Shijun Chen

International electric vehicle (EV) trade has expanded rapidly, but greater connectivity does not necessarily reduce structural concentration or dependence. This study constructs directed and weighted EV trade networks for 80 major economies from 2018 to 2025. Social network analysis, geospatial visualization, core–periphery analysis, structural hole measures, and the Hubness Measurement (HM) index are used to evaluate connectivity, spatial concentration, hierarchy, brokerage, and bilateral dependence. The number of directed trade links increased from 1804 in 2018 to 2884 in 2023, before declining modestly to 2820 in 2024 and 2740 in 2025. Clustering remained high throughout the period, while average path length declined, indicating greater structural accessibility despite persistent regional concentration. A relatively small group of economies continued to occupy core positions. At the same time, lower betweenness centrality and declining peak effective-size values suggest that brokerage became less exclusive as alternative connections expanded. In the bilateral case analysis, export dependence shifted toward a broader set of destination markets, although exposure to several important markets remained. Overall, the results show that stronger integration can coexist with persistent hierarchy, regional concentration, and bilateral dependence. These findings provide evidence for trade and industrial strategies aimed at improving diversification and the resilience of the global EV trade network.

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or on global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image-processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east, and the Chengdu–Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower, at 80.33%. This study provides an objective and accurate assessment of the distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east and the Chengdu-Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower at 80.33%. This study provides an objective and accurate assessment of distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

The carbon emissions trading system (CETS) is a crucial market-based tool aimed at achieving the goals of “carbon peaking” and “carbon neutrality”, and plays a pivotal role in promoting the green development of the logistics industry. The green total factor productivity (GTFP) of the logistics industry is a key indicator used to measure the green development level of the logistics industry. The research on the CETS and the GTFP in the logistics industry(LGTFP) is currently lacking. Accurately assessing the impact of CETS on LGFP is crucial for improving the construction of the CETS and promoting the green transformation of the logistics industry. Utilizing a combined approach of system dynamics and econometrics, this study examines the effects of CETS on the LGFTP. In this study, the impact mechanism of CETS on the LGTFP is analyzed by constructing a causal relationship diagram using system dynamics theory. And then, we construct a multi-period differencein- difference (DID) model using panel data from 30 Chinese provinces spanning from 2010 to 2020 to verify the impact of CETS on LGTFP. The findings reveal that: (1) CETS significantly promotes LGTFP, and this conclusion is consistent even after conducting robustness tests. (2) Green technological innovation is identified as the primary influencing mechanism of CETS in enhancing LGTFP, while industrial upgrading does not play a positive mediating role, contrary to established studies. (3) Heterogeneity analysis reveals that CETS has a significant promotion effect on LGTFP in central and western regions, and resource-based provinces, but not yet in eastern regions and non-resource-based provinces. Based on the empirical analysis, we propose relevant policy recommendations to improve China’s CETS and promote the improvement of LGTFP.

This paper presents an in-depth study of a statistical system using image feature extraction technology for cultural heritage tourism information. Based on the specificity of the tourism industry, it combines the technical architecture, framework of image feature extraction, GIS basic design, database design, and other related theories; realizes visual mapping through relevant tourism statistics in GIS; and uses the spatial analysis capability of ArcGIS software to complete the statistical scheme of cultural heritage tourism information data in the context of Internet big data. In this paper, the image preprocessing process is accelerated by the parallel optimization of the filtering operation and down-sampling operation in the relevant image preprocessing algorithm. The preprocessed images can effectively improve the recognition effect of the image feature extraction and classification stage. Based on cultural heritage tourism information mining results, the design and development of the tourism information statistics platform are carried out. The platform is built and developed using GIS secondary development technology and HTML + CSS front-end development technology API, Echarts, and other image feature components. The platform enables the massive and disorganized tourism text information to be displayed in a way that is easier to understand in both maps and text. This paper’s research on critical technologies provides technical support for image feature extraction of tourism statistics, which has specific practical value. Implementing the cultural heritage tourism platform offers a visual operating environment for tourism, enabling cultural heritage tourism businesses to tap into user preferences so that users can effectively access the travel options that interest them, which is conducive to promoting the development of travel recommendation platforms.

Many companies have paid attention to environmental preservation in recent years. Firms label their goods or services ecologically responsible, becoming a quickly growing trend. This article tries to establish the link between green elements and competitive advantage tactics to progress sustainable manufacturing. It aims to discover the relationship between green innovation, green market orientation, green entrepreneurial orientation, technology readiness, and product quality to attain sustainable business success. Employing simple random sampling, surveys were distributed via email. The present study is a quantitative analysis, and the analytical tool utilized is AMOS and smart PLS program applications. The empirical outcomes find that green factors help companies improve the quality of products and sustainable success. Also, technology readiness positively influences the quality of products and sustainable success in business. In addition, the impact of green entrepreneurial orientation on the quality of products and sustainable success is positive and significant. The findings illustrated that green market orientation is an essential factor in the quality of products and sustainable success in business. Another point is that green innovation positively affects the quality of products and sustainable success. Finally, the quality of products has significantly affected sustainable success in business.

From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government’s economic forecast and macro control. Therefore, it is necessary to strengthen the research on early warning and prediction of agricultural futures price. For the prediction of futures price, there are two kinds of common models: one is the traditional classic time series model, and the other is the neural network model under the wave of artificial intelligence. This paper selects the 1976 closing data of agricultural futures index from January 10, 2012, to February 27, 2020, and uses the time series differential autoregressive integrated moving average model (ARIMA model) and long short‐term memory model (LSTM model) to study this work, respectively, and compares the predicted effects of the two models in some metrics. Based on the predicted results of the two models, a simple trading strategy is established, and the trading effects of the two models are compared. The results show that the LSTM model has obvious advantage over ARIMA time series model in the price index prediction of agricultural futures market.