Alireza Taghipour; Mommad Mehdi Hajian; Garshasb Khazaeni; Javad Kashani; Atefeh Taklif
Abstract
With the change in policy by the Iranian Ministry of Petroleum in 2017 to outsource the operation and maintenance of oil and gas units to the private sector, the O&M outsourcing contract framework has drawn more attention than ever.This study first explores the current state of O&M contracts, ...
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With the change in policy by the Iranian Ministry of Petroleum in 2017 to outsource the operation and maintenance of oil and gas units to the private sector, the O&M outsourcing contract framework has drawn more attention than ever.This study first explores the current state of O&M contracts, in conjunction with the laws and regulations, and wraps up that the Ministry of Petroleum has an inherent duty to comprehensively outsourcing these operations to the private sector. The next step points out that the vast majority of Operation contracts, which are always claimed as O&M contracts, are basically manpower contracts.The next step is to categorize these contracts into two categories: independent and integrated. This study divided independent categories into seven categories: Manpower, Inspection, Package-based, Indexed packages, Hybrid, and Full Coverage. In opposed to the current method of making independent O&M contracts in the oil and gas industry, the results showed that optimal O&M outsourcing takes place through integrated contracts in which the operation is a partial part of that contract. Furthermore, a review of the framework of integrated upstream contracts, particularly the Iranian Upstream Petroleum Contract (IPC), and some types of integrated downstream contracts, such as the EPC+O&M and the FIDIC DBO model, conclusively demonstrates that "Build Operation and Transfer" and "Rehabilitate, Operate, Transfer" contracts are one of the most effective methods of outsourcing these operations.Introduction In the oil and gas sector, physical assets such as refineries, pipelines, and processing plants are essential for a stable energy supply. Increasing operational costs and production demands have prompted companies to outsource maintenance to the private sector. Efficient asset management is crucial for cost optimization, safety compliance, and reducing downtime, while strategic outsourcing can mitigate risks and enhance operational efficiency. This study examines optimal contract frameworks for outsourcing, particularly in the context of Iran's recent initiatives to enhance asset value through integrated management aligned with global standards. Although previous research supports the viability of outsourcing, significant gaps remain in contract models, cost structures, and execution quality, especially regarding Operations and Maintenance (O&M) contracts in the hydrocarbon industry. This research focuses on developing optimal O&M contract frameworks for the post-feasibility privatization phase, confronting the specific confidentiality challenges of the sector.Research MethodologyThis applied-descriptive study utilized a mixed-methods approach, integrating document analysis of over 35 Operations and Maintenance (O&M) contracts, including IPC, Buyback, PPP, and BOT agreements, with in-depth interviews involving more than 30 experts from NIOC subsidiaries, Oil and Gas companies, and E&P contractors. Additionally, focus group discussions with representatives from the Ministry of Energy enriched the data collection process. For the analysis of upstream contracts, we reviewed four active IPC and two buyback agreements, while infrastructure projects were assessed using simulated ROT frameworks, such as the revitalization of Bibi Hakimeh EPC project.Results and DiscussionAs shown in Figure 1, these are divided into independent and integrated outsourcing models. Independent O&M contracts split into partial and full-scope agreements, exemplified by rotating equipment maintenance in oilfields. Full-coverage contracts encompass entire facilities through seven risk/reward tiers: a) Resource provision b) Inspection c) Package-based d) Indexed packages e) Performance-based f) Hybrid (product-service-resources) g) Phased implementation.Figure 1:O&M Contract Classification Key FindingsMajor outsourcing barriers include distrust of private sector, resistance to change, and workforce social implications.O&M contracts require customized approaches; standardized frameworks should evolve from operational experience rather than being imposed uniformly.Integrated contracts (BOT, ROT, EPC+O&M) demonstrate superior operational outcomes compared to standalone O&M agreements, particularly when operational alignment begins early.Full-scope contracts should be risk-stratified across seven implementation models (resource provision, inspection, package-based, etc.), though current practice in oil/gas sectors overly relies on labor-supply arrangements.Hybrid contract structures focusing on service procurement (vs. manpower supply) are recommended for optimal operational flexibility.ConclusionAging infrastructure and high operational costs in oil & gas processing plants, combined with limited government budgets and organizational inefficiencies, highlight the critical need for performance-based integrated models. The Rehabilitate-Operate-Transfer (ROT) approach proves particularly effective by:○ Maintaining private-sector ownership until operational transfer, creating self-regulating quality control○ Eliminating the need for complex KPIs through built-in asset preservation incentives○ Ensuring production reliability while modernizing critical facilitiesAcknowledgmentsWe thank Majid Habibi (Servak Azar), Mohammad Kazem Rajabali Pourcharmi (Ministry of Petroleum), Amir Abbas Shokourian (Rampco), and Mohammad Eghbali & Reza Mohammadi Ardehali (NIGC) for their expert insights.
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Fatemeh Rastehmoghadam; Mohammad Nabi Shahiki Tash; Emad Kazemzadeh
Abstract
Air pollution has emerged as a critical concern for contemporary human societies due to its significant implications for both economic stability and public health. This research investigated the potential of renewable energy adoption to enhance air quality and mitigate mortality rates associated with ...
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Air pollution has emerged as a critical concern for contemporary human societies due to its significant implications for both economic stability and public health. This research investigated the potential of renewable energy adoption to enhance air quality and mitigate mortality rates associated with air pollution. Employing the quantile regression method, the study analyzed data spanning from 1996 to 2018 across the member nations of the Regional Comprehensive Economic Partnership. The findings indicated a statistically significant and negative correlation between renewable energy consumption and air pollution-induced mortality across all quantile ranges. Consequently, the implementation of renewable energy sources in managing air pollutants was found to contribute to a reduction in mortality and an improvement in air quality. Furthermore, increased gross domestic product was observed to correlate with decreased air pollution-related mortality, while carbon dioxide emissions and the rate of urbanization were associated with a rise in such mortality. These results underscore the potential of promoting renewable energy and managing emissions as effective strategies for bolstering public health and alleviating the adverse effects of air contamination.IntroductionRapid industrialization in recent decades has led to high levels of air pollution, causing numerous health problems. Air pollution originates from various sources, including the burning of solid fuels in homes, particulate matter from industry and transportation, and ground-level ozone. The emission of air pollutants and the resulting climate change pose a significant challenge that has garnered much attention. The overuse of fossil fuels for energy production is a primary cause of these pollutants, leading to respiratory and cardiovascular diseases, as well as global warming.To address these issues, reducing dependence on fossil fuels and adopting renewable energy sources is essential. Renewable energies not only do decrease air pollution but also help preserve the environment. Recent researches indicate that the use of renewable energy can improve human health by reducing pollutants. Economic collaborations, such as the "Regional Comprehensive Economic Partnership" (RCEP) agreement, can also be effective in reducing air pollution. RCEP member countries constitute a large portion of the global economy and population and account for a substantial share of global emissions.Given these points, the main research question of this study is:Can the consumption of renewable energy reduce the number of deaths caused by air pollution in RCEP member countries?This research also examines the impact of economic growth, urbanization rate, and carbon dioxide on deaths caused by air pollution. The main objective is to identify the effect of renewable energy consumption on air pollution-related mortality in RCEP member countries using the quantile regression method over the period from 1990 to 2018. This topic is important due to its direct impact on public health.Methods and MaterialIn this research, we employ quantile regression to investigate the conditional quantiles of the dependent variable. Introduced by Koenker and Bassett Jr. in the 1970s, quantile regression is a method in statistics and econometrics used to model and analyze the effects of variables on different quantiles of the response variable's distribution. It focuses on estimating "how much" of the lowest and highest possible values of a dependent variable are influenced by a set of independent variables, rather than just the mean.In quantile regression, a specific confidence level (τ, a value between 0 and 1) is chosen, and the estimator calculates the desired quantile for the dependent variable. For instance, setting τ to 0.05 allows the estimation of the 0.05th (lower tail) and implicitly the 0.95th (upper tail) quantiles. This method is particularly useful for modeling more complex relationships and the nonlinear impact of independent variables, serving as an alternative to mean regression (like linear regression) and aiding in the analysis of heterogeneously distributed data.Moment-based Quantile RegressionTo analyze the distributional heterogeneity across countries within a panel dataset, we utilize the moment-based quantile regression approach developed by Machado and Silva (2005, 2019). This method estimates different quantiles of the outcome distribution by accounting for unobserved effects across the distribution. Following Fouquau et al. (2021), this approach assumes that the independent variables influence the dependent variable solely through a location shift. Furthermore, it examines the conditional correlation effects of the determinants of air pollution-related mortality at various quantiles.The moment-based quantile regression model can be summarized as follows:Qit (τ∣Xit) = (αi + δiq(τ))+yit′β+Zit′γq(τ) (1)In Equation (1), αi(τ)=αi+δiq(τ) is a scalar coefficient indicating the τ-quantile fixed effects for a country. Since this coefficient is not location-invariant, the distributional impact differs from the classical fixed effect. Moreover, time-invariant characteristics captured by the distributional effect allow other variables to affect the countries under investigation in different ways (Machado & Silva, 2019).In this study, we use the quantile regression model to examine the effects of gross domestic product (GDP), renewable energy (RE), carbon dioxide (CO2) emissions, and urbanization rate (UP) on air pollution-related mortality (Y). The model is specified as:QYit (τ∣ξt , Xit) = αi+ξt+β1τCO2it+β2τ REit+β3τ GDPit+β4τ UPit(2)Where GDP, CO2, RE, and UP represent economic growth, carbon dioxide emissions, renewable energy consumption, and urbanization rate, respectively, in order to analyze their effects on mortality due to air pollution.Results and DiscussionThe purpose of tables and figures in documents is to enhance your readers’ understanding of the information presented in the document. It is much lucid and efficient if the information is communicated in tables or figures.Limit the use of borders or lines in a table to those needed for clarity. In general, use a border at the top and bottom of the table, beneath column headings, and above column spanners. You may also use a border to separate a row containing totals or other summary information from other rows in the table.Do not use vertical borders to separate data and do not use borders around every cell in a table. Use spacing between columns and rows and strict alignment to clarify relations among the elements in a table. Also, add one blank double-spaced line between the table and any text to improve the visual presentation. Note that the Table 12 presents the results of the panel quantile regression. The coefficients for each independent variable across different quantiles (10th, 25th, 50th, 75th, and 90th percentiles) can be interpreted as follows:Carbon Dioxide (lCO2): The coefficient for carbon dioxide is positive and statistically significant across all quantiles, indicating a positive relationship with mortality. The impact of carbon dioxide on mortality decreases as we move from lower to higher quantiles. For example, a 1% increase in CO2 leads to a 0.32% increase in mortality at the 10th percentile, while this effect reduces to 0.198% at the 90th percentile. This suggests that the effect of CO2 emissions on mortality is more pronounced in countries with lower levels of mortality. Overall, the findings suggest that increased carbon dioxide emissions can lead to a higher mortality rate.Gross Domestic Product (LGDP): The coefficient for GDP is negative and statistically significant across all quantiles, indicating an inverse relationship with mortality. The negative impact of GDP on mortality tends to decrease in magnitude as we move from lower to higher quantiles. This suggests that economic growth is associated with a reduction in mortality rates.Urbanization Rate (LUP): The coefficient for the urbanization rate is positive and statistically significant across all quantiles, suggesting that a higher urbanization rate is associated with increased mortality. However, the positive impact of urbanization gradually decreases across higher quantiles.Renewable Energy (LRE): The coefficient for renewable energy consumption is negative and statistically significant across all quantiles, indicating that increased consumption of renewable energy is associated with a decrease in mortality rates. The negative impact of renewable energy tends to increase in magnitude across higher quantiles.In summary, the results suggest that higher carbon dioxide emissions and urbanization rates are associated with increased mortality, while higher GDP and renewable energy consumption are associated with decreased mortality. The magnitude of these effects varies across different quantiles of the mortality distribution. Table 1. Quantile regression estimation resultsNvariables90th75th50th25th10thlCO20.1891***0.1052***0.3252***0.1029***0.5123***LGDP-0.5768***-0.6749***-0.0630***-1.1041***-1.2321***LUP0.9183**0.5175**0.2967**0.2030**0.9842**LRE-0.1932***-0.0122***-0.6902***-0.2491***-0.9181***Note that the symbols *** , ** and * indicate significance levels (1%) , (5%) and (10%), respectively. Table 2. Table Title GroupNMeanStd. DeviationStd. Error MeanPosttestFactor 1xxxx.xxx.xx.xxFactor 2xxxx.xxx.xx.xx*Here is a note on the table.ConclusionAir pollution is a complex mixture of gases and particulate matter containing organic and inorganic pollutants in the air. This pollution has serious negative effects on human health and can lead to respiratory diseases, heart conditions, and even premature death. Environmental changes and globalization play a significant role in increasing air pollution and can contribute to the spread of diseases and viruses worldwide. Improving air quality and reducing air pollution can occur through the transition to cleaner energy sources, support for renewable energies, and the reduction of pollutant production. These measures can help decrease mortality caused by air pollution.Given the importance of public health, studies on air pollution and its effects on humans are essential. Serious efforts to reduce air pollution and improve air quality can help protect public health and reduce premature mortality. In this paper, the impact of air pollution on the economy and human health has been investigated.In this research, using the quantile regression method and analyzing data from the RCEP member countries from 1996 to 2018, certain results have been obtained. The results indicate that in all quantile deciles, the use of renewable energy has had a significant and negative impact on mortality. The research findings suggest that the use of renewable energy sources can facilitate improved air quality and reduced air pollution, consequently lowering mortality due to air pollution.Furthermore, the research results show that an increase in gross domestic product can lead to a decrease in the mortality rate caused by air pollution. Increased gross domestic product improves economic and social conditions, which can, in turn, facilitate better health and a reduction in mortality rates. However, the emission of carbon dioxide and the rate of urbanization lead to an increase in the mortality rate. Carbon dioxide emissions and an increased rate of urbanization lead to serious problems in societies. Increased carbon dioxide contributes to global warming and climate change, which have negative impacts on public health. An increased rate of urbanization leads to a higher mortality rate in societies. The reasons for this include traffic and road accidents, air pollution, and reduced access to healthcare services.
مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Mahnaz Rezazadeh; Saeed Daei-Karimzadeh; Shahram Moeeni
Abstract
Energy security and environmental sustainability have been identified as key economic challenges in recent years. Most countries have shown a strong interest in achieving significant economic development through the development of exports and its diversification, and gradually the share of innovative ...
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Energy security and environmental sustainability have been identified as key economic challenges in recent years. Most countries have shown a strong interest in achieving significant economic development through the development of exports and its diversification, and gradually the share of innovative produced goods and services in total exports surpassed traditional exports. This change in the export pattern may change energy needs because the energy needed to produce new and industrial products (such as spacecraft, electrical equipment, telecommunication equipment, and..) is relatively higher. The exports diversification is a trade indicator and can play a role in encouraging the consumption of renewable energy. In this paper, the impact of diversification export, extensive export margin and intensive export margin as determinants of renewable energies, using the CS-ARDL model in countries with natural resource rents during the period of 2000-2020 has been investigated. The results of the study in the estimation of the first model show that the increase in export diversification has a positive effect on the consumption of clean energy, and the increased export diversification generally increases the consumption of these energies. Also, the estimation results of the second model indicate that the extensive export margin, which emphasizes the export of new products, has a positive and significant effect on the ratio of clean energy consumption, but the intensive export margin, which emphasizes the development of traditional trade, has a negative significant effect. It has a ratio of clean energy consumption. IntroductionEnergy demand and its determinants have been controversial in the energy economics literature. Since the 19th century, global energy demand has increased by 2.5% per year. On the other hand, the consumption of non-renewable energy is a factor in environmental degradation and can trigger climate change, which becomes a serious threat to sustainable growth (Kaygosuz, 2007). The rapid increase in energy intensity in many industries may cause the depletion of fossil resources, so experts warn that with the current energy consumption, non-renewable energy resources will probably end by 2040 (Bielska et al. cited in Asgari et al.2023).One of the important factors affecting energy demand is international trade, which, according to empirical evidence, energy demand, in addition to trade volume, is strongly dependent on the composition of countries' export baskets in terms of export diversity (Shehzad et al., 2021a). Export diversification is intended to increase the number of exported goods and reduce dependence on a single source of income. The more diversified a region's economy is, the less sensitive it is to fluctuations (Nasiri and Nunezhad, 2020). Export diversification can generate economic benefits through two channels: product diversification or partner diversification (Shehzad et al., 2021b). The International Monetary Fund and the World Bank recommend that developing and emerging economies adopt export diversification strategies to reduce dependence on specific exports and generate sustainable income. Export-related policies for developing and developed economies may conflict with sustainable development goals and environmental protection goals. This leads researchers to ask whether such trade strategies are good or bad for the environment and cleaner energy production. And this is because export diversification is closely related to energy consumption and the overall energy mix, while most developing and developed economies consume abundant fossil fuels and non-renewable energies (Shahbaz et al., 2019). Therefore, given the aforementioned conditions, it was necessary to investigate whether do developing countries that have high natural resource rents in their economies and relatively free energy have an incentive to use renewable energies by diversifying exports? And under what conditions, can these countries clean the decrease or increase of energy consumption?Methods and MaterialIn this study, information was collected using the library document method, using books, articles, theses, and databases to collect literature and statistical data. Theoretical foundations and research literature were collected using a data mining tool, and in order to obtain statistical data related to the indicators, the World Bank database and international institutions and organizations such as the IMF and the UNCTAD database were used. This article, as the first study, examines the effect of export diversification on renewable energy demand in developing countries with oil exports and medium to high pollution, including Iran, Saudi Arabia, Iraq, the United Arab Emirates, Kuwait, Nigeria, Kazakhstan, Mexico, Colombia, Algeria, Qatar, Malaysia, Gabon, Egypt, and Indonesia, along with other control variables, during the period 2000-2020, using the CS-ARDL model.To test the aforementioned relationship, following the model presented in the study by Sharma et al. (2021, b), the following models were used: (1) (2) Results and DiscussionAs mentioned, two models were used to examine the effect of export diversification on renewable energy consumption. In the first model, the effect of export diversity, economic growth, degree of openness, natural resource rent, and research and development variables on renewable energy consumption were examined, and in the second model, the effect of broad and narrow export margins was analyzed. Based on the results of the first model and in the long run, the export diversity variable of the selected countries has had a positive effect on the consumption of renewable energy, and in other words, with increasing export diversity, the consumption of renewable energy has increased. Therefore, export diversity and its growth have reduced the use of fossil fuels and increased the proportion of renewable energy use. Also, economic growth has had a positive and significant effect on the consumption of renewable energy. Another variable is natural resource rent, which is significant, and an increase in natural resource rent reduces the consumption of renewable energy. Finally, the results related to the research and development variable indicate that this variable has had a positive effect on the consumption of renewable energy.After estimating the second model and with the presence of extensive and intensive margin indicators in the long run, the coefficient of the extensive export margin variable has the necessary significance and its coefficient has become positive. Therefore, for the extensive margin variable, which in a way emphasizes the expansion of trade through the development of new export markets, the effect of the variable on the consumption of new energies has increased. Also, the coefficient of the intensive export margin variable has the necessary significance, but this coefficient has become negative and in a way emphasizes that the increase in the intensive margin has reduced the proportion of renewable fuel consumption. However, the reason for the negative effect of the intensive export margin on the consumption of renewable fuels is that because these countries have high natural resource rents, as a result, the development of exports of these countries to their traditional destinations mainly includes figures related to these resources, and since exports related to these products, including petrochemical, oil and gas products, in a way require a large consumption of fossil fuels, as a result, it has increased the proportion of fossil fuel consumption. The degree of openness has the necessary significance and its coefficient has also become positive, and in other words, with increasing integration in the global economy, the proportion of clean fuels has increased. Also, the coefficient of the natural resource rent variable has also been significant and its coefficient has also become negative, in other words, with the increase in the ratio of natural resource rent in the economy, the use of clean fuels has decreased. Also, the increase in clean energy with a short-term break has been able to improve the ratio of clean energy use in the economy. The variable of investment in research and development has also been significant. The coefficient of this variable has also been positive, in other words, with the increase in the ratio of research and development, the use of fossil fuels has decreased and the use of clean fuels has increased.ConclusionIn general, the sustainable development literature emphasizes that different countries, while developing their exports and economic growth, should be sensitive to the relationship between these variables due to the importance of the environment, because perhaps in the short term this growth and diversification of exports can improve welfare, but in the long term it will reduce the welfare of the people of the society through environmental problems. Accordingly, and according to the estimated results of this study, the export development of countries with natural resource rents has greatly increased the consumption of fossil fuels in this group of countries, and this can affect economic growth and export development in these countries in the future, and especially the consequences of that growth will confront society with environmental and health problems. Another point that is clearly visible in the results of the studies is the effects of technological development on the development of clean and renewable fuels, which the results of this study emphasize in both the short and long term. Also, the degree of openness, which shows the degree of economic integration of a country and to some extent emphasizes the development of trade, has caused a decrease in the proportion of fossil fuel consumption. Based on the results and as observed, trade development based on trade growth through traditional trade has had a negative impact on the proportion of renewable energy consumption in the long term and in a way emphasizes that the development of traditional trade, since it is related to natural resource rents and especially cheap energy rents, has ultimately increased the consumption of fossil fuels. However, changing this indicator to a wide margin, which is in a way an emphasis on the development of new export markets and distancing from traditional trade, and since the conquest and development of new markets requires the development of new knowledge and technology, the impact of this indicator in the long term has increased the proportion of clean fuel consumption. Therefore, and finally, it seems that in countries, especially with oil rents and natural resources, and due to the dependence of their competitive advantage in exports on the consumption of fossil fuels, in practice the development of exports and ultimately economic growth will lead to increased destruction of the environment and natural resources, and as mentioned in previous studies in this field, the path to sustainable development and sustainable export development in these countries will pass through the path of technological development, and through this path the consumption of clean fuels will also increase.Acknowledgments With due appreciation and thanks to the learned and wise professors who helped me in writing this article, as well as my dear family who supported me financially and spiritually.
مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Mohammad Rahim Soltani; Mohammad Ali Afsharkazemi; Reza Radfar
Abstract
Considering climate change, which has generated many studies today, including reducing fossil fuel consumption and using renewable energies to produce clean energy, in this research, with this aim, the design of a three-level biomass supply chain network model with two minimization functions in economic ...
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Considering climate change, which has generated many studies today, including reducing fossil fuel consumption and using renewable energies to produce clean energy, in this research, with this aim, the design of a three-level biomass supply chain network model with two minimization functions in economic and environmental costs has been considered. The main research gap solved in this study is the resilience of the model, which examines the disruption in the supply of raw materials with a scenario approach. The mathematical model of the research is mixed integer linear programming. To single-target the function, under uncertainty, the fuzzy TH mathematical model has been used and the validation of the model has been investigated in a real case study in Tehran province. According to the findings from the output of GAMS software, which shows the optimal economic cost equal to 791354423200 Tomans and the emission of 1420469 grams of carbon dioxide per year, the optimal mode of construction of 4 power plants in the cities of Pakdasht, Qarchak, Parand and Mallard have been proposed. The sensitivity analysis on the parameters of the TH method and on the change of biomass supply values met the expectations. As a result, the proposed model has the necessary efficiency and has been able to be optimal in terms of cost and reduce greenhouse gas emissions by combining economic and environmental approaches. Therefore, the model has the necessary resilience.IntroductionCorrect biomass management is becoming one of the most important factors to achieve a sustainable future for human society. Although biomass is highly dependent on regional climatic conditions, it is currently the only practical renewable source for direct supply of sustainable fuels for all countries in the world. Urban waste management is one of the most important tasks of urban management, which has many costs and implementation problems. Mismanagement of these biomass causes various environmental risks. Over the past half century, the world's electricity consumption has increased continuously. Between 1980 and 2023, electricity consumption has more than tripled. The growth of industrialization and access to electricity worldwide has further increased the demand for electricity. Worldwide electricity generation is projected to triple over the next three decades. The growth and expansion of a sustainable bio economy, is proposed as an important strategy that can help the world to meet many of these challenges. In support of this strategy, more than 50 countries worldwide are currently pursuing bio economy strategies. The production of renewable fuels requires long-term planning, which requires the design of a flexible supply chain network. Optimum biofuel supply chain network must deal with the time difference of fuel supply and demand. Seasonal variation is very important due to the availability of biomass and it is challenging not to consider the seasons. Therefore, the modeling of the biofuel supply chain network should consider both long-term planning and decisions such as seasons should also be considered in the modeling.Methods and MaterialThe method of this article is two-objective mixed integer linear programming. The two-objective model designed in this research has been converted into a non-fuzzy single-objective using the fuzzy TH method and it has been solved with the exact solution method and with the help of Games software. In the designed model, strategic and tactical decisions are made to achieve the set goals. Strategic decision variables include location and allocation. For location, it is meant to choose a place from among the proposed places for the construction of power plants so that the cost of transportation and as a result the cost of electricity production is kept to the minimum possible and reduces carbon emissions. In the discussion of allocation, the optimal capacities for each of the power plants are determined from among the proposed capacities. Tactical decision variables include determining the amount of biomass to be transferred from each supplier to each power plant, as well as the amount of electricity produced and transferred from each power plant to each applicant. Biofuel supply chains are subject to uncertainty due to their dynamic and complex nature. Here, according to the opinion of the experts, the uncertainties of the fuzzy type of the research model; the costs of ordering to the supplier are the costs of purchasing raw materials (biomass) and the costs of setting up the power plant. Also, according to the opinion of experts, the cost of repair and maintenance has uncertainty of a possible type.Results and DiscussionHere, the real data to determine the values of the first and second functions have been entered into the Gems software to obtain the exact solution for the desired problem. Solving the problem by TH method with beta (satisfaction coefficient) of 0.5 for W1=0.7 and W2=0.3 is considered for it. The findings from the software outputs suggest that the best situation or in other words the optimal situation is to build four power plants among the proposed points out of the seven points. These four power plants should be built in three different capacities. A power plant with a capacity of forty megawatt hours per day in Pakdasht city, with an annual production of 14,600 megawatt hours, a power plant with a capacity of twenty megawatt hours per day in the city of Mallard, equal to 7,300 megawatt hours of annual production power, a power plant with a capacity of forty megawatt hours per day in Qarchak city, With an annual production of 14,600 megawatt hours, and a power plant with a capacity of ten megawatt hours per day in Parand city, with an annual production of 3,650 megawatt hours, it produces and supplies electricity to all four residential towns in Tehran. Electricity has been supplied to three residential towns in Parand city, and electricity to a residential town in Rabat Karim will also be produced and its need will be met. The electricity of a residential town in Islamshahr and a residential town in Pardis, which were applicants, has not been supplied and both types of biomasses are consumed in different proportions in four power plants. Biomass is purchased from all ten suppliers in ten different cities.ConclusionIn this article, the presented model has two objective functions, one for reducing total costs and the other for reducing carbon emissions, both of which aim to achieve sustainable development in the waste supply chain network. Any model that can control uncertainties and turn them into certainty, that is, that can predict uncertainties so that the supply chain network does not suffer from disruption and disorder, is a resilient model. A model can be made resilient in various ways. The current model has turned uncertainties into certainty by creating scenarios, therefore the current model is also a resilient model to use the energy known as Biomass-to-X to increase the efficiency of the network. One of the biggest challenges in the biomass supply chain is logistics management, because biomass with high moisture and low density requires more expensive transportation. Therefore, to develop the model, it is suggested to manage logistics in the waste supply chain network, in sync with today's technologies, to reduce Economic costs and reducing carbon emissions should be investigated.AcknowledgmentsThe authors of the article are grateful to all those who contributed to the preparation and improvement of the quality of the article with their valuable comments.
• اقتصاد سیاسی انرژی به ویژه در حوزه خلیج فارس
Armin Sharifi; Fateh Habibi; Bakhtiar Javaheri
Abstract
Income inequality has become a political issue for most countries around the world. In recent decades, levels of income inequality have increased in most industrialized economies. Therefore, income inequality has been largely considered as an important social and economic problem, thus attracting plenty ...
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Income inequality has become a political issue for most countries around the world. In recent decades, levels of income inequality have increased in most industrialized economies. Therefore, income inequality has been largely considered as an important social and economic problem, thus attracting plenty of attention from policy-makers and scholars as to its resolution. The aim of this study is to investigate the impact of energy security in four dimensions (availability, accessibility, development capability and acceptability) on income inequality in Middle Eastern countries during the period 2000-2021 using threshold panel regression. The contributions of the current study can be summarized as follows: First, we fill the gap in the literature by examining the influence of energy security on income inequality. Second, unlike previous works, we delve into the non-linear impact of energy security on income inequality. Third, we employ a new DPT model to find the threshold variables. The results show that among the four dimensions of energy security, only the acceptability dimension has a negative and significant effect on income inequality, and other energy dimensions do not have a significant effect on income inequality. The threshold level for the Middle East countries is calculated at 42032 dollars per capita. Therefore, governments should focus more on improving the efficiency of energy use. Not only does this help to reduce the income gap, but also increases energy security optimally.
Introduction
The literature has studied various factors affecting income inequality and the influence of energy security on the economy, while knowledge is rather limited regarding the linkage between energy security and income inequality. The purpose of this paper is to offer new insights into whether and how energy security impacts income inequality via a global sample of 68 countries for the period 2000-2021. In current study, we capture four aspects of energy security (availability, accessibility, develop-ability, and acceptability) mentioned above in our empirical analysis. Previous studies are also aware of the non-linear effects in some predictors of the empirical models. It is also noted that the reduction of income inequality requires a proper long-term policy. With these considerations in mind, our analysis aims to assess the effect of energy security on income distribution, whether this effect of energy security on income inequality changes under different stages of economic development, and whether the inequality in the previous period influences current inequality.
Methods and Material
Most existing studies on the factors affecting income inequality are based on the Kuznets curve hypothesis, which postulates that inequality increases with economic growth under the early phase of economic development and subsequently decreases after achieving a certain development level. According to his theoretical framework, income inequality is hypothesized to be a function of linear and quadratic income terms.
Following the proposition of the Kuznets curve, we hypothesize that the influence of energy security on income distribution varies with the degree of economic development. On the one hand, a high level of energy security can guarantee stable economic growth in low development countries, but this inevitably leads to greater income inequality. On the other hand, higher energy security can guarantee the normal operations of enterprises’ production in high development countries. People can get more jobs to do, ultimately leading to a drop in income inequality. Our investigation assesses the influence of energy security on income inequality for a panel dataset of 12 Middle East countries over 2000-2021 owing to data availability.
When studying the issue of income inequality, it is thus required to introduce the lagged value of the income inequality on the right-hand side of the regression equation as an independent variable. This transforms the static panel data into dynamic panel data as largely applied in the literature. Second, the static threshold model requires the selection of threshold variables to be completely exogenous. In this regard, the use of an exogenous threshold variable may generate biased estimations.
Results and Discussion
Before proceeding with further analysis, we first examine the stationarity of the variables to avoid the issue of spurious regressions. To this end, we employ Levin-Lin-Chu and Im-Pesaran-Shin unit root tests. we conclude that all variables are stationary in levels. Therefore, non-stationarity of the variables is not a major concern for the following estimation. Before conducting a parameter estimation of the DPT model to examine the non-linear impact of energy security on income inequality, we first test the nonlinearity and the threshold effect. The null hypothesis is that the model is linear and there is no threshold effect. According to results, the null hypothesis that there is no threshold effect can be rejected at the 1% significance level. We now take GDP per capita as the threshold variable and estimate using the DPT model.
Table 1: Result of DPT estimations by using GDP per capita as a threshold
variable
Model ES1
Model ES2
Low
High
Low
High
Prob.
Coef.
Prob.
Coef.
Prob.
Coef.
Prob.
Coef.
Gini
0.0000
0.9398***
0.000
1.0536***
0.000
0.7065***
0.000
0.7908***
GDP
0.664
-0.0028
0.828
0.0065
0.365
-0.0203
0.001
-0.0689***
Trade
0.149
-0.0085
0.000
-0.1017***
0.050
0.0393*
0.466
- 0.0115
Fin.Dev.
0.957
0.0003
0.225
0.0281
0.657
-0.0051
0.457
0.0121
Availability
0.641
0.0010
0.347
-0.0091
-
-
-
-
Acceptability
-
-
-
-
0.386
-0.0184
0.009
- 0.0407***
Const.
0.000
1.1395***
0.000
1.4753***
Variable Gini
Model ES3
Model ES4
Low
High
Low
High
Prob.
Coef.
Prob.
Coef.
Prob.
Coef.
Prob.
Coef.
GDP
0.000
0.7320***
0.000
0.7894***
0.000
0.7301***
0.000
0.7931***
Trade
0.820
0.0038
0.006
-0.0475***
0.980
- 0.0004
0.002
-0.0491***
Fin. Dev.
0.047
-0.0397**
0.780
0.0041
0.055
- 0.0383**
0.456
0.0100
variable
0.487
0.0079
0.820
-0.0036
0.692
-0.0048
0.854
- 0.0028
Develop-ability
0.310
-0.0176
0.456
-0.0139
-
-
-
-
Accessibility
-
-
-
-
0.383
0.0182
0.940
0.0007
Const.
0.000
1.3942***
0.000
1.1686***
Notes: t-statistics. ***p < 0.01, **p < 0.05, and * p < 0.1
The coefficient of ES1, used to measure the availability of energy security, is insignificantly positive under the threshold estimate, while the effect above the threshold becomes significantly negative at the 5% significance level. The results indicate that greater availability of energy security improves income distribution only when a certain level of economic development is reached. This means that the availability of energy security has an inverted U-shape influence on income inequality with the growth of the economy, which is consistent with previous studies (Lee et al. 2022). On the one hand, higher availability of energy security leads to economic development, and economic development leads to an increase in income inequality when the level of economic development is low. On the other hand, higher availability of energy security makes the production of enterprises in countries with a high level of economic development more stable, and the income of people will also be more stable, thus reducing income inequality.
Second, ES2 allows us to quantify the acceptability of energy security. The results reveal that the coefficients of ES2 below the threshold are not significantly negative, while the effect above the threshold is significantly negative. The acceptability of energy security widens the degree of income inequality in a regime with low economic development, while it decreases income inequality in a regime with high development. In the case of an underdeveloped economy, the technology of non-fossil energy is not advanced, and the cost of using non-chemical energy is high. The higher the proportion is for non-fossil energy used, the more people spend on energy, which increases income inequality. Along with the development of an economy, the technology of non-fossil energy becomes advanced, and the price of non-fossil energy turns lower. Thus, the use of non-fossil energy reduces income inequality.
Third, both ES3 is negative indicators capturing the developability of energy security. The results reveal that the coefficients of them below the threshold are significantly positive, and the effect above the threshold is significantly positive. These results suggest that lower levels of developability of energy security decrease inequality in a regime with low and high development. In countries with a low level of economic development (Middle East), the government is not concerned about environmental issues. Some high-emission and high-polluting companies will thus continue to produce stably, and the income of employees will remain stable, thus leading to reduced inequality.
Fourth and finally, ES4 is a positive indicator used to measure the accessibility of energy security. The evidence reveals that its coefficient is significantly positive below and above the threshold, suggesting that the accessibility of energy security deteriorates income distribution in the early stages of economic development. One possible explanation is that greater accessibility of energy security in countries with lower levels of economic development is not conducive to domestic economic growth, thereby exacerbating income inequality.
Conclusion
Prior literature has broadly discussed the importance of income inequality and its determinants with little consensus due to the inconclusive results therein. In contrast with the conventional economic and financial aspects, an alternative energy perspective of how energy market activities affect income inequality still awaits a more in-depth exploration. To our knowledge, this research is the first to explore the impact of energy security on income inequality in the global context. Unlike most previous works, we do not artificially classify transnational data, but instead employ the DPT model developed by Seo and Shin (2016) to estimate the threshold value. In addition, we employ four measures of energy security to capture of energy security (availability, accessibility, developability, and acceptability). Using a Middle East sample of 12 countries, our analyses thus complement the existing research not only on the non-linear nexus between energy security and income inequality, but also on how different dimensions of energy security affect income distribution. Our empirical results suggest that the impact of energy security on income inequality involves a threshold effect.
The evidence shows that in all dimensions of energy security only in relation to the dimension of acceptability positively influence income inequality when a country's economic growth is lower than the threshold level. In other words, a high level of energy security will widen income inequality when a country's economy is underdeveloped. On the contrary, a higher level of energy security reduces income inequality when the economy is developed. Our findings can help policymakers formulate energy security policies based on their country's own development level. Governments should focus more on improving the efficiency of energy use. Through these measures, not only the income gap is reduced, but energy security is optimally increased.
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Sajad Piri; Zahra Farshadfar
Abstract
High fluctuations in the price of crude oil, as the main source of energy and an important raw material of the global chemical industry, has doubled the importance of accurate estimation and forecasting of its price trend in recent years. The purpose of this applied research, is to increase the ability ...
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High fluctuations in the price of crude oil, as the main source of energy and an important raw material of the global chemical industry, has doubled the importance of accurate estimation and forecasting of its price trend in recent years. The purpose of this applied research, is to increase the ability to predict crude oil prices using non-linear patterns by artificial intelligence. For this purpose, four artificial intelligence networks MLP, RNN, LSTM and GRU have been used and their capabilities compared to each other and the benchmark model, besides their prediction accuracy have been evaluated using the mean squared error method. The studied sample is North Sea Brent crude oil data from Aug 1st 2007 to May 31st 2024 on a daily, monthly and yearly basis.
The results of the research indicate that the network architecture in these models have several advantages in extracting information from the data in order to make more accurate predictions, and the time to obtain future prices is shorter and less error-prone. Also, among the selected non-linear models, GRU has more accurate predictions with less error in different frequencies and in a shorter time.
Introduction
As oil price fluctuations affect both oil exporting and importing countries in different ways, crude oil price is one of the most important key variables in international trade (Salik and Khorsandi, 2022), As a result, policymakers and oil market experts pay attention to its price and its fluctuations. The price of crude oil in the market is the result of many fundamental and non-fundamental factors (Shakri et al., 2018). Therefore, it is not simply possible to categorize and model all the factors affecting the price of crude oil. Since all the basic and non-basic factors that affect the price formation will finally appear in the price of crude oil, it is necessary to pay attention to the price and its fluctuations (Yadgari et al., 2022). Previous research indicate that the trend of oil price changes follows a non-linear pattern (Guo, 2019); and among the non-linear models used in predicting the price of oil, models based on artificial intelligence have shown better results (Gumus and Kiran, 2017; Zhao et al., 2017; Gao et al., 2022). Therefore, the purpose of this research is to improve crude oil prices out-of-sample prediction using non-linear machine learning algorithms. It is assumed that this non-linear long-short-term memory method has better performance than historical average method and multilayer perceptron network and recurrent network.
Methods and Material
The purpose of this applied research, is to increase the ability to predict crude oil prices using non-linear patterns by artificial intelligence. For this purpose, four artificial intelligence networks MLP, RNN, LSTM and GRU have been used and their capabilities compared to each other and the benchmark model, besides their prediction accuracy have been evaluated using the mean squared error method. The studied sample is North Sea Brent crude oil data from Aug 1st 2007 to May 31st 2024 on a daily, monthly and yearly basis.
Results and Discussion
The results of the research indicate that nonlinear neural network models have a better ability in predicting crude oil price in different daily, monthly and yearly frequencies with different volumes of training data compared to historical average linear model and it has less error. These findings are consistent with the results of Farshadfar and Prokopczuk (2019), Luo et al. (2022) and Zang et al. (2020).Calculations and estimation of the studied models show that the MSFE prediction criterion in all the samples used by the GRU is better than other networks. It also indicates that with the increase in training data amount, network prediction power increases.
Conclusion
It can be concluded that the network architecture in these models have several advantages in extracting information from the data in order to make more accurate predictions, and the time to obtain future prices is shorter and less error-prone. Besides that, among the selected non-linear models, GRU has provided more accurate predictions with less errors in different frequencies and in a shorter time.
Acknowledgments
Authors would like to appreciate Eng. Behzad Alipour for his kind collaboration in program coding.
