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The purpose of this paper is to investigate the statistical correlation properties of the Shanghai Interbank Offered Rate (SHIBOR) interbank lending market.

The authors apply methods of correlation analysis, random matrix theory (RMT) and minimum spanning tree (MST) to investigate the correlation properties of Chinese interbank lending market and analyze how the SHIBOR panel banks behave in different market periods.

First, the largest eigenvalue λ ₁ is the index to describe the market mode of the whole market when all banks behavior collectively and λ ₁/N is a good estimator of the average correlation <C> of the correlation matrix. Second, notably, the authors find the “market mode” is weakened in two crises periods of 2008 stock market crash and 2009 Global Financial Crisis. This is significantly different from other market where the “market mode” is normally strengthened in crises periods. Third, the authors subtract the contribution of λ ₁, the second and third eigenvalue, λ ₂ and λ ₃, will fall outside of the predicted interval. And both λ ₂ and λ ₃ are getting times larger in the crises periods than in “Non-Crisis” period. Fourth, and in the MST analysis, the authors find again that the average distances of the MST are the times larger in crises periods than in “Non-Crisis” period and the second largest eigenvalue is a good estimator of the average distance of the MST.

According to the best knowledge, this paper is the first work on the study of the statistical properties of an interbank lending market using quotation level data of panel banks, which allows us to analyze the properties of the interest rate formation and how all panel banks behavior in different periods. This work is also the first study on the SHIBOR market using econophysics methods of correlation analysis, RMT and MST.

Underground logistics system (ULS) is recognized as sustainable alleviator to road-dominated urban logistics infrastructure with various social and environmental benefits. The purpose of this study is to propose effective modeling and optimization method for planning a hub-and-spoke ULS network in urban region.

Underground freight tunnels and the last-mile ground delivery were organized as a hierarchical network. A mixed-integer programming model (MIP) with minimum system cost was developed. Then a two-phase optimization schema combining Genetic-based fuzzy C-means algorithm (GA-FCM), Depth-first-search FCM (DFS-FCM) algorithm and Dijkstra algorithm (DA), etc. was designed to optimize the location-allocation of ULS facilities and customer clusters. Finally, a real-world simulation was conducted for validation.

The multistage strategy and hybrid algorithms could efficiently yield hub-and-spoke network configurations at the lowest objective cost. GA-FCM performed better than K-means in customer-node clustering. The combination of DFS-FCM and DA achieved superior network configuration than that of combining K-means and minimum spanning tree technique. The results also provided some management insights: (1) greater scale economies effect in underground freight movement could reduce system budget, (2) changes in transportation cost would not have obvious impact on ULS network layout and (3) over 90% of transportation process in ULS network took place underground, giving remarkable alleviation to road freight traffic.

Demand pairs among customers were not considered due to lacking data. Heterogeneity of facilities capacity parameters was omitted.

This study has used an innovative hybrid optimization technique to address the two-phase network planning of urban ULS. The novel design and solution approaches offer insights for urban ULS development and management.

With the analysis of the causes of corruption, this study aims to investigate specific anti-corruption measures that can be implemented to reform the political system and the social climate of China.

This study examines 97 severe corruption cases of high-ranking officials in China, which occurred between 2012 and 2015. As this insinuates that both institutional and social corruption are major problems in China, the analysis delves into multiple facts of corruption, including different types, four primary underlying causes, and suggestions regarding the implementation of three significant governmental shifts that focus on investigation, prevention tactics and legal regulations.

China’s corruption is not only individual-based but also it has developed into institutional corruption and social corruption. Besides human nature and instinct, the causes of corruption can be organised into four categories, namely, social customs, social transitions, institutional designs and institutional operations. For the removed high-ranking officials, the formation of interest chains was an important underlying cause behind their corruption.

This study makes a significant contribution to the literature because this study provides a well-rounded approach to a complex issue by highlighting the significance of democracy and the rule of law as ways to regulate human behaviour to combat future corruption.

This article gives a brief introduction to computer applications in information retrieval, library management and Chinese character processing in some information organisations in China. It outlines the history, present status and future trends of these aspects.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This paper evaluates the level of the digital economy in Chinese cities based on digital industrialization and industrial digitalization. The research focuses on the effects of spatial mechanism of the urban digital economy on the quality of firms’ exported products.

The authors use the principal component analysis method to evaluate the level of China’s urban digital economy, and spatial metrology to measure the spatial effects of the digital economy on product quality.

The findings suggest that the urban digital economy can expand the quality of firms’ exports. The digital economy has spatial dependence, spatial spillover and spatial heterogeneity on product quality. At the same time, the spatial effect has a significant nonlinear effect and threshold effect. Further decomposition shows that industrial digitalization is the core factor of enterprises’ export products quality, and the micro-mechanism of this impact is mainly manifested in optimization of resource allocation.

The innovation of this paper is reflected explicitly in exploring the quality upgrading of export products from the background of the digital economy, providing a reference for the improvement of China’s export trade competitiveness and the cultivation of a trade power. The authors studied two different mechanisms (specialization division of labor and optimization of resource allocation) to explain the spatial imbalance of export product quality to provide empirical support for enterprises and government departments to formulate quality upgrading policies accurately.

This study aims to examine the impact of climate change on the total factor productivity (TFP) of tourism in Chinese cities. Using temperature and precipitation as proxies for climate change, the research analyzes both the direct negative effects of climate change on tourism productivity and the positive spillover effects on neighboring cities. In addition, it investigates how geographic location and economic development contribute to the variation in these effects. The study also explores the mechanisms through which government intervention and industrial structure upgrading influence these impacts.

This study uses a spatial Durbin model to analyze the relationship between climate change and tourism TFP in 287 Chinese cities from 2000 to 2020. Panel data is used, with temperature and precipitation serving as proxies for climate change. The model evaluates both the direct and spillover effects of climate change on tourism productivity, while also analyzing the mechanisms through which government intervention and industrial upgrading affect these relationships. The study further considers how geographic location and economic development impact the results.

This study finds that climate change directly reduces tourism TFP, while generating positive spillover effects for neighboring cities. Cities in the eastern and more economically developed regions are more sensitive to climate change, experiencing stronger impacts compared to cities in central and western regions. The findings suggest that government intervention and industrial structure upgrading are important mechanisms through which climate change affects tourism productivity in Chinese cities.

This research fills a gap in the literature regarding how climate change affects tourism productivity in developing countries, particularly in China. By applying a spatial Durbin model and panel data analysis, the study provides empirical evidence on both the direct and spillover effects of climate change on tourism productivity. It highlights the critical role of government intervention and industrial upgrading as mechanisms shaping the impact of climate change, offering new insights for policymakers and tourism businesses to address the challenges posed by climate change and enhance productivity and competitiveness.

本研究旨在探讨气候变化对中国城市旅游全要素生产率的影响。通过温度和降水量作为气候变化的代理变量, 研究分析了气候变化对旅游全要素生产率的直接抑制效应及其对邻近城市的积极溢出效应。此外, 研究考察了地理位置与经济发展水平如何导致这些效应的异质性。通过分析政府干预和产业结构升级的机制, 本研究为气候变化影响旅游全要素生产率的机制分析提供了理论支持, 为提升发展中国家旅游竞争力提供了指导。

本研究采用空间杜宾模型分析2000年至2020年期间, 中国287个城市的气候变化与旅游全要素生产率之间的关系。研究使用面板数据, 温度和降水量作为气候变化的代理变量。模型分析了气候变化对旅游全要素生产率的直接效应与溢出效应, 并研究了政府干预与产业结构升级的机制效应。研究还考察了基于地理位置与经济发展水平的异质性影响, 提供了气候变化对城市旅游全要素生产率影响的综合分析。

气候变化直接抑制旅游全要素生产率, 同时对邻近城市产生积极的溢出效应。东部城市及高经济水平地区对气候变化更为敏感, 影响强于中西部地区。研究发现, 政府干预与产业结构升级是气候变化影响中国城市旅游全要素生产率的关键机制。

本研究填补了气候变化如何影响发展中国家, 尤其是中国, 旅游全要素生产率领域的研究空白。通过运用空间杜宾模型和面板数据分析, 提供了气候变化对旅游全要素生产率的直接效应与溢出效应的实证证据。研究强调了政府干预和产业结构升级作为气候变化影响旅游全要素生产率的主要机制。通过关注区域异质性与经济发展水平, 本研究为旅游企业与政策制定者应对气候变化挑战,提升生产力和竞争力提供了新的思路。

Este estudio examina el impacto del cambio climático en la productividad total de los factores (PTF) del turismo en las ciudades chinas. Utilizando la temperatura y las precipitaciones como indicadores del cambio climático, la investigación analiza tanto los efectos negativos directos del cambio climático sobre la productividad del turismo como los efectos indirectos positivos sobre las ciudades vecinas. Además, investiga cómo la ubicación geográfica y el desarrollo económico contribuyen a la variación de estos efectos. El estudio también explora los mecanismos a través de los cuales la intervención gubernamental y la mejora de la estructura industrial influyen en estos impactos.

Este estudio utiliza un modelo espacial de Durbin (SDM) para analizar la relación entre el cambio climático y la productividad total de los factores del turismo en 287 ciudades chinas entre 2000 y 2020. Se emplean datos de panel, en los que la temperatura y las precipitaciones sirven como variables sustitutivas del cambio climático. El modelo evalúa tanto los efectos directos como los indirectos del cambio climático sobre la productividad del turismo, al tiempo que analiza los mecanismos a través de los cuales la intervención gubernamental y la modernización industrial afectan a estas relaciones. El estudio examina además cómo influyen en los resultados la ubicación geográfica y el desarrollo económico.

El estudio concluye que el cambio climático reduce directamente la productividad total de los factores del turismo, al tiempo que genera efectos indirectos positivos para las ciudades vecinas. Las ciudades de las regiones orientales y económicamente más desarrolladas son más sensibles al cambio climático y experimentan impactos más fuertes que las ciudades de las regiones centrales y occidentales. Los resultados sugieren que la intervención gubernamental y la mejora de la estructura industrial son mecanismos importantes a través de los cuales el cambio climático afecta a la productividad del turismo en las ciudades chinas.

Esta investigación llena un vacío en la literatura sobre cómo el cambio climático afecta a la productividad del turismo en los países en desarrollo, especialmente en China. Aplicando un modelo espacial de Durbin y un análisis de datos de panel, el estudio aporta pruebas empíricas sobre los efectos directos y indirectos del cambio climático en la productividad del turismo. Destaca el papel decisivo de la intervención pública y la modernización industrial como mecanismos que determinan el impacto del cambio climático, ofreciendo nuevas perspectivas a los responsables políticos y las empresas turísticas para afrontar los retos que plantea el cambio climático y mejorar la productividad y la competitividad.

Presents over sixty abstracts summarising the 1999 Employment Research Unit annual conference held at the University of Cardiff. Explores the multiple impacts of globalization on work and employment in contemporary organizations. Covers the human resource management implications of organizational responses to globalization. Examines the theoretical, methodological, empirical and comparative issues pertaining to competitiveness and the management of human resources, the impact of organisational strategies and international production on the workplace, the organization of labour markets, human resource development, cultural change in organisations, trade union responses, and trans‐national corporations. Cites many case studies showing how globalization has brought a lot of opportunities together with much change both to the employee and the employer. Considers the threats to existing cultures, structures and systems.