Decoding China's new-type industrialization: Insights from an XGBoost-SHAP analysis

Integrating machine learning and explainable artificial intelligence offers superior interpretability for indicator selection compared with traditional evaluation models. This study utilizes the XGBoost-SHAP model to assess the influencing factors, development trends, and spatiotemporal evolution of China’s new-type industrialization. The results demonstrate that digitalization emerges as the most critical factor, contributing 41.23% to the weight of indicators, followed by innovation drive at 34.31%, greenization at 16.38%, and aspects of servitization and fusion. China’s new industrialization level shows a positive trend, with an accelerated growth rate post-2020, despite significant regional disparities in industrial indices. A spatiotemporal evolution identified a three-tier regional distribution: the eastern coastal, central plain, and western plateau regions. Notably, the improvement in new-type industrialization has expanded from coastal to inland regions, with significant progress observed in the southwestern region, represented by Sichuan Province. Additionally, the analysis revealed that China’s level of new industrialization is converging toward the Hu Huanyong Line, suggesting that it may be influenced by population distribution density and natural environmental factors. The research results provide a scientific basis and decision-making guidance for evaluating the dynamics of China’s new industrialization. • XGBoost-SHAP model identifies digitalization & innovation as key drivers of new industrialization. • A positive trend in China's new industrialization, with accelerated growth after 2020. • While significant regional disparities exist, new industrialization is gradually expanding from coastal to inland regions. • China's new industrialization converging towards Hu Huanyong Line, influenced by population & environment. • AI-powered evaluation (ML & explainable AI) provides guidance for the dynamic assessment of China's new industrialization.

Authors

Institutions

Publication Details

Journal
Journal of Cleaner Production
Published
2024-10-13
DOI
https://doi.org/10.1016/j.jclepro.2024.143927
Citations
29
Primary Topic
China's Socioeconomic Reforms and Governance
Type
article
Field-Weighted Citation Impact
64.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decoding China's new-type industrialization: Insights from an XGBoost-SHAP analysis

Guochao Wan, Yawen Lai, Xiaoxia Qin
29 citations
Journal of Cleaner Production
China's Socioeconomic Reforms and Governance
64.70
article

Decoding China's new-type industrialization: Insights from an XGBoost-SHAP analysis

Guochao Wan, Yawen Lai, Xiaoxia Qin
article en
29 citations

Abstract

Integrating machine learning and explainable artificial intelligence offers superior interpretability for indicator selection compared with traditional evaluation models. This study utilizes the XGBoost-SHAP model to assess the influencing factors, development trends, and spatiotemporal evolution of China’s new-type industrialization. The results demonstrate that digitalization emerges as the most critical factor, contributing 41.23% to the weight of indicators, followed by innovation drive at 34.31%, greenization at 16.38%, and aspects of servitization and fusion. China’s new industrialization level shows a positive trend, with an accelerated growth rate post-2020, despite significant regional disparities in industrial indices. A spatiotemporal evolution identified a three-tier regional distribution: the eastern coastal, central plain, and western plateau regions. Notably, the improvement in new-type industrialization has expanded from coastal to inland regions, with significant progress observed in the southwestern region, represented by Sichuan Province. Additionally, the analysis revealed that China’s level of new industrialization is converging toward the Hu Huanyong Line, suggesting that it may be influenced by population distribution density and natural environmental factors. The research results provide a scientific basis and decision-making guidance for evaluating the dynamics of China’s new industrialization. • XGBoost-SHAP model identifies digitalization & innovation as key drivers of new industrialization. • A positive trend in China's new industrialization, with accelerated growth after 2020. • While significant regional disparities exist, new industrialization is gradually expanding from coastal to inland regions. • China's new industrialization converging towards Hu Huanyong Line, influenced by population & environment. • AI-powered evaluation (ML & explainable AI) provides guidance for the dynamic assessment of China's new industrialization.

Journal of Cleaner ProductionVol. 478
Chengdu University of Information Technology (CN)
Chengdu University of Information Technology
Openalex Percentile: Top 0%
China's Socioeconomic Reforms and Governance
64.70
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.