Identifying complex relationship in sub-fields between digital economy and urban agglomeration water pollution in Yangtze River Economic Belt: Evidence from original large-scale social media text analysis

Songhua Huan

Chaos, Solitons & Fractals, 2025, vol. 200, issue P2

Abstract: The specific sub-fields of urban agglomeration water pollution influenced by digital economy remain underexplored. To address this gap, this study conducts a novel analysis using large-scale original social media texts to capture public discourse. We construct two comprehensive text databases: one focusing on digital economy and the other on urban agglomeration water pollution within the Yangtze River Economic Belt. Natural language processing topic model, pre-trained deep learning model and a sliding correlation method are applied for topic identification and temporal analysis. The results reveal that the (1) digital economy and urban agglomeration water pollution can be classified into nine and five sub-fields, respectively, with more than 70 % of public sentiment being positive toward them. (2) The average response time of urban agglomeration water pollution to digital economy influences is approximately 6.07 months within provinces and 5.91 months across provinces. (3) From an economic perspective, digital investment and digital consumption are potentially associated with urban agglomeration water engineering programs within provinces. In cross-provincial contexts, global trade is identified as the primary digital economy component influencing these programs. This study provides new insights into the relationship between digital economy and urban agglomeration water pollution from a public perspective. The findings offer valuable implications for digital economy development, environmental policy-making in urban agglomerations and the promotion of sustainable urban development.

Keywords: Digital economy; Urban agglomeration; Water pollution; Text data mining; Public perspective (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:200:y:2025:i:p2:s096007792501029x

DOI: 10.1016/j.chaos.2025.117016

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