Open Access

ARTICLE

A Dual-Indicator Early Warning Model for Carbon Emissions Utilizing Electricity-Based Carbon Emission Estimation Data

Yuan Leng¹, Ao Chai^2,3, Yuliang Liu^2,3,*, Rongfeng Deng¹, Xi Liu¹, Xiaorui Hu^2,3
1 Energy Development Research Institute, China Southern Power Grid Co., Ltd., Guangzhou, China
2 Department of Electrical Engineering, Tsinghua University, Beijing, China
3 Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China
* Corresponding Author: Yuliang Liu. Email:
(This article belongs to the Special Issue: Hydrogen Energy Systems: Storage, Power-to-Hydrogen, and AI-Enabled Design, Planning, and Operation)

Energy Engineering https://doi.org/10.32604/ee.2026.083794

Received 10 April 2026; Accepted 03 June 2026; Published online 07 July 2026

Abstract

To address the critical issues of lagging monitoring and inaccurate early warning in regional carbon emissions, this paper constructs a DDPR dynamic early warning model. This model is supported by “electricity-based carbon emission estimation” technology and integrates an improved Grey Model GM(1,1)-BP neural network optimized by BOA with concept drift retraining. A case study was conducted using monthly data from China’s Guangdong Province from 2020 to 2024 to verify the calculation accuracy and applicability of the early warning framework. Results demonstrate that the improved electricity-to-carbon prediction model delivers high-frequency calculation with a relative error rate maintained below 4%. The dynamic early warning model successfully identifies explicit progress deviations and latent cross-period risks by capturing three core risk scenarios: production rushes in May drive the DI to a peak of 1.257 (Emergency level). Summer peak and end-of-year production sprints trigger a surge in the RF from 90.41% to 99.31% between September and November, providing a critical early warning window several months in advance. By transforming high-frequency power data into near real-time situational awareness, this model enhances the foresight of regional carbon control and establishes a closed-loop framework for differentiated administrative and technical interventions.

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Keywords

Electricity-based carbon emission estimation; dynamic carbon early warning; progress deviation index; probabilistic risk factor; carbon emission prediction

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