LZUFE-KMS

Extreme earthquake loss assessment using spliced marginal distributions and SJC Copula based joint modeling

Thu, 23 Apr 2026 08:34:10 GMT

Title: Extreme earthquake loss assessment using spliced marginal distributions and SJC Copula based joint modeling Authors: Zhao, Yu; Li, Yuhong; Li, Yani; Chen, Wenkai Description: Earthquake hazards, though occurring infrequently, can produce catastrophic losses with pronounced right-skewed and heavy-tailed characteristics in both economic damages and fatalities. These impacts often intensify jointly under extreme conditions, posing challenges for reliable regional catastrophe risk assessment. Using earthquake records from 1980 to 2024 in selected provinces (autonomous regions and municipalities) of China, this study develops variable weights spliced Gumbel-GPD and Weibull-GPD distributions to model the marginal behavior of extreme losses. The dependence between economic losses and casualties-particularly in the upper tail-is captured using the SJC Copula, allowing for asymmetric co-extremal behavior. Due to the limited number of high-loss historical events, we analyze the sensitivity of parameter estimates to sample size through numerical simulations. After confirming that the GAN-generated samples are statistically consistent with the original data, they are employed to strengthen the robustness of parameter estimation. Integrating the spliced marginal models with a copula-based dependence framework, this study evaluates extreme loss levels for different regions under historical seismic conditions. The resulting estimates offer quantitative support for identifying key areas requiring enhanced seismic protection and for informing regional disaster-risk management.

Can digital infrastructure increase the technical complexity of agricultural product exports?

Sat, 28 Feb 2026 04:00:17 GMT

Title: Can digital infrastructure increase the technical complexity of agricultural product exports? Authors: Zhang, Yutian; Liu, Xinyi; Wei, Feng Description: This article employs a bidirectional fixed-effect model and, based on panel data from 47 countries along the Belt and Road Initiative from 2006 to 2023, examines the impact of digital infrastructure development on the technical complexity of agricultural product exports and analyzes the moderating role of institutional quality. The results show that, first, the development of digital infrastructure can significantly enhance the technical complexity of agricultural product exports through three transmission mechanisms: improving human capital, promoting technological innovation, and increasing labor productivity. Second, national heterogeneity indicates that digital infrastructure has a significant positive impact on the technical complexity of agricultural product exports in both developed and developing countries. Product heterogeneity indicates that digital infrastructure significantly promotes the technical complexity of non-fresh agricultural product exports, but has no significant impact on fresh agricultural products. Third, the quality of the system plays a positive moderating role in the process of the impact of digital infrastructure construction on the technical complexity of agricultural product exports.

The digital economy and corporate financialization: evidence from China

Tue, 16 Jun 2026 06:55:46 GMT

Title: The digital economy and corporate financialization: evidence from China Authors: Yang, Qi; Feng, Hongxia; Li, Mutian Description: The digital economy exerts a multifaceted influence on corporate operations and strategic decision-making; however, its impact on corporate financialization remains unclear. Using data from Chinese A-share-listed firms spanning 2013 to 2023, this study examines the restraining effect of the digital economy on corporate financialization. The empirical findings reveal that the digital economy significantly curbs corporate financialization, primarily by alleviating financing constraints and boosting research and development expenditures. Further analysis shows that this restraining effect is more pronounced in cities characterized by deep integration between the digital and real economies and by advanced digital infrastructure, as well as among firms with strong political connections and firms that share a large number of common institutional investors. Based on these findings, we also conduct a heterogeneity analysis and offer targeted policy recommendations.

Unveiling the mechanisms: How does the new energy demonstration city pilot policy affect urban energy efficiency? Evidence from a quasi-natural experiment in China

Tue, 11 Aug 2026 02:46:44 GMT

Title: Unveiling the mechanisms: How does the new energy demonstration city pilot policy affect urban energy efficiency? Evidence from a quasi-natural experiment in China Authors: Deng, Guangyao; Liu, Yuting Description: The pilot program of new energy demonstration cities (NEDC) is a key strategic measure for China to promote sustainable and high-quality development. It is imperative to analyze this policy's impact and leverage it to foster a green transition of the socio-economy to realize the dual carbon goals of carbon peak and carbon neutrality. Based on panel data from 253 prefecture-level cities in China spanning 2008-2022, the difference-in-differences (DID)model is employed and a series of robustness checks are conducted to demonstrate that the NEDCpilot policy has significantly improved energy efficiency (EE). Specifically, thepolicy results in a gain of 0.15 units in energy efficiency.Our analysis identifies three primary channels through which the policy enhances EE: green technological innovation, structural transformation and upgrading, and government governance. In particular, the NEDC policy exerts a marked positive impact on both government fiscal support and environmental regulation.Heterogeneity analysis reveals the strongest effects in the eastern and western regions, non-resource-based cities, and municipalities or sub-provincial cities.Consequently, effective implementation calls for strategies that are adapted to local conditions.Further analysis confirms the presence of positive spatial spillover effects from the NEDC pilot program.Therefore, the effectiveness of the New Energy Demonstration City policy varies across regions, city types, and administrative levels. It is thus more necessary to rely on the synergistic promotion of green technology innovation, industrial transformation, and government guidance, while making good use of inter-city spillover effects to ensure that the demonstration experience benefits a wider range of areas.

Unsupervised feature selection via row-sparse local preserving projection

Mon, 13 Jul 2026 03:41:25 GMT

Title: Unsupervised feature selection via row-sparse local preserving projection Authors: Yang, Zhengguo; Li, Xiran; Zhou, Ruiting; Yi, Jihai; Wang, Jikui; Nie, Feiping Description: In order to effectively process high-dimensional unlabeled data, an increasing number of researchers are focusing on unsupervised dimensionality reduction methods, which consist of two types: feature extraction and feature selection. Local Preserving Projection (LPP) is a widely used unsupervised dimensionality reduction technique that performs feature extraction rather than feature selection. Some methods perform feature selection by applying sparse constraints to the projection matrix of the LPP method. Because the & ell;(2,0)-norm is difficult to optimize, these LPP-based feature selection methods introduce sparse regularization terms in the objective function by considering the & ell;(2,p)-norm (0 < p <= 1) in the projection matrix. Since the & ell;(2,p)-norm is only an approximation of the & ell;(2,0)-norm, the feature subset selected in this way is often suboptimal. To directly handle the & ell;(2,0)-norm constraint problem to obtain the optimal feature subset, we propose an unsupervised feature selection method termed Unsupervised Feature Selection via Row-Sparse Local Preserving Projection (UFSLP) in this paper. The proposed method preserves the local neighborhood structure in the feature selection process and effectively balances local and global information by introducing principal component analysis (PCA) as a regularization term. To optimize the & ell;(2,0)-norm problem, we reformulate it as an equivalent form and solve it via a coordinate descent method. Extensive experiments on nine benchmark datasets demonstrate that UFSLP outperforms other state-of-the-art unsupervised feature selection methods in terms of clustering accuracy and normalized mutual information.

Efficient estimation for the Greeks of Asian options under mixed fractional Brownian motion

Mon, 13 Jul 2026 03:41:20 GMT

Title: Efficient estimation for the Greeks of Asian options under mixed fractional Brownian motion Authors: Zhao, Dandan; Ai, Mingyao; Guo, Jingjun Description: Greeks are widely utilized to quantify the impact of diverse market factors on option prices, and the rapid, accurate estimation of these sensitivities is paramount for effective risk management. Among the available approaches, the finite difference method stands as the most prevalent, yet it suffers from a critical limitation: poor numerical stability. To address these inherent drawbacks, this paper uses a novel methodology called the Malliavin calculus approach. Specifically, we derive explicit expressions for the Greeks of continuous Asian options by leveraging Malliavin calculus, and transform the computation of Greeks into the calculation of Malliavin weight. Our analysis is built upon an underlying asset price model driven by a mixed fractional Brownian motion, which effectively captures key market characteristics such as long memory and self-similarity. This methodology circumvents the need for direct differentiation of the option price, thus offering enhanced generality and applicability. By applying this method, we successfully derive the Greeks for both continuous geometric and arithmetic Asian options, respectively. Some numerical results demonstrate that the Malliavin calculus approach outperforms the finite difference method in terms of computational efficiency.

Balanced symmetric non-negative matrix factorization

Mon, 13 Jul 2026 03:41:30 GMT

Title: Balanced symmetric non-negative matrix factorization Authors: Wang, Jikui; Yao, Baocheng; Ma, Yuqi; Wu, Genqiang; Zhao, Ruijuan; Nie, Feiping Description: Symmetric non-negative matrix factorization (SNMF) as a classical graph clustering method has demonstrated powerful clustering capabilities in an increasing number of studies. However, improper initialization may cause the algorithm to get stuck in a local optimum, resulting in a highly uneven distribution of samples across clus ters. To address the problem, we propose a Balanced Symmetric Non-negative Matrix Factorization (BSNMF) model that introduces a novel balance-regularization term to actively equalize cluster sizes and boost clustering performance. The proposed BSNMF incorporates a newly designed balance-regularization term that encourages more balanced cluster distributions to enhance clustering performance. Projection Gradient Descent (PGD) is employed to solve this optimization problem. Experiments conducted on eight benchmark datasets demonstrate the effectiveness of our algorithm.

Embedded fuzzy C-means joint row-sparse principal component analysis

Tue, 19 May 2026 03:21:22 GMT

Title: Embedded fuzzy C-means joint row-sparse principal component analysis Authors: Wang, Jikui; Li, Xiran; Ma, Yuqi; Liu, Feifei; Nie, Feiping Description: High-dimensional data clustering faces the well-known curse of dimensionality. Traditional methods usually adopt a two-stage strategy, which first reduces the dimension of the data, and then applies clustering algorithms to the reduced data. Traditional high-dimensional data clustering algorithms have two main drawbacks. The first drawback is that the goals of dimensionality reduction and clustering are not necessarily consistent, and the reduced data may not be suitable for clustering. The second drawback is that using feature extraction and feature selection methods alone for dimensionality reduction makes it difficult to find potential data structures that are more suitable for clustering in low-dimensional spaces. To tackle these issues, we propose an embedded fuzzy C-Means joint row-sparse principal component analysis (RS-EFCM), which simultaneously performs feature selection, feature extraction, and clustering tasks. To tackle the challenges posed by the non-smoothness and non-convexity of the /20-norm, we employ a coordinate descent approach to seek an optimal solution. The RS-EFCM algorithm has a linear time complexity with respect to the number of samples. We carried out comprehensive experiments on eight datasets to demonstrate the efficacy and convergence properties of the RS-EFCM algorithm. The code is available on the website: https://github.com/LZUFE-Machine-Learning/RS-EFCM.

A study of the impact of carbon accounting information disclosure on corporate ambidextrous innovation

Tue, 11 Aug 2026 02:46:47 GMT

Title: A study of the impact of carbon accounting information disclosure on corporate ambidextrous innovation Authors: Zhou, Deliang; Zhang, Dingyin; Wang, Ziqian Description: Carbon accounting information disclosure enables firms to communicate information about their production and operating activities to the capital market more effectively, thereby mitigating the adverse role of information dissymmetry on corporate ambidextrous innovation. Accordingly, the paper empirically tests the influence of carbon accounting information disclosure on corporate ambidextrous innovation, employing the sample data of all listed companies in the Shanghai and Shenzhen A-share markets (2014-2023). The test outcomes demonstrate that carbon accounting information disclosure is capable of significantly promoting exploratory innovation while significantly inhibiting exploitative innovation; this conclusion stays robust after relevant endogeneity and robustness tests. The mechanism analysis reveals that financing constraints serve as a significant mediator in the correlation between carbon accounting information disclosure and corporate ambidextrous innovation. An analysis of the differences in the effects and mechanisms of carbon accounting information disclosures and ESG information disclosures indicates that there are significant differences in the impact of carbon accounting information disclosures and ESG information disclosures on firms' ambidextrous innovation, as well as in their underlying mechanisms. Further analysis indicates that, based on heterogeneity tests, the promotional effect of carbon accounting information disclosure on exploratory innovation is more pronounced in state-owned enterprises, while its inhibitory effect on exploitative innovation is more pronounced in non-state-owned enterprises; furthermore, the impact of carbon accounting information disclosure on both exploratory and exploitative innovation is more pronounced in the eastern region, as well as during the growth and decline phases of a firm; Moderation effect tests revealed that environmental uncertainty amplifies the impact of carbon accounting information disclosure on firms' ambidextrous innovation, while marketization and analyst attention mitigate this effect; an analysis of economic consequences indicates that carbon accounting information disclosure not only promotes exploratory innovation and inhibits exploitative innovation but ultimately fosters high-quality development in firms. This paper not only diversifies research on carbon accounting information disclosure but also provides scientific empirical evidence and decision-making references for promoting corporate ambidextrous innovation.

Foreseeing snowmelt responses of the Tibetan Plateau to biomass-burning black carbon from South Asia

Tue, 19 May 2026 03:20:12 GMT

Title: Foreseeing snowmelt responses of the Tibetan Plateau to biomass-burning black carbon from South Asia Authors: Wu, Jiaqi; Huang, Tao; Wang, Runyu; Wu, Xingyu; Mei, Zihui; Ling, Zaili; Zhao, Yuan; Gao, Hong; Ma, Jianmin Description: Black carbon (BC), a major light-absorbing aerosol emitted from South Asian biomass burning, can be efficiently transported to the Tibetan Plateau (TP), substantially accelerating snowmelt. However, the relative contributions of different biomass burning sources and their future impacts on TP snowpack remain poorly quantified. Using the Community Integrated Earth System Model (CIESM), this study conducted a suite of sensitivity experiments to systematically quantify the impacts of BC emissions from South Asian biomass burning on snow cover fraction (SCF) and snow depth (SD) over the TP, and assessed the synergistic effects of sulfur dioxide (SOS) through aerosol-radiation interactions. The results show that BC emitted from South Asian biomass burning significantly enhances snowmelt over the TP. The strongest impacts occur in spring, during which SCF and SD are decreased by 9.7% and 4.6 cm, respectively. Source attribution reveals that forest fires dominate TP spring snowmelt, contributing 42.1% and 41.1% to the decreases in SCF and SD, respectively. Agricultural waste burning is the primary contributor in autumn, accounting for 67.7% and 46.0% of the decrease in SCF and SD, respectively. Under the SSP1-2.6 and SSP5-8.5 scenarios, BC emissions from South Asian biomass burning will lead to a 6.9% and 3.5 cm reduction, and an 11.5% and 7.3 cm reduction, respectively, in SCF and SD in spring over the TP in 2060. We show that SOS substantially amplifies BC-induced snowmelt through the aerosol lensing effect, reducing the annual mean SCF and SD from 5.2% to 6.4% and from 2.7 cm to 4.3 cm in 2020, respectively. These findings highlight the critical roles of source-specific emission control and multi-pollutant mitigation strategies in alleviating cryospheric degradation and safeguarding water resources in the Asian Water Tower.