A novel approach to integrate CCHP systems with desalination for sustainable energy and water solutions in educational buildings.

This study presents a novel approach to integrating combined cooling, heating, and power (CCHP) systems with water desalination for enhanced energy and water management in educational buildings. Two distinct layouts for CCHP and desalination systems are introduced: one prioritizing efficient power generation to meet electricity demands while providing waste heat for desalination, and the other focusing on balancing cooling and heating loads alongside water desalination. Both layouts are tailored to meet the building's energy and water demands while considering operational efficiency. Optimization of these layouts against traditional systems using the bat search algorithm emphasizes economic viability and the gas engine's operational flexibility, which are crucial for partial load operation. In addition, an environmental assessment compares the proposed CCHP-desalination systems with conventional setups, assessing CO2 emission reductions and overall sustainability. The evaluation encompasses key environmental metrics, such as resource consumption and the integration of renewable energy sources. Results highlight significant CO2 emission reductions across various gas engine capacities, with notable enhancements in economic and environmental performance achieved by selecting a 3,250 kW gas engine within the CCHP-desalination system. This choice not only maximizes the annual profit but also reduces CO2 emissions by 57% compared to conventional systems, underscoring the system's sustainability benefits.

Linkages between environmental sustainability, disaggregated emission, renewable energy, and energy efficiency: An evidence from BRI countries.

This paper examines the long-term and short-run causative relationship among environmental sustainability, energy efficiency, renewable energy and carbon emissions from all over sources (coal, oil and fossil fuels) and sector wise division (heat and power, transportation, residential, manufacturing and other sectors. The empirical evidence presented in this study is derived from a balanced panel dataset spanning the annual periods from 2000 to 2021. The dataset specifically focuses on a selection of BRI Countries. The Kao test demonstrates the presence of cointegration across variables such as carbon dioxide emissions, environmental suitability, energy efficiency and renewable energy. The Panel Pooled Mean Group-Autoregressive Distributed Lag (PMG-ARDL) model indicates a statistically significant positive association between the environmental sustainability and disaggregated CO2 emissions over a long-term period. The study found a positive relationship between disaggregated CO2 emissions and environmental sustainability and energy efficiency, with renewable energy sources reducing emissions. It suggests a need for a structural transition from an energy-intensive economy to a decarbonized one, with sectors like heat and power positively impacting sustainability. Implementing measures to reduce emissions is crucial for tackling climate change.

A puzzle on environmental quality and energy security.

i) This study examines the determinants of environmental quality. It is not possible to fully analyze the complex network that emerges from the set of interactions of these determinants, both with each other and with environmental security. Indeed, a number of variables and relationships hidden in the background of the puzzle such as 'game theoretical interactions between economies on energy security', characterize this network. However, this study, which includes energy security and environmental quality simultaneously, may open the door to revealing the key patterns of the current network. ii) This study, which investigates the network between environmental problems and energy security, provides empirical evidence that these two variables may well evolve by positively affecting each other under some conditions. iii) Using the current and sophisticated econometric methods such as CDw + based on Juodis and Reese (2022) test and CS-ARDL Model, over a panel of top 20 energy-using countries in the period 1980-2018, the empirical analysis of the article shows that an increase in energy security risk positively affects environmental quality in aggregate by motivating increased energy efficiency, triggering environmental awareness and regulations, and stimulating research and development activities for clean energy etc. Technologies. Therefore, this study concludes that potential policies and reforms, including reducing fossil fuel consumption, increasing energy efficiency in distribution and consumption, encouraging investments in clean energy are of key importance in making energy security sustainable in the long term by increasing environmental quality.

Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Unlocking a greener future: The role of digital finance in enhancing green total factor energy efficiency.

The development of digital finance provides new opportunities for improving energy efficiency and promoting green development. This paper calculates green total factor energy efficiency (GTFEE) using the super-efficiency SBM model and examines the impact of digital finance on GTFEE. Digital finance has a significant positive impact on GTFEE. Under a bank-dominated financial structure, the positive impact of digital finance on GTFEE is quite significant. In regions with intense banking competition, a large amount of green credit, and lower resource dependence, digital finance is conducive to enhancing GTFEE. Optimizing the allocation efficiency of production factors is an essential mechanism for digital finance to encourage improvements in GTFEE. Digital finance alleviates distortions in factor markets and enhances the matching of the marginal output and the price of capital, labor, and energy factors, thereby facilitating improvements in GTFEE. Further analysis indicates that digital finance has a significant, positive spatial spillover effect on GTFEE, enhancing GTFEE levels in both local and neighboring regions. This study enriches the research on the relationship between digital finance and energy efficiency and provides theoretical foundations and policy references for how digital finance can better serve the green transition of the economy.

Collaborative effect of the energy conservation and emission reduction fiscal policy in China.

Government-led national comprehensive demonstration cities for Energy Conservation and Emission Reduction Fiscal Policy (ECERFP) are pivotal for China in addressing environmental governance. Using a panel dataset covering 278 Chinese cities from 2003 to 2019, this study adopts the staggered difference-in-differences (DID) approach to investigate the synergistic impacts of ECERFP on pollution and carbon reduction. The findings indicate that ECERFP contributes to a 3% improvement in pollution reduction performance, a 1.5% enhancement in carbon reduction performance, and a 4% overall increase in combined pollution and carbon reduction efforts. Furthermore, the study examines the heterogeneous effects of ECERFP on environmental performance. ECERFP significantly influences the synergistic efforts in pollution and carbon reduction by fostering green innovation, enhancing energy allocation, and optimizing industrial structures. This study both theoretically and empirically outlines the specific pathways and mechanisms through which "incentive-based" green fiscal policy promotes synergistic pollution and carbon reduction, thus providing a pragmatic foundation for enhancing the role of fiscal policy in environmental governance.

Can energy saving and emission reduction policies promote green transformation of industrial enterprises--The Case of China.

In the context of the continued advancement of the green economy transition, the proactive pursuit of carbon emissions reduction and the early attainment of carbon neutrality goals have emerged as essential components in promoting high-quality economic development. Not only does it contribute to the creation of a community of human destiny, but it is also vital to the realization of sustainable development for human civilization. A dynamic evolutionary game model, which encompasses the interactions among government, enterprises, and the public, was constructed to examine the inherent impact mechanisms of the behavior of three players on the development of a green economy under the context of energy saving and emission reduction subsidies. The results showed that the incentive and punishment mechanisms served as effective tools for harmonizing the interests of system members. Within the mechanisms, the public demonstrated a higher sensitivity to rewards, while enterprises exhibited greater responsiveness to fines. Consequently, the government could influence the behavior of enterprises by incentivizing the public to serve as a third-party inquiry and oversight body. Simultaneously, the government could encourage enterprises to expedite green technology innovation by employing a combination of incentive and punishment mechanisms.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

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.

Environmental regulation, industrial structure and energy efficiency: Evidence from 30 provinces in China.

The government's environmental protection policy can significantly contribute to alleviating resource shortages and curbing environmental pollution, but the impact of various policy instruments implemented by the government on energy efficiency is unclear. Based on the panel data of 30 provinces in China from 2005 to 2021, this paper analyses the impact of environmental regulation and the industrial structure on energy efficiency from the perspective of resource taxes. The U-shaped relationship between environmental regulation and energy efficiency and between the optimization of industrial structure can significantly improve energy efficiency, and the optimization of industrial structure is conducive to weakening the initial inhibitory effect of environmental regulation. In addition, the analysis of regional heterogeneity showed that the impact of environmental regulation was stronger in the central and western regions, while the impact of industrial structure was stronger in the eastern and western regions. The conclusions of this study can help to expand the understanding of the relationship between environmental regulation and industrial structure on energy efficiency, provide policy enlightenment for the realization of green development and high-quality development, and provide Chinese examples and experiences for developing countries to improve energy efficiency.

Nexus of green energy, financial inclusion, militarization, and environmental sustainability: A global perspective.

This article investigates the dynamic impact of green energy consumption (GE), financial inclusion (FI), and military spending (MS) on environmental sustainability (ES) by utilizing a sample of 121 countries from 2003 to 2022. The dataset is divided into high-income, upper-middle income and low and lower-middle-income countries. We employed a two-step system GMM approach, which was further robust through panel Quantile and Driscoll-Kraay (D-K) regressions. The findings divulged that green energy resources benefit ES at global and all income levels because of having a significant negative impact of 5.9% on ecological footprints. At the same time, FI and MS significantly enhance ecological footprints by 7% and 6.9%, respectively, proving these factors detrimental to ES. Moreover, conflicts (CON), terrorism (TM), institutional quality (IQ), and socioeconomic conditions (SEC) also have a significantly positive association with global ecological footprints and most of the income level groups. Dissimilarly, financial inclusion and armed conflicts have a non-significant influence on ecological footprints in low-income and high-income countries, respectively. Furthermore, institutional quality enhances ES in upper-middle and low and lower-middle-income countries by negatively affecting ecological footprints. At the same time, terrorism significantly reduces ecological footprints in high-income countries. This research also provides the imperative policy inferences to accomplish various SDGs.

The role of financial innovation in carbon intensity reduction: perspectives from energy structure transition and fiscal policies.

Carbon emissions are important factors causing global warming, which requires global efforts to deal with. In this paper, we investigate the mechanism of financial innovation on reducing carbon emissions in China by constructing a financial innovation development index with factors of green finance as well as fintech development. Empirical results show that financial innovation contributes to reduce carbon intensity by promoting energy structure transition as well as public fiscal expenditure on energy conservation and environmental protection. Moreover, heterogeneity exists in the effect of financial innovation on carbon emission reduction. Financial innovation has a significant role in reducing carbon intensity in eastern regions, but has a relatively small influence on central and western regions. Furthermore, financial innovation has a lag effect on reducing carbon intensity.

An energy efficiency assessment of Yttrium-aluminum-garnet laser in vitro.

To study the effect range of the Nd:YAG laser through various levels of cloudy medium for targets with varying grayscale values in vitro. The coated paper cards with grayscale values of 0, 50, 100, and 150 were used as the laser's targets, which were struck straightly with varying energies using three burst modes (single pulse, double pulse, and triple pulse). Six filters (transmittances of 40, 50, 60, 70, 80, and 90) were applied to simulate various levels of cloudy refractive medium. Image J software was used to measure the diameters and regions of the laser spots. The ranges of the Nd:YAG laser spots increased with energy in the same burst mode (P < 0.05). Under the same amount of energy, the ranges of the Nd:YAG laser spot increased with the grayscale value of the targets (P < 0.05). The greater the transmittance of the filters employed, the larger the range of the Nd: YAG laser spots produced. Assuming that the total pulse energy is identical, the effect ranges of multi-pulse burst modes were significantly larger than those of single-pulse burst mode (P < 0.05). The effect range of a Nd:YAG laser grows with increasing energy and the target's grayscale value. A cloudy refractive medium has a negative impact on the effect range of the Nd: YAG laser. The single pulse mode has the narrowest and safest efficiency range.

Research on total factor energy efficiency in western China based on the three-stage DEA-Tobit model.

As an essential material basis and power source for economic and social development, Western China's low energy use efficiency has hindered its sustainable economic development. This study aims to evaluate the total factor energy efficiency of the region and identify its influencing factors. A three-stage DEA model was used to measure the efficiency of 11 provinces from 2006 to 2021, and the Tobit model was employed to investigate internal factors. The findings show that (i) external environmental factors and stochastic perturbations have a significant impact on TFEE in the western region, overestimating integrated efficiency and scale efficiency and underestimating pure technical efficiency. (ii) the study of external influencing factors finds that the level of economic development increases input redundancy; the industrial structure increases capital input and labor input redundancy while decreasing energy input redundancy; and the energy consumption structure increases capital input and energy input redundancy while decreasing labor input redundancy. (iii) the study of internal influencing factors finds that the level of scientific and technological innovation, the level of openness to the outside world, and the TFEE have a positive correlation. In contrast, the intensity of environmental regulation has a negative correlation.

A new strategy for improving the energy efficiency of electro-Fenton: Using N-doped activated carbon cathode with strong Fe(III) adsorption capacity to promote Fe(II) regeneration.

Fe(II) regeneration plays a crucial role in the electro-Fenton process, significantly influencing the rate of ·OH formation. In this study, a method is proposed to improve Fe(II) regeneration through N-doping aimed at enhancing the adsorption capacity of the activated carbon cathode for Fe(III). N-doping not only enriched the pore structure on the surface of activated carbon, providing numerous adsorption sites, but also significantly increased the adsorption energy for Fe(III). Among the types of nitrogen introduced, pyridine-N exhibited the most substantial enhancement effect, followed by pyrrole-N, while graphite-N showed a certain degree of inhibition. Furthermore, N-doping facilitated the adsorption of all forms of Fe(III) by activated carbon. The adsorption and electrosorption rates of the NAC-900 electrode for Fe(III) were 30.33% and 42.36%, respectively. Such modification markedly enhanced the Fe3+/Fe2+ cycle within the electro-Fenton system. The NAC-900 system demonstrated an impressive phenol degradation efficiency of 93.67%, alongside the lowest electricity consumption attributed to the effective "adsorption-reduction" synergy for Fe(III) on the NAC-900 electrode. Compared to the AC cathode electro-Fenton system, the degradation efficiency of the NAC-900 cathode electro-Fenton system at pH = levels ranging from 3 to 5 exceeded 90%; thus, extending the pH applicability of the electro-Fenton process. The degradation efficiency of phenol using the NAC-900 cathode electro-Fenton system in various water matrices approached 90%, indicating robust performance in real wastewater treatment scenarios. This research elucidates the impact of cathodic Fe(III) adsorption on Fe(II) regeneration within the electro-Fenton system, and clarifies the influence of different N- doping types on the cathodic adsorption of Fe(III).

Green energy innovation initiatives for environmental sustainability: current state and future research directions.

Worldwide, all countries have been facing the crisis of climate change problem. They have been addressing this issue by focusing on implementing green energy innovation initiatives and promoting a sustainable future through environmental sustainability. In this research study, we focus on examining the role of green finance through green energy innovations, which are taking place in several sectors across different regions to promote environmental sustainability. The study has analysed 152 articles on this research domain through a systematic literature review to understand the present state of existing knowledge. The current study examines the Scopus-indexed research articles from the time period 2002 to 2023. Six emerging themes have been examined to understand their development and the potential impact of green initiatives for environmental sustainability. Various institutional theories have been explored to understand their association with the investigated research area. The paper has discussed multiple challenges that need to be addressed for the speedy implementation of green innovations. Finally, future research questions have been proposed based on the findings from the extant literature and the existing research gaps.

Synergistic consideration of co-treatment of sewage sludge, low-rank coal, and straw for sustainable resource utilization and enhanced energy efficiency: a review.

This article provides a comprehensive exploration of the imperative necessity for coupling the utilization of low-rank coal, sewage sludge, and straw. It studies the challenges and limitations of individual utilization methods, addressing the unique hurdles associated with feedstocks. It focused on achieving integrated and sustainable resource management, emphasizing efficient resource utilization, waste minimization, and environmental impact reduction. The investigation extends to the intricate details of reaction processes in co-processing, with a specific emphasis on the drying of raw materials to enhance combustion characteristics. The molding and preparation of feedstock are dissected, encompassing raw material selection, mixing, and the crucial addition of additives and binders. The proportions and homogenization of these feedstocks are intricately examined for uniformity and effectiveness. Furthermore, it presents theoretical approaches for investigating the co-combustion of these diverse feedstocks, contributing a solid foundation for future studies in this dynamic field. The findings presented in it offer valuable insights for researchers, practitioners, and policymakers seeking sustainable solutions in the co-disposal technology of these feedstocks. Therefore, it provides a holistic understanding of the challenges and opportunities in coupling the utilization of these selected feedstocks. By addressing individual limitations and emphasizing integrated resource management, the article establishes the groundwork for sustainable and efficient co-processing practices. The exploration of reaction processes gives a comprehensive framework for future research and application in the field of co-combustion technology. The insights gleaned from this study contribute significantly to advancing knowledge in the sustainable utilization of diverse feedstocks, guiding efforts towards environmentally responsible and resource-efficient practices.

The impact of green innovation resilience on energy efficiency: A perspective based on the development of the digital economy.

As digital economy develops, its impact on green innovation and energy efficiency has become the focus of current research. To explore the impact of the current development of the digital economy on the energy industry, this paper selects the parameter of green innovation resilience, analyzes the impact mechanism of green innovation resilience on energy efficiency under the shock of digital economic development, and uses relevant data from 284 cities in China from 2011 to 2019 for empirical testing. It is found that: green innovation resilience promotes energy efficiency; low level of green innovation resilience inhibits the improvement of energy efficiency, while high level of resilience promotes energy efficiency; the initial stage of digital economic development generates resource grabbing and the effect of technological constraints, which weakens the role of green innovation resilience in promoting energy efficiency. The results indicate that the resilience of the green innovation system should be strengthened in order to fully tap the potential for promoting energy efficiency; the policy orientation of "digital greening-energy efficient" should be pursued in the development of digital economy; the rational allocation of resources and the implementation of green standards should be strengthened in the process of digital economic development; and the constraints on energy efficiency improvement in the early stage of digital economic development should be broken through by accelerating the digitalization process.

The synergy effect of energy security and carbon-haze collaborative management: From the perspective of biased technological progress.

It is important to ensure energy security and achieve carbon-haze collaborative management for sustainable development. Reducing imported energy dependence is necessary to maintain energy security, while its impact on environmental quality remains unclear. From the perspective of biased technological progress, this paper estimates the level of biased technological progress towards self-sufficient energy by a heterogeneous stochastic frontier analysis (SFA) function, and then empirically examines whether self-sufficient energy biased technological progress has a dampening effect on haze pollution and carbon emissions. It is found that: (1) Self-sufficient energy biased technological progress can effectively reduce haze pollution and carbon emissions, achieving a synergistic effect between energy security and carbon-haze collaborative management. (2) "Efficiency enhancement" and "quality improvement" are the essential mechanisms for the synergistic effect. (3) Environmental regulation, abundant resource and technology endowments can enhance the haze reduction effect. And the lower dependence on foreign trade and stable global economic policy environment are more conducive to achieving carbon-haze collaborative control. (4) In the Eastern and Western regions, self-sufficient energy biased technology can be sped up to alleviate haze pollution. The findings can enrich the research exploring pollution control from the perspective of biased technological progress, and provide policy recommendations for promoting high-quality development.