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The aluminum electrolysis industry is a typical high-energy-consumption and high-carbon-emission sector, and its low-carbon transformation is crucial for achieving “dual-carbon” goals. However, aluminum electrolysis is constrained by thermodynamic safety limits, and conventional dispatch models also often overlook carbon emission trading and the integrated utilization of waste heat. To address these challenges, a low-carbon economic dispatch method considering production safety constraints is proposed in the paper for integrated energy systems in aluminum electrolysis, aiming to enhance wind power utilization and ensure operational safety. First, a load model incorporating thermodynamic safety constraints is developed, and a thermal dynamics equation of electrolytic cells is established to characterize the temperature dynamics of aluminum loads. Then, a bi-level optimization framework for the power–aluminum system is constructed: the upper level minimizes grid power-supply costs by coordinating thermal, wind, and photovoltaic generation, while the lower level maximizes enterprise profit, balancing production safety and economic efficiency to achieve coordination between the system and enterprise layers. Finally, a tiered carbon trading mechanism and waste heat heating model are integrated into the framework, combined with a second-order RC building thermal inertia model to realize coordinated optimization among electricity, heat, and carbon flows. The simulation results demonstrate that the proposed method effectively reduces carbon emissions while ensuring electrolytic cell safety: with carbon trading, emissions decrease by 7.2%; when incorporating waste heat utilization reduces boiler heating emissions, they decrease by 74.7%; and further considering building thermal inertia increases wind power utilization to 99.6%, achieving the coordinated optimization of electricity–heat–carbon systems. Full article

Recent frequent food safety incidents have heightened consumer concern about agricultural product traceability, driving companies to build more robust supply chain traceability systems. However, enhancing traceability level is not only driven by consumer preferences but is also profoundly shaped by supply chain power structures and coordination mechanisms. In this study, we investigate how consumer preferences, power structures, and contractual mechanisms jointly shape traceability investment and coordination in agricultural supply chains. Using a two-tier supplier–retailer game-theoretic model, we compare traceability levels, pricing, and profit allocation under three governance structures: vertical Nash, supplier-led, and retailer-led. We also evaluate the effectiveness of cost-sharing and revenue-sharing contracts. The results reveal several key insights. First, consumer preference for traceable products serves as a critical market-driven force that enhances traceability investment across supply chain tiers. Second, power structures fundamentally determine traceability outcomes through threshold-dependent mechanisms: when consumer preference is weak, vertical Nash structures yield superior traceability via balanced cost-sharing; however, once preference intensity surpasses critical thresholds, retailer-led structures dominate in responsiveness, profit distribution, and capability building. In contrast, supplier-led structures deliver the weakest outcomes, as concentrated cost burdens suppress investment incentives, particularly in supply chains composed of small and medium-sized suppliers. Third, coordination contracts exhibit structure-specific efficacy. Cost-sharing contracts achieve full optimization in vertical Nash contexts and yield Pareto improvements in supplier-led chains, whereas traditional contracts exert minimal influence in retailer-led settings. These findings enrich our theoretical understanding of traceability governance and provide practical guidance for differentiated traceability design and contract formulation. Full article

From the interdisciplinary perspective of industrial economics and behavioral finance, this study establishes a noncooperative-cooperative biform game model between new energy enterprises and traditional energy enterprises. In this model, sales price is considered the non-cooperative strategy, while R&D expenses borne forms the basis of cooperative alliances. The Shapley value is applied to allocate profits, and numerical analysis is conducted to analyze the impact of factors, such as government subsidies and competitive intensity, on optimal strategies. The findings reveal the following: (1) Government subsidies effectively increase energy sales volume, promote technological advancements in new energy enterprises, and reduce the traditional energy enterprises’ proportion of R&D expenses borne. Moderate increases in competitive intensity are conducive to expanding market size, thereby enhancing both energy sales volume and profits. (2) Reasonably increasing the executive risk preference of energy enterprises encourages traditional energy enterprises to bear a higher proportion of R&D expenses and stimulates new energy enterprises to improve their production level, leading to increased sales value of energy. (3) Rising investment and production costs result in a higher proportion of R&D expenses borne for new energy enterprises. Consequently, the shrinking of new energy value reduces their profits, while the profits of traditional energy enterprises increase correspondingly. Full article

This study examines the impact of digital transformation on the total factor productivity (TFP) of Chinese listed companies. Using a firm-level panel dataset from 2007 to 2024 and applying the Levinsohn–Petrin method to estimate TFP, we find that digital transformation significantly enhances firm productivity. Both Digital Technology Application (DTA) and Underlying Technologies (UT) contribute positively to TFP, with the effect of UT being more pronounced. Heterogeneity analysis indicates that the productivity-boosting effect of digital transformation is stronger in firms with higher market value, greater industry competition, and those outside high-pollution industries (HPEs). Mechanism analysis shows that digital transformation promotes TFP through innovation, human capital optimization, cost reduction, and operational efficiency. Moreover, external macro factors such as digital infrastructure, intellectual property protection and marketization further moderate this relationship. Finally, the influence of DT on TFP shows a threshold effect related to profitability fluctuations and cash flow conditions. Our findings provide robust empirical evidence on how digital transformation reshapes firm-level productivity dynamics and highlight the key conditions under which it yields optimal economic returns. Full article

This study investigated the effect of co-fermentation of starch (1G) and lignocellulosic (2G) feedstocks on ethanol production and the profile of volatile by-products. Experiments were conducted using an integrated SHF/SSF method with separate pretreatment of each raw material. After 72 h, the ethanol concentration in the starch–lignocellulosic mash reached 49.39 g/L, which is 77% higher compared to the ethanol concentration from the lignocellulosic biomass alone (27.93 g/L). The ethanol yields were 31.32 and 17.72 L/100 kg of raw material, respectively. Co-fermentation significantly changed the profile of volatile compounds. In the starch–lignocellulosic mash, the content of aldehydes was 51.20 mg/L (43% lower vs. lignocellulose alone), higher alcohols was 2018.17 mg/L (64% lower), esters was 8.70 mg/L (73% lower), and methanol was 1.33 mg/L (98% lower). These results demonstrate that integrating 1G and 2G feedstocks reduces the formation of by-products during the fermentation process, while maintaining ethanol concentrations at an industrial profitability threshold (above 40 g/L). The findings provide important insights for optimizing integrated bioethanol production from whole corn plants, which is essential for improving the economic feasibility. Full article

This study assesses the feasibility and profitability of marine hybrid clusters, combining wave energy converters (WECs) and offshore wind turbines (OWTs) to power households and marine aquaculture. Researchers analyzed two coastal sites: La Serena, Chile, with high and consistent wave energy resources, and Ensenada, Mexico, with moderate and more variable wave power. Two WEC technologies, Wave Dragon (WD) and Pelamis (PEL), were evaluated alongside lithium-ion battery storage and green hydrogen production for surplus energy storage. Results show that La Serena’s high wave power (26.05 kW/m) requires less hybridization than Ensenada’s (13.88 kW/m). The WD device in La Serena achieved the highest energy production, while PEL arrays in Ensenada were more effective. The PEL-OWT cluster proved the most cost-effective in Ensenada, whereas the WD-OWT performed better in La Serena. Supplying electricity for seaweed aquaculture, particularly in La Serena, proves more profitable than for households. Ensenada’s clusters generate more surplus electricity, suitable for the electricity market or hydrogen conversion. This study emphasizes the importance of tailoring emerging WEC systems to local conditions, optimizing hybridization strategies, and integrating consolidated industries, such as aquaculture, to enhance both economic and environmental benefits. Full article

This work addresses the challenges of airline planning, which requires the integration of flight scheduling, aircraft availability, and maintenance to ensure both airworthiness and profitability. Current solutions, often developed by human experts, are susceptible to bias and may yield suboptimal results due to the inherent complexity of the problem. Furthermore, existing state-of-the-art approaches often inadequately address critical factors, such as maintenance, variable flight numbers, discrete time slots, and potential flight repetition. This paper presents a novel approach to aircraft routing optimization using a model that incorporates critical constraints, including path connectivity, flight duration, maintenance requirements, turnaround times, and closed routes. The proposed solution employs a simulated annealing algorithm enhanced with specialized perturbation operators and constraint-handling techniques. The main contributions are twofold: the development of an optimization model tailored to small airlines and the design of operators capable of efficiently solving large-scale, realistic scenarios. The method is validated using established benchmarks from the literature and a real case study from a Mexican commercial airline, demonstrating its ability to generate feasible and competitive routing configurations. Full article

The demand for submersible pumps for lifting the liquids from under the surface is growing day by day. The growing market of submersible pumps creates difficulties for managing their end of life. Recycling, the process of converting scraps material into new virgin material and fine products while the expenditures are used for carrying the scrap material from different sources to destinations, is the main focus area of this research. The aim of this study is to investigate and create strategies for minimizing the transportation cost in the reverse logistics of electrical appliances (submersible pumps) in order to increase profitability and economically sustainability. Data was gathered through interviews with two distinct individuals that are extremely knowledgeable and proficient in their fields. A case study of reverse logistics of submersible pumps is used to optimize the transit cost in a supply chain. To find an appropriate solution, the simplex linear programming approach was used. Microsoft Excel Solver was utilized to conduct data analysis. The findings revealed that optimizing the transportation cost not only reduces the operational cost, but also increases the profit margins. The study concludes that by integrating reverse logistics strategically, both environmental and financial benefits can be achieved by industries. Full article

The integration of electric vehicles (EVs) and fuel-cell electric vehicles (FCEVs) requires accessible and profitable facilities for fast charging. To promote fast-charging stations (FCSs), a systematic analysis that encompasses both planning and operation is required, including the incorporation of multi-energy resources and uncertainty. This paper presents an optimization framework that addresses a joint strategy for the configuration and operation of an EV/FCEV fast-charging station (FCS) integrated with distributed energy resources (DERs) and hydrogen systems. The framework incorporates uncertainties related to solar photovoltaic (PV) generation and demand for EVs/FCEVs. The proposed joint strategy comprises a four-phase decision-making framework. Phase 1 involves modeling EV/FECE demand, while Phase 2 focuses on determining an optimal long-term infrastructure configuration. Subsequently, in Phase 3, the operator optimizes daily power scheduling to maximize profit. A real-time uncertainty update is then executed in Phase 4 upon the realization of uncertainty. The proposed optimization framework, formulated as mixed-integer quadratic programming (MIQP), considers configuration investment, operational, maintenance, and penalty costs for excessive grid power usage. A heuristic algorithm is proposed to solve this problem. It yields good results with significantly less computational complexity. A case study shows that under the most adverse conditions, the proposed joint strategy increases the FCS owner’s profit by 3.32% compared with the deterministic benchmark. Full article

The severe degradation of thin-layer black soil in the Southern Songnen Plain threatens both regional agricultural sustainability and national food security. While various fertile topsoil restoration technologies have been proposed, a systematic evaluation of their comprehensive benefits is lacking, hindering effective policy and technology promotion. This study addresses this gap by employing an entropy weight–fuzzy comprehensive evaluation method to assess the economic, social, and ecological performance of four predominant restoration models—no-tillage, strip-tillage, deep-tillage, and indirect return—using survey data from 263 farmers. Results identify strip-tillage as the optimal model, achieving the highest integrated benefit score (8.153) by successfully balancing superior economic profitability and social acceptance with robust ecological performance. Although no-tillage excels in ecological benefits like moisture conservation (8.901) and pesticide reduction (8.524), its economic potential is constrained by higher management costs. Deep-tillage rapidly enhances soil fertility (8.628) but is limited by high operational costs, whereas the indirect model, despite high ecological sustainability (7.781), faces adoption barriers due to technical complexity and cost. The findings underscore the necessity of moving beyond one-size-fits-all approaches. We propose a targeted promotion system based on “categorized guidance and precision adaptation”, offering a practical framework for optimizing technology deployment to support both black soil conservation and sustainable agricultural development. Full article

The environmental pollution problem in the textile and garment industry has become increasingly severe, necessitating urgent mitigation. This study examines which supply chain member’s pollution abatement investment generates greater benefits for both the supply chain and the environment, particularly under demand uncertainty. We consider a three-tier textile and garment supply chain and employ uncertainty theory to model member risk attitudes via confidence levels. Based on this, uncertain demand and profit functions are formulated under different investment scenarios—single investment and joint investment. Our comparative analysis reveals that pollution abatement investment by either the fabric manufacturer or the garment manufacturer contributes to environmental sustainability. Which member’s investment is more advantageous for both the environment and supply chain performance depends on their respective cost coefficients. Interestingly, under certain conditions, the fabric manufacturer may derive greater benefit from the garment manufacturer’s investment than from its own. Furthermore, while both the fabric manufacturer and the garment retailer gain more from joint manufacturer investments than from a single manufacturer’s investment, the same does not hold for the garment manufacturer. Finally, as the three members become more risk-averse, both the optimal pollution abatement level and the profits of all members decrease. Full article

The flotation stage is a critical segment of mineral processing production. In copper concentrate flotation, predicting the concentrate grade is essential for maintaining a stable flotation process, ensuring concentrate quality, and enhancing profits. To improve the prediction accuracy for the concentrate grade, we propose a prediction method based on an improved eXtreme Gradient Boosting (XGBoost) model using real copper concentrate flotation data in the paper. To address the issues of outliers and missing values in the collected dataset, we firstly present an outlier detection and imputation method using the Inter-Quartile Range (IQR) method and the MissForest (MF) algorithm. An XGBoost-based model is developed for predicting the copper concentrate grade. The model is trained using some key indicators, including feed grade, ore throughput, reagent concentration, pulp flow rate, air flow rate, level, and pH value, as the input features. Moreover, hyper-parameter tuning is optimized based on a Tree-Structured Parzen Estimator (TPE). Combining the IQR/MissForest with TPE-optimized XGBoost can enable an end-to-end prediction pipeline for the copper concentrate grade in the flotation process to address the issues of data anomalies and missing values in the flotation process, as well as the low efficiency of multi-parameter tuning, ensuring the accuracy of data processing and the effectiveness of model training. The experimental results demonstrate that compared with some traditional prediction methods, such as support vector machines, the proposed method achieves about a 25.3% reduction in the Root Mean Square Error (RMSE), indicating our method’s superior performance. Full article

Against the backdrop of intensifying global technological competition and the deepening of the national innovation-driven strategy, private technology enterprises, as the core entities of technological innovation, have their sustainable innovation dynamics profoundly influenced by the strategic interactions among multiple parties such as the government, enterprises, and users. Based on evolutionary game theory, this paper constructs a tripartite evolutionary game model involving the government, private technology enterprises, and market users in the Chinese context. Through theoretical deduction and multi-scenario numerical simulation using Matlab, it systematically analyzes the logic of strategic choices and the laws of dynamic equilibrium of the three parties in the process of sustainable innovation. The research shows that the strategic evolution of multiple entities presents multiple equilibrium states. There exist critical thresholds for the intensity of policy support, the concentration of market competition, and users’ willingness to choose innovative products; beyond these thresholds, the marginal impact on sustainable innovation dynamics increases significantly. Further research finds that the government and enterprises need to compensate for the profit gap between users’ choice of innovative products and traditional products through a subsidy mechanism to form a positive cycle of “active innovation–market recognition–profit improvement”. This study enriches the theoretical system of multi-entity innovation dynamics by incorporating user behavior and provides a decision-making reference for optimizing innovation governance and fostering the development of sustainable innovation dynamics in private enterprises in China and other similar economies. Full article

Mitigating stress responses is crucial for maintaining optimal productivity and profitability in modern poultry production. The effects of early feed withdrawal (EFW) and vitamin C (Vit C) supplementation, both individually and in combination, on heat-stressed broilers’ productivity, stress responses, and metabolic markers were investigated. In total, 240 newly hatched Cobb-500 male chicks were randomly distributed to four treatment groups (six replicates × 10 birds per group). A basal diet was offered to the control group; meanwhile, the EFW group underwent a 24 h early feed withdrawal on day four of age. The Vit C group was given 200 mg/kg vitamin C daily, and the EFW + Vit C group received both interventions. The study was executed under hot summer conditions, where the average minimum and maximum temperature-humidity Index (THI) were 29.15 ± 0.78 and 33.34 ± 0.76, respectively. The results demonstrated a superior stress-mitigating effect when EFW was combined with Vit C supplementation, leading to a significant improvement in productive parameters and elevated blood metabolic hormone levels. Additionally, total antioxidant capacity was enhanced, hepatic endogenous antioxidant enzyme expression was upregulated, and stress biomarkers were reduced. Furthermore, the economic efficiency indicators were significantly improved with EFW, and when EFW was combined with Vit C addition. These findings suggest that integrating multiple stress mitigation strategies, such as EFW and Vit C supplementation, may be more effective in relieving the adverse effects of heat stress by restoring homeostasis and optimizing broilers’ productivity and profitability. Full article

Integrated agronomic optimization can synergistically enhance crop yields and resource use efficiency. This strategy incorporates suitable sowing date, planting density, and fertilization and irrigation management adapted to the local environment. However, there is a dearth of research on how integrated agronomic optimization practices enhance wheat productivity and water use efficiency (WUE) by improving population root distribution and canopy production capacity. Therefore, a two-year field experiment was conducted in the North China Plain. The experiment involved three integrated agronomic practice treatments with four replications: local farmer’s agronomic practice (FP); high-input agronomic practice (HP), which aimed to explore wheat yield potential regardless of resource input costs; and optimized high-input agronomic practice (OP), which was adapted to local conditions to revamp the wheat production system. Compared to FP and HP, OP involved a later sowing date, higher planting density, and lower N fertilizer or irrigation inputs. Results showed that OP significantly improved grain yield, WUE, N fertilizer productivity (NFP), and net profit compared to FP (p < 0.05). Although OP’s yield was 4.25% lower than that of HP, it achieved a 22.99% increase over FP. Compared to HP, OP increased average WUE, NFP, and net profit by 3.08%, 25.68%, and 9.12%, respectively. Over the 2 years, OP promoted deeper roots and higher root length density, which enhanced the uptake of soil water and N. Furthermore, the high transpiration under OP, required for canopy productivity, was sustained by efficient water extraction from deep soil. Additionally, the reduction in unproductive evaporation loss was attributed to increased population density and reduced irrigation. Moreover, OP sustained a higher canopy photosynthetic rate for a longer duration, facilitated by greater post-anthesis N uptake. These improvements in resource acquisition, combined with sustained photosynthetic capacity, ultimately led to more efficient water and N utilization and high grain yield. These indicate that integrated optimization of agronomic practices used under OP can synergistically enhance wheat yield, WUE, and NFP. This was achieved by enlarging and deepening population root distribution while supporting high canopy photosynthesis. Our findings may provide actionable insights into establishing high-yielding, efficient, and profitable wheat production systems in the region. Full article