Artificial intelligence for carbon emissions management: advances, challenges, and future directions across monitoring, prediction, and reduction.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a structured taxonomy of AI methods for carbon management and identifies unresolved challenges (data quality, model generalization) as future research directions.

🏢実務担当者:Offers a practical framework with a technology readiness level lens to select appropriate AI tools for emissions monitoring, prediction, and reduction in industrial settings.

🏛政策担当者:Demonstrates how AI can support quantifiable, verifiable carbon management, informing policies that integrate AI into emissions governance and reporting systems.

Rising anthropogenic carbon emissions are a major driver of climate change and pose a critical challenge to global sustainable development. As a rapidly advancing technology, artificial intelligence (AI) has shown strong potential to enhance carbon emissions management. This review provides a critical and comprehensive synthesis of recent advances in AI-enabled approaches for carbon emissions monitoring, prediction, and reduction. For monitoring, it explores the integration of satellite remote sensing, sensor networks, and machine learning (ML) algorithms, which can improve multi-scale, high-resolution, and near-real-time monitoring capabilities. For prediction, it categorizes prediction models into three groups, namely deep learning (DL), ensemble learning, and statistical learning, to facilitate the selection of appropriate technical approaches based on varying data characteristics and prediction requirements. For reduction, it examines the practical effectiveness of AI in industrial process optimization, energy structure transformation, transportation scheduling and management, construction energy efficiency improvement, and carbon capture, utilization, and storage (CCUS). We further reveal core challenges and potential solutions across the data layer, model layer, and application layer in AI deployment, including data availability and quality, model generalization and interpretability, and engineering and governance barriers that hinder the translation of AI methods into real-world applications. Furthermore, future research directions are discussed to promote the development of more reliable and scalable AI methods that can better support decision-making and practical governance in carbon emissions management. Overall, distinct from previous reviews that mainly focus on single tasks, specific model types, or sectoral applications, this review represents, to our knowledge, one of the first review-level attempts to develop a policy-relevant and interdisciplinary AI framework for carbon emissions management across the full process of monitoring, prediction, and reduction. By integrating unified evaluation metrics, evidence matrices, deployment-constraint analysis, and a technology readiness level (TRL)-based assessment, this framework links methodological performance, application readiness, and governance needs. It provides an integrated methodological foundation for fine-grained emissions sensing, predictive analysis, and emissions reduction decision support, while supporting quantifiable, verifiable, and actionable carbon balance and management.

• semanticscholar https://doi.org/10.1186/s13021-026-00479-5first seen 2026-07-04 05:29:56