Analysis on the Development of Digital Economy in Guangdong Province Based on Improved Entropy Method and Multivariate Statistical Analysis

A novel MADM model integrating hybrid information for evaluating the development prospects of urban new energy vehicles

As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information. This system incorporates five different types of evaluation information: exact numbers, interval numbers, triangular fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets (PLTS), enhancing the framework's ability to handle diverse data types. Subsequently, the improved entropy (IEntropy) weight method is employed to determine the objective weights of the evaluation indicators. These objective weights are then integrated with the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, facilitating a structured group decision-making approach that synthesizes hybrid evaluation information. Based on modular thinking, hybrid evaluation information is synthesized to evaluate and rank the NEVs development prospects of each city. Sensitivity analysis and comparative analysis further demonstrate the robustness and reliability of the proposed MADM framework. The ranking results indicate that Shanghai and Guangzhou lead in NEVs promotion, while cities like Harbin and Zhengzhou lag behind. Based on these findings, the study proposes targeted policy recommendations to promote the sustainable development of the NEVs industry in major Chinese cities.

Spatial analysis of digital economy and its driving factors: A case study of the Yangtze River Delta City Cluster in China

The digital economy (DE) has become a major breakthrough in promoting industrial upgrading and an important engine for high-quality economic growth. However, most studies have neglected the important driving effect of regional economic and social (RES) development on DE. In this paper, we discuss the mechanism of RES development promoting the development of DE, and establish a demand-driven regional DE development model to express the general idea. With the help of spatial analysis toolbox in ArcGIS software, the spatial development characteristics of DE in the Yangtze River Delta City Cluster (YRDCC) is explored. We find the imbalance of spatial development is very significant in YRDCC, no matter at the provincial level or city level. Quantitative analysis reveals that less than 1% likelihood that the imbalanced or clustered pattern of DE development in YRDCC could be the result of random chance. Geographically weighted regression (GWR) analysis with publicly available dataset of YRDCC indicates RES development significantly promotes the development of DE.

Research on safety early warning of uranium tailings dam based on abnormal radioactive indexes of water leachate

The radioactive index value of the leachate of the uranium tailings dam is affected by the internal damage of the dam. Therefore, a way of using the deviation of the radioactive index concentration in the leachate to warn the instability of the dam is innovatively proposed in this paper. Firstly, the SSA-BP algorithm is used to predict and analyze the five groups of parameters U, Ra, ∑ α, ∑ β and Rn. Then, the deviation between the actual value and the predicted value is computed. Finally, an early warning is given based on the entropy weight extension decision-making model. The model is verified by the leachate environment monitoring data of a uranium tailings dam in southern China from 2016 to 2020, which shows that the model can effectively caution of the instability of the uranium tailings dam and provides a reference for the subsequent decommissioning management.

Moving towards Environmental Sustainability: Can Digital Economy Reduce Environmental Degradation in China?

In the context of environmental sustainability and accelerated digital technology development, China attaches great importance to the prominent role of digital economy in addressing environmental degradation. Utilizing Chinese provincial panel data from 2011 to 2019, this study investigates whether the digital economy can improve China's environmental sustainability proxy by reducing carbon emission intensity. Based on the fixed effects model, the findings reveal that the digital economy has a significant negative effect on carbon emission intensity and the conclusion remains robust after conducting several robustness checks. However, this impact shows regional heterogeneity, which is more effective in resource-based eastern regions and the Belt and Road provinces. Moreover, mediating effect analyses indicate that the transmission mechanisms are energy consumption structure, total factor energy productivity, and green technology innovation. Furthermore, the results based on the spatial Durbin model (SDM) demonstrate that digital economy development has a significant spatial spillover effect. Finally, on the basis of results analysis and discussion, policy recommendations are provided for achieving environmental sustainability.

Estimation and Potential Analysis of Land Population Carrying Capacity in Shanghai Metropolis

It is of great practical significance to understand the current situation of urban land carrying capacity, explore its potential space, and continuously improve the economic adaptability and resilience and population carrying capacity of megacities. Based on the guiding principle of territorial spatial division and the concept of moderate-scale resilient cities, combined with GIS technology, this study aims to divide land spaces into three types and construct different index systems to evaluate the land carrying capacity of Shanghai in different spaces. Furthermore, we propose different schemes of estimating subspace land population carrying capacity, and the carrying potential of land population is analysed as well. The acquired results demonstrate three key points. Firstly, the total land population capacity of Shanghai is estimated at 25,476.61–32,047.27 people, with urban land space being the most dominant for the city’s population carrying capacity. Furthermore, the inner suburbs carry the largest population, and the urban centre carries a larger population density than other areas. Secondly, there are significant spatial differences in land population carrying potential. Compared with the demographic data from 2017, Shanghai still has a population carrying potential of 1293.30–7863.97 people and a suitable population carrying potential of 4578.64 people. The population of the urban centre is near the upper limit of the estimated population carrying capacity, and the suburbs, especially the outer suburbs, have large population carrying potential. Thirdly, the estimation method adopted in this study can effectively reveal the spatial differences in population carrying capacity and the potential of different land spaces and different regions in Shanghai, with the estimation results being highly credible. The results will provide references for the improvement of the multi-scenario population planning strategy in Shanghai, as well as enrich the research span and methods currently employed in land carrying capacity.

Entropy analysis and grey cluster analysis of multiple indexes of 5 kinds of genuine medicinal materials

5 kinds of genuine medicinal materials, including Diding (Latin name: Corydalis bungeana Turcz), Purslane (Latin name: Portulaca oleracea L.), straw sandal board (Latin name: Hoya carnosa (L.f.) R. Br), June snow (Latin name: Serissa japonica (Thunb.) Thunb.), pine vine rattan (Latin name: Lycopodiastrum casuarinoides (Spring) Holub. [Lycopodium casuarinoides Spring]), were selected as the research objects. The combustion heat, thermo gravimetric parameters, and fat content, calcium content, trace element content, ash content of 5 kinds of genuine medicinal materials were measured. The combustion heat, differential thermal gravimetric analysis, fat content, calcium content, trace elements content, and ash content of 5 kinds of genuine medicinal materials were used to build a systematic multi-index evaluation system by gray pattern recognition and grey correlation coefficient cluster analysis, which can make up for the gaps in this area and provide scientific basis and research significance for the study of genuine medicinal materials quality. The results showed that the order of combustion heat of 5 kinds of genuine medicinal materials, including Diding, Purslane, straw sandal board, June snow, pine vine rattan, was Diding > June snow > straw sandal board > Purslane > pine vine rattan, the order of fat content (%) of 5 kinds of genuine medicinal materials was straw sandal board > Diding > pine vine rattan > June snow > Purslane, the order of calcium content (%) was pine vine rattan > June snow > Purslane > straw sandal board > Diding, the order of ash content was June snow > Purslane > straw sandal board > pine vine rattan > Diding. From the analysis of thermogravimetric analysis results and thermogravimetric combustion stability, the order of combustion stability of 5 kinds of genuine medicinal materials was June snow > pine Vine rattan > straw sandal board > Diding > Portulaca oleracea. The order of the content of 12 trace elements in 5 kinds of genuine medicinal materials, in terms of trace element content, June snow contains the highest trace elements in all samples. According to combustion heat, combustibility (combustion stability of genuine medicinal materials), fat, calcium, ash, trace element content, the comprehensive evaluation results of multi-index analysis constructed by gray correlation degree, gray correlation coefficient factor analysis, and gray hierarchical cluster analysis showed that the comprehensive evaluation multi-index order of 5 genuine medicinal materials, including Diding, Purslane, straw sandal board, June snow and pine vine rattan, was June snow > straw sandal board > Diding > Purslane > pine vine rattan. Therefore, the comprehensive evaluation results of the quality of genuine medicinal materials selected in this study were June snow the best, followed by straw sandal board. This research has important theoretical and practical significance for the multi-index measurement and comprehensive evaluation of genuine medicinal materials, and can provide scientific basis and research significance for the research of multi-index quality control of genuine medicinal material.