Using a Geographically Weighted Regression Model to Explore the Influencing Factors of CO2 Emissions from Energy Consumption in the Industrial Sector
Rina Wu1, Jiquan Zhang1, Yuhai Bao2,3, Siqin Tong1
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1College of the Environment, Northeast Normal University, Jilin Changchun 130024, People’s Republic of China
2Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information,
Inner Mongolia Huhhot 010022, People’s Republic of China
3College of Geography, Inner Mongolia Normal University, Huhhot Inner Mongolia 010022,
People’s Republic of China
Submission date: 2016-05-19
Final revision date: 2016-07-07
Acceptance date: 2016-07-09
Publication date: 2016-11-24
Pol. J. Environ. Stud. 2016;25(6):2641-2651
This study presents the methodology as well as a quantitative analysis of the influence of social and economic factors, namely GDP, population, economic growth rate, urbanization rate, and industrial structure on CO2 emissions as a result of energy consumption in the 101 counties of Inner Mongolia’s industrial sector based on a geographically weighted regression model (GWR) and geographical information systems (GIS) from the perspectives of energy and environmental science. The results show significant differences in the measured CO2 emission levels among different counties. Utilizing the GWR method (which was tested on the smallest scale that has been published thus far), the relationship between CO2 emissions and these five explanatory variables produced an overall model fit of 99%. The GWR results showed that the parameters of variables in the GWR varied spatially, suggesting that the influencing factors had different effects on the CO2 emissions among the various counties. Overall, population, GDP, and urbanization rates positively affect CO2 emissions, industrial structure, and economic growth rate, and affect CO2 emissions both positively and negatively. We also characterize the fact that varying industrial structures and economic growth rates result in different effects on the CO2 emission of various regions.
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