Balanced Supply Research Articles

Multivariate Analysis and Optimization Scheme of the Relationship between Leaf Nutrients and Fruit Quality in ‘Bingtang’ Sweet Orange Orchards

Citrus trees require a balanced and adequate supply of macronutrient and micronutrient elements for high yield and fruit quality. Foliar nutrient analysis has been widely used in fruit-tree nutrient diagnosis and fertilization calculation. However, there is no information on ways to produce optimal fruit quality in sweet oranges. In the present study, fruit and leaf samples were collected from 120 ‘Bingtang’ sweet orange [Citrus sinensis (L.) Osbeck] orchards during four consecutive years (2019–2022). Parameters of leaf nutrition and fruit quality were analyzed based on these samples. Diagnostic results based on leaf classification standards indicated that the most deficient elements were Ca, Mg, and B, followed by N and Zn. Fruit quality, determined by single fruit weight (SFW), fruit shape index (FSI), total soluble solids (TSS), titratable acidity (TA), vitamin C (Vc), and maturation index (MI = TSS/TA) during fruit maturation, exhibited inconsistent responses to leaf mineral nutrition concentrations. The leaf-nutrient optimum values for high quality of the ‘Bingtang’ sweet orange fruit were ranges of 2.41–4.92% N, 0.10–0.28% P, 1.30–2.11% K, 2.99% Ca, 0.26–0.41% Mg, 340–640 mg/kg S, 89.65–127.46 mg/kg Fe, 13.48–51.93 mg/kg Mn, 2.60–13.84 mg/kg Cu, 15.59–51.48 mg/kg Zn, and 53.95 mg/kg for B. These results suggest the leaf-nutrient optimum values for diagnosis can be used not only to identify the nutrient constraints but also to provide guidance for the establishment of fertilization regimes in citrus cultivation.

Efficient prediction for Blast Furnace Gas holder level using novel preprocessing techniques and weight correction strategy

Accurately forecasting the level of Blast Furnace Gas (BFG) holder, managing the balance of gas supply and demand, strategically allocating BFG brings substantial economic and social benefits. In order to address the issues of volatile fluctuation, strong temporal variability, and high uncertainty of BFG, this paper proposes an efficient system for predicting the BFG holder level. Variational Mode Decomposition is utilized to separate components with different frequencies. The number of modes is determined according to Energy Ratio. Improved Discrete Wavelet Threshold is employed to eliminate noise. The grid search approach adjusts the activation functions. What is more, Particle Swarm Optimization is used to correct the weights of subsequences and emphasize critical information. Experimental results demonstrate that Root Mean Squared Error of the system are 0.0411, 0.0423 and 0.0416 for the three real-time monitoring datasets in April 2021. The average improvement percentage for the second dataset is 67.5% when compared to other commonly models. It is obvious that the prediction errors are minimized, and the stability and generalizability of the hybrid prediction system are enhanced. Additionally, the performance of the system is further verified through Diebold–Mariano test, improvement percentage test, and effectiveness test.

Ecological impacts and supply demand evolution of the Yangtze to Huaihe water transfer project in Anhui section

Human activities have profound impacts on land use and the supply–demand balance of ecosystem services (ESs). Various activities, such as urban construction, urban and rural planning, and inter-basin water transfer projects, continuously reshape land use patterns. This is a case study of the Anhui section of the Yangtze-Huaihe Water Diversion Project. Data from 2000, 2010, and 2020 is analyzed. Additionally, the patch-generating land use simulation (PLUS) model is utilized to quantify the specific impacts of the water diversion project construction on the supply and demand of ESs. The results indicate that the comprehensive dynamic attitude of land use during the project construction period significantly increased, rising from 0.16 to 13.79%, and mainly affected forest, water areas, construction land, and unused land. Specifically, the construction of the project led to significant changes in water purification, biodiversity, and, especially, hydrological regulation services. Additionally, the migration of residents significantly impacted the demand for ESs. The study also found a significant correlation between land use changes and the balance of ES supply and demand: the proportion of cultivated land and construction land is positively correlated with the balance, while the proportion of forest, grassland, and water areas is negatively correlated. This study provides empirical data for understanding the environmental and socio-economic impacts of large-scale water diversion projects and offers a scientific basis for local mitigation and control of adverse impacts. Through quantitative analysis and model prediction, this research effectively bridges the gap between theory and practice, providing important references for sustainable regional development.

Quantitative performance analysis of anion-exchange membrane fuel cells with in-plane and through-plane water transport

Effective water management is crucial in anion-exchange membrane fuel cells (AEMFCs) due to water's dual role as a reactant during alkaline oxygen reduction and a product of alkaline hydroxide oxidation. Meanwhile, both hydroxide conduction and water transport in polymer electrolytes are highly dependent on the hydration level. Therefore, a three-dimensional multi-physics model is developed to investigate the critical role of ionic and water transport parameters of the electrolyte on AEMFC performance. Additionally, detailed water distribution analysis is used to reveal its fundamental mechanism. The results reveal that extremely inhomogeneous water distribution and cathode water starvation are the main causes of the poor cell performance. Moreover, increasing membrane water diffusivity, water conversion, and decreasing the electro-osmotic drag coefficient all contribute to better cell performance through improved water balance and cathode water supply. Among these, cell performance is most sensitive to membrane water diffusivity, with a 40 % increase in peak power density when it is increased from 10−10 m2 s−1 to 10−9 m2 s−1. Although high ionic conductivity improves cell performance by directly reducing ohmic impedance, it also results in a more inhomogeneous distribution of water in the whole cell, so that a four-fold increase in ionic conductivity only yields a 62 % increase in peak power density.

Model-driven design and validation of glucose supply control in a tubular photobioreactor operated under oxygen-balanced mixotrophy

Scaling up bioprocesses remains challenging due to the partially unpredictable and suboptimal behavior of microbes during scale transition. Mathematical modelling serves as a valuable tool in navigating these challenges. In this study, we developed and validated a kinetic model based on the tank-in-series approach for a novel bioprocess known as oxygen-balanced mixotrophy with the microalga Galdieria sulphuraria. This model aims to facilitate the design of an effective glucose feeding control strategy tailored for two-phase tubular photobioreactors (TPBRs). The residence time distribution of our reference reactor was investigated by means of a tracer experiment. Qualitative analysis of the results indicated that our system was optimally represented by simulations with a 100 stirred-tank reactors (STRs).The controlled variable, oxygen concentration in the gas phase, showed a downward trend in simulations with simplified conditions: fixed oxygen production and balanced glucose supply. The decrease was caused by dominant net heterotrophic activity and accumulation of CO2 in the gas phase. Different configurations of a proportional-integral-derivative (PID) controller were designed by means of the ultimate gain method and compared under these simplified conditions. The most effective PID controller was implemented and refined in simulations with outdoor weather conditions. The model was validated successfully by simulating over time in a lab-scale STR the spatial fluctuations happening in a TPBR. The empirical oxygen consumption and production were faster and steeper than in the model, probably due to inaccuracies in parameter estimation or biomass adaptation.

Real-time pricing considering carbon constraints for smart grid with multiple energy sources

With the escalation of fossil fuel pollution and the challenges of climate change, the smart grid is forced to address the carbon emissions associated with fossil fuel use. Real-time pricing of smart grid is an effective means to regulate the balance of supply and demand in the grid as well as to achieve energy conservation and environmental protection. This paper takes traditional fossil energy and clean energy power supply as the background, takes social welfare maximization as the goal, combines carbon emission cost with traditional power generation cost, and constructs a real-time pricing model that takes into account carbon emission cost, so that thermal power enterprises participate in the power market and carbon market at the same time. By using duality methods, we transformed the original optimization problem into an equivalent one. Furthermore, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. The simulation results verify the feasibility and effectiveness of the proposed real-time pricing strategy.

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

Multi-nutrient and humic acid foliar applications enhance green gram growth and yield on alkaline soil by increasing nutrient use and physiological activity

Widespread multi-nutrient deficiency and water stress are the major yield-limiting factors in alkaline soil. Inorganic nutrient application in alkaline soil is ineffective for green gram due to the antagonistic interaction with alkalinity. In this setting, a balanced supply of foliar nutrients is a viable strategy to improve green gram nutrient utility and productivity in an alkaline environment. Field experiments were conducted for two consecutive years during Rabi 2019 and 2020. Water-soluble NPK (2%), 1% multi-micronutrient and 1% humic acid foliar nutrients in five combinations were applied at 30 and 45 days after sowing. The effect of multi-nutrient and humic acid foliar spray on green gram growth, physiology, nutrient accumulation, and yield were measured by adopting the standard protocol. Green gram seed yield increased by 20 per cent on the application of water soluble fertilizers 2% and liquid multi-micronutrients 1% with 1% humic acid. The same set of treatments augmented leaf area (428 cm2 plant−1), dry matter production (2087 kg ha−1), SPAD value (46.4), nitrogen (36.6 kg ha−1), phosphorous (4.9 kg ha−1), potassium (46.5 kg ha−1), and micronutrients uptake. Foliar application of multi-nutrients reduces nutrient stress and aids in the promotion of green gram crop growth and production in alkaline soil.

Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China

Influenced by increasing global extreme weather and the uneven spatiotemporal distribution of water resources in monsoon climate areas, the balance of agricultural water resources supply and demand currently faces significant challenges. Conducting research on the spatial allocation trade-offs and synergistic mechanisms of agricultural water resources in monsoon climate areas is extremely important. This study takes the spatial layout of reservoir site selection in water conservancy projects as an example, focusing on Shandong Province as the research area. During the site selection process, the concept of water resource demand is introduced, and the suitability of reservoir siting is integrated. It clarifies ten influencing factors for suitability degree and five influencing factors for demand. A bi-objective optimization model that includes suitability degree and demand degree is established. Utilizing machine learning methods such as the GA_BP neural network model and the GA-bi-objective optimization model to balance and coordinate the supply and demand relationship of agricultural water resources in the monsoon region. The study found that: (1) in the prediction of suitability degree, the influencing factors are most strongly correlated with the regulatory storage capacity (regulatory storage capacity > total storage capacity > regulating storage coefficient); (2) compared with single-objective optimization of suitability degree, the difference between water supply and demand can be reduced by 74.3% after bi-objective optimization; (3) according to the spatial layout optimization analysis, the utilization of water resources in the central and western parts of Shandong Province is not sufficient, and the construction of agricultural reservoirs should be carried out in a targeted manner. This study provides new ideas for promoting the efficient use of water resources in monsoon climate zones and the coordinated development of humans and nature, reflecting the importance of supply and demand balance in the spatial allocation of agricultural water resources, reducing the risk of agricultural production being affected by droughts and floods.

Assessment of Different Humate Ureas on Soil Mineral N Balanced Supply

Assessment of Different Humate Ureas on Soil Mineral N Balanced Supply

Metabolic Reprogramming of Astrocytes in Pathological Conditions: Implications for Neurodegenerative Diseases.

Astrocytes play a pivotal role in maintaining brain energy homeostasis, supporting neuronal function through glycolysis and lipid metabolism. This review explores the metabolic intricacies of astrocytes in both physiological and pathological conditions, highlighting their adaptive plasticity and diverse functions. Under normal conditions, astrocytes modulate synaptic activity, recycle neurotransmitters, and maintain the blood-brain barrier, ensuring a balanced energy supply and protection against oxidative stress. However, in response to central nervous system pathologies such as neurotrauma, stroke, infections, and neurodegenerative diseases like Alzheimer's and Huntington's disease, astrocytes undergo significant morphological, molecular, and metabolic changes. Reactive astrocytes upregulate glycolysis and fatty acid oxidation to meet increased energy demands, which can be protective in acute settings but may exacerbate chronic inflammation and disease progression. This review emphasizes the need for advanced molecular, genetic, and physiological tools to further understand astrocyte heterogeneity and their metabolic reprogramming in disease states.

A Study on the Construction and Evaluation of the Water Resource Reutilization System for Farmland Diversion and Drainage

Water is an essential resource for both rural and agricultural areas; it can be wisely distributed and used in the field to protect daily life, production, the natural environment, and the safety and stability of regional drainage and flood control systems. Our research selected a typical plains rural river network area with agriculture as the main industry to investigate the most effective method of farmland diversion and drainage. We comprehensively planned and transformed the water system flow, water conservation engineering, and the ecological environment in the irrigation area through the reutilization system. The reutilization system’s operation and scheduling design is implemented for four specific periods: the water replenishment cycle, agricultural irrigation, agricultural drainage and the rainy period of the flood season. The research period ranges from 2020 to 2023 after the completion of the system. We used monitoring, the recording of hydraulic equipment parameters and data collection to evaluate the balance of water supply and demand in the study area. At the same time, we have tracked and evaluated the four aspects of water quality enhancement, water conservation and flood control, and agricultural irrigation. The results show that the total agricultural water consumption decreased by 2.9%, and the amount of water saved increased by 9.6%. The current segment creates the rivers’ embankment standards. With a 92% irrigation guarantee rate, the current section forms and the embankment standards of the rivers satisfy the design storage volume and the flood level of one in twenty years. The water quality of all the rivers in the area has decreased by 5~10% compared to the average concentration prior to establishment. This study verifies the comprehensive effect and the suitability of the system by comparing the before and after effects, and provides a scientific basis for the method of efficient recycling and utilization of water resources in the rural plains river network area; we also propose the guidance of increasing the digital twin control and long-term operation mechanism to ensure the long-term stable operation of the technology.

Pengembangan Model Sistem Dinamis Suppy Chain di PT XYZ

PT.XYZ is an automotive company that has been operational since 1970, with over 400 suppliers for key components such as Stoper A and Stoper B. Annually, Stoper A sells 151,000 units (valued at IDR 4.077 billion), while Stoper B reaches 300,000 units (valued at IDR 8.100 billion). This research focuses on analyzing and optimizing the supply chain for these components, using sales data from January to December 2022. The goal is to create stock and flow diagrams, analyze the supply chain system modeling, and propose optimal scenarios for demand fulfillment. The modeling results indicate that a strategy of adding a 20% safety stock to the average sales is highly effective in preventing lost sales without burdening suppliers with overtime. However, the potential for overstock needs to be monitored. Based on the analysis, the second scenario proposing the addition of safety stock is recommended due to its effectiveness in meeting demand and efficiently managing inventory. This scenario is deemed most suitable for PT. XYZ in maintaining a balanced supply without unnecessary surplus.

Migration agenda of the Kaliningrad region in the context of socio-economic development of Russia

Relevance. The socio-economic context of the importance of migration policy is represented by a wide range of areas. First of all, the intensity of migration processes significantly correlates with the state of the labor market, influencing the balance of supply and demand. The relevance of the research topic is due to the importance of migration policy, both for the socio-economic development of the state and for ensuring national security in the strategic management horizon. The importance of building an effective migration policy has a significant level of importance for modern Russia. Taking into account the author’s position, the geopolitical situation and the retrospective history of international communications make Russia the center of gravity of migration flows, which requires the establishment of high-quality control not only over their intensity, but also over their structural content.The purpose is to determine the most effective tools for improving migration policy, taking into account territorial specifics.Objectives: to clarify the theoretical and methodological aspects of the implementation of state migration policy in Russia; reveal the features, analytical indicators and organizational mechanisms for implementing migration policy in Russia, taking into account the territorial specifics of the Kaliningrad region.Methodology. The methodological “portfolio” of the study is built on the basis of the provisions of socioeconomic theories of migration process management using quantitative methods of data collection and analysis.Results. A targeted analysis was carried out, as a result of which effective tools were identified for improving migration policy in the Kaliningrad region based on the adaptation of the universal migration project in the Kaliningrad city management system, creating the prerequisites for increasing its effectiveness and achieving target indicators.Conclusion. Improving migration policy is an objective necessity in order to solve a number of socio-economic problems and minimize risks to national security.

Optimizing Nutrient Management for Pearl Millet: Insights from a Long-term Field Experiment

The study investigates the impact of integrated nutrient management strategies on the yield and nutrient uptake of pearl millet (Pennisetum glaucum), a vital crop in arid and semi-arid regions. Pearl millet is a significant source of dietary energy and essential nutrients for rural populations in India. However, its productivity remains low compared to other millet-producing countries. This research aims to enhance pearl millet yield through a combination of organic manures, biofertilizers, and chemical fertilizers, addressing the need for sustainable agricultural practices. The experiment was conducted at the Research Farm of Rajmata Vijayaraje Scindia Krishi Vishwavidyalaya during the kharif seasons of 2022 and 2023. Twelve treatment combinations were tested, including 100% NPK along with farmyard manure (FYM) and seed treatments with biofertilizers. Results indicated that the treatment combining 100% NPK with FYM at 10 t/ha/year and biofertilizers significantly improved biological yield, nutrient uptake, and soil organic carbon content. Specifically, the highest biological yield recorded was 8026 kg/ha, with notable increases in total nitrogen, phosphorus, and potassium uptake. This study underscores the importance of integrated nutrient management in enhancing pearl millet productivity while maintaining soil health. By adopting these practices, farmers can improve crop yields and contribute to food security in regions reliant on pearl millet. The findings provide a framework for future research and practical applications in sustainable agriculture, emphasizing the need for balanced nutrient supply systems to optimize crop performance and soil fertility.

Analysis of Determinants of the Rupiah Exchange Rate (Exchange Rate) against the US Dollar in 2016: Q1-2023: Q4

In foreign trade, exchange rates are one of the big factors that make a big difference. And although each country has its own currency, they all have different monetary values. To show how much one country's currency is worth compared to another country's currency, we need a measure called the exchange rate. The aim of this research is to find out how the exchange rate is related to things such as the amount of money in circulation, non-oil and gas exports, the balance of payments, and Indonesia's benchmark interest rate. A multiple regression analysis model was used to conduct the research. This research shows that the IDR/USD Exchange Rate is closely related to Indonesia's Balance of Payments, Money Supply (M2), Non-Oil and Gas Exports, and Reference Interest Rates. This relationship is real and good.

Performance analysis and optimization of multi-energy complementary combined heat and power system considering the influence of time-of-use price

Abstract The multi-energy complementary system can meet the increasingly abundant and diversified energy consumption needs of users and, at the same time, help to improve energy utilization efficiency, reduce environmental pollution, and optimize the balance of energy supply and demand. In this paper, a multi-energy complementary cogeneration system with a gas turbine distributed photovoltaic, gas boiler, and heat storage tank is proposed, and the operation scheduling optimization model of the system is constructed. Aiming to minimize the comprehensive cost of economy and carbon emission, the model adopts a linear programming method to optimize the operation scheduling of multi-energy complementary systems. Taking a multi-energy complementary cogeneration system as an example, the key equipment of the system is optimized under three different scenarios with different time-of-use prices, and the influence of time division of time-of-use electricity price on the comprehensive cost of the system is analyzed. The results show that the time division method, which is more in line with the system net load curve, can effectively reduce the comprehensive cost of the complementary system and increase the consumption space of new energy.

Modifying row-configuration and vermicompost application reduces intercropped peanut (Arachis hypogaea L.) yield instability and penalty in sorghum at Babile, Eastern Ethiopia

The yield of intercropped peanut (Arachis hypogaea L.) in cereal crops was drastically reduced by 20–55 %, presumably due to high interspecific competition caused by illogical field layout and imbalanced fertilizer application. Field experiments were conducted in the Babile district of Eastern Ethiopia during the main cropping seasons of 2021 and 2022 to assess the possibilities of minimizing the peanut yield penalty and instability while improving sorghum production. The treatments consisted of two monocrops (SM = sole sorghum, GM = sole peanut), three row-configurations (S1G1 = 1-row sorghum with 1-row peanut, S1G2 = 1-row sorghum with 2-row peanut, MBILI = Managing Beneficial Interactions in Legume Intercrops via modifying 2-rows of sorghum with 2-rows of peanut), and four vermicompost levels (0, 1.5, 3, and 4.5 t/ha), which were factorial arranged in a randomized complete block design and replicated three times. Peanut under MBILI with 4.5 t/ha vermicompost boosted peanut seed/pod and dry pod yield by 17.5 % and 20 %, respectively, with a corresponding rise of sorghum grain yield by 72 % and net profit by 48 %. Unlike GM, intercropping peanut with sorghum significantly increases yield with time, which shows the high yield stability and sustainability of intercropping over monocropping. Interestingly, peanut yield in this treatment was statistically on par with pure stand, supporting the concept that MBILI row-configuration is necessary for maintaining the potential yield of the peanut crop. Similarly, the assessment of bio-ecological indices infers the superiority of the MBILI in terms of land use efficiency, yield advantage, and profitability compared to other combinations. This implies that modifying the planting geometry along with balanced nutrient supply could alleviate the detrimental effects of sorghum over peanut by minimizing interspecific competition, thereby giving better yield and economic value for subsistence farmers.

Towards a Sustainable Structure of an Urban Water–Energy–Food Nexus: Based on Network and Hierarchy Analysis

Water, energy, and food (WEF) are critical resources to sustain urban development, which requires a sustainable structure of the urban WEF nexus to address trade-offs and achieve synergies. Although interactions in the WEF nexus are widely explored, its sustainable structure has largely been ignored. This study constructs a framework of WEF nexus sustainability factors. Based on a literature review and expert opinions, 21 factors influencing urban WEF nexus sustainability were extracted and their interrelationships determined. We used social network analysis (SNA) and interpretive structural modeling (ISM) to analyze the structure of the urban WEF network. The results indicate that technological investment and industrial added-value energy consumption are the most critical and fundamental factors for promoting the sustainable development of the urban WEF nexus. Additionally, the balance of water supply and demand and the comprehensive management of waste pollutants are also driving and supporting factors for the sustainability of the urban WEF nexus. The results of this study complement the interaction mechanism research of the urban WEF nexus and provide practical references for sustainable decision-making in urban WEF nexus practices.