Behzad Sadeghvand; Hassan Heidari; Mehdi Nejati
Abstract
Iran simultaneously faces the dual challenges of economic sanctions and escalating environmental concerns. This study aims to examine how oil export sanctions contribute to increasing carbon dioxide (CO₂) emissions across various economic sectors. Using the dynamic GTAP-E-Power model—a ...
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Iran simultaneously faces the dual challenges of economic sanctions and escalating environmental concerns. This study aims to examine how oil export sanctions contribute to increasing carbon dioxide (CO₂) emissions across various economic sectors. Using the dynamic GTAP-E-Power model—a computable general equilibrium (CGE) model tailored for energy and environmental analysis—the impact of sanctions is assessed under three oil export reduction scenarios: 60%, 65%, and 70%. Results indicate a consistent increase in Iran’s total CO₂ emissions under all scenarios, with emissions rising further as the severity of sanctions intensifies. Sectoral analysis reveals that electricity production and distribution, low-tech manufacturing, base-load fossil fuel power generation, and petroleum refining are the most affected, showing substantial emission increases. Conversely, sectors such as renewable-based electricity generation and high-tech manufacturing either experienced a decline or only a marginal increase in emissions. These findings suggest that sanctions not only impact Iran’s economy but also exacerbate environmental degradation. Accordingly, the study recommends prioritizing the removal of sanctions as a macro-level policy objective, enhancing investment in oil extraction with modern technologies, expanding renewable energy infrastructure, and supporting high-tech industries that demonstrate greater resilience to sanctions and lower environmental costs.IntroductionIran is concurrently grappling with widespread economic sanctions and escalating environmental challenges. Among these, the impact of oil export sanctions on carbon dioxide (CO₂) emissions stands out as a critical concern. This study investigates the extent to which sanctions, particularly those reducing Iran's oil exports, exacerbate environmental degradation. Employing a computable general equilibrium (CGE) model—specifically, the dynamic GTAP-E-Power model—this study assesses CO₂ emissions under three scenarios of oil export reduction: 60%, 65%, and 70%. The results reveal a direct correlation between intensified sanctions and increased CO₂ emissions, emphasizing the environmental costs of economic isolation.Methods and MaterialsThe study utilizes the GTAP-E-Power model, a dynamic extension of the Global Trade Analysis Project framework, tailored to incorporate power sector and energy-related emissions. This model facilitates the analysis of environmental impacts resulting from shifts in economic and energy-related variables. The analysis simulates three scenarios wherein Iran’s oil exports are reduced by 60%, 65%, and 70%, respectively, over the period from 2019 to 2050. The model estimates CO₂ emissions across various economic sectors under these constrained export conditions, comparing them to a baseline of unrestricted oil trade.Results and DiscussionThe simulation results indicate a consistent increase in CO₂ emissions within Iran as oil export sanctions intensify. Specifically, emissions rise by an average of 0.187%, 0.193%, and 0.197% in the three respective scenarios. Conversely, sanctioning countries experience a decline in their own CO₂ emissions, likely due to increased adoption of clean energy alternatives.Sectoral analysis reveals that electricity production and distribution, low-tech industries, fossil fuel-based power generation, and oil product manufacturing are the most affected sectors. These sectors exhibit the highest CO2 emission increases, with emission intensity growing alongside the severity of sanctions. In contrast, sectors relying on renewable energy and high-tech industries display minimal or even negative emission growth, highlighting their resilience.Electricity generation using fossil fuels under base load conditions sees annual average CO2 increases of 0.602%, 0.641%, and 0.677%, while peak load shows slightly lower increases. However, renewable-based electricity generation demonstrates minimal or negative changes: +0.015% and +0.08% in the first two scenarios, and -0.001% in the third.Moreover, low-tech and medium-tech industries show significantly higher increases in emissions than high-tech industries, which remain relatively unaffected due to their access to advanced and efficient technologies. Similarly, sectors such as mining, coal, agriculture, and services all showConclusionThe findings underscore the substantial environmental costs of oil export sanctions on Iran. Sanctions lead to increased reliance on carbon-intensive industries and outdated technologies, thereby elevating national CO₂ emissions. In contrast, high-tech sectors and renewable energy-based power generation exhibit greater resilience.Key policy recommendations include:Prioritizing the removal of international sanctions to mitigate environmental and economic damage.Investing in oil product manufacturing with access to cleaner, advanced technologies.Expanding renewable energy sources in electricity production to reduce fossil fuel dependency.Strengthening high-tech industries to enhance technological resilience.Focusing development on sanction-resilient sectors, particularly those capable of maintaining efficiency and environmental standards under constrained conditions.Strategic investments in clean technology and the facilitation of knowledge transfer are vital to offsetting the negative environmental consequences of continued sanctions.
Ali Asghar Heidari; Sobhan Pirahan siah; Mohammad Ali Aghandeh
Abstract
International tourism plays a significant role in the economic development of countries, particularly in newly industrialized economies. The present study aimed to identify and assess the determinants influencing the demand for foreign tourism to Iran during the period 1990–2021. A quantitative ...
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International tourism plays a significant role in the economic development of countries, particularly in newly industrialized economies. The present study aimed to identify and assess the determinants influencing the demand for foreign tourism to Iran during the period 1990–2021. A quantitative and explanatory research design was employed using a gravity model combined with a panel data approach. The statistical population included countries with considerable tourist flows to Iran. Variables such as per capita income of the origin country, geographical distance, relative exchange rate, relative prices, household debt, and money supply were analyzed. The data were subjected to panel unit root tests, fixed-effects modeling, and Generalized Least Squares (GLS) estimation to handle heteroscedasticity and autocorrelation. The findings revealed that per capita income, relative exchange rate, and money supply had a positive effect on tourist arrivals, while geographical distance, relative prices, and household debt had negative impacts. It is concluded that a policy focus on regional markets, financial facilitation, competitive pricing, and enhanced infrastructure and promotion strategies can boost Iran’s tourism sector.IntroductionTourism has emerged as a dynamic driver of economic growth, income generation, and cultural exchange. In the global context, international tourism not only contributes significantly to GDP and employment but also plays a central role in enhancing cross-cultural communication and national image. In recent decades, the tourism industry has experienced a shift of flows towards newly industrialized countries, thereby necessitating precise modeling and forecasting of tourism demand for effective planning. Iran, with its cultural heritage, historical attractions, and diverse landscapes, holds strong potential for attracting international tourists. However, identifying and understanding the influencing factors behind tourism demand is crucial for capitalizing on this potential.Research QuestionsWhat are the most significant determinants influencing the demand for international tourism to Iran?How do economic and geographical variables such as income, distance, exchange rate, and prices affect inbound tourist arrivals?How can the gravity model and panel data analysis help in evaluating and forecasting tourism flows?Methods and MaterialsThis research adopted an applied and explanatory approach using panel data methodology covering the years 1990 to 2021. The gravity model was employed as the core theoretical framework, which posits that tourist flows between countries are directly related to their economic size and inversely related to the geographical distance. The study analyzed variables such as per capita income of the origin country, geographical distance (in kilometers between capitals), relative exchange rate, relative prices, household debt, and money supply. Data were obtained from reliable international databases, and statistical analysis included unit root testing, F-Limer and Hausman tests for model selection, and final estimation using Feasible Generalized Least Squares (FGLS) to control for autocorrelation and heteroscedasticity.Results and DiscussionThe empirical results indicated that all selected variables were statistically significant. The analysis confirmed:Positive effects:Per capita income of the origin country increased tourism demand.A favorable relative exchange rate enhanced tourists’ purchasing power.An increase in the money supply supported outbound travel.Negative effects:Greater geographical distance reduced tourist arrivals.Higher relative prices in Iran discouraged travel.Higher household debt in origin countries limited travel capacity.These findings are consistent with theoretical expectations from the gravity model and consumer behavior theories. The high explanatory power of the model underscores the reliability of the results for policy formulation.ConclusionThe study emphasizes the crucial role of economic and geographical factors in shaping international tourist flows to Iran. It is recommended that policymakers:Focus on attracting tourists from neighboring and geographically closer countries.Facilitate travel conditions and financial processes for tourists from high-income countries.Improve tourism infrastructure and promote competitive pricing.Enhance Iran's international image through targeted advertising campaigns.Implementing these strategies may strengthen Iran’s position in the global tourism market and support the goal of sustainable tourism development.
Mostafa Kazemi Najafabadi; Ahmad Ali Rezaei
Abstract
While individualism is often perceived as a negative factor that undermines social ethics and reduces participation in charitable activities, this study proposes the hypothesis that individualism—as a set of cultural values and norms that place the individual at the center of decision-making ...
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While individualism is often perceived as a negative factor that undermines social ethics and reduces participation in charitable activities, this study proposes the hypothesis that individualism—as a set of cultural values and norms that place the individual at the center of decision-making and social responsibility—can play a positive role in enhancing the level of generosity in society. Accordingly, two mechanisms are examined: a direct mechanism, where individualism fosters personal motivations for helping others, and an indirect mechanism, in which individualism promotes economic freedom, thereby facilitating the growth of charitable activities. To empirically test this hypothesis, the Autoregressive Distributed Lag (ARDL) model was applied using quarterly data from Iran over the period 2010 to 2021. The results indicate that individualism has a positive and significant effect on the level of generosity; specifically, a one-percent increase in individualism leads to a 0/43 percent rise in generosity. Furthermore, economic freedom also shows a positive and significant effect, with a coefficient of 1/16 percent. The findings support both proposed mechanisms and suggest that individualism, when properly institutionalized within Iran’s cultural context, can strengthen ethical behaviors such as generosity. Additionally, the variables of economic growth, income inequality, education level, and government size were found to have positive effects, while corruption showed a negative effect on the level of generosity in Iranian society.IntroductionCharitable giving is often considered a reflection of social solidarity and ethical responsibility within a society. Conventional wisdom suggests that collectivist cultures are more likely to foster altruistic behavior, while individualism is frequently associated with self-interest and reduced social cohesion. However, recent perspectives argue that individualistic values—by empowering personal responsibility—can also encourage prosocial actions such as charitable giving. This study focused on the Iranian context, where cultural transitions and economic fluctuations have created a unique environment for examining how individualism interacts with generosity.Methods and MaterialsThis study adopted a quantitative, longitudinal research design using quarterly time-series data from 2010 to 2021. The statistical population consisted of national economic and social indicators related to charitable giving in Iran. To examine the role of individualistic social norms in the level of charitable giving in society, following Kai et al. (2022), the following regression model was estimated:Philanthropyt = c0 + c1 Individualismt + c2 GDPt + c3 Freedomt + c4 GINIt + c5 Educationt + c6 Corruptiont + c7 Government sizet + etDependent Variable:Level of Philanthropy in Society (Philanthropy): To measure the level of generosity in society, the World Giving Index was used. This index is based on three components: helping a stranger, donating money, and volunteering time, which are measured using random sampling across different countries. Higher index scores indicate more altruistic behavior as a result of increased generosity. Data for this variable were extracted from the World Giving Index reports.Independent Variable:Individualism Index (Individualism): The level of individualism across countries was assessed using Hofstede's Individualism–Collectivism Index (2001), which reflects the degree of individuals’ integration into social groups. In individualistic societies, people tend to form looser bonds and assume responsibility for themselves and their immediate families. In contrast, collectivist societies feature stronger and more supportive in-group relationships. However, in this study, the focus is on a specific form of individualism, which is not equated with selfishness or social indifference, but is rather viewed as a set of ethical and cultural norms that place the individual at the center of social action. Therefore, while Hofstede’s index was used as the measurement tool, the emphasis was placed on the role of personal values and responsibility in prosocial behavior and generosity, particularly within the cultural context of Iran.Overall, the Individualism–Collectivism Index measures the extent to which a society endorses and promotes individualistic values as opposed to collectivist ones, ranging from 0 (most collectivist) to 100 (most individualist). Data for this variable were obtained from the World Values Survey database.Control Variables:GDP per capita (GDP): Measured based on the growth rate of per capita Gross Domestic Product. Data were extracted from the World Bank’s WDI database.Economic Freedom (Freedom): Defined and measured according to the Heritage Foundation’s Index of Economic Freedom.Income Inequality (GINI): Measured using the Gini coefficient. Data were sourced from the World Bank’s WDI database.Education Level (Education): Measured by the rate of higher education graduates in the country. Data were obtained from the World Bank’s WDI database.Corruption (Corruption): Measured based on the Control of Corruption Index. Data were sourced from the World Bank’s WDI database.Government Size (Government size): Defined as the share of government consumption expenditures in GDP. Data were extracted from the World Bank’s WDI database.Data were collected from official national and international databases. The Autoregressive Distributed Lag (ARDL) model was applied to estimate both short-term and long-term relationships between variables. The analysis investigated two causal pathways: a direct mechanism through which individualism influences personal motivations to donate, and an indirect mechanism where individualism affects generosity by enhancing economic freedom.Results and DiscussionThe results of the estimated model indicate that the variables of individualism, economic growth, economic freedom, income inequality, education level, corruption, and government size have significant effects on the level of charitable giving in Iran. Specifically, a 1% increase in individualism leads to a 0.43% rise in generosity, which is statistically significant at the 1% level. Additionally, a 1% increase in economic growth and economic freedom results in increases of 0.004% and 1.16% in generosity, respectively—both significant at the 1% level. Income inequality and government size also have positive and statistically significant effects of 0.85% and 0.87%, respectively, on generosity, at the 1% significance level. Moreover, education level has a positive effect of 0.28% on generosity, which is significant at the 5% level. In contrast, corruption has a negative effect of 0.34% on the level of generosity, significant at the 1% level.The empirical results supported both proposed mechanisms. Individualism showed a statistically significant positive effect on the level of charitable giving, indicating that personal responsibility and autonomy can enhance generosity. Economic freedom also positively influenced charitable activities by enabling a more supportive institutional environment. In addition, economic growth, higher education levels, reduced income inequality, and greater government size were positively associated with generosity, while corruption had a strong negative impact. These findings challenge the conventional assumption that collectivism is inherently more charitable and suggest that individualism, when properly contextualized within the cultural framework of Iran, can reinforce ethical norms and civic responsibility. The study contributes to a nuanced understanding of the cultural determinants of prosocial behavior in developing countries.ConclusionThe findings of this study challenged the traditional notion that individualism undermines prosocial behavior by revealing its positive role in promoting charitable giving within Iranian society. When institutionalized within the cultural framework, individualism encouraged personal responsibility and voluntary generosity, both directly and through the enhancement of economic freedom. The research concluded that individualism, rather than being inherently detrimental to social ethics, can foster civic engagement and ethical behavior when supported by appropriate economic and institutional structures. These insights highlight the importance of considering cultural values in policy-making aimed at increasing philanthropic activities in developing societies.AcknowledgmentsThe authors would like to express their gratitude to the editorial board of the journal for their support and consideration.
Fatemeh Sorkhedehi; Priya Kavianinia
Abstract
Achieving sustainable economic growth and reducing dependence on fossil fuels are strategic priorities for Iran’s economy. Economic complexity—reflecting the technological sophistication and knowledge embedded in production—and renewable energy—supporting environmental protection ...
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Achieving sustainable economic growth and reducing dependence on fossil fuels are strategic priorities for Iran’s economy. Economic complexity—reflecting the technological sophistication and knowledge embedded in production—and renewable energy—supporting environmental protection and energy diversification—are both critical drivers of sustainable growth. This study examines the asymmetric and nonlinear effects of these two factors on Iran’s economic growth from 1995 to 2022 using the Nonlinear Autoregressive Distributed Lag (NARDL) model. The model distinguishes between short- and long-term impacts of positive and negative changes in each variable. Results indicate that positive shocks in renewable energy consumption and economic complexity significantly boost economic growth, while negative shocks have comparatively weaker adverse effects. Over the long term, increases in both variables contribute to growth, whereas decreases hinder it. Additionally, investment and labor positively influence economic growth, while carbon dioxide emissions exert a negative impact. The findings highlight the importance of advancing renewable energy and enhancing economic complexity as strategies to foster sustainable growth and reduce reliance on fossil fuel resources.IntroductionAchieving sustainable economic growth and reducing dependence on fossil fuel resources are strategic objectives of Iran’s economy in the current context. Economic complexity, as an indicator of the level of technology and knowledge embedded in production, and renewable energies, as a tool to reduce environmental impacts and diversify the energy mix, both play vital roles in sustainable economic growth. This study analyzes the asymmetric and nonlinear effects of these two key variables on Iran’s economic growth over the period 1995 to 2022 using the Nonlinear Autoregressive Distributed Lag (NARDL) model. This model allows the separate examination of the positive and negative impacts of variables in the short and long run. The findings indicate that positive shocks in renewable energy consumption and economic complexity have significant and positive effects on economic growth, while negative shocks in these variables have smaller and adverse effects. In the long run, increases in renewable energy consumption and economic complexity enhance economic growth, whereas decreases reduce it. Furthermore, investment and labor have positive effects on economic growth, while carbon dioxide emissions have a negative impact. The results emphasize the importance of developing renewable energy and enhancing economic complexity in Iran. These two strategies not only strengthen economic growth but can also reduce environmental damage and the country’s reliance on fossil fuel resources.Research QuestionHow do renewable energy consumption and economic complexity affect Iran's economic growth, and what is the pattern (asymmetric and nonlinear) of their impact?Methods and MaterialsThis study utilizes annual time series data spanning from 1995 to 2022. The key variables include real gross domestic product (RGDP), renewable energy consumption (REC), economic complexity index (ECI), labor force (L), gross capital formation (RGCF), and carbon dioxide emissions (CO2). The econometric model is built on an augmented Cobb-Douglas production function and implemented using the Nonlinear Autoregressive Distributed Lag (NARDL) approach. This method allows the decomposition of positive and negative shocks in explanatory variables and assesses their separate effects on economic growth. The asymmetric nature of the model provides more precise insights into how increases and decreases in renewable energy and complexity impact the economy. The findings are based on rigorous testing for stationarity, cointegration, and dynamic adjustment mechanisms.Results and DiscussionThe empirical findings confirm the asymmetric effects of renewable energy consumption and economic complexity on economic growth in Iran. In the short term, positive shocks in renewable energy consumption have a statistically significant and favorable impact on growth, while negative shocks show weaker and sometimes statistically insignificant effects. Similarly, positive changes in the economic complexity index lead to higher economic growth, whereas negative deviations exert milder negative influences. In the long run, both renewable energy and economic complexity maintain strong and positive relationships with economic growth. An increase in renewable energy consumption improves sustainability by promoting green technologies and reducing fossil fuel dependency. Enhanced economic complexity fosters innovation, diversification of exports, and technological advancement, all contributing to a more resilient economic structure. Other control variables such as gross capital formation and labor force also demonstrate expected positive effects, reinforcing their roles as traditional growth factors. In contrast, CO2 emissions exhibit a significant negative relationship with economic growth, highlighting the economic costs of environmental degradation. These results suggest that the direction and magnitude of shocks in energy and complexity matter significantly, supporting the adoption of targeted policies to stimulate positive developments in these areas while mitigating potential adverse shocks. ConclusionThis study highlights the critical roles of renewable energy consumption and economic complexity in shaping Iran’s economic growth trajectory. The use of the NARDL model enabled the identification of asymmetric responses to positive and negative shocks in both variables. The findings indicate that increases in renewable energy use and complexity drive economic growth, while their reductions have more limited adverse impacts. The results emphasize the importance of integrated economic and energy policies aimed at expanding renewable energy infrastructure and enhancing technological capabilities. Such strategies not only strengthen economic resilience and sustainability but also support environmental objectives by curbing carbon emissions and reducing fossil fuel reliance. Promoting innovation, investing in renewable sectors, and fostering complex economic activities can thus serve as complementary pillars for sustainable development in Iran.AcknowledgmentsInclude your acknowledgments here, if any.
Parisa Gholipour Feizi; Ghodratollah Emamverdi; Marjan Damnkeshideh; Aliasghar Esmaeilnia Ketabi
Abstract
Since income tax may reduce the profits and optimal output of a production company, this research extends the concept to oil investor companies by analyzing the optimal oil production path for the Dalpari oil field between 2018 and 2037. Specifically, the study investigates the effects of income ...
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Since income tax may reduce the profits and optimal output of a production company, this research extends the concept to oil investor companies by analyzing the optimal oil production path for the Dalpari oil field between 2018 and 2037. Specifically, the study investigates the effects of income taxation on the investor company’s discounted profit, optimal production rate, and changes in the production trajectory. Using a dynamic optimization framework based on the BELMAN model, several scenarios are explored and compared with the proposed production plan for the field. The results reveal that tax policy adjustments can influence the optimal production path. Depending on the scenario, these changes may have adverse or counterproductive impacts on the producer’s interests.IntroductionIn projects for the exploration and extraction of resources such as oil, which require significant investments, governments, as the owners of the resource and the main employers, enter into contracts with domestic and mainly foreign oil investment companies to attract investment in this area. Therefore, the various financial and legal regulations present in different types of contracts can have significant effects on the behavior of the oil investor company and ultimately on the optimal production path. One of the key factors in evaluating investment opportunities is the existence of a transparent financial relationship between the host government and the investor through a clear and codified law. In this context, taxes play a decisive role in the financial relationship between the government and the investor. On one hand, taxes serve as a revenue-generating factor for governments and an incentive tool in international investment, while on the other hand, they are considered the most important factor in assessing the rate of return on investment, risk, and so on, making the evaluation of investment justification meaningless without taxes. Therefore, this research aims to examine and analyze how taxes are applied in various ways, including income tax and the imposition of royalties on the income of the oil investment company, and their effects on the optimal production path. Thus, the question before us is whether the consequences of such tax policies can impact the optimal production path.In this research, an appropriate optimization model has been specified within the framework of taxation for a depleting resource like oil for Iran, and through a case study, it addresses how the removal of taxes affects the discounted profit rate, optimal production rate, and the optimal oil production path. This study is essentially conducted to highlight and reference the necessity of having an independent tax law in Iran's oil industry. Therefore, firstly, considering the history of exploitation in the Dalpari oil field and using the results of studies on the optimal oil production exploitation model in this field, the optimal oil production path from the Dalpari field is extracted within the framework of a dynamic optimization model for the time period from 2018 to 2037. Subsequently, the scenario of how income tax is imposed on the oil company and its impact on the extraction process of the optimal oil production path is examined.Methodology and MethodsIn this research, the optimal extraction model is specified using the optimal extraction theory of Pindyck (1981), whose theoretical basis essentially includes the optimal control problem of Richard Bellman (the maximum principle technique, which is the most comprehensive method for solving continuous optimal control problems in resource extraction such as oil) or the dynamic programming method. The theory of optimal control is a mathematical optimization method that seeks to find a control law for a given system. Additionally, to specify the dynamic optimization model of oil in the presence of income tax on the oil-producing company, the optimal optimization framework of Zhao et al. (2019) has been utilized. Like any optimal control model aimed at maximizing discounted profit, it includes a state variable or state equation that shows how the remaining reserves in each period are obtained by subtracting the previous period's production from the remaining reserves of the previous period, as well as a control variable that indicates the field's production in each period, the discount factor, and the discount rate. The field's cost function is also dependent on oil production and the remaining reserves of the field in period t. In this model, the parameters of price, income tax rate, and the percentage of royalties or external ownership interest are considered, and the income tax is computable within the model. This research utilizes data related to the Dalpari oil field. The oil field under study is located in the southwest of Iran, 30 kilometers from the central processing unit of the Cheshmeh Khosh Dehloran field. Active oil extraction from this field began in 2000. The in-situ oil volume of the field is 315 million barrels, and the gas volume of the field is 122 billion cubic feet, with current production being approximately 20,000 barrels per day. Cumulative production from this field until 2017 was about 40 million barrels. Additionally, the estimated increase in production for the next 10 years is about 47 million barrels. The API of the oil in the field is 33, and the recovery factor of the field is 41.8 percent. It is worth mentioning that the data used in this project pertains to the existing data of the Dalpari field and the report related to the development and operation contract of the Dalpari oil field, which is modeled for a twenty-year period (2018-2037).Results and DiscussionWith the cost function and objective function specified, as well as the framework of the tax model, the optimal production path of oil from the Dalpari oil field is estimated based on various price scenarios, discount rates, and cost coefficient rates, along with testing various hypotheses such as the negative impacts of income tax on the optimal oil production rate in the first scenario, examining the simultaneous increase of the discount rate and oil price in the second scenario, and also the simultaneous increase of the discount rate and cost coefficient rate in the third scenario, addressing the enhancing and exacerbating effects of the mentioned factors. In the first scenario, the extraction of the optimal production path is executed with the help of changes in the discount rate, and then the effects of changes in the discount rate along with the imposition of income tax on the optimal production path are examined to determine the impact of income tax on the company's optimal production path. The execution of the discount rate scenario before applying the tax model and comparing the optimal production paths with the actual production values (proposed by the concerned oil company) show that although the actual path is more uneven than its simulated values, the slight distance between the simulated optimal paths and the planned path (proposed by the field) indicates that the optimal production rate of the model firstly corresponds with the optimal field rate and also remains constant with changes in the discount rate. With the imposition of tax constraints, the optimal production rate shows a more severe sensitivity compared to the state before the imposition of tax at various discount rates, and with further increases in the discount rate, extraction from the field declines to a lower level of production with a lower recovery factor. The results of this scenario indicate that, according to previous findings and research, the policy of imposing income tax on the producer negatively affects the extraction process and the optimal oil production rate. The price changes applied in the second scenario show that with consecutive changes in oil prices during extraction, the optimal production rate increases, and along with the increase in production level, the recovery factor also significantly increases, leading to greater exploitation in the early years of production. Therefore, it can be confidently stated that the simultaneous increase of the discount rate and oil price can significantly reduce the negative effects of tax imposition on the optimal production rate and improve the optimal production path. By comparing the present value of the discounted profit of the producer along with the simultaneous increase in oil prices, the producer's profit significantly increases compared to the first scenario, despite the imposition of tax constraints. Thus, it can be concluded that in this scenario, the NPV of the company will have a significant increase compared to the first scenario. In the third scenario, changes in the discount rate along with the simultaneous increase in the cost coefficient rate indicate the high sensitivity of the proposed optimization model to changes in the cost coefficient, such that it exacerbates the negative effect of tax imposition on the optimal production path and severely reduces the net present value of the production project. Therefore, firstly, continuous increases in the cost coefficient rate lead to a decrease in the optimal production quantity of the field, accumulated production, and the recovery factor of the field, which causes the optimal production path to shift towards lower production levels. Additionally, with the imposition of tax constraints on the producer, this sensitivity increases further, such that this trend, with continuous increases in the cost coefficient compared to the first scenario, significantly reduces the optimal production rate and negatively impacts the optimal production path.ConclusionThis research, conducted using real data from the Dalpari oil field, aims to simulate the optimal production path using a dynamic optimization model. This model not only provides a theoretical framework to demonstrate the dynamics of oil production but also effectively reflects the impacts of income tax on the oil investor company's optimal production path. To this end, the question is posed: Can the imposition of income tax on the oil investor company negatively affect the optimal production path? The modeling of this field has been simulated and analyzed under three scenarios: changes in the discount rate, price changes, and changes in the cost coefficient, to answer the research question and assumptions. As expected and in line with previous studies, the results from the sensitivity analysis of discount rates in the first scenario indicate that the imposition of income tax on the oil investor company can reduce the optimal production rate and negatively impact the optimal oil production path. Additionally, the results obtained in the second scenario show that a simultaneous increase in the discount rate and oil prices can significantly reduce the negative burden of taxation on the optimal oil production path. Finally, the results of the third scenario indicate that a simultaneous increase in production costs along with the imposition of a tax on the oil investor company exacerbates the reduction in the optimal production rate and ultimately decreases the optimal oil production path. Therefore, the results obtained from all three scenarios not only provide a positive answer to the question posed in this research but also confirm the consistency of the sensitivity analysis results across different scenarios with previous studies.
Gholam Reza Ghaffari; Ahmad Sarlak; Maryam Sharifnezhad
Abstract
Renewable energies have been proposed as a key solution to combat climate change and reduce dependence on alternative fossil fuels. The development and expansion of this type of energy depends on various factors, including good governance, green financing, technological innovation, and urbanization. ...
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Renewable energies have been proposed as a key solution to combat climate change and reduce dependence on alternative fossil fuels. The development and expansion of this type of energy depends on various factors, including good governance, green financing, technological innovation, and urbanization. Investment in the aforementioned factors can help accelerate the transition to renewable and sustainable energies. The present study examined the impact of factors affecting renewable energies in ECO member countries over the period 2000 to 2024 and based on the Cross Sectional Augmented Autoregressive Distributed Lag (CS-ARDL), the relationship between the research variables has been analyzed. The results of this study indicate that good governance, green financing, and technological innovation have a positive and significant effect on renewable energy consumption, while urbanization has a negative effect on renewable energy consumption in the ECO region. Since the use of renewable energies plays an important role in sustainable development, it is recommended to prepare space for the transition towards the use of green energies by considering and promoting effective factors. IntroductionIn the contemporary era, the main focus of many economies is to achieve the Sustainable Development Goals (United Nations, Sustainable Development Goals, 2016). Achieving the Sustainable Development Goals is essential for protecting and improving the environment and the economic well-being of the world's people (Awosusi et al., 2022). For this reason, the energy consumption of societies has increased from non-renewable energy sources to renewable energy consumption (Dogan et al., 2022). Since renewable energy and energy efficiency are dependent on the macroeconomics of a country and this type of energy is effective as a powerful marketing tool for commercial investment and domestic and foreign trade, the country's dependence on non-renewable energy should be minimized and by increasing the consumption of renewable energy, the life of existing domestic energy resources should be extended, and in this way, by reducing costs, it will make a useful contribution to the relevant companies, the government, and ultimately the national economy. In addition, renewable energy consumption has been identified as a mechanism to reduce environmental degradation, so it is essential to study the determinants and main drivers of this type of energy consumption. Analyzing these drivers can help provide appropriate policies for governments and energy policymakers. Governance is one of the fundamental drivers in the application of renewable energy technology. It is also responsible for many economic, social, political, institutional, health and environmental standards (Huang et al., 2022). Since renewable energy sources have specific cycles and mainly require storage or other mechanisms to save energy, many countries cannot afford this infrastructure. For this reason, conventional methods of energy production are mostly non-renewable. This leads to a discussion about the relationship between governance and the process of production and consumption of renewable energy. On the other hand, green financing also plays an important role in achieving sustainable development. Because it includes any structured financial activity for a product or service, including facilities, debt, and investment mechanisms to encourage and develop green projects or minimize the negative impacts of projects that affect the climate (Sampen et al., 2024). Green financing in the form of green bonds is a suitable initiative for the development of clean energy and can reduce capital risk, increase investment returns, and attract international and domestic investment in the field of renewable energy (Rasoulinezhad, Taghizadeh Hessari, 2022; Liu et al., 2021). Another essential mechanism for the development and transition to renewable energy consumption is to focus on investment opportunities in technological innovations. Improving technology reduces the cost of renewable energy and ultimately increases the amount of investment in this industry. Energy storage technology, therefore, offers a practical solution to the intermittent nature of renewable energy consumption (Yao et al., 2016). As a result, investment in technological innovations is essential for the effective implementation of renewable energy deployment (Qadir, et al., 2021). In total energy production, potential losses occur at the initial stage of conversion, extraction, transmission, transportation and final consumption. Due to technological advances in renewable energy generation and distribution, there are certain synergies between renewable energy consumption and technological innovations (United Nations, 2015). Another issue that can affect renewable energy consumption is urbanization, which has received less attention in the energy-urbanization literature. To create sustainable electricity integration and manage diverse patterns in urban and rural centers, it must be considered throughout the energy transition stages (Lantz et al., 2021). Theoretically, the energy-urbanization nexus is a transition to the theory of the compact city, the theory of the urban environment, and the theory of ecological renewal (Poumanyvong and Kaneko, 2010). The focal points of these theories are that the urbanization process has changed various renewal structures and has led to energy efficiency at a rapid pace with the use of environmentally friendly tools and technology (Lantz et al., 2021). Considering the above and the situation of the member countries of the Economic Cooperation Organization, it is necessary to move towards a fully sustainable energy system. Therefore, the present study has examined the factors affecting the consumption of renewable energies in the ECO member countries and analyzed the challenges and opportunities facing this area. The innovation of the present study is: examining the simultaneous effect of four independent variables: good governance, green financing, technological innovation, and urbanization on renewable energy consumption in the eco-region, as well as using the generalized distributed lag autoregression model as an econometric model. The present study was the first of its kind in the country.Methods and MaterialsThe present study used the Cross-sectional Augmented Autoregressive Distributed Lag method to examine cross-sectional problems. This approach has been found to be more satisfactory for solving cross-sectional problems than traditional models, such as least squares mean and pooled mean group (Sun et al., 2022; Addae, Sun, & Aban, 2022). In addition, studies have shown that this approach provides a more accurate and reliable estimate for panel data (Baydoun & Aga, 2021; Huang, Chien and Sadiq, 2021). The advantage of this estimation method is its ability to detect errors that may arise from collinearity between the indicators studied in this study (Sadiq et al., 2022). In addition, Cross-sectional Augmented Autoregressive Distributed Lag (CS-ARDL) represents a significant improvement over previous methods, such as pooled averages and linear regression, especially in the field of renewable energy studies. It estimates long-run and short-run elasticity more robustly and accurately, and addresses endogeneity and serial correlation issues more effectively than previous methods. Therefore, the use of this advanced model provides insight into dynamic relationships and sets a new standard for empirical analysis in this field (Sampen, Li, & Nashia, 2024). In cross-sectional econometrics, it is generally assumed that the data used are cross-sectionally independent. This assumption, like other assumptions, may not hold, so the first step in this method, before performing any test, is to detect cross-sectional dependence or independence by examining the "cross-sectional dependence test" and the "slope homogeneity or heterogeneity test". The reason for using the "cross-sectional dependence test" is to identify and solve the challenges of panel data, including residual interdependencies and unobserved parameters (Ngong et al., 2022), so the "cross-sectional dependence test" includes the corrected standard test of Pesaran (2007) and the Baltaji test (Baltagi, Feng, Kao, 2012) with a corrected scale and the standard test of Brosch and Pagan, and the "slope homogeneity or heterogeneity test" uses the Pesaran and Yamagata test (2008). In general, if the statistical value of the computational “test of cross-sectional dependence” at a certain significance level is greater than the critical value of the standard normal distribution, then the null hypothesis is rejected and cross-sectional dependence is confirmed (Pesran, 2004). If cross-sectional dependence is confirmed in the panel data, the second step is to examine the stationarity or calculate the unit root and then examine the cointegration. Using conventional panel unit root methods such as the Levine, Lin and Cho and Im, Pesran and Shin tests will increase the probability of false unit root results. To overcome this problem, multiple panel unit root tests with cross-sectional dependence have been proposed, including Pesran (Pesran, 2007). In this test, if the unit root test statistic is greater than the critical values, the null hypothesis (non-significance of the variable) will be rejected and the stationary variable will be accepted. The third step is to examine panel cointegration. Using conventional panel cointegration methods such as Pedroni (1996) and Kao (2006) will increase the probability of false cointegration results (Asadzadeh and Jalili, 2015). To solve this problem, the present study has used the method proposed by Westerland (2007). The null hypothesis of this test is the absence of a cointegration relationship, and if the null hypothesis is rejected, the variables will have a cointegration relationship. Finally, to determine the accuracy and precision of the estimation of the generalized distributed autoregressive model with cross-sectional lags, the generalized mean test (Eberhart and Bond, 2009) and the commonly correlated mean effect test (Chodik and Pesaran, 2013) were used. Similar to the original generalized distributed autoregressive model with cross-sectional lags, these tests examine the errors created by the “slope homogeneity or heterogeneity test” and the “cross-sectional dependence test”, including the correlation between indicators. Also, in addition to providing consistent and accurate predictions, these tests address multicollinearity, autocorrelation, and endogeneity issues with panel data (Aday et al., 2022; Rahman et al., 2022). The D-H causality test (Dumitrescu and Hurlin, 2012) was used to assess the interaction of the causal relationships of the parameters. In many studies, this test has been used to examine causal relationships between series (Chen et al., 2022-Islam, 2022).Results and DiscussionIn a world facing serious challenges such as climate change and limited fossil resources, the use of renewable energies has been proposed as a promising solution to achieve sustainable development, but the development and expansion of this type of energy depends on several factors, among which good governance, green financing, technological innovation and urbanization are of particular importance. For this purpose, the present study discussed and examined the impact of good governance, green financing, technological innovation and urbanization on the consumption of renewable energies in the ECO area. The adjusted model was estimated using annual data using the autoregressive method with generalized distributed lags (CS-ARDL) for the period 2000 to 2024. The results of the estimation indicate that the rule of law can be useful for reducing overall energy consumption in ECO member countries. Because a stronger regulatory regime imposes more restrictions on the energy sector and limits the use of various energy sources. This result is consistent with the studies of Mahmoud et al. (2021) and Wang and Dong (2022). Therefore, ECO member countries should focus on improving good governance and pursuing policies to control and limit non-renewable energy in the region. These measures will help reduce the use of non-renewable energy and improve the environment. On the other hand, green financing has a positive impact on renewable energy consumption in ECO member countries. In other words, increasing domestic credit through financial assistance from commercial banks and insurance companies provides investment through loans with long-term repayment and low interest to finance renewable energy projects. The use of crowdfunding and cooperatives can effectively fill the financing gap in the deployment of renewable energy sources. This result suggests that the financial system in ECO member countries can promote the financing of renewable energy consumption. This finding is consistent with the findings of Saadawi and Chetorou (2022), but contradicts the findings of Mukhtarov et al. (2020). Also, policymakers should emphasize the importance of investing in cleaner technologies and increasing R&D spending in renewable energy production due to the increasing digitalization, the Fourth Industrial Revolution, and artificial intelligence in the contemporary era. Furthermore, since technological innovation in cleaner energy sources is capital-intensive and requires huge financial support, this study suggests that ECO member countries should create the necessary governance channels and a conducive environment to attract other relevant stakeholders to invest in renewable energy consumption. This enabling environment includes inviting academic institutions to participate in the development of smart energy grids for current and future needs and risk consultants to assess the threat of such investment opportunities in renewable energy technologies. The research findings also showed that urbanization has an adverse effect on renewable energy consumption. Hence, city authorities should focus on expanding the use of electric vehicles, energy-efficient public transport systems and alternative fossil fuels for cars in cities. Urban planners and the government should implement appropriate awareness-raising measures to educate city residents about the necessity of using cleaner energy sources and maintaining a healthy environment.ConclusionThe use of renewable energy is crucial to achieving sustainable development goals and eliminating environmental pollution. Extensive studies such as this one can help create a more constructive debate on this issue and transform the energy industry of ECO member countries. To achieve a sustainable future and reduce dependence on fossil fuels, improved governance, green financing, technological innovation and urbanization in the field of renewable energy are essential. By strengthening the aforementioned factors, a clean, efficient and sustainable energy system can be achieved.AcknowledgmentsFinally, I would like to thank my beloved wife and my hardworking children who have chosen the path of education and refinement in their lives with me. I would also like to express my gratitude to the esteemed editorial staff of this prestigious magazine, who work to increase the knowledge of the educated people of this country.
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