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<p>The digital industrial revolution is needed for digital economy development. Green manufacturing is evolving due to this new influx. In this age of fast technological innovation, addressing economic and regional inequities and improving environmentally friendly industrial efficiency are major challenges. Environmental regulations make the impact of green total factor productivity (GTFP) on the digital economy harder. Fuzzy logic may address uncertainty caused by regional inequalities, digital adoption, and environmental restrictions. This research develops a fuzzy digital green productivity model (FDGPM) using a fuzzy rule-based algorithm to examine how regional digital adoption affects green manufacturing advantages. Fuzzy rules handle geographical differences and other uncertainties in the model. The Mamdani inference system handles several inputs for interpretative insights, including geographical disparities and environmental factors. The Sugeno inference system first converts these insights into correct facts to determine how the digital economy affects green production. This saves computer power and makes decisions’ data driven and interpretable. The combined Mamdani–Sugeno inference system helps grasp complex inputs for digital adoption-based environmental sustainability. The research shows that digital economy indicators may boost green productivity by evaluating resource consumption and reducing environmental impact. This study notion adds to academic understanding and practical implementations by helping industrial executives reconcile economic progress and environmental responsibility.</p>

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The Green Productivity Improvements in Manufacturing and the Geographical Impact of the Digital Economy Using a Fuzzy Rule-Based Approach

  • 期刊论文

  • 发表日期：

    2025年5月19日

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  • Qiansha Zhang

摘要

The digital industrial revolution is needed for digital economy development. Green manufacturing is evolving due to this new influx. In this age of fast technological innovation, addressing economic and regional inequities and improving environmentally friendly industrial efficiency are major challenges. Environmental regulations make the impact of green total factor productivity (GTFP) on the digital economy harder. Fuzzy logic may address uncertainty caused by regional inequalities, digital adoption, and environmental restrictions. This research develops a fuzzy digital green productivity model (FDGPM) using a fuzzy rule-based algorithm to examine how regional digital adoption affects green manufacturing advantages. Fuzzy rules handle geographical differences and other uncertainties in the model. The Mamdani inference system handles several inputs for interpretative insights, including geographical disparities and environmental factors. The Sugeno inference system first converts these insights into correct facts to determine how the digital economy affects green production. This saves computer power and makes decisions’ data driven and interpretable. The combined Mamdani–Sugeno inference system helps grasp complex inputs for digital adoption-based environmental sustainability. The research shows that digital economy indicators may boost green productivity by evaluating resource consumption and reducing environmental impact. This study notion adds to academic understanding and practical implementations by helping industrial executives reconcile economic progress and environmental responsibility.