Search Results (92)

Balancing efficiency and equity within agricultural supply chains is crucial for rural revitalization and sustainable development. This study focuses on the three-tiered chain of ‘farmers–cooperatives–retailers’, constructing a joint decision-making model linking pricing, sales effort, and order volume. It compares the performance differences between decentralized and centralized decision-making structures. Methodologically, we introduce four corrective factors—risk-bearing capacity, cooperation level, capital investment, and information access—to the traditional Shapley value. By employing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to calculate proximity, we derive an enhanced Shapley–TOPSIS allocation coefficient. Furthermore, we design a secondary distribution rule of ‘effort-based value-added distribution according to labor contribution,’ tightly binding the marginal returns of sales effort to input intensity, thereby reconciling structural fairness with incentive compatibility. Empirical findings indicate that, compared with decentralized approaches, centralized decision-making significantly enhances overall system revenue and reduces retail prices. The refined distribution scheme outperforms the baseline Shapley value in fairness and stability, effectively mitigating the misalignment where effort contributors receive disproportionately low returns. The optimal sales effort level is approximately 0.35. Under the ‘distribution according to labor’ approach, retailers (the primary effort providers) see a marked increase in their value-added share, whereas farmers and cooperatives also gain positive benefits, enhancing alliance stability. Unlike existing studies that rely mainly on revenue-sharing contracts or a single Shapley allocation, this study, on the one hand, explicitly endogenizes sales effort into demand and profit functions and systematically characterizes the joint mechanism between effort and profit allocation under both centralized and decentralized structures. On the other hand, an improved Shapley–TOPSIS modeling procedure and an ‘effort added-value allocation according to contribution’ rule are proposed. By adjusting demand parameters and the weights of the adjustment factors, the proposed framework can be readily extended to other agricultural products and green supply chain settings, providing a replicable tool and managerial implications for designing sustainable profit allocation schemes. Full article

Geothermal energy provides a stable baseload renewable source that is less affected by weather variability compared with solar and wind power, and is therefore increasingly considered in national energy transition and net-zero strategies. Yet its environmental externalities and associated social benefits are not fully priced in existing electricity markets, raising the question of how much the public is willing to pay for geothermal-based generation. This study applies non-market valuation theory to estimate citizens’ additional annual electricity payment required to replace coal-fired generation with geothermal energy. A contingent valuation method (CVM) survey was conducted through face-to-face interviews, employing a closed-ended single-bounded dichotomous choice format with incentive compatibility. Stratified random sampling yielded 678 valid observations. The estimated mean willingness to pay (WTP) per person per year is USD 56.18 (NTD 1792) under the Probit model and USD 52.16 (NTD 1663) under the Logit model, representing approximately 0.2–0.3% of average annual income and 16–20% of the average annual electricity bill. Aggregated to the population level, total annual WTP amounts to USD 688 million (NTD 21,934 billion; Probit) and USD 638 million (NTD 20,355 billion; Logit). These estimates correspond to support for developing approximately 108–335 MW of geothermal capacity, sufficient to supply around 202,000–624,000 four-person households. The findings indicate substantial public support for geothermal power as part of Taiwan’s renewable energy transition, and provide empirical evidence relevant to regions with comparable geothermal potential. Full article

This study investigates the accelerating adoption of electric vehicles (EVs) integrated with residential rooftop solar and battery storage in Australia, employing Actor–Network Theory (ANT) to elucidate socio-technical dynamics. Through purposive sampling, semi-structured interviews with 15 EV industry stakeholders were conducted and analysed using NVivo 14. Findings revealed EV–solar–storage adoption as a negotiated process shaped by alignments among human and non-human actors, structured by three interdependent obligatory passage points. First, technological integration hinges on interoperability among inverters, smart chargers, EV supply equipment, batteries, and home energy management systems. These are constrained by factors like off-street parking availability. Second, policy and market frameworks require clear interconnection standards, bidirectional charging protocols, streamlined approvals, and targeted incentives. Third, consumer engagement depends on energy literacy, equitable access for renters, and daytime charging infrastructure. Smart and bidirectional charging positions EVs as flexible energy assets, yet gaps in standards and awareness destabilise networks. This ANT-framed study offers a practice-oriented model for clean mobility integration, proposing targeted interventions such as device compatibility standards, equitable policies, and education to maximise environmental and economic benefits at household and system levels. Full article

Energy theft remains a major source of non-technical losses in smart grids, leading to significant economic damage and operational risks. Traditional detection methods often rely on fine-grained user consumption data, raising serious privacy concerns and limiting user willingness to cooperate. To address this conflict between detection accuracy and privacy protection, this paper proposes a novel incentive mechanism based on contract theory. We first quantify privacy-preserving levels using the differential privacy framework and analyze the resulting degradation in energy theft detection performance due to added noise. Then, we design an optimal contract menu that encourages users to report more accurate data by compensating them for privacy losses, while accounting for information asymmetry regarding individual privacy preferences. The proposed mechanism is analyzed under both discrete and continuous user types, and the optimization problem is simplified by reducing the number of incentive compatibility and individual rationality constraints. Simulation results demonstrate the feasibility of the proposed mechanism and show how it balances detection performance, aggregation accuracy, and user privacy. This work offers a theoretically grounded and feasible solution to the privacy–detection trade-off in smart grid energy theft detection. Full article

This study analyzes how blockchain technology can be interpreted through an economic perspective, viewing network nodes as rational agents whose strategic behavior affects the efficiency and sustainability of decentralized systems. Using a multi-player non-cooperative game with complete but imperfect information, we model validators’ decisions in voting-based consensus mechanisms and compare alternative incentive configurations through simulation results. The analysis shows how variations in reward schemes influence validators’ behavior and consensus reliability. Extending the framework to Decentralized Autonomous Organizations (DAOs), the study explores how blockchain-based incentives can enhance participation, accountability, and decentralized governance. The findings highlight that incentive design plays a decisive role in aligning individual motivations with collective goals, ensuring both network integrity and long-term sustainability. Overall, this study connects economic theory with blockchain governance, extending its relevance to business and organizational contexts beyond cryptocurrencies. Full article

Public transit subsidization often suffers from a double (or bilateral) moral hazard problem, where both regulators and operators may reduce their efforts due to information asymmetry, thereby compromising service quality despite significant public investment. This paper develops a continuous-time principal-agent model to investigate optimal subsidy contract design under such conditions, where both parties exert costly, unobservable efforts that jointly determine stochastic service outcomes. Using stochastic dynamic programming and exponential utility functions, we derive closed-form solutions for the optimal contracts. Our analysis yields three key findings. First, under standard technical assumptions, the optimal subsidy contract takes a simple linear form based on final service quality, facilitating practical implementation. Second, the contract’s incentive intensity decreases with environmental uncertainty, highlighting a fundamental trade-off between risk-sharing and effort inducement. Third, a unique and mutually agreeable contract emerges as the parties’ risk preferences and productivity levels converge. This study extends the classic principal-agent framework by incorporating bilateral moral hazard in a dynamic setting, offering new theoretical insights into public-sector contract design. For policymakers, the results suggest that performance-based subsidies should be calibrated to account for operational uncertainty, and that regulators are active co-producers of service quality whose own unobservable efforts—distinct from the subsidy itself—are critical to outcomes.The proposed framework provides actionable guidance for designing effective, incentive-compatible subsidies to enhance public transit service delivery. Full article

This paper proposes a coalition-based framework for the coordinated operation of multi-regional power systems subject to extreme uncertainty in demand surges, renewable variability, and resource mobilization delays. Methodologically, we integrate Bayesian learning with distributionally robust optimization (DRO), embedding dynamically updated scenario posteriors into a Wasserstein ambiguity set. This construction captures both stochastic variability from renewable and load realizations and epistemic uncertainty from incomplete knowledge of probability distributions. To align individual incentives with system-level efficiency, we design a risk-adjusted utility mechanism that combines VCG transfers, Shapley allocations, and nucleolus refinements. These mechanisms explicitly consider agent heterogeneity, risk aversion, and coalition stability, ensuring that cooperation remains both efficient and sustainable. The optimization model maximizes expected social welfare while incorporating constraints on transmission corridor capacities, mobilization logistics, demand–response rebound effects, and mobile energy storage operations. A hierarchical decomposition algorithm integrates the Bayesian-DRO dispatch layer with cooperative game-theoretic allocations to maintain tractability and robustness at large scale. A case study on a six-province interconnected system with 14–26 GW peak demand, 10.2 GW solar, 8.6 GW wind, 14 GW peaking units, and 6.8 GW mobile storage demonstrates the effectiveness of the approach. Results indicate that the proposed framework raises expected welfare by nearly 10% relative to a non-cooperative baseline, reduces the probability of unserved energy exceeding 1.5% from almost 2% to negligible levels, and narrows payment disparities across provinces to strengthen coalition stability. Demand response peaks at 250–300 MW with rebound averaging 25%, while mobile BESS units cycle frequently to enhance local reliability. Overall, the findings highlight a robust and incentive-compatible pathway for resilient inter-provincial operation, providing both methodological advances and policy-relevant insights for multi-regional energy governance. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

Global food systems face growing pressure from population expansion and climate change, making the identification of resilient crops a priority. The nopal cactus (Opuntia spp.) stands out for its capacity to thrive in arid environments and for its cultural and economic importance in Mexico. This study analyzes worldwide research trends and evaluates evidence from Mexico to identify opportunities and strategies for closing production cycles through residue valorization. Scientific output over the past decade shows steady growth and a thematic transition from basic agronomic and compositional studies toward sustainability, bioactive compounds, and circular economy approaches. In the Mexican context, applied studies demonstrate that Opuntia spp. cladodes residues can be transformed into composts with C/N ratios between 12 and 26, improving soil organic matter and nutrient availability. Biofertilizers produced through anaerobic fermentation enhanced phosphorus solubility in alkaline soils, while direct residue incorporation increased carrot and tomato yields up to threefold. Farmers recognize these practices as low-cost and compatible with local systems. Nevertheless, the lack of standardized protocols and scalable models limits widespread adoption. Strengthening research collaboration, policy incentives, and technology transfer could position Mexico as a leader in sustainable Opuntia value chains and advance circular economy practices in smallholder farming systems. Full article

Green design by manufacturers is essential for achieving supply chain sustainability, and large retailers may exhibit altruistic preferences to incentivize such efforts. Accordingly, this study develops three game-theoretic models of a two-echelon supply chain composed of a manufacturer and a dominant retailer, with and without altruistic preferences, to examine how altruism and green design affect firms’ optimal decisions and environmental impact. In addition, two coordination mechanisms—green design cost-sharing and two-part tariff contracts—are proposed under altruistic preferences. We find that the dominant retailer’s altruistic preference can motivate the manufacturer to improve the green design level and increase system profit. Although the dominant retailer has altruistic preference, they cannot always lower the total environmental impact of products, so it is helpful to motivate the manufacturer to reduce the environmental adverse impact by increasing investments in green design. Both the two contracts designed in this paper can achieve incentive compatibility and perfect coordination of supply chain. However, with the retailer’s altruistic preference enhancement, the feasible range of the two contracts will be reduced. Full article

This paper compares national hydrogen (H₂) infrastructure plans in Canada, the United States (the USA), Singapore, and Sri Lanka, four countries with varying geographic and economic outlooks but shared targets for reaching net-zero emissions by 2050. It examines how each country approaches hydrogen production, pipeline infrastructure, policy incentives, and international collaboration. Canada focuses on large-scale hydrogen production utilizing natural resources and retrofitted natural gas pipelines supplemented by carbon capture technology. The USA promotes regional hydrogen hubs with federal investment and intersectoral collaboration. Singapore suggests an innovation-based, import-dominant strategy featuring hydrogen-compatible infrastructure in a land-constrained region. Sri Lanka maintains an import-facilitated, pilot-scale model facilitated by donor funding and foreign collaboration. This study identifies common challenges such as hydrogen embrittlement, leakages, and infrastructure scalability, as well as fundamental differences based on local conditions. Based on these findings, strategic frameworks are proposed, including scalability, adaptability, partnership, policy architecture, digitalization, and equity. The findings highlight the importance of localized hydrogen solutions, supported by strong international cooperation and international partnerships. Full article

Designing fair and sustainable pricing mechanisms for health insurance requires accurate risk assessment and the formulation of incentive-compatible strategies among stakeholders. This study proposes a hybrid framework that integrates machine learning with game theory to determine optimal, risk-based premium rates. Using a comprehensive dataset of insured individuals, the XGBoost algorithm is employed to predict medical claim costs and calculate corresponding premiums. To enhance transparency and explainability, SHAP analysis is conducted across four risk-based groups, revealing key drivers, including healthcare utilization and demographic features. The strategic interactions among the insurer, insured, and employer are modeled as a repeated game. Using the Folk Theorem, the conditions under which long-term cooperation becomes a sustainable Nash equilibrium are explored. The results demonstrate that XGBoost achieves high predictive accuracy (R² ≈ 0.787) along with strong performance in error measures (RMSE ≈ 1.64 × 10⁷ IRR, MAE ≈ 1.08 × 10⁶ IRR), while SHAP analysis offers interpretable insights into the most influential predictors. Game-theoretic analysis further reveals that under appropriate discount rates, stable cooperation between stakeholders is achievable. These findings support the development of equitable, transparent, and data-driven health insurance systems that effectively align the incentives of all stakeholders. Full article

Digital interventions are widely promoted as levers of institutional change, yet their effects often prove fragile. We examine why some interventions persist while others fade. Using crisp-set Qualitative Comparative Analysis (csQCA) on 13 large-scale cases from India and abroad, we identify the configurations of conditions under which digital systems become self-sustaining. We conceptualise persistence as a shift in the Nash equilibrium: when incentives realign, the new behaviour maintains itself without continuing external push. The analysis shows that software openness is neither necessary nor sufficient for durable change. Instead, six non-technological conditions—regulatory enablement, a credible revenue model, substantial scale, a clearly targeted systemic barrier, presence of enabling prerequisites, and sufficient time—are each necessary and, in combination, sufficient for an equilibrium shift; no single condition is enough on its own. Successful cases (e.g., Aadhaar, UPI, Chalo, Swiggy) meet these conditions in combination, whereas others (e.g., ONDC, DIKSHA, ICDS-CAS) illustrate how missing elements limit institutional embedding. The paper contributes a theory-informed diagnostic that links game-theoretic stability to configurational evaluation and provides practical “if–then” decision rules for appraisal. We argue that policy and investment decisions should prioritise incentive-compatible ecosystems over software attributes, and judge success by whether interventions reconfigure the rules of the game rather than by short-term uptake. This perspective clarifies when digital systems can contribute to sustainable, inclusive institutional transformation. Full article

As global energy systems shift to low-carbon models, microgrid systems play an increasingly vital role in decentralized energy management. This study proposes a collaborative scheduling strategy, incorporating both power and carbon contribution for multi-microgrid systems. Through the utilization of a cooperative Stackelberg game and a Nash bargaining model, a bi-level game framework is established between grid operators and microgrid alliances, enabling efficient resource sharing and equitable benefit distribution. To accurately assess each microgrid’s impacts, a VCG (Vickrey–Clarke–Groves)-based mechanism is introduced to quantify its marginal contribution to both power supply and carbon mitigation. The contribution factors are then embedded into the bargaining process, guiding incentive-compatible allocation. Furthermore, to improve computational efficiency and enable distributed problem-solving, an enhanced analytical target cascading (ATC) algorithm is applied. Experimental results reveal that this approach improves both economic and environmental performance, effectively reducing carbon emissions and dependence on the main grid. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article