scholarly journals DEA BASED APPROACH TO SET ENERGY EFFICIENCY TARGET UNDER PAT FRAMEWORK: A CASE OF INDIAN CEMENT INDUSTRY

2018 ◽  
Vol 2 (1) ◽  
pp. 103 ◽  
Author(s):  
Anoop Singh ◽  
Bharat Sharma
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Energy Conversion ◽  
Production Process ◽  
Specific Energy Consumption ◽  
Cement Industry ◽  
Data Envelopment ◽  
Suggested Approach ◽  
Energy Efficiency Targets ◽  
Process Energy

Aim: Propose a Data Envelopment Analysis (DEA) based approach to set energy efficiency targets under the Perform, Achieve and Trade (PAT) framework of the Bureau of Energy Efficiency (BEE)  Design / Research methods: We adopt input-oriented non-controllable DEA model with variable return to scale DEA Conclusions / findings: Due to the implication of in-house energy conversion, we estimate separate energy efficiency targets based on “purchased energy” as well as “process energy”. The later accounts for energy finally used in the production process after in-house energy conversion. Originality / value of the article: The Bureau of Energy Efficiency (BEE) in India has introduced a market based energy efficiency mechanism under the Perform, Achieve and Trade (PAT) framework. Under this mechanism, energy efficiency certificates can be traded across eight identified sectors thus bringing cost effectiveness to achieve the energy efficiency targets. To implement the scheme, differentiated energy efficiency targets have been set using baseline specific energy consumption. This approach does not account for technical and operational aspects like vintage, scale, output mix and input mix. This study proposes an alternative target setting method based on Data Envelopment Analysis (DEA) which takes into account some of the above mentioned technical and operational differences across the industrial plants. A comparative assessment highlights the efficacy of DEA methodology in implementation of the PAT scheme. We estimate energy efficiency targets based on “purchased energy” as well as “process energy”, i.e. that used finally in the production process. Implications of the research: BEE may adopt the suggested approach to set energy efficiency targets for subsequent cycles under the Perform, Achieve and Trade (PAT) framework.

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.

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.

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

Evaluating two-stage holistic production systems using novel CSDEA approach

2018 ◽  
Vol 13 (1) ◽  
pp. 101-118 ◽  
Author(s):  
Dujun Zhai ◽  
Minyue Jin ◽  
Jennifer Shang ◽  
Chenfeng Ji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Production Process ◽  
Group Decision Making ◽  
Group Decision ◽  
Collective Decision ◽  
Data Envelopment ◽  
Two Stage ◽  
Decision Strategies ◽  
Content Type

Purpose The purpose of this paper is to apply data envelopment analysis (DEA) techniques to the collective decision-making environment to appraise two-stage production process under different decision preferences. Design/methodology/approach The authors propose a novel multi-criteria group decision-making approach that uses consensus-strategic data envelopment analysis (CSDEA) to appraise two-stage production process under two different decision strategies, which are efficiency- and fairness-based group decision preferences. Findings The authors find that the proposed CSDEA model evaluates the performance of the decision-making units (DMUs) not by diminishing other competitors but rather based on group interests of the entire decision set. Originality/value The authors extend Li’s two-stage model to cases that consider both intermediate inputs and outputs. The authors address the issue of incorporating collective managerial strategy into multi-criteria group decision-making and propose a novel CSDEA model that considers not only the individual-level performance of a DMU but also the group-level or collective decision strategies.

Determining the life cycle energy efficiency of six biofuel systems in China: A Data Envelopment Analysis

2014 ◽  
Vol 162 ◽  
pp. 1-7 ◽  
Author(s):  
Jingzheng Ren ◽  
Shiyu Tan ◽  
Lichun Dong ◽  
Anna Mazzi ◽  
Antonio Scipioni ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Life Cycle ◽  
Data Envelopment Analysis ◽  
Data Envelopment
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