Carbon dioxide emissions, GDP, energy use, and population growth

a multivariate and causality analysis for Ghana, 1971–2013

Samuel Asumadu-Sarkodie*, Phebe Asantewaa Owusu

*Corresponding author for this work

Research output: Contribution to journalArticle

83 Citations (Scopus)

Abstract

In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen’s multivariate co-integration and performed a variance decomposition analysis using Cholesky’s technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana’s energy mix can help mitigate climate change and its impact in the future.

Original languageEnglish
Pages (from-to)13508-13520
Number of pages13
JournalEnvironmental Science and Pollution Research
Volume23
Issue number13
DOIs
Publication statusPublished - 1 Jul 2016
Externally publishedYes

Keywords

  • ARDL bound test
  • Carbon dioxide emissions
  • Econometrics
  • Ghana
  • Multivariate co-integration
  • Variance decomposition

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