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基于化石能源消耗的重庆市二氧化碳排放峰值预测
引用本文:刘晴川,李强,郑旭煦.基于化石能源消耗的重庆市二氧化碳排放峰值预测[J].环境科学学报,2017,37(4):1582-1593.
作者姓名:刘晴川  李强  郑旭煦
作者单位:重庆工商大学环境与资源学院, 重庆 400067,重庆国际投资咨询集团, 重庆 400023,重庆工商大学环境与资源学院, 重庆 400067
摘    要:首先利用重庆市能源平衡表,采用IPCC方法 1对重庆市1997—2012年的碳排放进行核算;其次依据重庆市经济社会发展状况,通过LMDI因素分解法将影响碳排放的因素分解为:人口、人均GDP、产业结构、能源结构、能源强度和碳排放系数;然后利用扩展的重庆市STIRPAT碳排放模型,在9个情景模式下对2013—2050年重庆市碳排放进行预测;最后对比分析了各情景下的峰值大小及出现时间.研究发现:基准模式下的重庆市碳排放在2035年出现32135.38万t的峰值;提高能源利用技术、增加清洁能源使用比例和大力发展第三产业,能在不降低经济发展的情况下有效降低碳排放;消极因素中的第二产业占比下降比碳排放强度下降对碳排放的抑制作用更加明显;积极因素对碳排放峰值的影响比消极因素更有效.

关 键 词:碳排放  峰值预测  LMDI模型  STIRPAT模型  情景设置
收稿时间:2016/2/14 0:00:00
修稿时间:2016/3/23 0:00:00

The prediction of carbon dioxide emissions in Chongqing based on fossil fuel combustion
LIU Qingchuan,LI Qiang and ZHENG Xuxu.The prediction of carbon dioxide emissions in Chongqing based on fossil fuel combustion[J].Acta Scientiae Circumstantiae,2017,37(4):1582-1593.
Authors:LIU Qingchuan  LI Qiang and ZHENG Xuxu
Institution:College of Environment and Resources, Chongqing Technology and Business University, Chongqing 400067,Chongqing International Investment Consulting Group Co. Ltd, Chongqing 400023 and College of Environment and Resources, Chongqing Technology and Business University, Chongqing 400067
Abstract:Carbon emission from 1997 to 2012 in Chongqing were calculated by the IPCC method 1 using the energy balance sheets. Factors of carbon emissions were then decomposed into population, GDP per capita, industrial structure, energy structure, energy intensity and carbon emission coefficient by LMDI factor decomposition method according to the economic and social development of Chongqing City. The peak of carbon emission from 2013 to 2050 was predicted by the extended STIRPAT model under the nine scenarios. Finally, the occurrence time and sizes of different peaks were compared and analyzed. It was found that the peak under the Benchmark model (model M) would be 321353.8 kilotons appearing in 2035. Carbon emissions could be effectively reduced in the case of no reduction in economic development by improving energy utilization technology, increasing the proportion of clean energy use and developing the third industry. In the negative factors, the decline of the proportion of secondary industry would be more effective on emission reduction than the carbon emission intensity, and the positive factors would have greater influence on peaks than the negative factors.
Keywords:carbon emissions  peak prediction  LMDI model  STIRPAT model  scene setting
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