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Dynamic scenario simulations of carbon emission peak in China's city-scale urban residential building sector through 2050
被引:131
|作者:
Huo, Tengfei
[1
]
Xu, Linbo
[1
]
Feng, Wei
[2
]
Cai, Weiguang
[3
]
Liu, Bingsheng
[4
]
机构:
[1] Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China
[2] Lawrence Berkeley Natl Lab, China Energy Grp, 1 Cyclotron Rd, Berkeley, CA 94720 USA
[3] Chongqing Univ, Sch Management Sci & Real Estate, Chongqing 400044, Peoples R China
[4] Chongqing Univ, Sch Publ Affairs, Chongqing 400044, Peoples R China
来源:
基金:
中国国家自然科学基金;
国家重点研发计划;
关键词:
Urban residential building sector;
Carbon emission peak;
Dynamic scenario simulation;
System dynamics model;
Monte Carlo simulation;
China;
CO2;
EMISSIONS;
ENERGY-CONSUMPTION;
INDUSTRIAL-STRUCTURE;
OCCUPANT BEHAVIOR;
ECONOMIC-GROWTH;
STIRPAT MODEL;
REDUCTION;
IMPACT;
DECOMPOSITION;
MITIGATION;
D O I:
10.1016/j.enpol.2021.112612
中图分类号:
F [经济];
学科分类号:
02 ;
摘要:
Understanding future trajectory of urban residential building carbon emissions (URBCE) is essential to seeking effective carbon-abatement pathways to combat climate change. However, future evolutionary trajectory, possible emission peaks and peaking times in this sector are still unclear. This study innovatively develops an integrated dynamic simulation model by embedding a bottom-up building end-use energy model into the system dynamics model. Based on this, scenario analysis approach is combined with Monte Carlo simulation method to explore the possible emission peaks and peaking times considering the uncertainties of impact factors. We apply the integrated SD-LEAP model to Chongqing, a typical city in China's hot-summer and cold-winter zone. Results show that URBCE will probably peak at 22.8 (+/- 8.0) Mt CO2 in 2042 (+/- 3.4)-well beyond China's 2030 target. Different building end-uses present substantial disparities. The contribution of combined heating and cooling to URBCE mitigation will be over 60% between business-as-usual and low-carbon scenarios. Dynamic sensitivity analysis reveals that per capita gross domestic product, carbon emission factor, and residential floor space per capita can boost emission peaks and peaking time. This study can not only boost the theory and model development for carbon emission prediction, but also assist governments to set effective carbon-reduction targets and policies.
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页数:14
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