Financial holding companies(FHCs)in China leverage equity control to enhance oper-ational efficiency and synergies,yet excessive equity concentration often undermines these benefits.This study investigates the impact of equity structure-specifically concentration and balance-on the performance of 17 A-share listed Chinese FHCs from 2010 to 2022,using data from the CSMAR database.Empirical results reveal an inverted U-shaped relationship between equity concentration and performance,with moderate concentration optimizing decision-making efficiency,while excessive levels risk power abuse.Equity balance,however,negatively affects performance by fostering power struggles and delaying decisions.These findings underscore the need for a balanced equity structure in Chinese FHCs.Policy recommendations include listing parent companies to diversify equity,keeping subsidiaries unlisted with concentrated ownership for synergy,strengthening regulation,and encourag-ing small shareholder participation to enhance governance and stability.

The job-housing balance (JHB) significantly influences residents’ well-being and the high-quality development of cities. However, existing studies exhibit limitations in accurately measuring JHB, categorizing cities based on JHB patterns, and identifying its influencing factors. Leveraging large-scale datasets, this study offers a more comprehensive analysis of JHB across Chinese cities. Our work makes contributions in three key aspects: (1) We developed a set of bilateral job-housing balance indicators grounded in the “happy commuting” concept, capturing the balance of inflows and outflows at the sub-district level. (2) We conducted a classification of cities based on these indicators, providing a nuanced understanding of urban spatial structures. (3) We employed data analytics to examine the factors affecting the indicators by city type. The results demonstrate that cities with different JHB characteristics are influenced by distinct factors. These findings underscore the necessity of tailored policy recommendations to promote sustainable and high-quality urban development across various city types.

The study uses multiplex network analysis and a panel data model, based on over 100 million multi-sources data from 286 Chinese cities, to explore the impact of multiple factor flows on economic output in the digital age. New measures were developed to measure cities' multi-connectivity, indicative of their ability to connect and enable factor flows. Empirical analysis was subsequently conducted to investigate the impact of multi-connectivity on economic output. The results unveil positive effects of multi-connectivity on economic output, offering a novel perspective on promoting economic output through multiple factor flows.

This paper explores an emerging business model for Waste Recycling Platforms that collect users’ recycling demands online and provide door-to-door recycling services offline. Unlike existing literature, which typically assumes constant user demand, we account for demand uncertainty and develop a location optimization model using two-stage stochastic programming. By analyzing the relationship between random demand and recycling prices, we extend this model to a joint location-pricing model. We also design a Benders Decomposition-based algorithm to solve the model using actual operational data from a Waste Recycling Platform in Beijing. Our findings reveal that incorporating both location and pricing decisions into the model makes it more realistic and capable of yielding better optimal profits. Additionally, parameter analysis suggests that the platform could benefit from moderately relaxing the confidence level of its random constraints and focusing more on controlling operating costs.

This study investigates the evaluation of housing policies for migrants in China, focusing on the interplay between rental and purchase decisions under the rent-and-purchase policy (RPP) framework. Employing a system dynamics model, we simulate migrant housing choices from 2001 to 2023 and forecast market trends from 2024 to 2030. The results indicate that RPPs significantly improve housing quality and reduce costs for migrants by mitigating institutional disparities and market distortions. Scenario analyses demonstrate that a coordinated approach combining supply-side interventions (e.g., affordable housing expansion) with rights-based policies (e.g., equalizing renter and buyer rights) effectively balances affordability and demand stability. The findings emphasize the critical role of addressing rights inequalities and advocate for a holistic policy framework to tackle migrant housing challenges, offering actionable insights for policymakers in system science and urban planning.

This study aims to investigate the role of fintech in stimulating innovation and promoting regional economic growth. A Multi-Sector Endogenous Growth Model (MEGM) is constructed by incorporating fintech while internalizing technological progress and human capital accumulation within the framework of creative destruction. Based on this model, we theoretically analyze how fintech influences innovation and regional economic growth, followed by empirical testing using panel data from 246 prefecture-level cities in China during the period from 2012 to 2020. Furthermore, we contextualize these findings within China's fintech development landscape, trends in technological innovation, and patterns of regional economic growth. The results demonstrate that fintech exerts a significant incentive effect on regional innovation and effectively promotes economic growth. Heterogeneity analysis based on cities' endowments reveals that the impact of fintech is more pronounced in financially developed cities, those with high digital endowments, as well as in first- and second-tier cities. The analyses presented herein elucidate how fintech facilitates technology-driven regional economic growth in China, providing valuable insights for fostering innovation-driven development through fintech.

Based on the cross-generational data of the China Family Panel Studies (CFPS) in 2010, 2014, 2018, this paper empirically explores the intergenerational transmission of household housing preference and its underlying mechanism from a perspective of intergenerational transmission. It finds that: (1) There is a distinct intergenerational transmission of housing preference. (2) For offspring under the age of 45, or those who are female, the intergenerational transmission of housing preferences from parents to their children is more pronounced. (3) Fertility intentions exhibit a resource dilution effect on the intergenerational transmission of housing preference. (4) The stronger the belief within a family that a child’s future success depends on the family’s economic status, the more likely it is to negatively influence the child's housing preferences. (5) Parents not only affect their children's consumption preferences regarding housing but also shape their attitudes towards borrowing for home purchases. This paper offers new insights into understanding household housing preference, and provides a foundation for government initiatives aimed at guiding households in rational asset allocation through social mechanisms.

This paper studies the regional differences, dynamic evolution and influencing factors of regional carbon emission intensity（CEI） in 262 cities and 5 regional urban agglomerations（UAs） in China. The Dagum Gini coefficient is used to analyze the intra-regional and inter-regional differences in carbon emissions, and the temporal evolution of the absolute differences of CEI among regions is analyzed by means of kernel density estimation（KDE）. The paper provides an in-depth study on the spatial difference and temporal evolution of CEI in Chinese cities and major strategic regions.Through Moran index and LISA’s test, the spatial correlation of carbon emission in prefecture-level cities is tested, and its spatial agglomeration characteristics are described. It is found that China’s CEI is decreasing year by year, presenting a spatial pattern of “low in the south but high in the north”. Based on the calculation of carbon emission intensity at the urban level, this paper conducts LDMI factor decomposition research on carbon emission intensity at the national and key regions, and analyzes the impact of the impact factors on carbon emission intensity. The research results provide a path for China’s green development at the city level and urban agglomeration level, and a theoretical support for different regions and cities to introduce emission and carbon reduction policies.

In today's rapidly advancing technological landscape,science communication is closely intertwined with technological innovation,serving as a crucial element in influencing a country's technological competitiveness and competing for the global scientific and technological discourse.This study delves into the historical context of science communication and the theoretical research and paradigm shifts surrounding it.It explores the symbiotic relationship between science communication and technological innovation,uncovering the mechanisms of their interactions.On this basis,the study examines exemplary practices in promoting science communication in major developed countries.In light of China's current state of science communication,tentative strategies are proposed to enhance the nation's endeavors in the realm of science communication.

Notable Regional heterogeneity is commonly observed in housing markets. This study examines how multiple factor flows affect urban house prices and the resulting premium. It utilizes over 100 million pieces of Chinese intercity factor flow data to construct a multi-layer network and introduces a new metric called multiple connectivity quality to measure a city's comprehensive factor connection capability. Empirical analysis reveals that cities with higher multiple connectivity quality tend to exhibit higher price premiums, making them more resilient to market fluctuations. This study offers fresh insight into regional heterogeneity within housing markets and holds practical significance for stabilizing these markets.

Starting with the housing markets of large and medium-sized cities in China, this paper constructs housing market networks with the data of both the price index and the trading volume, and finds the overall decreasing trend of the connectivity of the networks. The analysis of the network characteristics reflected by the indices established in this paper reveals the extreme high level of global network indicators in the first quarter of 2015 and the first quarter of 2020, demonstrating the phenomenon of “bad news traveling fast”, which can be explained from the perspective of information dissemination and herding behavior. Further empirical analysis shows the negative impact on housing market connectivity from both macroeconomic conditions and the frequency of stock market transactions. Based on the findings, this paper makes policy recommendations for preventing systemic risk in the housing market from the perspective of information dissemination and macroeconomics.

Differences in progress across sustainable development goals (SDGs) are widespread globally; meanwhile, the rising call for prioritizing specific SDGs may exacerbate such gaps. Nevertheless, how these progress differences would influence global sustainable development has been long neglected. Here, we present the first quantitative assessment of SDGs’ progress differences globally by adopting the SDGs progress evenness index. Our results highlight that the uneven progress across SDGs has been a hindrance to sustainable development because (1) it is strongly associated with many public health risks (e.g., air pollution), social inequalities (e.g., gender inequality, modern slavery, wealth gap), and a reduction in life expectancy; (2) it is also associated with deforestation and habitat loss in terrestrial and marine ecosystems, increasing the challenges related to biodiversity conservation; (3) most countries with low average SDGs performance show lower progress evenness, which further hinders their fulfillment of SDGs; and (4) many countries with high average SDGs performance also showcase stagnation or even retrogression in progress evenness, which is partly ascribed to the antagonism between climate actions and other goals. These findings highlight that while setting SDGs priorities may be more realistic under the constraints of multiple global stressors, caution must be exercised to avoid new problems from intensifying uneven progress across goals. Moreover, our study reveals that the urgent needs regarding SDGs of different regions seem complementary, emphasizing that regional collaborations (e.g., demand-oriented carbon trading between SDGs poorly performed and well-performed countries) may promote sustainable development achievements at the global scale.

As common pursuits of human society, subjective well-being (SWB) strongly depends on economic factors, and the 17 Sustainable Development Goals (SDGs) emphasize promoting equilibrium between socio-economic development and environmental conservation. Therefore, trade-offs between narrowing existing progress differences across SDGs and improving SWB might exist, which could interfere with global policy-making for human development but remain unexplored. Here we investigate the changing effects of achieving balance across SDGs and other factors on SWB along the global sustainable development gradient. Results show that achieving balance across goals, rather than their average performance or per capita gross domestic product, is the primary factor supporting well-being in countries with poorly progressed SDGs. However, SWB in countries approaching fulfillment of sustainable development depends more on wealth rather than on achieving balance across SDGs. Given the trade-offs between economic development and poorly achieved goals (for example, SDG 13, Climate Action) in these countries, the strong dependence of well-being on wealth might impede the holistic achievement of the 17 goals. Overall, our study uncovers an essential but long-neglected subjective control factor in the global road map towards SDGs. While the economic effects of progress towards the Sustainable Development Goals (SDGs) have been studied extensively, this study examines how efforts to achieve balance across the SDGs affect subjective well-being within and across countries.

Digital platform is the most important form of organization in the digital era.How to clarify the boundary between platform autonomy and government regulation so as to exert the order maintenance function of platforms effectively is the key issue in the region of the digital economy governance.This study firstly introduces the basic model of platform autonomy and the regulatory challenges it faces,basing on the background of the emergence of digital platform autonomy.Secondly,through a comparative analysis of the regulatory theories and legal policies of the digital platform autonomy in the European Union and the United States,this study summarizes the advantages and disadvantages of different models.Finally,aiming at the fuzzy boundary of digital platform autonomy in China,as well as the dilemma between over-tolerance and over-regulation in government regulation,on the basis of the reconstruction of the governance boundary,this study puts forward policy recommendations such as changing extensive rigid supervision to prudent and flexible regulation,changing mandatory supervision to cooperative regulation,changing after supervision to whole-process regulation;at the same time,the study proposes to strengthen the compliance capacity building of digital platforms,in order to promote the standardized and healthy sustainable development of China's platform economy.

The gated structure of the long short-term memory (LSTM) alleviates the defects of gradient disappearance and explosion in the recurrent neural network (RNN). It has received widespread attention in sequence learning such as text analysis. Although LSTM has good performance in handling remote dependencies, information loss often occurs in long-distance transmission. We propose a new model called ELSTM based on the computational complexity and gradient dispersion in the traditional LSTM model. This model simplifies the input gate of LSTM, reduces some time complexity by reducing some components, and improves the output gate. By introducing the exponential linear unit activation layer, the problem of gradient dispersion is alleviated. Comparing the new model with multiple existing models, when predicting language sequences, the time used by the model has been greatly reduced, and the language confusion has been reduced, showing good performance.

The rapid growth of the commercial satellite industry is hindered by its vulnerability to launch failures, demanding the adoption of effective risk mitigation strategies. This research investigates such strategies, including the adoption of launch insurance, government subsidies, and blockchain technology integration within satellite launch supply chains. Utilizing Stackelberg games, we model scenarios with launch insurance (Model I), insurance plus government subsidies (Model IG), blockchain-embedded insurance (Model B), and blockchain-embedded insurance with government subsidies (Model BG), to investigate optimal launch and retail pricing strategies and to enhance launch success probabilities. Our findings demonstrate that government-subsidized launch insurance can create a win-win scenario, while the incorporation of blockchain technology fosters an all-win situation, benefiting all stakeholders, including consumers. Notably, the study reveals a synergistic relationship between government subsidies and blockchain technology, significantly enhancing supply chain efficiency and leading to positive spillover effects on profits and social welfare. This research contributes significantly to the understanding of managerial implications of these strategies within the commercial space launch market.

新时代对创新创业人才在品德素养、知识结构、专业能力等方面提出了新的更高要求.在此背景下,世界范围内创新创业人才培养实践呈现出多学科交叉会聚、培养过程贯通融合、教科产多主体开放协同、数字化转型赋能等新走向.依托中国科学院创新创业优势办学,中国科学院大学深入研究高素质创新创业人才尤其是科技领军人才的培养规律,从培养模式和组织模式创新、课程教学体系改革、完善"大思政"工作体系等方面,积极探索科教融合育人的新模式、新路径,以及为支撑育人目标而强化师资队伍建设的思路举措.

In this paper, we propose an extension to the barrier model, i.e., the Multi-Barriers Model, which could characterize an area of interest with different types of obstacles. In the proposed model, the area of interest is divided into two or more areas, which include a general area of interest with sampling points and the rest of the area with different types of obstacles. Firstly, the correlation between the points in space is characterized by the obstruction degree of the obstacle. Secondly, multiple Gaussian random fields are constructed. Then, continuous Gaussian fields are expressed by using stochastic partial differential equations (SPDEs). Finally, the integrated nested Laplace approximation (INLA) method is employed to calculate the posterior mean of parameters and the posterior parameters to establish a spatial regression model. In this paper, the Multi-Barriers Model is also verified by using the geostatistical model and log-Gaussian Cox model. Furthermore, the stationary Gaussian model, the barrier model and the Multi-Barriers Model are investigated in the geostatistical data, respectively. Real data sets of burglaries in a certain area are used to compare the performance of the stationary Gaussian model, barrier model and Multi-Barriers Model. The comparison results suggest that the three models achieve similar performance in the posterior mean and posterior distribution of the parameters, as well as the deviance information criteria (DIC) value. However, the Multi-Barriers Model can better interpret the spatial model established based on the spatial data of the research areas with multiple types of obstacles, and it is closer to reality.

With the rise of satellite-as-a-service subscriptions as technology improves, our paper examines how satellite operators (SOs) can choose between traditional channels for selling satellites, satellite-as-a-service (SataaS), and dual-channel strategies. In the space supply chain, commercial satellites display several distinct characteristics that differentiate them from other physical commodities in traditional supply chains, most notably their exposure to significant launch risks. In addition, SataaS suffers from the risk of secure data transmission. However, the emergence of novel technologies such as blockchain technology (BCT) could help to mitigate such risks. Therefore, our study analyzes the optimal strategy among the sell, SataaS, and dual-channel approaches for SOs by considering the above distinctive characteristics in the case without blockchain and the case with blockchain. Our paper reveals the existence of a dual-channel strategy that generates the highest profit for the SO and yields the highest consumer utility when launch success is high and when SataaS fixed costs are low. When the launch success rate is very high, the dual-channel strategy transforms into a pure sell strategy. After the adoption of BCT, when the cost of BCT is low, the SO’s profit is enhanced in all three sales channels compared to the case without blockchain. Notably, in SataaS as well as in the dual-channel approach, the adoption of BCT always improves consumer utility compared to the case without blockchain. However, in the sell channel, the consumer benefits depend on the relatively low cost of blockchain customization.