The peak value of carbon emissions in the Beijing-Tianjin-Hebei region based on the STIRPAT model and scenario design

• Autorzy: Wen L. , Liu Y.
• Języki publikacji: EN

Abstrakty

EN

The main objective of this paper was seeking suitable scenarios for the Beijing-Tianjin-Hebei region, where both socio-economic development and low-carbon targets would be achieved. Potential driven factors of carbon emissions, including population, affl uence, urbanization level, technology level, industrial construction, and energy consumption construction were selected to build an extended stochastic impacts by regression on population, affl uence, and technology (STIRPAT) model, where ridge regression was applied to ensure its stability. The STIRPAT model showed the significance of each independent variable, which was the foundation of CO2 emissions’ prediction. Furthermore, eight scenarios were established to explore the possible carbon footprints and the maximum of CO2 in the period from 2013 to 2050. This paper finally proposed the strategies that can be applied to reduce future carbon emissions in the Beijing-Tianjin-Hebei region. Applying reasonable policies about improvement of technological level, and adjustment of industry and energy consumption structures is a critical factor for the control of CO2 emissions.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2016
• Tom: 25
• Numer: 2
• Opis fizyczny: p.823-834,fig.,ref.

Twórcy

autor

Wen L.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei, 071003, China

autor

Liu Y.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei, 071003, China

Bibliografia

• 1. IEA. Energy technology perspectives 2012. International Energy Agency; 32-40, Paris, 2012.
• 2. United Nations Development Program (UNDP). China and a sustainable future: towards a low carbon economy and society. China human development report; 47-73, China, 2009.
• 3. EIA (US Energy Information Administration).Total Carbon Dioxide Emissions from the Consumption of Energy; The U.S., 2011.<http://tonto.eia.doe.gov/cfapps/ipdbproject/ IEDIndex3 .cfm?tid=90&pid=44& aid=8S>[accessed 11.06.15].
• 4. Carbon dioxide information analysis center (CDIAC).2013 Global Carbon Project; The U.S., 2013. <http://cdiac. ornl. gov/GCP/carbonbudget/2013/>[accessed 12.06.15].
• 5. DIETZ T., ROSA E.A. Rethinking the environmental impacts of population. Affluence and technology, Hum. Ecolo. Rev. 1, 277, 1994.
• 6. DIETZ T., ROSA E.A. Effects of population and affluence on CO2 emissions. Proc. Nat.Acad.Sci.U.S.A, 94 (1), 175, 1997.
• 7. WOLD S., ALBANO C., DUNN W.J., ESBENSEN K., HELLBERG S., Johansson E., Sjöström M. Pattern recognition: finding and using regularities in multivariate data. Food Research and Data Analysis. Applied Science Publishers, 176, 1983.
• 8. HOERL A.E., KENNARD R.W. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12 (1) 55, 1970.
• 9. KENNY T., GRAY N.F. Comparative performance of six carbon foot print models for use in Ireland. Environmental Impact Assessment Review, 29 (1), 1, 2009.
• 10. SUN J.W. The nature of CO2 emission Kuznets Curve. Energy Policy, 27 (12), 691, 1999.
• 11. MENG M., NIU, D.X. Modeling CO2 emissions from fossil fuel combustion using the logistic equation. Energy, 36 (5), 3355, 2011.
• 12. WANG S. J., FANG C. L., GUAN X. L., PANG B., MA H. Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China's provinces. Applied Energy; 136 (C), 738, 2014.
• 13. Research Group of energy and carbon emissions in China 2050. China energy and carbon emission report, 753, Beijing, 2009.
• 14. WANG T., WATSON J. China's energy transition: pathways for low carbon development. United Kingdom:University of Sussex, 2009 <http://www.sussex.ac.uk/>[accessed 12.05.15]
• 15. ZHOU N., FRIDLEY D., MCNEIL M.A., KHANNA N., KE J., LEVINE M.D. China's energy and carbon emissions outlook to 2050. CA: Lawrence Berkeley National Laboratory, 2012.
• 16. McKinsey&Company. Energy and environment technology options to achieve sustainable development. China's Green Revolution, 2009. <http://www.mckinsey.com/locations/ chinas implified/ mckonchina/reports/ china_green_ revolution_report_cn.pdf>[accessed 12.05.15]
• 17. EHRLICH P., HOLDREN J. Impact of population growth. Science, 171 (45), 1212, 1971.
• 18. WAGGONER P.E., AUSUBEL J.H. A framework for sustainability science: A renovated IPAT identity. Proc. Nat. Acad. Sci. USA, 12 (6), 7860, 2002.
• 19. SONG J., SONG Q., ZHANG D., LU Y., LUANET L. Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012. The Scientific World Journal, 2014 (1), 56, 2014.
• 20. ZHANG C., LIN Y. Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China. Energy Policy, 49, 488, 2012.
• 21. YORK R., ROSA E., DIETZ T. STIRPAT, IPAT, and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46, 351, 2003.
• 22. WANG Z., YIN F., ZHANG Y., ZHANG X. An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China. Energy Policy, 100 (4), 277, 2012.
• 23. LIN S., ZHAO D., MARINO VA D. Analysis of the environmental impact of China based on STIRPAT model. Environ. Impact Assess, 29 (6), 341, 2009.
• 24. HESSAMI M., GACHON P., OUARDA T., ST-HILAIRE A. Automated regression based statistical downscaling tool. Environmental Modelling and Software, 23 (6), 813, 2008.
• 25. YAN-FU L., MIN X., THONG-NGEE Goh. Adaptive ridge regression system for software cost estimating on multi-collinear datasets. The Journal of Systems and Software, 83 (11), 2332, 2010.
• 26. WANG P., WU W., ZHU B., WEI Y. Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China. Applied Energy, 106 (11), 65, 2013.
• 27. DINDA S. Environmental Kuznets Curve hypothesis: a survey. Ecological Economics, 49 (4), 431, 2004.
• 28. STERN D. The rise and fall of the Environmental Kuznets Curve. World Development, 32 (8), 1419, 2004.
• 29. GONG G., WANG D., CHUN L. Research on carbon emissions of energy in Shanghai. Chinese population Resources and Environment, 20 (2), 103, 2010.
• 30. AKDENIZ F., GÜZIN Y., ALAN T.K.W. The moments of the operational almost unbiased ridge regression estimator. Applied Mathematics and Computation, 153 (3), 673, 2004.
• 31. NGO S.H., KEMÉNY S., DEÁK A. Performance of the ridge regression method as applied to complex linear and nonlinear models. Chemometrics and Intelligent Laboratory Systems, 67 (1), 69, 2003.
• 32. JEFFERY R., RUHE M., WIECZOREK I. A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data. Information and Software Technology, 42 (14),1009, 2000.
• 33. KUTNER M.H., NACHTSHEIM C.J., NETER J., LI W. Applied Linear Statistical Models, 5th ed. McGraw-Hill, 408, Londo, 2005.
• 34. BELSLEY D.A., Condition Diagnostics Collinearity and Weak Data in Regression. Wiley, New York; 1991.
• 35. NBSC (National Bureau of Statistics of China). China Statistics Yearbook of 2013. China Statistics Press; 78-83, China, 2013.
• 36. ZANG Y., LI S., SUN Y., CHEN Y., ZHANG M. The trends of population and Regional Carrying Capacity in the Beijing-Tianjin-Hebei region, Journal of Academic Frontier, 2, 169, 2013.
• 37. XU X. China's Economic Growth in Future and Prospect of Its International Economic Position. Economic Research Journal, 3 (3), 27, 2002.
• 38. IMF. Uneven Growth: Short-Term and Long-Term Factors; USA, 2015. <http://www.imf.org/ External/pubs/ft/weo /2015/01/index.htm> [accessed 12.06.15].
• 39. MINX G., BAIOCCHI G. P., PETERS C. L., WEBER D., GUAN K., HUBACEK A. "Carbonizing Dragon": China's fast growing CO2 emissions revisited. Environ. Sci. Technol, 45 (21), 9144, 2011.

Identyfikatory

DOI

10.15244/pjoes/61244