Environmental Science and Pollution Research

, Volume 26, Issue 1, pp 706–720 | Cite as

The environmental Kuznets curve by considering asymmetric oil price shocks: evidence from the top two

  • Talel BoufatehEmail author
Research Article


This study is the first attempt to investigate the validity of the environmental Kuznets curve (EKC) hypothesis by considering the asymmetric oil price effects on the CO2 emission in the USA and China. The oil prices were incorporated as an indicator (proxy) of energy consumption in order to avoid potential endogeneity problems and allow exploring the asymmetric effects of the energy fluctuation on the CO2 release. The nonlinear autoregressive distributed lag (NARDL)–bound testing approach to cointegration of Shin et al. (2014) in the presence of structural break is used to identify both short-run and long-run dynamic relationships between real oil prices, per capita GDP, and per capita CO2 emissions over the period 1976–2013. The results indicate that the inverted U-shaped EKC hypothesis is not supported in the short and long terms in both countries. Asymmetric findings suggest that positive and negative fluctuations in crude oil prices affect CO2 emissions differently in the USA and China. Unlike China, rising energy prices in the USA could be a contributing factor in the fight against pollution. More taxation of fossil energy and renewable energy subsidies are recommended for the American economy. However, the growth priority seems to outweigh the environmental issue for the Chinese economy.


Environmental Kuznets curve Asymmetric oil price shocks CO2 emissions NARDL 

Supplementary material

11356_2018_3641_MOESM1_ESM.xlsx (11 kb)
ESM 1 (XLSX 11 kb)


  1. Agras J, Chapman D (1999) A dynamic approach to the environmental Kuznets curve hypothesis. Ecol Econ 28:267–277CrossRefGoogle Scholar
  2. Alam MM, Murad MW, Abu Hanifa MN, Ozturk I (2016) Relationships among carbon emissions, economic growth, energy consumption and population growth: testing environmental Kuznets curve hypothesis for Brazil, China, India and Indonesia. Ecol Indic 70:466–479CrossRefGoogle Scholar
  3. Apergis N, Aslan A, Goodness CA, Rangan G (2015) The asymmetric effect of oil price on growth across US states. Energy Explor Exploit 33(4):575–590CrossRefGoogle Scholar
  4. Aslan A, Destek MA, Okumus I (2018) Bootstrap rolling window estimation approach to analysis of the environment Kuznets curve hypothesis: evidence from the USA. Environ Sci Pollut Res 25:2402–2408CrossRefGoogle Scholar
  5. Azam M, Khan AQ (2016) Testing the environmental Kuznets curve hypothesis: a comparative empirical study for low, lower middle, upper middle and high income countries. Renew Sust Energ Rev 63:556–567CrossRefGoogle Scholar
  6. Bacon R (1991) Modeling the price of oil. Oxf Rev Econ Policy 7(2):17–34CrossRefGoogle Scholar
  7. Bai J, Perron P (2003) Critical values for multiple structural change tests. Econ J 6:72–78Google Scholar
  8. Bal DP, Rath BN (2015) Nonlinear causality between crude oil price and exchange rate: a comparative study of China and India. Energy Econ 51:149–156CrossRefGoogle Scholar
  9. Balaguer J, Cantavella M (2016) Estimating the environmental Kuznets curve for Spain by considering fuel oil prices (1874–2011). Ecol Indic 60:853–859CrossRefGoogle Scholar
  10. Bildirici M, Ersin Ö (2018) Markov-switching vector autoregressive neural networks and sensitivity analysis of environment, economic growth and petrol prices. Environ Sci Pollut Res 25:31630–31655CrossRefGoogle Scholar
  11. Brown RL, Durbin J, Evans JM (1975) Techniques for testing the constancy of regression relations over time. J R Stat Soc Ser B 37:149–163Google Scholar
  12. Burnett JW, Bergstrom JC, Wetzstein ME (2013) Carbon dioxide emissions and economic growth in the U.S. J Policy Model 35(6):1014–1028CrossRefGoogle Scholar
  13. Central Intelligence Agency (CIA), (2016) The world factbook, Available from:
  14. Cologni A, Manera M (2009) The asymmetric effects of oil shocks on output growth: a Markov-switching analysis for the G-7 countries. Econ Model 26:1–29CrossRefGoogle Scholar
  15. Congregado E, Feria-Gallardo J, Golpe AA, Iglesias J (2016) The environmental Kuznets curve and CO2 emissions in the USA. Environ Sci Pollut Res 23:18407–18420CrossRefGoogle Scholar
  16. Cross J, Nguyen BH (2017) The relationship between global oil price shocks and China’s output: a time-varying analysis. Energy Econ 62:79–91CrossRefGoogle Scholar
  17. Darby M (1982) The price of oil and world inflation and recession. Am Econ Rev 72(4):738–751Google Scholar
  18. Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431Google Scholar
  19. Fosten J (2012) Rising household diesel consumption in the United States: a cause for concern? Evidence on asymmetric pricing. Energy Econ 34(5):1514–1522CrossRefGoogle Scholar
  20. Gisser M, Goodwin TH (1986) Crude oil and the macroeconomy: tests of some popular notions. J Money Credit Bank 18(1):95–103CrossRefGoogle Scholar
  21. Gronwald M (2008) Large oil shocks and the US economy: infrequent incidents with large effects. Energy J 29:151–171CrossRefGoogle Scholar
  22. Grossman G, Krueger A (1991) Environmental impacts of North American free trade agreement. NBER Working Papers 3914, National Bureau of Economic Research, Inc.Google Scholar
  23. Hamilton J (1983) Oil and the macroeconomy since World War II. J Polit Econ 91(2):228–248CrossRefGoogle Scholar
  24. Hamilton J (1996) This is what happened to the oil price-macroeconomy relationship. J Monet Econ 38(2):215–220CrossRefGoogle Scholar
  25. Hamilton J (2003) What is an oil shock? J Econ 113(2):363–398CrossRefGoogle Scholar
  26. Hamilton J (2011) Nonlinearities and the macroeconomic effects of oil prices. Macroecon Dyn 15(3):364–378CrossRefGoogle Scholar
  27. Heil MT, Selden TM (2001) Carbon emissions and economic development: future trajectories based on historical experience. Environ Dev Econ 6:63–83CrossRefGoogle Scholar
  28. Huang BN (2008) Factors affecting an economy’s tolerance and delay of response to the impact of a positive oil price shock. Energy J 29:1–34CrossRefGoogle Scholar
  29. Intergovernmental Panel on Climate Change (IPCC) (2014) Climate change 2014: synthesis report. In: Pachauri RK, Meyer LA (eds) Contribution of working groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, GenevaGoogle Scholar
  30. Jaunky VC (2011) The CO2 emissions-income nexus: evidence from rich countries. Energy Policy 39(3):1228–1240CrossRefGoogle Scholar
  31. Kaika D, Zervas E (2013) The environmental Kuznets curve (EKC) theory—part A: concept, causes and the CO2 emissions case. Energy Policy 62:1392–1402CrossRefGoogle Scholar
  32. Kang Y-Q, Zhao T, Yang Y-Y (2016) Environmental Kuznets curve for CO2 emissions in China: a spatial panel data approach. Ecol Indic 63:231–239CrossRefGoogle Scholar
  33. Kilian L, Vigfusson RJ (2011) Are the responses of the US economy asymmetric in energy price increases and decreases? Quant Econ 2:419–453CrossRefGoogle Scholar
  34. Kilian L, Vigfusson RJ (2013) Do oil prices help forecast us real GDP? The role of nonlinearities and asymmetries. J Bus Econ Stat 31:78–93CrossRefGoogle Scholar
  35. Lee K, Shawn N, Ratti R (1995) Oil shocks and the macroeconomy: the role of price variability. Energy J 16:39–56CrossRefGoogle Scholar
  36. Li T, Wang Y, Zhao D (2016) Environmental Kuznets curve in China: new evidence from dynamic panel analysis. Energy Policy 91:138–147CrossRefGoogle Scholar
  37. Mork K (1989) Oil and the macroeconomy when prices go up and down: an ex-tension of Hamilton’s results. J Polit Econ 97(3):740–744CrossRefGoogle Scholar
  38. Narayan PK, Narayan S (2005) Estimating income and price elasticities of imports for Fiji in a cointegration framework. Econ Model 22(3):423–438CrossRefGoogle Scholar
  39. Olivier JGJ, Janssens-Maenhout G, Muntean M, Peters JAHW (2014) Trends in global CO2 emissions: 2014 report. PBL Netherlands Environmental Assessment Agency, The Hague; European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability (IES). Report No. PBL-1490, JRC-93171Google Scholar
  40. Panayotou T (1993) Empirical tests and policy analysis of environmental degradation at different stages of economic development, working paper. WP-238, Technology and Employment Programme. ILO, GenevaGoogle Scholar
  41. Pesaran H, Shin Y (1999) An autoregressive distributed lag modeling approach to cointegration analysis. In: Strom S (ed) Econometrics and economic theory in 20th century: the Ragnar–Frisch centennial symposium. Cambridge University Press, CambridgeGoogle Scholar
  42. Pesaran H, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econ 16:289–326CrossRefGoogle Scholar
  43. Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75:335–346CrossRefGoogle Scholar
  44. Rahman S, Serletis A (2010) The asymmetric effects of oil price and monetary policy shocks: a nonlinear VAR approach. Energy Econ 32(6):1460–1466CrossRefGoogle Scholar
  45. Richmond AK, Kaufman RK (2006) Energy prices and turning points: the relationship between income and energy use/carbon emissions. Energy J 7:157–180Google Scholar
  46. Rodríguez M, Pena-boquete Y, Pardo-fernández KC (2016) Revisiting environmental Kuznets curves through the energy price lens. Energy Policy 95:32–41CrossRefGoogle Scholar
  47. Saidi K, Mbarek MB (2017) The impact of income, trade, urbanization, and financial development on CO2 emissions in 19 emerging economies. Environ Sci Pollut Res 24:12748–12757. CrossRefGoogle Scholar
  48. Shafik N, Bandyopadhyay S (1992) Economic growth and environmental quality: time series and cross-country evidence. Background paper for world development report 1992. World Bank, Washington, DCGoogle Scholar
  49. Shin Y, Yu B, Greenwood-Nimmo MJ (2011) Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework.
  50. Shin Y, Yu B, Greenwood-Nimmo MJ (2014) Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In: Horrace WC, Sickles RC (eds) Festschrift in honor of Peter Schmidt. Springer Science & Business Media, New YorkGoogle Scholar
  51. Solarin SA, Lean HH (2016) Natural gas consumption, income, urbanization, and CO2 emissions in China and India. Environ Sci Pollut Res 23:18753–18765. CrossRefGoogle Scholar
  52. Stern N (2006) Stern review on the economics of climate change. Cambridge University Press, CambridgeGoogle Scholar
  53. United Nations Framework Convention on Climate Change (2015). Available from: [cited 02.05.15]
  54. World Development Indicators database, World Bank, 2017. GDP data source: Available from:
  55. Yeh F-Y, Hu J-L, Lin C-H (2012) Asymmetric impacts of international energy shocks on macroeconomic activities. Energy Policy 44:10–22CrossRefGoogle Scholar
  56. Yin J, Zheng M, Chen J (2015) The effects of environmental regulation and technical progress on CO2 Kuznets curve: an evidence from China. Energy Policy 77:97–108CrossRefGoogle Scholar
  57. Zhang YJ (2013) Speculative trading and WTI crude oil futures price movement: an empirical analysis. Appl Energy 107:394–402CrossRefGoogle Scholar
  58. Zhang T, Ma G, Liu G (2015) Nonlinear joint dynamics between prices of crude oil and refined products. Physica A 419(1):444–456CrossRefGoogle Scholar
  59. Zhao L, Zhang X, Wang S, Xu S (2016) The effects of oil price shocks on output and inflation in China. Energy Econ 53:101–110CrossRefGoogle Scholar
  60. Zhoumu Y, Wenping W, Yibo Y, Fen F (2015) An empirical study of environmental Kuznets curve in China. In: Qi E, Shen J, Dou R (eds) Proceedings of the 21st International Conference on Industrial Engineering and Engineering Management 2014. Proceedings of the International Conference on Industrial Engineering and Engineering Management. Atlantis Press, ParisGoogle Scholar
  61. Zivot E, Andrews D (1992) Further evidence of great crash, the oil price shock and the unit root hypothesis. J Bus Econ Stat 10:251–270Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ecole Supérieure de Commerce de TunisUniversité de la ManoubaManoubaTunisia

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