Iranian Energy Economics

Abstract The implementation of the mechanism of carbon border adjustments or carbon tariffs as a tool to deal with carbon leakage and reducing the competitiveness of production, was implemented by the European Union in October 2023 under the transitional phase and it will be implemented under the definitive phase from January 2026. Some countries, including the United States of America and Japan, have also predicted similar policies. This is while developing and developed countries have committed to take measures to combat climate change and reduce carbon emissions based on the Paris Agreement. This shows the concern of countries applying carbon tariffs because of carbon leakage even after the creation of the Paris Agreement. Considering that the European Union has announced that it will first apply carbon tariffs to energy industries, this study uses the GTAP-E model to investigate the change in the amount of carbon dioxide emissions in Iran's industries as a result of the imposing of carbon tariffs by the European Union, the Japan, the United States of America and all regions on Iran's energy intensive industries. The statistical of the research includes 141 regions and 65 section in the GTAP10 data base that published in 2019. The estimation of the model shows that under all four scenarios, the amount of production and carbon dioxide emissions will decrease in the energy-intensive industries sector and the entire industries of Iran.

Abstract The growing natural gas consumption has made it difficult to supply gas to power plants and large industries in the cold seasons and forces these industries to use polluting fuels. One of the consequences of this situation is air pollution. Due to the inelastic demand of natural gas, command policies cannot prevent the increasing trend of gas consumption. So it is necessary to use incentive policies to manage consumption. Recently, in developed countries, regulatory instruments based on exchange of certificates, such as tradable savings certificates based on the market, have been significantly developed and had positive results. Considering the importance of gas consumption management and with the aim of creating a mechanism for certificates of natural gas exchange, in this research, based on the experiences of the leading countries in this field and the results of consensus of the elites, design a mechanism for the exchange of consumption savings certificates of natural gas. The results of Delphi analysis showed that the mechanism of issuing and exchanging natural gas saving certificates is feasible and this mechanism makes households and business units find motivation to save natural gas consumption. Also, power plants and other large industries can fulfill part of their natural gas needs in the cold season by buying certificates from households in the established market, and in this way they can access clean fuel in the cold seasons of the year.

Abstract This study examined the impacts of economic policy uncertainty and oil price on carbon dioxide emissions as a criterion of environmental quality in seven OPEC member countries from 1990 until 2019. According to the theoretical framework, economic policy uncertainty can directly or indirectly affect the quality of the environment, such as through direct policy adjustment, consumption, and investment channels. In this regard, with the help of panel data, the experimental models of this study were estimated and evaluated by the Fully-modified Ordinary Least Squares (FMOLS) method. The results indicate that the economic policy uncertainty and the oil price variables are statistically significant in both models, and their coefficient is positive. In other words, higher policy-related economic uncertainty and oil price over this period has led to higher carbon dioxide emissions and, thus, lower environmental quality in OPEC member countries. Ultimately, the estimates in the second model confirm an inverse U-shaped relationship between economic growth and the quality of the environment of these countries in the mentioned period, based on the Environmental Kuznets Curve (EKC) hypothesis.

Abstract This study employs a vector autoregression approach with time-varying parameters (TVP-VAR) to investigate the spillover of volatility and risk among the stock markets of 13 OPEC and OPEC+ member countries, alongside Gold, Brent Oil, OPEC Oil, and the Dollar index. Daily data spanning from March 1, 2014, to March 1, 2023, is utilized for analysis. Our findings reveal several key insights. First, the average systemic risk within the network of investigated variables has escalated in the years following the onset of the COVID-19 pandemic. Second, among the investigated countries, the stock markets of Saudi Arabia, Kuwait, UAE, Russia, Malaysia, and Nigeria serve as transmitters of fluctuations within the network, while the stock markets of Bahrain, Kazakhstan, Venezuela, Oman, Iran, Iraq, and Mexico act as receivers of volatilities. Third, significant volatilities in Iran's stock market returns originate from idiosyncratic shocks, with variables such as OPEC oil prices and the stock markets of Bahrain, Iraq, and Kuwait playing pivotal roles in explaining these fluctuations. Fourth, approximately 60% of the volatility in gold returns and the dollar index can be attributed to idiosyncratic risks. Fifth, compared to other OPEC+ member countries, the Saudi stock market's volatility exerts a more substantial influence on the volatilities observed in the global oil, gold, and dollar markets.

Abstract Abstract:

Due to current sanctions, financing upstream oil industry projects faces significant challenges. To design an effective financing portfolio for these projects, we first identified financing criteria through a literature review. Using the fuzzy Delphi method, we screened and determined six main criteria. The fuzzy DEMATEL technique revealed that among these criteria, repayment structure constraints, currency exchange rate fluctuation risk, external monitoring factors (cause type), and The financing cost factors, the time required to acquire financial resources, and the risk of failing to obtain financial resources are of the effect (dependent) type. Additionally, the time required to acquire financial resources has a greater weight compared to other criteria. Considering the weighted criteria the fuzzy TOPSIS technique showed that issuing forward contracts had the highest priority, while obtaining international bank loans had the lowest priority. The study involved 12 experts from the oil industry and the banking and capital markets, who provided responses to structured questionnaires.

Abstract This study investigates the application of recurrent neural network (RNN) models—specifically RNN, long short-term memory (LSTM), and gated recurrent unit (GRU)—in predicting the stock indices of the Iranian energy industry. Using daily time series data from May 1, 2020, to May 1, 2024, the dataset was divided into a training period (80%) and a testing period (20%). In the first step, the optimal architectures of each model (estimating hyper-parameters) were determined for prediction horizons of 1, 2, 5 (one week), and 20 trading days (one month). Subsequently, prediction errors of the three machine learning models were compared with the linear econometric model (ARIMA) across various forecast horizons. The findings in two areas of cross validations of machine learning models as well as predication error reveal the following insights: First, as the forecast horizon increases, the batch size of optimal prediction decreases for all three machine learning models, and the larger the input training sample size leads to the smaller batch size. Second, in short-term forecast horizons (1, 2, and 5 trading days), machine learning models—particularly LSTM—demonstrate lower prediction errors than ARIMA, while in the 20-trading-day (1-month) forecast horizon, ARIMA's predictive accuracy approaches to the nonlinear machine learning models. Third, forecast accuracy decreases as the horizon lengthens, with accuracy dropping from approximately 98.5% (for a 1-day horizon) to 92.5% (for a 20-day horizon). Finally, selecting the appropriate forecasting method for the stock market indices of energy industries depends on the forecast horizon and data characteristics.

Abstract In the process of globalization, the interdependence between countries increases, and changes in economic policies introduce uncertainty shocks to both domestic and foreign economies. In oil-dependent countries, fluctuations in global oil prices significantly impact income and Gross Domestic Product (GDP). This study examines the impact of uncertainty in global economic policies and oil prices on Iraq's economic growth from 2008 to 2022. The research employs the nonlinear Autoregressive Distributed Lag (NARDL) econometric model. The results indicate that an increase in the uncertainty of global economic policies leads to a decrease in Iraq's economic growth in the short term, with this effect persisting in the long term. Conversely, a reduction in the uncertainty of global economic policies leads to an increase in Iraq's economic growth over both timeframes. Regarding oil prices, a reduction in uncertainty in the short term (at the first lag) fosters economic growth; however, at the second lag, it has a negative impact. In the long term, a decrease in oil price uncertainty contributes to an increase in economic growth. An increase in oil price uncertainty does not significantly affect economic growth in the short term; however, in the long term, it leads to a decline in Iraq's economic growth. These findings highlight the asymmetry in the effects of economic policies and oil prices on Iraq’s economic growth.

Abstract Commodity price fluctuations, particularly oil prices, significantly impact the stock markets of commodity-exporting countries. This study investigates the relationship between commodity price changes and stock market returns in selected commodity-oriented countries. The analysis, conducted using the ARDL approach over the period January 2, 2001, to February 16, 2023, reveals that in the long run, oil price changes have a direct and significant positive relationship with the stock market index in all studied countries except Iran. This suggests that rising oil prices generally lead to a rise in the stock market value of these countries. The impact of other commodities, however, varies across the selected countries. Interestingly, the study identifies oil as the most influential commodity affecting their stock markets. These findings highlight the critical importance of understanding the dynamics between commodity prices and stock market returns for both investors and policymakers in commodity-exporting countries. With this knowledge, investors can make informed decisions, while policymakers can develop strategies to enhance the resilience of their stock markets in the face of commodity price fluctuations.

Abstract With the increase in electricity consumption in the country over the last few years, despite the strategic nature of the steel industry in the country, the supply of electricity for steel companies has faced serious problems; In this study, we have first examined the strategies for improving the quality and increasing the production rate of Gol Gohar Steel Company through forming a SWOT matrix. Then, considering that sustainable electricity supply is identified as the key strategy to achieve this goal, using multi-criteria decision-making methods, different power plants are ranked for electricity supply of this company. For this purpose, using two main indicators of maintenance planning, i.e. reliability and availability, the options have been evaluated, and the ranking of power plants has been done using different methods: AHP, EDAS, TOPSIS, and MAUT. Also, for the weighting of criteria, two methods of forming a pairwise comparison matrix (PCM) and Shannon's method have been used. The results indicate that among the three power plants—Kahnouj, Goharan, and Samangan—the power supply from the Samangan power plant will be the most reliable for the Golgohar company. Specifically, in three out of four decision-making methods, the Samangan power plant was identified as the best option, while it ranked second in the remaining method. Additionally, the Kahnouj power plant and the Goharan power plant were identified as the second and third-best options, respectively.

Abstract The Ecological Footprint Index measures the human demand for natural resources, which leads to climate change, biodiversity loss, soil degradation, and environmental pollution. This study uses data from 1980 to 2021 and the Threshold Autoregressive (TAR) method to examine the effects of energy consumption, economic growth, globalization, and financial development on the ecological footprint in Iran. The results reveal that the impact of variables on the ecological footprint depends on the initial energy consumption threshold (3.6 exajoules). In both lower and higher regimes of this threshold, energy consumption and globalization positively and significantly contribute to increasing the ecological footprint, with these effects being significantly amplified in the higher regime. Additionally, economic growth significantly impacts the ecological footprint only in the higher regime, indicating the adverse effects of advanced economic development on the environment. Financial development, on the other hand, negatively and significantly affects the ecological footprint in both regimes, demonstrating its potential to reduce environmental pressures. However, this mitigating effect is stronger in the lower regime. Therefore, it is essential to devise effective strategies to reduce fossil fuel consumption and improve energy efficiency during advanced stages of economic growth to alleviate environmental pressures.

Abstract Iran has been facing economic sanctions for more than four decades. In general, sanctions affect the environment and sustainable development in several ways. Restrictions on financial flows, transfer of technology, pressure on government budgets, pressure on the environment, and many other issues are among the issues that have put pressure on the environment and prevented the process of sustainable development in Iran. The present study has used the "calculable general equilibrium" method to simulate the effect of sanctions on Iran's economy and is purposefully applied and developmental and descriptive-analytical in nature.

The research model is designed based on the Lafgren standard model according to the characteristics of Iran's economy and in accordance with the purpose of the research, and eight scenarios are applied to the research model. According to the results of the, if the government's goal is to "reduce the budget deficit while reducing the pressure on the environment", the best option is to increase the tax rate. If the government's goal is to "reduce pollution while increasing economic growth and development" but does not take into account the budget deficit, increasing research and development incentives is recommended. If the government's goal is to "grow and develop in the long run and increase the competitiveness of the business community in order to be more resilient to sanctions," the scenario is to increase research and development efficiency throughout the economy. Therefore, the results indicate that the strengthening of research and development in various economic sectors will be significantly important

Abstract This paper examines the impact of natural resource rents on financial development in selected MENSA (Middle East and North Africa) countries from 2000 to 2022 using an ARAL panel model. The results indicate that, in the short term, rising natural resource rents negatively affect financial development, primarily due to an over-dependence on natural resource revenues and a decline in non-oil sectors. However, in the long term, the adverse effects of these rents are alleviated, suggesting that effective management of natural resource revenues and investment in financial infrastructure can bolster financial development. The research findings reveal that trade openness has a positive and significant influence on financial development. Expanding international trade and attracting foreign investment through access to new technologies enhances the financial performance of MENSA countries. Conversely, increasing urbanization negatively impacts financial development, as the pressure on government finances to address infrastructure and municipal service needs restricts investment opportunities in the financial sector. Overall, effective management of natural resource revenues, enhancement of governance quality, and investment in financial infrastructure mitigate the negative effects of natural resource rents on financial development. Trade openness also contributes positively to financial development, while rising urbanization adversely affects it due to the strain on government finances.

Abstract This study aimed to design a strategic energy management model based on the Chinese approach. The research was conducted within an interpretivist philosophical framework and adopted an inductive reasoning approach. It is an applied-developmental study in terms of its purpose and is classified as a non-experimental (descriptive) study in terms of data collection methods. A qualitative research design was employed to achieve the research objective. The participant population included university professors and managers from the Electricity and Energy Deputy of the Ministry of Energy. Purposeful sampling was used, and theoretical saturation was reached with 10 participants. Data were collected through semi-structured interviews and a decision matrix-based questionnaire. The validity of the qualitative coding was confirmed using Holsti's method (0.756) and Cohen’s kappa coefficient (0.681). The face validity of the questionnaire was confirmed, and its reliability was supported through an estimated intra-class correlation coefficient (0.815). The interviews were analyzed and coded using thematic analysis in MaxQDA 20 software. In the second phase, Interpretive Structural Modeling (ISM) and MicMac software were used to determine the relationships among the constructs and to design the model. The findings indicated that China’s authoritarian political system and spatial planning framework influence the country’s cultural soft power projection. This cultural projection affects international communication, diplomacy, and the harmonization of domestic and global systems, ultimately leading to strategic energy management. In turn, strategic energy management contributes to environmental preservation and China’s accelerating economic growth.

Abstract In recent years, the development of eco-innovation has been recognized as one of the main drivers of reducing the non-renewable energy consumption, the importance of which is undeniable for combating climate change and promoting environmental sustainability. Research evidence shows that green innovations can change energy consumption pattern and lead to a reduction in the demand for fossil fuels by improving energy efficiency and utilizing renewable resources. Therefore, examining the effects of these innovations on energy consumption is not only necessary for developing energy management strategies, but it can also help identify the challenges and opportunities on the path to a green economy. However, this issue has not been addressed in the previous literature. Accordingly, the present study aims to investigate the asymmetric role of eco-innovation in the non-renewable energy consumption in Iran during the period 1975–2022. The findings, based on the Nonlinear Autoregressive Distributed Lag (NARDL) model, show that the effect of eco-innovation (EI) in the research model is asymmetrical, such that positive changes in eco-innovation lead to a decrease, while negative changes lead to an increase in the non-renewable energy consumption (NRE). In addition, the results indicate that human capital (HC), industrialization (IND), and foreign direct investment (FDI) have a negative effect, whereas economic growth (EG), trade openness (TO), urbanization (URB), and the dummy variable to the Islamic Revolution of Iran (DU) have a positive and significant effect on the non-renewable energy consumption.

Abstract Optimal fuel consumption management at petroleum product distribution stations is a major challenge in the oil and energy industry, with direct implications for the national economy and energy security. In this regard, software packages and accounting information systems can provide accurate and timely information and thereby support more effective managerial decision-making. This study aimed to examine the effect of software packages and accounting information systems on fuel consumption management, with the mediating roles of management efficiency, control and monitoring, and planning and optimization at petroleum product distribution stations. The research was applied in terms of purpose and descriptive-survey in terms of method. The statistical population included accounting experts, operations staff, station owners, and members of the relevant professional association; 80 individuals were selected through simple random sampling. Data were collected using a researcher-made questionnaire and analyzed using the partial least squares structural equation modeling (PLS-SEM) approach. The results showed that both software packages and accounting information systems had significant positive effects on fuel consumption management. In addition, accounting information systems had the strongest effect on management efficiency, while software packages had the greatest effect on control and monitoring. The findings indicate that the development of software infrastructure and the implementation of integrated accounting information systems can play an effective role in improving productivity, increasing financial transparency, and enhancing fuel consumption management at petroleum product distribution stations. Accordingly, policymakers and industry stakeholders are advised to prioritize integrated digital systems and invest in human resource training

Abstract This study investigates the impact of financial development on renewable energy technology deployment in resource-rich countries with varying levels of financial system maturity. Using panel data from 2000 to 2021, the models were estimated with the Generalized Method of Moments (GMM), while the robustness of results was validated through Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS). The findings indicate that financial development positively affects renewable energy growth in all countries studied. In developed resource-rich countries, particularly those with advanced financial markets, natural resource abundance has facilitated renewable energy development, providing no evidence of the “resource curse.” In contrast, developing resource-rich countries with less developed financial systems show signs of the resource curse. These results highlight the crucial role of financial system development in transforming resource wealth into an opportunity for renewable energy advancement.
Introduction
The sharp rise in energy prices, particularly oil, over the twentieth century has brought about profound challenges and opportunities for the global economy. The heavy reliance on fossil fuels has raised serious concerns regarding energy security and environmental sustainability, prompting countries to seek alternative energy solutions. Among these, the development of renewable energy technologies has emerged as a crucial strategy to mitigate climate change and ensure long-term energy security (Bhattacharya et al., 2017; Charfeddine & Kahia, 2017). In recent years, developed economies have adopted a variety of policy instruments—such as feed-in tariffs and renewable portfolio standards—to accelerate renewable energy deployment (Kim & Park, 2016). However, despite these efforts, the contribution of renewable energy to total energy consumption remains limited, particularly in developing economies. High upfront capital costs and inadequate financial support are frequently cited as major barriers to renewable energy investment. Financial market development plays a pivotal role in mobilizing resources for renewable energy projects by facilitating access to capital and reducing investment risks. Yet, in resource-rich developing countries, the interplay between financial development and renewable energy expansion is complicated by the “resource abundance” phenomenon. While natural resource wealth has the potential to finance large-scale renewable energy investments, empirical evidence indicates that it can also distort economic structures and hinder sustainable development (Nili & Ratad, 2007; Moradbeigi & Law, 2016). Countries such as Iran and the Gulf Cooperation Council (GCC) states, despite abundant oil and gas revenues, have made limited progress in renewable energy adoption.
This study aims to explore how financial development influences renewable energy growth in resource-rich economies, considering the dual role of natural resource abundance. By distinguishing between countries with varying levels of financial system development, this research seeks to provide new insights into the mechanisms through which resource wealth and financial markets interact to shape renewable energy transitions.
Methods and Material
Drawing on theoretical foundations and previous studies (e.g., Moradbeigi & Law, 2016; Nili & Rastad, 2007; Kim & Park, 2016), this study employs a dynamic model to investigate the impact of financial development on the deployment of renewable energy technologies, while accounting for the role of natural resource abundance and the level of financial system development across countries. The general model is specified as follows:
 
In this model,  represents the installed capacity of renewable energy technologies in country i at time t. The vector  includes explanatory variables such as financial development (FD), natural resource rents (NRR), real GDP per capita, population (POP), consumer price index (CPI), and greenhouse gas emissions (GHG). The interaction term FD∗NRR is incorporated to examine the indirect effect of resource abundance on renewable energy development through its influence on financial development.
Given the dynamic nature of the model, the estimation is conducted using the Generalized Method of Moments (GMM), as proposed by Arellano and Bond (1991) and further refined by Arellano and Bover (1995) and Blundell and Bond (1998). The sample comprises resource-rich developed and developing countries over the period 2000–2021. Countries are classified into two groups—those with developed and less-developed financial systems—based on the financial development index provided by the International Monetary Fund (IMF). Furthermore, the categorization of developed and developing economies follows the World Bank classification. Only countries with significant natural resource rents are included in the sample (Sachs & Warner, 2001; Moradbeigi & Law, 2016).
 
 
Results and Discussion
The empirical findings, based on dynamic panel GMM estimations, reveal significant insights into the relationship between financial development and the deployment of renewable energy technologies (RETs) in resource-rich countries. Diagnostic tests, including the Sargan test for instrument validity and Arellano-Bond serial correlation tests, confirm the robustness and reliability of the model estimations. In resource-rich developed countries, financial development exhibits a strong and positive impact on both annual and cumulative renewable energy capacities, regardless of the maturity level of their financial markets. This highlights the critical role of well-functioning financial systems in mobilizing investments for RETs. Furthermore, the positive and significant coefficients of natural resource rents (NRR) contradict the resource curse hypothesis in these countries, suggesting that revenues from natural resources have been effectively utilized to promote clean energy transitions. The interaction term (FD*NRR) is also significant in countries with highly developed financial systems, indicating that financial development amplifies the positive effect of resource abundance on renewable energy deployment.
In contrast, the results for resource-rich developing countries present a more nuanced picture. While financial development positively influences RET expansion in countries with advanced financial markets, its effect is statistically insignificant in those with underdeveloped financial systems. Moreover, the negative and significant coefficient of NRR, alongside the interaction term (FD*NRR), confirms the existence of the resource curse in developing economies with weak financial infrastructures. This suggests that inadequate financial systems hinder the ability of these countries to channel resource rents towards sustainable energy investments. Control variables such as GDP per capita and CPI show consistent positive effects on RET deployment across most models, underscoring the importance of economic development and energy price signals in driving renewable energy adoption. Conversely, greenhouse gas emissions are negatively associated with RETs, supporting the notion that increased fossil fuel dependency hampers clean energy progress.
Robustness checks using FMOLS and DOLS estimators corroborate the main GMM results, further strengthening the study’s findings. These results collectively emphasize the pivotal role of financial development in transforming natural resource wealth into a driver for renewable energy growth, particularly in countries with efficient financial systems.
Conclusion
This study examined the role of natural resource abundance (resource rents) in shaping the relationship between financial development and renewable energy technology (RET) deployment in resource-rich developed and developing countries, with particular attention to the level of financial market development during the period 2000–2021. The findings reveal that financial development positively influences RET growth in both groups of countries. However, in resource-rich developed countries, this effect is significant regardless of the level of financial market development, whereas in developing countries, the impact of financial development on RET is only significant in those with advanced financial markets. This underscores the critical role of efficient financial systems in mobilizing resources and reducing investment risks in renewable energy projects.
Moreover, the positive and significant effect of resource rents in developed countries rejects the “resource curse” hypothesis, highlighting the capacity of advanced financial markets to channel resource revenues toward RET advancement. Conversely, in resource-rich developing countries with underdeveloped financial markets, resource rents negatively affect RET, confirming the existence of the “resource curse.”
Based on these results, it is recommended that countries with less developed financial systems strengthen credit markets, support private sector investments, create financial incentives, and enhance institutional frameworks to facilitate renewable energy technology deployment. Additionally, policies aimed at fostering sustainable economic growth, ensuring fair energy pricing, and implementing environmental strategies can play a vital role in reducing dependence on fossil fuels and promoting RET expansion.
Acknowledgments
The author would like to express their sincere gratitude to the anonymous reviewers for their valuable comments and constructive suggestions, which have significantly improved the quality of this study.
Keywords: Renewable Energy Technologies (RET), Financial Development, Natural Resource Rent, GMM Estimator

Abstract The National Development Fund was established as a development fund with the aim of providing intergenerational benefits, preventing the spread of fluctuations in oil revenues to the economy, and also supporting the country's development plans. Despite this, until now, there has not been a detailed evaluation of how the allocation of resources of this fund affects macroeconomic variables. However, by studying and examining the successful global models of such funds, in addition to the limited impact of this fund on the macro-economic variables in Iran, there are also flaws in the way its resources are allocated. Based on this, the main goal of this research is to design a dynamic stochastic general equilibrium model to evaluate the impact of the allocation of National Development Fund resources on macroeconomic variables with the Bayesian estimation approach using quarterly data for the period 2011-2021. The results of the simulation show that if the National Development Fund spends part of its resources on direct and indirect investment, although at the beginning of the period its effects are the same as before (only facilities), but after that the level of production, capital and investment will increase, which will lead to higher economic growth. Also, the results obtained from the minimum variance portfolio method show that among the existing methods, buying shares of capital market companies directly and investing in various types of investment funds, can bring higher returns than the current method (facilities) for the Fund at a certain level of risk.
Introduction
Countries rich in natural resources often struggle with resource mismanagement, institutional inefficiency, and economic volatility. Iran, despite significant oil revenues, has faced low economic growth and macroeconomic instability. The NDF was created to mitigate these challenges by saving oil revenues and promoting productive investment. This research explores how the structure and allocation of NDF resources affect macroeconomic variables and seeks to identify optimal strategies for maximizing its impact.
Methods and Materials
The study employs a DSGE model based on Real Business Cycle (RBC) and New Keynesian foundations, integrating sectors such as households, firms, government, central bank, and the NDF. Bayesian estimation techniques were used to calibrate model parameters using quarterly macroeconomic data. Multiple policy scenarios were simulated, including pure loan-based allocation and mixed investment strategies, to examine their effects on output, inflation, employment, and capital accumulation.
Results and Discussion
Simulation results show that switching from a loan-only strategy to a mixed investment approach enhances capital accumulation, investment, and output growth. While the short-term effects (approximately the first year) of both approaches are similar, the investment-inclusive approach yields superior long-run results. Portfolio optimization through the MVP model recommends allocating 43.4% to equities, 49.6% to mutual funds, and 7% to real estate, maximizing returns under acceptable risk levels.
Conclusion
The findings emphasize that diversifying the NDF’s financial instruments beyond traditional loans enhances both fund profitability and macroeconomic stability. Strategic allocation toward capital markets and investment vehicles leads to sustainable growth and improved intergenerational equity. Future policies should integrate a balanced portfolio approach to optimize the Fund’s economic contribution.
Acknowledgments
The author extends sincere gratitude to Dr. Mehdi Sarem for his invaluable support in model development, and to the editorial board of the Journal of Energy Economics of Iran for their constructive feedback and publication support.
 

Abstract The Probability of Informed Trading (PIN) is one of the important measures of market microstructure that is generally used to estimate the level of information asymmetry. Estimating PIN can be challenging due to boundary solutions, local maxima, and Floating Point Exceptions (FPE). Additionally, the prevailing assumption of the existence of only one information layer per trading day in PIN is inconsistent with the real-world empirical evidence and exposes it to a considerable underestimation bias. In this paper, we estimate information asymmetry for 55 listed companies in the energy sector during the period from 1396:Q1 to 1402:Q1, utilizing the Multi-Layer Probability of Informed Trading (MPIN) model introduced by Ghachem and Ersan (2023). The findings indicate: First, the assumption of a single information layer is satisfied for only 2.67% of the 1,200 stock/season observations, which implies the necessity of using MPIN to estimate information asymmetry. Second, the use of PIN not only leads to significant underestimation bias, but also provides an inaccurate picture of the ranking of companies from the perspective of information asymmetry. Third, the energy sector faces an average information asymmetry of 34.4%, and estimations reveal that private information reached its peak in the summer of 2020, exceeding 49%. Fourth, the symbols "Bepeyvand" from the “electricity, gas, and steam” sector and "Shapna" from the refining sector hold the highest (64.75%) and lowest (18.9%) information asymmetry, respectively.
 
Introduction
The Probability of Informed Trading (PIN) is a prominent metric in market microstructure, utilized to assess the level of information asymmetry by estimating the probability of informed trading. Despite extensive international research on measuring information asymmetry and its applications across various domains, the Iranian academic landscape reveals two significant gaps. First, studies in this field remain limited in number. Second, corrective and generalized methodologies—whether in terms of estimation techniques or the underlying assumptions of the models—have not been sufficiently explored in domestic research.
The primary contribution of this study is to address this research gap and provide a more precise representation of information asymmetry levels within the stock market of companies operating in the energy sector. This investigation is structured around three central research questions: First, what differences exist between the average estimated levels of asymmetric information in the energy industry when using the PIN and MPIN models? Second, what is the magnitude of bias in the estimated asymmetric information derived from the PIN model across energy subsectors, including "chemical and petrochemical," "refining," and "electricity, gas, and steam"? Third, which companies in the energy sector exhibited the highest levels of asymmetric information, as estimated by the MPIN model, during each quarter from the first quarter of 1396 to the first quarter of 1402?
To answer these questions, asymmetric information will be estimated using the PIN and MPIN models, which are rooted in the extended methodologies proposed by Ersan and Alici (2016) and Ghachem and Ersan (2023). This approach aims to enhance the accuracy and reliability of the findings, contributing to a deeper understanding of asymmetric information dynamics in the energy sector.
 
Methods and Materials
To estimate information asymmetry in this study, high-frequency daily stock data from 55 companies operating in the energy sector were collected. These companies are categorized into three subsectors: "chemical and petrochemical," "refining," and "electricity, gas, and steam." The data spans the period from the first quarter of 1396 to the first quarter of 1402 and was sourced from the Tehran Stock Exchange website. The data was subsequently cleaned using Python software. The selection of these 55 companies was based on two critical criteria: the availability of high-quality, high-frequency data (with minimal non-trading days) and the inclusion of a diverse range of companies of varying sizes.
At any given moment, a vast number of bid and ask quotes exist at different price levels. Therefore, the initial step involved collecting order data, which amounted to over 30 billiard rows of data for each of the 55 symbols across the 25 quarters under study. Due to the irregular timing of trades, the bid and ask quotes were aggregated into one-second intervals. Subsequently, price data was aggregated using a weighted average, while trade volume data was summed up within each one-second interval. Following this, the traded prices and volumes were matched with the corresponding bid and ask quotes.
The second-by-second data for each day were processed using the Lee and Ready (1996) algorithm to identify the origin of each trade (i.e., whether it was buyer-initiated or seller-initiated). Finally, within the frameworks proposed by Ersan and Alici (2016) and Ghachem and Ersan (2023), the parameters of the PIN and MPIN models were estimated, respectively. These parameters were used to calculate the probability of informed trading for each quarter. The likelihood function was constructed separately for each company and each quarter, and the estimation was performed using parallel processing on a Core i9 processor in the R software environment.
 
Results and Discussion
In Figure (1), the average estimated values of PIN and MPIN for 1,387 quarter/stock pairs are presented, while Figure (2) illustrates the trend of PIN bias alongside the number of layers identified in the MPIN model. The findings reveal the following:




Figure (2). Average PIN bias and the number of layers identified in MPIN


Figure (1). Average values of PIN and MPIN in the energy industry from 1Q:1396 to 1Q:1402




 


 




 

The average PIN ranges between approximately 14% and 37%, while the average MPIN fluctuates between 21% and 51%.
The average MPIN values are consistently higher than PIN values across all quarters, with MPIN being, on average, approximately 46% higher than PIN. This observation suggests a higher probability of encountering informed traders when using the MPIN model, which aligns with theoretical expectations.
The average values of MPIN and PIN exhibit similar patterns of fluctuation over time. However, the difference between the two is not constant. Although they began the study period with relatively similar values, the gap between them gradually widened. As shown in Figure (2), the underestimation of PIN increased from approximately 5% in the first and second quarters of 1396 to around 12% in the first quarter of 1402. The highest bias was observed in the fourth quarter of 1398, where PIN underestimated information asymmetry by 18%.

These results highlight the importance of using the MPIN model for a more accurate estimation of information asymmetry, particularly in dynamic and complicated markets such as the energy sector. The increasing divergence between PIN and MPIN over time underscores the limitations of the PIN model in capturing the full extent of informed trading, especially in periods of heightened market activity or volatility.
 
Conclusion
The estimated PIN values in the energy industry fluctuate between 14% and 37% across different quarters, with an average of 22.9%. Among the three energy subsectors, the refining industry exhibits the lowest level of information asymmetry (20.4%), while the chemical and petrochemical sector shows the highest (23.5%). The electricity, gas, and steam subsector has an information asymmetry level of 23.1%. The estimated PIN values in the Iranian energy sector are higher than those reported by Easley et al. (2002) for the U.S. stock market (approximately 19.1%) and Hwang et al. (2013) for the South Korean stock market (20.1%), but are close to the estimates by Martins and Paulo (2014) for the Brazilian stock market (25%).
Given that the level of information asymmetry in Iran is relatively high compared to estimates from other countries and has been increasing over time, and considering that private information is more prevalent in subsectors with smaller market shares (such as electricity, gas, and steam) and smaller companies (e.g., Bepeyvand, Begilan, etc.), it is recommended that the Securities and Exchange Organization of Iran enhance its oversight of smaller companies and industries. These entities should be required to improve transparency by promptly disclosing material information that impacts current and future revenues and costs.
Additionally, analyzing the effectiveness of policies, particularly trading restrictions such as volume limits and price fluctuation limits—which are implemented in various countries to reduce information asymmetry and enhance retail investor confidence—could serve as a focus for future complementary research. Such studies would aid in designing and implementing optimal policies to address information asymmetry and improve market efficiency.
 
Keywords: Market Microstructure, Information Asymmetry, Multi-Layer Probability of Informed Trading (MPIN), Hierarchical Agglomerative Clustering (HAC)
JEL Classification: C13، G10، G14

Abstract There is good reason to expect crude oil prices to follow nonlinear models. However, previous research has considered the linear assumption to investigate the existence of a unit root. Unit root linear tests such as ADF, PP, and KPSS are provided for linear models. These tests are not suitable for nonlinear time series. Because the model deviation from the linear state may be considered as a random permanent deviation. The purpose of this article is to test the nonlinear unit root of crude oil prices, specifically Brent and WTI oil in the period 2019-2020 daily. For several decades now, various classes of nonlinear models have been introduced. These models introduce a wider range of dynamics than linear models in time series. A special type of these models that economists pay attention to are TAR models. In these models, as in linear models, valid statistical analysis requires distinguishing between the deterministic trend and the stochastic trend. In this study, the Bayesian unit root test for the general SETAR (1) model has been used with respect to the necessary and sufficient conditions for the maintenance of SETAR processes based on the article by Petrocyl and Wolford (1984). A nonlinear unit root test was performed using Bayesian validity interval. The results show that Brent crude oil prices in both regimes contain a unit root that is consistent with similar findings for the production or consumption of crude oil.
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Abstract Energy is a input in the production sector and used in the distribution and consumption of many goods and services, which should be used optimally. One of the indicators showing the optimal use of energy input is the EII. Obviously, the lower the EI, it means that less energy has been used for each unit of production of goods and services. Several factors can be mentioned that affect EI, of which innovation is one of the key ones. Purpose of this article is to investigate the influence of several factors on EI in selected countries of the MENA region during the period of 2010-2020. The panel data model for 13 selected MENA countries have been used. The results show that the influence of control variables including "government consumption expenditure", "energy price" and "trade openness" on EI is negative and significant. The findings of the first model indicate a negative and significant effect of the OII on EI, and the estimation results of the second and third models also indicate a negative and significant effect of the innovation sub-indices (ICT and financial access) on the energy intensity of the selected countries in the period. is under investigation. Considering the findings of the research, it is recommended that the economic policy makers pay special attention to the category of innovation and place their investments and policies with more focus in this area, because according to the findings of the research, innovation is an effective factor in reducing energy intensity

Abstract The development of Underground Gas Storage (UGS) in depleted hydrocarbon reservoirs has always been a priority for industrialized countries. This is mainly because UGS helps in balancing the seasonal supply and demand of gas, managing reservoirs efficiently over time, ensuring the security of gas supply, and fulfilling international contractual obligations. Providing a legal platform and an attractive contract model is crucial to encourage private sector participation in this strategic industry and accelerate UGS development. Concession arrangements are one of the most commonly used and attractive models for UGS development. However, the main question of this research is whether the use of concessional agreements faces any legal prohibitions. To answer this question, the legal challenges facing the application of this contractual model in the oil fields development have been investigated. In this regard, the research verifies whether this industry is upstream or downstream from the legal point of view, and considers the opinions of prominent jurists. After analyzing the unique features of the storage industry, the essential differences between UGS and the traditional development of oil fields, and especially the governmental ownership of the gas that is injected, the conclusion is that the use of concession agreements is allowed

Abstract This article was done with the aim of presenting the industrial and commercial development model in the country's oil and gas industry in Gachsaran Oil and Gas Company. The current article is an applied-developmental research in terms of its purpose, and it is a survey-cross-sectional research from the point of view of the data collection method. A mixed exploratory design was used to achieve the goal of the research. The community of participants in the qualitative section includes managers with experience in Gachsaran Oil and Gas Company. Sampling was done by theoretical sampling method and theoretical saturation was achieved with 11 interviews. The statistical population of the quantitative part also includes the experts of Gachsaran Oil and Gas Company, which was estimated to be 384 people using Cochran's formula. The required sample volume was provided by a simple random sampling method. A semi-structured interview and a researcher-made questionnaire were used to collect data. The basic categories of industrial and commercial development in the country's oil and gas industry were identified by the grounded theory method in Maxqda 20 software. The final model was validated by the partial least squares method in Smart PLS 3 software. The results have shown that financial and economic indicators affect the development of human, technical and managerial capabilities. These capabilities also affect commercialization strategies. In this regard, the legal and political atmosphere of the country provides the necessary platform and the risk-taking of the industry plays the role of an interventionist. Finally, commercialization strategies lead to industrial and commercial development

Abstract One of the innovations that has been formed in the insurance industry in recent years is risk transfer to the capital markets. Today, this possibility is provided by issuing insurance bonds and catastrophe bonds, which are the most important type of insurance-linked securities and can redress inefficiency in the insurance industry. Today, more and more catastrophe bonds are being issued worldwide, which is welcomed by investors and insurance companies. On the other hand, traditional insurance solutions to cover the risks of Iran's oil and gas industry are not efficient as well as sufficient, and using CAT bonds to transfer risks of this industry to capital markets is a necessary and inevitable issue. The aim of this research is to identify effective factors for issuing catastrophe bonds in Iran's oil and gas industry. Based on this and after reviewing the literature through library studies, 33 factors were identified in the form of seven categories, based on the similarities. Then, based on the Delphi method, experts were asked to express their opinions through an iterative questionnaire. After taking the experts' opinions in every round, the statistics analysis was performed and the Delphi process was stopped in the third round. Based on the results, 32 factors in six categories were recognized. Also, by using the method of analytical hierarchy process and reusing the opinions of experts, the criteria and sub-criteria were prioritized and in order of preference with the titles Legislation and Amendment of the Rules, Process Management, Transparency, Knowledge Management, Creation and Strengthening of Software Platforms and Cultivation. Based on the results of this research, policymakers and activists in this field should consider and apply the various dimensions that have been counted in this research in order to successfully publish papers on catastrophic bonds

Abstract Making long-term investments in the oil and gas industry to maintain the current production levels and increase its capacity is one of the ways to increase economic resilience in the country's upstream documents, including the resistance economy's general policies. In recent years, there have been intense sanctions on the country, especially in the energy field. The importance of proper use of China's allocated credit lines to finance these projects is revealed due to the high need for investment in oil and gas industry projects and the impossibility of covering the investment needs of this industry from domestic sources, and the challenge in effective communication with international monetary and financial institutions. Anyway, the conducted studies indicate serious challenges in using the capacity of these credit lines. For this purpose, this research tries to investigate these challenges from many aspects. After studying the background of the research and reviewing various sources, this article conducted targeted interviews with financing experts. Also, the challenges of using credit lines were extracted and classified into four categories: financial and economic, executive and operational, structural and institutional, and juridical and legal, utilizing the method of thematic analysis and focus group. The extracted challenges were exposed to experts to validate the findings. Finally, the formation of the focus group presented the corrective solutions for the use of credit lines to finance oil and gas industry projects.

Abstract Oil prices and other oil-products prices are connected to each other and their price volatilities are parallel. Firms which are using crude oil in their products are facing a risk of price volatility which has different reactions in each era and is known under different oil regimes. For example lubricant industry is completely connected to the oil price. With this philosophy when the economy faced volatility the market players faced loss and so to overcome this issue they began to hedge themselves with another commodity. This hedging process in different regimes has different rates. So there is a need to introduce a new model. From the work of Hamiltonian (1989) oil price has its own volatility and regimes so to this attitude there is an effort to calculate an efficient hedging ratio with regime switching dynamic constant correlation. In this article, monthly data of oil and gold prices for about 10 years from 2010 till 2020 is used and the model is programed with MATLAB. The result showed that the efficient hedge ratio for the first regime (first major change in price of two markets) is 66 percent and the second (second major change in price of two markets) one is 26 percent.