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低碳转型趋势下中国能源消费结构优化 被引量:6

Optimization of China′s Energy Consumption Structure under Trend of Low-carbon Transition
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摘要 运用偏最小二乘回归方法构建能源需求预测模型。结合经济新常态下的发展特征设置基准情景和约束情景,预测了分情景下"十三五"期间中国的能源需求总量、能源消费结构及CO_2排放量。考虑能源、经济和环境因素,构建了能源消费结构多目标优化模型,得到了"十三五"期间优化情景下中国的能源消费结构及其相应的CO_2排放量。研究表明:优化能源消费结构能够有效降低能源消费总量及CO_2排放量;具体而言,到2020年,优化能源消费结构能减少34708.6万吨标准煤的能源消费量(约占基准情景下的6.55%)、88977.9万吨的CO_2排放量(占基准情景下的8.15%)。指出:"十三五"期间中国应侧重天然气、一次电力及其他清洁能源的开发利用以实现低碳发展转型。 This paper applies partial least-squares regression method to build the model for forecasting energy demand.Combining with the development characteristics of the new normal of China′s economy,it sets the baseline scenario and constraints scenario,and forecasts China′s total energy demand,energy consumption structure and carbon dioxide emissions from 2015 to 2020.Considering the factors of energy,economy,environment,it constructs a multi-objective optimization model for energy structure,and obtains China′s energy consumption structure as well as under the corresponding carbon dioxide emissions under optimization scenario during the period from 2015 to 2020.Results show as follows optimizing energy consumption structure could effectively reduce total energy consumption and carbon dioxide emissions;specifically,by 2020,optimizing energy consumption structure could reduce China′s energy consumption by 347.086 million tons of standard coal(accounting for about6.55% of baseline scenario)and carbon dioxide emissions by 889.779 million tons(accounting for 8.15% of the baseline scenario).Therefore,China should focus on the development and utilization of nature gas,electricity and other clean energy to realize low-carbon development in the period of Thirteenth Five-year Development Planning.
出处 《技术经济》 CSSCI 北大核心 2016年第7期71-79,128,共10页 Journal of Technology Economics
基金 国家自然科学基金项目"基于拍卖理论的流域梯级水电站群组合竞价机制研究"(71103135) 国家自然科学基金项目"基于超网络的企业多主体合作创新与创新群体演化研究"(71373198) 国家自然科学基金项目"复杂不确定性多主体多目标合作博弈与协商谈判的合作机制与合作模式"(71231007)
关键词 能源消费结构 能源需求 碳排放 低碳转型 energy consumption structure energy demand carbon emission low carbon transformation
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