Search Results (16,769)

Urban forest fragmentation can weaken ecological connectivity and reduce the spatial continuity of cooling benefits, highlighting the need for structure-function integrated planning. Using 2020 as the baseline year, this study combined the Markov Chain-Future Land Use Simulation model, Morphological Spatial Pattern Analysis, the Minimum Cumulative Resistance model, and Kernel Density Estimation to simulate the evolution of Hangzhou’s forest-dominated ecological network under Business-as-Usual, Economic Development, and Ecological Conservation scenarios for 2030. A Random Forest model with Shapley Additive Explanations was further used to quantify the nonlinear relationship between landscape spatial context and land surface temperature. The results show that the Economic Development and Business-as-Usual scenarios intensify forest fragmentation and weaken the structural integrity of potential ecological corridors, whereas the Ecological Conservation scenario improves network stability through patch amalgamation, core area expansion, and reduced breakpoint density. Land surface temperature was strongly associated with built-up land adjacency. Built-up areas near forest patches showed a maximum relative cooling benefit of 8.04 degrees Celsius, and corridor cooling effects were most pronounced within 300 m, remaining detectable up to about 900 m under the Ecological Conservation scenario. These findings support structure-oriented planning for urban forest conservation and heat mitigation. Full article

The impact of PM_2.5 pollution on the health inequality of vulnerable groups is a core issue in environmental justice research. However, existing studies in China mostly focus on severely polluted areas in northern China. They lack comparative cases in economically developed eastern regions. They also rarely consider changes in the impact of air pollution on residents’ health amid rapid urbanization. Based on multi-source data, this study employed spatial visualization, spatial autocorrelation analysis and spatial regression models. It investigated the impact of PM_2.5 pollution on the health inequality of vulnerable elderly groups in 92 districts and counties of Jiangsu Province from 2010 to 2020. The results show that: first, the regional pattern of health inequality between PM_2.5 pollution and vulnerable elderly groups in Jiangsu has continuously evolved, with a “lower in the south and higher in the north” pollution pattern and high overlap between high-pollution areas and high elderly health risk areas in northern Jiangsu. Second, the spatial coupling between PM_2.5 and elderly health risks has gradually strengthened, showing significant positive spatial agglomeration in 2020, confirming obvious spatial agglomeration characteristics of air pollution’s health impact. Third, the adverse health impact of PM_2.5 on vulnerable elderly groups became significant in 2020, exhibiting cumulative and lagged characteristics; urbanization and regional coordinated development have played a positive role in alleviating regional health inequality, while a lagging energy structure further exacerbates the health vulnerability of the elderly. This study fills the gap of insufficient research on economically developed eastern regions and provides targeted empirical references for urban refined governance and precise prevention and control of environmental health inequality. Full article

Blue crab (Callinectes sapidus) populations are of substantial ecological and economic importance. As a keystone species, C. sapidus plays a critical role in maintaining estuarine food webs while also supporting one of the most consumed and economically valuable seafood industries in Delaware and Maryland. This study investigated the presence of Callinectes sapidus reovirus 1 (CsRV1) in C. sapidus collected from Rehoboth Bay, Delaware, USA, using reverse transcription–quantitative polymerase chain reaction (RT-qPCR), and evaluated potential associations between viral occurrence and physicochemical parameters, including temperature, salinity, pH, turbidity, alkalinity, calcium hardness, nitrite, and chlorophyll-a. A total of eighteen traps were deployed across six study sites encompassing oyster aquaculture areas, artificial oyster reefs, and control sites with minimal structural habitat. CsRV1 was detected in blue crabs from Rehoboth Bay, confirming the presence of the virus within the Delaware Inland Bays; however, detections were limited to a small subset of sampled individuals. Among the environmental parameters examined, salinity exhibited the greatest interannual variability, while other physicochemical conditions remained relatively consistent across site types and sampling periods. Overall, environmental conditions during the study period were within ranges considered suitable for C. sapidus, indicating that the population is likely to experience limited environmental stress and minimal disease-related impacts under current conditions. Full article

This study explores what motivates farmers in Fars Province, Iran, to consider adopting LCA practices, with a focus on behavioral, moral, and institutional influences. Data were collected from 386 farmers selected through stratified random sampling and analyzed using Structural Equation Modeling (SEM) in Smart Partial Least Squares (PLS) 3. The results confirmed that the measurement model was reliable and valid, and the structural model showed strong explanatory power, explaining 76% of the variance in adoption intention (R² = 0.766) and 64% in moral norms (R² = 0.642). Farmers’ intentions were significantly shaped by attitude (β = 0.210, p < 0.001), perceived behavioral control (β = 0.175, p < 0.001), moral norms (β = 0.307, p < 0.001), policy support (β = 0.202, p = 0.003), and perceived climate risk (β = 0.176, p < 0.001). In contrast, subjective norms and trust in institutions did not directly influence intention, although trust strongly strengthened moral norms (β = 0.387, p < 0.001). In general, the findings highlight that farmers’ decisions are shaped not only by practical and economic considerations but also by their sense of responsibility, confidence in their abilities, and perceptions of climate risk and institutional support. The study contributes to sustainability research by integrating moral and institutional perspectives into behavioral models and offers practical insights for policymakers to support the transition toward low-carbon, climate-resilient agriculture in Iran. Full article

The shift from the central place paradigm to the network paradigm in regional relation research emphasizes the need to elucidate the factors and mechanisms driving urban network dynamics. Leveraging firm-level big data—including a headquarters–branch relationships database (29,359 headquarters and 114,679 branches) and an investment relationships database (21,843 investing firms and 69,733 recipients)—this study constructs an urban network integrating both vertical and horizontal enterprise connections. Using exponential random graph models (ERGMs), it analyzes the influencing factors and driving mechanisms of urban network dynamics in the Yellow River Basin (YRB). This study found that the urban network in the YRB is characterized by multiple isolated “core–periphery” radial networks. Strong connections are concentrated within each province’s major cities and their immediate surroundings, while horizontal connections across provincial borders are weaker. From 2000 to 2020, the urban network has evolved from isolated “core–periphery” radial networks to corridor networks where some core nodes are interconnected. The urban network dynamics in the YRB result from the combined influences of the preferential attachment mechanism, the network self-organization mechanism, the multi-dimensional proximity mechanisms, and the geographical boundary effect. Enterprises tend to establish branches or investments in cities with spatial proximity and larger economic scales. Reciprocal and transitive structures significantly facilitate urban network formation. Additionally, institutional proximity, geographical proximity, cultural proximity, cognitive proximity, and geomorphological division all exert varying degrees of influence on enterprise connections between cities. Full article

Copy number variations (CNVs) are a major source of structural genomic diversity that influence adaptation, reproduction, and production traits in livestock. The Black Bengal goat, an economically important Indian breed known for its high fecundity, superior skin quality, and resilience to humid tropical climates, was studied to uncover its structural genomic landscape. We performed whole-genome CNV analysis using high-depth (10×) sequencing data from eight individuals. A total of 31,816 copy number variants (CNVs) were identified, predominantly duplications, with an average length of approximately 45 kb. These CNVs were combined into 8910 copy number variation regions (CNVRs) covering approximately 0.15 Gb (about 5.3% of the autosomal genome). CNVR hotspots were mainly located on chromosomes 1. Gene annotation showed that regions overlapping with CNVs and CNVRs contained over 1987 protein-coding genes that are involved in pathways related to immunity, reproduction, metabolism, and extracellular matrix (ECM) organization. The presence of copy number variations involving genes such as GDF9 and BMPR1B on chromosome 7 & 6, respectively, are important because it indicates that the breed has a high reproductive capacity due to dosage-sensitive duplications. Changes in the extracellular matrix and increased dermal strength have been linked to duplications of genes such as COL6A1, LAMC2, LAMB3, FMN1, and CLDN1. This helps explain the superior hide quality of the breed. This research offers a comprehensive map of CNVs and CNVRs within the genome of the Black Bengal goat. It demonstrates how these duplications lead to structural changes that enhance both reproductive performance and skin resilience. These findings provide a valuable genomic resource for future marker-assisted selection, comparative genomics, and conservation breeding programs aimed at preserving indigenous goat populations. Full article

This study investigates the effect of green innovation on environmental performance and the moderating role of ownership structure. A generalized method of moments regression approach was applied to a sample of 68 firms operating in the United Arab Emirates (UAE), observed from 2012 to 2024. The results indicate a significant and positive relationship between green innovation and environmental performance. In addition, institutional and state ownership strengthen this relationship. Splitting the sample according to key UAE characteristics (firms listed on the Abu Dhabi Securities Exchange versus the Dubai Financial Market, and the pre-UAE Vision versus post-UAE Vision period) as well as economic conditions (COVID-19) provides further interesting results. Our findings remain robust across alternative estimation methods. The results show significant differences in how ownership structures moderate green innovation effectiveness across the two markets. We also find that green innovation’s effectiveness on environmental performance significantly intensifies after the UAE Vision’s announcement. Our findings also indicate that the positive impact of green innovation on environmental performance becomes more pronounced in the post-COVID period. This paper provides an in-depth assessment of the role of sustainable tools (particularly green innovation) in enhancing environmental performance in the United Arab Emirates. It offers valuable insights for board members, CEOs, regulators, and policymakers who remain undecided or hesitant about implementing sustainability-oriented practices. Full article

Cañihua (Chenopodium pallidicaule) is an underutilized Andean pseudocereal of strategic interest for sustainable agriculture in high-altitude, climate-constrained environments, where its tolerance to frost, drought, and saline soils positions it as a potential climate-resilient crop. Despite its high nutritional value and potential for functional food applications, its research landscape remains fragmented and unevenly developed across agronomic, nutritional, and technological dimensions. This study aimed to systematically and bibliometrically analyze the scientific literature on cañihua published between 1995 and 2025. A total of 104 documents indexed in the Scopus database were evaluated following the PRISMA 2020 approach, including analyses of publication trends, geographic distribution, collaboration networks, and thematic structures, together with a qualitative critical appraisal of the included evidence. Results indicate a marked increase in scientific output since 2006, with research predominantly concentrated in food science and technology and limited development in agronomy, clinical nutrition, and socio-economic domains. Thematic analysis reveals a strong focus on bioactive compounds, nutritional composition, and processing technologies, while clinical, socio-economic, and large-scale agricultural studies remain limited. Processing strategies such as germination, malting, and fermentation enhance nutrient bioavailability, reduce antinutritional factors, and improve sensory properties, supporting the incorporation of cañihua into functional and gluten-free foods at levels of up to 25%. Significant gaps persist in clinical validation, agronomic standardization, production scalability, genetic improvement, and integration across research domains. Overall, cañihua shows strong potential to contribute to sustainable Andean agriculture, food security, and functional food innovation, although further interdisciplinary and translational research linking agricultural production with nutritional and technological outcomes is required to realize its full applied potential. Full article

Freezing rain is a high-impact winter weather phenomenon that acts as a major disturbance agent in forest ecosystems, causing canopy damage, stem breakage, tree mortality, and long-term changes in forest structure and functioning. Although ice storms have been studied for decades, research on freezing rain impacts on forests remains fragmented across multiple disciplines, and few studies have attempted an integrated synthesis that simultaneously combines climatological, ecological, and methodological perspectives. In this study, we present a systematic and integrative review of the scientific literature on freezing rain and forests, combining a large-scale bibliometric analysis with an in-depth qualitative synthesis. A total of 241 publications retrieved from the Scopus and Web of Science databases were analyzed following PRISMA guidelines. The bibliometric assessment examined publication trends, geographic distribution, institutional contributions, research domains, and keyword networks. The qualitative review synthesized current knowledge on freezing rain climatology, forest damage mechanisms, species-specific vulnerability, major ice storm events, detection and modeling approaches, and ecological consequences. Results reveal a strong increase in scientific output over the last two decades, dominated by research from North America and northern Europe. Ice accretion intensity emerges as the primary driver of forest damage, while species traits, crown architecture, tree size, stand structure, topography, and exposure strongly modulate damage severity. Freezing rain affects a wide range of forest types worldwide and triggers both immediate structural damage and long-term ecological effects, including altered successional dynamics and reduced forest productivity. Recent methodological advances—including passive remote sensing (e.g., optical satellite data), active remote sensing (e.g., LiDAR), experimental ice storm simulations, reanalysis datasets, and machine learning approaches—have significantly improved detection, monitoring, and forecasting capabilities. Despite these advances, major research gaps remain, particularly regarding long-term ecosystem recovery, trait-based vulnerability, socio-economic impacts, and future freezing rain regimes under climate change. This review highlights freezing rain as an increasingly important but underappreciated forest disturbance and underscores the need for interdisciplinary research and adaptive management strategies in ice-prone regions. Full article

Wood is a sustainable material, but hygroscopicity can affect dimensional stability and mechanical durability. Recent research has increasingly focused on combining citric acid with various polyols as eco-friendly crosslinking systems to improve wood properties. Herein, a solvent-free and catalyst-free method was used to synthesize bio-based polyesters from citric acid and mannitol. In situ curing was carried out after vacuum-pressure impregnation of fast-growing poplar wood (Populus deltoides Marshall). Morphological characterization showed that the polyester filled the cell lumen and penetrated the cell wall structure. It was confirmed by Fourier Transform Infrared (FTIR) and cross-polarization/magic angle spinning (CP/MAS) ¹³C nuclear magnetic resonance (NMR) analysis that the polyester formed covalent ester bonds with wood hydroxyl groups, which indicated successful chemical grafting. The dimensional stability and mechanical properties of the modified wood were greatly improved. The parallel compressive strength of the grain reached 41.5 MPa, which was 41.7% higher than that of the untreated wood. This research adopted a citric acid–mannitol polyester, providing a sustainable, economical, and scalable approach for the development of high-performance, degradable wood composites for construction/furniture applications. Full article

This study develops a lifecycle economic comparison framework for shared energy storage, in which multiple users share a common storage asset through capacity leasing. A multi-service revenue structure, including capacity leasing, spot-market arbitrage, auxiliary frequency regulation, peak shaving, and capacity compensation, is established for comparative evaluation. Case studies are conducted for lithium iron phosphate (LFP) and vanadium redox flow (VRF) batteries across six representative Chinese electricity markets and six standardized revenue-combination scenarios. The results show that, among the scenarios that more closely reflect current operating practices, P3 (capacity compensation + spot market + auxiliary frequency regulation) delivers the highest net present value (NPV). P6 combines all five revenue streams without explicitly modeling service-coupling dispatch constraints, and is therefore treated as a theoretical benchmark rather than an immediately deployable operating mode. Under this benchmark assumption, its calculated NPV is 21.1% and 41.7% higher than that of P3 for the two battery types, respectively. The study also shows that power-related services are more sensitive to rated power, while spot-market and peak-shaving revenues are more dependent on rated capacity. Full article

With the continuous decline in the cost of renewable energy such as wind power and photovoltaic power generation, the economic competitiveness in the power supply structure is increasing, and traditional thermal power units are gradually being replaced, resulting in a profound adjustment of the power supply structure. However, the unclear alternative between units may lead to system redundancy configuration or power supply shortage. At the same time, the volatility of the new energy output and the flexible allocation of resources and other factors work together, resulting in the cost of the power system showing complex evolution characteristics. Therefore, it is of great significance to study the evolution of system cost in the process of thermal power substitution. This paper first analyzes the internal mechanism of the cost change of the new power system. Second, the cost accounting model of the power system is constructed to reveal the relationship between ‘thermal power substitution mode-system cost’ in the process of thermal power installed capacity substitution. Finally, the Garver-6 system is taken as an example to carry out simulation analysis, solve the optimal thermal power substitution mode under different renewable energy penetration rates, and explore the evolution law of system cost. The results of the example show that with the increase of renewable energy penetration, the total cost of the system first decreases and then increases, and the optimal substitution method is ‘unit thermal power to replace more renewable energy’. Full article

As university campuses increasingly open to the public and become integrated into urban tourism systems, campus spaces are evolving into institutionalized multifunctional environments shared by students and visitors. However, existing tourism conflict research has mainly focused on traditional resident–tourist relations, with limited attention to institutionalized educational spaces. Taking Hunan University in the Yuelu Mountain Scenic Area, Changsha, as a case study, this study develops a perceived conflict–place attachment–overall impact evaluation framework and tests it using data from 438 student questionnaires and structural equation modeling. The results show that perceived conflict significantly weakens place attachment and reduces students’ positive evaluations of campus tourism impacts, with place attachment serving as a significant mediator. More importantly, the effects are not uniform: only economic and social conflict significantly reduce place attachment, campus belonging amplifies rather than buffers the negative effect of perceived conflict, and study duration does not significantly moderate this relationship. In addition, students whose daily study or living spaces are closer to tourism core areas report stronger perceived conflict and greater vulnerability to its consequences. By showing that tourism conflict in campus spaces is selective and context-dependent, this study extends tourism conflict theory beyond conventional community settings and offers practical implications for conflict-sensitive, spatially differentiated, and student-oriented campus tourism governance. Full article

In the context of global digital transformation, scientifically examining the spatio-temporal evolution patterns and transition mechanisms of the digital economy at the municipal level is crucial for promoting coordinated regional development. This study takes 281 prefecture-level cities in China from 2011 to 2023 as its research units. Exploratory Spatio-Temporal Data Analysis (ESTDA) is employed to analyze its spatio-temporal dynamics, while a panel quantile regression model nested with spatio-temporal transition types is used to reveal the driving mechanisms. The findings indicate that (1) the overall development level of China’s municipal digital economy has steadily increased, yet significant regional heterogeneity persists, characterized by a pattern of “eastern leading, central fastest-growing, and western lagging,” with considerable room for overall improvement. (2) The digital economy exhibits a significant positive spatial correlation. High–high agglomeration areas remain stable in the southeastern coast, whereas low–low agglomeration areas are concentrated in the central-western and northeastern regions. The spatial pattern demonstrates strong stability and path dependence. (3) LISA time paths reveal drastic changes in local spatial structures in provinces such as Heilongjiang, Inner Mongolia, Hubei, Guangdong, and Guangxi, while East and Central China remain relatively stable. Tortuosity analysis indicates that spatial linkages in the western region are becoming active yet unstable. (4) The quantile regression nested with transition types identifies four mechanisms: “Economic development-Technological innovation” serves as the fundamental driving mechanism across all regions. Low-quantile areas face a complex situation with dual suppression from “opening-up and urbanization” coexisting with drivers from “human capital, government intervention, and industrial structure.” High-quantile areas are synergistically driven by “urbanization, human capital, government intervention, and advanced industrial structure.” This study provides a decision-making reference for overcoming the dilemma of “low-level club convergence” in digital economy development and formulating differentiated regional policies. Full article

The decarbonization of multimodal transport systems requires assessment approaches that simultaneously address environmental impacts and economic performance at dynamic operational conditions. Conventional Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), including Total Cost of Ownership (TCO), are widely used for this purpose; however, they often rely on static assumptions and averaged data, limiting their ability to capture real-world variability. This study proposes an AI-enhanced LCA–LCC/TCO framework for the integrated evaluation of decarbonised multimodal Door-to-Port transport systems. Artificial intelligence is embedded directly into the life cycle inventory and cost inventory stages to generate scenario-specific estimates of energy consumption, greenhouse gas emissions, and operational costs. The framework is demonstrated through a case study of a multimodal Door-to-Port transport chain comprising road pre-haulage, rail line-haul, and port terminal operations. Three scenarios are analysed: conventional, partially decarbonised, and fully decarbonised configurations. The results indicate that partial decarbonization reduces greenhouse gas emissions by more than 60% compared to the baseline while achieving the lowest total cost of ownership. Full decarbonization achieves emission reductions exceeding 95% but is associated with slightly higher costs under current assumptions. Sensitivity analysis verifies the robustness of the relative scenario ranking under different energy prices, carbon pricing, and electricity carbon intensity. The proposed framework provides a structured decision-support framework for logistics operators, port authorities, and policymakers seeking cost-effective pathways to low-emission multimodal transport systems. Full article