Search Results (167)

In this study, we examine the impact of mergers and acquisitions (M&As) on firm environmental performance, aiming to address the gap in research and guide firms, investors, and policymakers toward more environmentally conscious decision-making in M&A. Using panel data from Chinese A-share listed firms (2008–2022), we estimate a two-way fixed effect model. The Propensity Score Matching and the instrumental variable method address potential endogeneity concerns, and robustness checks validate the findings. We found that M&As have a significantly positive effect on firm environmental performance, with heterogeneous impacts across regions, industries, and M&A types. The environmental benefits are most pronounced in heavily polluting industries and hybrid M&A deals. Eastern China shows more modest improvements. The results of mechanism tests revealed that M&As enhance environmental performance primarily by boosting total factor productivity and fostering innovation. This study offers a novel perspective by linking M&A activities to environmental sustainability, enriching the literature on both M&As and corporate environmental performance. We show that even conventional M&A deals (not sustainability-focused) can improve environmental performance through operational synergies. Expanding beyond polluting industries, we reveal how sector characteristics shape M&A’s environmental impacts. We identify practical mechanisms through which standard M&A activities can advance sustainability goals, helping firms balance economic and environmental objectives. It provides empirical evidence from China, an emerging market with distinct institutional and regulatory contexts. The findings offer guidance for firms engaging in M&A to strategically improve sustainability performance. Policymakers can leverage these insights to design incentives for M&A in pollution-intensive industries, aligning economic growth with environmental goals. By demonstrating that M&As can enhance environmental outcomes, this study supports the potential for market-driven mechanisms to contribute to broader societal sustainability objectives, such as reduced industrial pollution and greener production practices. Full article

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article

This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic behaviors. Anchored in the concept of technology trust, this study examines how different risk preferences affect BCT adoption decisions and freight rate strategies. A game-theoretic model is constructed using a mean-variance utility framework to analyze interactions between shipping companies and freight forwarders under three adoption scenarios: no adoption (NN), partial adoption (BN), and full adoption (BB). The results indicate that risk-seeking agents are more likely to adopt BCT early but face greater freight rate volatility in the initial stages. As the technology matures, strategic variability declines and the influence of adaptability on pricing becomes less pronounced. In contrast, risk-neutral and risk-averse participants tend to adopt more conservatively, resulting in slower but more stable pricing dynamics. These findings offer new insights into how technology trust and risk attitudes shape strategic decisions in digitally transforming supply chains. The study also provides practical implications for differentiated pricing strategies, BCT adoption incentives, and collaborative policy design among logistics stakeholders. Full article

The aim of this study was to compare IC and CSR and to examine ICr as a mediating variable. The study employed a relational survey design and involved participants drawn from industrial organizations based in Tehran. To evaluate participants’ perceptions of innovation climate, corporate social responsibility, and Individual Creativity, standardized questionnaires were used. In analyzing the findings, the structural equation modeling (SEM) approach was adopted, and the analyses were conducted using SPSS AMOS version 26. The analysis revealed that an Innovative Climate exerts a positive and statistically significant influence on CSR. In addition, a positive and significant association was identified between Innovative Climate and Individual Creativity. The results further demonstrated a meaningful relationship between Individual Creativity and CSR. Finally, to test the hypothesis of partial mediation, the study confirmed that Individual Creativity functions as a mediating mechanism between IC and CSR. Overall, these findings highlight the critical role of an innovative environment in enhancing CSR efforts from the perspective of Individual Creativity, and they provide insight for future studies aimed at developing strategies to strengthen creativity as a strategic means of achieving more effective CSR outcomes. The study advocates the development of an innovation-oriented mindset as a pathway to promoting socially responsible practices within Tehran’s industrial sector. Full article

Purpose: The acquirer’s corporate environmental performance (CEP) in mergers and acquisitions has been a subject of debate, yielding mixed results. This paper uses the US firm-level data of 1437 M&A deals from 2002–2019 to examine the impact of overall CEP, resource use, emissions, and innovation on the acquirers’ post-merger market value. Design/methodology/approach: This study employs multi-level fixed effects panel regression using Ordinary Least Squares (OLS) and the instrumental variable (IV) 2SLS method to estimate the models and compare the results with those from robust estimation. Absorbing the multiple levels of fixed effects (i.e., firm, industry, and year) offers a novel and robust algorithm for efficiently accounting for unobserved heterogeneity. The results from IV (2SLS) are more convincing, as the method overcomes the problem of endogeneity due to reverse causality and sample selection bias. Findings: The authors find that CEP has a significant impact on market value, particularly in the long term. While both resource use and emissions performance have positive effects, emissions performance has a stronger impact, presumably because external stakeholders and market participants are more concerned about emissions reduction. The performance of environmental innovation is relatively weak compared to other pillars. Descriptive analysis shows low average scores in environmental innovation compared to the resource use and emissions performance of the acquirers. However, large deals yield significant returns from investing in environmental innovation in both the short and long term compared to small deals. Practical implications: This paper offers several practical implications. First, environmental performance can help improve the acquirer’s long-term market value. Second, managers can focus on the strategic side of environmental performance, based on its pillars, and benchmark their relative position against peers. Third, environmental innovation can be considered a new potential, as the market as a whole in this area is still lagging. Given the growing pressure to improve environmental technology and innovation, prospective acquirers should confidently prioritise actions on green revenue, product innovation, and capital expenditure now rather than ticking these boxes later. Originality value: The key contribution is offering valuable insights into the impact of acquirers’ environmental performance on long-term value creation in mergers and acquisitions (M&A). These results fill the gap in the literature focusing mainly on the effect of environmental pillar and sub-pillar scores on acquirer’s firm value. The authors claim that analysing sub-pillar-level granularity is crucial for accurately measuring the effects on firm-level performance. Full article

Against the dual backdrop of the “digital countryside” initiative and the development of ecological civilization, the construction of smart green villages has increasingly emerged as a vital pathway for improving rural ecological environment. This study utilizes a three-dimensional framework—including strategic choice, influencing factors, and simulation practice—to construct an evolutionary game model involving the government, enterprises, and villagers. A systematic simulation is conducted based on a field case from Village P in the hilly region of Hunan Province, China. The results of the study reveal the following: (1) Under the combination of high financial incentives, low technical support, and high villager participation, the ecosystem achieves the most stable and positive evolution. Moreover, collaborative governance outperforms unilateral control. (2) Financial support, technological provision, and environmental awareness constitute the three core variables driving the evolution of ecological governance. (3) Cognitive feedback mechanisms significantly influence the dynamic trajectory of green behaviors in enterprises. (4) The simulation results indicate a risk of “sub-stable” divergence in the collaborative mechanism. Furthermore, the stability of the governance system heavily relies on the alignment between policy configuration and information structure. This study offers theoretical support and empirical validation for the institutional design of and strategic interventions for smart green villages, serving as a valuable reference for local-level implementation. Full article

The sustainable utilization of forest biomass for bioenergy production is increasingly challenged by the variability and unpredictability of raw material availability. These challenges are particularly critical in regions like Central Portugal, where seasonality, dispersed resources, and wildfire prevention policies disrupt procurement planning. This study investigates two flexibility strategies—dynamic network reconfiguration and operations postponement—as policy relevant tools to enhance resilience in forest-to-bioenergy supply chains. A novel mathematical model, the mobile Facility Location Problem with dynamic Operations Assignment (mFLP-dOA), is proposed and solved using a scalable matheuristic approach. Applying the model to a real case study, we demonstrate that incorporating temporary intermediate nodes and adaptable processing schedules can reduce costs by up to 17% while improving operational responsiveness and reducing non-productive machine time. The findings offer strategic insights for policymakers, biomass operators, and regional planners aiming to design more adaptive and cost-effective biomass supply systems, particularly under environmental risk scenarios such as summer operation bans. This work supports evidence-based planning and investment in flexible logistics infrastructure for cleaner and more resilient bioenergy supply chains. Full article

Phase stability at low radio frequencies is severely impacted by ionospheric propagation delays. Radio interferometers such as the giant metrewave radio telescope (GMRT) are capable of detecting changes in the ionosphere’s total electron content (TEC) over larger spatial scales and with greater sensitivity compared to conventional tools like the global navigation satellite system (GNSS). Thanks to its unique design, featuring both a dense central array and long outer arms, and its strategic location, the GMRT is particularly well-suited for studying the sensitive ionospheric region located between the northern peak of the equatorial ionization anomaly (EIA) and the magnetic equator. In this study, we observe the bright flux calibrator 3C48 for ten hours to characterize and study the low-latitude ionosphere with the upgraded GMRT (uGMRT). We outline the methods used for wideband data reduction and processing to accurately measure differential TEC ($\delta \mathrm{TEC}$) between antenna pairs, achieving a precision of< mTECU (1 mTECU = ${10}^{-3}$ TECU) for central square antennas and approximately mTECU for arm antennas. The measured $\delta \mathrm{TEC}$ values are used to estimate the TEC gradient across GMRT arm antennas. We measure the ionospheric phase structure function and find a power-law slope of $\beta =1.72\pm 0.07$, indicating deviations from pure Kolmogorov turbulence. The inferred diffractive scale, the spatial separation over which the phase variance reaches $1{\mathrm{rad}}^2$, is ∼6.66 km. The small diffractive scale implies high phase variability across the field of view and reduced temporal coherence, which poses challenges for calibration and imaging. Full article

Background: The Patient Outcome Assessment and Decision Support (PADS) model is a real-time framework designed to predict both mortality and the likelihood of discharge within 48 h in critically ill patients. By combining these predictions, PADS enables clinically meaningful stratification of patient trajectories, supporting bedside decision-making and the planning of critical care resources such as nursing allocation and surgical scheduling. Methods: PADS integrates routinely collected clinical data: SOFA variables, age, gender, admission type, and comorbidities. It consists of two Long Short-Term Memory (LSTM) neural networks—one predicting the probability of death and the other the probability of discharge within 48 h. The combination places each patient into one of four states: alive/discharged within 48 h, alive/not discharged, dead within 48 h, or dead later. The model was trained using MIMIC-IV data, emphasizing ease of implementation in units with electronic health records. Out of the 76,540 stays present in MIMIC-IV (53,150 patients), 32,875 (25,555 patients) were used after excluding those with short stays (<48 h) or life support treatment limitations. The code is open, well-documented, and designed for reproducibility and external validation. Results: The model achieved strong performance: AUCROC of 0.94 (±0.03) for mortality and 0.89 (±0.07) for discharge on training data, and 0.87 (±0.02) and 0.88 (±0.03), respectively, on the test set. As a comparison, benchmark models obtain worse accuracy (−13.4% for APS III, −19% for OASIS, and −7.4% for SAPS II). Predictions are visualized in an intuitive format to support clinical interpretation. Conclusions: PADS offers a transparent, reproducible, and practical tool that supports both individual patient care and the strategic organization of intensive care resources by anticipating short-term outcomes. Full article

To explore the feasibility of renewable hybrid energy systems for expressway infrastructure, this study proposes a scenario-based design methodology integrating solar, wind, and hydropower resources within the expressway corridor. A case study was conducted on a highway service area located in southern China, where a solar/wind/hydro hybrid energy system was developed based on the proposed approach. Using the HOMER Pro 3.14 software platform, the system was simulated and optimized under off-grid conditions, and a sensitivity analysis was conducted to evaluate performance variability. The results demonstrate that the strategic integration of corridor-based natural resources—solar irradiance, wind energy, and hydrodynamic potential—enables the construction of a technically and economically viable hybrid energy system. The system includes 382 kW of PV, 210 kW of wind, 80 kW of hydrokinetic power, a 500 kW diesel generator, and 180 kWh of battery storage, forming a hybrid configuration for a stable and reliable energy supply. The optimized configuration can supply up to 1,095,920 kWh of electricity annually at a minimum levelized cost of energy of USD 0.22/kWh. This system reduces CO₂ emissions by 23.2 tons/year and NO_x emissions by 23 kg/year. demonstrating strong environmental performance and long-term sustainability potential. Full article

Background/Objectives: Recent research has correlated an inability to integrate sensory information with several adverse clinical outcomes, including slow gait, poor balance, and falls. For this reason, a digital health iPhone app (CatchU^® v3.1.2) has been strategically designed to bring the measurement of visual–somatosensory integration into clinical settings. The purpose of this study was to determine whether CatchU could reliably capture the phenomenon of multisensory integration compared to a validated piece of laboratory apparatus (“tristimulator”). Methods: Using both the established tristimulator and CatchU, 50 participants (76.5 ± 6.2 years of age, 60% female) completed a simple reaction time test in response to visual, somatosensory, and combined visual–somatosensory stimulation. A reaction time cumulative distribution frequency (CDF) curve was calculated for each stimulus condition, and together these were used to calculate the CDF difference function (the multisensory visual–somatosensory CDF minus a magnitude-limited sum of the unisensory visual and somatosensory CDFs). From this, the magnitude of visual–somatosensory integration (VSI) was obtained. Results: CatchU captured multisensory integration in both average reaction times and the CDF difference function. It also produced a similar magnitude of VSI and showed no systematic bias compared to the laboratory stimulator. Additionally, CatchU responses were significantly less variable than responses recorded using the tristimulator. Conclusions: Despite using different forms of stimulation and different methods to record responses, these results reveal that CatchU can be used to produce the same inferences as laboratory apparatus. This confirms the ability of CatchU to reliably capture VSI. Full article

Air quality modeling has become a strategic area within data science, particularly in urban contexts where pollution exhibits high variability and nonlinear dynamics. This study provides a mathematical and computational comparison between two predictive paradigms: the classical Long Short-Term Memory (LSTM) model, designed for sequential analysis of time series, and the quantum model Quantum Support Vector Machine (QSVM), based on kernel methods applied in Hilbert spaces. Both approaches are applied to real PM2.5 concentration data collected at the Plaza Castilla monitoring station (Madrid) over the period 2017–2024. The LSTM model demonstrates moderate accuracy for smooth seasonal trends but shows limited performance in detecting extreme pollution events. In contrast, the QSVM achieves perfect binary classification through a quantum kernel based on angle encoding, with significantly lower training time and computational cost. Beyond the empirical results, this work highlights the growing potential of Quantum Artificial Intelligence as a hybrid paradigm capable of extending the boundaries of classical models in complex environmental prediction tasks. The implications point toward a promising transition to quantum-enhanced predictive systems aimed at advancing urban sustainability. Full article

In order to mitigate the risks of losing key personnel and their innate tacit knowledge resources, this paper explored a framework for reducing knowledge loss in South African public sector enterprises (PSEs) through the integration of knowledge management (KM) and human resource management (HRM) strategies. The study used a quantitative research design, administering survey questionnaires to 585 randomly selected employees in three South African PSEs. The survey yielded a 25% response rate and was analysed using Statistical Analysis Software, resulting in a Cronbach alpha of 0.94. The findings of the exploratory factor analysis showed that a framework for reducing knowledge loss can be developed by integrating HRM practices and focusing on seven factors: knowledge loss recognition, knowledge management practices, human resource training, organisational culture, recruitment practices, employee retention, and organisational barriers. Three important components constitute the developed framework for knowledge loss minimization. Tacit knowledge loss was recognised as a critical strategic issue based on the results of the chi-square test for independence and logistic regression. This realisation, along with control and intervention variables, created the three main components of the framework. This paper explored the Knowledge Loss Reduction framework, focusing on South African PSEs as a case, to help organisations address the complex tacit knowledge loss prevalent in public and commercial firms worldwide. It contributes to the knowledge-based view, focusing on knowledge-absorptive and -retentive capacities and praxis in knowledge (risk) management and human resource management. Full article

Tourism governance evaluates the participation of stakeholders in planning and development decisions within a territory. Understanding who these stakeholders are and their involvement is crucial, as tourism impacts social, economic, and cultural spaces, requiring equitable distribution of its costs and benefits to ensure sustainability. This study focuses on the UNESCO Environmental Corridor of Extremadura, using data from the Tourism Observatory of Extremadura and visitor surveys to define its tourist scope. A literature review identified dimensions and variables of tourism governance, leading to the design of questionnaires and evaluation scales, as well as the identification of stakeholders based on existing research. Results reveal variability in tourism governance across territories, with a notable lack of management concerning gender and functional diversity. While aligning with existing literature on the underdevelopment of shared governance policies, the study highlights progress in stakeholder participation strategies, particularly in territories with UNESCO designations. The UEC territories stand out for their strategic tourism development plans, stakeholder consultation, sector coordination, and diverse participatory decision-making mechanisms. Full article

Electricity markets with a high share of variable renewable energy require significant balancing reserves to ensure stability by preserving the balance of supply and demand. However, they were originally conceived for dispatchable technologies, which operate with predictable and controllable generation. As a result, adapting market mechanisms to accommodate the characteristics of variable renewables is essential for enhancing grid reliability and efficiency. This work studies the strategic behavior of a wind power producer (WPP) in the Iberian electricity market (MIBEL) and the Portuguese balancing markets (BMs), where wind farms are economically responsible for deviations and do not have support schemes. In addition to exploring current market dynamics, the study proposes new market designs for the balancing markets, with separate procurement of upward and downward secondary balancing capacity, aligning with European Electricity Regulation guidelines. The difference between market designs considers that the wind farm can hourly bid in both (New 1) or only one (New 2) balancing direction. The study considers seven strategies (S1–S7) for the participation of a wind farm in the past (S1), actual (S2 and S3), New 1 (S4) and New 2 (S5–S7) market designs. The results demonstrate that new market designs can increase the wind market value by 2% compared to the optimal scenario and by 31% compared to the operational scenario. Among the tested approaches, New 2 delivers the best operational and economic outcomes. In S7, the wind farm achieves the lowest imbalance and curtailment while maintaining the same remuneration of S4. Additionally, the difference between the optimal and operational remuneration of the WPP under the New 2 design is only 22%, indicating that this design enables the WPP to achieve remuneration levels close to the optimal case. Full article