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Exploring the effect of including the environment in the neoclassical growth model

  • Research Article
  • Growth and the Environment
  • Published:
Environmental Economics and Policy Studies Aims and scope Submit manuscript

Abstract

This study begins with a brief presentation of the Neoclassical Model of economic growth and continues with the inclusion of two additional variables that affect welfare in opposing ways: pollution and abatement expenditures. The optimal steady-state conditions are derived allowing for a preliminary comparison of the resulting balanced growth paths under the criterion of welfare maximization with and without environmental externalities. Finally, using a balanced panel data of 43 countries and for the time period 1990–2011 we test the validity of including the environment in the neoclassical growth model approximating pollution abatement with the electricity production from renewable sources and pollution with carbon dioxide emissions. With the help of adequate econometric panel data methods we test the validity of the environmental Kuznets curve hypothesis for the full sample, as well as for the OECD and non-OECD countries.

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Notes

  1. Brock and Taylor (2005) and Xepapadeas (2005) provide a full mathematical framework in this kind of analysis. Our study, using specific to the neoclassical growth model functional forms, examines the effects of including the environment in this model and tests empirically its validity.

  2. López-Menéndez et al. (2014) provide a review of the findings of the EKC empirical studies.

  3. It is typical to measure output in value terms of a single composite good whose price is set equal to 1. Thus, the final good becomes a measure of comparison of values for all other goods and services whose price is expressed in units of the final good (numéraire).

  4. Generally, the term ‘physical capital’ includes all accumulated or produced factors of production which are themselves output of some productive process.

  5. Given that the economy is closed, total savings is equal to total investment.

  6. The last case describes a situation defined as the steady state of the model.

  7. Note that all variables depend on time even though the time subscript is omitted whenever time dependence is easily understood.

  8. This guarantees a constant level of pollution in steady state.

  9. Notice that the additional term in (19) is the (constant) ratio of per-capita abatement expenditures \( b \), over per-capita physical capital \( k \).

  10. Another variable of interest for our purpose was the greenhouse gases (GHG) net emissions/removals by LUCF (in Mt of CO2 equivalent) referring to changes in levels of all GHG attributable to forest and land-use change activities. Due to many missing values this variable was omitted from our analysis.

  11. The full sample database used has 946 observations per variable. The countries used are the following: OECD countries (n = 21): Australia, Austria, Canada, Chile, Denmark, Finland, France, Greece, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, The Netherlands, Norway, Portugal, Sweden, Turkey, UK, USA Non-OECD countries (n = 22): Argentina, Bolivia, Brazil, China, Colombia, Costa Rica, Caribbean, Cuba, Dominican Rep, Gabon, Guatemala, Indonesia, Nicaragua, Panama, Peru, Philippines, Senegal, Singapore, El Salvador, Thailand, Trinidad and Tobago, Uruguay.

  12. For more details see http://data.worldbank.org/indicator/NY.GNP.PCAP.CD.

  13. The source of data is IEA Statistics © OECD/IEA 2012 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/.

  14. For more details see Halkos (2011).

  15. STATA’s “xtcsd” command was used (De Hoyos and Sarafidis 2006).

  16. Im et al. (2003) test is generally more powerful than the Fisher type and that proposed by Levin et al. (2002) tests (Barbieri 2006).

  17. Fisher type tests are based on combining the p values of the N cross-sectional tests rather than using appropriately scaled cross-sectional averages of the N independent test statistics (Verbeek 2005).

  18. As mentioned before, our dynamic model specification was reduced to an autoregressive distributed lag model [AD(1,0)], which for simplicity is called dynamic.

  19. The latter comes in line with the two-step Sargan test statistic. Additionally, for all specifications we test the validity of instruments with the Hansen test failing to reject the null hypothesis.

  20. The long-run coefficients of the GMM estimates may be calculated by dividing each estimated short-run coefficient by one minus the coefficient of the lagged dependent variable.

  21. For more information on global warming potential, the dimensions of the problem of climate change and its economic effects see Halkos (2014, 2015).

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  1. Laboratory of Operations Research, Department of Economics, University of Thessaly, Korai 43, 38333, Vólos, Greece

    George Halkos & Iacovos Psarianos

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  1. George Halkos
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  2. Iacovos Psarianos
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Correspondence to George Halkos.

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Halkos, G., Psarianos, I. Exploring the effect of including the environment in the neoclassical growth model. Environ Econ Policy Stud 18, 339–358 (2016). https://doi.org/10.1007/s10018-016-0146-5

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