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2016 | 25 | 6 |

Tytuł artykułu

Using a geographically weighted regression model to explore the influencing factors of CO2 emissions from energy consumption in the industrial sector

Autorzy

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
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.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

25

Numer

6

Opis fizyczny

p.2641-2651,fig.,ref

Twórcy

autor
  • College of the Environment, Northeast Normal University, Jilin Changchun 130024, People’s Republic of China
autor
  • College of the Environment, Northeast Normal University, Jilin Changchun 130024, People’s Republic of China
autor
  • Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information, Inner Mongolia Huhhot 010022, People’s Republic of China
  • College of Geography, Inner Mongolia Normal University, Huhhot Inner Mongolia 010022, People’s Republic of China
autor
  • College of the Environment, Northeast Normal University, Jilin Changchun 130024, People’s Republic of China

Bibliografia

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  • 3. Su Y.X., Chen X.Z., Li Y., Liao J.S., Ye Y.Y., Zhang H.O., Huang N.S., Kuang Y.Q. China's 19-year citylevel carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines. Renewable and Sustainable Energy Reviews, 35, 231, 2014.
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  • 5. Xiong Y.L., Zhang Z.Q., Qu J.S. Research on characteristics of provincial CO2 emissions from 2005 to 2009 in China. Journal of Natural Resources. 27, 1767, 2012.
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  • 8. Qian G.X. Decomposition analysis on changes of energyrelated CO2 emission in Inner Mongolia. Technology Economies, 29, 78, 2009.
  • 9. Tan Z.F., Li L., Wang J.J., Wang J.H. Examining the driving forces for improving China’s CO2 emission intensity using the decomposing method. Applied Energy, 88, 4496, 2011.
  • 10. Fan T.J., Luo R.L., Xia H.Y., Li X.P. Using LMDI method to analyze the influencing factors of carbon emissions in China’s petrochemical industries. Nat Hazards, 75, 319, 2015.
  • 11. Li B., Liu X.J., Li Z.H. Using the STIRPAT model to explore the factors driving regional CO2emissions: a case of Tianjin, China. Nat Hazards, 76, 1667, 2015.
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  • 17. National Bureau of Statistics of Inner Mongolia Autonomous Region. Inner Mongolia Autonomous Region Statistical Yearbook 2010; China Statistics Press: Beijing, China, 2010.
  • 18. National Bureau of Statistics of Inner Mongolia Autonomous Region. Inner Mongolia Autonomous Region Statistical Yearbook 2011; China Statistics Press: Beijing, China, 2011.
  • 19. National Bureau of Statistics of Inner Mongolia Autonomous Region. Inner Mongolia Autonomous Region Statistical Yearbook 2012; China Statistics Press: Beijing, China, 2012.
  • 20. IPCC. IPCC Guidelines for National Greenhouse Gas Inventories, Energy. 2, 2006.
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  • 22. Gilbert A., Chakraborty J. Using geographically weighted regression for environmental justice analysis: Cumulative cancer risks from air toxics in Florida. Social Science Research, 40, 273, 2010.
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  • 24. Brunsdon C., Fotheringham A.S. Charlton . M. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28, 281, 1996.
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Typ dokumentu

Bibliografia

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Identyfikator YADDA

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