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基于变参数模型的北京碳排放影响因素研究    

Research of Carbon Emissions Influence Factors in Beijing Based on Varying Parameters Model

文献类型:期刊文献

中文题名:基于变参数模型的北京碳排放影响因素研究

英文题名:Research of Carbon Emissions Influence Factors in Beijing Based on Varying Parameters Model

作者:张丽峰[1]

第一作者:张丽峰

机构:[1]北京联合大学旅游学院

第一机构:北京联合大学旅游学院

年份:2013

卷号:29

期号:12

起止页码:1247-1250

中文期刊名:资源开发与市场

外文期刊名:Resource Development & Market

收录:CSTPCD;;国家哲学社会科学学术期刊数据库;CSSCI:【CSSCI_E2012_2013】;

基金:国家自然科学基金项目(编号:71273275);北京联合大学校级科研项目(编号:SK50201303);北京学研究基地资助项目(编号:BJXJD-KT2013-A04)

语种:中文

中文关键词:STIRPAT模型;变参数模型;卡尔曼滤波

外文关键词:STIRPAT model ; varying parameter model ; Kalman filter

摘要:基于STIRPAT模型,建立了北京碳排放与经济发展水平、能源消耗强度、产业结构和能源消费结构间的变参数模型,得出以下结论:①经济发展水平、能源利用效率和能源消费结构与人均碳排放正相关;产业结构大多数年份与人均碳排放负相关。②经济发展水平对人均碳排放的变弹性系数最大,依次是能源消费结构、能源利用效率和产业结构。③经济发展水平、能源利用效率和产业结构的变弹性系数呈现先升后降再升的趋势,只是上升和下降转折点有差异,能源消费结构变弹性系数波动较大。
Based on the STIR_PAT model, this paper set up the varying parameter model among carbon emissions and economic development level, the energy intensity, the industrial structure and energy consumption structure, and estimated the parameters using Kalman filter method, and compared with fixed parameter model, it had the following conclusions: ①The variable elasticity coefficients of the economic development level, energy consumption intensity and energy consumption structure had the positive correlation with per capita carbon emissions, but industrial structure had a negative relation with the per capita carbon emissions in most years.②The variation elasticity coefficient of the economic development level was the largest, followed by the energy consumption structure, the energy use efficiency and the industrial structure. ③From the time trend, the variable elasticity coefficient of the economic development level, energy efficiency and industrial structure showed the increased trend at first, declined trend and to rise again at last, but the turning point of rise and fall was different, the variation elasticity coefficient of energy consumption structure fluctuated great.

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