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Digital transformation has emerged as a crucial driver of high-quality economic growth and represents one of China's key strategies for achieving sustainable development. Its role in enhancing total factor productivity (TFP) and promoting green and sustainable practices is of significant importance. Drawing on a comprehensive dataset spanning 1993 to 2023 in China, this study employs a combination of social network analysis (SNA) and deep learning techniques to investigate the impact of digital transformation on high-quality economic development, as main measured by green total factor productivity (GTFP). The findings reveal three key insights: First, leveraging location-based big data analysis, industrial automation (IR) and economic policy uncertainty (EPU) are identified as the primary factors significantly influencing China’s high-quality economic development. Second, while IR positively influences GTFP, EPU exerts a negative impact. Third, comparative evaluation of multiple models indicates that recurrent neural networks (RNN) outperform others in accurately predicting GTFP. This study introduces a novel methodological framework integrating data-driven forecasting with systemic policy interventions. By leveraging big data analysis to identify critical influencing factors and employing deep learning techniques to predict GTFP, this research broadens interdisciplinary approaches to sustainability. Additionally, the findings offer theoretical guide and actionable insights for strategic planning toward a green and sustainable economic future.
Lin et al. (Thu,) studied this question.
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