A DEA-based approach for allocation of emission reduction tasks

Abstract

Rapid economic growth has led to increasing pollution emission, leading governments to require emission reductions by specific amounts. The allocation of specific emission reduction tasks has become a significant issue and has drawn the attention of academia. Data envelopment analysis (DEA) has been extended to construct the allocation of emission reduction tasks model. These previous DEA-based approaches have strong assumptions about individual enterprise production. In this paper, we propose a new method to accurately assess the production, using each enterprise's previously observed production to construct its own production technology plan. With emission permits decreased, the enterprise can have new production strategy based on its own technology. Assuming emission permits can be freely bought and sold, we show how each enterprise can determine the optimal amount of emission allowance that should be used for production, which may leave some allowance to be sold for extra profit or may require the purchase of permits from other firms. Considering the limitation on the total allowance from emission permits, we introduce the concept of satisfaction degree and use it in maximising the minimum enterprise satisfaction degree. Last, a numerical example is presented and an empirical application is given to verify the proposed approach.

Allocating natural resource reduction amounts: A data envelopment analysis based‐approach considering production technology heterogeneity

“…Thus, it may be inappropriate to use the homogeneous production possibility set T , constructed by the efficient DMUs, to determine each DMU's change in production. In particular, when some DMUs are far from the efficient frontier, they will not easily achieve efficiency after the new resource allocation (Fang, ; Wu, Zhu, Chu, et al, ; Wu, Zhu, & Liang, ; Zhu et al, ). To tackle this limitation, which occurs using Model 6, in the following section, we incorporate the concept of meta‐frontier into the resource allocation model, which more exactly describes the possible production changes with the reduction or increase of natural resource consumption.…”

“…Accordingly, some scholars have considered the technology heterogeneity effect in their solutions for resource allocation. Wu, Zhu, Chu, et al () and Zhu et al () proposed using the evaluated DMU's previous production data to identify its production technology, but then the amount of historical data may affect the final allocation results. Additionally, Wu, Zhu, and Liang () used the concept of context‐dependent DEA to characterize the DMUs' production technology but ignored the problem that many levels may be clustered by the context‐dependent DEA model, and, even worse, a group may consist of only one DMU in some cases.…”

“…To properly solve the problem of resource allocation, many scholars have proposed approaches based on data envelopment analysis (DEA; e.g., Ding, Chen, Wu, & Wei, 2018;Du, Cook, Liang, & Zhu, 2014;Hadi-Vencheh, Foroughi, & Soleimani-damaneh, 2008;Lozano, Villa, & Brännlund, 2009;Shao, Bi, Yang, & Xia, 2018;Wu, Zhu, Chu, An, & Liang, 2016;Zhu, Wu, Li, & Xiong, 2017). DEA, which was first introduced by Charnes, Cooper, and Rhodes (1978), is an effective nonparametric method to evaluate the efficiency of a set of decision-making units (DMUs) using multiple inputs to produce multiple outputs.…”

City-Level Carbon Emission Abatement in the Subtropics of China: Evaluation and Reallocation for Zhejiang

“…Regionally, the allocation of carbon quotas has been investigated within one specific country or region (Wang, Zhang, Wei, & Yu, 2013). In studies focused on China, the majority of studies have been based on a provincial setting (Wang, Zhang, Wei, & Yu, 2013;Wu, Zhu, Chu, An, & Liang, 2016;Zhao, Shi, & Xu, 2018), while studies have only rarely been based on municipal settings (Zhou, Liu, Zeng, Jiang, & Liu, 2018) or factory settings (Sun, Wu, Liang, Zhong, & Huang, 2014;Sun, Fu, Ji, & Zhong, 2017;Li, Emrouznejad, Yang, & Li, 2019).…”

The impacts of the coal-electricity price linkage on the profit efficiency of China’s thermal power plants

“…The non-homogeneity of the DMUs were also considered to investigate the relationship between energy constraints and the enterprises' profit ratios (Wang, Chen, and Song 2018). Apart from efficiency analysis, DEAbased permits and resource allocation models have also been constructed and applied (Wu et al 2016b;Li et al 2017). Kroes, Subramanian, and Subramanyam (2012) used data on utility firms operating coal-fired generation units to examine the relationships between levers for compliance, environmental performance and firm performance.…”