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Writing Energy Economics Research for Impact

Author

Listed:
  • Michael Dowling

    (ESC [Rennes] - ESC Rennes School of Business)

  • Helmi Hammami

    (ESC [Rennes] - ESC Rennes School of Business)

  • Dima Tawil

    (ESC [Rennes] - ESC Rennes School of Business)

  • Ousayna Zreik

    (Université de Damas = Damascus University)

Abstract

We explore the drivers of impact for energy economics research based on an analysis of citations generated by The Energy Journal articles. The focus is on non-topic generators of impact. Our regression analysis shows that these non-topic measures can explain a substantial proportion (about 20%) of variation in future citations. We apply these findings, integrated with prior research on effective economics writing style, to recommend how energy economics articles should be written to increase their impact. These recommendations center particularly around the importance of initial article information provided to the reader and article structure.

Suggested Citation

  • Michael Dowling & Helmi Hammami & Dima Tawil & Ousayna Zreik, 2021. "Writing Energy Economics Research for Impact," Post-Print hal-03159699, HAL.
    • Handle: RePEc:hal:journl:hal-03159699
    DOI: 10.5547/01956574.42.3.mdow
    as

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    Cited by:

    1. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 369-385, January.

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