The impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution: evidence from Ghana

Abstract

In this study, the impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution from 1971 to 2011 is investigated using the statistically inspired modification of partial least squares (SIMPLS) regression model. There was evidence of a linear relationship between energy, agriculture, macroeconomic and human-induced indicators and carbon dioxide emissions. Evidence from the SIMPLS regression shows that a 1% increase in crop production index will reduce carbon dioxide emissions by 0.71%. Economic growth increased by 1% will reduce carbon dioxide emissions by 0.46%, which means that an increase in Ghana’s economic growth may lead to a reduction in environmental pollution. The increase in electricity production from hydroelectric sources by 1% will reduce carbon dioxide emissions by 0.30%; thus, increasing renewable energy sources in Ghana’s energy portfolio will help mitigate carbon dioxide emissions. Increasing enteric emissions by 1% will increase carbon dioxide emissions by 4.22%, and a 1% increase in the nitrogen content of manure management will increase carbon dioxide emissions by 6.69%. The SIMPLS regression forecasting exhibited a 5% MAPE from the prediction of carbon dioxide emissions.

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References

  1. Acaravci A, Ozturk I (2010) On the relationship between energy consumption, CO 2 emissions and economic growth in Europe. Energy 35:5412–5420

    Article  Google Scholar 

  2. Akon-Yamga G, Boadu P, Obiri BD, Amoako J, Mboob F (2011) Agricultural innovations for climate change adaptation and food security in Africa: the cases of Ghana and the Gambia. African Technology Policy Studies Network, Working paper series

  3. Apergis N, Payne JE (2011) The renewable energy consumption–growth nexus in Central America. Appl Energy 88:343–347. doi:10.1016/j.apenergy.2010.07.013

    Article  Google Scholar 

  4. Asumadu-Sarkodie S, Owusu P (2016a) A review of Ghana’s energy sector national energy statistics and policy framework. Cogent Eng 3:1155274. doi:10.1080/23311916.2016.1155274

    Google Scholar 

  5. Asumadu-Sarkodie S, Owusu PA (2016b) Carbon dioxide emissions, GDP, energy use and population growth: a multivariate and causality analysis for Ghana, 1971-2013. Environ Sci Pollut Res Int 23:13508–13520. doi:10.1007/s11356-016-6511-x

    CAS  Article  Google Scholar 

  6. Asumadu-Sarkodie S, Owusu PA (2016c) The causal nexus between carbon dioxide emissions and agricultural ecosystem—an econometric approach Environ Sci and Pollut Res International doi:10.1007/s11356-016-7908-2

  7. Asumadu-Sarkodie S, Owusu PA (2016d) Energy use, carbon dioxide emissions, GDP, industrialization, financial development, and population, a causal nexus in Sri Lanka: with a subsequent prediction of energy use using neural network. Energy Sources Part B Econ Plan Policy 11:889–899. doi:10.1080/15567249.2016.1217285

    Article  Google Scholar 

  8. Asumadu-Sarkodie S, Owusu PA (2016e) A multivariate analysis of carbon dioxide emissions, electricity consumption, economic growth, financial development, industrialization and urbanization in Senegal. Energy Sources Part B Econ Plan Policy. doi:10.1080/15567249.2016.1227886

    Google Scholar 

  9. Asumadu-Sarkodie S, Owusu PA (2016f) Multivariate co-integration analysis of the Kaya factors in Ghana. Environ Sci Pollut Res Int 23:9934–9943. doi:10.1007/s11356-016-6245-9

    CAS  Article  Google Scholar 

  10. Asumadu-Sarkodie S, Owusu PA (2016g) Recent evidence of the relationship between carbon dioxide emissions, energy use, GDP and population in Ghana: a linear regression approach. Energy Sources Part B Econ Plan Policy. doi:10.1080/15567249.2016.1208304

    Google Scholar 

  11. Asumadu-Sarkodie S, Owusu PA (2016h) The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model. Environ Sci Pollut Res Int 23:10968–10982. doi:10.1007/s11356-016-6252-x

    CAS  Article  Google Scholar 

  12. Ben Abdallah K, Belloumi M, De Wolf D (2013) Indicators for sustainable energy development: a multivariate cointegration and causality analysis from Tunisian road transport sector. Renew Sust Energ Rev 25:34–43. doi:10.1016/j.rser.2013.03.066

    Article  Google Scholar 

  13. Boulesteix A-L, Strimmer K (2007) Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief Bioinform 8:32–44

    CAS  Article  Google Scholar 

  14. Cawley GC, Talbot NL (2004) Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Netw 17:1467–1475

    Article  Google Scholar 

  15. Ceglar A, Toreti A, Lecerf R, Van der Velde M, Dentener F (2016) Impact of meteorological drivers on regional inter-annual crop yield variability in France. Agric For Meteorol 216:58–67

    Article  Google Scholar 

  16. Cerdeira Bento JP, 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. doi:10.1016/j.rser.2015.10.151

    Article  Google Scholar 

  17. De Jong S (1993) SIMPLS: an alternative approach to partial least squares regression. Chemom Intell Lab Syst 18:251–263

    CAS  Article  Google Scholar 

  18. Engle RF, Granger CW (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica 55:251–276

    Article  Google Scholar 

  19. EPA (2016) Methane emissions. https://www3.epa.gov/climatechange/ghgemissions/gases/ch4.html. Accessed July 7th, 2016

  20. Eriksson L, Johansson E, Kettaneh-Wold N, Wold S (2001) Multi-and megavariate data analysis: principles and applications. Umetrics

  21. FAO (2015) FAO statistical yearbooks—world food and agriculture. http://faostat3.fao. org/home/E. Accessed 24 Oct 2015

  22. FAO (2016) Introduction & status of the forestry sector in Ghana. http://www.fao.org/docrep/003/ab567e/AB567E02.htm. Accessed 16 Jul 2016

  23. Gosselin R, Rodrigue D, Duchesne C (2010) A bootstrap-VIP approach for selecting wavelength intervals in spectral imaging applications. Chemom Intell Lab Syst 100:12–21

    CAS  Article  Google Scholar 

  24. Graham MH (2003) Confronting multicollinearity in ecological multiple regression. Ecology 84:2809–2815

    Article  Google Scholar 

  25. Griffith DA, Harvey MG (2001) A resource perspective of global dynamic capabilities. J Int Bus Stud 32:597–606

    Article  Google Scholar 

  26. Gul S, Zou X, Hassan CH, Azam M, Zaman K (2015) Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach. Environ Sci Pollut Res 24:1–13

    Google Scholar 

  27. Huang B-N, Hwang MJ, Yang CW (2008) Causal relationship between energy consumption and GDP growth revisited: a dynamic panel data approach. Ecol Econ 67:41–54. doi:10.1016/j.ecolecon.2007.11.006

    Article  Google Scholar 

  28. IPCC (2015) Working Group III: mitigation. http://www.ipcc.ch/ipccreports/tar/wg3/index.php?idp=90. Accessed 25 September, 2015

  29. Jammazi R, Aloui C (2015) On the interplay between energy consumption, economic growth and CO2 emission nexus in the GCC countries: a comparative analysis through wavelet approaches. Renew Sust Energ Rev 51:1737–1751. doi:10.1016/j.rser.2015.07.073

    CAS  Article  Google Scholar 

  30. Johansen S (1995) Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, Oxford

  31. Lozano S, Gutiérrez E (2008) Non-parametric frontier approach to modelling the relationships among population, GDP, energy consumption and CO2 emissions. Ecol Econ 66:687–699. doi:10.1016/j.ecolecon.2007.11.003

    Article  Google Scholar 

  32. Mansfield ER, Helms BP (1982) Detecting multicollinearity. Am Stat 36:158–160

    Google Scholar 

  33. Mehmood T, Liland KH, Snipen L, Sæbø S (2012) A review of variable selection methods in partial least squares regression. Chemom Intell Lab Syst 118:62–69

    CAS  Article  Google Scholar 

  34. Mohiuddin O, Asumadu-Sarkodie S, Obaidullah M (2016) The relationship between carbon dioxide emissions, energy consumption, and GDP: a recent evidence from Pakistan. Cogent Eng 3:1210491. doi:10.1080/23311916.2016.1210491

    Google Scholar 

  35. ND-GAIN (2014) Ghana. http://index.gain.org/country/ghana. Accessed 16 Jul 2016

  36. O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690

    Article  Google Scholar 

  37. Owusu P, Asumadu-Sarkodie S (2016a) A review of renewable energy sources, sustainability issues and climate change. Mitigation Cogent Eng 3:1167990. doi:10.1080/23311916.2016.1167990

    Google Scholar 

  38. Owusu PA, Asumadu-Sarkodie S (2016b) Is there a causal effect between agricultural production and carbon dioxide emissions in Ghana? Environ Eng Res. doi:10.4491/eer.2016.092

    Google Scholar 

  39. 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 87:1938–1943

    Article  Google Scholar 

  40. Ozturk I, Acaravci A (2011) Electricity consumption and real GDP causality nexus: evidence from ARDL bounds testing approach for 11 MENA countries. Appl Energy 88:2885–2892

    Article  Google Scholar 

  41. Qureshi MI, Rasli AM, Zaman K (2016) Energy crisis, greenhouse gas emissions and sectoral growth reforms: repairing the fabricated mosaic. J Clean Prod 112:3657–3666. doi:10.1016/j.jclepro.2015.08.017

    Article  Google Scholar 

  42. Rafindadi AA, Ozturk I (2015) Natural gas consumption and economic growth nexus: is the 10th Malaysian plan attainable within the limits of its resource? Renew Sust Energ Rev 49:1221–1232. doi:10.1016/j.rser.2015.05.007

    Article  Google Scholar 

  43. Remuzgo L, Sarabia JM (2015) International inequality in CO2 emissions: a new factorial decomposition based on Kaya factors. Environ Sci Pol 54:15–24. doi:10.1016/j.envsci.2015.05.020

    CAS  Article  Google Scholar 

  44. 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 Sust Energ Rev 51:317–326. doi:10.1016/j.rser.2015.06.005

    CAS  Article  Google Scholar 

  45. Seker F, Ertugrul HM, Cetin M (2015) The impact of foreign direct investment on environmental quality: a bounds testing and causality analysis for Turkey. Renew Sust Energ Rev 52:347–356. doi:10.1016/j.rser.2015.07.118

    Article  Google Scholar 

  46. Shahbaz M, Lean HH, Shabbir MS (2012) Environmental Kuznets curve hypothesis in Pakistan: cointegration and granger causality. Renew Sust Energ Rev 16:2947–2953. doi:10.1016/j.rser.2012.02.015

    Article  Google Scholar 

  47. Shahbaz M, Khraief N, Jemaa MMB (2015) On the causal nexus of road transport CO2 emissions and macroeconomic variables in Tunisia: evidence from combined cointegration tests. Renew Sust Energ Rev 51:89–100. doi:10.1016/j.rser.2015.06.014

    CAS  Article  Google Scholar 

  48. Soytas U, Sari R (2009) Energy consumption, economic growth, and carbon emissions: challenges faced by an EU candidate member. Ecol Econ 68:1667–1675. doi:10.1016/j.ecolecon.2007.06.014

    Article  Google Scholar 

  49. Tiwari AK, Shahbaz M, Adnan Hye QM (2013) The environmental Kuznets curve and the role of coal consumption in India: cointegration and causality analysis in an open economy. Renew Sust Energ Rev 18:519–527. doi:10.1016/j.rser.2012.10.031

    Article  Google Scholar 

  50. Wise BM (2004) Properties of partial least squares (PLS) regression, and differences between algorithms. Technical Report

  51. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130

    CAS  Article  Google Scholar 

  52. World Bank (2014) World development indicators. http://data.worldbank.org/country. Accessed 24 Oct 2015

  53. Xu C, Yue D, Deng C (2012) Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis. Eng Appl Artif Intell 25:468–475

    Article  Google Scholar 

  54. Zhang X-P, Cheng X-M (2009) Energy consumption, carbon emissions, and economic growth in China. Ecol Econ 68:2706–2712. doi:10.1016/j.ecolecon.2009.05.011

    Article  Google Scholar 

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Correspondence to Samuel Asumadu-Sarkodie.

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Asumadu-Sarkodie, S., Owusu, P.A. The impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution: evidence from Ghana. Environ Sci Pollut Res 24, 6622–6633 (2017). https://doi.org/10.1007/s11356-016-8321-6

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Keywords

  • SIMPLS
  • Energy economics
  • Econometrics
  • Carbon dioxide emissions
  • Ghana