Copula Approach to Multivariate Energy Efficiency Analysis

2021 ◽  
Author(s):  
Mervenur Sözen ◽  
Mehmet Ali Cengiz
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Data Envelopment ◽  
Network Dea ◽  
Dependency Structure ◽  
Optimal Weights ◽  
Joint Efficiency ◽  
Copula Approach

Data envelopment analysis (DEA) is a method that finds the effectiveness of an existing system using a number of input and output variables. In this study, we obtained energy efficiencies of construction, industrial, power, and transportation sectors in OECD countries for 2011 using DEA. It is possible to achieve the efficiencies in different sectors. However, we aim to find joint energy efficiency scores for all sectors. One of the methods proposed in the literature to obtain joint efficiency is network data envelopment analysis (network DEA). Network DEA treats sectors as sub-processes and obtains system and process efficiencies through optimal weights. Alternatively, we used a novel copula-based approach to achieve common efficiency scores. In this approach, it is possible to demonstrate the dependency structure between the efficiency scores of similar qualities obtained with DEA by copula families. New efficiency scores are obtained with the help of joint probability distribution. Then, we obtained joint efficiency scores through the copula approach using these efficiency scores. Finally, we obtained the joint efficiency scores of the same sectors through network DEA. As a result, we compared network DEA with the copula approach and interpreted the efficiencies of each energy sector and joint efficiencies.

scholarly journals Modelling Carbon Emissions Efficiency from UK Higher Education Institutions Using Data Envelopment Analysis

2019 ◽  
pp. 1-18
Author(s):  
Adefarati Oloruntoba ◽  
Japhet Tomiwa Oladipo
Keyword(s):  
Higher Education ◽  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Carbon Emission ◽  
Scale Effects ◽  
Higher Education Institutions ◽  
High Energy ◽  
Data Envelopment ◽  
Emission Efficiency ◽  
The Uk

Aims: To correlate the energy and carbon emission efficiency relative to research income, gross internal area, and population for all the Higher Education Institutions (HEIs) in the UK and to assess the comparative carbon emission efficiency of HEIs relative to economic metrics. Study Design:  Analytical panel data study. Place and Duration of Study: This paper evaluates the energy efficiency of 131 HEIs in the UK subdivided into Russell and non-Russell groups from 2008 to 2015. Methodology: Data Envelopment Analysis (DEA) and Malmquist productivity indexes (MPI) are used for the efficiency calculations. Results: The empirical results indicate that UK HEIs have relatively high energy efficiency scores of 96.9% and 77.6% (CRS) and 98.5%, 86.3% (VRS) for Russell and non-Russell groups respectively. Conclusion: The evidence from this study reveals that HEIs are not significantly suffering from scale effects, hence, an increase in energy efficiency of these institutions is feasible with the present operating scale but would need to work on their technical improvements in energy use. Malmquist index analysis confirms the lack of substantial technological innovation, which impedes their energy efficiency and productivity gain. Findings show that pure technical efficiency accounts for the annual efficiency obtained in the DEA model, the technological progress in contrast is the source of their energy inefficiency.

Energy efficiency improvement and input cost saving in kiwifruit production using Data Envelopment Analysis approach

2011 ◽  
Vol 36 (9) ◽  
pp. 2573-2579 ◽  
Author(s):  
Ali Mohammadi ◽  
Shahin Rafiee ◽  
Seyed Saeid Mohtasebi ◽  
Seyed Hashem Mousavi Avval ◽  
Hamed Rafiee
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Cost Saving ◽  
Analysis Approach ◽  
Data Envelopment ◽  
Efficiency Improvement ◽  
Energy Efficiency Improvement ◽  
Using Data ◽  
Input Cost

scholarly journals Avaliação de desempenho e integração docente-assistencial nos hospitais universitários

2010 ◽  
Vol 44 (4) ◽  
pp. 581-590 ◽  
Author(s):  
Maria Stella de Castro Lobo ◽  
Marcos Pereira Estellita Lins ◽  
Angela Cristina Moreira da Silva ◽  
Roberto Fiszman
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Network Dea ◽  
De Medicina

OBJETIVO: Avaliar o desempenho e a integração entre as dimensões de assistência e de ensino dos hospitais universitários brasileiros. MÉTODOS: Um modelo de data envelopment analysis em redes (network DEA) foi elaborado para aferir o desempenho de hospitais universitários federais, o qual permite considerar a relação entre as dimensões de ensino e de assistência, simultaneamente. Foram utilizados os dados do Sistema de Informação dos Hospitais Universitários do Ministério da Educação, referentes ao segundo semestre de 2003, e os resultados do modelo network foram comparados àqueles dos modelos DEA tradicionais para avaliação das vantagens da nova proposta metodológica. RESULTADOS: A eficiência dos hospitais avaliados variou entre 0,19 e 1,00 (média = 0,54). O escore dimensional mostrou que os hospitais priorizam o ganho de eficiência assistencial. Observou-se que há necessidade de dobrar o número de alunos de medicina e de aumentar os residentes em 14% para que se tornem eficientes na dimensão de ensino. CONCLUSÕES: O modelo mostrou utilidade de aplicação tanto para os gestores das unidades, visando à integração docente-assistencial, como para os órgãos reguladores, na definição de políticas e incentivos.

An optimistic-pessimistic DEA model based on game cross efficiency approach

2020 ◽  
Vol 54 (4) ◽  
pp. 1215-1230
Author(s):  
Mediha Örkcü ◽  
Volkan Soner Özsoy ◽  
H. Hasan Örkcü
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Equilibrium Solution ◽  
Data Envelopment ◽  
Optimal Weights ◽  
Discriminating Power ◽  
Proposed Model ◽  
Essential Problem ◽  
Decision Making Units ◽  
Selection Of

The ranking of the decision making units (DMUs) is an essential problem in data envelopment analysis (DEA). Numerous approaches have been proposed for fully ranking of units. Majority of these methods consider DMUs with optimistic approach, whereas their weaknesses are ignored. In this study, for fully ranking of the units, a modified optimistic–pessimistic approach, which is based on game cross efficiency idea is proposed. The proposed game like iterative optimistic-pessimistic DEA procedure calculates the efficiency scores according to weaknesses and strengths of units and is based on non-cooperative game. This study extends the optimistic-pessimistic DEA approach to obtain robust rank values for DMUs. The proposed approach yields Nash equilibrium solution, thus overcomes the problem of non-uniqueness of the DEA optimal weights that can possibly reduce the usefulness of cross efficiency. Finally, in order to verify the validity of the proposed model and to show the practicability of algorithm, we apply a real-world example for selection of industrial R&D projects. The proposed model can increase the discriminating power of DMUs and can fully rank the DMUs.

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