FACTORS INFLUENCING DATA ELEMENTS EMPOWERING NEW QUALITY PRODUCTIVE FORCES AND THEIR SPATIOTEMPORAL EVOLUTION: A STUDY BASED ON PANEL DATA FROM 31 PROVINCES IN CHINA

In the context of the deep integration between the digital and real economies, data elements have become a core force driving economic growth and social transformation. China has positioned market-oriented allocation and value realization of data elements as a national development strategy; however, challenges such as regional development imbalances and widening digital divides persist in the empowerment of new-quality productive forces. This study employs panel data from 31 Chinese provinces and municipalities between 2012 and 2022, integrating entropy-based TOPSIS models, ARIMA models, and spatial econometric models to systematically explore the impact mechanisms and spatiotemporal evolution characteristics of data elements on new-quality productive forces. The ARIMA model predicts that China's national coupling coordination degree will show a yearly growth trend over the next three years, with eastern regions outperforming central and western regions, though regional disparities are gradually narrowing. The spatial econometric model further reveals heterogeneous response characteristics of data element policies: "rapid effectiveness in eastern regions with delayed effects in central and western areas." This study aims to bridge the digital divide, optimize data governance, and provide robust support for achieving high-quality development goals in China's digital economy during the 14th Five-Year Plan period.

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Publication Details

Journal
World journal of economics and business research.
Published
2025-08-18
DOI
https://doi.org/10.61784/wjebr3056
Primary Topic
Regional Economic and Spatial Analysis
Type
article
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article

FACTORS INFLUENCING DATA ELEMENTS EMPOWERING NEW QUALITY PRODUCTIVE FORCES AND THEIR SPATIOTEMPORAL EVOLUTION: A STUDY BASED ON PANEL DATA FROM 31 PROVINCES IN CHINA

World journal of economics and business research.
Regional Economic and Spatial Analysis
article

FACTORS INFLUENCING DATA ELEMENTS EMPOWERING NEW QUALITY PRODUCTIVE FORCES AND THEIR SPATIOTEMPORAL EVOLUTION: A STUDY BASED ON PANEL DATA FROM 31 PROVINCES IN CHINA

article en

Abstract

In the context of the deep integration between the digital and real economies, data elements have become a core force driving economic growth and social transformation. China has positioned market-oriented allocation and value realization of data elements as a national development strategy; however, challenges such as regional development imbalances and widening digital divides persist in the empowerment of new-quality productive forces. This study employs panel data from 31 Chinese provinces and municipalities between 2012 and 2022, integrating entropy-based TOPSIS models, ARIMA models, and spatial econometric models to systematically explore the impact mechanisms and spatiotemporal evolution characteristics of data elements on new-quality productive forces. The ARIMA model predicts that China's national coupling coordination degree will show a yearly growth trend over the next three years, with eastern regions outperforming central and western regions, though regional disparities are gradually narrowing. The spatial econometric model further reveals heterogeneous response characteristics of data element policies: "rapid effectiveness in eastern regions with delayed effects in central and western areas." This study aims to bridge the digital divide, optimize data governance, and provide robust support for achieving high-quality development goals in China's digital economy during the 14th Five-Year Plan period.

World journal of economics and business research.Vol. 3(3)
Tianjin University of Technology (CN)
Decent work and economic growth
Openalex Percentile: Top 71%
Regional Economic and Spatial Analysis
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FACTORS INFLUENCING DATA ELEMENTS EMPOWERING NEW QUALITY PRODUCTIVE FORCES AND THEIR SPATIOTEMPORAL EVOLUTION: A STUDY BASED ON PANEL DATA FROM 31 PROVINCES IN CHINA · World journal of economics and business research. (2025) | TGRS Research Map | TGRS