Skip to main content

Time Trends and Persistence in the Global CO2 Emissions Across Europe

Abstract

We analyze the evolution across time of CO2 emissions in the European Union (EU) using advanced econometric techniques in time series analysis. We estimate the time trends along with the orders of integration of series corresponding to global CO2 emissions in EU member states using both parametric and semiparametric methods. The results show that there is a significantly negative trend only in the case of the UK, this being also a country where the trend shows mean reversion. At the other extreme, Spain, Italy, Greece and Bulgaria are some of the countries where CO2 emissions show positive trends and orders of integration that are substantially above unity. Moreover, we examine the CO2 emissions of the EU as a whole, China and the US, finding some support for mean reversion only in the second case. Therefore, there is less urgent need for policy reforms in the U.K. and somehow China than in the rest of the EU or the US.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Notes

  1.  See the World Energy Outlook of the IEA (2016).

  2. Note that the time series for EU-28 sums up the data for all of its 28 member countries only from the early 1990s—when data are available for all the countries—onwards. Therefore, the EU-28 series must reflect some other kinds of estimations when it comes to the period preceding the 1990s.

  3. Though AIC and BIC can be used in this context, these criteria might not be appropriate with fractional integration since they may not give sufficient attention to the long-run properties of the models. See, for instance, Hosking (1981, 1984). Beran et al. (1998) propose versions of the AIC, BIC and the HQC (Hannan and Quinn 1979) in the case of fractional autoregressions but do not consider MA components.

  4. The choice of the bandwidth number (i.e., m) in the semiparametric estimation of the differencing parameter is still an unresolved issue. It balances the trade-off between bias and variance. As m increases, the asymptotic variance of the estimator decreases while the bias grows larger.

  5. Also these data are from the World Development Indicators database.

References

  • Abadir KM, Distaso W, Giraitis L (2007) Nonstationarity-extended local Whittle estimation. J Econom 141:1353–1384

    Article  Google Scholar 

  • Akbostanci E, Turut-Ask S, Tunc G (2009) The relationship between income and environment in Turkey: is there an environmental Kuznets curve? Energy Policy 37:861–867

    Article  Google Scholar 

  • Aldy JE (2006) Per capita carbon dioxide emissions: convergence or divergence? Environ Resour Econ 33:534–555

    Article  Google Scholar 

  • Aslanidis N (2009) Environmental Kuznets curves for carbon emissions: a critical survey. Nota di Lavoro 75‐2009, Fondazione Eni Enrico Mattei

  • Barassi MR, Cole MA, Elliot RJR (2011) The stochastic convergence of CO2 emissions: a long memory approach. Environ Resour Econ 49:367–385

    Article  Google Scholar 

  • Barros CP, Gil-Alana LA, de Gracia FP (2016) Stationarity and long range dependence of carbon dioxide emissions: evidence from disaggregated data. Environ Resour Econ 63(1):45–56

    Article  Google Scholar 

  • Beran J, Hansali B, Ocker RJ (1998) On unified model selection for stationary and nonstationary short and long memory autoregressive processes. Biometrika 85:921–934

    Article  Google Scholar 

  • Bloomfield P (1973) An exponential model in the spectrum of a scalar time series. Biometrika 60:217–226

    Article  Google Scholar 

  • Bloomfield P, Nychka D (1992) Climate spectra and detecting climate change. Clim Change 21:1–16

    Article  Google Scholar 

  • Christidou M, Panagiotidis T, Sharma A (2013) On the stationarity of per capita carbon dioxide emissions over a century. Econ Model 33:918–925

    Article  Google Scholar 

  • Dergiades T, Kaufmann R, Panagiotidis T (2016) Long-run changes in radiative forcing and surface temperature: the effect of human activity over the last five centuries. J Environ Econ Manag 76(C):67–85

    Article  Google Scholar 

  • Fodha M, Zaghdoud O (2010) Economic growth and pollutant emissions in Tunisia: an empirical analysis of the environmental Kuznets curve. Energy Policy 38:1150–1156

    Article  Google Scholar 

  • Galeotti M, Lanza A, Pauli F (2006) Reassessing the environmental Kuznets curve for CO2 emissions: a robustness exercise. Ecol Econ 57:452–463

    Article  Google Scholar 

  • Gil-Alana LA (2004) The use of the model of Bloomfield (1973) as an approximation to ARMA processes in the context of fractional integration. Math Comput Model 39:429–436

    Article  Google Scholar 

  • Gil-Alana L, Cunado J, Gupta R (2015) Persistence, mean-reversion, and non-linearities in CO2 emissions: the cases of China, India, UK and US. In: University of Pretoria Department of Economics Working Paper Series 2015–2028

  • Gil-Alana LA, Cuñado J, Gupta R (2017) Persistence, mean-reversion and non-linearities in CO2 emissions: evidence from the BRICS and G7 countries. Environ Resour Econ 67(4):869–883

    Article  Google Scholar 

  • Grenander U, Rosenblatt M (1957) Statistical analysis of stationary time series. Chelsea Publishing Company, New York

    Book  Google Scholar 

  • Hannan EJ, Quinn BQ (1979) The determination of the order of an autoregression. J Roy Stat Soc B 41:190–195

    Google Scholar 

  • Hendry DF, Juselius K (2000) Explaining cointegration analysis: part 1. Energy J 0(1):1–42

    Google Scholar 

  • Hendry DF, Juselius K (2001) Explaining cointegration analysis: part II. Energy J 0(1):75–120

    Google Scholar 

  • Hosking JRM (1981) Fractional differencing. Biometrika 68:165–176

    Article  Google Scholar 

  • Hosking JRM (1984) Modelling persistence in hydrological time series using fractional differencing. Water Resour Res 20:1898–1908

    Article  Google Scholar 

  • IEA (2016) World energy outlook. International Energy Agency, Paris

    Google Scholar 

  • Jaunky V (2011) The CO2 emissions-income nexus: evidence from rich countries. Energy Policy 39:1228–1240

    Article  Google Scholar 

  • Lee C-C, Chang C-P (2008) Energy consumption and economic growth in Asian economies: a more comprehensive analysis using panel data. Resour Energy Econ 30(1):50–65

    Article  Google Scholar 

  • Lee C, Chang C (2009) Stochastic convergence of per capita carbon dioxide emissions and multiple breaks in OECD countries. Econ Model 26:1375–1381

    Article  Google Scholar 

  • Liu H, Chen Y (2013) A Study on the volatility spillovers, long memory effects, and interactions between carbon and energy markets: the impact of extreme weather”. Econ Model 35:840–855

    Article  Google Scholar 

  • Magazzino C (2014) A panel VAR approach of the relationship among economic growth, CO2 emissions, and energy use in the ASEAN-6 countries. Int J Energy Econ Policy 4(4):546–553

    Google Scholar 

  • Nourry M (2009) Re-examining the empirical evidence for stochastic convergence of two air pollutants with a pair-wise approach. Environ Resour Econ 44:555–570

    Article  Google Scholar 

  • Olivier JGJ, Janssens-Maenhout G, Muntean M, Peters JAHW (2016) Trends in global CO2 emissions: 2016 report. PBL Netherlands Environmental Assessment Agency, The Hague

    Google Scholar 

  • Park RE, Mitchell BM (1980) Estimating the autocorrelated error model with trended data. J Econom 13:185–201

    Article  Google Scholar 

  • Perman R, Stern DI (2003) Evidence from panel unit root and cointegration tests that the environmental Kuznets curve does not exist. Aust J Agric Resour Econ 47:325–347

    Article  Google Scholar 

  • Prais SJ, Winsten CB (1954) Trend estimators and serial correlation. In: Cowles Commission Monograph, No. 23. Yale University Press, New Haven

  • Richmond A, Kaufmann R (2006) Is there a turning point in the relationship between income and energy use and/or carbón emissions? Ecol Econ 56:176–186

    Article  Google Scholar 

  • Robinson PM (1995) Gaussian semi-parametric estimation of long range dependence. Ann Stat 23:1630–1661

    Article  Google Scholar 

  • Shimotsu K, Phillips PCB (2005) Exact local Whittle estimation of fractional integration. Ann Stat 33(4):1890–1933

    Article  Google Scholar 

  • Slottje D, Nieswiadomy M, Redfearn M (2001) Economic inequality and the environment. Environ Model Softw 16:183–194

    Article  Google Scholar 

  • Strazicich MC, List JA (2003) Are CO2 emission levels converging among industrial countries? Environ Resour Econ 24:263–271

    Article  Google Scholar 

  • Sun L, Wang M (1996) Global warming and global dioxide emissions: an empirical study. J Environ Manag 46:327–343

    Article  Google Scholar 

  • Velasco C (1999) Gaussian semiparametric estimation of non-stationary time series. J Time Ser Anal 20(1):87–127

    Article  Google Scholar 

  • Woodward WA, Gray HL (1993) Global warming and the problem of testing for trend in time series data. J Clim 6:953–962

    Article  Google Scholar 

  • Yavuz NC, Yilanci V (2013) Convergence in per capita carbon dioxide emissions among G7 countries: a TAR panel unit root approach. Environ Resour Econ 54:283–291

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the financial support received from the Ministerio de Economía y Competitividad: ECO2017-85503-R (Luis Alberiko Gil-Alana) and ECO 2015-68815-P (Tommaso Trani). Comments from the Editor and three anonymous reviewers are gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luis A. Gil-Alana.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gil-Alana, L.A., Trani, T. Time Trends and Persistence in the Global CO2 Emissions Across Europe. Environ Resource Econ 73, 213–228 (2019). https://doi.org/10.1007/s10640-018-0257-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10640-018-0257-5

Keywords

  • CO2 emissions
  • Time trends
  • Fractional integration
  • Persistence

JEL Classification

  • C22
  • Q50
  • Q58