scholarly journals Review of Life-Cycle Approaches Coupled with Data Envelopment Analysis: Launching the CFP + DEA Method for Energy Policy Making

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ian Vázquez-Rowe ◽  
Diego Iribarren
Keyword(s):  
Life Cycle ◽  
Data Envelopment Analysis ◽  
Energy Policy ◽  
Environmental Impacts ◽  
Environmental Sustainability ◽  
Policy Making ◽  
Energy Systems ◽  
Data Envelopment ◽  
Dea Method ◽  
Operating Points

Life-cycle (LC) approaches play a significant role in energy policy making to determine the environmental impacts associated with the choice of energy source. Data envelopment analysis (DEA) can be combined with LC approaches to provide quantitative benchmarks that orientate the performance of energy systems towards environmental sustainability, with different implications depending on the selected LC + DEA method. The present paper examines currently available LC + DEA methods and develops a novel method combining carbon footprinting (CFP) and DEA. Thus, the CFP + DEA method is proposed, a five-step structure including data collection for multiple homogenous entities, calculation of target operating points, evaluation of current and target carbon footprints, and result interpretation. As the current context for energy policy implies an anthropocentric perspective with focus on the global warming impact of energy systems, the CFP + DEA method is foreseen to be the most consistent LC + DEA approach to provide benchmarks for energy policy making. The fact that this method relies on the definition of operating points with optimised resource intensity helps to moderate the concerns about the omission of other environmental impacts. Moreover, the CFP + DEA method benefits from CFP specifications in terms of flexibility, understanding, and reporting.

Joint Life Cycle Assessment and Data Envelopment Analysis for the benchmarking of environmental impacts in rice paddy production

2015 ◽  
Vol 106 ◽  
pp. 521-532 ◽  
Author(s):  
Ali Mohammadi ◽  
Shahin Rafiee ◽  
Ali Jafari ◽  
Alireza Keyhani ◽  
Tommy Dalgaard ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Data Envelopment Analysis ◽  
Environmental Impacts ◽  
Rice Paddy ◽  
Data Envelopment

scholarly journals Selecting Suitable, Green Port Crane Equipment for International Commercial Ports

2021 ◽  
Vol 13 (12) ◽  
pp. 6801
Author(s):  
Guo-Ya Gan ◽  
Hsuan-Shih Lee ◽  
Yu-Jwo Tao ◽  
Chang-Shu Tu
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Protection ◽  
Green Energy ◽  
Programming Approach ◽  
Data Envelopment ◽  
Container Handling ◽  
Dea Method ◽  
Green Port ◽  
Large Container ◽  
Gantry Cranes

Responding to the increasing global need for environmental protection, a green port balances economic vibrancy with environmental protection. However, because exhaust emissions (e.g., CO2 or sulfide) are difficult to monitor around ports, data on such emissions are often incomplete, which hinders research on this topic. The present study aimed to fill this gap in this topic. To remedy this problem, this study formulated a new data envelopment analysis (DEA) method for collecting CO2 emissions data at their source. This method was applied to collect real-world operating data from a large container-handling company in Taiwan. Specifically, we provide a real example using a novel green energy index to account for undesirable outputs. Our main objective was to formulate two methods that combine: (1) data envelopment analysis based on a modified slack-based measure, and (2) a multi-choice goal programming approach. The contributions of this paper included the finding that rubber-tired gantry cranes are the greenest and should be used in ports. Finally, our findings aid port managers in selecting port equipment that provides the best balance between environmental protection and profitability.

scholarly journals Benchmarking—A Way of Finding Risk Factors in Business Performance

2021 ◽  
Vol 14 (5) ◽  
pp. 221
Author(s):  
Jarmila Horváthová ◽  
Martina Mokrišová ◽  
Mária Vrábliková
Keyword(s):  
Risk Factors ◽  
Data Envelopment Analysis ◽  
Heat Supply ◽  
Business Performance ◽  
Data Envelopment ◽  
Performance Factors ◽  
Best Value ◽  
Target Values ◽  
Dea Method ◽  
Comparison Of The Results

The purpose of this study was to emphasize that the Data Envelopment Analysis (DEA) method is an important benchmarking tool which provides necessary information for improving business performance. To fulfil the abovementioned goal, we used a sample of 48 Slovak companies involved in the field of heat supply. As their position in the economic and social environment of the country is essential, considerable attention should be paid to improving their performance. In addition to the DEA method, we applied the Best Value Method (BVM). We found that DEA is a highly important benchmarking tool, as it provides benchmarks for units that have problems with performance and helps us to reveal risk performance factors. The DEA method also allows us to determine target values of indicators. The originality of this paper is in its comparison of the results of the BVM and the DEA methods.

scholarly journals The Efficiency of Non-Bank Financial Intermediaries: Empirical Evidence from Malaysia

2008 ◽  
Author(s):  
Fadzlan Sufian
Keyword(s):  
Risk Factors ◽  
Data Envelopment Analysis ◽  
Empirical Evidence ◽  
Financial Institutions ◽  
Economies Of Scale ◽  
Financial Intermediaries ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Parametric Data ◽  
Dea Method

This paper investigates the performance of Malaysian non-bank financial institutions during the period of 2000-2004. Several efficiency estimates of individual NBFIs are evaluated using the non-parametric Data Envelopment Analysis (DEA) method. The findings suggest that during the period of study, scale inefficiency outweighs pure technical inefficiency in the Malaysian NBFI sector. We find that the merchant banks have exhibited a higher, technical efficiency compared to their peers. The empirical findings suggest that scale efficiency tends to be more sensitive to the exclusion of risk factors, implying that potential economies of scale may be overestimated when risk factors are excluded.  

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