Search Results (15,737)

Electric vehicles represent a promising path toward reducing transportation-related greenhouse gas emissions, but partisan polarization presents a significant barrier to their widespread adoption in the United States. This study provides a detailed look at the auto industry’s strategies for reframing electric vehicles (EVs) to resonate with mainstream American consumers, and it contributes to scholarly understanding of how sustainable products are framed to politically diverse audiences. Through a comprehensive content analysis, we analyze the persuasive strategies in all available EV video advertisements run in the U.S. from 2018 to 2023. Spanning 263 unique advertisements and 62 vehicle models, our analyses reveal the ways that nature and the environment are used in EV ads. Our data show that 90% of EV ads do not make any reference to sustainability, and 71% do not employ nature in any way. Instead, EV ads tend to emphasize vehicle features and performance, and they portray EVs as a futuristic transportation revolution. We situate these findings within the broader literature on partisan polarization of environmental issues, identity signaling in green consumer behavior, and green marketing strategy. We argue that the near-total absence of sustainability messaging in EV advertising reflects an industry-wide strategy to decouple electric vehicles from environmental identity and reframe them as mainstream consumer products. Full article

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article

Urban tourism is frequently promoted as a driver of regeneration, competitiveness, and local economic growth. However, its expansion increasingly generates overtourism, environmental degradation, social inequality, gentrification pressures, and cultural commodification in densely populated cities. Although existing tourism research has examined these challenges from managerial, planning, and sustainability perspectives, less attention has been paid to their ethical foundations. This conceptual paper addresses that gap by developing an integrated ethical framework for sustainable urban tourism through a structured, theory-driven synthesis of literature in environmental ethics, social justice theory, virtue ethics, and urban tourism studies. The paper makes three main contributions: it reframes urban tourism growth as a moral and normative issue rather than merely an economic one; it organizes the key ethical dilemmas of urban tourism as interconnected outcomes of growth-oriented development; and it links ethical principles to stakeholder responsibilities and desired governance outcomes. The proposed framework positions tourists, businesses, and policymakers as moral agents and identifies ecological integrity, social equity, and cultural protection as core criteria for evaluating tourism development. As a conceptual study, however, the framework remains theoretical and requires future empirical application and testing across different urban contexts. Full article

Modern agriculture depends heavily on machinery to maximize operational efficiency and, consequently, profitability, but the wear-and-tear on the mechanical components of machinery due to ageing can lead to reduced efficiency, more downtime, and higher maintenance expenses, thus raising the operative costs. These problems have been addressed by the use of specific lubricant additives for machinery; however, additives have known disadvantages, such as compatibility restrictions and environmental concerns, which represent critical issues especially in case of possible dispersion in the environment. Modern industry is always looking for techniques and solutions to increase efficiency and productivity, and this study investigates the possible advantages of employing nanotechnology in lubricant formulations. Amongst all possible substances, SiO₂ nanoparticles are increasingly promising as lubricant additives due to their unique properties, which include heat resistance, high levels of stability, and good biocompatibility. Moreover, biolubricants, derived from renewable sources, offer an environmentally friendly alternative to conventional lubricants. This article contributes to the field of agricultural technology by demonstrating the potential of SiO₂ nanoparticles in formulations of biolubricants thought to be used in agricultural machines. Key degradation parameters, including density, viscosity, total acid number (TAN), total base number (TBN), oxidation, and elemental composition, were systematically analysed. The results showed that SiO₂ nanoparticles mitigate viscosity loss and density increase, optimize TAN and TBN, reduce oxidation of the biolubricants by up to 17.7% at 1.00 wt% SiO₂, and stabilize elemental composition during ageing. Nanoparticles remained uniformly dispersed without sedimentation for over 30 days. This provides insights that can prevent machinery performance degradation over time, reduce lubricant changes, and suggest a more sustainable and environmentally friendly lubrication solution, thus promoting more sustainable industry. Full article

The present Special Issue, entitled “Electrochemical (Bio)Sensors as Promising Analytical Tools in the Analysis of Soils, Plants and Environmental Monitoring”, aims to provide an up-to-date overview of recent advances in electroanalytical techniques and electrochemical (bio)sensors, with particular emphasis on their applications in environmental systems, agriculture, and biological matrices [...] Full article

This article examines The Mark of Cassandra by Chingiz Aitmatov through the emerging framework of Blue Humanities. While most prior studies have approached Aitmatov’s ecological concerns from a land-based ecocritical perspective, this article shifts the focus to his engagement with oceanic themes and marine environments. By combining literary interpretation with ecological philosophy, the study suggests that The Mark of Cassandra goes beyond the limits of traditional environmental fiction. It presents the ocean not only as a setting but as a source of knowledge and ethical reflection. In this way, Aitmatov’s work seems to anticipate current global discussions on climate change, biodiversity loss, and environmental justice. The novel encourages readers to reconsider the human-centered worldview and adopt a more ecocentric approach. Through its marine symbolism and critical stance on human exploitation of nature, the text offers valuable insights into ecological ethics that cross both national and species boundaries. Overall, this article argues that The Mark of Cassandra is an important literary contribution that challenges the usual borders of ecocriticism and calls for a more integrated and holistic understanding of environmental issues. Full article

Growing concerns about global climate change and its negative consequences for communities have put immense pressure on the building industry, which is one of the primary sources of greenhouse gas emissions. Due to the environmental issues associated with the manufacture of sustainable construction materials, alkali-activated concrete (AAC) has emerged as a competitive alternative to cement. To predict the compressive strength (CS) of AAC, four machine learning (ML) models, namely, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were employed in this study using 193 data points. The input variables include Precursor “P” (kg/m³), Blast Furnace Slag “BFS ratio”, Sodium hydroxide “Na” (kg/m³), silicate modulus “Ms”, water content “W” (kg/m³), fine aggregate “FA” (kg/m³), coarse aggregate “A” (kg/m³), and curing time “CT” (day), with CS (MPa) as the output variable. The dataset was checked for stationarity and then normalized to decrease data redundancy and increase integrity. Furthermore, three model combinations were developed based on the relationship between the input and target variables. The XGB-M3 model outperformed all other models with a high degree of accuracy, according to the study’s findings. Specifically, the Pearson correlation coefficient (PCC) was 0.9577, and the mean absolute percentage error (MAPE) was 14.95% during the calibration phase. SHAP, an explainable AI approach that provides interpretable insights into complex AI systems by assigning feature importance to model predictions, was employed. Results suggest the higher predictions from the XGB-M3 and RF-M3 models were largely driven by curing time (CT). Full article

The output power of photovoltaic (PV) systems is influenced by various environmental factors, exhibiting strong nonlinearity and non-stationarity, which poses significant challenges for accurate forecasting. To address these issues, this paper proposes a short-term PV power forecasting method based on wavelet convolutional neural networks and an improved Transformer. First, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to decompose the original PV power sequence into several intrinsic mode functions (IMFs). Fuzzy entropy is then utilized to evaluate the complexity of each component, and subsequences with similar entropy values are reconstructed to reduce the non-stationarity of the original series. Subsequently, Pearson correlation coefficients and the maximal information coefficient (MIC) are applied to capture both linear and nonlinear relationships between each reconstructed component and meteorological features, enabling the selection of strongly correlated variables. On this basis, a wavelet convolutional network (WTConv) is introduced to perform multi-scale decomposition and frequency-band feature extraction on the reconstructed components by integrating wavelet transform with convolution operations, effectively expanding the receptive field and extracting deep-seated features of the sequences. Finally, an improved iTransformer model is adopted for time-series modeling, leveraging its inverted encoding structure and self-attention mechanism to fully capture long-term dependencies among multivariate variables. The proposed model is validated using actual power data from a PV plant in Ningxia, China, across four seasons. Comprehensive experiments, including ablation studies, comparative analyses, loss function convergence evaluation, and Diebold–Mariano significance tests, are conducted to thoroughly assess the model’s effectiveness and superiority. Experimental results demonstrate that the proposed model achieves excellent prediction accuracy and stability in spring, summer, autumn, and winter, showing strong potential for engineering applications. Full article

Influenced by factors such as residents’ living habits, commuting patterns, and commercial activity cycles, the generation of domestic waste exhibits a distinct double-peak distribution. To meet the high demand during peak periods, collection companies typically deploy excess transportation capacity, which leads to severe resource idleness during off-peak periods, imposing significant economic and environmental burdens. To address this issue, this study develops a dynamic smart waste collection routing model aimed at minimizing the coordinated collection cost between self-owned and outsourced multi-compartment vehicles, and designs a two-phase algorithm to solve it. In the pre-optimization phase, an improved Harris Hawks Optimization algorithm integrated with multiple heuristic algorithms is employed to generate initial collection routes. In the re-optimization phase, a hybrid strategy combining periodic and continuous re-optimization is used to dynamically update collection routes. Finally, the effectiveness of the proposed model and algorithm is validated through case studies. Furthermore, a systematic sensitivity analysis is conducted to investigate the impact of key parameters, yielding practical insights for waste collection management. Full article

To address ship-sourced water pollutant issues resulting from shipping industry growth and achieve precise supervision and effective management in coastal ports, this study develops a method for calculating and predicting the generation volume of oily sewage, domestic sewage and solid waste based on Automatic Identification System (AIS) data. First, a questionnaire survey (“Survey on Ship Water Pollutants”) is designed and implemented. Through analysis of questionnaire data, the ranges of values for the generation of oily sewage, domestic sewage, and solid waste from different ship types at China’s coastal ports are established. Additionally, onboard sampling is conducted to determine average emission factors for domestic sewage and oily sewage from typical ship types. Second, ship activities are derived from AIS data and combined with the established generation volume ranges for spatiotemporal calculation. Finally, a ConvLSTM (Convolutional Long Short-Term Memory) model is developed to predict the generation volume of water pollutant based on their spatiotemporal characteristics. Taking a major Chinese port area as a case study, the results indicate that pollutant generation volumes are significant in coastal port zones and main navigation channels, particularly between 15:00 and 16:00. chemical oxygen demand (COD), suspended solids (SS), and 5-day biochemical oxygen demand (BOD₅) levels in domestic sewage exceeded China’s national regulatory limits by 0.35 times, 2.88 times and 1.07 times, respectively, which can easily lead to a decrease in dissolved oxygen content in the water, affecting the respiration and survival of aquatic organisms. Petroleum content in oily sewage remained below the standard threshold. For pollutant generation volume prediction, the proposed ConvLSTM model achieved MAE and RMSE values of 0.0824 and 0.1433, respectively, outperforming other prediction models such as LSTM and CNN-LSTM. This research provides technical support for the prevention and control of water pollution from ships in coastal ports. The proposed AIS-driven framework and ConvLSTM prediction method are transferable and globally applicable, offering a reference for the environmental sustainability of port ecosystems, the global maritime pollution prevention, and the sustainable development of the shipping industry worldwide. Full article

This study explores the environmental justice issues associated with landfill compensation policies in Bantargebang, Indonesia. Although compensation programs have been implemented for many years, communities living near landfills continue to experience ongoing environmental damage and significant health concerns. Using a qualitative descriptive method, this research explores systemic barriers through in-depth interviews, observations, and water quality analysis. The findings indicate that labeling the program as “Social Assistance” within the Local Government Information System (SIPD) redefines ecological compensation as a fixed form of charity, rather than as a mechanism for genuine environmental restitution. Laboratory data show severe bacteriological contamination, with Total Coliform levels reaching 95%, forcing residents to bear substantial “hidden costs” for clean water, perpetuating a cycle of financial dependence. The growing normalization of health hazards is evident in over 5000 annual cases of acute respiratory infections, and the deadly landslide in March 2026, in which claimed seven lives and injured six others. These incidents underscore the failure of existing remediation approaches to safeguard human dignity and well-being. To address these shortcomings, this study proposes the adoption of an Integrated Compensation Model based on Green Social Work. This model emphasizes structural investment, spatial risk-based indices using quantitative data, and budget coding adjustments within the SIPD. This approach highlights the urgent need to move beyond temporary charitable assistance and instead pursue meaningful environmental justice, while positioning social workers as “Social-Ecological Brokers” who help restore dignity and well-being in communities often treated as “sacrifice zones.” Full article

Accurate extraction of impervious surface areas (ISA) is essential for urban environmental monitoring, yet severe spectral confusion among complex urban land-cover types limits the performance of classifications based solely on optical imagery. To address this issue within a localized context, this study proposes a multi-source framework integrating UAV-based LiDAR (UAV-LiDAR) and high-resolution visible imagery for fine-scale ISA extraction. An improved segmentation optimization strategy, termed EGS-Optimizer, is developed to enhance boundary delineation within the object-based image analysis (OBIA) framework by coupling edge detection with global segmentation quality evaluation. A comprehensive feature set including spectral, index, texture, geometric, and terrain features is constructed, and Shapley Additive Explanations (SHAP) is applied to select the most informative variables while reducing dimensionality. The proposed framework is validated in a typical 1.45 km² built-up area in Deyang City, Sichuan Province. Experimental results demonstrate that, within this specific study area, multi-source data fusion improves classification accuracy by 3.59–5.79% compared with single-source data, while feature selection reduces the feature dimension from 45 to 21. Among the evaluated classifiers, the random forest (RF) model achieves the highest performance, with an overall accuracy of 97.24% (Kappa = 0.96). While the high accuracy highlights the efficacy of synergizing spectral and structural information for micro-landscape mapping, these findings are constrained to the demonstrated fine-scale local environment. The results provide an effective, interpretable solution for detailed neighborhood-level ISA mapping, though further validation is required before the framework can be generalized to larger or more heterogeneous urban scenarios. Full article

This paper addresses the issue of lighting in historic urban spaces, using Tarnow (Poland) as a case study. The aim is to assess the impact of artificial light sources on visual comfort within the area, with particular consideration given to light pollution. A comprehensive inventory of active street lighting in the Old Town was conducted. Measurements taken at ground and eye level revealed strong inconsistencies: some areas were under-lit (<1 lx), while others showed façade illuminance above 100 lx, far exceeding recommended thresholds. The highest environmental impact was shown by decorative and globe-type fixtures, with Sky Glow Contribution Index (SGCI) values of up to 0.62. Only suspended street luminaires met CIE requirements (ULR ≤ 15%). The findings reveal that several lighting installations do not meet recommended standards, adversely affecting both human comfort and ecological balance. The study proposes strategies to optimise urban lighting, such as replacing inefficient fixtures with full cut-off LED luminaires and implementing intelligent lighting control systems which could reduce energy consumption by 50-67% while preserving the architectural character of the historic centre. The results provide evidence-based strategies for sustainable lighting modernisation in heritage cities across Europe. Full article

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, with significant performance degradation observed in dense obstacle environments. To mitigate the adverse impacts imposed by environmental factors, a dual-RIS-assisted communication system exhibits superior adaptability to practical scenarios. This work focuses on investigating such a system. It is worth noting that fully passive RISs lack the capability to process signals independently. Furthermore, when employing pilot-aided algorithms to acquire channel state information (CSI), wireless systems often encounter challenges arising from large channel matrix dimensions, thereby leading to substantial pilot overhead. To address the aforementioned issues, this paper proposes a novel semi-blind channel estimation method for multiple-input multiple-output (MIMO) systems aided by double reconfigurable intelligent surfaces (D-RISs). Specifically, we construct two tensor models, namely the Parallel Factor (PARAFAC) model and the Parallel Tucker2 model, for the received signal in two separate stages. By means of tensor decomposition, the joint channel estimation and symbol detection problem is reformulated as a least squares problem and solved using a two-stage algorithm. In the first stage, the ALS algorithm is adopted to estimate the transmitted symbols and provide initialization for the second stage. Then, in the second stage, the TALS algorithm is employed to obtain the final estimation results of the three sub-channels. Simulation results verify the effectiveness of the proposed receiver. Full article

An effective strategy for significantly enhancing photocatalytic activity of composite materials is to construct heterojunctions. Herein, a series of imidazole-modified heterostructured g-C₃N₄/SnO (CNIS) Z-scheme photocatalysts were prepared by calcination methods, leading to superior photocatalytic performance than pure SnO and imidazole-modified g-C₃N₄. Rhodamine B (Rh B) aqueous solution was taken as the target pollutant, and the result presented that both imidazole modification and the Z-scheme heterojunction construction benefited from significant enhancement in photocatalytic activity. Moreover, it is revealed that electrons were transferred from imidazole-modified g-C₃N₄ to SnO through the interface of the composite by XPS analysis. Under visible light (>420 nm) irradiation, the built-in electric field, band edge bending, and Coulomb interaction work synergistically to drive the recombination of relatively useless electrons and holes in the hybrid. As a result, the residual electrons and holes, which possess enhanced reducibility and oxidizability, endow the composite with exceptional redox capability. This research can not only deepen our comprehension of designing and fabricating innovative Z-scheme heterojunction photocatalysts but also presents an effective approach to tackle environmental pollution issues in the future. Full article