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This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction. Full article

(This article belongs to the Special Issue Advancing Civil Engineering Construction and Management: Innovations in Green Building, Intelligent Construction, and Sustainable Infrastructure Development)

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27 pages, 6078 KB

Open AccessArticle

A Generative AI-Enhanced Case-Based Reasoning Method for Risk Assessment: Ontology Modeling and Similarity Calculation Framework

by Jiayi Sun and Liguo Fei

Mathematics 2025, 13(17), 2735; https://doi.org/10.3390/math13172735 (registering DOI) - 25 Aug 2025

Abstract

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

(This article belongs to the Special Issue Multi-Source Information Fusion and Fuzzy Uncertainty Reasoning in Interdisciplinary Applications)

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13 pages, 3987 KB

Open AccessArticle

CFD-Based Optimization of the Growth Zone in an Industrial Ammonothermal GaN Autoclave for Uniform Flow and Temperature Fields

by Marek Zak, Pawel Kempisty, Boleslaw Lucznik, Robert Kucharski and Michal Bockowski

Crystals 2025, 15(9), 754; https://doi.org/10.3390/cryst15090754 (registering DOI) - 25 Aug 2025

Abstract

This study presents a computational fluid dynamics (CFD) simulation to investigate fluid flow and heat transfer within the growth zone of gallium nitride crystals synthesized via the alkaline ammonothermal method, with particular emphasis on the influence of seed crystal arrangement and installation geometry. The model analyzes temperature and velocity distributions, highlighting how seed configuration affects turbulent and transitional flow behavior. Key findings demonstrate the effectiveness of CFD in evaluating and optimizing growth zone design. Both simulation and experimental results show that achieving more uniform flow and temperature fields leads to more consistent growth rates and improved structural crystal quality. Furthermore, the study underscores the critical role of installation geometry in shaping flow characteristics such as velocity distribution, temperature gradients, and their transient fluctuations, factors essential for optimizing the ammonothermal crystallization process. Full article

21 pages, 3413 KB

Open AccessArticle

Research on a Soil Mechanical Resistance Detection Device Based on Flexible Thin-Film Pressure Sensors

by Haojie Zhang, Wenyi Zhang, Bing Qi, Yunxia Wang, Youqiang Ding, Yue Deng and Maxat Amantayev

Agronomy 2025, 15(9), 2041; https://doi.org/10.3390/agronomy15092041 (registering DOI) - 25 Aug 2025

Abstract

Soil compaction is a pivotal factor influencing crop growth and yield, and its accurate assessment is imperative for precision agricultural management. Soil mechanical resistance is the key indicator of soil compaction, with accurate measurement enabling precise assessment. Dynamic soil mechanical resistance measurement outperforms conventional manual fixed-point sampling in data acquisition efficiency. In this paper, a methodology is proposed for the dynamic acquisition of soil mechanical resistance using a flexible thin-film pressure sensor. This study dynamically captures soil mechanical resistance at three depths (5 cm, 10 cm, and 15 cm) under dynamic machinery operating conditions. A device was designed for the detection of soil mechanical resistance, and a prediction model for soil mechanical resistance was developed based on the Kalman filter algorithm. Tests were conducted under steady-state and variable-load conditions, and the predicted values accurately tracked the reference pressure. Soil tank trials showed that at an operating speed of 0.69–0.72 km/h, the average prediction errors for the three soil layers were 2.03%, 1.48%, and 6.27%, with the coefficient of determination (R²) between predicted and measured values reaching 0.96. The system effectively predicts multi-depth soil resistance, providing novel theoretical and technical approaches for dynamic acquisition. Full article

(This article belongs to the Section Precision and Digital Agriculture)

18 pages, 854 KB

Open AccessArticle

Evolutionary Sampling for Knowledge Distillation in Multi-Agent Reinforcement Learning

by Ha Young Jo and Man-Je Kim

Mathematics 2025, 13(17), 2734; https://doi.org/10.3390/math13172734 (registering DOI) - 25 Aug 2025

Abstract

The Centralized Teacher with Decentralized Student (CTDS) framework is a multi-agent reinforcement learning (MARL) approach that utilizes knowledge distillation within the Centralized Training with Decentralized Execution (CTDE) paradigm. In this framework, a teacher module learns optimal Q-values using global observations and distills this knowledge to a student module that operates with only local information. However, CTDS has limitations including inefficient knowledge distillation processes and performance gaps between teacher and student modules. This paper proposes the evolutionary sampling method that employs genetic algorithms to optimize selective knowledge distillation in CTDS frameworks. Our approach utilizes a selective sampling strategy that focuses on samples with large Q-value differences between teacher and student models. The genetic algorithm optimizes adaptive sampling ratios through evolutionary processes, where the chromosome represent sampling ratio sequences. This evolutionary optimization discovers optimal adaptive sampling sequences that minimize teacher–student performance gaps. Experimental validation in the

S t a r C r a f t

Multi-Agent Challenge (SMAC) environment confirms that our method achieved superior performance compared to the existing CTDS methods. This approach addresses the inefficiency in knowledge distillation and performance gap issues while improving overall performance through the genetic algorithm. Full article

(This article belongs to the Section E1: Mathematics and Computer Science)

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24 pages, 48856 KB

Open AccessArticle

Dynamic Supply–Demand Relationships of Food Provision in China: A Supply–Demand–Flow Perspective

by Chen Ying and Ruolin Meng

Land 2025, 14(9), 1724; https://doi.org/10.3390/land14091724 (registering DOI) - 25 Aug 2025

Abstract

Understanding food production (FP) supply–demand relationships is crucial for achieving Sustainable Development Goal 2 (SDG 2). Previous studies often assessed these relationships by overlaying supply and demand without considering food production flow (FPF). This study developed a framework from the perspectives of supply, demand, and flow to analyze the Agrifood System (AFS) of four major urban agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu–Chongqing. It applied the enhanced two-step floating catchment area model to simulate the magnitude and direction of four types of FPF—grains, vegetables, fruits, and meat—under three scenarios: intra-city flow, intra-provincial flow, and free flow. Results revealed mismatches in the FP supply–demand, and incorporating FPF improved these relationships. As flow restrictions eased, intra-city flows decreased, cross-regional flows expanded, and supply–demand imbalances were alleviated. Enhancing regional cooperation plays a key role in addressing the spatial mismatch between food supply and demand. These findings provide useful insights for addressing food supply–demand mismatches through more proper agricultural land allocation, better alignment of consumption patterns, and improvements in the flow system. Full article

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19 pages, 9369 KB

Open AccessArticle

Heading and Path-Following Control of Autonomous Surface Ships Based on Generative Adversarial Imitation Learning

by Jialun Liu, Jianuo Cai, Shijie Li, Changwei Li and Yue Yu

J. Mar. Sci. Eng. 2025, 13(9), 1623; https://doi.org/10.3390/jmse13091623 (registering DOI) - 25 Aug 2025

Abstract

Autonomous ship control faces significant challenges due to the diversity of ship types, the complexity of task scenarios, and the uncertainty of dynamic marine environments. These factors limit the effectiveness of traditional control approaches that rely on explicit dynamics modeling and handcrafted control laws. With the rapid advancement of computing and artificial intelligence, imitation learning offers a promising alternative by directly learning expert behaviors from data. This paper proposes a Generative Adversarial Imitation Learning (GAIL) method for heading and path-following control of autonomous surface ships. It employs an adversarial learning structure, in which a generator learns control policies that reproduce expert behavior while a discriminator distinguishes between expert and learned trajectories. In this way, the control strategies can be learned from expert demonstrations without requiring explicit reward design. The proposed method is validated through simulations on a model-scale tug. Compared with a behavioral cloning (BC) baseline controller, the GAIL-based controller achieves superior performance in terms of path-following accuracy, heading stability, and control smoothness, confirming its effectiveness and potential for real-world deployment. Full article

(This article belongs to the Section Ocean Engineering)

19 pages, 1200 KB

Open AccessFeature PaperArticle

Wave Load Reduction and Tranquility Zone Formation Using an Elastic Plate and Double Porous Structures for Seawall Protection

by Gagan Sahoo, Harekrushna Behera and Tai-Wen Hsu

Mathematics 2025, 13(17), 2733; https://doi.org/10.3390/math13172733 (registering DOI) - 25 Aug 2025

Abstract

This study presents an analytical model to reduce the impact of wave-induced forces on a vertical seawall by introducing a floating elastic plate (EP) located at a specific distance from two bottom-standing porous structures (BSPs). The hydrodynamic interaction with the EP is described using thin plate theory, while the fluid flow through the porous medium is described by the model developed by Sollit and Cross. The resulting boundary value problem is addressed through linear potential theory combined with the eigenfunction expansion method (EEM), and model validation is achieved through consistency checks with recognized results from the literature. A comprehensive parametric analysis is performed to evaluate the influence of key system parameters such as the porosity and frictional coefficient of the BSPs, their height and width, the flexural rigidity of the EP, and the spacing between the EP and BSPs on vital hydrodynamic coefficients, including the wave force on the seawall, free surface elevation, wave reflection coefficient, and energy dissipation coefficient. The results indicate that higher frictional coefficients and higher BSP heights significantly enhance wave energy dissipation and reduce reflection, in accordance with the principle of energy conservation. Oscillatory trends observed with respect to wavenumbers in the reflection and dissipation coefficients highlight resonant interactions between the structures. Moreover, compared with a single BSP, the double BSP arrangement is more effective in minimizing the wave force on the seawall and free surface elevation in the region between the EP and the wall, even when the total volume of porous material remains unchanged. The inter-structural gap is found to play a crucial role in optimizing resonance conditions and supporting the formation of a tranquility zone. Overall, the proposed configuration demonstrates significant potential for coastal protection, offering a practical and effective solution for reducing wave loads on marine infrastructure. Full article

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23 pages, 3736 KB

Open AccessArticle

Accelerating Thermally Safe Operating Area Assessment of Ignition Coils for Hydrogen Engines via AI-Driven Power Loss Estimation

by Federico Ricci, Mario Picerno, Massimiliano Avana, Stefano Papi, Federico Tardini and Massimo Dal Re

Vehicles 2025, 7(3), 90; https://doi.org/10.3390/vehicles7030090 (registering DOI) - 25 Aug 2025

Abstract

In order to determine thermally safe driving parameters of ignition coils for hydrogen internal combustion engines (ICE), a reliable estimation of internal power losses is essential. These losses include resistive winding losses, magnetic core losses due to hysteresis and eddy currents, dielectric losses in the insulation, and electronic switching losses. Direct experimental assessment is difficult because the components are inaccessible, while conventional computer-aided engineering (CAE) approaches face challenges such as the need for accurate input data, the need for detailed 3D models, long computation times, and uncertainties in loss prediction for complex structures. To address these limitations, we propose an artificial intelligence (AI)-based framework for estimating internal losses from external temperature measurements. The method relies on an artificial neural network (ANN), trained to capture the relationship between external coil temperatures and internal power losses. The trained model is then employed within an optimization process to identify losses corresponding to experimental temperature values. Validation is performed by introducing the identified power losses into a CAE thermal model to compare predicted and experimental temperatures. The results show excellent agreement, with errors below 3% across the −30°C to 125°C range. This demonstrates that the proposed hybrid ANN–CAE approach achieves high accuracy while reducing experimental effort and computational demand. Furthermore, the methodology allows for a straightforward determination of the coil safe operating area (SOA). Starting from estimates derived from fitted linear trends, the SOA limits can be efficiently refined through iterative verification with the CAE model. Overall, the ANN–CAE framework provides a robust and practical tool to accelerate thermal analysis and support coil development for hydrogen ICE applications. Full article

32 pages, 5540 KB

Open AccessArticle

High-Accuracy Cotton Field Mapping and Spatiotemporal Evolution Analysis of Continuous Cropping Using Multi-Source Remote Sensing Feature Fusion and Advanced Deep Learning

by Xiao Zhang, Zenglu Liu, Xuan Li, Hao Bao, Nannan Zhang and Tiecheng Bai

Agriculture 2025, 15(17), 1814; https://doi.org/10.3390/agriculture15171814 (registering DOI) - 25 Aug 2025

Abstract

Cotton is a globally strategic crop that plays a crucial role in sustaining national economies and livelihoods. To address the challenges of accurate cotton field extraction in the complex planting environments of Xinjiang’s Alaer reclamation area, a cotton field identification model was developed that integrates multi-source satellite remote sensing data with machine learning methods. Using imagery from Sentinel-2, GF-1, and Landsat 8, we performed feature fusion using principal component, Gram–Schmidt (GS), and neural network techniques. Analyses of spectral, vegetation, and texture features revealed that the GS-fused blue bands of Sentinel-2 and Landsat 8 exhibited optimal performance, with a mean value of 16,725, a standard deviation of 2290, and an information entropy of 8.55. These metrics improved by 10,529, 168, and 0.28, respectively, compared with the original Landsat 8 data. In comparative classification experiments, the endmember-based random forest classifier (RFC) achieved the best traditional classification performance, with a kappa value of 0.963 and an overall accuracy (OA) of 97.22% based on 250 samples, resulting in a cotton-field extraction error of 38.58 km². By enhancing the deep learning model, we proposed a U-Net architecture that incorporated a Convolutional Block Attention Module and Atrous Spatial Pyramid Pooling. Using the GS-fused blue band data, the model achieved significantly improved accuracy, with a kappa coefficient of 0.988 and an OA of 98.56%. This advancement reduced the area estimation error to 25.42 km², representing a 34.1% decrease compared with that of the RFC. Based on the optimal model, we constructed a digital map of continuous cotton cropping from 2021 to 2023, which revealed a consistent decline in cotton acreage within the reclaimed areas. This finding underscores the effectiveness of crop rotation policies in mitigating the adverse effects of large-scale monoculture practices. This study confirms that the synergistic integration of multi-source satellite feature fusion and deep learning significantly improves crop identification accuracy, providing reliable technical support for agricultural policy formulation and sustainable farmland management. Full article

(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)

38 pages, 3747 KB

Open AccessArticle

Parametric Optimization of Artificial Neural Networks and Machine Learning Techniques Applied to Small Welding Datasets

by Vinícius Resende Rocha, Fran Sérgio Lobato, Pedro Augusto Queiroz de Assis, Carlos Roberto Ribeiro, Sebastião Simões da Cunha, Louriel Oliveira Vilarinho, João Rodrigo Andrade, Leonardo Rosa Ribeiro da Silva and Luiz Eduardo dos Santos Paes

Processes 2025, 13(9), 2711; https://doi.org/10.3390/pr13092711 (registering DOI) - 25 Aug 2025

Abstract

Establishing precise welding parameters is essential to achieving the desired bead geometry and ensuring consistent quality in manufacturing processes. However, determining the optimal configuration of parameters remains a challenge, particularly when relying on limited experimental data. This study proposes the use of artificial neural networks (ANNs), with their architecture optimized via differential evolution (DE), to predict key MAG welding parameters based on target bead geometry. To address data limitations, cross-validation and data augmentation techniques were employed to enhance model generalization. In addition to the ANN model, machine learning algorithms commonly recommended for small datasets, such as K-nearest neighbors (KNNs) and support vector machines (SVMs), were implemented for comparative evaluation. The results demonstrate that all models achieved good predictive performance, with SVM showing the highest accuracy among the techniques tested, reinforcing the value of integrating traditional ML models for benchmarking purposes in low-data scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence in Process Innovation and Optimization)

18 pages, 6433 KB

Open AccessArticle

Study on Nano-Grinding Characteristics and Formation Mechanism of Subsurface Damage in Monocrystalline Silicon

by Haipeng Yan, Haining Zhang, Siyuan Cao and Chao Wang

Micromachines 2025, 16(9), 976; https://doi.org/10.3390/mi16090976 (registering DOI) - 25 Aug 2025

Abstract

Monocrystalline silicon is an excellent semiconductor material for integrated circuits. Its surface quality has an enormous effect on its service life. The surfaces are formed by ultra-precision machining using nano-grinding, one of the technologies that can achieve surface roughness at the nano- or sub-nano-scale. Therefore, subsurface damage of monocrystalline silicon in nano-grinding was studied by establishing a molecular dynamics simulation model, and the impact of machining parameters on the force–thermal behavior was analyzed. The results reveal that the mechanism of subsurface damage is mainly structural phase transformation and amorphization. In nano-grinding of monocrystalline silicon, the tangential grinding force has a relatively major role in material removal. With increasing grinding depth and grinding speed, the grinding heat rises, and a certain degree of high temperature strengthens the toughness of the material, improving the subsurface quality of monocrystalline silicon. Therefore, subsurface damage in monocrystalline silicon can be controlled by reducing the grinding depth and increasing the grinding speed. Full article

(This article belongs to the Special Issue Functional Materials and Microdevices, 2nd Edition)

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15 pages, 299 KB

Open AccessArticle

The Moderating Effect of Social Capital on the Relationship Between Loneliness and Depression in Older Adults

by Eun Seo Jeong and Sung Man Bae

Behav. Sci. 2025, 15(9), 1157; https://doi.org/10.3390/bs15091157 (registering DOI) - 25 Aug 2025

Abstract

Loneliness contributes to depression in older adults, but research on effective intervention strategies remains limited. This study aims to examine the moderating effects of social capital on the relationship between loneliness and depression in older adults. Social capital was divided into structural and cognitive social categories. A self-report questionnaire was administered to 350 participants aged 65 or older, using the Korean version of the UCLA Loneliness Scale, Short-Form Geriatric Depression Scale, Structural Social Capital Scale, and Cognitive Social Capital Scale. Data from 334 patients were included in the final analysis, and the moderating effect was tested using Model 1 of the PROCESS Macro for SPSS. The results of this study are as follows: (1) it demonstrated that loneliness is a significant predictor of higher levels of depression; (2) structural social capital significantly moderated this relationship, indicating that the impact of loneliness on depressive symptoms varied according to the level of structural social capital; (3) cognitive social capital also moderated this relationship, suggesting that the influence of loneliness on depression differs based on the level of cognitive social capital. This study provides valuable foundational data for the development of prevention and intervention strategies targeting loneliness and depression in old age. Full article

(This article belongs to the Section Health Psychology)

41 pages, 9064 KB

Open AccessArticle

PLSCO: An Optimization-Driven Approach for Enhancing Predictive Maintenance Accuracy in Intelligent Manufacturing

by Aymen Ramadan Mohamed Alahwel Besha, Opeoluwa Seun Ojekemi, Tolga Oz and Oluwatayomi Adegboye

Processes 2025, 13(9), 2707; https://doi.org/10.3390/pr13092707 (registering DOI) - 25 Aug 2025

Abstract

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

(This article belongs to the Special Issue Machine Learning for Predictive Modeling and Optimization of Manufacturing Processes)

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35 pages, 4640 KB

Open AccessArticle

Electric Strategy: Evolutionary Game Analysis of Pricing Strategies for Battery-Swapping Electric Logistics Vehicles

by Guohao Li and Mengjie Wei

Sustainability 2025, 17(17), 7666; https://doi.org/10.3390/su17177666 (registering DOI) - 25 Aug 2025

Abstract

Driven by the urgent need to decarbonize the logistics sector—where conventional vehicles exhibit high energy consumption and emissions, posing significant environmental sustainability challenges—electrification represents a pivotal strategy for reducing emissions and achieving sustainable urban freight transport. Despite rising global electric vehicle sales, the penetration rate of electric logistics vehicles (ELVs) remains comparatively low, impeding progress toward sustainable logistics objectives. Battery-swapping mode (BSM) has emerged as a potential solution to enhance operational efficiency and economic viability, thereby accelerating sustainable adoption. This model improves ELV operational efficiency through rapid battery swaps at centralized stations. This study constructs a tripartite evolutionary game model involving government, consumers, and BSM-ELV manufacturers to analyze market dynamics under diverse strategies. Key considerations include market scale, government environmental benefits, battery leasing/purchasing costs, lifecycle cost analysis (via discount rates), and resource efficiency (reserve battery ratio

λ

). MATLAB-2021b-based simulations predict participant strategy evolution paths. Findings reveal that market size and manufacturer expectations significantly influence governmental and manufacturing strategies. Crucially, incorporating discount rates demonstrates that battery leasing reduces consumer enterprises’ initial investment, enhancing economic sustainability and cash flow while offering superior total cost of ownership. Furthermore, gradual reduction of government subsidies effectively stimulates market self-regulation, incentivizes leasing adoption, and bolsters long-term economic/operational sustainability. Market feedback can guide policy adjustments toward fiscally sustainable support mechanisms. This study proposes the following management implications for advancing sustainable logistics: 1. Governments should phase out subsidies systematically to foster market resilience; 2. Manufacturers must invest in BSM R&D to improve efficiency and resource circularity; 3. Consumer enterprises can achieve economic benefits and emission reductions by adopting BSM-ELVs. Full article

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