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China's social safety net is still underdeveloped, hence family support in the form of intergenerational transfers often serves as a substitute for the public transfer system. Using data from the China Health and Retirement Longitudinal Study, this paper finds that both upstream inter‐vivos transfers (from children to parents) and downstream inter‐vivos transfers (from parents to children) are prevalent in urban China. Moreover, the relative income status of the parent and children has an impact on inter‐vivos transfers. To investigate what economic factors generate the observed patterns of inter‐vivos transfers, this paper adopts a general equilibrium life‐cycle model in which overlapping generations are altruistically linked and calibrates the model to match data from urban China. Counterfactual experiments of removing one source of economic risk or modifying the social security replacement rate from the baseline model at a time reveal that intergenerational transfers mainly serve as informal insurance against the income risk of the children. [ABSTRACT FROM AUTHOR]

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

Macrophages play a crucial role in aiding all phases of the wound‐healing process and has garnered increasing attention recently. Although a substantial body of related studies has been published, there remains a lack of comprehensive bibliometric analysis. In this study, we collected 4296 papers from the Web of Science Core Collection database. Three tools including CiteSpace, VOSviewer and one online analytical platform were employed to conduct bibliometric analysis and data visualization. Our results revealed that the annual number of publications related to macrophage and wound healing has increased exponentially with the year. The United States and China stand as the primary driving forces within this field, collectively constituting 58.2% of the total publication output. The application of biomaterials was one of the most concerned research areas in this field. According to references analysis, the current research focus has shifted to diabetic wound healing and regulating macrophage polarization. Based on the keywords analysis, we identified the following research frontiers in the future: exosomes and other extracellular vesicles; bio‐derived materials and drug delivery methods such as nanoparticles, scaffolds and hydrogels; immunomodulation and macrophage polarization in the M2‐state; chronic wounds, particularly those associated with diabetes; antimicrobial peptides; and antioxidant. Additionally, TNF, IL‐6, IL‐10, TGF‐β1 and VEGF ranked as the five genes that have garnered the most research attention in the intersection of macrophage and wound healing. All in all, our findings offered researchers a holistic view of the ongoing progress in the field of macrophages and wound healing, serving as a valuable reference for scholars and policymakers in this domain. [ABSTRACT FROM AUTHOR]

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF). [ABSTRACT FROM AUTHOR]

Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

Background: For adults with inflammatory bowel disease (IBD), they experience many challenges in dietary decision‐making. Thus, this study examined the perspective and experiences of adults with IBD in dietary decision‐making. Objective: This study aimed to explore the perception and consideration of people with IBD in their daily dietary decisions through monitoring, interpretation and action during the decision‐making process. Design: A qualitative study of individuals affected by IBD was conducted through semistructured interviews. Results: Twenty patients were recruited from four tertiary hospitals in Nanjing, China, and each participant completed a semistructured interview. The majority of participants reported on the process and experience of dietary decision‐making. Key themes were categorised into three stages: (1) assessing needs, preferences and food cues (monitor); (2) moving from experience to expertise (interpret) and (3) balancing expectations amidst limitations (act). The majority of participants reported that their decisions were shaped by assessing current disease status and food cues. Those interviewed with IBD were willing to make tradeoffs for bowel stability, but their decisions were also influenced by past dietary experiences and traditional Chinese beliefs. The lack of awareness of dietary guidelines was a significant barrier to healthy eating decisions. Positive or negative feelings accompanied dietary decisions. Conclusion: Although most people with IBD change their diet after diagnosis, the changes made are often inconsistent with existing dietary recommendations. Several factors can influence the dietary decision‐making process. This study will help assess the experiences of people with IBD in dietary decision‐making to encourage the formation of targeted dietary health and well‐being interventions. Knowledge of nutrition and diet should be provided in education and training programmes for IBD management. Patient or Public Contribution: The first three authors of this paper were the lead researchers in this study's design. These authors were mentored by patient researchers who also contributed to the manuscript, and the research process was co‐lead and directed by other patient participants and consultants. The results of this paper were directly obtained from patient participants. [ABSTRACT FROM AUTHOR]

The green supply chain of agricultural products (GSCAP) is a key link for rural revitalization and sustainable development in China. However, it faces various risks from internal and external environments that threaten its performance and stability. This paper proposes a novel framework and system for identifying and evaluating the main risks in the GSCAP from the perspective of agricultural enterprises. The framework combines social network analysis (SNA) and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method. SNA is used to analyze the correlations and influences among different types of risks, while the improved TOPSIS method is used to rank the risks of different GSCAPs and identify the key risks in each supply chain. The framework and system are verified by a case study of CDYBIT, a leading platform of food safety big data service in China. The results show that the supermarket supply chain has the highest risk, followed by the group catering supply chain, and the five‐star hotel supply chain has the lowest risk. The main risk factors for each supply chain are also discussed, and some suggestions for risk management are provided. This paper contributes to the literature by providing a comprehensive and systematic risk assessment framework and system for the GSCAP, which can help agricultural enterprises improve their risk awareness and response capabilities. [ABSTRACT FROM AUTHOR]

In order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper. [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently. [ABSTRACT FROM AUTHOR]

This study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction. [ABSTRACT FROM AUTHOR]

In the chemical flooding process, the premature breakthrough of chemical agents in production wells results in a large waste of chemical agents and increases the volume and processing difficulty of the produced fluids. The early warning method of chemical agent channeling can predict the strength of agent channeling in advance. The practice of chemical flooding shows that the production performance can be used for early warning of chemical agent channeling. In this paper, we analyze the relationship between cumulative oil production and cumulative polymer production of production wells in polymer–surfactant flooding. Three types of curves according to the enhanced oil production characteristics of chemical flooding, including convex type, S‐type, and concave type. We use the drop speed of the water–oil ratio and the rapid‐decline speed of water cut as early warning indicators to predict the channeling coefficient. A Latin hypercube experimental design method is used to design a polymer–surfactant flooding scheme with the main control factors of the channeling coefficient and early warning indicators. Numerical simulation is used to calculate samples of the channeling coefficient and early warning indicators under various conditions. The drop speed of the water–oil ratio reference value model and the rapid‐decline speed of the water cut reference value model are determined with a multiple regression method. A prediction model for the chemical agent channeling coefficient is established using the deviation coefficient of an early warning index. The method is applied in the Ng54‐61 polymer–surfactant flooding pilot area in the west of the Gudong Seventh District, Shengli Oilfield, China, and the error between the predicted result and the actual value is less than 10%. This research is helpful in taking effective antichanneling measures and improving the oil recovery degree of chemical flooding reservoirs. [ABSTRACT FROM AUTHOR]

This paper qualitatively and quantitatively examines the development of the sovereign debt market in Prewar China under different governments. During the Beijing Era (1912–26), accompanied by the establishment of necessary financial institutions, the sovereign debt market emerged to meet fiscal needs. Surprisingly, the Nationalist government, in power from 1927, successfully cultivated a robust market characterized by its expanding size and liquidity. Setting itself apart from its predecessors, the government established credibility as a borrower in two key ways. Firstly, it demonstrated unwavering commitment to debt service by settling previous debts and offering well‐structured new ones, even during challenging times. Furthermore, the government escrowed fiscal revenue, pledged for debt repayments, to a semi‐independent committee of private bankers on behalf of debtholders, enhancing public confidence. Secondly, the government showcased its ability to secure tax revenues for debt repayments. However, starting from 1931/2, the debt market experienced a decline due to the government's compromised ability to pay resulting from external wars and shifting political priorities that weakened its commitment to debt repayments. Empirical evidence confirms the market's responsiveness to regime shifts and policy changes. This paper sheds light on how a nascent autocratic government can successfully borrow from the public. [ABSTRACT FROM AUTHOR]

Concerted effort to reduce ecological damages requires that the role of enterprises should be extended to incorporate environmental issues into their core business objectives. Despite huge developments in sustainable entrepreneurship research, outcomes of sustainable entrepreneurship uptake remain largely unclear. Drawing on data of Chinese firms, and utilizing fixed effects regression, our paper evaluates the conditions under which compliance with environmental regulation may enable or constrain corporate performance. We find that environmental compliance lowers corporate market performance and improves financial performance at the same time. We also find that the decline in market performance is reversed for large and privately owned enterprises, highlighting the role of firm size and ownership in corporate performance. When distinguishing between market‐ and financial performance, our evidence shows that environmental compliance will most likely improve market performance when enterprise carbon intensity level is low and improve financial performance when enterprise carbon intensity level is high. As we have discussed in our paper, these results have several implications from the point of view of managers, firm stakeholders, and policymakers. [ABSTRACT FROM AUTHOR]

Background: Kenny–Caffey syndrome type 2 (KCS2) is an extremely rare inherited disorder characterized by proportionate short stature, skeletal defects, ocular and dental abnormalities, and transient hypocalcemia. It is caused by variants in FAM111A gene. Diagnosis of KCS2 can be challenging because of its similarities to other syndromes, the absence of clear hallmarks and the deficient number of genetically confirmed cases. Here, we aimed to further delineate and summarize the genotype and phenotype of KCS2, in order to get a better understanding of this rare disorder, and promote early diagnosis and intervention. Methods: We present clinical and genetic characteristics of eight newly affected individuals with KCS2 from six families, including one family with three individuals found to be a father‐to‐daughter transmission, adding to the limited literature. Furthermore, we performed a review of genetically confirmed KCS2 cases in PubMed, MEDLINE and CNKI databases. Results: There were six females and two males in our cohort. All the patients presented with short stature (100.0%). Clinical manifestations included ocular defects such as hypermetropia (5/8), dental problems such as defective dentition (3/8) and dental caries (3/8), skeletal and brain anomalies such as delayed closure of anterior fontanelle (6/8), cerebral calcification (3/8), cortical thickening (3/8) and medullary stenosis (4/8) of tubular bones. Endocrinologic abnormalities included hypoparathyroidism (5/8) and hypocalcemia (3/8). One male patient had micropenis and microorchidism. All cases harboured missense variants of FAM111A, and nucleotides c.1706 arose as a mutational hotspot, with seven individuals harbouring a c.1706G>A (p.Arg569His) variant, and one child harbouring a c.1531T>C (p.Tyr511His) variant. Literature review yielded a total of 46 patients from 20 papers. Data analysis showed that short stature, hypoparathyroidism and hypocalcemia, ocular and dental defects, skeletal features including cortical thickening and medullary stenosis of tubular bones, and seizures/spasms were present in more than 70% of the reported KCS2 cases. Conclusion: We provide detailed characteristics of the largest KCS2 group in China and present the first genetically confirmed instance of father‐to‐daughter transmission of KCS2. Our study confirms that Arg569His is the hot spot variant and summarizes the typical phenotypes of KCS2, which would help early diagnosis and intervention. [ABSTRACT FROM AUTHOR]

This paper discusses the historical and social origins of the bifurcation in the political institutions of China and Western Europe. An important factor, recognized in the literature, is that China centralized state institutions very early on, while Europe remained politically fragmented for much longer. These initial differences, however, were amplified by the different social organizations (clans in China, corporate structures in Europe) that spread in these two societies at the turn of the first millennium AD. State institutions interacted with these organizations, and were shaped and influenced by this interaction. The paper discusses the many ways in which corporate organizations contributed to the emergence of representative institutions and gave prominence to the Rule of Law in the early stages of state formation in Europe, and how specific features of lineage organizations contributed to the consolidation of the Imperial regime in China. This publication follows on from the Economica Phillips 2022 lecture presented by Professor Guido Tabellini on 01 December 2022. [ABSTRACT FROM AUTHOR]

In recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results. [ABSTRACT FROM AUTHOR]

Drought is an important meteorological event in China and can cause severe damage to both livelihoods and socio‐ecological systems, but current seasonal prediction skill for drought is far from successful. This study used convolutional neural network (CNN) to build an effective seasonal forecast model for the summer consecutive dry days (CDD) over China. The principal components (PC) of the six leading empirical orthogonal function modes of CDD anomaly were predicted by CNN, using the previous winter precipitation, 2‐m temperature and 500 hPa geopotential height as predictors. These predicted PCs were then projected onto the observed spatial patterns to obtain the final predicted summer CDD anomaly over China. In the independent hindcast period of 2007–2018, the interannual variabilities of first six PCs were significantly predicted by CNN. The spatial patterns of CDD were then skillfully predicted with anomaly correlation coefficient skills generally higher than 0.2. The interannual variability of summer CDD over the middle and lower Yangtze River valley, northwestern China and northern China were significant predicted by our CNN model three months in advance. CNN identified that the previous winter precipitation was the important predictor for PC1–PC3, whereas the previous winter 2‐m temperature and 500 hPa geopotential height were important for the prediction of PC4–PC6. This research provides a new and effective method for the seasonal prediction of summer drought. Plain Language Summary: Drought can cause serious agricultural and ecosystem disasters, so its forecast is valuable for preventing and mitigating related natural disasters and regional socioeconomic sustainability. However, current prediction skill for drought is far from successful since its extreme feature. The gradually emerging deep learning methods offer new possibilities, but how to effectively apply deep learning models in climate prediction with a small sample size remains an open question. In this paper, we build seasonal prediction convolutional neural network model for summer consecutive dry days over China using previous winter predictors. This model achieves significant prediction skill three months in advance. The empirical orthogonal function decomposition is used to reduce the dimensionality of consecutive dry days data in our model. Our research provides a new perspective for drought prediction, and it is expected that such method will be also useful for other seasonal climate prediction problems. Key Points: Convolutional neural network (CNN) skillfully predicts summer consecutive dry days (CDD) over China three months in advanceThe principal components of CDD are predicted by CNN and then projected on the observed spatial patternsPrevious winter 2‐m temperature, geopotential height at 500 hPa and precipitation are the essential predictors in CNN [ABSTRACT FROM AUTHOR]

From core observations of shales of the Niutitang formation in the northern part of Guizhou Province, China, calcite was often found to act as a natural fracture filler and affect the extension of fractures in hydraulic fracturing. Therefore, it is crucial to understand the tensile mechanical behavior of shales by calcite veins. In this paper, by computed tomography scanning of shales containing calcite veins of different dip angles from the Niutitang formation, a three‐dimensional numerical model reflecting the internal fine structure of the shale was constructed. Direct tensile numerical simulations were carried out to investigate the effect of calcite veins at different angles on the fine‐scale damage process and mechanical properties of shale. The experimental results show that the tensile capacity of the shale increases with the increase in calcite veins. Depending on the dip angle of the calcite vein, the damage pattern of the shale is divided into three types of damage: pull‐off damage along the calcite vein, jagged damage, and horizontal damage perpendicular to the loading method. At high dip angles, the shale damage is more intense and the fracture network more complex. The temporal and spatial characteristics of the acoustic emissions provide a good indication of the microscopic behavior of the shale specimens during the damage process. The box dimension method was used to calculate the fractal dimension of the shale specimens at the final damage, and it was found that the damage was more intense and the fracture network was more complex at high dip angles, and there was an angular threshold. [ABSTRACT FROM AUTHOR]

This paper examines the impact of institutional pressure (IP), top management support (TMS), green supply chain management practices (GSCM), and supply chain competitive advantage (SCCA) on corporate environmental performance (EP). We also analyze the mediation effect of GSCM on the interplay between TMS and EP. Additionally, the paper also provides an analysis of the moderating role of SCCA between IP and EP. To attain the objective of this research, we assembled data from 710 business entities within the Shaanxi province of China utilizing a survey design approach. The structural equation model (SEM) was applied to test and assess the hypothetical outline. The study outcomes empirically show that TMS, GSCM, and SCCA positively and significantly impact EP. Interestingly, our study found an insignificant association between IP and EP. The study's results also demonstrate that IP directly relates to top management support. Moreover, the study's empirical findings reveal that GSCM positively mediates IP and EP. The study findings show that SCCA shapes IP and EP's connection. Accordingly, the practical implications of our study's findings suggest that business managers, investors, and government agencies must know the importance of adopting sustainable practices within the supply chain. Business managers must take action to integrate environmental criteria into supplier selection, evaluate suppliers' EP, and collaborate with eco‐friendly suppliers. Hence, government agencies, stakeholders, and business managers can use this information to shape regulations and policies that encourage businesses to adopt sustainable supply chain practices. Offering incentives such as tax benefits or grants for sustainability initiatives can also promote adoption. The study recommends that a business culture that targets improving EP due to IP and top management support is essential in achieving GSCM practices, thereby promising competitive advantage. [ABSTRACT FROM AUTHOR]

The platform economy is growing fast in the world. Its expansion, exemplified by industry giants like Amazon, Airbnb, and Uber, is reshaping societal lifestyles and commercial businesses. However, its environmental impacts are uncertain. Currently, the relationship between electronic commerce (e‐commerce) activities and environmental pollution has attracted a lot of attention. This paper attempts to construct a comprehensive theoretical model to investigate the nexus between e‐commerce and regional environmental pollution, with a particular focus on the effects of air pollution and waste pollution. Fixed effect models and China's provincial data during the period from 2008 to 2018 are employed to examine the roles of energy efficiency and resource consumption in such a nexus. The findings suggest that the development of e‐commerce is linked to a reduction in regional air pollution through the channel of improving transportation energy efficiency. The results also show that e‐commerce is associated with an increase in regional waste pollution due to the mechanism of resource consumption in the express packaging industry. As a result, the relationship between e‐commerce and environmental pollution is quite complex at the regional level, transcending a binary distinction of solely positive or negative impacts. This paper contributes to a better understanding of the complex environmental impacts of e‐commerce and helps governments design more efficient and targeted policies to reduce regional pollution. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the impact of government environmental concerns on green innovation and whether this has a "leverage effect" or a "crowding‐out effect." This study employs a two‐way fixed‐effects model to conduct an empirical test using panel data from 2010 to 2020 on Chinese firms listed in heavy‐polluting industries. The results suggest that an increase in government environmental concerns can promote corporate green innovation. However, government environmental concerns primarily stimulate strategic innovation rather than substantive innovation. After a series of robustness and endogeneity tests, the conclusions of this paper still hold. Corporate green innovation induced by government environmental concerns is not the "leverage effect" superimposed on existing innovation activities but the "crowding‐out effect" of other technological innovation. The heterogeneity tests in this paper indicate that the impact is more pronounced for firms with poorer environmental conditions, stronger political connections, and richer executive backgrounds. [ABSTRACT FROM AUTHOR]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [ABSTRACT FROM AUTHOR]

Nudging the amelioration of enterprise environmental performance is the micro‐foundation for effectively enhancing resource utilization and realizing sustainable development. Based on the upper echelons theory, the characteristics and previous experiences of managers determine their strategic choices and then affect the behavior of enterprises at the whole social layer. Nevertheless, the literature concerning the effect of CEO green experience on enterprise environmental performance is void. To bridge this gap, this paper uses the data of China's A‐share listed enterprises from 2010 to 2021 to empirically investigate the direct impact, influence mechanisms, and heterogeneities of CEO green experience on enterprise environmental performance. The study finds that CEO green experience can significantly promote the improvement of enterprise environmental performance. Based on the main effect, to exploring potential mechanisms behind the conclusion, we figure that environmental ethics commitment, environmental protection investment, and green technology innovation are the core mechanism approaches during the process of CEO green experience affects environmental performance. Heterogeneity analysis of CEO characteristics demonstrates that the green experience of female CEOs, older CEOs, and high educated CEOs both have a pronounced effect on ameliorating enterprise environmental performance. Furthermore, the positive effect of CEO green experience on environmental performance is more prominent in non‐state‐owned enterprises, heavily polluting industries, and regions with high environmental protection pressure. Besides providing an effective supplement for the research on managers and environmental performance, this paper also generates micro‐empirical support for government departments to formulate and optimize environmental policies. [ABSTRACT FROM AUTHOR]

A PDE‐constrained model predictive control (MPC) algorithm for open‐channel systems based on the Saint‐Vevant(S‐V) equations is investigated in this paper. The S‐V equations, which precisely model the dynamics of open‐channel systems, are quasi‐linear hyperbolic partial differential equations (PDEs) without analytical solutions. Directly applying the S‐V equations to an MPC controller design becomes sophisticated. In this work, the calculus of variation is used to obtain the adjoint equations and the adjoint analysis method is utilized to deduce the gradients of the MPC optimization problem. Particularly, the physical constraints involving both the state and control variables are also considered. A gradient‐based optimization algorithm in combination with the numerical computation of Preissmann implicit scheme is proposed to solve the constrained MPC optimization problem. The control performances of the developed PDE‐constrained MPC algorithm with respect to the controlled water levels and gate openings are compared with those of the MPC controller designed for the linearized model. All the simulation tests are carried out on an aqueduct reach in Yehe Irrigation District in Hebei Province, China. The results show that the proposed PDE‐constrained MPC algorithm is a promising method in dealing with the constraints in terms of hyperbolic PDEs, control variables and state variables simultaneously. [ABSTRACT FROM AUTHOR]

This paper explores the impact of the political hierarchies of cities in China from different perspectives. First, we examine the economic disparities between prefectural cities and municipalities. Furthermore, this paper draws upon a quasi‐ experiment to analyze the impact of upgrading Chongqing to a municipality in 1997 using the synthetic control method. The city‐upgrading policy significantly increased Chongqing's gross domestic product (GDP) in the following 4 years. Finally, we find that the policy increased GDP in treated cities within 1200 km of Chongqing by about 10%–13% relative to the control cities. [ABSTRACT FROM AUTHOR]

This paper analyses the integration of the Sustainable Development Goals (SDGs) in the revisions of China's primary, secondary and high school curriculum standards. Employing a word frequency analysis of SDGs‐related keywords across ten disciplines, the study reveals an increasing trend in SDGs integration, particularly in Physical Education and Health, and Biology, but notes a lack of growth in the Arts discipline. Key findings include the widespread presence of SDG3 (Good Health and Well‐being) and SDG11 (Sustainable Cities and Communities), with a notable absence of SDG5 (Gender Equality) and SDG12 (Responsible Consumption and Production). This study suggests enhancing SDGs coverage in Arts education, broadening the scope to include underrepresented SDGs and promoting a balanced integration of all SDGs across disciplines. It emphasizes the critical role of Geography Education in addressing global challenges and advocates for interdisciplinary teaching methods. The study highlights the importance of regular assessment in curriculum standards to ensure effective SDGs integration, aiming to align China's educational framework with global developmental objectives for a sustainable future. Education emerges as a potent agent for promoting Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

The Shanghai Soft X-ray Free-Electron Laser (SXFEL) is the first X-ray freeelectron laser facility in China. The SASE beamline, which consists of a pinkbeam branch and a mono-beam branch, is one of the two beamlines in the Phase-I construction. The pink-beam branch opened for users in 2023 after successful first-round beamline commissioning. In this paper, the design of the beamline is presented and the performance of the pink-beam branch is reported. The measured energy-resolving power of the online spectrometer is over 6000 @ 400 eV. The focusing spot size of the pink beam is less than 3 μm in both the horizontal and vertical at the endstation. [ABSTRACT FROM AUTHOR]

To systematically assess the effect of negative pressure wound therapy (NPWT) on postoperative surgical wound infection, length of hospital stay and postoperative complications after spinal surgery. Relevant studies on the application of NPWT in spinal surgery were conducted via a computerised database search, including PubMed, EMBASE, Web of Science, MEDLINE, Cochrane Library, China National Knowledge Infrastructure (CNKI) and Wanfang, from inception to June 2023. The identified literature was rigorously screened and data extraction was performed by two investigators independently. The quality of the relevant studies was evaluated using the Newcastle–Ottawa scale (NOS). The effect size for count data was determined by the odds ratio (OR), while the impact size for measurement data was expressed as the standardised mean difference (SMD). The 95% confidence interval (CI) was calculated for each effect magnitude. Stata 17.0 software was used for the meta‐analysis. Ten papers, totalling 1448 patients, were finally included. This study demonstrated that NPWT led to a statistically significant reduction in the occurrence of postoperative surgical wound infections (OR: 0.377, 95% CI: 0.238–0.598, p < 0.001), fewer postoperative complications (OR: 0.526, 95% CI: 0.360–0.770, p = 0.001) and a shortened hospital stay (SMD: −0.678, 95%CI: −1.324 to −0.031, p = 0.040) after spinal surgery compared with the control group. When compared with other treatment approaches, NPWT also demonstrated a substantial reduction in surgical wound infections and postoperative complications, as well as a shorter duration of hospitalisation after spinal surgery. [ABSTRACT FROM AUTHOR]

Prospecting for and exploiting buried mineral deposits is currently challenging. Given their high precision and resolution, reflection seismic methods might be useful in such applications involving deep mineral deposits. However, there are few open seismic datasets available from mineral deposit exploration, especially in hardrock environments. The world‐class Caosiyao porphyry Molybdenum deposit (1.76 Mt) in the Jining area of Inner Mongolia, China, is largely covered by loess layers, which poses challenges to its exploration. Seismic reflection surveys were conducted to help delineate the deep granite porphyry intrusions and associated orebodies. This paper presents the raw seismic reflection dataset from three profiles on the Caosiyao deposit area, which can be used as a standard dataset for reflection seismic processing in shallow coverage and hardrock areas. Situated at the juxtaposition of the Khondalite Belt and the Trans‐North China Orogen in the northern North China Craton, the Jining region hosts considerable known porphyry Mo deposits. As such, this open dataset can assist in research on deep geological structures and hence increase prospecting efficiency in geologically similar areas. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) is an important tool in probabilistic seismic risk analysis (PSRA) due to its holistic nature and powerful probabilistic inference capabilities. However, while the information that can be stored by BN includes variable values, probability distributions, and relationships between variables, it does not involve event quantities. The results obtained from PSRA and probabilistic seismic hazard analysis (PSHA) are commonly frequency‐related (the number of occurrences per unit time), fundamentally describing event numbers. BN assumes a fixed event number relationship between nodes, but the aftershock counts vary for different mainshock magnitudes. This limitation further restricts its effectiveness in lifespan PSRA for structures. Therefore, this study aims to propose an enhanced BN (EBN) for modeling aftershock PSHA (APSHA) considering frequency information. This paper first introduces the modeling method using BN, including PSHA and a simplified model of APSHA using Båth's law. Next, an EBN that can effectively address frequency issues between the mainshock and aftershocks is presented for modeling APSHA considering the modified Omori's law, while the mathematical principles of both forward and backward inferences for the EBN are elaborated in detail. Then, the article carefully analyzes the utilization of the EBN for APSHA modeling under different scenarios where the information regarding the mainshock is known to varying extents, followed by the extension of the EBN in a time domain to obtain time‐dependent aftershock hazard. Finally, an example of APSHA in a specific location in China is illustrated. [ABSTRACT FROM AUTHOR]

As urban population increases, integrated underground–aboveground complexes are being constructed at growing paces in major cities. The seismic analysis of such complexes is crucial for the safety and functionality in the threat of potential earthquake disasters. However, fine‐grained numerical modeling and analysis of such large and complex structures are still inefficient due to the consideration of the soil–structure interaction (SSI). To address this challenge, an efficient approach for numerical modeling of large‐scale seismic SSI analysis is presented in this paper to overcome the limitations of existing finite element analysis (FEA) software. Moreover, a multi‐graphic processing unit (GPU) parallel explicit algorithm is implemented for the nonlinear dynamic SSI problems to further increase the computational efficiency. A large underground–aboveground complex project in China is used as an example to demonstrate the capability of the integrated method. The accuracy and reliability of the multi‐GPU parallel explicit finite element algorithm for SSI analysis (GFEA‐SSIA) are verified through a comparative analysis of the linear‐elastic and nonlinear dynamic response of the building calculated by GFEA‐SSIA and common FEA software. Finally, the structural response and structural damage of the underground–aboveground complex are analyzed under multidirectional seismic motions, and the damage distributions of the structures are provided. [ABSTRACT FROM AUTHOR]

Aim: To investigate the seizure course of PCDH19 clustering epilepsy (PCDH19‐CE) in a cohort of female children in China. Method: This ambidirectional cohort study examined 113 female patients with PCDH19‐CE through multicentre collaboration. Prognostic factors for seizure freedom were evaluated by multivariate Cox regression analysis. Results: The median seizure course period from seizure onset was 6 years 6 months. Of 113 patients, 78% and 56% experienced seizure freedom for at least 1 year and at least 2 years respectively. In patients younger than 5 years (n = 30), 5 to 10 years (n = 52), and older than 10 years (n = 31), 57%, 81%, and 94% experienced at least 1 year of seizure freedom, and 32%, 52%, and 84% experienced at least 2 years of seizure freedom, respectively. However, 58% (65 out of 113) relapsed at least once after more than 1 year of seizure freedom without trigger exposure (40%) or because of common triggers, including fever (43%) and antiseizure medication (ASM) reduction (29%). There was an 84% risk of seizure relapse after ASM reduction attempts. The likelihood of seizure freedom decreased with early age at seizure onset and developmental delay. Interpretation: Patients with PCDH19‐CE exhibit increasing seizure freedom with age, but there is a risk of relapse. ASM reduction in children younger than 10 years old requires caution. Patients with early seizure onset and developmental delay have a reduced chance of seizure freedom. What this paper adds: The seizure freedom rate in PCDH19 clustering epilepsy gradually increases with age.The disease course is characterized by relapsing–remitting seizures.Antiseizure medication reduction requires caution for patients younger than 10 years of age.Patients with early seizure onset and developmental delay are less likely to achieve seizure freedom. [ABSTRACT FROM AUTHOR]

Through using 17,995 firm‐year observations of all the Chinese A‐share listed firms from 2008 to 2016 as a sample, we document that firms with vertical interlock have a lower likelihood of committing corporate fraud. We further test the underlying mechanisms, and we find that the effect of vertical interlock on the occurrence of corporate fraud is more pronounced when firms operate in a region with poor legal protection; with the legal environment being enhanced in China, the effect of vertical interlock has become less significant; and the effect of vertical interlock is also more pronounced when firms exhibit poor information environment at the firm level. Our results in this paper imply that the role of large shareholders and legal protection are substitutes in an emerging economy, and even with rapid economic and legal development in China, large shareholders keep playing a positive role in controlling fraudulent behaviours in Chinese listed firms. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of Chinese firms' environmental, social, and governance (ESG) performance on their financial constraint and financing activities. We find a negative association between firms' ESG performance and their financial constraint driven by the Chinese government's commitment to tackling climate change. Compared with state‐owned enterprises (SOEs), non‐SOEs have alleviated their financial constraint through both equity and debt issuance, thanks to the stock price appreciation and green credit. High‐pollution firms benefit from both equity and debt issuance, while low‐pollution firms mainly finance through equity issuance. Our findings demonstrate the leading role of the Chinese government in its domestic capital markets. [ABSTRACT FROM AUTHOR]

This paper examines whether state ownership and political connections affect the relationship between customer concentration and cash holdings for Chinese listed manufacturing firms. We show that non‐state‐owned firms, but not state‐owned firms, hold more cash as customer concentration increases. In addition, political connections weaken the positive effect of customer concentration on non‐state‐owned firms' cash holdings. Our supplemental analyses further show that for non‐state‐owned firms with limited access to finance—for instance, firms with low analyst following, low institutional ownership, or low government subsidies—the effects of political connections on weakening the positive association between customer concentration and cash holdings are more pronounced. Additional robustness tests support our arguments. [ABSTRACT FROM AUTHOR]

More than 50 earthquakes occurred in the Luding region, including a 6.8‐magnitude earthquake on September 5, 2022, which induced huge amounts of geological disasters. As the main triggered factors for the geological disasters, the vegetation, precipitation and temperature show regular changes on the time axis. Thus, this paper based on the spatial–temporal evolution of the precipitation, temperature and normalized difference vegetation index (NDVI), focuses on the susceptibility of the Luding area using the weighted information content method (ICM) and analytic hierarchy process (AHP) for the post‐earthquake regional planning and reconstruction. In the Luding area, it was found that there were 18 debris flow gullies, and 470 geological disasters scattered in the debris flow gullies. The NDVI of the study area has been declining in recent years, with only about 12% of the NDVI increasing from 2015 to 2020. From 2011 to 2015, the annual cumulative precipitation has been dropping overall, while the July average temperature has increasing quadratic functions after decreasing trend. Furthermore, the studied area's geological hazards susceptibility was classified into five categories, with extremely low susceptibility accounting for 5.82%, low susceptibility accounting for 13.61%, moderate susceptibility accounting for 38.54%, high susceptibility accounting for 39.96% and extremely high susceptibility accounting for 2.08%. The geological disasters in moderate, high and extremely high susceptibility accounted for approximately 95.08%, and the covered area of geological disasters in moderate, high and extremely high susceptibility accounted for 88.06%. Through the receiver operating characteristic (ROC) curve of the model verification found the AUC = 0.743. The geological hazards susceptibility study results show that the approach is useful for the analysis and prevention of geological disasters. [ABSTRACT FROM AUTHOR]

The Dunhuang Basin, situated in western China along the Altyn Tagh Fault (ATF) zone, intersects the Tethys and Paleo‐Asian Ocean tectonic domains. Influenced by both the ATF system and the far‐field effects of the Qiangtang‐Lhasa‐Eurasia collision during the Mesozoic, the mechanism of the Jurassic sedimentary migration of the basin in response to tectonic movements is unclear so far. The paper uses a comprehensive approach, including field geological surveys, lithologic and lithofacies discern, stratigraphic relationships analysis and 2D seismic profile interpretation, to examine the distribution of Jurassic residual strata in the basin. The comprehensive results of our study suggest that the Dunhuang Basin exists as an isolated block with unique tectonic and sedimentary evolution characteristics. In the Early Jurassic, the Dunhuang Basin underwent initial rifting, leading to the formation of small segmented intermontane sags. This phase was marked by the coarse particle sedimentary system of alluvial fans and braided rivers, represented the near source rapid deposition in the initial formation period of the basin. Stratigraphic distribution was primarily influenced by pre‐Jurassic basement topography and was not significantly constrained by faulting during this period. The formation of these isolated discontinuous small intermontane sags indicates the segmented activities of the ATF in the Early Jurassic period. In the Middle Jurassic period, influenced by the ATF dextral strike‐slip faulting, sedimentation extended eastward and the depocenter migrated clockwise compared with the distribution of the Middle Jurassic strata within the Dunhuang Basin. This period witnessed the development of coal measure strata at the basin's margins and lacustrine fine‐grained clastic deposition in the centre. The segmented fracture of the ATF gradually initiated a unified dextral strike‐slip tectonic movement. In the Late Jurassic period, sedimentary strata were locally present in the Wanyao Sag but absent in other sags. The depocenter migrated counterclockwise compared with the distribution of Middle Jurassic strata within the Dunhuang Basin, due to regional uplift accompanied by the ATF sinistral strike‐slip faulting caused by the collision between the Lhasa Block and the Eurasia Plate. The depocenter migration of the Dunhuang Basin constrained within the ATF system from the Early to Middle and Late Jurassic can be attributed to the transition of the ATF strike‐slip faulting in context of the stress relaxation and compression between the collision of the Qiangtang and Lhasa blocks to the Eurasia Plate, respectively. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality will be important policies to constrain China's economic and social development in the future. Based on the theory of computable general equilibrium, CGE model of carbon emission was constructed in this paper to simulate the influence of different scenarios of carbon emission reduction on economic and social development. The major contribution is to subdivide the energy sector according to primary energy and secondary energy, and construct the macro SAM table and micro SAM table of 21 energy sectors in 2017 to quantitatively simulate the impact of carbon emission reduction policies on China's economy and society, and put forward some policy suggestions based on this. [ABSTRACT FROM AUTHOR]

This paper studies the impact of collectivization on patricide in China during the Cultural Revolution. From 1955 to 1957, nearly 96 per cent of farmers were organized into communes. Consequently, fathers lost control over family wealth. We propose that this shift decreased fathers' bargaining power over their adult sons, which might increase family conflicts. On the basis of a novel dataset, we find that the speed of collectivization significantly increased patricide, and the result is robust by employing ruggedness to instrumenting for the speed of collectivization. Our study extends the literature on intra‐household bargaining from couples to intergenerational relationships. [ABSTRACT FROM AUTHOR]

In recent years, as China's economy has grown at a rapid pace and social welfare has increased dramatically, environmental issues have become increasingly prominent. Whether companies undertaking digital transformation will improve the environmental performance has drawn the focus of scholars. For this reason, this paper uses the microfirm data of China's manufacturing industry from 2011 to 2020 to explore the relationship between corporate digital transformation (CDT), market competition, and environmental performance, as well as its external mechanism. The study has found that in the analysis of environmental performance measured by firms' pollution emissions, the improvement of the level of CDT shows a nonlinear trajectory in its impact on environmental performance. With a highly centralized market environment, the impact of CDT on environmental performance is further diminished. By analyzing the variability of the degree of market competition, the CDT can promote the environmental performance by enhancing market competition (reducing market concentration). In addition, from the perspective of source reduction, the CDT also advances the environmental performance. The research conclusions contribute to the study of environmental performance, which can guide the transformation from end‐of‐pipe pollutant emissions to source reduction to promote the sustainable development of enterprises. [ABSTRACT FROM AUTHOR]

Based on social capital theory, this paper aims to investigate the impact of member enterprises of alien merchant chambers on enterprise innovation, within A‐share listed companies on the Shanghai and Shenzhen Stock Exchanges of China from 2010 to 2020. Additionally, the study seeks to explore the role played by social capital resulting from participation in these alien merchant chambers. Furthermore, we delve into the moderating effects of the number of alien merchant chambers and the degree of marketization in the enterprise's location. Moreover, we analyze the influence of heterogeneity in firm ownership and firm type. Our findings reveal that member enterprises of alien merchant chambers can increase their social capital and consequently promoting innovation; both the quantity of alien merchant chambers and the level of marketization in an enterprise's location positively influence this enhancement. Furthermore, the impact of state‐owned enterprises and non‐high‐tech enterprises on enterprise innovation, following their engagement with alien merchant chambers, surpasses that of non‐state‐owned enterprises and high‐tech enterprises. This research bridges the gap in understanding the corporate governance implications of alien merchant chambers, provides empirical evidence concerning Chinese alien merchant chambers, and addresses inquiries regarding the influence of merchant chambers' social capital. Additionally, it proposes a novel approach for listed companies to overcome resource constraints and to collaboratively pursue development, especially in light of the challenges posed by the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Using data from the 2015 China Household Financial Survey (CHFS) this paper examines the effect of culture on the gender gap in financial literacy. We exploit geographical differences in culture in China, comparing outcomes between rural and urban areas and between areas in the east and west (Shanghai and Chongqing). Using the Blinder‐Oaxaca decomposition we show that, nationally, the gender gap in financial literacy is entirely the product of differences in the way men and women acquire financial literacy. It is a result consistent with cultural effects. When considering just women in Shanghai and Chongqing we observe a raw financial literacy differential of 13% (favoring Shanghai). This gap is also the product of differences in the way financial literacy is acquired. It provides additional evidence as to the importance of culture when it comes to understanding financial literacy. [ABSTRACT FROM AUTHOR]

The high incidence of adolescent depression has become the focus of social and academic attention. Exercise is an important method to improve adolescent depression, but its intervention effect is still controversial. This study first compares and analyzes the relevant studies at home and abroad and finds that exercise prescription in adolescent depression intervention is not accurate enough. A meta‐analysis was conducted to develop a precise exercise intervention strategy for adolescent depression. Firstly, this thesis identified how to optimize five elements (exercise intensity, exercise frequency, exercise time, exercise cycle, and exercise type) of exercise prescription to improve depression in adolescents. This is the problem. Furthermore, the concept of "precision exercise" was proposed, and a precision exercise intervention strategy (moderate‐intensity aerobic exercise for 8–10 weeks, 3 times/week, 45–50 min/time) was constructed to improve adolescent depression. This paper also presents research that strengthens the cross‐sectional research and empirical research on adolescent depression and establishes a precision exercise prescription database for adolescent depression in China. In conclusion, this study not only puts forward the concept of "precision exercise" but also constructs a precision exercise intervention strategy for adolescent depression, which has important theoretical and practical significance for improving the high incidence of adolescent depression. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of institutional investor horizons on investment efficiency in China. By analysing 26,831 firm‐year observations from Chinese listed companies, we find that long‐term (short‐term) institutional ownership is negatively (positively) associated with the level of inefficient investments. A battery of robustness tests indicates causality. Further analyses reveal that the rise in environmental uncertainty and information asymmetry are transmission channels for increased inefficient investments. Moreover, we find that the single controlling shareholder and state owner can curb the management myopia induced by short‐term institutional investors while they do not impede the monitoring effort of long‐term institutional investors. [ABSTRACT FROM AUTHOR]

Using medical insurance (MI) to shift employees' health risks outside is an important risk management tool for modern firms. Existing studies usually treat firms' contributions to employees' MI only as a labor cost. However, contributing to MI also has indirect benefits, such as improved labor productivity and R&D innovation, which consequently increase corporate value. This paper studies the impact of firms' MI contributions for employees on corporate value, using social insurance collection system reform in China as a natural experiment. Results show that, first, the reform increases firms' contributions to employees' MI funds. Second, the increase has a positive impact on firms' market‐to‐book ratio. These effects can be explained by enhanced labor productivity, firm efficiency, and innovation. Heterogeneity analysis suggests that the effects are more pronounced for firms in high R&D industries, areas with high pollution, or areas with better medical and labor supplies. [ABSTRACT FROM AUTHOR]

Environmental sustainability has gained significant importance for organizations, highlighting the crucial role of business leaders in addressing environmental degradation. However, the connection between chief executive officer (CEO) characteristics and environmental sustainability remains understudied in academic literature. Understanding this relationship is crucial, considering the significance of environmental sustainability in organizational strategies. This paper presents the first systematic literature review (SLR) on this topic, aiming to identify gaps and opportunities for future research. The SLR analysed 139 studies on the CEO–environmental sustainability relationship. The findings indicate a recent increase in research activity, particularly peaking in 2022 and focusing on China and the United States. Most studies employed upper echelons theory and examined the influence of CEO demographic characteristics on environmental performance. However, less attention has been given to CEO psychological traits, such as personality and ethical leadership, and environmental disclosure. Furthermore, qualitative aspects of environmental disclosure, including reporting tone, readability and specificity, have received less attention. The findings offer valuable insights for academics, practitioners and policymakers. Recommendations for future research include exploring the impact of CEO psychological traits and environmental disclosure on a firm's environmental sustainability. Additionally, studying cross‐country and cross‐industry differences in this relationship is encouraged. This study makes a significant contribution to the field by shedding light on the crucial and relevant topic of environmental sustainability and its association with CEO characteristics, providing valuable insights to guide future research and inform decision‐makers. [ABSTRACT FROM AUTHOR]

Continuous improvement of technological innovation ability, adjustment of the development strategy, and enhancement of operational performance are of great theoretical and practical significance for logistics enterprises. This paper aims to analyze and evaluate the innovation efficiency of the logistics industry. The study utilizes the static three‐stage DEA model and the dynamic Malmquist index model, considering a total of 12 indicators related to innovation input, output, and environmental variables. A dataset of 2940 entries from 49 listed logistics enterprises from 2017 to 2021 was calculated. The analysis provides insights into the innovation efficiency of logistics enterprises from a static perspective and the innovation total factor productivity from a dynamic perspective and decomposition terms. Based on the analysis of environmental variables by the SFA model, it was found that DEA inefficiency is the combined result of environmental factors and management inefficiency. Environmental variables have both positive and negative effects on innovation. The improvement of the economic development level will lead to excess R&D investment. Increased government simple fund subsidies are not conducive to the efficient allocation of innovation resources within enterprises. The expansion of enterprise scale will increase R&D personnel and investment in fixed assets. A thriving technology market can encourage enterprises to improve their own conversion rate of scientific and technological output and give full play to their innovation ability. The dynamic Malmquist model analysis reveals a recution in the overall innovation efficiency of listed logistics enterprises over 5 years. The changes in total factor productivity and technological progress efficiency of all listed logistics enterprises are synchronized, with most enterprises exhibiting higher technological progress efficiency compared to comprehensive technical efficiency. The total factor productivity of logistics enterprise innovation is mainly affected by comprehensive technical efficiency. [ABSTRACT FROM AUTHOR]

The economic and financial risks of global warming have become a hot issue of academic interest. This article analyzes global warming, financial risk, and the economic statistics of 30 provinces in China from 2010 to 2020 as a sample to assess the influence of the severity of global warming on financial risk. Global warming is found to affect financial risk and to have a substantial favorable influence on the amount of financial risk. Global warming also has significant effects on households, enterprises, financial institutions, and governments. There is heterogeneity in the effects of climate variation on financial risk. Global warming will cause a greater accumulation of financial risk in economically developed regions. Additionally, it will lead to financial risks by causing a decline in the incomes of residents, a decline in the profits of enterprises, growth in the number of loans that financial institutions consider to be nonperforming, and a widening of the gap between government revenue and expenditures. All of these effects will have an adverse domino effect on the economy. To reduce the financial risks caused by global warming, countermeasures are being offered to ensure social cohesion, conduct risk stress testing, create risk mitigation instruments, and encourage environmentally responsible development. This paper discusses policies and provides policymakers with recommendations on how to cope with the systematic financial risks induced by climate variation. [ABSTRACT FROM AUTHOR]

This paper examines the impacts of a government‐led e‐commerce expansion project on household income. We find that the project has led to increases in rural household income. The positive impacts are more prominent for lower‐income rural households. The income‐increasing effects are driven by the reduction in information search costs and transportation costs. The provision of complementary interventions for e‐commerce, such as promoting the internet‐related skills of farmers and the expansion of formal financial credit support is also important for the program's success. The results provide policy implications for the design and implementation of government‐led e‐commerce policies in developing economies. [ABSTRACT FROM AUTHOR]