Research on the Spatiotemporal Evolution of the Coupled and Coordinated Development of Artificial Intelligence and Carbon Emission Reduction
- DOI
- 10.2991/978-94-6239-602-9_19How to use a DOI?
- Keywords
- Artificial Intelligence; Carbon Emission Reduction; Coupling Coordination; Spatial-Temporal Analysis
- Abstract
This study examines the coupling coordination between artificial intelligence and carbon emission reduction across Chinese provinces from 2016 to 2022. Using a coupling coordination degree model and K-means clustering, the research identifies significant spatial heterogeneity, which is characterized by a “high in the southeast, low in the northwest” pattern. Temporal analysis revealed a path-dependent trend, wherein provinces with high initial coordination levels consolidated their advantages, while others struggle with persistent barriers, leading to a widening of regional disparities. Four distinct provincial clusters are identified. The findings underscore the need for differentiated policies to address regional bottlenecks and promote synergistic development of artificial intelligence and carbon emission reduction, emphasizing targeted interventions for optimal technological empowerment and emission reduction.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ze Yang AU - Tingting Qian PY - 2026 DA - 2026/03/13 TI - Research on the Spatiotemporal Evolution of the Coupled and Coordinated Development of Artificial Intelligence and Carbon Emission Reduction BT - Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025) PB - Atlantis Press SP - 192 EP - 201 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6239-602-9_19 DO - 10.2991/978-94-6239-602-9_19 ID - Yang2026 ER -