Search Results (204)

Responsible tourism practices are essential for sustainable tourism development, yet evidence on their implementation across different types of tourism enterprises remains limited. This study examines reported responsible tourism practices, together with the motivations and barriers influencing their implementation among tourism enterprises in Kien Giang province, Vietnam. A mixed-methods approach was employed, involving focus group discussions with 24 participants to develop the measurement indicators and a questionnaire survey of 125 tourism business representatives and 70 employees. Descriptive analysis revealed that only a few responsible tourism practices were reported at very high levels, including employee welfare, customer protection, fair recruitment, price transparency, avoidance of legally protected wild flora and fauna, and the provision of multiple waste bins. Most economic, social, and environmental practices were reported at lower levels, indicating substantial room for improvement. The main motivations were environmental protection, long-term business sustainability, tourist satisfaction, operational efficiency, and ethical responsibility, whereas inadequate training, limited awareness, insufficient implementation knowledge, and weak institutional support were the principal barriers. These findings suggest that targeted capacity building and stronger institutional support are needed to address implementation gaps and strengthen responsible tourism practices in Kien Giang province and other emerging tourism destinations. Full article

The purpose of this study is to investigate the extent to which tourists appreciate sustainable tourism and what effect eco-friendly practices have on the decision-making process of selecting a hotel. Through the use of large-scale analysis of online reviews of hotels and the application of sentiment analysis techniques, the research investigates the impact of environmental factors (e.g., energy usage reduction, minimising waste, and promoting nature experiences) on customer perspectives and decision-making processes for lodging. This research adopts an approach that utilises machine-learning-based sentiment analysis as its source of understanding. The results of this research demonstrate that while more individuals are becoming aware of sustainable tourism, sustainability often plays a secondary role in determining whether or not to stay at a specific hotel compared to lodging attributes such as comfort, price, and quality service. Based upon these findings, this research indicates that while many tourists value sustainable tourism and make an effort to choose eco-friendly lodging establishments, the influence of sustainability on tourists’ lodging decisions is not as strong as other attributes. These results indicate important implications for hotel managers that will help them balance environmental stewardship with a competitive stance. Full article

Corporate social responsibility (CSR) has grown in importance as a means for companies to engage with customers who are increasingly environmentally and socially conscious. This study examines how CSR affects sustainable consumer buying tendencies, emphasizing the mediating role of consumer happiness and corporate image. Scientists contend that customers are more inclined to support businesses whose values align with CSR programs that foster positive feelings and trust. Therefore, a conceptual model was developed by following cognitive consistency theory. Data from 504 customers in Pakistan, an expanding market where awareness of sustainability issues is continually rising, were gathered to test this. The results demonstrate that CSR has a significant and favorable influence on consumer purchasing preferences, as assessed using partial least squares structural equation modeling (PLS-SEM). Crucially, the proposed relationship is not only direct: CSR improves consumer happiness and corporate image, leading to better purchase decisions. By emphasizing the emotional and perceptual processes involved, these findings provide a better understanding of how CSR influences consumer behavior. The study demonstrates how CSR can encourage more conscientious consumption habits from a sustainability standpoint, supporting Sustainable Development Goal 12 (Responsible Consumption and Production). Findings suggest that well-thought-out CSR programs may truly affect how and why customers make purchase decisions, especially in emerging countries, going beyond reputation-building. Full article

In today’s world, renewable energy sources (RESs) are crucial. Their role is growing year by year, for commercial enterprises, public institutions, and individuals alike. The aim of this study was to examine the competitiveness of solid biomass compared to other renewable energy sources in the opinion of entrepreneurs participating in the acquisition and processing of biomass. We did the research in 2024–2025. The number of companies participating in the research and involved in the production and sale of solid biomass was 37. The largest number of companies focus on two key stages of the biomass value chain: the acquisition and processing of biological raw materials. The most frequently indicated strategy is concluding long-term contracts with suppliers, which was chosen by 13 respondents. In total, 25 companies (representing approximately 68%) declared active investment in pro-ecological solutions and 12 companies (approximately 32%) indicated no such activities. The most noticeable factor influencing the sector was the development of regulations and certification at the European Union (EU) level, including the Renewable Energy Directives (RED II and RED III) and ESG requirements, as indicated by 10 respondents. The largest number of respondents (13 responses) indicated a moderate increase in the share of solid biomass. The most frequently cited barrier was high transportation and logistics costs, highlighted by as many as 13 companies. The increasing environmental awareness of customers, especially institutional ones, is fostering an increase in demand for certified biomass. The vast majority of companies confirmed that transportation costs pose a significant challenge, highlighting the importance of logistics in the biomass value chain. Maintaining and strengthening its market position requires overcoming the identified barriers and systemic political and economic support. Full article

Macro litter is a global environmental challenge with ecological, social, and economic implications for coastal zones such as the Escravos Estuary in Nigeria. This study examined community perceptions and mitigation preferences using a structured survey administered to 161 residents of the Escravos Estuary. Results indicated that awareness levels were substantial, with most respondents (76.6%) recognising macro litter as a major environmental concern. Macro litter was widely perceived to impose negative livelihood impacts, particularly among fishing-dependent households, where damaged gear, reduced catch rates, and income loss were frequently reported. Business-related effects were also identified, with most respondents (78.4%) noting increased operational costs and reduced customer patronage (58.0%) associated with littered surroundings. Social perceptions reinforced these findings, with some respondents (59.3%) strongly agreeing that macro litter poses a present and future environmental risk. Most respondents (79.0%) acknowledged the daily impacts of macro litter on quality of life. Ordinal analyses indicated limited demographic differentiation in awareness levels, although gender demonstrated a weak association. These findings suggest that awareness and concerns were broadly distributed within the surveyed population. Community-driven strategies received strong support, as most respondents (96.9%) affirmed the effectiveness of cleanups and supported policies promoting reusable products over single-use plastics. Building on these findings, a phased implementation roadmap is proposed, integrating community mobilisation, livelihood-sensitive interventions, infrastructure strengthening, regulatory enforcement, and measurable monitoring indicators. Such locally grounded strategies are essential for reducing macro litter through participatory approaches in estuarine systems while enhancing socioeconomic resilience and environmental sustainability. Full article

Rear-view human tracking and re-identification remain critical challenges for robotic visual sensing in unmanned vehicles, particularly under adverse weather conditions and severe occlusion. Conventional deep learning models often suffer from feature contamination and trajectory drift under dynamic illumination. To overcome these bottlenecks, we propose a lightweight tracking framework driven by spatiotemporal prediction and multimodal feature fusion. Specifically, an ego-motion-aware Kalman prediction mechanism maintains temporal continuity during complete occlusions. Upon target reappearance, a multi-factor descriptor—fusing color histograms with geometric constraints—is employed within a dynamic Mahalanobis search region. This is coupled with a specular-reflection-penalized adaptive learning rate (η_k) that actively freezes template updates during severe environmental degradation conditions. Evaluated on a custom Mecanum-wheeled robot, the proposed method achieves a peak precision of 94.2% and a tracking success rate of 93.4%. Extensive experiments in extreme rainy night scenarios demonstrate a 35% reduction in average tracking error, maintaining a Center Location Error (CLE) below 11 pixels. Furthermore, the system achieves a rapid target re-identification response of 72.83 ms during occlusion phases. Ultimately, this framework delivers a highly robust and real-time solution for autonomous navigation in complex dynamic environments. Full article

Energy efficiency in computing has emerged as a critical concern due to escalating environmental and financial costs, particularly in the context of cluster computing, where there is an ever-increasing software workload. Achieving meaningful improvements in energy efficiency requires a comprehensive understanding of the interplay between hardware and software. This research investigates how algorithmic optimisations, language choice, and parallelisation strategies influence energy efficiency and how hardware-level strategies such as underclocking, overclocking, cooling, and on-demand computing can further impact energy usage. A set of measures that can be used generally to show the impact trade-off of power and performance are defined, including the Energy Factor (EF) and a new Efficiency–Performance Score (EPS). Validation experiments on a custom-built Raspberry Pi Bramble cluster used workloads like Monte Carlo Pi simulations in Python and C. Energy and performance trade-offs were evaluated using the Energy Factor and Efficiency–Performance Score on a small example cluster to validate the approach. Results show parallelisation greatly improves energy efficiency over serial execution. Cooling slightly boosts speed under heavy loads but increases total energy use. Perhaps counter-intuitively, underclocking actually raises total energy consumption, while overclocking reduces it. Language choice also impacts efficiency, with C offering notable energy savings over Python. The findings support the hypothesis that software optimisation alone can improve energy efficiency, but the most impactful results are achieved when both software and hardware strategies are jointly considered. These insights contribute to the design of future energy-aware computing systems and provide a foundation for sustainable, high-performance computing architectures. Full article

Urban waste management remains a significant challenge for achieving environmental sustainability and advancing smart city infrastructures. This study proposes a multimodal vision–language framework that integrates real-time object detection with automated semantic interpretation and structured semantic analysis for intelligent urban waste monitoring. A custom dataset including 2247 manually annotated images was constructed from publicly available sources (TrashNet and TACO), enabling robust multi-class detection across six waste categories. Two state-of-the-art object detection models, YOLOv8m and YOLOv10m, were trained and evaluated using a fixed 70/15/15 train–validation–test split. Under this configuration, YOLOv8m achieved a mAP@50 of 90.5% and a mAP@50–95 of 87.1%, slightly outperforming YOLOv10m (89.5% and 86.0%, respectively). Moreover, YOLOv8m demonstrated superior inference efficiency, reaching 120 FPS compared to 105 FPS for YOLOv10m. To obtain a more reliable estimate of performance stability across data partitions, stratified 5-Fold Cross-Validation was conducted. YOLOv8m achieved an average Precision of 0.9324 and an average mAP@50–95 of 0.9315 ± 0.0575 across folds, suggesting generally stable performance across data partitions, while also revealing variability associated with dataset heterogeneity. Beyond object detection, the framework integrates MiniGPT-4 to generate context-aware textual descriptions of detected waste items, thereby enhancing semantic interpretability and user engagement. Furthermore, GPT-5 Vision is incorporated as a structured auxiliary semantic classification and category-suggestion module that analyzes object crops and multi-class scenes, producing constrained JSON-formatted outputs that include category labels, concise descriptions, and recyclability indicators. Overall, the proposed YOLOv8–MiniGPT-4–GPT-5 Vision pipeline shows that combining accurate real-time detection with multimodal semantic reasoning can improve interpretability and support interactive, semantically enriched waste analysis in smart-city and environmental monitoring scenarios. Full article

In disease control and precision management in aquaculture, rapid and accurate identification of common fish diseases is pivotal to mitigating economic losses and ensuring aquaculture profitability. However, fish diseases are characterized by subtle symptoms, polymorphic lesions, and high susceptibility to environmental perturbations such as water turbidity and illumination fluctuations. Existing detection models generally suffer from inadequate lightweight design, poor fine-grained lesion feature extraction, and deficient adaptability to class imbalance, failing to meet the stringent requirements of precise diagnosis in real-world aquaculture scenarios. To address these challenges, this study proposes FDR-Net: a fine-grained lesion detection model for tilapia via multi-scale feature enhancement and spatial attention fusion. Using image data of Nile tilapia (Oreochromis niloticus) covering 6 common diseases and healthy individuals (from the NTD-1 dataset), the model incorporates symmetry-aware design logic, leveraging the morphological and textural symmetry of healthy tilapia tissues to capture lesion-induced symmetry-breaking features, thereby improving fine-grained lesion detection accuracy. Through depth-width scaling coefficients, FDR-Net achieves lightweight optimization while integrating three core modules and a task-specific loss function for full-chain optimization: specifically, a Micro-lesion Feature Enhancement Module (MLFEM) is embedded in key feature layers of the backbone network to accurately extract edge and texture features of incipient fine-grained lesions via multi-scale frequency decomposition and residual fusion; subsequently, a Lightweight Multi-scale Position Attention Module (MS_PSA) and a Single-modal Intra-feature Contrastive Fusion Module (SMICFM) are collaboratively deployed—the former focusing on spatial localization of lesion features, and the latter enhancing lesion-background discriminability through channel-spatial feature recalibration and contrastive fusion; finally, a Class-Aware Weighted Hybrid Loss (CAWHL) function is combined with customized small-target anchor boxes to alleviate class imbalance and further improve localization and classification accuracy of fine-grained lesions. Empirical evaluations on the NTD-1 dataset demonstrate that compared with mainstream state-of-the-art baseline models, FDR-Net achieves a peak recognition accuracy of 90.1% with substantially enhanced mAP_50-95 performance. Retaining lightweight characteristics, it exhibits superior performance in identifying incipient fine-grained lesions and strong adaptability to simulated complex aquaculture scenarios. Collectively, this study provides an efficient technical backbone for the rapid and precise detection of tilapia fine-grained lesions, offering a potential solution for precise disease management in tilapia farming. Full article

Underwater Acoustic Sensor Networks (UASNs) are pivotal for environmental monitoring, surveillance, and marine data collection. However, their open and largely unattended operational settings, constrained communication capabilities, limited energy resources, and susceptibility to insider attacks make it difficult to achieve safe, secure, and efficient collaborative learning. Federated learning (FL) offers a privacy-preserving method for decentralized model training but is inherently vulnerable to Byzantine threats and malicious participants. This paper proposes trust-aware FL for underwater sensor networks (TAFL-UWSN), a trust-aware FL framework designed to improve security, reliability, and energy efficiency in UASNs by incorporating trust evaluation directly into the FL process. The goal is to mitigate the impact of adversarial nodes while maintaining model performance in low-resource underwater environments. TAFL-UWSN integrates continuous trust scoring based on packet forwarding reliability, sensing consistency, and model deviation. Trust scores are used to weight or filter model updates both at the node level and the edge layer, where Autonomous Underwater Vehicles (AUVs) act as mobile aggregators. A trust-aware federated averaging algorithm is implemented, and extensive simulations are conducted in a custom Python-based environment, comparing TAFL-UWSN to standard FedAvg and Byzantine-resilient FL approaches under various attack conditions. TAFL-UWSN achieved a model accuracy exceeding 92% with up to 30% malicious nodes while maintaining a false positive rate below 5.5%. Communication overhead was reduced by 28%, and energy usage per node dropped by 33% compared to baseline methods. The TAFL-UWSN framework demonstrates that integrating trust into FL enables secure, efficient, and resilient underwater intelligence, validating its potential for broader application in distributed, resource-constrained environments. Full article

Advances in artificial intelligence (AI), particularly agentic AI, have created opportunities to enhance global sustainability by improving the efficiency and accuracy of environmental monitoring and response systems. Agentic AIs autonomously plan and execute towards specific goals with minimal or no human intervention; however, accessing environmental data is challenging and requires expertise due to inherent fragmentation and the diversity of data formats. The Model Context Protocol (MCP) is an open standard that allows AI systems to securely access and interact with diverse software tools and data sources through unified interfaces, reducing the need for custom integrations while enabling more accurate, context-aware assistance. This study introduces WeatherInfo_MCP, an interface that provides the required expertise for AI agents to access National Weather Service (NWS) data. Built on a service-oriented architecture, the system uses a centralized engine to handle robust geocoding and data extraction while providing AI agents with simple, independent tools to retrieve weather data from the NWS API. The system was validated through 14 unit tests and 23 comprehensive protocol compliance tests against the MCP 2025-06-18 specification, achieving a 100% pass rate across all categories, demonstrating its reliability when working with AI agents. We also successfully tested our model alongside a memory MCP to showcase its performance in a multi-MCP environment. While in its earliest version, WeatherInfo_MCP connects to the NWS API, its modular design and compliance with software development and MCP standards facilitate immediate expansion to additional environmental data and tools. WeatherInfo_MCP is released as an open-source tool to support the sustainable development community, enabling broad adoption of AI agents for environmental use cases. Full article

Digital eco-labels are an effective means to shape customers’ decisions towards sustainable tourism and achieve the UN Sustainable Development Goals (SDGs). However, past studies have paid less attention to how digital eco-labels shape customers’ intention to visit green hotels. This study aims to explore the influence of digital eco-labels on customers’ psychological mechanism towards visiting green hotels. Based on an extended Norm Activation Model (NAM), the study employs purposive sampling and collected 640 participants’ data. Partial Least Squares–Structural Equation Modeling (PLS-SEM) was employed for data analysis. The findings indicate that digital eco-labels serve as powerful stimuli that activate awareness of consequences, as well as environmental concern that influences and develops personal credibility in visiting green hotels. These results highlight the significance of the interactive and information-rich digital platform to trigger environmental awareness and promote sustainable hotel practices. Furthermore, the study’s findings provide practical solutions for green hotel managers to design effective digital eco-labels that display clear and credible sustainability information to hotel customers. Full article

Entities using business models that integrate sustainability principles into business practice are gaining popularity through innovative measures. One such model is the subscription economy, in which customers pay regularly for access to products or services rather than purchasing them once. The study aims to present the subscription economy as a model that can help promote sustainable consumption. The paper uses a diagnostic survey method and a literature analysis and critique. Based on the literature on sustainable business models, the authors have shown that subscription economics, meeting the conditions of a sustainable model, can play an important role in promoting sustainable consumption (in terms of economic, social, and environmental rationality). The authors’ own research showed that Poles are very interested in the subscription model and its greatest importance in terms of economic rationality, which is the most important element of sustainable consumption. This also applies to the younger generation, which, despite being characterized by a high awareness of growing social and environmental problems, identifies sustainability as a secondary motivation to personal benefits such as financial security. Full article

Based on the assumptions and framework of relationship marketing, this research examines green banking methods. The present study is pertinent to management as it aims to improve customer expectations and maximize service quality, given that an increasing number of customers are becoming more eco-friendly, more environmentally conscious, and increasingly interested in green products and services. The present study investigated the impact of green banking (GB) practices on green awareness (GA), green image (GI), and service quality (SQ). Additionally, the study investigates how a positive GI and GA mediate the relationship between GB practices and SQ. This research utilized results from a quantitative survey administered to 470 consumers of the commercial banking industry in Nigeria. The relationship between the study’s variables was analyzed using a structural equation modeling technique. The result of this study showed a direct and significant link between GB and GI, GB and GA, and GB and SQ, respectively. Furthermore, confirmed the mediating influence of GI and GA in the link between GB and SQ. This study offers valuable insights to researchers, policymakers, and organizations. Full article

This article examines the phenomenon of greenwashing in the automotive industry and its perception by market participants, i.e., vehicle users and potential buyers. The main goal of this publication is to highlight greenwashing and determine how this concept is perceived by consumers in the context of the electric vehicle market and whether it may influence future purchasing decisions. A study was conducted using a proprietary questionnaire. Respondents were asked about their knowledge and awareness of greenwashing. Subsequent questions asked 417 respondents to provide their perceptions of greenwashing in the electric vehicle market. Greenwashing is a marketing practice that portrays a company’s products or activities as more environmentally friendly than they actually are, potentially misleading potential customers. This article concludes that greenwashing in the automotive industry has a real impact on consumer decisions and brand image. While green marketing can bring short-term benefits, long-term lack of transparency and misinformation can lead to a loss of trust and harm to both companies and the environment. Real benefits can only be achieved by combining these claims with actual environmentally friendly practices. Full article