ENERGY ECONOMICS REVIEW
Year:2012 | Volume:8 | Issue:31|Papers Count:10

While there have been many studies of the behavior of oil prices using various approaches such as Hoteling and fundamental models, uncertainty has been relatively ignored by most of the models. Stochastic models in the form of stochastic differential equations have the ability to track uncertainty. These equations include the non-differentiable Weiner process. These equations are associated with some complicated breaking methods, which can produce information about the level of uncertainty in prices.The goal of this paper is to analyze uncertainty of the price of Iran’s heavy oil and OPEC basket by stochastic differential equation for the period 1990-2010. To this end, we have specified stochastic differential equations, developed them by the Brownian motion through programming the process in MATLAB, and then estimated the value of oil price uncertainty (s). Comparing the results obtained for prices of Iran’s heavy oil and the OPEC basket, we find that, for the most years, Iran’s oil price uncertainty is lower than that of the OPEC basket. In addition, through this study, the variation of uncertainty in oil prices over time is identified.

The purpose of this paper is to analyze energy intensity of manufacturing industries in Iran. The analysis uses Laspeyres index and Arithmetic Mean Divisia decomposition techniques and data of Iranian manufacturing industries with more than 10 employees for the period 1374 to 1386 from the Statistical Center of Iran.The factors considered are changes in production structure (i.e. structural effect) and sectoral energy intensities (i.e. intensity effect). The results show that the changes in sectoral energy intensity play a greater role in the variation in total energy intensity of Iranian manufacturing compared to changes in the production structure of industries. However, in some cases both structural and intensity effects have been at work. In most cases the intensity effect has worked in favor of reducing energy intensity.

In this paper, we will study the impacts of a counterfactual scenario of oil revenues increase on the price levels, activity levels, import and export of goods in Iran. Our focus is on non-traded sectors, household welfare and expenditure indices in the framework of a Computable General Equilibrium (CGE) model. The model is calibrated based on 2001 Micro Consistent Matrix assuming Iran as a small open economy.The model consists of 11 production sectors, urban households, rural households, government, capital formation, export and import. We concentrate on non-traded products especially rental services, public services and construction sectors. Since part of the outputs of the construction sector relates to the capital value of building, we divided the demand for the construction sector into consumption and investment purposes.In this study, we simulate the impact of a 30% increase in annual oil revenues.Based on the results, this shock leads to an increase in activity levels in the nontraded sectors and a decline in the traded sectors activity levels. Services and manufacturing show the highest increases in import levels at respectively 24% and 22%. Except for the oil and gas sector, all productive sectors experience declining exports. Public services, water and construction sectors register the highest price increases. Results are robust to production elasticity of substitution choice, while they are sensitive to elasticity of substitution between imports and domestic output.

Because Iran’s economy is highly dependent on oil and gas export, long term energy planning with considerations for various management strategies appears to be necessary and vital. The objective of this study is to forecast energy consumption for a 20 year period (2010-2030) and examine various demand and supply side management strategies based on Iran energy balance for 2007. Bottom-up analysis is performed using LEAP and energy consumption forecasting is carried out by two methods: (a) multi layer perceptron artificial neural network (ANN) and (b) JD model. For training ANN, gross domestic product (GDP), population and energy consumption historical data for period (1990-2007) are used. For forecasting by ANN, inputs GDP and population are estimated for the forecasting horizon. Grey model (GM) and Trigonometric Grey Model with Rolling Mechanism (TGMRM) are applied methods to predict GDP and, for population estimation, linear regression is employed. These two estimations are also useful for JD model forecasting method, since it has terms including GDP as well as population. To achieve the objective four scenarios are examined: (a) replacement incandescent lamps with compact fluorescent lamps (CFL), (b) utilization of natural gas fueled stoves in place of electric stoves (ES), (c) replacement of gasoline fueled vehicles with electric vehicles (EV) and (d) employment of coal power plants (CPP). Further, nuclear power utilization and employment of upgrading gas turbine plants to combined cycle ones are also considered in (a), (b) and (c) scenarios. Based on forecasting method (ANN-GM), applying CFL, ES and EV scenarios up to year 2030 results in savings of 2634.5, 2170.2 and 2553.9 MBOE respectively, and CPP scenario results reduction of natural gas and oil usage by 2440.8 MBOE, all to compare with BAU. The noted figures are 1.85, 1.52, 1.80 and 1.72 times Iran’s TPES in 2007 respectively.

It is well known that while energy used for economic activities leads to production the process also creates environmental pollution through CO2 emission.As a result dealing with pollution and concern for environmental protection have become important political considerations. In this study we use panel data to assess the relationship between electricity consumption, emission of pollutants and GDP in low-income and high-income countries, for the period 1976- 2006. The results reveal that the three variables change in the same direction over time, indicating a positive relationship between electricity consumption, pollution and GDP.According to the results of the model estimation, using vector autoregressive method, there is a positive correlation between energy consumption and GDP levels in all countries, through the extent of the correlation varies between high and low income countries.

One of the policies for managing the spread of demand for electric energy during the day is use of Daylight Savings Time (DST). To reduce energy consumption and the peak load, the Iranian government moves the official clock forward one hour in the first month of each year and moves it back in the 7th month of the year. In 2006 the Government decided not to change the official clock.Incidentally the country experienced five times more blackouts in that year compared to the previous year. In this paper we apply Panel Data Analysis to the 16 electric companies operating in Iran to study the impact of DST on the total electricity consumption. We used an Iterated Seemingly Unrelated Regressions (ISUR) technique to estimate energy demand for the first six months of the years 2005 to 2008. The results of this investigation show that DST by shifting electricity use in evening hours to midnight and daybreak reduces the peak load and achieves a more even use of electric energy. However, our findings show that implementation DST increases the total electricity demand in Iran.

Power plants are one of the basic infrastructures for economic development.Currently, several power plant projects are under construction in Iran. Due to various reasons such as technical complexity, requiring substantial foreign exchange and local currency resources, the need for special equipment and dependency between the different phases, these projects are subject to numerous risks. Thus, while effective risk management is needed for these projects, there are gaps in our understanding of risk response assessment and selection. This paper proposes an optimization model that integrates the project work breakdown structure, risk events, risk abatement actions, secondary risks and their effect into a comprehensive framework. This model minimizes total expected loss. It consists of four components: abatement actions costs, expected time loss, expected financial loss and expected quality loss. We use a fuzzy analytic hierarchy process for calculating the coefficients of the objective function. Furthermore, a case study in Iranian power plant projects is presented to demonstrate applicability of the proposed model. The results show that the approach can lead to considerable savings in time and cost and improve the quality of projects under implementation.

The share of oil and gas insurance coverage compared to its value added (3% and 27% of insurance revenue and gross national product respectively) is very low.Some argue that this fact can be explained by the poor insurance culture of oil and gas managers. In this paper, based on a theoretical model, we assess the relationship between concentration of ownership in the oil and gas sector and insurance cover in the sector. This may explain the low insurance cover in the oil sector despite the low cost of such coverage. The analysis shows that whle the oil and gas industry is heavily concentrated in Government hands, the insurance industry has the characteristics of a competitive activity. We notice that concentration of ownership in any sector has a negative impact on insurance coverage. This implies that privatization and resultant reduced concentration of ownership in the oil and gas sector can have the greatest impact on insurance coverage of the sector. In addition, in subsectors that are likely to remain in the public sector, self-insurance and creating an insurance fund is the most feasible option for providing insurance cover to the concerned activities.