Utility Supply Research Articles

Prediction and Evaluation of Industrial Land Intensive Use: Adding Policy Impact to System Dynamics Model

Nowadays, urban development prioritizes improving quality over mere quantitative expansion, which requires a shift in land use from previous extensive use patterns to a more refined and efficient intensive use pattern. The intensive use of industrial land (IUIL) is a resource-utilization strategy for industrial land that aims at maximizing the dual growth of ecological and economic benefits by minimizing resource consumption and factor costs. In this research, IUIL is deconstructed into four aspects: input intensity, utilization intensity, economic benefits, and environmental benefits. Grounded in a qualitative analysis of the interconnected casual feedback among the various factors influencing IUIL, a system dynamics model with 36 indicators for IUIL evaluation and prediction is constructed. Policy impacts on IUIL from the supply side, utilization side, and supervision side are analyzed and embedded for a scenario simulation based on the theory of the entire life cycle of land use. The simulation shows its efficiency in reporting the variation tendency of industrial land use with varied policy impact strength. The results reveal a dynamic understanding of the evolving patterns of land intensification and present different characteristics of IUIL. This study further proposes policy recommendations to provide experience and references for the authorities to promote high-quality urbanization development in practice.

Variable Capital Utilization and Indeterminacy

Variable capital utilization has been widely used in business cycle research based on the observation that utilization rate is variable over time. The common assumption is that capital depreciates faster when it is utilized more extensively. This paper shows analytically that variable capital utilization makes it easier for indeterminacy to arise. In particular, when both capital utilization and labor supply become infinitely elastic, the required degree of external increasing returns can be arbitrarily small.

Time is money: An analysis of cost drivers in ambulatory sinus surgery.

Functional endoscopic sinus surgery (FESS) is one of the most commonly performed otolaryngologic procedures and is associated with significant cost variability. We performed a retrospective analysis of all inflammatory sinus surgeries at a single tertiary care medical center from July 2021 to July 2023. The electronic medical record was reviewed for patient factors and cost variables for each procedure, and multivariable analysis was performed. A total of 221 patients were included in analysis with a mean age of 48.2 years. There was a 44.8% incidence (n=99) of nasal polyps and 31.2% (n=69) of cases were revision surgeries. The average total cost for the surgical encounter was $8960.31 (standard deviation $1967.97). Operating room time represented $4912.46 (54.8% of all costs), while average operating room supply costs were $1296.06 (14.5%) and recovery room costs were $919.48 (10.3%). Total costs were significantly associated with length of surgery ($7.83/min, p=0.04), in addition to presence of nasal polyps ($531.96, p=0.04). There was no significant association between total costs and the remaining clinical and demographic factors. Costs associated with ambulatory FESS for inflammatory sinus disease vary across patients and this cost variability is predominantly driven by time efficiency within the operating room, as well as supply utilization and nasal polyposis to a lesser degree. As a result, operating room efficiency represents a primary target for cost-related interventions. Additionally, our data provide a framework for surgeons and hospitals to make evidence-based decisions on intraoperative equipment in a tradeoff between efficiency and supply costs. Our findings indicate that an approach focused on streamlining efficiency across the entire ambulatory surgery encounter will have the greatest impact on reducing healthcare expenses for both the patient and the health system.

Safety-driven design of carbon capture utilization and storage (CCUS) supply chains: A multi-objective optimization approach

Carbon capture, utilization, and storage supply chains (CCUS) play a pivotal role in achieving sustainability targets but necessitate meticulous risk identification and mitigation measures. Traditional safety assessments often occur post-design, constraining proactive risk management efforts. Hence, there is a pressing need to optimize safety performance during the design stages. To address this challenge, a framework for evaluating and optimizing CCUS supply chain safety performance using inherent safety index system (ISI) is introduced. Recognizing the trade-offs between total cost, environmental impact reduction, and risk mitigation, our approach considers multi-objective optimization to concurrently address these sustainability objectives and generate a Pareto set of solutions. Utilizing the augmented ε-constraint method, we applied this framework to optimize CCUS networks and develop sustainable designs across three key objectives. The method was applied to a CCUS system that includes various CO2 utilization pathways to minimize the total annual cost, CO2 emissions, and safety risks. The resulting Pareto surface illustrates unique network configurations, each representing a distinct trade-off scenario. Through a case study, we optimized a CCUS network to achieve economic, environmental, and safety objectives. The most economically viable design, with a total annual cost of $97 million and a 40 % net carbon reduction, prioritizes CO2 utilization for value-added products, while limiting CO2 sequestration. Conversely, safety-focused designs shift utilization towards safer routes, including CO2 sequestration and algae production. The proposed framework offers a systematic approach to developing sustainable CCUS supply chain designs, balancing economic viability, environmental sustainability, and safety.

The influence of regulation on preference for utility infrastructure investment to generate income for Australian water corporations

ABSTRACT Effects of price regulation and preference for utility supply infrastructure on Australian urban water utilities and urban water markets are examinated using historical data, models of the future and a case study of Greater Sydney. Australian regulators utilise the building block method based on operating and capital costs, and a Regulatory Asset Base to set nominal revenue requirements and ultimately prices for water utility services. Regulation of water utilities that is dependent on a Regulatory Asset Base drives preference for utility infrastructure and is remote from market mechanisms of consumer demands for water and sewage services. These regulatory processes are not linked to the operation of the urban water market of government owned utility and distributed solutions, and act to crowd out viable complementary solutions including water efficiency, distributed water sources and alternative pricing models. Government regulation, ownership and operation of utilities may produce strong performance from the perspective of urban water corporations but decrease economic efficiency, resilience and social welfare in urban water markets. The role of major water corporations needs to be redefined in a market recognising multiple complementary water sources and services. Regulation of utility services should have regard to the entire market, market demand, environmental health and consumer welfare.

Study on controllability assessment of a new type of power system based on lagrangian function integrated assessment method

The complex structure and multiple controllability metrics of the new power system require a comprehensive assessment approach. We propose a controllability assessment method based on the Lagrangian function, analyzing key factors that influence controllability. Our index system includes distributed power supply utilization rate, energy storage station charge state, power factor, and voltage offset. Using Lagrange shadow price theory, we construct a guideline layer index system treating bottom indexes as constraints. We formulate an optimization function utilizing the Lagrangian shadow price theory and considering weights calculated from the bottom index’s shadow price. These weights inform primary evaluation. By combining primary evaluation results, weights, and variable coefficients using the percentage system method, we determine comprehensive evaluation results for the new power system’s controllability. Experimental results demonstrate the high reliability of this method’s evaluation indexes, indicating their effectiveness. This research provides valuable insight for the advancement of the new power system. Region 1 had the lowest overall evaluation score of 51.57 and was rated general. The overall score of Region 2 was 77.24, which was considered good. Region 3 received the highest overall score of 86.29 and was rated as excellent.Graphical

Research on Electricity Balance and Measurement Optimization of New Energy Power System Considering Renewable Energy Consumption Mechanism

With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.

Optimal supply chain performance: risk aversion to green innovation

PurposeAlthough many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.Design/methodology/approachUsing Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.FindingsThe conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.Originality/value(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

Efficiency analysis of China's urban water supply utilities using a dynamic multiactivity network DEA model

Urban water scarcity and severe consumption challenges are crucial for improving performance in the urban water supply industry. Urban Water Supply Utilities (UWSUs) perform the primary task of providing tap water to city residents, and regulators use benchmarks to monitor UWSU operations. To assess the efficiency of UWSUs in detail, this study introduces a dynamic two-stage network data envelopment analysis (DEA) model with shared inputs and shared carryover. This model decomposes UWSU operations into water production (WP) and water distribution (WD) subprocesses. By combining dynamic features with network DEA, the study extends the static network DEA to a more general case for evaluating UWSU efficiency. The study sample includes 246 UWSUs from 24 provinces in China over the period 2016–2018. The analysis covers overall efficiency, period efficiency, subprocess efficiency, and regional disparities among the sampled water utilities. The findings indicate that the average efficiency of the UWSUs was 0.643 during 2016–2018, with slightly lower efficiency in the WP subprocess. UWSUs in the eastern provinces outperformed those in the central and western provinces, which require significant improvements. To enhance the performance of China's water supply sector, accelerated market competition and infrastructure improvements are necessary.

A parameter design method of LC filter circuit for closed‐loop dynamic power supply system based on linear power amplifier

AbstractThe linear power amplifier (LPA) suffers from low power supply utilization in the DC power supply mode, leading to reduced output efficiency. This paper constructs an efficient power supply mode based on closed‐loop dynamic power supply system (CLDPSs). The CLDPSs can generate a pair of bipolar dynamic power supplies based on a predetermined signal to provide power to LPA, which can maintain a small voltage drop between the power supply and the output of LPA, thereby improving the power supply utilization of LPA. In addition, the study also focuses on the conflicting relationship between the output ripple and the dynamic performance of the CLDPSs, for which a new parametric design of the LC filter circuit is proposed, aiming to achieve a balance between these two aspects. Finally, a voice coil motor driving platform is constructed for experimental testing. The results demonstrate that the output ripple and dynamic performance of the CLDPSs can be balanced. In comparison to the DC power supply mode, the CLDPSs power supply mode can enhance the efficiency of LPA by 2 times while the driving accuracy of voice coil motor is basically unchanged.

The challenge of closing the climate adaptation gap for water supply utilities

Many drinking water utilities face immense challenges in supplying sustainable, drought-resilient services to households. Here we propose a quantified framework to perform drought risk analysis on ~5600 potable water supply utilities and evaluate the benefit of adaptation actions. We identify global hotspots of present-day and mid-century drought risk under future scenarios of climate change and demand growth (namely, SSP1-2.6, SSP3-7.0, SSP5-8.5). We estimate the mean rate of unsustainable or disrupted utility supply at 15% (interquartile range, 0–26%) and project a global increase in risk of between 30–45% under future scenarios. Implementing the most cost-effective adaptation action identified per utility would mitigate additional future risk by 75–80%. However, implementing the subset of cost-effective options that generate sufficient tariff revenue to provide a benefit-cost ratio that is greater than 1 would only achieve 5–20% of this benefit. The results underline the challenge of attracting the financing required to close the climate adaptation gap for water supply utilities.

Economic optimization scheduling of virtual power plants considering an incentive based tiered carbon price

Against the backdrop of China's carbon peak and carbon neutrality goals, the use of virtual power plants for electricity economic dispatch has gradually become a research hotspot. The virtual power plant includes both supply and demand sides. If the inherent conflicts of interest between both parties cannot be effectively addressed, there are significant hidden dangers in the sustainable operation of the virtual power plant. On the basis of existing research, this article constructs a regional virtual power plant. With the goal of maximizing supply and demand utility, a two-stage Stackelberg dynamic game model was established between virtual power plant operators and users in the electricity market and carbon trading market mechanisms, and an incentive based hierarchical carbon price constraint was designed in the optimization model. In the model constructed in this article, the operator of a virtual power plant will consider environmental benefits based on expected returns, adjust the output of controllable units to change the electricity supply strategy, and formulate a pricing strategy. At the same time, users will adjust their electricity consumption strategy based on their own electricity consumption characteristics, and both will achieve a game equilibrium. Based on the optimization results, this article conducted a sensitivity analysis on the incentive coefficient of graded carbon prices and obtained the optimal incentive coefficient under the case conditions. The research results indicate that the model can coordinate and meet the interests and needs of both the power generation and consumption sides, and the implementation of incentive based hierarchical carbon pricing strategies has certain advantages compared to traditional carbon pricing strategies in different user types scenarios. The research conclusion of this article has good reference value for the sustainable green operation of virtual power plants.

A decision-making model for echelon utilization of retired batteries in competitive duopoly: the role of government subsidy

As the wave of retirements in the new energy vehicle sector approaches, China’s emphasis on echelon utilization has grown to optimize battery reuse and recycling. However, the burgeoning industry will inevitably face the challenges of competition, influencing stakeholders across the supply chain. In this study, we employ game theoretic models to investigate the interplay between competitive dynamics and government subsidy policies within this industry. Through the development of a competitive duopoly echelon utilization supply chain model, our analysis offers valuable insights and recommendations for the advancement of the industry. We find that increases in echelon utilization costs prompt adjustments in retail prices by echelon utilization enterprises, third-party recyclers, and new energy vehicle manufacturers. As competition intensifies, overall supply chain profitability diminishes, resulting in a lose-lose situation, favoring only third-party recyclers, who raise wholesale prices as new energy vehicle manufacturers reduce transfer payments. Conversely, government subsidies stimulate higher total demand, benefiting subsidized echelon utilization enterprises, which enhance consumer and social value. Non-subsidized enterprises are compelled to raise retail prices. Given the positive impact of subsidies, governments should prioritize support for enterprises demonstrating superior echelon utilization practices.

Study on synergistic and antagonistic interactions of P and Zn fertilization on sweet pepper

The present study was carried out to investigate the synergistic and antagonistic interactions of combined application of Phosphorus and Zinc fertilizers on sweet pepper in net house conditions. Pot experiment consists of thirteen treatments combinations set up in a completely randomized design. The treatment combinations comprising of four levels of phosphorus (0, 237.5, 355.5 and 475 kg Single Super Phosphate ha−1) and three levels of Zn (5, 7.5 and 10 kg Zinc Sulfate ha−1) including one control (100% NPK). The available macro and micronutrients content in potting mixture was confirmed in medium range before conducting the experiment. The results clearly demonstrate the treatment comprising 475 kg SSP ha−1 with 10 kg ZnSO4 ha−1 (T13) showed synergistic effects on vegetative characteristics, above and below ground nutrient content as well as nutrient uptake. When balanced phosphatic and zinc fertilizers (T13) are applied to soil, there is a low possibility of an antagonistic reaction. Antagonism problems arise most frequently when the soil’s Zn content is extremely low in combination with excess application of P fertilizer limiting their availability to plants. To avoid P-Zn antagonism in the soil system, utilization of appropriate fertilizer methods, supplies, and rates was done. As a result, the present study confirms that recommended fertilizer doses combined with phosphorus @ 475 kg SSP ha−1 and zinc @ 10 kg ZnSO4 ha−1, is the best nutrient combination for improving plant growth parameters; however, in terms of soil parameters, P and Zn fertilizers affected micronutrient content in soil, which further interacted antagonistically.

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

Highly accurate wind power prediction is important for the operation and management of wind farms, energy utilization and power supply. In recent years, in order to further improve prediction accuracy, some researchers have considered utilizing factors such as wind speed and wind direction to establish prediction models. However, this often results in input redundancy due to a lack of technical means. In this paper, a multivariate prediction model for wind power based on variational mode decomposition (VMD), fuzzy entropy (FE), partial auto-correlation function and cross-correlation function (PACF&CCF, abbreviated as PC), improved artificial hummingbird algorithm (IAHA), temporal convolutional network (TCN), and regularized online sequential extreme learning machine (ReOSELM) is proposed. To address the nonlinearity and non-stationarity of the original wind power time series and decrease computational costs, the VMD combined with FE data preprocessing technique is employed. Considering the information redundancy in the multivariate data, the PC technique is utilized to conduct correlation analysis on the input data and select highly correlated features as inputs. When the input is multivariate and high-dimensional, the ReOSELM model struggles to achieve satisfactory prediction performance. Therefore, a new model based on TCN-ReOSELM is constructed. Additionally, to overcome the drawbacks of manual parameter adjustment, an IAHA algorithm is proposed, which integrates Latin hypercube sampling and chaotic local search strategies, to optimize the crucial parameters of TCN-ReOSELM. The experimental results demonstrate that the multivariate method proposed in this paper reduces the RMSE by 31% compared to the univariate method. Specifically, the proposed VMD-FE-PC-TCN-ReOSELM model reduces the RMSE, MAE, and SMAPE by 60–64%, 55–58%, and 10–36%, respectively, compared to the PC-TCN-ReOSELM model.

Interface engineering of Z-scheme heterojunction for photocatalytic water splitting

Photocatalytic water splitting technology can directly convert solar energy into H2 via a zero-carbon route, offering a sustainable solution for solar utilization and H2 supply. Among various developed photocatalysts, Z-scheme heterojunction mimicking natural photosynthesis by combining two dissimilar semiconductors for redox reactions in series has unequivocally demonstrated its superiority in enhanced charge transfer, robust redox driving force, and wide optical absorption range. A comprehensive understanding on the fundamental principles of interface engineering between semiconductor components is the key to construct an efficient Z-scheme heterojunction. By focusing on different types of semiconductors, this article thoroughly expounds the coupling principles of components in binary mediator-free and ternary solid-mediator Z-scheme heterojunctions for photocatalytic water splitting, from the viewpoint of band structure alignment and interfacial electric field design. In addition to the well summarized research progresses in recent years, perspectives on the challenges and opportunities for developing advanced Z-scheme heterojunctions are provided.

A partial siphon operational strategy strengthens nitrogen removal performance in partially saturated vertical flow constructed wetlands

The carbon‒oxygen balance has always been problematic in constructed wetlands (CWs), putting pressure on stable and efficient nitrogen removal. In this study, a novel partial siphon operational strategy was developed to further optimize the carbon and oxygen distributions of a partially saturated vertical flow CW (SVFCW) to enhance nitrogen removal. The removal performances of the partial siphon SVFCW (S-SVFCW) were monitored and compared with those of the SVFCWs at different partial siphon depths (15 cm, 25 cm and 35 cm) in both the warm and cold seasons. The results showed that the partial siphon operating strategy significantly facilitated the removal of ammonia and total nitrogen (TN) in both the warm and cold seasons. When the partial siphon depth was 25 cm, the S-SVFCWs had the highest TN removal efficiency in both the warm (71%) and cold (56%) seasons, with an average improvement of 46% and 52%, respectively, compared with those of the SVFCWs. The oxidation‒reduction potential (ORP) results indicated that richer OPR environments and longer hydraulic detention times were obtained in the S-SVFCWs, which enriched the denitrification bacteria. Microbial analysis revealed greater nitrification and denitrification potentials in the unsaturated zone with enriched functional genes (e.g., amo_AOA, amo_AOB, nxrA and nirK), which are related to nitrification and denitrification processes. Moreover, the strengthening mechanism was the intensified oxygen supply and carbon utilization efficiency based on the cyclic nitrogen profile analysis. This study provides a novel partial siphon operational strategy for enhancing the nitrogen removal capacity of SVFCWs without additional energy or land requirements.

Effects of manufacturer fairness concerns and carbon emission reduction investment on pricing decisions under countervailing power

Government policies have focused on carbon emissions from the green supply chain (GSC). To discuss how price decision-making in GSC is affected by the manufacturer's fairness concerns, retailer's countervailing power, and carbon emissions reduction, the Stackelberg model assumes that consumers are environmentally sensitive. To explore this problem considering the type of manufacturer's behavior preferences, we built four models that pertain to the following two situations: the retailer has countervailing power, and the retailer has no countervailing power. The results show that (1) the manufacturer's disadvantageous fairness concerns reduce the overall supply chain profits and utility; influenced by countervailing power, the members witness a win-win situation; (2) moderate advantageous fairness concerns benefit GSC members; furthermore, under advantageous fairness concerns, the manufacturer can “give profits” to the retailer; (3) affected by the retailer's countervailing power, no bargaining agreement is made in the GSC under advantageous fairness concerns. Finally, the interplay between member decisions and consumer sensitivity is explored. This study can help manufacturers achieve emission reduction targets in response to consumer environmental awareness and government regulation.

Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management

Given the practical implications of El Niño-Southern Oscillation (ENSO) forecasting for developing decision support systems and its importance in global climate prediction on seasonal to interannual scales, significant efforts have been made to enhance ENSO forecasts. This study presents an event-based evaluation of ENSO forecasts and discusses the implications of such forecasts to water resources management. Using ENSO forecasts for the period 2002–2020, this study evaluates and compares the results of two types of forecasting models, i.e., statistical and dynamical models, in predicting Sea Surface Temperatures (SSTs). Forecasting skills are evaluated for distinct target seasons via two metrics, spearman correlation and mean squared error. In addition, the performance of the two types of models at different lead times are also evaluated. Results reveal that the forecasting skills of these models are comparable, both of which exhibit higher forecasting skills for the boreal fall-winter season and lower skills for the boreal spring season. Event-based analyses show that dynamical and statistical models under-forecast SST anomaly at the onset month for El Niño events, although the forecasting error diminishes with a reduced forecasting lead for most occasions. For La Niña events, SST anomaly forecasting errors could be positive or negative. It is also difficult for the models to accurately predict the quick shift from one ENSO phase to another. Factors that contribute to such challenges are discussed. The implication of ENSO forecasts for water resources management, mainly streamflow forecasts, for a regional water supply utility in the southeastern United States is also discussed. Results from this study provide insights into ENSO forecasting skills and their practical implications.

Hospital care direct costs due to ambulatory care sensitive conditions related to diabetes mellitus in the Mexican public healthcare system

BackgroundHospitalizations for ambulatory care sensitive conditions (ACSC) incur substantial costs on the health system that could be partially avoided with adequate outpatient care. Complications of chronic diseases, such as diabetes mellitus (DM), are considered ACSC. Previous studies have shown that hospitalizations due to diabetes have a significant financial burden. In Mexico, DM is a major health concern and a leading cause of death, but there is limited evidence available. This study aimed to estimate the direct costs of hospitalizations by DM-related ACSC in the Mexican public health system.MethodsWe selected three hospitals from each of Mexico’s main public institutions: the Mexican Social Security Institute (IMSS), the Ministry of Health (MoH), and the Institute of Social Security and Services for State Workers (ISSSTE). We employed a bottom-up microcosting approach from the healthcare provider perspective to estimate the total direct costs of hospitalizations for DM-related ACSC. Input data regarding length of stay (LoS), consultations, medications, colloid/crystalloid solutions, procedures, and laboratory/medical imaging studies were obtained from clinical records of a random sample of 532 hospitalizations out of a total of 1,803 DM-related ACSC (ICD-10 codes) discharges during 2016.ResultsThe average cost per DM-related ACSC hospitalization varies among institutions, ranging from $1,427 in the MoH to $1,677 in the IMSS and $1,754 in the ISSSTE. The three institutions’ largest expenses are LoS and procedures. Peripheral circulatory and renal complications were the major drivers of hospitalization costs for patients with DM-related ACSC. Direct costs due to hospitalizations for DM-related ACSC in these three institutions represent 1% of the gross domestic product (GDP) dedicated to health and social services and 2% of total hospital care expenses.ConclusionsThe direct costs of hospitalizations for DM-related ACSC vary considerably across institutions. Disparities in such costs for the same ACSC among different institutions suggest potential disparities in care quality across primary and hospital settings (processes and resource utilization), which should be further investigated to ensure optimal supply utilization. Prioritizing preventive measures for peripheral circulatory and renal complications in DM patients could be highly beneficial.