Search Results (2,596)

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Carbon flow tracking and spatial pattern optimization at the scale of urban functional zones are key scientific challenges in achieving carbon neutrality. However, due to the complexity of carbon metabolism processes within urban functional zones, related studies remain limited. To address these scientific challenges, this study, based on the “source–sink–flow” ecosystem services framework, develops an integrated analytical approach at the scale of urban functional zones. The carbon balance is quantified using the CASA model in combination with multi-source data. A network model is employed to trace carbon flow pathways, identify critical nodes and interruption points, and optimize the urban spatial pattern through a low-carbon land use structure model. The research results indicate that the overall carbon balance in Hangzhou exhibits a spatial pattern of “deficit in the center and surplus in the periphery.” The main urban area shows a significant carbon deficit and relatively poor connectivity in the carbon flow network. Carbon sequestration services primarily flow from peripheral areas (such as Fuyang and Yuhang) with green spaces and agricultural functional zones toward high-emission residential–commercial and commercial–public functional zones in the central area. However, due to the interruption of multiple carbon flow paths, the overall carbon flow transmission capacity is significantly constrained. Through spatial optimization, some carbon deficit nodes were successfully converted into carbon surplus nodes, and disrupted carbon flow edges were repaired, particularly in the main urban area, where 369 carbon flow edges were restored, resulting in a significant improvement in the overall transmission efficiency of the carbon flow network. The carbon flow visualization and spatial optimization methods proposed in this paper provide a new perspective for urban carbon metabolism analysis and offer theoretical support for low-carbon city planning practices. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

Coupled grey and green infrastructure (CGGI) offers a promising pathway toward sustainable stormwater management in historic urban environments. This study compares CGGI and conventional grey infrastructure (GREI)-only strategies across four degrees of layout centralization (0%, 33.3%, 66.7%, and 100%) in the Quanzhou West Street Historic Reserve, China. Using a multi-objective optimization framework integrating SWMM simulations, life-cycle cost (LCC) modeling, and resilience metrics, we found that the decentralized CGGI layouts reduced the total LCC by up to 29.6% and required 60.7% less green infrastructure (GI) area than centralized schemes. Under nine extreme rainfall scenarios, the GREI-only systems showed slightly higher technical resilience (Tech-R: max 99.6%) than CGGI (Tech-R: max 99.1%). However, the CGGI systems outperformed GREI in operational resilience (Oper-R), reducing overflow volume by up to 22.6% under 50% network failure. These findings demonstrate that decentralized CGGI provides a more resilient and cost-effective drainage solution, well-suited for heritage districts with spatial and cultural constraints. Full article

Existing research predominantly focuses on the preservation or renewal models of the physical forms of historic cultural districts, with limited exploration of their roles in stimulating tourists’ cognitive, affective resonance, and behavioral interactions. This study addresses historic cultural districts by evaluating the space quality and its impact on tourist experiences through the “cognition-affect-behavior” framework, integrating GIS, street view semantic segmentation, VR eye-tracking, and web crawling technologies. The findings reveal significant multidimensional differences in how space quality influences tourist experiences: the impact intensities of functional diversity, sky visibility, road network accessibility, green visibility, interface openness, and public facility convenience decrease sequentially, with path coefficients of 0.261, 0.206, 0.205, 0.204, 0.201, and 0.155, respectively. Additionally, space quality exerts an indirect effect on tourist experiences through the mediating roles of cognitive, affective, and behavioral dimensions, with a path coefficient of 0.143. This research provides theoretical support and practical insights for empowering cultural heritage space governance with digital technologies in the context of cultural and tourism integration. Full article

The synthesis of “green ammonia” from “green hydrogen” represents a critical pathway for renewable energy integration and industrial decarbonization. This study investigates the green ammonia synthesis process using an axial–radial fixed-bed reactor equipped with three catalyst layers. A simplified two-dimensional physical model was developed, and a multiscale simulation approach combining computational fluid dynamics (CFD) with physics-informed neural networks (PINNs) employed. The simulation results demonstrate that the majority of fluid flows axially through the catalyst beds, leading to significantly higher temperatures in the upper bed regions. The reactor exhibits excellent heat exchange performance, ensuring effective preheating of the feed gas. High-pressure zones are concentrated near the top and bottom gas outlets, while the ammonia mole fraction approaches 100% near the bottom outlet, confirming superior conversion efficiency. By integrating PINNs, the prediction accuracy was substantially improved, with flow field errors in the catalyst beds below 4.5% and ammonia concentration prediction accuracy above 97.2%. Key reaction kinetic parameters (pre-exponential factor k₀ and activation energy Ea) were successfully inverted with errors within 7%, while computational efficiency increased by 200 times compared to traditional CFD. The proposed CFD–PINN integrated framework provides a high-fidelity and computationally efficient simulation tool for green ammonia reactor design, particularly suitable for scenarios with fluctuating hydrogen supply. The reactor design reduces energy per unit ammonia and improves conversion efficiency. Its radial flow configuration enhances operational stability by damping feed fluctuations, thereby accelerating green hydrogen adoption. By reducing fossil fuel dependence, it promotes industrial decarbonization. Full article

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms. Full article

The implementation of the trans-European tourist cycle route network “EuroVelo” is fostering new strategic importance for non-motorized mobility and the associated practice of cycling tourism. Indeed, slow tourism offers a pathway for the development of inland areas. The infrastructure supporting it, such as long-distance cycling and walking paths, can act as a vital connection, stimulating regeneration in peripheral territories by enhancing environmental and landscape assets, as well as preserving heritage, local identity, and culture. The regeneration of peri-urban landscapes through soft mobility is recognized as the cornerstone for accessibility to material and immaterial resources (including ecosystem services) for multiple categories of users, including the most vulnerable, especially following the restoration of green-area systems and non-urbanized areas with degraded ecosystems. Considering the forthcoming implementation of the Magna Grecia cycling route, the southernmost segment of the “EuroVelo” network traversing three regions in southern Italy, this contribution briefly examines the necessity of defining new development policies to effectively integrate sustainable slow tourism with the enhancement of environmental and landscape values in the coastal areas along the route. Specifically, this case study focuses on a coastal stretch characterized by significant morphological and environmental features and notable landscapes interwoven with densely built environments. In this area, environmental and landscape values face considerable threats from scattered, irregular, low-density settlements, abandoned sites, and other inappropriate constructions along the coastline. Full article

In response to the challenges posed by soil degradation in the arid regions of Xinjiang, China, green and organic management practices have emerged as effective alternatives to conventional agricultural management methods, helping to mitigate soil degradation by promoting natural soil recovery and ecological balance. However, most of the existing studies focus on a single management practice or indicator and lack a systematic assessment of the effects of integrated orchard management in arid zones. This study aims to investigate how different agricultural management practices influence soil physicochemical properties and inter-root microbial communities in apple orchards in Xinjiang and to identify the main physicochemical factors affecting the composition of inter-root microbial communities. Inter-root soil samples were collected from apple orchards under green management (GM), organic management (OM), and conventional management (CM) in major apple-producing regions of Xinjiang. Microbial diversity and community composition of the samples were analyzed using high-throughput amplicon sequencing. The results revealed significant differences (p < 0.05) in soil physicochemical properties across different management practices. Specifically, GM significantly reduced soil pH and C:N compared with OM. Both OM and GM significantly decreased soil available nutrient content compared with CM. Moreover, GM and OM significantly increased bacterial diversity and changed the community composition of bacteria and fungi. Proteobacteria and Ascomycota were identified as the dominant bacteria and fungi, respectively, in all management practices. Linear discriminant analysis (LEfSe) showed that biomarkers were more abundant under OM, suggesting that OM may contribute to ecological functions through specific microbial taxa. Co-occurrence network analysis (building a network of microbial interactions) demonstrated that the topologies of bacteria and fungi varied across different management practices and that OM increased the complexity of microbial co-occurrence networks. Mantel test analysis (analyzing soil factors and microbial community correlations) showed that C:N and available potassium (AK) were significantly and positively correlated with the community composition of bacteria and fungi, and that C:N, soil organic carbon (SOC), and alkaline hydrolyzable nitrogen (AN) were significantly and positively correlated with the diversity of fungi. Redundancy analysis (RDA) further indicated that SOC, C:N, and AK were the primary soil physicochemical factors influencing the composition of microbial communities. This study provides theoretical guidance for the sustainable management of orchards in arid zones. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

Developing multifunctional wound dressings with excellent mechanical properties, strong tissue adhesion, and efficient antibacterial activity is crucial for promoting wound healing. This study prepared a novel nanocomposite hydrogel dressing based on sodium alginate-polyacrylic acid dual crosslinking networks, incorporating tannic acid-coated cellulose nanocrystals (TA@CNC) and in-situ reduced silver nanoparticles for multifunctional enhancement. The rigid CNC framework significantly improved mechanical properties (elastic modulus of 146 kPa at 1 wt%), while TA catechol groups provided excellent adhesion (36.4 kPa to pigskin, 122% improvement over pure system) through dynamic hydrogen bonding and coordination interactions. TA served as a green reducing agent for uniform AgNPs loading, with CNC negative charges preventing particle aggregation. Antibacterial studies revealed synergistic effects between TA-induced membrane disruption and Ag⁺-triggered reactive oxygen species generation, achieving >99.5% inhibition against Staphylococcus aureus and Escherichia coli. The TA@CNC-regulated porous structure balanced swelling performance and water vapor transmission, facilitating wound exudate management and moist healing. This composite hydrogel successfully integrates mechanical toughness, tissue adhesion, antibacterial activity, and biocompatibility, providing a novel strategy for advanced wound dressing development. Full article

Road density is a key indicator of human activity, causing habitat loss and fragmentation. Soil fungi, essential for ecosystem functioning, are sensitive bioindicators. Yet their responses to road density in urban green spaces are poorly characterized. Here, we analyzed the composition of the dominant fungal community, examined both the direct and indirect effects of road density on soil fungal communities, and identified specialist species. Focusing on Shanghai, China, a rapidly urbanizing city, we considered both edaphic factor and the road network. Through machine learning and Spearman correlation regression analyses, we quantified the relative importance of road density and edaphic factor in shaping fungal community composition and employed occupancy-specificity modeling to identify specialist taxa. Our results revealed that Ascomycota, Basidiomycota, Zygomycota, Rozellomycota, Chytridiomycota, and Glomeromycota were the dominant phyla, accounting for 93% of the retrieved ITS sequences. Road density was found to be the primary driver of fungal community composition, followed by soil lead and potassium concentrations. Notably, opportunistic pathogens (Acremonium spp.) correlated positively with road density (p < 0.001). Specialist species in high-density areas were primarily pathotrophic fungi, while saprotrophic fungi dominated in low-density areas. These findings highlight the need for urban planning strategies to mitigate the ecological impact of road density. Full article

Research in on-orbit processing enables edge AI deployment over satellite optical networks. However, these operations induce frequent battery discharge cycles, particularly depth-of-discharge (DoD) events, which accelerate degradation and curtail satellite longevity. To address this, we propose green task-aware routing planning (GreenRP), a task-aware routing framework that achieves sustainable edge AI through dynamic task offloading and discharge-resilient path orchestration. GreenRP employs a novel battery aging model explicitly coupling DoD effects with laser inter-satellite link dynamics under AI workloads, enhancing system sustainability. Comprehensive evaluation on a 1152-satellite constellation demonstrates that GreenRP extends network lifetime by 176% over shortest-path routing while meeting latency and completion rate targets. This work enables reliable edge AI via sustainable satellite resource utilization. Full article

High-quality green gardens can markedly enhance the quality of life and mental well-being of their users. However, health and lifestyle constraints make it difficult for people to enjoy urban gardens, and traditional methods struggle to offer the high-fidelity experiences they need. This study introduces a 3D scene reconstruction and rendering strategy based on implicit neural representation through the efficient and removable neural radiation fields model (NeRF-RE). Leveraging neural radiance fields (NeRF), the model incorporates a multi-resolution hash grid and proposal network to improve training efficiency and modeling accuracy, while integrating a segment-anything model to safeguard public privacy. Take the crabapple tree, extensively utilized in urban garden design across temperate regions of the Northern Hemisphere. A dataset comprising 660 images of crabapple trees exhibiting three distinct geometric forms is collected to assess the NeRF-RE model’s performance. The results demonstrated that the ‘harvest gold’ crabapple scene had the highest reconstruction accuracy, with PSNR, LPIPS and SSIM of 24.80 dB, 0.34 and 0.74, respectively. Compared to the Mip-NeRF 360 model, the NeRF-RE model not only showed an up to 21-fold increase in training efficiency for three types of crabapple trees, but also exhibited a less pronounced impact of dataset size on reconstruction accuracy. This study reconstructs real scenes with high fidelity using virtual reality technology. It not only facilitates people’s personal enjoyment of the beauty of natural gardens at home, but also makes certain contributions to the publicity and promotion of urban landscapes. Full article

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article