On dealing with the pollution costs in agriculture: A case study of paddy fields

Yaqubi, M.R.; Shahraki, Javad; Sabouni, Mahmood Sabouhi

doi:10.1016/j.scitotenv.2016.02.193

Cited by 13 publications

(7 citation statements)

References 52 publications

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“…According to the present research results, agricultural pollution is the most attractive topic for scholars, including Falavigna et al (2013), Kuhn et al (2018), Yaqubi et al (2016), Berre et al (2013), Skevas et al (2014), Reinhard et al (2000), Wu et al (2013), , Buckley and Carney (2013), Coelli et al (2007), Zare-Haghighi et al (2014), Dong et al (2018), Sun et al (2016), working on productivity measurement with undesirable outputs. In agriculture, water, soil, and Greenhouse Gas (GHG) are three significant pollution sources (Chen et al, 2017).…”

Section: Introductionmentioning

confidence: 80%

“…Australian scholars utilised the directional distance function (Hoang & Coelli, 2011, Azad & Ancev, 2014, CCR-DEA (Coelli et al, 2007, Hoang & Alauddin, 2012, BCC-DEA (Pagotto & Halog, 2016). Iranian scholars utilised the directional distance function (Yaqubi et al, 2016), non-radial DEA (Babazadeh et al, 2015, Zare-Haghighi et al, 2014, BCC-DEA (Khoshroo et al, 2018).…”

Section: Distribution Of Articles By Authors' Nationalitymentioning

confidence: 99%

“…Li et al (2013) used constant returns-to-scale (CRS) and variable returnsto-scale (VRS)-DEA to allocate resources to reduce CO 2 emission effectively. Yaqubi et al (2016) used Directional Distance Functions (DDF)-DEA to assess environmental practices' efficiency and shadow values.…”

Section: Introductionmentioning

confidence: 99%

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Green productivity and undesirable outputs in agriculture: a systematic review of DEA approach and policy recommendations

Štreimikis¹,

Saraji

2021

Economic Research-Ekonomska Istraživanja

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Measuring efficiency in the presence of undesirable outputs could be difficult depending on how to treat these outputs; thus, undesirable outputs modelling has been an exciting subject of several studies in the Data envelopment analysis (DEA) literature in the last two decades. The present study aims to illustrate a thorough overlook of studies in which DEA has applied for measuring efficiency with undesirable outputs. Fifty-eight articles were published from 2000 to 2020 have been systematically reviewed through PRISMA protocol. The results indicated that "Journal of Cleaner Production" ranked first with six published articles, and Chinese scholars have the most contributions to this field, with twenty-third articles. Also, almost a quarter of the published articles' scope was related to agricultural pollution, and thirteen articles were published in 2016, the highest number of published articles annually. Taken together, the theoretical and empirical implications of research in the field of Green Productivity are discussed, and some policies were recommended.

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Section: Introductionmentioning

confidence: 80%

Section: Distribution Of Articles By Authors' Nationalitymentioning

confidence: 99%

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Green productivity and undesirable outputs in agriculture: a systematic review of DEA approach and policy recommendations

Štreimikis¹,

Saraji

2021

Economic Research-Ekonomska Istraživanja

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“…In addition, they do not provide information regarding the efficiency of the specific inputs and outputs included in the process [18,20,47]. To overcome such weaknesses, non-radial models have been developed are widely used in empirical research [21,48]. According to Lui et al [21], the non-radial DEA model also causes less bias.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Energy-Environment Efficiency of European Transport Sectors: Non-Radial DEA and TOPSIS Approach

Djordjević

Krmac

2019

Energies

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Transport is recognized as a major energy consumer and environment pollutant. Recently scholars have paid considerable attention to the evaluation of transport energy and environmental efficiency (EEE). In this paper, the non-radial Data Envelopment Analysis (DEA) model was employed to evaluate EEE on a macro level-i.e., of European road, rail and air sectors. The evaluation was conducted under the joint production framework, which considers energy and non-energy inputs, and desirable and undesirable outputs for the last ten years period. To rank decision-making units and check the aptness of this non-radial DEA model in transport EEE evaluation, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been proposed. An empirical study has been conducted for as many European countries as possible, depending on availability of data. Based on the non-radial DEA model, it could be said that the level of EEE is improving for the road sector, while many evaluated countries have low EEE for the rail transport sector. Additionally, results have indicated that the TOPSIS method is more suitable than the non-radial DEA model in transport EEE evaluation and for identification of best practices.Energies 2019, 12, 2907 The Aim and the Scope of the PaperThe aim of this paper is twofold. The first is to evaluate and analyze the changes of EEE of European road, air, and rail transport sectors, where the methodology for evaluating EEE is based on a non-radial DEA model proposed by Wu et al. [6] for 2006-2008, 2010, 2012, and 2014-2016, using the available data for the European countries which represent DMUs. The second aim of the paper is the introduction of the TOPSIS method in the evaluation of EEE, where the TOPSIS method is used for the ranking of DMUs. The evaluation of transport EEE has been done under the joint production framework, using non-energy inputs (labor and transport assets) and energy input (energy consumption) to produce desirable outputs (volume of passenger and freight transport) and undesirable output (GHG emissions). Aside from other widely used non-radial DEA models such as Slack-based models, Russell measure models, and Directional distance function, in this paper, the non-radial DEA model has been chosen due to its ability to use different non-proportional adjustments, with decision maker specified weights assigned to each efficiency score, and because of its ability to proportionally decrease the amounts of energy inputs and undesirable outputs simultaneously as much as possible [5,6].The main contributions of this study are: (i) a newly systematic literature review in the field of transport EEE evaluation, (ii) a new definition of transport EEE, (iii) the evaluation of EEE with an extended set of used inputs, (iv) the evaluation of EEE of road, air, and rail transport sectors of European countries and their changing tendencies in terms of the EEE, (v) use of non-radial DEA and introduction of the TOPSIS method through DMUs ranking in the evaluation of transport EEE, ...

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“…Malana and Malano (2006) have applied DEA to evaluate and rank productivity performance of wheat growing area in selected regions of Pakistan and India. Yaqubi et al (2016) have evaluated the marginal abatement cost of the main agricultural pollutants using DEA. The results showed that DEA was useful for benchmarking and analyzing the efficiency of agricultural units.…”

Section: Introductionmentioning

confidence: 99%

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

Khoshroo

Izadikhah

Emrouznejad

2018

Journal of Cleaner Production

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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 CO 2 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.

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On dealing with the pollution costs in agriculture: A case study of paddy fields

Cited by 13 publications

References 52 publications

Green productivity and undesirable outputs in agriculture: a systematic review of DEA approach and policy recommendations

Green productivity and undesirable outputs in agriculture: a systematic review of DEA approach and policy recommendations

Evaluation of Energy-Environment Efficiency of European Transport Sectors: Non-Radial DEA and TOPSIS Approach

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

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