Climate-driven shifts in freshwater biodiversity will impact mitigation costs for hydropower.

Climate change is forecasted to drastically alter freshwater fish and mussel species distribution. Hydropower dam reservoirs, which modify downstream thermal regimes, may interact with climate change's impact on species distribution. This distribution shift may feedback, affecting hydropower operation costs through environmental compliance. We investigated how freshwater species distribution will shift due to climate change and hydropower reservoirs in the conterminous United States (CONUS), and how this will affect biodiversity mitigation costs for privately-owned hydropower plants. In general, using environmental niche modeling, we found that climate change increased the range of both freshwater fish and mussel species on average. For fish, this was mainly due to the expanded habitat for warm-water and cool-water fish species despite the diminish in habitat for cold-water species. Compared to climate change, thermal stratification of hydropower reservoirs had a small impact on the future range changes of these species in the tailwaters but showed an interaction with the effect of climate change on species range. Geographically, we projected an increase of species richness in the west and a decrease in the central and east of CONUS for fish, while projecting uniform increase for mussels. With this shift in species distribution, we estimated that the Northwest region will face the largest increase in mitigation cost, while the majority of plants in the Southeast will experience a decrease in cost.

Modelling energy trilemma and economic growth on renewables in N11 economies: Do economic complexity matter?

Utilizing renewable energy is a necessity for accomplishing global agendas, including combating climate change and promoting sustainable development programs. Although much literature has investigated the nexus between energy sources and their affected regressors during the last few years, no appreciable emphasis is available in the previous studies respecting the influence of the energy trilemma index and economic expansion on the influence of the energy trilemma index and economic the renewables in N11 economies. Therefore, the current study analyzes the crucial influencing factors, including the energy trilemma, economic growth, economic complexity, financial development, and urban population, as drivers of renewable energy in N11 economics from 1990 to 2021 by utilizing a panel quantile regression approach. The empirical outcomes certify that renewable energy is positively connected with the energy trilemma, economic growth, financial development, and urban population, but not with economic complexity, which has the inverse result. As a result, legislators responsible for monitoring the deployment of renewables should stimulate their attempts to consider the energy trilemma dimensions into account when determining energy structural policies, increase the use of greener energy subsidies, pose high-carbon taxes, promote green financial innovation, and improve energy efficiency.

Green innovation imperative for natural resource-driven sustainable economic recovery: Linking rights Structure, corporate social responsibility, and renewable energy contracts.

This study examines the complex relationships necessary for a sustainable economic recovery, focusing on the interplay between contracts for renewable energy, natural resource use, corporate social responsibility (CSR), and rights frameworks. Motivated by the increasing scrutiny of environmental practices, this research aims to highlight the need for sustainable business models during the transition to a more environmentally sensitive economy. The study area encompasses diverse sectors where CSR goals can be aligned with renewable energy project frameworks through natural resource utilization. Methodologies include a novel composite CSR evaluation indicator designed to complement industry rankings and a thorough analysis of CSR within the mining industry. Results demonstrate how aligning CSR with renewable energy initiatives can reshape profit models for stakeholders and emphasize the changing green product market as a catalyst for economic resurgence. Recommendations in the area of policies focus on the reasoned utilization of natural resources and the application of innovations following the principles of CSR. This research provides critical guidance to relevant authorities and institutions charged with ethical responsibility, ensuring the proper utilization and implementation of renewable energy sources to create a more ecological future based on green technology and sustainable resource management.

Exploring the impact of renewable energy, green taxes and trade openness on carbon neutrality: New insights from BRICS countries.

The world faces two significant challenges: promoting sustainable economic growth and reaching carbon neutrality. In BRICS countries, these challenges are shaped by renewable energy, green taxes, and trade openness. These countries were selected for their strategic location and the abundance of relevant data collected over the period of 1990-2021, providing a distinctive window into the energy and economic dynamics of the area. The link between renewable energy consumption, green taxes, trade openness, and natural resources and their effects on carbon emissions in BRICS countries is examined in this study using the Fully Modified Ordinary Least Square Method (FMOLS) estimator and the Drisc Kraay estimator for the robustness test. The findings indicate that using renewable energy and green taxes primarily contribute to reducing emissions, particularly at higher emissions levels. The study reveals that various factors, namely financial globalization, trade openness, efficient resource management, and population growth, substantially impact carbon neutrality. Population growth positively impacts carbon neutrality, while using renewable energy sources mitigates it. Furthermore, the empirical findings show a statistically significant positive association between financial globalization, efficient resource management, and carbon neutrality in BRICS nations. Therefore, it is necessary to implement an integrated ecological governance strategy to control and direct financial resources towards sustainable development and green energy.

The impact of renewable and non-renewable energy consumption on aggregate output in Pakistan: robust evidence from the RALS cointegration test.

Over the past three decades, Pakistan's energy consumption has surged due to industrialization, population growth, and development activities. To meet the escalating energy demands, the country has primarily relied on thermal power projects, which are financially burdensome and environmentally detrimental, compared to hydropower projects. This reliance exposes Pakistan to global oil price shocks and environmental degradation. To address this dilemma, this empirical research investigates the impact of both non-energy factors (labour and capital) and energy-specific factors (renewable and non-renewable) on Pakistan's aggregate output, using annual time-series data from 1980 to 2021. The analysis employs the newly established Residual Augmented Least Square (RALS) cointegration test and the Autoregressive Distributed Lag (ARDL) methodology to estimate the long-term cointegrating relationship among the examined variables. The empirical findings demonstrate that both non-energy and energy-specific factors positively and significantly influence Pakistan's long-term aggregate output. However, petroleum consumption exerts a positive but insignificant influence on Pakistan's long-term aggregate output. The study recommends diversifying the energy supply mix to include more hydroelectricity, non-hydroelectric renewables (mainly solar and wind), and natural gas. Specifically, transitioning from imported, expensive, and more greenhouse gas (GHG)-generating petroleum products to domestically produced natural gas could potentially reduce Pakistan's trade deficit and its vulnerability to global oil price shocks. Besides the economic benefits, shifting from non-renewable energy sources (specifically oil) to renewable energy would enhance Pakistan's image and increase its geopolitical influence over neighboring countries. Additionally, the study emphasizes the need to encourage private sector participation in renewable energy projects and suggests implementing effective carbon tax policies to mitigate CO2 emissions and foster economic growth.

Improving environmental and economic sustainability of cutlery manufacturing in a developing nation through energy reduction and energy transition initiatives.

Manufacturing industries are vital for economic development, but they cause significant environmental damages. As there are scarce research studies for this industrial sector from developing countries, this article reports a comprehensive environmental and economic analyses for cutlery manufacturing in Pakistan. SimaPro 9.5 was used as a modelling software tool, while ReCiPe 2016 methods were used to evaluate various midpoint and endpoint environmental impacts. Various economic indicators were used to evaluate the economic performance of different alternative scenarios. The results revealed that injection molding process, due to its energy-intensive nature caused the most environmental impacts as compared to other manufacturing processes. Global warming and terrestrial ecotoxicity were the most affected impact categories with values of 11.8 kg CO2 eq and 12.0 kg 1,4-DCB, respectively. Meanwhile, at endpoint level, human health category was most damaged as compared to others. Based on technical process intervention and energy transition, four different alternative scenarios were developed. In comparison with baseline scenario, the alternative scenario with double-cavity mold resulted in a reduction of more than 30% for various impact categories. The other three alternatives were grounded on the use of solar energy (50% or 100%) and injection mold with double or single cavity. Overall, the alternative scenario with 50% solar energy and double-cavity mold was the best solution that showed more than 50% reduction in most of the impact categories, less than 3-year payback time, 2.12 million (Pakistani Rupees) net present value, and 36.3% of return on investment. This study clearly shows the importance of renewable energy resources and simple changes in process technology for improving sustainability performance. The relevant stakeholders can effectively use the results and methodology of this study as a reference and guide for future research and practical interventions, especially in developing countries.

Financial inclusion and environmental pollution in sub-Saharan Africa: moderating effects of economic growth and renewable energy.

A thriving literature exists about the role of financial inclusion in socio-economic development. Nevertheless, the environmental effects of financial inclusion are largely unknown in the literature, especially in sub-Saharan African countries. Therefore, this study explores the association between financial inclusion and CO2 emissions utilizing data from 23 sub-Saharan Africa for the period 2004-2019. Based on different estimation methods such as dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), canonical correlation regression (CCR), and an instrumental variable generalized-method of moment (IV-GMM), the results show that financial inclusion is responsible for a substantial increase in CO2 emissions. In addition, financial inclusion moderates economic growth, resulting in higher CO2 emissions. Alternatively, financial inclusion moderates renewable energy use to lower CO2 emissions. The outcomes also verify the presence of the Environmental Kuznets Curve hypothesis (EKC). This study proposes uniting financial inclusion and environmental policies as a strategy for reducing CO2 emissions in sub-Saharan Africa.

Applications of blockchain technology in peer-to-peer energy markets and green hydrogen supply chains: a topical review.

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.

Contributions of heterogeneous catalysis enabling resource efficiency and circular economy.

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'.

The potential of magnesium oxide nanoparticles (MgONPs) in the transesterification of lipids produced by Rhodotorula minuta.

The urgent need to address energy security risks and global warming has led to exploration of renewable energy sources. One such avenue is biodiesel specifically focusing on the potential of Rhodotorula minuta, a type of yeast known for producing lipids that can be used as a sustainable alternative for production of biodiesel. In the current study, this promising yeast was evaluated for its potential to produce lipids. The morphological characterization was carried out by scanning electron microscope (SEM), and intracellular detail was studied by transmission electron microscope (TEM). Changes in content and cellular biomass were monitored at time intervals with the highest biomass yield of 12.4 g/l and lipid content of 6.2 g/l achieved after 72 h. In the present work, magnesium oxide nanoparticles (MgO NPs) were synthesized and extensively characterized through Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), Brunauer-Emmett-Teller (BET), SEM, TEM, and X-ray diffraction (XRD). By employing response surface methodology (RSM) with Box-Behnken design (BBD), optimal process conditions for transesterification could be determined. The best result achieved was a yield of 88.6% when the conditions were optimized, using methanol to oil ratio of 18:1 and 8% (w/w) amount of catalyst maintaining a reaction temperature of 55 °C and allowing the reaction to proceed for 120 min.

Impact of the circular economy on ecological footprint: evidence from Germany.

The circular economy practices contribute to sustainable development by maximising efficiency, utilising renewable resources, extending product lifespans, and implementing waste reduction strategies. This study investigates the individual impacts of four sources of the circular economy on the ecological footprint in Germany, a country that is among the pioneers in establishing a comprehensive roadmap for the circular economy. The four sources examined are renewable energy consumption (REC), recycling, reuse, and repair of materials. Using time series data from 1990 to 2021, the study employed the dynamic autoregressive distributed lag (ARDL) simulation technique and also applied kernel-based linear regression (KRLS) to test the robustness of the results. The findings revealed that reuse practices significantly reduce the ecological footprint in both the short and long run. REC and repair also substantially decrease the ecological footprint, as shown by the simulation analysis. Conversely, while recycling is generally considered crucial for minimising environmental impact, in this study, it was found to contribute to environmental degradation. This paradox may be attributed to the nascent state of the recycling industry and data limitations. The results from KRLS confirm the findings of the dynamic ARDL. It is recommended that policymakers develop measures that are appropriate, efficient, and targeted to enhance the role of each source of the circular economy in reducing the ecological footprint in Germany. The major limitation of the study is its reliance on the indirect measures of circular economy attributed to the non-availability of data on direct measures.

Prioritization of renewable energy for offshore ship charging stations based on intuitionistic fuzzy GLDS method: A case of China.

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts' fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

Are hybrid approaches combining EKF-UKF and artificial neural networks key to unlocking the full potential of sustainable energy technologies and reducing environmental footprint?

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.

Accelerating transmission capacity expansion by using advanced conductors in existing right-of-way.

As countries pursue decarbonization goals, the rapid expansion of transmission capacity for renewable energy (RE) integration poses a significant challenge due to hurdles such as permitting and cost allocation. However, we find that large-scale reconductoring with advanced composite-core conductors can cost-effectively double transmission capacity within existing right-of-way, with limited additional permitting. This strategy unlocks a high availability of increasingly economically viable RE resources in close proximity to the existing network. We implement reconductoring in a model of the US power system, showing that reconductoring can help meet over 80% of the new interzonal transmission needed to reach over 90% clean electricity by 2035 given restrictions on greenfield transmission build-out. With $180 billion in system cost savings by 2050, reconductoring presents a cost-effective and time-efficient, yet underutilized, opportunity to accelerate global transmission expansion.

Do various financial resources ensure renewable energy production and environmental protection in OECD countries: modelling for insight.

The recent global energy crisis scenario echoes the past, and the vulnerability of traditional fuels remains the proximate cause of environmental degradation. The fundamental changes in mobilising finance resources are predominant in an energy transition. So, acknowledging finance and energy transition via financial and environmental technology is necessary to meet sustainable development goals. Accordingly, this study employs panel econometric models to investigate how financial resources can ensure the production of renewable energy and environmental protection in 38 OECD member nations over a time period from 2000 to 2021. The empirical finding shows that foreign direct investment and financial technology ameliorate environmental performance by alleviating CO2 emissions. Environmental technologies positively impact environmental protection by eliminating CO2 emissions. The financial resources ensure renewable energy production and environmental protection by reducing the environmental externalities and encouraging renewable energy consumption. Similarly, the consumption of renewable energy catalyses environmental improvement. Economic globalisation spurs environmental externalities. The D-H causality test also shows a reciprocal movement from environmental performance to financial technology, environmental technology and renewable energy consumption. The study's outcomes offer a new model of insight for governments as well as financial and energy policymakers to protect the environment. Enable sustainable development in OECD countries by empowering financial innovation through fintech and incentivising environmental technology to increase renewable energy production.

Spatial effects of renewable and fossil energy consumption on the ecological footprint for the EU Countries.

This study examines the spillover of pollution among the 26 European Union (EU) countries from 1995 to 2020. In order to quantify pollution spillovers among the countries, we estimated the Environmental Kuznets Curve (EKC) using spatial econometric methods. Our research is unique in that it investigates ecological footprint spillovers for EU countries. This study also considers the direct and indirect effects of renewable and fossil energy consumption and globalization on environmental degradation in EU countries. The empirical results favor the validity of the EKC hypothesis. Our results support the presence of positive and significant ecological footprint spillovers among EU countries. Our spatial estimates also reveal the significant spillover impact of explanatory variables on the ecological footprint. The ecological footprint of the local country is declining owing to the consumption of renewable energy in neighboring countries. Furthermore, the fossil energy consumption of the local and neighboring countries has a positive impact on the ecological footprint. Evidence obtained from our spatial estimates provides useful insights to policymakers in developing appropriate environmental policies to combat climate change.

Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions.

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability.

Offshore wind farm operation contributed to a slight improvement in seawater quality along the Jiangsu Coast, China.

The rapid growth of offshore wind farms (OWFs) is driven by concerns for energy security and climate change mitigation. However, their impact on marine environments remains poorly understood due to limited research. This study analyzes the effects of an OWF along China's Jiangsu Coast on seawater quality using data from different development phases. Results show the major pollutants were different across phases. Heavy metal pollution reached alert levels during construction compared to the safe levels observed in the pre-construction and operational phases, mainly due to increases in Pb, Cd, and Hg concentrations. Eutrophication was mild throughout all periods but exhibited a continuous decrease, primarily attributed to reductions in PH and COD concentrations. As a result, the comprehensive pollution level during construction was increased, but it was improved to a clean level during the operational phase. Besides, significant variations were observed in the spatial distribution patterns of major pollutant indices across different scenarios. These changes may stem from a combination effect of land-based pollution, aquaculture, OWF-induced disturbances to atmosphere and hydrodynamics, OWF-related drain and leakage contamination, and marine management policies. Understanding these effects informs OWF optimization, rational wind resource utilization, and marine ecology protection.

Performance comparison between PID and Fuzzy logic controllers for the hardware implementation of traditional high voltage DC-DC boost converter.

This research introduces a hardware implementation of DC-DC boost converter designed to elevate the DC voltage generated by renewable sources while effectively regulating it against line and load fluctuations for inverter application. The main objective is to boost the DC link voltage to the level of Vmax in the output AC voltage obtained from inverter circuits. This enables the inverters for transformer-less power conversion from DC to AC to reduce magnetic losses, size and weight of the inverter circuits used in the utility application. The proposed converter's topology and switching sequences play a crucial role in enhancing overall performance. Utilizing a Zero Current Switching (ZCS) technique, the converter efficiently recovers stored energy from the magnetics. The proposed converter attained the output voltage of 350 V at its current of 1A from the input voltage of 20 V at its current of 19 A. The ZCS technique and the topology of the converter enhances the efficiency to 92 %. The study employs traditional Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers for effective voltage regulation, analysing time domain specifications. Additionally, a Fuzzy logic controller is introduced as an alternative to PID controllers to compare their performance metrics, evaluating the optimization of the converter's transient and steady-state behaviours. The proposed converter is designed, simulated and their performance metrics are analysed using MATLAB for both with and without controllers. The step-time characteristics of the proposed converter with load resistance of RL = 500 Ω and an input voltage of Vi = 20 V has been determined and analysed. The PID system attained a rise time of 88.781 ms, an overshoot value of 9.341 %, and a steady-state error of 0.00043. The fuzzy system achieved a low-rise time of 10.624 ms, a low overshoot of 0.55 %, and a steady-state error of 0.0584. The hardware prototype of the proposed converter is implemented with a FPGA based PID and Fuzzy logic controllers for providing better voltage regulation and to improve the performance metrics of the converter. The simulation and experimental findings are contrasted, examined, and confirmed to ensure improved consistency in performance measures.

Design of a high voltage gain converter using coupled inductor with reduced voltage stress for photovoltaic energy based systems.

This paper presents the design and analysis of a high voltage gain converter utilizing a coupled inductor with reduced voltage stress, specifically for photovoltaic energy-based systems. The proposed converter employs a two-winding coupled inductor and voltage multiplier cells to achieve an increase in output voltage while mitigating voltage stress across semiconductor components. Additionally, the voltage multiplier cells function as voltage clamps for the power switch, further enhancing the converter's performance. The converter features a single switch design, which simplifies control, reduces cost, and improves reliability. Key advantages of the converter include a low component count, a common ground between input and output ports, and high efficiency. The converter's performance is thoroughly investigated through mode analysis and steady-state analysis. Comparative evaluations with similar converters are conducted to highlight the benefits and performance of the proposed design. To validate the theoretical analysis, a 125 W prototype with 26 V input and 200 V output voltages operating at a 50 kHz switching frequency is developed, and experimental results are presented, demonstrating the effectiveness and practicality of the proposed high voltage gain converter.