Synergy degree measurement of traditional manufacturing and green industry driven by digital economy in Yangtze River Delta region

Measuring the collaborative development of the manufacturing industry with green industry can sort out the green development context of the traditional manufacturing industry in the digital economy era and provide a reference for realizing the green transformation of traditional manufacturing industry. This paper proposes the coordinated development evaluation model from the perspective of industrial, social and environmental subsystems based on the theory of the social shaping of technology. Furthermore, this paper applies the entropy method and order parameter method to measure the synergy degree of composite system of traditional manufacturing industry, digital economy and green industry in Yangtze River Delta region based on the panel data of China Statistical Yearbook from 2013 to 2021. The research shows that the order degree of traditional manufacturing industry, digital economy and green industry subsystem has been effectively increased year by year, while the synergy degree is still low in different provinces. The traditional manufacturing industries should build the digital innovation ecosystem and utilize digital technology to improve the high energy consumption production process and achieve the dual goals of “low-carbon development and digital transformation”.

Authors

Institutions

Publication Details

Journal
Data Science and Informetrics
Published
2024-09-01
DOI
https://doi.org/10.1016/j.dsim.2025.02.001
Citations
3
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
1.05
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Synergy degree measurement of traditional manufacturing and green industry driven by digital economy in Yangtze River Delta region

Zhao Ming, Yuanyuan Wang, Yi Liu
3 citations
Data Science and Informetrics
Digital Transformation in Industry
1.05
article

Synergy degree measurement of traditional manufacturing and green industry driven by digital economy in Yangtze River Delta region

Zhao Ming, Yuanyuan Wang, Yi Liu
article en
3 citations

Abstract

Measuring the collaborative development of the manufacturing industry with green industry can sort out the green development context of the traditional manufacturing industry in the digital economy era and provide a reference for realizing the green transformation of traditional manufacturing industry. This paper proposes the coordinated development evaluation model from the perspective of industrial, social and environmental subsystems based on the theory of the social shaping of technology. Furthermore, this paper applies the entropy method and order parameter method to measure the synergy degree of composite system of traditional manufacturing industry, digital economy and green industry in Yangtze River Delta region based on the panel data of China Statistical Yearbook from 2013 to 2021. The research shows that the order degree of traditional manufacturing industry, digital economy and green industry subsystem has been effectively increased year by year, while the synergy degree is still low in different provinces. The traditional manufacturing industries should build the digital innovation ecosystem and utilize digital technology to improve the high energy consumption production process and achieve the dual goals of “low-carbon development and digital transformation”.

Data Science and InformetricsVol. 4(3)
Zhejiang Energy Research Institute (CN), Hangzhou Dianzi University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Digital Transformation in Industry
1.05
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.