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Parametric and non-parametric convergence analysis of electricity intensity in developed and developing countries

Abstract

This paper examines the pattern of convergence in electricity intensity in a sample of 79 countries. We apply the residual augmented least squares regression to the convergence of energy intensity. This method has been used in the convergence of per capita energy consumption but not convergence of energy intensity. Furthermore, in contrast to the previous studies which mainly used the conventional beta convergence approach to examine conditional convergence, we use a beta convergence method that is capable of identifying the actual number of countries that contribute to conditional convergence. The sigma and gamma convergences of electricity intensity are also examined. In addition to the full sample of countries, we also examine convergence in African countries, Asian and Oceanic countries, American countries and European countries, separately. Convergences in OECD and non-OECD countries are also examined, separately. In the full sample, the results show convergence exists in 54% of the countries in the total sample. There is convergence in 65% of the African countries, 61% of the American countries, 43% of the Asian and Oceanic countries and 33% of the European countries. In terms of the regional classification, it is also observed that convergence exists for 58% of the non-OECD countries and 31% of the OECD countries. There is evidence for sigma convergence in all the blocs with the exception of European and non-OECD countries. With the exception of African countries, there is evidence for gamma convergence in all the countries and the various blocs. The policy implications of the results are discussed.

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Notes

  1. Decreasing energy intensity (i.e. the ratio of energy input to economic activity) is vital in combating the current concerns about greenhouse gas emissions and ensuring energy security.

  2. Another approach is the club clustering convergence method, which has the ability of capturing the comparative convergence of cross-sectional midpoints.

  3. We are aware that several authors have used the method to evaluate the convergence of relative per capita energy consumption. However, no previous studies on energy intensity has adopted this method.

  4. Another related area that has been studied in the literature is energy productivity. Due to space concern, we only concentrate on the convergence of energy consumption and intensity (Ulucak and Apergis 2018).

  5. Some of these authors have utilised several methods, but we only review the methods that are closely related to ours.

  6. Algeria, Argentina, Australia, Austria, Bangladesh, Belgium, Bolivia, Brazil, Cameroon, Canada, Chile, China, Colombia, Congo Democratic Republic, Congo Republic, Costa Rica, Cote , Cuba, Denmark, Dominican Republic, Ecuador, Egypt, El Salvador, Finland, France, Gabon, Germany, Ghana, Greece, Guatemala, Honduras, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Kenya, Korea, Luxembourg., Malaysia, Malta, Mexico, Morocco, Myanmar, Nepal, Netherlands, Nicaragua, Nigeria, Norway, Oman, Pakistan, Panama, Paraguay, Peru, Philippines, Portugal, Saudi Arabia, Senegal, Singapore, South Africa, Spain, Sri Lanka, Sudan, Sweden, Thailand, Trinidad and Tobago, Tunisia, Turkey, United Kingdom, United States, Uruguay, Venezuela, Zambia and Zimbabwe.

  7. Electricity consumption includes the production of power plants and combined heat and power plants less transmission, distribution and transformation losses and own use by heat and power plants.

  8. In the process of data extraction, we observe that some countries have consistent data for only one of the two variables. For instance, while countries such as Bahamas, Burundi and Puerto Rico have consistent data for real GDP per capita but not electricity consumption per capita, Albania, Mozambique and Poland have consistent data for electricity consumption per capita but not real GDP per capita. We ignore all the countries that failed to produce consistent datasets for the two variables.

  9. Although Liddle (2009) conducted an exercise that normalises an intercept and trend to the log of electricity series, the method was not used to identify convergence as adopted in our study.

  10. We have used the structural breaks resulting from the RALS test. The only difference we observed for structural breaks between LM and RALS test is the case of Indonesia. According to the LM test, there are two structural breaks for Indonesia, which are 1994 and 2000.

  11. For the purpose of this test, we only picked the intercept, linear or the dummy variable that the t-statistic suggests that it is significant, which is consistent with the work of Galvao and Reis Gomes (2007). Hence, we have not reported the intercept terms and the two dummies that represent(s) two breaks in the intercepts as none of the terms are individually significant according to the t-statistics in Cameroon, Iraq and Israel.

  12. According to the LM test, there are two structural breaks for Congo Democratic Republic, which are 1986 and 1998.

  13. According to the LM test, there is only one break for Ireland, which is 1997.

  14. For details on Zimbabwe, see footnote 10.

  15. For details on Iraq, see footnote 10.

  16. According to the LM test, there are two structural breaks for Algeria, which are 1981 and 2005.

  17. According to the LM test, there are two structural breaks for Austria, which are 1990 and 2001, and there is structural break in Ireland, which is in 1997.

  18. For details on Iraq, see footnote 10.

  19. We have also estimated the total sample and the adopted blocs with the traditional beta convergence and the results provide evidence for convergence. We do not report the results because of space. It is available from the author upon request.

  20. We cannot do a direct comparison with the results of Liddle (2009) because out of the 22 countries adopted in his dataset, we do not have the required data for New Zealand and Switzerland.

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Correspondence to Sakiru Adebola Solarin.

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Solarin, S.A. Parametric and non-parametric convergence analysis of electricity intensity in developed and developing countries. Environ Sci Pollut Res 26, 8552–8574 (2019). https://doi.org/10.1007/s11356-019-04225-y

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Keywords

  • Electricity intensity
  • Conditional convergence
  • Sigma convergence
  • Gamma convergence
  • Policy implications

JEL Classification

  • C32
  • Q49