An Evaluation System for Carbon Emission Disclosure Based on DSR-C Model and BP Neural Network: A Case Study of Sichuan Changhong
- DOI
- 10.2991/978-2-38476-551-5_69How to use a DOI?
- Keywords
- Carbon emission disclosure; DSR-C model; BP neural network; Evaluation system; Sichuan Changhong
- Abstract
Against the backdrop of global carbon neutrality goals, evaluating corporate carbon emission disclosure (CCD) is crucial. This study constructs an innovative evaluation system by integrating an improved Driving-State-Response model with a Supply Chain dimension (DSR-C) and a Backpropagation Neural Network (BPNN). Taking Sichuan Changhong Group as a case study, we designed a four-layer indicator system with 25 specific metrics. The BPNN was trained on 2023-2024 data and tested on 2022 data. The model demonstrated high accuracy and effective generalization. We extracted the weights of all indicators, revealing that “Energy Consumption per Unit Revenue” held the highest weight. A comprehensive evaluation index was calculated, showing a clear upward trend in Changhong’s CCD performance from 2022to 2024. This system provides a quantitative, data-driven tool for assessing corporate CCD practices.
- 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 - Wei Li AU - Guochao Wan PY - 2026 DA - 2026/03/26 TI - An Evaluation System for Carbon Emission Disclosure Based on DSR-C Model and BP Neural Network: A Case Study of Sichuan Changhong BT - Proceeding of 2025 8th International Conference on Humanities Education and Social Sciences (ICHESS 2025) PB - Atlantis Press SP - 651 EP - 657 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-551-5_69 DO - 10.2991/978-2-38476-551-5_69 ID - Li2026 ER -