Carbon Management

AI-Powered Paradigm Shift in Global Carbon Management: Impacts, Frontiers, and Challenges

Climate change has emerged as one of the most formidable challenges confronting human society in the 21st century. Carbon management, as a central pillar of climate crisis response, is undergoing a profound transformation—shifting from traditional manual accounting and static reporting toward intelligent, dynamic, and end-to-end governance. In recent years, the rapid advancement of artificial intelligence technologies, exemplified by large language models, deep learning, and reinforcement learning, has introduced an entirely new technological paradigm for carbon management (Li et al., 2026; Peng et al., 2026). AI-driven innovations not only substantially enhance the accuracy and efficiency of carbon emissions accounting but also demonstrate disruptive potential in domains such as carbon footprint tracking, carbon market forecasting, carbon capture, utilization and storage (CCUS) optimization, and intelligent energy system dispatch. From corporate-level carbon asset management to national-level carbon peaking pathway planning, and from supply chain carbon transparency to global carbon governance coordination, AI is reshaping the theoretical frameworks and practical boundaries of carbon management with unprecedented depth and breadth. This paradigm shift represents not merely a technological upgrade at the instrumental level, but a systematic restructuring of governance philosophies, institutional designs, and organizational models.

Against the backdrop of accelerating progress toward global "dual carbon" goals, conventional carbon management faces multiple bottlenecks—uneven data quality, lagging accounting methodologies, insufficient predictive capacity, and difficulties in cross-regional coordination—rendering it increasingly inadequate for the growing demand for granular, real-time governance. The integration of AI technologies offers a key to overcoming these bottlenecks: on the one hand, intelligent accounting systems built upon multi-source heterogeneous data fusion can significantly reduce the uncertainty of carbon data, establishing a credible data foundation for carbon pricing, carbon trading, and climate policy formulation; on the other hand, AI-driven scenario simulation and optimization decision models can provide scientific support for emission reduction pathway selection across different scales, thereby enhancing the cost-effectiveness and implementation efficiency of climate action. More importantly, amid the increasingly complex landscape of global carbon governance, AI holds promise as a critical technological infrastructure for advancing North–South cooperation, enhancing carbon data transparency, and building a fair and effective global carbon governance system. An in-depth exploration of the deep integration of AI and carbon management is both theoretically urgent and practically necessary.

This Collection welcomes original research addressing, but not limited to, the following topics:

• AI-driven innovations in carbon emissions accounting methods and data quality enhancement;
• applications of machine learning in life-cycle assessment of carbon footprints;
• the role of large language models and natural language processing in carbon information disclosure and ESG report analysis;
• frontier exploration of reinforcement learning in energy system optimization and carbon emission reduction pathway planning;
• AI-enabled carbon market forecasting, trading strategies, and price mechanism design;
• applications of digital twin technology in carbon management across industrial, building, and transportation sectors;
• co-optimization of AI and carbon capture, utilization, and storage (CCUS) technologies;
• AI-driven climate policy simulation and governance evaluation;
• algorithmic fairness, data privacy, and ethical challenges in AI carbon governance;
• pathways for AI carbon management capacity building and technology transfer in developing countries;
• and the impact of AI carbon governance at the corporate governance level.

This Collection particularly encourages frontier exploration employing interdisciplinary methodologies; manuscripts relying solely on econometric analysis should be submitted with caution.

Keywords:

1. AI technology innovation
2. Carbon management
3. Carbon policy
4. Carbon emissions
5. Carbon markets

­­All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is May 28, 2027.

Please contact Kara Roberts at kara.roberts@taylorandfrancis.com with any queries on discount codes regarding this Article Collection.

Please be sure to select the appropriate Article Collection from the drop-down menu in the submission system.

Guest Advisors declare no potential conflicts of interests in line with our Editorial Policies.

Dr. Jiachao Peng is an associate professor with a Ph.D. in Applied Economics and a postdoctoral fellowship in Business Administration. He is a Chutian Scholar and an outstanding talent at Wuhan Institute of Technology. He has been a visiting scholar at Macquarie University and has led numerous funded projects. His research focuses on applied economics and business administration, contributing significantly to the field through various high-impact studies and collaborations. Dr. Peng's work has influenced both academic circles and practical applications in economics and business.

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