مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Mohammad Sadegh Adibian; Taghi Ebrahimi Salari; Hadi Esmaeilpour Moghadam
Abstract
Uncertainties are an intrinsic element of economic systems, exerting substantial and complex effects on various economic structures. Oil prices, in addition to being a crucial factor in production, are also a key indicator of oil revenues within Iran's economy. This study investigates the impacts of ...
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Uncertainties are an intrinsic element of economic systems, exerting substantial and complex effects on various economic structures. Oil prices, in addition to being a crucial factor in production, are also a key indicator of oil revenues within Iran's economy. This study investigates the impacts of economic policy uncertainty and geopolitical uncertainty, originating globally as well as from China, the United States, and Russia, on Iran’s crude oil prices. The analysis is conducted using the Generalized Additive Model (GAM) with monthly data from 1997 to 2022. The results indicate that economic policy uncertainty at the global level, as well as that originating from China and Russia, has a significant and nonlinear effect on Iran’s crude oil prices. Similarly, geopolitical uncertainty originating globally, from China, and from Russia has a direct and nonlinear influence. In contrast, geopolitical uncertainty from the United States shows a linear and inve
rse relationship with Iran’s crude oil prices. Moreover, the study explores the effects of concurrent uncertainties from the same source, revealing that simultaneous uncertainties originating globally, from Russia, and from the United States, have substantial and nonlinear impacts on Iran’s crude oil prices. These findings highlight the importance of employing advanced models capable of accounting for the joint effects and interactions of multiple variables. The insights provided by this research are valuable for stakeholders in oil markets and policymakers involved in managing the complexities of financial and geopolitical dynamics.
Introduction
Numerous studies have demonstrated that economic uncertainties, by creating unstable expectations, significantly impact key macroeconomic variables including exchange rates (Balli et al., 2016; Bartsch, 2019; Chen et al., 2020), employment (Caggiano et al., 2017), investment (Barro et al., 2017), exports (Borio et al., 2022; Jia et al., 2020), cryptocurrency markets (Fassina et al., 2021), and inflation (Binder, 2017). Since these variables are directly and indirectly connected to energy markets and oil prices, uncertainties consequently affect these markets as well (Jiantiella & Vataja, 2018; Pham & Nguyen, 2022; Wen et al., 2022). Among various uncertainty indices, geopolitical uncertainty and economic policy uncertainty have gained particular research attention due to their greater applicability. Oil, as a strategic commodity and the "lifeblood of industry" (Zhang, 2011), influences both macroeconomic stability and global financial markets (Apergis & Miller, 2009; Hu et al., 2016; Kilian & Park, 2009; Sadorsky, 2009), with its political nature making it susceptible to geopolitical tensions and policy changes (Bloomberg et al., 2009; Su et al., 2021). Given Iran's 5.5% share in global oil trade, this study examines the impact of geopolitical and economic policy uncertainties originating from global, Russian, Chinese, and US sources on Iranian oil prices, extending existing literature in three dimensions: (1) measuring these uncertainty effects, (2) examining linear/nonlinear relationships through econometric methods, and (3) analyzing pairwise interactions between uncertainty indicators from common sources.
Methodology and Data
This study employs Generalized Additive Models (GAM) to analyze nonlinear relationships between variables (Wood, 2006). The key advantage of GAM lies in its ability to automatically identify relationship patterns (via spline smoothing functions) without requiring linearity assumptions (Wood et al., 2015). The model utilizes cubic splines (Hastie & Tibshirani, 1987) and tensor products (Wood, 2006) to estimate both univariate and multivariate smoothing functions, incorporating roughness penalties (Wood & Augustin, 2002) to prevent overfitting. Monthly data (1997-2022) includes Iranian oil prices (OPEC) alongside economic policy uncertainty (EPU: Baker et al., 2016) and geopolitical risk (GPR: Caldara & Iacoviello, 2022) indices from four regions (global, China, Russia, US). The final model evaluates both individual and interactive effects of these indicators on oil prices through a combination of linear (S) and nonlinear (f) smoothing functions.
Findings and Conclusion
The investigation reveals no consistent pattern in the relationship between uncertainties and oil prices, with divergent results across studies. While three primary factors - supply changes, demand fluctuations, and future expectations - fundamentally influence oil prices, the study emphasizes that uncertainties primarily affect prices indirectly through altering financial market participants' sentiment. For instance, simultaneous increases in both geopolitical and economic policy uncertainties from US sources drive up Iranian oil prices, whereas the same uncertainties from Russian sources lead to price declines. These variations underscore how investor sentiment toward uncertainty origins plays a pivotal role in price volatility. Notably, some uncertainties don't directly affect supply/demand but induce price fluctuations through systematic biases in financial decision-making. These findings align with behavioral economics principles, suggesting traditional models may inadequately explain such dynamics.
The study offers three key recommendations:
Adopt Generalized Additive Models (GAM) for analyzing multiple uncertainties' simultaneous effects on oil prices, particularly leveraging their capability to identify nonlinear and interactive relationships.
Alert oil market participants that concurrent rises in US-sourced geopolitical and policy uncertainties may signal imminent price surges.
Monitor price drop risks when either US-sourced policy or geopolitical uncertainty increases independently, as this may trigger sudden declines in Iranian oil prices.
These findings highlight the critical importance of incorporating market sentiment and uncertainty origins in economic analyses, demonstrating that policymakers and market participants should complement fundamental analysis with attention to investors' systematic behavioral biases.
Acknowledgments
The 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.
Keywords: Uncertainty of Economic Policy, Geopolitical Uncertainty,
سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
Majid Aghaei
Abstract
This study investigates the impact of financial development on renewable energy technology deployment in resource-rich countries with varying levels of financial system maturity. Using panel data from 2000 to 2021, the models were estimated with the Generalized Method of Moments (GMM), while the robustness ...
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This study investigates the impact of financial development on renewable energy technology deployment in resource-rich countries with varying levels of financial system maturity. Using panel data from 2000 to 2021, the models were estimated with the Generalized Method of Moments (GMM), while the robustness of results was validated through Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS). The findings indicate that financial development positively affects renewable energy growth in all countries studied. In developed resource-rich countries, particularly those with advanced financial markets, natural resource abundance has facilitated renewable energy development, providing no evidence of the “resource curse.” In contrast, developing resource-rich countries with less developed financial systems show signs of the resource curse. These results highlight the crucial role of financial system development in transforming resource wealth into an opportunity for renewable energy advancement.IntroductionThe sharp rise in energy prices, particularly oil, over the twentieth century has brought about profound challenges and opportunities for the global economy. The heavy reliance on fossil fuels has raised serious concerns regarding energy security and environmental sustainability, prompting countries to seek alternative energy solutions. Among these, the development of renewable energy technologies has emerged as a crucial strategy to mitigate climate change and ensure long-term energy security (Bhattacharya et al., 2017; Charfeddine & Kahia, 2017). In recent years, developed economies have adopted a variety of policy instruments—such as feed-in tariffs and renewable portfolio standards—to accelerate renewable energy deployment (Kim & Park, 2016). However, despite these efforts, the contribution of renewable energy to total energy consumption remains limited, particularly in developing economies. High upfront capital costs and inadequate financial support are frequently cited as major barriers to renewable energy investment. Financial market development plays a pivotal role in mobilizing resources for renewable energy projects by facilitating access to capital and reducing investment risks. Yet, in resource-rich developing countries, the interplay between financial development and renewable energy expansion is complicated by the “resource abundance” phenomenon. While natural resource wealth has the potential to finance large-scale renewable energy investments, empirical evidence indicates that it can also distort economic structures and hinder sustainable development (Nili & Ratad, 2007; Moradbeigi & Law, 2016). Countries such as Iran and the Gulf Cooperation Council (GCC) states, despite abundant oil and gas revenues, have made limited progress in renewable energy adoption.This study aims to explore how financial development influences renewable energy growth in resource-rich economies, considering the dual role of natural resource abundance. By distinguishing between countries with varying levels of financial system development, this research seeks to provide new insights into the mechanisms through which resource wealth and financial markets interact to shape renewable energy transitions.Methods and MaterialDrawing on theoretical foundations and previous studies (e.g., Moradbeigi & Law, 2016; Nili & Rastad, 2007; Kim & Park, 2016), this study employs a dynamic model to investigate the impact of financial development on the deployment of renewable energy technologies, while accounting for the role of natural resource abundance and the level of financial system development across countries. The general model is specified as follows: In this model, represents the installed capacity of renewable energy technologies in country i at time t. The vector includes explanatory variables such as financial development (FD), natural resource rents (NRR), real GDP per capita, population (POP), consumer price index (CPI), and greenhouse gas emissions (GHG). The interaction term FD∗NRR is incorporated to examine the indirect effect of resource abundance on renewable energy development through its influence on financial development.Given the dynamic nature of the model, the estimation is conducted using the Generalized Method of Moments (GMM), as proposed by Arellano and Bond (1991) and further refined by Arellano and Bover (1995) and Blundell and Bond (1998). The sample comprises resource-rich developed and developing countries over the period 2000–2021. Countries are classified into two groups—those with developed and less-developed financial systems—based on the financial development index provided by the International Monetary Fund (IMF). Furthermore, the categorization of developed and developing economies follows the World Bank classification. Only countries with significant natural resource rents are included in the sample (Sachs & Warner, 2001; Moradbeigi & Law, 2016). Results and DiscussionThe empirical findings, based on dynamic panel GMM estimations, reveal significant insights into the relationship between financial development and the deployment of renewable energy technologies (RETs) in resource-rich countries. Diagnostic tests, including the Sargan test for instrument validity and Arellano-Bond serial correlation tests, confirm the robustness and reliability of the model estimations. In resource-rich developed countries, financial development exhibits a strong and positive impact on both annual and cumulative renewable energy capacities, regardless of the maturity level of their financial markets. This highlights the critical role of well-functioning financial systems in mobilizing investments for RETs. Furthermore, the positive and significant coefficients of natural resource rents (NRR) contradict the resource curse hypothesis in these countries, suggesting that revenues from natural resources have been effectively utilized to promote clean energy transitions. The interaction term (FD*NRR) is also significant in countries with highly developed financial systems, indicating that financial development amplifies the positive effect of resource abundance on renewable energy deployment.In contrast, the results for resource-rich developing countries present a more nuanced picture. While financial development positively influences RET expansion in countries with advanced financial markets, its effect is statistically insignificant in those with underdeveloped financial systems. Moreover, the negative and significant coefficient of NRR, alongside the interaction term (FD*NRR), confirms the existence of the resource curse in developing economies with weak financial infrastructures. This suggests that inadequate financial systems hinder the ability of these countries to channel resource rents towards sustainable energy investments. Control variables such as GDP per capita and CPI show consistent positive effects on RET deployment across most models, underscoring the importance of economic development and energy price signals in driving renewable energy adoption. Conversely, greenhouse gas emissions are negatively associated with RETs, supporting the notion that increased fossil fuel dependency hampers clean energy progress.Robustness checks using FMOLS and DOLS estimators corroborate the main GMM results, further strengthening the study’s findings. These results collectively emphasize the pivotal role of financial development in transforming natural resource wealth into a driver for renewable energy growth, particularly in countries with efficient financial systems.ConclusionThis study examined the role of natural resource abundance (resource rents) in shaping the relationship between financial development and renewable energy technology (RET) deployment in resource-rich developed and developing countries, with particular attention to the level of financial market development during the period 2000–2021. The findings reveal that financial development positively influences RET growth in both groups of countries. However, in resource-rich developed countries, this effect is significant regardless of the level of financial market development, whereas in developing countries, the impact of financial development on RET is only significant in those with advanced financial markets. This underscores the critical role of efficient financial systems in mobilizing resources and reducing investment risks in renewable energy projects.Moreover, the positive and significant effect of resource rents in developed countries rejects the “resource curse” hypothesis, highlighting the capacity of advanced financial markets to channel resource revenues toward RET advancement. Conversely, in resource-rich developing countries with underdeveloped financial markets, resource rents negatively affect RET, confirming the existence of the “resource curse.”Based on these results, it is recommended that countries with less developed financial systems strengthen credit markets, support private sector investments, create financial incentives, and enhance institutional frameworks to facilitate renewable energy technology deployment. Additionally, policies aimed at fostering sustainable economic growth, ensuring fair energy pricing, and implementing environmental strategies can play a vital role in reducing dependence on fossil fuels and promoting RET expansion.Acknowledgments The author would like to express their sincere gratitude to the anonymous reviewers for their valuable comments and constructive suggestions, which have significantly improved the quality of this study.Keywords: Renewable Energy Technologies (RET), Financial Development, Natural Resource Rent, GMM Estimator
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Ashkan Rahimzadeh
Abstract
The main goal of the research is to investigate the impact of various factors on energy intensity with an emphasis on economic complexity and mutual relationship between financial risk and financial development. The statistical data used in this research are from the International Country Risk Guide ...
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The main goal of the research is to investigate the impact of various factors on energy intensity with an emphasis on economic complexity and mutual relationship between financial risk and financial development. The statistical data used in this research are from the International Country Risk Guide (ICRG), World Bank, energy balance and MIT University website during the years 2000-2022. In order to estimate the target model, the Auto Regressive distributed Lags approach (ARDL approach) has been used in the framework of short-term dynamic model, long-term relationships and error correction model. ARDL model (1,0,0,0,0,0,0,0,0,0,0) was selected with one interval for energy intensity variable and zero interval for all independent variables based on Schwartz-Bayesian criterion. The results of the ARDL dynamic model in the short-term and long-term show: the effect of energy price and capital per capita on energy intensity is indirect. The effect of variables of economic complexity, trade liberalization, urbanization rate and internet users on energy intensity in the short and long term is direct. Despite its statistical significance, the coefficients of domestic investment and labor force are very small and close to zero. The mutual effects of financial risk and financial development, as well as the foreign direct investment variable, did not have a significant effect on energy intensity in both time periods.
Introduction
In line with the special importance given to environmental issues, rationalizing energy consumption is becoming more and more necessary. The continuity of the supply of finite energies, such as oil, is facing serious doubts, and the revelation of the realities of energy supply is creating anxiety and anomalies in countries. Reducing energy intensity, or in other words, optimizing energy consumption, is considered one of the development goals and aspirations of policymakers and economic planners in every country, and achieving this goal is not possible except by recognizing its determining factors and providing thoughtful solutions. Energy price The main factor in choosing between energy-efficient technology or environmental technology is the price of energy. Economic complexity can affect energy intensity through scale, composition, and technical effects, which manifest themselves over different time periods. There are various channels proposed regarding the impact of financial risk on energy intensity. Some channels imply a decrease in energy intensity and others imply an increase in energy intensity. These three effects may also appear in the case of foreign direct investment and Trade liberalization. Information and communication technology affects energy intensity through substitution and income effects. Investment may have different effects on energy intensity depending on the structure of the economy. Urbanization can be examined from different perspectives, such as economies of scale and the expansion of economic activities.
Methods and Materials
The analysis used in this research is the ARDL method, which uses three dynamic equations: short-term, long-term, and error correction. The model variables are logarithmic. The dependent variable is energy intensity and the independent variables are energy price, economic complexity, the interrelationship of financial risk and financial development, trade liberalization, urbanization rate, Internet users, capital per capita, foreign direct investment, domestic investment, and labor force. Statistics on energy intensity, domestic investment, foreign direct investment, labor, capital per capita, trade liberalization (trade as a percentage of GDP), and urbanization rate were obtained from the World Bank. Information on bank facilities to the non-governmental sector (Financial Development Index) was obtained from the Central Bank, information on economic complexity was obtained from the MIT website, and financial risk information was obtained from the International Country Risk Guide database. Regarding energy prices in Iran, the main energy carriers include petroleum products, natural gas, and electricity. In this study, the energy price index is obtained as a weighted average (based on the share of carriers) of the three price indices of petroleum products, natural gas, and electricity. Each of these sub-indices is calculated using the Laspeyres method. The period under study is 2000-2022. EViews 9 software was also used in the estimation.
Results and Discussion
The results of the ARDL model estimation show that: The impact of energy prices on energy intensity in the short and long term is indirect. Such a result is consistent with the theoretical foundations that increasing energy prices provides an incentive to increase energy efficiency. As economic complexity increases, energy intensity increases in both the short and long term. Therefore, it can be said that the country is not yet at the stage of strong emergence of technical or combination effects, or their magnitude is lower compared to the scale effect. The long-term positive coefficient (0.53) is slightly lower than the short-term (0.775). The urbanization rate has a direct impact on energy intensity in the short and long term. This result indicates that with the expansion of urbanization, on the one hand, urban density in the country has not been able to reduce energy intensity for public urban infrastructure through economies of scale, and on the other hand, with the expansion of economic activities, energy intensity has increased. The value of the long-term positive coefficient (0.563) is lower compared to the short-term (0.82). An increase in capital per capita has a negative impact on energy intensity in the short and long term. Labor, despite its negative impact, has a very small impact in both time periods. That is, a higher share of capital is associated with energy-intensive technologies, and a lower share of capital is associated with technologies with a higher share of energy input. Internet users have a positive impact on energy intensity in the short and long term, indicating that the income effect is dominant over the substitution effect. Foreign direct investment did not have a significant impact on energy intensity in both time periods, which indicates that economic growth is not affected by foreign direct investment. The interaction effects of financial risk and financial development on energy intensity did not have a significant impact in either time period, and it can be said that financial development in the country has not yet reached a level that can significantly increase investment and economic growth.
Conclusion
The energy price variable coefficient implies that each 1% increase in energy prices reduces energy intensity by 0.018 and 0.013% in the short and long run, respectively. The economic complexity coefficient implies that each 1% increase in this variable increases energy intensity by 0.775 and 0.53% in the short and long term, respectively. Trade liberalization has a positive and significant effect on energy intensity, such that each one percent increase in this variable increases energy intensity by 0.038 and 0.026 percent in the short and long term, respectively. The urbanization rate has a positive effect on energy intensity, such that each one percent increase in this variable increases energy intensity by 0.82 and 0.563 percent in the short and long term, respectively. The coefficient of Internet users implies that a one percent increase in Internet users increases energy intensity by 0.023 and 0.016 percent in the short and long run, respectively. The coefficient of domestic investment and labor, despite being statistically significant, is very small and close to zero. The variables of foreign direct investment and the interaction effects of financial risk and financial development did not have a significant effect on energy intensity.
سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
Ezatollah Tayebi; Teymur Mohammadi; Morteza Khorsandi; Abdolrasol Ghasemi; Mohammad Sayedi
Abstract
The National Development Fund was established as a development fund with the aim of providing intergenerational benefits, preventing the spread of fluctuations in oil revenues to the economy, and also supporting the country's development plans. Despite this, until now, there has not been a detailed evaluation ...
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The National Development Fund was established as a development fund with the aim of providing intergenerational benefits, preventing the spread of fluctuations in oil revenues to the economy, and also supporting the country's development plans. Despite this, until now, there has not been a detailed evaluation of how the allocation of resources of this fund affects macroeconomic variables. However, by studying and examining the successful global models of such funds, in addition to the limited impact of this fund on the macro-economic variables in Iran, there are also flaws in the way its resources are allocated. Based on this, the main goal of this research is to design a dynamic stochastic general equilibrium model to evaluate the impact of the allocation of National Development Fund resources on macroeconomic variables with the Bayesian estimation approach using quarterly data for the period 2011-2021. The results of the simulation show that if the National Development Fund spends part of its resources on direct and indirect investment, although at the beginning of the period its effects are the same as before (only facilities), but after that the level of production, capital and investment will increase, which will lead to higher economic growth. Also, the results obtained from the minimum variance portfolio method show that among the existing methods, buying shares of capital market companies directly and investing in various types of investment funds, can bring higher returns than the current method (facilities) for the Fund at a certain level of risk.IntroductionCountries rich in natural resources often struggle with resource mismanagement, institutional inefficiency, and economic volatility. Iran, despite significant oil revenues, has faced low economic growth and macroeconomic instability. The NDF was created to mitigate these challenges by saving oil revenues and promoting productive investment. This research explores how the structure and allocation of NDF resources affect macroeconomic variables and seeks to identify optimal strategies for maximizing its impact.Methods and MaterialsThe study employs a DSGE model based on Real Business Cycle (RBC) and New Keynesian foundations, integrating sectors such as households, firms, government, central bank, and the NDF. Bayesian estimation techniques were used to calibrate model parameters using quarterly macroeconomic data. Multiple policy scenarios were simulated, including pure loan-based allocation and mixed investment strategies, to examine their effects on output, inflation, employment, and capital accumulation.Results and DiscussionSimulation results show that switching from a loan-only strategy to a mixed investment approach enhances capital accumulation, investment, and output growth. While the short-term effects (approximately the first year) of both approaches are similar, the investment-inclusive approach yields superior long-run results. Portfolio optimization through the MVP model recommends allocating 43.4% to equities, 49.6% to mutual funds, and 7% to real estate, maximizing returns under acceptable risk levels.ConclusionThe findings emphasize that diversifying the NDF’s financial instruments beyond traditional loans enhances both fund profitability and macroeconomic stability. Strategic allocation toward capital markets and investment vehicles leads to sustainable growth and improved intergenerational equity. Future policies should integrate a balanced portfolio approach to optimize the Fund’s economic contribution.AcknowledgmentsThe author extends sincere gratitude to Dr. Mehdi Sarem for his invaluable support in model development, and to the editorial board of the Journal of Energy Economics of Iran for their constructive feedback and publication support.
تمرکز بر هریک از موارد فوق الذکر با توجه به جایگاه و نقش جمهوری اسلامی ایران
Abouzar Fattahizadeh; Shirin Andarkhord
Abstract
Renewable energies are gradually replacing fossil fuels as the primary sources of energy. The transition from non-renewable resources to renewable sources, such as solar, wind, geothermal, biomass, etc., has significantly transformed productive, commercial, and financial aspects of the international ...
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Renewable energies are gradually replacing fossil fuels as the primary sources of energy. The transition from non-renewable resources to renewable sources, such as solar, wind, geothermal, biomass, etc., has significantly transformed productive, commercial, and financial aspects of the international energy market. This transformation, in turn, has created new challenges and opportunities for energy security of states. Iran, as a major producer of fossil fuels and energy consumer, is also grappling with these challenges and opportunities. Here we aim to answer the question that what strategies and policies Iran has adopted in the field of renewable energies to ensure its future energy security? In other words, what role and position does Iran envisioned for renewable energies in its future energy security? Answering this question requires addressing several sub-questions. First, what is energy security and its components? Second, what opportunities and threats do renewable energies pose to the energy security of states? Third, what is the current status of Iran in terms of energy security indicators, and how does the shift in the energy market from fossil fuels to renewable energies affect Iran's energy security? Fourth, what strategies and policies has Iran specifically adopted in the field of renewable energies to prevent these threats and risks? Fifth, what are the shortcomings of these strategies and policies, and what solutions can be proposed to overcome them? Literature ReviewThe diversity in definitions of energy security has led scholars to propose various indicators for assessing energy security. In Table 1, we attempt to compile all the indicators proposed by researchers in this field. Table 1. Indicators of Energy SecurityResearch examplesIndicator Kruyt, Van Vuuren, de Vries & Groenenberg, 2009; Yao & Chang, 2014; Chuang & Ma, 2013; Fang, Shi &Yu, 2018; Lixia, 2021 Acceptability1Fang, Shi. &Yu, 2018; Kruyt, Van Vuuren, de Vries & Groenenberg, 2009Sustainability2Karatayev & Hall, 2020; Radovanović, Filipović & Pavlović, 2017Demand continuity3Radovanović, Filipović & Pavlović, 2017; Fu &et al, 2021Environmental sustainability4Paravantis, Kontoulis, Ballis, Tsirigotis & Dourmas, 2018; Kruyt , Van Vuuren, de Vries & Groenenberg, 2009Supply continuity5Kruyt , Van Vuuren, de Vries & Groenenberg, 2009; Erahman, Purwanto, Sudibandriyo & Hidayatno, 2016; Fang, Shi &Yu, 2018Accessibility6Azzuni &Breyer, 2018; Szulecki, 2018Democracy7Lee, Xing & Lee, 2022Distribution of incomes8Lin & Raza, 2020; Radovanović, Filipović & Pavlović, 2017; Kruyt, Van Vuuren, de Vries & Groenenberg, 2009Energy import dependency9Jewell, Cherp &Riahi, 2014Diversity of resources10Sovacool &Mukherjee, 2011; Kruyt, Van Vuuren, de Vries & Groenenberg, 2009; Martchamadol & Kumar, 2013Estimating resources and Reserve-to-production ratio11Kruyt, Van Vuuren, de Vries & Groenenberg, 2009; Shah, Zhou, Walasai &Mohsin, 2019; Novikau, 2019Political stability12Kruyt, Van Vuuren, de Vries & Groenenberg, 2009; Chuang & Ma, 2013; Radovanović, Filipović & Pavlović, 2017; Yao & Chang, 2014; Kruyt et al., 2009Affordability13 Martchamadol & Kumar, 2013; Dźwigoł, Dźwigoł-Barosz, Zhyvko, Miśkiewicz & Pushak, 2019Energy consumption intensity14 MethodologyIn response to the main research question and using rational-conceptual modeling method, we first identified fourteen indicators for assessing energy security. Then, we identified the threats and opportunities arising from the transition to renewable resources in each of these indicators. Next, with documentary and descriptive content analysis methods, we demonstrated which of these threats and opportunities Iran has faced or will face, and to which of them it has paid attention in its macro-policy-making and high-level documents. ResultsIn Table 2, we categorized the most important threats and opportunities affecting the stability or improvement of Iran's energy security.Table 2. Threats and opportunities of renewable resources for Iran's energy securityThreats and opportunitiesIndicator Change in public perception towards non-renewable resources and domestic and international public opinion pressuresAcceptability1Positive public perception of renewable resource exploitationLoss of oil and gas resources during production processDe-legitimization of governmental oil and gas derivatives consumption methodsUtilization of the country's capacity in wind, solar, hydro, and nuclear energy productionSustainability2Probable future reduction in oil and gas resourcesInvestment in export of renewable energyDemand continuity3Reducing dependency on international oil and gas demandAggravation of climate crises and increasing pollution of biochemical cycles due to fossil resource production and consumptionEnvironmental sustainability4Enhancement of environmental sustainability with renewable resourcesInternational sanctions on oil and gas technologies and renewable energy technologiesSupply continuity5International sanctions on oil and gas salesDevelopment of unconventional oil and gas resources exploitationGlobal prices increaseDecrease in job opportunities in oil and gas industriesNew job opportunities in renewable energy sectorIncreased public access to renewable resources to expand local developmentAccessibility6Rentier state and the need to reduce dependency on oil and gas revenuesDemocracy7Reduction in distribution of oil and gas incomesDistribution of incomes8Possibility of creating new public revenues through renewable energy sourcesIncrease in the role of other energy sources versus oil and gasEnergy import dependency9Diversity of resources10Relying solely on Estimating resources and reserves volumeEstimating resources and Reserve-to-production ratio11Social protests due to energy-related issuesPolitical stability12Increase in energy carrier pricesAffordability13Optimizing energy consumptionEnergy consumption intensity14 Examining high-level documents of Iran’s energy shows that the greatest attention has been paid to Affordability and Energy consumption intensity indicators, while the least attention has been given to Acceptability and political stability indicators, and to some extent, Supply continuity indicators. ConclusionIt seems that understanding the critical situation and deficiencies of Iran's energy security can only be achieved through recourse to the foundations of good governance, particularly good energy governance. Based on a general rule in good governance, such governance entails a tripartite relationship between the government, civil society and stakeholders. However, in high-level energy documents and general energy security policies, two other actors of good governance are absent. Acceptability, political stability, and to some extent, Supply continuity are indicators directly related to these other two kinds of actors.Acknowledgments The authors of this research are grateful to the referees for their valuable comments and suggestions.
• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Pouyan Kiani; Kioumars Heydari; Maryam Nafisi Moghadam
Abstract
The purpose of this study is to investigate the price elasticity of household and non-household electricity demand across 31 provinces of Iran from 2011 to 2021. Due to the skewness of the dependent variable, the panel quantile regression method was chosen. The results show that the price elasticity ...
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The purpose of this study is to investigate the price elasticity of household and non-household electricity demand across 31 provinces of Iran from 2011 to 2021. Due to the skewness of the dependent variable, the panel quantile regression method was chosen. The results show that the price elasticity of household electricity demand ranges of -0.069 to -0.115. The price elasticity demand of non-household ranges from -0.021 to -0.043. It reveals that price elasticities are less than one for both groups. According to the results, electricity is an inelastic good in Iran. Also, the elasticity of electricity demand is higher for households than for non-household. Moreover, the results show that an increase in the price of natural gas, which is the closest substitute for electricity, has had a negligible impact on the electricity demand of the household and non-household sectors. Among other model results, we can mention the incredible influence of demand habits on household and non-household electricity demand. IntroductionElectricity consumption in Iran has shown a consistent upward trend over time, particularly in the household sector. This increase is largely attributable to expanded access to the electricity grid, improved supply reliability, and the growing use of electrical appliances. Similarly, rising electricity intensity in industry and the increased use of groundwater through electric pumps in agriculture have deepened the reliance of non-household sectors on electricity. Given electricity’s vital role in enhancing welfare and driving economic development, analyzing its demand is of considerable importance.Although numerous studies have estimated Iran’s electricity demand using various econometric methods, the novelty of this study lies in its application of the quantile regression approach to estimate demand functions for both the household and non-household sectors. This method enables the estimation of price, income, and cross-price elasticities across different demand levels, allowing for a nuanced evaluation of the impacts of pricing and income policies. Consequently, this study provides valuable insights for policymakers in designing effective and timely interventions.Methods and MaterialThe primary advantage of quantile regression lies in its ability to capture how changes in independent variables affect different points of the dependent variable’s distribution. An analysis of electricity demand in the household and non-household sectors reveals that their distributions are non-normal, right-skewed, and contain numerous outliers. As a result, the ordinary least squares (OLS) method is not well-suited for identifying the determinants of these variables. Moreover, because quantile regression examines the entire distribution and provides a detailed depiction of the regression relationship, it is particularly appropriate for modeling skewed variables. Unlike conventional regression, which estimates the average effect of explanatory variables, quantile regression assesses these effects across various points of the conditional distribution (Coad and Rao, 2006; Mosteller and Tukey, 1977). Accordingly, this study applies the quantile regression approach to estimate electricity demand functions in both sectors.Results and DiscussionIn this study, electricity demand functions for both household and non-household sectors were estimated across 31 provinces in Iran during the period 2011–2021. The results show that the price elasticity of household electricity demand ranges from –0.069 to –0.115, whereas for the non-household sector it ranges from –0.021 to –0.043. This indicates that household electricity consumption is more sensitive to price changes than the non-household sector. Although substitution elasticities are low in both sectors, the household sector demonstrates greater responsiveness, particularly at higher consumption levels.Another key finding is the positive effect of cooling degree days on household electricity consumption, suggesting that rising average temperatures and the consequent increase in cooling requirements are likely to boost household electricity demand. Furthermore, the income elasticity of household electricity demand is positive and significant at lower consumption levels, implying that in provinces with lower per capita electricity use, increases in household income lead to a more substantial rise in consumption. In contrast, the income elasticity of non-household electricity demand is higher at upper consumption levels.ConclusionA sound understanding of price elasticity enables policymakers to design pricing strategies that effectively influence consumption patterns and promote energy efficiency and conservation. The findings of this study indicate that the price elasticity of household electricity demand is greater than that of the non-household sector, suggesting that households are more responsive to changes in electricity prices. Accordingly, during the study period, electricity pricing policies proved more effective in curbing consumption in the household sector compared to the non-household sector.The results also show that the price elasticity of non-household electricity demand declines at higher levels of consumption. This implies that pricing policies are more effective in reducing demand among lower consumption quantiles within the non-household sector than among higher ones.Cross-price elasticity is positive for both sectors. Notably, the cross-price elasticity of household electricity demand is consistently higher than that of the non-household sector across all quantiles. This difference can be attributed to the nature of electricity use in the non-household sector, where substituting electricity with alternative energy sources is often more difficult and costly. For instance, in the industrial and agricultural sectors, switching from electricity to gas involves significant financial and logistical challenges. In contrast, households may more easily respond to rising gas prices by switching to electric stoves or heating appliances.Based on the findings of this study, the following policy recommendations are offered:Since consumption habits have a substantially greater influence on electricity use in both household and non-household sectors than electricity prices, there is significant potential for electricity savings through behavioral change. Thus, promoting the development of conscious, energy-saving habits is strongly recommended.Reforming electricity tariffs is essential to ensure that price signals function effectively. Such reforms should reflect the true cost of electricity supply to support more efficient demand management, enhance energy efficiency, and improve resource allocation
