Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas

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. (C) 2018 Elsevier Ltd. All rights reserved.