西安市工业碳排放的空间分异特征与驱动因子

栗新巧; 张艳芳

doi:10.13961/j.cnki.stbctb.2014.05.048

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西安市工业碳排放的空间分异特征与驱动因子

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- 西安市工业碳排放的空间分异特征与驱动因子
- Space Differentiation Characteristics and Driving Factors of Industuial Carbon Emissions in Xi'an City
- 水土保持通报 2014年33卷第5期页码：226-231
- 作者机构：
  
  陕西师范大学旅游与环境学院,陕西,西安,710062
- 作者简介：
- 基金信息：
  
  教育部人文社会科学研究规划基金项目"区域土地利用的低碳效应与低碳经济发展模式研究:以西安市为例"(10XJA790011);陕西师范大学中央高校基本科研业务费专项"西安城市空间增长下碳足迹时空变化与优化模拟研究"(GK201302031);国家自然科学基金项目"我国生态脆弱区能源开发生态效应测评与调控研究"(41371523)
- DOI：10.13961/j.cnki.stbctb.2014.05.048
  中图分类号：
- 纸质出版：2014
- 稿件说明：
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栗新巧, 张艳芳. 西安市工业碳排放的空间分异特征与驱动因子[J]. 水土保持通报, 2014,33(5):226-231.

LI Xin-qiao, ZHANG Yan-fang. Space Differentiation Characteristics and Driving Factors of Industuial Carbon Emissions in Xi'an City[J]. Bulletin of Soiland Water Conservation, 2014, 33(5): 226-231.
栗新巧, 张艳芳. 西安市工业碳排放的空间分异特征与驱动因子[J]. 水土保持通报, 2014,33(5):226-231. DOI： 10.13961/j.cnki.stbctb.2014.05.048.

LI Xin-qiao, ZHANG Yan-fang. Space Differentiation Characteristics and Driving Factors of Industuial Carbon Emissions in Xi'an City[J]. Bulletin of Soiland Water Conservation, 2014, 33(5): 226-231. DOI： 10.13961/j.cnki.stbctb.2014.05.048.

摘要

采用西安市1997-2011年工业能耗数据和2010年各区县单位GDP能耗数据进行了碳排放量的估算

通过STIRPAT模型构建驱动因子分析模型。研究表明:(1)西安市1997-2011年各类碳排放量表现出总体上升的特征。(2)各区县碳排放量、碳排放产值、人均碳排放量、地均碳排放量呈现出明显的空间分异现象。Moran指数显示

西安市各区县碳排放量存在显著地空间正相关性。碳排放量的空间分布特征是:碳排放量较高的区县趋向于和碳排放量较高的区县集聚

碳排放量较低的区县趋向于和碳排放量较低的区县集聚。(3) STIRPAT模型表明:经济发展、人口规模、产业结构和技术水平对碳排放量影响程度不同

其中经济发展对碳排放量增加具有决定作用

产业结构优化对碳排放量增加具有抑制作用。

Abstract

Applying the data of industrial energy consumption from 1997 to 2011

the data of energy consumption per unit of GDP in districts or counties of Xi'an City in 2010 were estimated

and the model to analyze the carbon emissions driving factors was built by using STIRPAT model. The results show that all kinds of carbon emissions presented generally

an increasing trend during 1997-2011. Carbon emissions

carbon emissions output and per capita carbon emissions and carbon emissions per unit area of land presented significant regional differences in each district or county. Moran index showed that the spatial correlation of carbon emissions in each district or county was positive correlation

and the spatial distribution feature of carbon emissions was that:the district or county with higher carbon emissions tended to aggregate to the one's of the higher

the district or county of lower carbon emissions tended to aggregate to the one's of the lower. The STIRPAT model showed that:the influence degree of economic development

population scale

industrial structure and technical level on carbon emissions was different. Moreover

economic development played the decisive role on increasing carbon emissions

but the industrial structure optimization had inhibitory effect on the increasing of carbon emissions.

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