Economia Politica

, Volume 34, Issue 3, pp 389–405 | Cite as

The effect of Rio Convention and other structural breaks on long-run economic development-CO2 relationships

  • Massimiliano MazzantiEmail author
  • Antonio Musolesi


This paper assesses the effect of the 1992 United Nations Rio Convention on Environment and Development and other unknown structural time breaks on the long-run carbon dioxide—economic development relationship for different groups of advanced countries. By taking into account the possible size distortion of standard unit roots tests and allowing nonlinearities in the trend function, we provide evidence suggesting that the time-series are nonlinear trend stationary. From this result, we then develop our analysis without moving to cointegration or first-differencing, and using an interrupted time-series approach, we identify three patterns in the dynamics of carbon dioxide: one is market-led, one is market- and policy-led, and one is more development-oriented.


Carbon Kuznets curves UN Rio convention Policy events Oil shocks Intervention analysis Structural breaks 

JEL Classification

C22 Q53 


  1. Almer, C., & Winkler, R. (2017). Analyzing the effectiveness of international environmental policies: The case of the Kyoto protocol. Journal of Environmental Economics and Managment, 82, 125–151.CrossRefGoogle Scholar
  2. Andersen, M. S., & Ekins, P. (2009). Carbon taxation: Lessons from Europe. Oxford: Oxford University Press.CrossRefGoogle Scholar
  3. Barrett, S. (2003). Environment and statecraft. Oxford: Oxford University Press.CrossRefGoogle Scholar
  4. Bierens, H. J. (1997). Testing the unit root with drift hypothesis against nonlinear trend stationarity, with an application to the US price level and interest rate. Journal of Econometrics, 81, 29–64.CrossRefGoogle Scholar
  5. Borghesi, S., et al. (2016). The European emission trading system and its followers: comparative analysis and linking perspectives. Berlin: Springer.CrossRefGoogle Scholar
  6. Box, G. E. P., & Tiao, G. C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70, 70–92.CrossRefGoogle Scholar
  7. Brock, W., & Taylor, S. (2010). The green Solow model. Journal of Economic Growth, 15, 127–153.CrossRefGoogle Scholar
  8. Carson, R. T. (2010). The environmental Kuznets curves: Seeking empirical regularity and theoretical structure. Review of environmental Economics and Policy, 4(1), 3–23.CrossRefGoogle Scholar
  9. de Jong, P., & Penzer, J. (1998). Diagnosing shocks in time series. Journal of the American Statistical Association, 93, 442.Google Scholar
  10. Dechezlepretre, A., Glachant, M., Hascic, I., Johnstone, N., Meniere, N. (2011). Invention and transfer of climate change mitigation technologies on a global scale: a study drawing on patent data. In The review of environmental economics and policy, January (previous version FEEM working paper 82, FEEM, Milan), vol. 7.Google Scholar
  11. Delgado, M. A., & Hidalgo, J. (2000). Nonparametric inference on structural breaks. Journal of Econometrics, 96(1), 113–144.CrossRefGoogle Scholar
  12. Ekins, P., & Speck, S. (2011). Environmental tax reform (ETR). A policy for green growth. Oxford: Oxford University Press.CrossRefGoogle Scholar
  13. European Environment Agency. (2016). The European Environment—environmental taxation and EU environmental policies. Copenhagen: European Environment Agency.Google Scholar
  14. European Environment Agency. (2014). Resource efficient green economy and EU policies. Copenhagen: European Environment Agency.Google Scholar
  15. Ertur, C., & Musolesi, A. (2017). Weak and strong cross-sectional dependence: A panel data analysis of international technology diffusion. Journal of Applied Econometrics, 32, 477–503.CrossRefGoogle Scholar
  16. Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110(2), 353–377.CrossRefGoogle Scholar
  17. Johnstone, N., Hascic, I., Kalamova, M. (2010). Environmental Policy design characteristics and technological innovation. In OECD environment working paper 15. OECD Paris.Google Scholar
  18. Johnstone, N., Hascic, I., & Popp, D. (2010). Renewable energy policies and technological innovation: Evidence based on patent counts. Environmental and Resource Economics, 45, 133–155.CrossRefGoogle Scholar
  19. Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54, 159–178.CrossRefGoogle Scholar
  20. Hamilton, J. D. (1994). Time series analysis. Princeton: Princeton University Press.Google Scholar
  21. Hamilton, J. D. (2003). What is an oil shock? Journal of Econometrics, 113(2), 363–398.CrossRefGoogle Scholar
  22. Harbaugh, W. T., Levinson, A., & Wilson, D. M. (2002). Reexamining the empirical evidence for an environmental Kuznets curve. Review of Economics and Statistics, 84(3), 541–551.CrossRefGoogle Scholar
  23. Härdle, W., & Mammen, E. (1991). Bootstrap methods in nonparametric regression. In Nonparametric functional estimation and related topics (pp. 111–123). Springer Netherlands.Google Scholar
  24. Heil, M. T., & Selden, T. M. (1999). Panel stationarity with structural breaks: Carbon emissions and GDP. Applied Economics Letters, 6(4), 223–225.CrossRefGoogle Scholar
  25. Lee, C., Wu, J. L., & Yang, L. (2016). A simple panel unit-root test with smooth breaks in the presence of a multifactor error structure. Oxford Bulletin of Economics and Statistics, 78(3), 365–393.Google Scholar
  26. Mazzanti, M., & Musolesi, A. (2013). The heterogeneity of carbon Kuznets curves for advanced countries: Comparing homogeneous, heterogeneous and shrinkage/Bayesian estimators. Applied Economics, 45, 3827–3842.CrossRefGoogle Scholar
  27. Mazzanti, M., & Musolesi, A. (2014). Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries. Studies in Nonlinear Dynamics and Economics, 18, 521–541.Google Scholar
  28. McGlade, C., & Ekins, P. (2015). The geographical distribution of fossil fuels unused when limiting global warming to 2 \({^\circ }\) C. Nature, 517, 187–190.CrossRefGoogle Scholar
  29. McKitrick, R. (2007). Why did US air pollution decline after 1970? Empirical Economics, 33(3), 491–513.CrossRefGoogle Scholar
  30. Panopolou, E., & Pantelidis, T. (2009). Club convergence in carbon dioxide emissions. Environmental and Resource Economics, 44, 47–70.CrossRefGoogle Scholar
  31. Pankratz, A. (1991). Forecasting with dynamic regression models. New York: Wiley.CrossRefGoogle Scholar
  32. Pearce, D. W. (2005). The political economy of an energy tax: The United Kingdom’s Climate Change Levy. Energy Economics, 28(2), 149–58.CrossRefGoogle Scholar
  33. Perman, R., & Stern, D. I. (2003). Evidence from panel unit root and cointegration tests that the environmental Kuznets curve does not exist. Australian Journal of Agricultural and Resource Economics, 47(3), 325–347.CrossRefGoogle Scholar
  34. Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57, 1361–401.CrossRefGoogle Scholar
  35. Perron, P. (2006). Dealing with structural breaks. Palgrave Handbook of Econometrics, 1(2), 278–352.Google Scholar
  36. Popp, D. (2002). Induced innovation and energy prices. American Economic Review, 92, 160–180.CrossRefGoogle Scholar
  37. Su, L., & Xiao, Z. (2008). Testing structural change in time-series nonparametric regression models. Statistics and its Interface, 1(2), 347–366.CrossRefGoogle Scholar
  38. Zivot, E., & Andrews, D. (1992). Further evidence of the great crash, the oil-price shock and the unit-root hypothesis. Journal of Business and Economic Statistics, 10, 251–70.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.SEEDSFerraraItaly

Personalised recommendations