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.

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.

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.

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.

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.

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.

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.

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.

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.

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.