through anaerobic digestion is one of the parameters that directly affect biogas yield. The most commonly used mixing model for bioreactors in biogas-production processes is mechanical mixing. In the present study, we focus on the geometry of the tank, where the mechanical mixing actually takes place. In this context, by using the six-blade standard Rushton impeller in two different types of tank, flow patterns involving velocity, dead zone volume, turbulent kinetic energy, and turbulent eddy dissipation rate in the angular velocity range of 25-100 rpm were observed, and the possible effects of the results on biogas production were interpreted. A new impeller design was proposed that maximizes the interface between the fluid inside the reactor tank and the impeller, which has the potential to reduce the dead zone volume to significantly lower levels. Our results showed that the lowest dead zone volume was achieved for a 60° slope reactor tank compared to the conventional 90° slope reactor tank at an angular velocity of 100 rpm. The dead zone volume decreased to 0.000094 m3 at 100 rpm in the 60° slope reactor tank with a total volume of 0.0305 m3, which by comparison was 0.000374 m3 in the 90° slope reactor tank. The magnitudes of both maximum turbulent kinetic energy and maximum turbulent eddy dissipation were higher in the 60° slope reactor tank at all angular velocities examined, which would be expected to enhance mixing performance. It is hoped that the reader will benefit from the results of this study; however, further studies should be conducted on the use of actual biowaste as the working fluid instead of water.

Reinforced Nacre-Like MXene/Sodium Alginate Composite Films for Bioinspired Actuators Driven by Moisture and Sunlight.

MXene-based soft actuators have attracted increasing attention and shown competitive performance in various intelligent devices such as supercapacitors, bionic robots and artificial muscles. However, the development of robust MXene-based actuators with multi-stimuli responsiveness remains challenging. In this study, a nacre-like structure soft actuator based on MXene and sodium alginate (SA) composite films is prepared using a straightforward solvent casting self-assembly method, which not only enhances the mechanical performance (tensile strength of 72 MPa) but also diversifies the stimuli responsiveness of the material. The composite actuators can be powered by external stimuli from renewable energy sources, from moisture inducing a maximum bending angle of 190 degrees at a relative humidity (RH) of 91%, and sunlight irradiation generating a maximum curvature of 1.45 cm-1 under 100 mW cm-2. The feasibility of practical applications, including moisture-responsive flowers and walkers, sunlight-responsive oscillators, and smart switches, is demonstrated through comprehensive experimental characterization and performance evaluation. The work presented here provides insight into the design of robust actuators via the utilization and conversion of environmentally renewable energy sources.

Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources.

Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80-90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

Improved biohydrogen production using Ni/ZrxCeyO2 loaded on foam reactor through steam gasification of sewage sludge.

The pervasive generation of sewage sludge (SES) and deficiencies in its disposal methods have resulted in several significant environmental and human health challenges. This study explored the catalytic effect of nickel (Ni)-based CeO2, ZrO2, Zr0.8Ce0.2O2, Zr0.4Ce0.6O2, and γ-Al2O3 supports in fixed beds and foam reactors in the steam gasification of SES. A comparison of the hydrogen selectivity and gas yield of the synthesized catalysts confirmed that the metal composite support, especially Zr0.8Ce0.2O2, had a positive effect on the catalytic activity and stability. This can be attributed to the enhanced oxygen vacancies and oxygen mobility, resistance to coke deposition, uniform morphology, improved dispersion, and increased number of Ni sites on the Zr0.8Ce0.2O2 support. Furthermore, foam reactors offer unique advantages in improving hydrogen production. This study provides an advanced strategy for SES valorization that fulfills the requirements of an economically and environmentally sustainable technology.

Analyzing nonlinear and asymmetric effects of green finance and renewable energy on energy efficiency amidst technological innovation in E7 countries.

The study aims to explore the relationship between green funding, green energy, and energy efficiency in E7 countries, guided by the SDG-7 guidelines recommended by the United Nations General Assembly. Methodologically, the study employs the Nonlinear Autoregressive Distributed Lag (NARDL) and Two-Stage Least Squares (2SLS) techniques on data collected between 1988 and 2022. The rationale for this approach lies in its ability to capture both short-term and long-term dynamics in the relationship between green funding, green energy, and energy efficiency. Analysis of the data reveals varying stages of green funding growth among E7 countries, with China (1.52), Brazil (1.44), India (1.35), Indonesia (1.94), Mexico (1.73), and Russia (1.93) exhibiting different levels of progress. Russia and Turkey are identified as having the highest Gini coefficients in 2019, indicating disparities in green funding distribution within these countries. The empirical findings underscore the critical role of investment in the energy sector by both corporations and the public sector to enhance access to electricity, bolster energy security, and foster environmentally sustainable economic development. However, the study identifies insufficient investment as a fundamental obstacle hindering progress in green energy efficiency in E7 nations. Despite the potential for implementing energy efficiency measures and renewable energy sources in E7 countries, the future remains uncertain due to existing obstacles in green financing and regulatory frameworks. Consequently, the paper emphasizes the imperative of addressing these obstacles to unlock the full funding potential for energy efficiency initiatives in E7 nations.

A comprehensive analysis of real options in solar photovoltaic projects: A cluster-based approach.

Solar photovoltaic (PV) projects are pivotal in addressing climate change and fostering a sustainable energy future. However, the complex landscape of renewable energy investments, characterized by high upfront costs, market uncertainties, and evolving technologies, demands innovative evaluation methods. The Real Options Approach has emerged as a powerful tool, offering strategic flexibility in decision-making under uncertainty. This paper comprehensively analyzes the application of real options for evaluating solar photovoltaic projects in 2008-2023. Analysis of document descriptors (author keywords, index keywords, and noun phrases extracted from titles and abstracts) reveals that the dominant research topics in the last ten years (2014-2023) include investment optimization, strategic analysis, energy policy, optimization of energy generation and investments in wind energy. These descriptors are used to analyze the evolution of research interests on a two-year basis and reveal the yearly evolution of the research topics. Finally, the concept of emergence is used to unveil emerging research trends, providing valuable insights for researchers and practitioners in the renewable energy sector. Ultimately, this work contributes to a deeper understanding of how real options analysis empowers decision-makers to make informed choices in advancing clean and sustainable energy solutions.

The utilization of renewable energy and the economic potential of offshore wind power supported by digital finance.

Colombia is well-positioned for the development of sustainable energy due to its abundance of natural resources, which include water, wind, and sun. Regulating the safe and sustainable use of offshore wind energy, which is considered non-conventional, is lacking in the nation, nonetheless. The development of offshore wind technology in Colombia shows potential to meet energy needs during dry hydrological conditions and El Niño/Southern Oscillation events when the hydroelectric system power supply is low. This study examines global initiatives that have encouraged nations to develop plans for cutting their CO2 emissions, stressing both their successes and shortcomings in putting offshore wind technology into practice. An examination of Colombia's renewable energy administrative framework finds a lack of data required to carry out offshore wind projects. Furthermore, a review of previous research on marine energy emphasizes how important it is to expand our knowledge of offshore wind generation. Although the majority of local renewable energy projects concentrate on terrestrial sources, an analysis of wind speed and wind power density in Colombia at different altitudes shows promising magnitudes and good trends.Digital finance plays a crucial role in this context by providing innovative funding mechanisms, enhancing financial accessibility, and reducing investment risks through improved financial technologies. These advancements support the mobilization of capital necessary for the development and expansion of offshore wind energy projects.As a result, the technical, economic, administrative, and legal data pertinent to renewable energy in Colombia is compiled in this study. It proposes to provide information to stakeholders involved in decision-making processes and promotes the possible installation of offshore wind farms in regions close to Colombia's Caribbean coast. Because of its plentiful resources, Colombia offers a great chance to implement offshore wind energy technology, which will lessen dependency on fossil fuels and provide a backup energy source in case of supply shortages. The integration of digital finance is key to unlocking the economic potential of these projects, ensuring sustainable and scalable energy solutions for the future.