Digital infrastructure (DI) has become a critical driver of economic growth, social inclusion, and coordinated regional development. Using panel data for Chinese prefecture-level cities, this study exploits the "Broadband China" pilot policy as a quasi-natural experiment and employs a staggered difference-in-differences design to identify the effect of DI on common prosperity (CP). The results show that digital infrastructure significantly improves the level of CP, and this finding remains robust to a series of tests, including parallel-trend and placebo tests. Mechanism analysis indicates that DI promotes CP by advancing digital economy level and digital inclusive finance. In addition, human capital level positively moderates the effect of DI on CP. Heterogeneity analysis further shows that the positive effect of DI is stronger in southern cities and inland cities. This study provides empirical evidence on how DI contributes to CP.

As sustainability challenges, such as environmental pollution, become increasingly severe, businesses must balance stakeholder interests while achieving economic objectives. Applying stakeholder theory, this study finds that active participation in corporate social responsibility (CSR) significantly improves supply chain efficiency (SCE), with an indirect effect through mechanisms such as green technology innovation and information sharing. Heterogeneity experiments reveal that CSR has a greater effect on SCE in nonstate-owned businesses, companies with highly competitive positions, strong media attention, and regions with lower market segmentation. These findings offer practical insights for businesses aiming to integrate CSR into supply chain strategies, providing a new perspective on the economic effects of CSR under varying market and competitive conditions.

Mix sparse structure is inherited in a wide class of practical applications, namely, the sparse structure appears as the inter-group and intra-group manners simultaneously. In this paper, we propose an iterative mix thresholding algorithm with continuation technique (IMTC) to solve the ℓ _0 regularized mix sparse optimization. The significant advantage of the IMTC is that it has a closed-form expression and low storage requirement, and it is able to promote the mix sparse structure of the solution. We prove the convergence property and the linear convergence rate of the ITMC to a local minimum; moreover, we show that the ITMC approaches an approximate true mix sparse solution within a tolerance relevant to the noise level under an assumption of restricted isometry property. We also apply the mix sparse optimization to model the differential optical absorption spectroscopy analysis with the wavelength misalignment, and numerical results indicate that the IMTC can exactly and quantitatively predict the existing materials and the factual wavelength misalignment simultaneously within 0.1 s, which meets the demand of improvement of the automatic analysis software.

This study employs text data analysis of annual government work reports from various provinces in China for the years 2015–2020. It constructs an index measuring the extent of government attention allocated to digital economic development (DED) at the government level. Simultaneously, it establishes a DED index system to measure the level of DED. The aim is to examine the impact of government attention on the level of DED. The research findings demonstrate that government attention significantly promotes the development of the DED. For every one-unit increase in government attention to the DED, the level of DED increases by 0.043 points. This result remains robust even after conducting sensitivity tests using instrumental variable methods and variable substitution methods. In further investigation, a threshold effect model is applied, revealing a marginal increasing effect of government attention on the level of DED. As the level of DED rises, the marginal contribution of government attention increases from 0.048 to 0.112. Using a spatial durbin model, it is found that government attention exhibits a positive spatial spillover effect on the level of DED.

Severe global climate change has resulted in the focus of social attention shifting to the manufacturing industry's low-carbon transformation. Digital intelligent transformation (DIT) in enterprises is identified as a crucial driver in mitigating carbon emissions. An estimation of DIT's impact on manufacturing industries' carbon emission intensity (CEI) and its underlying mechanisms was conducted using data from Chinese A-share listed companies. Research findings indicate that DIT significantly reduces corporate CEI. Robustness tests, such as the instrumental variable method and variable substitution method, confirm this conclusion. By enhancing labor productivity and accelerating capital renewal, DIT indirectly lowers CEI. Furthermore, non-state-owned enterprises with superior market competitiveness have been observed to be markedly adept at harnessing DIT for CEI. The heterogeneity test found that DIT has a considerably significant effect on reducing CEI in enterprises that are not low-carbon pilots, non-broadband pilots, smart pilots, non-provincial capitals, and eastern cities. This study provides new evidence supporting the promotion of enterprise DIT for achieving green development, offering insights for corporate policy making.

Using the data of the 2018 China Family Panel Studies (CFPS), this paper empirically tests the impact of the "Big Five" personality characteristics on household charitable donation behavior. The benchmark regression results show that after controlling the individual characteristics and family characteristics of the household heads, the conscientiousness and openness of the household heads have a significant positive impact on the social donation behavior of the family. On this basis, this paper takes the openness personality as an example, selects the identification strategy of processing effect, and tests the robustness of the causal effect of personality on household donation behavior. The openness personality has a significant positive impact on household external donation behavior. In the further study, it is found that with the improvement of the level of household charitable donation, the positive effect of the household head 's openness personality on household charitable donation behavior is gradually weakening; The influence of openness personality on household charitable donation has the nonlinear characteristics of "marginal effect" increasing and obvious life cycle characteristics.

The influence mechanism of digital economy on collaborative innovation still needs further exploration in terms of theoretical and empirical evidence. Based on the panel data of 30 provincial-level administrative regions in China from 2013-2019, this paper uses the entropy value method to measure the digital economy index and the level of collaborative innovation. Then we conduct a more in-depth discussion on the temporal and spatial evolution characteristics and influence relationship of the digital economy and collaborative innovation from the perspective of entrepreneurship. The results show that, firstly, the digital economy has a significant positive impact on the level of collaborative innovation, and entrepreneurship, specifically entrepreneurial entrepreneurship and entrepreneurial innovation, plays a partly mediating role in this process. Secondly, by using the Spatial Durbin model, we found that the digital economy has a significant siphon effect on the level of collaborative innovation in neighboring provinces. Finally, there is a significant double threshold effect of entrepreneurship on the impact of digital economy on collaborative innovation, and the positive impact of digital economy is characterized by a non-linear incremental marginal effect. The findings of this paper provide a new way of thinking to reveal the inner mechanism of the digital economy's influence on collaborative innovation.

Forecasting accurate traffic conditions is essential to regional traffic management. Since congestions are usually caused by regular activities, capturing speed-cycle patterns for congestions is useful for short-term traffic forecasting. In this study, we propose a novel approach, namely Neighbor Subset Deep Neutral Network (NSDNN), to forecast spatio-temporal data; the approach conjoins Deep Neutral Network (DNN) and the subset selection method, in order to extract useful inputs from nearby roads. Appropriate input subsets can be selected for DNN training via congestion cycle patterns, in order to reduce input data dimensions and to avoid artificial high correlations from free-flow traffic in off-peak hours. Furthermore, speed data with time lag is also embedded into the DNN model to generate the multi-timestep forecast model. Experimental results demonstrate that the proposed NSDNN achieves higher accuracy, compared to other conventional methods including the Autoregressive Integrated Moving Average (ARIMA), the correlation method and the k-Nearest Neighbor (kNN). In addition, NSDNN is also comparable to the Long Short Term Memory (LSTM) Neural Network, namely NSLSTM, when the same selected input subset is used. The forecasting system can be used by logistic companies to produce better route planning and fleet management in assigning vehicles.

This study examines the impact of green finance on export technological complexity by using panel data from 30 provincial-level administrative units in China from 2011 to 2019. The study finds that green finance significantly promotes export sophistication; with the promotion effect varying by the geographical location and institutional environment, the mechanism test shows that upgrading industrial structure and enhancing technological innovation are the two transmission paths for green finance to enhance export sophistication. Additionally, the study finds that green tax moderates the impact of green finance on export sophistication. The threshold effect test reveals that industrial structures, as well as their upgrades and technological innovation, have a single threshold. However, they need to reach a certain threshold value before they can play their role to the fullest, while green tax has a marginal increasing effect. The study provides a new perspective on the relationship between green finance and export sophistication, and the empirical evidence for current green finance policies promotes the development of the real economy.

In this paper, we consider a truck loading problem with a fixed-plus-linear charge scheme, which is thought to be more suitable for short-distance freight transportation than fixed charge schemes. Here, we study a mixed-integer linear program (MILP) formulation of this problem and develop two methods for solving it. We discuss the property of optimal solutions and tighten the formulation. Based on the polyhedral knowledge of orbit opes, we also provide a symmetry-breaking heuristic for this problem. We conduct a series of numerical experiments to indicate that the computational performance of the MILP formulation can be significantly improved by applying our method.

家庭财务脆弱性影响居民财富福利,债务杠杆对家庭财务变化存在着不可忽视的影响,数字金融作为新兴金融模式改善了金融获得环境.利用2014年、2016年及2018年三轮CFPS调查数据,探究债务杠杆、数字金融对家庭财务脆弱性的影响.由于具有相同债务杠杆的家庭,其财务陷入困境的可能不同,因此着重分析数字金融在缓解债务杠杆对家庭财务脆弱性负面影响中的作用.结果表明:债务杠杆升高加剧了家庭财务脆弱性,数字金融能够有效缓解债务杠杆对家庭财务脆弱性的负面冲击,且其对正规渠道借贷的杠杆率具有更大的缓释效应.进一步研究发现,数字金融对债务杠杆与家庭财务脆弱性关系的调节可以通过降低资金流动性约束实现,这其中起到关键作用的是数字金融的投融资功能.同时,在传统金融较为发达的地区、城市以及教育水平、资产水平较高的优势家庭中,数字金融更容易发挥其缓释效应,体现了破除弱势群体"数字鸿沟"的重要性.

We develop a power penalty approach to a finite-dimensional double obstacle problem. This problem is first approximated by a system of nonlinear equations containing two penalty terms. We show that the solution to this penalized equation converges to that of the original obstacle problem at an exponential rate when the coefficient matrices are \begin{document}$ M $\end{document} -matrices. Numerical examples are presented to confirm the theoretical findings and illustrate the efficiency and effectiveness of the new method.

In this paper we develop a PDE-based mathematical model for valuing real options on the expansion of an investment project whose underlying commodity price and its volatility follow their respective geometric Brownian motions. This mathematical model is of the form of a 2-dimensional Black-Scholes equation whose payoff condition is determined also by a PDE system. A novel 9-point finite difference scheme is proposed for the discretiza-tion of the spatial derivatives and the fully implicit time-stepping scheme is used for the time discretization of the PDE systems. We show that the coefficient matrix of the fully discretized system is an M-matrix and prove that the solution generated by this finite dif-ference scheme converges to the exact one when the mesh sizes approach zero. To demon-strate the usefulness and effectiveness of the mathematical model and numerical method, we present a case study on a real option pricing problem in the iron-ore mining industry. Numerical experiments show that our model and methods are able to produce numerical results which are financially meaningful.(c) 2022 Elsevier Inc. All rights reserved.

本文从普惠金融资金供给端和需求端出发,分别阐释了定向降准政策和企业所得税优惠政策影响小微企业信贷融资的作用机制.在此基础上,利用2012-2016年全国企业调查数据,采用三重差分法检验定向降准和企业所得税优惠政策同时实施时对小微企业获取信贷资源产生的引导作用,并进一步探讨了地区金融发展水平和企业属性对普惠金融发展政策实施效果的异质性影响.研究发现,定向降准和企业所得税优惠政策配套实施能够产生"1+1＞2"的叠加"普惠"效应,小微企业信贷可得性显著增加、信贷融资成本显著降低,但是负债结构合理性没有显著提高.异质性检验结果表明,实施普惠金融发展政策有利于减弱"信贷歧视",从而优化金融资源配置.此外,金融发展水平较高地区,普惠金融发展政策调控效果更佳.

高质量发展视域下,科技创新成为经济发展第一生产力.以技术市场成交额为表征的技术市场发展情况反映了区域科技研发及应用转化水平,其存在究竟能否促进经济高质量发展?又是通过怎样的传导机制产生影响?通过SPCA方法计算得到2007-2019年30个省市区经济高质量得分,以此为基础构建面板数据以考察技术市场对经济高质量的影响及作用路径.结果 表明,技术市场是助推经济高质量发展的重要因素,并可以通过创新驱动和绿色经济两条中介路径间接支持经济高质量发展.此外,通过门槛检验发现,随着技术市场规模不断扩大,其对经济高质量的助益呈增强趋势.为此,提出相应对策建议以完善技术市场,进而助推经济走上高质量发展道路.

We develop a power penalty approach to the discrete Hamilton–Jacobi–Bellman (HJB) equation in $$ \mathbb {R}^N $$ in which the HJB equation is approximated by a nonlinear equation containing a power penalty term. We prove that the solution to this penalized equation converges to that of the HJB equation at an exponential rate with respect to the penalty parameter when the control set is finite and the coefficient matrices are M-matrices. Examples are presented to confirm the theoretical findings and to show the efficiency of the new method.

Nonlinear Hamilton–Jacobi–Bellman (HJB) equation commonly occurs in financial modeling. Implicit numerical scheme is usually applied to the discretization of the continuous HJB so as to find its numerical solution, since it is generally difficult to obtain its analytic viscosity solution. This type of discretization results in a nonlinear discrete HJB equation. We propose a power penalty method to approximate this discrete equation by a nonlinear algebraic equation containing a power penalty term. Under some mild conditions, we give the unique solvability of the penalized equation and show its convergence to the original discrete HJB equation. Moreover, we establish a sharp convergence rate of the power penalty method, which is of an exponential order with respect to the power of the penalty term. We further develop a damped Newton algorithm to iteratively solve the lower order penalized equation. Finally, we present a numerical experiment solving an incomplete market optimal investment problem to demonstrate the rates of convergence and effectiveness of the new method. We also numerically verify the efficiency of the power penalty method by comparing it with the widely used policy iteration method.

We investigate the determinants of excessive/under-borrowing. Our study differs from the literature by measuring excessive/under-borrowing, and estimating the effect of self-control, financial literacy, and misperception of interest rates, in addition to present bias. We observe both excessive borrowing and under-borrowing, with higher proportion of subjects exhibiting the latter. Subjects with better self-control are less likely to exhibit excessive borrowing, and are more likely to exhibit under-borrowing. It suggests that excessive (under-) borrowing can be due to over-estimation (under-estimation) of self-control. Subjects with better financial literacy are less likely to exhibit excessive borrowing. An additional level of better self-control leads to 10.34% lower borrowing interest rate in real-life. An additional level of better financial literacy leads to 3.44% lower interest rate. Borrowers exhibiting interest rate misperception pay about 11.49% higher interest rate. The findings on the role of financial literacy and misperception of interest rate suggest that excessive-borrowing is not merely a self-control problem, and it can be reduced through appropriate financial education.

在阐释经济高质量发展内涵的基础上,分别构建了经济高质量增长和税收健康发展指标体系,并通过因子分析法降维处理得到经济高质量和税收健康的综合评分,构造出省级面板数据,对高质量视角下经济增长与税收发展的关系进行实证分析.研究结果表明,现阶段经济高质量增长与税收健康发展之间具有双向格兰杰影响;重点税源企业发展、税收产业结构优化、不同类型企业税收贡献比重改善和税收绿色化发展能有效促进经济高质量增长,而税收规模增加、增速过快对经济高质量增长具有抑制作用.

Purpose The purpose of this study was to compare the effect of bipolar radiofrequency energy (bRFE) on chondroplasty at the different time durations in an in vitro experiment that simulated an arthroscopic procedure. Methods Six fresh bovine knees were used in our study. Six squares were marked on both the medical and lateral femoral condyles of each femur. Each square was respectively treated with bRFE for 0 s, 10 s, 20 s, 30 s, 40 s and 50 s. Full-thickness articular cartilage specimens were harvested from the treatment areas. Each specimen was divided into three distinct parts: one for hematoxylin/eosin staining histology, another for cartilage surface contouring assessment via scanning electron microscopy (SEM), and the last one for glycosaminoglycan (GAG) content measurement. Results bRFE caused time-correlated damage to chondrocytes, and GAG content in the cartilage was negatively correlated to exposure time. bRFE caused time-correlated damage to chondrocytes. The GAG content in the cartilage negatively correlated with the exposure time. The sealing effect positively correlated with the exposure time. Additionally, it took at least 20 s of radiofrequency exposure to render a smooth cartilage surface and a score of 2 (normal) in the scoring system used. Conclusion bRFE usage in chondroplasty could effectively trim and polish the cartilage lesion area; however, it induces a dose-dependent detrimental effect on chondrocytes and metabolic activity that negatively correlated with the treatment time. Therefore, cautions should be taken in the use of bRFE for treatment of articular cartilage injury.