Search Results (262)

We consider the problem of Bayesian parameter inference in the Merton structural credit risk model, where the posterior is induced by a jump-diffusion likelihood and the marginal evidence is not available in closed form. To approximate this posterior, we construct a variational family based on triangular sum-of-squares (SOS) polynomial flows, in which each component map is monotone by construction: its diagonal derivative is a positive definite quadratic form on a monomial basis, yielding a closed-form log-Jacobian and explicit gradients with respect to all flow parameters. The symmetric positive definite matrices parametrizing the flow are optimized by intrinsic Riemannian gradient ascent on the positive definite cone equipped with the affine-invariant metric, which preserves feasibility at every iterate without projection. We show that the rank-one Jacobian gradients produced by the SOS structure have unit norm in the affine-invariant metric, establishing a direct algebraic coupling between the transport family and the optimization geometry and implying a universal 1-Lipschitz bound for the log-Jacobian along geodesics. On the likelihood side, we derive exact score identities for all five structural parameters of the Merton model—drift, volatility, jump intensity, jump mean, and jump volatility—through both the Poisson log-normal mixture and the Fourier inversion representations. Strictly positive parameters are handled via exponential reparametrization, and the resulting gradients propagate end-to-end through the flow. We establish uniform truncation bounds on compact parameter sets for the infinite mixture and its associated score series, providing rigorous control over the finite approximations used in practice. The base distribution is chosen to be uniform on ${[0,1]}^5$, whose bounded support ensures uniform control of the monomial basis and stabilizes the polynomial calculus. These ingredients are assembled into a fully explicit modified ELBO with implementable gradients, combining Euclidean updates for vector parameters and intrinsic manifold updates for matrix parameters. Full article

Tree ensembles—Random Forests (RFs) and Gradient Boosting Machines (GBMs)—often stabilize before all trees are evaluated. We study early stopping as a nonparametric change-point problem on prediction increments. The P²-STOP method family monitors a robust interquartile-range (IQR) scale of prediction increments online and stops when a relative-scale criterion is met. The default variant uses a rolling-window exact-quantile estimator ($O\left(w\right)$ memory), which provides a clean finite-sample stopping guarantee; a full-prefix P² streaming approximation ($O\left(1\right)$ memory) is available as a memory-light alternative. The stopping rule applies to both RFs and GBMs without model-specific distributional assumptions. On four RF benchmarks (MNIST, Covertype, HIGGS, and Credit Card Fraud), P²-STOP achieves 44.8% mean work reduction (range: 0.7–71.7%) with an accuracy change from $-0.53$ to $+0.02$ percentage points versus full-ensemble inference. On XGBoost ($T=500$), work reduction is dataset-dependent (41.4% on Covertype up to 89.0% on Credit Card), with corresponding accuracy trade-offs. Under random-tree contamination conditions (5%, 15%, and 25%), performance remains stable, whereas IQR-versus-standard-deviation baseline differences are mixed rather than uniformly dominant. Designed for compiled inference engines (e.g., C++/Numba), P²-STOP translates theoretical work reduction into consistent wall-clock speedups ($4.14\times$–$4.82\times$ versus compiled full RF on MNIST/Covertype/HIGGS for $T=500$). Native Python implementations serve purely as logical baselines due to loop overhead, while Credit Card exhibits the expected slowdown when work reduction is near zero. All comparisons use five seeds with 95% confidence intervals and seed-level paired tests. With only five seeds, inferential power is limited, and p-values should be interpreted cautiously. Relative to the Dirichlet RF baseline, our contribution is not larger RF-specific work reduction; it is a robust nonparametric IQR-scale stopping criterion, cast as a change-point/sequential-inference problem, that works as a post hoc wrapper across RF and GBM settings. Full article

Smallholder farming remains a critical livelihood source for rural communities in South Africa, particularly in the Eastern Cape Province. However, cropland abandonment has become an escalating concern, undermining food security, household incomes, and the long-term sustainability of agricultural systems. This study assessed the socio-economic and climate-related factors influencing cropland abandonment in the former homelands of Transkei. A mixed-methods approach was used, combining a quantitative survey, a qualitative focus group discussion, and a key informant interview. Data were analysed using descriptive statistics, a double-hurdle model, and thematic analysis. The descriptive results revealed that the average respondent was 57 years, with a predominantly male majority (57.47%), a primary education (40.27%), and a mean average household size of 5.4. About 51.58% of household heads were married and 48.42% were single, with a mean household income of R63 155 (3680.26 USD). The econometric results from the first hurdle model indicated that education level, farming experience, rainfall variability, access to irrigation, and off-farm income significantly influenced the decision to abandon cropland. The second hurdle model demonstrated that the extent of cropland abandonment was shaped by labour availability, access to credit, rainfall patterns, cooperative membership, and farming experience. The study concluded that cropland abandonment in the former Transkei was influenced by different factors. Therefore, the study would recommend targeted policy interventions that strengthen human capital, improve access to agricultural support services, and promote youth participation and collective farming structures to revitalise smallholder agriculture and enhance rural food security. Full article

To mitigate credit risk, banks are required to set aside a specific amount as a safety net to absorb the expected loss on a banks’ loan portfolio called loan loss provisions (LLPs) or provisions for bad debts. All banks worldwide had to adopt International Financial Reporting Standard 9 (IFRS 9) as the financial reporting standard. Unlike its predecessor (i.e., International Accounting Standard 39, IAS 39), IFRS 9 accelerates the recognition of losses by requiring provisions to cover both already-incurred losses and some losses expected in the future by calculating the expected credit loss (ECL). To evaluate if the obligor’s credit quality has deteriorated, the IFRS 9 standard requires banks to compare the obligor’s probability of default (PD) at the inception phase of the loan and at the reporting date. Thus, three methodologies are explored in this study (i.e., Cox proportional hazard (PH), Extended Cox PH, and Random Boosting Forest (RBF)) for computation of the PD term structures using Kaplan–Meier as the benchmark model under IFRS 9. The purpose of this research is to illustrate the application of three methodologies on the publicly available mortgage loan portfolio from Freddie Mac using different measures of goodness-of-fit and the predictive accuracy measure, i.e., the Concordance index (C-index). The comparison analysis reveals that the extended Cox PH and RBF models provide better predictive accuracy (higher C-index) but at the cost of increased complexity and potential overfitting (higher information criteria). However, Cox PH has shown the most efficient fit, and offers a stable and understandable hazard trajectory. Finally, for reproducibility, the SAS and R codes are included to illustrate how each of the results (in form of a table or figure) were obtained. Full article

Stock market efficiency plays a vital role in financial economics, as it reflects how quickly and accurately asset prices incorporate available information. This study investigates stock market efficiency and banking sector stability in the MENA region, focusing on the dynamic interactions between macroeconomic indicators, financial depth, and bank-specific variables. Using panel data from 21 countries over the period 2003–2021, the analysis employs both fixed-effects regression and a Panel Vector Autoregression (PVAR) framework to capture cross-country heterogeneity, temporal dynamics, and systemic interdependencies. The findings reveal that traditional macroeconomic variables, including inflation, GDP per capita, and domestic credit to the private sector, exert limited direct influence on banking sector stability as measured by the Z-score. Instead, the results highlight the importance of country-specific characteristics, institutional quality, and regulatory frameworks in shaping financial resilience across MENA countries. Overall, the findings confirm that effective risk management plays a central role in strengthening bank stability. By enhancing financial resilience, improving operational discipline, and reducing vulnerability to economic and financial shocks, sound risk management practices support the ability of banks to maintain consistent performance over time. The results further suggest that stability is not solely driven by internal mechanisms but also depends on the broader economic and institutional environment in which banks operate. Together, these elements reinforce the capacity of banking systems to contribute to long-term financial stability in the region. Full article

Energy Communities (ECs) have emerged as central legal instruments for decentralized renewable energy deployment across Europe; however, their long-term viability depends critically on financial sustainability mechanisms that remain inadequately understood. This study examines the economic foundations of ECs through a narrative literature review of revenue generation, cost allocation, and the capital mobilization pathways in three representative European markets (Germany, Spain, and Italy). A structured Scopus database search identified 280 peer-reviewed studies published between 2019 and 2025. Following systematic screening, 89 articles were selected for analysis through bibliometric mapping in R (Biblioshiny) and qualitative synthesis in NVivo. The analysis reveals that stable feed-in tariffs, tax incentives, and self-consumption remuneration schemes form the primary revenue mechanisms, while cost management effectiveness varies substantially across countries due to differing grid-charge structures and administrative frameworks. Capital access remains constrained for smaller communities despite hybrid financing innovations combining public grants, cooperative equity, and emerging crowdfunding mechanisms. Regulatory heterogeneity, high upfront investment requirements, and limited institutional credit availability continue to impede scalability. The findings emphasize that achieving widespread EC adoption requires harmonized policy frameworks, transparent cost-sharing arrangements, and diversified investment instruments that align local participation with national decarbonization objectives while ensuring equitable access across diverse socio-economic contexts. Full article

In a context of increasing wildfire risk and highly fragmented forest ownership, this work investigates two relatively recent monetary policy instruments in Catalonia that require grouped applications: a subsidy for fuel reduction, which prioritises collective applications in wildfire-strategic areas, and a climate credit system that promotes territorially coordinated, multifunctional forest management that, i.a., decreases wildfire risk through fuel management. Through in-depth interviews with beneficiaries and consultations with key informants, we analysed whether these measures have triggered adjacent forest management, and how they have interacted with joint action rules to facilitate concerted interventions. The qualitative content analysis indicates that these measures represent a significant step towards landscape-level management and that pre-existing forest owners’ associations play a crucial role in capturing the available funds. The eligibility of coordination costs is also appreciated for covering the transaction costs of catalysing landowners. Yet, areas with weaker social capital may become disadvantaged if there is no external support for their organisation. These findings contribute to the emerging field of policy tools for effective landscape-level interventions. Full article

A two-parameter environmental (measured in CO2eq—CO₂ is used in this paper to represent the carbon dioxide molecule as opposed to the chemical formula CO₂ as is common practice in LCA studies; CO2eq is an abbreviation for CO₂ equivalent and may be written as CO2e in the literature) and economic (measured in USD) analysis using life cycle analysis (LCA) and techno-economic analysis (TEA) of repurposed wind turbine blades for structural use in recreational trail bridges (e.g., on hiking trails and golf courses) is described in this paper. The US Department of Energy’s TECHTEST TEA/LCA software (v1.0) platform was used to compare three commercially available trail bridges (a steel truss bridge, an FRP pultruded truss bridge, and a glulam stringer bridge) with a bridge made from retired wind turbine blades (known as a BladeBridge). All bridges had a 50 ft (15.24 m) long by 6 ft (1.83 m) wide deck and were designed for a 90 psf (4.3 kN/m²) live load. The LCA functional unit was the assembled bridge, which was made ready to be shipped from the fabricator. Cradle-to-gate (A1–A3, i.e., raw material extraction, transportation, and manufacturing) system boundaries were used. For the BladeBridge, no embodied carbon was attributed to the blade itself (cut-off system allocation). For the TEA, a USD 660/tonne credit was attributed to the blade. The raw materials for each bridge were determined from detailed construction documents. Manufacturing and transportation energy were determined based on the equipment used for fabrication and geographical location. Direct labor for fabrication was calculated based on a weighted average of salaries taken from the US Bureau of Labor Statistics. The results indicate that raw materials had the biggest effect on embodied CO2eq and that labor had the largest impact on cost for all bridges. The results indicate that the BladeBridge is significantly less expensive to produce and releases less CO2eq into the environment (less Global Warming Potential (GWP)) than the three commercially available bridges. Additional TEA metrics for the BladeBridge, including Technology Readiness Level (TRL) and future market potential, were also evaluated and found to be positive for the BladeBridge technology. Full article

Digital twins, understood as computational replicas of poultry production systems updated in real time by sensor data, are increasingly invoked as transformative tools for precision livestock farming and sustainable agriculture. They are credited with enhancing feed efficiency, reducing greenhouse gas emissions, enabling disease detection earlier and improving animal welfare. Yet close examination of the published evidence reveals that these promises rest on a surprisingly narrow empirical foundation. Across the available literature, no peer reviewed study has quantified the full lifecycle carbon footprint of digital twin infrastructure in poultry production. Only one field validated investigation reports a measurable improvement in feed conversion ratio attributable to digital optimization, and that study’s design constrains its general applicability. A standardized performance assessment framework specific to poultry has not been established. Quantitative evaluations of reliability are scarce, limited to a small number of studies reporting data loss, sensor degradation and cloud system downtime, and no work has documented abandonment timelines or reasons for discontinuation. The result is a pronounced gap between technological aspiration and verified performance. Progress in this domain will depend on small-scale, deeply instrumented deployments capable of generating the longitudinal, multidimensional evidence required to substantiate the environmental and operational benefits attributed to digital twins. Full article

Electrochemical ocean alkalinity enhancement is a form of marine carbon dioxide removal, a rapidly growing industry that is powered by efficient onshore or offshore energy sources. As more and larger deployments are being planned, it is important to consider how variable energy sources like tidal energy can impact plant performance and costs. An open-source Python-based generalizable model for electrodialysis-based ocean alkalinity enhancement has been developed that can capture key system-level insights of the electrochemistry, ocean chemistry, acid disposal, and co-product creation of these plants under various conditions. The model additionally accounts for hybrid energy system performance profiles and costs via the National Laboratory of the Rockies’ H2Integrate tool. The model was used to analyze an example theoretical plant deployment in North Admiralty Inlet, including how the plant is impacted by the available energy sources in the region and the scale at which plant costs are covered by the co-products it generates, such as recycled concrete aggregates, without requiring carbon credits. The results show that the example plant could be profitable without carbon credits at commercial scales of 100,000 to 1 million tons of carbon dioxide removal per year, so long as it uses low-cost electricity sources and either sells acid or recovers recycled concrete aggregates with about 1 molar acid concentrations, though more research is needed to confirm these results. Full article

In the context of climate change and greenhouse gas emissions, the forestry sector holds significant potential to contribute to global mitigation efforts. One of the primary drivers of deforestation is land expansion for livestock production. However, both sectors are closely linked to issues of food security and food sovereignty, with the livestock sector playing a crucial role in ensuring food availability. Integrating these two sectors through silvopastoral systems offers a promising solution that supports forest conservation while simultaneously addressing the global food crisis. Among the leading initiatives in forest conservation is REDD+, a mechanism under the UNFCCC that has proven effective in reducing deforestation and forest degradation, as well as in enhancing carbon stock conservation. Following the ratification of Article 6 of the Paris Agreement in 2024, REDD+ has gained recognition as a viable approach for generating international carbon credits. Given the intersection of the livestock and forestry sectors, and the potential of carbon credits to advance the goals of the Paris Agreement, silvopastoral systems could be considered for inclusion in REDD+ strategies under the framework of Article 6. Full article

This study estimates the prevalence of food insecurity and coping mechanisms among low-income rural households in Sri Lanka, collecting primary data from 400 households in the Ayagama Divisional Secretariat in Rathnapura District. The results uncover that around 38.1% of the households faced food scarcity within a year prior to the survey date, with 77.9% being uncertain about maintaining a nutritious diet in the next 30 days. Notably, household dietary diversity scores reveal that they are either moderately (62%) or severely (22.3%) lacking essential nutrients, irrespective of the gender of the household head. The leading cause is the unaffordability of protein-rich foods and certain fruits. Coping strategies are primarily short-term and consumption-based, such as purchasing food on credit and reducing meal sizes, which propagate future food insecurity. The findings underscore the need for government interventions that combine short-term safety nets with long-term agricultural productivity improvements, alongside nutrition-sensitive practices and market stabilisation to enhance food availability and affordability. Consequently, targeted social protection programmes for vulnerable groups, combined with livelihood support and climate-resilient agriculture, could reduce reliance on harmful coping mechanisms. Lastly, this study proposes integrating food security goals within broader development frameworks and community initiatives as pivotal for long-term stability and resilience. Full article

This study examines the association between environmental, social, and governance (ESG) controversies and abnormal ESG performance using a sample of listed Chinese firms from 2015 to 2021. We find a significant positive association between ESG controversies and abnormal ESG performance levels. Specifically, management cost is the channel through which ESG controversies affect abnormal ESG performance. Furthermore, heterogeneity tests indicate that financial performance and executive green cognition have a significant impact on the ESG controversies-abnormal ESG association. Strong financial efficiency and customer relationship stability mitigate the negative effects of ESG controversies. Moreover, while ESG controversies cannot affect environmental subsidies, ESG controversies are associated with higher firm profit volatility, lower asset utilization efficiency, and reduced credit availability, which leads to deteriorating financial performance and increased operational risks. However, analyst attention and investor scrutiny can positively moderate these negative effects. The findings of this study enrich relevant theories and empirical evidence, as well as provide new perspectives and policy suggestions for firm ESG performance management practices in China and other emerging economies. Full article

Digital tools and platforms offer significant potential to address critical gaps in market access, credit availability, and agricultural knowledge, particularly in urban and peri-urban areas. This is especially relevant in regions like Sudan, where these opportunities remain largely underexplored. By providing real-time market information, facilitating financial access, and offering essential agricultural training, these tools can help bridge traditional barriers, improve decision-making capabilities, and contribute to sustainable agriculture. Such advancements strengthen economic resilience and promote equity in agriculture, enabling these farmers to drive innovation and sustainability in the industry. Our study was conducted in Omdurman’s Algamwai area during 2022 and 2023, and involved interviews with 100 female farmers. It explored the intersection of gender, technology, and socioeconomic equity. It highlighted how technological advancements can enhance agricultural productivity and market access while addressing challenges such as limited digital literacy and socioeconomic constraints. Despite structural inequalities—including restricted land ownership (45%), limited credit access (5%), and inadequate extension services—female farmers are driving innovation and sustainability by adopting sustainable practices, enhancing food security, and building community resilience. Digital urban agriculture provides income opportunities (76% rely on it) and serves as a platform for equitable participation. From a social science perspective, this research underscores the need to address systemic disparities to unlock the full potential of farmers. Policies ensuring equitable access to resources, credit, and technology are essential for fostering participation and maximizing the socio-economic benefits of digital agriculture in Sudan and similar contexts. Full article

Gastrointestinal disorders, such as colorectal cancer (CRC), pose a substantial health burden worldwide, showing increased incidence rates across different age groups. Detecting and removing polyps promptly, recognized as CRC precursors, are crucial for prevention. While traditional colonoscopy works well, it is vulnerable to specialist errors. This study suggests an AI-based diminutive sessile polyp localization assistant utilizing the YOLO-V8 family. Comprehensive evaluations were conducted using a diverse dataset that was assembled from various available datasets to support our investigation. The final dataset contains images obtained using two imaging methods: white light endoscopy (WLE) and narrow-band imaging (NBI). The research demonstrated remarkable results, boasting a precision of 96.4%, recall of 93.89%, and F1-score of 94.46%. This success can be credited to a meticulously balanced combination of hyperparameters and the specific attributes of the comprehensive dataset designed for the colorectal polyp localization in colonoscopy images. Also, it was proved that the dataset selection was acceptable by analyzing the polyp sizes and their coordinates using a special matrix. This study brings forth significant insights for augmenting the detection of diminutive sessile colorectal polyps, thereby advancing technology-driven colorectal cancer diagnosis in offline scenarios. This is particularly beneficial for gastroenterologists analyzing endoscopy capsule images to detect gastrointestinal polyps. Full article