پژوهشنامه اقتصاد انرژی ایران
پیاپی 45 (زمستان 1401)

In recent years, the share of renewables in the global energy basket has increased significantly. This will have geopolitical consequences, especially for a country like Iran, whose economy is largely dependent on fossil fuel revenues. By examining the transition process of the global and regional economy towards renewable and clean energy, this article tries to outline Iran's position in the energy transition perspective. The main question is: how will Iran position itself in the global energy transition outlook to 2050? Through scenario planning, the authors identified four possible scenarios, including "meeting sustainable development and transition to green gold", "gradual and independent transition to sustainable development and green gold", "development based on black gold" and "development locked with black gold". They cover a wide range of possible futures. By knowing these scenarios and comparing their desirability, policymakers will be able to properly assess the costs and opportunities of the transition to clean energy in different situations.s

This paper focuses on the analysis of the petrochemical products manufacturing value chain and the presentation of petrochemical products' optimal manufacturing pattern in petrochemical industries affiliates of pension funds. The case study in this research is JAM and MASJIDSOLEYMAN Petrochemical companies. Period of this research is 5 years. In this case study, the determiner faces paradoxical objectives simultaneously. The fuzzy multi-objective linear programming (FMOLP) method has been used based on the α-cut of fuzzy parameters in this research. The goal of this research is to extract the amount of products that have maximized profit and value-added and minimized risk simultaneously which face technological, market, financial, budget, legal, and national interest-related limitations. This method is often interested in the proper POSs that have finite tradeoffs between objective functions. One numerical sample has been used to show the possibility of using the said method in multi-objective optimizing issues for the production of propylene. Because of the efficiency and satisfaction of the obtained new solutions for this method, this method undoubtedly can be useful to solve the FMOLP problems. A decrease in the production of ethylene and light polyethylene and an increase in propylene and butadiene production is the most important policy recommendation of this research.

Managing energy consumption in smart buildings has become an increasingly important challenge. Efficient energy management can have a positive impact on both micro and macroeconomics. Moreover, it is essential to ensure that the comfort of smart building residents is maintained at an acceptable level. Optimization algorithms can be used to achieve user convenience while minimizing energy consumption. In this study, we propose an optimization approach that utilizes an agent-based architecture. This architecture comprises intelligent agents that communicate with each other via message exchange in a network structure consisting of three layers: (1) The switch layer monitors user preferences and comfort levels. (2) The coordination layer includes a coordinating agent that determines the optimal timing for electrical appliances to minimize electricity consumption costs and maximize user comfort. (3) The execution layer contains performer agents. Our focus in this research is on the coordination layer with the aim of reducing energy consumption costs and peak average rates, while increasing user comfort to the highest possible level. However, this optimization problem is highly complex due to the large number of electrical devices and their capabilities. To address this, we propose a hybrid method based on genetic and bat algorithms. We evaluated its performance based on objective functions and compared it with recent research on SmartHome and CU-Bems datasets. Our results demonstrate an improvement in performance

The curse of natural resources is an emerging phenomenon that is of interest to economic researchers. The attention of researchers is about the direct effects of resources on economic factors. The subjects of these researches are around axes such as the impact on growth, productivity, and production incentives. In the research conducted, special attention has been paid to how the abundance of resources affects macroeconomic factors such as the Dutch disease; but what is not considered is the investigation and analysis of the side effects of abundant resources on economic, social, and political indicators. One of the side effects of the abundance of resources can be its effect on the spread of economic corruption. In case of an abundance of natural resources, the potential for widespread corruption is created. This research was done in a descriptive and analytical way. It has investigated the relationship between the extent of economic corruption and the abundance of natural resources. To conduct this research, the statistics of selected developing countries in the period of 2000-2021 have been used. The information used in this research is the indicators of the perception of corruption, fuel export of countries, and economic freedom during the research period, which is from reliable sources such as the United Nations, Transparency International, and the Heritage Foundation. Multivariate panel data regression method with random effects has been used to estimate model parameters. The results of this research show that there is a negative and significant relationship between the corruption perception index and the fuel export index of selected countries, so that in the 95% confidence interval, the estimated coefficient of the independent variable is negative 0.01. For economic freedom, it is 0.07, which is in line with the results of other researches in this field. Therefore, it can be stated that the abundance of natural resources has a direct relationship with the extent of economic corruption.

The impact of technological advances on energy consumption is one of the topics that has been considered by many researchers and policymakers. Numerous researchers have tried to evaluate this relationship based on various technology indicators. The index of economic complexity is one of the new indicators that has been used in recent years to measure the level of knowledge and technology in the production structure. In this paper, the index of economic complexity along with energy prices and GDP have been used as determining factors of energy consumption in Iran during the period 1976 to 2018. Quantile regression results show that the coefficients of the variables are different in the deciles. The impact of economic complexity on energy consumption in all deciles has been positive, indicating the dominance of the rebound effect on energy consumption. Price elasticity is less than one in all deciles and less in the higher consumption deciles. In contrast, the income elasticity of energy demand was higher in the upper deciles

Crude oil is the main source of energy and accounts for about a third of world energy production. Turmoil in this market will have far-reaching economic and financial consequences. Because of this, investors attach great importance to predicting volatility when investing in crude oil markets to hedge risk and portfolio diversification. However, their investment strategies are often strongly influenced by volatility because, in different periods of crude oil markets, there are high and low fluctuations that are attributed to the movement of economic cycles. Accordingly, the present study compares the Markov Regime Switching (MRS) and Hidden Markov (HM) volatility models with the GJR-GARCH asymmetric model on their forecasting capabilities in the WTI and Brent crude oil markets. Empirical results show that the MRS-GJRGARCH model performs better than the HM_GJRGARCH model in predicting volatility in both markets. Accordingly, using the two criteria of value at risk and the expected deficit, the minimum loss and the expected loss for December 2021 were predicted. The results show that the expected shortfall from investing in the WTI market is greater than the Brent oil market