Prioritizing and Investigating Economic-Environmental Dimensions of Renewable Energy Use in Agriculture

Document Type : Research Paper

Authors

• Mahsa Taslimi ¹
• HAMID AMIRNEJAD ²
• Seyed Mojtaba Mojaverian ³
• Hossein Azadi ⁴

¹ Sari Agricultural Sciences and Natural Resources

² Agricultural Economics Department Sari Agricultural Sciences and Natural Resources University

³ Associated Prof.وSari Agricultural Sciences and Natural Resources University.

⁴ Associated Prof., Gent University.

10.30495/jae.2022.22990.2079

Abstract

The agricultural sector relies heavily on energy consumption to meet the growing need for food. One of the challenges of sustainable agriculture is that the majority of farmers still use fossil energy. Carbon dioxide emissions in Iran have caused $ 26.27 billion in damage to the country, of which $ 1.57 billion is the share of the agricultural sector. The final energy consumption per capita in Iran's agricultural sector is 3.3 times the global average. Therefore, energy use in the country is very important. This study prioritized renewable energies in the agricultural sector of the north of Iran (Golestan, Mazandaran, and Guilan) using the Entropy weighting technique and the TOPSIS method. The information required for this study was collected using a Delphi method from 39 experts and five types of renewable energy and five criteria. The results showed that the Prioritization of energies are solar energy, wind energy, biomass energy, hydropower, and geothermal energy and Environmental, political, social, technical and economic criteria rank first to fifth in importance, respectively.

Keywords

• Sustainable Development
• TOPSIS
• Environmental Criteria
• Solar Energy

---

References

1. Razzaghi M, Rezaei R, Shabanali H. Identifying the Inhibiting Factors of Application Development of Renewable Energies in Smallholder Farming Systems of the Tafresh Township. IJE. 2012; 15 (3): 1-18.

2. United Nations (UN). 2015. https://www.Un.org.

3. Moghaddasi R. Ziaee G. Study of Relationships between Carbon Dioxide Emissions and GDP Per Capita Based on Panel Data. Journal of Agricultural Economics and Development (Agricultural Science and Technology). 2011; 25(4):480-487. [DOI: 10.22067/JEAD2.V0I0.12187]

4. Muntean M, Guizzardi D, Schaaf E, Crippa M, Solazzo E, Olivier JGJ, Vignati E. Fossil CO2 Emissions of All World Countries - 2018 Report, EUR 29433 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-79-97240-9. 2018. [DOI:10.2760/30158]

5. WHO. World Health Organization. Public Health and Environment. Ambient air pollution. 2019. https:// . www.who.int

6. International Energy Agency (IEA). 2019. https://www.iea.org.

7. Tashkini A, Oriani B, Saburi Deilami M. Energy carrier subsidy system: problems and the need to review it. Economic Journal (bi-monthly review of economic issues and policies). 2009; 9 (101) : 140-161.

8. Asiae M, Khiabani N, Mousavi B. The Environmental Effects of the Omission of Energy Carriers Subsidies in Iranian Manufacturing Sector. Iranian Energy Economics. 2012; 1(4): 1-24.

9. Amadeh H, Ghafari AR, Farajzadeh Z. Analysis of Environmental and Welfare Effects of Energy Subsidy Refor Application of Computable General Equilibrium Model. Iranian Energy Economics. 2014; 4(13): 33-62.

10. Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H. Reduction of CO2 emission by improving energy use efficiency of greenhouse cucumber production using DEA approach. Journal of Energy. 2013; 55: 676-682. [DOI:10.1016/j.energy.2013.04.021]

11. Hatirili SA, Ozkan B, Fert K. An econometric analysis of energy input-output in Turkish agriculture. Journal of Renewable and Sustainable Energy Reviews. 2005; 9: 608- 623.
[DOI:10.1016/j.rser.2004.07.001]

12. Shabanali Fami H, Ghasemi J, Malekipoor R, Rashidi R, Nazari S, Mirzaee A. Renewable energy use in smallholder farming systems: A case study in Tafresh Township of Iran. Sustainability. 2010; 2: 702-716. [DOI: 10.3390/su2030702]

13. Soheili K. Analysis of results of production technological improvements in agricultural sector on long-run energy demand in this sector by using a technical economic model (MEDEE-S). Agricultural Economics and Development. 2008: 15(4):45-70.

14. Energy balance sheet. Deputy Minister of Electricity and Energy, Ministry of Energy. 2015.

15. World Bank Group. State and Trends of Carbon Pricing 2019. Washington, DC: World Bank. World Bank. https://openknowledge.worldbank.org/handle/10986/31755 License: CC BY 3.0 IGO. 2019. www.worldbank.org.

16. Medusa D, Randers J. Medusa D. Growth restrictions (updated after thirty years). Habibi, A. and Pourasaghrangachin, F. Institute of Higher Education and Research Management and Planning. Tehran. 2009.

17. Lolaavar N, Niknami M. Investigation of Factors Affecting the Possibility of Using Solar Energy in Agriculture as Perceived by Experts of Tehran Province Agriculture Jihad Organization. 2015; 11(2): 135-148. [DOI:20.1001.1.20081758.1394.11.2.9.5]

18. Hamdollahi A, Mohammadi H, Khatib Semnani MA. Economic Evaluation of Solar Energy in Iran's Agricultural and Rural Sector. Journal of Rural Economics Research. 2016; 2(5): 83-100.

19. Jafar Kazemi F. The importance of solar energy in agriculture. Monthly Livestock, Agriculture and Industry. 2014; (175).

20. Razeghei S, Sha’ban Ali Femi H, Rezaei R. Factors Influencing on Farmers' Willingness in Equipping Farm to Renewable Energies Technology. Agricultural Extension and Education Research. 2013; 6(4):87-106.

21. Statistical Yearbook of Iran. Statistics Center of Iran. 2017.

22. Statistics of consumption of energy products. Iran Petroleum Products Distribution Company. 2016.

23. Razini S, Batahai SMT, Moghaddas Tafreshi SM. Prioritizing Iranian Renewable Energy Sources with the MCDM Approach. 19th Iranian Conference on Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, May 27-29. 2011.

24. Sadeghi A, Larimian T, Molabashi A. Evaluation of Renewable Energy Sources for Generating Electricity in Province of Yazd: a FUZZY MCDM Approach. Procedia, Social and Behavioral Sciences. 2012; 62: 1095 – 1099.[DOI:10.1016/j.sbspro.2012.09.187]

25. Janetipour M, Mohammad Sirus K. Prioritizing renewable energies in Iran with a multi-criteria decision-making approach. Third National Conference on Fuel, Energy and Environment, Energy Research Institute, Karaj. 2013.

26. Ijabi E, Bayat R, Shirvani M. Prioritization energy types in Iran with the aim of increasing energy security in the 1404 horizon (using hierarchical analysis method). Strategic Studies of Public Policy. 2018; 8 (29):135-157.

27. Ertay T, Kahraman C, Kaya I. Evaluations of Renewable Energy Alternatives Using MACBETH and FUZZY AHP Multi Criteria Methods: The Case of Turkey. Technological and Economic Development of Economy. 2013; 19(1): 38–62. [DOI: 10.3846/20294913.2012.762950]

28. Çolak M, Kaya I. Prioritization of Renewable Energy Alternatives by Using an Integrated Fuzzy MCDM Model: A Real Case Application for Turkey. Renewable and Sustainable Energy Reviews. 2017; 80: 840-853.[DOI: 10.1016/j.rser.2017.05.194]

29. Toklu MC, Taşkın H. A Fuzzy Hybrid Decision Model for Renewable Energy Sources Selection. International Journal of Computational and Experimental Science and Engineering. 2018; 4(1), 6-10. [DOI: 10.22399/ijcesen.399976]

30. Solangi YA, Tan Q, Mirjat NH, Valasai GD, Khan MW. A. and Ikram M.  An Integrated Delphi-AHP and Fuzzy TOPSIS Approach Toward Ranking and Selection of Renewable Energy Resources in Pakistan. Processes. 2019; 7(2): 118. [DOI: 10.3390/pr7020118]

31. Jahangiri Balataghi V, Asakereh A. Ranking of Renewable Energy Sources for Power Generation in Khuzestan Province Using TOPSIS Method. 10th National Congress of Biosystems Engineering (Agricultural Machinery) and Mechanization of Iran, Mashhad. 2016.

32. Kuleli Pak B, Albayrak YE, Erensal YC. Renewable Energy Perspective for Turkey Using Sustainability Indicators. International Journal of Computational Intelligence Systems. 2015; 8(1): 187-197.[DOI:10.1080/18756891.2014.963987].

33. Şengül Ü, Eren M, Shiraz SE, Gezder V, Şengül AB. Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable Energy. 2015; 75: 617-625. [DOI:10.1016/j.renene.2014.10.045].

34. Lee HC, Chang CT. Comparative Analysis of MCDM Methods for Ranking Renewable Energy Sources in Taiwan. Renewable and Sustainable Energy Reviews. 2018; 92: 883-896. [DOI: 10.1016/j.rser.2018.05.007].

35. Asgharpour MJ. Multi-Criteria Decision Making (16th Edition). University of Tehran Press, Tehran .2018.

36. Alimohamadiyan E, Shafiee MA. fuzzy multi-criteria decision approach for performance evaluation and improve the gaps among Shiraz University of Medical Sciences’ teaching hospitals based on balanced score card approach. Razi Journal of Medical Science. 2016; 22 (140) :12-24.

37. Mohammadizanjirani D, Salimifard KHK, Yousefi Dah Bidi Sh. Investigating the performance of the most common multi-attribute decision making techniques with an optimization approach. Journal of Operations Research in its Applications. 2014; 11 (1): 65-84.

38. Razavi hajiagha SH, amoozad mahdiraji H, Akrami H, Hashemi Sh.  Topsis- based non-Iinear programming model for calculating the ldeal weights of the decision. Industrial Management Studies. 2013; 11(29): 21-39. [DOI:20.1001.1.22518029.1392.11.29.2.9]

39. Amen M. Cost-Oriented Assembly Line Balancing Model Formulations, Solution Difficulty, Upper and Lower Bounds. European Journal of Operational Research. 2006; 168: 747-770. [DOI: 10.1016/j.ejor.2004.07.026]

40. Sabeti H, Akbari M. Cost-Oriented U-Shaped Mixed-Model Assembly Line Balancing and Sequencing, 7th International Industrial Engineering Conference. 2009.

41. Mousavi SJ, Kazemi A. Ranking of Private Banks Using Multilevel Decision Making Methods. Few studies in management. 2013; 4(3): 121-140.

42. Azar A, Rajabzadeh A. Applied Decision Making MADM Approach, Tehran, Negah Danesh. 2012.

43. Lin HT. Fuzzy Application in Service Quality Analysis: An Empirical Study. Expert systems with Applications. 2010; 37(1): 517-526. [DOI: 10.1016/j.eswa.2009.05.030].

44. Momeni M, Sharifi Salim A. Multi-criteria decision making models and software. Tehran, University of Tehran. 2011.

45. Mirzaei M, Bagheri Nejad J. Presenting a hierarchical model for prioritizing renewable energies using the AHP-Fuzzy method. Second Conference on Environmental Planning and Management, Tehran. 2012.

46. Esfandiari A. Rangzan A., Saberi A. and Fattahi Moghaddam, M. Potential assessment of construction of solar power plants by studying climatic parameters in Khuzestan province using GIS. National Geomatics Conference, 2-7, Shahid Chamran University of Ahvaz. 2011.

47. Iran Deserts and Deserts Group. 2019. https://www.irandeserts.com.

48. Islamic Parliament Research Center of The Islamic Republic Of IRAN (IPRC). About energy subsidies in Iran. 5. Image of energy consumption and water carriers in agriculture. Deputy of Research and Infrastructure Affairs, Office of Infrastructure Studies, Subject Code 250, No. 16656, October 2019. https://rc.majlis.ir/en.