Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter December 10, 2013

Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries

  • Antonio Musolesi EMAIL logo and Massimiliano Mazzanti


We study long run carbon dioxide emissions-economic development relationships for advanced countries grouped in policy relevant groups: North America and Oceania, South Europe, North Europe. By relying on recent advances on Generalized Additive Mixed Models (GAMMs) and adopting interaction models, we handle simultaneously three main econometric issues, named here as functional form bias, heterogeneity bias and omitted time related factors bias, which have been proved to be relevant but have been addressed separately in previous papers. The model incorporates nonlinear effects, eventually heterogeneous across countries, for both income and time. We also handle serial correlation by using autoregressive moving average (ARMA) processes. We find that country-specific time related factors weight more than income in driving the northern EU Environmental Kuznets. Overall, the countries differ more on their carbon-time relation than on the carbon-income relation which is in almost all cases monotonic positive. Once serial correlation and (heterogeneous) time effects have been accounted for, only three Scandinavian countries – Denmark, Finland and Sweden – present some threshold effect on the CO2-development relation.

JEL classification: C14; C23; Q53

Corresponding author: Antonio Musolesi, INRA, Univ. Grenoble Alpes, UMR 1215 GAEL, F-38000 Grenoble, France, e-mail:


We thank Simone Borghesi, Hervé Cardot, Valeria Costantini, Michel Simioni, the Editor-in-Chief Bruce Mizrach and an anonymous referee for useful comments.


Andersen, M. S., and P. Ekins. 2009. Carbon Taxation: Lessons from Europe. Oxford/NY: Oxford University Press.Search in Google Scholar

Andersen, M. S., T. Barker, E. Christie, P. Ekins, J. F. Gerald, J. Jilkova, S. Junankar, M. Landesmann, H. Pollitt, R. Salmons, S. Scott, and S. Speck. 2007. Competitiveness Effects of Environmental Tax Reforms (COMETR): Publishable Final Report to the European Commission. in Google Scholar

Andreoni, J., and A. Levinson. 2001. “The Simple Analytics of the Environmental Kuznets Curve.” Journal of Public Economics 80: 269–286.10.1016/S0047-2727(00)00110-9Search in Google Scholar

Augustin, N. H., M. Musio, K. von Wilpert, E. Kublin, S. N. Wood, and M. Schumacher. 2009. “Modeling Spatiotemporal Forest Health Monitoring Data.” Journal of the American Statistical Association 104 (487): 899–911.10.1198/jasa.2009.ap07058Search in Google Scholar

Azomahou, T., and T. Mishra. 2008. “Age Dynamics and Economic Growth: Revisiting the Nexus In a Nonparametric Setting.” Economics Letters 99 (1): 67–71.10.1016/j.econlet.2007.05.031Search in Google Scholar

Azomahou, T., F. Laisney, and V. Phu Ngayen. 2006. “Economic development and CO2 emissions: a non parametric panel approach.” Journal of Public Economics 90: 1347–1363.10.1016/j.jpubeco.2005.09.005Search in Google Scholar

Azomahou, T., M. Goedhuys, and V. Phu Ngayen. 2009. “A Structural Nonparametric Reappraisal of the CO2 Missions-Income Relationship.” ONU-MERIT working paper.Search in Google Scholar

Barrett, S. 2003. Environment and Statecraft: The Strategy of Environmental Treaty-making. Oxford: Oxford University Press.10.1108/meq.2003.14.5.622.3Search in Google Scholar

Borghesi, S. 2001. “The Environmental Kuznets curve: a Critical Survey.” In Economic Institutions and Environmental Policy, edited by M. Franzini and A. Nicita, 201–224. Farnham, UK: Ashgate Publishing.10.4324/9781315203270-11Search in Google Scholar

Breslow, N. E., and D. G. Clayton. 1993. “Approximate Inference in Generalized Linear Mixed Models.” Journal of the American Statistical Association 88: 9–25.Search in Google Scholar

Brock, W., and S. Taylor. 2010. “The Green Solow Model.” Journal of Economic Growth 15: 127–153.10.1007/s10887-010-9051-0Search in Google Scholar

Chamberlain, G. 1982. “Multivariate Regression Models for Panel Data.” Journal of Econometrics 18 (1): 5–46.10.1016/0304-4076(82)90094-XSearch in Google Scholar

Costantini, V., and M. Mazzanti. 2012. “On the Green side of Trade competitiveness?” Research Policy 41: 132–153.10.1016/j.respol.2011.08.004Search in Google Scholar

Dechezlepretre, A., M. Glachant, I. Hascic, N. Johnstone, and N. Meniere. 2011. “Invention and transfer of climate change mitigation technologies on a global scale: a study drawing on patent data.” Review of Environmental Economics and Policy 5 (1): 109–130.10.1093/reep/req023Search in Google Scholar

Dietz, S. 2011. “The Stern Review.” In The Encyclopedia of Climate and Weather, edited by Stephen H. Schneider, Oxford: Oxford University Press.Search in Google Scholar

EEA. 2008. Greenhouse Gas Emission Trends and Projections in Europe 2008. Copenhagen: European Environment Agency.Search in Google Scholar

EEA. 2013. Towards a Green Economy in Europe. Copenhagen: European Environment Agency.Search in Google Scholar

Egli, H., and T. Steger. 2007. “A Dynamic Model of the Environmental Kuznets curve: Turning Point and Public Policy.” Environmental & Resource Economics 36: 15–34.10.1007/s10640-006-9044-9Search in Google Scholar

Evdokimov, K. 2010. Identification and Estimation of a Nonparametric Panel Data Model with Unobserved Heterogeneity. mimeoSearch in Google Scholar

Figueroa, E., and R. Pasten. 2013. “A Tale of Two Elasticities: A General Theoretical Framework for The Environmental Kuznets Curve Analysis.” Economics Letters 119: 85–88.10.1016/j.econlet.2013.01.019Search in Google Scholar

Friedberg, L. 1998. “Did Unilateral Divorce Raise Divorce Rates?” American Economic Review 88: 608–627.10.3386/w6398Search in Google Scholar

Galeotti, M., M. Manera, and A. Lanza. 2009. “On the Robustness of Robustness Checks of the EKC Hypothesis.” Environmental and Resource Economics 43: 369–390.Search in Google Scholar

Gilli, M., M. Mazzanti, and F. Nicolli. 2013. “Sustainability, Environmental Innovations and Competitiveness in Evolutionary Perspectives. Evidence from the EU.” Journal of Socio Economics 45 (C): 204–215.Search in Google Scholar

Grossman, G. M., and A. B. Krueger. 1995. “Economic Growth and the Environment.” Quarterly Journal of Economics 110: 353–357.10.2307/2118443Search in Google Scholar

Gu, C., and G. Wahba. 1993. “Semiparametric Analysis of Variance With Tensor Product Thin-Plate Splines.” Journal of the Royal Statistical Society Series (B) 55 (2): 353–368.10.1111/j.2517-6161.1993.tb01906.xSearch in Google Scholar

Hamilton, James D. 1994. Time Series Analysis. Princeton: Princeton University Press.Search in Google Scholar

Hastie, T., and R. Tibshirani. 1990. Generalized Additive Models. Chapman and Hall.Search in Google Scholar

Heckman, J. J., and V. J. Hotz. 1989. “Choosing Among Alternative Non-experimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training.” Journal of The American Statistical Association 84 (408): 862–874.10.1080/01621459.1989.10478848Search in Google Scholar

Henderson, D. J., R. J. Carroll, and Q. Li. 2008. “Nonparametric Estimation and Testing of Fixed Effects Panel Data Models.” Journal of Econometrics 144: 257–275.10.1016/j.jeconom.2008.01.005Search in Google Scholar PubMed PubMed Central

Hoderline, S., and H. White. 2012. “Nonparametric Identification in Nonseparable Panel Data Models with Generalized Fixed Effects.” Working Paper, Dept. of Economics, Brown University.10.1016/j.jeconom.2012.01.033Search in Google Scholar

Hsiao, C. 2003. Analysis of Panel Data. Cambridge: Cambridge University Press.10.1017/CBO9780511754203Search in Google Scholar

ICCG. 2012. The State of Compliance in the Kyoto Protocol, ICCG Reflection 12, ICCG.Search in Google Scholar

Johnstone, N., I. Hascic, and D. Popp. 2010. “Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts.” Environmental & Resource Economics 45: 133–155.10.1007/s10640-009-9309-1Search in Google Scholar

Johnstone, N., I. Haščič, J. Poirier, M. Hemar, and C. Michel. 2012. “Environmental Policy Stringency and Technological Innovation: Evidence from Survey Data and Patent Counts.” Applied Economics 44 (17): 2157–2170.10.1080/00036846.2011.560110Search in Google Scholar

Kauermann, G., T. Krivobokova, and L. Fahrmeir. 2009. “Some Asymptotic Results on Generalized Penalized Spline Smoothing.” Journal of the Royal Statistical Society Series (B) 71: 487–503.10.1111/j.1467-9868.2008.00691.xSearch in Google Scholar

Kijima, M., K. Nishide, and A. Ohyama. 2010. “Economic Models for the EKC: a Survey.” Journal of Economic Dynamics and Control 34: 1187–1201.10.1016/j.jedc.2010.03.010Search in Google Scholar

Kim, Y. J., and C. Gu. 2004. “Smoothing spline Gaussian regression: more scalable computation via efficient approximation.” Journal of the Royal Statistical Society Series B 66: 337–356.10.1046/j.1369-7412.2003.05316.xSearch in Google Scholar

Levinson, A. 2009. “Technology, International Trade, and Pollution from US Manufacturing.” American Economic Review 99 (5): 2177–219210.1257/aer.99.5.2177Search in Google Scholar

Li, Q., and T. Stengos. 1996. “Semiparametric Estimation of Partially Linear Panel Data Models.” Journal of Econometrics 71: 289–397.10.1016/0304-4076(94)01711-5Search in Google Scholar

Mammen, E., B. Stove, and D. Tjostheim. 2009. “Nonparametric Additive Models for Panels of Time Series.” Econometric Theory 25: 442–481.10.1017/S0266466608090142Search in Google Scholar

Marin, G., and M. Mazzanti. 2013. “The Evolution of Environmental and Labour Productivity Dynamics.” Journal of Evolutionary Economics 23 (2): 357–399.10.1007/s00191-010-0199-8Search in Google Scholar

Marra, G., and S. N. Wood. 2012. “Coverage Properties of Confidence Intervals for Generalized Additive Model Components.” Scandinavian Journal of Statistics 39 (1): 53–74.10.1111/j.1467-9469.2011.00760.xSearch in Google Scholar

Mazzanti, M., and A. Musolesi. 2013. “The Heterogeneity of Carbon Kuznets Curves for Advanced Countries: Comparing Homogeneous, Heterogeneous and Shrinkage/Bayesian Estimators.” Applied Economics 45: 3827–3842.10.1080/00036846.2012.734597Search in Google Scholar

Melenberg, B., E. Dijkgraaf, and H. Vollebergh. 2009. “Identifying Reduced-form Relations with Panel Data: The Case of Pollution and Income.” Journal of Environmental Economics and Management 58: 27–42.10.1016/j.jeem.2008.12.005Search in Google Scholar

Millimet, D., J. List, and T. Stengos. 2003. “The EKC: Real Progress or Misspecified Models?” The Review of Economics and Statistics 85: 1038–1047.10.1162/003465303772815916Search in Google Scholar

Musolesi, A., M. Mazzanti, and R. Zoboli. 2010. “A Bayesian Approach to the Estimation of EKC for CO2.” Applied Economics 42: 2275–2287.10.1080/00036840701858034Search in Google Scholar

OECD. 2002. Indicators to Measure Decoupling of Environmental Pressure from Economic Growth. Paris: OECD.Search in Google Scholar

OECD. 2010. Taxation, Innovation and the Environment, OECD Green Growth Studies.Paris: OECD Publishing.Search in Google Scholar

OECD. 2011. Fostering Innovation for Green Growth, OECD Green Growth Studies. Paris: OECD Publishing.Search in Google Scholar

OECD. 2013. Choosing Fiscal Consolidation Instruments Compatible with Growth and Equity. Paris: OECD Publishing.Search in Google Scholar

Ordas Criado, C., S. Valente, and T. Stengos. 2011. “Growth and the Pollution Convergence Hypothesis: Theory and Evidence.” Journal of Environmental Economics and Management 62: 199–214.10.1016/j.jeem.2010.10.009Search in Google Scholar

Papke, L. E. 1994. “Tax Policy and Urban Development: Evidence from the Indiana Enterprise Zone Program.” Journal of Public Economics 54: 37–49.10.1016/0047-2727(94)90069-8Search in Google Scholar

Pesaran, M. H. 2006. “Estimation and Inference in Large Heterogenous Panels with Multifactor Error Structure.” Econometrica 74: 967–1012.10.1111/j.1468-0262.2006.00692.xSearch in Google Scholar

Pinheiro, J., and D. Bates. 2000. Mixed-effects models in S and S-PLUS. New York: Springer-Verlag.10.1007/978-1-4419-0318-1Search in Google Scholar

Pinheiro, J., D. Bates, S. DebRoy, D. Sarkar, and the R Core team. 2013. nlme: Linear and Nonlinear Mixed Effects Models.Search in Google Scholar

Ruppert, D., M. P. Wand, and R. J. Carroll. 2003. Semiparametric Regression. Cambridge: Cambridge University Press.10.1017/CBO9780511755453Search in Google Scholar

Stone, C. J. 1985. “Additive Regression and Other Nonparametric Models.” Annals of Statistics 13: 689–705.10.1214/aos/1176349548Search in Google Scholar

Su, L., and A. Ullah. 2006. “Profile likelihood estimation of partially linear panel data models with fixed effects.” Economics Letters 92: 75–81.10.1016/j.econlet.2006.01.019Search in Google Scholar

Su, L., and A. Ullah. 2010. Nonparametric and Semiparametric Panel Econometric Models: Estimation and Testing. mimeo.Search in Google Scholar

UNDP. 2009. Human Development Report 2007/2008. Fighting climate change: Human solidarity in a divided world, UNDP.Search in Google Scholar

Wood, S. N. 2000. “Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties.” Journal of the Royal Statistical Society Series (B) 62: 413–428.10.1111/1467-9868.00240Search in Google Scholar

Wood, S. N. 2003. “Thin Plate Regression Splines.” J.R. Statist.Soc.B 65 (1): 95–114.Search in Google Scholar

Wood, S. N. 2004. “Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models.” Journal of the American Statistical Association 99: 673–686.10.1198/016214504000000980Search in Google Scholar

Wood S. N. 2006a. Generalized Additive Models: An Introductionwith R. Boca Raton, Florida: Chapman and Hall/CRC Press.Search in Google Scholar

Wood, S. N. 2006b. “On Confidence Intervals for Generalized Additive Models Based on Penalized Regression Splines.” Australian and New Zealand Journal of Statistics 48 (4): 445–464.10.1111/j.1467-842X.2006.00450.xSearch in Google Scholar

Wood, S. N. 2008. “Fast Stable Direct Fitting and Smoothness Selection for Generalized Additive Models.” Journal of the Royal Statistical Society Series (B) 70 (3): 495–518.10.1111/j.1467-9868.2007.00646.xSearch in Google Scholar

Wood, S. N. 2013. mgcv R package, CRAN.Search in Google Scholar

Wooldridge, J. M. 2005. “Fixed-Effects and Related Estimators for Correlated Random-Coefficient and Treatment-Effect Panel Data Models.” The Review of Economics and StatisticsMIT Press 87 (2): 385–390.10.1162/0034653053970320Search in Google Scholar

Yoshida, T., and K. Naito. 2012. “Asymptotics for Penalized Additive B-Spline Regression.” Journal of the Japan Statistical Society 42 (1): 81–107.10.14490/jjss.42.81Search in Google Scholar

Yoshida, T., and K. Naito. 2013. Asymptotics for Penalized Splines in Generalized Additive Models. mimeo.Search in Google Scholar

Published Online: 2013-12-10
Published in Print: 2014-12-1

©2014 by De Gruyter

Downloaded on 3.12.2023 from
Scroll to top button