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As research into wave energy converters progresses and new developers enter the field, there arises a growing requirement for a standardized modelling approach. This article presents a novel design and dynamic emulation for a hybrid solar-wind-wave energy converter (SWWEC) which is the combination of three very well-known renewable energies: solar, wind and wave energy. Photovoltaic (PV) panels and vertical axis wind turbine (VAWT) are installed on top of the floating WEC that harness the energies from the sun and wind respectively. The SWWEC is designed with a point absorber capture system. An electrical motor is used to dynamically emulate the performance of the SWWEC under real world conditions to drive the DC generator. The present paper shows the importance and necessity of the required control schemes for the proper control of generator side converters which is present in the offshore marine substation and the most required grid connected onshore converters. The better switching signal generation for the converter control and generated harmonics elimination techniques are also presented in the paper. Outcomes of the present study are discussed and verified. [ABSTRACT FROM AUTHOR]

Amid growing concerns about global environmental sustainability, the focus on understanding the interplay between environmental policies, investments in sustainable practices, and renewable energy sources has intensified. This study delves into the relationship between environmental policy stringency, green investment, and the utilization of renewable energy, specifically examining their impact on the load capacity factor (LCF) within G11 countries utilized from 2000 to 2021. Manifestly, LCF integrates both demand‐ and supply‐side aspects of environmental quality. It also utilizes a comprehensive indicator of environmental policy stringency, evaluating its impact alongside green investment. Employing the Cross Sectional‐Augmented Autoregressive Distributed Lag (CS‐ARDL) model and the Common Correlated Effect Mean Group (CCEMG) approach, this research demonstrates that environmental policy stringency, green investment, and renewable energy consumption positively and significantly influence the LCF, consequently fostering improvements in environmental sustainability in both short‐ and long‐term scenarios. It is worth mentioning that environmental policy stringency has the strongest effects on LCF followed by renewable energy. The findings strongly indicate that reinforcing environmental policies, promoting green investment, and embracing renewable energy usage can collectively yield a substantial reduction in environmental pressure for G11 countries. [ABSTRACT FROM AUTHOR]

In pursuit of Saudi Arabia's Vision 2030 objectives for a sustainable and low-carbon future, this study examines the intricate dynamics between carbon emissions and key variables crucial to the nation's decarbonisation strategy. Grounded in the Triple Bottom Line (TBL) Theory, the chosen variables – sustainable tourism, renewable energy demand, and natural resource management – take centre stage. Applying Markov switching regime regression to data spanning 1990–2022, our findings reveal a positive association between international tourism and carbon emissions, aligning with the Tourism-induced Environmental Impact theory. Incorporating the Environmental Kuznets Curve (EKC), our results illustrate a U-shaped relationship. Renewable energy consumption (REC) exhibits a negative impact, supporting EKC's premise that economic development and cleaner energy adoption mitigate environmental degradation. Identified as influential factors affecting carbon emissions, Economic growth and natural resource capital find theoretical backing. The second power of economic expansion positively influences carbon emissions, while the square of natural resources has a negative impact, aligning with resource scarcity theories. Granger causality estimates validate Tourism-led Growth, Resource Capital, and Emissions-led Growth hypotheses. Integrating insights from the Community-Based Tourism (CBT) and stakeholder theory, this study provides actionable insights for policymakers steering Saudi Arabia toward a sustainable, low-carbon future. [ABSTRACT FROM AUTHOR]

District heating systems play a crucial role in the European Union for sustainable energy practices by contributing to reducing carbon emissions and thus counteracting climate change. In most European countries, the energy consumption for heating is a topic that needs to be considered. Germany has recognized the need for more awareness among its citizens about district heating systems. Therefore, an interactive website was developed in close cooperation with the city of Weilheim in Bavaria, Germany. The goal is to inform citizens about the district heating system under construction. The website aims to improve user engagement through interactive features, including a quiz, an interactive calculator, and various animations and images. The application focuses on the primary energy sources biomethane, solar energy, wood chips, and the use of environmental heat through heat pumps. The website was evaluated at a local secondary school, which yielded positive feedback. [ABSTRACT FROM AUTHOR]

Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy and power electronics equipment continues to rise, the level of rotational inertia decreases considerably, resulting in severe frequency stability challenges to the power grid. It is of great significance to accurately predict the frequency nadir following a large disturbance. This paper proposes a novel data-model fusion-driven approach for the prediction of frequency nadir. As the physics-driven part, a Simplified Prediction Model (SPM) based on power-frequency polynomial fitting is developed to quickly produce the frequency nadir. As the data-driven part, Back Propagation Neural Network (BPNN) is deployed to correct the errors of the SPM to achieve more accurate results. This serial integration scheme not only obtains the final prediction result with higher accuracy, but also meets the computational efficiency requirements of online prediction. Compared with existing integration-driven methods, SPM only focuses on the active power-frequency characteristics of the system, which retains the most critical effects and greatly reduces the dependence of BPNN on sample data quality. Case studies on a modified IEEE 39-bus system verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In Kenya's dynamic renewable energy landscape, characterized by complex policy frameworks, complex land tenure regimes, and diverse community dynamics, this qualitative research investigates the mechanisms and motivations guiding community decision-making when trading land for electricity access within the context of renewable energy projects. Through the lens of the Institutional Analysis and Development (IAD) framework, particularly the rules in use, this study unravels the complexities of rural communities' trade-offs inherent in both large-scale and small-scale renewable energy projects. Data was collected through in-depth interviews, focus group discussions, and participant observations in rural communities engaged in these projects. The findings offer new insights into communities' decision-making processes and institutional dynamics in shaping outcomes, with a focus on land rights and land use implications. The analysis highlights the relational nature of the trade-offs, influenced by factors such as land tenure systems, project scale, electricity access, traditional knowledge, and local context, supporting the importance of understanding communities' diverse roles and positions, power dynamics, and governance structures. Overall, this study contributes to a deeper understanding of the complexities surrounding land-electricity trade-offs in renewable energy projects in rural areas, emphasizing the need for adaptable strategies to address evolving community needs and challenges. Article highlights: Large and small renewable energy projects present unique trade-offs in rural areas, influencing land use decisions and community dynamics. Land tenure systems shape rural communities' decision-making and benefit distribution, influencing their trust, support and involvement in renewable energy development. Strategic planning is essential to balance immediate energy needs with long-term rural communities' development and sustainability. [ABSTRACT FROM AUTHOR]

It is the goal of this article to define and implement the strategic policies to avoid negative environmental impact by planning, implementing, monitoring, and controlling activities based on the environmental management. The forthcoming work does not involve experimental and characterization studies, and it does not even involve laboratory experiments. Environmental risks have been mitigated by a number of laws and regulations, including those that recognize, evaluate, and enforce environmental hazards. A sustainable policy is an innovative way of establishing policies and plans which are sustainable for the environment. This study provided metrics and indicators for measuring the environmental performance. As a result, the key priorities will be protecting the environment, growing the economy, and utilizing resources in the most effective manner without compromising the needs of the future. To ensure sustainable development, the environment and society will be managed to achieve their well-being because of implementing strategies. This report explores some of the practices and principles of sustainability that businesses, organizations, and individuals can employ to improve their sustainability. [ABSTRACT FROM AUTHOR]

Photosynthesis converts solar energy, carbon dioxide, and water into biomass, offering a renewable energy and chemical source. Biomass photo(electro)refining (BPeR) emerges as a sustainable alternative to petrochemicals for fuel and chemical production. However, BPeR faces challenges due to photocatalyst limitations, necessitating the integration of cocatalysts. Cocatalysts significantly impact the efficiency, selectivity, and durability of BPeR reactions, yet their roles require systematic elucidation and understanding. This review explores how cocatalysts impact the carbon bond functionalization, cleavage, and formation in biomass and its derivatives. It discusses their crucial roles in charge carrier generation, separation, and transportation, particularly in catalyzing surface reactions such as hydrogen‐involved reactions, generation, and manipulation of oxygen or carbon radicals, and the C─C/O/H bond transformations. Additionally, it outlines challenges and prospects for developing cocatalysts to enhance BPeR efficiency and durability, boosting the viability of biomass as a sustainable source of energy and materials. [ABSTRACT FROM AUTHOR]

Our industry today is predominantly based on linear value chains. Raw materials are extracted from primary sources, processed into products, used, and disposed of at the end of their life cycle. This linear economy causes a wide range of negative environmental impacts owing to the resulting greenhouse gas emissions and pollution of marine and terrestrial ecosystems. Closed carbon cycles and climate-neutral energy production are essential for the production not only of fuels but also of all chemicals, including plastics and fertilizers, to counteract climate change and further damage to the environment. In this regard, this article discusses the importance of heterogeneous catalysts for selected technologies associated with this transformation of the resource base and energy supply. It discusses the technological framework conditions of a net CO2-neutral industry, with a focus on electrocatalytic water-splitting for hydrogen production, as well as the catalytic challenges of production of chemicals for the whole value chain using biomass, CO2 and plastic waste as raw materials. This article is part of the discussion meeting issue 'Green carbon for the chemical industry of the future'. [ABSTRACT FROM AUTHOR]

Background: Sustainability science has become a crucial aspect of interdisciplinary research aimed at finding practical solutions and driving lasting change. This article examines the challenges facing sustainability science to move beyond the analysis of problems within interconnected systems and critically evaluate the interplay of social, political, and technological factors in the application of knowledge to action. Results: The study highlights the influence of innovation implemented by companies on their sustainability performance, the establishment of innovations that positively affect sustainability performance, and analyzes the youth and local community perspectives on scientific innovations for a sustainable future. Conclusions: The main conclusion is that sustainable development needs to focus on four areas of research: the impact of values ​​on research and decisions; community perspectives on sustainability; fostering socio-technical progress; and promoting learning for sustainable development. By decoding science, positive change can be achieved through the implementation of strategies such as transparency, understandability, and openness to scientific knowledge. [ABSTRACT FROM AUTHOR]

This work evaluates the use of zinc oxide nanorods as intensifiers of a latent heat thermal energy storage system working with adipic acid as the phase change material (PCM). By virtue of not participating directly in the solid–liquid and liquid–solid phase transition, ZnO-adipic acid composites (ZnO-adipic acid) possessed lower specific heat and latent heat. Our results have shown that the overall heat transfer coefficient during the freezing of PCM through heat transfer to a well-mixed liquid bath is amplified by 61%, when adipic acid is replaced with 2 wt.% ZnO-adipic acid. Heterogenous nucleation due to well-dispersed, ZnO nanorods caused this enhancement. The large enhancement in discharge rate of 2 wt.% ZnO-adipic acid during freezing overweighs higher degree of latent heat loss due to its repeated thermal cycling. The enhancement in overall heat transfer coefficient reported here (61%) is the highest reported so far for any latent heat thermal energy system employing adipic acid or its composites. [ABSTRACT FROM AUTHOR]

This paper investigates the predictive relationships among climate policy uncertainty (CPU), oil prices, and renewable energy (RE) stock market returns, particularly highlighting the challenges posed by the varying data frequencies of these variables. The study utilizes a comprehensive dataset comprising monthly CPU, daily oil prices, and RE stock returns, sourced globally. By applying a mixed-frequency causality test (MFCT), the analysis reveals significant predictability across different time horizons, particularly highlighting the strong influence of oil prices on RE stock returns over short-term horizons, while CPU demonstrates a more pronounced effect over medium to long-term horizons. In contrast, the application of the classical Granger causality test on low-frequency (monthly) data indicates an insignificant relationship between CPU and RE stocks, suggesting that traditional models may overlook important predictive dynamics. The analysis was conducted using Matlab code, and the findings provide valuable insights for policymakers in designing effective climate policies and for investors in optimizing portfolio strategies and hedging against risks. [ABSTRACT FROM AUTHOR]

Several efforts have been made to explore the income inequality influence on environmental degradation, but the query regarding the nexus of income inequality and renewable energy consumption (REC) is still unanswered. This vacuum in existing literature has raised the need to inspect the relationship between income inequality and REC. Thus, this study purposes to investigate the asymmetric effect of income inequality on REC for emerging Asian economies over the period 1990–2019 by adopting the nonlinear autoregressive distributed lag (NARDL) approach. The panel-wise long-run results infer that positive shock in income inequality exerts a significant negative effect on REC, while negative shock in income inequality has an insignificant effect on REC. Likewise, economy-wise long-run findings reveal that a positive shock in income inequality brings a negative and statistically significant influence on REC in the case of China, India, and Iran, while a negative shock in income inequality brings a positive influence on REC only in case of Russia. [ABSTRACT FROM AUTHOR]

In accordance with the Korean 2050 carbon neutrality scenarios, renewable energy accounts for 70.8 % and 60.9 % of the total future electricity production in scenarios A and B, respectively. However, renewable energy is relatively more uncontrollable than other generators, such as gas turbines and power plants, and this condition can lead to grid instability, including electricity waste and blackouts. Here, we analyzed the issues by designing and simulating a South Korean energy supply and demand system. Uncontrollable renewable energy causes a mismatch between power supply and demand. We resolved this issue by increasing the capacity of controllable generators, ultimately resulting in unwanted excess energy generation of 302 TWh and 281 TWh for Scenarios A and B, respectively. Therefore, an energy storage system (ESS), including a battery and electrolysis facility, was added to stabilize the total electrical grid. A stabilized electrical grid provides economic benefits, such as reduced levelized cost of electricity and net present cost, particularly by the battery ESS. Electrolysis can generate hydrogen to be used as a fuel of controllable energy, increasing the stability of the total grid by reducing excess energy more practically than the battery ESS does. [ABSTRACT FROM AUTHOR]

The global energy demand is growing, and the world is shifting towards using more renewable energy, like increased onshore wind power development. We used Global Positioning System (GPS) and Very High Frequency (VHF) location data from adult, territorial wolves Canis lupus in Scandinavia (Sweden and Norway; 1999–2021), to examine the potential for wind power development to affect wolf behavioural ecology. We examined the spatial overlap of areas proposed for wind power development with wolf territory activity centres prior to construction, to test to what extent overlap varies with season, time of day and social status (breeding versus non‐breeding wolves). Measures of overlap were the distance between wolf activity centre points and nearest proposed wind turbine, the probability of proposed wind turbines being within the activity centre, and the density of proposed wind turbines within the activity centre. The wolf activity centre points were closer to sites of proposed turbines in early summer than in late winter and the density of proposed turbines in the activity centre was higher in early summer than in late winter. These findings probably result from an altitudinal shift in wolf area use between summer and winter. We also found that the probability for proposed turbines to be within the activity centre was higher for non‐breeding than for breeding wolves during early summer, whereas it was higher for breeding compared to non‐breeding wolves during late winter. This difference might be an effect of that breeding wolves have a restricted area use during the early summer season (denning period), resulting in a lower probability of turbines being inside their activity centre as compared to late winter. There was no clear pattern for other seasonal and social status differences. The results should be viewed as a starting point for further research and supplemented with before‐after studies. [ABSTRACT FROM AUTHOR]

Scholars and policy makers are paying close attention to reduce climate problems for sustainable growth. The failure to enhance ecological integrity could increase greenhouse gas emissions. Therefore, this study examines the effect of economic complexity, high‐tech industries, renewables, natural resource abundance, and financial globalization on CO2 and ecological footprint for 10 selected newly industrializing countries for the period 1990 to 2018. We use Common Correlated Error Mean Group, Augmented Mean Group, panel causality, and Westerlund Error Correction Model of cointegration techniques that account for short‐run, and long‐run relationships across cross‐sectionally dependent and heterogeneous countries. The findings reveal long‐run interrelationships across the series of underlying observations. The long‐run empirical results show the development of high‐tech industries has statistically significant environmental welfare‐enhancing impact, whereas renewables and natural resource exploitation mitigate environmental challenges. However, economic complexity and financial globalization increase emissions and ecological footprint (ECF). The results confirm unidirectional causality from renewable energy to CO2 and ECF and further from natural resources to CO2 and ECF. Additionally, there exists bidirectional causality between financial globalization and ECF. Based on these findings, we suggest that these countries should revise their energy policies in order to allocate more funds for the renewable energy technologies so that environmental problems can be mitigated. Along with renewable energy, further investments should be made in high‐tech sector but economic complexity and financial globalization should be carefully handled. [ABSTRACT FROM AUTHOR]

The increasing concerns about climate change demand a more profound understanding of the elements influencing carbon dioxide (CO2) emissions, particularly in nations with high carbon footprints. This study embarks on a quantitative exploration of how renewable energy consumption, economic growth, and environmental technologies impact CO2 emissions across eight nations with the highest carbon emission levels. Leveraging annual data from the period 1990–2019, we undertake a meticulous empirical analysis to unearth the factors shaping the environmental quality in these countries. Our findings reveal that per capita income and environmental technologies wield a significant influence on CO2 emission levels. Delving further into causality relationships, we discern a fascinating bidirectional causal link between CO2 emissions and renewable energy consumption. In parallel, a similar bidirectional causality is spotted between renewable energy consumption and environmental technology. From CO2 emissions to environmental technology and GDP per capita to CO2 emissions and environmental technology, unidirectional causal links are also perceived. In light of these compelling results, we propose several policy recommendations aimed at promoting sustainability and fostering a healthier environmental future. Our research underscores the potential of renewable energy consumption and environmental technologies in mitigating carbon emissions and spurring a greener economy. [ABSTRACT FROM AUTHOR]

This study examines the impacts of digitalization through information and communication technology (ICT) and environmental taxes on greenhouse gas (GHG) emissions in 23 European Union (EU) countries between 2000 and 2017. Using the Pooled Mean Group estimator, the empirical results provide evidence that ICT development and environmental taxes improve environmental sustainability while research and development investments and income per capita deteriorate environmental sustainability. Furthermore, the results based on the Dynamic Panel Threshold Regression model show that the relationship between ICT and GHG emissions is dependent on the level of environmental taxes. During the period of low environmental taxes, the effect of ICT on GHG emissions is positive and insignificant but once environmental taxes cross the threshold value, the effect of ICT becomes negatively related to greenhouse gas emissions. This suggests that the period of low environmental taxes does not support the environmental friendliness of ICT development in the European region. The policy implication of these findings is that ICT, environmental taxes, and renewable energy can be possibly stirred up to achieve long‐term environmental sustainability in the EU region. [ABSTRACT FROM AUTHOR]

This study investigates the valorization of spent coffee grounds (SCGs) through protein extraction and their application in mycelium-based packaging and renewable energy pellets. Three extraction methods—mechanical stirring, ultrasound-assisted, and CO2-assisted extraction—were applied to SCGs. CO2-assisted extraction yielded the highest protein content at 34.24%, followed by mechanical stirring (31.46%) and ultrasound-assisted extraction (28.51%). The total polyphenol content and antioxidant capacity were also highest in the CO2 extracts, suggesting that this method preserves bioactive compounds most effectively. After protein extraction, SCGs were tested as a component in mycelium-based packaging, with results showing an apparent density of 0.551 g/cm3 and compression resistance of 3.354 MPa, indicating its suitability for structural applications. The energy value of SCGs remained high, with a calorific value of 19,887 J/g DW, slightly decreasing after extraction but still sufficient for renewable energy production. These findings highlight the potential of SCGs as a multi-functional resource, contributing to sustainable solutions across various industries. [ABSTRACT FROM AUTHOR]

Miskolc, which is the focus of our investigation, is the fourth most populous city in Hungary and the center of one of the most underdeveloped NUTS2 (basic territorial category for the regional policy of the European Union) regions in the European Union. The socialist heavy industry played a decisive role in the development of the city, the decline of which also left deep traces in the city. In its current position, the city tries to manage its available resources as efficiently as possible, and the city management is open to the use of modern urban development tools. This is supported by the fact that Miskolc was the first Hungarian city to join the Green Cities for Sustainable Europe movement in 2011, and then in 2015, it joined the Triangulum project of the EU Smart Cities and Communities program as a follower city. In the process of becoming a smart city, the dimensions of environmental sustainability and energy efficiency were given a prominent role, which should not be surprising considering the traditions of the city. Within this, we must first mention the construction of the geothermal central heating system, with which the city really took significant steps in this field. The main goal of the study is to develop a new smart local concept closely linked to regional development and the key energy sector, through which the local adaptation of the defining elements of the internationally defined smart city in several forms for the city of Miskolc will be presented. In our study, we review how the results achieved by Miskolc so far and the development plans for the future fit in with the smart energy developments of smart cities. Before exploring the processes in Miskolc, we will deal in more detail with the possibilities inherent in district heating and geothermal energy utilization and Hungary's capabilities. [ABSTRACT FROM AUTHOR]

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK's target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions. [ABSTRACT FROM AUTHOR]

In this study, an in-depth analysis is presented on forecasting aggregated wind power production at the regional level, using advanced Machine-Learning (ML) techniques and feature-selection methods. The main problem consists of selecting the wind speed measuring points within a large region, as the wind plant locations are assumed to be unknown. For this purpose, the main cities (province capitals) are considered as possible features and four feature-selection methods are explored: Pearson correlation, Spearman correlation, mutual information, and Chi-squared test with Fisher score. The results demonstrate that proper feature selection significantly improves prediction performance, particularly when dealing with high-dimensional data and regional forecasting challenges. Additionally, the performance of five prominent machine-learning models is analyzed: Long Short-Term Memory (LSTM) networks, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Extreme-Learning Machines (ELMs). Through rigorous testing, LSTM is identified as the most effective model for the case study in northern Italy. This study offers valuable insights into optimizing wind power forecasting models and underscores the importance of feature selection in achieving reliable and accurate predictions. [ABSTRACT FROM AUTHOR]

Countries have turned to developing renewable energy production, avoiding the risks posed by the disruptions in global energy trade, the high volatility in energy prices, and the remarkable environmental impairment. Numerous economic, environmental, institutional, and social factors have been put forward as driving factors toward renewable energy. The goal of this research article is to study the causal nexus among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy of the 27 EU members between 2000 and 2020 through Emirmahmutoglu and Kose causality test. The results of the panel-level causality tests demonstrate feedback interplay among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy use. However, the results of the country-level causality analysis unveil that the interplay among renewable energy utilization, energy dependency, real GDP per capita, CO2 emissions, and human capital remarkably varies among EU members. The results of this study suggest that renewable energy investments are significant instruments to make progress in energy security, human capital, real GDP per capita, and CO2 emissions. Furthermore, energy security, human capital, real GDP per capita, and CO2 emissions are significant drivers of renewable energy development. [ABSTRACT FROM AUTHOR]

Emerging states' path to enhancing the welfare of their citizens has been strongly accompanied by environmental degradation; climate change effects often abrogate their economic results. This zero-sum game must change, and environmental concerns should be considered when the development of a country is discussed and assessed. In this sense, this study's objective is to analyze the impact of economic complexity and renewable energy consumption in the presence of economic growth and urbanization in selected emerging European countries from 1995 to 2021. We used a multiple-methodologic approach to highlight the supportive effects of economic complexity and renewable energy consumption in mitigating carbon emissions. Furthermore, the effects of economic growth and urbanization were emphasized by applying the cointegration regression (CCR), fully modified OLS, and dynamic OLS (FMOLS–DOLS) approaches. Additionally, we used Driscoll–Kraay estimation regression to test the robustness of our results. The results reveal the beneficial role of renewable energy consumption and economic complexity in the decarbonization process of selected countries. Furthermore, the study highlighted the detrimental influence of urbanization and economic growth, which were feasible considering the emerging status of the countries included in the panel. [ABSTRACT FROM AUTHOR]

Large CO2 emissions constitute a significant problem today due to their effect on climate change, and the need to design appropriate energy policies to mitigate their consequences and reduce emissions requires a detailed analysis of one of the main sources of such emissions: the electricity system. Thus, this paper presents a study on the effects of energy policies on decarbonization by comparing the detailed phase-out of coal-fired power plants across a range of cases with the implementation of a carbon tax to meet Nationally Determined Contributions (NDCs). The case study focuses on the Chilean electricity system, using a long-term generation and transmission expansion planning model (GTEP) that incorporates a wide range of generation technologies. The study examines the long-term effects of these policies, including costs, investments, and CO2 emissions, as well as their impact on consumer prices reflected in the marginal costs of the system. The transmission system modeling covers various regions of Chile and significant projections for renewable energy sources. It evaluates three economic scenarios based on generation technology costs, fuel prices, and electricity demand under four different closure schemes and fourteen different carbon tax levels. The results indicate that implementing a carbon tax can be more cost-effective for the system than the implementation of a phase-out schedule for coal plants, taking the form of reduced CO2 emission and overall system costs, with an optimal carbon tax value of 37 USD/tCO2. Additionally, the study reveals significant effects on consumer prices, showing that a carbon tax as an energy policy leads to lower prices compared to a phase-out scheme. [ABSTRACT FROM AUTHOR]

Energy transition in the electricity sector is central to limiting global warming to 1.5 °C. Countries around the world rely on official planning documents, such as strategies, special programs, and action plans, to guide these efforts. However, there are few systematic approaches and tools that can help policy makers determine comprehensiveness, coherence, consistency, and coordination when designing these planning documents. This represents a risk to meet energy transition and climate change goals if the relationships of policies and actions included in these documents are not addressed properly. To this end, this paper proposes an approach to model hierarchical relationships using the theory of change, while causal relationships are allocated using policy makers' experience (yet can also be exogenously allocated using other models). As a case study, this work investigates Mexico's energy transition policy of the electricity sector over the 2013–2018 period. The results show that Mexico's official planning documents lack a good design since most policies and actions are not articulated in the right sequence and privilege clean centralized and distributed generation and energy efficiency, while others related to the modernization of electrical grids and climate and environmental policies are barely included. [ABSTRACT FROM AUTHOR]

Lithium-based battery systems (LBS) are used in various applications, from the smallest electronic devices to power generation plants. LBS energy storage technology, which can offer high power and high energy density simultaneously, can respond to continuous energy needs and meet sudden power demands. The lifetime of LBSs, which are seen as a high-cost storage technology, depends on many parameters such as usage habits, temperature and charge rate. Since LBSs store energy electrochemically, they are seriously affected by temperature. High-temperature environments increase the thermal stress exerted on LBS and cause its chemical structure to deteriorate much faster. In addition, the fast charging feature of LBSs, which is generally presented as an advantage, increases the internal temperature of the cell and negatively affects the battery life. The proposed energy management approach ensures that the ambient temperature affects the charging speed of the battery and that the charging speed is adaptively updated continuously. So, the two parameters that harm battery health absorb each other, and the battery has a longer life. A new differential approach has been created for the proposed energy management system. The total amount of energy that can be withdrawn from LBS is increased by 14.18% as compared to the LBS controlled with the standard energy management system using the genetic algorithm optimized parameters. Thus the LBS replacement period is extended, providing both cost benefits and environmentally friendly management by LBSs turning into chemical waste distinctly later. [ABSTRACT FROM AUTHOR]

This study evaluates the factors contributing to carbon emissions in China between 2002 and 2021, considering the impact of GDP growth, shocks in oil prices, trade liberalization and energy use. Using the dynamic simulated ARDL (SARDL) model, we indicate a long‐term link between trade liberalization, oil prices (WTI) shocks, GDP growth, energy use and CO2. The novel SARDL revealed a direct relationship between trade liberalization and CO2 in China in the long term. However, the results show a negative impact of WTI shocks on CO2 emissions. The study suggests that renewable energy (RE) significantly and negatively affects China's CO2 emissions. In addition, the findings conclude that, in the case of China, GDP has an insignificant long‐run relationship with CO2 emissions. The outcomes provide valuable insights for policymakers, researchers and industry stakeholders to enhance RE and promote sustainable energy practices. [ABSTRACT FROM AUTHOR]

Objective: The aim was to systematically evaluate the usability of all public electric vehicles (EV) direct current fast chargers (DCFC) in the San Francisco region. Background: To achieve a rapid transition to EVs, a highly reliable and easy to use charging infrastructure is critical to building confidence among consumers. Methods: The functionality and usability of all 182 open, public DCFC charging stations with CCS connectors (combined charging system) in the 9 counties of the Bay Area were tested (655 electric vehicle service equipment (EVSE) ports). An EVSE was classified as functional if it charged an EV for 2 minutes. Results: Overall, 73.3% of the 655 EVSEs were functional. The causes of the nonfunctioning EVSEs (23.5%) were blank or unresponsive screens or error messages; payment system failures; charge initiation failures; network failures; or broken connectors. In addition, the cable was too short to reach the EV inlet for 3.2% of the EVSEs. A random sampling of 10% of the EVSEs, approximately 8 days after the first evaluation, found no overall change in functionality. Conclusions: The level of functionality found with field testing conflicts with the 95–98% uptime reported by the EV service providers (EVSPs) who operate the EV charging stations. There is a need for precise and verifiable definitions of uptime, downtime, and excluded time, as applied to public EV chargers. Application: The level of failure of the existing public EV DCFC charge infrastructure highlights the importance of improving the system design and maintenance to improve adoption of EVs. [ABSTRACT FROM AUTHOR]

The impact of the climate and environmental problems experienced in the world with the Industrial Revolution has prominently begun to be felt today, and the consequences of climate change on the environment and public health have now become visible. The increase in greenhouse gas emissions resulting from human activities, which is the main cause of global climate change, caused the global surface temperature to be 1.1 °C higher between 2011 and 2020 compared to 1850–1900. In parallel with this global problem, the transition to clean energy has increased significantly with Russia's invasion of Ukraine, more aggressive energy and climate policies, technological developments, and increasing concerns about energy security. In this study, global climate change indicators, including land and sea surface air temperatures, sea level rise, sea ice extent, ocean heat content, surface humidity, and total column water vapor, are reviewed and updated in parallel with a comprehensive analysis of the progress in renewable energy. The results showed that if no measures are taken to reduce human-induced greenhouse gas emissions, the global average temperature will increase further in the coming years and the negative effects of other climate parameters will be felt even more. It has been emphasized that limiting human-induced global warming requires renewable and sustainable energy sources and net zero CO2 emissions and that the simultaneous adoption of emission reduction and adaptation strategies will be the most effective economic and technical solution to the global warming problem. [ABSTRACT FROM AUTHOR]

Purpose: Expansion of the consumption of renewable energy is a significant issue for reducing global warming, to cope with climate change and achieve sustainable development. This study aims to examine how research and development expenditure (R&D) affects renewable energy development in developed G-7 countries over the period from 2000 to 2019. Variables of trade liberalization and CO2 emissions are considered control variables. Design/methodology/approach: This study has adopted a panel quantile regression. The impact of the variables on renewable development has been examined in quantiles of 0.1, 0.25, 0.5, 0.75 and 0.9. Also, a robust examination is accomplished by applying generalized quantile regression (GQR). Findings: The empirical findings reveal a positive and significant relationship between R&D and the consumption of renewable energy in 0.1, 0.25, 0.5 and 0.75 quantiles. Also, the findings describe that the expansion of trade liberalization and CO2 emissions can significantly increase the development of renewable energy in G-7 countries. Furthermore, GQR verifies the main outcomes. Practical implications: These results have very momentous policy consequences for the governments of G-7 countries. Therefore, investment and support for the R&D section to promote the development of renewable energy are recommended. Originality/value: This paper, in comparison to other research, used panel quantile regression to investigate the impact of factors affecting renewable energy consumption. Also, to the best of the authors' knowledge, no study has perused the effect of R&D along with trade liberalization and carbon emissions on renewable energy consumption in G-7 countries. Also, in this paper, as a robustness check for panel quantile regression, the GQR has been used. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study comprehensively analyzed the economic development, agricultural production, renewable energy consumption, and urbanization linkage with carbon dioxide (CO2) emissions in China spanning the years 1995–2022. Utilizing advanced econometric models, including DOLS, FMOLS, and CCR, the research provides a comprehensive analysis. The findings show that agricultural production has a significant inverse correlation with CO2 emissions, showcasing the potential of sustainable agricultural practices in environmental mitigation. Economic growth, while statistically insignificant in its impact on emissions, underscores the need for sustainable and technologically advanced development strategies. The study emphasizes the role of renewable energy utilization in decreasing CO2 emissions in China, aligning with global efforts for cleaner production. However, the positive correlation between urbanization and CO2 emissions suggests challenges in balancing urban development with environmental conservation. The research offers valuable insights into crafting targeted environmental policies for China, contributing to its commitment to achieving net-zero emissions by 2060. As China's experiences resonate with those of other developing economies, the study provides a roadmap for sustainable growth, emphasizing the positive aspects of agriculture and renewable energy, while addressing the complexities introduced by urbanization. This comprehensive analysis contributes to the global discourse on combating climate change and fostering sustainable development. [ABSTRACT FROM AUTHOR]

Local contestation to the deployment of large-scale renewable energy infrastructures has been increasing. Right-wing populism has also been on the rise across the world. This article aims to explore the potential relations between these two socio-political issues, by analyzing Portuguese media discourses on the renewable energy transition and if and how those expose associations with right-wing populist rhetoric. 465 articles published by Portuguese newspapers were analyzed through Thematic Analysis, which revealed three main themes: Portugal at the forefront of the green transition; the dark side of the transition; and, less pervasively, energy justice as crucial for a green transition. These themes and how they are discursively organized resonate with far-right wing populist rhetoric, such as nationalist, anti-elitist and anti-establishment views. This might find echo in rural communities affected by the green energy transition feelings of marginalization. Key policy highlights Portuguese media communication on the green transition and its injustices, uses similar devices and tropes to right-wing populist rhetoric, which might incentivize support to right-wing populist parties; The media have a key role in communicating about the green energy transition and related justice issues in a politicized and multivocal way; Policy-makers need to promote a more just transition and contest the commodification of renewable energy and the discourse of renewable energy infrastructures as inherently sustainable. [ABSTRACT FROM AUTHOR]

Multi-rotor system (MRS) wind turbines can provide a competitive alternative to large-scale wind turbines due to their significant advantages in reducing capital, transportation, and operating costs. The main challenges of MRS wind turbines include the complexity of the supporting structure, mathematical modeling of the aerodynamic interaction between the rotors, and the yaw control mechanism. In this work, MATLAB 2018b/Simulink® software was used to model and simulate a twin-rotor wind turbine (TRWT), and an NREL 5 MW wind turbine was used to verify the model outputs. Different random signals of wind velocities and directions were used as inputs to each rotor to generate different thrust loads, inducing twisting moments on the main tower. A yaw controller system was adapted to ensure that the turbine constantly faced the wind to maximize the power output. A DC motor was used as the mechanism's actuator. The goal was to achieve a compromise between aligning the rotors with the wind direction and reducing the torque induced on the main tower. A comparison between linear and nonlinear controllers was performed to test the yaw system actuator's response at different wind speeds and directions. Sliding mode control (SMC) was chosen, as it was suitable for the nonlinearity of the system, and its performance showed a faster response compared with the PID controller, with a settling time of 0.17 sec and a very low overshoot. The controller used the transfer function of the motor to generate a sliding surface. The dynamic responses of the controlled angle are shown and discussed. The controller showed promising results, with a suitable response and low chattering signals. [ABSTRACT FROM AUTHOR]

The integration of wind and solar energy with green hydrogen technologies represents an innovative approach toward achieving sustainable energy solutions. This review examines state-of-the-art strategies for synthesizing renewable energy sources, aimed at improving the efficiency of hydrogen (H2) generation, storage, and utilization. The complementary characteristics of solar and wind energy, where solar power typically peaks during daylight hours while wind energy becomes more accessible at night or during overcast conditions, facilitate more reliable and stable hydrogen production. Quantitatively, hybrid systems can realize a reduction in the levelized cost of hydrogen (LCOH) ranging from EUR 3.5 to EUR 8.9 per kilogram, thereby maximizing the use of renewable resources but also minimizing the overall H2 production and infrastructure costs. Furthermore, advancements such as enhanced electrolysis technologies, with overall efficiencies rising from 6% in 2008 to over 20% in the near future, illustrate significant progress in this domain. The review also addresses operational challenges, including intermittency and scalability, and introduces system topologies that enhance both efficiency and performance. However, it is essential to consider these challenges carefully, because they can significantly impact the overall effectiveness of hydrogen production systems. By providing a comprehensive assessment of these hybrid systems (which are gaining traction), this study highlights their potential to address the increasing global energy demands. However, it also aims to support the transition toward a carbon-neutral future. This potential is significant, because it aligns with both environmental goals and energy requirements. Although challenges remain, the promise of these systems is evident. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability. [ABSTRACT FROM AUTHOR]

"Ensure access to affordable, reliable, sustainable, and modern energy for all" is one of the United Nations' 17 Sustainable Development Goals. A key target for achieving this goal is increasing the share of renewable energy. In the United States, many states are evaluating the environmental and economic impacts of the renewable energy transition. This study provides an in-depth evaluation of the economic impact of Ohio's proposed Community Solar Pilot Program, which plans to install 1750 MW of new solar capacity, including 250 MW on distressed Appalachian sites. We combine two input–output models to estimate the economic impact of community solar projects: the Jobs and Economic Development Impact (JEDI) model from the National Renewable Energy Laboratory (NREL) for the construction phase, and IMPLAN for the projects' 25-year lifespan. Data for this study were gathered through surveys of local solar developers, who are familiar with the regional costs of installing and operating solar projects. Our findings indicate that the community solar program could support 32,430 full-time job years and contribute USD 4.37 billion to Ohio's Gross State Product (GSP). Additionally, the program could generate USD 409.5 million in local tax revenue over its lifetime. The study highlights the potential of renewable energy initiatives to foster economic growth, particularly in economically distressed regions like Appalachian Ohio. [ABSTRACT FROM AUTHOR]

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2024, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. The analysis utilizes stepwise regression to systematically identify the most significant predictors, ensuring a parsimonious model. Robust least squares (ROLSs) are employed to account for potential outliers and heteroscedasticity in the data, providing more reliable estimates. Fully Modified Least Squares (FMOLSs) is applied to address issues of endogeneity and serial correlation, offering robust long-term coefficient estimates. Canonical cointegrating regression (CCR) further refines these estimates by handling non-stationary variables and ensuring consistency in the presence of cointegration. Cointegration tests, including the Johansen and Engle–Granger methods, confirm long-term equilibrium relationships among the variables; this study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, the Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle–Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman's efforts towards sustainable development and decarbonization. [ABSTRACT FROM AUTHOR]

This study aimed to identify the adoption of circular economy (CE) practices at thermoelectric power plants (TPPs) fueled by forest biomass in Brazil and determine the degree of implementation; social, environmental, and economic impacts; motivations; challenges; and facilitating factors for the adoption of such practices. Data were collected through a questionnaire applied to a sample of 32 TPPs in Brazil employing the ReSOLVE framework from October 2023 to January 2024. The data were analyzed using descriptive statistics, with the assignment of scores, and principal component analysis. The results indicate that optimization practices are the most widespread (6.7) and sharing practices were the least adopted (4.0). The greatest motivators for adopting CE practices are promoting sustainability (9.5) and enhancing corporate image (9.5). One of the most relevant positive impacts of such practices is the improvement in social relations between companies and the community (9.1). A major challenge to CE adoption is government neglect, whereas certification systems constitute one of the major facilitators (with 20 indications). This study provides indicators for decision-makers in the private sector and public managers interested in promoting sustainable practices in the renewable energy industry. [ABSTRACT FROM AUTHOR]

This paper examines the impact of renewable energy heating on air quality in China, using the Qinling Mountains–Huaihe River line as a quasi-natural experiment to distinguish between regions with central heating and those without. Employing a difference-in-differences approach and analyzing panel data from 298 cities between 2014 and 2022, our findings indicate that the renewable energy heating policy has significantly improved air quality. Specifically, the policy led to substantial improvements in air quality, reducing concentrations of key pollutants: SO2 by 28.31%, CO by 7.57%, NO2 by 5.72%, and PM2.5 by 7.15%. The policy's effects are most pronounced in regions with lower temperatures and in the eastern parts of the country. Further analysis emphasizes the critical role of energy transition, environmental regulations, and government investment in technology as key drivers of these air quality improvements. [ABSTRACT FROM AUTHOR]

This study aimed to investigate how Industrial Symbiosis (IS) built around thermoelectric power plants (TPPs) located in forest-based clusters in the southern plateau region of the state of Santa Catarina, Brazil, can stimulate the adoption of circular economy (CE) practices. Data were collected to identify the presence of forest-based clusters and the purchase and sale of logs, processed wood, and forest residues from companies within the clusters, and to investigate CE practices using the ReSOLVE framework adopted in two TPPs, which used residues and black liquor as fuel. The results indicate that the nature of the forest-based activities enhances the formation of clusters, especially when TPPs use forest residues for energy production with IS intensified in this process. Brazil has 140 TTPs that use forest biomass with the capacity to generate 5.5 million wK. Three clusters were identified in the study region with purchase and sale, respectively, of logs (78.6 and 100%), processed wood (75 and 66.7%), and wood residues (45.5 and 83.3%) within the region. The case studies indicated the strong occurrence of CE practices in the cases analyzed that contribute to four of the Sustainable Development Goals (SDG) proposed by the United Nations (7, 8, 9, and 12). [ABSTRACT FROM AUTHOR]

This study develops the heliostat curve tracing parametric model (HCTPM) to predict solar energy distribution in concentrated solar power (CSP) systems with sun-tracking capabilities. HCTPM uses curve tracing techniques to visualize flux distribution on mirrors and receivers, producing results that align closely with established models like HFLCAL, which use Gaussian and Tonatiuh ray-tracing methods. Simulations revealed that deviations in energy distribution increase as Sun shape error decreases, with greater impact on flux density and sensitivity. Variations in Sun disk radius caused notable deviations, especially in elliptical projections. The model's flexibility in adjusting mirror shapes and sizes allows for the evaluation of spill losses, optimizing mirror designs for different positions. Spill loss analysis showed that larger mirrors reduce spill loss on mirrors but increase it on receivers, particularly when mirrors deviate from the north. Although total spill loss decreases with larger mirrors, this effect weakens as receiver spill loss grows. These findings emphasize the importance of optimizing mirror and receiver design to maximize energy efficiency and minimize resource waste, contributing to more sustainable solar energy systems. The HCTPM model plays a crucial role in improving the sustainability of CSP systems by optimizing configurations based on Sun disk characteristics, reducing energy losses, and promoting efficient resource use. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the PRISMA framework to conduct a systematic review, focusing on the role of AI in renewable energy and sustainable development. This research utilized Scopus's curated AI research area, which employs text mining to refine AI concepts into unique keywords. Further refinement via the All Science Journals Classification system and SDG-mapping filters narrowed the focus to publications relevant to renewable energy and SDGs. By employing the BERTopic modeling approach, our study identifies major topics, such as enhancing wind speed forecasts, performance analysis of fuel cells, energy management in elective vehicles, solar irradiance prediction, optimizing biofuel production, and improving energy efficiency in buildings. AI-driven models offer promising solutions to address the dynamic challenges of sustainable energy. Insights from academia-industry collaborations indicate that such partnerships significantly accelerate sustainable-energy transitions, with a focus on AI-driven energy storage, grid management, and renewable-energy forecasting. A global consensus on the critical role of investing in technology-driven solutions for energy sustainability was underscored by the relationship between funding data and global R&D spending patterns. This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI's accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development. [ABSTRACT FROM AUTHOR]

The design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions. [ABSTRACT FROM AUTHOR]

This study highlights the main concerns regarding firms' carbon accounting and decarbonization practices. Under institutional, legitimacy and stakeholder theories, this study analyses the linkages between carbon accounting outputs and integrated reporting (IR), as well as between disclosure assurance systems in the transition towards net‐zero business models (n‐ZBMs). This study emphasizes the awareness of accounting tools to be considered environmental and social measures due to decarbonization practices. This study developed a systematic literature review (SLR) of 433 English articles published in Scopus database and Google Scholar between the years 1990 and 2024. It is a consolidated methodological approach in the 'sustainability accounting' research field to fill the existing gaps that justify this study. The results of this study highlight that IR and carbon accounting awareness enhance firms' performance and have positive social and environmental impacts with the adoption of decarbonization practices towards n‐ZBMs through an improved understanding of stakeholders and regulatory pressures. This study is the first to explore the relationship between carbon accounting awareness and IR about the decarbonization practices, as well as the role of assurance in the transition for n‐ZBMs. This study tries to move from the concept of legitimacy of the decarbonization practices that firms aim to achieve through sustainable disclosure to the legitimacy of business models towards net‐zero emissions. [ABSTRACT FROM AUTHOR]

This work aims to explore the potential of available renewable energy resources such as solar, wind, biomass and ocean energy and select the best suitable renewable energy resource considering technical, economic, environmental, and social constraints as the main criteria. Five sub-criteria are considered for each main criterion, and these twenty sub-criteria are considered for ranking renewable energy resources using Analytical Hierarchy Process (AHP). The investment cost of the system having the relative weight of 57.94%. Hence it is recommended to choose the renewable energy source having higher value over the economic aspect. The results also showed that the efficiency of the system is the first prioritized criteria among the technical sub criteria with a relative weight of 38.86%. Similarly, land requirement for the implementation of renewable energy system is the first prioritized criteria (43.38%) among the five environmental sub criteria.The overall results from the AHP showed that economic criteria are the more important aspect with a relative weight of 45.04% followed by Technical, environmental and social criteria with relative weights of 22.5%, 14.16% and 12.62% respectively.For Prioritization of renewable energy resources, the study region divided into nine smaller blocks for investigation using AHP and the hierarchical results showed that solar energy is of highest priority in block 2 whereas wind energy potential is higher in blocks 5 and 9. Due to the dense population and widespread agricultural plantations, Bioenergy has average priority over the entire study region. [ABSTRACT FROM AUTHOR]

This study aims at exploring the role of water investment, agricultural land expansion, economic growth, and renewable energy in improving environmental quality (CO2 emissions) in China under the linear and nonlinear Autoregressive Distributed Lags (ARDL and the NARDL) methods covering the 1994–2019 period. The motivation for choosing China for empirical research is that optimum control of pollution in China can improve global environmental quality and combat global warming. The empirical results provide strong evidence for a cointegration relationship between the variables considered. Our study contributes to the existing literature by rejecting two hypotheses. First, we demonstrate from the literature that water investments in China can improve environmental quality. Second, our findings suggest that agricultural land expansion can help reduce greenhouse gas emissions. Our findings suggest that Chinese policymakers should further promote "green agriculture" and encourage the use of renewable energy through "taxes" to control polluting activities and "subsidies," establishing a "punishment and relief" strategy. [ABSTRACT FROM AUTHOR]