Carbon emission abatement quota allocation in Chinese manufacturing industries: An integrated cooperative game data envelopment analysis approach

Li, Feng; Emrouznejad, Ali; Yang, Guo Liang; Li, Yongjun

doi:10.1080/01605682.2019.1609892

Cited by 49 publications

(14 citation statements)

References 110 publications

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“…The basic logic of the DEA method is to construct a set of homogeneous DMU convex combinations based on input-output data to obtain an effective production frontier. The relative efficiency of the DMU can be evaluated by judging the projection of the actual input-output data of the DMU onto the frontier [16]. Since the DEA method was proposed, it has gained the attention of many scholars and has been widely used in different fields [4,5].…”

Section: Literature Reviewmentioning

confidence: 99%

Efficiency of China’s Listed Securities Companies: Estimation through a DEA-Based Method

You

Shao

2020

Mathematics

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Accurate assessment of the efficiency of securities companies is of great significance to improve the competitiveness of companies, due to their increasingly important role in supporting economic development. As the main contribution, this paper proposes a novel efficiency estimation framework for securities companies based on data envelopment analysis (DEA), which takes into account operational risks and technical heterogeneity. First, the risk variable is incorporated in the evaluation system as an undesirable output through the setting of weak disposability. Subsequently, the meta-frontier model is introduced to consider the impact of the technical heterogeneity of different companies to improve the accuracy of the assessment. Furthermore, this article also provides the meta-frontier Malmquist model, which can be utilized to analyze in detail technological progress. Finally, the securities companies listed in the Chinese stock market were selected as samples for empirical analysis. The efficiency evaluation model for securities companies proposed in this paper will provide a reference for related evaluation issues.

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Section: Literature Reviewmentioning

confidence: 99%

Efficiency of China’s Listed Securities Companies: Estimation through a DEA-Based Method

You

Shao

2020

Mathematics

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“…It is a non-parametric frontier technique where the efficiency of a specific entity is calculated by its distance from the highest performance practice frontier created by the most exceptional performance entities inside the group. DEA is a general method for assessing the efficiency of ecological systems [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

et al. 2020

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Machine learning approaches have been developed rapidly and also they have been involved in many academic findings and discoveries. Additionally, they are widely assessed in numerous industries such as cement companies. Cement companies in developing countries, despite many profits such as valuable mines, face many challenges. Optimization, as a key part of machine learning, has attracted more attention. The main purpose of this paper is to combine a novel Data Envelopment Analysis (DEA) approach in optimization at the first step to find the Decision-Making Unit (DMU) with innovative clustering algorithms in machine learning at the second step introduce the model and algorithm with higher accuracy. At the optimization section with converting two-stage to a simple standard single-stage model, 24 cement companies from five developing countries over 2014–2019 are compared. Window-DEA analysis is used since it leads to increase judgment on the consequences, mainly when applied to small samples followed by allowing year-by-year comparisons of the results. Applying window analysis can be beneficial for managers to expand their comparison and evaluation. To find the most accurate model CCR (Charnes, Cooper and Rhodes model), BBC (Banker, Charnes and Cooper model) and Free Disposal Hull (FDH) DEA model for measuring the efficiency of decision processes are used. FDH model allows the free disposability to construct the production possibility set. At the machine learning section, a novel three-layers data mining filtering pre-processes proposed by expert judgment for clustering algorithms to increase the accuracy and to eliminate unrelated attributes and data. Finally, the most efficient company, best performance model and the most accurate algorithm are introduced. The results indicate that the 22nd company has the highest efficiency score with an efficiency score of 1 for all years. FDH model has the highest efficiency scores during all periods compared with other suggested models. K-means algorithm receives the highest accuracy in all three suggested filtering layers. The BCC and CCR models have the second and third places, respectively. The hierarchical clustering and density-based clustering algorithms have the second and third places, correspondingly.

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“…With dramatic increases in the emission of greenhouse gases (GHGs, mainly as carbon dioxide), climate change and environmental protection have become hot global issues (Intergovernmental Panel on Climate Change, 2006) and draw the common attention of policymakers, private enterprises, and scholars (Li, Emrouznejad, Yang, & Li, 2019;Song & Cui, 2016;Sun, Wang, & Li, 2018; just to name a few). There are two main methods to deal with the problem of GHGs emission reduction and allocation (Sun, Fu, Ji, & Zhong, 2017;Wu, Zhu, Chu, An, & Liang, 2016): One is allocation for value, take carbon tax (Cui & Song, 2017) as representative; the other is allocation for free, take quotas allocation (Gomes & Lins, 2008) as representative.…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Fang²,

Feng

et al. 2019

Tropical Conservation Science

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Zhejiang province, one of the classic subtropical regions in China, has promoted the establishment of a carbon trading market in recent years. The appropriate allocation of carbon emission abatement (CEA) quotas is the precondition for constructing a carbon trading market. This article mainly allocates municipal CEA quotas in Zhejiang province during the 12th Five-Year period based on data envelopment analysis approach. The main results reveal that Zhejiang exhibits relatively high environmental efficiency; carbon emission reduction in moderate level would bring gross domestic product growth for certain cities; the actual CEA quotas allocation of Zhejiang during 12th Five-Year period could be further optimized under the precondition of the national requirement of carbon intensity. Possible policy suggestions are provided in terms of the results.

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Carbon emission abatement quota allocation in Chinese manufacturing industries: An integrated cooperative game data envelopment analysis approach

Cited by 49 publications

References 110 publications

Efficiency of China’s Listed Securities Companies: Estimation through a DEA-Based Method

Efficiency of China’s Listed Securities Companies: Estimation through a DEA-Based Method

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

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

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