Based on the theory of organizational learning, supply chain practice view, and stakeholder theory, this paper presents an empirical analysis of the influence of learning orientation on corporate sustainability through supply chain relationships and supply chain agility. A learning orientation strengthens supply chain practices, potentially extending its impact beyond mere business transactions to enhance sustainability through knowledge transfer. Empirical results reveal the relationships among the constructs and serial mediation of supply chain relationship and supply chain agility. The empirical data were collected from organizations in the United Arab Emirates. The structural equation modeling approach is applied to validate the models in the study. Mediation analysis was performed to better understand the organizational and supply chain phenomenon. In addition, this study provides empirical support for serial mediation, with supply chain relationships and supply chain agility mediating the relationship between learning orientation and corporate sustainability. The study draws managers' attention to the importance of learning orientation as well as supply chain relationship and supply chain agility post-COVID-19. The findings may guide firms toward designing their sustainable supply chain strategies under organizational learning. This paper contributes to the sustainable supply chain management literature.

Hydrogen Fuel Cell Vehicle (HFCV) is developing as a major tool for the transport electrification and hydrogen transition. However, the HFCV market still faces a consumption dilemma, with limited research investigating the behavioral mechanisms underlying HFCV adoption. Using Behavioral Reasoning Theory, we identified key factors influencing consumer adoption of HFCVs and revealed the causal relationships among them. By analyzing data from 1001 respondents in Chongqing, China, we found that self-direction (SD) and stimulation (ST) are key antecedents shaping reasons for (RF) and reasons against (RA) adopting HFCVs, which in turn influence adoption attitude (ATT) and purchase intention (INT) toward HFCVs. These findings hold regardless of control variables such as age, gender, education, and monthly income. We also found that SD, RF, and ATT have higher importance and performance, making them the main variables influencing HFCV adoption, and confirmed both the applicability and predictive power of our model. Based on these findings, stakeholders in the HFCV market should focus on enhancing SD and ST to better motivate potential users for adoption, while removing adoption barriers through improved infrastructure and security measures.

This study constructs and develops a valid hierarchical structure for life cycle assessments (LCA) of circular supply chain (CSC) enablers within the automobile industry in Indonesia. Prior studies have focused on developing LCA of CSC enablers metrics; however, there is insufficient focus on integrated assessments of materials, parts, and product design in conjunction with waste management, aimed at enhancing the entire life cycle from raw material extraction and sourcing to end-of-life management. This study adds to the LCA literature by integrating the essential circular materials, parts and product design to optimize waste management, while enhancing the hierarchical measures of CSC enablers for improved policy recommendations and decision-making process, employing a data-driven approach to develop the structure. This data-driven approach that utilizes both qualitative and quantitative techniques integrates content and bibliographic analyses, the entropy weighted method, the fuzzy Delphi method, exploratory factor analysis, and fuzzy decision-making trial and evaluation laboratory to construct a hierarchical LCA structure by analyzing the interrelationships among attributes. The findings indicate that prioritizing assessments of waste management and circular materials, parts and product design are essential for improving CSC. Prioritize product refurbishment, eco-design, recycling process improvement, information management systems, and risk assessment management are as key CSC enablers for policy recommendations.

This study establishes a Stackelberg game model with Cloud Energy Storage Operators (CESO) as the leader, collaborating with industrial park users to achieve mutual benefit. CESO and industrial park user. The cloud energy storage (CES) effectively addresses the high self-investment costs and underutilization of resources in the energy internet context. This study proposes a time-based pricing strategy for CES leasing services. CESO determines the hourly capacity and power leasing prices over 24 h The aim is to minimize the discrepancy between user declarations and actual usage through penalty measures. Industrial Park users determine leased energy storage capacity and charging/discharging power based on CESO's prices, their own loads, and renewable energy availability. This study proposes an improved snow ablation optimizer (ISAO) to obtain the global optimal solution, i.e., the optimal price for the time-based pricing of CES leasing services. In a multi-user, multi-scenario analysis, adopting this strategy increased CESO's benefits by 44.80 % and users' benefits by 6.76 %. Users increased the electricity sales during peak hours by 51.53 % and reduced the electricity purchases during valley hours by 19.9 %.

This study contributes to identifying supply chain disruption indicators and resilience strategies, with the aim of enhancing issues related to disruption in the garment industry. Supply chain disruption influences a series of problems, including transportation delays, delivery times slow down and cost increases, etc. Additionally, resilience strategies mitigate and enable a supply chain to respond to disruption while recovering or improving so that risks are reduced, and operations are more efficient. However, previous studies have failed to identify supply chain disruption indicators, construct a hierarchical framework with causal interrelationships among the attributes, and develop resilience strategies to handle disruption situations, especially in the garment industry. This study proposes the creation of 6 aspects and 18 criteria of supply chain disruption and resilience strategy attributes via the fuzzy Delphi method and fuzzy decision-making trial and evaluation laboratory method. The findings show that the causal group consists of supply risk, flexible business strategies and collaborative strategies, whereas the effect group consists of human issues, transportation failure and preventive resilience strategies. In practice, managers should focus on resilience strategies such as backup suppliers, risk and revenue sharing, risk management culture, partnership management, and cooperation with stakeholders to enhance supply chain disruption. This study contributes to synthesize indicators related to supply chain disruption and resilience strategies for adapting and responding to disturbances during disruptions with minimal impacts on performance and future sustainability in the garment industry.

This study develops multi-regional integrated energy system (IES) optimization for the distribution network and undertakes intra-energy management of multiple IESs. Energy providers and IESs are constructed to optimize the regional performance in terms of the internal electricity price mechanism to give priority to the energy transaction with the upper-level energy management and to make the lower IES give priority to new energy equipment. The intra-tier energy management model combines upper tier energy trading prices with IES energy optimization to create a dynamic interaction of facility energy based on tariffs. This method reduces the energy purchase cost and increases the energy management income to enhance the regional IES. IES types are summarized and merged to form a large centralized IES. Power equipment in the lower IES is optimized by the CPLEX solver. The upper energy management optimizes the internal transaction price through the genetic algorithm. Model comparisons are presented.

cost is introduced to change the corresponding control strategy. A hybrid energy storage is used in this model to smooth out the solar power and wind power fluctuations. Hence, a multi-objective artificial hummingbird optimization algorithm is proposed and uses to solve the optimal operation strategy of the microgrid. The final optimal operation strategy is obtained from the Pareto solution set using TOPSIS. The results show that the proposed microgrid system has 20.2 % lower total operating costs, 4.5 % lower carbon emissions, and 32.6 % longer battery life than the conventional microgrid system, which is critical for improving the operation stability, economy, low carbon of the system, and extending the service life of the battery.

The large-scale expansion of vehicle-to-grid (V2G) technology requires the full support of electric vehicle (EV) users. However, existing studies lack a comprehensive review of V2G technology acceptance, especially the preferences and attitudes of potential consumers. To this end, our study conducts a systematic literature review to understand V2G acceptance behaviour and explore its future research directions. By reviewing 87 related literatures, we obtain key information about V2G adoption in terms of publication trends, keywords, theories, contexts, methods, antecedents, decisions, and outcomes. Results show that the antecedents mainly influencing V2G acceptance are those related to the product (e.g., battery life and EV flexibility), the individual (e.g., range anxiety and risk awareness), and the economy (e.g., V2G costs). The three most important decisions for V2G acceptance are the intention to join an aggregator, the decision to sign a contract, and the willingness to support EVs. The outcome with the most votes for V2G acceptance is its impact on energy, in particular by enhancing grid flexibility, efficiency, and stability. In addition, the research context is primarily focused on the United States, China, and the Netherlands, with a notable lack of studies from other countries. Based on these results, we also further discuss potential research directions for V2G acceptance.

In the realm of global supply chains, the optimization of floating crane operations for bulk product transshipment via inland waterways emerges as a crucial necessity to address economic, operational, and environmental imperatives. This research identifies a significant gap in existing methodologies for the scheduling, routing, and assignment of floating cranes, which are essential for improving efficiency and sustainability in maritime logistics. To bridge this gap, we propose the Reinforcement Learning Variable Neighbourhood Strategy Adaptive Search (RL-VaNSAS) algorithm, a novel integration of reinforcement learning with variable neighbourhood search strategies. This advanced model aims to holistically minimize energy consumption, labor costs, and penalty costs, while simultaneously enhancing service efficiency. Through rigorous simulations, RL-VaNSAS was benchmarked against conventional methods such as Differential Evolution (DE), Genetic Algorithm (GA), and the original Variable Neighbourhood Search Adaptive Strategy (VaNSAS), revealing its superior capability in significantly reducing annual energy costs to $1,211,948, labor costs to $270,948, penalty costs to $19,948, and operational hours to 12,087. Demonstrating notable advancements in operational efficiency and cost reduction, RL-VaNSAS offers a sustainable solution to the dynamic challenges of maritime logistics, characterized by fluctuating vessel arrivals and diverse cargo requirements. The findings illuminate the critical need for innovative optimization techniques in enhancing the sustainability and efficiency of maritime logistics operations. RL-VaNSAS not only fills the identified research gap but also sets a new standard for future endeavors in global supply chain management, underlining the importance of adopting advanced optimization strategies for sustainable production and economic growth.

As the automotive market continues to expand, the automotive industry needs perfect scheduling techniques to guarantee the timely delivery of automobiles. However, no effective scheduling method has been formed due to the backward construction of information. To address the above challenge, this study suggests an intelligent scheduling model for green automotive outbound logistics that mixes two distribution modes to encourage resource integration, including direct distribution through the manufacturer and indirect distribution through the warehouse. The model is built with the goal of minimizing operating costs and greenhouse gas (GHG) emissions. Specifically, the operating costs are composed of vehicle fixed cost, vehicle transportation cost, warehouse cost, and highway access cost, and the GHG emissions refer to the total amount of carbon dioxide, methane, and nitrous oxide. To efficiently solve the constructed model, this study takes full advantage of the strong local search ability of the mayfly algorithm (MA) and the strong global search ability of the non-dominated sorting genetic algorithm II (NSGA II) to create a hybrid MA-NSGA II algorithm. Furthermore, an actual case is used to verify the effectiveness of the proposed model and solution algorithm. The results demonstrate that the multi-objective model is better at balancing the economy and environment than single-objective optimization. In theory, this study innovatively develops an intelligent scheduling model for green automotive outbound logistics. The relevant practitioners can use the built model as a support tool to accomplish green scheduling.

The rapid growth of fast fashion has led to a thriving secondhand clothing market over the past decade, presenting both challenges and opportunities for sustainable energy practices in the fashion industry. However, the diverse stances adopted by developing countries on importing secondhand clothing, due to potential environmental threats and impacts on local garment manufacturing, have made international trade relationships increasingly sensitive and fragile. This study employs complex trade network analysis and temporal exponential random graph model to comprehensively analyze the dynamic evolution and driving factors of the global secondhand clothing trade network (GSCTN) from 1995 to 2022, with a focus on its implications for energy consumption and sustainability. The findings reveal consistent growth and significant expansion in the GSCTN, with developed countries like the United States, United Kingdom, and Germany remaining major exporters, while African countries such as Ghana and Kenya have become primary importers. Notably, Africa's import sources have gradually shifted from Europe to Asia, underscoring a trend of regionalization that may impact transportation-related energy consumption. The formation and evolution of the GSCTN are shaped by its endogenous structure, including delayed reciprocity, transitivity, and stability, as well as factors like environmental regulations, industrialization levels, and the fast fashion industry. Moreover, countries with colonial ties or regional trade agreements are more likely to engage in secondhand clothing trade. These findings provide valuable insights for policymakers to develop effective strategies that promote sustainable trade practices, support the transition towards a circular economy, and enhance energy efficiency in the fashion industry.

Bioplastics, which are made from crops and are biodegradable, are considered a sustainable alternative to petroleum-based plastics for reducing emissions and pollution. However, there is currently insufficient systematic assessment of the long-term economic, health, and environmental impacts, as well as the long-term policy effects and consumer diffusion patterns, of large-scale promotion of bioplastics in different regions of China. To address this gap, this study developed a multi-regional dynamic simulation framework integrating a recursive dynamic computable general equilibrium model (CGE), life cycle assessment (LCA), and consumer social network analysis. It employed the value of statistical life (VSL) method to quantify health benefits. The results indicate that the promotion of bio-based plastics may lead to a decrease of approximately 0.0157 % in China's gross domestic product (GDP) by 2052, with reductions in income for enterprises, residents, and the government of approximately 0.014 %, 0.012 %, and 0.013 %, respectively. However, over 30 years, health costs could be reduced by approximately 44.8 billion yuan (a decrease of 8.68 %), partially offsetting the economic losses. Additionally, bio-based plastics significantly reduce carbon dioxide (CO2) emissions intensity by 7.71 % and terrestrial ecological toxicity by 9.74 %, among other emissions, but significant regional variations exist. Coastal provinces with high production capacity (such as Guangdong and Jiangsu) exhibit non-linear relationships between emission reduction effects and production capacity due to differences in consumption patterns. Policy analysis results indicate that government subsidies are more effective than carbon taxes, and combining plastic bans with green education can further enhance emission reduction effects. Oil price fluctuations and high waste management costs also impact regional emission reduction outcomes. This study comprehensively assesses the multi-dimensional trade-offs and synergistic effects of promoting bio-based plastics in China, aiming to provide practical pathways and policy recommendations for decision-makers and the industry to achieve a sustainable transition of plastics.

With increasing resource shortages and environmental pollution, firms are increasingly relying on green technology application (GTA) to improve both environmental and financial performance. However, previous research found that the impact of GTA on both types of performance can be either positive or negative. Therefore, to understand the underlying mechanisms, we employ the meta-SEM method to explore the impact of GTA on environmental performance and financial performance, considering the natural resource-based view and socio-technical system theory. A meta-analysis is conducted to obtain the correlation matrix and establish a structural equation model to test the hypotheses. The results show that, first, it is difficult for firms to directly promote performance from GTA. GTA can only promote environmental performance through green innovation and green cooperation, and the mediating effect of green innovation is greater. Second, despite the direct negative impact of GTA on financial performance, it can promote financial performance through green innovation and the chain path. Third, internal environmental management can enhance all paths. Regarding the total effect, GTA has a significant positive impact on performance in both the environmental and financial dimensions. These results enrich extant knowledge on the relationship between GTA and firm performance.

The literature on green supply chain management (GSCM) has gained attention due to the growing concern for environmental sustainability, as evidenced in literature reviews. Classifying literature reviews is essential for organising research, identifying trends, focusing on specific areas, and guiding future studies. However, limited studies have classified previous GSCM literature review studies. This study classifies GSCM literature reviews. A three-step systematic method is employed for collecting the relevant samples. The method effectively selects 69 review papers for quantitative and qualitative synthesis. The findings offer a classification by categorising different types of review papers into three broad categories: methodological, generic, and domain-based reviews. The findings also reveal a growing interest in GSCM research, with Scopus and Web of Science being the most commonly used databases in this context. This study contributes by offering a comprehensive classification, identifying common databases and keywords, and specific future research directions.

This study proposes a hybrid improved butterfly optimization algorithm-support vector machine (SVM) to address the nonlinear and nonstationary characteristics of short-term wind power signals caused by uncertain wind speed factors. (1) This study proposes the Levy flight strategy after each iteration to improve the optimization performance of the butterfly algorithm because the dynamic switching probability strategy and adaptive weight are considered in the traditional butterfly algorithm; (2) the influence of different meteorological factors on wind power output is analyzed, and the input features of the short-term wind power prediction model are determined; and (3) the short-term wind power prediction model is applied to predict the wind power in different seasons and compared with existing prediction methods. High-precision wind power prediction is the solution for promoting the exploitation and utilization of wind. Large-scale wind power grid connections lead to challenges for the safe operation of power grids. The testing results reveal that the proposed improved butterfly optimization algorithm-SVM model improves the short-term wind power prediction accuracy, with a mean value of less than 0.21 compared with those of the butterfly optimization algorithm-SVM, particle swarm optimization-SVM, genetic algorithm-SVM, and back propagation neural network models. High-precision short-term wind power prediction can be used to establish a reasonable economic dispatch plan for power systems and improve the economic benefits of wind farms.

This study aims to develop and evaluate generative artificial intelligence (AI) capabilities to enhance green supply chain management (GSCM) in the automotive industry, Indonesia. Prior studies have concentrated on constructing generative AI metrics; however, there is a lack of emphasis on developing the capabilities to address dynamic environmental challenges in GSCM. This study integrates dynamic capabilities view with organisational learning theory and employs the integrated fuzzy Delphi method and fuzzy synthetic evaluation-decision-making trial and evaluation laboratory approach to ascertain the valid attributes to facilitate GSCM improvement. The findings indicate that dynamic knowledge and innovative learning capabilities and reflexive control and measurement capabilities from the perspective of sensing capabilities, as well as co-evolution capabilities from the perspective of seising capabilities, are key capabilities that need to be prioritised to enhance GSCM. In practices, data gathering and analysis for predictive maintenance, and sales and operations strategy identification must be prioritised.

This study contributes to select the system voltage fluctuation as the optimization objective and uses Improved Archimedes optimization algorithm (IAOA) to analyze the control parameters for DC microgrid. DC microgrids containing hybrid energy storage play an important role in energy utilization efficiency, system stability, operating costs, intelligent management and clean energy development. ESS significantly enhances the stability of the DC microgrid by regulating power balance, suppressing voltage fluctuations, and providing rapid power support. Hybrid photovoltaic storage system (HESS) is controlled by low-pass filter and double closed-loop control. The control strategy based on synchronized generator characteristic is adopted for the microgrid inverter. Yet, prior studies are neglecting to involve the control strategy optimization for the microgrid inverter. This study proposes an improved Archimedean optimization algorithm control method based on the Levy flight strategy to improve the DC microgrid system stability incorporating HESS. IAOA is used to find the optimal control parameters adapted to the overall system considering the interaction between microgrid inverter and HESS. This study substitutes the optimized control parameters into the model for simulation to verify the effectiveness of DC microgrid optimization. The control strategy based on IAOA optimization reduces the voltage fluctuation amplitude by 1.6 %-6.98 % when the simulated model is disturbed by analyzing with the traditional sag control.

This study explores the relationship between supply chain resilience (SCR) and sustainable supply chain performance (SSCP) within the context of the Chinese construction industry, a sector known for its complexity and vulnerability to disruptions. Drawing on data from 525 construction companies, the research investigates how SCR directly influences SSCP and examines the mediating role of dynamic capabilities in this relationship. The results highlight those dynamic capabilities, specifically the ability to seize and reconfigure, serve as critical mediators in the resilience-sustainability relationship while sensing does not have a significant mediating effect. This suggests that in highly dynamic environments like China's construction sector, focusing on capturing opportunities and reconfiguring resources is more effective for achieving sustainable outcomes than relying solely on sensing capabilities. This finding contributes to the growing body of knowledge by providing a nuanced understanding of how resilience practices can be leveraged to promote long-term sustainability.

Tugboats play a crucial role in connecting maritime and inland logistics by transferring goods from large vessels. However, managing their energy consumption is a major challenge due to factors such as barge capacity, cargo weight, tidal schedules, navigational complexities, and regulatory constraints. Efficient scheduling is essential to minimizing costs and enhancing sustainability. To address this challenge, this study introduces a mixed-integer programming model to optimize tugboat scheduling, incorporating real-world constraints to reduce energy consumption and operational inefficiencies. To address industrial scale problems, we propose a Hybrid Transformer-Attention Mechanism and Artificial Multiple Intelligence System (HT-AMIS), combined with metaheuristic-inspired intelligence boxes (IBs), to enhance adaptability and efficiency. The results show that HTAMIS reduces tugboat operating costs by 11.75%, with energy costs reduced by 10.73% and penalty costs reduced by 21.96%. The model demonstrated robustness, effectively handling challenging scenarios such as tugboat breakdowns and severe weather conditions.

This special issue (SI) addresses the sustainable semiconductor supply chain amid industrial disruption and this editorial note discusses the domain in technological solutions, management models, business performance, supply chain toward sustainability, carbon emissions, digital transformation, and resource recycling. The SI collected the studies from those perspectives and addressed the challenges from the semiconductor supply chain.