Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach

Mousavi‐Avval, Seyed Hashem; Rafiee, Shahin; Jafari, Ali; Mohammadi, Ali

doi:10.1016/j.energy.2011.02.016

Cited by 151 publications

(72 citation statements)

References 33 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There was no study to calculate the energy equivalents of inputs in Iranian condition; therefore in this study the coefficients were obtained from the previous studies conducted in similar conditions. The energy equivalents were computed for all inputs and outputs using the conversion factors for farm machinery and equipments (Canakci et al, 2005), diesel fuel, human labor, chemical fertilizers (Mousavi-Avval et al, 2011), FYM, water for irrigation, chemicals , electricity (Mobtaker et al, 2010), canola seed and output (Beheshti Tabar et al, 2010).…”

Section: Energy Balance Analysis Methodsmentioning

confidence: 99%

Energy flow modeling and sensitivity analysis of inputs for canola production in Iran

Mousavi‐Avval

Rafiee

Jafari

et al. 2011

Journal of Cleaner Production

Self Cite

115

View full text Add to dashboard Cite

Section: Energy Balance Analysis Methodsmentioning

confidence: 99%

Energy flow modeling and sensitivity analysis of inputs for canola production in Iran

Mousavi‐Avval

Rafiee

Jafari

et al. 2011

Journal of Cleaner Production

Self Cite

115

View full text Add to dashboard Cite

“…An inefficient DMU, in turn, receives a score that reflects its improvement potential: a 0.7 score, for example, means that DMU j needs to reduce its inputs by 30% in order to reach the efficiency frontier. Equation (4) can be optimized either by reducing the virtual input while maintaining the output or increasing the virtual output while maintaining the inputs; the first approach is called input-oriented model, and the second is called output-oriented model [26].…”

Section: Data Envelopment Analysis Overviewmentioning

confidence: 99%

“…The main objectives of DEA can be resumed as [24][25][26][27][28] (i) compare DMUs that perform the same activity and differ in inputs and outputs quantity; (ii) identify efficient DMUs, which will become benchmarks for inefficient DMUs; (iii) calculate the relative efficiency of all DMUs; (iv) provide strategies to improve the score of inefficient DMUs, optimizing their inputs/outputs. These strategies should not be taken as rigid objectives, but as suggestions of how the DMUs' inputs and outputs can be optimized [29].…”

Section: Data Envelopment Analysis Overviewmentioning

confidence: 99%

Dynamic Evaluation of the Energy Efficiency of Environments in Brazilian University Classrooms Using DEA

et al. 2017

View full text Add to dashboard Cite

This paper presents an experience applied to a public university campus using Data Envelopment Analysis (DEA) to evaluate and improve the energy efficiency of the indoor spaces of the buildings within the limits of the Federal University of Piauí, considering the lighting (installed power and luminous flux) and air conditioning (absorbed electric power and cooling capacity) input/output variables. Using Brazilian energy efficiency evaluation methods, a comparison was made between DEA and Brazilian standards, with the goal of examining DEA's performance and feasibility in efficiency improvement. The results revealed that all of the analyzed university classrooms were inefficient, which is coherent with the classification obtained by applying Brazilian standards; the calculated efficiency scores for these rooms varied from 0.7182 to 0.8477, a 0.7848 average. The DEA model, while operating in lighting and air conditioning systems, achieved a reduction of installed power of 43.5% and 22.7%, respectively, totaling a decrease of 25.6%, being able to maintain the standard characteristics of the systems mentioned. According to the DEA models, it was found that the generated targets effectively improved the efficiency of lighting and air-conditioning systems, reducing excessive inputs such as air conditioners' consumption and increasing deficient outputs such as luminous flux. It is possible to expand this successful application in the layout of the building in the whole campus area to concept small smart city projects; both have been achieved in the public buildings of the administrative body. Results from this paper revealed DEA's potential in assessing and optimizing the energy efficiency of buildings, improving their sustainability indexes, acting as a tool to support decision-making and benchmarking.

show abstract

“…Mousavi-Avval et al described the energy use pattern for canola production in the Golestan province of Iran and analyzed the degrees of technical and scale efficiency of producers using DEA [15]. Shi et al considered both undesirable outputs and minimization of energy consumption in measuring Chinese industrial energy efficiency and investigates the maximum energy-saving potential in 28 administrative regions in China [16].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Analysis of Integrated Energy and Environment Efficiency in China Based on a Green GDP Index

2011

View full text Add to dashboard Cite

Abstract:China is experiencing a high speed economic development which may exert great pressure on the environment and energy systems. To measure the environmental and energy performance during the economic development process, this paper selected 30 provinces, cities or autonomous regions as the decision making unit (DMU), and proposed a Green GDP index (GGI) in view of energy intensity and pollution intensity using the generalized Data Envelopment Analysis (DEA) method, and the developing trends of integrated energy and environment efficiency of DMUs from 2006 to 2010 are also demonstrated by the Malmquist index. Results show that the integrated energy and environment efficiency varies for each DMU. GGI were both 1 in Beijing and Shanghai. GGI values for the developed cities in Eastern China, such as Guangdong, Fujian, Zhejiang, Tianjin, Jiangsu, and Hainan, ranked high, while those in the Northeast and Middle China remained relatively low. Moreover, there is a positive relationship between the GGI and per capita GDP with a correlation coefficient of 0.75. Increases in GGI are also observed in the results, representing great achievements are acquired in energy conservation and emission reduction. However, the GGIs do not converge to the green frontier across the provinces.

show abstract

Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach

Cited by 151 publications

References 33 publications

Energy flow modeling and sensitivity analysis of inputs for canola production in Iran

Energy flow modeling and sensitivity analysis of inputs for canola production in Iran

Dynamic Evaluation of the Energy Efficiency of Environments in Brazilian University Classrooms Using DEA

Temporal and Spatial Analysis of Integrated Energy and Environment Efficiency in China Based on a Green GDP Index

Contact Info

Product

Resources

About