Search Results (307)

The rapid growth of Türkiye’s savings finance sector under Law No. 7292 has created demand for objective, multidimensional performance evaluation tools. This study assesses the financial performance of six savings finance companies in Türkiye over 2022–2024 using a hybrid CRITIC-EDAS multi-criteria decision-making model. Criterion weights for eight financial indicators—spanning profitability, operational efficiency, growth, and financial structure—were derived objectively via CRITIC, while EDAS produced the rankings, which were applied to criterion-direction-normalized data because the dataset contains negative values. The operating expense-to-revenue ratio carried the greatest weight in 2022 and 2023; gross profit margin became dominant in 2024. Katılımevim led the rankings in 2022 (ASi = 1.000); Eminevim then took the lead in 2023 (ASi = 1.000) and held it in 2024 (ASi = 0.968), with Fuzulev second (ASi = 0.815). A normalization artifact affecting two revenue-denominator ratios for one company (İmece) in 2024 was corrected through winsorization. Validity was confirmed in two stages: Spearman correlations between EDAS and the TOPSIS, MABAC, and MARCOS rankings exceeded the 0.89 reliability threshold in all three years, and a nine-scenario sensitivity analysis supported the rankings’ robustness. These findings give regulators, investors, and managers a replicable framework for evaluating performance in this young, underexamined sector and point to operational efficiency and outlier management as priorities for oversight. Full article

Low-carbon transformation requires substantial investments, challenging capital-constrained manufacturers to adopt carbon emission reduction (CER) technologies. While external financing alleviates capital shortages, it cannot address potential profit imbalances that trigger fairness concerns. We investigate a low-carbon supply chain where a capital-constrained manufacturer adopts CER technologies via a preferential bank loan and sells to a capital-abundant retailer. Unlike prior studies treating CER investments as one-time costs, we model CER technology as a quadratic per-unit royalty licensing fee. We find that, given consumers’ willingness to pay for low-carbon products, financing encourages CER upgrades but creates profit disparities unfavorable to the retailer. Incorporating the retailer’s fairness concerns, results show that compared to the non-fairness scenario, the manufacturer sets a lower wholesale price and cannot earn more. Conversely, the retailer strategically maintains or increases its order quantity, attaining higher profits. Furthermore, the optimal CER level remains invariant regardless of fairness preferences. Finally, supply chain coordination is achievable under specific conditions, yielding a win–win outcome where the manufacturer adopts CER technologies and the retailer’s fairness concerns are accommodated. The quadratic per-unit technology licensing fee we investigated maintains the manufacturer’s motivation and ensures the retailer’s fairness, contributing to the stable and sustainable evolution of low-carbon supply chains. Full article

Mortality in smallholder cattle and buffalo systems is climate-driven, but the signal is not uniform: heat and cold stress, flooding, and climate-sensitive disease act through different pathways, yet livestock loss models usually compress them into one elevated-mortality state. The paper builds a climate-state-dependent mortality model for the Thai national herd, separating an endemic baseline from a temperature-extreme and a moisture- and disease-driven regime. A 100,000-iteration Monte Carlo model, calibrated to the 2024 herd and a 2017 farmer survey at 2026 prices, generates the annual loss distribution and decomposes it by driver. The study is a calibrated scenario analysis, not an empirical estimation, so every result is conditional on the calibration and bounded by sensitivity analysis. Endemic mortality governs the average year, about 81% of expected loss but none of the extreme tail; the tail belongs entirely to the two climate regimes, with the moisture- and disease-driven regime carrying roughly 69% of losses beyond the 95th percentile and the temperature regime about 31%. This split holds across low-, medium-, and high-severity scenarios and a baseline range from 0.07 to 0.12, so it is structural: the driver of the typical year is not the driver of the catastrophe. Under a reduced-form behavioral layer with an assumed destocking response, generous payouts would raise stocking pressure 10% to 16% above a sustainable carrying capacity benchmark, so an adaptation instrument could degrade the rangeland it protects. The findings argue for regime-specific risk financing, for pairing insurance with heat and animal health adaptation, and for treating the carrying capacity externality as a design parameter. Full article

We develop a tensor-based dynamical framework for monetary flows in multi-sector, multi-agent economies and quantify the information destroyed when the monetary state is reduced to a scalar aggregate. The state is a third-order tensor encoding capital flows across sectors, agent classes, and time; deviations from equilibrium obey a tensor-indexed Langevin (multivariate Ornstein–Uhlenbeck) equation with a coupling operator and channel-specific friction rates. Using standard Lyapunov theory, we assemble a stability and convergence framework for the induced vectorized system, with a bound stated so as to remain valid for the non-normal system matrices generated by asymmetric economic coupling, and characterize the stochastically forced case in the mean-square sense. Shannon entropy, Kullback–Leibler divergence, and sector–agent mutual information measure the structural information discarded by scalar aggregation. We then study a stylized, heuristically calibrated $3\times 3$ economy subject to a shock inspired by the 2007–2009 crisis; we emphasize at the outset that the figures reported below are properties of that calibration and are not empirical estimates. In this scenario Finance absorbs an 18.9% peak capital loss while Manufacturing and Services suffer 5.8% and 3.9% secondary drops, against an aggregate contraction of only 8.6%; the Kullback–Leibler divergence of the sector–agent flow distribution recovers systematically later than the aggregate signal, a lag that is positive in 96.6% of a 1000-draw Monte Carlo ensemble, although its magnitude is calibration-dependent. Under a symmetric exit rule, a deficit-targeted stimulus restores equilibrium substantially faster than a share-weighted uniform stimulus in 100% of the ensemble while spending strictly less—its realized expenditure saturates below the uniform budget because it self-terminates as deficits close—and attains integrated disequilibrium within 18% of the exact linear-quadratic optimum at equal control effort while requiring no knowledge of the system matrix. The ordinal conclusions—aggregation masks the epicenter, structure lags the aggregate, and deficit targeting dominates uniformity—are robust across a wide neighborhood of the calibration, and identify the disaggregated state as the object that stabilization policy needs and that scalar aggregation destroys. Full article

Background: Progress toward universal health coverage in Central Asia depends on the interaction between public healthcare financing, broader social protection, and the fiscal capacity of the state. The region is analytically important because five post-Soviet health systems share a common institutional legacy while differing markedly in income, informality, migration dependence, and public financing arrangements. Methods: The study examines a country–year panel for Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan over 2015–2024. Fixed-effects estimates are treated as the principal benchmark. Two-step System Generalized Method of Moments estimates are retained only as sensitivity evidence because the cross-sectional dimension is very small (N = 5). Out-of-pocket expenditure as a share of current health expenditure represents household financial burden, while changes in general government debt measure macro-fiscal pressure. Results: In the reported models, higher social protection expenditure and higher domestic general government health expenditure are negatively associated with the out-of-pocket share. The fixed-effects coefficients are −1.245 and −3.810, respectively; the corresponding System GMM sensitivity estimates are −1.830 and −4.102. Combined social and health expenditure is positively associated with the debt ratio in the fiscal model. These findings are associations rather than causal effects. An illustrative scenario analysis shows that the estimated financing gap to a 5% of GDP public health benchmark varies substantially across countries and assumptions. Conclusions: The results are consistent with a dual-channel framework in which public financing can shift health risk away from households while creating fiscal pressure when revenue mobilization and expenditure efficiency do not adjust. Policy implications therefore concern the composition, targeting, and financing of expenditure rather than spending expansion alone. The limited sample, incomplete interpolation audit trail, and incomplete archived GMM diagnostics require cautious interpretation and motivate replication with household and subnational data. Full article

Infrastructure project reports contain rich narrative evidence on delay causes, yet transforming such unstructured text into reliable causal knowledge remains challenging because reports mix confirmed events with hypothetical, conditional, or localized statements. This study proposes an eight-stage computational pipeline that converts infrastructure project evaluation reports into a Bayesian-network model for exploratory structure learning and probabilistic dependency modeling. The central methodological contribution is a modality-aware extraction layer that distinguishes confirmed, project-wide delay evidence from conditional, hypothetical, or component-level statements before causal analysis. The pipeline was evaluated on 55 road infrastructure project reports financed by the Asian Development Bank, the African Development Bank, and JICA, from which delay events across 15 cause categories were extracted and stratified by epistemic modality and scope. Ablation analysis shows that the principal dependency structure recovered by the Bayesian network is not recoverable without modality-aware filtering, indicating that evidence-quality stratification materially shapes downstream causal-structure exploration. Among the recovered dependencies, a financial-to-project-management pathway was the most consistent signal: its undirected skeleton edge was the only relationship recovered by all four causal-discovery algorithms tested (with the orientation determined only by the score-based search), its association was nominally positive—though weak and not uniformly discernible—across nine extraction models spanning three commercial vendors and open-weight families, and it is consistent with prior delay-factor literature. Its model-based scenario contrast ($\Delta P=+0.638$, 95% CI $[0.470,0.764]$) is reported as hypothesis-generating rather than as a validated policy effect: under structure-learning uncertainty, the interval extends to zero, and the effect magnitude and the specific learned edge depend on the extraction model and the small effective sample. These findings suggest that incorporating modality awareness into narrative-evidence extraction improves the reliability of exploratory causal-structure analysis from infrastructure project reports. Full article

This study quantifies the transmission of global gold price shocks and US monetary shocks to the refining and trade sector of the United Arab Emirates, and the resulting response of real GDP. As an intermediate node in the global gold value chain, the UAE is exposed to upstream prices, Asian demand, logistics frictions, and, through the AED-USD peg, the US monetary stance. Quarterly data from 2005Q1 to 2025Q4 are used to construct cointegration and error-correction analysis, a structural VAR, and a two-regime threshold error-correction specification. Three cointegrating vectors link gold prices, Asian demand, and logistics costs to UAE refining throughput, re-exports, and gold-linked finance. A US monetary-tightening shock is associated with a contraction across all three UAE variables, whereas a positive gold-price shock is associated with an expansion of similar magnitude. Monetary shocks account for a rising share of forecast error variance at longer horizons, and adjustment is faster in high-rate regimes than in low-rate regimes. Long-run GDP estimates for re-exports are positive and stable; the gold-linked finance estimate is conditional on a calibrated proxy. Scenario simulations shift quarterly GDP growth by 0.4 to 0.6 percentage points, supporting commodity-hub indicators in the macroeconomic monitoring of small open economies under fixed exchange rates. Full article

Photovoltaic (PV) enhancement technologies, including cooling systems, reflectors, and tracking mechanisms, are widely employed to improve the electrical performance of PV systems. However, the effectiveness of these technologies should be evaluated not only in terms of performance improvement but also by considering the associated implementation costs. To address this need, previous studies introduced the Cost Effectiveness Factor (${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$), which integrates power output and manufacturing cost into a single performance indicator. While ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$ is useful for short-term and experimental assessments, it is based on instantaneous power output and does not account for the cumulative energy generated over extended operating periods. Furthermore, many experimental and field studies on PV enhancement technologies, particularly PV cooling systems, report their performance in terms of energy generation (kWh) rather than instantaneous power output (W), creating a need for an energy-based assessment methodology. Therefore, this study proposes a new Cost–Energy Effectiveness Factor (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$) that extends the concept of ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$ by incorporating energy output instead of power output, thereby enabling a more comprehensive evaluation of long-term techno-economic performance. The proposed indicator integrates the output energy of PV systems with and without enhancers, the manufacturing cost of PV enhancers, and the unit cost of PV electricity into a single dimensionless factor. In addition, a theoretical minimum value (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E},\mathrm{m}\mathrm{i}\mathrm{n}}$) is introduced to establish a benchmark for performance evaluation. Comprehensive sensitivity analyses were performed to investigate the influence of key technical and economic parameters, including the output energy of the enhanced and unenhanced PV systems, manufacturing cost, the unit PV electricity cost, and maximum output power under standard test conditions. The results indicate that ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ decreases with increasing enhanced PV energy output and electricity value; however, it increases with higher manufacturing costs and greater energy production from the reference PV system. In contrast, variations in the maximum output power affect only the benchmark value (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E},\mathrm{m}\mathrm{i}\mathrm{n}}$) without influencing the actual ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ values. The proposed indicator was further validated using data obtained from real photovoltaic cooling systems, demonstrating its applicability under practical operating conditions and confirming its suitability for real-world PV enhancement scenarios. Compared with ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$, the proposed ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ provides a more realistic representation of the long-term benefits of PV enhancement technologies because it evaluates accumulated energy generation rather than instantaneous power output. The indicator successfully differentiates between effective, neutral, and ineffective PV enhancers and offers a practical tool for researchers, designers, manufacturers, and investors seeking to compare PV enhancement technologies from both energy and economic perspectives. Consequently, ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ can serve as an effective preliminary screening and comparative assessment tool for PV enhancement technologies, thereby promoting the efficient utilization of sustainable energy resources, while detailed investment decisions should be supported by comprehensive techno-economic analyses that consider lifecycle costs, discount rates, financing conditions, and other project-specific economic factors. Full article

This study develops a scenario-based analytical framework for assessing smart microgrids in industrial zones as infrastructure investments and for analyzing multi-actor risk allocation and governance. No empirically validated return is reported for a specific Bulgarian industrial zone. Public institutional, market, financial, technology and environmental sources are instead used to benchmark the scenario assumptions. Three scenarios are evaluated for a reference zone with annual consumption of 12,000 MWh. The model incorporates photovoltaic (PV) degradation, battery round-trip efficiency of 85–90%, annual usable-capacity degradation, one modeled battery replacement within a 12–15-year service interval, component-based capital and operating expenditures, and an author-defined semi-quantitative likelihood–impact risk matrix. Net present value (NPV) is EUR −1,114,726 in the conservative scenario, EUR 697,836 in the baseline scenario and EUR 3,424,835 in the favorable scenario. Discounted payback is not achieved within 20 years in the conservative scenario and is approximately 14.1 and 7.0 years in the baseline and favorable scenarios, respectively. Deterministic one-at-a-time sensitivity analysis identifies electricity price, capital expenditures, and the direct PV self-consumption ratio as the dominant financial drivers.The indicative reduction in location-based emissions associated with grid electricity purchases is 802.9–1385.5 tonnes of carbon dioxide equivalent (tCO₂e) per year under the selected grid-average electricity emission factor. The contribution lies in integrating public-data availability assessment, external parameter benchmarking, battery service life, degradation and replacement economics, investment appraisal, threshold analysis, semi-quantitative risk prioritization and contractual risk allocation within one reproducible framework. The outputs remain illustrative and require project-level validation using measured hourly loads, binding prices, financing terms and enforceable contracts. Full article

This study develops an integrated generative artificial intelligence (GAI) framework for improving business process performance in industrial enterprises. The framework treats GAI not as the isolated use of generative tools, but as a governable information systems capability embedded in recurring workflows, enterprise architectures, documented knowledge, and human decision roles. It integrates four functional subframeworks—manufacturing, marketing and sales, accounting and finance, and human resource management—with a shared orchestration and governance layer. This layer coordinates process architecture, approved data and knowledge sources, reusable GAI capabilities, human-in-the-loop validation, traceability, escalation, and performance measurement. A proof-of-concept maturity-readiness validation is conducted in an electronics company using maturity-readiness logic inspired by the Smart Industry Readiness Index (SIRI). The assessment shows an increase in the overall readiness score from 41.60 in the pre-GAI baseline to 79.08 in the post-GAI implementation scenario. Accordingly, the score increase is interpreted as expert-assessed maturity-readiness evidence rather than as a measured causal effect on operational performance. This study contributes a process-centric reference architecture designed for technical implementability, traceability, auditability, and human-supervised enterprise-scale GAI adoption. Full article

Digital development is becoming increasingly embedded in agricultural production, agricultural product circulation, and market service systems. The relationship between digital resources and the stable operation, coordinated response, and recovery adjustment of agricultural product supply chains under external shocks has become an important issue in agricultural modernization and sustainable agricultural development. Using provincial-level data from Zhejiang, Fujian, and Guangdong covering the period from 2014 to 2023, this study applies the entropy weight method, necessary condition analysis (NCA), and fsQCA. Based on the resource-based view and dynamic capability theory, this study constructs an analytical framework linking resource configuration, scenario embeddedness, and capability transformation for agricultural product supply chain resilience, and examines multiple configurational paths through which different digital conditions are associated with agricultural product supply chain resilience. The results show that high agricultural product supply chain resilience is closely related to the coordinated matching of digital infrastructure, rural logistics networks, information and communication services, digital finance, and governance support. Zhejiang, Fujian, and Guangdong present three paths: information service and circulation synergy, logistics network support, and digital circulation synergy. The degree of fit between digital resources and specific supply chain scenarios, including agricultural production, agricultural product circulation, financial services, and governance support, helps explain differences in agricultural product supply chain resilience among the three provinces. Full article

To quantify the potential of carbon pricing and green finance to jointly drive deep decarbonization in the building sector, this paper extends the MESSAGEix-Buildings model using a policy-endogenous approach. It incorporates the internalization of carbon pricing and carbon emission costs, as well as mechanisms to relax capital constraints in green finance, into the system optimization framework, thereby enabling the quantification of marginal abatement costs and policy interactions. Using civil buildings in Beijing as case studies, four scenarios were devised for simulation analysis. The results show that: (1) carbon pricing alone can achieve a 11.44% reduction in carbon emissions by 2050, whereas green finance can only alleviate investment barriers and deliver only modest additional emission reductions; (2) when the two policies are implemented in tandem, the joint framework generates complementary price constraints and financial incentives, achieving an additional 4.68% reduction in final energy consumption and a nearly 5.27% saving in total investment compared with the carbon-pricing-only scenario. The study elucidates the complementary mechanism of ‘price constraints and cost incentives’, highlighting the methodological value of an endogenous policy approach, and provides quantitative support for the implementation of carbon neutrality in construction and green finance in megacities. Full article

This prospective study investigated the association of dog ownership with the onset of disabling dementia and mortality using propensity score weighting based on the physical, social, and psychological characteristics of dog owners, and then examined the potential causality of this relationship using an instrumental variable model. Additionally, we examined the costs and effects of dog ownership using cost-effectiveness analysis. Overall, 11,224 older adults selected using stratified and random sampling strategies in 2016 were analyzed. Dog ownership was defined as current, past, and never. Disabling dementia was defined according to a physician rating under the long-term care insurance system in Japan and mortality was ascertained by the Japanese national vital statistics during the approximately 7.5-year follow-up period. Current and past dog owners had an odds ratio (OR) of 0.53 (95% CI: 0.43–0.66) and 0.97 (0.86–1.09) of the onset of disabling dementia compared to never owners. ORs for mortality were 0.63 (0.51–0.79) in current dog owners and 0.88 (0.78–0.99) in past dog owners. Causal analysis showed that current dog owners (OR = 0.52, 95% CI: 0.27–0.98) and past dog owners (OR = 0.75, 95% CI: 0.55–1.03) had low ORs for incident dementia compared to never dog ownership. Corresponding values for mortality showed low ORs, but no significant effect on current dog owners (OR = 0.59, 95% CI: 0.35–1.01) and past dog owners (OR = 0.77, 95% CI: 0.60–1.00) compared to never owners. Results of cost-effectiveness analysis showed that dog ownership produced small but positive gains in quality-adjusted life years (QALYs) and was dominant (cost-saving and QALY-increasing) from the public payer perspective, reducing publicly financed medical and long-term care costs while increasing QALYs; when private dog-ownership costs were included in a scenario analysis, the incremental cost-effectiveness ratio (ICER) was ¥6,744,174 per QALY compared with no-dog status. This prospective study shows that dog ownership has a potential protective effect against the onset of disabling dementia in older adults. The study also revealed the potential of dog ownership to reduce public spending with regard to long-term care and medical expenditures. Full article

Fiscal shocks in post-land-finance China are weakening the funding basis of capital-intensive metro public-private partnership (PPP) projects, but the system-level mechanism through which public fiscal stress becomes subcontractor-level financial viability pressure remains underexplained. This study examines a section-level metro PPP project ecosystem in a sub-provincial Chinese city to trace this transmission mechanism and its financial implications. The analysis combines de-identified audit evidence and interviews with a scenario-based structural NPV model and 800,000 model-generated Monte Carlo realizations under calibrated institutional scenarios. The evidence indicates that quasi-bureaucratic SPV internal capital-market arrangements convert fiscal shortfalls into vertical and horizontal cross-subsidization practices, preserving short-term project continuity while shifting cash-flow pressure downstream. This condition is defined as a strategic resilience trap: practices that preserve short-term project continuity while potentially eroding the project ecosystem’s long-term adaptive capacity. Under calibrated assumptions, improving the contract-payment channel reduces model-generated losses by approximately 4%, suggesting that payment punctuality addresses only one part of the wider internal capital-market mechanism. Full article

The rise of artificial intelligence offers a critical opportunity to drive green innovation and advance sustainable development. However, the existing literature lacks sufficient systematic exploration of the relationship between the two from the resource allocation perspective. Taking initial application scenarios of artificial intelligence among Chinese enterprises as the research context, this paper employs a multiple mediation and threshold model to systematically examine its linear and nonlinear influencing mechanisms, so as to further enhance the green innovation-driven effect of artificial intelligence. The research findings show that (1) at the initial application stage, artificial intelligence can facilitate both substantive and strategic green innovation simultaneously, and such impacts present significant heterogeneity across industries and enterprise property rights. (2) Mechanism analysis verifies the existence of parallel and chain mediating effects of financing constraints, R&D investment, and labor force quality between artificial intelligence and green innovation. (3) The threshold effect test suggests that the impacts of artificial intelligence on green innovation and substantive green innovation feature a single threshold centered on corporate ESG performance, while no threshold effect is observed for strategic green innovation. This study provides a reliable theoretical and practical reference for enterprises to leverage artificial intelligence to advance green transformation and sustainable development. Full article