Skip to main content

Advertisement

SpringerLink
Log in

Investigation on key contributors of energy consumption in dynamic heterogeneous panel data (DHPD) model for African countries: fresh evidence from dynamic common correlated effect (DCCE) approach

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

The main aim of this current study is to empirically scrutinize the determinants of energy consumption for 24 African countries sub-grouped into three panels based on income levels: low-, lower-middle-, and upper-middle-income countries, from 1990 to 2015. Due to the presence of heterogeneity and cross-sectional reliance among country groups, recently developed econometric approaches, which include cross-sectional Im, Pesaran, and Shin together with cross-sectional Augmented Dickey-Fuller stationarity tests, Pedroni and Westerlund–Edgerton cointegration assessment, dynamic common correlated effect estimation approach and Dumitrescu–Hurlin Granger causality test are employed. Empirically, our findings depict analyzed variables are stationary and characterized by long-term stability affiliations for all panels. Economic growth, urbanization, population growth, and oil price with labor and capital stock as intermittent variables had palpable significant positive sway on energy consumption for all panels though their respective weight of contribution differed from one country group to another. The granger test of causation unveiled that (i) among all panels, urbanization and energy consumption are connected bidirectionally, whereas population growth causes energy consumption; (ii) a one-way causal link from economic growth to energy use is evidenced in low-income African countries, whereas a two-sided connection is confirmed in both lower-middle- and upper-middle-income economies; (iii) a bilateral causal association in low-income African nations is observed amid oil price and energy use, while a uni-lateral relationship extends from oil price to energy consumption in both lower-middle- and upper-middle-income nations in Africa. Such new methodologies and findings reveal that the long-term estimated effects as well as causal affiliations amid variables are skewed by different income levels of African countries in an attempt to conserve energy. Policy recommendations are further propose.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Emphatically, country’s income level has been found to affect its consumption of energy. This so because, from the demand perspective, countries with higher income levels can afford more energy consuming equipments and machineries compared with those with low levels of income. Similarly, from the supply angle, firms in countries with high-income levels keep demand for goods and services of the market relatively low. On the other hand, individuals and businesses in countries with high income can easily invest in energy efficient equipment and machineries to reduce the consumption of energy. In countries with lower income levels, although individuals demand for energy consuming equipment and companies supply of goods might be relatively low, the use of old equipment and machineries could increase energy consumption. These aforestated assertions therefore insinuate that country’s income level is highly linked to its rate of energy use in the process of moving from one level to the other. This thus justifies the employment of income levels classifications as sub-groups in our study.

  2. The lagged dependent variable is regarded as not strictly exogenous when estimating the DHPD model; thus, to make the DCCE estimator more efficient cross-sectional averages are added to the model with respect to the explanatory variables.

  3. Due to the large nature of the values pertaining the value of GDP, URB, POPg, and OP as variable of interest, we expressed all in millions with the exception of EC

  4. Detail results of the D-H Granger causality test for all the country groups are provided in the Appendix.

Abbreviations

CADF:

cross-sectional augmented Dickey–Fuller test

CIPS:

cross-sectional Im Pesaran and Shin

DCCE:

dynamic common correlated effect estimator

DHPD:

dynamic heterogeneous panel data model

POPg:

population growth

GDP:

gross domestic product

URB:

urbanization

L:

labor force

K:

capital stock

OP:

oil price

CD:

cross-sectional dependence

CDLM :

cross-sectional dependence Lagrange multiplier

CDLMadj :

Cross-sectional dependence Lagrange multiplier adjusted

P-Y:

Pesaran–Yamagata homogeneity test

W-E:

Westerlund–Edgerton bootstrap cointegration test

D-H :

Dumitrescu and Hurlin

PCA:

principal component analysis

KMO test :

Kaiser–Meyer–Oklin test

B-S test :

Bartlett sphericity test

OPEC:

Organization of Petroleum Exporting Countries

WDI:

World Bank Development Indicators

References

  • Ahmed M, Azam M (2016) Causal nexus between energy consumption and economic growth for high, middle and low-income countries using frequency domain analysis. Renew Sust Energ Rev 60:653–678

    Google Scholar 

  • Alam MJ, Begum IA, Buysse J, Van Huylenbroeck G (2012) Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis. Energy Policy 45:217–225

    Google Scholar 

  • Al-Bajjali SK, Shamayleh AY (2018) Estimating the determinants of electricity consumption in Jordan. Energy 147:1311–1320

    Google Scholar 

  • Al-Mulali U, Fereidouni HG, Lee JY, Sab CNBC (2013) Exploring the relationship between urbanization, energy consumption, and CO2 emission in MENA countries. Renew Sust Energ Rev 23:107–112

    Google Scholar 

  • Al-Mulali U, Ozturk I (2015) The effect of energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation in the MENA (Middle East and North African) region. Energy 84:382–389

    Google Scholar 

  • 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

  • Asongu S, El Montasser G, Toumi H (2016) Testing the relationships between energy consumption, CO 2 emissions, and economic growth in 24 African countries: a panel ARDL approach. Environ Sci Pollut Res 23(7):6563–6573

    CAS  Google Scholar 

  • Asumadu-Sarkodie S, Owusu PA (2016) Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971–2013. Environ Sci Pollut Res 23(13):13508–13520

    CAS  Google Scholar 

  • Bakirtas T, Akpolat AG (2018) The relationship between energy consumption, urbanization, and economic growth in new emerging-market countries. Energy 147:110–121

    Google Scholar 

  • Barreto H (2006) Omitted variable bias. Introductory econometrics: using Monte Carlo simulation with Microsoft excel. Cambridge University Press. https://www.cambridge.org/9780521843195 . Accessed in 17 June 2020

  • Chang T, Chu HP, Deale FW, Gupta R, Miller SM (2017) The relationship between population growth and standard-of-living growth over 1870–2013: evidence from a bootstrapped panel Granger causality test. Empirica 44(1):175–201

    Google Scholar 

  • Chudik A, Pesaran MH (2015) Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. J Econ 188(2):393–420

    Google Scholar 

  • Dikkaya M, Doyar BV (2017) Causality among oil prices, GDP and exchange rate: evidence from Azerbaijan and Kazakhstan. Bilig 83:79–98

    Google Scholar 

  • Dogan E, Aslan A (2017) Exploring the relationship among CO2 emissions, real GDP, energy consumption and tourism in the EU and candidate countries: evidence from panel models robust to heterogeneity and cross-sectional dependence. Renew Sust Energ Rev 77:239–245

    Google Scholar 

  • Dumitrescu EI, Hurlin C (2012) Testing for Granger non-causality in heterogeneous panels. Econ Model 29(4):1450–1460

    Google Scholar 

  • Eberhardt M, Presbitero AF (2015) Public debt and growth: heterogeneity and non-linearity. J Int Econ 97(1):45–58

    Google Scholar 

  • Eggoh JC, Bangaké C, Rault C (2011) Energy consumption and economic growth revisited in African countries. Energy Policy 39(11):7408–7421

    Google Scholar 

  • Elfaki KE, Poernomo A, Anwar N, Ahmad AA (2018) Energy consumption and economic growth: empirical evidence for Sudan. Int J Energy Econ Policy 8(5):35–41

    Google Scholar 

  • Faisal F, Tursoy T, Ercantan O (2017) The relationship between energy consumption and economic growth: evidence from non-Granger causality test. Procedia Computer Science 120:671–675

    Google Scholar 

  • Gam I, Rejeb JB (2012) Electricity demand in Tunisia. Energy Policy 45:714–720

    Google Scholar 

  • Gorus MS, Aydin M (2019) The relationship between energy consumption, economic growth, and CO2 emission in MENA countries: causality analysis in the frequency domain. Energy 168:815–822

    Google Scholar 

  • Gummi UM, Buhari AI, Muhammad A (2017) Oil price fluctuations and economic growth in Nigeria (evidence from granger causality test). Australasian Journal of Social Science 3(1):1–16

    Google Scholar 

  • Hair JF, Tomas G, Hult M, Ringle CM, Sarstedt M, Thiele KO (2017) Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J Acad Mark Sci 45(5):616–632

  • Ibrahiem DM (2018) Road energy consumption, economic growth, population and urbanization in Egypt: cointegration and causality analysis. Environ Dev Sustain 20(3):1053–1066

    Google Scholar 

  • Iddrisu S.K., 2017. Energy consumption and economic growth: evidence from the west African sub-region. Thesis

    Google Scholar 

  • International Energy Agency (IEA), 2014. World energy outlook. (WEO-2014)

  • Jamil F, Ahmad E (2010) The relationship between electricity consumption, electricity prices and GDP in Pakistan. Energy Policy 38(10):6016–6025

    Google Scholar 

  • Keho Y (2016) What drives energy consumption in developing countries? The experience of selected African countries. Energy Policy 91:233–246

    Google Scholar 

  • Khobai HB, Le Roux P (2017) The relationship between energy consumption, economic growth and carbon dioxide emission: the case of South Africa. Int J Energy Econ Policy 7(3):102–109

    Google Scholar 

  • Kremer M (1993) Population growth and technological change: one million BC to 1990. Q J Econ 108(3):681–716

    Google Scholar 

  • Liddle B, Messinis G (2015) Which comes first–urbanization or economic growth? Evidence from heterogeneous panel causality tests. Appl Econ Lett 22(5):349–355

    Google Scholar 

  • Lin B, Moubarak M (2014) Renewable energy consumption–economic growth nexus for China. Renew Sust Energ Rev 40:111–117

    Google Scholar 

  • Mei X (2014) Research on the relationship between urbanization and economic growth in China-econometric analysis based on data from 1952 to 2013. Bio Technology An Indian Journal (BTAIJ) 10(22):13949–13954

    Google Scholar 

  • Mensah IA, Sun M, Gao C, Omari-Sasu AY, Zhu D, Ampimah BC, Quarcoo A (2019) Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach. J Clean Prod 228:161–174

    Google Scholar 

  • Mohammed Albiman M, Nassor Suleiman N, Omar Baka H (2015) The relationship between energy consumption, CO2 emissions and economic growth in Tanzania. International Journal of Energy Sector Management 9(3):361–375

    Google Scholar 

  • Nguyen HM, Nguyen LD (2018) The relationship between urbanization and economic growth: an empirical study on ASEAN countries. Int J Soc Econ 45(2):316–339

    Google Scholar 

  • Nindi AG, Odhiambo NM (2014) Energy consumption and economic growth in Mozambique: an empirical investigation. Economics 5:4

    Google Scholar 

  • Nwani C (2017) Causal relationship between crude oil price, energy consumption and carbon dioxide (CO2) emissions in Ecuador. OPEC Energy Review 41(3):201–225

    Google Scholar 

  • Nyasha S, Gwenhure Y, Odhiambo NM (2018) Energy consumption and economic growth in Ethiopia: a dynamic causal linkage. Energy & Environment 29(8):1393–1412

    Google Scholar 

  • Omri A, Kahouli B (2014) Causal relationships between energy consumption, foreign direct investment and economic growth: fresh evidence from dynamic simultaneous-equations models. Energy Policy 67:913–922

    Google Scholar 

  • Ozturk I, Acaravci A (2010) CO2 emissions, energy consumption and economic growth in Turkey. Renew Sust Energ Rev 14(9):3220–3225

    Google Scholar 

  • Pablo-Romero MDP, De Jesús J (2016) Economic growth and energy consumption: the energy-environmental Kuznets curve for Latin America and the Caribbean. Renew Sust Energ Rev 60:1343–1350

    Google Scholar 

  • Pedroni P (2004) Panel cointegration: asymptotic and finite sample properties of pooled time series test with an application to the PPP hypothesis. Econometric theory 20(3):597–625

    Google Scholar 

  • Pesaran MH (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74(4):967–1012

  • Pesaran MH (2007) A simple panel unit root test in the presence of cross-section dependence. J Appl Econ 22(2):265–312

    Google Scholar 

  • Pesaran MH, Ullah A, Yamagata T (2008) A bias-adjusted LM test of error cross-section independence. Econ J 11(1):105–127

    Google Scholar 

  • Pesaran MH, Yamagata T (2008) Testing slope homogeneity in large panels. J Econ 142(1):50–93

    Google Scholar 

  • Pesaran, M. H, 2004. General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics. Series No. 1229, Available at SSRN; https://ssrn.com/abstract=572504

  • Poveda A, Martinez C (2011) Trends in economic growth, poverty, and energy in Colombia: long-run and short-run effects. Energy System 2:281–298

    Google Scholar 

  • Raheem ID, Yusuf AH (2015) Energy consumption-economic growth nexus: evidence from linear and nonlinear models in selected African countries. Int J Energy Econ Policy 5(2):558–564

    Google Scholar 

  • Saibu OM (2013) Causal implication of oil price shock, energy consumption on sustainable economic growth in Africa: the case of Nigeria. Journal of sustainable development in Africa 15(5):1520–5509

    Google Scholar 

  • Saidi K, Hammami S (2015) The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Rep 1:62–70

    Google Scholar 

  • Sarkodie SA, Adom PK (2018) Determinants of energy consumption in Kenya: a NIPALS approach. Energy 159:696–705

    Google Scholar 

  • Sarwar S, Chen W, Waheed R (2017) Electricity consumption, oil price and economic growth: global perspective. Renew Sust Energ Rev 76:9–18

    Google Scholar 

  • Sbia R, Shahbaz M, Ozturk I (2017) Economic growth, financial development, urbanization and electricity consumption nexus in UAE. Economic research-Ekonomska istraživanja 30(1):527–549

    Google Scholar 

  • Shaari MS, Rahim HA, Rashid IMA (2013) Relationship among population, energy consumption and economic growth in Malaysia. Int J Soc Sci 13(1)

  • Shabbir MS, Shahbaz M, Zeshan M (2014) Renewable and non-renewable energy consumption, real GDP and CO2 emissions nexus: a structural VAR approach in Pakistan. Bulletin of Energy Economics 2:91–105

    Google Scholar 

  • Shahbaz M, Van Hoang TH, Mahalik MK, Roubaud D (2017) Energy consumption, financial development and economic growth in India: new evidence from a nonlinear and asymmetric analysis. Energy Econ 63:199–212

    Google Scholar 

  • Sibe JP, Chiatchoua C, Megne MN (2016) The long run relationship between population growth and economic growth: a panel data analysis of 30 of the most populated countries of the world. Análisis Económico 31(77):205–218

    Google Scholar 

  • Sineviciene L, Sotnyk I, Kubatko O (2017) Determinants of energy efficiency and energy consumption of Eastern Europe post-communist economies. Energy & Environment 28(8):870–884

    Google Scholar 

  • Stambuli BB (2014) Oil consumption and economic growth nexus in Tanzania cointegration causality analysis. International Journal of Academic Research in Economic and Management Sciences 3(2):113–123

    Google Scholar 

  • Sunny Oputa, 2018. Is Sub-Saharan Africa producing nations ready for the oil price Armagedon? [online]. Available at http://energycorperateafrica.com (assessed 29, July 2019)

  • Wang S, Li Q, Fang C, Zhou C (2016) The relationship between economic growth, energy consumption, and CO2 emissions: empirical evidence from China. Sci Total Environ 542:360–371

    CAS  Google Scholar 

  • Westerlund J, Edgerton DL (2007) A panel bootstrap cointegration test. Econ Lett 97:185–900

    Google Scholar 

  • WHO Kobe 2019. Available online: https://extranet.who.int/kobe_centre/en (accessed on 20th October)

  • Wooldridge JM (2015) Business & economics. https://books.google.com/books?isbn=1305446380 . Accessed in 17 June 2020

  • World Bank, 2017. World bank development indicators on CD-ROM, Washington, DC

    Google Scholar 

  • World Bank 2011, World development indicators on CD-ROM, Washington, DC

    Google Scholar 

  • Yardimcioğlu F, Gülmez A (2013) OPEC Ülkelerinde Hollanda Hastalığı: Petrol Fiyatları ve Ekonomik Büyüme İlişkisinin Ekonometrik Bir Analizi. Sosyoekonomi 19(1)

  • Zaher, Z., Maayan, S., 2015. The impact of global oil prices on the Israelâ s GDP per capita: an empirical analysis. Journal of Global Economics

    Google Scholar 

  • Zhao H, Zhao H, Han X, He Z, Guo S (2016) Economic growth, electricity consumption, labor force and capital input: a more comprehensive analysis on North China using panel data. Energies 9(11):891

    Google Scholar 

  • Zhao Y, Wang S (2015) The relationship between urbanization, economic growth and energy consumption in China: an econometric perspective analysis. Sustainability 7(5):5609–5627

    Google Scholar 

Download references

Funding

This research fund was supported by the National Science Foundation of China (Nos. 71774070, 71804060) and Jiangsu Province Graduate Scientific Research Innovation Project (China) (KYCX17_KYZZ16_0336).

Author information

Authors and Affiliations

  1. Institute of Applied Systems Analysis (IASA), Jiangsu University, Zhenjiang, 212013, Jiangsu, People’s Republic of China

    Isaac Adjei Mensah, Mei Sun, Cuixia Gao, Benjamin Chris Ampimah & Alfred Quarcoo

  2. Department of Mathematics and Statistics, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana

    Akoto Yaw Omari-Sasu

  3. Institute of Industrial Economics, Jiangsu University, Zhenjiang, 212013, Jiangsu, People’s Republic of China

    Huaping Sun

Authors
  1. Isaac Adjei Mensah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cuixia Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akoto Yaw Omari-Sasu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huaping Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Benjamin Chris Ampimah
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alfred Quarcoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mei Sun.

Additional information

Responsible Editor: Nicholas Apergis

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 34 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adjei Mensah, I., Sun, M., Gao, C. et al. Investigation on key contributors of energy consumption in dynamic heterogeneous panel data (DHPD) model for African countries: fresh evidence from dynamic common correlated effect (DCCE) approach. Environ Sci Pollut Res 27, 38674–38694 (2020). https://doi.org/10.1007/s11356-020-09880-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-020-09880-0

Keywords

Search

Navigation