Abstract
The topic of climate change is so crucial that experts, world leaders, and international organizations are constantly working on how to solve this problem. One of the recommendations lies in using renewable energy to protect the global ecosystem and promote environmental sustainability. This study, therefore, examines the impact of renewable energy consumption (RNEW) on economic growth (RGDP) in Nigeria within the period of 1990Q1–2019Q4 using a non-linear autoregressive distributed lag (NARDL) model. This research contributes to existing literature by focusing on a single-country analysis using the NARDL methodology. Nigeria has the highest GDP in Africa and can drive continental growth. In addition, the NARDL approach examines the positive and negative shocks of the independent variables on the dependent variable. The bound test result confirms cointegration among the variables. Further results show that a positive shock of RNEW decreases RGDP, while a negative shock increases RGDP in the long run. This result differs from existing literature in which a majority of the studies found a positive relationship between RNEW and RGDP. In the short run, a positive shock of RNEW increases RGDP, while a negative shock decreases RGDP, although not significant. A positive shock in RNEW hurts RGDP because of the nature and source of renewable energy used in Nigeria, majorly wood biomass. Therefore, this study recommends that cleaner technologies be utilized to maximize the advantages of renewable energy sources, especially wood biomass, while minimizing their adverse effects.
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Data availability
This study utilized annual data converted to quarterly data from 1990Q1 to 2019Q4. Data was sourced from World Bank Development Indicators (2018). The variables selected include gross domestic product (constant 2010 US$), renewable energy consumption (% of total final energy consumption), gross fixed capital formation (constant 2010 US$) (GFCF), and labor force participation rate, female (% of female population ages 15–64) (modeled ILO estimate). The independent variable is RGDP while the independent variables are RNEW, GFCF, and LFOW. This study utilized data from 1990 because labor force data starts from 1990.
Abbreviations
- ADF:
-
Augmented Dickey–Fuller
- ARDL:
-
Autoregressive distributed lag
- DHC:
-
Dumitrescu–Hurlin causality
- DOLS:
-
Dynamic ordinary least square
- FMOLS:
-
Fully modified ordinary least square
- GMM:
-
General method of moments
- NARDL:
-
Non-linear autoregressive distributed lag
- PP:
-
Phillips and Perron
- TFEC:
-
Total final energy consumption
- VECM:
-
Vector error correction model
- WDI:
-
World Development Indicators
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Somoye, O.A., Ozdeser, H. & Seraj, M. The impact of renewable energy consumption on economic growth in Nigeria: fresh evidence from a non-linear ARDL approach. Environ Sci Pollut Res 29, 62611–62625 (2022). https://doi.org/10.1007/s11356-022-20110-7
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DOI: https://doi.org/10.1007/s11356-022-20110-7