• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Batoul Zargar; Ali Emami Meibodi; Hosein Jahangirnia; Mozhgan Safa
Abstract
It is necessary to develop the photovoltaic industry due to the criticality of reducing economic dependence on fossil fuels and mitigating air pollution. Therefore, the present study aims to propose a financing model for this industry in Iran. This is an applied-developmental study, in terms of purpose. ...
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It is necessary to develop the photovoltaic industry due to the criticality of reducing economic dependence on fossil fuels and mitigating air pollution. Therefore, the present study aims to propose a financing model for this industry in Iran. This is an applied-developmental study, in terms of purpose. It is developmental because it provides a framework for financing the industry. On the other hand, it is of an applied nature, as its results find direct application in developing this industry. The modeling draws on a mixed-method approach combining the qualitative methodology of grounded theory and the quantitative method of artificial neural networks. The study's population comprises the financial, economic, and technical experts of the photovoltaic industry. Semi-structured interviews took place with 25 experts chosen through targeted sampling, combining maximum variation with snowball sampling methods. The findings indicate that the investment funds (one of the primary strategies of community financing), bank loans (one of the private financing strategies), power purchase agreements (a government incentive), public funding by modifying the fossil power tariffs, along with guarantees and insurances are among the strategic priorities for financing this industry. In sum, the financing model of the photovoltaic industry demonstrates that based on the current context in Iran, it is possible to create a profitability perspective and a supportive atmosphere for the photovoltaic industry by adopting diverse strategies.
Mohammed Goli Yousefi; Timur Mohammadi; Navid Maarefzadeh
Volume 2, Issue 7 , July 2013, , Pages 147-170
Abstract
The purpose of this paper is to forecast demand for crude oil of Iran using Artificial Neural Networks and ARMAX models. The result indicates that Artificial Neural Networks provides an accurate and better picture compared with ARMAX. In order to show whether the variables used in this study are true ...
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The purpose of this paper is to forecast demand for crude oil of Iran using Artificial Neural Networks and ARMAX models. The result indicates that Artificial Neural Networks provides an accurate and better picture compared with ARMAX. In order to show whether the variables used in this study are true determinants of Crude oil demand, we have also applied the same techniques with the same variables to forecast crude oil demand of five selected OPEC countries. The result confirms our earlier findings for Iran. Applying rank correlation coefficient for these findings, show high correlation coefficients between the result for Iran and other countries. Therefore we may say that the variables such as GDP, population, net exports and the number of vehicles are key variables for any forecasting relating to crude oil demands in similar countries.
