ORIGINAL RESEARCH article
Front. Appl. Math. Stat.
Sec. Mathematics of Computation and Data Science
Volume 11 - 2025 | doi: 10.3389/fams.2025.1595365
From Energy to Ecology: Decarbonization Pathways for Sustainable High-performance Computing through Global Carbon-Energy Nexus Analysis
Provisionally accepted
- South China Normal University, Guangzhou, China
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High-performance computing (HPC) has been a pivotal driving force of technological development. This study evaluates the environmental impact of HPC by analyzing energy consumption and carbon emissions across major global centers. We analyzed data from the top 500 HPC centers, using linear regression to fill in missing values for max power due to a high Pearson correlation. The representativeness of the TOP500 dataset was validated via distribution fitting and Monte Carlo simulations, confirming that it captures over 99.8% of high-end global HPC power consumption. ( 1) Applying a logistic model to relate the average utilization rate of the four major countries to the ratio of HPC market size to the number of centers (R 2 =0.775). Global annual energy consumption ranges from 2.3-4.2 billion kW•h at average utilization, with the US accounting for 1.68 billion kW•h. (2) Carbon footprint calculations using energy mix data (2016-2022) incorporated an Environmental Impact Index (EII) to weigh ecological sensitivity, linking CO2 emissions to a 0.5% GDP loss per trillion tons, totaling $2.18 million in economic losses. (3) Forecasting models projected 2030 emissions at 1.071×10 20 kg under average utilization with sobol analysis demonstrating marginal energy consumption fluctuations due to uncertainty. (4) Renewable energy adoption analysis showed strong inverse correlations between clean energy use and emissions in the US (R²=0.904), China (R²=0.99), and Germany (R²=0.779), while quantifying air pollutants like SO₂, NOx and PM10. (5) The combined differential equation and regression models captured the dynamic evolution of energy efficiency and its impact on energy consumption, revealing through 2025 projections that policy incentives can substantially enhance energy efficiency (from 21.22 to 30.90) while reducing energy consumption (from 0.3449 to 0.3278). This study underscores the urgency of balancing HPC growth with sustainability through renewable integration and operational efficiency.
Keywords: high-performance computing, energy consumption, carbon emissions, Regression Analysis, Analytic hierarchy process
Received: 18 Mar 2025; Accepted: 28 May 2025.
Copyright: © 2025 Yu, Wang, Xu, Javan Shun and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Susan Chen, South China Normal University, Guangzhou, China
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