Search Results (394)

Shared bikes are frequently parked in disorder, resulting in low efficiency of manual collection and transfer and heavy workload for maintenance staff. Random parking across various areas forces shared bikes to occupy sidewalks and fire exits, damaging urban landscapes and disrupting traffic order. To tackle these industrial pain points, this paper develops an integrated intelligent robot system equipped with functions of multi-pose grasping, automatic transfer and fixed-point delivery of shared bikes, which can effectively address the drawbacks of low efficiency and high labor costs in traditional manual maintenance. This paper focuses on the completion of the robot’s overall mechanical structure design, stiffness–precision collaborative optimization model construction, finite-element static simulation verification, 1:7 scaled prototype development and performance testing. Firstly, the overall layout design of the multi-posture adaptive floating clamping mechanism, transfer-bearing frame, and Mecanum wheel omnidirectional mobile chassis is completed, and the structural parameters and assembly benchmarks of the core components are clarified. Secondly, a stiffness–precision coupling optimization model is established, and the static analysis under extreme load conditions is carried out through Abaqus finite-element software, which verifies the rationality of 45# carbon steel material selection and the safety of structural strength. Subsequently, a 1:7 scaled principle prototype is developed, and repetitive grabbing and transfer tests are carried out to verify the system operation feasibility, stability and grabbing accuracy. Finally, the statistical analysis of the test data and the horizontal comparison of similar schemes are completed. The test and simulation results show that the maximum stress of the system under extreme working conditions is 131.21 MPa, which is far lower than the allowable stress of 355 MPa of 45# steel, and the safety factor reaches 2.71. The maximum total deformation is 4.0552 mm, which is concentrated at the end of the front-end clamping mechanism, and is within the allowable stiffness deviation range of the transfer system. The average value of the single clamping positioning error of the scaled prototype is 0.476 mm, with a 95% confidence interval of 0.457–0.495 mm, which is converted to a positioning error of ≤3.4 mm for the full-scale prototype, which is far better than similar industry solutions. The average time of a single complete grabbing and transfer operation is 12.38 s, which is more than 45% higher than the traditional manual mode. The structural design, grabbing accuracy and operation stability of the robot designed in this paper all meet the requirements of actual working conditions of urban sidewalks, which can effectively reduce the intensity of manual labor and improve the operation and maintenance efficiency of shared bicycles. It has strong engineering application value and can provide reference for the design and manufacturing of intelligent collection and transfer systems for shared two-wheelers. Full article

Ecological porous concrete (EPC) offers both structural performance and ecosystem services, yet an inherent contradiction exists between the ecological benefits of high porosity and mechanical performance. Traditional design methods focusing solely on macro-scale porosity fail to achieve synergistic optimization. This review comprehensively synthesizes the intrinsic correlations between EPC’s multi-scale pore structures and key properties from micro-, meso-, and macro-scale perspectives, drawing upon representative studies across experimental, numerical, and theoretical approaches. The microscale reveals interfacial transition zone bonding, capillary pore effects, and alkalinity regulation for vegetation compatibility. The mesoscale clarifies the control of effective porosity, tortuosity, and pore throats on fluid transport and root penetration. The macro-scale analyzes skeletal pore support for plant growth, hydrology, and slope stability. A cross-scale collaborative design approach is proposed, featuring microscopic reinforcement, mesoscopic continuity, and macroscopic moderation. This paper provides theoretical support for EPC’s transition from empirical to precision design, promoting low-carbon and large-scale applications in revetments, Sponge Cities, and slope restoration. Full article

Against the background of carbon neutrality and the global energy transition, biomass-derived nanocellulose has attracted increasing attention because of its renewability, biodegradability, low density, high mechanical strength, and tunable surface chemistry. This study reviews the global development of biomass-derived nanocellulose from the perspectives of research trends, hotspot themes, technological frontiers, patent deployment, and industrial applications. The results show that research output has grown rapidly in recent years, with China leading in publication volume, while the United States and several European countries perform more strongly in highly cited studies and international collaboration. Current research focuses mainly on green preparation technologies, chemical functionalization, multifunctional composites, adsorption and environmental remediation, and energy-related applications. Patent analysis reveals clear international differentiation: China leads in patent quantity and large-scale production technologies, whereas the United States and Japan show stronger advantages in patent quality, overseas patent layout, and high-end applications. Overall, biomass-derived nanocellulose is accelerating from laboratory research to industrial application, but further progress still depends on achieving breakthroughs in cost reduction, continuous manufacturing, functional modification, and standardization. Full article

Driven by China’s green and low-carbon development strategy, sustainable urban construction has become a crucial issue in the construction industry. In recent years, building information modeling (BIM) has been widely applied in the construction industry due to its advantages in information management, project coordination, and construction optimization. Although numerous studies have explored the application of BIM in sustainable construction, existing research findings are scattered across different research topics and project stages. Therefore, it is still necessary to develop a clearer understanding of the BIM functions that can support sustainable urban construction and their practical applications, especially in the context of China’s construction industry. To address this issue, this study provides a systematic literature review of BIM functions related to sustainable urban construction. Using the PRISMA framework, this study identified and analyzed relevant research to examine the main BIM functions reported in the literature, their application methods, and their contributions to sustainable urban construction. The review identified twelve main BIM functions, including 3D modeling and visualization, data management, multi-party collaboration, cost control, schedule optimization, environmental management, and construction safety management. Based on published research, this study further explores the potential relationships between different BIM functions and proposes several conceptual frameworks to clarify how to integrate these functions in sustainable urban construction. The research results indicate that BIM can support sustainable urban development through information integration, lifecycle coordination, data-driven decision-making, and optimized project management. These findings also provide a reference for the future implementation of BIM and the promotion of sustainable urban development in China, especially in the context of digital transformation and green development in the construction industry. Full article

The rapid proliferation of electric vehicles (EVs) has positioned Vehicle-to-Grid (V2G) technology as an important enabler for mitigating grid congestion, accelerating the energy transition, and supporting the sustainable transition of regional energy systems. However, recent incentive mechanisms often fail to balance EV users’ willingness to participate with the economic viability of intermediary operators, thereby hindering effective multi-party collaboration in Regional Integrated Energy System (RIES). To address this challenge, this paper proposes a novel commission-based service fee mechanism for V2G incentive mechanisms to dynamically regulate revenue distribution among Integrated Energy System Operator (IESO), Energy Supplier (ES), Charging Station Operator (CSO), and Electric Vehicle Aggregator (EVA). The study further examines how different incentive strategies affect V2G market liquidity. Case studies indicate that the proposed strategy significantly increases effective V2G transaction power while preserving CSO profit margins and encouraging EV participation. The results also indicate that the reward rate, commission rate, and subsidy have nonlinear effects on V2G transaction performance and should be set within reasonable ranges. The proposed model also exhibits superior performance in enhancing system economic benefits and promoting multi-agent coordination. It provides an actionable framework for sustaining CSO participation under upper-level subsidy mechanisms while improving the long-term commercial viability and ecological sustainability of smart-grid ecosystems. These findings provide practical guidance for designing incentive policies that facilitate the low-carbon energy transition and sustainable smart-grid development. Full article

To address the key challenges of distributed generation and loads, insufficient edge computing capacity, significant data privacy risks among multiple participants, and immature market mechanisms in county-level distribution networks, this paper presents a transaction-driven two-tier distributed collaborative optimization approach for interconnected integrated energy systems. We develop a market-oriented architecture that combines upper-layer price coordination with lower-layer autonomous optimization. The overall system is decoupled using just two types of non-sensitive data—local electricity prices and regional net power—while preserving the operational independence and data privacy of all stakeholders. We further devise a Two-Stage Distributed Transactional Optimization (TSDTO) mechanism. This mechanism reformulates the intraday multi-variable collaborative optimization into a single-variable electricity price search problem, substantially reducing algorithm iterations and communication overhead. Simulations are conducted on three typical interconnected integrated energy systems in a county in northern China. The results demonstrate that the proposed method maintains main transformer power within safe limits, effectively lowers daily operating costs, and boosts the renewable energy accommodation rate. Compared with the conventional subgradient method, our algorithm offers higher computational efficiency, along with improved convergence and real-time performance. The proposed approach is capable of achieving a relatively satisfactory balance among privacy protection, low computational complexity, on-site renewable energy utilization, and rapid real-time operation. This paper provides theoretical references and guidance for the low-carbon, cost-effective, coordinated and sustainable operation of modern county-level power systems and integrated energy systems. Full article

Enhancing urban carbon productivity (UCP) is crucial for achieving the dual carbon goals in China. This study investigates the spatiotemporal patterns and underlying drivers of UCP in the Yangtze River Economic Belt (YREB) from 2010 and 2020. Utilizing a comprehensive dataset of 110 cities, we employ kernel density estimation, spatial autocorrelation analysis, and the Spatial Durbin Model (SDM). The results reveal a significant overall improvement in UCP alongside intensified internal disparities and a fundamental spatial restructuring—from a monocentric eastern-led pattern to a multipolar network driven by the Yangtze River Delta, middle Yangtze, and Chengdu-Chongqing agglomerations. The SDM decomposition reveals a shift in core drivers towards green technological innovation and advanced industrial structure, while energy consumption remains the primary constraint. Crucially, complex spatial spillover effects are identified: factors like advanced industrial structure and digital governance are associated with positive synergistic spillovers, whereas government intervention (government public budget expenditure) and urban sprawl exhibit negative competitive spillovers, collectively corresponding to the polarized regional pattern. Furthermore, urban form shows strong spatial externalities: urban compactness is linked to a “local-neighborhood” double dividend, while urban sprawl is associated with a “local-neighborhood” double curse. The influence of digital factors appears to evolve from early widespread spillovers to later localized deepening. The findings suggest the necessity of implementing spatially differentiated policies, strengthening regional collaborative governance to manage spatial externalities, and promoting compact regional spatial planning to foster synergistic and equitable low-carbon transitions across the YREB. Full article

Low-carbon development (LCD) and intensive urban land use (IULU) are critical objectives for sustainable urban development. Existing studies have usually evaluated LCD or IULU separately, whereas the dynamic relationship between carbon-transition capacity and land-use intensification under rapid urbanization remains insufficiently clarified. This gap limits the ability of policymakers to design spatially differentiated and synergistic actions for achieving the Sustainable Development Goals (SDGs). This study investigates the relationship between LCD and IULU and its transformation within the sustainable development framework, using the Middle Reaches of the Yangtze River Urban Agglomeration (MRYRUA) in central China as a case study. Results indicate a strong positive correlation between LCD and IULU. Crucially, their coupling exhibited a distinct U-shape trajectory from 2005 to 2020; it decreased from 0.89 in 2005 to 0.73 in 2013 and then recovered to 0.84 in 2020, suggesting a relative weakening of the interaction followed by recoupling rather than complete decoupling. The identified U-shaped trajectory holds vital implications for other developing nations, suggesting that integrating low-carbon goals into spatial planning and land policies from the early stages of urbanization can pave the way for a faster transition to a green, intensive, and high-quality development model. Moreover, although both LCD and IULU exhibited positive trends, a widening gap was observed between provincial capitals and non-provincial cities. We, therefore, recommend integrating multi-stakeholder collaboration and implementing differentiated strategies to enhance the synergistic effects of LCD and IULU for cities at different phases of the LCD–IULU transition. Full article

Addressing critical bottlenecks in traditional hydrogen refueling station operations—specifically supply–demand imbalances and suboptimal scheduling—this paper presents a systematic review of the advancements and practical implementations of intelligent transportation platforms (ITPs). We explore how these platforms catalyze enhancing operational efficiency within the hydrogen ecosystem. This paper first outlines the technical foundations of Vehicle-to-Everything communication, edge computing, and multi-source data fusion, and provides an in-depth analysis of core challenges, such as demand uncertainty and resource scheduling complexity, as well as existing optimization algorithms. Through typical case studies, the significant value of such platforms in breaking down data silos, reducing equipment idle rates, and achieving end-to-end energy efficiency optimization is demonstrated. This study notes that current bottlenecks include fragmented standards, difficulties in implementing algorithms, commercial challenges, and the retrofitting of existing infrastructure. Moving forward, efforts should shift from isolated technological breakthroughs to ecosystem development. This includes improving demand forecasting accuracy in low-penetration regions, implementing lightweight retrofits to revitalize the existing market, establishing cross-domain data collaboration standards, building a trustworthy cross-platform settlement system, and exploring innovative pathways that integrate “hydrogen, carbon, and computing.” Full article

Against the backdrop of the deep advancement of the carbon peak and carbon neutrality goals and the superposition of the transportation power strategy, leveraging the spatial restructuring of highway networks to optimize the low-carbon layout of county-level industries has become a crucial lever for balancing economic quality improvement with carbon intensity control. This study selects panel data from 129 counties in Yunnan Province spanning 2015–2024, constructing a comprehensive highway network development index from four dimensions: highway density, road network connectivity, weighted hierarchical structure, and county accessibility. Using a two-way fixed effects benchmark model, a stepwise mediation effect testing framework, and a regional heterogeneity identification strategy, the paper systematically examines the marginal effects, transmission pathways, and spatially differentiated characteristics of highway network development on county-level industrial carbon emission intensity. Key findings are as follows: Enhanced highway network development significantly suppresses the increase in county-level industrial carbon emission intensity, and a well-developed road network can provide long-term empowerment for the low-carbon transformation of county-level industries. Mechanism analysis confirms that highway network development reduces emissions through two core pathways: first, a direct emission reduction effect achieved by optimizing the county-wide freight organization system, reducing inefficient transport energy consumption, and improving overall transport efficiency; second, an indirect low-carbon enabling effect realized by breaking down administrative barriers in county markets, lowering cross-regional business transaction costs, deepening industrial division of labor and collaboration, and forcing resource allocation improvements. Heterogeneity analysis reveals that the low-carbon dividends of highway network development exhibit significant gradient differentiation: the emission reduction enabling effect is strongest in counties within the Central Yunnan urban agglomeration, followed by cultural tourism counties in western Yunnan and border counties in southern Yunnan, with the weakest marginal enabling effect observed in traditional agricultural counties in northeastern Yunnan. Full article

To address the deepening electro-carbon coupling and flexibility shortages in active distribution networks with high renewable energy penetration, this paper proposes a bi-level collaborative planning strategy considering dynamic electro-carbon factors. First, considering the spatial–temporal correlation of wind and solar outputs, typical renewable energy scenarios are generated using the Frank-Copula function and clustering algorithms. Second, a bi-level planning model for the Soft Open Point integrated with an Energy Storage System (E-SOP) is established: the upper level optimizes the siting and sizing of E-SOPs to minimize the annualized comprehensive cost; the lower level incorporates a dynamic stepped carbon trading mechanism and a continuous price-based demand response (PBDR) mechanism to achieve optimal operational economy. For model solving, a hybrid bi-level decomposition strategy combining the Dhole Optimization Algorithm (DOA) and second-order cone programming (SOCP) is adopted, utilizing a coordinated dual-level solution interaction to favorably support numerical stability. Case studies on a modified IEEE 33-node system demonstrate that the proposed scheme reduces the annualized comprehensive cost by 12.3% and transforms the carbon trading expenditure into a net revenue, thereby significantly enhancing the low-carbon economic efficiency and operational flexibility of the distribution network. Full article

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

Against the background of global climate change and China’s dual-carbon strategic goal, agricultural carbon emission reduction and low-carbon transformation have become urgent practical issues. As an important characteristic cash crop in China, apple cultivation faces significant carbon emission pressure, and an obvious spatial imbalance exists in carbon productivity across major producing areas. Using the Dagum Gini coefficient, kernel density estimation, and Markov-chain analysis, this study analyzes regional differences in and the dynamic distribution of carbon productivity in China’s main apple-growing provinces from 2008 to 2024. The results indicate the following: (1) Overall, carbon productivity in China’s apple industry shows an upward trend, with a “rising–declining–rising–declining” M-shaped evolution during the study period. (2) The main reason for the overall differences is variation between regions, which shows a continuous inverted V-shaped change pattern of “rising–declining–rising–declining–rising–declining–rising.” (3) High-carbon-productivity areas have a positive effect on surrounding areas, while low-productivity areas have a negative effect. Therefore, to improve carbon productivity in apple cultivation, it is essential to not only understand regional differences and their causes but also leverage the positive effects of neighboring high-carbon-productivity areas to positively influence local conditions. This will help achieve cross-regional collaborative improvement in carbon productivity in China’s main apple-producing provinces. Full article

Plant functional traits are central to regulating ecosystem multifunctionality (EMF), yet how coordinated above- and below-ground resource acquisition strategies mediate the effects of forest structural diversity on EMF remain insufficiently understood, particularly in typical south subtropical forests. Here, we applied a trait-based framework to disentangle the pathways linking forest structural diversity to EMF through plant resource acquisition strategies. Typical south subtropical forests were sampled for community-level leaf and root traits, including leaf total nitrogen and total phosphorus content, specific leaf area, leaf dry matter content, root diameter, specific root length, root tissue density, root total nitrogen and root total phosphorus content. EMF was quantified using 13 indicators associated with carbon storage, litter decomposition, primary productivity, and nutrient cycling, evaluated using both averaging and multi-threshold approaches. Principal component analysis was used to summarize trait variation along major functional axes representing the leaf and root economics spectra, and structural equation modeling was employed to quantify direct and trait-mediated pathways linking forest structural diversity to EMF. We found pronounced variation in EMF among forest types, with multifunctionality increasing along the classical fast-slow plant economics spectrum. Communities dominated by fast-growing species exhibited consistently higher EMF than those dominated by slow-growing species, with below-ground traits showing stronger associations with EMF than above-ground traits. In contrast, EMF was unrelated to the root collaboration gradient, suggesting that alternative below-ground foraging strategies contributed little to multifunctionality. Moreover, the positive effects of structural diversity on EMF were indirectly mediated through both leaf and root conservation gradients. Notably, the relative importance of these trait-mediated pathways was threshold-dependent. Root conservation gradients dominated EMF at low multifunctionality thresholds, whereas leaf conservation gradients became increasingly important at higher thresholds. Our findings show that forest structural diversity enhances ecosystem multifunctionality through coordinated leaf and root strategies. By revealing trait-mediated links between biodiversity and EMF, this study clarifies how community composition and species turnover shape multifunctionality in typical south subtropical forests. Full article

In this study, three types of industrial solid waste—granulated blast furnace slag (GBFS), fly ash, and desulfurization gypsum (DG)—are utilized to collaboratively prepare low-carbon cementitious materials. The effects of steam curing temperature, constant temperature time, and fly ash content on the mechanical properties of multi-source solid waste cementitious materials are systematically investigated, and the optimal mix proportion ratio for low-carbon cementitious materials is determined. The results indicate that as steam curing temperature and constant temperature time increase, the compressive strength of the ternary cementitious material generally shows an upward trend, while the fly ash content exhibits a negative correlation. When the steam curing temperature is 70 °C, the constant temperature time is 10 h, the fly ash content is 20%, and the strength can reach 24 MPa, with both its engineering performance and economic benefits meeting the requirements of practical applications. Meanwhile, the steam curing temperature shows a tendency of first decreasing and then increasing shrinkage rate after 28 d, with the lowest shrinkage rate at 70 °C. Extending the constant temperature time can slightly reduce shrinkage, and the addition of 20–30% fly ash can optimize shrinkage performance. Moreover, the TG/DTG and SEM-EDS microscopic testing demonstrates that the ternary system achieves synergistic activation by accelerated mineral dissolution, ion release and enhanced alkalinity under steam curing, which jointly promotes the formation of AFt and C-A-S-H gel to refine microstructure and improve compactness. This study can not only reduce the consumption of cement, but also facilitate the recycling of industrial waste, providing theoretical support for the application of multi-source solid waste low-carbon materials in practical engineering. Full article