Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method
Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method
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中国省际碳强度减排潜力估算：PSO-GA优化的多因素环境学习曲线方法
DOI：
10.1016/j.enpol.2014.11.035
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发表时间：
2015-02
期刊：
影响因子：
9
通讯作者：
Han Sun
中科院分区：
文献类型：
--
作者：
Shiwei Yu;Junjie Zhang;Shuhong Zheng;Han Sun
本研究旨在通过提出粒子群优化-遗传算法（PSO-GA）多元环境学习曲线估计方法来估计中国区域层面的碳强度减排潜力。该模型使用人均国内生产总值（GDP）和第三产业占GDP比重两个自变量，构建碳强度学习曲线（CILCs），即，中国30个省份单位GDP二氧化碳排放量。用PSO-GA智能优化算法代替传统的普通最小二乘（OLS）方法来优化学习曲线的系数。利用PSO-GA方法对中国30个省（市、自治区）在“一切照旧”情景下的碳强度减排潜力进行了估算。估计揭示了以下结果。(1)对于大多数省份来说，提高单位第三产业占GDP比重的减排潜力大于提高单位人均GDP的减排潜力。(2)在2005年的基础上，到2020年，30个省份的平均排放潜力为37.6%。江苏、天津、山东、北京、黑龙江的潜力超过60%。宁夏是唯一一个没有强度减缓潜力的省份。(3)2020年，按30个省份GDP比重加权的中国总碳强度比2005年下降39.4%。这一强度无法实现中国政府设定的碳强度降低40%-45%的目标。应制定额外的缓解政策，以发掘宁夏和内蒙古的潜力。此外，PSO-GA优化的CILCs的仿真精度高于传统的OLS方法优化的CILCs。
This study aims to estimate carbon intensity abatement potential in China at the regional level by proposing a particle swarm optimization–genetic algorithm (PSO–GA) multivariate environmental learning curve estimation method. The model uses two independent variables, namely, per capita gross domestic product (GDP) and the proportion of the tertiary industry in GDP, to construct carbon intensity learning curves (CILCs), i.e., CO2emissions per unit of GDP, of 30 provinces in China. Instead of the traditional ordinary least squares (OLS) method, a PSO–GA intelligent optimization algorithm is used to optimize the coefficients of a learning curve. The carbon intensity abatement potentials of the 30 Chinese provinces are estimated via PSO–GA under the business-as-usual scenario. The estimation reveals the following results. (1) For most provinces, the abatement potentials from improving a unit of the proportion of the tertiary industry in GDP are higher than the potentials from raising a unit of per capita GDP. (2) The average potential of the 30 provinces in 2020 will be 37.6% based on the emission's level of 2005. The potentials of Jiangsu, Tianjin, Shandong, Beijing, and Heilongjiang are over 60%. Ningxia is the only province without intensity abatement potential. (3) The total carbon intensity in China weighted by the GDP shares of the 30 provinces will decline by 39.4% in 2020 compared with that in 2005. This intensity cannot achieve the 40%–45% carbon intensity reduction target set by the Chinese government. Additional mitigation policies should be developed to uncover the potentials of Ningxia and Inner Mongolia. In addition, the simulation accuracy of the CILCs optimized by PSO–GA is higher than that of the CILCs optimized by the traditional OLS method.
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影响因子：
12.8
作者：
Ying Fan;Qian Liang;Yi-Ming Wei
通讯作者：
Ying Fan;Qian Liang;Yi-Ming Wei
影响因子：
6.7
作者：
K. Promjiraprawat;P. Winyuchakrit;B. Limmeechokchai;T. Masui;T. Hanaoka;Y. Matsuoka
通讯作者：
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影响因子：
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作者：
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通讯作者：
Jianzhou Wang;Yao Dong;Jie Wu;Ren Mu;He Jiang
DOI：
--
发表时间：
2013
期刊：
Mathematics in Practice and Theory
影响因子：
--
作者：
Wang Wei-guo
通讯作者：
Wang Wei-guo
影响因子：
15.9
作者：
B. Limmeechokchai;Saichit Chawana
通讯作者：
B. Limmeechokchai;Saichit Chawana