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Estimation of gross refining margin of Indian petroleum refineries using Driscoll-Kraay standard error estimator

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  • Joshi, Jignesh M.
  • Dalei, Narendra N.
  • Mehta, Pratik

Abstract

The prudent combination of technological, economic, and other factors can enhance the performance of any manufacturing unit. Indian petroleum refineries are no exception to this. In this background, an effort has been made to estimate the gross refining margin using the Fixed Effect and Random Effect Models as well as the Models of White, Rogers, and Driscoll-Kraay standard error Estimators for seven Indian refineries for the period between 2008–09 and 2018–19. The study found that complexity, specific energy consumption, and distillate yield significantly and positively influenced gross refining margin, whereas refinery fuel & loss (loss due to evaporation, flaring, and seepage and chemical losses) and heavy yield significantly and negatively influenced it. The study recommends that Indian refiners should invest more in secondary process units as well as advanced technology to enhance the complexity and distillate yield in order to increase the gross refining margin. They should adopt waste heat recovery, flare gas recovery, and other best operating practices in order to reduce fuel and loss of refinery to increase the gross refining margin. Therefore, policies to optimize the utilization of resources through technological intervention and investment will certainly enhance the gross refining margin of Indian refineries.

Suggested Citation

  • Joshi, Jignesh M. & Dalei, Narendra N. & Mehta, Pratik, 2021. "Estimation of gross refining margin of Indian petroleum refineries using Driscoll-Kraay standard error estimator," Energy Policy, Elsevier, vol. 150(C).
  • Handle: RePEc:eee:enepol:v:150:y:2021:i:c:s0301421521000173
    DOI: 10.1016/j.enpol.2021.112148
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    References listed on IDEAS

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    3. Mohammed Atris, Amani, 2020. "Assessment of oil refinery performance: Application of data envelopment analysis-discriminant analysis," Resources Policy, Elsevier, vol. 65(C).
    4. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
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