Carbon emission estimation for computing infrastructure: Methods, progress, and prospects

Authors

Xiaohong CHEN, School of Management Science and Engineering, Hunan University of Technology and Business, Changsha 410205, China; School of Advanced Interdisciplinary Studies (School of Future Technologies), Hunan University of Technology and Business, Changsha 410205, China; Business School, Central South University, Changsha 410083, ChinaFollow
Liaoying CAO, School of Management Science and Engineering, Hunan University of Technology and Business, Changsha 410205, China; School of Advanced Interdisciplinary Studies (School of Future Technologies), Hunan University of Technology and Business, Changsha 410205, China
Yangjie WANG, Business School, Central South University, Changsha 410083, China
Zhuqian HE, Business School, Central South University, Changsha 410083, China
Wenzhi CAO, School of Management Science and Engineering, Hunan University of Technology and Business, Changsha 410205, China; School of Advanced Interdisciplinary Studies (School of Future Technologies), Hunan University of Technology and Business, Changsha 410205, ChinaFollow

Keywords

computing infrastructure; carbon emission reduction; green computility; green transition

Document Type

Policy & Management Research

Abstract

As a foundational pillar of an increasingly digital and intelligent society, computing infrastructure embodies a dual nature—marked by high energy consumption and potential for emission reduction simultaneously. This duality positions it as a critical domain requiring balance under China’s dual-carbon goals. Nevertheless, there remains no clear consensus on methods for estimating its carbon emissions. Its ambiguity stems from differences in the applicability of assessment methods across various lifecycle stages, as well as a fragmented understanding of its intrinsic emissions and enabling effects on carbon mitigation. This study systematically reviews estimation methodologies from both broad and narrow perspectives, highlighting the heterogeneity of emissions across different lifecycle stages and revealing the green enabling potential of computing infrastructure. Findings indicate that indirect emissions from purchased electricity during the operational phase account for nearly 90% of total emissions. Despite its high energy intensity, computing infrastructure holds considerable emission reduction potential—achieved through energy efficiency improvements, intelligent resource scheduling, and digital enablement of traditional sectors. These factors collectively support a structural net effect where reductions can outweigh emissions. To address the current gaps—namely, incomplete lifecycle data, weak methodological adaptability, and the absence of models suited to emerging scenarios—this study proposes an “optimize–evaluate–coordinate” research framework. It advocates for the construction of a data governance system supported by institutional, technological, and methodological pillars; the development of a dynamic carbon accounting system spanning the full lifecycle; and the creation of a multi-scale, adaptive path for collaborative governance. The study offers methodological guidance for quantifying the carbon neutrality contribution of computing infrastructure and provides strategic insights for promoting a green transition in computing and synergizing digital and ecological transformation.

First page

1777

Last Page

1791

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

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Recommended Citation

CHEN, Xiaohong; CAO, Liaoying; WANG, Yangjie; HE, Zhuqian; and CAO, Wenzhi (2024) "Carbon emission estimation for computing infrastructure: Methods, progress, and prospects," Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 40 : Iss. 10 , Article 12.
DOI: https://doi.org/10.3724/j.issn.1000-3045.20250409002
Available at: https://bulletinofcas.researchcommons.org/journal/vol40/iss10/12