Search

[Display omitted] • 28 out of 57 target PFAS were detected in paper products from China. • 9 groups of PFAS with 1–3 confidence levels were identified by non-target analysis. • Ethanol in food simulants can increase the migration of PFAS. • Long-chain PFAS in popcorn bags can be degraded by microwave heating. • PFAS in some paper products exceeded the recommended daily intake values. The contamination characteristics, migration patterns and health risks of per- and polyfluoroalkyl substances (PFAS) were investigated in 66 Chinese paper products by using target and non-target screening methods. Among 57 target PFASs, 5 and 6 PFASs were found in the hygiene paper products (

Urbanization and the release of carbon emission are two significant features of contemporary economic and social progress in China. However, there are few studies that consider how urbanization policy goals affect carbon emission. This paper aims to study the nonlinear effects of urbanization policy goals on carbon emission in 13 cities of the Beijing-Tianjin-Hebei region. In this paper, a multi-dimensional spatial disturbance grey model is proposed. Based on matrix perturbation theory, this paper explores the spatial perturbation bounds of the novel model and verifies that the spatial property of the model is consistent with the viewpoint of the First Law of Geography. Then, the novel model is used to predict the urban carbon emission under different urbanization policy goal scenarios. The results showed that the novel model has spatial regional correlation in spatial region prediction. With the development of urbanization level, carbon emissions show a downward trend in more than 50% of cities from 2022 to 2025. In addition, the improvement of urbanization policy goals will promote the carbon peak time of most cities. The carbon emission intensity will exhibit an inverted U-shaped trend distribution in space. The research results are conducive to providing scientific guidance for the formulation of new-type urbanization policies and helping to achieve the dual carbon goals. • A multi-dimensional spatial disturbance grey model is proposed. • The novel grey model is consistent with the viewpoint of the First Law of Geography. • The spatial distribution of carbon intensity showed an inverted U-shaped pattern. [ABSTRACT FROM AUTHOR]

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models. [ABSTRACT FROM AUTHOR]

High precision ionospheric Total Electron Content(TEC) prediction is of great significance for improving the accuracy of Global Navigation Satellite System(GNSS), preventing natural disasters, and ensuring wireless communication. Given the varying frequencies of TEC signals, a hybrid CEEMDAN-BiLSTM-PSO-LSSVM-FE model for predicting ionospheric TEC content is proposed in this paper. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the extracted hourly TEC sequence and calculate the fuzzy entropy (FE) of the subsequences. Then, the signal is divided into high-frequency and low-frequency parts based on the fuzzy entropy value, for the high-frequency component, Bidirectional Long Short-Term Memory network (BiLSTM) is used for prediction; for the low-frequency component, Particle Swarm Optimization-based Least Squares Support Vector Machine (PSO-LSSVM) is used for prediction. The hourly TEC values collected from six evenly distributed GPS stations in China are used as the main input variable for the proposed model, with solar and geomagnetic activity data used as auxiliary data, using the TEC data from the previous 48 h to forecast the TEC content for the next hour. The performance of the model is evaluated by comparing it with other models under different geographical locations, seasons, solar and geomagnetic activity conditions. Experimental results show that the proposed hybrid model outperforms other models, with a correlation coefficient R2 and root mean square error (RMSE) of 0.99 and 0.20 TECU, respectively. The model effectively overcomes the problem of low TEC prediction accuracy and can provide more precise ionospheric delay correction services. [ABSTRACT FROM AUTHOR]

• Variable stiffness design for a 3-legged asteroid probe. • Landing dynamics for soft landing on the 2016HO3 asteroid is modeled. • Response surface surrogate model is established for optimization. • Variable stiffness leg and pressing force are optimized. China plans to launch a probe to detect and sampling on the near-Earth asteroid 2016HO3 around 2025. In view of this, this paper discusses an alternative landing strategy with passive variable stiffness landing gears, which help to land the probe stably on the asteroid for sufficient sampling time. First, a three-legged lander is introduced, following which, a four-bar-linkage-based variable stiffness leg is designed considering the design constraint of landing clearance. Then, a numerical model of the probe for landing simulation is established. Moreover, a surrogate model is obtained using the second-order response surface method (RSM) to improve computational efficiency. Based on the surrogate, the variable stiffness leg and the pressing force are optimized to reduce the impact forces at touchdown and the landing time. Finally, the optimal solution is validated using the dynamics analysis model. [ABSTRACT FROM AUTHOR]

In contrast to the rising technological applications of China's satellite systems, their applications and impacts in modern Chinese Society, particularly in the legal and administrative forums, have largely escaped under the radar. Specifically, the use of satellite outputs can now be found within a number of administrative and legal activities within China - including policy and rule-making, administrative investigations, and the production of court evidence. At the same time however, outdated legal practices, ambiguous status, and lack of guidance has prevented China's legal system from fully capitalizing on the advantages of satellite technologies. Therefore, to fill these legislative gaps, this review provides a comprehensive overview on the applications of satellite technologies within China's legal system, and provide important insights as to their current developments, legal framework, and legal issues. Additionally, having identified the aforementioned issues, this paper offers policy recommendations for the purposes of legislation. • Satellite applications in China's policy and rule-making processes. • Investigatory applications of satellite technologies within China's administrative organs. • Emerging evidentiary applications of satellite technologies in Chinese court systems. [ABSTRACT FROM AUTHOR]

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]

• The model for multiple horizontal wells considers heterogeneous fracture network. • The number of frac hits is determined by pressure differentials and derivatives. • Higher conductivity promotes inter-well pressure equilibrium. • Lower conductivity is beneficial to extended interference testing. Frac hits can significantly impact the final development results. Therefore, accurately and quickly understanding the frac-hit interference between wells is crucial for efficient development. At present, traditional pressure/production data analysis methods and numerical simulation methods can characterize the frac-hit interference. However, challenges such as quantitatively assess interference, high uncertainty in modeling parameters and computational time still exist. A quantitative evaluation method for the interference between wells with complex fracture networks has not been reported yet. This paper takes shale oil reservoir with three production wells as an example and establishes a semi-analytical model (frac-hit semi-analytical model, FSM) based on the linear flow characteristics. The model accurately characterizes the non-uniform distribution of complex fracture networks in the stimulated reservoir volume by introducing fractal theory. A quantitative evaluation index, interference coefficient, is defined to assess the interference. The coupled flow model is applied to evaluate the effects of interference in three typical wells in a continental shale oil field in China. We can see from the actual data fitting of the FSM model that the interference coefficients between target well A and adjacent wells B and C are 0.1 and 0.05, respectively, which means there are about 8–10 and 3–5 frac hits, respectively. Comprehensive analysis for the effects of interference shows that the current inter-well interference is beneficial for production in low-pressure areas but detrimental to new well production. Based on this, sensitivity analysis is generated to illustrate the relationship between the conductivity of combined fractures arising from hits and the interference coefficient. As the interference increases, the propagation of inter-well pressure accelerates, significantly reducing the time required for inter-well pressure equilibrium. The research findings have important guiding implications for the quantitative evaluation of inter-well interference in field operations of horizontal shale oil and gas wells and optimization of fracturing operations. [ABSTRACT FROM AUTHOR]

Satellite Laser Ranging (SLR) technology is one of the main technologies in the field of space geodesy, it has played an extremely important role in laser ranging data application and research. As a commonly used geodynamic satellites, the main contribution of LAGEOS and LAGEOS-2 is the solution of orbit determination and station coordinates. At present, there are 8 analysis centers of the International Laser Ranging Service (ILRS) to release the precise orbit products for the two LAGEOS satellites. However, there is currently no relevant research on the analysis of the consistency of these orbits. So, the authors would evaluate the accuracies of them, and the results show that the orbital accuracies of the two LAGEOS satellites are 3 ∼ 5 cm. SLR is regarded as one of the important input data of the International Terrestrial Reference Frame (ITRF), the analysis center of ILRS provides weekly solutions of station coordinates for ITRF. But, due to the limitation of SLR observation conditions, for example most observations of Chinese SLR stations can be made at night, and laser ranging test cannot be conducted in rainy days, the orbit solution of the seven-day arc may have the problem of less data. Therefore, this paper proposed a method for computing the station coordinates based on long arc orbit determination, and we developed the LAODGEO software (Long Arc Orbit Determination Software for Geodynamic Satellite), which is used to solve the orbits of two LAGEOS satellites and station coordinates of five SLR stations in China. The results show that the obtained orbital precision is basically superior to 3.70 cm, and the 3DRMS values between station coordinates solved by us and the coordinates published by SLRF2014 are mostly smaller than 0.013 m, which is consistent with research results domestic and overseas. [ABSTRACT FROM AUTHOR]

Many studies have investigated the spatial variations of the ionosphere, but the quantitative characteristics of the ionosphere are rarely reported. In this paper, we utilize the total electron content (TEC) data to evaluate the latitudinal gradient of the ionosphere within 10°-50° N over the China sector. It is found that the magnitudes of latitudinal gradient are significantly higher within 10°-40° N and 45°-50° N, respectively. The database of TEC from 1 November 2018 to 31 October 2022 is processed to figure out the local time, seasonal, and solar activity dependency of the latitudinal gradient. The results suggest that the gradient within 10°-40° N is higher in the daytime and during high solar activity period. They are more noticeable in the spring and autumn, and least visible in the summer. Conversely, the gradient within 45°-50° N strengthens in the nighttime and under lower solar activity, and has larger values in the summer months. Furthermore, the International Reference Ionosphere (IRI-2020) model is assessed in terms of the reproducibility of latitudinal gradient. The IRI-2020 basically represents the latitudinal gradient within 10°-40° N, whereas it overestimates the gradient in the low solar activity period and misses the gradient features near 45°–50° N. [ABSTRACT FROM AUTHOR]

This paper investigates the ionospheric responses to the February and April 2023 geomagnetic storms using Total Electron Content (TEC) derived from the 10 Global Navigation Satellite System (GNSS) stations at the transition of low to mid-latitudes (Approx. 20 ∼ 30°N) across different longitudes in the American and Asian sectors. Significant variations in both sectors were recorded during the recovery phase and were mainly attributed to the Prompt Penetration Electric Field (PPEF), Disturbance Dynamo Electric Field (DDEF) and the thermospheric neutral composition changes as O/N 2 depletion. Similarly, the American and Asian sectors showed TEC enhancements, primarily attributed to the PPEF, during the main phase of the April storm. The negative TEC variations during the recovery phase were observed due to the DDEF and changes in O/N 2. In the Asian sector, positive and negative variations were recorded over Pakistan and China, respectively, during the main phase of the April storm. These variations resulted in high and low concentrations of the O/N 2 ratio due to Magnetosphere-Ionosphere-Thermosphere coupling. [ABSTRACT FROM AUTHOR]

In recent years, extreme rainfall events have frequently occurred frequently, and heavy rainfall can cause drastic changes in the troposphere. Therefore, achieving to achieve real-time high-precision numerical prediction of key tropospheric parameters during heavy rainfall has become a major problem in global navigation satellite system (GNSS) meteorology. In this paper, two extreme rainfall events in southern China (Guangdong region) and northern China (Shandong region) in 2022 are used as case studies. Twenty-four-hour real-time numerical forecasts of key tropospheric parameters (atmospheric weighted mean temperature (Tm), precipitable water vapor (PWV), and GNSS zenith tropospheric delay (ZTD)) are obtained using three models, namely, the HGPT2, GPT3, and WRF models. Two optimization models, i.e., WRFDA (am) and WRFDA (pre), are then constructed by assimilating two types of data (global upper air and surface weather observations and daily advanced microwave sounding unit A (AMSU-A) brightness temperature) based on the WRF model. The experimental results for heavy rainfall show that (1) the WRF model predicts the key tropospheric parameters with better accuracy than the HGPT2 and GPT3 models, and the WRFDA (pre) model predicts PWV and ZTD with the highest accuracy; (2) the WRFDA (pre) model achieves a higher accuracy than the WRF model in predicting PWV and ZTD, where the PWV prediction accuracy is improved relative to the WRF model (in the south: MAE: 32.7 %; RMSE: 33.9 %; MAPE: 36.8 %; in the north: MAE: 27.3 %; RMSE: 24.2 %; MAPE: 28.0 %); this model achieves an MAE of 2.17 cm and an RMSE of 2.70 cm in 24-h ZTD prediction in the south, while the MAE reaches 2.48 cm, and the RMSE is 3.18 cm in the north; (3) the models provide a higher forecast accuracy in the southern region than in the northern region for heavy rainfall. The WRFDA (pre) model provides a favourable ZTD accuracy at GNSS stations near the ocean, while the WRFDA (am) model provides a satisfactory ZTD accuracy at inland GNSS stations, and the WRFDA (am) model provides the highest ZTD prediction accuracy at GNSS stations above 100 m. [ABSTRACT FROM AUTHOR]

The ever-increasing challenges related to water security and climate mitigation underscore the pressing need for forward-thinking systems planning tools to drive urban sustainability transitions. In this study, we propose a novel city-level multi-objective planning framework that explores the nexus between carbon trading and water management, with the aim of facilitating water-efficient urban industrial restructuring. The framework integrates cap-and-trade mechanisms into a two-layered optimization model, creating an integrated approach to optimizing carbon emissions and generating economic opportunities for improving water efficiency. To illustrate the potential application of this framework, we conducted a case study focusing on Dongguan City, a water-stressed industrial metropolis in southern China. The results illustrate potential synergies between water planning strategies and carbon trading schemes, which could be harnessed to enable targeted reductions in water usage and carbon emissions. Furthermore, our findings identify the textile, apparel, and paper products manufacturing industries as primary candidates for strategic production scale reduction, emphasizing the importance of sustained support for the development of the computer and electronic manufacturing sector as a catalyst for urban sustainability transitions. By pioneering this new nexus-based perspective, our study offers valuable insights into long-term strategic planning for a low-carbon and resource-efficient urban economy. • Synergizing carbon trading and water management within a novel integrated planning framework. • Implementing resource-efficiency-driven optimization for water- and carbon-intensive manufacturing sectors. • Gaining insights into the specific roles of key manufacturing sectors in water management and carbon trading. • Introducing carbon trading systems may help in promoting water-efficient industrial restructuring. [Display omitted] [ABSTRACT FROM AUTHOR]

Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's future low-carbon and green development plan. This paper proposes a novel multivariable grey model based on historical data on China's renewable energy generation and three industries. A novel information accumulation mechanism with two adaptive factors is designed to improve the traditional multivariable grey modeling defect. Based on the proposed mechanism, this paper optimizes the initial and background values and nonlinear model structure with the whale optimization algorithm. The forecasting results show that the fitting MAPE is 1.13%, comprehensive MAPE is 2.60%, MSE is 50.86, and RMSE is 7.13, which significantly improve the forecasting accuracy of traditional GM(1,N) and are better than other compared models. The forecasting results show that China's renewable energy generation will gradually increase to 5834.02 TWh. The Chinese government should keep the previous Five-Year Plans rising trend of the three industries in the future Five-Year Plans to support renewable energy industries. In China's future energy system, it is necessary to promote incentive policies and capital investment for actively accelerated development to make renewable energy the leading force. [ABSTRACT FROM AUTHOR]

The research investigates how the resumption of the China certified emission reduction (CCER) market affects Chinese manufacturers' stock market valuations from an operations and supply chain management (OSCM) perspective. The paper draws from a resource-based view to examine the moderating effects of supply chain concentration (SCC), operational slack, and operational efficiency (OE) on the relationship between the CCER market and manufacturers' stock market valuations. This paper employs the event study method to examine market responses to CCER. The results reveal that the resumption of the CCER market significantly enhances the market valuations of high-pollution manufacturers to a greater extent than those of manufacturers with lower pollution levels. Additionally, the research findings indicate that SCC and operational slack attenuate the positive effect of CCER market resumption while OE amplifies it. This paper contributes to the OSCM literature by urging businesses to focus on the roles of SCC, operational slack, and OE in the CCER framework to harness the advantages of the CCER system better. • Resumption of China certified emission reduction positively affects manufacturers. • Operations and supply chain management factors are important moderators. • Manufacturers with varying pollution levels face different institutional impacts. • The event study method is employed to examine manufacturers' market responses. [ABSTRACT FROM AUTHOR]

• Fracture evolution in dual gob-side entries varies spatially and temporally. • A formula to calculate arbitrary dip rock layer cutting angles was derived. • A technology based on the fracture evolution's turning points was proposed. • Differential roof cutting-anchor grouting boosts stability in dual gob-side entries. The coal pillar-free gob-side entry retention technology (GER), which enhances resource recovery rates and reduces the amount of tunnel excavation, has been applied in multiple mining areas in China. However, cases of implementing gob-side entry retention on both sides of a single working face are rare. The asynchronous extraction of long-distance working faces leads to significant differences in the stress environment of roadways on both sides, and uniform technical means are poorly adapted to the control of surrounding rock on both sides, affecting the safe and efficient production. This paper, taking the dual gob-side entry retention on both sides of the 61001 W working face with thick mudstone roofs in Zhao Guan Coal Mine, Shandong Province, as the engineering background, utilizes a combination of theoretical calculations, numerical simulations, and on-site detailed monitoring to analyze the uncoordinated deformation characteristics of the upper part bulging and the lower part concaving of the two roadways. It clarifies the differentiated spatiotemporal evolution law of the number of fractures and the development turning points at different depths of the surrounding rock of the two entries, revealing the differential deformation mechanism of the surrounding rock of the two entries caused by stress state and fracture development differences. A "cutting roof to relieve pressure-multilevel continuous anchor-grouting" combined control technology system is proposed. By cutting off the stress transmission of the roof through drilling and blasting, and then reconstructing the integrity of the surrounding rock and improving the rock strength with the help of anchor-grouting. With the application of this new technology, the deformation of the surrounding rocks of the two entries was reduced by more than 60 % and 29 %, respectively, and the maximum crack depth of the roof was controlled within 3 m, significantly enhancing the stability of the roadways. The research results of this paper provide a reference for the control of surrounding rock in gob-side entry retention under similar conditions. [ABSTRACT FROM AUTHOR]

Global environmental problems are worsening. Among various regulatory measures, market-oriented approaches are most favored by governments worldwide. Using provincial panel data from 2004 to 2020, a multi-period DID model was hereby constructed to empirically study the pollution reduction effects of the pilot policy for carbon emissions trading in China. Findings of this paper could be listed as follows: (1) The pilot policy has achieved notable reductions in waste gas and solid waste, although its effect on wastewater pollutants has been less pronounced; (2) Pollution reduction in the pilot regions can be achieved by optimizing the energy consumption structure; (3) Significant variations in pollution reduction effects are observed between the Northern and Southern pilot regions, with notably stronger policy effects in the north across various pollutants compared to the south. Overall, this paper offers pertinent policy recommendations to aid the Chinese government in enhancing its carbon market. [ABSTRACT FROM AUTHOR]

Green finance can effectively support industrial upgrading and technology development, such as artificial intelligence (AI). In this paper, we attempt to pursue the possibility of a win-win situation for achieving an eco- and tech-friendly society. By asking where and how we could develop green finance and AI innovation simultaneously, this paper conducts an empirical investigation on the influence of green finance on the development of AI in China from 2011 to 2020. Based on the panel data from the 30 provinces, we introduce spatial measurement, policy effect, heterogeneity, and threshold analyses to present deeper insights into the impact of green finance on AI innovations. Indeed, green finance could promote the progress of AI innovation. China's green finance pilot policy is verified to promote the progress of local AI innovations. Furthermore, the spatial spillover effect and regional heterogeneity are observed as well. The promotion effect is most significant in the western area, where the green finance index is relatively low. Besides, the threshold analysis also considers how to increase the marginal effect of green finance in different areas. Finally, several policy recommendations are proposed, which contribute to providing specific directions for the policymakers to improve AI innovation and achieve sustainable development at the same time. [ABSTRACT FROM AUTHOR]

As China's economy continues to grow rapidly, the importance of green and sustainable development is increasingly prominent. In this context, enterprises have undergone significant changes in all aspects of the value creation process. Based on the data of Chinese a-share listed companies from 2011 to 2021, this paper links the digital transformation of enterprises with the green technology innovation of enterprises. The focuses on the relationship between the enterprises' digital transformation and the enterprises' green innovation and its Local-neighborhood effect. Research shows that the digital transformation of enterprises will promote the enterprise green technology innovation, and this relationship will have a Local-neighborhood effect in different regions, and this spatial spillover effect is mainly achieved through the transfer of high-tech industries between regions. The results of heterogeneity test show that the effect of green technology innovation is the strongest in Beijing-Tianjin-Hebei economic circle, and the weakest in Pearl River Delta economic circle. Based on the spatial econometric model, this paper provides empirical evidence for the government and the state to formulate the regional development strategy of enterprises' digital transformation and green innovation. • Spatial panel Durbin model reveals digital transformation's impact on green technology innovation and regional spillover. • The effect of digital transformation of enterprises on their own green technology innovation has a "local-neighborhood" effect. • The spatial spillover effect is mainly realized through the transfer of high-tech industries between regions. • The strongest green technology innovation effect is found in the Beijing-Tianjin-Hebei economic circle. [ABSTRACT FROM AUTHOR]

• The cause of fatigue failure in steel wire rope suspenders on suspension bridges has been identified. • The recommended approach of utilizing auxiliary cables is successful in decreasing tensile-bending fatigue stress levels. • These discoveries are significant for guiding the development and upkeep of comparable infrastructure ventures. In consideration of the significant impact of train loads on the fatigue of suspenders in railway bridges, this paper focuses on the first railway suspension bridge in China − the Jinsha River Railway Suspension Bridge. The study analyzes the force characteristics and bending failure features of the steel wire ropes in railway suspension bridges, aiming to provide insights into the design of such structures. Firstly, a finite element model of the Jinsha River Railway Suspension Bridge is established in this paper. The natural frequencies are analyzed and compared with on-site measurements to validate the accuracy of the finite element model. Secondly, the tension force amplitude and angular displacement amplitude of the suspenders are analyzed under design loads, and the failure characteristics of the cables are experimentally examined. Subsequently, a wear fatigue test for steel wire rope is designed, evaluating the fatigue life of the suspender in the bridge. The computational results are compared with experimental data to validate their accuracy. Finally, structural measures to reduce the bending fatigue of railway suspension bridge suspenders are proposed to ensure their safety during the service life. The study reveals that the presence of bending stress exacerbates the fatigue failure of suspension cables in railway suspension bridges, with the failure mode identified as fatigue failure due to the tension-bending-wear coupling effect. Furthermore, our findings indicate that the implementation of auxiliary cables can reduce the displacement differential at both ends of the suspension cables by approximately 4/5, and decrease the axial tensile stress amplitude by about 3/4. Consequently, this significantly enhances the fatigue life of the suspension cables. [ABSTRACT FROM AUTHOR]

High-rise residential building façades (HRBFs), given their size and abundant façade information, pose a challenge for conventional parsing methods. This paper presents FaçadeGraph, an approach for parsing the information of HRBFs into hierarchical attributed graphs. The method decomposes HRBF information into five hierarchical layers: ternary, floor, unit, space, and component. The façade elements are identified as semantics information with geometric attributes. The topological relationships between the elements are classified into affiliation, connection, aggregation, and decoration. The efficacy of FaçadeGraph was evaluated through the analysis of 36 HRBFs in China. The result showed that FaçadeGraph is effective in transforming diverse façade designs into consolidated graphs for automated syntax analyses. The paper contributes to the knowledge body of façade design by serving as an analytical tool for design feature analysis and underlying the development of generative HRBF design. • An attributed graph-based façade parsing approach is proposed. • A hierarchical structure is used to indicate façade elements for semantic analysis. • Universally applicable rules are defined for façade representation. • The framework is validated by a dataset of façades on 36 high-rise buildings. [ABSTRACT FROM AUTHOR]

The outer bank slump blocks play a fundamental role in modulating the rate of bank erosion, and consequently, have a profound influence on many geomorphic and ecologic processes in meandering streams, e.g., channel evolution and habitat creation. To date, no adequate research has been developed regarding how these blocks, partially strengthened by roots of herbaceous plants, impact the near-bank flow velocity, bank erosion, and migration of streambanks. This paper analyzes 246 slump blocks in a meandering stream on the Marqu Meadow, China. Aerial surveys were conducted once a year from 2018 to 2021 and the lateral migration rates (M) of streambanks are estimated by superimposing the current-year drone imagery onto its previous-year counterpart. When compared to the outer bank segments lacking slumps, our research findings indicate that the bank segments exhibiting slumps demonstrate a reduction of 57 % and 43 % in the maximum and average bank erosion rates, respectively. Overall, the wider blocks provide stronger bank protection; nevertheless, they may also introduce or enhance local scouring in their vicinity, thereby increasing the heterogeneity of bank erosion rates. To elucidate this complex influence mechanism, we simulate the three-dimensional flow velocity field of a ~100 m meandering bend containing three slump blocks using the MIKE-3 model. Furthermore, we compare one-year bank migration distance with and without the blocks by correlating the lateral migration distance of outer banks to the longitudinal gradient of near-bank velocity. The paper provides valuable insights into the complex interplay between flow dynamics and streambank behavior in real-world settings, thereby enhancing the effectiveness of future management and restoration practices. • 246 slump blocks were analyzed in a meander on Marqu Meadow, China. • Slump blocks can persist for years but may be washed away in high flows. • The outer bank slump blocks play a vital role in modulating bank erosion rates. • Slump blocks protect outer banks by redistributing the high flow and secondary flow. • Slump blocks help protect bank overall but it may introduce scour in their vicinity. [ABSTRACT FROM AUTHOR]

• Explain the economic value of the quadratic cost of MECM in terms of marginal cost and elasticity. • Adopt an opinion modification mechanism based on social network. • The MECMs with uncertain adjustment costs are developed under three uncertain scenarios. • The proposed models are applied to the agricultural insurance premiums subsidy policymaking in China. In the realm of group decision making (GDM), the maximum expert consensus model (MECM) emerges as a potent tool for consensus optimization. The complexity of decision-making environment leads to the uncertainty of adjustment costs and the intricate social relationships between decision makers (DMs). Therefore, this paper aims to develop the MECM that integrates both social relationships and uncertain adjustment costs to support social network group decision making (SNGDM) problems. Specifically, we propose a MECM with quadratic cost that can more accurately reflect DMs' sensitivity for opinion adjustment. Additionally, we adopt an opinion modification mechanism based on the information obtained from the social network. The paper also develops the robust MECM (RMECM) to handle the uncertainty of the unit adjustment cost under three uncertain scenarios. Finally, the efficiency of the proposed models is demonstrated by applying them to the agricultural insurance premiums subsidy policymaking in China, further substantiated by sensitivity analysis and comparative analysis showcasing their robust performance. [ABSTRACT FROM AUTHOR]

• We have devised a composite gear transmission system, which comprises a RV reducer, two parallel shaft transmissions, and a planetary gear transmission. • A rescue module has been integrated into the driving system to ensure the necessary rescue operations for the manipulator. • The structural configuration of the relevant shells has been developed, with comprehensive explanation on dimensions, manufacturing methodology and strength assessment. • A detailed exposition on the internal architecture and rotational capabilities of these joints has been provided. • An effective internal cable routing scheme for the root joints has been proposed. This study concentrates on the engineering design and analysis of the root joints for the CFETR Multi-Purpose Overload Robot (CMOR), a crucial component in the China Fusion Engineering Test Reactor (CFETR). The CFETR tokamak is an engineering test reactor in China for magnet confinement fusion research. CMOR is a 7-DOF manipulator characterized by heavy-load capacity, high precision, and compact structures, designed for internal maintenance inside the CFETR. This presents significant challenges in its design. The design of the root joints is significant for the CMOR. This paper provides an in-depth exploration of the engineering design and analysis of the root joints. It offers a thorough explanation of the driving systems' design, including an integrated rescue module to address potential manipulator faults. Additionally, this paper delves into the conceptual design and manufacturing schemes of the corresponding shells, which are validated under various loading conditions—including regular and earthquake scenarios—through finite element analysis. The assembled root joints' internal structure and cable routing scheme are also discussed in-depth. This research provides valuable insights for future joint designs of the CMOR heavy load manipulator, advancing maintenance operations for fusion reactors. [ABSTRACT FROM AUTHOR]

The widespread use of digital technology has driven a huge change in the way payments are made. Based on the micro-data of the China Family Panel Studies (CFPS), this paper explores the relationship between mobile payment and carbon emissions from the consumption side. With the exogenous impact of Alipay's "Collecting Five Blessings" campaign, this paper finds that mobile payment significantly increases household consumption carbon emissions, but the marginal effect is gradually decreasing. Mechanism analysis shows that mobile payment mainly realizes the above impact through promoting the increase of consumption expenditure and upgrading of consumption structure. Further analysis indicates that carbon reduction policies introduced by the Chinese government in the personal consumption sector can mitigate the carbon emissions growth associated with mobile payments. Still, there is a need to strengthen policy measures further to effectively address the issue of rising carbon emissions from household consumption. [ABSTRACT FROM AUTHOR]

Due to the non-homology problem and the simple structural characteristics, a grey prediction model will have defects in modeling. In this paper, the structure of the GM (1 , 1 , x (1) ) model is deformed, and additional parameters are added. A novel four-parameter grey prediction model NFGM(1,1) is established to avoid the non-homology problem. The accumulation order of the NFGM(1,1) model is optimized to enhance its performance. This paper first introduces a nonlinear term and a linear term into the GM (1 , 1 , x (1) ) model to compensate for its structural defects, which can enhance the accuracy of the model in modeling complex modeling sequences. Secondly, a simplified basic formula of the model is proposed to estimate its parameters and iteratively establish the model, which can avoid the problem of non-homologous errors during modeling. Then a novel four-parameter grey prediction model NFGM(1,1) is constructed. Thirdly, the unbiasedness of NFGM(1,1) is proved and verified by matrix theory. Fourthly, by optimizing the order of the NFGM(1,1) model, the model is more flexible and adjustable, and a novel fractional-order four-parameter grey prediction model FNFGM(1,1) can be obtained. Finally, the FNFGM(1,1) model is applied to the prediction of natural gas production in China. The model results show that the FNFGM(1,1) model exhibits superior performance compared to the NFGM(1,1), TWGM(1,1), TDGM(1,1), DGM(1,1), and GM(1,1) models, with the mean relative simulation/prediction/comprehensive percentage errors of 0.92%/1.42%/1.07%, respectively. According to the predicted results, China's natural gas production will reach 3542.9 × 108 m3 in 2027 and some relevant policy recommendations are put forwarded. • On the basis of GM (1 , 1 , x (1) ) , a nonlinear term and a linear term are introduced to expand the model structure, and a novel four-parameter grey prediction model is proposed. • In order to avoid the non-homology problem of the differential equation and difference equation in traditional modeling, this paper uses the simplified basic formula of the model to estimate parameters, and does not directly derive the time response, but directly uses the iterative recurrence method to model. • The new model is proved to be unbiased by the matrix theory method and verified by arithmetic examples. • According to the objective function and constraints of fractional-order solving, PSO algorithm is used to optimize the order of the new model, which makes the model parameters changeable and improves the model performance. • The comprehensive performance of the new model is verified by a case study, the new model is applied to forecast China's natural gas production in the context of Xi Jinping's new era, and the results show that the new model has better accuracy, and the results obtained can help the government to formulate relevant policies. [ABSTRACT FROM AUTHOR]

As the battery recycling market in China is still in an embroynic stage, the mechanisms, channels, pricing, and efficiency of battery recycling are shrouded in opacity. Battery recycling in China is currently dominated by electric vehicle manufacturer, supplemented by other market players such as battery producers and electric vehicle retailers. Due to the lack of information, battery recycling lacks cooperation and communication, and the whole market is dealing with "Information Island." Therefore, based on information sharing and channel competition, this paper designs a Stackelberg game model with an electric vehicle manufacturer, an electric vehicle retailer, and a third-party recycler as the primary participants. This study found that effective information sharing can reduce intensified market competition and promote the transformation of market players into "collaboration state". To promote partnership in the battery recycling market, this paper proposes a revenue-sharing contract incentive for the electric vehicle retailer to share demand information about battery recycling. It finds a revenue-sharing contract based on information sharing can help companies and competitors make appropriate behavioral choices in the battery recycling market. This study demonstrates that information sharing benefits the electric vehicle manufacturer and the third-party recycler while offering no advantages to the electric vehicle retailer. The decreasing accuracy of demand signals results in the diminishing value of information sharing for the electric vehicle manufacturer and the third-party recycler. The revenue-sharing contract can incentivize the electric vehicle retailer to share information. [Display omitted] • A battery recycling mode of electric vehicles is constructed. • Effective information sharing would encourage market players to be more collaborative. • A revenue-sharing contract is designed to incentivize the electric vehicle retailer to share information. • Inaccurate information and channel competition reduce the attractiveness of information sharing. • Inter-firm information sharing is beneficial for building an efficient battery recycling system. [ABSTRACT FROM AUTHOR]

The surge of machine learning (ML) applications and increasing usage of data driven approach for resilience enhancement provide great opportunities for applying ML techniques to power outage prediction (POP) during hurricanes. Considering the substantial damage that wind hazards can cause to power system network (PSN) in countries such as the USA and China, researchers have devised numerous approaches for disaster prevention to reduce their susceptibility. Since the selection of optimal ML algorithms for POP during hurricanes is a complex task, this paper helps researchers easily comprehend the numerous prediction techniques used worldwide for power outages during hurricanes. The paper provides a brief review of several categories of hurricanes and the damage it causes to the PSN more specifically to distribution side, the necessary measures to improve the PSN resilience, and finally a sequential approach for optimal ML model selection techniques that researchers and engineers can use to improve the POP during hurricanes. In this study the effectiveness of ML algorithms has been explored with the help of performance evaluation metrics which serve as the basic deciding criteria for optimal model selection. This research also highlights some of the key issues and challenges of integrating machine learning algorithms with existing PSN. • Reviewed various ML algorithms used for distribution system POP during hurricanes. • Effects of damage experienced by power system networks during hurricanes are widely investigated. • Provides an insight into the measures for improving power system resilience against hurricanes. • Provides a stepwise approach for using various ML algorithms in the context of POP. • Identification of key issues, challenges, and discussion of possible future research directions. [ABSTRACT FROM AUTHOR]

This paper examines the effects of privatizing China's state-owned enterprises (SOEs) on wage distribution and the welfare of the economy. Privatizing profitable SOEs can narrow wage inequality and improve welfare, and it is however accompanied by a business dynamism effect that can widen the skilled-unskilled wage gap. Using China's split share structure reform in 2005 as a quasi-natural experiment, we empirically demonstrate for profitable SOEs a positive relationship between partial privatization and wage gap. Our findings are consistent with the plan to wind down unprofitable SOEs that would not survive without government subsidies. • This paper examines the effects of privatizing China's SOEs on wages and welfare. • Privatization accompanied by firm entry can widen the skilled-unskilled wage gap. • Predictions regarding the distributive effect of privatizing SOEs are confirmed. [ABSTRACT FROM AUTHOR]

• A practical and feasible protection strategy for EC maintaining in highly urbanized areas was proposed. • Key patches for EC maintaining were identified by using stepping stone and complex network theories. • The protection rate for key patches by the new policies were assessed. Maintaining and restoring ecological connectivity (EC) are important measures of mitigating habitat isolation caused by urbanization. Considering the prominent contradiction of land use in highly urbanized areas, the EC protection measures proposed in the previous studies, such as expanding the ecological sources and widening the ecological corridors, are unrealistic. By using the stepping-stone and complex network theories, this paper simulated the ecological network (EN), evaluated the centrality of each ecological source and stepping-stone, and analysed the network robustness under different damage scenarios, then identified the optimal strategy and key patches for EC maintaining. Meanwhile, this paper evaluated whether the key patches are protected by China's new policies, which are, Ecological Protection Red Line (EPRL) and the Three Lines One Permit (TLOP). The results were as follows: (1) The EN is composed of 53 ecological sources and 291 stepping-stones, with 591 links between them. (2) The network stability can be optimally maintained by prioritizing the protection of stepping-stones with high centrality. (3) The 53 ecological sources and 51 stepping-stones with the highest centrality can ensure the stable operation of the EN, that is, they are key patches for EC maintaining. (4) Compared with EPRL, the protection rate for ecological sources and stepping-stones are significantly improved by TLOP; however, stepping-stones with high centrality require stronger protection. This study enriched the research cases of complex network theory applied in landscape ecology, and provided references for the improvement of policies in the future. [ABSTRACT FROM AUTHOR]

• Ecosystem services are quantified at large scales and long time series in NE China. • The trade-offs and synergies among ecosystem services are characterized. • Human activities are the dominant driving factors of provisioning services. • Synergistic optimization paths of production-ecological functions are proposed. In Northeast (NE) China, the largest commodity grain base and natural forest area in China, both agricultural production and ecological protection require attention. In recent years, land degradation in NE China has increased, seriously affecting the production-ecology synergy. In this paper, four key ecosystem services (ESs) in NE China are quantified: provisioning, water conservation (WC), soil conservation (SC) and sand fixation (SF), and the spatiotemporal evolution characteristics and trade-offs and synergies (T&S) of ESs are analyzed. The driving factors of provisioning services and the characteristics of T&S are considered, and the county zoning scheme and the corresponding production-ecological function synergistic optimization paths are proposed. The results show that (1) the provisioning, WC and SF services in NE China have slightly increased in the past 20 years, and the provisioning in the Greater-Khingan-Mountains-Region (GKMR) and Lesser-Khingan-Mountains-Region (LKMR), the WC in the LKMR, and the SF in the Liao-River-Plain (LRP) have more obviously increased. The SC service has slightly decreased, especially in the Changbai-Mountains-Region (CBMR). (2) The provisioning service in NE China mainly shows a synergistic relationship with WC and SF, accounting for 79.1 % and 62.9 % of the area, respectively, and a trade-off relationship with SC, accounting for 52.54 % of the area. (3) Human activities (HA) are the dominant driving factor of the provisioning service change in NE China, with an impact degree of 62.65 %. The LKMR is an exception, as climate change (CC) dominates. The LRP, Songnen-Plain (SNP) and GKMR have high degrees of HA. (4) The number of synergistic optimized (SO), light optimized (LO) and trade-off limited (TL) counties accounted for 19.0 %, 61.7 % and 19.3 %, respectively. Among them, the SO type counties should prioritize improving provision and grain production capacity, the LO type counties should protect basic farmland and decrease trade-offs between ESs, and the TL type counties should improve ecological functions and strengthen ecological protection and restoration. This paper clarifies the main contradictions of ESs in NE China and provides an important scientific basis for ecological protection and restoration, which is important for ensuring food and ecological security and realizing the ecological and efficient sustainable utilization of black soil. [ABSTRACT FROM AUTHOR]

The surge of machine learning (ML) applications and increasing usage of data driven approach for resilience enhancement provide great opportunities for applying ML techniques to power outage prediction (POP) during hurricanes. Considering the substantial damage that wind hazards can cause to power system network (PSN) in countries such as the USA and China, researchers have devised numerous approaches for disaster prevention to reduce their susceptibility. Since the selection of optimal ML algorithms for POP during hurricanes is a complex task, this paper helps researchers easily comprehend the numerous prediction techniques used worldwide for power outages during hurricanes. The paper provides a brief review of several categories of hurricanes and the damage it causes to the PSN more specifically to distribution side, the necessary measures to improve the PSN resilience, and finally a sequential approach for optimal ML model selection techniques that researchers and engineers can use to improve the POP during hurricanes. In this study the effectiveness of ML algorithms has been explored with the help of performance evaluation metrics which serve as the basic deciding criteria for optimal model selection. This research also highlights some of the key issues and challenges of integrating machine learning algorithms with existing PSN. • Reviewed various ML algorithms used for distribution system POP during hurricanes. • Effects of damage experienced by power system networks during hurricanes are widely investigated. • Provides an insight into the measures for improving power system resilience against hurricanes. • Provides a stepwise approach for using various ML algorithms in the context of POP. • Identification of key issues, challenges, and discussion of possible future research directions. [ABSTRACT FROM AUTHOR]

Freshwater resources are scarce globally, and the increase in carbon dioxide (CO 2) concentration leads to both a decrease in soil moisture and arid climate, further limiting agricultural production. Therefore, it is critical to achieve water efficiency in agriculture under elevated CO 2 concentration. A comprehensive analysis was conducted on the research topic of efficient agricultural water under elevated CO 2 concentration using bibliometric methods. The results show that the number of papers on this research has changed from an accumulation phase (1992–2005, 395) to a growth phase (2006–2023, 963). "Carbon dioxide", "water-stress", "growth", "photosynthesis", "yield", and other keywords have been the focus of past research in this area. Kimball BA and Ainsworth EA are the most influential authors in this area. Leakey's (2009) paper in "Experimental Botany" was the most contributing study, summarizing six lessons about the effects of CO 2 enrichment on the relationship among carbon, nitrogen, and water in plants. The United States and China were the most influential countries. Over time, research has shifted from an early focus on atmospheric CO 2 change itself to the response of crops to elevated CO 2 in agricultural production. The efficient crop production strategy under the interaction of environmental factors is becoming a hot spot for future research, and the emission and use of greenhouse gases, the improvement of crop quality, and the efficient guidance of models are also worth exploring. Overall, this study presents a quantitative analysis and comprehensive review of past research conducted on the effect of water and fertilizer on agricultural production under CO 2 enrichment. It also offers suggestions and expectations for future research on the hot spot direction of efficient crop production under climate change. • The continuous research attention is closely related to climate change caused by CO 2 enrichment. • The main research fields included CO 2 enrichment, water stress, crop growth and photosynthesis. • The co-citation analysis reveals broad research clusters in the literature. • The new research focus will be interactive climatic factors, greenhouse gas recycling, and modeling. [ABSTRACT FROM AUTHOR]

This paper studies examines how a firm's position in the global value chain affects its pollutant emissions. This paper integrates China's firm-level trade data into China's multi-regional input-output tables to provide an index to measure a firm's position in global value chains. Based on a firm-level pollution dataset (2000–2014) in China, this paper conducts an empirical analysis and find that the most-downstream firms on the global value chain have lower pollutant emission intensity. Our results also suggest that the production factor structure effect, the knowledge absorption effect and the abatement investment effect together contribute to the emission intensity gap between the most-upstream firms and the most-downstream firms. These novel results are significant in guiding firms towards reducing their emissions and promoting a green global value chain, with the ultimate goal of achieving multinational environmental protection. [Display omitted] • The most-downstream firms have lower pollutant emission intensity. • A supply chain disruption shock is used as an instrumental variable. • Factor structure, knowledge absorption and abatement investment are channels. • Effects on firms with diverse ownership and trade patterns are heterogeneous. [ABSTRACT FROM AUTHOR]

With the growing global energy demand and limited production of conventional crude oil, the extraction of unconventional heavy and extra-heavy oil is receiving unprecedented attention. China is the fourth largest producer of heavy oil. High viscosity and complex formation environments increase the difficulty of exploiting heavy oil. To alleviate the economic cost and environmental pollution pressure brought by heavy oil production, China has achieved significant advancement in the innovation of heavy oil recovery, including accelerating the transformation of thermal technology to non-thermal technology and the use of new energy technology to achieve the goal of low-carbon economy and green environmental protection. Based on the background of heavy oil in China, this paper recharacterizes the high-viscosity mechanism of heavy oil. From different aspects, including basic principles, main characteristics, applicability, limitations, and challenges, this paper comprehensively reviews existing heavy oil recovery technologies, including thermal recovery, in-situ upgrading, cold recovery, and new energy technologies. Currently, in China, the main target of heavy oil recovery is achieving the "carbon peaking and carbon neutrality" goals, which can be described as low energy consumption, low emission, and low pollution. To facilitate the low-carbon transition in the extraction of heavy/extra-heavy oil and attain environmental sustainability goals, it is essential to increase the proportion of non-thermal factors in thermal recovery technologies, develop efficient in-situ catalysts, adopt clean cold extraction techniques, and advance the development of new energy technologies. [ABSTRACT FROM AUTHOR]

Compared to cast-in-situ construction, prefabricated construction reduces the generation of construction waste; however, construction waste cannot be entirely eliminated through prefabrication and can be generated at each stage of its life cycle. This paper generates a system dynamics model, comprising two causal-loop diagrams and one stock-flow diagram, to simulate the effectiveness of policies on decarbonizing prefabricated concrete building waste. The model can predict the annual and total construction waste, along with the associated carbon emissions, under different policy scenarios in China by 2030. This paper focuses on prefabricated concrete buildings in China, with specific attention to the wastage of concrete, bricks, mortar, and metal. In single-factor scenario simulations, increasing landfilling fee is the most effective policy for waste decarbonization, potentially reducing carbon emissions by 31%. Increasing prefabrication rate and enhancing regulation degree at the design phase of prefabrication can directly reduce prefabricated building waste generation, subsequently resulting in an 18% and 7% carbon emissions reduction. Multi-factor scenario simulations demonstrate that the simultaneous application of all four policies is most effective, achieving a 44% reduction in carbon emissions. This research establishes a theoretical framework for prefabricated concrete building waste management and provides the Chinese government with practical insights on optimizing policies for waste decarbonization in the dynamic landscape of prefabrication. • This paper develops the prefabricated building waste generation and disposal systems. • The model predicts the construction waste and associated carbon emissions by 2030. • Raising landfill fee is an effective policy for decarbonizing construction waste by 31.06%. • Prefabrication rate reaching 100% can reduce 18.04% of carbon emissions from waste. • Applying all policies is most effective, achieving a 44.34% carbon emissions reduction. [ABSTRACT FROM AUTHOR]

In the domain of global socio-economic dynamics, the pivotal role of food production (FP) cannot be overstated. It constitutes the bedrock of fundamental human necessities while being inexorably linked with critical aspects such as economic expansion, societal equilibrium, and geopolitical interplays. The multifaceted nature of FP appears through its direct impact on macroeconomic indicators and its latent influence on international relations and policy-making. This critical nexus underscores the imperative for a comprehensive understanding and strategic management of food production processes to sustain and enhance global socio-economic stability and growth. Investigating the nexus between green finance (GF) and FP is pivotal for ensuring national security and promoting sustainable economic development. This research thus paper examines GF's impact on FP in China as outlined in the 2017 green finance policies. It employs the difference-in-differences (DID) method and analyzes a balanced panel dataset comprising 282 cities in China from 2009 to 2020. The results show that GF has an inhibitory effect on FP, and this remains valid after a range of reliability checks, including parallel trend assessments and placebo tests. Further findings of transmission mechanism analysis indicate that non-agricultural factors and financial exclusion are critical for GF's inhibitory effect on FP. Heterogeneity analyses show that GF's inhibitory effect on FP is more pronounced in the main food-producing areas, the main food-marketing areas, and cities with better infrastructure. The empirical results of this paper comprehensively illustrate GF's impact on FP and how it contributes to the achievement of food security objectives. [ABSTRACT FROM AUTHOR]

The fluctuation of oil futures price greatly influences the market investment sentiment, which can cause credit risk and capital pressure risk of various financial institutions, and ultimately affect the idiosyncratic risk and systemic risk of financial institutions. This paper adopts MES to measure the systemic risk of Chinese financial institutions and proposes a high-dimensional time-varying parameter spillover network matrix to identify the infection route and source of systemic risk. Finally, the paper investigates the mechanism by that oil futures affect the contagion effect of systemic risk across institutions. The results show that banking financial institutions are the main exporters of risk while insurance is the main absorber, and after the outbreak of COVID-19, the risk contagion capacity of institutions generally increased, the network structure became more complex, and the risk spillover level was pro-cyclical. The empirical results show that the rise of oil futures will promote the (net) risk spillover capacity of institutions and inhibit the risk absorption capacity, and the above process can be transmitted through the stock market, bond market, and foreign exchange market. • Banking institutions are transmitters of risk, while insurance are receivers; • Financial institutions' risk contagion network becomes more complex after COVID-19; • The systemic risk spillover level of Chinese financial institutions has obvious pro-cyclical; • Higher oil futures price promotes the (net) risk spillover capacity of institutions and inhibit the risk absorption capacity; • The above impacts involve three possible transmission mechanisms: the stock market, bond market and foreign exchange market; [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations. [ABSTRACT FROM AUTHOR]

Early childhood education programs have been found to effectively promote children's social and cognitive development. However, the health impact of these programs is less understood. Using a quasi-experiment of the first universal child care reform in China from 2010, this paper aims to identify whether the preschool reform produces any short-term effects on health-related outcomes of preschoolers (3-6 years old). Specifically, this reform expands access to affordable preschools that provide full-day center-based education, with school meals and essential health services on campus. I exploit the variation in the number of newly established preschools across provinces and implement difference-in-differences and triple-difference strategies. Results confirm the effectiveness of this reform by showing a strong and positive impact on preschool attendance. This paper then documents the benefits to alleviating underweight among preschoolers. Estimates show a larger effect in rural areas, suggesting that the reform narrows rural-urban disparities in education access and undernutrition prevention. I also explore the impact on caregivers' health consciousness and find improved health-seeking behavior when children get sick. [ABSTRACT FROM AUTHOR]

This paper highlights the evolution and impact of the SWAP model (Soil – Water – Atmosphere – Plant), which was initiated by R.A. Feddes and colleagues fifty years ago, in 1974. Since then, the SWAP model has played a crucial role in the advancement of agrohydrology. This paper highlights some major advances that have been made, especially focussing on the last fifteen years. The domain of the SWAP model deals with the simulation of the soil water balance in both unsaturated and saturated conditions. The model solves the Richards equation using the water retention and hydraulic conductivity functions as described by the Van Genuchten – Mualem equations. Bimodal extensions of the Van Genuchten - Mualem relationships have been implemented, as well as modifications near saturation and addressing hysteresis. An important sink term in the Richards equation is root water uptake. Crop development plays an important role in a robust simulation of root water uptake. That is why a link has been made with the dynamic crop growth model WOFOST. Instead of using a prescribed crop development, a distinction between potential and actual crop development is calculated by reducing the potential photosynthesis as a result of water or oxygen stress. Since the early days of SWAP, empirical and macroscopic concepts have been used to simulate root water uptake. Recently two process-based concepts of root water uptake and oxygen stress have also been implemented. Another important sink-source term in the Richards equation is the interaction with artificial drains. In SWAP, drainage can be simulated by either using prescribed or simulated drain heads and simulation of controlled drainage with subirrigation is possible. Finally, we briefly elaborate on three studies using SWAP: water stresses in agriculture in the Netherlands, regional water productivity in China, and controlled drainage with subirrigation. We finish discussing promising developments for the near future. • Advances in soil-vegetation-atmosphere transfer modelling in the SWAP model. • Coupling of soil water dynamics with dynamic crop growth model. • Process-based root water uptake and oxygen uptake functions. • Climate-adaptive drainage with subirrigation. [ABSTRACT FROM AUTHOR]

• Most of China still faces notable water poverty with obvious spatial variations. • Unconventional water use does not always have a positive impact on water poverty. • The impact shows an inverted U-shaped tendency as per capita GDP increases. • Sustainable water management plans should be tailored to local economy. Unconventional water serves as a bridge to water sustainability. However, many developing nations still have a long way to go in terms of unconventional water utilization. Different regional characteristics lead to different consumption capacities for unconventional water. Consequently, more unconventional water use (UWU) may not necessarily be positive for alleviating water poverty. However, there is a significant research gap in this field. This paper aims to answer three key questions: "Whether UWU consistently alleviates water poverty?" "Is this effect spatially heterogeneous?" and "What role do economic factors play in addressing water poverty?" For that, this paper focused on 30 provinces in China, with the sample period spanning from 2006 to 2021. To identify the mechanisms through which UWU alleviates water poverty, this paper employed a panel fixed-effects model using the Water Poverty Index (WPI) as the dependent variable and the square of UWU amount as an independent variable. The results indicate a non-linear impact of UWU on WPI, with the significance of impact influenced by GDP per capita (PGDP). Furthermore, threshold regressions were conducted with PGDP as the threshold variable. The results revealed a double-threshold effect of PGDP in the impact of UWU on WPI, displaying "U" or inverted "U" shape as PGDP develops: When PGDP is less than or equal to RMB 13,574, UWU demonstrates a negative effect on WPI; when PGDP ranges from RMB 13,574 to RMB 53,653, the effect turns positive; however, when PGDP exceeds RMB 53,653, the effect turns back to negative. The findings confirm that more UWU is not always better. Sustainable utilization plans of water resources must be tailored to local conditions, especially considering the unique characteristics of the regional economy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Measure carbon emission inequalities (CEIs) of urban agglomerations under rapid urbanization. • Identify the key factors determining CEIs within the three urban agglomerations. • Account for the differences between adjacent or distant urban agglomerations. • The declining CEI is mainly due to changes in industrial structure and economic development. • CEI in the upper reaches of the YREB increases due to the changes in energy intensity. Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China's Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future. [ABSTRACT FROM AUTHOR]

Here we present our workflow, numerical models and results of the spatial distribution of chalcopyrite mineralization under the different porosity and permeability conditions of the host rocks of the Shaxi deposit. During the numerical modeling research, we tried to coupling heat transfer, fluid flow, material migration, mineralization reaction, and their coupling with porosity and permeability of rock. The results show that the porosity and permeability of the host rocks play a significant role in the mineralization of porphyry-type systems. The chemical properties of the strata restrict significantly on mineralization intensity and spatial extent rather than affecting the depth of mineralization. This paper also indicates that the numerical modeling method employed in this study can be used to predict the mineralization depth and spatial location of deep concealed porphyry-type deposits, aiding research in deep-seated mineral exploration. [Display omitted] • The porosity and permeability of the host rocks play a significant role in the mineralization of Shaxi porphyry-type deposit. • The chemical properties of the strata restrict significantly on mineralization intensity and spatial extent rather than affecting the depth of mineralization. • The numerical modeling may be used to predict the mineralization depth and spatial location of deep concealed porphyry-type deposits. The Shaxi porphyry-type Cu-Au deposit is located in the northwestern outer margin of the Luzong volcanic basin, which is situated in the Middle-Lower Yangtze River Metallogenic Belt (MLYRMB), Eastern China. It is a large porphyry-type Cu-Au deposit with confirmed copper resources exceeding 1 Mt and gold resources exceeding 40 tons. Previous studies have extensively investigated the genesis, controlling structures, geochemical characteristics, fluid evolution, alteration zone, and ages of mineralization and host rocks in this deposit. However, research on the influence of the physical and chemical properties of the host rocks on the mineralizing processes has not been explored in detail. This study employs the finite element method to apply numerical modeling of multiple physical and chemical fields, interlinking heat transfer, fluid flow, material migration, mineralization reaction, and their coupling with porosity and permeability of rock, to investigation of the influence of different porosities, permeabilities, and chemical properties of the host rocks on the mineralizing processes in the Shaxi deposit. The results show that the porosity and permeability of the host rocks play a significant role in the mineralization of porphyry-type systems. The different porosity and permeability of the intrusions and strata control the depth, the mineralization intensity, the morphology of the ore bodies, and the spatial location of the mineralization center. The chemical properties of the strata significantly restrict the mineralization intensity and horizontal spatial rather than affecting the depth of mineralization. Furthermore, the formation of skarn-type deposits may require a longer period of time than that of porphyry-type deposits under the same mineralization conditions and ore grade. This paper also indicates that the numerical modeling method employed in this study can be used to predict the mineralization depth and spatial location of deep concealed porphyry-type deposits, aiding research in deep-seated mineral exploration. [ABSTRACT FROM AUTHOR]

This paper is the second in a two-part series on the concavity-convexity of the distribution system security region (DSSR). In Part I, the concave-convex characteristics and mechanism of DSSR are revealed by observing many case grids. In Part II (this paper), the concave-convex principle of DSSR is revealed through mathematical deduction. Firstly, the relevant concepts of sub-regions are proposed, and a DSSR model based on the union of sub-regions is developed. Secondly, the concave-convex principle of DSSR is proposed. The proposed criteria show that the security region of a distribution system with a single load transfer path for each feeder is convex, while that with multiple load transfer paths for some feeders can be concave or convex. Thirdly, the proposed criteria are deduced mathematically. Fourthly, based on the criteria, a method is proposed to judge whether a DSSR is concave or convex. Fifthly, the proposed criteria and method are verified through several cases, and the DSSR concavity-convexity of real distribution systems in China is also analyzed. Finally, the applications of DSSR concavity-convexity are illustrated. This work lays a mathematical foundation for the research of the topological properties of DSSR and is also useful for the application of DSSR. • The DSSR mathematical model based on the union of sub-regions is developed. • Three criteria are proposed and proved, revealing the concave-convex principle of DSSR. • A judgment method for DSSR concavity-convexity is proposed based on these criteria. • The DSSR concavity-convexity of real distribution systems in China is analyzed. • The application values of DSSR concavity-convexity are demonstrated. [ABSTRACT FROM AUTHOR]

• The magnitude of current and electromagnetic force was calculated through simulation analysis combined with theoretical formulas. • The target plate structure was calculated through finite element analysis under the influence of eddy currents and halo current electromagnetic forces. • Transient dynamic analysis was conducted using modal superposition method. • Based on the safety performance evaluation criterion 3Sm for internal components of fusion devices, the safety performance assessment of the structure was conducted. China Fusion Engineering Test Reactor (CFETR) is a fully superconducting tokamak device independently designed and developed by China. The divertor of CFETR is one of the core components, which will withstand huge and complex electromagnetic loads. Electromagnetic loads are often of large magnitude and short duration, which will cause impact damage to internal components.During the plasma disruption event, the plasma current drops rapidly in milliseconds, which creates large eddy currents on the divertor, and eddy current will generate electromagnetic force and torque under the action of toroidal and poloidal magnetic field, which will cause the divertor to be pulled, compressed or twisted; during the plasma vertical displacement event, halo current is generated on the plasma surface, then a large electromagnetic force will be generated on the divertor under the action of the toroidal and the poloidal magnetic field.In this paper, the electromagnetic analysis under the action of halo current and eddy current is carried out for the hypervapotron structure, and transient dynamic analysis of the real stress state during the electromagnetic load impact process is simulated.Strength analysis based on the results of transient analysis shows that the structure has a high reliability. The work in this paper is based on the evaluation of reliability of the hypervapotron structure under the maximum electromagnetic load. [ABSTRACT FROM AUTHOR]

To addreess the challenge of overcrowding in the urban rail transit system, this paper proposes a novel joint optimization strategy that optimizes the timetable and the passing facility's working status (PFWS) simultaneously according to the passenger demands. To this end, a mixed-integer nonlinear programming (MINLP) model is formulated to jointly minimize overcrowding on trains and platforms, as well as the passenger travel time. The passing facility setting and the passenger flow activities depiction are particularly taken into account by the rigorous constraints. Subsequently, we design a simulation-based hybrid variable neighborhood search (SHVNS) algorithm to efficiently generate near-optimal solution. This algorithm incorporates a variable neighborhood search (VNS) algorithm, a randomly reset strategy, and a simulation-based algorithm. The VNS algorithm includes five neighborhood operators to enhance search efficiency. Finally, various experiments with the real-world data from Chongqing rail transit line 6 in China are tested to prove the applicability and the performance of our proposed model and algorithm. The computational results show that the joint optimization strategy outperforms a current operation strategy in terms of the overcrowded situation on trains and platforms, often by more than 60%, and some management implications and theoretical support can be obtained for the urban rail transit operator. [ABSTRACT FROM AUTHOR]

Digital transformation has the potential to significantly impact household energy consumption (HEC) by promoting the adoption of energy-efficient technologies. This paper utilizes the time-varying Difference-in-Differences (DID) model to evaluate the causal effect of digital transformation on HEC within China's households. We find that HEC is positively impacted by digital transformation, as embodied by the "Broadband China" strategy; and the results remain reliable when many robustness tests are taken into account. Specifically, digital transformation has the potential to indirectly increase HEC by consumption upgrade and internet development mechanisms. The heterogeneity analysis reveals that the impact of digital transformation on HEC varies significantly across different household and regional. High-income, low skilled, rural areas, as well as households in areas with higher levels of development and marketization, are more likely to be affected by the digital revolution in production and consumption, which will inevitably lead to an increase in HEC. These insights are crucial for developing effective strategies to reduce HEC and promote sustainable development. [Display omitted] • Assesses digital transformation's impact on China's household energy use. • Identifies significant increase in energy consumption due to digitalization. • Examines mechanisms of how digital transformation affects energy use. • Provides insights for harmonizing digital and green transformations. [ABSTRACT FROM AUTHOR]

The energy system and its energy performance of R-CELLS, a residential zero energy building from team Tianjin U+ in the Solar Decathlon China 2022, is introduced in this paper. When designing and constructing the R-CELLS energy system, two sets of challenges are encountered. Firstly, from an architectural perspective, the limited construction timeframe (only 21 days) and constrained space for housing the equipment, also ease of maintenance should be taken into account. Secondly, from an operational perspective, a constrained period for tests before the competition, a large number of devices dependent on electricity, heat and hot water supply should be considered. In consideration of the ease of assembly and maintenance of the energy system components and the aesthetic requirements of R-CELLS, an improved "photovoltaics, energy storage, direct current, flexibility" based energy system is utilized in R-CELLS. The R-CELLS energy system integrates various renewable energy sources (building integrated photovoltaics, photovoltaic-thermal, and building integrated wind turbines). Furthermore, it incorporates energy storage systems such as battery energy storage system, hot water tank, and phase change material thermal storage. In order to satisfy user requirements in grid-connected, off-grid and outage scenarios, including electricity, heat and domestic hot water, a tri-period energy management strategy based on optimized scheduling and rule-based control is proposed to achieve weekly, daily, and hourly net zero goals. Furthermore, the energy performance of R-CELLS is simulated for a whole year, including typical days in winter and summer, as well as the competition period. During the Solar Decathlon China 2022 competition, R-CELLS was subjected to testing by the committee. The results demonstrated that R-CELLS can be operated off-grid and with zero energy consumption, while meeting the test requirements for indoor environment, heating and cooling, and home life. • An improved PEDF based energy system of residential ZEB is utilized in R-CELLS, the champion competition entry from team Tianjin U+ in the Solar Decathlon China 2022. • A three-period EMS based on optimized scheduling and rule-based control is proposed. • Simulation data demonstrate that R-CELLS meets the ZEB standards. • The actual operation during the competition validates the reliable performance of the energy system, the effectiveness of the EMS, and the fulfillment of various energy needs of the residents. [ABSTRACT FROM AUTHOR]