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Modelling the economic role of hydropower: Evidence from bootstrap autoregressive distributed lag approach

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  • Bello, Mufutau Opeyemi
  • Solarin, Sakiru Adebola
  • Yen, Yuen Yee

Abstract

Despite being one of the most researched areas within the general field of energy economics, the energy-growth nexus remains largely inconclusive with four testable hypotheses that include the feedback, neutrality, conservation, and growth hypothesis. This study contributes to the extant literature of energy-growth nexus by investigating the causal dynamics between hydroelectricity consumption and economic growth in Malaysia in an augmented production function framework that incorporates human capital as additional regressor with an updated yearly time series data that span the period 1971–2017. Importantly, we have employed a recently developed bootstrap autoregressive distributed lag (BARDL) modelling technique of McNown et al. (2018) [1]; to probe the long-run relationship between the variables and Granger causality test based on the new method was conducted to ascertain the causal dynamics in the model. The new technique is a significant improvement over the conventional ARDL method as it incorporates an additional F-test on the lagged independent variables (or t-test in the case of a single independent variable) which helps in avoiding a scenario of treating a degenerate case as a cointegrating case. The empirical findings show evidence that in the long run hydroelectricity consumption Granger causes gross domestic product (GDP), and physical capital Granger causes human capital while the short run analysis show that GDP and physical capital Granger cause hydroelectricity consumption. The study recommends more funding to improve hydroelectric facilities and investment in capital expenditure that can promote the development of human capital in the country.

Suggested Citation

  • Bello, Mufutau Opeyemi & Solarin, Sakiru Adebola & Yen, Yuen Yee, 2021. "Modelling the economic role of hydropower: Evidence from bootstrap autoregressive distributed lag approach," Renewable Energy, Elsevier, vol. 168(C), pages 76-84.
  • Handle: RePEc:eee:renene:v:168:y:2021:i:c:p:76-84
    DOI: 10.1016/j.renene.2020.12.031
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