Under the background of global climate governance and China’s “double carbon” strategy, the modern environmental governance system puts forward six core requirements for enterprise carbon information management: comprehensiveness, accuracy, real-time, traceability, disclosure and supply chain collaboration. However, the traditional carbon management model that relies on manual statistics and offline reporting has outstanding problems such as data fragmentation, large deviation, lagging supervision and coordination difficulties. Artificial intelligence technology provides key technical support for solving the above dilemma by virtue of its massive data processing, real-time dynamic monitoring and intelligent decision analysis capabilities. This paper takes Industrial Fulian as a single case study object to explore the influence mechanism, technical path and practical effect of artificial intelligence on carbon information management under modern environmental governance. The research finds that artificial intelligence is deeply embedded in the whole process of carbon management through technical architecture, which significantly improves the efficiency and accuracy of carbon management, strengthens the ability of supply chain coordination and compliance disclosure, and helps enterprises achieve the dual goals of carbon emission reduction and cost reduction and efficiency increase. This paper enriches the technical theory of modern environmental governance and carbon information management, and provides a practical paradigm and policy reference for the transformation of intelligent carbon management in manufacturing enterprises.
| Published in | Science Innovation (Volume 14, Issue 3) |
| DOI | 10.11648/j.si.20261403.12 |
| Page(s) | 72-77 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Modern Environmental Governance, Artificial Intelligence, Carbon Information Management, Supply Chain Collaboration
| [1] | 钱琼. 企业数字化转型对碳信息披露的影响研究 [D]. 大连理工大学, 2025. |
| [2] | 史芳仪. “双碳”目标下企业碳信息披露的法律规制研究 [D]. 广东财经大学, 2025. |
| [3] | 敦志刚, 刘凡菁, 张文英. 信息还是噪声?碳信息披露如何影响股权融资成本 [J]. 投资研究, 2025, 44(01): 95-113. |
| [4] | 李玥儒. 大数据政策对企业碳信息披露水平的影响研究[D]. 北京化工大学, 2025. |
| [5] | 坚瑞. 企业数字化转型对碳信息披露的影响 [J]. 东南学术, 2024(02): 86-96. |
| [6] | 甄皓晴, 何理, 陈迎. 企业碳信息披露与客户集中度 [J]. 首都经济贸易大学学报, 2026, 28(01):115-128. |
| [7] | 王晓珂, 于晓淇.碳信息披露对分析师行为的影响研究 [J].上海金融, 2025, (03): 55-68. |
| [8] | 赵舒蕴. 碳信息披露和企业创新效率 [J]. 中小企业管理与科技, 2024, (09): 52-55. |
| [9] | 宋子欣. 碳信息披露对企业融资约束的影响研究 [D]. 吉林大学, 2024. |
| [10] | 胡楠, 华昊辰. 碳信息披露对企业绿色投资能够产生倒逼效应吗?[J]. 财会通讯, 2026, (03): 57-61. |
| [11] | 林阳. 人工智能在新能源领域中的应用与前景分析[J].中国科技纵横, 2025, (14): 20-22. |
| [12] | 黄和金. 面向新能源领域的智能电网发电预测与调度管理方法研究 [D]. 电子科技大学, 2023. |
| [13] | 石鑫, 刘奇央, 高峰. 深度神经网络在新型能源系统中的应用及展望 [J]. 综合智慧能源, 2025, 47(02): 88-101. |
| [14] | 陈晓云. 智能电表数据在电力营销精准服务中的作用 [J].信息与电脑, 2025, 37(01): 179-181. |
| [15] | 方世南. 现代环境治理体系的系统性构建研究 [J]. 湖湘论坛, 2024, 37(04): 80-87. |
APA Style
Jin, H., Haowei, X., Yuxin, L., Yijing, L. (2026). Research on the Impact of Artificial Intelligence on Carbon Information Management in Modern Environmental Governance—Study of Foxconn Industrial Internet. Science Innovation, 14(3), 72-77. https://doi.org/10.11648/j.si.20261403.12
ACS Style
Jin, H.; Haowei, X.; Yuxin, L.; Yijing, L. Research on the Impact of Artificial Intelligence on Carbon Information Management in Modern Environmental Governance—Study of Foxconn Industrial Internet. Sci. Innov. 2026, 14(3), 72-77. doi: 10.11648/j.si.20261403.12
@article{10.11648/j.si.20261403.12,
author = {He Jin and Xu Haowei and Li Yuxin and Liu Yijing},
title = {Research on the Impact of Artificial Intelligence on Carbon Information Management in Modern Environmental Governance—Study of Foxconn Industrial Internet},
journal = {Science Innovation},
volume = {14},
number = {3},
pages = {72-77},
doi = {10.11648/j.si.20261403.12},
url = {https://doi.org/10.11648/j.si.20261403.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20261403.12},
abstract = {Under the background of global climate governance and China’s “double carbon” strategy, the modern environmental governance system puts forward six core requirements for enterprise carbon information management: comprehensiveness, accuracy, real-time, traceability, disclosure and supply chain collaboration. However, the traditional carbon management model that relies on manual statistics and offline reporting has outstanding problems such as data fragmentation, large deviation, lagging supervision and coordination difficulties. Artificial intelligence technology provides key technical support for solving the above dilemma by virtue of its massive data processing, real-time dynamic monitoring and intelligent decision analysis capabilities. This paper takes Industrial Fulian as a single case study object to explore the influence mechanism, technical path and practical effect of artificial intelligence on carbon information management under modern environmental governance. The research finds that artificial intelligence is deeply embedded in the whole process of carbon management through technical architecture, which significantly improves the efficiency and accuracy of carbon management, strengthens the ability of supply chain coordination and compliance disclosure, and helps enterprises achieve the dual goals of carbon emission reduction and cost reduction and efficiency increase. This paper enriches the technical theory of modern environmental governance and carbon information management, and provides a practical paradigm and policy reference for the transformation of intelligent carbon management in manufacturing enterprises.},
year = {2026}
}
TY - JOUR T1 - Research on the Impact of Artificial Intelligence on Carbon Information Management in Modern Environmental Governance—Study of Foxconn Industrial Internet AU - He Jin AU - Xu Haowei AU - Li Yuxin AU - Liu Yijing Y1 - 2026/05/15 PY - 2026 N1 - https://doi.org/10.11648/j.si.20261403.12 DO - 10.11648/j.si.20261403.12 T2 - Science Innovation JF - Science Innovation JO - Science Innovation SP - 72 EP - 77 PB - Science Publishing Group SN - 2328-787X UR - https://doi.org/10.11648/j.si.20261403.12 AB - Under the background of global climate governance and China’s “double carbon” strategy, the modern environmental governance system puts forward six core requirements for enterprise carbon information management: comprehensiveness, accuracy, real-time, traceability, disclosure and supply chain collaboration. However, the traditional carbon management model that relies on manual statistics and offline reporting has outstanding problems such as data fragmentation, large deviation, lagging supervision and coordination difficulties. Artificial intelligence technology provides key technical support for solving the above dilemma by virtue of its massive data processing, real-time dynamic monitoring and intelligent decision analysis capabilities. This paper takes Industrial Fulian as a single case study object to explore the influence mechanism, technical path and practical effect of artificial intelligence on carbon information management under modern environmental governance. The research finds that artificial intelligence is deeply embedded in the whole process of carbon management through technical architecture, which significantly improves the efficiency and accuracy of carbon management, strengthens the ability of supply chain coordination and compliance disclosure, and helps enterprises achieve the dual goals of carbon emission reduction and cost reduction and efficiency increase. This paper enriches the technical theory of modern environmental governance and carbon information management, and provides a practical paradigm and policy reference for the transformation of intelligent carbon management in manufacturing enterprises. VL - 14 IS - 3 ER -