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

被引：0
|
作者：
Lai, Yawen [1 ,2 ]
Wan, Guochao [1 ]
Qin, Xiaoxia [1 ]
机构：
[1] School of Management, Chengdu University of Information Technology, Chengdu,610103, China
[2] School of Statistics, Chengdu University of Information Technology, Chengdu,610103, China
来源：
Journal of Cleaner Production | 2024年 / 478卷
关键词：
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. © 2024 The Authors;
D O I：
10.1016/j.jclepro.2024.143927
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