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The impact of biomass energy consumption on pollution: evidence from 80 developed and developing countries

Abstract

The aim of this research is to explore the effect of biomass energy consumption on CO2 emissions in 80 developed and developing countries. To achieve robustness, the system generalised method of moment was used and several control variables were incorporated into the model including real GDP, fossil fuel consumption, hydroelectricity production, urbanisation, population, foreign direct investment, financial development, institutional quality and the Kyoto protocol. Relying on the classification of the World Bank, the countries were categorised to developed and developing countries. We also used a dynamic common correlated effects estimator. The results consistently show that biomass energy as well as fossil fuel consumption generate more CO2 emissions. A closer look at the results show that a 100% increase in biomass consumption (tonnes per capita) will increase CO2 emissions (metric tons per capita) within the range of 2 to 47%. An increase of biomass energy intensity (biomass consumption in tonnes divided by real gross domestic product) of 100% will increase CO2 emissions (metric tons per capita) within the range of 4 to 47%. An increase of fossil fuel consumption (tonnes of oil equivalent per capita) by 100% will increase CO2 emissions (metric tons per capita) within the range of 35 to 55%. The results further show that real GDP urbanisation and population increase CO2 emissions. However, hydroelectricity and institutional quality decrease CO2 emissions. It is further observed that financial development, foreign direct investment and openness decrease CO2 emissions in the developed countries, but the opposite results are found for the developing nations. The results also show that the Kyoto Protocol reduces emission and that Environmental Kuznets Curve exists. Among the policy implications of the foregoing results is the necessity of substituting fossil fuels with other types of renewable energy (such as hydropower) rather than biomass energy for reduction of emission to be achieved.

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Notes

  1. 1.

    In a very strict sense, biofuels come in many forms including liquid fuels, biogases, and solid biomass. These forms of energy generate energy that is required to operate several machinery available in the modern era.

  2. 2.

    The fuel market rebound effect in this case refers to the decrease in anticipated benefits from new technologies that increase the efficiency of biomass use. This might be due to the possibility of biomass generating greenhouse gases emissions.

  3. 3.

    Some of these studies have also used causality methods in the estimation process. However, we do not report the causality results due to space concerns.

  4. 4.

    Institutional quality is the average of the level civil liberties and political rights available in the country. The institutional quality index is based on a one-to-seven scale, with the score of one (1) suggesting the maximum form of institutional quality and a score of seven (7) suggesting the lowest level of institutional quality.

  5. 5.

    Panel data methods do not usually provide for individual country coefficients

  6. 6.

    Economic globalisation refers to an index that captures the volume of foreign trade as well as the size of barriers to trade in a country. For details, please see Dreher (2006). We report economic globalisation results because of the deficiencies associated with the traditional indicator of globalisation—trade openness and the overall globalisation index of Dreher (2006). The traditional trade openness, which has been used in the existing literature may somewhat be misleading, since the ratio does not necessarily reflect the size of (tariff or non-tariff) barriers to foreign trade. The overall globalisation index comprised economic, political and social dimensions of globalisation. We are concern with the economic dimension of emissions and globalisation. Hence, we report economic globalisation results.

  7. 7.

    This is due to the fact that FDIit and KYOTOt contain negative figures.

  8. 8.

    Albania, Argentina, Australia, Austria, Bangladesh, Belgium, Benin, Bolivia, Brazil, Bulgaria, Cameroon, Canada, Chile, China, Colombia, Congo, Rep., Costa Rica, Cote d’Ivoire, Cuba, Cyprus, Denmark, Dominican Republic, Ecuador, Egypt, El Salvador, Finland, France, Gabon, Ghana, Greece, Guatemala, Honduras, Iceland, India, Indonesia, Iran, Iraq, Ireland, Italy, Jamaica, Japan, Kenya, Korea, Malaysia, Mauritius, Mexico, Morocco, Mozambique, Myanmar, Nepal, Netherlands, New Zealand, Nicaragua, Nigeria, Norway, Pakistan, Panama, Paraguay, Peru, Philippines, Portugal, Saudi Arabia, Senegal, South Africa, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, United Kingdom, United States, Uruguay, Venezuela, Zambia and Zimbabwe.

  9. 9.

    The complete dataset for the baseline variables- emission, RGDP, urbanisation and biomass consumption-for the period, 1980–2010 are only available in the selected 80 countries. We focus on this sample because regression with inconsistent data may lead to multicollinearity and spurious results (Solarin 2017)

  10. 10.

    We have tested the endogeneity of the independent variables by using the Granger Causality and the results suggest that they are endogenous. We have not reported here due to space but they are available upon request

  11. 11.

    A key setback with the two-step method is that it is usually biased downwards. We utilise the corrected standard errors of Windmeijer (2005) in the estimation process to control for this deficiency.

  12. 12.

    In a bid to circumvent over-identifying restrictions in the estimations, we follow the procedures of Al-Mulali et al. (2016c) by introducing each control variable at a time rather than incorporating all the variables at once.

  13. 13.

    We resort to the use of hydroelectricity generation because hydroelectricity consumption is not available for most of these countries.

  14. 14.

    We have used the The Centre for Systemic Peace Database as against the Freedom House because The Centre for Systemic Peace Database provides more consistent and comprehensive data.

  15. 15.

    We have also used the alternative POLITY data provided in The Centre for Systemic Peace Database and the results are not material different from those generate in this study. The results are available upon request.

  16. 16.

    The classification is based on the World Bank Country and Lending Groups, 2015, which can be obtained at https://datahelpdesk.worldbank.org/knowledgebase/articles/906519. The developed countries are Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Greece, Iceland, Ireland, Italy, Japan, Korea, Rep., Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and United States. The developing countries are Albania, Argentina, Bangladesh, Benin, Bolivia, Brazil, Bulgaria, Cameroon, Chile, China, Colombia, Congo, Rep., Costa Rica, Cote d’Ivoire, Cuba, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Gabon, Ghana, Guatemala, Honduras, India, Indonesia, Iran, Islamic Rep., Iraq, Jamaica, Kenya, Malaysia, Mauritius, Mexico, Morocco, Mozambique, Myanmar, Nepal, Nicaragua, Nigeria, Pakistan, Panama, Paraguay, Peru, Philippines, Saudi Arabia, Senegal, South Africa, Sri Lanka, Sudan, Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, Uruguay, Venezuela, RB, Zambia and Zimbabwe.

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Correspondence to Sakiru Adebola Solarin.

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Solarin, S.A., Al-Mulali, U., Gan, G.G.G. et al. The impact of biomass energy consumption on pollution: evidence from 80 developed and developing countries. Environ Sci Pollut Res 25, 22641–22657 (2018). https://doi.org/10.1007/s11356-018-2392-5

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Keywords

  • Biomass energy consumption
  • CO2 emissions
  • Generalised method of moments (GMM)