Search Results (3,886)

Aiming to improve nighttime safety for motorcycle riders, this study evaluates the visibility and usability of LED and retroreflective-equipped protective motorcycle clothing versus conventional retroreflective gear. Ten male participants with driving experience were selected based on specific criteria, including normal or corrected visual acuity. Utilizing a simulated driving environment with a 75-inch screen and electric bicycles, the study employed an eye tracker to define recognition distances. It was found that LED and retroreflective-equipped clothing significantly increased the recognition distance in various nighttime scenarios, with the experimental group’s gear being visible from substantially further away than the control group’s gear. Additionally, subjective assessments showed that the LED gear scored higher in visibility and overall satisfaction, though no significant differences in wearability and activity performance were noted between the two groups. These results indicate that LED clothing could enhance rider safety at night, emphasizing the importance of such innovations for safety gear. Despite its focus on SUV drivers and specific conditions, the study provides foundational data for the development of effective protective motorcycle clothing, suggesting future research should include a broader array of vehicle types and environmental conditions. Full article

Enhancing forest biodiversity and carbon sinks in the face of climate change is a high priority on the global agenda. The aim of our study was to explore the feasibility and potential of enhancing biodiversity and stand biomass productivity, which are strongly linked to forest ecosystem functioning and services in temperate forests. Based on data from the 5th to 7th National Forest Inventory of South Korea, 1760 natural forest plots (0.16 ha) were used, of which 344 plots belonged to conifer stands, 711 plots belonged to broadleaved stands, and 705 plots belonged to mixed stands. Forest succession-related factor (i.e., stand age), and abiotic (i.e., climatic and topographic conditions, and soil properties) and biotic drivers (i.e., species diversity, functional trait diversity, functional trait identity, and stand structural diversity) were jointly included as independent variables in an integrated model to explain variations in stand biomass productivity. In order to reveal the key drivers and relationships that regulate stand biomass productivity across forest stand types, we applied a multi-model averaging approach and piecewise structural equation modelling (pSEM). As a key finding, across all forest stand types, forest stand age-induced tree size inequality (i.e., DBH STD) in all forest stand types commonly increased stand biomass productivity, showing strong positive standardized effects (β > 0.5, p < 0.001). We also found that the functional trait identities controlling stand biomass productivity within each forest stand type differed according to their functional traits of dominant species, and that these mechanisms were controlled directly or indirectly by environmental conditions. Our research suggests that appropriate forest management plans should be developed in accordance with environmental gradients to simultaneously promote biodiversity and stand biomass productivity in different forest stand types. Full article

This paper investigates the determinants of economic growth in EU member states that joined the Union in 2004 and later, focusing on institutional quality, competitiveness, and the green transition. Three composite indices are constructed using principal component analysis (PCA) and incorporated into a panel ARDL-PMG model, complemented by robustness checks with fixed-effects and system-GMM estimators. The results highlight competitiveness as the most robust driver of growth across specifications, while institutional quality emerges as an enabling factor, particularly under dynamic specifications that account for endogeneity. The green transition shows significant long-run benefits, although its short-run effects are weaker, reflecting the gradual payoff of environmental investments. Policy implications emphasize the importance of strengthening institutional frameworks, fostering innovation and productivity, and sustaining commitments to the green transition as pillars of sustainable convergence. The findings enrich the literature on EU integration and provide evidence-based insights for aligning cohesion policy and the European Green Deal with growth objectives. Full article

Based on panel data from China’s prefecture-level cities spanning 2009–2023, this study thoroughly examines the impact of digital technologies on urban land use efficiency and its underlying mechanisms. Findings reveal that advancements in digital technologies significantly enhance urban land use efficiency. This conclusion remains robust after undergoing a series of stability tests and endogeneity treatments, demonstrating its reliability. Further heterogeneity analysis revealed regional variations and structural characteristics in the impact of digital technologies. The study found that digital technologies most significantly boosted land use efficiency in western regions and cities with higher levels of centralization. Meanwhile, in cities with higher levels of land industrialization and digital workforce capabilities, the positive impact of digital technologies is more pronounced. The analysis of intermediary mechanisms from both micro-level resource allocation and macro-level structural transformation perspectives reveals that digital technologies effectively enhance urban land use efficiency through four dimensions: increasing the number of startups, strengthening innovation support intensity, elevating green technology levels, and driving industrial structure upgrading. Additionally, the study examined synergistic mechanisms and found that government signaling and environmental policy intensity can all significantly amplify the enabling effects of digital technologies, providing multiple drivers for enhancing urban land use efficiency. Full article

In Latin America, coffee is cultivated in distinct coffee agroecosystems (CASs), ranging from traditional agroforestry (“shade”) systems (CAFSs) to intensive, unshaded (“sun”) monocultures (UCASs). While various socioenvironmental impacts of these systems have been studied, their implications have not yet been integrated within a planetary health perspective. This review of 146 studies applies the Planetary Boundaries and Nature’s Contributions to People frameworks and the DPSEEA (Drivers, Pressures, State, Exposure, Effects, Actions) model to map the relationships between socioenvironmental drivers of change, different CASs, the state of natural systems at local and global scales, and human health and well-being. The analysis shows that conventional intensification, driven by low revenues for producers, climate change, and disease outbreaks, has accelerated deforestation, biodiversity loss, greenhouse gas emissions, agrochemical use and leakage, and water pressures. These changes create health risks for coffee-growing communities, such as pesticide exposure and increased vulnerability to external shocks. Conversely, agroecological practices can mitigate environmental pressures while reducing exposure to health hazards and improving resilience, food security, and income stability. However, mainstreaming these practices requires addressing structural inequities in the global coffee value chain to ensure fairer revenue distribution, stronger institutional support, and the protection of coffee-growing communities. Full article

Background: Diarrheal disease (DD) remains a major public health challenge and is the leading cause of malnutrition and the second leading cause of death among children under five globally. Although DD can be caused by a wide range of pathogens, its primary drivers are often linked to unimproved sanitation, limited access to clean drinking water, and poor hygiene practices. Low- and middle-income countries, particularly those in South Asia, experience the highest burden. These regions are also increasingly vulnerable to climate change and land use/cover changes, which may further exacerbate DD risk. However, the relative influence of environmental and social drivers at localized scales is not well understood. This gap presents a critical opportunity to identify scalable, data-informed interventions that address environmental determinants of health in the context of a changing climate. Methods: To investigate these dynamics, we analyzed 21,779 records from the Demographic and Health Surveys (DHS) for Bangladesh, integrating them with remotely sensed data on forest cover change, temperature, and rainfall. Using Random Forest machine learning models, we assessed the relative importance of both environmental and socio-demographic variables at household and regional (village) levels. Results: The results show that DD risk varies across scales: household-level outcomes are primarily associated with socio-demographic characteristics, while regional-level outcomes are more strongly influenced by environmental and geographic features, including precipitation, elevation, and proximity to water bodies. Conclusions: These findings underscore the importance of scale-sensitive approaches when assessing environmental health risks and developing climate-adaptive public health strategies. Full article

Located in a transitional climatic zone surrounded by sea on three sides, Kunyu Mountain provides an ideal region to study the drivers of plant distribution. The study examined the distribution patterns and environmental drivers of three dominant tree species—Pinus densiflora, Cunninghamia lanceolata, and Pinus koraiensis—using data from 55 permanent plots. A total of 52 plant species were recorded, primarily in warm-temperate areas, with some in other climatic zones. While the native P. densiflora was widespread, the introduced C. lanceolata and P. koraiensis showed limited distribution, with lower richness and growth rates than in their core ranges, yet both regenerated naturally. C. lanceolata mainly occurred below 400 m on sunny slopes, whereas P. koraiensis was concentrated at 400–500 m on shaded slopes. Climate similarity analysis confirmed that local microhabitats created by transitional climate and complex topography offer suitable conditions for both species. These findings improve the understanding of microhabitat roles in species distribution and offer insights for future species introduction strategies. Full article

This study examines the influence of perceived inclusion and accessibility dimensions on user satisfaction within the Valparaíso Metro system in Chile. The research focuses on a quantitative survey conducted with 192 regular passengers along the Limache–Puerto corridor of the EFE Valparaíso railway network. A structured questionnaire comprising 58 Likert-scale items assessed perceived accessibility, inclusion, intermodality, safety, environmental effectiveness, and overall satisfaction. Data were analyzed using Confirmatory Factor Analysis (CFA) with the WLSMV estimator based on polychoric correlations, followed by multiple linear regression with robust standard errors. Results show that the proposed model explains 72% of the variance in overall satisfaction (Adjusted R² = 0.71). Among the five predictors, perceived inclusion emerged as the most influential factor (β = 0.64, p < 0.001), surpassing perceived accessibility (β = 0.18, p < 0.01) and intermodality (β = 0.11, p < 0.05). Safety and environmental conditions showed weaker but significant associations. These findings provide empirical evidence that inclusive perceptions—rather than merely physical or operational aspects—constitute the primary driver of satisfaction in urban railway systems. The study contributes to accessibility research by integrating psychosocial and perceptual dimensions into the evaluation of public transport performance. It also offers actionable implications for inclusive design, passenger communication, and service management strategies in metropolitan rail systems, particularly in Latin American contexts undergoing infrastructure expansion and modernization. Full article

This study investigates the impact of integration into the international economic cycle (IEC) on green innovation in China’s manufacturing sector, a key factor in the country’s green strategic transformation. Using multi-regional input–output tables for both global and Chinese contexts from 2012 to 2017, alongside data from listed manufacturing firms, the analysis demonstrates that IEC integration significantly promotes green innovation in Chinese manufacturing enterprises. The mechanisms of innovation-driven development and the upgrading of production capital structure are central to this effect. Economic cycles involving Europe and developing economies exert a strong positive influence on green innovation, whereas demand from North America and East Asia has a comparatively weaker effect. State-owned and high-tech enterprises are identified as primary drivers of green innovation through IEC integration. The findings also indicate a high degree of dependence of China’s economy on the IEC. However, reliance on IEC integration alone may result in market failure, underscoring the essential role of government environmental regulation and macroeconomic guidance. The study provides valuable insights into the transformation and advancement of manufacturing and high-quality development within the context of the modernization of China. Full article

As environmental sustainability pressures intensify and AI technologies rapidly evolve, the integration of AI into green marketing strategies has become increasingly prominent. This systematic review examines the application of artificial intelligence (AI) in green marketing, with a focus on studies published between 2020 and 2024. This review addresses two key research questions: the effectiveness of different types of artificial intelligence in green marketing applications, and the role of AI in supporting enterprise development in this context. A comprehensive search of SpringerLink, Web of Science, and Google Scholar initially identified 200 records. After duplicate removal and multi-stage screening, 47 articles were deemed to meet the inclusion criteria. Only peer-reviewed journal articles in English were included. Study quality was appraised using established evaluation criteria to ensure methodological rigor. Among these, Thinking AI, Mechanical AI and Feeling AI appeared in 45 studies, 23 studies and 15 studies, respectively. The selected studies span 34 journals and 28 countries, reflecting both the rising academic interest and the interdisciplinary character of this emerging field. However, this review also identifies notable deficiencies in the current body of work. This review integrates these AI types with the 4Ps framework to form a concise conceptual mapping of their respective functions. Although AI has been positioned as a powerful driver of green marketing, research remains fragmented, with limited assessment of AI’s sustainability, weak data and ethical safeguards, and insufficient long-term and global perspectives. This underscores the need for a deeper and more systematic understanding of AI to better achieve the goals of green marketing and improve its practices. Full article

Banisteriopsis and Stigmaphyllon are among the most species-rich liana genera in the Neotropical Malpighiaceae family, known for their diverse chemical and biological activities. However, metabolomic information correlating these genera with their biome of occurrence, humidity levels, and plant habit (liana vs. shrub) remains limited. This study explored in detail the metabolomic profiles of 15 species (29 specimens) of Banisteriopsis and 26 species (35 specimens) of Stigmaphyllon using Partial Least Squares Discriminant Analysis (PLS-DA). Leaf extracts obtained with ethanol/water (4:1, v/v) were analyzed by Ultra Performance Liquid Chromatography coupled to Quadrupole Time-of-Flight Mass Spectrometry (UHPLC-QTOF-MS/MS). Comparative analyses were performed at the genus level and across biome, humidity, and plant habit variables. The UHPLC-MS/MS profiling enabled the annotation of compounds, including previously unreported metabolites identified in B. quadriglandula and S. saxicola. Shared metabolites between the two genera were also characterized. PLS-DA revealed discriminant metabolites associated with the different comparative parameters, notably glucose (plant habit), coumaroyl hexoside, myricetin-3-galactoside, quercetin (genus), and quercetin-3-O-robinobioside (environment). With 95% confidence, our results demonstrate that environmental and morphological factors significantly influence metabolite biosynthesis in Banisteriopsis and Stigmaphyllon, providing valuable insights for future phytochemical and ecological research in the Neotropics. Full article

Ice krill (Euphausia crystallorophias) play a key role in the Antarctic coastal ecosystem, yet its spatial growth variability remains poorly understood. This study examined 5298 krill individuals from 52 stations across the Amundsen Sea, transitional waters, and the Ross Sea, collected between 2020 and 2024. Length–weight relationships (LWR) were constructed to derive the condition factor a and the allometric growth exponent b, followed by regional comparisons and environmental response analyses using boxplots, redundancy analysis (RDA), and generalized additive models (GAM). Boxplots revealed that a was significantly higher in the Amundsen Sea and transitional zone than in the Ross Sea, while b was highest and most variable in the Amundsen Sea. RDA indicated that a was primarily associated with depth, latitude, mean temperature, and mean salinity, whereas b was influenced by sea surface temperature, chlorophyll-a, sea ice concentration, and longitude. GAM further showed nonlinear responses of a to mean temperature, mean salinity, and depth, with peaks near −0.5 °C, 34.2 PSU, and 3500 m, respectively. These results suggest that krill in deep, cold, and less-productive transitional zone allocate more energy to body condition (high value a), while those in warmer, moderately productive regions like the Amundsen Sea invest more in structural growth (high value b). This study provides new insights into the environmentally driven growth strategies of ice krill and contributes to understanding its ecological adaptability under changing climatic and oceanographic conditions. Full article

Land use change is a key driver of regional carbon emissions. Understanding the mechanisms through which regional land use changes influence carbon emissions, as well as their spatiotemporal evolution, is of great significance for the optimization of land use structure and the formulation of low-carbon policies. This study, based on land use data and socio-economic data from 2002 to 2022, combines decoupling analysis models with carbon carrying capacity assessment frameworks to systematically analyze the dynamic evolution of carbon emissions from land use in Jilin Province. The results show the following: (1) From 2002 to 2022, the cultivated land area in Jilin Province remained stable and accounted for the largest proportion; the areas of water bodies and construction land expanded, while forest, grassland, and unutilized land continued to decline. (2) Total carbon emissions exhibited a “growth-stabilization-slight decline” trend, with construction land contributing the most to emissions. Spatially, carbon emissions were concentrated in the central region with Changchun at its core. (3) The overall carbon ecological carrying capacity of Jilin Province showed a fluctuating upward trend, with notable differences in carbon ecological carrying capacity across cities. (4) Cultivated land showed the highest correlation with carbon emissions, followed by woodland. The decoupling relationship between carbon emissions and economic development exhibited phase fluctuations, evolving from weak decoupling to strong decoupling and then transitioning back to weak negative decoupling. Therefore, it is recommended that effective measures be adopted to curb the excessive expansion of construction land, enhance ecological carbon sink functions, and facilitate the transformation of cultivated land from a carbon source to a carbon sink. This will promote the efficient and green utilization of land resources, advance the synergistic progress of economic development and environmental protection, and achieve the goal of regional sustainable development. Full article

Green buildings play a pivotal role in advancing urban sustainability and fostering a green economy. This study constructs an integrated model to examine the key drivers of green building development and their influence on urban greening. Data were gathered from 403 stakeholders across Shandong Province, China, and analyzed using Structural Equation Modeling through SPSS and AMOS. The results demonstrate that policy, market demand, technological advancement, and long-term economic benefits are critical drivers, among which policy exerts the most significant influence, while economic viability serves as a fundamental incentive for market adoption. Quantitatively, the SEM path coefficients indicate that policy (β ≈ 0.56) and market forces (β ≈ 0.55) exert the strongest effects, followed by technology (β ≈ 0.49) and economic factors (β ≈ 0.38). The study further identifies eight specific sub-factors—including streamlined approval processes and enhanced regulatory measures—and proposes nine targeted strategies to foster sustainable and economically viable building practices. These findings align with several Sustainable Development Goals, such as affordable and clean energy, sustainable cities and communities, and climate action, offering a strategic framework to guide policymakers and industry stakeholders toward synergistic achievement of environmental and economic objectives. Full article

In applied battery research, use-case-driven prediction is becoming increasingly important, particularly for predicting real-life load profiles. This study proposes techniques to forecast lifetime load profiles for traction batteries, comparing urban- and highway-dominated vehicular use cases. Both charging and discharging scenarios are analyzed. We examine the uncertainty in these profiles and conduct a sensitivity analysis to understand the relationship between load profiles and user behavior. In this study, we introduce a novel methodology that maps behavioral and environmental parameters to battery load clusters, enabling us to identify high-risk aging scenarios. Based on parameter studies, we perform load profile clustering to identify critical use case groups and observe key parameter interactions. We present a case study of an idealized driver under Hungarian environmental conditions to predict outlier battery usage in fleets. This novel approach enables more robust predictions of aging and performance degradation for automotive traction batteries across different user clusters. Full article