Digital empowerment and win-win co-operation for green and low-carbon industrial development: Analysis of regional differences based on GMM-ANN intelligence models

Abstract

The study employs two sophisticated computational model validation mechanisms, Artificial Neural Network (ANN) and System Generalized Method of Moments (SYS-GMM), as the theoretical foundation for sustainable development theory. Within this research framework, ANN serves as an intelligent model for autonomously learning input data features by capturing the nonlinear and dynamic relationships within the data. This study thoroughly simulates and analyzes the intricate relationship between digital empowerment and win-win cooperation in green and low-carbon industrial development. The findings indicate a regional decreasing trend in digital empowerment in China, with percentages of 87.39%, 79.05%, and 65.85%, respectively. These regional variations may result from policy, economic structure, and social factors. Notably, a significant trend of win-win cooperation exists between digital empowerment and green low-carbon initiatives. This discovery implies that promoting digital empowerment contributes to reducing green carbon emissions, offering empirical evidence for the positive impact of digital technology on promoting environmental sustainability. This study unveils the empirical trend of digitally-enabled green, low-carbon industry development within the win-win cooperation framework and presents specific recommendations for future sustainability strategies. In addition to offering a scientific foundation for policymakers, this study provides a forward-looking perspective for academics engaged in the study of digital transformation and environmental sustainability. Through a comprehensive analysis of the intricate relationship between digital empowerment and win-win cooperation in the context of green and low-carbon industrial development, this study offers a profound understanding of the application of digital technology in sustainable development across diverse regions, contributing empirical research findings to advance sustainable development in China.