基于STIRPAT模型的北京能源压力区域空间变化分析

doi:10.3969/j.issn.1004-1524.2016.09.21

浙江农业学报 ›› 2016, Vol. 28 ›› Issue (9): 1603-1608.DOI: 10.3969/j.issn.1004-1524.2016.09.21

基于STIRPAT模型的北京能源压力区域空间变化分析

李虹¹, 冯仲科¹, 唐秀美^{2, 3}, 潘瑜春^{2, 3}, 刘玉^{2, 3, *}, 郝星耀^{2, 3}

1.北京林业大学林学院,精准林业北京市重点实验室,北京 100083;
2.北京农业信息技术研究中心,北京 100097;
3.国家农业信息化工程技术研究中心,北京 100097

收稿日期:2016-04-20 出版日期:2016-09-15 发布日期:2016-11-23
通讯作者: 刘玉,E-mail: liuyu@nercita.org.cn
作者简介:李虹(1985—),女,黑龙江哈尔滨人,博士研究生,从事地理信息与遥感技术研究。E-mail: fairyleeh@163.com
基金资助:
项目基金:国家自然科学基金资助项目(41301093); 北京林业大学青年教师科学研究中长期项目(2015ZCQ-LX-01)

Regional variation analysis of energy pressures of Beijing based on STIRPAT model

LI Hong¹, FENG Zhong-ke¹, TANG Xiu-mei^{2, 3}, PAN Yu-chun^{2, 3}, LIU Yu^{2, 3, *}, HAO Xing-yao^{2, 3}

1. College of Forestry, Beijing Forestry University, Precision Forestry Key Laboratory of Beijing ,Beijing 100083,China;
2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;
3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

Received:2016-04-20 Online:2016-09-15 Published:2016-11-23

摘要/Abstract

摘要： 综合考虑地理空间因素,以STIRPAT模型作为基础,采用能源消费总量作为环境压力的指标,以人口密度、GDP和第二产业增加值比重分别代表人口、富裕度和技术项,估计人口、富裕度、技术指标的弹性系数。将能源消费的空间差异性纳入模型,采用地理加权回归模型,从市区的尺度估计北京市16个区各驱动力因素弹性变化的差异性,得到北京市区域内部能源消费变化在空间上的变化规律。结果显示,各驱动力因素在不同区的变化并不均衡,每种驱动力因素的变化也具有一定的空间规律。由此,可针对不同区经济发展和城市化进程的差异制定个性化的调控措施。

关键词: 能源消费, 驱动因素, STIRPAT模型, 地理加权回归

Abstract: Considering spatial factors, the present study took geographical space effects into STIRPAT model to reveal the stochastic impacts by regression on P (population), A (affluence) and T (technology). Total energy consumption was used as indicators of environmental pressure, and population density, GDP and the proportion of secondary industry were adopted to represent population, affluence and technology, respectively. Geographical weighted regression (GWR) model was applied to estimate the elasticity of driving factors in 16 districts of Beijing. It was shown that the change of driving factors was not balanced in different districts. But, the changes in every district exhibited certain rule. Therefore, it is possible to formulate characterized regulation and control policies of energy production for different districts.

Key words: energy consumption, driving factors, STIRPAT model, geographically weighted regression

中图分类号:

F424.1

李虹, 冯仲科, 唐秀美, 潘瑜春, 刘玉, 郝星耀. 基于STIRPAT模型的北京能源压力区域空间变化分析[J]. 浙江农业学报, 2016, 28(9): 1603-1608.

LI Hong, FENG Zhong-ke, TANG Xiu-mei, PAN Yu-chun, LIU Yu, HAO Xing-yao. Regional variation analysis of energy pressures of Beijing based on STIRPAT model[J]. , 2016, 28(9): 1603-1608.

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基于STIRPAT模型的北京能源压力区域空间变化分析

Regional variation analysis of energy pressures of Beijing based on STIRPAT model

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