ENERGY ECONOMICS REVIEW
Year:2010 | Volume:7 | Issue:24|Papers Count:11

The price of energy carriers in Iran is determined by the government. The government intervention in energy price determination imposes a heavy expenditure on subsidies in the government budget and distorts relative prices. In this paper, we use the Input-Output table and household demand function to assess the impact of the adjustment of energy prices on cost of living and consumption under four different price increases. For this purpose, we have used 10 table of 1384 and household budget data from 1353-1386. The 10 model and panel data method are used to investigate and estimate the parameters. The results show that increase of gasoline and gas oil prices under all have the greatest impact on household expenditures on transport and communications and food and beverage. The recreation, entertainment, education and cultural services groups are the second groups where consumption is expected to be reduced the most in response to the increased prices.

This paper introduces a method to define an optimized set of various technologies of producing hydrogen gas, consistent with available potentials in different regions, with a view to minimize production costs. The costs covered by this model include costs of utilizing a hydrogen gas production system, costs of gas transfer and those of greenhouse gases' emission related to each technology. Available potentials in each region means the extent of availability of initial materials required for each technology, acceptable limit of greenhouse gases' emission, the financial resources at hand for investing in production plants and demand in the relevant region, which are all considered in this model. Furthermore, in this paper different methods of hydrogen gas production are categorized based on production system capacity and in a lower level, based on the type of production process, which are used in model planning stages. The model introduced in this paper can be utilized in future research, with a bottom-up analytical viewpoint, using the optimization methodology of production technologies in the regions that are to be investigated.

There are some forecasting models derived from logistic growth curves. This paper compares the predictive power of three relatively new models namely, Logistic, Harvey Logistic, and Harvey models in the prediction of electricity consumption in Iran. These models are employed to calculate and to compare actual and predicted values of domestic, non-domestic and total consumption of electricity in Iran. The results suggest that Harvey model outperforms other models in forecasting Iranian electricity consumption.

The first step to optimize natural gas consumption is to prioritize consumption and optimize allocation of limited natural gas resources to different consuming sectors. This paper attempts to use operations research to identify different consumption sectors and the associated attributes that can be used to prioritize amongst them, utilizing desk studies and interviews with oil and gas industry experts. The significance of different attributes and quantitative values of important attributes for prioritizing amongst natural gas consumption sectors are identified through use of two groups of qualitative questionnaires to elicit the views of oil and gas ihdustry experts, to which the entropy method is applied in order to arrive at weights for the important attributes. Subsequently, consumption sectors are prioritized by utilizing multi-attribute decision making technique of TOPSIS. Eventually, multi-objective Decision Making technique of goal programming is applied to existing information in the energy and fossil balance reports and the gas sector vision document to arrive at optimal allocation of limited natural gas resources to various consumption sectors.Based on the results obtained, energy security is the most important factor, followed respectively by energy consumption efficiency, value added, national security and national developmental impacts as second to fifth most important factors for prioritizing amongst consumption sectors. The injection of gas into oil fields is the top consumption priority, followed in descending order by sectors of transportation and gas intensive industries as equal second, then electrical power, household business and general consumption, LNG exports by tanker, natural gas export by pipeline and petrochemical industries.

Significant increase of greenhouse gases, especially Carbon Dioxide in recent decades due to industrial growth and development has attracted attention to the serious threat this poses to sustainable development. The objective of this study is to identify the factors that contribute to changes in CO2 emissions. The complete decomposition method is used to review factors responsible for CO2 emissions in major economic sectors of Iran during 1994-2007. To this end the impact of four factors namely pollution coefficient, energy intensity, structural changes and economic activity on CO2 emissions changes are analyzed. The results of aggregate analysis show that economic activity is the largest contributor to CO2 increase while structural changes have the least effect on carbon emissions. The pollution coefficient and energy intensity have lesser impacts on CO2 emissions. The sectoral analysis shows different results based on each sector's characteristics. The economic activity effect is positive in all sectors. Sectors which have decreasing share in total GDP show negative structural effect and consequently have contributed to lowering of CO2 emissions.

Even though Iran was spared major impact from the global financial crisis at the outset, due to the particular situation of its financial sector, the crisis can still impact the economy through the medium of exports of goods.This research studies the impact of the change in the income of Iran's main trading partners on exports of various groups of commodities. Given the high versatility of fuzzy logic models in forecasting the behavior of economic systems, we use such a model in our research. We clarify the export commodities into 8 groups and Iran's main trading partners into 11 groups. We use the change of GDP of main trading partners as an input in the model and changes in exports of various commodity groups as outputs.We develop and test the model using data for the period of 1370-1387. The test results had a mean squared error (MSE) of 0.074 and R2 of 99.06%. The results indicate differential response of various commodity groups to changes in GDP of trading partners. Some commodity groups are more adversely affected by the change in of trading partners, while others were partially affected.

The purpose of this study is Gasoil price forecasting in Persian Gulf Market by Using GMDH neural network based on technical analysis rules include short-term and long-term moving averages for different trends in market. The result shows that in stable market (smooth trend) can achieve to best prediction. Also the comparison reveals that the GMDH neural networks consistently outperformed the ARIMA time series model developed in this study.

The rate of growth of natural gas consumption in Iran over the past decade has consistently outpaced the global average rate of increase. It is self evident that temperature variation has an impact on the use of raw materials that supply the required energy for heating and cooling systems, including natural gas. In this research we investigate the impact of changes in temperature on different components of natural gas consumption such as industrial, agricultural, power plant, public, commercial and transportation sectors in Iran's provinces in order to - develop a predictive model for the country.The paper offers a clear process for identifying an appropriate model to assess the impact of changes in temperature on each of the seven sectors using natural gas. The study considers the use of natural gas in each sector as a dependent variable and average temperature as the independent variable. The values of temperature and use of natural gas in each sector for each of the country's 25 provinces are collected on a monthly basis. Provinces are considered as cross sections in the model, while months provide time series data that are combined together in regression analysis to understand the relationship among these two variables for each of sub sector of natural gas demand. In order to enhance the accuracy of the model, the process of selecting and estimating parameters of the model is repeated after eliminating parts of data and the results compared to each other. Finally the correlation between consumption and temperature in each of the seven consumption fields are analyzed.