Search Results (241)

To enhance spatial resource complementarity and cross-entity coordination among multi-regional integrated energy systems (MRIESs), an optimized operation strategy is developed based on a bilevel Stackelberg game framework. In this framework, the integrated energy system operator (IESO) and MRIES act as the leader and followers, respectively. Guided by an integrated demand response (IDR) mechanism and a collaborative green certificate and carbon emission trading (GC–CET) scheme, energy prices and consumption strategies are optimized through iterative game interactions. Inter-regional electricity transaction prices and volumes are modeled as coupling variables. The solution is obtained using a hybrid algorithm combining particle swarm optimization (PSO) with mixed-integer programming (MIP). Simulation results indicate that the proposed strategy effectively enhances energy complementarity and optimizes consumption structures across regions. It also balances the interests of the IESO and MRIES, reducing operating costs by 9.97%, 27.7%, and 4.87% in the respective regions. Moreover, in the case study, renewable energy utilization rates in different regions—including an urban residential zone, a renewable-rich suburban area, and an industrial zone—are improved significantly, with Region 2 increasing from 95.06% and Region 3 from 77.47% to full consumption (100%), contributing to notable reductions in carbon emissions. Full article

The rapid development of telematics technology has greatly supported high-computing applications like autonomous driving and real-time road condition prediction. However, the limited computational resources and dynamic topology of in-vehicle terminals pose challenges such as delay, load imbalance, and bandwidth consumption. To address these, a three-layer vehicular network architecture based on cloud–edge–end collaboration was proposed, with V2X technology used for multi-hop transmission. Models for delay, energy consumption, and edge caching were designed to meet the requirements for low delay, energy efficiency, and effective caching. Additionally, a dynamic pricing model for edge resources, based on load-awareness, was proposed to balance service quality and cost-effectiveness. The enhanced NSGA-III algorithm (ADP-NSGA-III) was applied to optimize system delay, energy consumption, and system resource pricing. The experimental results (mean of 30 independent runs) indicate that, compared with the NSGA-II, NSGA-III, MOEA-D, and SPEA2 optimization schemes, the proposed scheme reduced system delay by 21.63%, 5.96%, 17.84%, and 8.30%, respectively, in a system with 55 tasks. The energy consumption was reduced by 11.87%, 7.58%, 15.59%, and 9.94%, respectively. Full article

Alternative proteins (APs) have the potential to contribute to the sustainable transition of food systems. This study aims to assess the current stage of development of the alternative protein industry, identifying factors, both barriers and enablers, that influence business growth. The analysis adopts a systematic literature review, following the PRISMA guidelines, identifying 50 studies. The S-curve model is then applied as an analytical tool to determine the development stage of the AP industry concerning plant-based, insect, and algae segments. The application of the S-curve provides a perspective on the evolution of innovative business such as AP. The results reveal significant differences in the maturity of the AP industry. The plant-based one is the most advanced, characterized by strong market organization and collaborations, despite challenges such as price competitiveness. The algae industry is promising due to its versatility, yet it is hindered due to production costs and unstable demand. Insects face the greatest barriers, including consumer acceptance and ethical concerns. The study emphasizes the importance of creating business strategies suited to each AP source to transform barriers into opportunities. This review contributes to the literature by comparing the unique peculiarities of the AP industry and providing insights from a cross-analysis of plant-based, algae, and insect development stages. Full article

Illegal wildlife trade is a major global driver of biodiversity loss, shaped by high consumer demand, transboundary networks, and uneven enforcement. In the Middle East, particularly the Gulf Cooperation Council (GCC) region, factors such as high purchasing power, cultural traditions (e.g., falconry, prestige pets), and expanding digital marketplaces sustain both legal and illegal flows. We present a nine-year (2017–2025) assessment based on weekly, repeated field surveys at the Friday Market, adjacent pet shops, and private farms, complemented by systematic monitoring of online advertisements on Haraj.com.sa. We recorded 1063 individual animals across 88 species, birds (39.4%), reptiles (52.0%), and mammals (8.6%), and analyzed prices, conservation status, and venue-specific patterns. The most frequently recorded taxa included the white-eared bulbul (Pycnonotus leucotis), common slider (Trachemys scripta), and Egyptian mastigure (Uromastyx aegyptia). Mammals, though fewer in number, commanded the highest prices, particularly cheetahs (Acinonyx jubatus) and lions (Panthera leo). About 26% of species were IUCN-listed as threatened, with CITES Appendix I taxa fetching higher prices. Findings underscore the need for real-time monitoring, targeted enforcement, and cross-border collaboration to address escalating trade in rare and protected species. Full article

Fueled by advances in cloud technologies and industrial platforms, networked collaborative manufacturing platforms (NCMPs) are reshaping how products are priced and customized. As decision rights increasingly shape value creation within these platforms, platform leadership—whether driven by the manufacturer or the designer—emerges as a critical determinant of product strategy. However, the effects of different leadership structures on pricing and customization remain unclear. To address this issue, we develop game models comparing manufacturer-led and designer-led platforms. Our analysis reveals that under manufacturer-led platforms, dual-product strategies remain viable across a wider range of customization conditions, ensuring pricing stability and broader demand coverage. In contrast, designer-led platforms are more sensitive to the commission rate—excessive commissions tend to crowd out standard product offerings and distort pricing incentives. Moreover, platform control does not always guarantee superior profit: while designers consistently outperform manufacturers under manufacturer-led platforms, profit dominance in designer-led settings shifts with commission rates. Notably, by jointly optimizing product strategy and pricing mechanisms, firms can achieve more balanced value distribution and sustain collaboration. These findings offer a strategic framework for manufacturers and designers to align platform governance with product architecture, contributing new insights into collaborative pricing, platform leadership, and dual-product innovation in industrial platform ecosystems. Full article

In the context of the coordinated operation of microgrids and community energy storage systems, achieving optimal resource allocation under complex and uncertain conditions has emerged as a prominent research focus. This study proposes a robust collaborative optimization model for microgrids and community energy storage systems under a game-theoretic environment where potential fraudulent behavior is considered. A multi-energy collaborative system model is first constructed, integrating multiple uncertainties in source-load pricing, and a max-min robust optimization strategy is employed to improve scheduling resilience. Secondly, a game-theoretic model is introduced to identify and suppress manipulative behaviors by dishonest microgrids in energy transactions, based on a Nash bargaining mechanism. Finally, a distributed collaborative solution framework is developed using the Alternating Direction Method of Multipliers and Column-and-Constraint Generation to enable efficient parallel computation. Simulation results indicate that the framework reduces the alliance’s total cost from CNY 66,319.37 to CNY 57,924.89, saving CNY 8394.48. Specifically, the operational costs of MG1, MG2, and MG3 were reduced by CNY 742.60, CNY 1069.92, and CNY 1451.40, respectively, while CES achieved an additional revenue of CNY 5130.56 through peak shaving and valley filling operations. Furthermore, this distributed algorithm converges within 6–15 iterations and demonstrates high computational efficiency and robustness across various uncertain scenarios. Full article

To mitigate the risks posed by uncertainties in renewable energy output and Electric Vehicle (EV) travel patterns on the scheduling of Virtual Power Plants (VPPs), this paper proposes an optimal scheduling model for a VPP incorporating EVs based on Information Gap Decision Theory (IGDT). First, a Monte Carlo load forecasting model is established based on the behavioral characteristics of EV users, and a Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive prices. Second, within the VPP framework, an economic optimal scheduling model considering multi-source collaboration is developed by integrating wind power, photovoltaics, gas turbines, energy storage systems, and EV clusters with Vehicle-to-Grid (V2G) capabilities. Subsequently, to address the uncertain parameters within the model, IGDT is employed to construct a bi-level decision-making mechanism that encompasses both risk-averse and opportunity-seeking strategies. Finally, a case study on a VPP is conducted to verify the correctness and effectiveness of the proposed model and algorithm. The results demonstrate that the proposed method can effectively achieve a 7.94% reduction in the VPP’s comprehensive dispatch cost under typical scenarios, exhibiting superiority in terms of both economy and stability. Full article

With the progress of green transformation, government subsidies have become an important incentive for enterprises to invest in green technologies. However, their effectiveness differs markedly under alternative decision-making structures. This study develops a two-tier green supply chain game model comprising manufacturers and e-commerce platform self-operators. Six game structures are examined, covering both scenarios without subsidies and those in which manufacturers receive subsidies. The analysis focuses on product greenness, service levels, retail prices, and the profits of supply chain members. The results show that government subsidies substantially enhance manufacturers’ green investments and motivate platform self-operators to provide higher levels of green services, thereby improving market performance and overall supply chain profitability. Among the different structures, centralized decision-making demonstrates the strongest coordination effect and maximizes the subsidy impact. In contrast, within decentralized structures, subsidies help alleviate double marginalization, but their effectiveness is constrained by the distribution of power. These findings highlight the heterogeneous impacts of subsidies on green supply chain performance, offering theoretical support for targeted government policy design and practical guidance for enterprises to optimize green collaborative strategies. Full article

This paper presents the SMALLDERS project, a European initiative aimed at transforming smallholders’ business models through an innovative technological platform. The platform functions as an e-marketplace that connects small farmers directly with consumers while simultaneously promoting environmental sustainability and collaboration across the agri-food value chain. The study evaluates the platform’s commercial viability and acceptance through a mixed-methods approach, incorporating qualitative and quantitative data. Research methods include focus group sessions, interviews with key stakeholders—such as transport companies, large distributors, and public administrations—and a consumer survey assessing intentions and attitudes toward the e-marketplace. Results indicate limited overall consumer readiness to adopt the platform; however, 48.6% of respondents expressed willingness to use it provided competitive prices and personal benefits are assured. Smallholders regard e-commerce as a promising opportunity, yet they face significant barriers, including limited resources, low digital literacy, and logistical constraints. Stakeholders generally view the platform positively, emphasizing that its success depends on achieving a critical mass of business volume. To foster adoption, SMALLDERS proposes three business models for smallholders: sustainable, cooperative, and technological. The platform includes a user-friendly feature to assist smallholders in transitioning among these models, complemented by training and support services designed to encourage more resilient and innovative agricultural practices. Full article

This paper addresses the synergy between environmental and economic benefits in the green power trading market by constructing a collaborative game model for environmental rights value and electricity energy value. Based on this, a model for maximizing the benefits of flexible resource operation is proposed. Through the combination of non-cooperative and cooperative games, the conflict and synergy mechanisms of multiple stakeholders are quantified, and the Shapley value allocation rule is designed to achieve Pareto optimality. Simultaneously, considering the spatiotemporal regulation capability of flexible resources, dynamic weight adjustment, cross-period environmental rights reserve, and risk diversification strategies are proposed. Simulation results show that under the scenario of a carbon price of 50 CNY/ton (≈7.25 USD/ton) and a peak–valley electricity price difference of 0.9 CNY/kWh (≈0.13 USD/kWh), when the environmental weight coefficient α = 0.5, the total revenue reaches 6.857 × 10⁷ CNY (≈9.94 × 10⁶ USD), with environmental benefits accounting for 90%, a 15.3% reduction in carbon emission intensity, and a 1.74-fold increase in energy storage cycle utilization rate. This research provides theoretical support for green power market mechanism design and resource optimization scheduling under “dual-carbon” goals. Full article

As a crucial vehicle for advancing the transition to a green low-carbon economy, the green supply chain plays a pivotal role in alleviating pollution pressures and facilitating the green transformation of products. Existing studies mainly focus on static optimization and cost coordination in green supply chains, with limited attention to the dynamic impact of consumer behavior on green production and channel coordination. Based on consumer green preferences and the evolution of reference prices, we developed a differential game model for a two-tier green supply chain composed of a manufacturer and a retailer. The model incorporates green goodwill and consumer memory variables to capture the dynamic interaction among product greenness, sales effort, and consumer perception. By comparing the dynamic optimal response paths under integrated and non-integrated strategies, the study analyzes how reference price effects and goodwill accumulation influence decision-making and system performance. The results show that the stable reference price of green products is significantly higher than the actual selling price. When consumer environmental awareness is strong, cooperative strategies can markedly improve both green performance and supply chain profits, offering potential for Pareto improvement. This research enhances behavior-oriented modeling in green supply chains and provides theoretical and empirical support for designing collaboration mechanisms in green product promotion. Full article

Against the backdrop of global low-carbon transition, the integrated development of electricity and carbon markets demands higher efficiency in the optimal operation of virtual power plants (VPPs) and distribution networks, yet conventional trading mechanisms face limitations such as inadequate recognition of differentiated contributions and inequitable benefit allocation. To address these challenges, this paper proposes a collaborative optimal trading mechanism for VPP clusters and distribution networks in an electricity–carbon coupled market environment by first establishing a joint operation framework to systematically coordinate multi-agent interactions, then developing a bi-level optimization model where the upper level formulates peer-to-peer (P2P) trading plans for electrical energy and carbon allowances through cooperative gaming among VPPs while the lower level optimizes distribution network power flow and feeds back the electro-carbon comprehensive price (EACP). By introducing an asymmetric Nash bargaining model for fair benefit distribution and employing the Alternating Direction Method of Multipliers (ADMM) for efficient computation, case studies demonstrate that the proposed method overcomes traditional models’ shortcomings in contribution evaluation and profit allocation, achieving 2794.8 units in cost savings for VPP clusters while enhancing cooperation stability and ensuring secure, economical distribution network operation, thereby providing a universal technical pathway for the synergistic advancement of global electricity and carbon markets. Full article

In a collaborative effort with private agents of the oilseed industry, INIA conducted a research study to determine the feasibility of framing soybean production in Uruguay into a sustainable development pathway. A spatial model based on land suitability analysis and the imposition of other soil restrictions (risk erosion, current regulations, and permanent soil uses) was adopted to estimate potential soybean yields and the most suitable cropping areas in the country. Assuming a national average production cost for soybeans, total costs were calculated by adding location-specific logistics and land rent costs. Crop economic margins were estimated using a combination of price, technology, and climate-change scenarios. Only areas exhibiting non-negative margins were considered suitable for sustainable cultivation. With all restrictions imposed, the potential soybean area on rotation with other crops and pastures in Uruguay would range between 2.1 and 2.9 million hectares, depending on the prevailing producer price level. Climate change effects did not show significant differences on their own. This ad-hoc approach can be useful for private and public decision-makers. It can be applied to any crop situation or region where the objective is to define how far it is possible to expand and intensify production sustainably, without compromising the environment. Full article

Optimizing agricultural structure serves as a crucial pathway to promote sustainable rural economic development. This study focuses on a representative village in the mountainous region of North China, where agricultural production is constrained by perennial low-temperature conditions, resulting in widespread adoption of single-cropping systems. There exists an urgent need to enhance both economic returns and risk resilience of limited arable land through refined cultivation planning. However, traditional planting strategies face difficulties in synergistically optimizing long-term benefits from multi-crop combinations, while remaining vulnerable to climate fluctuations, market volatility, and complex inter-crop relationships. These limitations lead to constrained land productivity and inadequate economic resilience. To address these challenges, we propose an integrated decision-making approach combining stochastic programming, robust optimization, and data-driven modeling. The methodology unfolds in three phases: First, we construct a stochastic programming model targeting seven-year total profit maximization, which quantitatively analyzes relationships between decision variables (crop planting areas) and stochastic variables (climate/market factors), with optimal planting solutions derived through robust optimization algorithms. Second, to address natural uncertainties, we develop an integer programming model for ideal scenarios, obtaining deterministic optimization solutions via genetic algorithms. Furthermore, this study conducts correlation analyses between expected sales volumes and cost/unit price for three crop categories (staples, vegetables, and edible fungi), establishing both linear and nonlinear regression models to quantify how crop complementarity–substitution effects influence profitability. Experimental results demonstrate that the optimized strategy significantly improves land-use efficiency, achieving a 16.93% increase in projected total revenue. Moreover, the multi-scenario collaborative optimization enhances production system resilience, effectively mitigating market and environmental risks. Our proposal provides a replicable decision-making framework for sustainable intensification of agriculture in cold-region rural areas. Full article

To address the challenges of power generation rights trading and profit distribution in the integrated energy system of multi-park combined cooling, heating, and power (CCHP) with new energy grid integration, we constructed a hierarchical game model involving multi-energy system aggregators. By having aggregators price electricity, heat, cold, and carbon costs, the model establishes a hierarchical game framework with the linkage of the four prices (electricity, heat, cold, and carbon), achieving inter-park peer-to-peer (P2P) multi-energy dynamic price matching for the first time. It aims to coordinate distribution network dispatching, renewable energy, energy storage, gas turbine units, demand response, cooling–heating–power coupling, and inter-park P2P multi-energy interaction. With the goal of optimizing the profits of integrated energy aggregators, a hierarchical game mechanism is established, which integrates power generation rights trading models and incentive-based demand response. The upper layer of this mechanism is the profit function of integrated energy aggregators, while the lower layer is the cost function of park microgrid alliances. A hierarchical game mechanism with Two-Level Optimization, integrating the Adaptive Disturbance Quantum Particle Swarm Optimization (ADQPSO) algorithm and the branch and bound method (ADQPSO-Driven Branch and Bound Two-Level Optimization), is used to determine dynamic prices, thereby realizing dynamic matching of energy supply and demand and cross-park collaborative optimal allocation. Under the hierarchical game mechanism, the convergence speed of the ADQPSO-driven branch and bound method is 40% faster than that of traditional methods, and the optimization profit accuracy is improved by 1.59%. Moreover, compared with a single mechanism, the hierarchical game mechanism (Scenario 4) increases profits by 17.17%. This study provides technical support for the efficient operation of new energy grid integration and the achievement of “dual-carbon” goals. Full article