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

Abstract

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.

This is a preview of subscription content, log in to check access.

Fig. 1

Notes

  1. 1.

    Carson (2010) comments on the relative paucity of time series analysis within the EKC literature since its inception, while discussing causality issues and econometric models.

  2. 2.

    Linked to the Iranian war and related to a recession.

  3. 3.

    The methodologically oriented message is that polynomial functions of time and GDP are not adequate to capture CO2 evolution. The specific economic messages are that the shock negatively influences the CO2 pattern, which then reaches an equilibrium level unless another shock intervenes.

  4. 4.

    It is true that in the early 90s, the invasion of Kuwait by Iraq and the following Gulf war caused an oil peak. Nevertheless, that peak in constant prices was much lower than the one caused by the Iran–Iraq war and Iranian revolution (that exceeded 100$ per barrel in 2013 prices). It was comparable to the post first oil shock (Arab oil embargo) price of around 60$. It is worth noting that in the early 70s, before the first oil shock that opened the way to stagflation periods, the oil price was lower than 20$.

  5. 5.

    This highlights that assumed and unknown time-related events dynamically explain the overall EKC shape and are interrelated in certain circumstances (NE).

  6. 6.

    A ‘shock’ might be a sudden unexpected jump in a variable (covariate) or a credible policy that develops from a given time T onwards. The second case is historically rare but possible.

References

  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.

    Article  Google Scholar 

  2. Andersen, M. S., & Ekins, P. (2009). Carbon taxation: Lessons from Europe. Oxford: Oxford University Press.

    Google Scholar 

  3. Barrett, S. (2003). Environment and statecraft. Oxford: Oxford University Press.

    Google 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.

    Article  Google Scholar 

  5. Borghesi, S., et al. (2016). The European emission trading system and its followers: comparative analysis and linking perspectives. Berlin: Springer.

    Google 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.

    Article  Google Scholar 

  7. Brock, W., & Taylor, S. (2010). The green Solow model. Journal of Economic Growth, 15, 127–153.

    Article  Google 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.

    Article  Google 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.

  11. Delgado, M. A., & Hidalgo, J. (2000). Nonparametric inference on structural breaks. Journal of Econometrics, 96(1), 113–144.

    Article  Google Scholar 

  12. Ekins, P., & Speck, S. (2011). Environmental tax reform (ETR). A policy for green growth. Oxford: Oxford University Press.

    Google Scholar 

  13. European Environment Agency. (2016). The European Environment—environmental taxation and EU environmental policies. Copenhagen: European Environment Agency.

  14. European Environment Agency. (2014). Resource efficient green economy and EU policies. Copenhagen: European Environment Agency.

  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.

    Article  Google Scholar 

  16. Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110(2), 353–377.

    Article  Google Scholar 

  17. Johnstone, N., Hascic, I., Kalamova, M. (2010). Environmental Policy design characteristics and technological innovation. In OECD environment working paper 15. OECD Paris.

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  23. Härdle, W., & Mammen, E. (1991). Bootstrap methods in nonparametric regression. In Nonparametric functional estimation and related topics (pp. 111–123). Springer Netherlands.

  24. Heil, M. T., & Selden, T. M. (1999). Panel stationarity with structural breaks: Carbon emissions and GDP. Applied Economics Letters, 6(4), 223–225.

    Article  Google 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.

  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.

    Article  Google 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.

    Article  Google Scholar 

  29. McKitrick, R. (2007). Why did US air pollution decline after 1970? Empirical Economics, 33(3), 491–513.

    Article  Google Scholar 

  30. Panopolou, E., & Pantelidis, T. (2009). Club convergence in carbon dioxide emissions. Environmental and Resource Economics, 44, 47–70.

    Article  Google Scholar 

  31. Pankratz, A. (1991). Forecasting with dynamic regression models. New York: Wiley.

    Google 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.

    Article  Google 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.

    Article  Google Scholar 

  34. Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57, 1361–401.

    Article  Google 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.

    Article  Google Scholar 

  37. Su, L., & Xiao, Z. (2008). Testing structural change in time-series nonparametric regression models. Statistics and its Interface, 1(2), 347–366.

    Article  Google 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 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Massimiliano Mazzanti.

Additional information

The manuscript is available as a working paper on “Econpapers-RePEc organization” https://ideas.repec.org/p/srt/wpaper/1815.html.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mazzanti, M., Musolesi, A. The effect of Rio Convention and other structural breaks on long-run economic development-CO2 relationships . Econ Polit 34, 389–405 (2017). https://doi.org/10.1007/s40888-017-0069-z

Download citation

Keywords

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

JEL Classification

  • C22
  • Q53