ArticlePloS one2023
The impact of digital logistics under the big environment of economy.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed, 12 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital logistics techniques are important for business applications that contribute to economic growth. The modern supply chain or logistics seeks to implement a large-scale smart infrastructure incorporating data, physical objects, information, products, and business progressions. The business applications are utilized various intelligent techniques to maximize the logistic process. However, the logistic process suffers due to transportation costs, quality, and multinational transportation. These factors frequently affect the region's economic growth. In addition, most cities are located in remote areas that receive improper logistic support, which minimizes business growth. So, this work analyzes the impact of digital logistics on the region's economy. The Yangtze River economic belt region, which includes almost 11 cities, is chosen for analysis. The gathered information is processed by Dynamic Stochastic Equilibrium with Statistical Analysis Modelling (DSE-SAM), which predicts the correlation and influence of digital logistics on economic development. Here, the judgment matrix is constructed to reduce the difficulties of data standardization and normalization processes. Then entropy model and statistical correlation analysis are utilized to improve the overall impact analysis process. Finally, the developed DSE-SAM based created system efficiency is compared with the other economic models, such as Spatial Durbin Model (SDM), Coupling Coordination Degree Model (CCDM), and Collaborative Degree Model (CDM). The results of the suggested DSE-SAM model achieve a high correlation of urbanization, logistics, and ecology in the Yangtze River economic belt region compared to other regions.
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Registered trials
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