Search Results (87)

Cross-border remanufacturing trade is developing steadily. Carbon tariffs and carbon cap-and-trade regulation have emerged as two important instruments for carbon governance, yet their independent and combined effects on supply chain operations remain insufficiently discussed, which delivers supplementary analytical space for cross-border remanufacturing supply chain research. This paper examines a cross-border supply chain consisting of an exporting manufacturer and an importing retailer that distributes both new and remanufactured products. Four research scenarios are established: no regulation, carbon tariffs only, cap-and-trade only, and dual mixed regulation. Adopting a two-stage Stackelberg game, we analyze equilibrium pricing, production, and carbon abatement decisions, and further evaluate environmental performance and overall social welfare. Firms with different initial carbon emission levels respond differently to regulatory stringency. The results show that carbon tariffs reduce total emissions but erode corporate profits and social welfare, while cap-and-trade regulation can mitigate such adverse effects. When carbon tariffs are stringent and carbon quotas are sufficient, dual regulation improves all participants’ profitability alongside better environmental quality and higher consumer surplus only within this paper’s simplified analytical context. This study offers tentative operational references for remanufacturing firms and theoretical and analytical implications for governments to design compatible cross-border carbon regulatory systems. Full article

This paper studies the effects of price-cap regulation in an oral healthcare market by developing a duopoly model of price–quality competition between a public provider and a private provider. The results show that, in the absence of regulation, the public provider charges a high price and offers high quality, whereas the private provider adopts a low-price, low-quality strategy. This vertical differentiation softens price competition but leaves some highly price-sensitive patients untreated. Under price-cap regulation, market outcomes depend critically on the stringency of the cap. An intermediate price cap narrows the quality gap by inducing the private provider to improve quality while only weakly reducing the public provider’s quality, thereby expanding the market from partial to full coverage. A high price cap generates heterogeneous quality responses depending on the public provider’s unregulated quality level but does not improve market coverage. By contrast, a low price cap expands coverage but compresses profit margins, leading both providers to reduce quality and resulting in a low-price, low-quality equilibrium. These findings highlight the trade-offs between affordability, access, and quality in healthcare price regulation and suggest that intermediate price caps may achieve a better balance between these objectives. Full article

Steel production accounts for approximately 7% of global GHG emissions. Brazil is the largest steel producer in Latin America, and carbon pricing is rapidly moving from a policy debate to an operational reality, making the financial exposure of Brazilian steelmakers to carbon regulation one of the most pressing industrial sustainability questions in an emerging market context. This study evaluates the financial exposure of the Brazilian steel industry to three carbon pricing scenarios: (i) a domestic cap-and-trade mechanism under Brazil’s Greenhouse Gas Emissions Trading System (SBCE); (ii) Carbon Border Adjustment Mechanisms (CBAMs) applied by key trading partners (EU, a hypothetical USA scenario, and a global scenario); and (iii) supply chain (Scope 3) exposure, relevant under net-zero corporate commitments and the expected expansion of EU-CBAM coverage. Using a scenario-based financial impact approach, with both macro-level (industry) and micro-level (company) analyses, results show that domestic carbon pricing could increase production costs by 7–21%, generating USD 1.6–4.9 billion in additional annual costs (equivalent to 4.3–13.3% of annual industry revenue). International CBAM exposure could reduce Brazilian steel export revenues by USD 570 million to USD 1.7 billion in a global scenario (1.5–4.6% of annual industry revenue). Supply chain emissions represent 68% of the industry’s total carbon pricing exposure, equivalent to USD 1.1–3.3 billion in domestic pricing costs and USD 388 million–1.16 billion in CBAM export revenue reduction. A company-level case study confirms the pattern, with lower Scope 3 intensity yielding a comparatively smaller but still material exposure. These findings offer practical decision support for steel companies and policymakers navigating the transition to a low-carbon economy. Full article

Honey maturity is increasingly discussed in relation to product integrity, fair trade, and the classification of immature honey production as a form of adulteration. This narrative critical review examines honey maturity using evidence from peer-reviewed microbiological, physicochemical, and metabolomic studies, combined with an analysis of international and European regulatory frameworks, including Codex Alimentarius CXS 12-1981, Council Directive 2001/110/EC, Regulation (EC) No 178/2002, and Regulation (EC) No 852/2004. Particular attention is given to the interpretation of osmophilic yeast counts, water activity ($a_w$), moisture content, comb cell capping, fermentation, and technological dehumidification. The reviewed evidence indicates that osmophilic yeasts are natural components of honey and that their presence, expressed as colony-forming units per gram (CFU/g), should not be treated as an independent non-compliance criterion in the absence of active fermentation. Existing honey standards define compositional and quality requirements, including moisture, hydroxymethylfurfural, enzymatic activity, and absence of fermentation or effervescence, but do not establish a honey-specific CFU/g limit for yeasts. On this basis, the review formulates a functional maturity assessment framework integrating $a_w$, moisture, enzymatic indicators, and metabolomic biomarkers. The proposed framework is presented as a conceptual model derived from the synthesis of the existing literature and requiring further multilaboratory validation prior to adoption in official control practice. This approach may improve proportionality in honey quality assessment and reduce the risk of misclassifying microbiologically stable honeys as immature or adulterated. Full article

Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article

This article integrates sustainability principles into a three-echelon supply chain for deteriorating items with imperfect quality, consisting of a single vendor, a third-party logistics enterprise (3PL), and a single buyer, with a focus on balancing economic efficiency with environmental responsibility. The vendor is assumed to operate an imperfect production system, resulting in products of imperfect quality. The 3PL undertakes all transportation activities, while the buyer conducts a quality inspection process to detect defective items, which is subject to Type-I and Type-II errors. Aside from that, the inventory model also assesses carbon emissions arising from various operational activities including energy usage during production, warehousing, and disposal processes, and fuel consumption in transportation, for which the above members of the supply chain are accountable. Afterward, carbon management policies such as a carbon tax and carbon cap-and-trade are considered to regulate total supply chain emissions. The objective is to minimize the joint expected total cost by simultaneously optimizing shipment frequencies and the replenishment cycle for the buyer within carbon emission constraints. An iterative solution procedure is developed to address the problem. A numerical example and sensitivity analysis are provided to demonstrate the model’s applicability and to explore the influence of critical parameters. Finally, the study presents managerial insights, along with conclusions and recommendations for future research directions. Full article

This study investigates trade-in channel selection in a closed-loop supply chain under a hybrid carbon policy framework that integrates cap-and-trade and carbon taxation. Game-theoretic models are developed for three manufacturer-led channels: manufacturer trade-in (M-CX), retailer trade-in (R-CX), and third-party trade-in (T-CX). The analysis examines pricing strategies, profitability, and carbon emission reductions across these channels. The key findings are as follows: (1) Carbon tax consistently compresses manufacturer profits, whereas cap-and-trade mechanisms exhibit a non-linear U-shaped effect. Manufacturer profits remain highest under the M-CX channel, irrespective of policy intensity. (2) Retail prices are most sensitive to carbon policies under the T-CX channel, where trade-in rebates increase with carbon intensity. The R-CX channel sustains higher retail prices and rebates than M-CX, while T-CX surpasses both under conditions of high carbon intensity. (3) Carbon emission reductions decline sharply under M-CX and R-CX as policy stringency increases. In contrast, the T-CX channel establishes a buffering mechanism through rising rebates, exhibiting the slowest rate of decline. At low carbon intensity, T-CX yields the lowest reduction levels; however, under high intensity, it overtakes the other channels to achieve the highest reduction. This study offers insights for manufacturers’ channel selection and government policy coordination under hybrid carbon regulation regimes. Full article

Under the background of the carbon cap and trading mechanism, the shipping logistics service supply chain faces pressure to reduce carbon emissions, and artificial intelligence technology provides a new technological path for emission reduction. In the context of a carbon cap-and-trade system, this study examines a shipping logistics service supply chain comprising a service provider and a service integrator, where the provider adopts AI technologies for direct emission reduction and the integrator contributes indirectly. It investigates optimal decision-making under two models: a single emission-reduction model (only provider uses AI) and a joint-emission-reduction model (both adopt AI), while also exploring one-way and two-way cost-sharing contracts between them. The study establishes these models to analyze the impact of cost-sharing contracts on emission reduction levels, total service volume, and profits, and further examines how government regulation of carbon trading prices can promote reduction. Findings reveal that cost-sharing contracts effectively enhance emission reduction, output, and member benefits; one-way contracts are conducive to operations, while two-way contracts are effective only within a small cost-sharing ratio range. The joint model outperforms the single model under specific parameter thresholds, and cost-sharing ratios influence decentralized versus centralized decision-making. Government carbon price regulation can encourage reduction but must consider its effects on low-carbon logistics volume and profits. Full article

To enhance the low-carbon operational performance of integrated energy systems (IESs) under multi-source uncertainties, this study proposes a bilevel stochastic optimization framework incorporating a dynamic mean–CVaR risk model and a tiered carbon pricing mechanism. The upper level adopts an improved NSGA-II to jointly optimize economic cost, carbon emissions, and system flexibility through capacity planning decisions. The lower level performs scenario-based operation evaluation with a time-varying risk aversion coefficient, enabling differentiated risk responses across operating periods. A stepwise carbon price function and a capped carbon revenue mechanism are introduced to represent real carbon market regulations and avoid excessive emission reduction benefits. Multidimensional uncertainty scenarios—covering renewable variability, load fluctuations, and market price disturbances—are generated for risk-aware evaluation. Simulation results show that the proposed approach effectively reduces cost and emission volatility and achieves a more balanced trade-off between economy and low-carbon performance compared with conventional static-risk models. Sensitivity analyses further reveal that increased risk aversion shifts system operation strategies from economy-oriented to robustness-oriented modes, highlighting the importance of dynamic risk modeling and carbon policy design for future low-carbon multi-energy systems. Full article

Within closed-loop supply chains (CLSCs), limited attention has been given to firms’ production and abatement decisions involving carbon permit transfer between an original equipment manufacturer (OEM) and an independent remanufacturer (IR) under a cap-and-trade policy (CTP). Several questions remain unresolved: How does CTP reshape production and abatement decisions through carbon permit transfer under binding emission constraints? Can such regulation reallocate abatement responsibilities between firms and generate environmental and economic benefits? To address these questions, this paper develops a constrained Cournot game model capturing competitive interactions between an OEM and an IR under regulation. The results show that CTP reallocates abatement responsibilities toward the firm with a lower abatement difficulty, inducing full abatement as a corner solution. When remanufactured products exhibit a high low-carbon level, a moderate increase in cap stringency promotes remanufacturing output and market share. By contrast, once full abatement is reached, stricter regulation expands output. Moreover, when remanufacturing features substantial cost savings and a high low-carbon level, CTP consistently improves social welfare. This study provides insights into how cap-and-trade policies shape production and abatement decisions in CLSCs. Full article

In this study, we develop a game-theoretic optimization framework to analyze competing vessels’ technology choices between shore power (SP) and low-sulfur fuel oil (LSFO) within a maritime supply chain which is regulated by a cap-and-trade mechanism. Using a Stackelberg game approach, we construct two models—one port-led and the other vessel-led—to derive closed-form equilibrium for pricing, service quantities, profits, emissions, and social welfare. The results reveal three key findings. First, the leader in either Stackelberg structure always achieves higher profits, while total supply chain profits remain identical across power structures. Second, at low carbon prices, LSFO-equipped vessels provide more services and earn higher profits due to cost advantages. As the carbon price rises—which directly incentivizes emission reduction and accelerates maritime decarbonization—SP becomes more attractive and eventually dominates in profitability despite higher initial investment. Notably, although SP has lower unit emissions, its total emissions may surpass those of LSFO at certain carbon-price thresholds because the SP-equipped vessel optimally expands output. Third, intensified competition reduces service quantities, profits, and emissions, with a more substantial reduction effect on LSFO vessels. Overall, our results provide mathematically grounded insights for optimizing low-carbon technology adoption in maritime transport and offer actionable policy implications for carbon pricing that balance environmental objectives and supply chain efficiency. This research contributes specifically to the United Nations’ Sustainable Development Goals (SDGs), specifically SDG 13 (Climate Action) and SDG 9 (Industry, Innovation and Infrastructure). Full article

Growing climate concerns and rising consumer awareness of sustainability have reshaped strategic interactions in platform-based supply chains. This study examines how a manufacturer and an e-commerce platform make channel and fulfillment decisions under cap-and-trade regulation. The manufacturer chooses between non-encroachment and agency encroachment, while the platform decides between conventional and sustainable fulfillment. To capture the dynamic adaptation of boundedly rational agents, we develop an evolutionary game model (EGT) and characterize the evolutionary stable strategies. The findings indicate the following: (1) Platform investment in sustainable fulfillment exerts a strategic stabilizer effect, effectively protecting the reselling channel by reducing the manufacturer’s incentive to encroach even under moderate commission rates; (2) there exists a regulatory substitution effect between carbon pricing and commissions, where high carbon prices force manufacturers to encroach for survival, while low commissions encourage encroachment for profit; (3) consumer sensitivity exhibits a critical threshold behavior, where a synchronized transition to joint sustainability is impossible unless awareness exceeds a specific tipping point. Managerial insights suggest that platforms should view green logistics as a retention strategy to prevent channel fragmentation, while policymakers must coordinate carbon taxation with consumer awareness campaigns to avoid locking the system into non-green equilibria. Full article

As low-carbon supply chains increasingly integrate green transition strategies with digital transformation, coordinating high-cost green technology investments with data-driven marketing (DDM) becomes a complex managerial task. While these dual investments are essential for market growth, the inherent tension between economic efficiency and fairness concerns often triggers strategic friction phenomenon whose impact under cap-and-trade regulations remains insufficiently explored. This paper investigates the strategic implications of fairness concerns in a low-carbon supply chain in which a manufacturer invests in carbon emission reduction and a retailer engages in data-driven marketing (DDM), under a cap-and-trade regulation. We formulate four Stackelberg game models—Neutral Benchmark (NF), Retailer Fairness (RF), Manufacturer Fairness (MF), and Bilateral Fairness (BF)—to analyze the interplay between behavioral equity and economic efficiency. The main analytical results indicate that (1) fairness concerns universally function as an “efficiency tax” on the supply chain system, where the rational benchmark consistently yields the highest system efficiency. In contrast, bilateral fairness concerns lead to the worst performance due to double friction effects. (2) Counter-intuitively, the retailer can “weaponize” fairness concerns to extract surplus from the leader. Specifically, in environments with high carbon emission reduction costs, a fairness-concerned retailer compels the manufacturer to grant significant wholesale price concessions, thereby achieving higher profits than in a purely rational setting. (3) The manufacturer’s fairness creates a “Benevolence Trap” for the follower; to balance equity, a fair manufacturer tends to underinvest in green technologies, which severely contracts market demand and, unlike the retailer fairness scenario, fails to yield economic benefits for the retailer. (4) A critical “regime-switching” dynamic exists regarding the carbon trading price. While the retailer benefits from fairness strategies in nascent carbon markets, a pivot to rationality becomes optimal as carbon prices surge and efficiency dividends dominate. These findings offer novel managerial insights for supply chain members to navigate behavioral complexities and for policymakers to align incentive mechanisms. Full article

The paper introduces a real-time digital-twin controller that manages evacuation routes while operating GEEM for emergency energy management during building fires. The system consists of three interconnected parts which include (i) a physics-based hazard surrogate for short-term smoke and temperature field prediction from sensor data (ii), a router system that manages path updates for individual users and controls exposure and network congestion (iii), and an energy management system that regulates the exchange between PV power and battery storage and diesel fuel and grid electricity to preserve vital life-safety operations while reducing both power usage and environmental carbon output. The system operates through independent modules that function autonomously to preserve operational stability when sensors face delays or communication failures, and it meets Industry 5.0 requirements through its implementation of auditable policy controls for hazard penalties, fairness weight, and battery reserve floor settings. We evaluate the controller in co-simulation across multiple building layouts and feeder constraints. The proposed method achieves superior performance to existing AI/RL baselines because it reduces near-worst-case egress time ($T_{95}$ and worst-case exposure) and decreases both event energy $E_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ and CO₂-equivalent ${{CO}_2}_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ while upholding all capacity, exposure cap, and grid import limit constraints. A high-VRE, tight-feeder stress test shows how reserve management, flexible-load shedding, and PV curtailment can achieve trade-offs between unserved critical load $U_{\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}$ and emissions. The team delivers implementation details together with reporting templates to assist researchers in reaching reproducibility goals. The research shows that emergency energy systems, which integrate evacuation systems, achieve better safety results and environmental advantages that enable smart-city integration through digital thread operations throughout design, commissioning, and operational stages. Full article

Under the tightening carbon reduction policies, port microgrids face the challenge of optimizing the installed capacity of multiple power generation types to reduce operating costs and increase renewable energy penetration. We develop a bi-level cost-optimization framework in which the upper level decides long-term capacities (PV, wind, gas turbine, bio-fuel unit, and battery energy storage), and the lower level dispatches a multi-energy port microgrid (electricity–heat–cold) on an hourly basis with frequency regulation services. To ensure rigor and reproducibility, we (i) move the methodology upfront and formalize all constraints, (ii) provide a dedicated data–preprocessing pipeline for multi-region 50/60 Hz frequency time series, and (iii) map a policy intensity index to a carbon price and/or an annual cap used in the objective/constraints. The bi-level MILP is solved by a column-and-constraint generation algorithm with optimality gap control. We report quantitative metrics—annualized total cost, CO₂ emissions (t), renewable shares (%), and regulation cycles—across scenarios. Results show consistent cost–carbon trade-offs and robust capacity shifts toward storage and biofuel as policy tightens. All inputs and scripts are organized for exact replication. Full article