What question did this study set out to answer?

This review aims to synthesize advances in AI for managing carbon emissions across monitoring, prediction, and reduction stages.

July 4, 2026Open Access

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

Key Points

This review aims to synthesize advances in AI for managing carbon emissions across monitoring, prediction, and reduction stages.
Review of AI-enabled approaches for carbon emissions monitoring, prediction, and reduction.
Categorization of prediction models into deep learning, ensemble learning, and statistical learning.
Discussion of challenges in data availability, model generalization, and governance barriers.
Improved monitoring capabilities using satellite remote sensing, sensor networks, and machine learning.
Categorization of predictive models assists in selecting appropriate approaches for diverse data requirements.
Insights into practical applications of AI in industrial optimization, energy management, and carbon capture.

Abstract

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.

Perguntar à IA

Bookmark

View Full Paper