Exploitation Of Renewable Energy Sources Research Articles

Valuation of Internet of Energy (IoE) Platforms in Smart Cities: A Hybrid Multi-Criteria Decision Making Approach

A smart city is a place where people live using computing, sensing, and networking technology in a variety of ways, including smart buildings, smart homes, smart transportation, smart workplaces, smart offices, smart industries, and smart agriculture. These smart technologies need to connect so; we need to use the Internet of Things (IoT) that has applications in many areas of our smart lives. IoT has been used due to the growing usage of renewable energy sources and the challenges of network management. Internet of Energy (IoE) integrates the IoT and smart grid capabilities. The basic role of smart grids or IoE is an essential element in creating smart cities. In this work, we present some IoE platforms that represent the alternatives and their criteria. The proposed study treats the uncertainty of multi-criteria decision-making (MCDM) modified by single value triangular neutrosophic number (SVTrN) by choosing the optimal IoE platform based on the criteria importance through inter-criteria correlation (CRITIC) method and multi-attributive ideal-real comparative analysis (MAIRCA). The proposed model proved its effectiveness by applying it to an experimental case. The results show that the DCC (A3) platform is the best one.

Minimizing the Effect of Integrating Wind Farms on Transmission Power Systems

Expanding the renewable energy sources usage in power systems will help the government meet its energy policy goals of lowering emissions, ensuring high power supply reliability, and promoting spot markets with high competitivity. However, due to the intermittent nature of these sources, this will necessitate the mitigation of network vulnerabilities. High wind energy penetration in the grid could have an impact on the system frequency, voltage, real and reactive power response, and power quality of the system. The main objective of this paper is to investigate how the integration of wind farms in the Jalan Bani Bu Ali (JBBA) area will affect Oman's main interconnected system (MIS). Results show that the effect is minor on voltage and frequency stability by all disturbances taken into account.

Technical innovation, renewable energy consumption, and CO2 emissions in the USA: a cross-quantile approach.

This study investigates whether technological innovation and the consumption of renewable energy tend to reduce the emissions of CO2 in the USA by analyzing datasets from January 2010 to May 2022. The main contribution to this study is that we applied a cross-quantile approach, which possesses several strengths compared to other methods used for directional predictability. The empirical results of this research can be concluded as three points: (1) both the consumption of renewable energy and technological innovation significantly and negatively impacted the emissions of CO2 in the short run (i.e., 1month) across high quantiles, which gradually diminished over time (i.e., 3months, 12months, and 24months), implying that technological innovation and the consumption of renewable energy possess a short-lived effect on CO2 emissions, respectively; (2) this relationship remains significant for causal links spanning 1 and 3months and 1 and 2years when the consumption of renewable energy and technological innovation are treated as control variables respectively; (3) a recursive cross-quantilogram was constructed to support further our findings, which showed that the consumption of renewable energy and technological innovation tend to negatively impact the emissions of CO2 across all quantiles. These results imply that an increase in the consumption of renewable energy and technological innovation can curb CO2 emissions in the USA; these effects tend to be more lasting when technological innovation and the consumption of renewable energy are combined. Therefore, future policies focused on curbing the emissions of CO2 should pay attention to the combined effect, which is the promotion of technological innovation and the exploitation of renewable energy sources in the USA.

Modeling the Green Cloud Continuum: integrating energy considerations into Cloud–Edge models

AbstractThe energy consumption of Cloud–Edge systems is becoming a critical concern economically, environmentally, and societally; some studies suggest data centers and networks will collectively consume 18% of global electrical power by 2030. New methods are needed to mitigate this consumption, e.g. energy-aware workload scheduling, improved usage of renewable energy sources, etc. These schemes need to understand the interaction between energy considerations and Cloud–Edge components. Model-based approaches are an effective way to do this; however, current theoretical Cloud–Edge models are limited, and few consider energy factors. This paper analyses all relevant models proposed between 2016 and 2023, discovers key omissions, and identifies the major energy considerations that need to be addressed for Green Cloud–Edge systems (including interaction with energy providers). We investigate how these can be integrated into existing and aggregated models, and conclude with the high-level architecture of our proposed solution to integrate energy and Cloud–Edge models together.

Does tourism development, financial development and renewable energy drive high-quality economic development?

Tourism development is generally agreed upon to be a key tool in promoting economic growth, and green development has emerged as a significant idea and an efficient approach to accomplish this goal in a manner that is environmentally responsible. It is common knowledge that making the switch to renewable sources of energy may act as a catalyst for economic development in both developed and developing nations. Therefore, people all over the globe are beginning to realize the significance of advancing renewable energy's rising importance that suggests that it will be used extensively in the years to come. The purpose of this study is to examine the effect that increasing tourism and adopting renewable energy sources impact on green economic growth development in the region using the AMG test BRICS nations from 2001 to 2022. This research additionally makes use of a rigorous check by means of the CCEMG exam and the DCCEMG test. According to the findings, green economic growth is influenced favorably by the expansion of the tourist industry, renewable energy, and the digital economy, but urbanization and the rise financial industry are detrimental to green economic growth. D-H panel causality test results show that tourism development is causally related to green economic growth, green economic growth to urbanization, and green economic growth to the usage of renewable energy sources. According to these results, the management authorities of BRICS nations should embrace policies of green growth while also controlling environmental pollution in order to achieve sustainable economic development whereas in rural areas. The findings have major policy implications for the nations that make up the BRICS bloc. These implications pertain to the enhancement of tourist development, the digital economy (DIG), and financial institutions, all of which have the potential to improve environmental quality.

Does Higher Institutional Quality Result in Environmental Sustainability? A Comparative Study of India and China

There is a lack of comprehensive research comparing India and China in terms of institutional quality and environmental degradation. This special issue aims to make threefold distinct contributions. By comparing India and China, we can learn from each other's experiences and gain insight into the shared difficulties both nations face when trying to achieve environmental sustainability in the face of high-quality institutional frameworks. The impact of corruption, economic development, ecological risk, and renewable energy sources on carbon dioxide emissions in China and India is investigated in this study. For this reason, we also employ the ARDL model and pairwise Granger causality to prove that something caused another. Applying the ARDL long-run bound test, the empirical results reveal a long-run connection between variables in both China and India. In the case of China and India, the estimated results also reveal a negative correlation between carbon emissions and the usage of renewable energy sources, but a positive correlation between carbon emissions and economic growth, corruption, and environmental risk. Improving institutional quality is the goal, and the present theoretical and applied discourse on comparative environmental sustainability is a good place to start. As a result, policymakers in China and India should oppose unfair practices that undermine competition laws and policies by instituting stringent anti-corruption measures and environmental rules. Energy efficiency measures that reduce carbon emissions without slowing economic growth should also be the government's primary focus.

The Intricate Impression of Tourism, Patent, And Renewable Energy on Environmental Sustainability

The growing global attention to indicating climate conversion and plummeting carbon dioxide (CO2) emanations has spurred researchers to examine the influence of several factors on promoting environmental sustainability. This research examines the intricate role of tourism, patent applications, rent from natural resources, and utilization of renewable power in carbon dioxide secretions in Bangladesh. For this purpose, data has been collected from 1995 to 2019 from World Development Indicators (WDI). Augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP) and the ARDL bound cointegration procedures are employed to determine the existence of stationary data and cointegration among variables respectively. The Autoregressive Distributive Lag (ARDL) procedure is used to consider the effects of the chosen variables on CO2 emissions over the short and long run. VIF test, White's test, and the Breusch-Godfrey test are employed to check multicolinearity, homoscedasticity, and autocorrelation respectively. The research discloses that it is vital to expand the usage of renewable energy sources while simultaneously decreasing natural resource rent by limiting natural resource extraction for a sustainable environment. Although the statistical insignificance of the long-term coefficient of tourism has been observed, the study reveals a negative coefficient for tourism, suggesting the possible efficacy of sustainable tourism practices for mitigating CO2 emissions. The findings also demonstrate the long-term contribution of innovation activities to environmental degradation through the positive coefficient of patent applications. The research results offer significant insights for policymakers and stakeholders involved in the pursuit of a sustainable and ecologically conscious future in Bangladesh.

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.

Effect of green taxation on renewable energy technologies: an analysis of commonwealth and non-commonwealth countries in Sub-Saharan Africa.

African nations encounter difficulties enforcing regulations and providing incentives for using renewable energy sources. However, several nations are making efforts to encourage renewable energy through financial and tax advantages. Therefore, a shift to renewable energy is essential for African nations to experience sustainable growth and lessen environmental deterioration. Similarly, the extant literature examining green taxes' influence on renewable energy technology has documented equivocal findings. Hence, there is a need for a more thorough investigation. This study, therefore, explores the influence of green taxation on renewable energy technologies of emerging countries in Sub-Saharan Africa. We employed data from a sample of 28 countries of 54 African countries spanning 21years from 2001 to 2021, providing a panel of 588 country-year observations. The Organisation for Economic Co-operation and Development (OECD) and the World Bank Dataset provided all the study's data. A heterogeneous dynamic panel data modelling using the autoregressive distributed lag (ARDL) has been adopted. The study found that green taxes might be used to mitigate the adverse effects of non-renewable energy activities on the environment in Africa. Considering the findings of the components of green taxes, it was recognised that an increase in energy-related tariffs would lead to a growth in Africa's use of renewable energy. It was further established that an increase in transport taxes increases the adoption of renewable energy technologies in Africa. A comparative analysis between the commonwealth and non-commonwealth countries showed that green taxes of commonwealth countries in Africa significantly contribute to the growth of renewable energy technologies compared to non-commonwealth countries in Africa. Primarily, the results of this study can be a valuable resource for African governments and policymakers as they develop policies and evaluate legislation about the usage of renewable energy sources and other green practices. Finally, the study can shed light on creating and using efficient tax laws that support renewable energy sources.

Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques

This study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including economic efficacy, environmental viability, and microgrid robustness. The use of GA showcases a significant 10% decrease in overall system expenses, highlighting its efficacy in augmenting economic feasibility. PSO diligently tracks, attaining an 8% decrease, while SA and LP make significant contributions but provide somewhat lesser cost reductions at 7% and 6%, correspondingly. Within the domain of renewable energy integration, GA and PSO have emerged as frontrunners, with remarkable advancements of 12% and 10%, respectively. SA and LP provide commendable contributions, demonstrating their effectiveness in optimizing the usage of renewable energy sources inside the microgrid, as seen by their respective increases of 8% and 7%. The environmental factor, as quantified by the decrease of carbon emissions, highlights the commendable efficacy of GA and PSO, resulting in significant reductions of 15% and 12% respectively. SA and LP provide praiseworthy environmental efforts, achieving reductions of 10% and 8% respectively. The resilience index highlights the strength of GA and PSO in assessing the resilience of the microgrid, with GA showing an increase of 0.05 and PSO showing an increase of 0.04. SA and LP make a significant contribution, with increments of 0.03 and 0.02, underscoring the potential of evolutionary and swarm-based methodologies to bolster the microgrid’s resilience against disturbances. Scenario analysis effectively brings unpredictability into the operational environment of the microgrid, continually showcasing the remarkable flexibility of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) over a wide range of situations. SA and LP demonstrate consistent efficacy but with somewhat reduced flexibility. Statistical evaluations provide compelling evidence confirming the exceptional efficacy of GA and PSO in improving microgrid metrics. Ultimately, this research provides valuable perspectives on the intricate trade-offs between various optimization techniques, empowering decision-makers to choose strategies that align with specific microgrid objectives. Moreover, it contributes to the wider discussion on resilient, sustainable, and economically feasible energy infrastructures.

Fuzzy Logic-Based Energy Storage Management for Grid Resilience

In this study, we present and examine the implementation of a fuzzy logic-driven energy storage management system devised to enhance the efficiency of charging and discharging activities in modern power grids. By using real data derived from the installation of the system, our research aims to assess its effectiveness in bolstering grid resilience and stability. The system integrates linguistic factors and employs rule-based decision-making, enabling it to adapt charging and discharging techniques in real-time to the prevailing grid circumstances. The results unequivocally indicate a noteworthy enhancement in the efficiency of the system, exhibiting a percentage alteration spanning from 8% to 12%, hence confirming the system’s inherent ability to mitigate energy losses throughout its operating cycles. Moreover, the use of the fuzzy logic controller significantly enhances the Grid Reliability Index, exhibiting a percentage variation ranging from 10% to 15%, so demonstrating a higher level of stability and responsiveness to grid demand. The controller’s flexibility is highlighted by doing sensitivity analysis, which demonstrates its strong decision-making skills across many contexts. The charging and discharging techniques are meticulously tuned, demonstrating a percentage fluctuation of 15% to 20% in accordance with diverse grid situations, effectively optimizing the exploitation of renewable energy sources. The practical usefulness of the proposed system is confirmed by the validation of simulation results against historical data. The research incorporates ethical issues, such as clear communication, user permission, and socio-economic implications, to underscore the appropriate application of modern energy management technology. The results of this study significantly enhance the overall comprehension of fuzzy logic-driven systems, presenting a very promising resolution for the enhancement of energy storage operations and the progression of robust and environmentallyfriendly energy infrastructures.

Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks

Today, many countries are focused on smart grids due to their positive effects on all sectors of a power system, including those of operators, utilities, and consumers. Furthermore, the usage of renewable energy sources for power production is quickly expanding due to the depletion of fossil fuels and the emissions caused by their use. Additionally, intermittent power generation from renewable energy sources, such as wind and solar, necessitates the use of energy storage devices with which to ensure a continuous power supply to meet demand. This can be accomplished by employing an appropriate storage device with a sufficient storage capacity, thus enabling a grid-connected solar PV and wind system to have enhanced performance and to reduce adverse effects on the power quality of the grid. In this study, a probabilistic planning model that takes the intermittent natures of solar irradiances, wind speeds, and system demands into account is introduced. A novel criterion is also adopted to map the three-dimensional spaces of intermittency with the proposed model for optimizing BESS charging/discharging decisions. This planning model is intended to minimize the economic costs of investment and operation of a battery energy storage system (BESS) for a planning period. Moreover, the substation and feeder upgrade costs, as well as the overall system loss costs, are included in the proposed model. Particle swarm optimization (PSO) is utilized to find the optimal sizing, location, and operation of energy storage systems. The proposed methodology was validated using a 69-bus distribution system.

NSGA-T: A novel evaluation method for renewable energy plans

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods.

Increasing Distributed Generation Hosting Capacity Based on a Sequential Optimization Approach Using an Improved Salp Swarm Algorithm

In recent years, a pronounced transition to the exploitation of renewable energy sources has be observed worldwide, driven by current climate concerns and the scarcity of conventional fuels. However, this paradigm shift is accompanied by new challenges for existing power systems. Therefore, the hosting capacity must be exhaustively assessed in order to maximize the penetration of distributed generation while mitigating any adverse impact on the electrical grid in terms of voltage and the operational boundaries of the equipment. In this regard, multiple aspects must be addressed in order to maintain the proper functioning of the system following the new installations’ capacities. This paper introduces a sequential methodology designed to determine the maximum hosting capacity of a power system through the optimal allocation of both active and reactive power. To achieve this goal, an Improved Salp Swarm Algorithm is proposed, aiming to establish the appropriate operational planning of the power grid considering extensive distributed generation integration, while still ensuring a safe operation. The case study validates the relevance of the proposed model, demonstrating a successful enhancement of hosting capacity by 14.5% relative to standard models.

Landscape-based spatial energy planning: minimization of renewables footprint in the energy transition

Landscape transformations derived by renewable energy sources exploitation may induce public resistance and loss of quality of the existing environment. Integrated approaches are needed to inform and guide transformation processes, relying on empirical evidence regarding spatio-technological feasibility, acceptance by the community, and integration in the landscape. To address this issue, the paper aims to propose a methodological procedure for the development of local spatial plans to implement photovoltaic systems at the local level. The procedure is spatially explicit, combining qualitative considerations of inhabitants and experts with quantitative data on energy potentials, and associating site selection with solar integration strategies. The outcome is a planning framework combining spatial areas with quality requirements for the implementation of solar power plants, thereby allowing for the envisioning of future scenarios. The application of the method is tested in Arcos de la Frontera, Spain, considering both on-ground and on-roof distributed energy systems.

Development of N doped C/MnAl2O4 spinel as environmental friendly energy storage devices

The tremendous use of fossil fuels has a detrimental impact on the environment and living organisms. Scientists stive to replace nonrenewable fossil fuels with renewable sources of energy that are environmentally friendly. However, the usage of renewable energy sources is limited due to their dependency on the climate and geographical position. Researchers focused on electrochemical saving equipment have developed supercapacitor devices with outstanding and unique physiochemical features. Herein, we utilized a straightforward two-step method for producing N-doped C MnAl2O4 spinel from hydrothermally synthesized Mn–Al layered double hydroxide (LDH), and pyrolysis of LDH created a thin spinel material and carbon composite. Various physical characteristics were used to determine the material properties, and various physical analyses were employed to ensure the fabrication of doped materials. MnAl2O4/C has the potential to generate a high specific capacitance of 1231.42 F g−1, energy density (Ed) of 37.94 Wh kg−1 as well as a rate capability of 96.01 % at 1 A g−1. The chronoamperometry analysis showed a straight line indicating the stability of the fabricated nanocomposite electrode over 50 h. Our findings suggest that nitrogen-doped carbon/MnAl2O4 exhibited a larger specific capacitance and stability than many other spinel materials and may potentially be employed in other energy storage applications.

Water flow simulation in a pelton turbine bucket with variable bucket dimensions using computational fluid dynamic

Today, especially among industrialized and developing nations, the usage of renewable energy sources is growing in popularity as a means of supplying energy needs. One of the renewable energy sources that is now being heavily exploited in Indonesia is the potential of water that may be used as a source of electrical energy by establishing a hydroelectric power plant (PLTA). One of the turbines that is frequently used in hydropower plants as a part that may transform water's kinetic energy into mechanical energy is the Pelton turbine. There is not a lot of literature on the design of Pelton turbine buckets. In order to determine the most ideal bucket dimensions that can be used with Pelton turbines, this study presents the conceptual and experimental components of the design and analysis of Pelton turbines based on bucket variations. AutoCAD was used to model the bucket and Pelton turbine, and ANSYS Fluent was used to perform the simulation so that results could be analyzed later. By comparing the simulated data of the blade variants 1 and 2, the ideal blade dimension variation is identified by the blade simulation of variant 1. The blade variation 2 with average torque of 5.859 Nm, average angular velocity of 40.822 rad/s, and average power of 242,970 watts is the most ideal blade. With an average torque of 4,735 Nm, an average corner speed of 40,404 rad/s, and an average power of 196,794 W, the blade 3 model has lower values

A novel evaluation method of renewable energy development based on improved rough set theory and grey cloud model

Renewable energy is an effective solution to alleviate the energy crisis and achieve sustainable development, and is gradually growing into an important energy source to support economic and social development. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. Given this background, this paper proposes a novel evaluation method for renewable energy technical plans based on improved rough set theory and grey cloud model. Firstly, based on the normalized evaluation data, self-organizing map (SOM) clustering algorithm and system clustering analysis are adopted to discretize the evaluation attributes indexes and decision attributes indexes, respectively. Next, the improved rough set conditional information entropy method is used to calculate the index weights. Finally, the grey cloud model is employed to determine the grade classification of each evaluated plan. At the same time, each grade is quantified by assigning a corresponding score, and the comprehensive performances of renewable energy plans can be calculated, and then the ranking of different plans is achieved. Based on the actual renewable energy development data from a province in China, experiments were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than some evaluation methods for renewable energy technical plans.

Short-Loop Recycling of Nd-Fe-B Permanent Magnets: A Sustainable Solution for the RE2Fe14B Matrix Phase Recovery.

The green transition initiatives and exploitation of renewable energy sources require the sustainable development of rare earth (RE)-based permanent magnets prominent technologies like wind turbine generators and electric vehicles. The recycling of RE-based permanent magnets is necessary for the future supply of critical rare-earth elements. The short-loop recycling strategies to directly reprocess Nd-Fe-B magnet waste are economically attractive and practical alternatives to conventional hydro- and pyrometallurgical processes. This study focuses on the development of a procedure to extract the (Nd, Pr)2Fe14B hard-magnetic phase from sintered Nd-Fe-B magnets. The extraction is achieved through preferential chemical leaching of the secondary, RE-rich phases using 1 M citric acid. Before the acid treatment, the magnets were pulverized through hydrogen decrepitation (HD) to increase the material's surface-to-volume ratio. The as-pulverized Nd-Fe-B powder was subsequently exposed to a 1 M citric acid solution. The effect of leaching time (5-120 min) on the phase composition and magnetic properties was studied. The results of the microstructural (SEM) and compositional (ICP-MS) analyses and the study of thermal degassing profiles revealed that the RE-rich phase is preferentially leached within 5-15 min of reaction time. Leaching of the secondary phases from the magnet's multi-phase microstructure is governed by the negative electrochemical potential of Nd and Pr. The extraction of (Nd, Pr)2Fe14B grains by the proposed acid leaching approach is compatible with the existing hydrogen processing of magnetic scrap (HPMS) technologies. The use of mild organic acid as a leaching medium makes the leaching process environmentally friendly, as the leaching medium can be easily neutralized after the reaction is completed.

The impacts of renewable energy and institutional quality in environmental sustainability in the context of the sustainable development goals: A novel approach with the inverted load capacity factor.

It is crucial to fulfill sustainable development goals in combating environmental pollution. Recently, there has been a growing literature on environmental pollution; however, while many proxies represent environmental pollution, few proxies represent environmental sustainability. In this paper, we examine the effects of institutional quality (SDG-16), economic growth (SDG-8), and renewable energy (SDG-7) on the inverted load capacity factor (SDG-13) in OECD countries from 1999 to 2018. The objective is to ensure environmental sustainability within the Sustainable Development Goals (SDGs) framework. In this respect, the study differs from the existing literature by approaching the sustainable environment literature from a broader perspective. Long-term empirical estimates from the PMG-ARDL technique have shown that institutional quality, reel income, and population increase the inverted load capacity factor, that is, decrease environmental sustainability. However, on the contrary, renewable energy decreases the inverted load capacity factor. Therefore, renewable energy consumption helps reach SDG-7 and SDG-13 in OECD countries. In addition, it is found that economic growth is significant both in the long run and in the short run, and the impact of economic growth on the environment is greater in the short run than in the long run. This result supports the environmental Kuznets curve (EKC) hypothesis for OECD countries. The panel causality test results find a bidirectional causality relationship from renewable energy and population to inverted load capacity factor and a unidirectional causality relationship from institutional quality to inverted load capacity factor. This study argues that policymakers should concentrate on deploying environmentally friendly technology to slow down environmental degradation, increase the usage of renewable energy sources, and promote sustainable development in line with the SDGs.