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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
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.
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.
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.
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.
Panel data methods do not usually provide for individual country coefficients
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.
This is due to the fact that FDIit and KYOTOt contain negative figures.
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.
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)
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
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.
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.
We resort to the use of hydroelectricity generation because hydroelectricity consumption is not available for most of these countries.
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.
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.
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.
Adewuyi AO, Awodumi OB (2017) Biomass energy consumption, economic growth and carbon emissions: fresh evidence from West Africa using a simultaneous equation model. Energy 119:453–471
Al-Mulali U, Ozturk I, Lean HH (2015) The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on pollution in Europe. Nat Hazards 79(1):621–644
Al-Mulali U, Solarin SA, Ozturk I (2016a) Biofuel energy consumption-economic growth relationship: an empirical investigation of Brazil. Biofuels Bioprod Biorefin 10(6):753–775
Al-Mulali U, Ozturk I, Solarin SA (2016b) Investigating the environmental Kuznets curve hypothesis in seven regions: the role of renewable energy. Ecol Indic 67:267–282
Al-Mulali U, Solarin SA, Sheau-Ting L, Ozturk I (2016c) Does moving towards renewable energy causes water and land inefficiency? An empirical investigation. Energy Policy 93:303–314
Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297
Arellano M, Bover O (1995) Another look at the instrumental-variable estimation of error components models. J Econ 68:29–52
Bento JPC, Moutinho V (2016) CO2 emissions, non-renewable and renewable electricity production, economic growth, and international trade in Italy. Renew Sust Energ Rev 55:142–155
Bildirici ME (2017) The effects of militarization on biofuel consumption and CO2 emission. J Clean Prod 152:420–428
Bilgili F, Koçak E, Bulut Ü (2016) The dynamic impact of renewable energy consumption on CO2 emissions: a revisited Environmental Kuznets Curve approach. Renew Sust Energ Rev 54:838–845
Bloomberg (2016) China to slow green growth for first time after record boom. Bloomberg, New York
Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econ 87:115–143
British Petroleum (2016) BP Statistical Review of World Energy 2016. Available at http://www.bp.com/content/dam/bp/pdf/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-full-report.pdf
Bryce P (2016) Corn Ethanol Is Now a Climate-Change Scandal. Energy & Environment: Climate, Regulations. Available at https://www.manhattaninstitute.org/html/cornethanolnowclimatechangescandal9244.html
Chudik A, Pesaran MH (2015a) Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. J Econ 188(2):393–420
Chudik A, Pesaran MH (2015b) Large panel data models with cross-sectional dependence: a survey. In The Oxford handbook of panel data. Baltagi BH (eds), chap. 1, pp 2–45. Oxford University Press
Chum HL, Warner E, Seabra JEA, Macedo IC (2014) A comparison of commercial ethanol production systems from Brazilian sugarcane and US corn. Biofuels Bioprod Biorefin 8(2):205–223
DeCicco JM, Liu DY, Heo J, Krishnan R, Kurthen A, Wang L (2016) Carbon balance effects of U.S. biofuel production and use. Clim Chang 138(3):667–680
Dietz T, Rosa EA (1997) Effects of population and affluence on CO2 emissions. Proc Natl Acad Sci 94(1):175–179
Ditzen J (2016) xtdcce: Estimating dynamic common correlated effects in Stata. The Spatial Economics and Econometrics Centre (SEEC). Available at http://fmwww.bc.edu/RePEc/usug2016/ditzen_uksug16.pdf
Dogan E, Ozturk I (2017) The influence of renewable and non-renewable energy consumption and real income on CO2 emissions in the USA: evidence from structural break tests. Environ Sci Pollut Res 24(11):10846–10854
Dreher A (2006) Does globalization affect growth? Evidence from a new index of globalization. Appl Econ 38(10):1091–1110
Energy Information Administration (2016) Energy information administration analysis. Retrieved from https://www.eia.gov/beta/international/analysis.cfm
Flórez-Orrego D, da Silva JAM, Velásquez H, de Oliveira S (2015) Renewable and non-renewable exergy costs and CO2 emissions in the production of fuels for Brazilian transportation sector. Energy 88:18–36
Goel RK, Herrala R, Mazhar U (2013) Institutional quality and environmental pollution: MENA countries versus the rest of the world. Econ Syst 37(4):508–521
Hill J, Tajibaeva L, Polasky S (2016) Climate consequences of low-carbon fuels: the United States Renewable Fuel Standard. Energy Policy 97:351–353
Hondroyiannis G (2006) Private saving determinants in European countries: a panel cointegration approach. Soc Sci J 43(4):553–569
International Renewable Energy Agency (2012) Biomass for power generation. International Renewable Energy Agency Working Paper Series, 1, (5/5), pp 1–50
IPCC (2014) Climate Change 2014: Mitigation of Climate Change - Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, Stechow CV, Zwickel T, Minx JC (Series edn). Cambridge University Press, New York
Iwata H, Okada K (2012) Greenhouse gas emissions and the role of the Kyoto protocol. Environ Econ Policy Stud 16(4):325–342
Jaforullah M, King A (2015) Does the use of renewable energy sources mitigate CO2 emissions? A reassessment of the US evidence. Energy Econ 49:711–717
Jebli MB, Belloumi M (2017) Investigation of the causal relationships between combustible renewables and waste consumption and CO2 emissions in the case of Tunisian maritime and rail transport. Renew Sust Energ Rev 71:820–829
Jebli MB, Youssef SB (2017) The role of renewable energy and agriculture in reducing CO2 emissions: evidence for North Africa countries. Ecol Indic 74:295–301
Jebli MB, Youssef SB, Ozturk I (2015) The role of renewable energy consumption and trade: environmental Kuznets curve analysis for sub-Saharan Africa countries. Afr Dev Rev 27(3):288–300
Jebli MB, Youssef SB, Ozturk I (2016) Testing environmental Kuznets curve hypothesis: the role of renewable and non-renewable energy consumption and trade in OECD countries. Ecol Indic 60:824–831
Johansen S (2002) A small sample correction for the test of cointegrating rank in the vector autoregressive model. Econometrica 70(5):1929–1961
Kao C, Chiang MH (2001) On the estimation and inference of a cointegrated regression in panel data. In Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, Bingley, pp 179–222
Ouyang X, Lin B (2017) Carbon dioxide (CO2) emissions during urbanization: a comparative study between China and Japan. J Clean Prod 143:356–368
Özbuğday FC, Erbas BC (2015) How effective are energy efficiency and renewable energy in curbing CO2 emissions in the long run? A heterogeneous panel data analysis. Energy 82:734–745
Pedroni P (2000) Fully modified OLS for heterogeneous cointegrated panels. Adv Econ 15:93–130
Pesaran MH (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74(4):967–1012
Pesaran MH, Smith R (1995) Estimating long-run relationships from dynamic heterogeneous panels. J Econ 68(1):79–113
REN21 (2005) Renewables 2005 Global status report. Retrieved from Paris: http://www.ren21.net/Portals/0/documents/activities/gsr/RE2005_Global_Status_Report.pdf
REN21 (2017) Renewables 2017 Global Status Report. Retrieved from Paris. http://www.ren21.net/wp-content/uploads/2017/06/GSR2017_Full-Report.pdf
Rosenzweig C, Casassa G, Karoly DJ, Imeson A, Liu C, Menzel A, Rawlins S, Root TL, Seguin B, Tryjanowski P (2007) Assessment of observed changes and responses in natural and managed systems. In: Parry ML, Canziani OF, Palutikof JP, Linden PJVD, Hanson CE (eds) Climate Change 2007: Impacts, Adaptation and Vulnerability - Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 79–131
Shahbaz M, Solarin SA, Sbia R, Bibi S (2015a) Does energy intensity contribute to CO2 emissions? A trivariate analysis in selected African countries. Ecol Indic 50:215–224
Shahbaz M, Nasreen S, Abbas F, Anis O (2015b) Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries? Energy Econ 51:275–287
Shahbaz M, Nasreen S, Ahmed K, Hammoudeh S (2017) Trade openness–carbon emissions nexus: the importance of turning points of trade openness for country panels. Energy Econ 61:221–232
Solarin S (2015) Assessing the effectiveness of the policies to boost hydropower consumption. Int J Energy Sect Manag 9(2):136–155
Solarin SA (2017) The role of urbanisation in the economic development process: evidence from Nigeria. J Appl Econ Res 11(3):223–255
Solarin SA, Al-Mulali U, Musah I, Ozturk I (2017) Investigating the pollution haven hypothesis in Ghana: an empirical investigation. Energy 124:706–719
The Guardian (2012) Has the Kyoto protocol made any difference to carbon emissions? Retrieved from https://www.theguardian.com/environment/blog/2012/nov/26/kyoto-protocol-carbon-emissions
Tiba S, Omri A (2017) Literature survey on the relationships between energy, environment and economic growth. Renew Sust Energ Rev 69:1129–1146
Timilsina GR, Shrestha A (2011) How much hope should we have for biofuels? Energy 36(4):2055–2069
UNFCCC (2017a) Kyoto Protocol. Retrieved from http://unfccc.int/kyoto_protocol/items/2830.php
UNFCCC (2017b) Status of the Doha Amendment. Retrieved from http://unfccc.int/kyoto_protocol/doha_amendment/items/7362.php
United States Environmental Protection Agency (2017) Greenhouse Gas Emissions. Retrieved from https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data
Windmeijer F (2005) A finite sample correction for the variance of linear efficient two-step GMM estimators. J Econ 126(1):25–51
World Bank (2017) Hydropower Overview. Retrieved from http://www.worldbank.org/en/topic/hydropower/overview
Xu B, Lin B (2017) Does the high–tech industry consistently reduce CO2 emissions? Results from nonparametric additive regression model. Environ Impact Assess Rev 63:44–58
York R, Rosa EA, Dietz T (2003) STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ 46(3):351–365
Zhu Q, Peng X (2012) The impacts of population change on carbon emissions in China during 1978–2008. Environ Impact Assess Rev 36:1–8
Zoundi Z (2017) CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach. Renew Sust Energ Rev 72:1067–1075
Responsible editor: Philippe Garrigues
About this article
Cite this article
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
- Biomass energy consumption
- CO2 emissions
- Generalised method of moments (GMM)