scholarly journals Improving efficiency assessments using additive data envelopment analysis models: an application to contrasting dairy farming systems

2015 ◽  
Vol 24 (3) ◽  
pp. 235-248 ◽  
Author(s):  
Andreas Diomedes Soteriades ◽  
Philippe Faverdin ◽  
Margaret March ◽  
Alistair William Stott
Keyword(s):  
Data Envelopment Analysis ◽  
Feeding Strategy ◽  
Additive Model ◽  
Dairy Farm ◽  
Farming Systems ◽  
Environmental Efficiency ◽  
Feeding Strategies ◽  
Data Envelopment ◽  
Detailed Data ◽  
Dea Models

Applying holistic indicators to assess dairy farm efficiency is essential for sustainable milk production. Data Envelopment Analysis (DEA) has been instrumental for the calculation of such indicators. However, ‘additive’ DEA models have been rarely used in dairy research. This study presented an additive model known as slacks-based measure (SBM) of efficiency and its advantages over DEA models used in most past dairy studies. First, SBM incorporates undesirable outputs as actual outputs of the production process. Second, it identifies the main production factors causing inefficiency. Third, these factors can be ‘priced’ to estimate the cost of inefficiency. The value of SBM for efficiency analyses was demonstrated with a comparison of four contrasting dairy management systems in terms of technical and environmental efficiency. These systems were part of a multiple-year breeding and feeding systems experiment (two genetic lines: select vs. control; and two feeding strategies: high forage vs. low forage, where the latter involved a higher proportion of concentrated feeds) where detailed data were collected to strict protocols. The select genetic herd was more technically and environmentally efficient than the control herd, regardless of feeding strategy. However, the efficiency performance of the select herd was more volatile from year to year than that of the control herd. Overall, technical and environmental efficiency were strongly and positively correlated, suggesting that when technically efficient, the four systems were also efficient in terms of undesirable output reduction. Detailed data such as those used in this study are increasingly becoming available for commercial herds through precision farming. Therefore, the methods presented in this study are growing in importance.

scholarly journals Evaluating Environmental Efficiency of Turkish Cities by Data Envelopment Analysis

2015 ◽  
Vol 17 (2) ◽  
pp. 281-290 ◽  
Keyword(s):  
Water Pollution ◽  
Air Pollution ◽  
Economic Development ◽  
Data Envelopment Analysis ◽  
Empirical Study ◽  
Rapid Growth ◽  
Environmental Efficiency ◽  
Data Envelopment ◽  
Dea Models ◽  
The City

<p>Turkey is a developing country and has achieved impressive economic development in recent years. But this rapid growth has brought in many environmental problems in Turkish cities, such as air pollution, <a href="http://en.wikipedia.org/wiki/Water_pollution" title="Water pollution">water pollution</a> etc. In order to eliminate these problems, environmental performances of the city administrations must be evaluated. The objective of this empirical study is to evaluate the environmental efficiency of 81 Turkish provinces for the year 2010 by using by Data Envelopment Analysis (DEA) technique. Efficient and inefficient units were determined in the system by four proposed DEA models. According to each model, the environmental efficiency maps of Turkey are constructed and the risky regions of the country are determined.&nbsp;</p>

scholarly journals Assessing between and within Product Group Variance of Environmental Efficiency of Swiss Agriculture Using Life Cycle Assessment and Data Envelopment Analysis

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1862
Author(s):  
Dario Pedolin ◽  
Johan Six ◽  
Thomas Nemecek
Keyword(s):  
Life Cycle Assessment ◽  
Data Envelopment Analysis ◽  
Environmental Impacts ◽  
Food Production ◽  
Production Systems ◽  
Farming Systems ◽  
Environmental Efficiency ◽  
Agricultural Products ◽  
Data Envelopment ◽  
Product Group

Food production systems can contribute to the degradation of the environment; thereby endangering the very resource, they depend on. However, while overall large, the environmental impacts of individual agricultural products are disparate. Therefore, in order to gain a better understanding of the impact different food production systems have on the environment, we should start at the produce level. In this study, we combine life cycle assessment (LCA) methodology and data envelopment analysis to calculate environmental efficiency scores (i.e., agricultural output divided by environmental impacts) for eight product groups (Milk, Cattle, Pig fattening, Cereals, Beets, Potatoes, Vegetables, Fruits) in Switzerland. First, LCA is used to calculate “cradle to farm-gate” environmental impacts. These impacts are then used as inputs in a data envelopment analysis, with the amount of produced agricultural products as outputs. The resulting environmental efficiency scores reflect the relative efficiency (i.e., related to the best-observed performance) of the observed product groups. We find large differences in environmental impacts and environmental efficiency score distribution between the product groups. While we find some variability of environmental efficiency between farming systems (Organic and Proof of Ecological Performance) within a product group (difference in coefficient of variation between farming systems: Fruits = 48%, Vegetables = 13%, Cereals, Potatoes = 8%), we did not find any significant differences in environmental efficiency between organic and integrated farming systems for any of the considered product groups. Furthermore, we did not find a negative effect of multifunctionality of Swiss farms (i.e., multiple simultaneously produced product groups), but found a small positive effect for Milk in the presence of other product groups. However, the high within product group variance of environmental efficiency suggests the potential for improvements (notably >40% for Fruits and >30% for Cattle and Potatoes).

Efficiency changes index in the network data envelopment analysis with non-radial model

2018 ◽  
Vol 13 (02) ◽  
pp. 2050031
Author(s):  
Samaneh Esfidani ◽  
Farhad Hosseinzadeh Lotfi ◽  
Shabnam Razavyan ◽  
Ali Ebrahimnejad
Keyword(s):  
Data Envelopment Analysis ◽  
Insurance Industry ◽  
Returns To Scale ◽  
Additive Model ◽  
Stage Structure ◽  
Data Envelopment ◽  
Two Stage ◽  
Dea Models ◽  
The Difference ◽  
Constant Returns To Scale

Evaluating the efficiency and the performance of decision making units (DMUs) at different time periods is one of the most critical and important issues of managers. Data envelopment analysis (DEA) is a powerful non-parametric technique to measure the relative efficiency of a set of DMUs where each DMU consumes multiple inputs to produce multiple outputs. In many DEA applications, DMUs are considered as systems with a two-stage structure. In these situations, two-stage DEA models are used to measure the efficiencies of these systems. In many of such systems, the simultaneous presence of two stages is not necessary for the final product and the shortcoming of one stage is compensated by another stage. Therefore, this paper will use compensatory property of the sum operator and will propose the additive model to measure the multi-period efficiency of these systems under the constant returns to scale (CRS) assumption. In addition, based on the obtained efficiencies, the new efficiency changes Indexes (ECIs) related to the whole system and the first and second stages between two periods will be proposed that have circularity property. Furthermore, ECI of the whole system (and stages) for two periods is defined as the difference between the efficiencies in these periods. Moreover, positive changes (or negative changes), or unchanged in the efficiency of stages will be concluded by the positive changes (or negative changes), or unchanged of the whole system. Finally, the data of 21 non-life insurance industry in Taiwan are used to describe our suggested model that extracted from the extant literature.

scholarly journals The Environmental Efficiency Analysis Based on the Three-Step Method for Two-Stage Data Envelopment Analysis

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7028
Author(s):  
Qingyou Yan ◽  
Fei Zhao ◽  
Xu Wang ◽  
Tomas Balezentis
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Cooperative Game ◽  
Environmental Efficiency ◽  
Data Envelopment ◽  
Step Method ◽  
Two Stage ◽  
Dea Model ◽  
The Status ◽  
Dea Models

This paper suggests that the efficiency of a system (decision-making unit) and its subsystem cannot be properly measured using a two-stage data envelopment analysis (DEA) model either in cooperative or non-cooperative evaluation. Indeed, the existing methods subjectively determine the status of the subsystems in the whole system. The two-stage DEA models, either cooperative game or non-cooperative game, are used to analyze the environmental efficiency. However, when the actual relationship between the two subsystems is inconsistent with the subjective relationship assumptions, the overall efficiency of the system and the efficiency of each subsystem will be biased. The conventional two-stage DEA models require predetermining the relationship between the subsystems within the system based on the subjective judgment of the decision-maker. Based on this, this paper proposes a three-step method to solve the two-stage DEA. First, the position relation among subsystems is determined according to the optimal weights through the model. According to the status relationship among subsystems, the decision units are grouped, and the two-stage DEA model of cooperative game or non-cooperative game is used to analyze the efficiency in each group. This method reduces the subjectivity of decision making and analyzes the efficiency of each decision unit applying the most appropriate two-stage DEA model to find the source of inefficiency. Finally, this paper verifies the rationality and validity of the method by analyzing the water use efficiency of industrial systems in China. It is found that most regions in China value economic development more than environmental protection (as evidenced by the DEA weights). What is more, the method proposed by the paper can be generalized for any two-stage DEA problem.

Measuring Performance of a Three-Stage Network Structure Using Data Envelopment Analysis and Nash Bargaining Game: A Supply Chain Application

2018 ◽  
Vol 17 (05) ◽  
pp. 1429-1467 ◽  
Author(s):  
Mohammad Amirkhan ◽  
Hosein Didehkhani ◽  
Kaveh Khalili-Damghani ◽  
Ashkan Hafezalkotob
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Nash Bargaining ◽  
Data Envelopment ◽  
Second Stage ◽  
Proposed Model ◽  
Nash Bargaining Game ◽  
Dea Models ◽  
Overall Efficiency

The issue of efficiency analysis of network and multi-stage systems, as one of the most interesting fields in data envelopment analysis (DEA), has attracted much attention in recent years. A pure serial three-stage (PSTS) process is a specific kind of network in which all the outputs of the first stage are used as the only inputs in the second stage and in addition, all the outputs of the second stage are applied as the only inputs in the third stage. In this paper, a new three-stage DEA model is developed using the concept of three-player Nash bargaining game for PSTS processes. In this model, all of the stages cooperate together to improve the overall efficiency of main decision-making unit (DMU). In contrast to the centralized DEA models, the proposed model of this study provides a unique and fair decomposition of the overall efficiency among all three stages and eliminates probable confusion of centralized models for decomposing the overall efficiency score. Some theoretical aspects of proposed model, including convexity and compactness of feasible region, are discussed. Since the proposed bargaining model is a nonlinear mathematical programming, a heuristic linearization approach is also provided. A numerical example and a real-life case study in supply chain are provided to check the efficacy and applicability of the proposed model. The results of proposed model on both numerical example and real case study are compared with those of existing centralized DEA models in the literature. The comparison reveals the efficacy and suitability of proposed model while the pitfalls of centralized DEA model are also resolved. A comprehensive sensitivity analysis is also conducted on the breakdown point associated with each stage.

scholarly journals A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs

2021 ◽  
Vol 9 (4) ◽  
pp. 378-398
Author(s):  
Chunhua Chen ◽  
Haohua Liu ◽  
Lijun Tang ◽  
Jianwei Ren
Keyword(s):  
Data Envelopment Analysis ◽  
Transportation Systems ◽  
Decision Makers ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Super Efficiency ◽  
Dea Model ◽  
Dea Models ◽  
Analysis Models ◽  
Transport Accidents

Abstract DEA (data envelopment analysis) models can be divided into two groups: Radial DEA and non-radial DEA, and the latter has higher discriminatory power than the former. The range adjusted measure (RAM) is an effective and widely used non-radial DEA approach. However, to the best of our knowledge, there is no literature on the integer-valued super-efficiency RAM-DEA model, especially when undesirable outputs are included. We first propose an integer-valued RAM-DEA model with undesirable outputs and then extend this model to an integer-valued super-efficiency RAM-DEA model with undesirable outputs. Compared with other DEA models, the two novel models have many advantages: 1) They are non-oriented and non-radial DEA models, which enable decision makers to simultaneously and non-proportionally improve inputs and outputs; 2) They can handle integer-valued variables and undesirable outputs, so the results obtained are more reliable; 3) The results can be easily obtained as it is based on linear programming; 4) The integer-valued super-efficiency RAM-DEA model with undesirable outputs can be used to accurately rank efficient DMUs. The proposed models are applied to evaluate the efficiency of China’s regional transportation systems (RTSs) considering the number of transport accidents (an undesirable output). The results help decision makers improve the performance of inefficient RTSs and analyze the strengths of efficient RTSs.

scholarly journals The Efficiency of Waste Sector in Italy: An Application by Data Envelopment Analysis

2020 ◽  
Vol 24 (3) ◽  
pp. 225-238
Author(s):  
Massimo Gastaldi ◽  
Ginevra Virginia Lombardi ◽  
Agnese Rapposelli ◽  
Giulia Romano
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Performance ◽  
Returns To Scale ◽  
Sustainable Growth ◽  
Waste Reduction ◽  
Data Envelopment ◽  
Environmental Legislation ◽  
Dea Models ◽  
Developed Nations ◽  
Variable Returns To Scale

AbstractWith growing environmental legislation and mounting popular concern for the need to pursuing a sustainable growth, there has been an increasing recognition in developed nations of the importance of waste reduction, recycling and reuse maximization. This empirical study investigates both ecological and economic performances of urban waste systems in 78 major Italian towns for the years 2015 and 2016. To this purpose the study employs the non-parametric approach to efficiency measurement, represented by Data Envelopment Analysis (DEA) technique. More specifically, in the context of environmental performance we implement two output-oriented DEA models in order to consider both constant and variable returns to scale. In addition, we include an undesirable output – the total amount of waste collected – in the two models considered. The results show that there is variability among the municipalities analysed: Northern and Central major towns show higher efficiency scores than Southern and Islands ones.

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