TY - JOUR
T1 - Allocation of emission permits in large data sets
T2 - a robust multi-criteria approach
AU - Ji, Xiang
AU - Sun, Jiasen
AU - Wang, Yaoyu
AU - Yuan, Qianqian
N1 - Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2017/1/20
Y1 - 2017/1/20
N2 - 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.
AB - 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.
KW - Allocation of emission permits (AEP)
KW - Data envelopment analysis (DEA)
KW - Large data set
KW - Multi-criteria
KW - Robust
UR - http://www.scopus.com/inward/record.url?scp=85006942595&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2016.02.117
DO - 10.1016/j.jclepro.2016.02.117
M3 - Article
AN - SCOPUS:85006942595
SN - 0959-6526
VL - 142
SP - 894
EP - 906
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
ER -