Carbon efficiency evaluation: An analytical framework using fuzzy DEA

Ignatius, Joshua; Ghasemi, Mohammad; Zhang, Feng; Emrouznejad, Ali; Hatami-Marbini, Adel

doi:10.1016/j.ejor.2016.02.014

Cited by 50 publications

(16 citation statements)

References 43 publications

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“…The results show that there are some disparities across groups, provinces and also plant level. Xie et al [34]; Iftikhar et al [35]; Ignatius et al [10] and Zhou et al [17] examined a country level energy efficiency using DEA model. Wang et al [13]; Wu et al [36]; Zeng et al [37]; Zha et al [38]; Meng et al [12] and Zhang et al [16] applied DEA models at regional level to evaluate the energy and environmental efficiency of China's regions.…”

Section: Literature Reviewmentioning

confidence: 99%

A two-stage analytical approach to assess sustainable energy efficiency

Ervural

Zaim

2018

Energy

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Section: Literature Reviewmentioning

confidence: 99%

A two-stage analytical approach to assess sustainable energy efficiency

Ervural

Zaim

2018

Energy

View full text Add to dashboard Cite

“…Charnes and Cooper (1985) combined the DEA with window analysis and proposed the DEA window analysis to estimate the dynamic effect of data. The fuzzy DEA model was proposed by Ignatius et al (2016) to evaluate CEE in 23 European Union (EU) countries. Slack-based measure (SBM) DEA model was applied by Choi et al (2012) to calculate the efficiency and potential CO 2 emissions reduction (PCR) (the slack of CO 2 emissions) in China.…”

Section: Introductionmentioning

confidence: 99%

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

Huang

Kwan

et al. 2018

Journal of Cleaner Production

142

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a b s t r a c tCities have been one of the most important areas of CO 2 emissions. It is increasingly important to research the effect of urbanization on CO 2 emissions, especially in large emerging and developing economies, due to the indispensable need for understanding the effect of urbanization on CO 2 emissions, evaluating carbon reduction tasks and providing the scientific basis for low-carbon urbanization. Utilizing a balanced panel dataset in the Yangtze River Delta (YRD), China, during the period of 2000e2010, this paper employed data envelopment analysis (DEA) window analysis and a spatial lag panel Tobit model to investigate the effect of urbanization on CO 2 emissions efficiency (the ratio of the target CO 2 emissions to the actual CO 2 emissions). The results show that the average CO 2 emissions efficiency was 0.959 in 2010, and CO 2 emissions efficiency ranged from 0.816 to 1 and exhibited spatial clustering in the region. The larger potential of CO 2 emissions reduction appeared in Zhenjiang and Yangzhou, indicating that more CO 2 emissions reduction tasks should be allocated to these two cities. Urbanization has negative effects on improving CO 2 emissions efficiency, and there is a U-curve relation between CO 2 emissions efficiency and urbanization, indicating that CO 2 emissions efficiency decreases at the early stage of urbanization, then increases when urbanization reach a high level. There is spatial spillover effect among the prefecture-level cities, suggesting that different prefecture-level governments should coordinate with each other to improve CO 2 emissions efficiency in the whole area. Gross domestic product (GDP) per capita also plays a markedly positive role in improving CO 2 emissions efficiency. This research highlights the effect of urbanization on CO 2 emissions efficiency and the importance of improving CO 2 emissions efficiency in developing countries.

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“…Sengupta 1992), (2) the α-level based approach (e.g. Kao and Liu, 2000;Hatami-Marbini and Saati, 2009;Ignatius et al 2016), (3) the fuzzy ranking approach (e.g., Guo and Tanaka, 2001), (4) the possibility approach (Lertworasirikul et al, 2003) (5) the fuzzy arithmetic (e.g., Wang et al 2009), and (6) the fuzzy random/type-2 (e.g., Qin et al 2009).…”

Section: Introductionmentioning

confidence: 99%

The role of multiplier bounds in fuzzy data envelopment analysis

et al. 2017

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The non-Archimedean epsilon ε is commonly considered as a lower bound for the dual input weights and output weights in multiplier data envelopment analysis (DEA) models. The amount of ε can be effectively used to differentiate between strongly and weakly efficient decision making units (DMUs). The problem of weak dominance particularly occurs in Fuzzy Data Envelopment Analysis where the reference set is fully or partially defined in terms of fuzzy numbers. In this paper, we propose a new four-step fuzzy DEA method to reshape weakly efficient frontiers along with revisiting the efficiency score of DMUs in terms of perturbing the weakly efficient frontier. This approach eliminates the non-zero slacks in fuzzy DEA while keeping the strongly efficient frontiers unaltered. In comparing our proposed algorithm to an existing method in the recent literature we show three important flaws in their approach that our method addresses. Finally, we present a numerical example in banking with a combination of crisp and fuzzy data to illustrate the efficacy and advantages of the proposed approach.

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Carbon efficiency evaluation: An analytical framework using fuzzy DEA

Cited by 50 publications

References 43 publications

A two-stage analytical approach to assess sustainable energy efficiency

A two-stage analytical approach to assess sustainable energy efficiency

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China

The role of multiplier bounds in fuzzy data envelopment analysis

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