人工智能减轻碳足迹：高性能计算协同迈向净零排放

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

🔬研究者:Provides a comprehensive overview of AI applications for emissions reduction, useful for identifying research gaps.

🏢実務担当者:Highlights practical AI use cases for carbon management, which can be adapted for corporate sustainability strategies.

🏛政策担当者:Discusses policy alignment with NDCs and the role of AI in climate action, relevant for regulatory frameworks.

The chapter reviews the transformative power of Artificial Intelligence (AI) in improving the environmental effects of anthropogenic activities by lowering carbon footprints in key sectors of the economy. As the problem of climate change becomes an existential crisis, introducing AI into climate action plans has created new capabilities in real-time emissions monitoring, predictive modeling, and sustainable decisionmaking. The chapter introduces the discussion of carbon emissions in the context of the Anthropocene epoch and states the central role of AI in environmental monitoring systems. It provides a systematic examination of direct and indirect sources of emissions and sectoral patterns of emissions in energy, transportation, agriculture, and industry, with the help of indicators such as CO2e and life cycle assessments. The main part of the chapter examines how the latest AI methods, such as machine learning and deep learning, and edge computing, are being used to monitor, forecast, and minimize emissions in a more precise and timely fashion. Scalable carbon reductions are seen through smart grids, precision agriculture, and AI-optimized industrial operations. It is followed by a set of international case studies, such as the optimization of Google data centers, the Green Horizon Project by IBM, or AI-powered efforts of Climate TRACE, that demonstrate how it can be done in practice and what practical results can be achieved. The issue of High-Performance Computing (HPC) in promoting the scalability and efficiency of AI-enabled climate simulations is also discussed in the chapter. Ethical issues, including the energy requirements of AI itself, algorithmic bias, and global fairness, are also reviewed considerably to deploy AI responsibly. In addition, it goes further to policy frameworks where there is a mention of the compatibility of the AI technologies with the Nationally Determined Contributions (NDCs), international standards of sustainability, and the role of public-private partnerships. The final segments make some predictions about the future, such as the rise of low-power machine learning models, AI-blockchain convergence in carbon credit validation, and sustainable AI strategic road mapping. The chapter gives a thorough and future-focused overview of how AI can be both an instrument and a priority on the path to a net-zero carbon future.

• openalex https://doi.org/10.2174/9798898816032126010012first seen 2026-09-03 05:17:02