Translation invariance in data envelopment analysis

Ali, Agha Iqbal; Seiford, Lawrence M.

doi:10.1016/0167-6377(90)90061-9

Cited by 329 publications

(27 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, such treatment does not conform to physical laws and standard production theory as it incurs conceptual confusion and does not reflect the true production process in the DEA result [21]. The "translation invariance method" adds a large scalar to each of the undesired output values to make the resulting output positive [22]. However, one drawback of this approach is that the scalar selected can change the efficient frontier and shift the position of zero.…”

Section: Dea Introductionmentioning

confidence: 99%

Eco-Efficiency Assessment for Some European Countries Using Slacks-Based Measure Data Envelopment Analysis

et al. 2020

View full text Add to dashboard Cite

One problem raised by the lack of energy efficiency is the generation of more greenhouse gases (GHGs) that can cause air pollution and climate change. Ecological efficiency (eco-efficiency) means the efficiency of resources used. A poor performance from this efficiency can then be detected for further improvement. In this research, we conduct an assessment on the eco-efficiency for some European countries as they consume a large part of global energy annually. A total of 17 European countries were selected as decision making units (DMUs) and assessed by the Slacks-based measure (SBM) Data Envelopment Analysis (DEA) model. Indices including Catch-Up, Frontier-Shift, and Malmquist Productivity Index (MPI) have been used to evaluate eco-efficiency, as well as efficiency change, technological change, and productivity change, over 2013-2017. In the model, energy consumption and share of renewable energy are used as energy inputs, and labor productivity and gross capital formation are used as economy inputs. On the other hand, GDP is used as a desired output, and CO 2 emissions is used as one undesired output. The experimental results show that the 17 countries as a whole lacked eco-efficiency in 2013-2017, implying more efforts are required to improve their eco-efficiency.Eco-efficiency is measurable based on selected input and output variables. For example, the authors of reference [2] measured the co-efficiency as the ratio between the value of what has been produced (e.g., GDP) and the environment impacts of the product or service (e.g., SO 2 emissions). The purpose of eco-efficiency measurement is to best utilize inputs such as energy, water, soil, and raw materials so as to maximize the benefit to human beings [4] or minimize bad outputs such as pollution and CO 2 , which can damage our environment. For every country in the world, improving eco-efficiency is necessary, as the protection of the environment is everyone's responsibility. Problem DefinitionOne problem raised by the lack of eco-efficiency is the generation of more greenhouse gases (GHGs), which can further lead to the global warming effect. The global warming effect, in turn, can lead to climate change, which might result in disasters and threaten the lives of human beings. International organizations have developed some programs to deal with the global warming effect. For example, the Paris Agreement initiated at the COP21 meeting held in December 2015 has set an objective to keep the rise in average global temperature below 2 • C [5]. The achievement of this difficult objective requires collective efforts from all countries. To achieve this objective, eco-efficiency is considered one of the effective approaches [6,7]. This is why the assessment of eco-efficiency has become the focus of this research. Though every country in the world needs to improve eco-efficiency, in this research, we focused on European countries, as they consume a lot of global energy annually. In fact, energy consumption, especially the use of fossil fuels, is one of the main s...

show abstract

Section: Dea Introductionmentioning

confidence: 99%

Eco-Efficiency Assessment for Some European Countries Using Slacks-Based Measure Data Envelopment Analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Since COD emission is an undesirable or negative output, it is necessary to transform the negative index to a positive index in order to use the Shephard radial distance function and the identifying criteria of biased technical change in Table 2, which is derived on the assumption of Shephard radial distance function. One main method for transforming a negative indicator to a positive indicator is to transform the negative indicator to the form of additive inverses (-y), and add to the additive inverses a sufficient large positive constant c, then construct the transformed positive indictor y' by y'= −y + c [44,45]. The advantage of this transformation method is that it does not change the internal linear structure of the original data.…”

Section: Indicators and Datamentioning

confidence: 99%

Green Biased Technical Change in Terms of Industrial Water Resources in China’s Yangtze River Economic Belt

Xi-yue

Sun

Wang

2020

IJERPH

View full text Add to dashboard Cite

As a significant ecological corridor from west to east across China, the Yangtze River Economical Belt (YREB) is in great need of green development and transformation. Rather than only focusing on the overall growth of green productivity, it is important to identify whether the technical change is biased towards economic performance or green performance in promoting green productivity. By employing the biased technical change theory and Malmquist index decomposition method, we analyze the green biased technical change in terms of industrial water resources in YREB at the output side and the input side respectively. We find that the green biased technical change varies during 2006–2015 at both the input side and output side in YREB. At the input side, water-saving biased technical change is generally dominant compared to water-using biased technical change during 2006–2015, presenting the substitution effects of non-water production factors. At the output side, the economy-growth biased technical change is the main force to promote green productivity, whereas the role of water-conservation biased technical change is insufficient. The green performance at the output side needs to be strengthened compared to the economic performance in YREB. A series of water-related environmental policies introduced in China since 2008 have promoted the green biased technical change both at the input side and the output side in YREB, but the policy effects at the output side is still inadequate compared to that at the input side. The technological innovation in sewage treatment and control need to catch up with the economic growth in YREB. Our research gives insights to enable a deeper understanding of the green biased technical change in YREB and will benefit more focused policy-making of green innovation.

show abstract

“…Another type of transformation is based on f (U) = −U + β, called TRβ, in which everything is dependent on β. Among those who have used this method, one can mention Ali and Seiford [33], Pastor [34], Scheel [35], Seiford and Zhu [30], Lin et al [36], Zhou and Hu [37], and Liu et al [38]. Another type of transformation is based on f (U) = 1/U, called multiplicative inverse, MLT (Multiplicative inverse), which was suggested by Golany and Roll [39].…”

Section: Introductionmentioning

confidence: 99%

Cost Efficiency Evaluation Based on a Data Envelopment Analysis Approach by Considering Undesirable Outputs on the Basis of the Semi-Disposability Assumption

Khorramabadi

Yarahmadi

Ghiyasi

2020

MCA

View full text Add to dashboard Cite

It is considerably important to calculate the cost efficiency in data envelopment analysis for the efficiency evaluation of decision-making units. The present paper develops the classical cost efficiency model in which all the input prices are constant and certain for each decision-making unit, considering undesirable outputs under the semi-disposability assumption. The proposed models are interval and uncertain under the constant returns to scale and also variable returns to scale assumptions, for the easy solution of which, their lower and upper bounds are obtained on the basis of the theorem presented in the text. In order to simulate the proposed models and show their scientific capabilities, additionally, 56 electricity producing thermal power plants in Iran were studied in 2015. Results of the present study show that under both assumptions of constant returns to scale and variable returns to scale, the highest cost efficiency bounds belonged to the combined and steam cycle power plants. Moreover, the average of lower and upper cost efficiency bounds of the power plants under study were 34% and 35%, respectively, in 2015, under the constant returns to scale assumption, and 52% and 54%, respectively, under the variable returns to scale assumption.

show abstract

Translation invariance in data envelopment analysis

Cited by 329 publications

References 11 publications

Eco-Efficiency Assessment for Some European Countries Using Slacks-Based Measure Data Envelopment Analysis

Eco-Efficiency Assessment for Some European Countries Using Slacks-Based Measure Data Envelopment Analysis

Green Biased Technical Change in Terms of Industrial Water Resources in China’s Yangtze River Economic Belt

Cost Efficiency Evaluation Based on a Data Envelopment Analysis Approach by Considering Undesirable Outputs on the Basis of the Semi-Disposability Assumption

Contact Info

Product

Resources

About