عنوان مقاله [English]
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.