Provincial allocation of China's commercial building operational carbon toward carbon neutrality

National carbon peak track and optimized provincial carbon allocations are crucial for mitigating regional inequality within the commercial building sector during China's transition to carbon neutrality. This study proposes a top-down model to evaluate carbon trajectories in operational commercial buildings up to 2060. Through Monte Carlo simulation, scenario analysis is conducted to assess carbon peak values and the corresponding peaking year, thereby optimizing carbon allocation schemes both nationwide and provincially. The results reveal that (1) the nationwide carbon peak for commercial building operations is projected to reach 890 (± 50) megatons of carbon dioxide (MtCO 2 ) by 2028 (± 3.7 years) in the case of the business-as-usual scenario, with a 7.87 % probability of achieving the carbon peak under the decarbonization scenario. (2) Significant disparities will exist among provinces, with Shandong's carbon peak projected at 69.6 (± 4.0) MtCO 2 by 2029, approximately 11 times higher than Ningxia's peak of 6.0 (± 0.3) MtCO 2 by 2027. (3) Guided by the principle of maximizing the emission reduction potential, the optimal provincial allocation scheme reveals the top three provinces requiring the most significant reductions in the commercial sector: Xinjiang (5.6 MtCO 2 ), Shandong (4.8 MtCO 2 ), and Henan (4.7 MtCO 2 ). Overall, this study offers optimized provincial carbon allocation strategies within the commercial building sector in China via dynamic scenario simulations, with the goal of hitting the carbon peak target and progressing toward a low-carbon future for the building sector. Graphical abstract: (a) distributions of the carbon peak and peaking time for China's commercial building operations nationwide; (b) national emission trends and projections from 2000 to 2060; (c) provincial distributions of carbon peaks and the corresponding peaking time under BAU and decarbonization scenarios; (d) carbon peaks and peaking time for the four provinces with the highest emission levels • National carbon peak and provincial carbon allocation are optimized by the dynamic scenario analysis. • Operational commercial building emissions in China will peak in 2028 (± 3.7 yrs) at 890 (± 50) MtCO 2 . • Emissions across provinces are expected to peak before 2046 (± 4.8 yrs) under dynamic scenario analysis. • The future emission peak in Shandong [69.6 (± 4.0) MtCO 2 )] will be 11 times greater than that in Ningxia. • East China's reduction allocation of 18.1 MtCO 2 will exceed the lowest emission regions by 6.7 times.

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Publication Details

Journal
Applied Energy
Published
2025-02-06
DOI
https://doi.org/10.1016/j.apenergy.2025.125450
Citations
34
Primary Topic
Environmental Impact and Sustainability
Type
article
Field-Weighted Citation Impact
27.17

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article

Provincial allocation of China's commercial building operational carbon toward carbon neutrality

Minda Ma, Chenchen Zou, Yanqiao Deng, Xin Ma et al.
34 citations
Applied Energy
Environmental Impact and Sustainability
27.17
article

Provincial allocation of China's commercial building operational carbon toward carbon neutrality

Minda Ma, Chenchen Zou, Yanqiao Deng, Xin Ma, Yan Ran, Nan Zhou, Zhili Ma
article en
34 citations

Abstract

National carbon peak track and optimized provincial carbon allocations are crucial for mitigating regional inequality within the commercial building sector during China's transition to carbon neutrality. This study proposes a top-down model to evaluate carbon trajectories in operational commercial buildings up to 2060. Through Monte Carlo simulation, scenario analysis is conducted to assess carbon peak values and the corresponding peaking year, thereby optimizing carbon allocation schemes both nationwide and provincially. The results reveal that (1) the nationwide carbon peak for commercial building operations is projected to reach 890 (± 50) megatons of carbon dioxide (MtCO 2 ) by 2028 (± 3.7 years) in the case of the business-as-usual scenario, with a 7.87 % probability of achieving the carbon peak under the decarbonization scenario. (2) Significant disparities will exist among provinces, with Shandong's carbon peak projected at 69.6 (± 4.0) MtCO 2 by 2029, approximately 11 times higher than Ningxia's peak of 6.0 (± 0.3) MtCO 2 by 2027. (3) Guided by the principle of maximizing the emission reduction potential, the optimal provincial allocation scheme reveals the top three provinces requiring the most significant reductions in the commercial sector: Xinjiang (5.6 MtCO 2 ), Shandong (4.8 MtCO 2 ), and Henan (4.7 MtCO 2 ). Overall, this study offers optimized provincial carbon allocation strategies within the commercial building sector in China via dynamic scenario simulations, with the goal of hitting the carbon peak target and progressing toward a low-carbon future for the building sector. Graphical abstract: (a) distributions of the carbon peak and peaking time for China's commercial building operations nationwide; (b) national emission trends and projections from 2000 to 2060; (c) provincial distributions of carbon peaks and the corresponding peaking time under BAU and decarbonization scenarios; (d) carbon peaks and peaking time for the four provinces with the highest emission levels • National carbon peak and provincial carbon allocation are optimized by the dynamic scenario analysis. • Operational commercial building emissions in China will peak in 2028 (± 3.7 yrs) at 890 (± 50) MtCO 2 . • Emissions across provinces are expected to peak before 2046 (± 4.8 yrs) under dynamic scenario analysis. • The future emission peak in Shandong [69.6 (± 4.0) MtCO 2 )] will be 11 times greater than that in Ningxia. • East China's reduction allocation of 18.1 MtCO 2 will exceed the lowest emission regions by 6.7 times.

Applied EnergyVol. 384
Southwest University of Science and Technology (CN), Lawrence Berkeley National Laboratory (US), Chongqing University (CN)
U.S. Department of Energy, National Planning Office of Philosophy and Social Science
Openalex Percentile: Top 0%
Environmental Impact and Sustainability
27.17
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