Search Results (26)

This study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors. Full article

The integration of artificial intelligence (AI) into environmental, social, and governance (ESG) disclosure represents a critical frontier for corporate transparency in emerging markets. This study investigates the relationship between AI adoption in ESG reporting, disclosure quality, and financial performance among 180 Saudi-listed companies (2021–2024) within Vision 2030’s transformative context. Using the System Generalized Method of Moments (GMM) estimation with panel unit root and cointegration testing to ensure stationarity assumptions and addressing endogeneity through bounding analysis, the study finds that AI adoption intensity significantly enhances ESG disclosure quality (β = 0.289, p < 0.001), with coefficient significance assessed through t-tests using firm-clustered robust standard errors. Enhanced disclosure quality translates into meaningful financial performance improvements: 0.094 percentage points in return on assets (ROA), 0.156 in return on equity (ROE), and 0.0073 units in Tobin’s Q. Mediation analysis reveals that 73% of AI’s total effect operates through improved ESG quality rather than direct operational benefits. The findings demonstrate parametric bounds robust to macroeconomic confounders, suggesting AI-enhanced transparency creates substantial shareholder value through strengthened stakeholder relationships and reduced information asymmetries. Full article

Digital innovation is rapidly transforming the agriculture sector, drawing attention from global development institutions, policymakers, tech firms, and scholars aimed at aligning food systems with international goals like Zero Hunger and the FAO agendas. Small and medium enterprises in agriculture (Agri-SMEs) represent a significant portion of processing and production units but face challenges in digital transformation despite their importance. Technologies such as Artificial Intelligence (AI), blockchain, cloud services, IoT, and mobile platforms offer tools to improve efficiency, access, value creation, and traceability. However, the patterns and applications of these transformations in Agri-SMEs remain fragmented and under-theorized. This paper presents a systematic review of interactions between digital transformation and innovation in Agri-SMEs based on findings from ninety-five peer-reviewed studies. Key themes identified include AI-based decision support, blockchain traceability, cloud platforms, IoT precision agriculture, and mobile technologies for financial integration. The review maps these themes against business model values and highlights barriers like capacity gaps and infrastructure deficiencies that hinder scalable adoption. It concludes with recommendations for future research, policy, and ecosystem coordination to promote the sustainable development of digitally robust Agri-SMEs. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

The review provides the results of the analysis of research publications in the field of energy economy on a global scale. The review aims to test three hypotheses and build and analyze the main trend lines and clusters to determine the direction of movement of new knowledge in the energy economy research. This review delves into the multifaceted nature of energy transitions, highlighting the pivotal role of policy frameworks, financial instruments, and technological innovation. By examining the socio-economic implications of renewable energy deployment and addressing the challenges associated with energy storage and grid integration, this study contributes to the ongoing discourse on sustainable energy development. The review used scientometric, correlation, and bibliometric methods of analysis. Artificial Intelligence was used to process 411,396 units of information (a special prompt was created). There is a new scientific result: (a) research hypotheses 1 and 3 were accepted, research Hypothesis 2 was rejected; (b) new trend lines showed the directions where the global energy economy is heading; (c) new clusters showed the top five leading countries and top 10 keywords in the field of energy economy; (d) leading journals in the studied area were found. The review indicated an underrepresentation of specialized journals in this field among the top journals. The review also showed that several keywords characterizing the sixth technological paradigm are missing from the top 10 keywords. This result suggests that these research areas were underrepresented in citation-based bibliometric data in energy in 2021–2024. The review is useful to identify promising and problematic areas for future research in the global energy economy. Full article

This paper examines the uptake of AI-driven fraud detection systems among financial institutions in the UAE and Qatar, with a special focus on trust, transparency, and perceptions of fairness. Despite the promise of AI operations in identifying financial anomalies, unclear decision-making processes and algorithmic bias constrain its extensive acceptance, especially in regulation-driven banking sectors. This study uses a quantitative strategy based on Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) of survey responses from 409 bank professionals, such as auditors and compliance officers. This study shows that transparency greatly enhances trust, which is the leading predictor of AI uptake. Fairness perception mediates the negative impacts of algorithmic bias, emphasizing its important role in establishing system credibility. The analysis of subgroups shows differential regional and professional variations in trust and fairness sensitivity, where internal auditors and highly AI-exposed subjects are found to exhibit higher adoption preparedness. Compliance with regulations also emerges as a positive enabler of adoption. This paper concludes with suggestions for practical implementation by banks, developers, and regulators to align AI deployment with ethical and regulatory aspirations. It recommends transparent, explainable, and fairness-sensitive AI tools as essential for promoting adoption in regulation-driven sectors. The findings provide a guide for promoting responsible, trust-driven AI implementation in fraud detection. Full article

A significant portion of small and medium-sized enterprises (SMEs) are usually allocated to the construction sector, which plays a vital role in many economies. SMEs currently face serious concerns regarding the pursuit of sustainability. Limited financial resources (FRs) frequently prevent SMEs from implementing sustainable practices. Therefore, these enterprises should mitigate expenses to invest in environmentally friendly initiatives. Enhancing resources and developing ways to accelerate Turkish SMEs’ shift toward sustainability is vital. Moreover, adopting artificial intelligence (AI) and green innovation strategies (GISs) can boost sustainable competitive advantage (SCA) and lead them to success. This study utilized the natural resource-based view theory (NRBV), developed to compensate for the RBV’s shortcomings by incorporating the natural environment into the RBV’s framework. This study uses structural equation modeling (SEM) to examine the causal effect between the study variables based on the responses received from 228 executives within SMEs in Turkey’s construction sector. The findings of this study reveal that FRs significantly impact the SCA among SMEs, while GIS serves as a mediator in the relationship. Additionally, the moderating impact of AI adoption promotes sustainability development in this industry. This study is significant because it contributes to the body of knowledge regarding the relationship between the study’s constructs that align with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure), presented by the United Nations in 2015. This goal promotes robust infrastructure, encourages sustainable and inclusive industrialization, and stimulates innovation in the SME construction industry. Although these variables have been studied individually in previous studies, this study integrates them into a thorough framework that emphasizes the function of GIS as a mediator in the relationship between FRs and SCA, and the interaction effect of AI adoption. This study offers useful information to managers, stakeholders, politicians, and SME leaders, enabling them to make well-informed decisions about sustainable practices. Full article

The wastewater conveyance systems in the United States are facing severe structural challenges, with the nation’s overall wastewater infrastructure receiving a critically low grade of D- from the American Society of Civil Engineers (ASCE). Innovative trenchless technologies, such as Cured-in-Place Pipe Renewal Technology (CIPPRT), offer a cost-efficient substitute for traditional open-cut construction methods (OCCM). However, the possibility of a comprehensive life-cycle cost analysis (LCCA) comparing these methods remains unexplored. LCCA examines the comprehensive financial impact, encompassing installation, operation, maintenance, rehabilitation, and replacement expenses, using net present value (NPV) over a set duration. The objective of this study is to systematically review the existing literature to explore advancements in calculating the LCCA for CIPPRT and compare the latter approach to OCCM. A rigorous PRISMA-guided methodology applied to academic databases identified 845 publications (1995–2024), with 83 documents being selected after stringent screening. The findings reveal limited use of artificial intelligence (AI) or machine learning (ML) in predicting CIPPRT costs. A bibliometric analysis using VOSviewer visualizes the results. The study underscores the potential of intelligent, data-driven approaches, such as spreadsheet models and AI, to enhance decision-making in selecting rehabilitation methods tailored to project conditions. These advancements promise more sustainable and cost-effective management of sanitary sewer systems, offering vital insights for decision-makers in addressing critical infrastructure challenges. Full article

Current Environmental, Social, and Governance (ESG) indices are flawed because the data are incomplete and not reported consistently, and some measured factors may be irrelevant to the industry. Regulators in the financial services industry emphasize reporting CO₂ emissions (environmental factor), yet the key resources leveraged for production are rented offices, and internet–governance issues like money laundering, corruption, and unethical behavior would be more relevant. To investigate this problem, we sampled the finance and insurance industry firms in the USA with the greatest economic impact, i.e., those managing at least USD 1 trillion in assets. We used artificial intelligence to collect data about undisclosed legal decisions against firms to measure the ESG anti-corruption governance factor GRI 206-1, defined by the Global Reporting Institute (GRI) for global sustainable development goals (SDGs), which correspond to the United Nations’ SDGs. We applied Bayesian correlation with bootstrapping to test our hypotheses, followed by root cause analysis. We found that ESG ratings from providers did not reflect legal cases decided against firms; the Bayesian BF₊₀ odds ratio was 3005 (99% confidence intervals were 0.617, 0.965). Also, misconduct fines and arbitration legal case counts were significantly related for the same firm (the Vovk-Selke maximum p-ratio was 4411), but most ESG scores were significantly different for the same firm. We found three other studies in the literature that corroborated some of our findings that specific firms in our sample were considered to be unethical. We propose deeper study of the implications related to our findings based on public interest and stakeholder theory. Full article

This study employs a bibliometric analysis of emerging trends in money laundering for terrorism financing (ML/TF) to provide direction for future research. The authors used VOSviewer and analyzed 2577 published documents retrieved from the SCOPUS database using the PRISMA methodology. The findings reveal a growing research interest in understanding the complex interplay between money laundering and terrorism financing. This research emphasizes the significance of ML/TF for economic stability, as understanding terrorism financing mechanisms allows authorities to trace and block funds going to terrorist groups, which is crucial for national security. Critical insights for policymakers underscore the need for robust legislative frameworks, effective Financial Intelligence Units (FIUs), and international collaboration to combat these global threats. This analysis offers a foundation for future research, mapping the evolving knowledge landscape in ML/TF. Full article

This study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature review of recent research is conducted, exploring AI applications, benefits, challenges, regulations, and relevant theories. This study identifies key trends and suggests future research directions. The major findings include an overview of AI applications, benefits, challenges, and ethical issues in the banking and finance industries. Recommendations are provided to address these challenges and ethical issues, along with examples of existing regulations and strategies for implementing AI governance frameworks within organizations. This paper highlights innovation, regulation, and ethical issues in relation to AI within the banking and finance sectors. Analyzes the previous literature, and suggests strategies for AI governance framework implementation and future research directions. Innovation in the applications of AI integrates with fintech, such as preventing financial crimes, credit risk assessment, customer service, and investment management. These applications improve decision making and enhance the customer experience, particularly in banks. Existing AI regulations and guidelines include those from Hong Kong SAR, the United States, China, the United Kingdom, the European Union, and Singapore. Challenges include data privacy and security, bias and fairness, accountability and transparency, and the skill gap. Therefore, implementing an AI governance framework requires rules and guidelines to address these issues. This paper makes recommendations for policymakers and suggests practical implications in reference to the ASEAN guidelines for AI development at the national and regional levels. Future research directions, a combination of extended UTAUT, change theory, and institutional theory, as well as the critical success factor, can fill the theoretical gap through mixed-method research. In terms of the population gap can be addressed by research undertaken in a nation where fintech services are projected to be less accepted, such as a developing or Islamic country. In summary, this study presents a novel approach using descriptive analysis, offering four main contributions that make this research novel: (1) the applications of AI in the banking and finance industries, (2) the benefits and challenges of AI adoption in these industries, (3) the current AI regulations and governance, and (4) the types of theories relevant for further research. The research findings are expected to contribute to policy and offer practical implications for fintech development in a country. Full article

In horses, quantitative assessment of gait parameters, as with the use of inertial measurement units (IMUs) systems, might help in the decision-making process. However, it requires financial investment, is time-consuming, and lacks accuracy if displaced. An innovative artificial intelligence marker-less motion tracking system (AI-MTS) may overcome these limitations in the field. Our aim was to compare the level of agreement and accuracy between both systems and visual clinical assessment. Twenty horses underwent locomotion analysis by visual assessment, IMUs, and AI-MTS systems, under the following conditions: straight hard (SH), straight soft (SS), left and right circle hard (LCH, RCH), and soft (LCS, RCS). A greater number of horses were considered sound by clinical examination, compared to those identified as symmetric by the two gait analysis systems. More limbs were considered asymmetric by the AI-MTS compared to IMUs, suggesting its greater sensitivity. The greatest agreement between the two systems was found for the difference between two minima in vertical head position in SH, while the lowest for the difference between two minima in vertical pelvis position in SS, reflecting the difficulties in assessing asymmetry of the hindlimbs. It is unknown what degree of asymmetry is clinically relevant, suggesting that more consistent use in training horses may help determine the thresholds for asymmetry. Some degree of asymmetry may be clinically relevant, suggesting its regular use in training horses. Full article

Stock prediction has garnered considerable attention among investors, with a recent focus on the application of machine learning techniques to enhance predictive accuracy. Prior research has established the effectiveness of machine learning in forecasting stock market trends, irrespective of the analytical approach employed, be it technical, fundamental, or sentiment analysis. In the context of fiscal year-end selection, the decision may initially seem straightforward, with December 31 being the apparent choice, as discussed by B. Kamp in 2002. The primary argument for a uniform fiscal year-end centers around comparability. When assessing the financial performance of two firms with differing fiscal year-ends, substantial shifts in the business environment during non-overlapping periods can impede meaningful comparisons. Moreover, when two firms merge, the need to synchronize their annual reporting often results in shorter or longer fiscal years, complicating time series analysis. In the US S&P stock market, misaligned fiscal years lead to variations in report publication dates across different industries and market segments. Since the financial reporting dates of US S&P companies are determined independently by each listed entity, relying solely on these dates for investment decisions may prove less than entirely reliable and impact the accuracy of return prediction models. Hence, our interest lies in the synchronized fiscal year of the TW stock market, leveraging machine learning models for fundamental analysis to forecast returns. We employed four machine learning models: Random Forest (RF), Feedforward Neural Network (FNN), Gated Recurrent Unit (GRU), and Financial Graph Attention Network (FinGAT). We crafted portfolios by selecting stocks with higher predicted returns using these machine learning models. These portfolios outperformed the TW50 index benchmarks in the Taiwan stock market, demonstrating superior returns and portfolio scores. Our study’s findings underscore the advantages of using aligned financial ratios for predicting the top 20 high-return stocks in a mid-to-long-term investment context, delivering over 50% excess returns across the four models while maintaining lower risk profiles. Using the top 10 high-return stocks produced over 100% relative returns with an acceptable level of risk, highlighting the effectiveness of employing machine learning techniques based on financial ratios for stock prediction. Full article

Non-technical losses (NTLs) verified in the power distribution grids cause great financial losses to power utilities. In rural distribution grids, fraudulent consumers contribute to technical problems. The Southern region in Brazil contains more than 70% of the total rice production and power irrigation systems. These systems operate seasonally in distribution grids with high NTL conditions. This work aimed to present an artificial intelligence-based system to help power distribution companies detect potential consumers causing NTLs. This minimizes the challenge of maintaining compliance with current regulations and ensuring the quality of services and products. In the proposed methodology, historical energy consumption information, meteorological data, satellite images, and data from energy suppliers are processed by artificial intelligence, indicating the suspicious consumer units of NTL. This work presents every step developed in the proposed methodology and the tool application in a pilot area. We detected a high number of consumers responsible for NTLs, with an accuracy of 63% and an average reduction of 78% in the search area. These results corroborated the effectiveness of the tool and instigated the research team to expand the application to other rice production areas. Full article