Allocation of emission permits in large data sets: a robust multi-criteria approach

This paper addressed the issue of the allocation of emission permits (AEP) in large data sets, with the goal of providing government strategies to practically operate the AEP in a group of organizations, and realize economic, social and environmental goals at the same time. We propose a robust multi-criteria AEP approach, together with its tractable algorithm, by extending the classical theory of data envelopment analysis (DEA) for large data sets. Reasonable AEP mechanisms adjusted to the large data set can be derived from this approach. The main advantages of this approach are as follows. First, this approach shows real-world tractability of large data sets, as it takes the characteristics of large data sets into full consideration. Second, the proposed AEP mechanism can help centralized decision makers to achieve the lowest total group-level emission while keeping group-level outputs invariant, and the mechanism is proved to be sustainable theoretically. Third, besides obtaining an optimal allocation plan for emission permits, the proposed approach can be used to calculate the optimal emission standard and optimal total amount of permits to be allocated. The proposed approach was used in an empirical study of SO2 emission permits allocation among 202 prefecture-level cities in mainland China. The results further demonstrated theoretical and practical values of our method. One valuable policy suggestion resulted from the empirical analysis is presented as well.