Study on Driving Factors of Carbon Emissions in Inner Mongolia of China Based on Geographically Weighted Regression Model

Article Preview

Abstract:

In this paper, based on the data of carbon emissions of county-level in Inner Mongolia autonomous region of China, using the Geographically Weighted Regression (GWR) model, we quantitatively analyze the effects of six social-economic driving factors, including Gross Domestic Product (GDP), population (Popu), economic growth rate (EconGR), urbanization (Urba), industrial structure (InduS) and road density (RoadD) on regional carbon emissions. The results were achieved as follow:(1) The spatial heterogeneity of carbon emissions of Inner Mongolia and the social-economic factors of affecting carbon emissions are obviously; (2) the correlation among the six factors is low. (3) GDP, InduS and Popu have significant effect on carbon emissions, and effects of EconGR, Urba and RoadD are smaller. The impacts of different factors on carbon emissions at different spatial region show spatial heterogeneity.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 962-965)

Pages:

2355-2359

Citation:

Online since:

June 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Yong-Lan Xiong, Zhi-Qiang Zhang & Jian-Sheng Qu. Research on Characteristics of Provincial CO2 Emissions from 2005 to 2009 in China. Journal of Natrure Resources, 27(10), pp.1767-1776, (2010).

Google Scholar

[2] Huanan Li, HailinMua, MingZhang, NanLi. Analysis on influence factors of China's CO2emissions based on Path–STIRPAT model . Energy Policy , (39), p.6906–6911. (2011).

DOI: 10.1016/j.enpol.2011.08.056

Google Scholar

[3] Shoufu Lin, Dingtao Zhao , Dora Marinova. Analysis of the environmental impact of Chinabased on STIRPAT model. Environmental Impact Assessment Review , (29), p.341–347. (2009).

DOI: 10.1016/j.eiar.2009.01.009

Google Scholar

[4] York R, Rosa E A, Dieta T. STIRPAT, IPAT and IMPACT: analytic tools for unpacking the driving forces of environmental impacts . Journal of Ecological Economics, 46(3), pp.351-365. (2003).

DOI: 10.1016/s0921-8009(03)00188-5

Google Scholar

[5] Gui-Xia Qian, Yi-Pin Zhang & Jian-Guo Wu. Decomposition Analysis on Changes of Energy-related CO2Emission in Inner Mongolia. Journal of Technology Economic, 29(12), pp.77-84, (2010).

Google Scholar

[6] Xiao-Hui Jia, Qiong Yao & Jun-Jun Guo. An GWR Empirical Study of China's carbon emissions factors -Spatial measurement analysis of regional differences based on eastern, central and western cities.

Google Scholar

[7] http: /www. nmg. gov. cn/main/nmg/zjnmg/nmggk/shjj/2013-07-04/s2_287893.

Google Scholar

[8] Gilbert A, Chakraborty J (2010) Using geographically weighted regression for environmental justice analysis: Cumulative cancer risks from air toxics in Florida. Social Science Research 40, p.273–286. (2010).

DOI: 10.1016/j.ssresearch.2010.08.006

Google Scholar