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Net radiation (Rn), a critical component in land surface energy cycling, is calculated as the difference between net shortwave radiation and longwave radiation at the Earth's surface and holds significant importance in crop models for precision agriculture management. In this study, we examined the performance of four machine learning models, including extreme learning machine (ELM), hybrid artificial neural networks with genetic algorithm models (GANN), generalized regression neural networks (GRNN), and random forests (RF), in estimating daily Rn at four representative sites across different climatic zones of China. The input variables included common meteorological factors such as minimum and maximum temperature, relative humidity, sunshine duration, and shortwave solar radiation. Model performance was assessed and compared using statistical parameters such as the correlation coefficient (R2), root mean square errors (RMSE), mean absolute errors (MAE), and Nash-Sutcliffe coefficient (NS). The results indicated that all models slightly underestimated actual Rn, with linear regression slopes ranging from 0.810 to 0.870 across different zones. The estimated Rn was found to be comparable to observed values in terms of data distribution characteristics. Among the models, the ELM and GANN demonstrated higher consistency with observed values, exhibiting R2 values ranging from 0.838 to 0.963 and 0.836 to 0.963, respectively, across varying climatic zones. These values surpassed those of the RF (0.809-0.959) and GRNN (0.812-0.949) models. Additionally, the ELM and GANN models showed smaller simulation errors in terms of RMSE, MAE, and NS across the four climatic zones compared to the RF and GRNN models. Overall, the ELM and GANN models outperformed the RF and GRNN models. Notably, the ELM model's faster computational speed makes it a strong recommendation for Rn estimates across different climatic zones of China.

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This study explores the recycling challenges of industrial sludge, owing to its non-recyclable properties and associated environmental problems. To promote sustainable energy utilization, a novel approach combining hydrothermal carbonization and co-gasification was employed to facilitate the conversion from waste to energy. The industrial sludge was pretreated in the batch-type hydrothermal treatment unit at 180-220 °C, followed by co-gasification. The experimental results indicate that pretreating the sludge at the hydrothermal temperature of 200 °C maximized its thermal decomposition, leading to a rougher structure with obvious cracks, eventually transforming into numerous fragmented small particles. At 1100 °C with a blending mass ratio of 1:1, the sludge hydrochar at 200 °C significantly enhanced the reactivity of coal char, exhibiting the gasification reactivity index R0.9 of 1.57 times higher than that of untreated char. Using the in-situ technique with the heating stage microscope, it was first observed that the addition of pretreated sludge coal chars underwent gasification in the shrinking core mode, displaying a significant ash melt flow phenomenon. Based on the in-situ X-ray diffraction, it was discovered that more amorphous structures were formed by the reaction of Fe with other minerals in the sludge-coal blended char after hydrothermal carbonization at 200 °C. With pretreatment at the hydrothermal temperature of 200 °C, the sludge can increase the specific surface area of the blended char and facilitate the cracking of carbon crystals during co-gasification. Its specific surface area and the Raman spectroscopic ratio ID1/IG were 1.76 and 1.17 times that of coal char, respectively. Collectively, this study highlights the potential for energy recovery from industrial sludge, contributing to sustainable waste management in the chemical industry.

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Plastic waste poses a critical environmental challenge for the world. The proliferation of waste plastic coffee pods exacerbates this issue. Traditional disposal methods such as incineration and landfills are environmentally unfriendly, necessitating the exploration of alternative management strategies. One promising avenue is the pyrolysis in-line reforming process, which converts plastic waste into hydrogen. However, traditional pyrolysis methods are costly due to inefficiencies and heat losses. To address this, for the first time, our study investigates the use of microwave to enhance the pyrolysis process. We explored microwave pyrolysis for polypropylene (PP), high-density polypropylene (HDPE), and waste coffee pods, with the latter primarily comprising polypropylene. Additionally, catalytic ex-situ pyrolysis of coffee pod pyrolysis over a nickel-based catalyst was investigated to convert the evolved gas into hydrogen. The single-stage microwave pyrolysis results revealed the highest gas yield at 500 °C for HDPE, and 41 % and 58 % (by mass) for waste coffee pods and polypropylene at 700 °C, respectively. Polypropylene exhibited the highest gaseous yield, suggesting its readiness for pyrolytic degradation. Waste coffee pods uniquely produced carbon dioxide and carbon monoxide gases because of the oxygen present in their structure. Catalytic reforming of evolved gas from waste coffee pods using a 5 % nickel loaded activated carbon catalyst, yielded 76 % (by volume) hydrogen at 900 °C. These observed results were supported by elemental balance analysis. These findings highlight that two-stage microwave and catalysis assisted pyrolysis could be a promising method for the efficient management of waste coffee pods, particularly for producing clean energy.

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I examine which extraordinary international events coincide with pronounced changes in the equity markets for some of the world's largest publicly traded suppliers on opposite sides of the global energy mix - oil and environmentally clean energy companies. First, I adapt an intuitively appealing non-parametric filter to empirically timestamp unexpected and prominent increases and decreases in a wide range of global indicators relevant to the international energy market. Then, I use such extraordinary conditions to characterise the performance of oil and environmentally clean energy equities, and their relationships. My findings suggest that jumps in the global stock market, international crude oil market shocks, and the US dollar real effective exchange rate, are the indicators that define the financial landscape during which considerable gains, losses, and instability across both types of energy markets materialise. In contrast, major elevated uncertainties related to geo-political risk and climate policy reflect relative stability in the equities of both oil and environmentally clean energy companies. Although these results imply that both energy assets are potentially lucrative hedging strategies for investors to exploit during heightened geo-political and climate policy uncertainties, clean energy equities offer market participants the option to combine profit maximising and sustainability objectives while minimising global energy security risks.

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Development of highly porous and robust HOFs for high-pressure methane and hydrogen storage remains a grand challenge due to the fragile nature of hydrogen bonds. Herein, we report a strategy of constructing double-walled framework to target highly porous and robust HOF (ZJU-HOF-5a) for extraordinary CH4 and H2 storage. ZJU-HOF-5a features a minimized twofold interpenetration with double-walled structure, in which multiple supramolecular interactions are existed between the interpenetrated walls. This structural configuration can notably enhance the framework robustness while maintaining its high porosity, affording one of the highest gravimetric and volumetric surface areas of 3102 m2 g-1 and 1976 m2 cm-3 among the reported HOFs so far. ZJU-HOF-5a exhibits an extremely high volumetric H2 uptake of 43.6 g L-1 at 77 K/100 bar and working capacity of 41.3 g L-1 under combined swing conditions, and also impressive methane storage performance with a 5-100 bar working capacity of 187 (or 159) cm3 cm-3 at 270 K (or 296 K). SCXRD studies on CH4-loaded ZJU-HOF-5a reveal that abundant supramolecular binding sites combined with ultrahigh porosities account for its high CH4 storage capacities. Combined with high stability, super-hydrophobicity, and easy-recovery, ZJU-HOF-5a is placed among the most promising materials for H2 and CH4 storage applications.

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Developing clean energy is a key pathway and an inevitable choice for achieving the goals of carbon peak and carbon neutrality. From a global perspective, technology is increasingly affecting the trajectory of energy transition, driving clean energy into a stage of rapid development. Therefore, this paper focuses on exploring the dynamic evolutionary characteristics of clean energy transitions driven by different productivity. Using panel data from 171 economies from 1990 to 2019, this paper systematically examines the impact of Total Factor Productivity (TFP) and Green Total Factor Productivity (GTFP) on clean energy transitions. The empirical results indicate that both TFP and GTFP positively impact clean energy transition. Specifically, clean energy consumption increases by 3.35% and 6.03%with a one standard deviation change in TFP and GTFP respectively. Upon decomposing TFP and GTFP, it was found that Green Efficiency Change (GECH) and Green Technical Change (GHCH), especially GECH, are the main factors driving the clean energy transition. Heterogeneity analysis shows that, in developed economies, GTFP has a larger positive impact on clean energy transition than TFP. Furthermore, GTFP demonstrates a significant positive impact on the clean energy transition both before and after the 2008 financial crisis, whereas TFP's positive impact is only evident before the crisis. Our findings emphasize the social benefits of further investments in GTFP.

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In this study, the authors projected the impacts of clean energy investment on environmental degradation by applying a novel and dynamic Autoregressive Distributed Lag (DARDL) model for Pakistan from 1990 to 2022. Most researchers have used ecological footprint or CO2 emissions indicators to look at how clean energy investment affects environmental degradation, which primarily represents contamination induced by humans' consumption patterns and does not consider the impact of the supply side. Against this background, the study scrutinized the dynamic interaction between clean energy investment and environmental sustainability using the load capacity factor (LCF) as an ecological indicator in Pakistan, including economic growth, population density, trade openness, urbanization, and industrialization in the analysis. The long-run estimates from DARDL indicate that a 1 percent upsurge in clean energy investment mitigates environmental degradation by approximately 0.42 percent on average, controlling for other factors. Further, the study also revealed that a 1 percent increase in clean energy investment diminishes dirty energy consumption by approximately 0.45 percent. The validity of the findings is confirmed using alternate methods, i.e., KRLS. The study recommends that Pakistan prioritize investment in clean energy projects to promote environmental sustainability and enforce environmental regulations to reduce the adverse externalities associated with dirty energy activities.

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A sustainable future, concerning the energy transformation of a country, heavily relies on the availability of energy resources, particularly renewables such as solar, wind, hydropower, and clean hydrogen. Among these, hydrogen is the most promising energy source due to its high calorific value, ranging between 120 and 140 MJ/kg. It has the potential to lead the market in various industries such as power generation, steel, chemical, petrochemical, and automotive. Significant research has been going on in hydrogen production technologies to reduce costs and improve competitiveness with fossil fuels. One such potential approach includes the use of metal-water reactions, which offer unique opportunities for producing clean hydrogen and other valuable byproducts. However, the quantity of hydrogen produced varies depending on the metal feedstock, type of electrolyte, and the activator or catalyst, used in combination with water. This latest work discusses recent progress on hydrogen production and the effects of variations in different parameters on the process, with a focus on aluminum (Al)-water reactions. Investigations have been conducted and reported on the effect of various activators with different concentrations, the quantity of aluminum scrap feedstock, and the volume of the electrolyte on the kinetics of the metal-water reactions and hydrogen production. Sodium hydroxide (NaOH) was observed to be more effective than potassium hydroxide (KOH) in promoting metal-water reactions. These activator-assisted metal-water reactions help produce clean hydrogen, along with other value-added products such as hydroxides. This work clearly sheds light on the potential utilization of industrial aluminum scrap as feedstock for producing clean hydrogen.

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The water-energy nexus has garnered worldwide interest. Current dual-functional research aimed at co-producing freshwater and electricity faces significant challenges, including sub-optimal capacities ("1 + 1 < 2"), poor inter-functional coordination, high carbon footprints, and large costs. Mainstream water-to-electricity conversions are often compromised owing to functionality separation and erratic gradients. Herein, we present a sustainable strategy based on renewable biomass that addresses these issues by jointly achieving competitive solar-evaporative desalination and robust clean electricity generation. Using hydrothermally activated basswood, our solar desalination exceeded the 100% efficiency bottleneck even under reduced solar illumination. Through simple size-tuning, we achieved a high evaporation rate of 3.56 kg h-1 m-2 and an efficiency of 149.1%, representing 128%-251% of recent values without sophisticated surface engineering. By incorporating an electron-ion nexus with interfacial Faradaic electron circulation and co-ion-predominated micro-tunnel hydrodynamic flow, we leveraged free energy from evaporation to generate long-term electricity (0.38 W m-3 for over 14d), approximately 322% of peer performance levels. This inter-functional nexus strengthened dual functionalities and validated general engineering practices. Our presented strategy holds significant promise for global human-society-environment sustainability.

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The effectiveness of green finance in driving clean energy and environmental sustainability in the current era is receiving attention. Therefore, this study proposes an empirical framework highlighting the effects of green bonds (GB) on clean energy investment (CEI), clean energy investment efficiency (CEE) and environmental sustainability of 29 green bond issuing countries between 2014 and 2022. Using system and difference GMM approaches, this study finds that (i) green bond issuance drives clean energy investment. (ii) Green bonds sufficiently enhance the selected countries' environmental quality. These results supplement the promotion of green bonds in increasing the transfer of funds towards renewable energy projects by reducing reliance on fossil fuels. (iii) Using Driscoll & Kraay, Fully Modified-OLS, and changing the dependent variable, this study further supported the idea that green bonds effectively promote the CEE and environmental sustainability of the chosen countries. (iv) Similarly, this study conducted income heterogeneity, showing that green bonds improve high- and middle-income countries' CEI and environmental quality. (v) Finally, the results indicate that resource consumption escalates CO2 emissions by declining the CEI. Technological innovations increase CEI, whereas they do not mitigate CO2 emissions directly, hinting at the requirement for a comprehensive approach. Therefore, inclusive policies on green bond frameworks, robust incentives, and rigorous environmental criteria should be implemented to attract investment in clean energy development and ensure the environmental sustainability of the selected countries.

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Magnesium-based hydrogen storage alloys have attracted significant attention as promising materials for solid-state hydrogen storage due to their high hydrogen storage capacity, abundant reserves, low cost, and reversibility. However, the widespread application of these alloys is hindered by several challenges, including slow hydrogen absorption/desorption kinetics, high thermodynamic stability of magnesium hydride, and limited cycle life. This comprehensive review provides an in-depth overview of the recent advances in magnesium-based hydrogen storage alloys, covering their fundamental properties, synthesis methods, modification strategies, hydrogen storage performance, and potential applications. The review discusses the thermodynamic and kinetic properties of magnesium-based alloys, as well as the effects of alloying, nanostructuring, and surface modification on their hydrogen storage performance. The hydrogen absorption/desorption properties of different magnesium-based alloy systems are compared, and the influence of various modification strategies on these properties is examined. The review also explores the potential applications of magnesium-based hydrogen storage alloys, including mobile and stationary hydrogen storage, rechargeable batteries, and thermal energy storage. Finally, the current challenges and future research directions in this field are discussed, highlighting the need for fundamental understanding of hydrogen storage mechanisms, development of novel alloy compositions, optimization of modification strategies, integration of magnesium-based alloys into hydrogen storage systems, and collaboration between academia and industry.

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Crafting an inorganic semiconductor heterojunction with defect engineering and morphology modulation is a strategic approach to produce clean energy by the highly efficient light-driven splitting of water. In this paper, a novel Z-scheme sulfur-vacancy containing Zn3In2S6 (Vs-Zn3In2S6) nanosheets/In2O3 hollow hexagonal prisms heterostructrue (Vs-ZIS6INO) was firstly constructed by an oil bath method, in which Vs-Zn3In2S6 nanosheets grew on the surfaces of In2O3 hollow hexagonal prisms to form a hollow core-shell structure. The obtained Vs-ZIS6INO heterostructrue exhibited much enhanced activity of the production of H2 and H2O2 by the light-driven water splitting. In particular, under visible light irradiation (λ > 420 nm), the rate of generation of H2 of Vs-ZIS6INO sample containing 30 wt% Vs-Zn3In2S6 (30Vs-ZIS6INO) could reach 3721 µmol g-1h-1, which was 87 and 6 times higher than those of Zn3In2S6 (43 µmol g-1h-1) and Vs-Zn3In2S6 (586 µmol g-1h-1), respectively. Meanwhile, 30Vs-ZIS6INO could exhibit the rate of H2O2 production of 483 µmol g-1h-1 through the dual pathways of indirect 2e- oxygen reduction (ORR) and water oxidation (WOR) without adding any sacrifice agents, far exceeding In2O3 (7 µmol g-1h-1) and Vs-Zn3In2S6 (58 µmol g-1h-1). The excellent photocatalytic activities of H2 and H2O2 generations of Vs-ZIS6INO sample might result from the synergistic effect of the sulfur vacancy, hollow core-shell structure, and Z-scheme heterostructure, which accelerated the electron delocalization, enhanced the absorption and conversion of solar energy, reduced the carrier diffusion distance, and ensured high REDOX ability. In addition, the possible photocatalytic mechanisms for the production of H2 and H2O2 were discussed in detail. This study provided a new idea and reference for constructing the novel and efficient inorganic semiconductor heterostructures by coordinating vacancy defect and morphology design to adequately utilize water splitting for the production of clean energy.

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Objective: Studies of the association between clean energy consumption, sleep duration, and cancer are still extremely limited. We aim to investigate the individual or joint role of clean energy consumption, and sleep duration in cancer onset. Methods: We used data from the China Health and Retirement Longitudinal Study. Multivariable locally weighted regression (LOESS) models were first used to assess the individual association of daily sleep time with the risk to develop cancer. Multivariate logistic regression models were conducted on the individual and interaction effects of daily sleep time and cooking fuel. Results: We found that short sleep duration (≤6 hours) and non-clean energy consumption were respectively associated with an increased risk of cancer among older Chinese(p<0.05). We assessed daily sleep time in four quartiles, the adjusted odds ratios (AOR), and 95% confidence intervals (95% CI) for participants in the second (5.0 to <6.5 hours), third (6.5 to <8.0 hours), and fourth quartiles (≥8.0 hours) were 0.88 (95% CI: 0.65-1.20), 0.61 (95% CI: 0.40-0.91), and 0.53 (95% CI: 0.37-0.77), respectively. When we set the cutoff point(6.5 hours), participants who slept more than 6 hours had a 39% lower risk of cancer (AOR: 0.61, 95% CI: 0.46-0.79) compared with others. On the other hand, we conducted that exposure to clean fuel from cooking was negatively associated with cancer incidence (AOR: 0.73, 95% CI: 0.54, 0.97). Furthermore, the combination of longer sleep and cleaner fuels showed the lowest OR for cancer (AOR: 0.39, 95% CI: 0.24, 0.65). Conclusion: Our study showed that sleep duration and clean energy consumption were significantly associated with cancer in elderly Chinese people. In addition, the prevalence of cancer was higher among people who slept less than six hours and used non-clean energy sources. Paying greater attention to the effects of sleep duration and clean energy on the risk of cancer may yield practical implications for cancer prevention.

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This study investigates the impact of public-private partnerships investment in energy and FDI on environmental quality in global investment countries during 1995-2018. Economic growth, technological innovations and consumption of clean energy are also considered as additional determinants of environmental quality. The study applied advanced panel econometric models. Our empirical results affirm the evidence of a long-run association between environmental quality and its determinants. Specifically, economic growth as well as clean energy use improves quality of environment by lowering carbon emissions. Public-private partnerships investment in energy, FDI and technological innovations decrease carbon emissions. Energy consumption (generated from fossil fuel) increases carbon emissions. Heterogeneous causality evidence indicates the presence of a unidirectional causality relation from carbon emissions to public-private partnerships investment in energy and a feedback causality occurs between consumption of clean energy and CO2 emissions. This empirical evidence provides new insights for both policymakers and governments to support public-private partnership investments in energy for the improvement of quality of environment in global investment countries.

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As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

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This study designs a novel electroneutralization desalination cell using reaction heat from acidic-alkaline wastewater neutralization to desalinate wastewater and generates chemoelectric power. Several key performance indicators are measured in terms of the energy, environmental and economic aspects of the system, including the ionic flux, the electrical energy produced, the electrical energy consumption for desalination, parasitic losses, overall energy conversion efficiency and desalination performance. The maximum peak power density is ∼31.5 mW/cm2 at 83.5 mA/cm2 and the desalination efficiency is 62 % using brine. The overall energy conversion efficiency is ∼81.8 % and the desalination followed the zero-order reaction. Assuming a 1.5 million litres per day treatment capacity integrated with reverse osmosis, the system has environmental and economic benefits, with 44.5 kg-CO2eq greenhouse gas emissions per cubic meter of treated brine, and a discounted payback period of 4.2 years. This study demonstrates a pioneering electroneutralization technique for self-sufficient brine valorization and wastewater reclamation.

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Population growth and environmental degradation are major concerns for sustainable development worldwide. Hydrogen is a clean and eco-friendly alternative to fossil fuels, with a heating value almost three times higher than other fossil fuels. It also has a clean production process, which helps to reduce the emission of hazardous pollutants and save the environment. Among the various production methodologies described in this review, biochemical production of hydrogen is considered more suitable as it uses waste organic matter instead of fossil fuels. This technology not only produces clean energy but also helps to manage waste more efficiently. However, the production of hydrogen obtained from this method is currently more expensive due to its early stage of development. Nevertheless, various research projects are underway to develop this method on a commercial scale.

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The development of rural clean energy is the key to cope with the shortage of traditional energy supply in the rural revitalization strategy and improve the sustainability of rural energy supply. Under the background of digital age, the development and utilization of rural clean energy Internet has become the focus of rural economic development. The government partners of the Rural clean Energy Internet PPP project (RCEIPPPP) are the key to promoting the green and intelligent development of rural energy. In this paper, the index system of project partner selection is constructed, and the problem of government partner selection for RCEIPPPP is studied by AHP and fuzzy comprehensive evaluation. The results of this study are as follows: 1) Partners' financial ability, technical ability, management ability, performance experience, corporate reputation, cooperation ability and risk management are the influencing factors for government partner selection of rural clean energy Internet PPP projects (RCEIPPPPs); 2) Compared with other factors, financial ability, technical ability, management ability and performance experience are the four key factors that are more important in choosing partners; 3) The empirical research shows that AHP, fuzzy comprehensive evaluation and the index system constructed by this research can be applied to the practice of government partner selection for RCEIPPPPs. This study puts forward the evaluation system of government cooperation selection of energy Internet PPP projects from the theoretical level, improves the existing research methods, and makes the theoretical system in this field more complete. From the practical level, it provides scientific basis and suggestions for the government to make decisions on energy Internet PPP projects, and improves the engineering efficiency and quality of rural clean energy Internet construction. This study demonstrates the complexity of clean energy projects, the need for an integrated approach to decision-making, and the need for project managers to actively manage communication and collaboration with partners to ensure successful project implementation.

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An ionic liquid (IL, [DMAPA]HSO4) was prepared to facilitate the removal of heavy metals by hydrothermal carbonization (HTC) in sewage sludge (SS) and to obtain a positive energy recovery (ER, (Energyoutput/Energyinput - 1) > 0). The results found that the removal efficiencies of the Fe, Mn, Zn, Co, and Cd from SS exceeded 75 % with positive ER (6 %) at 20 wt% IL dosage (IL:SS). IL promoted the HTC reactions of proteins and polysaccharides to produce fixed carbon and small molecule polymers. The process mainly relies on IL to catalyze the dehydration and graphitization of SS and to destroy the heavy metal binding sites such as carboxyl and hydroxyl groups. Additionally, IL aids in constructing the macropore structures in hydrochar, thereby facilitating the release of heavy metals and water during the HTC process. This discovery holds promise for removing heavy metals from SS by one-pot HTC processes with positive energy recovery.

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The understanding of the geographical variability of biomass energy is an essential requirement for the optimal location of biomass energy conversion plants. This research presents a multicriteria GIS-based assessment of biomass energy potentials and the appropriate siting of biomass plants in Nigeria. The study applies the weighted overlay multicriteria decision analysis method. Crop and forest areas, settlement (energy supply areas), shrub/grasslands, barren land, water bodies, distance from water sources, road accessibility, topography, and aspect are the criteria that were considered for locating a biomass facility in this study. The results suggest that the theoretical, technical, and economical energy potentials of crop residues are highest in the North-East region of Nigeria and estimated at 1,163.32, 399.73, and 110.56 PJ/yr, respectively, and lowest in the South-East at 52.36, 17.99, and 4.98 PJ/yr, respectively. The theoretical, technical, and economical energy potentials of forest residues are highest in the North-West, estimated at 260.18, 156.11, and 43.18 PJ/yr, respectively, and lowest in the South-East at 1.79, 1.08, and 0.30 PJ/yr, respectively. Although most areas were identified to be suitable for siting biomass plants across Nigeria, the most suitable areas are located in the northern part of the country and include Niger, Zamfara, the Federal Capital Territory, Nassarawa, Kano, Kebbi, Kaduna, and Borno State. The study supports the Nigerian bio-energy policy that proposes to effectively utilize Nigeria's non-fuelwood as a substitute for the felling of trees. This is very important to strengthen its commitment at the COP26 International Climate Conference, which is to conserve and restore its forest. Furthermore, this study will serve as a good reference for policymakers to make well-informed decisions on tackling the energy insecurity in Nigeria.