ENERGY ECONOMICS REVIEW
Year:2011 | Volume:8 | Issue:28|Papers Count:9

Efficiency improvement in electricity uses leads to a decrease in its demand and consequently a decline in market price of electricity. It is expected that the induced increase in electricity demand due to this price effect offsets part of the primary reduction in consumption, a phenomena known as "Rebound Effects". Hence, ignorance of these effects in policy making causes overestimation of the benefits of efficiency improvement policies.This paper aims to determine the parameters that influence the magnitude of rebound effects theoretically and to evaluate the consequences of an exogenous and costless efficiency improvement in electricity use in the context of a computable general equilibrium model. This model is calibrated using Micro Consistent Matrix (MCM) constructed based on 2001 Social Accounting Matrix (SAM) of I.R.Iran assuming a small open economy.We found that electricity efficiency improvement will result in rebound effects of 14.2%. This means that 14.2% of primary decrease in demand is offset by rebound effects. According to our results, there are significant differences of rebound effects across electricity consuming sectors. Oil and Gas sector demonstrates highest rebound effects. Sensitivity analysis to test the response of rebounds to the specification of elasticity of substitution between electricity and fossil fuels shows that, economy-wide rebound effects changes from 11.6% to 14.2% due to changes in elasticity of substitution from 0.1 to 0.9.

Oil price has a determining role in the World Economy due to its strategic role in international production systems. This is why there has been a lot of interest in understanding its price dynamics and modeling its behavior and factors that lead to its volatility. In this context, Artificial Neural Networks have a high potential in modeling of random and complex processes and forecasting of dynamic nonlinear trends. In this paper we model and forecast the daily price of the OPEC oil basket through use of Artificial Neural Networks based on daily price expectations and compare its results with those of the ARIMA model based on prediction accuracy measure criteria. The results demonstrate that Neural Networks have better forecasting power than the ARIMA model, with the daily price of oil being a function of the price in the preceding five days.

The main purpose of this paper is to analyze the factors that affect the extent and intensity of CO2 emissions due to energy consumption. To this end, the index decomposition analysis (IDA) is applied to analyze the factors that influence CO2 emissions, namely: economic activity, structural changes, coefficient of CO2 emissions and energy intensity. Due to the differentiated nature of this relationship across economic sectors, energy consumers are divided into five aggregated units: (1)domestic-public-commercial, (2)industrial, (3)transportation, (4) agriculture, and (5)other sectors. The study covers the period of 1995 to 2005. The results indicate that economic activity has the largest positive effect on CO2 emissions in the economy as a whole and all sectors except industry and transportation. Structural changes in industry and transport have a dominant impact on CO2 emissions increase. Energy intensity has a relatively large effect on changes in domestic-public-commercial CO2 emissions, while it has a negligible and at time negative impact in other sectors. Changes in the coefficient of CO2 emissions has negligible effect in all economic sectors except for the “other sectors (including power plants, refineries). Analysis of CO2 emission intensity shows that about 82 percent of changes in the intensity of CO2 emissions are due to structural changes.

The European Union, as the third most important consumer of gas in the World, after the USA and the ex-Soviet Union, is heavily dependent on gas imports. Russia, as the most important gas exporter to Europe has turned into an unreliable trading partner, following the gas disputes of 2006 and 2009.It is in this context that as part of its policy of energy security, Europe is attempting to diversify its sources of energy, notably natural gas. The idea of constructing a pipeline that does not originate from Russia and does not cross unstable Ukraine is one such measure. The Nabucco line has to be understood in this context.Despite initial indications from European countries that Iran should participate in the project, eventually the Nabucco line contract was signed in Turkey without Iran's participation. This study is focused on determining whether the participating countries can provide the required volume of natural gas to make full use of the pipeline. In case this is possible, the study assesses the consequences for Iran of being absent from the project.

The aim of this study is to introduce more optimal models to forecast Iranian crude oil prices. The study uses weekly data for the period 1987-2010, to separately forecast 10, 20 and 30 percent of data variables. The study applies 4 Artificial Neural Networks and one ARIMA regression model. The selected Artificial Neural Networks are Feed-Forward Back Propagation, Cascade Back Propagation, Elman Back Propagation and Generalized Regression. The experimental functions are Levenberg-Marquardt and Quasi-Newton BFG. The findings indicate that for 10 percent of price data networks of Generalized Regression and Quasi-Newton BFG based Cascade Back Propagation networks give the best forecasts with errors of less than 1 and 2 percent respectively. To forecast 20 percent of Iranian Crude oil prices Feed-Forward Back Propagation and Elman Back Propagation networks based on the Levenberg-Marquardt experimental functions had the best performance. In the case of 30 percent of price data also Feed-Forward Back Propagation was found more optimal. The results also indicate that as the percentage of data forecast increases prediction accuracy tends to decrease and this happens most markedly when we increase the percentage of data used from 10 percent to 20 percent. The study also reveals a lower forecasting power for the ARIMA model compared to all the other models.

While there is much evidence to support the resource curse hypothesis for resource abundant countries, some studies have found that oil booms raise the economic growth of oil exporting countries. This paper examines the issue of the existence of the threshold effects in the relationship between oil revenues and output growth in oil-exporting countries, applying panel regressions. The empirical results strongly suggest the existence of a threshold beyond which oil revenues growth exerts a negative effect on output. The results indicate that the threshold of growth rate of oil revenues above which oil revenues significantly slows growth is around 17-21 percent. In contrast, linear estimation without any allowance for threshold effects would misleadingly have us believe that an increase in the oil revenues increase the growth rate. Failure to account for nonlinearities conceal the resource curse in these countries particularly during extreme oil booms as suggested in previous studies.

The paper investigates the sources of real and nominal exchange rate movements in Iran by decomposing real exchange rate movements into those attributable to real and nominal shocks. Since the real exchange rate is related to the relative price of domestic goods in terms of foreign goods, it is important to know to what extent it is influenced by real shocks versus nominal shocks. Quarterly data from the period 1369:01 to 1387:02 were utilized in this study. Using a popular structural VAR model and assuming long-run neutrality of nominal shocks, we find that real shocks dominate nominal shocks in explaining real exchange rate movements in Iran. On the other hand, variance decompositions show that nominal shocks explain about 53 percent of nominal exchange rate movements in the short run and about 39 percent in the long run. Our results imply that it is important to minimize nominal shock by following a stable monetary policy in order to achieve real exchange rate stability, at least, in the short run. Our results also imply that given the importance of real shocks in explaining exchange rate changes, in order to improve the competitiveness of the economy through a real exchange rate policy, the government needs to focus on the real side of the economy, such as improving efficiency and productivity.

Given the engagement of Iran in the process for gaining membership in the WTO, the question that is worth considering is can Iran benefit from membership in WTO in order to accelerate its socio-economic development plans, given the dependence of the economy on crude oil and oil derivatives, which in total account for 90% of the country's exports.Many developing countries, including a number of oil exporting countries, joined the WTO during the 1990s in the hope of accelerating their socio-economic development by virtue of trade liberalization. The current study which uses the Balassa Revealed Comparative Advantage (RCA) to study differences between countries that are members of WTO and non-member countries, does not demonstrate a meaningful difference in terms of diversity of export items or increasing specialization in the production of more complex products. It shows that oil exporting countries continue to be involved in exports of oil and its derivatives. It shows that WTO membership in itself does not generate any changes, but rather its beneficial effects are a function of specific policy interventions for changing the comparative advantage of the country away from production and export of primary products to production and export of complex products.