气候变化领域集成服务门户(CCPortal): Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach

CCPortal

DOI	10.1007/s11069-020-04434-6
	Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach
	Qu S.; Cai H.; Xu D.; Mohamed N.
发表日期	2021
ISSN	0921030X
起始页码	2419
结束页码	2438
卷号	107 期号:3
英文摘要	CO2 emission control is one of the most vital parts of environment management. China owns the largest CO2 emission in the world. For the sake of clarifying China’s emission sharing responsibilities and set emission reduction targets, a considerable number of scholars have worked to project China’s embodied CO2 emission. Single Regional Input–output (SRIO) model is widely used for investigating CO2 emission issues. Considering the ubiquitous time lag of input–output data, entropy optimization model is introduced to estimate SRIO tables. However, the uncertainty in the corresponding model parameters necessarily has a serious impact on the estimation results. To consider the impact of uncertainties, we introduce robust optimization into entropy minimization model for SRIO table estimation. Based on three different uncertainty sets, we constructed three robust entropy minimization models to construct 2016 China’s SRIO tables and calculate China’s embodied CO2 emission based on those tables. The estimation results show that the model based on the ball uncertainty set has the best performance with less uncertainty, while the model based on the budgeted uncertainty set performances more ‘robust’ facing greater uncertainty, which means its performance is less volatile at different levels of uncertainty. Moreover, the embodied carbon emission is predicted to reach 9632.57 Mt CO2. The top emitter is the sector of supply of electricity, heating and water, which accounts for more than 40% of total CO2 emission. © 2021, Springer Nature B.V.
关键词	CO2 emission Entropy minimization Environment management Robust optimization Single regional Input–output model
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206511
作者单位	University of Shanghai for Science and Technology, Shanghai, China
推荐引用方式 GB/T 7714	Qu S.,Cai H.,Xu D.,et al. Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach[J],2021,107(3).
APA	Qu S.,Cai H.,Xu D.,&Mohamed N..(2021).Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach.Natural Hazards,107(3).
MLA	Qu S.,et al."Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach".Natural Hazards 107.3(2021).