发射强度
析因实验
还原(数学)
升级
冶炼
阶乘
强度(物理)
环境科学
碳纤维
相(物质)
数学
计算机科学
计量经济学
统计
工程类
化学
算法
材料科学
电气工程
冶金
物理
数学分析
操作系统
复合数
有机化学
激发
量子力学
几何学
作者
G.Y. Wang,Y.P. Li,Jiaguo Liu,Guohe Huang,L.R. Chen,Y.J. Yang,Peng Gao
出处
期刊:Energy
[Elsevier]
日期:2022-03-01
卷期号:248: 123615-123615
被引量:1
标识
DOI:10.1016/j.energy.2022.123615
摘要
In this study, a two-phase factorial input-output model (TFIOM) is developed for carbon dioxide (CO 2 ) emission mitigation, through integrating input-output model (IOM) and two-phase factorial analysis (TFA) into a general framework. TFIOM can identify the main sectors of CO 2 emission, quantify the individual and interactive effects of the main factors (i.e. various CO 2 management strategies implemented on different sectors) on reduction target, as well as seek CO 2 -emission reduction pathways and strategies from multiple perspectives (i.e. energy structure adjustment strategy (ESAS), technology upgrade strategy (TUS), and industrial structure adjustment strategy (ISAS)). The developed TFIOM is applied to Fujian province (in China), and results reveal that industries of chemical, non-metallic, metal smelting (MSI), and electric heating (EGW) are the main CO 2 -emission sectors (i.e. occupying 57.6% of direct emission and 56.4% of indirect emission). The joint application of ESAS (applied to EGW) and TUS (applied to MSI) has the best effect in reducing carbon emission intensity (decreasing by 24.5%). Among all scenarios, Scenario 115 shows the most desirable emission-reduction effect (i.e. carbon emission intensity, energy intensity, total direct emission, and total indirect emission would respectively decrease by 22.3%, 7.6%, 21.3% and 5.1%, and the cost of reducing CO 2 per ton would be 182 RMB¥). • A two-phase factorial input-output model (TFIOM) is developed. • Input-output model is employed to identify the main emission sectors. • Definitive screening design is adopted to screen out the main factors of main sectors. • The proportion of electricity consumption should be maintained at more than 40%. • Application of energy-technology strategies would reduce energy intensity by 9.8%.
科研通智能强力驱动
Strongly Powered by AbleSci AI