Search Results (391)

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

Strengthening climate resilience constitutes a foundational approach through which cities adapt to climate change and mitigate associated environmental risks. However, research on the influence of economic policy environments on climate resilience remains limited. Guided by institutional theory and dynamic capability theory, this study employs a panel dataset comprising 272 Chinese cities at the prefecture level and above, covering the period from 2009 to 2023. It constructs a composite index framework for evaluating the green business environment (GBE) and urban climate resilience (UCR) using the entropy weight method. Employing a two-way fixed-effect regression model, it examined the impact of GBE optimization on UCR empirically and also explored the underlying mechanisms. The results show that improvements in the GBE significantly enhance UCR, with green innovation (GI) in technology functioning as an intermediary mechanism within this relationship. Moreover, climate policy uncertainty (CPU) exerts a moderating effect along this transmission pathway: on the one hand, it amplifies the beneficial effect of the GBE on GI; on the other hand, it hampers the transformation of GI into improved GBEs. The former effect dominates, indicating that optimizing the GBE becomes particularly critical for enhancing UCR under high CPU. To eliminate potential endogenous issues, this paper adopts a two-stage regression model based on the instrumental variable method (2SLS). The above conclusion still holds after undergoing a series of robustness tests. This study reveals the mechanism by which a GBE enhances its growth through GI. By incorporating CPU as a heterogeneous factor, the findings suggest that governments should balance policy incentives with environmental regulations in climate resilience governance. Furthermore, maintaining awareness of the risks stemming from climate policy volatility is of critical importance. By providing a stable and supportive institutional environment, governments can foster steady progress in green innovation and comprehensively improve urban adaptive capacity to climate change. Full article

Background/Objectives: Weight loss and its subsequent regain pose significant challenges for those dealing with overweight and obesity. This study explores weight loss strategies among adults in Saudi Arabia and evaluates factors linked to weight regain. Methods: This cross-sectional study focused on adults residing in Jazan, located in southwest Saudi Arabia. Data collection was conducted using a self-administered questionnaire that assessed participants’ demographics, medical history, perceptions of body weight, weight loss methods, and the incidence of weight regain. Logistic regression was used to determine whether there were statistically significant differences related to the occurrence of weight regain. Results: A total of 368 participants reported efforts to lose weight over the past 3 years. The average age of these participants was 32.7 years (standard deviation: 11.3), and the gender distribution was almost equal. The majority of the sample (65%) voiced dissatisfaction with their body weight. Some participants employed a combination of weight loss methods, with exercise, reduced food intake, and intermittent fasting being the most frequently mentioned. The findings also indicate that a minority sought professional help, whether from a physician or a nutritionist. Over 90% claimed to have successfully lost weight at least once during their attempts, but more than half (139 individuals) experienced weight regain following their weight loss efforts. Within the univariate logistic regression, higher odds ratios of weight regain were detected among men, older participants, those living in rural areas, individuals with higher levels of education, employed persons or business owners, those with higher monthly incomes, smokers, khat chewers, and those diagnosed with a chronic condition (p values < 0.05). However, the multivariate logistic regression revealed that only residence, monthly income, smoking status, and being diagnosed with a chronic disease remained statistically significant as predictors of weight regain after adjusting for other variables (p values < 0.05). Conclusions: These findings highlight the significance of incorporating weight regain prevention into body weight management for individuals dealing with overweight and obesity. Further research is needed to evaluate specific dietary, physical activity, and psychological factors that may increase the risk of weight regain in certain participants. Full article

As many companies adopted hybrid work arrangements during and after the COVID-19 outbreak, interest in Metaverse applications for virtual offices grew considerably. Along with this growing interest, the risk of data breaches has also increased, as virtual offices often handle confidential documents for businesses. For this reason, existing studies have explored Metaverse user authentication methods; however, their methods suffer from several limitations, such as the need for additional sensors and one-time authentication. Therefore, this paper proposes a novel behavioral authentication framework for secure Metaverse workspaces. The proposed framework adopts keyboard typing behavior that is common in the office and does not cause fatigue to users as an authentication factor to afford active and continuous user authentication. Based on our evaluation, the user identification accuracy achieved an average of approximately 95% among 11 of 15 participants, with the highest-performing user reaching an accuracy of 99.77%. In addition, the proposed framework achieved an average false acceptance rate of 0.41% and a false rejection rate of 4.02%. It was also evaluated with existing studies using requirements for user authentication in the Metaverse to demonstrate its strengths. Therefore, this framework can fully ensure a secure Metaverse office by preventing unauthenticated users. Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article

This study conducts a comparative assessment of the environmental, social, and governance (ESG) integration strategies of three leading companies in Hong Kong—H&M Group, China Gas Company Limited (Towngas), and MTR Corporation Limited (MTR)—each operating in distinct sectors with unique sustainability challenges and opportunities. The analysis adopts the Challenge–Evaluation–Planning–Action–Review (CEPAR^®) framework developed by the International Chamber of Sustainable Development to examine how these companies identify and evaluate ESG-related risks, formulate action plans, implement sustainability initiatives, and refine their strategies. The findings reveal H&M’s strong emphasis on sustainable fashion, with a target of using 100% sustainable materials by 2030 and reducing greenhouse gas emissions by 56%. Towngas faces the complex challenge of transitioning from fossil fuels to cleaner energy and is investing in zero-carbon technologies to meet regulatory standards and stakeholder expectations. MTR focuses on sustainable urban development and efficient mass transit, prioritizing community engagement and reducing environmental impact. This study underscores the importance of sector-specific ESG approaches tailored to a company’s operational context. It also demonstrates how ESG integration is enhanced by proactive planning, transparent reporting, and alignment with long-term corporate values. By showcasing both successful practices and areas requiring further attention, this research contributes to the broader discourse on sustainable business practices in Hong Kong. Moreover, it provides actionable policy implications for government agencies and regulatory bodies. The insights gained can inform strategic decision-making across sectors and support the development of a more sustainable, resilient, and inclusive economy aligned with Hong Kong’s long-term climate and governance goals. Full article

Geometallurgy is an approach that utilizes predictive models that can support business decisions, mitigate risks, and enhance production efficiency. To develop an accurate geometallurgical model, it is essential to understand the behavior of each lithology within the ore body through geometallurgical testing. In this context, the present study aims to evaluate the performance of bench-scale tests conducted on the main mining fronts of a zinc mine operation located in Brazil. The mineral processing plant was designed to process lead and zinc sulfide ores without material stockpiling, where all ores extracted from the underground mine are immediately processed. The geometallurgical characterization was conducted through the following steps: sampling, crushing, grinding, and flotation. The recovery, concentrate, and tailing contents during the flotation stages of galena and sphalerite were analyzed. A mineralogical characterization using a Mineral Liberation Analyzer (MLA) was performed to assess the degree of particle liberation and mineral associations within the studied mining fronts. The results indicate that a higher degree of pyrite liberation leads to greater metallurgical recovery of mineralized bodies A (breccia-hosted orebody), B (sphalerite-rich doloarenite orebody), and C (upper replaced stratiform orebody). Among these, mineralized body C presents the highest recovery in the zinc and lead stages, with 99.5% and 86.2%, respectively. Full article

As an emerging e-commerce model, live streaming e-commerce integrates instant interaction, content marketing, and online sales to bring consumers a new shopping experience. However, there are many risks in the process of live e-commerce transactions. Identifying key risk factors and implementing targeted control measures are crucial for promoting the sustainable and healthy development of live streaming e-commerce. This paper firstly constructs a business model of live streaming e-commerce transactions according to the transaction scenario and summarizes 24 risk factors from the three dimensions of live streaming e-commerce platforms, merchants, and anchors based on relevant national standards and other relevant literature. Secondly, the Delphi method is employed to modify and optimize the initial risk factors. On this basis, the social network model of risk factors is constructed to determine the influence relationship among risk factors. By calculating the degree centrality, factor types are segmented, and key risk factors as well as influence paths are identified. Finally, corresponding countermeasures and suggestions are proposed. The results indicate that Credit Evaluation System Perfection, Service Evaluation System Perfection, Qualification Audit Mechanism Perfection, Dispute Complaint Handling Channels Perfection, Risk Identification Mechanism Perfection, Platform Qualification, Merchant Qualification, and Merchant Credit are the critical risk factors affecting live streaming e-commerce transactions. Full article

Purpose: This study aims to provide a comprehensive and objective view to investigate whether the motives of strong financial managers to adopt an accounting conservatism strategy have significant effects on improving financial growth opportunities in the context of banks listed on the Saudi Stock Exchange, while knowing how this relationship is affected by litigation risks. Design/Methodology/Approach: Using data from Saudi financial databases, this study examines how litigation risk moderates the relationship between accounting conservatism and financial growth in Saudi listed banks. Basu’s (1997) model and accrual-based metrics measure conservatism, whereas assets, liabilities, and business age are used to measure financial growth. Litigation risk factors included previous lawsuits. Validity was ensured using fixed-effects regression and robustness tests. Findings: The study found that accounting conservatism has a mixed impact on financial growth, litigation risk moderates the relationship between conservatism and financial growth, and litigation risk has a positive impact on accounting conservatism. Practical Implications: Use a balanced strategy to maintain accounting conservatism, lower litigation risk while maintaining the accuracy of financial statements, take legal risk into account when evaluating the quality of financial reporting, increase transparency without impeding growth, create guidelines tailored to a particular bank, and fortify governance to reduce lawsuits while permitting long-term financial growth. Originality/Value: In order to bridge the gap between conservatism strategies and long-term financial stability in emerging economies, this study examines how managerial decisions in accounting conservatism affect the financial growth of Saudi banks, incorporating litigation risk as a moderating factor. It also contributes to financial policies, risk management, and regulations. Full article

In the development process of the power industry, accurately assessing the level of development of the electricity business environment is of great significance. However, traditional evaluation systems have limitations, with the issue of “data silos” being prominent, and user privacy under federated learning is also at risk. This paper proposes a federated learning-based data protection method for the electricity business environment to address these challenges. Based on the World Bank’s B-READY framework, this paper constructs an electricity business environment evaluation system containing nine indicators, focusing on three aspects: electricity regulations, public services, and operational efficiency. The indicators are weighted using the Sequence Relation and Entropy Weight Method. To address the issue of sensitive data protection, we first use federated learning technology to build a distributed modeling framework, ensuring that raw data never leaves the local environment during the collaborative modeling process. Next, we embed a differential privacy mechanism in the model parameter transmission stage, encrypting the model parameters by adding controlled noise. Finally, an incentive mechanism based on contribution quantification is implemented to encourage participation from all parties. This paper conducts experiments using the data of Shenzhen City, Guangdong Province. Compared with the FNN model and the SVR model, the MLP model reduces MAE by 78.9% and 94.12%, respectively, and increases R² by 37.95% and 55.62%, respectively. The superiority of the method proposed in this paper has been proved. Full article

In today’s volatile market environment, supply chain management (SCM) must address complex challenges such as fluctuating demand, fraud, and delivery delays. This study applies machine learning techniques—Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks (RNNs)—to optimize demand forecasting, inventory policies, and risk mitigation within a unified framework. XGBoost achieves high forecasting accuracy (MAE = 0.1571, MAPE = 0.48%), while RNNs excel at fraud detection and late delivery prediction (F1-score ≈ 98%). To evaluate models beyond accuracy, we introduce two novel metrics: Cost–Accuracy Efficiency (CAE) and CAE-ESG, which combine predictive performance with cost-efficiency and ESG alignment. These holistic measures support sustainable model selection aligned with the ISO 14001, GRI, and SASB benchmarks; they also demonstrate that, despite lower accuracy, Random Forest achieves the highest CAE-ESG score due to its low complexity and strong ESG profile. We also apply SHAP analysis to improve model interpretability and demonstrate business impact through enhanced Customer Lifetime Value (CLV) and reduced churn. This research offers a practical, interpretable, and sustainability-aware ML framework for supply chains, enabling more resilient, cost-effective, and responsible decision-making. Full article

In the context of the globalization process, the interplay between geopolitical dynamics and international trade fluctuations has had significant effects on global economic and business stability. Recent crises, such as the US–China trade war, the invasion of Ukraine, and the COVID-19 pandemic, have highlighted how changes in the structure of international trade can amplify the risks of business failure and reshape global competitiveness. This study aims to analyze in depth the transmission of business failure risk within the global trade network by assessing the sensitivity of industrial sectors in different countries to disruptive/critical/significant events. Through the integration of data from sources such as the World Trade Organization, national customs, and international relations research centers, a quantitative, exploratory, and descriptive approach based on graph theory, random forest, multivariate regression models, and neural networks is developed. This quantitative system makes it possible to identify patterns of risk propagation and to evaluate the degree of vulnerability of each country according to its commercial and financial structure. The mechanisms that relate geopolitical factors, such as trade sanctions and international conflicts, with the oscillations in the global market are analyzed. This study not only contributes to our understanding of how the macroeconomic environment influences business survival, but also provides analytical tools for strategic decision making. By providing an empirical and theoretical framework for early risk identification, it brings a novel perspective to academia and business, facilitating better adaptation to an increasingly volatile and uncertain business environment. Full article

This paper explores the link between the results of the UK’s Research Evaluation Exercises (REEs) and university decisions on which Units of Assessment (UOA) to submit to in future REEs. How the raw data from REEs can be converted into two novel measurements of research performance—an internal and an external measurement—is explained. Data on two UOAs, Business and Management Studies (BMS) and Economics and Econometrics (E&E), from five consecutive REEs undertaken in the United Kingdom (UK) between 1992 and 2014, was then used to assess whether and how the results of one REE were related to UOA submissions in the next. The findings reveal that both the internal and external assessments of performance were associated with changes in the probability of resubmission to the same UOA in the next REE, with the external comparisons being particularly important. It also appears that while one instance of poor performance might be tolerated by a university, repeated poor performance was associated with a heightened risk of withdrawal from both the BMS and E&E UOAs in the next REE. In addition, holding research performance constant, universities were significantly more likely to withdraw from the E&E UOA than the BMS UOA. New (post-1992) universities were also more likely to continue to submit to a UOA in the next REE than pre-1992 institutions. There is also some evidence that the quality of submissions to the BMS UOA is catching up with that of submissions to the E&E UOA. The somewhat worrying implications of these findings for the health of the Economics discipline in UK universities are assessed. Full article

Past and current advances in artificial intelligence (AI) have resulted in a significant impact on business and accounting. Over time, AI has slowly transformed from the 1950s to today, from rule-based systems, also known as expert systems, to the deep learning architectures and sophisticated neural networks of modern generative AI. Early AI accounting applications of expert systems included a GAAP-based expert system to assess the appropriate accounting treatment for business combinations and an expert system to determine the proper type of audit report to issue. Recent accounting expert systems have been developed for document analysis, fraud detection, evaluating credit risk, and corporate default forecasting. The purpose of this study is to examine key events in the history of AI, current applications, and potential future effects pertaining to management accounting and society overall. In addition, the relationship of AI with economic and social factors will be evaluated. The study’s findings will be of interest to management accountants, businesspersons, academic researchers, and others who are concerned with artificial intelligence and its impact on management accounting and society overall. Full article

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. Using a seven-year dataset from S&P Capital IQ Pro, corporate credit ratings across 20 countries were analyzed, leveraging 51 financial and business risk variables. The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. Results indicate that Artificial Neural Networks (ANN) and GB consistently outperform traditional models, particularly in capturing non-linear relationships and complex interactions among predictive factors. This study advances financial risk management by demonstrating the efficacy of ML-driven credit rating systems, offering a more accurate, efficient, and scalable solution. Additionally, it provides practical insights for financial institutions aiming to enhance their risk assessment frameworks. Future research should explore alternative data sources, real-time analytics, and model explainability to facilitate regulatory adoption. Full article