Environmental Engineering Research

Korean Society of Environmental Engineers (대한환경공학회)
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Energy-related CO2 emissions in Hebei province: Driven factors and policy implications

  • Wen, Lei (The Academy of Baoding Low-Carbon Development) ;
  • Liu, Yanjun (Department of Economics and Management, North China Electric Power University)
  • Received : 2015.11.16
  • Accepted : 2015.12.23
  • Published : 2016.03.31
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Abstract

The purpose of this study is to identify the driven factors affecting the changes in energy-related $CO_2$ emissions in Hebei Province of China from 1995 to 2013. This study confirmed that energy-related $CO_2$ emissions are correlated with the population, urbanization level, economic development degree, industry structure, foreign trade degree, technology level and energy proportion through an improved STIRPAT model. A reasonable and more reliable outcome of STIRPAT model can be obtained with the introducing of the Ridge Regression, which shows that population is the most important factor for $CO_2$ emissions in Hebei with the coefficient 2.4528. Rely on these discussions about affect abilities of each driven factors, we conclude several proposals to arrive targets for reductions in Hebei's energy-related $CO_2$ emissions. The method improved and relative policy advance improved pointing at empirical results also can be applied by other province to make study about driven factors of the growth of carbon emissions.

Keywords

References

  1. United Nations Development Program (UNDP). China and a sustainable future: towards a low carbon economy and society. China human development report: 2009. p. 47-73.
  2. Jiang KJ. Secure low-carbon development in China. Carbon Manage. 2012;3:333-335. https://doi.org/10.4155/cmt.12.42
  3. Guo ZQ, Liu HB, Carbon footprint of the households' consumption in China. Int. J. Glob. Ener. Issue 2013;36:181-196. https://doi.org/10.1504/IJGEI.2013.061787
  4. Chevallier J, Delarue E, Lujan E, D'Haeseleer W. A counterfactual simulation exercise of $CO_2$ emissions abatement through fuel-switching in the UK (2008-2012). Int. J. Glob. Ener. Issues 2012;35:311-331. https://doi.org/10.1504/IJGEI.2012.046712
  5. Valipour M. Analysis of potential evapotranspiration using limited weather data. Appl. Water Sci. 2014:1-11.
  6. Valipour M, Mousavi SM, Valipour R, Rezaei E. Deal with environmental challenges in civil and energy engineering projects using a new technology. J. Civ. Env. Eng. 2013;3:1.
  7. Valipour M, Mousavi SM, Valipour R, et al. Air, water, and soil pollution study in industrial units using environmental flow diagram. J. Basi. C Appl. Sci. Res. 2012;2:12365-12372.
  8. Zhang N, Zhou P, Kung C. Total-factor carbon emission performance of the Chinese transportation industry: A bootstrapped non-radial Malmquist index analysis. Renew. Sust. Energ. Rev. 2015;41:584-593. https://doi.org/10.1016/j.rser.2014.08.076
  9. Zhang N, Choi YR. A comparative study of dynamic changes in $CO_2$ emission performance of fossil fuel power plants in China and Korea. Energ. Policy 2013;62:324-332. https://doi.org/10.1016/j.enpol.2013.07.060
  10. Ang BW. Decomposition analysis for policymaking in energy: Which is the preferred method. Energ. Policy 2004;32:1131-1139. https://doi.org/10.1016/S0301-4215(03)00076-4
  11. Song JK, Song Q, Zhang D. Study on influencing factors of carbon emissions from energy consumption of Shandong Province of China from 1995 to 2009. Int. J. Glob. Ener. Issues 2013;36:130-148. https://doi.org/10.1504/IJGEI.2013.061819
  12. Hatzigeorgiou E, Polatidis H, Haralambopoulos D. $CO_2$ emissions in Greece for 1990-2002: a decomposition analysis and comparison of results using the arithmetic mean Divisia index and logarithmic mean Divisia index techniques. Energy 2008;33:492-499. https://doi.org/10.1016/j.energy.2007.09.014
  13. Sheinbaum C, Ozawa L, Castillo D. Using logarithmic mean Divisia index to analyze changes in energy use and carbon dioxide emissions in Mexico's iron and steel industry. Energ. Econ. 2010;32:1337-1344. https://doi.org/10.1016/j.eneco.2010.02.011
  14. Xu JH, Tobias F, Wolfgang E, Fan Y. Energy consumption and $CO_2$ emissions in China's cement industry: A perspective from LMDI decomposition analysis. Energ. Policy 2012;50:821-832. https://doi.org/10.1016/j.enpol.2012.08.038
  15. Ehrlich P, Holdren J. Impact of population growth. Science 1971;171:1212-1217. https://doi.org/10.1126/science.171.3977.1212
  16. Waggoner PE, Ausubel JH. A framework for sustainability science: A renovated IPAT identity. In: Proceedings of the National Academy of Sciences of the United States of America. 2002;12:7860-7856.
  17. Dietz T, Rosa EA. Rethinking the environmental impacts of population, Affluence and technology. Human Ecol. Rev. 1994;1:277-300.
  18. Shao S, Yang L, Yu M, Yu M. Estimation, characteristics, and determinants of energy-related industrial $CO_2$ emissions in Shanghai (China), 1994-2009. Energ. Policy 2011;39:6476-6494. https://doi.org/10.1016/j.enpol.2011.07.049
  19. O'Neill BC, Liddle B, Jiang LW, et al. Demographic change and carbon dioxide emissions. Lancet. 2012;380:157-164. https://doi.org/10.1016/S0140-6736(12)60958-1
  20. Wang P, Wu W, Zhu B. Examining the impact factors of energy- related $CO_2$ emissions using the STIRPAT model in Guangdong Province, China. Appl. Energ. 2013;106:65-71. https://doi.org/10.1016/j.apenergy.2013.01.036
  21. Li H, Mu H, Zhang M, Gui S. Analysis of regional difference on impact factors of China's energy - Related $CO_2$ emissions. Energy 2012;39:319-326. https://doi.org/10.1016/j.energy.2012.01.008
  22. Wang Z, Yin F, Zhang Y, Zhang X. An empirical research on the influencing factors of regional $CO_2$ emissions: Evidence from Beijing city, China. Energ. Policy 2012;100:277-284.
  23. Lin S, Zhao D, Marinova D. Analysis of the environmental impact of China based on STIRPAT model. Environ. Impact Assess. Rev. 2009;29:341-347. https://doi.org/10.1016/j.eiar.2009.01.009
  24. Ponce de Leon Barido D, Marshall JD. Relationship between Urbanization and $CO_2$ Emissions Depends on Income Level and Policy. Environ. Sci. Technol. 2014;48:3632-3639. https://doi.org/10.1021/es405117n
  25. Liddle B. Impact of population, age structure, and urbanization on carbon emissions/ energy consumption: evidence from macro- level, cross-country analyses. Pop. Environ. 2014;35:286-304. https://doi.org/10.1007/s11111-013-0198-4
  26. Al-Mulali U, Fereidouni HG, Lee JYM, Che NBCS. Exploring the relationship between urbanization, energy consumption, and $CO_2$ emission in MENA countries. Renew. Sust. Energ. Rev. 2013;23:107-112. https://doi.org/10.1016/j.rser.2013.02.041
  27. Roberts TD. Intergenerational transfers in US county-level $CO_2$ emissions, 2007. Pop. Environ. 2014;35:365-390. https://doi.org/10.1007/s11111-013-0193-9
  28. Chikaraishi M, Fujiwara A, Kaneko S, Poumanyvong P, Komatsu S, Kalugin A. The moderating effects of urbanization on carbon dioxide emissions: A latent class modeling approach. Technol. Forecast. Soc. Change. 2015;90:302-317. https://doi.org/10.1016/j.techfore.2013.12.025
  29. Yuan R, Zhao T, Xu XS, Kang JD. Regional Characteristics of Impact Factors for Energy-Related $CO_2$ Emissions in China, 1997-2010: Evidence from Tests for Threshold Effects Based on the STIRPAT Model. Environ. Model. Assess. 2015;20:129-144. https://doi.org/10.1007/s10666-014-9424-4
  30. Yan H, Guo YG, Lin FC. Analyzing the developing model of Chinese cities under the control of $CO_2$ emissions using the STIRPAT model: a case study of Shanghai. Acta Geogr. Sin. 2010;65:983-90 [in Chinese].
  31. Wang Z, Yin F, Zhang Y, Zhang X. An empirical research on the influencing factors of regional $CO_2$ emissions: Evidence from Beijing city, China. Energ. Policy 2012;100:277-284.
  32. Kaika D, Zervas E. The Environmental Kuznets Curve (EKC) theory-part A: concept, causes and the C$CO_2$ emissions case. Energ. Policy 2013;62:1392-1402. https://doi.org/10.1016/j.enpol.2013.07.131
  33. Al-mulali U, Tang CF, Ozturk I. Estimating the Environment Kuznets Curve hypothesis: Evidence from Latin America and the Caribbean countries. Renew. Sust. Energ. Rev. 2015;50:918-924. https://doi.org/10.1016/j.rser.2015.05.017
  34. Tan Q, Wen Z, Chen J. The relationships between industrial pollution intensity and economic growth based on intensity environment Kuznets curve: study on China's pilot cities. Int. J. Sust. Dev. World Ecol. 2015;22:231-241. https://doi.org/10.1080/13504509.2014.994233
  35. Valipour M. A comprehensive study on irrigation management in Asia and oceania. Arch. Agron. Soil Sci. 2015;61:1247-1271. https://doi.org/10.1080/03650340.2014.986471
  36. Valipour M. Future of agricultural water management in africa. Arch. Agron. Soil Sci. 2014;52:245-268.
  37. Valipour M, Ahmadi MZ, Raeini-Sarjaz M, et al. Agricultural water management in the world during past half century. Arch. Agron. Soil Sci. 2014;61:1-22.
  38. Valipour M, Valipour M. Future of the area equipped for irrigation. Arch. Agron. Soil Sci. 2014;60:1641-1660. https://doi.org/10.1080/03650340.2014.905675
  39. Akdeniz F, Güzin Y, Alan TKW. The moments of the operational almost unbiased ridge regression estimator. Appl. Math. Comput. 2004;153:673-684.
  40. Ngo SH, Kemeny S. Deak A. Performance of the ridge regression method as applied to complex linear and nonlinear models. Chemometr. Intell. Lab. 2003;67:69-78. https://doi.org/10.1016/S0169-7439(03)00062-5
  41. Jeffery R, Ruhe M, Wieczorek I. A comparative study of two software development cost modeling techniques using multi- organizational and company-specific data. Inform. Software Tech. 2000;42:1009-1016. https://doi.org/10.1016/S0950-5849(00)00153-1
  42. Hoerl AE. Application of ridge analysis to regression problems. Chem. Eng. Prog. 1962;58:54-59.
  43. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models. McGraw-Hill/Irwin: 2005. p. 408-409.
  44. Ye Z, Li J, Zhang M. Application of ridge regression and factor analysis in design and production of alloy wheels. J. Appl. Stat. 2014;41:1436-1452. https://doi.org/10.1080/02664763.2013.872233

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