Environmental Science and Pollution Research

, Volume 23, Issue 13, pp 13508–13520 | Cite as

Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971–2013

Research Article

Abstract

In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen’s multivariate co-integration and performed a variance decomposition analysis using Cholesky’s technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana’s energy mix can help mitigate climate change and its impact in the future.

Keywords

Variance decomposition Carbon dioxide emissions Ghana Multivariate co-integration ARDL bound test Econometrics 

Abbreviations

Chi2

Chi square

Parms

Parameter

df

Difference

Prob

Probability

_ce

Co-integrated equation

Coef.

Coefficient

_cons

Constant

Std. Err.

Standard error

L1._ce

Error correction term

Acronyms

VECM

Vector error correction model

ECT

Error correction term

SR

Short run

LRE

Long-run elasticities

LL

Log likelihood

LR

Sequential likelihood ratio

AIC

Akaike information criterion

KPSS

Kwiatkowski-Phillips-Schmidt-Shin

SC

Schwarz information criterion

HQ

Hannan-Quinn information criteria

VIF

Variance inflation factor

LCUs

Local currency units

P

Population

GT

Grubbs’ test

GMM

Generalized Method of Moments

FMOLS

Fully-Modified Ordinary Least Squares

DOLS

Dynamic Ordinary Least Squares

JEL classification

Q43 C33 O13 Q43 

References

  1. Acaravci A, Ozturk I (2010) On the relationship between energy consumption, CO2 emissions and economic growth in Europe. Energy 35:5412–5420CrossRefGoogle Scholar
  2. Adom PK, Bekoe W (2012) Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: a comparison of ARDL and PAM. Energy 44:367–380CrossRefGoogle Scholar
  3. Apergis N, Ozturk I (2015) Testing environmental Kuznets curve hypothesis in Asian countries. Ecological Indicators 52:16–22. doi:10.1016/j.ecolind.2014.11.026 CrossRefGoogle Scholar
  4. Apergis N, Payne JE (2011) The renewable energy consumption–growth nexus in Central America. Appl Energy 88:343–347Google Scholar
  5. Asafu-Adjaye J (2000) The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries. Energy Economics 22:615–625. doi:10.1016/S0140-9883(00)00050-5 CrossRefGoogle Scholar
  6. Asumadu-Sarkodie S, Owusu P (2016a) A review of Ghana’s energy sector national energy statistics and policy framework. Cogent Engineering. doi:10.1080/23311916.2016.1155274 Google Scholar
  7. Asumadu-Sarkodie S, Owusu PA (2015) Media impact on students’ body image. International Journal for Research in Applied Science and Engineering Technology 3:460–469Google Scholar
  8. Asumadu-Sarkodie S, Owusu PA (2016b) Feasibility of biomass heating system in Middle East Technical University, Northern Cyprus Campus. Cogent Engineering 3:1134304. doi:10.1080/23311916.2015.1134304 CrossRefGoogle Scholar
  9. Asumadu-Sarkodie S, Owusu PA (2016c) Multivariate co-integration analysis of the Kaya factors in Ghana. Environmental Science and Pollution Research. doi:10.1007/s11356-016-6245-9 Google Scholar
  10. Asumadu-Sarkodie S, Owusu PA (2016d) The potential and economic viability of solar photovoltaic in Ghana. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2015.1122682 Google Scholar
  11. Asumadu-Sarkodie S, Owusu PA (2016e) The potential and economic viability of wind farms in Ghana. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2015.1122680 Google Scholar
  12. Asumadu-Sarkodie S, Owusu PA (2016f) The relationship between carbon dioxide and agriculture in Ghana, a comparison of VECM and ARDL model. Environmental Science and Pollution Research. doi:10.1007/s11356-016-6252-x Google Scholar
  13. Asumadu-Sarkodie S, Owusu PA, Jayaweera HM (2015a) Flood risk management in Ghana: a case study in Accra. Advances in Applied Science Research 6:196–201Google Scholar
  14. Asumadu-Sarkodie S, Owusu PA, Rufangura P (2015b) Impact analysis of flood in Accra, Ghana. Advances in Applied Science Research 6:53–78Google Scholar
  15. Azhar Khan M, Zahir Khan M, Zaman K, Naz L (2014) Global estimates of energy consumption and greenhouse gas emissions. Renewable and Sustainable Energy Reviews 29:336–344. doi:10.1016/j.rser.2013.08.091 CrossRefGoogle Scholar
  16. Baek J (2015) A panel cointegration analysis of CO2 emissions, nuclear energy and income in major nuclear generating countries. Applied Energy 145:133–138. doi:10.1016/j.apenergy.2015.01.074 CrossRefGoogle Scholar
  17. Caraiani C, Lungu CI, Dascălu C (2015) Energy consumption and GDP causality: a three-step analysis for emerging European countries. Renewable and Sustainable Energy Reviews 44:198–210. doi:10.1016/j.rser.2014.12.017 CrossRefGoogle Scholar
  18. Cerdeira Bento JP, Moutinho V (2016) CO2 emissions, non-renewable and renewable electricity production, economic growth, and international trade in Italy. Renew Sustain Energy Rev 55:142–155Google Scholar
  19. Chang C-C (2010) A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China. Applied Energy 87:3533–3537CrossRefGoogle Scholar
  20. Chen S-T, Kuo H-I, Chen C-C (2007) The relationship between GDP and electricity consumption in 10 Asian countries. Energ Policy 35:2611–2621CrossRefGoogle Scholar
  21. Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association 74:427–431Google Scholar
  22. Earth System Research Laboratory (2015) The NOAA Annual Greenhouse Gas Index (AGGI)., http://www.esrl.noaa.gov/gmd/aggi/aggi.html. Accessed October 24, 2015Google Scholar
  23. Edenhofer O et al (2011) Renewable energy sources and climate change mitigation: special report of the intergovernmental panel on climate change. Cambridge University PressCrossRefGoogle Scholar
  24. Fuinhas JA, Marques AC (2012) Energy consumption and economic growth nexus in Portugal, Italy, Greece, Spain and Turkey: an ARDL bounds test approach (1965–2009). Energy Economics 34:511–517. doi:10.1016/j.eneco.2011.10.003 CrossRefGoogle Scholar
  25. Granger CW (1988) Some recent development in a concept of causality. Journal of econometrics 39:199–211CrossRefGoogle Scholar
  26. Hatzigeorgiou E, Polatidis H, Haralambopoulos D (2008) CO2 emissions in Greece for 1990–2002: a decomposition analysis and comparison of results using the arithmetic mean Divisia index and logarithmic mean Divisia index techniques. Energy 33:492–499CrossRefGoogle Scholar
  27. Hatzigeorgiou E, Polatidis H, Haralambopoulos D (2011) CO2 emissions, GDP and energy intensity: a multivariate cointegration and causality analysis for Greece, 1977–2007. Applied Energy 88:1377–1385. doi:10.1016/j.apenergy.2010.10.008 CrossRefGoogle Scholar
  28. Herrerias MJ, Joyeux R, Girardin E (2013) Short- and long-run causality between energy consumption and economic growth: evidence across regions in China. Applied Energy 112:1483–1492. doi:10.1016/j.apenergy.2013.04.054 CrossRefGoogle Scholar
  29. Johansen S (1995) Likelihood-based inference in cointegrated vector autoregressive models OUP CatalogueCrossRefGoogle Scholar
  30. Lin B, Omoju OE, Okonkwo JU (2015) Impact of industrialisation on CO2 emissions in Nigeria. Renewable and Sustainable Energy Reviews 52:1228–1239. doi:10.1016/j.rser.2015.07.164 CrossRefGoogle Scholar
  31. Mahadeva L, Robinson P (2004) Unit root testing to help model buildingGoogle Scholar
  32. Ohler A, Fetters I (2014) The causal relationship between renewable electricity generation and GDP growth: a study of energy sources. Energy Economics 43:125–139. doi:10.1016/j.eneco.2014.02.009 CrossRefGoogle Scholar
  33. Osabuohien ES, Efobi UR, Gitau CMW (2014) Beyond the environmental Kuznets curve in Africa: evidence from panel cointegration. Journal of Environmental Policy & Planning 16:517–538. doi:10.1080/1523908x.2013.867802 CrossRefGoogle Scholar
  34. Owusu P, Asumadu-Sarkodie S (2016) A Review of Renewable Energy Sources, Sustainability Issues and Climate Change Mitigation. Cogent Engineering. doi:10.1080/23311916.2016.1167990
  35. Owusu PA, Asumadu-Sarkodie S, Ameyo P (2016) A review of Ghana’s water resource management and the future prospect. Cogent Engineering. doi:10.1080/23311916.2016.1164275 Google Scholar
  36. Ozturk I, Acaravci A (2010) The causal relationship between energy consumption and GDP in Albania, Bulgaria, Hungary and Romania: Evidence from ARDL bound testing approach. Appl Energy 87:1938–1943Google Scholar
  37. Ozturk I, Acaravci A (2011) Electricity consumption and real GDP causality nexus: evidence from ARDL bounds testing approach for 11 MENA countries. Applied Energy 88:2885–2892. doi:10.1016/j.apenergy.2011.01.065 CrossRefGoogle Scholar
  38. Pan Y, Jackson R (2008) Ethnic difference in the relationship between acute inflammation and serum ferritin in US adult males. Epidemiology and infection 136:421–431CrossRefGoogle Scholar
  39. Pesaran MH, Shin Y (1998) An autoregressive distributed-lag modelling approach to cointegration analysis. Econometric Society Monographs 31:371–413Google Scholar
  40. Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics 16:289–326CrossRefGoogle Scholar
  41. Salahuddin M, Gow J, Ozturk I (2015) Is the long-run relationship between economic growth, electricity consumption, carbon dioxide emissions and financial development in Gulf Cooperation Council Countries robust? Renew Sustain Energy Rev 51:317–326Google Scholar
  42. Sadorsky P (2011) Trade and energy consumption in the Middle East. Energy Economics 33:739–749. doi:10.1016/j.eneco.2010.12.012 CrossRefGoogle Scholar
  43. Seker F, Ertugrul HM, Cetin M (2015) The impact of foreign direct investment on environmental quality: a bounds testing and causality analysis for Turkey. Renewable and Sustainable Energy Reviews 52:347–356. doi:10.1016/j.rser.2015.07.118 CrossRefGoogle Scholar
  44. United Nations (2015) Sustainable development goals., https://sustainabledevelopment.un.org/?menu=1300. Accessed October 24, 2015Google Scholar
  45. UNTC (2015) United Nations Treaty Collection., https://treaties.un.org/. Accessed November14, 2015Google Scholar
  46. World Bank (2015) Ghana|data., http://data.worldbank.org/country/ghana. Accessed November 12, 2015Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Sustainable Environment and Energy SystemsMiddle East Technical UniversityGuzelyurtTurkey

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