Investigating the Heterogeneity of Farmers' Preferences for Solar Energy Use

Document Type : Research Article

Authors

• M. Taslimi ¹
• H. Amirnejad ¹
• S.M. Mojaverian ¹
• H. Azadi ²

¹ Department of Agricultural Economics, Sari Agricultural Sciences, and Natural resources, Sari, Iran

² Ghent University, Department of Geography

https://doi.org/10.22067/jead.2021.68131.1009

Abstract

Introduction: The final energy consumption per capita in Iran in the agricultural sector is 3.4, as well for household sector is 2, besides the commercial and public sectors are 1.6, and transportation and industry are 1.4 times the global average. This is due to low efficiency in operation, high energy consumption, as well as the use of energy goods and services. The use of renewable energy in the agricultural sector, while increasing the security of energy supply, will reduce global warming, stimulate economic growth, create jobs, and increase per capita income and social justice and environmental protection in all areas. The purpose of this study is to investigate farmers' preferences for using solar energy in Sari.
Materials and Methods: The Choice Experiment methods allow researchers to focus on valuing final changes as multidimensional features rather than discrete changes. Choosing between options encourages respondents to examine their preferences in detail related to different management programs. The Choice Experiment test approach consists of several steps, which include designing the Choice Experiment test, determining the sample size and method of data collection, estimation process, and modeling the Choice Experiment test. Designing a Choice Experiment test consists of five important steps which are defining attributes, determining the relevant levels, conducting an experimental design, constructing Choice sets, and measuring preferences. After determining the criteria affecting the prioritization of renewable energy, liketechnical, environmental, economic, social, and political criteria, in order to investigate the willingness to Pay of Sari farmers, a test questionnaire was designed. The criteria obtained from the review of prioritization of renewable energy were considered as the attributes of the Choice Experiment and the price attribute was added to the above criteria. A total of six technical, economic, social, political, environmental, and price attributes were considered to investigate farmers' willingness to pay. In the review of the studies and the current situation, the levels of each of the attributes were determined. To determine the levels of price attribute, these points were considered; the price of agricultural electricity per kilowatt-hour is 383 Rials, which was approximately 400 Rials for the current situation.
Results and Discussion: To investigate the farmers' preferences for using solar energy, 98 questionnaires of farmers in Sari were completed in September 2019. Each questionnaire included 8 choice set cards and each card included three options, based on which, the number of observations in Sari is equal to 2352 observations. The purpose of this study is to investigate the preferences of farmers in Sari for the use of solar energy. For this purpose, the Multinomial logit, the Random parameter logit, the latent class, and the Random parameter logit latent class are used. Based on the results of the Multinomial logit method, environmental and price attributes at the level of one percent and economic attribute at the level of five percent are statistically significant, but political, social, and technical attributes are not statistically significant. The Alternative-specific Constants (ASC) in the first and second options are not statistically significant. Based on the results of the Random Parameter Logit estimation method, environmental, economic and price attributes are statistically significant at the level of one percent. Technical, political, and social attributes are not statistically significant, which shows that farmers do not make a significant difference between these two attributes. The Alternative-specific Constants (ASC) are significant in the first option at the level of five percent and the second option at the level of one percent. The results of latent class estimation show that in the first class, environmental, economic, political, social, and price attributes are statistically significant at the level of one percent and technical attribute at the level of ten percent. The Alternative-specific Constants (ASC) are statistically significant at the level of one percent in the first class. In the second class, technical attribute at the level of five percent and environmental attribute at the level of ten percent are significant, besides other attributes in the second class are not statistically significant. The most sensitive class is the first class and farmers of the second class are considered the base class. The results obtained from the Bayesian and Akaike criteria of different classes showed that the two classes have the lowest values of BIC and AIC criteria and the class is appropriate. After determining the appropriate class, the model was estimated. The results of model estimation were calculated by the Latent Class Random Parameter logit method. In the first class, environmental attributes and price are significant at the level of one percent and economical attributes at the level of five percent. Also, the Alternative-specific Constants (ASC) is significant at the level of one percent, but, in the second class, the attributes are not statistically significant. Technical, environmental, economic, political, social, and price attributes, as well as the option of status quo or the Alternative-specific Constants (ASC) in the second class, do not affect farmers' utility due to the lack of statistical significance.
Conclusion: A comparison of the results obtained from the four methods shows that the highest value of the estimated coefficient for environmental attributes was in the latent class method and the lowest value was in the multinomial logit method; Comparison of fitted methods shows that the highest Log-likelihood is related to the latent class random parameter logit method and the lowest value is related to the multinomial logit method. Accordingly, the highest value of Akaike and Bayesian criteria is related to the multinomial logit method and the lowest value is related to the latent class random parameter logit method which is better than other methods according to the good fit criterion.

Keywords

• Choice experiment
• Latent class random parameter Logit
• Farmers
• Willingness to pay
• Renewable energy

20.1001.1.20084722.1400.35.3.2.6

Main Subjects

• Environment and Natural Resources Economics

References

1. Alpizar F., Carlsson F., and Martinsson P. 2001. Using Choice Experiments for Non-Market Valuation. Working Papers in Economics/Göteborg University, Dept. of Economics; No. 52.
2. Amirhajloo R., Fatahi Ardakani A., Fehresti M., and Neshat A. 2018. Estimation of Economic Value of Hydraulic Structures Damages on the Environment (Case Study: Zayandeh Rood Dam). Journal of Agricultural Economics and Development 32(2): 167-183. (In Persian)
3. Azarova V., Cohen J., Friedl C., and Reichl J. 2019. Designing Local Renewable Energy Communities to Increase Social Acceptance: Evidence from a Choice Experiment in Austria, Germany, Italy, and Switzerland. Energy Policy 132: 1176–1183. 
4. Bateman I.J., Carson R.T., Day B., Hanemann M., Hanley N., Hett T., Jones-Lee M., Loomes G., Mourato S., Özdemiroglu E., and Pearce D.W. 2002. Economic Valuation with Stated Preference Techniques: a Manual. Massachusetts: Edward Elgar Publishing.
5. Bayani A., Abolhasani L., Shahnoushi N., and Mohammadi H. 2017. Estimation of Economic Value of Recreational Services of Naharkhoran Park Using Choice Experiment Method. Journal of Natural Environment 70(4): 799-812. (In Persian).
6. Campbell R.M., Venn T.J., and Anderson N.M. 2018. Heterogeneity in Preferences for Woody Biomass Energy in the US Mountain West. Ecological Economics 145: 27-37.
7. Chaikaew P., Hodges A. W. and Grunwald S. 2017. Estimating the Value of Ecosystem Services in a Mixed-Use Watershed: A Choice Experiment Approach. Ecosystem Services 23: 228-237.
8. Economic Report of Mazandaran Province. 2017. General Department of Economy and Finance of Mazandaran Province. (In Persian)
9. Energy balance sheet. 2016. Deputy Minister of Electricity and Energy, Ministry of Energy. (In Persian)
10. Eskandari Damaneh H., Noroozi H., Naybandi Atashi M.R., Kalhori S., and Rafiee H. 2019. Estimating the Willingness to Pay for Air Quality Improvement with Emphasis on Agriculture and Natural Resources in Ahvaz County. Iranian Journal of Agricultural Economics and Development Research 50-2(3): 451-465. (In Persian)
11. Ghorbani M., and Mohammad Rezazadeh Bazaz N. 2017. Willingness to Pay for Environment-Oriented Energies in Khorasan-Razavi Province: Application of Spatial Tobit model. Journal of Natural Environment 70(2): 385-396. (In Persian)
12. Hamdollahi A., Mohammadi H., and Khatib Semnani M. 2016. Economic Evaluation of Solar Energy in Iran's Agricultural and Rural Sector 2(5): 83-100. (In Persian)
13. Hayati B., Salehnia M., and Molaei M. 2017. Dealing with Heterogeneous Preferences Concerned with Lake Urmia Restoration Using Multilevel Latent Class Model. Journal of Agricultural Economics and Development 30(4): 285-296. (In Persian)
14. Jalili Kamjoo S. P., Maboudi R., Khouchani R., and Nademi Y. 2017. Choice Experiment –Conditional Logit: a New Approach in Estimate of Visitor’s WTP Environmental. Environmental Research 7(14): 115-126. (In Persian)
15. Kjær T. 2005. A Review of the Discrete Choice Experiment-With Emphasis on its Application in Health Care.
16. Ku S.J., and Yoo S.H. 2010. Willingness to Pay for Renewable Energy Investment in Korea: A Choice Experiment Study. Renewable and Sustainable Energy Reviews 14(8): 2196-2201.
17. Lee H.J., Huh S.Y., and Yoo S.H. 2018. Social Preferences for Small-S'cale Solar Photovoltaic Power Plants in South Korea: A Choice Experiment Study. Sustainability 10(10): 3589.
18. Louviere J.J., and Woodworth G. 1983. Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data. Journal of Marketing Research 20(4): 350-367.
19. Mohammadi M., and Sabouri M.S. 2016. Analyzing the Role of Increased Energy Prices on the Renewable Energy Adoption by Birder of Garmsar Township. Iranian Journal of Agricultural Economics and Development Research 47(4): 913-927. (In Persian)
20. Murakami K., Ida T., Tanaka M., and Freidman L. 2014. Consumers' Willingness to Pay for Renewable and Nuclear Energy: A Comparative Analysis between the US and Japan. Energy Economics 50: 178-189.
21. Pishbahar A., Mahmoudi H., and Hayati B. 2019. Investigating Non-Attendance of Attributes in Choice Experiment with Endogenous Attribute Non-Attendance (Case Study: Organic Tea Consumers in Tehran). Iranian Journal of Agricultural Economics and Development Research 50-2(3): 437-449. (In Persian)
22. Razeghi M., Sha’banali Fami H., and Rezaiee R.A. 2013. Factors Influencing on Farmers' Willingness in Equipping Farm to Renewable Energies Technology. Agricultural Extension and Education Research 6(4): 87-106. (In Persian)
23. Renewable Energy and Electricity Efficiency Organization (SATBA). 2018. www. Satba.gov.ir. (In Persian)
24. Rezende C.E., Kahn J.R., Passareli L., and Vásquez W.F. 2015. An Economic Valuation of Mangrove Restoration in Brazil. Ecological Economics 120: 296-302.
25. Sagebiel J., Müller J.R., and Rommel J. 2014. Are Consumers willing to pay more for Electricity from Cooperatives? Results from an Online Choice Experiment in Germany. Energy Research & Social Science 2: 90-101.
26. Samadi S., Jalili Kamjoo S.P., Rahimi T., and Shirinkhah Y. 2015. Assessing Preferences and Estimating the Willingness of Isfahani Citizens to Pay for the Use of Clean Air: A Selection Modeling Approach and a Conditional Logit Model. Urban and Regional Studies and Researches 7(25): 141-162. (In Persian)
27. Sharzehi G., and Jalili Kamjoo S.P. 2013. Choice Modeling: A New Approach to Valuation of Environmental Commodity; Case Study: Ganjnameh, Hamadan. Quarterly Journal of Economic Research 13(3): 1-18. (In Persian)
28. Soliño M., Farizo B.A., Vázquez M.X., and Prada A. 2012. Generating Electricity with Forest Biomass: Consistency and Payment Timeframe Effects in Choice Experiments. Energy Policy 41: 798-806.
29. Statistical Yearbook of Mazandaran Province. 2017. Management and Planning Organization of Mazandaran province. (In Persian)
30. Tehran Air Quality Report. 2016. http://air.tehran.ir. (In Persian)
31. Vahhabi rad S., Khodaverdizadeh M., and Hashemibonab S. 2019. Estimate the Value of Improving Air Quality in the Tehran City: Application Choice Experiment Method. Journal of Environmental Studies 45(2): 269-286. (In Persian)
32. Vakili Ghaserian N., Molaei M., and Khodaverdizadeh M. 2017. Application of Choice Experiment in Determining the Value of Natural Functions of Zarivar Lake. Agricultural Economics Research 9(35): 183-206. (In Persian)
33. Vecchiato D., and Tempesta T. 2015. Public Preferences for Electricity Contracts Including Renewable Energy: A Marketing Analysis with Choice Experiments. Energy 88: 168-179.
34. World Bank Group. 2019. 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.
35. World Bank. (WB). 2017. Available at: http://data.worldbank.org/indicator.
36. World Health Organization (WHO). 2019. www.Who.int
37. Yoo J., and Ready R.C. 2014. Preference Heterogeneity for Renewable Energy Technology, Energy Economics 42: 101-114.
38. Yoo J.W. 2011. Advances in Nonmarket Valuation Econometrics: Spatial Heterogeneity in Hedonic Pricing Models and Preference Heterogeneity in Stated Preference Models. Dissertation of Agricultural Economics, Pennsylvania State University.