Estimating the carbon abatement potential of economic sectors in China
Estimating the carbon abatement potential of economic sectors in China
复制标题
估算中国经济部门的碳减排潜力
DOI：
10.1016/j.apenergy.2015.12.064
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发表时间：
2016-03
期刊：
影响因子：
11.2
通讯作者：
Junjie Zhang
中科院分区：
文献类型：
--
作者：
Shiwei Yu;Lawrence Agbemabiese;Junjie Zhang
本文通过建立和利用碳强度的环境学习曲线（ELC）模型，估算了中国43个经济部门的碳减排潜力。该模型选取能源强度、人均增加值和燃料消费结构作为自变量，通过面板数据回归得到其学习系数。基于该模型，在一切照旧（BAU）和计划情景下估计了43个经济部门2020年的碳减排潜力。研究结果表明：（1）所建立的学习曲线能够较好地模拟不同行业的碳强度;（2）能源强度对所有行业的正学习能力最强。能源密集度的降低将导致42个部门（除农业部门外）的碳密集度降低。然而，部门增值的增加将有可能降低34个部门的碳强度。降低煤炭能源比重仅会导致10个行业的碳强度下降;（3）在两种不同情景下，43个行业2020年的平均碳强度下降潜力分别为33.0%和39.0%。在43个行业中，与食品、医药、饮料和化学纤维制造相关的行业碳强度潜力最大。
This study estimates the carbon abatement potential of 43 Chinese economic sectors by establishing and utilizing an environmental learning curve (ELC) model of carbon intensity. The model selects energy intensity, per capita value added and fuel consumption mix as the independent variables and obtains its learning coefficients using panel data regression. Based on this model, the carbon abatement potential of 43 economic sectors in 2020 is estimated for business-as-usual (BAU) and planned scenarios. The findings show that: (1) the established learning curves adequately simulate the carbon intensity of different sectors; (2) energy intensity has the strongest positive learning ability among the three variables for all sectors. A reduction in energy intensity will lead to reduced carbon intensities for 42 sectors (all except the agriculture sector). However, an increase in sectoral value added will make it possible to reduce carbon intensity in 34 sectors. Reducing the proportion of coal energy will result in decreased carbon intensities in only ten sectors; (3) the average carbon intensity reduction potential for 43 sectors in 2020 will be 33.0% and 39.0% based on 2012 in two different scenarios. Sectors related to the manufacture of food, medicine, beverages and chemical fiber have the largest carbon intensity potential among the 43 sectors.
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影响因子：
9
作者：
Yun-Hsun Huang;Jung-hua Wu
通讯作者：
Yun-Hsun Huang;Jung-hua Wu
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影响因子：
9
作者：
Shiwei Yu;Junjie Zhang;Shuhong Zheng;Han Sun
通讯作者：
Han Sun
影响因子：
6.7
作者：
K. Promjiraprawat;P. Winyuchakrit;B. Limmeechokchai;T. Masui;T. Hanaoka;Y. Matsuoka
通讯作者：
K. Promjiraprawat;P. Winyuchakrit;B. Limmeechokchai;T. Masui;T. Hanaoka;Y. Matsuoka
DOI：
10.1016/j.energy.2004.03.092
发表时间：
2004-07
期刊：
--
影响因子：
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作者：
E. Rubin;Margaret R. Taylor;S. Yeh;David A. Hounshell
通讯作者：
E. Rubin;Margaret R. Taylor;S. Yeh;David A. Hounshell
影响因子：
15.9
作者：
B. Limmeechokchai;Saichit Chawana
通讯作者：
B. Limmeechokchai;Saichit Chawana