The rapid advancement of artificial intelligence (AI) has significantly increased the computational load on data centers, resulting in substantial carbon emissions. To mitigate these emissions, future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands. This study aims to investigate how much carbon emission reduction can be achieved by using a carbon-oriented demand response to guide the optimal planning and operation of data centers. An empirical study based on the proposed models is conducted on real-world data from China. The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province, Ningxia Hui Autonomous Region, Sichuan Province, Inner Mongolia Autonomous Region, and Qinghai Province, accounting for 57% of the total national increase in server capacity. 33% of the computational load from Eastern China should be transferred to the West, which could reduce the overall load carbon emissions by 26%.
The authors published their study on March 24, 2025, in iEnergy.
"We propose a carbon-oriented data center planning model that considers the carbon-oriented demand response of the AI load. In the planning model, future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service (QoS). Additionally, we conduct an empirical study on real-world data from China based on the proposed model", says Bojun Du, a Ph.D. student at the Department of Electrical Engineering at Tsinghua University.
Optimal planning results of data centers considering the carbon-oriented demand response
This study provides practical insights into how to configure data centers across different provinces, autonomous regions and municipalities directly under the Central Government. The empirical results indicate that Gansu Province, Ningxia Hui Autonomous Region, Inner Mongolia Autonomous Region, Sichuan Province, and Qinghai Province will have the highest number of newly built data centers, accounting for 57% of the total new server racks.
"However, owing to longer distances and data transmission times, batch loads are processed primarily by servers in the west, whereas online workload transfer from the east is relatively limited." Yaowang Li, a researcher majoring in carbon reduction of power systems, says.
Data centers in the eastern region are used mainly to handle online loads. And data centers in the western region, with more renewable energy outputs and greater variability in carbon emissions at different times, are suitable for batch load processing.
Carbon emission reduction through AI load shifting of data centers
This study shows the carbon reduction through carbon-oriented demand response. Among all computational demands nationwide, 83% are generated in the eastern region, and 17% are generated in the western region. On the basis of the planning and operation simulation results, 32% of the computational demand in the east will be transferred to the west for processing.
After spatial transfers of computational demand, the total national electricity carbon emissions were reduced by 26%. In East China, Central China, and South China, computational demand is significant, but due to high carbon emission factors and limited renewable energy, a large amount of demand has been transferred out, resulting in significant reductions in carbon emissions. Southwest China and Northwest China have large renewable energy installations and low carbon emission factors. Thus, a substantial amount of load is transferred from Eastern China, leading to a significant carbon emission increase in Southwest China and Northwest China.
The above research is published in iEnergy, which is a fully open access journal published by Tsinghua University Press. iEnergy publishes peer-reviewed high-quality research representing important advances of significance to emerging power systems. At its discretion, Tsinghua University Press will pay the open access fee for all published papers from 2022 to 2026.
About iEnergy
iEnergy is a quarterly journal launched on March 2022. It has published 4 volumes (13 issues). Authors come from 21 countries, including China, the United States, Australia, etc., and world's top universities and research institutes, including University of Nebraska Lincoln, Columbia University, Imperial College of Science and Technology, Tsinghua University, etc. 12 published articles are written by academicians from various countries. The published papers have also attracted an overwhelming response and have been cited by 179 journals, including top journals in the field of power and energy like Nature Materials, Advanced Materials, Advanced Functional Materials, Advanced Energy Materials, etc., from 45 countries.
About Tsinghua University Press
Established in 1980, as a department of Tsinghua University, Tsinghua University Press (TUP) is a leading comprehensive higher education and professional publisher in China. TUP publishes 58 journals and 42 of them are in English. There are 18 journals indexed by SCIE/ESCI. Three of them have the highest impact factor in its field. In 2022, TUP launched SciOpen . As a publishing platform of TUP, SciOpen provides free access to an online collection of journals across diverse academic disciplines and serves to meet the research needs of scientific communities. SciOpen provides end-to-end services across manuscript submission, peer review, content hosting, analytics, identity management, and expert advice to ensure each journal's development.
