Sustainability, Volume 16, Issue 4 (February-2 2024) – 359 articles

In the burgeoning field of sustainable energy, this research introduces a novel approach to accurate medium- and long-term load forecasting in large-scale power systems, a critical component for optimizing energy distribution and reducing environmental impacts. This study breaks new ground by integrating Causal Convolutional Neural Networks (Causal CNN) and Variational Autoencoders (VAE), among other advanced forecasting models, surpassing conventional methodologies in this domain. Methodologically, the power of these cutting-edge models is harnessed to assimilate and analyze a wide array of influential factors, including economic trends, demographic shifts, and natural phenomena. This approach enables a more nuanced and comprehensive understanding of power load dynamics, essential for accurate forecasting. The results demonstrate a remarkable improvement in forecasting accuracy, with a 15% increase in precision over traditional models. Additionally, the robustness of the forecasting under varying conditions showcases a significant advancement in predicting power loads more reliably. In conclusion, the findings not only contribute substantially to the field of load forecasting but also highlight the pivotal role of innovative methodologies in promoting sustainable energy practices. This work establishes a foundational framework for future research in sustainable energy systems, addressing the immediate challenges and exploring potential future avenues in large-scale power system management. Full article

To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction. Full article

The unit commitment (UC) problem aims to reduce the power generation costs of power generation units in the traditional power system structure. However, under the current arrangement, the problem of cutting the cost of producing electricity has turned into an opportunity to boost power generation units’ profits. Emission concerns are now given considerable weight when talking about the performance planning of power generation units, in addition to economic objectives. Because emissions are viewed as a limitation rather than an objective function in the majority of recent research that has been published in the literature, this paper solves the multi-objective profit-based unit commitment (PBUC) problem while taking into account energy storage systems (ESSs) and renewable energy systems (RESs) in the presence of uncertainty sources, such as demand and energy prices, in order to minimize generated emissions and maximize profits by power generation units in the fiercely competitive energy market. Owing to the intricacy of the optimization problem, a novel mutation-based modified version of the shuffled frog leaping algorithm (SFLA) is suggested as a way to get around the PBUC problem’s difficulty. A 10-unit test system is used for the simulation, which is run for a whole day to demonstrate the effectiveness of the suggested approach. The proposed algorithm’s output is compared with the best-known approaches from various references. The simulated results generated by the suggested algorithms and the previously reported algorithms to solve the PBUC problem show that the proposed method is better than other evolutionary methods utilized in this study and prior investigations. For example, the overall profit from the suggested MSFLA is around 4% and 5.5% higher than that from other algorithms like the ICA and Muller methods in the presence and absence of reserve allocation, respectively. Furthermore, the MSFLA emissions value is approximately 2% and 8% lower than the optimum emissions values obtained using the PSO and ICA approaches, respectively. Full article

Wind energy, renowned for cost-effectiveness and eco-friendliness, addresses global energy needs amid fossil fuel scarcity and environmental concerns. In low-wind speed regions, optimising wind turbine performance becomes vital and achievable by augmenting wind velocity at the turbine rotor using augmentation systems such as concentrators and diffusers. This study focuses on developing a velocity augmentation model that correctly predicts the throat velocity in an empty concentrator-diffuser-augmented wind turbine (CDaugWT) design and determines optimal geometrical parameters. Utilising response surface methodology (RSM) in Design Expert 13 and computational fluid dynamics (CFD) in ANSYS Fluent, 86 runs were analysed, optimising parameters such as diffuser and concentrator angles and lengths, throat length, and flange height. The ANOVA analysis confirmed the model’s significance (p < 0.05). Notably, the interaction between the concentrator’s length and the diffuser’s length had the highest impact on the throat velocity. The model showed a strong correlation (R² = 0.9581) and adequate precision (ratio value of 49.655). A low coefficient of variation (C.V.% = 0.1149) highlighted the model’s reliability. The findings revealed a 1.953-fold increase in inlet wind speed at the throat position. Optimal geometrical parameters for the CDaugWT included a diffuser angle of ${10}^{°}$, concentrator angle of ${20}^{°}$, concentrator length of 375 mm (0.62$R_{th}$), diffuser length of 975 mm (1.61$R_{th}$), throat length of 70 mm (0.12$R_{th}$), and flange height of 100 mm (0.17$R_{th}$) where $R_{th}$ is the throat radius. A desirability value of 0.9, close to 1, showed a successful optimisation. CFD simulations and RSM reduced calculation cost and time when determining optimal geometrical parameters for the CDaugWT design. Full article

Inter-provincial trade leads to changes in CO₂ and air pollutant emissions. However, there is a research gap regarding the coordinated effects (co-effects) between embodied CO₂ and air pollutant emissions in trade. Understanding co-effects in inter-provincial trade is a prerequisite for driving the green transformation of trade and achieving coordination between pollution and carbon reduction. Here, we calculated provincial-level CO₂ and air pollutant emission leakage in 2012 and 2017 based on a modified input–output model and, for the first time, investigated the co-effects between CO₂ and air pollutant emission leakage caused by emissions transfers in China. Three types of co-effects, categorized as co-benefits, trade-offs, and co-damage, were discovered and defined to reveal the provincial differences. Furthermore, combined with structural decomposition analysis (SDA), we calculated the interannual variation in trade-induced emissions and identified the key driving factors of provincial-level co-effects from 2012 to 2017. Optimizing the energy structure has led to the greatest co-benefits, while changes in the industrial structure and emission coefficients have led to limited co-benefits in specific provinces. Variations in trade volume have led to co-damages across all provinces, and changes in emission coefficients have led to trade-offs in the majority of provinces. The case analysis confirmed that identifying and adjusting the key driving factors of co-effects can promote the transformation from co-damage and trade-offs to co-benefits. The findings implied a new approach for the reduction in pollution and carbon through inter-provincial trade. Full article

As global warming intensifies, the development of offshore wind farms is swiftly progressing, especially deep-water Floating Offshore Wind Turbines (FOWTs) capable of energy capture in deep-sea regions, which have emerged as a focal point of both academic and industrial interest. Although numerous researchers have conducted comprehensive and multifaceted studies on various components of wind turbines, less attention has been paid to the operational stage responses of FOWTs to wind, waves, and currents and the reliability of their structural components. This study primarily employs a theoretical analysis to establish mathematical models under a series of reasonable assumptions, examining the possibility of collisions between FOWT transport fleets and other vessels in the passage area during the towing process. Using the model, this paper takes the Wanning Floating Offshore Wind Farm (FOWF) project, which is scheduled to be deployed in the South China Sea, as its research object and calculates the probability of collisions between FOWTs and other vessels in three months from the pier near Wanning, Hainan, to a predetermined position 22 km away. The findings of the analysis indicate that the mathematical model developed in this study integrates the quantities and velocities of navigational vessels within the target maritime area as well as the speeds, routes, and schedules of the FOWT transport fleet. By employing statistical techniques and geometric calculations, the model can determine the frequency of collisions between various types of vessels and the FOWT transport fleet during the transportation period. This has substantial relevance for future risk assessments and disaster prevention and mitigation measures in the context of FOWT transportation. Full article

Under the background of climate change, the problems of water resource allocation and desertification in arid areas are becoming increasingly prominent, which seriously threatens the sustainable development of society. Constructing an ecological network is an important measure to improve the ecological environment and maintain ecological service function. This study takes the Hexi Corridor as an example and relies on land use data from 2000 to 2020, and comprehensively applies methods, such as morphological spatial pattern analysis (MSPA), the minimum cumulative resistance model (MCR), and the network evaluation index to construct and optimize the ecological network of the Hexi Corridor. Our results show: (1) the spatial distribution of the landscape elements in the Hexi Corridor was not uniform and that the ecological foundation in the north was poor; (2) the resistance surface was “low in the south and high in the north”, with low-value areas mainly located to the south of Jiuquan City, Zhangye City, and Wuwei City, and the high-value areas were mainly located in the middle and to the north of Jiuquan City and Wuwei City; (3) the ecological source areas, corridors, and nodes showed a fluctuating upward trend, and they were mainly located to the southwest of Zhangye City, Jiuquan City, and Wuwei City; (4) the network closure (α), line point rate (β), and network connectivity (γ) showed a W-shaped change trend; (5) after the ecological network optimization, 22 new ecological source areas, 78 new corridors, and 61 new nodes were added, as a result, the α, β, and γ indices all increased. Our results provide a reference for ecological environment restoration research and serve as a regionally balanced means of sustainably developing the Hexi Corridor. Full article

Screening remediation technologies through the lens of green, low-carbon, and sustainable development is crucial for contaminated land management. To better understand the applicability of remediation technologies, this paper explored their application in China based on a survey of 643 cases. By employing coupled analysis and local spatial autocorrelation methods, this study reveals the alignment between remediation technologies and pollutants, along with their spatial distribution and clustering patterns. Specifically, the four primary remediation technologies identified were cement kiln co-processing (CKCP), chemical oxidation/reduction (CO/CR), thermal desorption (TR), and solidification and stabilization (S/S), collectively accounting for over 90% of the cases. Additionally, our findings indicated significant variation in how different pollutants respond to remediation technologies, largely attributable to the characteristics of the pollutants. We observed High–High clustering patterns for CKCP, CO/CR, TR, and S/S. These were predominantly found in Jiangsu, Chongqing, Shandong, and Guizhou for CKCP and CO/CR and in Hebei, Jiangsu, Shanghai, and Chongqing for CO/CR. TR exhibited a High–High clustering in Shanghai, as did S/S. This research contributes to reducing the economic and resource costs associated with the trial-and-error of screening contaminated soil remediation technologies, offering valuable scientific and technological guidance for contaminated land regulation. Full article

The development of the sharing economy is accelerated using digital technologies. Such a topic is not widely discussed in the literature and requires knowledge to fill the existing gaps. The authors analyzed technology-driven variables which have the highest impact on expanding sharing activities. The research helps to examine the degree of integration of society into the process of sharing economy development. This paper aims to create a methodology that helps to evaluate the development of sharing platforms dependent on technological variables such as society’s access to digital services. Two activities are foreseen to achieve the goal. The first activity includes the steps necessary for revising technological variables (the compilation of an initial list of variables, the selection of variables, normalization, and the formation of correlation matrix). The second activity is designed to form a panel regression model using several sharing platform cases. Using the developed methodology, the revision of technological variables is carried out to expand the knowledge of economic science about the intensifying processes of the digitization of society, the resulting changes in consumption, and the redistribution of conventional economic solutions in the markets for goods and services. The authors compared the technological variables which had the highest impact on sharing platforms. The study results demonstrated that among ten sharing platforms, the highest dependence on technological variables is evident in the number of visitors visiting the Uber sharing platform. Full article

Pluvial flooding (PF), resulting from intense short-duration rainfall events, is challenging in urban areas amidst climate change and rapid urbanization. Identifying flood-prone zones and implementing collaborative mitigation strategies with the local population are crucial aspects of PF management. This study aims to enhance the understanding of urban PF in Croatia by collecting historical PF data, creating the GIS-MCDA susceptibility model, and conducting a risk perception survey for the study area of Gospić. Susceptibility zones were generated utilizing topographical, environmental, and hydrological criteria using the AHP method. To examine the risk perception, a face-to-face survey was conducted among 5% of the city’s population (N = 64). Five factors were defined: (F1) risk awareness, (F2) anthropogenic and (F3) natural causes of PF, (F4) potential consequences, and (F5) preparedness. The reliability of the questionnaire was very high (>0.71). Most respondents believe they are ill-equipped to defend against flooding independently and express a lack of confidence in the measures taken by local authorities. The highly susceptible zones encompass not only agricultural areas but also residential zones of city. Among all respondents, 36% live in a flood-prone area and half of them have no flood insurance or other mitigation measures. Incorporating locals’ suggestions and problems, mitigation measures were proposed. Results from this research can be a starting point for further research in Croatia and can provide guidelines for decision-makers in implementing a risk mitigation strategy. Full article

The integrated development of social media makes enterprise public opinion spread across multiple social platforms. The safety of enterprise public opinion affects the sustainability of enterprise development and social stability. The risk assessment of enterprise public opinion in a cross social media context and sustainable strategies is researched to help enterprises and governments better regulate enterprise public opinion and improve their ability to respond to public opinion. We established an enterprise public opinion risk assessment index system in a cross social media context, and an enterprise public opinion risk assessment model was established by using a combination of the entropy method, TOPSIS, grey relational analysis and Fuzzy C-means method. The research results show that, compared with the context of single social media, the analysis of enterprise public opinion in a cross social media context is more comprehensive and accurate. The risk assessment model of enterprise public opinion proposed in our research is more suitable for the judgment of enterprise public opinion in a cross social media context and can comprehensively and accurately grasp the situation of enterprise public opinion. The management significance of public opinion risk management for the sustainable development of enterprises is also discussed. Full article

For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression–cellular automata (LR-CA) and artificial neural network–cellular automata (ANN-CA), for the prediction of LULCC (2019–2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986–2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (−2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land–water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies. Full article

In order to explore the positive impact of the joint distribution model on the reduction in logistics costs in small-scale logistics enterprises, considering the demand on enterprises for simultaneous pick-up and delivery, as well as the cost of carbon emissions, this study considers the vehicle routing problem of simultaneous pick-up and delivery under a joint distribution model. First of all, an independent distribution model and a joint distribution model including fixed transportation, variable transportation, time penalty, and carbon emissions costs are established; second, by adding the self-adaption cross-mutation probability and the destruction and repair mechanism in the large-scale neighborhood search algorithm, the genetic algorithm is improved to adapt to the solution of the model in this paper, and the effectiveness of the improved algorithm is verified and analyzed. It is found that the improved genetic algorithm is more advantageous than the original algorithm for solving the problems of both models designed in this paper. Finally, the improved genetic algorithm is used to solve the two models, and the results are compared and analyzed. It is found that the joint distribution model can reduce the total cost by 6.61% and the carbon emissions cost by 5.73%. Additionally, the impact of the carbon trading mechanism on the simultaneous pick-up and delivery vehicle routing problem under the joint distribution model is further explored. The results of this study prove that enterprises can effectively reduce costs, improve profits, reduce carbon emissions, and promote the sustainable development of logistics enterprises under the condition of joint distribution. Full article

The quality of drinking water is crucial for human health and the sustainable development of societies. The Aksu River Basin, a typical inland river system, has areas where groundwater arsenic levels exceed safe drinking water standards (i.e., arsenic concentrations greater than 10 μg/L). Identifying the causes of high arsenic levels in the basin’s groundwater requires further study. Analyzing the hydrogeochemical composition of the Aksu River basin helps us to understand the spatial distribution of groundwater environments and locate areas with dangerously high arsenic levels. In this research, we collected 196 groundwater samples from along the river. Out of these, 38 samples had arsenic levels above 10 μg/L, which represents 19.4% of the total samples collected. By examining the slope changes in the cumulative frequency curves of major ion ratios and employing geostatistics (specifically, the Kriging interpolation), and taking into account the environmental characteristics of the entire basin, we divided the study area into five sub-regions (Zone I through Zone V). The geostatistical analysis showed a significant spatial variability in groundwater arsenic levels, with a clear spatial correlation. Our findings demonstrate that arsenic concentrations in the Aksu River basin’s groundwater vary widely, with Zones II and III—mainly located in the northeastern part of the basin and in Awat County—being hotspots for high-arsenic water. Factors such as a weak reducing environment, intense evaporation, strong cation exchange, and the low-permeability recharge of surface water contribute to the accumulation of arsenic in the basin’s groundwater. The results of this study are vital for assessing the risk of arsenic contamination in groundwater in similar basins and for identifying critical areas for further investigation and research. Full article

Market convergence challenges socially sustainable supply chain management (SSSCM) due to the increasing competition. Identifying market convergence trends allows companies to respond quickly to market changes and improve supply chain resilience (SCR). Conventional approaches are one-sided and biased and cannot predict market convergence trends comprehensively and accurately. To address this issue, we propose a framework based on info2vec that solves the problem of matching multidimensional data by using the technology layer as the focal layer and the supply chain as the supporting layer. The framework enriches the supply chain dimension with the technology dimension. A knowledge graph is constructed to facilitate cross-domain information connectivity by integrating different data sources. The nodes in the knowledge graph were characterized using a representation learning algorithm, which enhanced feature mining during supply chain and market convergence. Changes in market demand were predicted based on link prediction experiments. Market convergence has an impact on firm cooperation and, thus, on SCR. The framework recommends potential technological and innovative cooperation opportunities for firms. In this way, it has been demonstrated to improve SSSCM through network resilience experiments. This method predicts market convergence efficiently based on the supply chain knowledge graph, which provides decision support for enterprise development. Full article

As urban tourism increases, digital signage landmarks are frequently utilized to develop and enhance the attractiveness of cities for tourism. However, the benefits of this development for local residents have not been fully explored from a sustainability perspective. Thus, this study aims to quantitatively analyze local residents’ perceptions of digital signage landmarks in urban areas using one of the prominent icons, the Samseong-dong free display zone in Seoul, Korea. To measure the overall value of the landmarks, this study used a double-bounded dichotomous choice contingent valuation method and spike model. Based on the surveys of 600 respondents in Korea, the results show that a household’s willingness to pay to support the landmark annually is KRW 5401 (USD 4) on average in the form of income tax. The perceived annual value for the landmark is about KRW 790 million (USD 60 million), surpassing that of typical tourism attractions in Korea. Full article

Family small and medium enterprises (FSMEs) differ from non-family SMEs regarding leadership type, human resource management practices, innovation orientation, change management, information and communication technology deployment, process maturity, and resource availability. These differences present challenges when leading any change. Process mining (PM) tools can optimize process value and eliminate non-added-value activities in FSMEs based on “Event Logs”. The present study investigates how a PM project is implemented in an FSME operating in the agri-food sector, focusing on challenges faced in every project phase to extract the most appropriate process that eliminates all sources of waste and bottleneck cases. Drawing upon the L*Lifecycle methodology combined with quality and lean management tools such as the fishbone diagram, Pareto diagram, and overall equipment efficiency (OEE), this study applied a PM project to a manufacturing process for an FSME operating in the agri-food sector. To achieve theoretical production capacity (TPC) and customer satisfaction, the method was analyzed and optimized using Disco and ProM toolkits. The results analysis using Disco and ProM toolkits gave clues about the organizational and technical causes behind the manufacturing process’s inefficiency. First, OEE showed that the studied FSME is struggling with equipment availability. Then, the implementation of the L*Lifecycle methodology allowed for the identification of five critical causes. An action plan to eliminate causes was proposed to the FSME managers. Full article

This paper presents a method for assortment–quantity production scheduling in a printing company. The company uses specialized machinery to make prints on clothing. The method is based on a study of the company’s practical operations and the production technologies used. It involves the construction of simulation and optimization models of the process. The simulation models reflect the technical aspects of the production process and the business requirements. Optimization models provide solutions that balance product sales revenue with appropriate production schedules. On this basis, managers can make resource-balanced decisions on the implementation of selected production plans, taking into account the current economic conditions of the company. The experiments used the FlexSim simulation program (by FlexSim Software Products, Inc., Orem, UT 84097 USA; v. 20.1.3.1) and the OptQuest optimization package (embedded in FlexSim), resulting in a cost-effective solution in a short time. The proposed method, thanks to the optimization of the production program, provides savings in the use of materials for production, as well as water and energy savings in the production process. Thanks to the possibility of analyzing the process without interfering with it, provided by simulation modelling, the method practically eliminates the costs and time needed to prepare the execution of new production orders. This contributes to the sustainable development of the company and provides an opportunity to assess the impact of potential business decisions in the company prior to their implementation. The method has been directly applied in a company to improve its performance. The method is scalable and can be applied to problems of varying complexity and production systems of different types and sizes. This is especially true for small- and medium-sized companies that use discrete manufacturing in the textile, metal, and furniture industries. Full article

This paper presents an assessment of land stability using fallout environmental radioisotopes ⁷Be and ¹³⁷Cs. The measurement of both isotopes was carried out in samples of soil collected from twenty-five sites covering the studied region. At each site, the samples were taken from five consecutive vertical depth levels to show the vertical displacement or compactness of the soil column. The collected samples were carefully transferred for radioactivity measurement at Alexandria University’s Institute of Graduate Studies and Research. A high-resolution gamma-ray spectrometer utilizing high-purity germanium was employed for the measurements. Surface distribution of the radionuclides levels was used to show the studied lands’ stability over the short- and long-term based on the used radionuclides’ nuclear half-life. For short-term (months) stability, ⁷Be (half-life: 35.5 days) levels showed that about 73% of the area is very low in stability, while the areas that recorded low, moderate, and high stability are at 18%, 4%, and 5%, respectively. For long-term (years) stability, ¹³⁷Cs (half-life: 30 years) levels showed that about 80% of the areas are very low in stability, while the remaining areas, predicted as 12.8%, 5.6%, and 1.6%, are low, moderate, and high stability, respectively. It is clear that the eastern side of Alexandria is suffering from soil erosion and subsidence; on the other hand, the western side is more stable. Consequently, due to the origin of the soil, the nature of soil geological formations, and the environmental prevailing conditions, Alexandria is found to be more vulnerable to the consequences of sea-level rise and climate change. Therefore, adequate strategic management, including mitigation measures and adaptation, should be planned and implemented. Full article

This study addresses the challenge of accurately estimating hourly flow and soil moisture by integrating the Soil and Water Assessment Tool (SWAT) with a Land Surface Model (LSM). Our approach enhances SWAT by incorporating spatially distributed soil properties and a physically-based soil moisture process, using the Noah LSM for hourly soil moisture estimation. This integration captures spatial variations in soil moisture and hydraulic properties from remote sensing across the watershed. The parameter sensitivity analysis and the calibration of hourly flow were significantly impacted by the physically-based hourly soil moisture routing and the incorporation of spatially distributed soil properties. Consequently, the modified SWAT model showed improved accuracy in hourly flow simulations for long-term and multiple rainfall events. Validation results showed significant improvements, with Coefficient of Determination (R²) and Nash and Sutcliffe Efficiency (NSE) increasing by 25.95% and 33.3%, respectively, and Percent Bias (PBIAS) decreasing by 85.8%. Notably, the average error for peak flows across eight events decreased by 49%. These findings highlight the importance of initializing soil parameters based on spatial soil moisture distribution and incorporating physical process-based moisture routing to enhance hourly flow simulation accuracy. Future research should focus on validating the physical feasibility of the soil parameter set in the study area with detailed hourly flow and soil moisture data and exploring its applicability in various regions. This study provides valuable insights for the scientific community, water resources, and agricultural decision-makers regarding integrated modeling of soil moisture and hourly flow, which can inform dam operation management, disaster planning, and crop yield improvement. Full article

The environmental impacts from energy consumption account for a high percentage of the environmental impacts of wastewater treatment plants (WWTPs) throughout their life cycle; therefore, controlling energy use in WWTPs could bring substantial benefits to the environment. In this study, according to the different percentages of electricity generation from renewable energy compared to fossil energy, the global warming, acidification, eutrophication, human toxicity, and photochemical smog, the environmental impacts of WWTP operation were considered. Furthermore, to explore a more sustainable way of operating WWTPs under the “dual-carbon” strategic decision, the environmental impacts and energy utilization efficiency of different power allocation scenarios at present and in the next 40 years were compared based on the LCA+DEA integrated model. The study revealed that in scenarios 1–5, as the proportion of renewable energy power generation gradually increased, all LCA results showed a gradual decrease, of which GWP decreased by 83.32% and human toxicity decreased by 93.34%. However, in scenarios 2–5, the contribution ratio (proportion) of gas and electricity to GWP and POCP gradually increased, reaching 77.11% and 59.44%, respectively, in scenario 5. The contribution ratio (proportion) of biomass generation to AP and EP gradually increased as well, reaching 65.22% and 68.75%, respectively, in scenario 5. Meanwhile, the combined technical efficiency in energy utilization in the five scenarios showed a decreasing trend; only scenario 1 was fully efficient, and the combined efficiency was 1. The values of combined technical efficiency in scenarios 2, 3, 4, and 5 gradually decreased and were 0.7386, 0.4771, 0.2967, and 0.1673, respectively. This study discusses whether the use of renewable energy in place of fossil energy power elicits an environmental impact in WWTPs. We explore the feasibility of achieving energy savings and emission reductions in WWTPs within the Tumen River Basin, to provide a theoretical basis for their sustainable development. Full article

Analyzing the cause is crucial for recognizing the risks associated with potentially harmful substances found in soil, such as toxic elements. These substances can have adverse effects on both the ecological environment and human health, as they can migrate and transform within food chain networks. Therefore, it is imperative to address and prioritize the risks associated with these elements. Dongdagou, Baiyin City, Gansu Province, is a typical area of potentially toxic element pollution in farmland soil, which has attracted much attention and urgently needs to be controlled. Therefore, the main objective of this investigation is to analyze the concentrations of As, Cd, Pb, Hg, Cu, and Zn in the agricultural soil found in Dongdagou. Using statistical analysis, ecological and human health risk, principal component analysis, and the PMF model, we found that (1) there are varying degrees of accumulation in the soil in the study area, with Cu being the main component. (2) The soil in the study area has high and extremely high concentrations of Cd, posing significant risks. On the other hand, Hg presents mild and medium risks. However, there are no risks associated with As, Pb, Cu, and Zn. Overall, the ecological risks in the study area’s soil due to potentially toxic elements are predominantly extremely high (49.65%) and high (38.25%). A small proportion of the soil exhibits low risks (2.76%) and medium risks (9.33%). (3) As has a moderate acceptable carcinogenic risk for local residents, Cd has a moderate acceptable carcinogenic risk for local children, and other potentially toxic elements do not have carcinogenic or non-carcinogenic risks. (4) The source analysis shows that Cd in the soil in the study area mainly comes from agricultural activities and sewage irrigation, As mainly comes from industrial production, and Zn, Cu, Pb, and Hg are multiple sources. We recommend adopting targeted and differentiated safety utilization and control measures based on the pollution level and potential risks of potentially toxic elements in the research area, combined with the sources of potentially toxic elements. Full article

A novel cloud-based collaborative estimation framework for traffic management, utilizing a Gaussian Process Regression approach is introduced in this work. Central to addressing contemporary challenges in sustainable transportation, the framework is engineered to enhance traffic flow efficiency, reduce vehicular emissions, and support the maintenance of urban infrastructure. By leveraging real-time data from Priority Vehicles (PVs), the system optimizes road usage and condition assessments, contributing significantly to environmental sustainability in urban transport. The adoption of advanced data analysis techniques not only improves accuracy in traffic and road condition predictions but also aligns with global efforts to transition towards more eco-friendly transportation systems. This research, therefore, provides a pivotal step towards realizing efficient, sustainable urban mobility solutions. Full article

Sustainable use of biomass energy sources can reduce dependency on fossil fuels. Wood biomass is the primary source for heating in Slovenia, with firewood and wood pellets having the highest share. Slovenia’s largest consumers of wood fuels are households primarily using wood from their forests or imported wood pellets. This research used a life cycle assessment to analyze and evaluate the environmental impacts of using firewood and wood pellets for household heating in Slovenia for the first time. The results showed that wood logs have a considerably greater effect on stratospheric ozone depletion, ozone formation, and fine particulate matter (PM) formation. The impact on global warming was lower due to short transportation distances and using log boilers with high combustion efficiency (0.016 and 0.041 kg CO₂ eq for wood log and wood pellet combustion, respectively). An increase in transportation distance from 100 km to 1000 km resulted in an 84.9% increase in the values for the categories ozone formation and human health, a 120.4% increase for fossil resource scarcity, and a 102.4% increase in global warming, supporting the premise that short distribution routes are necessary for more sustainable use of the energy source. Full article

Before COVID-19, universities in the Philippines sparingly used online learning instructional methods. Online learning is now widely known, and universities are increasingly keen to adopt it as a mainstream instructional method. Accounting is a popular discipline of study undertaken by students, but its online adoption is less well known. This study investigated university accounting students’ perceptions of the cognitive load of learning and how it influences their effect on learning memory at a university in the Philippines. During the COVID-19 period, after introducing online learning, 482 university undergraduate accounting students provided their perceptions using a five-point Likert scale survey questionnaire. The study measured teaching quality, learning content quality, and learning management system (LMS) quality, representing the cognitive load of learning. It measured electronic learning (e-learning) quality, learner satisfaction, and behavioral intentions to adopt online learning, continually representing the learning memory framework. The data analyzed using a structural equation model showed that students managing their cognitive load positively influenced their short-term learning. Learning content, teaching, and LMS quality positively influenced e-learning quality and student satisfaction. Student satisfaction positively influenced, but e-learning quality did not influence, students’ continued willingness for online learning. The findings were largely consistent across the second- and third-year enrolments. Findings from the first-year students showed that teaching quality did not influence student satisfaction and e-learning quality. This is the first study to test the influence of the cognitive load of learning on the learning memory of accounting students in an online learning environment. Full article

The Lagunas de Campillos Natural Reserve and adjacent ponds are fundamentally surrounded by regularly fertilized crop fields and livestock industry, producing leachates which can be found in the ponds. The interest in this Site of European Importance and the RAMSAR wetland complex lies in the habitats within it, which are included in the Directive on Habitats of Community Interest. It is essential to determine the trophic status of the ponds and the quality of these habitats, as well as whether corrective measures need to be established in order to maintain a good environmental status. To characterize and compare the ponds, different parameters were measured, such as conductivity, pH, nutrient concentration, Chl-a concentration, phytoplankton composition, phytoplankton abundance (<20 µm), and the quantification of heterotrophic microorganisms indicating contamination of the aquifers. The obtained results showed that all ponds, except a mesotrophic pond, are eutrophic or even hypertrophic, with high levels of total nitrogen (>8 mg L⁻¹), total phosphorous (>165 μg L⁻¹), and chlorophyll-a concentration. These findings explain the high densities of phytoplankton observed, with the predominant presence of small cells (<3.6 μm ESD). In addition, concentrations of heterotrophs and coliforms are, in some ponds, higher than expected. Eutrophication hinders ecological functions and ecosystem services, which finally affects biodiversity and human wellbeing. Five of the six analyzed ponds are within various protection figures for their essential importance to local and migrating avifauna. Therefore, ponds’ status analysis and the implementation of measures for maintaining ecosystem services and trophic state are fundamental for the sustainable management of the studied area. Full article

Sustainable development means taking care of the environment, which also means promoting green transport, which involves the systematic development of personal transport in its broadest sense. The positive aspects associated with cheap and convenient electric transport are intertwined with the problem of collisions and accidents. While developing road infrastructure for electric vehicles such as scooters, bicycles, and others, research should be conducted in parallel to ensure the highest possible level of safety for users. There is also an increase in the number of people using bicycles and electric scooters, which develop significant speeds. The problem of accidents among users of classic and electric bicycles and scooters is evident, and post-accident injuries pose a serious challenge to medical practitioners. The literature is rich in statistical analyses of accidents among users of scooters and bicycles, but there are no studies where the behaviour of users of bicycles, scooters, etc. is analysed. The authors of this study set out to develop a measurement system to assess the traffic safety of people using bicycles and scooters. The device uses LIDAR to record the speed of the vehicle and a camera, the images of which are processed by an algorithm in order to classify the user as being on a bicycle or scooter and using or not using head protection with a helmet. It is also possible to analyse the behaviour of the vehicle users under study. The article describes the built measurement device and presents the results of the initial measurements made by the device. Full article

This work introduces the synthesis of hybrid polymer electrolytes based on polyethylene oxide (PEO) and electrolyte solution bis(trifluoromethane)sulfonimide lithium salt/ionic liquid 1-ethyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide (LiTFSI/EMIMTFSI) with in situ produced and dispersed silica particles by the sol–gel method. Conventional preparation of solid polymer electrolytes was followed by desolvation of lithium salt in a polymer matrix of PEO, which, in some cases, additionally contains plasticizers. This one-pot synthesis is an alternative route for fabricating a solid polymer electrolyte for solid-state batteries. The presence of TFSI- reduces the crystallinity of the PEO matrix (plasticizing effect), increases the dissociation and solubility of LiTFSI in the PEO matrix because of a highly delocalized charge distribution, and reveals excellent thermal, chemical, and electrochemical stability. Tetraethylorthosilicate (TEOS) was chosen due to the slow reaction rate, with the addition of (3-glycidyoxypropyl)trimethoxysilane (GLYMO), which contributes to the formation of a silica network. FTIR studies confirmed the interactions between the silica, the polymer salt, and EMIMTFSI. Impedance spectroscopy measurements were performed in a wide range of temperatures from 25 to 70 °C. The electrochemical performance was explored by assembling electrolytes in LiCoO₂ (LCO), NMC(811), and LiFePO₄ (LFP) coin half-cells. The HPEf15 shows a discharge capacity of 143 mA/g for NMC(811) at 0.1 C, 134 mA/g for LCO, and 139 mA/g for LFP half-cells at 0.1 C and 55 °C. The LFP half-cell with a discharge capacity of 135 mA/g at 0.1 C (safety potential range of 2.8 to 3.8) obtained a cyclability of 97.5% at 55 °C after 100 cycles. Such a type of electrolyte with high safety and good electrochemical performance provides a potential approach for developing a safer lithium-ion battery. Full article

The coronavirus pandemic crisis highlighted the critical importance of comprehensive safety management for all organizations. Safety management literature delineates two approaches to achieving safety, characterized as safety management through centralized control, known as the safety management system (SMS), and safety management through guided adaptability, known as organizational resilience (OR). Each of these approaches plays a pivotal role in establishing and maintaining the safety and sustainability of an organization. This paper aimed to compare the maturity of SMS with the maturity of OR, identifying the relationship between aspects of SMS and OR in the context of the crisis of the pandemic. Based on a literature review, the author presents adopted concepts of SMS and OR, as well as a customized maturity model for both. The survey methodology involved two questionnaires on SMS and OR, consisting of 26 and 18 questions, respectively. The survey was conducted in three approved training organizations (ATOs) in the Greater Poland voivodeship. When comparing key aspects of both approaches to safety management (SMS vs. OR), significant differences in ratings were observed. Additionally, a moderate correlation was found between aspects of SMS and OR. This discrepancy was reflected in the maturity models. According to the survey results, SMS achieved the fourth level of maturity, labeled proactive safety management, while OR attained the third level of maturity, termed a fairly agile organization. Furthermore, the results showed that while the guided adaptability approach is more difficult to achieve in an organization, the centralized control approach is insufficient. Therefore, both components are necessary to ensure the comprehensive safety of the organization. Full article

The research aims to find out the crucial factors in the design phase of packaging products, which are related to the determination of environmental influences on sustainable materials. The paper presents the results of research into the influence of environmentally friendly cardboard and the separation of yellow offset ink on the optical properties of prints exposed to thermal aging without and with exposure to NO₂. The samples were obtained under real conditions on a Roland 705 printing machine. The colorimetric characteristics of the print and its stability were determined in the research. The research is significant for graphic reproduction in the domain of testing the quality of the print itself, which is defined by certain raster characteristics. The research covers prints in 100%, 70% RTV, 50% RTV, and 30% RTV. The intensity of the tonal experience will depend on the interaction of the substrate with the raster and different types of inks in offset printing as a function of the experimental conditions. The ink characteristics of prints ∆L*, ∆a*, ∆b*, and ∆E were determined. The research results show that ink I1, with about 80% renewable raw materials, achieves the best stability under the specified experimental conditions. Full article