CORRECTION article

Front. Environ. Sci., 21 March 2025

Sec. Big Data, AI, and the Environment

Volume 13 - 2025 | https://doi.org/10.3389/fenvs.2025.1592508

Corrigendum: Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm

This article is a correction to:

  1. Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-Research on ARIMA-BP Neural Network Algorithm

    1. Read original article
Sanglin Zhao Sanglin Zhao1*Zhetong LiZhetong Li2Hao DengHao Deng1Xing YouXing You3Jiaang TongJiaang Tong4Bingkun YuanBingkun Yuan5Zihao ZengZihao Zeng1
  • 1School of Engineering Management, Hunan University of Finance and Economics, Changsha, China
  • 2School of Mathematics and Statistics, Hunan University of Finance and Economics, Changsha, China
  • 3School of Economics, Hunan University of Finance and Economics, Changsha, China
  • 4School of Economics, Fudan University, Shanghai, China
  • 5School of Energy Science and Engineering, Central South University, Changsha, China

A Corrigendum on
Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm

by Zhao S, Li Z, Deng H, You X, Tong J, Yuan B and Zeng Z (2024). Front. Environ. Sci. 12:1497941. doi: 10.3389/fenvs.2024.1497941

In the published article, Jiaang Tong and Bingkun Yuan were incorrectly labeled as corresponding authors.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: carbon emissions, ARIMA-BP model, LMDI decomposition, temporal and spatial evolution, standard elliptic difference

Citation: Zhao S, Li Z, Deng H, You X, Tong J, Yuan B and Zeng Z (2025) Corrigendum: Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm. Front. Environ. Sci. 13:1592508. doi: 10.3389/fenvs.2025.1592508

Received: 12 March 2025; Accepted: 14 March 2025;
Published: 21 March 2025.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2025 Zhao, Li, Deng, You, Tong, Yuan and Zeng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sanglin Zhao, MjAyMjA1NjQwMTA4QG1haWxzLmh1ZmUuZWR1LmNu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.