Search Results (250)

Shrinking and aging societies undermine the economic viability of network-based infrastructure once supported by economies of scale and network externalities. This paper develops a conceptual framing of “Denetworking” as a possible reconfiguration strategy in the contraction phase: reducing dependence on highly asset-specific dedicated networks (e.g., pipes and rail tracks) and shifting service functions to distributed systems or generic shared networks (e.g., roads) while maintaining minimum service standards. Rather than presenting a calibrated optimization model or full life-cycle cost (LCC) estimation, the paper proposes a heuristic decision condition for comparing a “keep” scenario (renew and maintain the dedicated network) with a “shift” scenario (Denetworking) and uses quantitative anchors from public sources to illustrate the associated fiscal and institutional trade-offs. Two Japanese cases are used as contrasting illustrations: physical Denetworking, referring to the reduction in or substitution of dedicated physical network assets, in wastewater services (centralized sewerage to decentralized treatment); and functional Denetworking, referring to the transfer of service functions from dedicated networks to more generic shared networks, in regional mobility (local rail to bus/BRT on the road network). The cross-case discussion suggests that Denetworking may become a rational policy option under certain conditions, particularly when demand density declines near renewal-investment peaks and asset specificity increases lock-in. The paper contributes a conceptual vocabulary and comparative policy framing for discussing infrastructure reconfiguration in shrinking societies and highlights practical issues of timing, cost sharing, phased implementation, and stakeholder engagement. Full article

The production and leasing of electric construction machinery play a critical role in the low-carbon transition. However, from a multi-cycle dynamic perspective, there is a lack of targeted research on how to enhance electric goodwill and AI-enabled maintenance service levels while maximizing enterprise profits. To fill this gap, this study incorporates AI-enabled O&M effort, R&D technology, AI-enabled maintenance effort, and advertising effort into a long-term dynamic framework to examine optimal decisions for the manufacturer and the lessor. We assume that the information in the leasing supply chain is symmetric, that the marginal profits of the manufacturer and the lessor are fixed parameters, and that the AI-enabled maintenance service effort level and the electric goodwill are taken as state variables. We develop differential game models across four decision cases: centralized (Case C), decentralized (Case D), unilateral cost-sharing contract (Case U), and bilateral cost-sharing contract (Case B). Results demonstrate monotonic state variable trajectories. Both Case U and Case B can achieve supply chain coordination, with the profit-sharing mechanism in Case B proving superior. In addition, the optimal cost-sharing proportion depends on the relative sizes of the manufacturer’s and the lessor’s marginal profits in both Case U and Case B. The AI-enabled maintenance service plays a significant role in enhancing equipment reliability and supply chain resilience. In addition, the impacts of key parameters on optimal decision variables, state variables, profits, and coordination of the leasing supply chain are comprehensively discussed. 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

Against the background of carbon neutrality, the government’s carbon regulations have had a profound impact on the distributed energy sharing behavior of industrial parks. To deeply explore the interactive relationship between distributed energy sharing in industrial parks and government regulation, this paper constructs a three-party evolutionary game model composed of the government, core enterprises and supporting enterprises; endogenizes government behavior; and integrates inter-enterprise contractual mechanisms into the evolutionary framework. By establishing a revenue payment matrix and a replication dynamic equation, the stability conditions and system evolution paths of the strategy choices of each subject are analyzed, and numerical simulations are conducted. The results show that there are multiple evolutionary stable equilibria in the system, among which the equilibrium where core enterprises actively share, supporting enterprises actively share, and the government actively regulates carbon is the ideal state. Cost-sharing contracts and cooperative penalty contracts play a significant role in promoting the participation of supporting enterprises in sharing and curbing “free-riding” behavior, respectively. The changes in government subsidy rates and carbon tax rates have a crucial impact on the evolution of corporate strategies. Quantitatively, the carbon tax rate exhibits a threshold effect; enterprises shift to positive energy sharing when the tax rate exceeds 0.8, while a subsidy rate above 0.4 leads the government to withdraw from regulation. This indicates that a reasonable design of carbon regulations can help achieve coordinated energy emission reduction between the government and enterprises. The findings provide theoretical support for optimizing carbon regulations and designing cooperation strategies. Full article

This paper proposes a physics-informed hybrid digital CO₂ emission-control technology for maritime transport, designed for adaptive ship speed optimization along a predefined geographical route between two ports, discretized into quasi-stationary segments and evaluated under forecasted metocean conditions, subject to economic and regulatory constraints associated with maritime decarbonization. The framework integrates two exact optimization methods, Backtracking (BT) and Dynamic Programming (DP), with a reinforcement learning approach based on Proximal Policy Optimization (PPO), operating on a unified physical, economic, and regulatory modeling core. By reducing propulsion fuel demand, the system acts as an upstream CO₂ emission-control mechanism for ship propulsion. This operational stabilization of the engine load creates favourable boundary conditions for advanced combustion processes and reduces the volumetric flow of exhaust gas, thereby lowering the technical burden on potential post-combustion carbon capture systems. Segment-wise speed profiles are optimized subject to propulsion limits, Estimated Time of Arrival (ETA) feasibility, and regulatory constraints, including the Carbon Intensity Indicator (CII), the European Union Emissions Trading System (EU ETS) and FuelEU Maritime. The physics-based propulsion and energy model is validated using full-scale operational data from four real voyages of an oil/chemical tanker. A detailed case study on the Milazzo–Motril route demonstrates that adaptive speed optimization consistently outperforms conventional cruise operation. Exact optimization methods achieve voyage time reductions of approximately 10% and fuel and CO₂ emission reductions of about 9–10%. The reinforcement learning approach provides the best overall performance, reducing voyage time by approximately 15% and achieving fuel savings and CO₂ emission reductions of about 13%. At the route level, the Carbon Intensity Indicator is reduced by approximately 10% for the exact methods and by about 13% for PPO. Backtracking and Dynamic Programming converge to nearly identical globally optimal solutions within the discretized decision space, while PPO identifies solutions located on the most favourable region of the cost–time Pareto front. By benchmarking reinforcement learning against exact discrete solvers within a shared physics-informed structure, the proposed digital platform provides transparent validation of learning-based optimization and offers a scalable decision-support technology for pre-fixture evaluation of fixed-route voyages. The system enables quantitative assessment of CO₂ emissions, ETA feasibility, and regulatory exposure (CII, EU ETS, FuelEU Maritime penalties) prior to transport contracting, thereby supporting economically and environmentally informed operational decisions. Full article

Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without sharing representative evaluation data with vendors, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero-Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, cryptographically verifiable competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Vendors cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation, while the on-chain workflow automates escrow, proof verification, and best-vendor selection with arbiter-based dispute resolution. ZKP privacy is scoped to vendor model weights; operator-side evaluation-data confidentiality is managed separately via synthetic, de-identified, or public benchmark data. We analyze three ZKP workflow variations and evaluate them on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation, while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling. Full article

Methane emissions from dairy production constitute a significant share of agricultural greenhouse gas emissions in Poland and represent a key challenge under EU climate policy and the Common Agricultural Policy (CAP). This study evaluates dairy farmers’ acceptance of alternative methane mitigation measures (MMMs) and examines the cost-efficient design of agri-environmental contracts from a public-budget perspective. A Discrete Choice Experiment (DCE) conducted among 302 dairy farmers was used to estimate participation probabilities for different mitigation measures and contract attributes, including result-based (RB) and input-based (IB) payment schemes. These preference-based probabilities were subsequently embedded into a cost-minimisation optimisation framework that identifies the least-cost portfolios of MMMs capable of achieving increasing methane-reduction targets while remaining behaviourally feasible. The DCE results show significantly higher acceptance of RB contracts compared with IB schemes, strong resistance to vaccination-based measures, and relatively favourable preferences for biofiltration. Payment levels and environmental attitudes significantly influence participation decisions. When behavioural constraints are incorporated into the optimisation model, RB contracts allow for higher achievable methane reductions under the adopted assumptions, primarily due to higher participation rates of farmers in result-based contracts. The model indicates that, beyond moderate mitigation targets, IB schemes face participation limits that constrain scalability. Biofiltration consistently forms the backbone of cost-efficient portfolios, while less accepted measures enter optimal solutions only when ambition levels exceed the feasible potential of high-acceptance options, revealing a potential ambition–acceptance gap. Methodologically, the study integrates stated-preference data into a policy optimisation model, demonstrating how farmers’ quantified perceptions can be treated as structural inputs to environmental policy design rather than assuming full adoption of technically efficient measures. Conceptually, the framework links farmer participation, environmental effectiveness, and budget efficiency within a unified decision-support structure. The proposed framework contributes to sustainability-oriented policy design by linking environmental effectiveness, behavioural feasibility, and public-budget efficiency in methane mitigation strategies for the dairy sector. Although the results are scenario-based and conditional on assumed mitigation and cost parameters, they underline the importance of aligning environmental ambition with empirically grounded participation patterns when designing methane mitigation policies for the dairy sector. Full article

The intelligent upgrading of photovoltaic (PV) power stations can improve their power-generating capacity, reduce operation and maintenance (O&M) costs, and effectively conserve energy and reduce emissions. However, the pressure of research and development (R&D) investment, as well as market demand uncertainty faced by PV technical suppliers, has become an obstacle to intelligent upgrading. From the perspective of R&D cost sharing between PV project operators and technical equipment suppliers in the PV supply chain, this study presents game models for suppliers and operators under the modes of R&D cost sharing and non-sharing, and compares the benefits to both parties under the cost-sharing contract. The results show that under the cost-sharing mode, the operators share a certain proportion of R&D costs with the suppliers, which can improve the effort level of R&D. The greater the impact of the power generation increased by intelligent products on market demand, the better the operator’s maintenance efforts, and the more motivated the operator is to choose the cost-sharing strategy. By setting a reasonable wholesale price for products, both parties can balance their profits. Full article

Addressing the high investment costs and market revenue uncertainties faced by hydrogen energy storage projects, this study examines the economic implications of green bond financing on their participation in electricity market transactions. A two-level optimization decision model is constructed: the upper level aims to minimize the total cost over the project’s lifetime by optimizing the proportion of green bond financing, while the lower level aims to minimize daily operational costs by optimizing the hydrogen storage system’s charging and discharging strategy. The model comprehensively accounts for factors including medium-to-long-term contracted electricity volumes, tiered carbon pricing, and forecasting errors for wind and solar generation, utilizing the CPLEX solver for optimization. Case study analysis demonstrates that green bonds can substantially reduce financing costs, achieving optimal net present value within a financing share range of 60–80% and a storage capacity range of 1000–2000 MWh. This enhances the full lifecycle economics of hydrogen storage projects, providing theoretical support for integrated ‘financing–investment–operation’ decision-making. Full article

Agri-food supply chains are highly exposed to freshness deterioration, demand uncertainty, and information asymmetry. In practice, upstream suppliers may strategically misreport freshness-related information to influence downstream procurement decisions, which can amplify inefficiency and increase food loss and waste. This study develops an analytical framework that integrates (i) strategic freshness misreporting by an informed supplier, (ii) endogenous investment in blockchain-enabled traceability that improves information credibility at a cost, and (iii) contract design for supply chain coordination. We consider a two-echelon agri-food supply chain with stochastic demand and freshness-dependent valuation, and characterize equilibrium operational decisions under centralized and decentralized settings. The results reveal how misreporting reshapes optimal order quantities, wholesale prices, and profit allocation, and identify conditions under which misreporting increases expected waste and undermines sustainability performance. We then examine how traceability investment changes the incentives of both parties, leading to adoption thresholds and potential incentive misalignment under decentralization. Finally, we design revenue-sharing, cost-sharing, and combined contracts and derive parameter regions that coordinate the blockchain-enabled agri-food supply chain and generate Pareto improvements for both the supplier and the retailer. Numerical experiments illustrate the comparative statics and quantify the trade-offs among profitability, transparency, and waste reduction. Relative to existing blockchain-enabled agri-food supply chain models, the framework jointly endogenizes supplier misreporting of freshness, blockchain-based traceability investment, and contract parameters, thereby uncovering new adoption thresholds and coordination regions that tightly link transparency decisions to food loss and waste. The findings provide actionable guidance for using digital traceability and contract mechanisms to curb opportunism, enhance coordination, and support sustainable agri-food supply chains. Full article

Modern passport systems face significant challenges in secure data sharing, real-time verification, and user-controlled authorization, particularly in cross-border scenarios. Existing digital passport solutions, often built on permissioned blockchains, suffer from limited transparency, scalability, and high operational costs. This paper proposes a decentralized passport management system based on an Ethereum Layer 2 architecture that combines global governance with high-throughput and cost-efficient passport operations. The system adopts a hybrid design in which a Global Passport Registry smart contract is deployed on the Ethereum mainnet for cross-country coordination, while passport issuance, access control, and identity management are handled on Layer 2 networks through country-operated Passport Managers and user-specific Personal Passport smart contracts. Extensive performance evaluations show that Ethereum Layer 1 throughput saturates at approximately 40–50 transactions per second (TPS), whereas the proposed Layer 2 deployment consistently exceeds 150 TPS and reaches up to 300 TPS under higher-performance environments, significantly surpassing the estimated system requirement of 70 TPS. These improvements result in faster response times, reduced congestion, and substantially lower transaction costs, demonstrating that public Ethereum Layer 2 infrastructures can effectively support a scalable, self-sovereign, privacy-preserving, and globally verifiable digital passport system suitable for real-world deployment. Full article

Information asymmetry regarding freshness has become a critical issue in the fresh produce supply chain. This study focuses on a fresh produce e-commerce supply chain comprising suppliers, third-party logistics (TPL) providers, and e-commerce platforms. Considering consumer preferences for freshness, it employs a Stackelberg game model to examine the impact of TPL exaggerating freshness preservation efforts on the supply chain. Subsequently, contract design is employed to achieve supply chain coordination. Findings indicate that when TPL misrepresents preservation effort information, profits decline across all supply chain members. A cost-sharing-profit-sharing contract facilitates redistribution of costs and benefits between upstream and downstream entities, thereby increasing preservation effort levels. Although preservation costs increase under this arrangement, contractual terms ultimately enhance profits for all supply chain members. This study incorporates freshness preferences to enhance model realism, providing theoretical foundations for decision-making under information asymmetry regarding freshness preservation efforts. It holds significant practical value for fostering collaboration among members in fresh produce e-commerce supply chains and promoting sustainable supply chain development. Full article

With the rapid development of the new energy vehicle industry, the efficiency of information sharing in the power battery recycling supply chain greatly affects resource utilization and sustainability. This paper examines battery manufacturers and third-party recyclers as game participants. We analyze incentive mechanisms for sharing technical information, considering both information quality and leakage risks. This study constructs three types of Stackelberg game models: contract mechanisms, profit-sharing mechanisms, and cost-sharing mechanisms. We analyze the impact of technical information quality and leakage costs on supply chain decisions. Results show that manufacturer profits increase with growing leakage costs, following optimal transitions through profit-sharing, contract, and cost-sharing mechanisms. Recycler profits are influenced by both the quality of technical information and leakage costs. Overall supply chain profits trend toward cost-sharing mechanisms when technical information quality is low and favor profit-sharing mechanisms when quality is high. Under low leakage risk, cost-sharing mechanisms dominate at the technological level and in terms of recycling quantity. Under high leakage risk, profit-sharing mechanisms share leakage costs and lead in technology investment and recycling quantity. Contract mechanisms consistently have the lowest levels and volumes because they lack cost sharing and profit compensation. This study provides a theoretical foundation and practical guidance for information-sharing strategies in power battery recycling supply chains. Full article

Global energy markets have experienced persistent dispersion in real energy prices, creating structural competitiveness pressures that standard indicators often fail to capture in real time. These pressures have intensified as energy-intensive sectors face asymmetric exposure across advanced and emerging economies. This study addresses two critical gaps in international energy cost competitiveness. The first is a frequency gap: conventional indicators such as the Real Unit Energy Cost (RUEC) are typically published with delays of 2–5 years, limiting their usefulness for timely policy evaluation. Here, both RUEC and the Real Price Level Index for energy (Real PLI)—the ratio of the Purchasing Power Parity (PPP) for energy to that for GDP—are measured with only a 2–3-month lag for nine countries—four in Asia, four in Europe, and the U.S. The second is a competitiveness gap that calls for policy responses. Real PLIs indicate that the energy price disadvantages of Japan, Korea, France, Germany, Italy, and the UK have widened from 1.76–2.91 times the U.S. level before the pandemic to 2.14–3.28 times by Q3 2025, with the gaps relative to China and India also widening. Once country-specific thresholds are exceeded, output in energy-intensive and trade-exposed (EITE) industries tends to contract disproportionately. These findings highlight that sustainable transitions require not only internationally differentiated burden-sharing but also structural reforms to avoid persistent widening of energy price gaps. The Real PLI framework provides a timely indicator of competitiveness and an early-warning tool, signaling when growing asymmetries may undermine policy feasibility. Policy implications include the need to monitor real energy price dispersion as a core source of competitiveness risk, to strengthen structural measures that stabilize marginal energy costs, and to design transition pathways that account for heterogeneous adjustment pressures across countries. Full article

Against the backdrop of the rapid growth in the scale of the prepared food market, safety issues have gradually become prominent. Establishing a traceability system has become crucial to safeguarding consumer rights and promoting the sustainable development of the industry, with traceability information sharing serving as the core link. However, affected by differences in interest demands and information asymmetry between manufacturers and retailers in the prepared food supply chain, there are obstacles to traceability information sharing. To explore the coordination mechanism of traceability information-sharing behavior in the prepared food supply chain under different decision-making models and its impact on profit distribution, this paper constructs a two-level supply chain model including manufacturers and retailers, comprehensively considers the online–offline dual-channel sales model, and distinguishes four scenarios: centralized decision-making, decentralized decision-making, retailer-led cost-sharing contract decision-making, and manufacturer-led cost-sharing contract decision-making. Using a differential game model, the equilibrium results under different decision-making models are discussed. The validity of the model is verified through fitting with empirical analysis and numerical example analysis. The research results show the following: (1) The centralized decision-making model has the best effect on increasing the market share of the prepared food supply chain, and although the cost-sharing contract model can improve it, there is still a gap. (2) The centralized decision-making model is not the one with the maximum profit, and manufacturer-led cost-sharing decision-making basically achieves Pareto optimality. The main reasons are the insufficient incentive mechanism, high coordination costs, and uneven profit distribution in centralized decision-making. (3) The impact of manufacturers’ offline channel traceability information-sharing behavior on profits is more significant than that of online channels. (4) In a market environment with information asymmetry, the impact of goodwill on the profits of prepared foods is more prominent. This research provides a theoretical basis for the management of the prepared food supply chain, helps optimize the traceability information-sharing mechanism and profit distribution plan, and promotes the healthy development of the industry. (5) When the coefficient measuring the intensity of traceability information sharing’s impact on product quality across manufacturers’ online and offline channels increases, only under the retailer-led model does product quality and goodwill exhibit a fluctuating trend of “rising from the bottom to the second place and then falling back to the bottom,” while the profits of all subjects increase simultaneously. (6) As the system attenuation coefficient increases, the evolution of product quality and goodwill under different cooperation models shows significant differences; in terms of profits, the profits of manufacturers’ online channels increase over time, while those of other subjects decrease. (7) When the discount rate rises, the manufacturer-led model presents distinct characteristics: both the ranking and absolute value of product quality decline synchronously, the ranking of goodwill falls, but its absolute value rises against the trend, the evolution of product quality and goodwill shows obvious model heterogeneity, and the profits of all subjects generally decrease. Full article