Modeling and Multi-objective Optimization of Carbon Emissions Throughout the Lifecycle of Zero Carbon Buildings
DOI:
https://doi.org/10.13052/spee1048-5236.4524Keywords:
Decarbonization and emission reduction, carbon emissions, non-dominated genetic algorithm, building information modeling, multi-objective optimizationAbstract
To manage the carbon emissions of zero carbon buildings, a building information model is used to construct a carbon emission model for zero carbon buildings, and a multi-objective optimization method based on non dominated genetic algorithm is developed to optimize the carbon emissions. The performance of the carbon emission model is analyzed using the China Energy and Carbon Emission Database (MEIC) public database, and the outcomes reveal that the data matching error rate of the model is less than 5%, and the model’s coverage of the whole span of carbon emissions reaches 87.9%. By reusing the data from Global Carbon Budget (GCB) database to predict the carbon reduction effect of the optimization plan, the outcomes reveal that the optimization plan can reduce the carbon emissions throughout the whole span by 35% to 45%, and the carbon reduction during the operation phase can reach 43.7%. From the above outcomes, the emission reduction plan based on carbon emission model and multi-objective optimization method can effectively reduce the carbon emissions. This can foster sustainable development, and provide ideas for carbon reduction plans in other fields.
Downloads
References
Wang W, Tang Q, Gao B. Exploration of CO2 emission reduction pathways: identification of influencing factors of CO2 emission and CO2 emission reduction potential of power industry. Clean Technologies and Environmental Policy, 2023, 25(5): 1589–1603. https://doi.org/10.1007/s10098-022-02456-1.
Wu X, Xu C, Ma T, Xu J, and Zhang C. Carbon emission of China’s power industry: driving factors and emission reduction path. Environmental Science and Pollution Research, 2022, 29(52): 78345–78360. https://doi.org/10.1007/s11356-022-21297-5.
Watari T, Yamashita N, Serrenho A C. Net-Zero Embodied Carbon in Buildings with Today’s Available Technologies. Environmental Science And Technology, 2024, 58(4): 1793–1801. https://doi.org/10.1021/acs.est.3c04618.
Yu K, van Son P. Review of trans-Mediterranean power grid interconnection: A regional roadmap towards energy sector decarbonization. Global Energy Interconnection, 2023, 6(1): 115–126. https://doi.org/10.1016/j.gloei.2023.02.010.
Roijen E V, Miller S A, Davis S J. Building materials could store more than 16 billion tonnes of CO2 annually. Science, 2025, 387(6730): 176–182. https://doi.org/10.1126/science.adq8594.
Liu J, Ma H, Wang Q, Tian, S., Xu, Y., Zhang, Y, and Yang, S. Optimization of energy consumption structure based on carbon emission reduction target: A case study in Shandong Province, China. Chinese Journal of Population, Resources and Environment, 2022, 20(2): 125–135. https://doi.org/10.1016/j.cjpre.2022.06.003.
Mullen D, Herraiz L, Gibbins J, and Lucquiaud M. On the cost of zero carbon hydrogen: A techno-economic analysis of steam methane reforming with carbon capture and storage. International Journal of Greenhouse Gas Control, 2023, 126(c): 103904–103904. https://doi.org/10.1016/j.ijggc.2023.103904.
Li H, Hao T, Li Z, Zhao, E., Wang, C & Xu, L. Research on a self-coordinated optimization method for distributed energy resources targeting risk mitigation. Distributed Generation and Alternative Energy Journal, 2024, 39(3): 659–690. https://doi.Org/10.13052/dgaej2156-3306.39312.
Shu Y, Zhang L, Zhang Y, Wang, Y., Lu, G., Yuan, B. Carbon peak and carbon neutrality path for China’s power industry. Strategic Study of Chinese Academy of Engineering, 2021, 23(6): 1–14. https://doi.org/10.15302/J-SSCAE-2021.06.001.
Ding C, Ke J, Levine M, Granderson, J. and Zhou, N. Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale. Nature Communications, 2024, 15(1): 5916–5920. https://doi.org/10.1038/s41467-024-50088-4.
Zhuo Z, Du E, Zhang N, Nielsen, C. P., Lu, X., Kang, C. Cost increase in the electricity supply to achieve carbon neutrality in China. Nature communications, 2022, 13(1): 3172–3180. https://doi.org/10.1038/s41467-022-30747-0.
Cheng D, Reiner D M, Yang F, Cui, C., Meng, J., Shan, Y & Guan, D. Projecting future carbon emissions from cement production in developing countries. Nature Communications, 2023, 14(1): 8213–8215. https://doi.org/10.1038/s41467-023-43660-x.
Zhang S, Chen W. Assessing the energy transition in China towards carbon neutrality with a probabilistic framework. Nature communications, 2022, 13(1): 87–95. https://doi.org/10.1038/s41467-021-27671-0.
Bistline J E T, Blanford G J. Impact of carbon dioxide removal technologies on deep decarbonization of the electric power sector. Nature Communications, 2021, 12(1): 3732–3741. https://doi.org/10.1038/s41467-021-23554-6.
Liu Z, Deng Z, Davis S J, Giron, C., and Ciais, P. Monitoring global carbon emissions in 2021. Nature Reviews Earth & Environment, 2022, 3(4): 217–219. https://doi.org/10.1038/s43017-022-00285-w.
Gao Z, Xie H, Yang X, Yang, X., Zhang, L., Yu, H., Wang, W and Chen, S. Electric vehicle lifecycle carbon emission reduction: A review. Carbon Neutralization, 2023, 2(5): 528–550. https://doi.org/10.1002/cnl2.81.
Wang P, Lin C K, Wang Y, Liu, D., Song, D., and Wu, T. Location-specific co-benefits of carbon emissions reduction from coal-fired power plants in China. Nature communications, 2021, 12(1): 6948–6952. https://doi.org/10.1038/s41467-021-27252-1.
Slameršak A, Kallis G, O’Neill D W. Energy requirements and carbon emissions for a low-carbon energy transition. Nature communications, 2022, 13(1): 6932–6937. https://doi.org/10.1038/s41467-022-33976-5.
Disney O, Roupé M, Johansson M, and Domenico Leto, A. Embracing BIM in its totality: a Total BIM case study. Smart and Sustainable Built Environment, 2024, 13(3): 512–531. https://doi.Org/10.1108/SASBE-06-2022-0124.
Canbolat A S, Albak E İ. Multi-Objective Optimization of Building Design Parameters for Cost Reduction and CO2 Emission Control Using Four Different Algorithms. Applied Sciences, 2024, 14(17): 7668–7672. https://doi.org/10.3390/app14177668.
https://aeeeuropeenergy.com/
