Search Results (552)

Considering the supply chain of new energy vehicles composed of a local manufacturer, an authorized distributor in the domestic market, and a competitive manufacturer in the export market, this paper studies three different cases of parallel export as well as their decisions about prices, sales scale, and the degree of production differentiation. Three game models are constructed and solved under the cases of no parallel exports (CN), authorized distributors’ parallel exports (CR), and third-party parallel exports (CT), respectively, and the equilibrium analysis is carried out, and finally, the influence of relevant parameters is explored through numerical simulation. It is found that (1) the manufacturer’s decisions on production and sales are influenced by the characteristics of consumer preferences in local and export markets, the cost of differentiated production, and the consumer recognition of parallel exports; (2) the manufacturers’ profits will always be damaged by parallel exports; (3) differentiated production can reduce the negative impact of parallel exports under certain conditions, and then improve the profits of manufacturers; (4) manufacturers can increase their profits by improving the purchase intention of consumers in the local market, improve the level of production differentiation in the export market, or reducing the cost of differentiation. Full article

Driven by carbon neutrality and peak carbon policies, hydrogen energy, due to its zero-emission and renewable properties, is increasingly being used in hydrogen fuel cell vehicles (H-FCVs). However, the high cost and limited durability of H-FCVs hinder large-scale deployment. Hydrogen internal combustion engine hybrid electric vehicles (H-HEVs) are emerging as a viable alternative. Research on the techno-economics of H-HEVs remains limited, particularly in systematic comparisons with H-FCVs. This paper provides a comprehensive comparison of H-FCVs and H-HEVs in terms of total cost of ownership (TCO) and hydrogen consumption while proposing a multi-objective powertrain parameter optimization model. First, a quantitative model evaluates TCO from vehicle purchase to disposal. Second, a global dynamic programming method optimizes hydrogen consumption by incorporating cumulative energy costs into the TCO model. Finally, a genetic algorithm co-optimizes key design parameters to minimize TCO. Results show that with a battery capacity of 20.5 Ah and an H-FC peak power of 55 kW, H-FCV can achieve optimal fuel economy and hydrogen consumption. However, even with advanced technology, their TCO remains higher than that of H-HEVs. H-FCVs can only become cost-competitive if the unit power price of the fuel cell system is less than 4.6 times that of the hydrogen engine system, assuming negligible fuel cell degradation. In the short term, H-HEVs should be prioritized. Their adoption can also support the long-term development of H-FCVs through a complementary relationship. Full article

In response to the increasing demand for environmentally friendly products and the parallel rise of deceptive green marketing practices, this study examines the impact of greenwashing awareness and green perceived benefits on consumers’ propensity to purchase green products, with a focus on the mediating role of green consumer confusion. Drawing upon data collected from 300 consumers in Greece through an online questionnaire, this study employed validated measurement scales and used multiple regression analyses to test its hypotheses. The findings reveal that both greenwashing awareness and green perceived benefits positively influence green purchase propensity. Additionally, green consumer confusion mediates the relationship between greenwashing awareness and green purchase propensity, indicating that the awareness of greenwashing reduces confusion and enhances consumers’ likelihood to choose genuinely green products. This study contributes to the literature by offering an integrated model that connects greenwashing awareness, green consumer confusion, and green perceived benefits in shaping green purchase propensity. Finally, the findings offer valuable insights for organizations to design clearer, more trustworthy green marketing strategies that minimize consumer confusion and foster informed green purchasing decisions. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

With the acceleration of global climate warming and agricultural modernization, the energy and carbon emission issues of agricultural parks (APs) have drawn increasing attention. An AP equipped with biogas-based combined heat and power (CHP) generation and photovoltaic systems serves as a prosumer terminal in a distribution network (DN). This paper introduces carbon emission flow (CEF) theory into the coordinated dispatch of APs and DNs. First, a CEF model for APs is established. Then, based on this model, a carbon–energy coordinated dispatch is carried out under bidirectional CEF interaction between the park and DN. A bidirectional carbon tax mechanism is adopted to explore the low-carbon synergy potential between them. Finally, the Stackelberg game approach is employed to address the pricing of electricity purchase/sale and carbon taxes in a DN, and the particle swarm optimization algorithm is used for rapid generating solutions. The case study shows that the proposed CEF model can effectively determine CEF distribution in the park. Moreover, the proposed bidirectional carbon tax mechanism significantly enhances the low-carbon economic benefits of both the AP and the DN. Full article

The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article

Preventing food waste is a pressing global policy concern, with households being the main producers of food waste along the food supply chain. This study aims to analyze consumers’ food waste behavior and identify how different consumer profiles and sociodemographic characteristics influence food waste. A survey was carried out in Spain with a representative sample of 717 participants, and the Theory of Planned Behavior (TPB) was applied to understand the influence of consumers’ attitudes, subjective norms, and perceived behavior control on their intention to reduce food waste and to find out the main drivers of their food waste behaviors. Results demonstrated that food waste reduction is mainly predicted by attitudes, followed by perceived behavior control, and lastly subjective norms. Finally, characteristics such as responsibility in food purchasing and cooking at home as well as sociodemographic factors played a relevant role in how much the intention to reduce food waste affects the final behavior. Our results suggest the potential of communication to reshape individual preferences towards valuing food conservation. Tailored strategies are recommended for specific groups, emphasizing the importance of targeted approaches in addressing food waste at various levels of society. Full article

In recent years, population growth, the enhancement of carbon emissions generation, and higher energy consumption have caused the movement to nearly zero-energy buildings. Additionally, the various strategies, phase change materials (PCMs) are suitable for reducing the energy consumption of a building. The focus of this study is to investigate the results of three scenarios that explore all the effective parameters for selecting a suitable Phase Change Material (PCM) for hot climate conditions in Saudi Arabia. The first scenario worked on choosing the best phase change material based on the climatic conditions and the selected area. To complete the optimization, the best thickness and placement of the two-layer phase change material were investigated in the second and third scenarios. The results indicated that optimized building using PCM 29 with 50 mm thickness reduced the energy consumption and carbon dioxide production by 20.72% and 21.05%, respectively. Furthermore, the outcomes of the study on two-layer phase change materials with different arrangements illustrated that the most proper location of PCMs caused 255.38 MWh of electricity consumption and 155.71 × 10³ kg of carbon dioxide production. Finally, as a novel integration, the results of using one-layer and two-layer PCM were added to the HOMER software to find the optimal hybrid energy systems. The findings showed that by integrating photovoltaic panels, diesel generation, batteries, and the grid, the cost of energy reached USD 0.162. Additionally, the grid purchase by using one-layer and two-layer phase change material was decreased by 21.2% and 24.3% compared to the base case. Full article

With the increasing popularity of electric vehicles (EVs) through purchase subsidy (PS) policies, the personal carbon tax (PCT) policy has been adopted by some countries due to its characteristics of restraining the diffusion of fuel vehicles (FVs) from the consumer side. This paper constructs a three-stage game model consisting of government, manufacturers, and consumers to investigate the impact of basic utility valuation heterogeneity differences on the optimal decisions and to compare the implementation effects of two policies. The results are as follows. First, conventional wisdom suggests that EV consumer surplus under PS policy will exceed that under PCT policy. Surprisingly, our results show that when the basic utility valuation difference is small, the EV consumer surplus under PCT policy exceeds that under PS policy. Second, for manufacturers, it is interesting to note that the sustained impact of PCT policy on promoting the diffusion of the EV market and the profit of the EV manufacturer is related to the basic utility valuation heterogeneity difference. However, compared with PS policy, the implementation of PCT policy has a better restraining effect on the diffusion of the FV market, effectively reducing the demand for FV and the profit of FV manufacturers. Finally, contrary to the common belief that increasing subsidies or raising carbon taxes can increase overall social welfare, this paper shows that subsidies and carbon taxes have a dual impact on overall social welfare, and only when their positive effects outweigh the negative ones can such policies become effective ways of promoting industrial transformation. Full article

Digital franchising is increasingly recognized as a technological advancement and a specialized subset of e-commerce, yet its unique entrepreneurial dynamics remain insufficiently explored in the existing literature. Previous studies have primarily focused on platform usability or general e-commerce adoption, often overlooking the motivational, contextual, and capability-based factors that influence individuals’ willingness to engage in digital franchising as either entrepreneurs or consumers. To address this research gap, the present study applies the Motivation-Opportunity-Ability (MOA) framework to examine how personal motivations (e.g., self-expression, financial rewards), perceived platform opportunities (e.g., support, attractiveness), and individual capabilities (e.g., digital literacy, self-efficacy) shape entrepreneurial intention and, in turn, influence consumption adoption intention in digital franchising environments. An online survey was conducted using a non-probability purposive sampling method. The final sample consisted of 491 respondents from Taiwan, all of whom were either entrepreneurs operating digital franchises in the fashion industry or consumers who had purchased fashion products through digital franchising platforms, thereby ensuring contextual relevance to the study’s focus. Data were analyzed using structural equation modeling (SEM). The results indicate that expected external rewards (β = 0.456, p < 0.001) and platform support (β = 0.315, p < 0.001) are the most influential factors in shaping entrepreneurial intention. Furthermore, entrepreneurial intention significantly mediates the relationship between MOA antecedents and consumption adoption intention (β = 0.176, p < 0.001), highlighting its role as a key behavioral mechanism. These findings extend the MOA framework to a new empirical setting and offer practical implications for platform developers, franchisors, and policymakers seeking to promote participation in digital franchising. Future research is encouraged to explore cross-industry comparisons, generational differences, and longitudinal approaches to further enrich the understanding of digital franchising adoption dynamics. Full article

This study focuses on the compression area of a cryogenic air separation unit (ASU). The mechanism of exergy consumption in the compressor was revealed. The influence of the compression ratio and of the isentropic efficiency per stage give arguments for proper choice of these decisional parameters. For the purchase cost of the compressor, an exergoeconomic correlation based on the exergetic product represented by the compression ratio and the isentropic efficiency as the Second Law coefficient of performance was used instead of the common thermo-economic one based only on the cost of materials. The impact of the suction temperature on the compressor operating performance is shown, making the gap between the compression stage and the associated intercooler. After optimization of the global system, a specific exergy destruction is assigned to each inter-stage compression cooler. To fit this optimum exergy consumption, a design procedure for the inter-stages and final coolers based on the number of heat transfer units (NTU-ε) method and the number of exergy units destroyed (NEUD) is shown. Graphs are provided that make the application of the method straightforward and much easier to use compared to the usual logarithmic mean temperature difference. A 25% increase in the compression ratio per stage leads to a decrease in the exergy efficiency of 3%, while the purchase cost of the compressor rises by 80%. An increase in the isentropic efficiency of the compressor from 0.7 to 0.85 leads to an increase in the exergetic performance coefficient of 21%, while the compressor purchase cost triples. Full article

Short-term load forecasting is important for proxy electricity purchasing in the electricity spot trading market. In this paper, a model SFPFMformer for short-term power load forecasting is proposed to address the issue of balancing accuracy and timeliness. In SFPFMformer, the random forest algorithm is applied to select the most important attributes, which reduces redundant attributes and improves performance and efficiency; then, multiple timescale segmentation is used to extract load data features from multiple time dimensions to learn feature representations at different levels. In addition, fusion time location encoding is adopted in Transformer to ensure that the model can accurately capture time-position information. Finally, we utilize a depthwise separable convolution block to extract features from power load data, which efficiently captures the pattern of change in load. We conducted extensive experiment on real datasets, and the experimental results show that in 4 h prediction, the RMSE, MAE, and MAPE of our model are 1128.69, 803.91, and 2.63%, respectively. For 24 h forecast, the RMSE, MAE and MAPE of our model are 1190.51, 897.26, and 2.97%, respectively. Compared with existing methods, such as Informer, Autoformer, ETSformer, LSTM, and Seq2seq, our model has better precision and time performance for short-term power load forecasting for proxy spot trading. Full article

With the increasing connection between integrated natural gas, thermal energy, and electric power systems, the integrated energy system (IES) needs to coordinate the internal unit scheduling and meet the different load demands of customers. However, when the energy subjects involved in scheduling are engaged in conflicts of interest, aspects such as hierarchical status relationships and cooperative and competitive relationships must be considered. Therefore, this paper studies the problem of achieving optimal energy scheduling for multiple subjects of source, storage, and load under the same distribution network while ensuring that their benefits are not impaired. First, this paper establishes a dual master-slave game model with a shared energy storage system (SESS), IES, and the alliance of prosumers (APs) as the main subjects. Second, based on the Nash negotiation theory and considering the sharing of electric energy among prosumers, the APs model is equated into two sub-problems of coalition cost minimization and cooperative benefit distribution to ensure that the coalition members distribute the cooperative benefits equitably. Further, the Stackelberg-Stackelberg-Nash three-layer game model is established, and the dichotomous distributed optimization algorithm combined with the alternating direction multiplier method (ADMM) is used to solve this three-layer game model. Finally, in the simulation results of the arithmetic example, the natural gas consumption is reduced by 9.32%, the economic efficiency of IES is improved by 3.95%, and the comprehensive energy purchase cost of APs is reduced by 12.16%, the proposed model verifies the sustainability co-optimization and mutual benefits of source, storage and load multi-interested subjects. Full article

This study aims to explore the impact of the green marketing mix (at the macro and micro level) implemented by outdoor brands on the attitudes and brand loyalty of Millennial and Generation Z (MZ generation) consumers toward these outdoor brands. Additionally, it seeks to examine the moderating effect of MZ generation consumers’ involvement in outdoor activities on the relationship between the green marketing mix (macro level) and brand attitudes. Using a cross-sectional research design, a quota random sampling method was employed to extract a sample of 500 MZ generation consumers who had purchased an outdoor brand at least once in South Korea. To evaluate the reliability and validity of the measurement model, a confirmatory factor analysis was conducted, followed by a bifactor structural model analysis with a moderator of outdoor involvement to test the hypotheses. The results indicate that the green marketing mix implemented by outdoor brands had a significantly positive effect on consumers’ attitudes toward outdoor brands. Specifically, green products, green pricing, and green promotion positively influenced consumers’ attitudes toward outdoor brands. Furthermore, the level of involvement in outdoor activities significantly moderated the effect of the green marketing mix on brand attitudes. Notably, the higher the MZ generation’s involvement in outdoor activities, the stronger the impact of the green marketing mix on their attitudes toward outdoor brands. Finally, brand attitudes were found to have a significant positive effect on brand loyalty. Based on these findings, meaningful theoretical and practical implications are discussed. Full article

The Internet Shopping Optimization Problem with multiple item Units looks for the best selection of stores where to buy various or individual units in a required list of items to minimize the final purchase cost. The problem belongs to the most challenging complexity class of optimization problems (NP-Hard). This paper adds to the already complex problem a more difficult situation with a second objective conflicting with the purchase cost minimization. As far as we know, this is the first state-of-the-art proposal with conflicting objectives for the Internet Shopping Optimization Problem or its variants. The objective in conflict with the minimization of the purchase final cost is the cash-back or reward points on personal or corporate credit cards, the most common payment method for online purchases. Due to the nature of the conflicting objectives, this paper proposes using evolutionary multi-objective optimization algorithms. We perform an experimental comparison using eight algorithms from the literature. The experimental results show that NSGA-II achieves the best overall performance for the studied instances from the state of the art. Full article