Improving energy efficiency considering reduction of CO2 emission of turnip production: A novel data envelopment analysis model with undesirable output approach

Alireza Khoshroo, Mohammad Izadikhah, Ali Emrouznejad

Research output: Contribution to journalArticle

Abstract

Modern Turnip production methods need significant amount of direct and indirect energy. The optimum use of agricultural input resources results in the increase of efficiency and the decrease of the carbon footprint of turnip production. Data Envelopment Analysis (DEA) approach is a well-known technique utilized to evaluate the efficiency for peer units compared with the best practice frontier, widely used by researches to analyze the performance of agricultural sector. In this regard, a new non-radial DEA-based efficiency model is designed to investigate the efficiency of turnip farms. For this purpose, five inputs and two outputs are considered. The outputs consist turnip yield as a desirable output and greenhouse gas emission as an undesirable output. The new model projects each DMU on the strong efficient frontier. Several important properties are stated and proved which show the capabilities of our proposed model. The new models are applied in evaluating 30 turnip farms in Fars, Iran. This case study demonstrates the efficiency of our proposed models. The target inputs and outputs for these farms are also calculated and the benchmark farm for each DMU is determined. Finally, the reduction of CO2 emission for each turnip farm is evaluated. Compared with other factors like human labor, diesel fuel, seed and fertilizers, one of the most important findings is that machinery has the highest contribution to the total target energy saving. Besides, the average target emission of turnip production in the region is 7% less than the current emission.
Original languageEnglish
Pages (from-to)605-615
JournalJournal of Cleaner Production
Volume187
Early online date23 Mar 2018
DOIs
Publication statusE-pub ahead of print - 23 Mar 2018

Bibliographical note

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

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