Eco-efficiency measurement of industrial sectors in China: A hybrid super-efficiency DEA analysis

Wang, Xinmin; Huang, Ding; Liu, Ling

doi:10.1016/j.jclepro.2019.05.014

Cited by 85 publications

(28 citation statements)

References 42 publications

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“…Economic development and industrialisation rely on high resource input, while the capacities of the environment and resources are neglected, which caused undesirable outputs and ecological crises (Wang et al, 2019). Undesirable outputs comprise wastewater, CO 2 emission, air pollution, etc., which are dangerous for the environment (Tohidi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Green productivity and undesirable outputs in agriculture: a systematic review of DEA approach and policy recommendations

Štreimikis¹,

Saraji

2021

Economic Research-Ekonomska Istraživanja

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Measuring efficiency in the presence of undesirable outputs could be difficult depending on how to treat these outputs; thus, undesirable outputs modelling has been an exciting subject of several studies in the Data envelopment analysis (DEA) literature in the last two decades. The present study aims to illustrate a thorough overlook of studies in which DEA has applied for measuring efficiency with undesirable outputs. Fifty-eight articles were published from 2000 to 2020 have been systematically reviewed through PRISMA protocol. The results indicated that "Journal of Cleaner Production" ranked first with six published articles, and Chinese scholars have the most contributions to this field, with twenty-third articles. Also, almost a quarter of the published articles' scope was related to agricultural pollution, and thirteen articles were published in 2016, the highest number of published articles annually. Taken together, the theoretical and empirical implications of research in the field of Green Productivity are discussed, and some policies were recommended.

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Section: Introductionmentioning

confidence: 99%

Green productivity and undesirable outputs in agriculture: a systematic review of DEA approach and policy recommendations

Štreimikis¹,

Saraji

2021

Economic Research-Ekonomska Istraživanja

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“…The performance assessment in the empirical literature can be generally divided into two groups, with the first one focusing on the use of accounting ratios and the second stream concentrating on the use of parametric or non-parametric operational research methods. In particular, the use of non-parametric data envelopment analysis has been gaining popularity in estimating efficiency in various economic sectors, including the high technology industry (An et al, 2020); the hotel sector (Assaf et al, 2010;Yin et al, 2020;Yu & Chen, 2020); insurance industry (Barros et al, 2010;Cummins et al, 2010;Eling & Jia, 2019;Eling & Luhnen, 2010): telecommunications industry (Bayraktar et al, 2012;Sahoo & Sahoo, 2020); transportation industry (Chang et al, 2013;Merkert & Hensher, 2011;Stefaniec et al, 2020;Wu & Goh, 2010;Yu, 2010); iron and steel industry (He et al, 2013;Lee et al, 2019); the tourism industry (Pestana et al, 2011;Song & Li, 2019); the agricultural sector (Picazo-Tadeo et al, 2011;Wang & Feng, 2020); the hospital sector (Rouyendegh et al, 2019); petroleum industry (Sueyoshi & Goto, 2012;Wang et al, 2019); the education sector (Aparicio et al, 2019;Thanassoulis et al, 2011); and the energy industry (Wang et al, 2013;Wanke et al, 2020;Zhou et al, 2012Zhou et al, , 2013. Also, the DEA models have been applied to analyse the efficiency level in various business-related operational issues, including supply chain management (Azadi et al, 2015;Malesios et al, 2020;Mirheda...…”

Section: Literature Reviewmentioning

confidence: 99%

Bank efficiency estimation in China: DEA-RENNA approach

et al. 2021

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The current study proposes a new DEA model to evaluate the efficiency of 39 Chinese commercial banks over the period 2010–2018. The paper also, in the second stage, investigates the inter-relationships between efficiency and some bank-specific variables (i.e. bank profitability, bank size, expenses management, traditional business and non-traditional business) under the Robust Endogenous Neural Network Analysis. The findings suggest that the sample of Chinese banks experiences a consistent increase in the level of bank efficiency up to 2015; the efficiency score is 0.915, after which the efficiency level declines and then experiences a slight volatility, while finally ending up with an efficiency score of 0.746 by the end of 2018. We also find that among different bank ownership types, the state-owned banks have the highest efficiency, the rural commercial banks are found to be least efficient and the foreign banks experience the strongest volatility over the examined period. The second-stage analysis shows that bank size exerts a positive influence on the development of non-traditional banking business and a proactive expense management, bank size and non-traditional businesses have a positive impact on efficiency levels, while bank profitability, traditional businesses and expenses management have negative influences on bank efficiency.

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“…, cost-effectiveness (Alizadeh, Majidpour, et al, 2016;Azadi, et al, 2015;Badiezadeh, et al, 2018;Luthra, et al, 2017;X. Zhou, et al, 2016), resource efficiency (Bai, et al, 2019;Cagliano, et al, 2017;Halkos, et al, 2019;Kiani Mavi, et al, 2019;Xiaohong Liu, et al, 2019;Ross, et al, 2010;Soltanisehat, et al, 2020;Zhang, et al, 2019;, and eco-efficiency (Alizadeh, et al, 2014;Bang, et al, 2019;Ezici, et al, 2020;Hu, et al, 2019;Xiuli Liu, et al, 2019;Mavi, et al, 2019;Shao, et al, 2019;Torres-Ruiz, et al, 2019;Wang, et al, 2019;Yu, et al, 2019;Zhu, et al, 2019), which only have focused on a limited set of energy sources in their analysis. However, in this paper, we consider all the possible electricity generation sources.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy production efficiency assessment using network data envelopment analysis

Beiragh¹,

Alizadeh²,

Beiragh³

et al. 2021

Preprint

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The performance of an energy generation system depends on three sectors of generation, transmission, and distribution. Especially when the power generation system has a decentralized structure in which different companies have different types of power plants, transmission, and distribution architectures. In this study, a network data envelopment analysis (NDEA) model is extended to assess the energy generation system's performance considering all three sectors of the regional electricity companies. There is a gap in the published DEA literature on energy network efficiency in studying the function of individual processes of these subsystems. After identifying the companies' structure, inputs, and outputs for all sectors, the extended NDEA model is implemented to estimate the efficiency of the whole energy generation network including production, transfer, and circulation sectors of the firms. The proposed model's network-based features allow computing the efficiency of individual subsystems and the whole system simultaneously. Iran’s energy generation system, including all three sectors during 2015–2019, is used to verify the model. The results indicate that while the annual average efficiency score of electricity companies has been increasing by 2018, it was decreased during 2018–2019. The model can be easily applied to energy generation systems in other countries.

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Eco-efficiency measurement of industrial sectors in China: A hybrid super-efficiency DEA analysis

Cited by 85 publications

References 42 publications

Green productivity and undesirable outputs in agriculture: a systematic review of DEA approach and policy recommendations

Green productivity and undesirable outputs in agriculture: a systematic review of DEA approach and policy recommendations

Bank efficiency estimation in China: DEA-RENNA approach

Energy production efficiency assessment using network data envelopment analysis

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