Bridge expansion joints play a crucial role in bridges. In order to solve problems such as the relatively high maintenance costs of traditional expansion joints, an assembled and quickly installed multi-directional displacement bridge expansion joint (hereinafter referred to as the ZPF assembled expansion joint) was now proposed. According to the “General Technical Requirements for Highway Bridge Expansion Joints” (JT/T 327—2016), on-site fatigue tests were designed, and finite element analysis was also carried out at the same time. The test results show that the fatigue life of the ZPF assembled expansion joint decreases as the fatigue load increases. The fatigue life of the ZPF assembled expansion joint depends largely on the concrete under the embedded steel plate. During the loading of the fatigue performance test for 2 million cycles, the overall structure is safe and stable without any abnormal phenomena. The research results can provide a basis for the application of the ZPF assembled expansion joint.

Addressing the issue of large rock block detachment during TBM (Tunnel Boring Machine) construction in unfavorable geological conditions at a metal mine in Jiangxi Province, China, the horizontal thrust force of the gripper shoes was systematically analyzed for the first time to reveal its mechanism of inducing tensile failure in surrounding rock, based on the PLAXIS 3D finite element simulation platform and field observations. An optimized support scheme combining “pre-grouting reinforcement and rock bolt-mesh-shotcrete support” was proposed. The study reveales that during TBM construction in Class V rock mass conditions, tensile failure zones developes on the tunnel roof and progressively expandes as the TBM advanced, leading to roof collapse phenomena observed in the project. The support optimization analysis compared three scenarios, such as no support, “rock bolt+mesh+shotcrete” (referred to as bolt-mesh-shotcrete support), and “pre-grouting reinforcement+bolt-mesh-shotcrete support.” Results show that the pre-grouting reinforcement scheme significantly enhances the stability of the surrounding rock, reducing the maximum roof deformation by 21.83% and the final deformation by 36.72%. While bolt-mesh-shotcrete support alone has limited effectiveness, it effectively restraines further deformation of the surrounding rock and prevents the detachment of loose rock masses. It is recommended that in similar TBM tunnel construction scenarios in metal mines encountering unfavorable geological conditions, the combined use of pre-grouting reinforcement and bolt-mesh-shotcrete support should be adopted to improve construction safety and efficiency.

The utilization of photovoltaic power generation represents a pivotal strategy for the transition of traditional energy systems towards decarbonization, and is of paramount importance in achieving global climate goals. A social network analysis was employed to examine the evolutionary characteristics of the global PV power generation trade network, based on the trade data of global photovoltaic devices from 2012 to 2021. The analysis aims to elucidate the basic situation and development trend of this network. It was found that during the period from 2012 to 2021, the global PV power generation trade network underwent substantial development and expansion, with trade links between countries becoming increasingly close. The trade network is distinguished by high aggregation and short path lengths, exhibiting pronounced “small-world” characteristics. The nodes of the global PV power generation trade network exhibit a stable “core-edge” structure. Concurrently, China’s central position is becoming increasingly evident, and its pivotal role in the global PV power generation trade network is intensifying. This has positioned China as a pivotal force in the advancement of trade in this field, with the center of gravity of trade activities demonstrating a tendency towards an eastward ascendance and a westward decline.

Regarding th special seismic response characteristics exhibited by super long-span bridge, a long-span continuous rigid-frame bridge was focused on as the research subject. A three-dimensional finite element model was established, and incremental dynamic analysis (IDA) was performed to systematically investigate the dynamic response characteristics under bi-directional and tri-directional seismic excitations. The results indicate that under bi-directional seismic excitation, the main pier exhibits a 43% probability of slight damage at PGA=0.4g. After incorporating vertical seismic excitation, the main pier shows a 22% probability of slight damage at PGA=0.2g, with the exceedance probability of complete damage state increasing by approximately 25%. Regarding the bearing system, the introduction of vertical seismic excitation significantly accelerates the damage progression rate, reaching a 100% probability of slight damage at PGA=0.4g.

In order to solve the problem of low accuracy and great lag in the inspection of hollow filter bar end face, a vision inspection system for hollow filter bar end face was studied based on machine vision technology. Using PLC to read the pulse signal on the encoder in real time, the industrial array camera was triggered to collect the image of the end face of the hollow filter bar. Image optimization technology was used to process the image of the filter bar end face to ensure the image quality. The dimension and roundness of the end face of the hollow filter rod were measured by using the least square method. Through the method of template matching difference, whether there are defects in the end face of the hollow filter rod is detected, and the alarm is displayed on the man-machine interface. The test results show that the system can detect the dimensions, roundness and defects of the end face of the hollow filter rod in real time, and the detection accuracy can reach 99% at different speeds, which lays the foundation for automatic visual inspection of the filter rod and improves the qualified rate of the product.

With the development of artificial intelligence technology, the unequal access to artificial intelligence, known as the “AI divide” phenomenon, is gradually becoming prominent. Using literature from the Web of Science database as corpus and identifies relevant topics under the topic of “Artificial Intelligence Divide” based on LDA model, text analysis was conducted through manual encoding. Finally, four core research themes such as intellectual barriers, digital education, healthcare and social effects are identified and summarized, the current situation and gaps in technological knowledge gaps, awareness gaps, digital education inequality and the unfair application of artificial intelligence in the healthcare field is revealed. Meanwhile, the social effects of the artificial intelligence divide, including ethical and cultural dilemmas, cultural convergence and social inequality, are also important factors affecting social structure and individual lives. This article aims to deepen the understanding of the core theme of the “artificial intelligence divide” and help artificial intelligence enhance human well-being and social progress.

Forecasting the stock market is a difficult and intricate task, as price series often display traits like significant noise, nonlinearity and non-stationarity. In order to improve the accuracy of predictions, a new method that combined the fuzzy C-means (FCM) clustering algorithm to identify and utilize local trend features in stock price prediction sequences was proposed. In the analysis, key market data of stocks, including opening price, highest price, lowest price, closing price, trading volume, and trading amount, was comprehensively considered as input features for the prediction model. Through experiments, an empirical analysis was conducted to compare the impact of different sliding window sizes (16, 32, 64) on the model’s predictive capability. It is found that the FCM-LSTM-Transformer method, which integrates FCM clustering with the LSTM-Transformer combination model, achieves higher prediction accuracy than both the standalone deep learning models and the LSTM-Transformer combination model. The evaluation metrics MAE, MAPE, MSE and RMSE reach their minimum errors, and the coefficient of determination R² improved by 2.75%, 2.4% and 2.19%, respectively. These results indicate that the proposed model has a significant advantage in handling the complexity of stock market data.

Railway transportation safety provides guarantees for ensuring the safety of passengers’ lives and property and the smooth transportation of goods. The current status and future directions of railway transportation safety were studied in China through two bibliometric analysis methods. The results show that railway transportation safety research can be divided into passive constraint stage, active constraint stage and active management improvement stage. The research focuses on the safety operation protection system, grassroots station safety management, prediction and evaluation of transportation safety accidents, and factors affecting transportation safety accidents. The future of railway transportation safety should focus on the integration of information technology for safety management, intelligent technology for train operation safety, and dynamic monitoring of the external environment of railways.

The tourism industry is undergoing a profound transformation, with tourists’ demands and preferences showing a diversified trend. Accurately identifying tourists’ preferences and satisfaction levels is of crucial importance. Taking the Longji Terraced Fields Scenic Area in Guilin as a case study, deep learning technology and cluster analysis methods were employed to extract the themes of tourists’ service preferences from online reviews and identify groups of tourists with similar preferences. Combined with questionnaire survey data, the service satisfaction was quantitatively evaluated. The research finds that tourists’ service preferences cover accommodation, catering, transportation and information services. The improvement space for information services and accommodation services is the largest, while transportation services and catering services also have room for improvement. Based on this, corresponding suggestions are put forward.

Considering the process of digital technology adoption by construction enterprises, where imitation learning and herd effect were factored in, game theory was employed to construct both a decision model and a diffusion model for enterprise groups concerning digital technology issues. Through numerical simulation, the impact of exchange benefits, exchange intensity, and initial adoption ratio of construction enterprises on their digital technology behavior was studied, and further strategies to promote the adoption and diffusion behavior of digital technology in enterprises were explored. The result shows that the higher the exchange benefits, the more companies tend to adopt digital technology. The exchange intensity has a positive driving effect on accelerating the adoption and diffusion of digital technology. The higher the initial adoption ratio, the more effective it is in promoting the widespread formation of digital technology adoption decisions.

As the difficulty of financing for manufacturing enterprises in the capital market becomes increasingly prominent, improving the quality of information disclosure and enhancing institutional investors’ confidence have become crucial issues in corporate financing strategies. Based on data from Chinese manufacturing listed companies from 2013 to 2022, the impact of the quality of Management Discussion and Analysis(MD&A) on institutional investment intentions and the mediating role of internal control in this relationship were examined. The results indicate that there is a significant positive correlation between the quality of MD&A and institutional investment intentions, with internal control quality playing a partial mediating role. High-quality MD&A improves the transparency and credibility of a company’s information disclosure, thereby enhancing institutional investors’ confidence. Effective internal control further ensures the accuracy and completeness of the information disclosed, boosting institutional investors’ confidence and willingness to invest. Additionally, non-state-owned enterprises disclose higher-quality MD&A than state-owned enterprises, with institutional investors particularly focusing on transparency and information quality. This research provides important theoretical foundations and practical guidance for manufacturing enterprises to improve financing efficiency and strengthen the management of non-financial information disclosure.

Digital economy has become an irreversible development trend, and the national level actively advocates the cluster development of digital economy related industries. Given that Guangdong Province occupies a leading position in the development of digital economy in China, it is of profound practical significance to explore the specific impact and mechanism of different levels of industrial agglomeration and knowledge spillover effect on the innovation ability of digital economy enterprises in Guangdong Province. Based on the digital economy patent data of listed enterprises and the economic indicators of prefecture-level cities in Guangdong Province,threshold regression model and spatial Durbin model were applied to conduct in-depth analysis. The research conclusions are as follows. The agglomeration level of digital economy industry shows a significant single threshold effect on the innovation ability of enterprises, and the relationship between the two shows an inverted “U” shape. The innovation capability of digital economy enterprises presents a distinct spatial agglomeration characteristic, and the knowledge spillover effect has a significant positive promoting effect on the improvement of enterprise innovation capability.

In the future, the characteristics of technology transformation and industrialization in the industrial field are long, and the risks are large, which puts forward new requirements for incubators and other business incubation carriers. Using field investigation, case analysis and other research methods, this paper sorted out the future technological innovation, future industrial growth mechanism and demand for incubation services, analyzed the current situation of incubation carrier construction and development, and the challenges faced by the supply of technological innovation and entrepreneurship services in the future. The research shows that the incubation vector is relatively insufficient in quality project screening and mining, innovation resource matching, professional service supply and other aspects. It is suggested to adjust and optimize from the aspects of promoting market-oriented operation, strengthening the recruitment of quality projects, and building professional service platforms.

In the context of rising trade protectionism and intensifying geopolitical competition, both endogenous drivers and exogenous pressures for the outward transfer of China’s manufacturing sector have increased. Based on the estimation of the scale of China’s manufacturing outward transfer using the ADB input-output table, text analysis and empirical analysis methods were employed to examine the “endogenous drivers” and “exogenous pressures” of China’s manufacturing outward transfer. The results indicate that both endogenous drivers and exogenous pressures coexist in the outward transfer of China’s manufacturing sector, with endogenous drivers being the primary force, but exogenous pressures are gradually becoming an important driving factor. The overall endogenous drivers for the outward transfer of China’s manufacturing include market seeking, technology acquisition, resource acquisition, utilizing cheap labor costs and host country institutional environments. Meanwhile, the exogenous pressures include manufacturing reshoring and trade frictions. However, when considering the motivations of utilizing cheap labor costs and resource acquisition, China also takes into account labor quality and the host country’s infrastructure conditions. Heterogeneity analysis reveals that the transfer to developing countries is mainly driven by endogenous factors, while the transfer to developed countries, as well as the outward transfer of low-tech and medium-high-tech manufacturing, is influenced by both endogenous drivers and exogenous pressures. Additionally, manufacturing reshoring has a greater driving effect on the outward transfer of China’s medium-high-tech manufacturing.

New-generation information technologies, such as big data and cloud computing, are leading industrial transformation and propelling the global shift towards the digital economy era. Digital transformation has emerged as a new competitive frontier in the global arena and a new engine for China’s economic development. By integrating traditional production factors, optimizing resource allocation, enhancing automation and intelligence, and reducing production costs, digital transformation is fostering efficiency gains. The real estate industry faces complex challenges, prompting real estate enterprises(REEs) to compete in leveraging new technologies for digital transformation to cut costs, enhance efficiency, and facilitate diverse industrial layouts. A shared service system based on a digital middle platform architecture was widely applied in diversified businesses, aiding REEs in nurturing data assets, achieving full-link system integration, and responding to business innovation needs, thereby reshaping their core competitiveness. By systematically reviewing theories on digital transformation and digital middle platform architectures, and conducting in-depth research on REEs to analyze the process and application logic of digital transformation based on a middle platform architecture, business scenarios before and after transformation were compared to reveal the challenges and value creation in the digital transformation of REEs, providing recommendations for their digital transformation journey. The research indicates that the digital transformation of REEs involves establishing a digital foundation through three stages of informatization, online enablement and intelligence, gradually building a digital middle platform, adjusting organizational structures, integrating ecological resources and enabling intelligent decision-making. This transformation holds profound significance for enhancing their digital leadership, business innovation capabilities, and promoting high-quality and sustainable enterprise development.

By constructing a comprehensive evaluation system for the digital transformation of manufacturing and the development of the digital economy, the coupling and coordination status of these two systems across 30 provinces in China from 2011 to 2019 were analyzed. Their dynamic changes and regional disparities from multiple dimensions were further examined. The research reveals that during this period, both the level of digitalization in manufacturing and the development of the digital economy show an upward trend across all provinces. However, there remains a significant gap between the achieved level of system coupling and the benchmark for high coupling. Despite this, the overall coupling and coordination degree has been on an upward trend, indicating a positive interaction and progress between the two domains. Regional analysis demonstrates that the coupling degree among provinces first increases and then decreases from east to central and western regions. Conversely, the coupling and coordination degree decreases progressively from the eastern to the western region.

In the financial business, effective risk avoidance and mitigation are perennial themes. In the face of the international economic and financial environment has become more complex and severe, extreme events caused by the tail risk brought about by the harm is very strong, China’s banking industry risk prevention and control work will face new challenges. Therefore, under the current globalization and complex and severe economic and financial environment, it is of great practical significance to improve the prediction ability of yield risks of commercial banks and take timely measures to prevent and resolve the risks. Selecting the monthly data of China’s A-share listed commercial banks from January 2013 to December 2022, a nonparametric Expectile Regression Forest(ERF) risk prediction model based on the Bagging algorithm to measure the tail risk of China’s commercial banks, and simultaneously incorporates the bank leverage ratio, asset size, economic policy uncertainty, financial market volatility and liquidity into the model at the same time were constructed, and then the mean absolute error and root mean square error of the training set and test set data under different models and risk levels respectively was calculateed. Finally, the results were compared with the traditional expected quantile regression(ER) model and Expectile Regression Tree(ERT) model to determine the respective predictive performance. The results show that the ERF model has outstanding performance in measuring the tail risk of commercial banks, and the estimation and prediction ability of the ERF model is significantly better than that of the ERT and ER models under different levels of risk. Further analysis reveals that the smallest and the largest errors in the prediction of tail risk of the four major types of commercial banks are those of the national large-scale commercial banks and the local rural commercial banks, respectively, and the error values of the joint-stock commercial banks and the local urban commercial banks are comparable. The error values of joint-stock commercial banks and local urban commercial banks are comparable.

Through the study of the panel data of 17 listed city commercial banks in the five years from 2017 to 2022, the DEA-SBM-DDF model was used to measure their efficiency. The results show that the operational efficiency of urban commercial banks is generally high, but it shows a downward trend, and there are great differences in the management level among banks, and the operational efficiency of Bank of Beijing is the highest. Finally, the Tobit model was used to analyze the influencing factors affecting the operational efficiency of urban commercial banks, and the results show that the scale of Internet payment, asset scale and non-interest income non-performing loan ratio have a significant impact on the operational efficiency of urban commercial banks. The analysis results also have a certain reference role for other commercial banks’ goal setting, performance evaluation and job candidates’ selection of target commercial banks.

Since the advent of the reform and opening-up policy, the establishment of national-level new areas has had varying degrees of impact on regional development. Shenzhen Special Economic Zone has contributed to the development of the Pearl River Delta, Pudong New Area has facilitated the growth of the Yangtze River Delta, and the Binhai New Area has supported the development of the Beijing-Tianjin-Hebei region. The starting point and situation of Qingdao West Coast New Area bear many similarities to Shanghai’s Pudong New Area. Quantitative analysis was employed to reveal that industrial structure, innovation, leadership in openness, and domestic trade are the primary influencing factors of the inherent mechanisms of development and openness in Pudong New Area, as well as its role as a regional growth pole. It also offers policy recommendations for Qingdao West Coast New Area to establish itself as a regional development growth pole.

Selecting 48 national-level rural tourism key villages in Shandong Province as the research samples, spatial analysis methods were employed to describe their spatial distribution characteristics and structural features, while the Geodetector, a quantitative analysis tool, was utilized to explore the mechanism of influencing factors. The results indicate that the rural tourism key villages in Shandong Province exhibit an aggregated distribution pattern, with an overall uneven distribution and a relatively concentrated distribution at the municipal scale, particularly clustering at municipal boundaries and along coastal areas. The spatial distribution of these key villages is jointly influenced by natural environment, transportation conditions, socio-economic factors, and resource endowments. Among them, socio-economic factors are the dominant ones affecting the spatial distribution of rural tourism key villages in Shandong Province.

Land-sea integration is a new concept derived from the concepts of “land sea coordination”. It is a strategic guiding ideology for China to handle the relationship between ocean and land development, and is of great significance for promoting coordinated development between land and sea. As the closest super large city to the deep waters of the South China Sea in China, Shenzhen has not yet been able to effectively transform its location advantages and land technology industry advantages into advantages for the development of the marine economy, compared to its strong foundation in land warfare and technological innovation. The marine economy faces problems such as small total volume and emerging industry scale, low degree of integration of the sea land industrial chain and uncoordinated development. Starting from the core connotation of land sea integration, the current situation and existing problems of Shenzhen’s land sea economic integration development, and the strategy integrated development of land-sea economy were deeply analyzed.

The terrain and geological conditions in the mountainous area of western Sichuan are complex and changeable. Dangerous rocks are mostly developed in high and steep slope sections, which pose a serious threat to the safety of highway traffic after collapse. Taking the tail perilous rock of Qiaoqi Reservoir in Baoxing County of Ya’an City as an example, the basic characteristics of perilous rock were analyzed by means of UAV aerial survey and field investigation. Rocpro3 D software was used to simulate the three-dimensional kinematics to evaluate the collapse damage. Finally, the treatment suggestions are given for the dangerous rock in the working area. The results show that the formation of dangerous rock in the working area is caused by the combined action of tectonic compression, rainfall erosion and weathering unloading. Strong rainfall and strong earthquake are the typical causes of dangerous rock collapse. The maximum impact energy after the collapse of dangerous rock in the working area is more than 5000 kJ, and the removal of large-size dangerous rock can effectively reduce the impact energy value. After the collapse of the dangerous rock in the working area, it shows the stage motion characteristics of rolling along the slope → bouncing after hitting the road surface near the surface of the 351 national highway → stopping the accumulation of the riverbed and bank slope of the East River. The treatment scheme of dangerous rock removal+passive protection network+open hole+frame anchor rod can be used for prevention and control.

Whether industrial structure upgrading and postgraduate training are coordinated is the key to the revitalization and development of Liaoning Province. By constructing a comprehensive evaluation index system, the evolutionary characteristics of the coupled and coordinated development of the two systems in Liaoning Province from 2012 to 2022 and their influencing factors were analyzed. It is found that the development level of both systems in Liaoning Province has made significant progress, but the trend of industrial structure upgrading lagging behind graduate training is becoming more and more obvious. The overall level of coordination in Liaoning Province has improved year by year, transitioning from on the verge of dissonance to good coordination. Among them, Shenyang and Dalian have reached the state of good coordination, while other areas are still in the state of dysfunction. In the industrial structure upgrading system, the Tel index and total factor productivity have a greater impediment to coordinated development. And in the system of postgraduate training, the number of enrolled students, education expenditure and the number of master’s degrees granted are the main hindering factors. In addition, the level of economic development and the development of the central city have a positive effect on the coordinated development of the two systems.

To understand and grasp the impact of the planned project on the atmospheric environment of Yumen City and the surrounding counties and cities after its completion, the WRF-Chem model method was used to study the PM2.5 and O₃ concentrations in Yumen City after the completion of the project. Two scenarios were predicted. Scenario One, where the total amount of waste gas generated in the industrial park after the completion of the project is 3 127.75 t/a, and Scenario Two, where the total amount of waste gas generated is 5 112.74 t/a. The model simulation results indicate that during the planning period, the 8-hour average concentration of O₃ at each environmentally sensitive point, when added to the current concentration, meets the standards under the guaranteed rate. Under Scenario One, both the daily average and annual average concentrations of PM2.5, when added to the current concentration, meet the standards. Under Scenario Two, the daily average and annual average concentrations of PM2.5 at the boundaries of the Mahuangtan core area, Mahuangtan buffer zone, and the experimental area of the Nanshan Nature Reserve exceed the standards when added to the current concentration, while other sensitive points meet the standards. To meet the control targets of the planning area, it is recommended to implement stricter pollution source reduction measures to ensure continuous improvement of PM2.5 concentrations.

It is of great practical significance to explore how B2B enterprises select suitable third-party logistics providers in the context of e-commerce. A multi-criteria decision-making model was established to evaluate and compare the comprehensive competitiveness of multiple third-party logistics providers by using the fuzzy analytic hierarchy process. The model considers key factors such as service quality, cost optimization, flexibility, technical capabilities and corporate responsibility. Subsequently, the selection process is further optimized by combining stochastic integer programming and robust optimization methods to ensure that business needs are met while maximizing cost-effectiveness. Finally, the effectiveness of the proposed method is verified through case studies, providing scientific decision-making support for B2B enterprises in selecting third-party logistics in an e-commerce environment.

Under the backdrop of the Chinese government’s emphasis on the digital economy and data elements, relevant ministries and commissions further clarify the policy framework for the inclusion of data assets in corporate financial statements in 2024, thereby unlocking greater potential for data elements. Against this background, the panel data firstly used from 18 companies that had incorporated data assets into their financial statements, employing the Difference-in-Differences(DID) method to examine the impact of data asset inclusion on profitability metrics of listed companies. The findings reveal that, under the current data management framework, while the inclusion of data assets positively influences the profitability of listed companies, the impact remains relatively limited. Despite the undeniable positive significance of data asset inclusion for enterprises, the process has yet to significantly boost overall corporate profitability. This limitation arises from the fact that the value assessment system for data assets is not yet fully reflected in the existing accounting standards, and the maturity of the inclusion process requires further improvement. To better harness the potential of data assets and enable companies to reap the benefits of their inclusion, future policies should focus on optimizing the management and valuation systems for data assets, enhancing the market-driven application of these assets, and ensuring deeper integration of relevant accounting standards and regulations. These measures would help unlock the value of data assets, activate corporate data resources, and provide robust support for profit growth.

Based on data from all A-share listed companies from 2014 to 2023, the impact of corporate social responsibility(CSR) information disclosure on supply chain resilience was empirically examined. CSR information disclosure mainly promotes the enhancement of supply chain resilience through two pathways, such as reputation effect and resource aggregation effect. This conclusion remains valid after robustness andendogeneity tests. Heterogeneity analysis reveals that the impact is more significant for state-owned enterprises and small and medium-sized enterprises. These findings enrich the theories related to CSR information disclosure and supply chain resilience, and assist companies in formulating more scientific CSR strategies during optimization and upgrading processes to improve supply chain resilience.

ESG ratings from Bloomberg(2006—2022), Huazheng(2009—2023) and China’s listed companies as samples were used. Correlations of ESG scores, ESG in different industries, and heterogeneity analysis of ESG was explored. Empirical results show that Environment(E), Social(S) and Governance(G) affect ESG scores significantly, with G having the greatest impact, followed by E and then S. ESG performance varies across industries. The finance-insurance sector is good at environment but weak in social aspects. The accommodation industry improves ESG ranking through social efforts. The agriculture and forestry service industry has an imbalance in governance and social development. ESG strategy advice are given for the government and companies.

The impact of economic policy uncertainty on M&A performance was examined. Taking the M&A data of A-share listed companies from 2007 to 2023 as the study sample, it is found that the impact is negative and statistically significant, and the moderating effect factor investor sentiment exacerbates this impact. Further analysis shows that during economic upturns, economic policy uncertainty has the greater significant negative effect on M&A performance compared with the economic downturns. The similar condition happens in the enterprises with non-concentrated institutional investor holdings. But economic policy uncertainty does not seem to influence the M&A performance of the enterprises with green M&A type or located in first-tier or new first-tier cities.

With the development of cross-border e-commerce, enterprises often face institutional pressure from external sources when promoting cross-channel integration. 298 data through Smartpls software was analyzed to explore the mechanism of institutional pressures on cross-channel integration, and verify the moderating role of organizational learning in it. It is found that normative, coercive and imitative pressures all positively affect cross-channel integration in firms. Organizational learning moderates the positive relationship between coercive and imitative pressures and cross-channel integration, while the moderating effect of normative pressure is not significant. The article reveals the complex interaction between institutional pressure and cross-channel integration, emphasizes the key moderating role of organizational learning in this process, and provides a theoretical basis for cross-border e-commerce firms to cope with institutional pressure and optimize cross-channel integration strategies.

Agriculture is the cornerstone of building a modern country. As the impact of global climate change on agricultural production intensifies, studying how agricultural enterprises can respond to challenges such as extreme weather and resource scarcity is crucial for improving agricultural adaptability and mitigating the negative effects of climate change. Based on empirical data of A-share agricultural listed companies from 2013 to 2022, fixed effect and mediation effect models were selected to test the impact of climate risk on business performance. The research results indicate that climate risk has a negative impact on the operational performance of agricultural enterprises, and green innovation plays a mediating role in it. Heterogeneity results show that non-state-owned enterprises are more significantly impacted by climate risk than state-owned enterprises. Important theoretical basis and practical guidance are provided for agricultural enterprises in addressing climate risks.

Digital resilience, as the ability of enterprises using digital technology to respond to major shocks, has received extensive attention from society. Through a review of domestic and foreign literature, it is found that the digital resilience of enterprises mainly consists of the ability to absorb shocks, the ability to adapt to shocks, and the ability to recover to a steady state. The multi-case study method was used to identify the typical practices adopted by chemical enterprises in the process of building digital resilience. On this basis, these scattered typical practices were further summarized and integrated to construct a universal model of the digital resilience building path for chemical enterprises. Through dynamic management and control based on digital innovation, organizational collaboration based on digital platforms, resource aggregation based on industrial Internet platforms, and agile response based on business model innovation, it helps enterprises effectively defuse major shocks and promotes their transition to a new steady state.

Abnormal events involving potential safety hazards and near misses are used as early warnings and signs for the escalation of minor accidents to major accidents, which can be used to establish accident models to identify source events and correct unsafe factors in the protection system. Tailored to the process characteristics and accident features of liquefied natural gas(LNG) storage areas, the system hazard identification, prediction and prevention(SHIPP) model was improved, and a novel risk assessment modeling method integrating fault trees, Bayesian networks, and the A-star algorithm was proposed. Firstly, based on expert experience and abnormal events in the accident alarm database, a safety barrier model and fault tree were established. Then, following the chain rule, the fault tree was mapped to a Bayesian network. Finally, the improved A-star algorithm was integrated to determine the accident occurrence pathways. Research based on the LNG accident alarm database indicates that this method, compared to the traditional SHIPP model, can achieve dynamic forward risk assessment and quantify the conditional probabilities between accidents, as well as simulate the accident occurrence process when safety barriers fail in reverse. The research results can provide reasonable design and decision-making for the system safety and risk avoidance of LNG storage areas.

Consumption upgrading is an inevitable trend of national development to a certain extent, and the differentiated research of different regions is the inevitable basis for formulating policies. The framework of residents’ consumption upgrading system was constructed from five dimensions: consumption scale and level, consumption structure, consumption capacity, consumption environment and consumption mode. The results show that the consumption upgrading of residents in the whole country and the three major regions shows a relatively flat upward trend, and the eastern region > the whole country> the central region > the western region. The difference in the level of consumption upgrading of Chinese residents mainly comes from the internal differences in the eastern region and the imbalance in the development of the eastern-central and eastern-western regions. There isσdevelopment in the whole country and the three major regions, there isβconvergence between the whole country and the eastern and western regions, and there is noβconvergence in the central region.

As one of the core policy tools for achieving the “dual-carbon” goals, the carbon market possesses financial, environmental and policy attributes. These characteristic attributes prompt the establishment of an effective regulatory system for carbon emission rights trading. Currently, in China’s carbon emission rights trading and its subsequent supervision, there are problems such as inefficient supervision and opaque trading information. Based on the application requirements of modern environmental governance for new digital technologies, applying technologies such as blockchain’s distributed storage, consensus mechanism, and smart contract encryption to the supervision of carbon emission rights trading will help improve the supervision efficiency and the security of the circulation of carbon trading information, and also alleviate the problems existing in the process of carbon emission monitoring and verification. However, due to the dual nature of technology, while blockchain empowers the supervision of carbon emission rights trading with its technology, there are also certain hidden concerns in aspects such as data security and the protection of the rights and interests of trading entities. In this regard, it is necessary to further improve the smart contract review mechanism and perfect the relief rules, construct a risk prevention and evaluation system, and clarify the main responsibilities of blockchain system development and maintenance to ensure the effective supervision of carbon emission rights trading.

Advanced nuclear energy technology and green computing power technology are both typical representatives of new quality productive forces, and are of great significance for the future development of artificial intelligence and the transformation of green energy. In recent years, the collaborative development model between nuclear energy and computing power centers has been burgeoning. The formation basis of this collaborative development model, as well as the advantages of the business model in the fields of energy conservation and carbon emission reduction, economic profitability, safety and stability, resource saving and market absorption were introduced. The two potential coupling models of electricity purchase-sale model and off-grid direct supply model were also discussed. At the same time, the article also focused on the challenges from the fields of policy, regulations, technology, cost and market, and outlined the typical business experiments abroad. Finally, recommendations are elaborated on in three aspects including technological advancement, commercial innovation and policy support.

Accounting to the goal of achieving the carbon peak by 2030, based on the existing relevant policies, the kaya carbon emission identity, combined with China’s “14th Five-Year Plan” and long-term planning and other relevant policies, the scenario analysis method was used to estimate China’s total carbon emissions in 2030. On the basis of the total amount, taking into account the two principles of fairness and efficiency, entropy method and zero-sum income data Envelopment (ZSG-DEA) were used for empirical testing, and a provincial carbon emission quota allocation scheme under the multi-index system was constructed. The allocation plan that takes into account the two principles of fairness and efficiency is conducive to the establishment of a reasonable cross-regional carbon emission quota allocation mechanism, and has important reference value for promoting China’s cross-provincial collaborative emission reduction strategy, establishing and improving the national carbon trading market, and achieving the goal of carbon peak in 2030.

Innovation-driven development provides new impetus for economic and social development and is the core approach to leading future economic transformation. Government R&D investment reflects the national will, and it is necessary to evaluate its output efficiency. Using panel data from 31 provinces(due to the lack of data, the statistical data mentioned here do not include the Hong Kong Special Administrative Region, the Macao Special Administrative Region and Taiwan Province) in China from 2018 to 2022, 13 specific indicators from the input and output sides were selected to build a performance evaluation system for government R&D investment. Factor analysis was used to study the government R&D investment performance. Combining the model construction and the results analysis, improving government R&D investment performance is proposed by promoting diversification of R&D funding sources, optimizing the execution structure of government R&D funding, and increasing R&D personnel input.

From “decoupling” to “de-risking”, the US policy towards China has not fundamentally reversed, and its essence is still to curb China’s economic development and maintain its own global hegemony. By combing the United States’ measures towards China, it is found that the United States focuses on the containment and suppression of China in the economic, technological and financial fields, which leads to the weakening of Sino-US import and export trade links and the acceleration of market diversification. At the industrial chain level, China and the United States face the risk of “chain breaking”, which is manifested in the decline of trade dependence on intermediate goods, the intensification of supply chain relocation, the weakening of manufacturing complementarity, and the acceleration of supply chain restructuring, especially in high-tech industries. At the same time, the cultural exchanges between the two countries are limited, and the two-way investment is also declining. To this end, China needs to manage differences through negotiation and expand the space for Sino-US cooperation. At the same time, China should further deepen its opening to the outside world, improve the multilateral trading system, promote the construction of the domestic market, and improve its ability to resist risks. On this basis, China should gradually improve the scientific and technological innovation system, accelerate the cultivation of new productivity, and continuously improve the level of investment at home and abroad.

With the booming development of big data, the demand for data management capabilities has gradually penetrated into various fields. Based on a deep reflection on the practical work of data management in universities, starting from the perspective of comprehensively optimizing university data management, three core issues in university data management were firstly analyzed and confirmed. Then, referring to the maturity evaluation model of data management capabilities, combined with the current situation of university data management capabilities, it focused on selecting three capability domains to solve the core issues one by one. Finally, from the above capability domains, it focused on six capability items and plans the implementation path of university data governance.

With the widespread application of artificial intelligence technology across various industries, our society has embarked on the journey towards the 4.0 intelligent era. To foster high-quality development in teaching reform at colleges and universities, which is empowered by information technologies such as artificial intelligence, in-depth interviews with 20 managers, implementers and educates involved in teaching reform across 11 colleges and universities were conducted. Qualitative analysis was performed using NVIVO software. Through analyzing the current application of artificial intelligence in teaching reform at colleges and universities, both achievements and challenges are identified. Finally, it is proposed to advance the intelligent application of teaching reform in colleges and universities from four perspectives such as people-oriented philosophy, talent support, financial support and institutional support.

Withthe rise of knowledge economy, “knowledge” has replaced the traditional production factors such as machinery, equipment, labor, capital and raw materials, bringing about changes in knowledge organization and management. In order to ensure the safety of intellectual achievements and meet the individual needs of all stages, it is necessary to improve the intellectual property information service mode under the guidance of the innovation and development of knowledge management. The characteristics of the era of knowledge economy, the expression form of knowledge management innovation and intellectual property service were analyzed, selecting Shanghai Jiao Tong University as the advanced case, and the practical effect of its library intellectual property information service innovation was analyzed. The beneficial experience covering the life cycle of knowledge management and meeting the needs of scientific research, management and teaching is organized out, in order to promote the integration of knowledge management innovation into the intellectual property information service of universities.

The Scientific evaluation of urban emergency response capacity is a critical step in enhancing urban resilience and the ability to response to unexpected events. Using the analytic hierarchy process (AHP) and the life-cycle theory of emergency management, this study constructs an evaluation index system for urban emergency capability, encompassing the four stages of prevention, preparedness, response, and recovery. The weights of 16 secondary indicators are determined, and a systematic analysis and quantitative assessment are conducted for each stage of emergency management and its key indicators. Case studies demonstrate that the constructed evaluation system not only comprehensively reflects the multidimensional characteristics of urban emergency capability but also reveals the strengths and weaknesses across the four stages.

Green development cannot be achieved without innovation in green technology. Only when green technology is innovated can a country achieve high-quality development. Regarding the construction of ecological civilization as a quasi natural experiment, green patent data was used to empirically analyze the impact of ecological civilization construction on green technology innovation through the double difference method. It is found that the construction of ecological civilization significantly promotes green technology innovation in the experimental zone. Ecological civilization construction can attract environmental investment and human capital, and is conducive to infrastructure improvement and industrial structure upgrading, thereby promoting the level of green technology innovation.

As the main body of developing new quality productivity, the level of environmental information disclosure of enterprises has a significant impact on the development of new quality productivity. Using data from Chinese A-share listed companies from 2011 to 2023, and calculates the environmental information disclosure index and the new quality productivity index of each listed company were respectively calculated through content analysis and entropy method to study the impact of corporate environmental information disclosure on new quality productivity. It is found that corporate environmental information disclosure can significantly promote the development of new quality productivity. Mechanism analysis shows that environmental information disclosure can promote the development of new quality productivity by alleviating financing constraints and enhancing green innovation capabilities. Heterogeneity analysis shows that state-owned enterprises and large-scale enterprises have a more significant effect on developing new quality productivity. Further discussion reveals that the promotion effect of developing new quality productivity in heavily polluting enterprises and enterprises located in the eastern region is more significant. At the same time, it is found that the level of environmental information disclosure and enterprise performance mutually promote each other, forming a virtuous cycle and providing assistance for the development of new quality productivity in enterprises. The research results have enriched the study of the consequences of environmental information disclosure and provide inspiration for improving new quality productivity in practice.

Based on panel data from 30 provinces(due to the lack of data, the statistical data mentioned here do not include the Tibet Region, the Hong Kong Special Administrative Region, the Macao Special Administrative Region and Taiwan Province) in China from 2013 to 2022, a new quality productivity system was constructed and a two-way fixed-effects model was used to examine the impact of new quality productivity on regional coordinated development, as well as to test the mediating role of industrial structure upgrading. The results show that new quality productivity significantly promotes regional coordinated development, with noticeable regional heterogeneity. Industrial structure upgrading plays a mediating role in the process of new quality productivity affecting regional coordinated development. Based on this, corresponding policy recommendations are proposed to promote the development of new quality productivity, reduce regional economic disparities, and achieve regional coordinated development.

Taking 30 provinces(due to the lack of data, the statistical data mentioned here do not include the Tibet Autonomous Region, the Hong Kong Special Administrative Region, the Macao Special Administrative Region and Taiwan Province) in China as samples, the TOE framework and dynamic QCA method was used to study the synergetic effect of artificial intelligence elements on the improvement of new quality productivity. The results show that all six antecedent conditions are non-essential conditions, and the lack of intelligent organizational capabilities is the core obstacle to high new quality productivity. Five configuration paths are identified including ability-driven-financial synergy type, technology-driven-ability synergy type, ability-driven type, ability-driven-government synergy type and organization-led type. According to the consistency analysis results within and between groups, the consistency in some years fluctuates, and there is no obvious time effect or case effect. The explanatory power of the results is relatively strong. Based on this, it is proposed to adopt multi-dimensional comprehensive measures, customize specific strategies for different configuration types, maintain policy continuity and stability, and focus on the core of organizational capabilities, aiming to enhance the development of new quality productivity in China.

With the rapid development of digital technology, short video platform is playing an important role in empowering rural new quality productivity. By optimizing and improving rural workers, labor tools, labor materials and the combination of this three elements, short video platform enables rural new quality productivity. However, practical problems such as the counterfeiting of agriculture-related short video, lack of industrial integration in some regions, and lack of talents in agriculture-related short video operations, restrict the sustainable development of rural new quality productivity. In this regard, a breakthrough path was given for short video platform to enable rural new quality productivity from four aspects, such as innovation and development, industrial layout, policy environment and talent support. Suggestions on promoting innovation at all levels are put forward for promoting deep industrial integration, doing a good job in system innovation, and cultivating and introducing talents.

Based on theoretical analysis, this paper selects panel data from 31 provinces(due to the lack of data, the statistical data mentioned here do not include the Hong Kong Special Administrative Region, the Macao Special Administrative Region and Taiwan Province)in China to study the impact of population aging on the effectiveness of monetary policy. The results show that the aging of the population significantly weakens the effectiveness of monetary policy. From the perspective of regional heterogeneity, the more severe the population aging, the stronger the inhibitory effect of population aging on monetary policy. Further research finds that population aging affects the effectiveness of monetary policy by weakening the credit channels of monetary policy. Finally, it is proposed that the age factor of the population should be fully considered, and the relevant policy suggestions should be carefully evaluated such as the intermediary target and the prediction effect of monetary policy.