A meta-analysis of the economic impact of carbon emissions in Africa

  • Received August 14, 2022;
    Accepted September 29, 2022;
    Published November 9, 2022
  • Author(s)
  • DOI
  • Article Info
    Volume 13 2022, Issue #1, pp. 89-100
  • Cited by
    7 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

The economic impact of carbon emissions in Africa is gaining traction in the extant literature. This study adopted Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to concomitantly track data on carbon emissions versus economic growth in Africa from 2018 to 2022 providing evidence from a meta-analysis. Through database searches, 591 publications were identified. A machine learning algorithm called Latent Dirichlet Allocation (LDA) was used as a visualization technique for reporting trends in the eleven papers selected for the analysis. Identifying, evaluating, and summarizing the findings of all relevant individual studies conducted in Africa on the impact of economic growth on carbon emissions contributes to the existing body of knowledge. This study fills a critical gap by surveying the studies conducted in Africa in the last five years, implying that economic growth negatively and significantly triggers CO2 emissions in Africa. The debate on the economic impact of CO2 emissions in Africa, the most vulnerable continent to climate change, is elucidated. The findings tracked sources of data for carbon emissions in Africa. The results showed that although some studies reported a positive correlation (and some a negative correlation) between economic growth and carbon emissions, most studies concur that the economic impact of carbon emissions over a timeline can be explained by the Environmental Kuznets Curve (EKC) hypothesis. Therefore, there is a dire need for African countries to strengthen economic growth without deteriorating their environment or having ecological footprint. Future research must assess whether this trend on the economic impact of carbon emissions in Africa continues.

The authors express their appreciation to the Durban University of Technology for providing the resources to conduct this study.

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    • Figure 1. PRISMA flow diagram of literature retrieval
    • Figure 2. Topics and weights produced by Latent Dirichlet Allocation (LDA)
    • Figure 3. Topic keywords in terms of their importance (weights)
    • Figure 4. Word clouds of top keywords in each topic
    • Figure 5. Sentence topic coloring for documents
    • Figure 6. Visualization of inter-related topics
    • Table 1. Selected papers on the economic growth in Africa versus emissions
    • Table 2. Selected papers by country, time series, methodology, and data source
    • Table 3. Impacting factors on carbon emissions in Africa
    • Conceptualization
      Mogiveny Rajkoomar, Ferina Marimuthu, Nalindren Naicker, Jean Damascene Mvunabandi
    • Investigation
      Mogiveny Rajkoomar, Ferina Marimuthu
    • Validation
      Mogiveny Rajkoomar, Ferina Marimuthu, Nalindren Naicker
    • Writing – original draft
      Mogiveny Rajkoomar, Ferina Marimuthu, Nalindren Naicker
    • Formal Analysis
      Ferina Marimuthu, Nalindren Naicker
    • Supervision
      Ferina Marimuthu, Nalindren Naicker
    • Visualization
      Ferina Marimuthu, Nalindren Naicker
    • Data curation
      Nalindren Naicker
    • Methodology
      Nalindren Naicker, Jean Damascene Mvunabandi
    • Writing – review & editing
      Jean Damascene Mvunabandi