Search Results (669)

Water blockage severely restricts gas transport in deep shale reservoirs, while effective mitigation requires a precise balance of multiple operational variables. This study utilizes core-flooding experiments to optimize the treatment processes of an amphiphobic fluorinated copolymer, focusing on the coupled roles of surfactant concentration, injected volume, and shut-in duration. The results show that permeability damage decreases rapidly with surfactant concentration, optimizing at 0.5 wt.%. Conversely, excessive liquid retention beyond a critical injection threshold of 1.0 PV triggers secondary water-blocking. Extending the shut-in duration to 8 days facilitates surfactant redistribution and interfacial equilibrium, gradually reversing rock wettability to a stable amphiphobic state. Crucially, the concurrent reduction in interfacial tension markedly lowers capillary resistance, allowing trapped water to detach and flow back under significantly lower driving pressures. This optimization effectively minimizes the energetic barrier for fluid displacement and creates a gas-preferential flow environment. The proposed laboratory operational window balances surfactant dosage, injection volume, and shut-in duration under the tested conditions, providing an experimental reference for optimizing post-fracturing cleanup, controlling liquid retention, and improving early-time gas flowback in shale gas reservoirs. Full article

With intensifying measures related to investor and policy requirements, corporate governance and sectoral environmental performance became a focal point for sustainability disclosure, especially in energy-intensive industries with high environmental externalities. This study evaluates whether corporate environmental governance practices in key sectors correspond to their pollution intensity and economic output, analysing a panel dataset across EU member states, for the 2000–2021 period. The empirical methodology includes ordinary least squares (OLS), fixed- and random-effects models, and dynamic system generalised method of moments (GMM) panel estimation to account for sectoral heterogeneity. Results prove that sectoral value added is an influential factor of greenhouse gas emissions, with carbon dioxide exhibiting the highest elasticity to economic activity, followed by methane emissions, and nitrous oxide displaying cross-country variations due to structural and regulatory differences. While services and manufacturing sectors partially decouple via cleaner technologies, overall growth positively correlates with emissions, and renewable energy offers limited mitigation due to scale and integration challenges. Conclusions emphasise robust governance frameworks in high-value energy sectors to meet EU climate-neutrality goals, as stronger environmental accountability attracts capital and supports sustainable development, underscoring the needs for targeted decarbonisation, regulatory coordination, and accelerated technological innovation within persistent industry disparities. Full article

This study investigates how eco-efficiency and socio-efficiency influence firms’ default risk across the European financial, industrial, and consumer service sectors from 2010 to 2024. This study aims to determine whether integrating environmental and social performance into corporate strategies mitigates financial distress over time. The Pooled Mean Group ARDL estimator was employed to capture the short- and long-term dynamics. The results indicate that higher eco- and socio-efficiency significantly reduce long-term default risk, particularly in the financial and industrial sectors. Short-term effects were found to be insignificant, suggesting that sustainability benefits gradually emerged. This study offers novel sector-specific evidence linking sustainability efficiency to default risk in European firms and provides insights into how environmental and social efficiencies enhance corporate resilience and financial stability. Full article

Autonomous robotic systems are being deployed in commercial, healthcare, logistics, and mixed-use built environments at a rate that significantly outpaces the adaptive capacity of existing building design and management paradigms. Buildings have historically been conceived exclusively for human occupants, and the resulting absence of a structured, scalable framework for evaluating or designing robot-ready facilities constitutes a critical gap in both research and professional practice. This article introduces the Robot-Friendly Buildings Level of Service (RFB-LOS) framework: a five-tier hierarchical classification system that characterises the degree to which a built environment supports autonomous robotic operations across six evaluative dimensions—building intelligence, active infrastructure, architectural planning, accessibility, observability, and safety. The framework spans a continuum from Robot Excluded (RFB-LOS-1), in which a building has no awareness of its robotic occupants, to Physical AI Robot Optimised (RFB-LOS-5), in which a Physical AI middleware layer assumes the highest command authority within a coordinated human–robot–building ecosystem. Drawing structural inspiration from the SAE J3016 Levels of Driving Automation, the EU Smart Readiness Indicator, HIMSS EMRAM, and BREEAM/LEED sustainability certification, the RFB-LOS framework is positioned as a foundational standard for the built environment and systems engineering community. Five real-world case studies spanning retail, hospitality, healthcare, and corporate sectors across four countries validate the framework’s tier assignments against observed operational outcomes. Full article

Although corporate social responsibility (CSR) research in hospitality has grown substantially, most evidence comes from large corporations in high-income countries, leaving food-service micro and small enterprises (MSEs) in emerging economies largely unexplored. This study investigated which sociodemographic and organizational factors predict operational CSR practices in 150 formal restaurants in Piura, Peru, using a quantitative, cross-sectional, associative-predictive design. Data were analyzed using IBM SPSS Statistics v.28 for descriptive, bivariate, and regression analyses, and IBM SPSS AMOS v.27 for confirmatory factor analysis. Grounded in an integrative framework combining human capital theory, institutional theory, and stakeholder theory, the study operationalized CSR through three dimensions validated for the Peruvian context: supplier relations, customer relations, and food safety. Multiple regression analysis revealed that manager academic education, certifications, and monthly sales were significant predictors, jointly explaining 23.9% of CSR variance, while firm size and service mode were not significant. Nearly all establishments scored at an intermediate CSR level, with none reaching the optimal category. Theoretically, these findings demonstrate that managerial cognitive capabilities and institutional routinization mechanisms are more powerful drivers of sustainability adoption than firm size in resource-constrained contexts. Practically, the results suggest that subsidized certification programs and targeted management training represent more efficient sustainability levers than generic business growth loans for food-service MSEs, contributing to SDG targets 8.3, 12.3, and 12.6. Full article

AI, specifically Bayesian networks, was applied in this study to diagnose and predict interruptions (whether due to faults or maintenance) that most significantly impact the time of interruption per kilowatt and frequency of maintenance intervention per kilowatt indicators in electrical distribution systems. Bayesian networks were employed to identify which functional stages, such as sub-transmission lines, distribution substations, and medium-voltage networks, exert the greatest influence on these performance metrics. Additionally, the analysis was conducted to categorize the interruption catalog and contribute to these impacts. By disaggregating the service quality and maintenance indicators reported monthly by the Guayas Los Ríos Business Unit of the National Electricity Corporation to Ecuador’s electricity sector regulatory bodies, the developed framework in this study enhances service reliability, optimizes maintenance planning, and reduces interruption times. Bayesian network models generated using R illustrate relationships between interruption causes and their impact on service quality indicators. Furthermore, a comparison of several models, including Naive Bayes, Tree Augmented Naive Bayes (TAN), and Backward Sequential Elimination and Joining (BSEJ), demonstrated that TAN and BSEJ achieved the highest accuracy in predicting interruption outcomes. These insights allow for more efficient targeting of maintenance resources, ultimately reducing the most impactful categories of interruptions and improving overall technical service quality. Full article

This study aims to explore the impact of artificial intelligence adoption in accounting systems (AIAS) on organizational performance (OP). Further, the study explores the mediating role of financial decision-making quality (FDMQ) on the AIAS-OP relationship. The sample comprises 583 accountants, finance managers, CFOs, and auditors in all firms listed on the Egyptian Stock Exchange (EGX), covering banking, IT, manufacturing, and service sectors. Data were analyzed using Smart-PLS 4 software. The results revealed a positive and significant impact of AIAS on both FDMQ and OP. Further, the results revealed a positive and significant impact of FDMQ on OP. Finally, FDMQ showed a significant mediating role between AIAS and OP. These results have significant implications for policymakers, investors, regulators, and corporate executives, emphasizing the crucial role played by AIAS and FDMQ in shaping OP, particularly within emerging markets such as Egypt. This study provides a valuable contribution to the accounting literature by highlighting the impactful consequences of AIAS and FDMQ on OP in a unique and unexplored context. Furthermore, this research underscores the vital role that FDMQ assumes in mediating the relationship between AIAS and OP, contrasting with earlier studies in the literature which primarily examined the direct impact of AIAS or FDMQ on OP. Full article

Agri-food systems account for a substantial share of global greenhouse gas (GHG) emissions, with a significant proportion arising from upstream supply-chain activities beyond direct operational control. In this context, effective decarbonization requires systematic assessment of emissions across all life-cycle stages. This study applies an ISO 14040/44-compliant cradle-to-grave Life Cycle Assessment (LCA) to CERELAC^® infant cereal, a processed dairy-based product, to quantify Scope 1, Scope 2, and Scope 3 emissions and identify mitigation pathways across the full product life cycle. Results indicate that Scope 3 emissions account for 94.3% of total product emissions, with product use (44.7%) and purchased goods and services (36.9%) as the primary contributors. Upstream agricultural inputs—particularly milk powder—emerge as the dominant hotspot due to methane emissions and energy-intensive processing. Scenario-based evaluation suggests that regenerative sourcing, ingredient optimization, packaging redesign, logistics improvements, and consumer-phase engagement could significantly reduce life cycle emissions. The findings demonstrate how product-level LCA can operationalize Scope 3 decarbonization strategies in processed food systems, bridging corporate net-zero ambitions with actionable supply chain interventions. These results provide transferable insights for cleaner production transitions within the agri-food sector. Full article

The growing emphasis on environmental sustainability within the European Union raises important questions about the internal structure of corporate environmental effort. This study examines environmental expenditures (intermediate consumption of environmental protection services) and environmental investments (gross fixed capital formation for environmental protection) in ten EU member states over 2015–2022, using Eurostat Environmental Protection Expenditure Accounts data at both the national and sectoral levels (NACE Rev.2 sectors A, B, C, D). Two hypotheses are tested empirically. First, sectoral differences in the investment-to-expenditure ratio are statistically significant (Kruskal–Wallis H = 27.72, p < 0.0001): electricity, mining, and manufacturing each display a higher ratio than agriculture, with the most pronounced contrast for electricity. Second, Eastern European member states exhibit a systematically higher investment-to-expenditure ratio than the remaining countries in the sample (level difference: β = +1.01, p < 0.001), although the two groups follow parallel trajectories without convergence or divergence over the period examined. Building on the relative intensity of the two indicators, the study proposes a four-quadrant typology—active transformation, investment focus, maintenance model, and low-intensity profile—whose stability is confirmed by bootstrap resampling, sub-sample analysis, and an alternative deflator specification. The findings suggest that the internal composition of environmental effort is as informative as its overall level and that sectoral disaggregation is essential for characterising patterns of environmental effort in the EU. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

Hydraulic oscillator tools (HOTs) are effective solutions for mitigating excessive drag encountered during sliding drilling in horizontal wells. However, their field performance remains unpredictable due to theoretical limitations in modeling nonlinear friction behavior under axial vibration. To address this gap, a series of friction tests was conducted on sandstone–steel pairs under water-based mud lubrication. Experimental results demonstrate that steady-state sliding friction follows the velocity-dependent Dieterich–Ruina model, while vibration–sliding coupled friction is accurately described by the Dahl model. Integrating these findings, a comprehensive drillstring dynamic model was developed. The model was solved using an explicit central difference method and validated against field hook load data from Well XX-1, with prediction errors below 9%. Parametric studies further quantified HOT performance, revealing that excitation force amplitude and HOT placement significantly impact drag reduction, whereas vibration frequency exerts a relatively modest influence. Meanwhile, the effective propagation distance induced by the hydraulic oscillator is relatively limited, resulting in a drag reduction rate of no more than 30% even under optimal parameter conditions. This work establishes a validated theoretical framework for optimizing hydraulic oscillator parameters in horizontal drilling. Full article

Adverse Childhood Experiences (ACEs), which encompass a broad range of adverse events during childhood, are prevalent in rural China. However, help-seeking among adults with ACEs remains limited and underexplored. This study aims to examine the barriers to help-seeking behaviors among adults with ACEs in rural China. Qualitative interviews were conducted with 20 adults affected by ACEs in rural mainland China between October 2024 and December 2024. Data were collected through semi-structured interviews in Mandarin, and transcripts were analyzed using the Theory of Planned Behavior (TPB) framework, focusing on behavioral beliefs, normative beliefs, and perceived behavioral control. The findings reveal barriers to help-seeking among individuals with ACEs in rural China, categorized into three key dimensions: (1) Behavioral Beliefs: Beliefs that corporal punishment is the responsibility as well as love of parents, and misconceptions attributing ACEs to personal faults significantly hindered help-seeking. (2) Normative Beliefs: Respect for parental authority in China culture context hinder help-seeking for adults with ACEs. Moreover, gender differences were evident, with men avoiding help-seeking due to perceived shame, while women were more likely to confide in friends and family. Finally, stigmatization of mental health services further inhibited help-seeking behaviors. (3) Perceived Behavioral Control: The lack of formal and informal support systems in rural areas exacerbated the issue, highlighting significant gaps in resource accessibility and cultural acceptance of mental health support. Addressing these barriers through public education, destigmatization of mental health services, and improved resource allocation could facilitate help-seeking behaviors and improve outcomes for individuals affected by ACEs. Full article

Gas-lift reverse circulation drilling technology is one of the typical “bottom-hole negative pressure” drilling technologies. This technology can significantly reduce wellbore circulation pressure loss, alleviate the bottom-hole pressure holding effect, and effectively lower the probability of lost circulation. The core theory underlying this technology is multiphase flow in the wellbore. Based on gas–liquid two-phase flow theory, this paper develops a method for calculating bottom-hole pressure during gas-lift reverse circulation. The effects of key operational parameters on bottom-hole pressure were analyzed. The results show that bottom-hole pressure decreases as gas injection rate increases and as the gas injection point deepens. Moreover, the deeper the gas injection point, the greater the pressure reduction. Compared with the results from gas-lift reverse circulation drilling design and monitoring software applied to a shale gas well in southern Sichuan, the two sets of data differ by approximately 3%. The proposed calculation method can predict bottom-hole pressure under gas-lift reverse circulation conditions, overcoming the low accuracy of empirical formulas traditionally used in such operations. This has significant implications for advancing gas-lift reverse circulation technology in oil and gas well drilling. Full article

The German industrial and energy sectors accounted for over 52% of national greenhouse gas emissions in 2024. This is influenced both by an ongoing demand for fossil fuels and the usage of emission-intensive raw and processed materials. With the current European directive on corporate sustainability reporting, a push is being made for companies to publish annual emission reports. However, as per a study conducted by the authors, small and medium-sized companies have difficulties accurately calculating emissions across their supply chain without relying on external service providers. As a scientific institute with a real production facility for metal machining, the ETA (Energy Technologies and Applications) Factory bridges the gap between academia and manufacturing enterprises. The authors have used this disposition to calculate scope 1–3 emissions for the factory as per the Greenhouse Gas Protocol across three years, while progressively attempting to automate data collection for all scopes. CO₂e emissions for the years 2022–2024 were 86.3 tCO₂e, 146.9 tCO₂e, and 86.1 tCO₂e, respectively. Emission categories were assessed in terms of relevance to the institute and subsequently used to analyze the emission activities of the factory. The highest contributor to emissions was electricity purchasing for 2022 and 2024, along with business travel for 2023. Within scope 3, the emissions produced by business travel showed the highest impact across all years, followed by either energy-related activities or purchased goods. The sensitivity of CO₂e factors was also investigated, showing discrepancies between 25% and 130% for the utilized CO₂e factor for steel. Automation of data collection benefits largely from implemented manufacturing systems, such as manufacturing execution systems or enterprise resource planning systems. Full article

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making and overlooking place-specific social and ecological consequences. As the level of government closest to everyday urban life, cities are uniquely positioned to steer AI toward public value, but face persistent tensions between efficiency, equity, accountability, and sustainability. This paper argues that responsible urban AI cannot be governed through top-down or one-size-fits-all approaches. To address this, the study aims to conceptualise and advance a ground-up model of responsible urban AI governance that places cities and local governments at the centre of decision-making. It addresses the following research question: How can municipal authorities translate high-level ethical principles into practical, context-sensitive governance arrangements that respond to local capacities, risks, and public values? Drawing on global governance principles and illustrative city experiences, we propose a locally grounded, stage-based framework for municipal AI governance. The framework addresses institutional capacity gaps, fragmented responsibilities, and algorithmic externalities, advancing a participatory, place-sensitive, and adaptive model that aligns urban AI innovation with democratic legitimacy, social justice, and sustainable urban futures. Full article