| 摘要: |
| LMDI模型、Shapley值模型和MRCI模型均是能源消费碳排放的零残差因素分解模型,对三种模型的基本形式进行拓展,提出基于多层次多因素分解的通用表述形式,给出各分解模型中因素的累积效应、逐年效应和效应贡献度的测算方法,并对三种模型特点进行对比。运用Kendall协调系数法对三种模型结果进行相容性检验,输出相容模型集；基于各相容单一分解模型,构建能源消费碳排放的最优加权组合分解模型。应用上述模型对青岛市能源消费碳排放进行分解实证,结果表明,人均GDP和人口是青岛市碳排放增加的驱动因素,能源消费强度下降和能源消费结构优化则对碳排放增长具有抑制作用。 |
| 关键词: 能源管理 能源消费 碳排放 零残差 因素分解 组合分解模型 |
| DOI：10.3969/j.issn.1673-5005.2013.01.032 |
| 分类号::X 32 |
| 基金项目:山东省自然科学基金项目(ZR2011GQ004);中央高校基本科研业务费专项资金资助项目(10CX04012B,11CX04034B);教育部人文社会科学研究青年基金项目(10YJC630207);山东省高校人文社会科学研究计划项目(J10WG94);大学生创新创业训练计划创新训练项目(111042555) |
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| Zero-residual factor decomposition models of carbon emissions from energy consumption |
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SONG Jie-kun, DOU Ji-fang
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(School of Economics and Management in China University of Petroleum, Qingdao 266580, China)
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| Abstract: |
| LMDI model, Shapley value model and MRCI model are all zero-residual factor decomposition models of carbon emissions from energy consumption. The three basic forms of decomposition models were expanded, and their general forms based on multiple levels and multiple factors were proposed. The calculation methods of accumulation effect, year after year effect and effect contribution of different factor were provided respectively. Meanwhile, the characteristics of three models were compared. Kendall coordination coefficient method was employed for compatibility test on different models results, and the compatible model set can be output. Based on compatible single decomposition models, an optimal weighted combination decomposition model of carbon emissions from energy consumption was constructed. The above models were applied to make an empirical analysis on factor decomposition of carbon emissions from energy consumption of Qingdao city. The results show that per capita GDP and population are the driving factors of carbon emissions, while reducing energy consumption intensity and optimizing energy consumption structure have inhibition role on the growth of carbon emissions. |
| Key words: energy management energy consumption carbon emissions zero-residual factor decomposition combination decomposition model |
