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Applying optimization techniques to improve of energy efficiency and GHG (greenhouse gas) emissions of wheat production

机译:应用优化技术来提高小麦生产的能源效率和GHG(温室气体)排放

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In this study a non-parametric method of DEA (Data Envelopment Analysis) and MOGA (Multi-Objective Genetic Algorithm) were used to estimate the energy efficiency and greenhouse gas emissions reduction of wheat farmers in Ahvaz county of Iran. Data were collected using a face-to-face questionnaire method from 39 farmers. The results showed that based on constant returns to scale model, 41.02% of wheat farms were efficient, though based on variable returns to scale model it was 53.23%. The average of technical, pure technical and scale efficiency of wheat farms were 0.94, 0.95 and 0.98, respectively. By following the recommendations of this study, 3640.90 MJ ha(-1) could be saved (9.13% of total input energy). Moreover, 42 optimal units were found by MOGA. The total energy required and GHG (greenhouse gas) emissions of the best generation of MOGA were about 23105 MJ ha(-1) and 340 kgCO(2eq), ha(-1), respectively. The results revealed that the total energy required of MOGA was less than DEA, significantly. Also, the GHG emissions of present, DEA and MOGA farms were about 903, 837 and 340 kgCO(2eq). ha(-1), respectively. (C) 2016 Elsevier Ltd. All rights reserved.
机译:在这项研究中,采用DEA(数据包络分析)和MOGA(多目标遗传算法)的非参数方法来估算伊朗阿瓦兹县小麦农民的能源效率和温室气体减排量。使用面对面问卷法从39个农民那里收集了数据。结果表明,基于不变规模收益模型,有41.02%的小麦农场有效,而基于可变规模收益模型则为53.23%。小麦农场的平均技术效率,纯技术效率和规模效率分别为0.94、0.95和0.98。通过遵循本研究的建议,可以节省364.90 MJ ha(-1)(占总输入能量的9.13%)。此外,MOGA找到了42个最佳单位。最新一代的MOGA所需的总能量和温室气体排放分别约为23105 MJ ha(-1)和340 kgCO(2eq),ha(-1)。结果表明,MOGA所需的总能量明显小于DEA。此外,目前,DEA和MOGA农场的温室气体排放量分别约为903、837和340 kgCO(2eq)。 ha(-1)。 (C)2016 Elsevier Ltd.保留所有权利。

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