Study on the Dynamic Prediction of Green Total Factor Productivity by Artificial Intelligence under the “Dual-Carbon” Target Based on Grey Prediction Models
Year: 2025
Type: article
Abstract: 党的二十大报告明确指出推动经济社会发展绿色化、低碳化是实现高质量发展的关键，绿色可持续发展已成为中国经济新的增长引擎。在此背景下，本研究基于2011~2023年中国30个省级(不含港澳台和西藏地区)面板指标数据，运用基准回归模型深入探究人工智能发展水平对绿色全要素生产率的影响，并借助灰色预测模型对二者未来发展趋势展开预测。研究发现，人工智能发展对绿色全要素生产率具有显著正向促进作用，且通过了内生性及稳健性检验后，说明该结论可靠。灰色预测模型显示，2025年后人工智能发展水平将强劲增长，绿色全要素生产率也将小幅增加，但省域层面存在明显区域分化，为推进人工智能技术创新与绿色发展协同提供了理论依据和政策建议。The report to the 20th National Congress of the Communist Party of China explicitly states that promoting green and low-carbon development in economic and social progress is crucial for achieving high-quality development, with green sustainable development having become a new growth engine for China’s economy. Against this backdrop, this study utilizes panel data from 30 provincial-level regions in China (excluding Hong Kong, Macao, Taiwan, and Tibet) spanning 2011 to 2023. It employs a benchmark regression model to thoroughly investigate the impact of artificial intelligence development levels on green total factor productivity (GTFP), while applying a grey prediction model to forecast their future development trends. The research findings reveal that artificial intelligence development exerts a significant positive promoting effect on green total factor productivity, and this conclusion remains robust after passing endogeneity and robustness tests. Grey prediction models indicate that artificial intelligence development levels will experience strong growth post-2025, accompanied by moderate increases in green total factor productivity. However, significant regional disparities at the provincial level highlight the necessity for coordinated advancement of artificial intelligence technological innovation and green development, providing both theoretical foundations and policy recommendations for synergistic development.
Source: Statistics and Applications
Author: 紫纯 石
Institution: Qufu Normal University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4411910146