Search Results (3,875)

Medical cannabis cultivation requires substantial energy for heating, lighting, and climate control. This study evaluates the economic feasibility of an innovative biomass-fired micro-CHP system in a greenhouse facility for medicinal cannabis cultivation. The system comprises an 80 kW_th boiler retrofitted for biomass and a 7 kW_el ORC engine and is assessed against a diesel-boiler Business-As-Usual (BAU) benchmark. Thermal load simulations for two growing periods (1 March–30 June and 1 September–30 December) estimate an annual heating demand of 91,065.20 kWh_th. The micro-CHP system delivers 8195.87 kWh_el per year, exceeding the greenhouse’s 7839.90 kWh_el consumption. Over a 30-year lifespan at a 7% discount rate, Life Cycle Costing yields EUR 196,421.33 for micro-CHP versus EUR 229,468.46 for BAU, a 14.4% reduction. Under all-equity financing, the project achieves an NPV of EUR 59,591.88, IRR of 27.32%, and a DPBP of 12.1 years; with 70% debt financing, NPV rises to EUR 61,211.39 and DPBP shortens to 10.5 years. Levelized Cost of Energy (LCOE) and Heat (LCOH) are EUR 0.122 per kWh_el and EUR 0.062 per kWh_th, respectively. While the LCOE is below the Greek and EU non-household averages (EUR 0.1578 and EUR 0.1515 per kWh_el), the LCOH exceeds the corresponding heat price benchmarks (EUR 0.0401 and EUR 0.0535 per kWh_th). These results indicate that, in the modeled context, biomass-ORC cogeneration can be a financially attractive and lower-carbon option for medicinal cannabis greenhouse operations. Full article

This study establishes a structured framework to identify and evaluate risk factors that may hinder the achievement of sustainable development goals in green buildings and sustainable infrastructure projects. Fifty-six risk factors are identified and categorized into four risk groups, including stakeholder and management, financial and economic, technological and resource, and process and regulatory risks. The risk factors are evaluated across four risk indices related to probability of occurrence, manageability, impact on building performance, and project cost. Further, the severity of risks based on combining the four indices’ effects is quantified using a new Green Risk Index (GRI), while the relationships among all risk indices are determined. The strongest positive correlation is observed between the probability and the impact on cost, whereas a negative relationship is found between the probability and manageability. The analysis demonstrates that a risk factor related to the lack of knowledge about energy-saving procedures and environmental concerns during the design phase is the most critical, as it has the highest severity based on the GRI. “Non-compliance with environmental standards in project design” is also identified as a critical risk factor due to its high effect on building performance. Additionally, the risk factor associated with unstable funds from investors shows the highest effect on manageability. Process and regulatory is identified as the most critical risk group, encompassing the maximum number of key risk factors, and has the highest average weight related to the GRI. These findings reveal crucial vulnerabilities and underline the importance of targeted strategies to strengthen the use of nature-based solution frameworks for mitigating the risk effects in green buildings and sustainable infrastructures. Full article

A scientifically systematic credit evaluation system serves as a crucial safeguard mechanism for maintaining a healthy business environment in the construction market, effectively regulating industry entities’ behaviors and promoting ecosystem optimization. Current credit risk assessment relies excessively on financial data, neglecting the importance of corporate operational conditions. This study focuses on constructing a credit risk assessment model for construction general contractors. Innovatively incorporating both short-term financial status and long-term operational development factors, the research integrates grey relational analysis with a two-dimensional cloud model to establish a comprehensive credit risk assessment system featuring visualization of evaluation results. The methodology involves three key steps: (1) establishing a dual-dimensional credit risk indicator system covering financial and operational aspects; (2) determining risk factor weights through grey relational analysis and generating three-dimensional cloud diagrams using reverse cloud generators; (3) visualizing corporate credit risk levels through cloud mapping. Empirical analysis of representative Contractor A, utilizing Wind Financial Database data and field research, demonstrates the model’s significant advantages in critical risk factor identification and comprehensive credit risk assessment. Full article

Climate change is driving the need for forest management strategies that simultaneously enhance ecosystem resilience and contribute to climate change mitigation. Voluntary carbon markets (VCMs), regulated in the European Union by the Carbon Removal Certification Framework (CRCF), offer potential financial incentives for such management, but eligibility criteria—particularly biodiversity requirements—limit the applicability of certain species. This study assessed the ecological and economic outcomes of six alternative management scenarios for a 4.7 ha, 99-year-old Scots pine (Pinus sylvestris) stand in western Hungary, comparing them against a business-as-usual (BAU) regeneration baseline. Using field inventory data, species-specific yield tables, and the Forest Industry Carbon Model, we modelled living and dead biomass carbon stocks for 2025–2050 and calculated potential CO₂ credit generation. Economic evaluation employed total discounted contribution margin (TDCM) analyses under varying carbon credit prices (€0–150/tCO₂). Results showed that an extended rotation yielded the highest carbon sequestration (958 tCO₂ above BAU) and TDCM but was deemed operationally unfeasible due to declining stand health. Black locust (Robinia pseudoacacia) regeneration provided high mitigation potential (690 tCO₂) but was ineligible under CRCF rules. Grey poplar (Populus × canescens) regeneration emerged as the most viable option, balancing biodiversity compliance, climate adaptability, and economic return (TDCM = EUR 22,900 at €50/tCO₂). The findings underscore the importance of integrating ecological suitability, market regulations, and economic performance in planning carbon farming projects, and highlight that regulatory biodiversity safeguards can significantly shape feasible mitigation pathways. Full article

The corporate finance field is inherently engaging, with a strong focus on factors influencing various performance indicators. This study analyzes 66 companies from the Information and Technology sector, all part of the Standard and Poor’s 500 index, over a 22-year period from 2003 to 2024. I applied linear, nonlinear, and interaction-variable models to identify the causal relationship between profitability and key influencing factors. The results reveal that firm size, sales growth rate, current ratio, long-term debt to total capital, free cash flow, asset turnover, receivable turnover, number of board meetings, percentage of women on the board, CEO age, audit committee independence, the presence of compensation and nomination committees, and a pandemic dummy variable all had positive effects on performance. In contrast, firm age, dividend payout ratio, effective tax rate, board size, CEO duality, and the presence of a corporate social responsibility committee negatively impacted firm performance. This research also explores corporate governance by evaluating the role of regulations and internal policies designed to promote financial transparency and protect shareholders’ interests. Additionally, it highlights the importance of board independence, the effectiveness of specialized committees, and the role of ethical leadership in driving long-term corporate success. Full article

With the growing advantages and conveniences provided by digital transactions, the financial sectors also face a loss of billions of dollars each year. While the use of credit cards has made life easier and convenient, it has also become a significant threat. Detecting fraudulent transactions in financial sectors, such as banking, is a major issue because existing fraud detection methods are rule-based and unable to detect unknown patterns. The tactics and techniques used by fraudsters are far more advanced than they are, making machine learning (ML) a valuable approach to improve detection efficiency. While numerous studies have explored machine learning models for credit card fraud detection, most have prioritized accuracy metrics alone, offering little attention to how or why models make decisions. This lack of interpretability creates barriers for financial institutions, where regulatory compliance and user trust are critical. In particular, the systematic application of explainable AI (XAI) techniques such as SHAP and LIME to fraud detection remains scarce. This study addresses this gap by combining high-performing ensemble models (Random Forest and XGBoost) with advanced interpretability methods (SHAP and LIME), providing both strong predictive performance and transparent feature-level explanations. Such integration not only improves fraud detection but also strengthens the trustworthiness and deployability of AI systems in real-world financial contexts. A real-world credit card dataset is used to evaluate both models, and experimental results show that Random Forest achieved higher precision (89.09%) and F1 score (0.9159), while XGBoost yielded better recall (95.56%) and ROC AUC (0.9997). To address the crucial need for interpretability, SHAP and LIME analyses were applied, revealing the most influential features behind model predictions and enhancing transparency in decision-making. Overall, this study demonstrates the potential of integrating explainable artificial intelligence (XAI) into fraud detection systems, thereby enhancing trust and reliability in financial institutions. Full article

Amid increasing pressure on sustainability across sectors, the wine industry is also being called to adopt sustainable and responsible practices. However, a potential concern for firms is whether and to what extent sustainability efforts, while beneficial for the environment and society, require some sacrifice on the economic side. Specifically, this study investigates whether adopting a holistic sustainability approach by wine firms leads to economic issues in the short term. We focus on Italian wineries certified under Equalitas certification, a three-pillar certification integrating environmental, social, and economic dimensions, and evaluate their financial performance using a difference-in-differences (DiD) approach. The analysis relies on firm-level economic data from the AIDA database, covering a sample of 631 companies observed over a six-year period from 2018 to 2023. Overall, the results show no statistically significant short-term changes in profitability or liquidity indicators across the entire sample. These findings suggest that, while short-term economic gains are not guaranteed, embarking on a sustainability path does not undermine financial performance and may offer benefits under certain organizational conditions. The study contributes to the literature by providing robust empirical evidence on the economic implications of the adoption holistic sustainability approaches in the wine sector. Full article

This article presents a systemic framework for integrating Industry 4.0 technologies with sustainability practices, structured around three strategic pillars: technological selection, technological integration, and sustainability assessment. To support its development, a systematic literature review was conducted, applying the PICO methodology (Population, Intervention, Comparison, Outcome) to ensure structured and reproducible research, and following PRISMA guidelines to guarantee methodological transparency and rigor. Relevant studies focusing on Industry 4.0 and sustainability integration were identified, analyzed, and synthesized. The proposed framework comprises five iterative stages—diagnosis, selection and prioritization, integration, assessment, and continuous improvement—complemented by practical guidelines to facilitate implementation across diverse organizational contexts, including administrative, financial, and human resources departments. It enables organizations to select appropriate technologies, evaluate multidimensional sustainability impacts, and align innovation with environmental, economic, and social objectives, providing a structured roadmap for decision-making. Comparative analysis with selected literature highlights that the framework fills existing gaps in systemic integration, multidimensional assessment, and iterative adaptation. Although conceptual, it integrates literature review insights and three illustrative case studies, offering a practical pathway for sustainable technological adoption. Future research should focus on empirical validation and metric development to consolidate its applicability across industrial sectors. Full article

Within the circular economy, repair is increasingly recognised as a crucial yet underexplored strategy that extends product lifespans and reduces waste. Service design offers approaches to support this transition by addressing technical, social, and systemic dimensions. This review aimed to synthesise how service design contributes to repair practices and identify research gaps. Following PRISMA 2020 guidelines, we systematically searched Scopus and Web of Science, applied inclusion criteria focusing on service design and repair within the circular economy, and conducted multi-step screening and snowballing. From 132 initial records, 73 studies were included (journal articles, conference papers, book chapters). Thematic synthesis identified three areas: micro-level interactions between producers, products, and users (e.g., motivations, trust, communication); meso-level tools, frameworks, and platforms enhancing accessibility and efficiency; and macro-level societal transformation through regulations, standards, and communities. Results highlight service design’s potential to foster systemic change by integrating environmental, social, and economic aspects, while also revealing notable research gaps related to the limited engagement of repairers, policymakers, and cross-level collaboration. Compared to previous studies, this review contributes a novel integrated framework linking micro-, meso-, and macro-level dimensions of repair within the circular economy, offering both conceptual insights and actionable directions for practitioners and policymakers. The study is limited by language constraints and the lack of a formal bias evaluation. All reviewed materials are publicly accessible on OSF. This research was conducted without external financial support. Full article

In the context of sustainable banking, this study examines the relationships between customers’ perceptions of corporate social responsibility (CSR), their evaluations of a bank’s financial performance, and the positive and negative emotions they associate with a bank, underscoring the importance of managing customer psychology. The dataset consists of 426 completed questionnaires collected from private bank customers in Ankara, the capital of Turkey. SPSS 24.0 was used for data entry, and SPSS and AMOS 24.0 were employed for statistical analyses. The analytical procedures included preliminary analyses, confirmatory factor analysis, Harman’s single-factor test, common latent factor analysis, internal consistency, composite reliability, convergent and discriminant validity, Pearson correlation analysis, structural equation modeling, the Sobel test, and a bootstrap method with 5000 resamples. The findings indicate that customers’ perceptions of CSR have a significant and positive direct effect on their perceptions of a bank’s financial performance. In turn, perceived financial performance positively influences customers’ positive emotions and negatively influences their negative emotions. Moreover, perceptions of CSR affect emotional responses both directly and indirectly through perceived financial performance, enhancing positive emotions and reducing negative ones. In conclusion, within the proposed model, perceived financial performance functions as a partial mediating variable between CSR perceptions and customer emotions. These findings advance CSR scholarship by mapping its financial and emotional impact pathways in banking, yielding strategic insights for practitioners. Full article

This paper applies a hybrid multi-criteria decision-making (MCDM) model that integrates the Analytic Hierarchy Process (AHP) for structured weighting of evaluation criteria with the Net Worth Analysis (NWA) method for value-based aggregation of scores. The proposed framework was employed to evaluate Learning Management Systems (LMS) in higher education, involving two independent expert panels representing management and IT perspectives. Results of the AHP analysis show that cost (28%), security (22%), and usability (17%) are the most influential criteria in the decision-making process, reflecting institutional priorities for financial efficiency, safety and ease of use. Based on the combined AHP-NWA model, Moodle 4.3 emerged as the most sustainable choice (0.586), followed by Atutor 2.2.1 (0.541) and Blackboard (SaaS edition) (0.490). The inclusion of sensitivity and scenario analyses confirmed the robustness of the model, demonstrating that the ranking of alternatives remains stable under variations in weighting factors and different strategic priorities. By framing LMS evaluation within the context of sustainable digital transformation, the study emphasizes how transparent and systematic decision-making supports long-term institutional resilience and aligns with the principles of Education for Sustainable Development (ESD). In addition, the framework contributes to the achievement of Sustainable Development Goal 4 (Quality Education), by guiding higher education institutions toward inclusive, resilient and cost-effective digital solutions. Full article

This research proposes an algorithmic machine learning framework aimed at the early evaluation of business ideas. The framework integrates fifteen critical variables organized into five dimensions—innovation, sustainability, the entrepreneurial team, scalability, and initial finances—identified from a systematic review of the literature. Unlike traditional approaches that focus on financial metrics or one-dimensional indicators, this model provides a comprehensive, multidimensional view of entrepreneurial viability in uncertain contexts. Methodologically, the study presents a structured pipeline that incorporates Random Forest, Gradient Boosting, and XGBoost ensemble algorithms, as well as SMOTE data balancing techniques. These techniques address common problems, such as class imbalance and generalization limitations. Theoretically, innovation and sustainability constructs are operationalized alongside entrepreneurial and financial factors, contributing to more consistent, integrative evaluation models. In practical terms, this proposal provides incubators, accelerators, and public policy designers with a replicable and adaptable tool for the early stages of entrepreneurship. While empirical validation is planned for the future, this work lays the methodological groundwork to bridge gaps in the literature and advance more robust predictive models for entrepreneurial evaluation. Full article

This study employs a mixed-methods approach to examine the relationship between sustainability reporting and brand value in Türkiye’s most valuable brands. In the first phase, panel data regression analysis is utilized to evaluate the association between the presence of sustainability reports and firms’ brand values, with a focus on the impact of sustainability reporting on brand value over time. The findings showed that sustainability reports have a positive impact on brand value in four sectors among ten. These are construction, manufacturing, financial institutions, and wholesale & retail sectors. To better interpret these results, the second phase employs qualitative content analysis, utilizing Leximancer software (LexiDesktop 5) to analyze sustainability reports and identify evolving thematic patterns in reports across various sectors from 2020 to 2023. The results suggest that ESG reporting is evolving to become more comprehensive, multi-faceted, and responsive to sector-specific contexts, driven by heightened stakeholder attention and the continuous development of global standards. The findings provide insights into the role of sustainability reporting in shaping corporate brand value and highlight sector-specific trends in sustainability practices. Full article

The triple shocks of the financial crisis, sovereign debt crisis, and COVID-19 pandemic have exerted significant impact on the financial markets in the Eurozone. Since the 2008 recession, the European Central Bank (ECB) has implemented an array of unconventional monetary policies (UMPs). These policies aim to address issues such as financing constraints and low inflation rates that the traditional monetary policy framework could not handle. The data indicated that when the ECB implemented its quantitative easing (QE) programs (e.g., the pandemic emergency purchase program), inflation in the Eurozone bounced back. It went up from −0.3% in August 2020 to 5% by December 2021. These measures prevented the pandemic from pushing the economy into a long-lasting deflation pressure. As the world’s second-largest economy, China’s monetary policy decisions play a crucial role in maintaining economic stability and fostering sustainable growth. This study examines ECB’S major unconventional monetary policy measures, evaluates their effects, and explores how these align with China’s monetary policy formulation and reforms. This research can provide useful insights for shaping monetary policy in the Eurozone and emerging economies such as China, especially during times of economic uncertainty. Full article

Sustainable development in rural communities faces significant challenges, such as inadequate infrastructure, poor resource management, and weak governance, especially in developing countries like Peru. This study aimed to develop a management model tailored to the local needs of smallholder communities in the Junín region, Peru, addressing social, economic, and environmental dimensions to improve quality of life. Using a descriptive mixed-method design with non-experimental and cross-sectional methods, 60 smallholder communities were evaluated based on criteria of access to information and relevance to sustainable development. Data collected through structured surveys and semi-structured interviews revealed a lack of inclusive participation, insufficient economic income, lack of financial transparency, and inadequate environmental practices. The proposed management model integrates strategies to improve community governance, foster inclusive participation, promote sustainable economic practices, and conserve the environment. It concludes that a comprehensive, flexible, and locally adapted approach, emphasizing transparency and community participation, is essential to achieving long-term sustainability in smallholder communities. Full article