The fashion industry is one of the most polluting sectors globally, and a key challenge it faces is the integration of sustainable practices into green product design. In this study, we examine a two-echelon green supply chain (GSC) model, assuming both environmentally conscious members and consumers. We consider that the customer’s utility depends on maximum tendency to pay for traditional item, price of eco-friendly and traditional items and their quality levels. Moreover buyers are influenced by both the environmental and functional qualities of products. Thus, demand for eco-friendly and traditional items is shaped by retail price, green and functional quality, marketing efforts, and consumer reference behaviors. We analyze this scenario across five models: decentralized, centralized, cost-sharing, revenue-sharing, and two-part tariff contracts. Closed-form solutions for optimal green marketing efforts, green and traditional quality levels, and wholesale and retail prices are obtained for all cases. Furthermore, we employ Nash bargaining processes to set contractual parameters for each contract. Our computational analysis reveals that the proposed two-part tariff contract simultaneously enhances environmental and economic outcomes, showing that there is no trade-off between improvements in green and functional quality when an appropriate contract is used to coordinate the supply chain.
Adopting circular rice supply chains requires addressing interrelated technological, technical, regulatory, economic, organisational, operational, market, and social barriers. Existing research provides limited integration of relevance screening, priority weighting, and structural relationship mapping. This study examines these functions together to inform intervention planning in Malang district, East Java, Indonesia. The framework combines the Fuzzy Delphi Method, Fuzzy Analytical Hierarchy Process, Interpretive Structural Modelling, and MICMAC analysis. Three experts assessed 36 literature-derived barriers, of which 25 met the relevance threshold. Regulatory and economic barriers received the highest dimensional weights. High investment costs had the highest global priority, followed by lack of tax incentives. Low circular-economy awareness and absence of reverse logistics shared the next-highest weight. The ISM hierarchy contained eight levels. Lack of government support and low circular-economy awareness occupied Level 8, while supply-chain complexity was another source barrier at Level 7. Low return on investment and inaccurate waste estimates were terminal barriers with the highest dependence scores. MICMAC classified ten barriers as independent, two as linkage, nine as dependent, and four as autonomous. The findings show that perceived importance and structural influence provide different information for intervention planning. The integrated framework supports coordinated consideration of institutional support, investment, capabilities, and recovery arrangements. Its empirical results remain conditional on the three-expert panel and local context; applications elsewhere require renewed relevance assessment, weighting, and structural analysis.
The urgent need for supply chain decarbonization has intensified interest in industrial symbiosis as a circular economy strategy. This study prioritizes industrial symbiosis-based carbon reduction strategies in agro-industries, represented by the food and beverage, tobacco, and paper sectors, which are interlinked through biomass, packaging, water, and logistics flows. An integrated DEMATEL–BWM–MARCOS framework was employed, drawing on evaluations from an 11-member expert panel with expertise spanning sustainability, supply chain management, circular economy, logistics, and environmental engineering. Eight factors emerged as causal drivers, led by workforce capacity, managerial support, and technological readiness. Digital Traceability and Monitoring, Technological Readiness and Process Integration, and Investment Feasibility received the highest weights, jointly accounting for 31.4
Successful electric-vehicle (EV) innovation in emerging markets requires coordination of vehicle attributes, ownership services, charging infrastructure, financing, and circular-economy support. Existing EV research commonly examines adoption, online discourse, Kano classification, or product–service systems separately, leaving limited methodological guidance for translating heterogeneous consumer signals into generation-specific product–service–ecosystem configurations. This study proposes an integrative Generational Open Product–Service–Ecosystem Configuration with Supply–Demand Flow (G-OPSEC-SDF) framework. The methodological novelty lies in the structured orchestration of established methods—cross-source social-media mining, Kano satisfaction criticality, generational normalization, rule-based product–service–ecosystem breadth mapping, ecosystem-service demand loading, and a configuration-priority index—rather than in claiming a new topic-modeling or Kano algorithm. Empirically, 27,189 user-generated contents from YouTube and X and Kano survey responses from 353 Indonesian respondents (Generation X, Y, and Z) were analyzed. Eight EV attributes were identified: price, brand, performance, battery capacity, charging capability, ergonomics, safety, and environmental impact. Statistical comparison confirms significant cohort heterogeneity for most attributes, while sensitivity tests show that the principal configuration priorities are generally robust to reasonable changes in index weights. Safety illustrates the value of cross-source integration: it has low digital salience but high satisfaction criticality. The framework translates these demand patterns into product, service, and ecosystem-support priorities such as cost-efficient design, battery warranty, charging access, financing, after-sales support, insurance, renewable-energy linkage, and battery recycling. The framework is therefore a decision-support approach for sustainability-oriented EV configuration; it does not directly measure lifecycle environmental outcomes or ecosystem supply readiness.
The shift to a circular economy (CE) has motivated inventory models that embed a CE index as a decision variable governing demand and profit, but existing formulations treat it as a single scalar without operational meaning for how a firm recovers value from inventory otherwise lost to deterioration - a gap that matters for perishables, where a reduce-redirect-redistribute framework has been proposed but never embedded in a replenishment model. This paper disaggregates the scalar CE index into two recovery-flow decisions: a fraction redirected to a secondary market at a resale revenue, and a fraction redistributed through donation at a net cost. The model is a carbon-constrained, expiration-date-dependent EOQ framework with partial backlogging, and introduces a carbon-cap-contingent subsidy a regulator can calibrate to preserve profitability as caps tighten. We prove the model reduces exactly to the scalar-index formulation as a special case. Experiments show redirect-redistribute decisions raise profit modestly over the scalar-index model (0.61
