Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas. (15th May 2019)

• Record Type: Journal Article
• Authors: Wegener, Matthew; Amin, Gholam R.
• Is Part Of: Expert systems with applications. Volume 122(2019)
• Page Start: 369
• Page End: 375
• Publication Date: 2019-05-15
• Subjects: Data envelopment analysis -- Inverse DEA -- Undesirable outputs -- Greenhouse gas emissions -- Oil and gas; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.12.058
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel inverse problem in environmental efficiency is suggested. A new inverse DEA model for minimizing GHG emissions is proposed. For a set of firms and certain production, the model obtains minimum GHG emissions. An application in oil and gas sector is proposed. Abstract: The industry dynamic creates a difficult problem that must be solved if we are to address climate change. How can we lower greenhouse gas (GHG) emissions while simultaneously increasing production? This paper develops a new inverse data envelopment analysis (DEA) model for optimizing GHG emissions. The inverse DEA model minimizes the overall GHG emissions generated by a set of decision making units (DMUs) for producing a certain level of outputs, given that the DMUs maintain at least their existing performance status. The usefulness of the proposed method in this paper is demonstrated through an application in the oil and gas industry. We find that roughly 57% of our sample DMUs are inefficient. Ample room exists within the current efficiency frontier to lower GHG emissions in the oil and gas sector. We recommend that environmental regulators look into targeting regulations at these inefficiencies that exist within the industry as low bearing fruit for potential GHG emissions reductions.