Airport efficiency in Pakistan - A Data Envelopment Analysis with weight restrictions

Ennen, David; Batool, Irem

doi:10.1016/j.jairtraman.2018.02.007

Cited by 30 publications

(18 citation statements)

References 19 publications

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“…These advantages are possible because the DEA method is based on the efficient production surfaces rather than central tendencies (Cooper et al., 2011). For these reasons, DEA has been widely applied to analyze the performance of numerous entities such as air ports (Ennen & Batool, 2018), schools (Charnes, Cooper, & Rhodes, 1981), products (Laso et al., 2018; Sanjuan et al., 2011), industrial parks (Guan, Huang, Liu, Zhai, & Zheng, 2019; Liu, Tian, Chen, Lu, & Gao, 2015), national policies (Lee & Mogi, 2018), cities (Moutinho, Madaleno, Robaina, & Villar, 2018; Sanjuan et al., 2011), countries (Camarero, Castillo, Picazo‐Tadeo, & Tamarit, 2013), and regions (Halkos et al., 2016).…”

Section: Materials Methods and Datamentioning

confidence: 99%

“…In a traditional DEA, inputs (such as energy, materials, and capital) and outputs (such as GDP, value‐added, and waste emissions) can be used to construct an efficiency frontier (Lee & Mogi, 2018; Liu et al., 2015; Yu, Huang, & Zhang, 2018). All points lying on the efficiency frontier are considered “technically efficient” while those lying away from the frontier are relatively inefficient (Ennen & Batool, 2018; Fan et al., 2017). In this context, “technical efficiency” is defined as the ability to produce highest possible output from the available set of inputs (Farrell, 1957).…”

Section: Materials Methods and Datamentioning

confidence: 99%

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Chronological change of resource metabolism and decarbonization patterns in Pakistan: Perspectives from a typical developing country

Shah

Park

2020

J of Industrial Ecology

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With economic growth in many developing countries, not all are making similar progress with regard to material and environmental efficiencies. This study examines material use and CO2 emission patterns and intensities from 1971 to 2015 in a typical developing country, Pakistan, and investigates national‐level and multi‐country‐level efficiency improvements using data envelopment analysis. The results are used to derive key policy insights for a sustainable economic transition with higher resource and carbon efficiencies. Results show that material intensity has reduced by 39.1% while CO2 intensity has risen by 21.5% in the country. Pakistan, when compared with its top 10 export countries, was relatively more material and CO2 intensive. National‐level efficiency was found to be low in most of the periods due to material/energy intensive agriculture and industries, low value‐added exports, etc. Insights from the national‐level efficiency analysis indicate that surging CO2 intensities have started to decline since 2010 and the economy has greatly stabilized. Multi‐country analysis revealed that the efficiency gap between Pakistan and its developed export countries (such as the United Kingdom and France) has widened during the study period. Insights from the multi‐country analysis suggest that the economic growth and industrialization improves material and environmental efficiencies to some extent, yet these improvements are not equally distributed among all countries. As a way forward, integrated policies on sustainable resource consumption, carbon mitigation, and economic growth are necessary for accruing higher benefits from rising global trade and resource connectedness.

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Section: Materials Methods and Datamentioning

confidence: 99%

Section: Materials Methods and Datamentioning

confidence: 99%

Chronological change of resource metabolism and decarbonization patterns in Pakistan: Perspectives from a typical developing country

Shah

Park

2020

J of Industrial Ecology

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“…In recent years a variety of application areas of DEAs have been applied widely in various forms to evaluate the performance of such entities as hospitals, business firms, universities, regions, etc. (Asandului et al, 2014;Ennen & Batool, 2018;Fazlollahi & Franke, 2018;Hosseinzadeh-Bandbafha et al, 2018;Khushalani & Ozcan, 2017;Leleu et al, 2014). DEA is one of the MADM techniques and the relationship between DEA and MADM has been highlighted by a group of researchers (Doyle & Green, 1993;Hu et al, 2017;Lin et al, 2011Lin et al, , 2017Sinuany-Stern et al, 2000;Stewart, 1996).…”

Section: Literature Reviewmentioning

confidence: 99%

A novel holistic approach for solving the multi-criteria transshipment problem for infectious waste management

Wichapa¹,

Khokhajaikiat²

2019

10.5267/j.dsl

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Effective transshipment network is currently recognized as an important success determinant for most manufacturing organizations, because the transshipment management has significant impact on cost and environmental impact. Due to the complexity of the multi-criteria transshipment problem for infectious waste management (IWM) for this case, forty hospitals and three candidate disposal municipalities in Northeastern Thailand, a novel holistic approach (combination of fuzzy AHP, transshipment model and DEA) was developed for solving this problem. We first utilized the fuzzy AHP technique to calculate the location weights of each candidate disposal municipalities. Secondly, a new cost-based transshipment model was formulated and solved in order to provide the set of feasible solutions. These solutions can be viewed as decision making units (DMUs), inputs and outputs. Finally, DEA-CCR model was applied to calculate the efficiency scores of candidate DMUs. The study results demonstrated that the proposed holistic approach can help decision makers (DMs) to choose a suitable transshipment network for IWM. The major advantage of the proposed holistic approach is that both costs and environmental impacts under constraints are focused on simultaneously. Future work will apply the developed approach with other real-world complex problems to enhance the validity of the research output further. For large-size transshipment problems in which an exact solution cannot be found, meta-heuristics must be applied.

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“…He used assurance region to reduce weight flexibility. Ennen and Batool [35] applied weights restrictions for inputs and outputs in the DEA procedure to increase the ability of DEA to differentiate performance levels. Wang et al [36] developed a method to rank DMUs by imposing an appropriate minimum weight constraint on all input and output factors.…”

Section: Literature Reviewmentioning

confidence: 99%

A heuristic method for choosing 'virtual best' DMUs to enhance discrimination power of augmented DEA model

Rezaei

Haeri

2019

Scientia Iranica

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Despite its intrinsic advantageous features as a tool for increasing discrimination power of the basic DEA (data envelopment analysis) model, augmented DEA has two main drawbacks including the presence of unrealistic efficiency scores and the presence of great distance between its efficiency scores and scores obtained by primary model. In this regard, this paper extends a heuristic method for dealing with both issues and improving the power of augmented DEA model in performance evaluation. Since different virtual DMUs lead to different results for ranking, the hierarchical clustering algorithm is applied in this study to select the best virtual DMUs in order to reduce the possibility of having inappropriate efficiency scores. Finally, to demonstrate the superiority of the proposed approach over previous approaches in literature, two numerical examples are provided.

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Airport efficiency in Pakistan - A Data Envelopment Analysis with weight restrictions

Cited by 30 publications

References 19 publications

Chronological change of resource metabolism and decarbonization patterns in Pakistan: Perspectives from a typical developing country

Chronological change of resource metabolism and decarbonization patterns in Pakistan: Perspectives from a typical developing country

A novel holistic approach for solving the multi-criteria transshipment problem for infectious waste management

A heuristic method for choosing 'virtual best' DMUs to enhance discrimination power of augmented DEA model

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