A Study on the Prospects of Regional Artificial Intelligence Development Based on Carbon Emission and Development Indicators

Qiaochu Li; Xinyu Zhuang

doi:10.62051/q91cn145

Authors

Qiaochu Li
Xinyu Zhuang

DOI:

https://doi.org/10.62051/q91cn145

Keywords:

New Quality Productive Forces; PCA (Principal Component Analysis); GB-DT; LSTM; Artificial Intelligence Development Potential.

Abstract

Artificial Intelligence today demonstrates an astonishing productivity, and its development has become one of the directions for many countries. However, while developing, it is also necessary to consider the adverse impact of AI development on carbon emissions and to seek environments and regions suitable for AI development. To address this issue, this paper innovatively proposes the concept of "fertility" to describe the AI development potential of a region, and fully considers the adverse impact of AI development on carbon emissions. Based on the carbon emission and development data of various provinces in China in recent years, the "fertility" is modeled through PCA (Principal Component Analysis) and GB-DT model, and combined with LSTM for predicting the future AI development potential, thus deriving the future AI development potential of various provinces in China.

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