Timetable and roadmap for achieving carbon peak and carbon neutrality of China's building sector

被引:63
|
作者
Huo, Tengfei [1 ]
Du, Qianxi [1 ]
Xu, Linbo [1 ]
Shi, Qingwei [2 ]
Cong, Xiaobo [1 ]
Cai, Weiguang [3 ]
机构
[1] Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China
[2] Southwest Univ, Business Coll, Chongqing 400715, Peoples R China
[3] Chongqing Univ, Sch Management Sci & Real Estate, Chongqing 400044, Peoples R China
基金
中国国家自然科学基金;
关键词
Carbon peak and carbon neutrality; Timetable; Roadmap; Building sector; Dynamic scenario simulation; China; ECONOMIC-GROWTH; ENERGY-CONSUMPTION; CO2; EMISSIONS; STIRPAT; IMPACT; IPAT; MODEL; PLAN;
D O I
10.1016/j.energy.2023.127330
中图分类号
O414.1 [热力学];
学科分类号
摘要
Carbon mitigation effectiveness in the building sector profoundly determines the achievement of the "Dual carbon" target. However, the timetable and roadmap for the building sector to achieve such a target are still lacking. This study constructs a STIRPAT-PLS model framework to clarify the impact of the key factors from various dimensions on building carbon emissions across sub-sectors. Then, a dynamic scenario simulation model is established and applied in China to explore the dynamic evolutionary paths, and the timetable and roadmap are ultimately formulated. The results show that building sector will probably peak at 2.99 (+/- 0.09) Gt CO2 in 2036 (+/- 1). Rural residential buildings will peak earliest in 2026 (+/- 1), followed by urban residential and commercial buildings: in 2036 (+/- 2) and 2038 (+/- 1). Achieving carbon neutrality will require the building sector to peak its carbon emissions in 2026 with 2.16-2.40 Gt CO2. The remaining 0.56-0.81 Gt CO2 by 2060 will be offset by negative carbon technologies. Dynamic sensitivity analysis shows that building floor space per capita and energy intensity significantly promote the emission peaks, and population and building floor space per capita markedly impact the peaking times. This study can promote the development of the theory and model for carbon emissions prediction. It can also offer a scientific basis for governments to set effective carbon-reduction targets and paths.
引用
收藏
页数:14
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