Search Results (8,128)

In light of the increasing focus on global climate change and environmental issues, countries around the world are collaboratively working towards the establishment of a low-carbon economy (LCE). As the most populous developing nation, China is proactively advocating for low-carbon economic development as a means to achieve sustainable growth. Nevertheless, the efficiency of the low-carbon economy (LCEE) exhibits considerable variation across different regions within China. This article seeks to explore the regional disparities in LCEE throughout the country and to identify the factors that contribute to these variations. Firstly, this paper examines the advancements in LCEE research, concentrating on an analysis of 30 Chinese provinces. Employing the Multi-directional Efficiency Analysis (MEA) framework alongside the global Malmquist (GM) index, this study evaluates the efficiency of the low-carbon economy across the 30 provinces from 2010 to 2021. Secondly, by integrating spatial autocorrelation analysis techniques, the research encompasses a multifaceted examination, including spatiotemporal analysis, regional disparities, driving factors, and potential for improvement. The findings indicate significant discrepancies in LCEE among various provinces in China. Notably, LCEE tends to be higher in the eastern coastal regions, attributed to their advanced economic development, whereas the western inland areas generally exhibit lower efficiency levels due to comparatively limited economic progress. Thirdly, LCEE exhibits significant spatial heterogeneity, with clear high–high and low–low clustering patterns, revealing systemic coordination gaps between eastern coastal and central/western regions. Fourthly, from the decomposition results of the global Malmquist index, it can be seen that efficiency change (EC) is less than 1 and technology change (TC) is greater than 1, which promotes the improvement of LCEE. Technical efficiency is the main factor affecting the improvement of LCEE. Full article

In the context of the global climate transition, managerial climate attention is influencing the risk posture of new energy vehicle firms as a key non-economic cognitive factor. This paper investigates the mechanism of managerial climate attention (MCA) on the systemic risk of firms using panel data from 111 listed NEV firms in China from 2013 to 2022. The results show that first, the systemic risk of NEV firms is significantly reduced as managerial climate attention increases. Second, the negative influence of MCA on the systemic risk of NEV firms is more significant among state-owned enterprises, firms in non-first-tier cities and in the machinery, equipment and computer communication sub-sectors. Third, MCA negatively affects the systemic risk of NEV firms by increasing market competition, environmental performance and investor sentiment. The difference-in-differences analysis based on the Paris Agreement shows that the systemic risk of the treatment group enterprises increased significantly after policy implementation, confirming the link between climate-related policies and risk. The management of NEV firms should be concerned about climate change, thus providing practical implications for financial stability and sustainable economic development. Full article

Background: In high-altitude regions, sporadic two-year-old immature Chinese mitten crabs (Eriocheir sinensis) would overwinter and mature in their third year, developing into three-year-old crabs (THCs) with a cold-adaptive strategy. Compared to two-year-old crabs (TWCs) from low-altitude Jiangsu, THCs from Karst landform and high-altitude Guizhou exhibit significantly larger final size but lower gonadosomatic index (GSI) (p < 0.01). Methods: To elucidate the molecular mechanisms underlying this delayed ovarian development, integrated transcriptomic and proteomic analyses were conducted. Results: Results showed downregulation of PI3K-Akt and FoxO signaling pathways, as well as upregulation of protein digestion and absorption pathways. Differentially expressed proteins indicated alterations in mitochondrial energy transduction and nutrient assimilation. Integrated omics analysis revealed significant changes in nucleic acid metabolism, proteostasis, and stress response, indicating systemic reorganization in energy-nutrient coordination and developmental plasticity. Conclusions: The observed growth-reproductive inverse relationship reflects an adaptive life-history trade-off under chronic cold stress, whereby energy repartitioning prioritizes somatic growth over gonadal investment. Our transcriptomic and proteomic data further suggest a pivotal regulatory role for FOXO3 dephosphorylation in potentially coupling altered energy sensing to reproductive suppression. This inferred mechanism reveals a potential conserved pathway for environmental adaptation in crustaceans, warranting further functional validation. Full article

The survival status of Lumnitzera littorea is near threatened globally and critically endangered in China. Clarifying its global distribution pattern and its changing trends under different future climate models is of great significance for the protection and restoration of its endangered status. To build a model for this purpose, this study selected 73 actual distribution points of Lumnitzera littorea worldwide, combined with 12 environmental factors, and simulated its potential suitable habitats in six periods: the Last Interglacial (130,000–115,000 years ago), the Last Glacial Maximum (27,000–19,000 years ago), the Mid-Holocene (6000 years ago), the present (1970–2000), and the future 2050s (2041–2060) and 2070s (2061–2080). The results show that the optimal model parameter combination is the regularization multiplier RM = 4.0 and the feature combination FC (Feature class) = L (Linear) + Q (Quadratic) + P (Product). The MaxEnt model has a low omission rate and a more concise model structure. The AUC values in each period are between 0.981 and 0.985, indicating relatively high prediction accuracy. Min temperature of the coldest month, mean diurnal range, clay content, precipitation of the warmest quarter, and elevation are the dominant environmental factors affecting its distribution. The environmental conditions for min temperature of the coldest month at ≥19.6 °C, mean diurnal range at <7.66 °C, clay content at 34.14%, precipitation of the warmest quarter at ≥570.04 mm, and elevation at >1.39 m are conducive to Lumnitzera littorea’s survival and distribution. The global potential distribution areas are located along coasts. Starting from the paleoclimate, the plant’s distribution has gradually expanded, and its adaptability has gradually improved. In China, the range of potential highly suitable habitats is relatively narrow. Hainan Island is the core potential habitat, but there are fragmented areas in regions such as Guangdong, Guangxi, and Taiwan. The modern centroid of Lumnitzera littorea is located at (109.81° E, 2.56° N), and it will shift to (108.44° E, 3.22° N) in the later stage of the high-emission scenario (2070s (SSP585)). Under global warming trends, it has a tendency to migrate to higher latitudes. The development of the aquaculture industry and human deforestation has damaged the habitats of Lumnitzera littorea, and its population size has been sharply and continuously decreasing. The breeding and renewal system has collapsed, seed abortion and seedling establishment failure are common, and genetic variation is too scarce. This may indicate why Lumnitzera littorea is near threatened globally and critically endangered in China. Therefore, the protection and restoration strategies we propose are as follows: strengthen the legislative guarantee and law enforcement supervision of the native distribution areas of Lumnitzera littorea, expanding its population size outside the native environment, and explore measures to improve its seed germination rate, systematically collecting and introducing foreign germplasm resources to increase its genetic diversity. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Petroleum hydrocarbon pollution is a serious environmental and human health problem. In recent decades, the impact of this substance has been profound and persistent, affecting the balance of aquatic and terrestrial ecosystems and leading to significant physical and psychosocial effects among the population. Natural sources (crude oil, natural gas, forest fires, and volcanic eruptions) and anthropogenic (road traffic, smoking, pesticide use, oil drilling, underground water leaks, improper oil spills, industrial and mining waste water washing, etc.), the molar weight of the hydrocarbon, and the physicochemical properties are important factors in determining the degree of pollution. The effects of pollution on the environment consist of altering the fundamental structures for sustaining life (infertile lands, climate change, and loss of biodiversity). In terms of human health, diseases of the following systems occur: respiratory (asthma, bronchitis), cardiovascular (stroke, heart attack), pulmonary (infections, cancer), and premature death. To reduce contamination, sustainable intervention must be carried out in the early stages of the pollution-control process. These include physical techniques (isolation, soil vapor extraction, solvent extraction, soil washing), chemical techniques (dispersants–surfactants, chemical oxidation, solidification/stabilization, thermal desorption), biological techniques (bioremediation, phytoremediation), and indigenous absorbents (peat, straw, wood sawdust, natural zeolites, clays, hemp fibers, granular slag, Adabline II OS). Due to the significant environmental consequences, decisions regarding the treatment of contaminated sites should be made by environmental experts, who must consider factors such as treatment costs, environmental protection regulations, resource recovery, and social implications. Public awareness is also crucial, as citizens need to understand the severity of the issue. They must address the sources of pollution to develop sustainable solutions for ecosystem decontamination. By protecting the environment, we are also safeguarding human nature. Full article

Nowadays, sustainable agriculture is emerging as a critical framework within which food production, environmental protection and resilience to climate change must go hand in hand. At the core of this framework are the linkages between soil, water, crops, and energy (SWCE). As pressures from climate change, population growth and agricultural land degradation intensify, environmental management strategies are called upon to become more interdisciplinary, targeted and cost-effective. This review article synthesizes recent scientific findings shaping the contemporary understanding of hydro-environmental agriculture and critically examines the conceptual foundation of the SWCE nexus under climate complexity. In addition to reviewing methodological approaches, it highlights both successful global practice examples—such as integrated solar-powered irrigation and conservation-oriented soil–water management systems—and failed or problematic implementations where institutional fragmentation, unsustainable groundwater use, or energy trade-offs undermined outcomes. By analyzing these contrasting experiences, the article identifies key limiting factors and enabling conditions for scaling up nexus-based solutions. Finally, it provides recommendations for future research, integration, and policy-making, emphasizing the importance of adaptive governance, participatory approaches, and cross-sectoral collaboration to enhance the sustainability and resilience of agriculture. Full article

The widespread production and poor management of plastic waste have led to the pervasive presence of microplastics (MPs) in environmental and biological systems. Among various polymers, polyethylene (PE) is the most widely produced plastic globally, primarily due to its use in single-use packaging. Its persistence in ecosystems and resistance to degradation processes result in the continuous formation of PE-derived MPs. These particles have been detected in human biological matrices, including blood, lungs, placenta, and even the brain, raising increasing concerns about their bioavailability and potential health effects. Once internalized, PE MPs can interact with cellular membranes, induce oxidative stress, inflammation, and apoptosis, and interfere with epigenetic regulatory pathways. In vitro studies on epithelial, immune, and neuronal cells reveal concentration-dependent cytotoxicity, mitochondrial dysfunction, membrane disruption, and activation of pro-inflammatory cytokines. Moreover, recent findings suggest that PE MPs can induce epithelial-to-mesenchymal transition (EMT), senescence, and epigenetic dysregulation, including altered expression of miRNAs and DNA methyltransferases. These cellular changes highlight the potential role of MPs in disease development, especially in cardiovascular, metabolic, and possibly cancer-related conditions. Despite growing evidence, no standardized method currently exists for quantifying MPs in human samples, complicating comparisons across studies. Further, MPs can carry harmful additives and environmental contaminants such as bisphenols, phthalates, dioxins, and heavy metals, which enhance their toxicity. Global estimates indicate that humans ingest and inhale tens of thousands of MPs particles each year, yet long-term human research remains limited. Given these findings, it is crucial to expand research on PE MP toxicodynamics and to establish regulatory policies to reduce their release. Promoting alternative biodegradable materials and improved waste management practices will be vital in decreasing human exposure to MPs and minimizing potential health risks. Full article

This study examines the upper-ocean response to Typhoon Khanun, which traversed the northern South China Sea in October 2017, by integrating multi-satellite observations with numerical simulations from the Regional Ocean Modeling System (ROMS). For the ROMS simulations, an Arakawa C-grid was adopted with a 4-km horizontal resolution and 40 vertical terrain-following $\sigma$-layers, covering the domain of 105° E to 119° E and 15° N to 23° N. Typhoons significantly influence ocean dynamics, altering sea surface temperature (SST), sea surface salinity (SSS), and ocean currents, thereby modulating air–sea exchange processes and marine ecosystem dynamics. High-resolution satellite datasets, including GHRSSST for SST, SMAP for SSS, GPM IMERG for precipitation, and GLORYS12 for sea surface height, were combined with ROMS simulations configured at a 4-km horizontal resolution with 40 vertical layers to analyze ocean changes from 11 to 18 October 2017. The results show that Typhoon Khanun induced substantial SST cooling, with ROMS simulations indicating a maximum decrease of 1.94 °C and satellite data confirming up to 1.5 °C, primarily on the right side of the storm track due to wind-driven upwelling and vertical mixing. SSS exhibited a complex response: nearshore regions, such as the Beibu Gulf, experienced freshening of up to 0.1 psu driven by intense rainfall, while the right side of the storm track showed a salinity increase of 0.6 psu due to upwelling of saltier deep water. Ocean currents intensified significantly, reaching speeds of 0.5–1 m/s near coastal areas, with pronounced vertical mixing in the upper 70 m driven by Ekman pumping and wave-current interactions. By effectively capturing typhoon-induced oceanic responses, the integration of satellite data and the ROMS model enhances understanding of typhoon–ocean interaction mechanisms, providing a scientific basis for risk assessment and disaster management in typhoon-prone regions. Future research should focus on refining model parameterizations and advancing data assimilation techniques to improve predictions of typhoon–ocean interactions, providing valuable insights for disaster preparedness and environmental management in typhoon-prone regions. Full article

Honeybee populations are increasingly threatened by various environmental stressors, including pesticides, pathogens, and climate change. Emerging research highlights the vital role of pollen polyphenols in supporting honeybee health through a network of antioxidants, immune responses, and detoxification mechanisms. This review synthesizes current findings on the chemical diversity, bioactivity, and functional relevance of polyphenolic compounds in honeybee nutrition. Pollen polyphenols, which include flavonoids and phenolic acids, possess remarkably high antioxidant potential, up to 235 times greater than that of nectar. They also significantly increase the expression of antioxidant enzymes, immune system genes, and detoxification pathways such as cytochrome P450s and glutathione-S-transferases. These compounds also demonstrate antimicrobial effects against key pathogens and mitigate the toxic effects of pesticides. The content and composition of polyphenols vary seasonally and geographically, impacting the resilience of honeybee colonies. Field and laboratory studies confirm that polyphenol-rich diets improve survival, gland development, and stress resistance. Advanced analytical techniques, including metabolomics, have expanded our understanding of polyphenol profiles and their effects on honeybee physiology. However, knowledge gaps remain in pharmacokinetics and structure–function relationships. Integrating this evidence into conservation strategies and good beekeeping practices, such as habitat diversification and targeted feed supplementation, is crucial for maintaining honeybee health and ecosystem services in a rapidly changing environment. Full article

With the escalating frequency of urban and forest fires driven by climate change, the development of intelligent and robust fire detection systems has become imperative for ensuring public safety and ecological protection. This paper presents a comprehensive multi-module fire detection framework based on visual computing, encompassing image enhancement and lightweight object detection. To address data scarcity and to enhance generalization, a projected generative adversarial network (Projected GAN) is employed to synthesize diverse and realistic fire scenarios under varying environmental conditions. For the detection module, an improved YOLOv8n architecture is proposed by integrating BiFormer Attention, Agent Attention, and CCC (Compact Channel Compression) modules, which collectively enhance detection accuracy and robustness under low visibility and dynamic disturbance conditions. Extensive experiments on both synthetic and real-world fire datasets demonstrated notable improvements in image restoration quality (achieving a PSNR up to 34.67 dB and an SSIM up to 0.968) and detection performance (mAP reaching 0.858), significantly outperforming the baseline. The proposed system offers a reliable and deployable solution for real-time fire monitoring and early warning in complex visual environments. Full article

Smart cities and urban planning have succeeded in gathering the attention of researchers worldwide, especially in the last decade, as a result of a series of technological, social and economic developments that have shaped the need for evolution from the traditional way in which the cities were viewed. Technology has been incorporated in many sectors associated with smart cities, such as communications, transportation, energy, and water, resulting in increasing people’s quality of life and satisfying the needs of a society in continuous change. Furthermore, with the rise in machine learning (ML) and artificial intelligence (AI), as well as Geographic Information Systems (GIS), the applications of big data analytics in the context of smart cities and urban planning have diversified, covering a wide range of applications starting with traffic management, environmental monitoring, public safety, and adjusting power distribution based on consumption patterns. In this context, the present paper brings to the fore the papers written in the 2015–2024 period and indexed in Clarivate Analytics’ Web of Science Core Collection and analyzes them from a bibliometric point of view. As a result, an annual growth rate of 10.72% has been observed, showing an increased interest from the scientific community in this area. Through the use of specific bibliometric analyses, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors, data are extracted and discussed in depth. Thematic maps and topic discovery through Latent Dirichlet Allocation (LDA) and doubled by a BERTopic analysis, n-gram analysis, factorial analysis, and a review of the most cited papers complete the picture on the research carried on in the last decade in this area. The importance of big data analytics in the area of urban planning and smart cities is underlined, resulting in an increase in their ability to enhance urban living by providing personalized and efficient solutions to everyday life situations. Full article

With the advancement of deep engineering (e.g., deep resource development, tunnel excavation), the deep rock mass is in a high in situ stress environment, leading to a critical engineering challenge: traditional blasting often causes disordered blast-induced crack propagation (severe deviation from the target direction) and unstable expansion rates, which reduce the directional blasting efficiency, trigger over-excavation/under-excavation, and threaten construction safety. Water jet notching is a promising directional control technique, but its coupling effect with vertical stress (a dominant component of in situ stress) on blasting crack characteristics remains unclear—hindering its application in deep engineering. To address this problem, reveal the law of blasting crack expansion in deep rock, explore the mechanism of controlled blasting for deep rock fractures, and clarify the effect of deep environmental water jet notching on the blasting effect, this study carried out experimental research on the crack extension velocity of the directional blasting of prefabricated grooved concrete under vertical stress (based on the crack extension strain gauge test system and perimeter pressure loading system) and verified the results by numerical simulations. The main conclusions are as follows: (1) Within the experimental test range, with the increase in vertical stress, the deviation of cracks from the prefabricated groove center in the vertical direction gradually decreases, indicating that vertical stress can further guide the direction of the crack extension on the basis of prefabricated grooves. (2) The experimentally measured crack expansion velocity shows a decreasing trend with the increase in the crack expansion length; the average crack expansion velocity is enhanced with the increase in vertical stress, while the change in the crack tip velocity is suppressed as a whole and gradually tends to be flat at approximately 555.6 m/s. (3) Numerical simulation results (using a model replicating the experimental concrete specimens) further verify the accuracy of the experimental results: the increase in vertical stress further guides the vertical crack expansion, enhances the average crack expansion velocity, and slows down the decay of the crack extension velocity. The core value of this research lies in “converting theoretical experimental data into engineering control capabilities.” Its findings can be directly applied to key areas such as deep resource development, tunnel engineering, and water conservancy projects. While ensuring engineering safety, improving efficiency, and reducing costs, it also provides scientific support for engineering construction in complex geological conditions. Full article

This study analyzes the factors that affect the technical efficiency (TE) of firms in the supporting industry in the context of Vietnam’s digitalized economy. Stochastic frontier analysis (SFA), Fixed Effect Models, and System-GMM methods are applied to reach the findings that the quality of human resources, capital intensity, and firm size have positive effects on TE. Furthermore, exogenous environmental factors, such as the domestic demand of an industry impacting all upstream businesses, which use inputs that are products of that industry (BSpill-ratio), and the FDI backward effect (BFSpill), also exhibit positive effects. These confirm that the linkage between domestic supporting industry suppliers and FDI assembly enterprises plays an important role in improving TE. Vietnam’s digital transformation since 2020 has also created some interesting changes in the correlation coefficient. Location, sectors, competitiveness, and investment environment are also considered, and the results suggest that they are all determinants to be considered in management policies at both the firm level and the government level. Our contribution in this study is new policies aligned with many major changes in the world economic context, such as the tough tariff policy implemented by recent presidential administrations and a series of reforms of the Vietnamese Government, as well as strong digital transformation in Vietnam. The key findings of this research are important as they confirm which factors are really determinants for the Vietnamese government to implement investment policies for this industry effectively. Full article

Abalone is among the most highly prized seafoods, valued for its delicate flavor and texture. As abalone aquaculture continues to expand, addressing its environmental impacts has become increasingly important. Although aquaculture is recognized as a contributor to greenhouse gas (GHG) emissions, the specific mechanisms and pathways of GHG emissions—particularly in abalone farming—remain poorly understood. To clarify the patterns and drivers of GHG emissions in abalone (Haliotis discus) culture systems, this study was conducted in three aquaculture ponds located in Gongliao District, New Taipei City, Taiwan. We measured CO₂, CH₄, and N₂O fluxes along with key environmental parameters to assess variation across sampling locations, times, and seasons. The results showed that sampling time had no significant effect on GHG flux variations, whereas seasonal changes influenced all three gases, and sampling location significantly affected N₂O flux only. During the culture period, average fluxes were 2.19 ± 10.83 mmol m⁻² day⁻¹ for CO₂, 2.11 ± 2.81 µmol m⁻² day⁻¹ for CH₄, and 1.65 ± 2.73 µmol m⁻² day⁻¹ for N₂O, indicating that the abalone ponds served as net sources of these GHGs. When converted to CO₂-equivalents (CO₂-eq), the total average CO₂-eq flux from the ponds was 0.02 ± 0.09 mg CO₂-eq m⁻² day⁻¹, calculated using global warming potential (GWP₂₀ and GWP₁₀₀) metrics. This study provides the first comprehensive assessment of GHG emissions in abalone pond systems and offers valuable insights into their emission dynamics. The findings contribute to the scientific basis needed to improve aquaculture GHG inventories. Full article