Avoided Emissions and Rebound Robustness in Data Center Heat Reuse
DOI:
https://doi.org/10.21512/emacsjournal.v8i2.16055Keywords:
avoided emissions, break-even rebound, data centers, waste heat recovery, rebound effects, rebound robustnessAbstract
Data centers are becoming increasingly important components of modern digital infrastructure, but their growing electricity demand has raised concerns regarding their environmental footprint. At the same time, data centers generate substantial quantities of low-grade waste heat that may be reused in nearby buildings or district heating systems. Such applications have attracted growing interest as examples of ICT-enabled heat-substitution solutions. However, methodologies for quantifying the associated avoided emissions remain under development, particularly in relation to emerging standards and guidance documents. This study applies internationally-aligned avoided-emissions frameworks to a published case study of direct waste heat reuse from a free-air-cooled data center. The engineering basis is derived from a previously published simulation of a 1 MW data center operating without heat pumps and reusing excess heat directly. The avoided-emissions calculation follows a standardized formulation including emissions from conventional heat production and emissions associated with the waste heat reuse solution. For the case with an Energy Reuse Factor (ERF) of 0.59 and a 40°C exhaust temperature, the avoided emissions are estimated to around 783 tCO₂e per year. Moreover, a new rebound robustness metric is proposed to quantify the magnitude of rebound effects required to overturn the avoided-emissions conclusion. The results indicate a break-even rebound of nearly 100% of the useful heat service, suggesting that the avoided-emissions claim is highly robust. The study reveals how ICT-enabled heat reuse can be assessed consistently using internationally standardized concepts and guidance while introducing a complementary break-even rebound and rebound robustness indicator for future research.
References
Ao, X., Zhang, J., Yan, R., He, Y., Long, C., Geng, X., & Liu, T. (2025). More flexibility and waste heat recovery of a combined heat and power system for renewable consumption and higher efficiency. Energy, 315, 134392. https://doi.org/10.1016/j.energy.2025.134392
Andrae, A. S. G. (2025). Evaluating use-phase emissions of liquid vs. air cooling in telecom base stations: Attributional and consequential LCA variants with process correlation. International Journal of Environmental Engineering and Development, 3, 282–290. https://doi.org/10.37394/232033.2025.3.23
Boix Rodríguez, N., Rossi, M., Cappelletti, F., & Favi, C. (2024). Engineering eco-design of heat exchangers in domestic heating systems using life cycle assessment methodology. International Journal on Interactive Design and Manufacturing, 18(8), 5749–5771. https://doi.org/10.1007/s12008-023-01301-z
da Silva, L. A., Vasconcelos, E. S., de Paiva, L. F. R., Collaço, M. H. D. V. R., Joaquim, W. M., de Lima, A. D., & Teixeira, E. P. (2025). Energy consumption of data centers and the primary energy metric: Global analysis 2000–2024 and projections until 2030. Editora Impacto Científico, 21–41. https://doi.org/10.56238/edimpacto2025.072-002
Deng, Y. (2025). A reinforcement learning approach to traffic scheduling in complex data center topologies. Journal of Computer Technology and Software, 4(3). https://doi.org/10.5281/zenodo.15164624
Dimitropoulou, A. M. N., Maroulis, V. Z., & Giannini, E. N. (2023). A simple and effective model for predicting the thermal energy requirements of greenhouses in Europe. Energies, 16(19), 6788. https://doi.org/10.3390/en16196788
European Telecommunications Standards Institute. (2025). ETSI TS 104 134 V1.1.1: Environmental engineering (EE); Simplified method for including uncertainty and sensitivity aspects in calculations of the avoided environmental impact of information and communication technology solutions. https://www.etsi.org/deliver/etsi_ts/104100_104199/104134/01.01.01_60/ts_104134v010101p.pdf
International Electrotechnical Commission. (2026). IEC 63372:2026: Quantification and communication of carbon footprint, GHG emission reductions and avoided emissions from electric and electronic products and systems—Principles, methodologies, requirements and guidance. https://webstore.iec.ch/en/publication/67618
Jha, R., Jha, R., & Islam, M. (2025). Forecasting US data center CO₂ emissions using AI models: Emissions reduction strategies and policy recommendations. Frontiers in Sustainability, 5, 1507030. https://doi.org/10.3389/frsus.2024.1507030
Latif, I., Ashraf, M. M., Haider, U., Reeves, G., Untaroiu, A., Coelho, F., & Browne, D. (2025). Advancing sustainability in data centers: Evaluation of hybrid air/liquid cooling schemes for IT payload using sea water. IEEE Transactions on Cloud Computing, 13(1), 184–197. https://doi.org/10.1109/TCC.2024.3521666
Lei, X., Zhao, K., Song, Z., & Yue, S. (2026). Digital infrastructure and carbon emission intensity: Evidence from enterprise cloud adoption. The Singapore Economic Review. https://doi.org/10.1142/S0217590826500219
Liao, X., Li, Y., Zhang, S., Wei, X., & Hu, J. (2026). Predicting GPU training energy consumption in data centers using task metadata via symbolic regression. Energies, 19(2), 448. https://doi.org/10.3390/en19020448
Liu, Y., Wei, X., Xiao, J., Liu, Z., Xu, Y., & Tian, Y. (2020). Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers. Global Energy Interconnection, 3(3), 272–282. https://doi.org/10.1016/j.gloei.2020.07.008
Liu, Z., Yang, X., Sun, D., & Xia, J. (2026). Assessment of waste heat in China: Characteristics and national potential. Energy, 139860. https://doi.org/10.1016/j.energy.2025.139860
Ljungqvist, H. M., Risberg, M., Toffolo, A., & Vesterlund, M. (2023). A realistic view on heat reuse from direct free air-cooled data centers. Energy Conversion and Management: X, 20, 100473. https://doi.org/10.1016/j.ecmx.2023.100473
Mural, R., Pherwani, D., Gupta, C., Yu, Y., Takahashi, A., Kim, D., & Xie, L. (2026). AI, data centers, and the US electric grid: A watershed moment. Belfer Center for Science and International Affairs, Harvard Kennedy School. https://minlanyu.seas.harvard.edu/writeup/belfer26.pdf
Ravi, S. S., Löffler, T. S., Pina, E. A., Sharma, S., Lepour, D., Terrier, C., & Maréchal, F. (2026). From servers to services: Modeling data centers as heat-active urban energy prosumers. Applied Energy, 402, 127049. https://doi.org/10.1016/j.apenergy.2025.127049
Sheng, Y., Zhang, C., Zhu, Z., Xu, H., Wen, J., Wang, R., & Bu, S. (2026). Power for AI data centers: Energy demand, grid impacts, challenges and perspectives. Energies, 19(3), 722. https://doi.org/10.3390/en19030722
Wang, G., Mi, D., He, P., Wang, J., Wang, F., Fotuhi-Firuzabad, M., & Sun, S. (2026). Deep reinforcement learning-based migration and scheduling strategy for data centers in electricity and carbon markets. IEEE Transactions on Industry Applications. https://doi.org/10.1109/TIA.2026.3691718
World Business Council for Sustainable Development. (2025). Guidance on avoided emissions v2.0: Drive innovations and scale solutions toward net zero. https://www.wbcsd.org/resources/guidance-on-avoided-emissions-helping-business-drive-innovations-and-scale-solutions-toward-net-zero/
Zhang, Y., Wang, Z., Filonenko, K., Dominković, D. F., & Wang, S. (2026). Physics-guided deep reinforcement learning for optimized data center cooling and waste heat recovery utilizing aquifer thermal energy storage. Applied Energy, 402, 126984. https://doi.org/10.1016/j.apenergy.2025.126984
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