Search Results (81)

This paper studies the balancedness and core properties of stochastic cooperative games under proportional distribution mechanisms. Different from decomposed payoff structures in the existing literature, where individual payoffs are separated into deterministic and stochastic components, we consider a restricted allocation environment in which each player’s payoff is a fixed proportional share of the realized stochastic coalition payoff. This proportional allocation rule is relevant for institutional settings where exposure shares are determined ex ante and cannot be adjusted after uncertainty is realized. Under homogeneous preference structures, we employ expectation–variance and quantile-based evaluation criteria to transform random coalition outcomes into comparable deterministic indices. We show that, under the proportional distribution mechanism, the equivalence between balancedness and core non-emptiness is not unconditional. Rather, it can be recovered only under additional structural assumptions, especially homogeneous preferences and positivity of evaluated coalition values. These results clarify the boundary under which fixed proportional sharing can support stable cooperation in stochastic cooperative games. Full article

The recycling of pesticide packaging waste is crucial for the sustainable development of agriculture and the advancement of ecological civilization. However, the current recycling management still faces challenges. This study adopts a dynamic analytical framework of “ex-ante behavioral cognition and post-event outcome perception” to investigate the impact of digitalization on farmers’ recycling behavior of pesticide packaging waste. The analysis draws on data from the 2020 China Rural Revitalization Survey and examines two dimensions of digitalization: digital technology access and digital technology usage. The findings indicate that integrating digital technologies into farming practices significantly increases the likelihood of farmers participating in pesticide packaging waste recycling programs. These results remain robust after conducting robustness checks and addressing potential endogeneity issues. A heterogeneity analysis reveals that the promotional effect of digitalization varies significantly across different categories of rural elite status, cooperative membership, education level, pesticide spraying methods, and income structure. Mechanism testing further indicates that hazard cognition regarding pesticide packaging serves as a mediating factor in the impact of both digital technology access and usage on farmers’ recycling behavior. In contrast, farmers’ satisfaction with their living environment mediates only the effect of digital technology usage on recycling behavior. Overall, these findings provide both theoretical and empirical support for the hypothesis that digitalization can facilitate the recycling of pesticide packaging waste and enhance the ecological effectiveness of agricultural policy governance. Full article

The dynamic process through which latent differences in business models of partners escalate into daily operational conflicts within exploitation alliances remains insufficiently explained. This study examines how alignment in partner business models influences operational conflicts, a key determinant of exploitation alliance sustainability. Questionnaire data from 110 experts in Chinese residential joint ventures (JVs) were used to test the proposed hypotheses. The findings indicate that key resources (KRs) and profit formula (PF) indirectly affect operational conflicts through jointly established core business standards (CBSs). Counterintuitively, these standards significantly increase operational conflict risks (OCRs) when they institutionalize underlying misalignments, thereby acting as a full mediator. The results advance theory by clarifying the micro-process of institutionalized misalignment and refining the Resource-Based View (RBV) in alliance contexts. Practically, the study highlights the importance of conducting thorough ex-ante business model analysis, co-creating operational standards, and undertaking continuous alignment reviews to mitigate conflict and enhance JV viability. Full article

Agricultural credit rationing remains a persistent systemic friction driven by information opacity and limited collateral. This study develops a credit risk early-warning system by fusing multi-source institutional digital footprints (tax compliance signals, judicial enforcement records, and credit history indicators) for 1021 agricultural enterprises in China. Methodologically, we propose a Default Event Isolation protocol to enforce strict ex ante validity by discarding observations at and after the event month, and implement a two-step feature optimization pipeline that reduces 138 predictors to a parsimonious set of 50 features. Empirically, the optimized LightGBM (version 4.6.0) model achieves an AUC = 0.9345 (95% bootstrap CI: 0.8745–0.9563) and PR-AUC = 0.4421, representing a 47× lift over the random baseline under extreme class imbalance (0.94% event rate), and captures 87.4% of early-warning events by monitoring only the top 10% highest-risk firms. The interpretability analysis consistently highlights judicial boundary constraints and tax stability signals as dominant predictors, forming a “judicial baseline + tax stability” dual-core structure. A strict credit-only robustness check using bank-recorded NPL labels maintains strong predictive performance (AUC = 0.9089, 95% bootstrap CI: 0.8255–0.9591), mitigating concerns that the model’s signal is driven by label overlap. These findings suggest that integrating institutional records into automated screening pipelines can enable the earlier and more targeted identification of distressed borrowers in rural lending, even when traditional financial statements are unavailable. Full article

Digital transformation in public institutions is increasingly understood as a socio-technical and organizational process rather than a purely technological upgrade. This study presents the design of an ICT-based digital transformation roadmap aimed at improving administrative efficiency and citizen service delivery in a municipal public utility in Ecuador. A mixed-methods diagnostic approach was adopted, combining qualitative evidence from direct observation and a semi-structured interview with the head of the IT department, and quantitative data from a structured online survey administered to citizens. Baseline Key Performance Indicators (KPIs) were established using institutional records, service logs, and workflow analysis conducted over a three-month diagnostic window. Post-implementation KPI values are explicitly treated as ex ante projections, derived from process redesign analysis, benchmarking with comparable public utilities, and scenario-based assumptions, rather than empirically observed outcomes. The empirical results demonstrate high citizen readiness and acceptance of proposed digital services, including remote service portals, electronic invoicing, and automated support channels. The projected operational improvements—such as reductions in response and administrative processing times and increased digital transaction rates—are therefore presented as expected performance scenarios. A risk and alternative scenario analysis further examines how organizational constraints, resource availability, governance capacity, and change-management factors may moderate these outcomes. The study contributes a transparent and replicable framework for diagnosing digital readiness and planning ICT-driven transformation initiatives in resource-constrained public utilities, while emphasizing the need for future longitudinal validation using post-implementation data. Full article

This study investigates whether financial markets efficiently incorporate information related to new product releases in industries where fundamental signals become available only after consumer engagement. Analyzing 49 commercial game launches by 13 publicly listed publishers in South Korea from 2001 to 2024, the research examines short-term return and volatility patterns around the official release date. In contrast to the pre-announcement drift in macroeconomic contexts, there is no evidence of abnormal price or volatility movements prior to launch, which is consistent with the limited informativeness of pre-release marketing for experience goods. Instead, stock prices display a significant negative return and a marked increase in volatility on the day following the launch, rather than on the launch day itself. This pattern corresponds to the delayed emergence of verifiable performance indicators, such as app store revenue rankings and early user-generated content, which typically appear only after consumer interaction with the product. These results indicate that price discovery for digital experience goods is influenced by industry-specific information frictions, which leads to delayed and discontinuous market adjustments. The study contributes to the literature by showing that ex-ante price discovery does not generalize across industries and by emphasizing the critical role of post-release signal timing in shaping event-driven asset price dynamics. Full article

Cost–benefit analysis (CBA), particularly the economic internal rate of return (EIRR), continues to play an important role in infrastructure project appraisal. Using EIRR data from 387 infrastructure projects financed under Japan’s ODA loans (Japanese fiscal years 2001–2020), this study identifies clear sectoral trends: economic infrastructure and brownfield projects generally exhibit higher EIRRs, while no significant differences are observed between tied projects (i.e., projects whose contracts are primarily tied to Japanese firms) and untied projects. A comparison of ex ante and ex post EIRRs for 84 projects shows that estimates may vary due to factors such as changes in demand, project costs, and implementation periods, indicating the practical challenges involved in estimating EIRRs. Qualitative analysis further suggests that non-economic considerations—such as humanitarian needs, national development priorities, and diplomatic interests—may also influence financing decisions. Overall, while CBA remains a valuable and widely used tool, the findings highlight the importance of complementing it with broader sustainability-oriented appraisal approaches that capture the multidimensional value of infrastructure projects. Full article

In the absence of ex-post empirical evidence on the implementation effects of the EU-Mercosur agreement, assessments of expected risks and benefits for the agri-food sector must rely on ex-ante reasoning rather than statistical identification. This paper develops a deductive ex-ante framework to assess how partial market integration under EU–Mercosur may affect the prices and profitability of two groups: agri-food processors and agricultural producers. Methodologically, we formalize a two-market setting (final food products and agricultural raw materials) and derive comparative-statics implications for microeconomic profitability indicators that guide agents’ choices. The main propositions are as follows. First, the integration of the sourcing base for processors is likely to increase the relative profitability of processing by improving the ratio of output to raw-material inputs and, crucially, by widening the price wedge between final food prices and agricultural input prices. Second, the same mechanism implies that agricultural producers in the EU face greater downside risk, as increased competition on the raw-material market tends to depress farm-gate prices; the resulting revenue effect is unlikely to be fully offset by higher sales volumes in the short run. Third, these asymmetric effects rationalize the divergence of perceived risks and benefits across processors and farmers, even when both operate within the same integrated market environment. In addition, we highlight a complementary risk channel: market integration can affect not only price levels but also price volatility in raw-material markets, which may further increase downside risk for farms. The proposed framework provides a disciplined basis for scenario and simulation analyses relevant to agricultural and trade policy, and yields testable predictions for future ex-post evaluation. Full article

This paper develops and applies an ex-ante methodological framework to assess the societal optimisation of eBRT innovations within the Horizon Europe eBRT2030 project, using Multi-Criteria Decision Analysis (MCDA) and the PROMETHEE method. The study evaluates 11 eBRT innovations to be deployed in five demonstration sites in Europe and one in Colombia. Twenty social parameters, including 10 risks and 10 benefits, were weighted and scored through expert and stakeholder engagement, to calculate the Societal Optimisation Index (SOI). Positive SOI values indicate that societal benefits outweigh risks, and negative values indicate the opposite, while close-to-zero values indicate socially neutral or ambiguous options requiring case-specific judgement. The results indicate that innovations such as Adaptive Fleet Scheduling and Planning, Intelligent Driver Support Systems, and IoT Monitoring Platforms provide strong societal benefits with manageable risks, while charging-related innovations are associated with social concerns. The study emphasises the importance of social impact assessment prior to implementing innovations, to enable inclusive decision-making for policymakers and transport planners and enable the development of socially optimised eBRT systems. Embedding experts’ perspectives and social criteria ensures that technological innovations are aligned with societal needs, assisting the transition towards more equitable, low-carbon transport systems. Full article

The transformation towards a sustainable bioeconomy presents new challenges and opportunities for the agricultural sector. This paper investigates how the environmental impacts of this transformation can be effectively monitored using existing agricultural models and sustainability indicators. Drawing on comprehensive model and indicator reviews from the SYMOBIO project, we identify a set of suitable models and propose a harmonised set of 21 SMART-aligned indicators for immediate use for national and regional monitoring. The key findings highlight strong capabilities for modelling GHG and nutrient indicators, persistent gaps in biodiversity and water indicators, and the need for improved methods for using biomass as well as for representing scenario-based spatially disaggregated approaches. The findings highlight the importance of having harmonised indicators, quantitative targets, and integrated monitoring systems in place. The results mark an intermediate step in the ongoing development of national bioeconomy monitoring in Germany. Full article

With the continuous advancement of sustainable agriculture, drone technology has become a focus of attention. Current research primarily relies on classical models for questionnaire surveys and analyses within specific regions, rather than implementing macro-level investigations that incorporate innovative algorithms. This study designed a survey questionnaire to investigate Chinese farmers’ preferences for agricultural drones and their technology adoption mechanisms under sustainable agriculture context. The Ant Colony Optimization-Decision Tree (ACO-DT) model and SHAP (Shapley Additive exPlanations) value analysis are applied to analyze the contribution of different indicators to technology adoption. The ACO-DT model outperformed traditional machine learning models with approximate accuracy 0.85, recall 0.98, and F1 Score 0.90, effectively identifying potential drone users compared to other traditional machine learning models. The SHAP analysis showed “Time Required for Promotion” (average SHAP value exceeds 1.25) and “Understanding of UAV Agriculture” (average SHAP value is about 1.0) were core influencing factors. Specifically, high-cognition farmers preferred shorter promotion cycles, while low-cognition group favored longer cycles to reduce decision-making uncertainty. Practically, the study enriches agricultural technology adoption research methodologically and offers references for advancing smart agriculture and optimizing rural production factors. Full article

Achieving Sustainable Development Goals (SDGs) requires integrated interventions that leverage synergies and minimize trade-offs across sectors and institutions. However, siloed institutional structures often prevent such alignment. Using panel data from 361 households and a difference-in-differences approach, this study examines how an integrated landscape restoration intervention, combining homestead gardening, soil and water conservation (SWC), and credit provision, affects SDG outcomes in rural Ethiopia. The study evaluated impacts on SDG-1 (no poverty), SDG-2 (zero-hunger), SDG-13 (climate-action), and SDG-15 (life-on-land) outcomes. Results indicate no statistically significant outcomes from single-intervention participation. Among dual interventions, SWC + credit improved all SDG indicators except SDG-1, while homestead gardening + SWC showed limited impacts. These results suggest that credit provision plays a critical catalyst in widening the impact of biophysical interventions across multiple SDGs. Participation in the full tripartite intervention induced significant, synergistic improvements across all SDG outcomes. These findings provide empirical evidence that bundling biophysical restoration with socio-economic interventions maximizes synergies. The results also underscore the need to inform integrated development approaches using ex-ante analysis of potential synergies and trade-offs among interventions to optimize efficacy and avoid unintended consequences. The findings offer critical guidance for evidence-based multi-objective policy formulation to advance the 2030 Agenda. Full article

Despite broad consensus on the importance of participatory processes in disaster risk management and urban resilience building, substantial gaps persist, including scarce research on monitoring and evaluating participation, lack of comparative studies, underexplored policy and institutional roles. The paper provides methodological and empirical insights by developing and validating two indicator-based tools: one for ex ante assessment of institutional capacity and the other for supporting monitoring and ex post evaluation of participatory processes. The paper also tests them through a comparative study employing a standardizable and reproducible methodology and synthesizes findings from a systematic review of case studies and a semi-systematic review of grey literature to compile a comprehensive pool of criteria and indicators. These are screened, assigned a weight (either by Equal Weight or Best Worst Method) and are aggregated in the two innovative tools mentioned above. These are tested on four case studies: recent local-scale participatory processes aimed at reducing disaster risk and promoting urban resilience addressing multi-hazard scenarios. The research quali-quantitatively demonstrates how, in the four case studies, greater institutional capacity turns into a higher-quality participatory process. Furthermore, the paper improves practical knowledge on participatory processes in disaster risk management and urban resilience building and lays the foundation for evidence-based innovative guidelines for their planning a priori. Full article

The governance of artificial intelligence (AI) in education requires theoretical grounding that extends beyond system compliance toward outcome-focused accountability. The EU AI Act classifies AI-based learning outcome assessment (AIB-LOA) as a high-risk application (Annex III, point 3b), underscoring the importance of algorithmic decision-making in student evaluation. Current regulatory frameworks such as GDPR and ALTAI focus primarily on ex-ante and system-focused approaches. ALTAI applications in education concentrate on compliance and vulnerability analysis while often failing to integrate governance principles with established educational evaluation practices. While explainable AI research demonstrates methodological sophistication (e.g., LIME, SHAP), it often fails to deliver pedagogically meaningful transparency. This study develops the XAI-ED Consequential Assessment Framework (XAI-ED CAF) as a sector-specific, outcome-focused governance model for AIB-LOA. The framework reinterprets ALTAI’s seven requirements (human agency, robustness, privacy, transparency, fairness, societal well-being, and accountability) through three evaluation theories: Messick’s consequential validity, Kirkpatrick’s four-level model, and Stufflebeam’s CIPP framework. Through this theoretical integration, the study identifies indicators and potential evidence types for institutional self-assessment. The analysis indicates that trustworthy AI in education extends beyond technical transparency or legal compliance. Governance must address student autonomy, pedagogical validity, interpretability, fairness, institutional culture, and accountability. The XAI-ED CAF reconfigures ALTAI as a pedagogically grounded accountability model, establishing structured evaluative criteria that align with both regulatory and educational standards. The framework contributes to AI governance in education by connecting regulatory obligations with pedagogical evaluation theory. It supports policymakers, institutions, and researchers in developing outcome-focused self-assessment practices. Future research should test and refine the framework through Delphi studies and institutional applications across various contexts. Full article

In the paper, using the three-statement financial modelling methodology as applied to a representative development project, we aim to analyse, ex ante, the industry-level impact of transition to mandatory escrow schemes in residential and mixed-use construction in Tashkent city (due to be implemented in Uzbekistan from 2026). Modelling single-milestone escrow plans against the current steep-discount advance-based system of off-plans as a baseline, the model accounts for salient institutional features of the Tashkent city development market, including land auctioning, full-cycle Value-added tax (VAT) accounting, and Tax loss carryforward provisions. It also incorporates a framework for demand-driven residual valuations for the development land element. Our findings indicate practically unchanged cashflow profitability of developers on the market in question. Around 30% p.a. in nominal Free-cashflow-to-equity based IRRs expressed in the national currency, provided that the transition to the greater use of leverage in funding unfolds as expected. The disappearance of steep off-plan discounts while the transition to escrows unfolds will be countervailed by the reliance on costly loans from escrow banks. Absent the greater use of leverage, the IRR (FCFE) profitability of the developers is expected to decline by some 5%. For the apartment buyers, this is effectively equivalent to increasing property transaction prices on the primary market in line with their headline asking amounts. Thus-generated economic surplus will be partially captured by the developers and partially passed through to escrow banks, increasing their gross profits by up to $50M, p.a. due to their new role in financing Tashkent city residential developments that are still largely equity-driven. Apart from this effect, we find only a moderate financial leverage influence on developers’ profitability due to the high-interest-rate environment prevailing in Uzbekistan. We also find a demand-driven pressure on land auction prices suggested by increasingly back-loaded alterations in project cashflow profiles. This study also purports to make a material contribution to the evolving body of literature on financial modelling of apartment and mixed-use property developments by offering a flexible three-statement modelling framework with innovative endogenised equity management features. Full article