Striving for carbon neutrality in EU region: examining the role of environmental policy stringency in geopolitical risk and energy transition

Global economic policymakers are facing increasing demands from diverse environmental constituents to prioritize sustainable development through the implementation of strict environmental policies, energy transition strategies, and the reduction of fossil fuel consumption by fostering technological advancement. This study aims to assess the moderating role of environmental Policy Stringency (EPS), among Geopolitical Risks (GPR), Energy Transition (ET), Technological Innovation (INV), and environmental degradation (CO2 emission). This study relies on empirical datasets spanning the years 1990 to 2022 in 13 EU countries and employed an extensive range of econometric techniques., Dynamic Ordinary Least Square (DOLS), Fully modified Ordinary Least Square (FMOLS), Canonical Cointegration Regression (CCR), and Method of Moment Quantile Regression (MMQR) to examine the data. The empirical findings demonstrate that interaction of GPR*EPS, INV*EPS, and ET*EPS plays a crucial role in promoting environmental sustainability and lowering CO2 emissions. Whereas, environmental degradation is attributed to higher geopolitical instability and increased economic activities. Further, Quantile Regression (QR) and Feasible Generalized least square (FGLS) methods are employed to check the robustness empirical findings of the primary techniques. Based on our comprehensive empirical research findings, we can put forth constructive policy recommendations aimed at addressing environmental concerns and achievement of SDGs 7 & 13 in EU region

A horizon on the evolution of machine learning applications in real estate

Machine learning (ML) in the real estate industry has transformed property assessment, administration, and promotion, tackling significant issues including market instability and pricing precision. Notwithstanding considerable progress in predictive, descriptive, prescriptive analytics, and automation, current research mostly emphasises technological and operational efficiencies, overlooking the integration of environmental, social, economic, and governance (ESEG) sustainability dimensions. This monitoring constrains the advancement of comprehensive and accountable real estate solutions corresponding to sustainable development objectives. This study aims to address these gaps by systematically analyzing publication trends, key contributors, and thematic clusters, incorporating sustainability principles via a combination of bibliometric and content analysis approaches. The study uncovers publication trends, key research themes, and their alignment with ESEG criteria. The results highlight significant research clusters in predictive and descriptive analytics while revealing a notable deficiency in sustainability-focused studies. Implications of this study underscore the necessity for incorporating ESEG dimensions into ML-driven real estate practices, promoting resilient, equitable, and environmentally responsible industry advancements. This study provides actionable insights for stakeholders to enhance sustainable ML adoption, fostering long-term viability and societal well-being in the real estate sector

Psychic distance and other determinants of the EU new member states’ outward foreign direct investment

Research on outward Foreign Direct Investment (OFDI) has so far been focused on developed countries. However, the significance of emerging and transition economies is increasing. The literature review shows the gap in research on determinants of the EU NMS (European Union New Member States) OFDI, in particular regarding Psychic Distance (PD). The paper motivates theoretically and uses a gravity model to investigate the drivers behind OFDI from NMS. We apply Hausman-Taylor (HT), Poisson Pseudo-Maximum Likelihood (PPML), and Gamma Pseudo-Maximum Likelihood (GPML) estimators. These are the characteristics of the destination country that exert more significant influence than the origin, which raises scepticism about OFDI being stimulated by policy measures in the case of the NMS. A tax-avoiding motive strongly drives their OFDI. We contribute to the literature by showing that PD affects OFDI from the NMS and is an alternative to distance in kilometres, which is traditionally used in gravity models. Socioeconomic and psychic distances are robust alternatives, while other single-dimensional ones (language, industrial, democratic, or religious) are not. Our findings confirm that NMS’s OFDI strongly depends on the Inward Foreign Direct Investment (IFDI) from the counterpart economies

The role of strategic management accounting in digital capabilities and competitive advantage in the tourism industry

This research investigates the extent to which the essential aspects of digital capabilities like Big Data Analytics (BDA), Digital Competence (DCT), and Quality of Information Technology (QIT) influence Strategic Management Accounting (SMA) and Competitive Advantage (CA) in Vietnamese tourism businesses. Data were gathered from 474 tourism businesses in Ho Chi Minh City, Vietnam, and analyzed using PLS-SEM. The results indicate, the three elements of digital capability have significant positive impacts on both SMA and CA, while SMA itself exerts a strong positive impact on CA. Furthermore, DCT serves as the strongest determinant of SMA. Additionally, mediating analyses indicate, SMA mediates the relationships between digital capabilities and CA, suggesting digital capabilities are a greater source of competitive returns when they are integrated into SMA. The results underline SMA as an important process for turning digital capability into better performance. This research contributes to the current literature by integrating digital capabilities and SMA in an investigation of the tourism industry. It presents a capability-to-advantage chain from BDA/DCT/QIT → SMA → CA. In practical terms, the results suggest that Vietnamese tourism businesses should invest in BDA, DCT, and IT infrastructure and institutionalize SMA routines to sustain CA in an increasingly digitized era

Navigating success: portfolio management through risk and performance analysis

Purpose – this study explores the growing interest in Exchange-Traded Funds (ETFs) and public shares during and after the COVID-19 pandemic, aiming to understand the factors driving this investment trend. Research methodology – an actively managed portfolio of U.S. stocks is constructed and compared to a passively managed S&P 500 index portfolio. Active management is conducted using three approaches: intuition-based selection, the Black-Litterman model, and Modern Portfolio Theory. A quantitative analysis assesses risk, performance, and the impact of rein- vestment and transaction costs. Findings – results show that active investing can outperform passive strategies in the short term but involves higher risk, transaction costs, and ongoing oversight. Passive investing offers stability and lower costs but may yield lower returns during volatile periods. Research implications – the study deepens understanding of how costs and reinvestment in- fluence investment outcomes and how active and passive strategies perform under varying market conditions, especially post-pandemic. Practical implications – the findings provide practical guidance for portfolio construction and management, helping investors balance risk and return in a changing financial landscape. Originality/Value – this paper uniquely compares three active investment approaches against a passive benchmark within a post-COVID context, offering integrated insights on performance, cost, and strategy for both academics and practitioners

Exploring the technology-organization-environment configurations to enhance stakeholder collaboration in the off-site construction projects

Stakeholder collaboration is critical in empowering off-site construction (OSC) projects to reduce the cost overrun, conflicts and quality problems. Prior studies have explored the factors affecting stakeholder collaboration but focused on the net effects, ignoring their interaction relations. Our study aims to explore the configuration conditions to enhance stakeholder collaboration in OSC projects using fuzzy-set qualitative comparative analysis (fsQCA). 103 valid questionnaires are collected to conduct fsQCA to explore the configurational conditions to achieve the high stakeholder collaboration. The findings reveal that there are no necessary conditions as a bottleneck factor constraining stakeholder collaboration emergence in the OSC projects. Three equivalent configurational conditions are identified to achieve high stakeholder collaboration, referring to environment-driven organizational configuration, technology-organizational configuration, and organizational-driven environment configuration. Our study enriches the literature on the antecedent identification of stakeholder collaboration in the OSC project. It also expands the underlying mechanisms between antecedents and stakeholder collaboration from the configurational perspective. Meanwhile, our study provides practical guidance for policymakers and project managers to make decisions by emphasizing the interaction effects of different project governance mechanisms

Physics-informed neural network–integrated point cloud decimation and 3D mesh modeling for thermo-mechanical simulation of rail fasteners in digital twin systems

High-density point cloud data of infrastructure components, such as railway fasteners, often contain excessive noise and redundant points, making them challenging to process for simulation and analysis. This study introduces an integrated framework that transforms raw point clouds into simulation-ready 3D meshes and couples them with PhysicsInformed Neural Networks (PINNs) for thermo-mechanical analysis, enabling applications within digital twin environments. The pipeline begins with a RANSAC-based iterative segmentation and outlier removal, achieving an 82.35% reduction in point count while preserving essential geometry. Poisson surface reconstruction and targeted post-processing then produce a high-fidelity, watertight mesh normalized for consistent simulation input. Leveraging this mesh, PINNs solve the steady-state heat equation to model thermal conduction and linear elasticity equations to estimate stress and displacement fields under defined Dirichlet boundary conditions. The resulting temperature and stress distributions are visualized directly on the mesh, providing interpretable, physics-consistent insights into fastener performance. By unifying geometric simplification with data-driven, physics-aware simulation, this approach supports accurate and computationally efficient digital twin development for railway asset monitoring and maintenance

Refreshing the design of a regional economic growth model in the context of the new digital decade

The digital economy is a significant objective of future development of the European space and is included alongside other strategic objectives in the 2030 and 2050 Agenda of the European Union. According to the European Union’s Digital Economy and Society Index (DESI), the Nordic countries (Finland, Denmark, the Netherlands, and Sweden) are the top performers of the European Union. In the same time, Romania ranks last in the 2022 ranking. Current research analyses the four areas of the digital economy (human capital, digital technology integration, connectivity, and digital public services) in a mix that is correlated with regional indicators of sustainable development to design a regional model of digital economy growth. The methods of study include restructuring the DESI rankings and regional reclassification based on the criteria of correlative ranking of digital economy indicators in correlation with sustainable development indicators and the design of structural equations. The study results will form the basis of a public policy proposal to accelerate digital development for disadvantaged European regions. First published online 24 September 202

The meaning of being a creative teacher through discovering

The research problem relates to the lack of research in terms of perceiving the meaning of being a creative teacher. The study aimed to highlight the phenomenological structure meaning of being a creative teacher. The methodology was based on the approach of integral phenomenology. A total of 26 interviews were obtained. Data were collected via semi-structured interviews and analyzed by integrating approaches of practical and epistemological phenomenologies. Findings showed that for teachers the core component in the phenomenological meaning of being a creative teacher was discovering. The conclusions summarized that discovering within the phenomenological structure of being a creative teacher involves pursuing, updating, engaging, being spontaneous, and experimenting

Does corporate social responsibility of pharmaceutical manufacturing enterprises decrease debt financing costs? Economic implications through a moderated mediation model

In today’s complex economy, debt financing costs play a crucial role in shaping corporate competitiveness and are significantly shaped by stakeholder interests. This study focuses on the pharmaceutical manufacturing industry, exploring how Corporate Social Responsibility (CSR) affects the debt financing costs, with CSR further divided into strategic CSR and altruistic CSR. Drawing on the data of 286 pharmaceutical enterprises publicly traded on China’s A-share market during the 2010–2021 period, we construct a moderated-mediation framework to examine the complex mechanisms by which CSR affects debt financing costs. The findings show that stronger CSR efforts are linked to decreased debt financing costs. Financial performance acts as a mediator in this relationship, while media attention serves as a moderator. Specifically, strategic CSR initiatives, enterprises that are privately-owned, companies positioned in the eastern areas of China, and strong medical regulation witness a more significant reduction in financing costs. Furthermore, we find that compared to other industries, the fulfilment of social responsibility by pharmaceutical manufacturing enterprises plays a more crucial role in the sustainable development of the enterprise. This study examines the mechanisms through which pharmaceutical manufacturing firms reduce debt financing costs by fulfilling Corporate Social Responsibility (CSR), contributing to understanding of CSR’s economic value creation China’s emerging economy context. By incorporating firm-level and regional economic variations, the research addresses a gap in the existing literature and provides insights for pharmaceutical companies to optimize their financing strategies and attain sustainable economic development in emerging economies. First published online 16 March 202