Research on Energy Carbon Emission Situation Prediction Technology: A Case Study of Fujian Province
Authors
Bidan Qiu1, Yusong Sun2, Yiqiu Zheng3, *
1Quanzhou Electric Power Skill Institute, Quanzhou, China
2State Grid Shanghai Electric Power Company Marketing Service Center, Shanghai, China
3State Grid Anxi County Power Supply Company, Quanzhou, China
*Corresponding author.
Email: aimer_wish@163.com
Corresponding Author
Yiqiu Zheng
Available Online 9 October 2023.
- DOI
- 10.2991/978-94-6463-256-9_79How to use a DOI?
- Keywords
- Carbon emission decomposition; Carbon emission projections; LMDI decomposition method; Scenario analysis; Energy and power industry
- Abstract
In this paper, Fujian Province was taken as an example to build a general analysis framework for urban carbon emission analysis. Firstly, carbon emission was measured, and then the LMDI method was used to decompose the influencing factors of carbon emission from the aspects of energy structure, industrial structure, social and economic development level, etc. On this basis, the carbon emission trend was analyzed and predicted. A data mining method for carbon emission situation prediction of energy and electric power is proposed.
- Copyright
- © 2024 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 - Bidan Qiu AU - Yusong Sun AU - Yiqiu Zheng PY - 2023 DA - 2023/10/09 TI - Research on Energy Carbon Emission Situation Prediction Technology: A Case Study of Fujian Province BT - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023) PB - Atlantis Press SP - 780 EP - 796 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-256-9_79 DO - 10.2991/978-94-6463-256-9_79 ID - Qiu2023 ER -