Abstract
This paper investigates the nexus between carbon emissions (CO2) and economic growth in West Africa based on the Environment Kuznets Curve (EKC) hypothesis by utilizing spatial panel data technique to check the possible effect of spatial dependence among countries in West Africa. Our empirical findings suggest the presence of spatial dependence of carbon emissions distribution in West Africa. By examining the existence of EKC embedded within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) approach, we conclude an inverse N-trajectory of the relationship between carbon emissions and economic growth. Furthermore, to mitigate global carbon emissions, we utilize a recurrent neural network (RNN) bidirectional long short-term memory (BiLSTM) algorithm devoid of exogenous variables and assumptions to forecast carbon emissions from the year 2015 to the year 2030 based on the predictive accuracy of our formulated algorithm. Due to the upward trends in future emission levels, we propose emissions mitigation pathways for countries in West Africa to still hold carbon emissions-related global warming well below 1.5 and 2 °C. Such mitigation pathways proposed could help implement strategic policies to minimize carbon emissions to a considerable level. As a policy implication, drafting strict environmental regulations and utilizing renewable energy technologies will help mitigate carbon emissions for all West African countries.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdullahi AO, Safiyanu SS, Soja T (2016) International trade and economic growth : an empirical analysis of West Africa. J Econ Financ 7:12–15. https://doi.org/10.9790/5933-07211215
Aboagye S (2017) Economic expansion and environmental sustainability nexus in Ghana. Afr Dev Rev 29:155–168. https://doi.org/10.1111/1467-8268.12247
Acheampong AO (2018) Economic growth , CO2 emissions and energy consumption : what causes what and where ? Energy Econ 74:677–692. https://doi.org/10.1016/j.eneco.2018.07.022
Adu DT, Denkyirah EK (2018) Economic growth and environmental pollution in West Africa: testing the environmental Kuznets curve hypothesis. Kasetsart J Soc Sci:8–15. https://doi.org/10.1016/j.kjss.2017.12.008
AfDB (2015) Regional Integration in the Context of Climate Change
Aguado I, Echebarria C, Barrutia J (2011) The impact of globalization on CO2 emissions in China. Munich Pers RePEc Arch:6–25
Aguir Bargaoui S, Liouane N, Nouri FZ (2014) Environmental impact determinants: an empirical analysis based on the STIRPAT model. Procedia Soc Behav Sci 109:449–458. https://doi.org/10.1016/j.sbspro.2013.12.489
Al-Ahmadi K, Al-Zahrani A (2013) Spatial autocorrelation of cancer incidence in Saudi Arabia. Int J Environ Res Public Health 10:7207–7228. https://doi.org/10.3390/ijerph10127207
Alege PO, Ogundipe A (2013) Environmental quality and economic growth in Nigeria: a fractional cointegration analysis. Int J Dev Sustain 2:580–596. https://doi.org/10.1111/j.1540-6520.2006.00149.x
Allard A, Takman J, Uddin GS, Ahmed A (2018) The N-shaped environmental Kuznets curve : an empirical evaluation using a panel quantile regression approach. Environ Sci Pollut Res 25:5848–5861
Alvarez-herranz A, Balsalobre-lorente D, Shahbaz M (2017) Energy innovation and renewable energy consumption in the correction of air pollution levels. Energy Policy 105:386–397. https://doi.org/10.1016/j.enpol.2017.03.009
Ameyaw B, Yao L (2018a) Analyzing the impact of GDP on CO2 emissions and forecasting Africa’s total CO2 emissions with non-assumption driven bidirectional long short-term memory. Sustain 10:1–23. https://doi.org/10.3390/su10093110
Ameyaw B, Yao L (2018b) Sectoral energy demand forecasting under an assumption-free data-driven technique. Sustainability 10:2348. https://doi.org/10.3390/su10072348
Ameyaw B, Yao L, Oppong A, Korang J (2019) Investigating , forecasting and proposing emission mitigation pathways for CO 2 emissions from fossil fuel combustion only : a case study of selected countries. Energy Policy 130:7–21. https://doi.org/10.1016/j.enpol.2019.03.056
Anselin L (2002) Under the hood. Issues in the Specification and Interpretation of Spatial Regression Models
Ara R, Sohag K, Mastura S et al (2015) CO2 emissions , energy consumption , economic and population growth in Malaysia. Renew Sust Energ Rev 41:594–601. https://doi.org/10.1016/j.rser.2014.07.205
Arbulu I, Lozano J, Rey-Maquieira J (2017) Waste generation flows and tourism growth: a STIRPAT model for Mallorca. J Ind Ecol 21:272–281. https://doi.org/10.1111/jiec.12420
Armeanu D, Vintil G, Andrei JV et al (2018) Exploring the link between environmental pollution and economic growth in EU-28 countries : is there an environmental Kuznets curve ? PLoS One 13:1–28
Aye GC, Edoja PE (2017) Effect of economic growth on CO2 emission in developing countries : evidence from a dynamic panel threshold model. Cogent Econ Financ 90:1–22. https://doi.org/10.1080/23322039.2017.1379239
Baiocchi G, Creutzig F, Minx J, Pichler PP (2015) A spatial typology of human settlements and their CO2 emissions in England. Glob Environ Chang 34:13–21. https://doi.org/10.1016/j.gloenvcha.2015.06.001
Balogh JM, Jámbor A (2017) Determinants of CO 2 emission: a global evidence. Int J Energy Econ Policy 7:217–226
Baltagi BH, Fingleton B, Pirotte A (2014) Estimating and forecasting with a dynamic spatial panel data model. Oxf Bull Econ Stat 76:112–138. https://doi.org/10.1111/obes.12011
Beckerman W (1992) Economic growth and the environment: whose growth? Whose environment? World Dev 20:481–496. https://doi.org/10.1016/0305-750X(92)90038-W
Beer C, Reichstein M, Tomelleri E et al (2010) Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science (80- ) 329:834–838. https://doi.org/10.1126/science.1184984
Bildirici ME (2017) The effects of militarization on biofuel consumption and CO2 emission. J Clean Prod 152:420–428. https://doi.org/10.1016/j.jclepro.2017.03.103
Bivand RS, Wong DWS (2018) Comparing implementations of global and local indicators of spatial association. Test 27:716–748. https://doi.org/10.1007/s11749-018-0599-x
Boamah KB, Du J, Bediako IA et al (2017) Carbon dioxide emission and economic growth of China — the role of international trade. Environ Sci Pollut Res 24. https://doi.org/10.1007/s11356-017-8955-z
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? -arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
Chang N (2015) Changing industrial structure to reduce carbon dioxide emissions: a Chinese application. J Clean Prod 103:40–48. https://doi.org/10.1016/j.jclepro.2014.03.003
Chen Y (2013) New approaches for calculating Moran’s index of spatial autocorrelation. PLoS One 8. https://doi.org/10.1371/journal.pone.0068336
Ghana Energy Commision (2015) Renewable energy policy review, Identification of Gaps and Solutions in Ghana
Dietz T, Rosa EA (2002) Effects of population and affluence on CO2 emissions. Proc Natl Acad Sci 94:175–179. https://doi.org/10.1073/pnas.94.1.175
Ebrahimi R, Salehi M (2015) Investigation of CO2 emission reduction and improving energy use efficiency of button mushroom production using data envelopment analysis. J Clean Prod 103:112–119. https://doi.org/10.1016/j.jclepro.2014.02.032
Ehrlich PR, Holdren JP (1971) Impact of population growth. Science. Sci New Ser 3977:1212–1217
Elamir EAH (2012) Mean absolute deviation about median as a tool of explanatory data analysis. IJRRAS 2197:324–329
Fan C, Myint S (2014) A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation. Landsc Urban Plan 121:117–128. https://doi.org/10.1016/j.landurbplan.2013.10.002
FAO (2018) Countries urged to adopt economically viable low-emission development options
Fu WJ, Jiang PK, Zhou GM, Zhao KL (2014) Using Moran’s i and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences 11:2401–2409. https://doi.org/10.5194/bg-11-2401-2014
Fu B, Wu M, Che Y et al (2015) The strategy of a low-carbon economy based on the STIRPAT and SD models. Acta Ecol Sin 35:76–82. https://doi.org/10.1016/j.chnaes.2015.06.008
Gillingham K, Stock JH (2018) The cost of reducing greenhouse gas emissions. J Econ Perspect 32:53–72. https://doi.org/10.1257/jep.32.4.53
Grossman GM, Krueger AB (1995) Economic growth and the environment. Q J Econ 110:353–377
Hamzaçebi C (2007) Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy 35:2009–2016. https://doi.org/10.1016/j.enpol.2006.03.014
Hao Y, Liu YM (2016) The influential factors of urban PM2.5 concentrations in China: a spatial econometric analysis. J Clean Prod 112:1443–1453. https://doi.org/10.1016/j.jclepro.2015.05.005
ICF International (2016) Analysis of intended nationally determined contributions (INDCs). New Clim Insitute
IFC (2018) Unlocking private investment: a roadmap to achieve Côte d’Ivoire’s 42 percent renewable energy target by 2030. Washington: https://doi.org/10.1596/30173
IRENA (2015) Ghana renewables readiness assessment
Kang YQ, Zhao T, Yang YY (2016) Environmental Kuznets curve for CO2 emissions in China: a spatial panel data approach. Ecol Indic 63:231–239. https://doi.org/10.1016/j.ecolind.2015.12.011
Kialashaki A (2014) Evaluation and forecast of energy consumption in different sectors of the United States using artificial neural networks
Kim S, Kim H (2016) A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast 32:669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
Kone AI, Buke T (2010) Forecasting of CO2 emissions from fuel combustion using trend analysis. Renew Sust Energ Rev 14:2906–2915. https://doi.org/10.1016/j.rser.2010.06.006
Landrigan P, Fuller R, Haines A et al (2018) Pollution prevention and climate change mitigation: measuring the health benefits of comprehensive interventions. Lancet Planet Heal 2:e515–e516. https://doi.org/10.1016/S2542-5196(18)30226-2
Lau LS, Choong CK, Eng YK (2014) Investigation of the environmental Kuznets curve for carbon emissions in Malaysia: DO foreign direct investment and trade matter? Energy Policy 68:490–497. https://doi.org/10.1016/j.enpol.2014.01.002
Lee J, Li S (2017) Extending Moran’s index for measuring spatiotemporal clustering of geographic events. Geogr Anal 49:36–57. https://doi.org/10.1111/gean.12106
Li K (2018) Spatial panel data models with structural change. Munich Pers RePEc Arch
Li Y, Xiong W (2019) A spatial panel data analysis of China’s urban land expansion, 2004–2014. Pap Reg Sci 98:393–407. https://doi.org/10.1111/pirs.12340
Liddle B (2013) Urban density and climate change: a STIRPAT analysis using city-level data. J Transp Geogr 28:22–29. https://doi.org/10.1016/j.jtrangeo.2012.10.010
Liddle B (2015) What are the carbon emissions elasticities for income and population? Bridging STIRPAT and EKC via robust heterogeneous panel estimates. Glob Environ Chang 31:62–73. https://doi.org/10.1016/j.gloenvcha.2014.10.016
Liousse C, Assamoi E, Criqui P et al (2014) Explosive growth in African combustion emissions from 2005 to 2030. Environ Res Lett 9. https://doi.org/10.1088/1748-9326/9/3/035003
Liu Y, Xiao H, Zikhali P, Lv Y (2014) Carbon emissions in China: a spatial econometric analysis at the regional level. Sustain 6:6005–6023. https://doi.org/10.3390/su6096005
Liu Y, Manyin ME, Gatti LV et al (2017) A global synthesis inversion analysis of recent variability in CO2 fluxes using GOSAT and in situ observations. Atmos Chem Phys Discuss:1–76. https://doi.org/10.5194/acp-2017-960
Liu Q, Wang S, Zhang W et al (2019) Examining the effects of income inequality on CO2 emissions: evidence from non-spatial and spatial perspectives. Appl Energy 236:163–171. https://doi.org/10.1016/j.apenergy.2018.11.082
Manganelli S (2006) A new theory of forecasting. Soc Sci Res
Martínez-Zarzoso I, Maruotti A (2011) The impact of urbanization on CO2 emissions: evidence from developing countries. Ecol Econ 70:1344–1353. https://doi.org/10.1016/j.ecolecon.2011.02.009
Martínez-Zarzoso I, Bengochea-Morancho A, Morales-Lage R (2007) The impact of population on CO2 emissions: evidence from European countries. Environ Resour Econ 38:497–512. https://doi.org/10.1007/s10640-007-9096-5
Mur J, Angulo A (2006) Le modèle de durbin spatial et les tests de facteur commun. Spat Econ Anal 1:207–226. https://doi.org/10.1080/17421770601009841
Nwodo OS, Ozor JO, Okekpa UE, Agu VC (2017) Environmental degradation and Nigeria’s macroeconomic space. Environ Manag Sustain Dev 7:37. https://doi.org/10.5296/emsd.v7i1.12155
Ogundipe A, Olurinola O, Odebiyi JT (2014) Examining the validity of EKC in Western Africa: different pollutants option. Environ Manag Sustain Dev 4:69–90. https://doi.org/10.2139/ssrn.2512152
Omojolaibi JA (2010) Environmental quality and economic growth in some selected west African countries : a panel data assessment of the environmental Kuznets curve. J Sustain Dev Africa 12:35–48
Oppong A, Acheampong KN, Abruquah LA (2018) Forecasting renewable energy consumption under zero assumptions. https://doi.org/10.3390/su10030576
Ouoba Y (2017) CO2 emissions and economic growth in the west African economic and monetary union ( WAEMU ) countries. Environ Manag Sustain Dev 6:174–197. https://doi.org/10.5296/emsd.v6i2.11145
Panayotou T (1997) Demystifying the environmental Kuznets curve: turning a black box into a policy tool. Environ Dev Econ 2:465–484. https://doi.org/10.1017/S1355770X97000259
Peres-Neto PR, Legendre P (2010) Estimating and controlling for spatial structure in the study of ecological communities. Glob Ecol Biogeogr 19:174–184. https://doi.org/10.1111/j.1466-8238.2009.00506.x
Rafindadi AA (2016) Does the need for economic growth influence energy consumption and CO2 emissions in Nigeria? Evidence from the innovation accounting test. Renew Sust Energ Rev 62:1209–1225. https://doi.org/10.1016/j.rser.2016.05.028
Reiss MK (2015) ECOWAS programs addressing gender and energy in climate change mitigation. ECOWAS Cent Renew Energy Energy Effic
Roberts TD (2011) Applying the STIRPAT model in a post-Fordist landscape: can a traditional econometric model work at the local level? Appl Geogr 31:731–739. https://doi.org/10.1016/j.apgeog.2010.06.010
Joeri Rogelj, Drew Shindell KJ (2018) Mitigation pathways compatible with 1.5°C in the context of sustainable development. 82
Shafiei S, Salim RA (2014) Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: a comparative analysis. Energy Policy 66:547–556. https://doi.org/10.1016/j.enpol.2013.10.064
Shahbaz M, Loganathan N, Muzaffar AT et al (2016) How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model. Renew Sust Energ Rev 57:83–93. https://doi.org/10.1016/j.rser.2015.12.096
Suganthi L, Samuel AA (2012) Energy models for demand forecasting - a review. Renew Sust Energ Rev 16:1223–1240
Takemura T, Suzuki K (2019) Weak global warming mitigation by reducing black carbon emissions. Sci Rep 9:1–6. https://doi.org/10.1038/s41598-019-41181-6
Tiwari C, Mishra M (2017) Testing the CO 2 emissions convergence: evidence from Asian countries. IIM Kozhikode Soc Manag Rev 6:67–72. https://doi.org/10.1177/2277975216674073
UNFCCC (2015) Nigeria’s intended national determined contribution
United Nations (2015) Malaysia submits its climate action plan ahead of 2015 Paris agreement. United Nations Framew Conv Clim Chang
Wang S, Li G, Fang C (2017) Urbanization , economic growth , energy consumption , and CO 2 emissions : empirical evidence from countries with di ff erent income levels. Renew Sust Energ Rev 1–16. https://doi.org/10.1016/j.rser.2017.06.025
Wang WC, Chang YJ, Wang HC (2019) An application of the spatial autocorrelation method on the change of real estate prices in Taitung city. ISPRS Int J Geo-Information 8. https://doi.org/10.3390/ijgi8060249
Yeh JC, Liao CH (2017) Impact of population and economic growth on carbon emissions in Taiwan using an analytic tool STIRPAT. Sustain Environ Res 27:41–48. https://doi.org/10.1016/j.serj.2016.10.001
Zhang Y, McCarl B, Jones J (2017) An overview of mitigation and adaptation needs and strategies for the livestock sector. Climate 5:95. https://doi.org/10.3390/cli5040095
Zhao J, Ji G, Yue Y et al (2019) Spatio-temporal dynamics of urban residential CO 2 emissions and their driving forces in China using the integrated two nighttime light datasets. Appl Energy 235:612–624. https://doi.org/10.1016/j.apenergy.2018.09.180
Zheng H, Yuan J, Chen L (2017) Short-term load forecasting using EMD-LSTM neural networks with a xgboost algorithm for feature importance evaluation. Energies 10. https://doi.org/10.3390/en10081168
Zhou C, Wang S (2017) Examining the determinants and the spatial nexus of city-level CO2 emissions in China: a dynamic spatial panel analysis of China’s cities. J Clean Prod. https://doi.org/10.1016/j.jclepro.2017.10.096
Zhou Z, Ye X, Ge X (2017) The impacts of technical progress on sulfur dioxide kuznets curve in China: a spatial panel data approach. Sustain 9. https://doi.org/10.3390/su9040674
Zhu H, Duan L, Guo Y, Yu K (2016) The effects of FDI , economic growth and energy consumption on carbon emissions in ASEAN-5 : evidence from panel quantile regression. Econ Model 58:237–248. https://doi.org/10.1016/j.econmod.2016.05.003
Acknowledgments
I want to thank Professor Li Yao for offering her expert advice throughout the manuscript.
Funding
This research is funded by the University of Electronic Science and Technology of China.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Responsible editor: Eyup Dogan
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ameyaw, B., Li, Y., Annan, A. et al. West Africa’s CO2 emissions: investigating the economic indicators, forecasting, and proposing pathways to reduce carbon emission levels. Environ Sci Pollut Res 27, 13276–13300 (2020). https://doi.org/10.1007/s11356-020-07849-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-020-07849-7