International Science Index


Nonlinear Multivariable Analysis of CO2 Emissions in China


This paper addressed the impacts of energy consumption, economic growth, financial development, and population size on environmental degradation using grey relational analysis (GRA) for China, where foreign direct investment (FDI) inflows is the proxy variable for financial development. The more recent historical data during the period 2004–2011 are used, because the use of very old data for data analysis may not be suitable for rapidly developing countries. The results of the GRA indicate that the linkage effects of energy consumption–emissions and GDP–emissions are ranked first and second, respectively. These reveal that energy consumption and economic growth are strongly correlated with emissions. Higher economic growth requires more energy consumption and increasing environmental pollution. Likewise, more efficient energy use needs a higher level of economic development. Therefore, policies to improve energy efficiency and create a low-carbon economy can reduce emissions without hurting economic growth. The finding of FDI–emissions linkage is ranked third. This indicates that China do not apply weak environmental regulations to attract inward FDI. Furthermore, China’s government in attracting inward FDI should strengthen environmental policy. The finding of population–emissions linkage effect is ranked fourth, implying that population size does not directly affect CO2 emissions, even though China has the world’s largest population, and Chinese people are very economical use of energy-related products. Overall, the energy conservation, improving efficiency, managing demand, and financial development, which aim at curtailing waste of energy, reducing both energy consumption and emissions, and without loss of the country’s competitiveness, can be adopted for developing economies. The GRA is one of the best way to use a lower data to build a dynamic analysis model.

[1] Miomir J, Ljiljana K, Aleksandra D, and Vladimir K. The Impact of Agro-Economic Factors on GHG Emissions: Evidence from European Developing and Advanced Economies. Sustainability 2015, 7, 16290-16310.
[2] World Bank, Growth and CO2 emissions: how do different countries fare, Environment Department, Washington, D.C., 2007.
[3] B. Chen, Where does China rank in the world? China Business review, 2015.
[4] Z. Liu, China’s carbon emissions report 2015, Harvard Kennedy School.
[5] COP21, China has promised to cut emissions from its coal power plants by 60% by 2020, 2015.
[6] C.D. Kolstad, J.A. Krautkraemer, Natural resource use and the environment. In: A.V. Kneese, J.L. Sweeney, (Eds.), Handbook of Natural Resource and Energy Economics 3 1993, pp.1219-1265.
[7] J. Frankel, D. Romer, Does trade cause growth?, Am. Econ. Rev. 89, 1999, 379-399.
[8] N. Birdsall, D. Wheeler, Trade policy and industrial pollution in Latin America: where are the pollution havens?, J. Environ. Dev. 2, 1993, 137-149.
[9] J. Frankel, A. Rose, An estimate of the effect of common currencies on trade and income, Q. J. Econ. 117, 2002, 437-466.
[10] V. Jensen, The pollution haven hypothesis and the industrial flight hypothesis: some perspectives on theory and empirics, Working Paper 1996.
[11] World Bank, Is globalization causing a ‘race to the bottom’ in environmental standard? PREM economic policy group and development economics group, Washington, D.C., 2000.
[12] A. Tamazian, J.P. Chousa, K.C. Vadlamannati, Does higher economic and financial development lead to environmental degradation: evidence from BRIC countries, Energy Policy. 37, 2009, 246-253.
[13] D. Hinrichsen and B. Robey, Population and the Environment: The Global Challenge. Actionbioscience, 2000.
[14] D.I. Stern, The rise and fall of the environmental Kuznets curve, World Dev. 32, 2004, 1419-1439.
[15] J.L. Deng, Control problems of grey systems, Syst. Control Lett. 5, 1982, 288-294.
[16] G.H. Huang, B.W. Baetz, G.G. Patry, Grey dynamic programming for waste-management planning under uncertainty, J. Urban. Plann. Dev. 120, 1994, 132-156.
[17] G.H. Huang, B.W. Baetz, G.G. Patry, A grey hop, skip and jump approach: generating alternatives for expansion planning of waste management facilities. Can. J. Civ. Eng.. 23, 1996, 1207-1219.
[18] Y.Y. Yin, G.H. Huang, K.W. Hipel, Fuzzy relation analysis for multicriteria water resources management, J. Water Resour. Plann. Manag. 125, 1999, 41-47.
[19] J.S. Yeomans, G.H. Huang, An evolutionary grey, hop, skip, and jump approach: generating alternative policies for the expansion of waste management facilities, J. Environ. Inform. 1, 2003, 37-51.
[20] Z. Wang, L. Zhu, J.H. Wu, Grey relational analysis of correlation of errors in measurement, J. Grey Syst. 8, 1996, 73-78.
[21] F. Zhu, M. Yi, L. Ma, J. Du, The grey relational analysis of the dielectric constant and others, J. Grey Syst. 8, 1996, 287-290.
[22] X. Tan, Y. Yang, J. Deng, Grey relational analysis factors in hypertensive with cardiac insufficiency, J. Grey Syst. 10, 1998, 75-80.
[23] I.J. Lu, S.J. Lin, C. Lewis, Grey relation analysis of motor vehicular energy consumption in Taiwan, Energy Policy 36, 2008, 2556-2561.
[24] S.J. Lin, I.J. Lu, C. Lewis, Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan, Energy Policy 35, 2007, 1948-1955.
[25] Y. Kuo, T. Yang, G.W. Huang, The use of grey relational analysis in solving multiple attribute decision-making problems, Computers & industrial engineering 55, 2008, 80-93.
[26] C.T. Ho, Measuring bank operations performance: an approach based on grey relation analysis, J. Oper. Res. Soc. 57, 2006, 337-349.
[27] H. T. Pao, Y.Y. Li, and H.C. Fu, Intelligent data analysis and modeling for the determinants of carbon dioxide emissions. Advances in Environmental Development, Geomatics Engineering and Tourism, 2014, 26-30.
[28] EIA (Energy Information Administrator), Statistical review of world energy. Energy Information Administrator, US Department of Energy, 2009.