| 经济增长与减排视角下电力行业碳峰值预测 |
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| 引用本文： | 丁甜甜,李玮.经济增长与减排视角下电力行业碳峰值预测[J].科技管理研究,2019,39(18). |
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| 作者姓名： | 丁甜甜 李玮 |
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| 作者单位： | 太原理工大学经济管理学院,山西太原,030024;太原理工大学经济管理学院,山西太原,030024 |
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| 基金项目： | 国家自然科学基金资助项目“区域碳减排潜力调控机制与政策研究”（编号：71373170）；山西省软科学项目“山西省产业升级促进低碳转型的作用机理与能力评估”（编号：2017041006-5） |
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| 摘 要： | 基于2000-2015年中国电力行业数据，利用递阶LMDI法分解出电力碳排放的九个影响因素，得出人均GDP贡献度为正向最大，对碳排放的增长起促进作用，生产结构贡献度为负向最大，起抑制作用。根据影响效应的大小，利用情景分析法设置出九种不同的情景模式，借助STIRPAT模型进行建模，对不同情景下电力碳峰值进行预测与分析。结果表明：基准模式（中增长中减排）下，碳排放量在2030年达峰，峰值为494058万吨，但高于2030年的目标排放量；如果火力发电占比一直处于65%以上，仅靠经济增长速率的降低，无法出现峰值，电力碳排放处于持续上升状态；优化电力生产结构，降低火电占比，有效开发利用新能源，完善CCS等减排技术，能够在不降低经济发展速率的情况下实现2030年达峰的目标要求。
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| 关 键 词： | 电力行业 碳峰值 递阶LMDI分解法 STIRPAT模型 情景预测 |
| 收稿时间： | 2018/10/16 0:00:00 |
| 修稿时间： | 2018/11/4 0:00:00 |
Peak Forecast of Carbon Emissions in the Power Industry from the Perspective of Economic Growth and Emission Reduction |
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| Abstract: | Based on China''s 2000-2015 power industry data, this paper uses the hierarchical LMDI method to decompose the nine major influencing factors of China''s power industry carbon emissions. Among them, the per capita GDP contribution is the largest in the positive direction, which promotes the growth of carbon emissions, and the contribution of production structure is the negative and the inhibition. According to the influence effect, nine different scenarios were set up by scenario analysis, and the STIRPAT model was used to model and predict the power carbon emissions and carbon emission intensity under different scenarios. The results showthat,under the baseline model (middle growth and emission reduction), carbon emissions peaked in 2030, with a peak value of 4940.58 million tons, but higher than the target emissions in 2030; if the proportion of thermal power generation has beenMore than 65%, only by the decline in economic growth rate, there is no peak, and carbon emissions have been rising. Optimizing the power production structure, reducing the proportion of thermal power, effectively developing and utilizing new energy, improving CCS and other reductions, The platoon technology can achieve the target of 2030 peaks without reducing the rate of economic development. |
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| Keywords: | Power Industry Carbon Emission Peak Hierarchical LMDI Method STIRPAT model Scenario Prediction |
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