Abstract
The building sector contributes significantly to carbon emissions, impeding China’s progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China’s construction industry to chart a low-carbon roadmap for the sector’s future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China’s building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector’s carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.
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The data used in the study are available from the corresponding author upon request.
References
Alam M, Murad W, Noman AH, Ozturk I (2016) Relationships among carbon emissions, economic growth, energy consumption and population growth: Testing Environmental Kuznets Curve hypothesis for Brazil, China, India and Indonesia. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2016.06.043
Ang BW, Choi KH (1997) Decomposition of aggregate energy and gas emission intensities for industry: a refined divisia index method. The Energy Journal. https://doi.org/10.5547/issn0195-6574-ej-vol18-no3-3
Ang YQ, Berzolla ZM, Samuele LD, Reinhart CF (2023) Carbon reduction technology pathways for existing buildings in eight cities. Nat Commun. https://doi.org/10.1038/s41467-023-37131-6
Arababadi R, Naganathan H, Pour MS et al (2020) Building stock energy modeling: feasibility study on selection of important input parameters using stepwise regression. Energy Science & Engineering. https://doi.org/10.1002/ese3.847
Besir AB, Cuce E (2018) Green roofs and facades: a comprehensive review. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2017.09.106
Calise F, Cappiello FL, d’Accadia MD et al (2021) A solar-driven 5th generation district heating and cooling network with ground-source heat pumps: a thermo-economic analysis. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2021.103438
Chen L, Huang L, Hua J et al (2023a) Green construction for low-carbon cities: a review. Environ Chem Lett 21:1627–1657. https://doi.org/10.1007/s10311-022-01544-4
Chen L, Ma M, Xiang X (2023b) Decarbonizing or illusion? How carbon emissions of commercial building operations change worldwide. Sustain Cities Soc 96:104654. https://doi.org/10.1016/j.scs.2023.104654
Chen R, Ye M, Li Z et al (2023c) Empirical assessment of carbon emissions in Guangdong Province within the framework of carbon peaking and carbon neutrality: a lasso-TPE-BP neural network approach. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-30882-1
Chen X, Zhou C, Wang T (2023d) China’s energy consumption and carbon peak path under different scenarios. Environmental Science 44:5464–5477. https://doi.org/10.13227/j.hjkx.202211293
Dijkstra TK (2014) Ridge regression and its degrees of freedom. Qual Quant 48:3185–3193. https://doi.org/10.1007/s11135-013-9949-7
Dong K, Dong X, Jiang Q (2020) How renewable energy consumption lower global CO2 emissions? Evidence from countries with different income levels The World Economy https://doi.org/10.1111/twec.12898
Fan R, Zhang X, Bizimana A et al (2022) Achieving China’s carbon neutrality: predicting driving factors of CO2 emission by artificial neural network. J Clean Prod. https://doi.org/10.1016/j.jclepro.2022.132331
Fang Q, Qian L, Lu Z (2021) Measure carbon emission amount of China in the context of carbon peak and carbon neutrality. Environmental Protection 49:49–54. https://doi.org/10.14026/j.cnki.0253-9705.2021.16.012
Filippidou F, Navarro J (2019) Achieving the cost-effective energy transformation of Europe’s buildings. Publications Office of the European Union, Luxembourg
Garcia J, Salmeron R, Garcia C, Martin M (2016) Standardization of variables and collinearity diagnostic in ridge regression. Int Stat Rev 84:245–266. https://doi.org/10.1111/insr.12099
Guo C, Bian C, Liu Q et al (2022) A new method of evaluating energy efficiency of public buildings in China. Journal of Building Engineering. https://doi.org/10.1016/j.jobe.2021.103776
Guo D, Chen H, Long R (2018) Can China fulfill its commitment to reducing carbon dioxide emissions in the Paris Agreement? Analysis based on a back-propagation neural network Environmental Science and Pollution Research https://doi.org/10.1007/s11356-018-2762-z
Han W, Nan L, Su M et al (2019) Research on the prediction method of centrifugal pump performance based on a double hidden layer BP neural network. Energies. https://doi.org/10.3390/en12142709
Hoekstra R, Bergh J (2003) Comparing structural decomposition analysis and index. Energy Economics. https://doi.org/10.1016/s0140-9883(02)00059-2
Huo T, Ma Y, Cai W, et al (2020) Will the urbanization process influence the peak of carbon emissions in the building sector? A dynamic scenario simulation Energy and Buildings https://doi.org/10.1016/j.enbuild.2020.110590
Huo T, Ma Y, Yu T et al (2021) Decoupling and decomposition analysis of residential building carbon emissions from residential income: evidence from the provincial level in China. Environ Impact Assess Rev. https://doi.org/10.1016/j.eiar.2020.106487
IEA (2021) World energy statistics in 2021. International Energy Agency. https://www.iea.org/data-and-statistics/data-browser?country=WORLD&fuel=Energy%20supply&indicator=TESbySource
Jelle M, Sarah DV, Nele S et al (2021) Renewable electricity support in perfect markets: economic incentives under diverse subsidy instruments. Energy Economics. https://doi.org/10.1016/j.eneco.2020.105066
Jessica GA, Veronica V, Yris O (2021) Simulating the effect of sustainable buildings and energy efficiency standards on electricity consumption in four cities in Colombia: a system dynamics approach. J Clean Prod. https://doi.org/10.1016/j.jclepro.2021.128041
Jiang B, Sun L, Zhang X et al (2023) The impacts of driving variables on energy-related carbon emissions reduction in the building sector based on an extended LMDI model: a case study in China. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-30952-4
Jing R, Kuriyan K, Lin J et al (2020) Quantifying the contribution of individual technologies in integrated urban energy systems - a system value approach. Appl Energy. https://doi.org/10.1016/j.apenergy.2020.114859
Li D, Huang G, Zhang G, Wang J (2020) Driving factors of total carbon emissions from the construction industry in Jiangsu Province, China Journal of Cleaner Production https://doi.org/10.1016/j.jclepro.2020.123179
Li H, Qiu P, Wu T (2021) The regional disparity of per-capita CO2 emissions in China’s building sector: an analysis of macroeconomic drivers and policy implications. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2021.111011
Li Y, Wang J, Deng B et al (2023) Emission reduction analysis of China’s building operations from provincial perspective: factor decomposition and peak prediction. Energy and Buildings 296:113366. https://doi.org/10.1016/j.enbuild.2023.113366
Ma M, Cai W, Wu Y (2019) China Act on the Energy Efficiency of Civil Buildings (2008): a decade review. Sci Total Environ 651:42–60. https://doi.org/10.1016/j.scitotenv.2018.09.118
Ma M, Feng W, Huo J, Xiang X (2022) Operational carbon transition in the megalopolises’ commercial buildings. Build Environ. https://doi.org/10.1016/j.buildenv.2022.109705
Meijer RJ, Goeman JJ (2013) Efficient approximate k-fold and leave-one-out cross-validation for ridge regression. Biom J 55:141–155. https://doi.org/10.1002/bimj.201200088
Niu D, Wang K, Wu J, et al (2020) Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network Journal of Cleaner Production https://doi.org/10.1016/j.jclepro.2019.118558
Nosheen M, Abbasi MA, Iqbal J (2020) Analyzing extended STIRPAT model of urbanization and CO2 emissions in Asian countries. Environ Sci Pollut Res 27:45911–45924. https://doi.org/10.1007/s11356-020-10276-3
Ohene E, Chan APC, Darko A (2023) Navigating toward net zero by 2050: drivers, barriers, and strategies for net zero carbon buildings in an emerging market. Build Environ 242:110472. https://doi.org/10.1016/j.buildenv.2023.110472
Rao C, Huang Q, Chen L et al (2023) Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis. Environ Sci Pollut Res 30:57460–57480. https://doi.org/10.1007/s11356-023-26599-w
Rathore PKS, Gupta NK, Yadav D et al (2022) Thermal performance of the building envelope integrated with phase change material for thermal energy storage: an updated review. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2022.103690
Rocío RC, Any Viviana MC (2018) Towards a sustainable growth in Latin America: a multiregional spatial decomposition analysis of the driving forces behind CO2 emissions changes. Energy Policy. https://doi.org/10.1016/j.enpol.2018.01.019
Schwarz M, Nakhle C, Knoeri C (2020) Innovative designs of building energy codes for building decarbonization and their implementation challenges. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.119260
Scrivener KL, John VM, Gartner EM (2018) Eco-efficient cements: Potential economically viable solutions for a low-CO2 cement-based materials industry. Cem Concr Res. https://doi.org/10.1016/j.cemconres.2018.03.015
Senarathne LR, Nanda G, Sundararajan R (2022) Influence of building parameters on energy efficiency levels: a Bayesian network study. Advances in Building Energy Research 16:780–805. https://doi.org/10.1080/17512549.2022.2108142
Shi Q, Ren H, Cai W, Gao J (2019) How to set the proper level of carbon tax in the context of Chinese construction sector? A CGE analysis J Clean Prod https://doi.org/10.1016/j.jclepro.2019.117955
Tan X, Lai H, Gu B et al (2018) Carbon emission and abatement potential outlook in China’s building sector through 2050. Energy Policy 118:429–439. https://doi.org/10.1016/j.enpol.2018.03.072
Tang X, Liu J (2023) Forecast of peak carbon emissions of buildings based on PSO-LSTM model. Science and Technology Management Research 43:191–198. https://doi.org/10.3969/j.issn.1000-7695.2023.1.024
UN (2020) General debate of the 75th session of the general assembly. United Nations. https://www.un.org/en/delegate/general-debate-75th-session-general-assembly
Ustaoglu A, Kurtoglu K, Yaras A (2020) A comparative study of thermal and fuel performance of an energy-efficient building in different climate regions of Turkey. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2020.102163
Wang H, Ang BW, Su B (2017) Assessing drivers of economy-wide energy use and emissions: IDA versus SDA. Energy Policy. https://doi.org/10.1016/j.enpol.2017.05.034
Wang Z, Li Z, Lu G et al (2022) Experimental study on phase change heat storage of valley electricity and economic evaluation of commercial building heating. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2022.104098
Wei W, Hao S, Yao M et al (2020) Unbalanced economic benefits and the electricity-related carbon emissions embodied in China’s interprovincial trade. J Environ Manage. https://doi.org/10.1016/j.jenvman.2020.110390
Wen Y, Lau SK, Leng J, Liu K (2022) Sustainable underground environment integrating hybrid ventilation, photovoltaic thermal and ground source heat pump. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2022.104383
Xi C, Ding J, Ren C et al (2022) Green glass space based design for the driven of sustainable cities: a case study. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2022.103809
Xing Y, Li F (2020) Research on the influence of hidden layers on the prediction accuracy of GA-BP neural network. J Phys Conf Ser 1486:022010. https://doi.org/10.1088/1742-6596/1486/2/022010
Yan H, Fan Z, Zhang Y et al (2022) A city-level analysis of the spatial distribution differences of green buildings and the economic forces - a case study in China. J Clean Prod. https://doi.org/10.1016/j.jclepro.2022.133433
Yang J, Cai W, Ma M, Li L, Liu C, Ma X et al (2020) Driving forces of China’s CO2 emissions from energy consumption based on Kaya-LMDI methods. Sci Total Environ 711:134569. https://doi.org/10.1016/j.scitotenv.2019.134569
Yang J, Deng Z, Guo S, Chen Y (2022) Development of bottom-up model to estimate dynamic carbon emission for city-scale buildings. Appl Energy. https://doi.org/10.1016/j.apenergy.2022.120410
Yang T, Pan Y, Yang Y et al (2017) CO2 emissions in China’s building sector through 2050: a scenario analysis based on a bottom-up model. Energy. https://doi.org/10.1016/j.energy.2017.03.098
You K, Yu Y, Cai W, Liu Z (2023) The change in temporal trend and spatial distribution of CO2 emissions of China’s public and commercial buildings. Build Environ 229:109956. https://doi.org/10.1016/j.buildenv.2022.109956
Zhang S, Ma M, Li K et al (2022a) Historical carbon abatement in the commercial building operation: China versus the US. Energy Economics 105:105712. https://doi.org/10.1016/j.eneco.2021.105712
Zhang S, Yang XY, Xu W, Fu YJ (2021) Contribution of nearly-zero energy buildings standards enforcement to achieve carbon neutral in urban area by 2060. Adv Clim Chang Res 12:734–743. https://doi.org/10.1016/j.accre.2021.07.004
Zhang Y, Hu S, Guo F et al (2022b) Assessing the potential of decarbonizing China’s building construction by 2060 and synergy with industry sector. J Clean Prod. https://doi.org/10.1016/j.jclepro.2022.132086
Zhou N, Khanna N, Feng W et al (2018) Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050. Nat Energy. https://doi.org/10.1038/s41560-018-0253-6
Zou C, Ma M, Feng W et al (2023) Toward carbon free by 2060: a decarbonization roadmap of operational residential buildings in China. Energy. https://doi.org/10.1016/j.energy.2023.127689
Funding
The research was financially supported by the National Natural Science Foundation of China (No. 51468022), the Humanities and Social Sciences Research Project of Jiangxi Provincial Higher Education Institutions (No. GL21118), Doctor Start-up Fund of Jiangxi Science & Technology Normal University, China (No.2020BSQD018), and Graduate Innovation Special Fund Funding Project of Jiangxi Science & Technology Normal University (No. YC2022- × 08).
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Junjie Xia: conceptualization of study, methodology, and writing—original draft; Hao Cui: conceptualization of study, methodology, and writing—reviewing and editing.
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Cui, H., Xia, J. Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model. Environ Sci Pollut Res 31, 22694–22714 (2024). https://doi.org/10.1007/s11356-024-32591-9
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DOI: https://doi.org/10.1007/s11356-024-32591-9