| 吉林省2020年CO2排放情景预测 |
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| 作者姓名： | 张亚欣 张平宇 |
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| 作者单位： | 1. 中国科学院东北地理与农业生态研究所, 长春 130012;
2. 中国科学院研究生院, 北京 100049 |
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| 基金项目： | 国家自然科学基金(41071108)、吉林省科技引导计划软科学项目(20100640)和中国科学院东北地理与农业生态研究所前沿领域项目(KZCX3-SW-NA09-07)资助 |
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| 摘 要： | 使用中国吉林省1978~2009年人口、GDP和单位GDP能耗数据,采用BP神经网络模型分2种情景预测了吉林省2020年CO2排放量.结果表明,如果以吉林省2005年单位GDP的CO2排放为参照,2种情景下,吉林省2020年单位GDP的CO2排放分别降低55.17%和58.79%;如果以中国2005年平均水平为参照,吉林省2020年单位GDP的CO2排放分别降低35.40%和40.62%.
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| 关 键 词： | CO2排放 情景分析 BP神经网络 吉林省 |
| 收稿时间： | 2010-08-03 |
| 修稿时间： | 2010-12-20 |
Scenario prediction for CO2 emissions in 2020 in Jilin province |
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| Authors: | ZHANG Ya-Xin ZHANG Ping-Yu |
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| Institution: | 1. Northeast Institute of Geography and Agricultural Ecology,Chinese Academy of Sciences,Changchun 130012,China;
2. Graduate University,Chinese Academy of Sciences,Beijing 100049,China |
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| Abstract: | Using the data of population, GDP, and energy intensity in Jilin province from 1978 to 2009 as input data, we use BP neural network to predict carbon dioxide emissions in 2020 in Jilin province under reference and emission mitigation scenarios. The results show that, if we use carbon dioxide emissions per unit of GDP in 2005 in Jilin province as a reference, CO2 emissions per GDP in 2020 will be reduced by 55.17% and 58.79% under the two scenarios, respectively. The results also show that, if we use the national average level in 2005 as a reference, CO2 emissions per unit of GDP in 2020 in Jilin province will be reduced by 35.40% and 40.62%, respectively. |
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| Keywords: | CO2 emissions scenario prediction BP artificial neural network Jilin province |
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