The widespread occurrence of corporate greenwashing can precipitate adverse selection within the green market, thereby undermining the efficacy of sustainable development initiatives. Existing research has predominantly concentrated on the effects of external regulation and internal organizational governance mechanisms on corporate greenwashing. However, limited attention has been paid to the influence of micro-level, individual factors. This study, therefore, shifts focus to the cognitive characteristics of management, specifically examining whether managerial myopia serves as a significant determinant of corporate greenwashing. Utilizing a sample of Chinese A-share listed firms from 2009 to 2022, this study empirically investigates the relationship between managerial myopia and greenwashing practices. The findings reveal a positive correlation: as the degree of managerial myopia increases, the severity of corporate greenwashing also intensifies. Furthermore, the analysis indicates that managerial myopia exerts a detrimental effect on corporate green transformation. Additionally, heterogeneity analysis indicates that the adverse impact of managerial myopia on corporate greenwashing is accentuated by factors such as weaker internal controls, a higher degree of ownership separation, intense analytical focus, and insufficient government regulation. These results underscore the importance of addressing individual managerial characteristics in the context of corporate sustainability and the potential implications of such characteristics for greenwashing behaviors.
Global water scarcity has emerged as a pressing concern, exacerbated by climate change and increasing human demand for freshwater resources. This study conducts a comprehensive literature review to assess the impacts of climate change on freshwater availability, utilizing remote sensing technologies as a pivotal tool for evaluation. By synthesizing findings from various research articles, reports, and case studies, we analyze how climate-induced alterations in precipitation patterns, temperature fluctuations, and extreme weather events contribute to the depletion of freshwater resources. The review highlights the efficacy of remote sensing in monitoring water bodies, assessing water quality, and predicting future water scarcity scenarios. Key findings reveal that regions already facing water stress are likely to experience intensified scarcity due to climate change, with significant implications for agriculture, ecosystems, and human health. Furthermore, the study emphasizes the need for integrated water resource management strategies that incorporate remote sensing data to enhance resilience against the impacts of climate change. This assessment not only underscores the urgency of addressing global water scarcity but also advocates for the adoption of innovative technologies to ensure sustainable freshwater management in the face of ongoing environmental changes.
Business development and environmental preservation are the two aspects of today’s global concerns. SDG’s implementation at the root level is the only solution to achieve the two ends. This has created an urge for green investment, which can contribute to Sustainable Development Goals (SDGs). The present research focuses on finding the impact of Green Investment Capacity, Green Risk Tolerance and green risk appetite on Environmental Stewardship Investment Focus and Green Portfolio Strategy. Furthermore, the current study also attempts to analyse the influence of Environmental Stewardship Investment Focus and Green Portfolio Strategy on SDG-12-aligned investment decisions. Based on responses collected through the questionnaire and application of SMART PLS-4, it was found that a significant relationship exists between the variables. The present study provides a framework for achieving SDG-12 through appropriate investment pathways.
This research investigates the complex interplay between consumer behavior and green product adoption, uncovering key drivers and implications for sustainable consumption strategies. Green products, geared towards reducing environmental impacts, are gaining prominence in the wake of heightened environmental consciousness. However, understanding the multifaceted factors influencing consumers' decisions to embrace such products remains a challenge. Leveraging a binary logit model and drawing on the theory of consumption value, this study explores the intricate dynamics of consumers' choices within the context of environmentally friendly products. Data from a Likert scale questionnaire, completed by 922 participants across three Indian cities, forms the foundation for analysis. The study reveals that consumers' adoption of green products is significantly influenced by attributes such as Social Value, Environmental Concern, Pricing, and Conditional Value. These factors reflect the intricate balance between societal impact, emotional connections, and economic considerations in shaping purchasing behavior. While Quality, Emotional Value, and Epistemic Value exhibit nuanced impacts, they collectively underscore the complex decision-making process. The binary logit model is used in predicting green product buying behavior in modelling consumer preferences based on diverse predictor variables. Leveraging a binary logit model and the theory of consumption value, the study analyzed survey data from 922 participants across Mumbai, Pune, and Jaipur. The model revealed that Social Value ($p = 0.0048$), Environmental Concern ($p = 0.0444$), Conditional Value ($p = 0.0887$), and Pricing ($p = 0.05339$) significantly influence green product adoption. The model achieved an accuracy of 88.73%, confirming its robustness in predicting green purchasing behavior. These findings hold practical relevance for policymakers and marketers aiming to bridge the intention-action gap. Strategies such as enhancing the social appeal of green products, transparent pricing communication, and targeted environmental awareness campaigns are recommended to drive sustainable consumer choices. Collaborative partnerships among stakeholders, incentives, and addressing misconceptions are recommended strategies to bridge the gap between intention and action in adopting environmentally conscious behavior.
Dried fruits are a popular and widely consumed food due to their high nutritional value and long shelf life. However, their contamination with heavy metals poses a health and environmental concern. This study aims to estimate the levels of some heavy metals (copper, nickel, manganese, iron, lead, and chromium) in selected dried fruit samples available in local markets and assess their compliance with international standards approved by the World Health Organization (WHO) and the Codex Alimentarius (CAC). Samples were collected from multiple sources and analyzed using advanced techniques (Atomic Absorption Spectroscopy (AAS)). The results showed that some samples contained concentrations exceeding permissible limits, indicating potential health risks, especially in cases of long-term consumption. The sources of this contamination are often attributed to the use of chemical pesticides and fertilizers, and unsafe drying and packaging methods. The research suggests implementing stricter surveillance of food products and increasing consumer awareness on selecting safe dried fruits. Research for different area basis needs to be pursued for better apprehension of menace.
This study analyzes the safety risk transmission mechanism in urban logistics drone last-mile delivery within the policy-driven low-altitude economy. To address the limitations of traditional risk identification methods, which rely heavily on accident data, this research integrates the Fuzzy Decision Analysis Laboratory Method (Fuzzy-DEMATEL),Interpretive Structural Modeling (ISM), and the Matrix of Cross-Impact Multiplication (MICMAC) to construct a three-dimensional analytical framework based on causal relationships, structural hierarchy, and attribute classification.First, Fuzzy-DEMATEL is employed to quantify the strength of causal relationships among risk factors. Next, ISM is used to deconstruct the multi-level hierarchical network and identify fundamental causes within the risk system. Finally, MICMAC is applied to calculate the dependencies and driving forces of each influencing factor, helping prioritize risk governance measures. The research findings indicate that: (1) The safety risk system of urban logistics drones for last-mile delivery exhibits a “dual-core driven – multi-loop coupled” characteristic. Equipment failures act as the physical carriers of systemic failures, while the root-cause risks stem from institutional factors such as inadequate pre-service training and violations of laws and regulations. (2) The risk hierarchy follows a pyramid-shaped transmission path, with risks propagating from the root layer to the surface in successive layers. Open airspace serves as an accelerator, transforming environmental disturbances into institutional defects, which in turn lead to technical failures. (3) The dependency attributes of each factor indicate the priority order for risk prevention and control: management leverage points serve as the strategic control core, the environment-technology interaction network is central to joint prevention, standardized processes solidify basic operations, and systemic risk levels are reduced.
Accurate prediction of Standard Penetration Test (SPT) blow counts from Cone Penetration Test (CPT) data is critical for reliable geotechnical characterization, particularly when SPT data are scarce or difficult to obtain. This study presents a data-driven framework that employs an Artificial Neural Network (ANN) to estimate the corrected SPT blow number ($N_{60}$) using key CPT parameters. The database was compiled from two construction sites in Nasiriyah, Iraq, comprising cone tip resistance ($q_c$), sleeve friction ($fs$), and effective overburden pressure ($\sigma_{vo}^{\prime}$) as input variables. Multiple ANN architectures were trained and validated, and optimal performance was achieved using one hidden layer with eight neurons, yielding a coefficient of determination ($R^2$) of 0.9967, and two hidden layers with six and sixteen neurons, achieving $R^2$ = 0.9976. Relative importance analysis indicated that cone tip resistance contributed 44% to the model’s predictive strength, followed by sleeve friction and effective overburden pressure, each accounting for approximately 26%. Sensitivity analysis confirmed that $N_{60}$ increases with higher input parameters, consistent with soil behavior principles. The ANN model demonstrated high accuracy and generalization capability across both sandy and clayey soils. Design charts derived from the trained model enable practical estimation of $SPT-N$ from CPT results, providing geotechnical engineers with a rapid and reliable tool for site characterization and preliminary design.
Extreme precipitation events driven by climate change significantly accelerate sediment delivery into alluvial rivers, resulting in substantial morphological alteration and downstream channel instability. This study investigates bed evolution within the downstream segment of the Palu River in Central Sulawesi, Indonesia, by applying a two-dimensional hydrodynamic and sediment transport model in HEC-RAS 2D. Three discharge scenarios representing dry, wet, and extreme rainfall conditions were simulated using river geometry derived from high-resolution DEM and bathymetric measurements. Model performance was calibrated against observed water levels, achieving optimal agreement at a Manning’s roughness coefficient of 0.0295 with a Root Mean Square Error of 0.15. The results demonstrate pronounced spatial variability in riverbed response. Under extreme rainfall, degradation is dominant in the upstream bend zone with maximum erosion depth reaching 0.40 m, while deposition intensifies near the river mouth, producing aggradation up to 0.75 m. Although the spatial patterns remained consistent across all simulated scenarios, the magnitude of morphological change during extreme rainfall reached approximately twice that observed under wet-season discharge. These findings highlight the critical role of extreme flow events in shaping alluvial river morphology and provide essential quantitative benchmarks for river management strategies targeting flood mitigation and navigation safety.
Axial micro-hydro turbines' performance is sensitive to design and operational parameters. This study investigates the performance of micro-hydro turbines through advanced computational fluid dynamics simulations utilizing ANSYS FLUENT. A three-dimensional, steady-state RANS approach with the SST $k-\omega$ turbulence model was employed to simulate fluid flow interactions in turbines of 3, 4, and 5 blades, multiple rotational speeds of 100 and 1000 RPM, and flow rates. The research uniquely investigates a design that integrates internal flow control features by incorporating secondary guide blades and axial flow straighteners, which effectively reduce vortex formation and energy dissipation. Results show that increasing blade count significantly boosts mechanical power output and hydraulic head across flow conditions. Turbines operating at higher rotational speeds demonstrate markedly enhanced power generation, indicating the importance of mechanical design for durability under elevated stress. Comparative analyses between water and oil as working fluids reveal interesting fluid-dependent performance trends, with oil exhibiting superior energy transfer at higher RPMs. Validation against empirical data confirms the computational procedure's accuracy, with RMSE below 4\%. The best performance was observed with the 5-blade turbine, reaching 95\%, 92\%, and 95\% at rotational speeds of 100, 500, and 1000 RPM, respectively, at 60,700 Reynolds number. This configuration consistently outperformed others, demonstrating superior energy conversion. The addition of axial flow straighteners slightly improved efficiency by minimizing turbulence and vortex losses, confirming that structural enhancements combined with high rotational speeds are key to achieving maximum turbine performance.
To achieve the European energy and climate goals by 2050, meaningful refurbishment actions are necessary for the existing stock sector, including historic buildings. A relatively new trend in contemporary design emphasizes reusing existing structures instead of constructing new ones, revisiting and reinterpreting practices that have been experimented with in the past. The pertinent question today is how to reconcile the requirements for building protection with the implementation of energy efficiency measures. In this contest stands Villa Manganelli, a creation of the renowned architect Ernesto Basile, showcasing Italian Art Nouveau in Catania from the early twentieth century. Currently, Villa Manganelli needs to be re-functionalized to enable its reuse. A master plan has been developed that encompasses both the functional changes needed to accommodate new activities and energy-efficient measures aimed at reducing the building's energy demands, all while preserving the architectural appeal of this remarkable structure. Due to the lack of data regarding the thermo-physical features of the building envelopes, the first phase of this study consists in an experimental set of measurements carried out to characterize the thermal properties of the building masonry and the indoor thermal conditions. A second phase entails the dynamic numerical simulation of the investigated building through the Design Builder software and its calibration and validation through the comparison with the experimental data. Finally, based on the developed analysis, a first hypothesis of not invasive energy efficient measures for re-functionalization of this building is presented.
Biomass-derived syngas is a promising alternative to fossil fuels for various applications, including internal combustion engines for electricity generation and direct combustion for thermal energy. However, numerical modeling of syngas burners is still scarcely addressed in literature compared to more common fuels, posing challenges for the design of high-efficiency combustors with low pollutant emissions. This study presents a two-dimensional computational fluid dynamics (CFD) simulation of a syngas burner, validated against experimental data. The syngas adopted in the combustion tests is produced using a small-scale gasifier prototype fueled by wood pellets. At first, the syngas is premixed with air, then it moves in a cylindrical burner where the key parameters are monitored, including syngas and air volume flow rates, temperature, and syngas composition. Additionally, an emission analyzer is used to measure O$_2$, CO, NO, and NO$_2$, concentrations in the exhaust gases. Given the axial symmetry of the problem, a two-dimensional model is developed to save the computational effort. The simulation results are compared to the experimental measurements including burner temperature and emissions. Despite the simplicity and the reduced effort compared to high-fidelity simulations, the 2D model proves to be able to properly predict both temperature inside the burner and emissions at the exhaust. Therefore, it turns out to be a valuable tool to guide the design of syngas burners.
Open Access
