Measuring energy supply chains' efficiency with emission trading: A two-stage frontier-shift data envelopment analysis

Abstract

Ecological performance of an energy supply chain (ESC) is an important yet understudied field, which differs from the traditional manufacturing supply chain. This research proposes a novel two-stage data envelopment analysis (DEA) model that employs frontier-shift methodology to measure ESC ecological efficiency when carbon permits are tradable. The proposed frontier-shift DEA (FSDEA) approach uses the carbon trading quantitative information to determine to whom the trade should be made and how much to trade. In addition to estimating ESC efficiency performance, this research also stipulates the efforts required for a particular decision-making units (DMU) to become efficient and discuss the benefits that derived from the proposed exchange. It also reveals why certain DMUs are more likely involved in trading than others. Under the two-stage ESC, this research shows that either stage can improve the overall efficiency, which can be optimally derived if and only if both stages obtain the optimal solutions. In an application study of cities in one of the Chinese provinces, the proposed FSDEA model compares each city's potential percentage efficiency improvement with minimal possible efforts (carbon reduction), which differs from existing DEA models that rank solely by efficiency scores. Results show that on average the amount of carbon credits a city should trade to its competitors is 392.21 tons, resulting in a 6.61% improvement in efficiency. Among cities, Xuancheng and Fuyang is ranked in the first and the last place, respectively, according to their effectiveness in potential efficiency improvement with possible reduction efforts. Our findings offer valuable insights into ESC efficiency and help businesses make advantageous ecological decisions.