Search Results (2,327)

Artificial intelligence (AI) has become an integral driver of digital transformation in the banking sector, fundamentally influencing operational efficiency, resource allocation, and profitability. This study investigates how AI adoption affects the profitability of Chinese commercial banks and through which mechanisms these effects occur, within the context of the country’s broader financial digitalization process. Using panel data for 17 A-share listed banks in China from 2009 to 2022, we employ a multi-period difference-in-differences (DID) framework—whose validity rests on the parallel trend assumption, empirically verified through an event-study specification—and combine it with propensity score matching (PSM) and placebo simulations to ensure credible causal identification. The results indicate that AI adoption significantly improves bank profitability. Mechanism analyses suggest that AI enhances profitability through two overarching channels—operational efficiency and resource allocation—manifested in (i) higher cost elasticity of income, (ii) improved deposit–loan turnover adaptability via more efficient liquidity and funding-cycle management, and (iii) optimized cross-business capital allocation efficiency through better risk–return matching in diversified operations. The effects are stronger for banks with higher digital investment intensity and tighter customer stickiness–liability cost coupling, and vary systematically across ownership types, bank sizes, and policy cycles. Overall, the findings provide policy-relevant evidence on how AI-driven digital transformation can enhance bank performance and risk management in modern financial systems. This study contributes by constructing a disclosure-based AI adoption measure from bank annual reports and exploiting staggered adoption with a multi-period DID design to provide causal evidence from China’s listed banking sector. Full article

This research focuses on the challenge of measuring the socio-economic impact of road traffic accidents (RTAs) by examining how losses are redistributed across major institutional sectors, including the government, businesses, and households. Unlike traditional cost-based approaches, the analysis relies on a modified input–output framework that captures not only the direct losses but also the indirect damage flows transmitted from one sector to another. This methodology makes it possible to reveal the multiplicative propagation of losses, determine the proportion of net costs, and quantify the transfer dependencies between institutional agents. Using compiled and adapted data for the Azerbaijani economy, the study estimates the net economic damage from RTAs at 2268.17 million manats after adjusting for internal transfers. The results show that households bear more than 47% of total losses, the enterprise sector accounts for approximately 39%, and the government absorbs nearly 13%. The model also isolates an “additional damage” component, reflecting lost income, profits, and tax revenues, and demonstrates that every 1000 RTA generates a chain reaction of interlinked costs that substantially amplifies the overall effect. The findings highlight the necessity of integrating input–output analytical approaches into the practical assessment of RTA-related economic consequences, particularly in countries with limited statistical capacity and structurally diverse institutional linkages. Full article

The smart grid concept is based on the full integration of different types of energy sources and intelligent devices. Due to the short- and long-term volatility of these sources, new flexibility measures are necessary to ensure the smart grid operates stably and reliably. One option is to convert renewable energy into hydrogen, especially during periods of generation overcapacity, in order that the hydrogen that is produced can be stored effectively and used “just in time” to stabilize the power system by undergoing a reverse conversion process in gas turbines or fuel cells which then supply power to the network. On the other hand, in order to achieve a sustainable general energy system (GES), it is necessary to replace other forms of fossil energy use, such as that used for heating and other industrial processes. Research indicates that a comprehensive hydrogen supply infrastructure is required. This infrastructure would include electrolyzers, conversion stations, pipelines, storage facilities, and hydrogen gas turbines and/or fuel cell power stations. Some studies in Germany suggest that the existing gas infrastructure could be used for this purpose. Further, nuclear and coal power plants are not considered reserve power plants (as in the German case), and an additional 20–30 GW of generation capacity in H₂-operated gas turbines and strong H₂ transportation infrastructure will be required over the next 10 years. The novelty of the approach presented in this article lies in the development of a unified modeling framework that enables the simultaneous and coherent representation of both economic and technical aspects of hydrogen production systems which will be used for planning and pre-decision making. From the technical perspective, the model, based on the black box approach, captures the key operational characteristics of hydrogen production, including energy consumption, system efficiency, and operational constraints. In parallel, the economic layer incorporates capital expenditures (CAPEX), operational expenditures (OPEX), and cost-related performance indicators, allowing for a direct linkage between technical operation and economic outcomes. This paper describes the systematic transformation from today’s power system to one that includes a hydrogen economy, with a particular focus on practical experiences and developments, especially in the German energy system. It discusses the components of this new system in depth, focusing on current challenges and applications. Some scaled current applications demonstrate the state of the art in this area, including not only technical requirements (reliability, risks) and possibilities, but also economic aspects (cost, business models, impact factors). Full article

Digital construction implementation has not yet realized its promised potential after three decades. Across the entire project lifecycle, adoption has encountered difficulties from high-level standard guidance, a lack of strategies, fragmented delivery approaches, and insufficient digital delivery competency. Establishing digital workflows tailored to organizations’ contexts is an essential linkage of the information layer to synthesize the business and technology layers to address these challenges within the ISO 19650 framework. The uneven implementation of building information modelling (BIM) in the Architecture, Engineering, Construction, and Operation (AECO) industry provides a holistic perspective to consider the digitalization workflow dynamics. This report performs a case study through a parallel approach to examining multiple projects’ digital construction implementation of an organization in the Forest City development. Applying an observation research method and real-world data of project records, it analyses its workflows’ digitalization and process digitization, combining with its organization’s structure and overall project strategy. Moreover, it highlights bespoke digital construction ecosystems and relevant stakeholders to streamline workflows. The digital construction implementation results and project benefits as project context indicators verify that fundamental digital workflows of design quality checking, project optimization, asset data collection, and defect management have significant applicability compared with the advanced workflows of integrated 5D cost management and precast design and production. Their adoptability keeps consistency with those of applicability using the extra cost, application complexity, and disruption level indicators from the technology–organization–environment (TOE) framework to measure. These multiple project studies reveal the feasibility for organizations to achieve lifecycle digital construction implementation competency. The feasibility is underpinned by introducing an in-house digital engineering team to organization structure, cultivating applicable digital delivery capabilities through workflows digitalization and process digitization, and synthesizing ISO 19650 with workflows to enable more contextualized digital construction implementation. Full article

Fluorinated greenhouse gases (FGGs) are classified as worldwide pollutants and have a high global warming potential compared to other greenhouse gases. Detecting the existence and concentration of new and older refrigerant gases is crucial for assessing system functionality and determining whether they can be recycled or need to be disposed of. Additional justifications for the necessity of quantitative measurements of these gases include the manufacturing of air conditioning components; leak detection is conducted to ensure they are free of leaks. Classical laboratory Fast Fourier transform spectrometers enable the detection and measurement of substances while being delicate, unwieldy, and costly, and typically requiring a skilled technician to operate them. For the estimation of refrigerants in the field, a portable, user-friendly, and cost-effective detection device must be deployed. This article provides an in-depth analysis of the categorization of refrigerant gases using an Internet of Things (IoT) gas detection device. The functionality in effectively differentiating between important refrigerant gases, like R-32 and R-134a, with low delay, is demonstrated through practical tests. With the portable device, this study utilizes Fourier-Transformed infrared spectra measured from the refrigerants R-32 and R-134a, collected using a custom-made 3D-printed tubular reactor equipped with two BaF₂ windows, suitable for use in the beamline of a Bruker IR Spectrometer. Calibration was performed by exposing the infrared sensor to controlled gas environments with varying amounts of refrigerant gases using accurately produced gas mixtures. Following the on-field analysis of the reclaimed refrigerants, the obtained data was immediately processed, and both the data and the results were uploaded to an IoT platform, making them available to business-to-business (B2B) clients. The functionality of the device is demonstrated. Full article

Artificial intelligence is increasingly being applied not only in the economy but also across various social sectors. As a result, research into maintenance activities is justified, particularly in the context of complex corporate systems. These systems often involve significant investments in fixed assets and advanced technologies, which implies high maintenance costs. Therefore, maintenance should be considered both in the formulation and implementation of business strategies. The research hypothesis proposes that the application of artificial intelligence can enhance business and production processes, particularly by optimizing maintenance and reducing costs. Accordingly, maintenance should be integrated into the broader business strategy as a key implementation process. To ensure effective application, all available AI capabilities should be thoroughly explored. Through analysis and discussion, the advantages of using artificial intelligence in maintenance are to be identified, ultimately leading to the validation of the hypothesis. Given the rapid development of information technology especially, this topic offers significant potential for further research. Full article

According to Fortune Business Insights, the market share of the Internet of Vehicless is expected to grow from USD 95.62 billion in 2021 to USD 369.61 billion in 2028, at a compound annual growth rate of 21.4%. However, the Internet of Vehicles system still faces several challenges, including regulation, scalability, data management, connectivity, interoperability, privacy, and security. To improve communication security within the Internet of Vehicle system, we have implemented a secure cryptographic algorithm called Optimized Certificateless Key-Encapsulated Encryption, resulting from a fusion of the key-insulated cryptosystem and the cryptographic key-encapsulated mechanism. The formal security analysis of our algorithm using the AVISPA version 1.1 software shows us that our protocol is safe. Informal analysis shows that our algorithm ensures authenticity, confidentiality, integrity, and non-repudiation and resists several other attacks. Our algorithm’s computational and communicational costs are slightly better than those at which it inherits the functionalities. Full article

Background: In the era of rapid technological advancement, digital transformation has emerged as a pivotal strategy for enhancing operational efficiency and competitiveness in logistics enterprises, particularly amid globalization and post pandemic recovery; this study aims to evaluate its multifaceted impact on business performance using a multi-criteria framework focused on Vietnamese firms. Methods: Employing structural equation modeling on primary survey data from 346 middle and senior level managers, alongside the Malmquist productivity index derived from data envelopment analysis on secondary financial indicators spanning 2020 to 2024, the research integrates latent variables such as organizational capability, technological innovation capability, institutional pressure, digital transformation, and business performance. Results: Key findings reveal a strong positive correlation between technological innovation capability and organizational capability (path coefficient 0.522), with organizational capability directly influencing business performance (0.359), while institutional pressure positively affects digital transformation (0.321) but negatively impacts business performance (−0.152); overall, digital transformation exhibits limited optimization, contributing to modest productivity gains and a potential 23% cost reduction through technologies like Internet of Things and artificial intelligence. Conclusions: These results underscore the necessity for logistics enterprises to strengthen organizational integration and training to maximize digital transformation benefits, thereby fostering sustainable competitiveness in global supply chains. Full article

This study investigates the crucial role energy leaders play in driving strategic energy management (SEM) and accelerating cost savings within a manufacturing organization and consequently, the industrial sector. Whereas energy efficiency can be seen as an innovative business practice with irrefutable cost benefits, its effective implementation requires strategic leadership and a structured approach. This research analyzes data collected from 120 participants representing 71 companies attending the Energy Bootcamp events organized by the U.S. Department of Energy’s (DOE) Better Plants program. The collected data focused on the state of SEM implementation, the presence and responsibilities of energy leaders, and the formation and function of energy teams. The findings reveal a significant gap between the perceived importance of SEM and its actual adoption, highlighting the need for strong leadership to drive behavioral changes by championing energy efficiency initiatives. Results indicate that effective energy leaders possess a diverse skill set, including the ability to secure top management buy-in, foster a culture of energy consciousness, and collaborate across departments. This study emphasizes the importance of empowering energy leaders with clearly defined roles and responsibilities as well as the authority to build and lead cross-functional energy teams. Furthermore, integrating energy management into existing organizational structures and leveraging readily available resources are identified as key factors for successful implementation. This research underscores how dedicated leadership and effective SEM practices help achieve industrial energy efficiency goals, providing practical insights for organizations seeking to improve performance and contribute to a resilient future. Full article

The rapid advancement of artificial intelligence (AI) in e-commerce has intensified data-driven personalization, raising important questions about its psychological implications for consumers and its role in shaping sustainable and responsible digital business practices. This study examines how AI-driven personalization affects consumer psychological well-being in the Turkish e-commerce market and investigates the roles of privacy concerns and brand trust in shaping this relationship from a social sustainability and responsible AI perspective. The research develops and empirically tests an integrated model comprising perceived personalization, privacy concerns, psychological well-being, and brand trust. Survey data from 400 active e-commerce customers were analyzed using partial least squares structural equation modeling (PLS-SEM). Findings show that both perceived relevance and perceived specificity significantly enhance psychological well-being by reducing cognitive overload and increasing perceived value. However, these personalization dimensions also increase privacy concerns, with perceived specificity exerting a notably stronger effect. Privacy concerns negatively affect psychological well-being and competitively mediate the relationship between personalization and well-being, reflecting the Personalization–Privacy Paradox in AI-driven e-commerce contexts. Moreover, brand trust significantly moderates this dynamic by weakening the harmful impact of privacy concerns on psychological well-being. Overall, the findings indicate that privacy concerns represent a latent social cost that can undermine the long-term sustainability of data-intensive business models when not governed by trust-based mechanisms. Full article

The classic set covering problem (SCP) is an NP-hard binary integer optimization problem with diverse business and industrial applications. Its primary goal is to consolidate resources by selecting a minimal cost subset of columns in a matrix that covers all required rows. Traditionally, conflicts between selected resources were resolved after generating a solution, often adding managerial effort and inefficiency. Recently, two papers have tried to handle conflict constraints explicitly as part of the SCP solution generation process. This paper focuses on SCPs with soft conflict constraints (SCP-SCC), where violations are allowed but with penalties, and proposes a simple hybrid solution approach that combines a GRASP-based heuristic with Gurobi optimization. Using 360 test instances (160 from the literature and 200 new instances), this hybrid approach results in a 7.4% performance improvement over Gurobi, demonstrating the benefit of integrating heuristic and exact solution methods. In addition, classification tree analysis is applied as an attempt to identify problem features (such as conflict graph density and size) that can be used to predict when SCP-SCC instances will likely be difficult to solve to proven optimality efficiently using Gurobi. These insights provide practical guidance for operations research practitioners, enabling informed decisions among heuristic, exact, or hybrid solution approaches and improving efficiency in real-world applications. Full article

The proposed research aims to analyse the effects of the relationship between Sustainability Governance (SG) and Climate Impact (CI), taking into consideration Green Finance (GF). Furthermore, it examines how Institutional Support (IS) enhances the governance systems governing these variables. The research provides a holistic approach for analysing the effects of financial dynamics on climate impacts. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed in this research study. The data were collected from various industries using a standardised questionnaire. The structural model examined the direct and indirect relationships between variables such as GF, SG, and CI. IS emerged as the moderated variable. The outcomes of the study confirmed that “GF has an important and direct as well as indirect (through SG as the mediator) impact on CI. IS significantly increases SG and thus exerts an overall enhancing effect on the impact of GF on the climate.” The study has supported the research objectives and aims. The limitations of this study comprised constraints related to both time and cost. The researchers encountered limitations in accessing senior managers and directors of various organisations for the study. IS emerged as an important intermediate factor that can significantly link various actions and activities that impact the climate. This study supports both global and local research objectives. The study offers significant insights, underscoring the critical role of SG within Green Business (GB). Additionally, IS emerges as a vital enabling tool that strengthens the overall governance framework. The study contributes significantly to the development of integrated frameworks for institutions seeking to effectively address environmental challenges. The implications for action indicate that furthering entrenched institutional structures and instilling good governance practices can add tremendous value to the transformation potential of GF and usher in accelerated efforts to achieve national and international objectives on climate change. Full article

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

Methane emissions from underground coal mines are a significant source of greenhouse gases (GHGs) and a major safety concern. In highly methane-prone operations, a large proportion of emissions comes from low-content abandoned mine methane (LCAMM) accumulated in post-mining goafs, where concentrations usually stay below 30% CH₄. Building on the Research Fund for Coal and Steel (RFCS) REM project, this paper presents a cost–benefit analysis of a comprehensive scheme for capturing, transporting, and utilising LCAMM from post-mining goafs for electricity generation. The concept involves long-reach directional boreholes drilled behind isolation dams, a dedicated methane-reduced drainage system connected to a surface methane drainage station, and four 2 MWe gas engines designed to run on a 20–40% CH₄ mixture. Greenhouse gas performance is evaluated by comparing a “business-as-usual” scenario in which post-mining methane is combusted in gas engines to produce electricity without further GHG cost–benefit consideration. The results indicate that the project can achieve a positive net present value, highlighting the role of LCAMM utilisation for methane-intensive coal mines. The paper also explores the monetisation of non-emitted methane using the European Union Emissions Trading System (EU ETS), as well as social cost benchmarks and penalty levels consistent with the emerging EU Methane Emissions Regulation (EU MER). Full article

The growing dominance of low-cost carriers (LCCs) in global air transport has intensified the need for airport terminal facilities that reflect their simplified, efficiency-driven operating principles. Traditional Level of Service (LOS) standards, based on International Air Transport Association’s Airport Development Reference Manual (IATA ADRM), were primarily designed for traditional air carriers or full-service network carriers (FSNCs) and may lead to over-dimensioned or misaligned airport terminal facilities when applied to airports with dominance of LCCs. This study presents the first newly developed computational tool called Airport Terminal Facilities Software (ATFS) as a methodological and conceptual advance in airport terminal planning, that integrates LOS guidelines differentiated by airline business models. The methodology integrates spatial–temporal LOS parameters, specific facility capacity formulas, and peak-hour demand calculations of airport terminal facilities. Results from the case study conducted at Pula Airport show substantial differences between IATA and LCC LOS outcomes, i.e., applying LCC LOS guidelines can significantly reduce required areas for the several airport terminal facilities. Findings confirm that new LCC LOS guidelines and the ATFS tool can optimize airport terminal facilities, reduce or reconfigure excessive or empty space, and improve passenger flow efficiency at LCC-dominant airports. Full article