• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Seyyed Mohammad Ghaem Zabihi; Rasta Kamalian; Fatemeh Akbari; Ali Akbar Naji Meidani
Abstract
The current study has studied the threshold effects of energy consumption structure and GDP per capita variables on carbon emissions from 2002 to 2019 for 37 selected countries (with middle to high-income levels) using the non-linear approach of Panel Smooth Transition Regression Models. For this purpose, ...
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The current study has studied the threshold effects of energy consumption structure and GDP per capita variables on carbon emissions from 2002 to 2019 for 37 selected countries (with middle to high-income levels) using the non-linear approach of Panel Smooth Transition Regression Models. For this purpose, two separate models have been estimated by considering energy consumption structure transfer and GDP per capita variables. The results indicate a non-linear relationship between the studied variables in both models. The estimation results of both models show that GDP per capita (in the threshold state of energy consumption structure) and energy consumption structure (in the threshold state of GDP per capita) positively affect carbon emissions. Also, urbanization and trade openness have a positive effect on carbon emissions in both models. Thus, the results show that increasing efficiency in energy consumption and GDP per capita structure can significantly reduce carbon emissions. These findings point to the importance of optimizing energy policies and the crucial role of changes in the economic structure in managing greenhouse gas emissions..
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Mahboobeh Farahati; Leyla Salimi; Mehdi Gholizadeh Eratbeni
Abstract
The lack of security, political dependencies, the formation, and the increase in environmental problems are the main reasons for changing the approach to energy supply from fossil fuels to renewable energies. This alteration requires financial support for the extraction of renewable energies. Foreign ...
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The lack of security, political dependencies, the formation, and the increase in environmental problems are the main reasons for changing the approach to energy supply from fossil fuels to renewable energies. This alteration requires financial support for the extraction of renewable energies. Foreign direct investment, investment in research and development, and financial market development are among the Financing methods that also impact the consumption of renewable energies. This study aims to assess the impact of these financing methods on the consumption of renewable energies across 26 developing countries during the period from 2008 to 2019. Findings from panel model estimations indicate that foreign direct investment and investment in research and development have a positive and significant effect on the consumption of renewable energy, while the development of financial markets does not have a significant effect on the consumption of renewable energies. Based on the results, since the defined methods of financing do not have an adverse effect on renewable energy consumption, it is recommended that the government, to guarantee the indicators of the nation's welfare, including environmental quality, put the laws covering these financing methods in the main plan and support
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Zinat Goli; Hamid Amadeh; taymoor mohamadi
Abstract
Global greenhouse gas emissions have risen from 31,553 million tons of CO2 equivalent in 1990 to 46,187 million tons in 2022. According to the United Nations Intergovernmental Panel on Climate Change (IPCC), since the late 19th century, the Earth’s average temperature has increased by 1.1 degrees ...
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Global greenhouse gas emissions have risen from 31,553 million tons of CO2 equivalent in 1990 to 46,187 million tons in 2022. According to the United Nations Intergovernmental Panel on Climate Change (IPCC), since the late 19th century, the Earth’s average temperature has increased by 1.1 degrees Celsius.Every decade since 1960 has been warmer than the previous one, with the last decade being the hottest on record. The warming caused by human activities and greenhouse gas emissions has currently reached about 1 degree Celsius above pre-industrial levels. Over the past two decades, global scientific and political communities have increasingly focused on the issue of global warming and its associated climate changes. The historic Paris Agreement, signed on December 12, 2015, during the 21st Conference of the Parties (COP21) to the UN Climate Change Convention, was a significant step toward combating climate change and addressing the challenges of reducing emissions and investing in a low-carbon, resilient, flexible, and sustainable economy. The agreement, signed by 195 countries, came into force on November 4, 2016. Under the Paris Agreement, countries committed to reducing greenhouse gas emissions to prevent the global average temperature from rising more than 2 degrees Celsius above pre-industrial levels, and to pursue efforts to limit the increase to 1.5 degrees Celsius above pre-industrial levels.Following the agreement, countries through the UN Climate Change Convention asked the IPCC to provide a special report on the impacts of global warming of 1.5 degrees Celsius above pre-industrial levels and related global greenhouse gas pathways. In the IPCC report, supported by 133 researchers, various greenhouse gas emission pathways to achieve the 1.5-degree goal were outlined. Achieving this goal will require significant reductions in greenhouse gas emissions, with a major focus on the energy sector. Four proposed scenarios, which aim to reach net-zero carbon emissions by 2050, predict a sharp decline in the use of fossil fuels. However, the type of fuel and the speed of the transition in fuel consumption vary considerably, especially for coal, oil, and gas, through 2030. Coal faces the most severe reductions, with consumption needing to decrease by 59% to 78% by 2030 compared to 2010. Natural gas has a better outlook, with predictions ranging from a one-third increase to a one-quarter decrease in different scenarios. Oil has the most uncertain future, with the fourth scenario, based on bioenergy combined with carbon capture and storage (BECCS), predicting an 86% increase in oil consumption compared to 2010. Given the uncertain future of oil in these scenarios, analyzing the impact of implementing each of the IPCC's proposed scenarios on OPEC member countries, whose economies are heavily reliant on oil revenues, is crucial. The innovation of this research lies in examining the effects of climate change policies on oil-producing and exporting OPEC countries, including Iran, using a time-series econometric approach, co-integration equations, and a vector error correction model.Methods and MaterialIn this research, to examine the effects of the IPCC scenarios, which are based on reducing global fossil fuel consumption, on OPEC’s oil demand and supply, a time-series econometric approach was used. Co-integration equations were employed to estimate long-term relationships, and the vector error correction model was applied for short-term estimates. Given the significance of reduced demand for OPEC countries, which are economically dependent on oil export revenues, data on the production and price of OPEC oil were used. Additionally, the long-term effects of environmental actions under the IPCC scenarios, which replace fossil fuels with renewable energy by 2030 and 2050, were incorporated into the model using renewable energy price variables. Variables used in the supply and demand functions include OPEC oil production, OPEC oil prices adjusted for the U.S. consumer price index, industrial production indices for developed and emerging countries, and renewable energy price indices. The research data were gathered monthly from 1986 to 2022. OPEC oil price and production statistics were obtained from OPEC, and the U.S. consumer price index data were sourced from the World Bank. The industrial production index (IP) for developed countries was calculated as a weighted average of IP from the U.S., Japan, Germany, France, the U.K., Italy, Canada, Spain, the Netherlands, Sweden, Norway, Belgium, Austria, Denmark, Finland, Greece, Ireland, Portugal, and Luxembourg, with weights based on the GDP share of each country in total GDP. For emerging countries, the IP index was similarly calculated for China, Brazil, India, South Korea, Mexico, Turkey, and Indonesia. The GDP data were obtained from the World Bank, and IP data from the International Monetary Fund. Renewable energy prices were based on the weighted average levelized cost of energy (LCOE) for renewable sources such as concentrated solar power, offshore and onshore wind power, and photovoltaic solar energy. The weights were based on each energy type's share of total renewable energy production, and the LCOE data were published by the International Renewable Energy Agency. Initially, the industrial production indices for developed and emerging countries, as well as the renewable energy price index, were seasonally adjusted.Table 1. Long-term supply and demand relationships for oil based on Johansen's method. variablesOPEC Oil Supply functionOPEC Oil Supply functionOPEC oil production11Real price of OPEC oil0.22(0.05)-0.05(0.02)Non-OPEC oil production1.56(0.41)0IP(Advanced economic)00.76(0.16)IP(Emerging economic)00.58(0.07)Renewable energy price00.26(0.06)Error correction term0.03-)0.009)0.08-(0.003)Results and DiscussionOPEC adopts two approaches in the global oil market: a strategic approach, where OPEC acts similarly to non-OPEC producers and amplifies the effect of price shocks, and an adaptive approach, where OPEC seeks to balance non-OPEC production changes and stabilize oil price fluctuations. The estimated coefficients indicate that during the study period, OPEC countries, alongside the increase in non-OPEC production, attempted to maintain their market share, often increasing production to force high-cost producers out of the market. This finding is consistent with those of Bog, Pal, and colleagues (2016), who viewed OPEC as a dominant producer seeking to protect market share by limiting competitors like shale oil producers.The results of the model estimation indicate a direct relationship between OPEC oil supply and real oil prices, with a price elasticity of oil supply of 0.22. Additionally, a 1% increase in non-OPEC production leads to a 1.56% increase in OPEC oil production. The price elasticity of oil demand is negative at -0.05, with demand from developed countries having a more significant impact on OPEC oil demand than demand from emerging countries. Furthermore, a 1% decrease in renewable energy prices reduces OPEC oil demand by 0.26%. Therefore, in the pessimistic IPCC scenario, where oil consumption declines by 37%, OPEC’s oil supply could decrease by 40% by 2030.Based on the findings, it is recommended that OPEC regularly monitor the pace of renewable energy development up to 2030 and adjust its strategies accordingly. Although the growth of industrial production in developed countries has a more significant effect on OPEC oil demand, trends in oil imports from China and India, which accounted for about 40% of OPEC’s exports in 2019, versus declining imports from the U.S. and European OECD countries, which have dropped by 40%, should also be considered by OPEC.
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Yazdan Gudarzi farahani; Zoleikha Morsali Arzanagh; Mohsen Mehrara
Abstract
The purpose of this paper is to investigate the effect of investment in renewable energy on Iran's macroeconomic variables. In this regard, statistical information related to the period 1991-2022 was used. For this purpose, the stochastic dynamic general equilibrium method was used. The information used ...
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The purpose of this paper is to investigate the effect of investment in renewable energy on Iran's macroeconomic variables. In this regard, statistical information related to the period 1991-2022 was used. For this purpose, the stochastic dynamic general equilibrium method was used. The information used in this article was collected from the Central Bank of Iran and the Ministry of Energy. The theoretical framework of the present study will be based on investment models, optimization and inter-sectoral balance. In this study, the effects of investment in the field of renewable energy through public and private companies are included in the model. The results obtained from the investment shock in the field of renewable energy indicated that investment in this sector had the greatest impact on the growth of economic added value in the industry, services, agriculture, and oil and gas sectors. Also, the obtained results indicate that in order to increase social welfare and achieve economic development, a 4-year investment period with a 50% growth in the field of renewable energy infrastructure in the country is necessary.
سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
parisa Mohajeri; Reza Taleblou
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 ...
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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. IntroductionThe 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 MaterialsTo 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 DiscussionIn 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 MPINFigure (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. ConclusionThe 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
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Asghar Vahedi; Esmaiel Abounoori; parviz malekzadeh
Abstract
In this research, the effect of oil price shock on the return of the Iranian stock market has been evaluated using a new quantile-on-quantile approach. To do this, first, the oil price shock has been calculated using the structural vector autoregression method, and then the effect of the oil price shock ...
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In this research, the effect of oil price shock on the return of the Iranian stock market has been evaluated using a new quantile-on-quantile approach. To do this, first, the oil price shock has been calculated using the structural vector autoregression method, and then the effect of the oil price shock on the return of the Iranian stock market has been investigated using the quantile-on-quantile approach. The statistical population consists of the data related to oil variables and the stock price index of the Iranian stock market. The statistical sample includes 200 observations of the monthly data related to the oil variables and the stock price index of the Iranian stock market during the period of 1385: 1 -1401: 12. The results of this research show that the effect of the oil price shock on the Iranian stock market varies across different quantiles of the Iranian stock market returns. A negative oil price shock has a larger effect on stock market returns when the stock market is bullish. Also, in the normal state of the stock market, a positive oil price shock has a large negative effect on stock market returns. Based on these observations, it is concluded that the relationship between oil price and stock market returns can depend on the nature of oil price shocks and the performance of the stock market.
