AI is facilitating green technology innovation, which is expected to improve energy efficiency and aid the transition to cleaner production methods, thereby supporting carbon reduction goals.

Baseline regression analysis reveals that a 1% increase in AI application is associated with a 0.0413% reduction in carbon emission intensity, highlighting a significant inverse relationship bolstered by regulatory mechanisms.

To assess the levels of AI application, the study utilizes text analysis of annual reports from listed companies, employing techniques like Latent Dirichlet Allocation (LDA) for topic modeling and GloVe for word vector analysis.

A recent article explores how artificial intelligence (AI) is influencing carbon emission intensity in China's industrial sector, emphasizing its potential to mitigate emissions even as energy consumption and carbon output rise.

With industrial enterprises in China responsible for about 70% of energy consumption and 80% of carbon emissions in 2023, the need for sustainable practices in this sector is increasingly urgent.

AI's integration into industrial processes has advanced significantly, evidenced by an 80.1% penetration of digital design tools and a 62.9% numerical control rate in critical processes as of 2023.

The study employs a comprehensive methodology, analyzing data from 2011 to 2022 to evaluate AI's impact on key factors such as total factor productivity, energy consumption, and green technology innovation.

The article proposes three hypotheses: that AI applications will lower carbon emission intensity, achieve this through supply chain optimization, and foster green technology innovation.

Research indicates that AI can substantially decrease carbon emissions, especially in regions with advanced AI capabilities, although its effectiveness varies across different industries and levels of industrial development.

Despite AI's potential to reduce emissions, there are concerns that inadequate digital infrastructure could inadvertently raise carbon intensity in certain sectors, creating a 'low-end lock-in' scenario for downstream industries.