درخت حوزه‌های تخصصی

Purpose: This study examines the impact of product diversification on asset returns in petrochemical companies using a kernel density approach, addressing contradictory findings in the diversification-performance literature within this capital-intensive sector. Design/methodology/approach: Employing an ex-post facto research design, we analyzed data from petrochemical companies listed on the Tehran Stock Exchange from 2011 to 2021. The study utilized a two-stage analytical approach: first classifying companies into life cycle stages (growth, maturity, decline) using discriminant analysis, then testing hypotheses through multivariate regression and cross-sectional analysis. The kernel density estimation provided a non-parametric assessment of diversification effects. Results: The results demonstrate that product diversification significantly enhances asset returns in petrochemical firms, with the Herfindahl index revealing seven distinct market concentration levels. The positive effect of diversification was particularly pronounced in monopolistic market structures. Empirical evidence confirms that diversification serves as an effective strategy for improving financial performance and mitigating market dependence. Originality/value: This research contributes to the literature by introducing kernel density estimation to diversification analysis, providing a more nuanced understanding of return distributions beyond traditional parametric methods. The study also offers unique insights into the petrochemical sector's dynamics in emerging economies, highlighting how market structure moderates the diversification-performance relationship. Practical implications: The findings suggest that managers should prioritize diversification strategies, especially in monopolistic environments, to enhance asset utilization and financial resilience. Policymakers can use these insights to design industrial policies that encourage strategic diversification in response to Iran's economic conditions.

The need to speed up the creation of innovation in the oil industry and support it properly in order to respond to the increasing needs of the world today has caused activists in this field to use various forms of protection. Patent systems and trade secrets are the main ways of protecting intellectual achievements in the oil industry. In recession conditions such as the Corona epidemic, which had a negative impact on the production and income generation of oil companies, intangible assets such as trade secrets or registered inventions could be a very good source of income for these companies. The complexity and longevity of technology in the upstream field of the oil industry has caused the reluctance of companies active in this field to register patents, which will slow down the dissemination of knowledge, creation and development of innovation. Therefore, due to the necessity of energy production, lack of resources and the need for complex and combined technologies in this field, patenting becomes inevitable.

West Texas Intermediate (WTI) crude oil is a pivotal benchmark in the global energy market, exerting a decisive influence on economic expectations and national macroeconomic policies. As the primary pricing basis on the New York Mercantile Exchange and numerous energy futures contracts, WTI is subject to persistent and severe price volatility. Such fluctuations, often appearing as abrupt upward or downward shocks, profoundly affect key macroeconomic indicators. These include inflation, economic growth, trade balances, corporate profitability, production costs, and government budgets. Consequently, variations in WTI prices influence oil and gas markets, financial stability, energy security, and even international geopolitical relations. To address these challenges, this study develops a hybrid VMD+SVR framework to model and forecast WTI crude oil futures prices across short-, medium-, and long-run horizons. Empirical findings reveal that across all three horizons, the proposed hybrid model consistently achieves the lowest forecasting errors compared with alternative approaches. Moreover, the Diebold–Mariano and Wilcoxon tests statistically confirm the superior predictive performance of the hybrid VMD+SVR model. These results highlight the importance of integrating advanced adaptive signal decomposition (VMD) with powerful nonlinear learning algorithms (SVR) for accurate oil price forecasting. The proposed approach not only enhances forecasting accuracy but also provides practical insights for policymakers in managing economic risks, stabilizing budgets dependent on oil revenues, and formulating sustainable energy strategies. It opens a new avenue for developing financial forecasting models inspired by advanced signal processing.

The exchange rate in Iran’s economy, is one of the most critical indicators influencing fiscal, trade, and monetary policymaking. Given that a substantial portion of the country’s foreign exchange earnings is dependent on crude oil exports, any fluctuations in oil revenues can directly or indirectly affect the exchange rate. This relationship becomes especially significant under circumstances such as international sanctions, global oil price volatility, and the structural dependency of the government budget on oil revenues. Consequently, a precise understanding of the mechanisms by which oil income influences the exchange rate is essential for designing sustainable currency policies and ensuring the country’s macroeconomic stability. The present study investigates the long-term relationship between oil revenues and the exchange rate in Iran during the years 2002 to 2024, utilizing the Fully Modified Ordinary Least Squares (FMOLS) econometric method. Through the analysis of quarterly time series data and unit root tests, the stationarity and then cointegration of the variables were examined. The test results indicate that the variables are integrated of order one, and a cointegration relationship exists among them, justifying the estimation of the model using FMOLS.The findings reveal that, according to the model estimates, oil revenues and non-oil exports exert a significant negative effect on the exchange rate. Specifically, a one-percent increase in oil revenues leads to a 0.41 percent decrease in the exchange rate (signifying an appreciation of the Rial), while an increase in non-oil exports results in a 0.72 percent decrease in the exchange rate.

The present study examines the investment model of the National Development Fund (NDF) in the upstream oil and gas industries, focusing on the risks associated with field development. Given the NDF's recent entry into the investment arena, the challenges in this field have been analyzed in this article. Furthermore, the NDF 's investment policies and guidelines, project prioritization, and the Non-Meddle Investment model (I-HOPE) as the NDF’s main strategy to reduce risk and increase resource effectiveness are introduced and analyzed in detail. Finally, alongside a comparative review of the NDF’s investment model and field development challenges, solutions are proposed to improve the NDF’s investment process in this sector. This study, by evaluating the strengths and weaknesses of the I-HOPE model in confronting the inherent risks of field development, provides a framework for optimizing future investments. These measures help the NDF to optimally manage risks, increase resource returns, and play a more effective role in the country's economic development. Keywords: Non-Meddle Investment Model, Risk, Uncertainty

The compilation of a list of disqualified entities by NIOC serves as both a supervisory and deterrent mechanism to prevent violations and mitigate legal and financial risks associated with high-risk contracts. This directive has been designed with the objective of enhancing the integrity of the tendering process, ensuring fair competition, enforcing regulations governing public procurement and governmental transactions, and guaranteeing that work is not repeatedly delegated to companies that have demonstrated an unsatisfactory performance history in prior projects. However, an exclusive focus on the legal personality of companies may reduce the deterrent effect, as offending entities may continue their activities under subsidiary or shell companies. This study proposes the inclusion of the managing director and members of the board of directors—as the key natural persons influencing a company’s decision-making—alongside the legal entity itself in the blacklist of disqualified contractors, presenting this measure as a practical and low-cost approach to enhancing the effectiveness of the disqualification system. Using a descriptive–analytical method, the research reviews the current directive, outlines the legal foundations of managerial accountability, and evaluates both the feasibility and effectiveness of this proposed reform, while also addressing the practical and legal challenges associated with piercing the corporate veil. The findings indicate that extending the disqualification list to relevant individuals can meaningfully strengthen the deterrent function of the existing supervisory mechanism

Climate change is a global issue, driven largely by greenhouse gas emission, with carbon dioxide (CO2) being the biggest contributor. Iran, one of the top 10 emitters globally, faces unique challenges in reducing its CO2 emission due to economic sanctions, data gaps, and limited experience with climate policy. To help guide decision-makers, this study uses the Simplified DICE model—DICE model is a widely accepted tool for analyzing climate and economic interactions—to evaluate different strategies for emission reduction. These strategies mainly include carbon pricing, investment in renewables as mitigation of emission growth, and improving energy efficiency. The results show that such policies can positively impact GDP, temperature stabilization, and emission levels. This research provides the first numerical framework tailored to Iran’s specific context, offering practical insights for policymakers. Key policy implications include the necessity to overcome sanctions in aspects of Investment and technology absorption, improved data collection to refine future analyses, and adopting cost-effective measures that balance economic growth with environmental sustainability.

In this paper, the competitive market of crude oil exports of Iran and the world is modeled using game theory. In the present research, we have focused on OPEC's internal competition within the game theory framework to analyze the economic conflicts between OPEC members in detail. In the conditions where each of the producers has their own goals and limitations, the amount of access to the desired goals for each producer depends not only on the decisions they make, but also is affected by the prevailing atmosphere in the oil market, including the strategy of other competitors, sanctions, and different situations of economic growth and oil sale price level. In recent years, Western countries, especially the United States, have imposed extensive sanctions against Iran, mainly targeting. In this regard, three players of Iran, Saudi Arabia, and the United States of America (USA) were defined and a model for the game between them was presented. Then the presented model was solved as a cooperative game and the equilibrium point was obtained for it. The results obtained showed that the three main players have the conditions for cooperation by choosing the options of "reducing pressure and fewer sanctions", "reducing pressure and cooperation", and "cooperation", and Iran can only improve its conditions by competing with Saudi Arabia in OPEC.

Bifurcation constitutes a procedural mechanism in arbitration whereby complex disputes are segmented into separate and distinct issues to be addressed individually over the course of the proceedings. This stratification enables a more organized and methodical approach, commonly by isolating jurisdictional questions from the substantive merits of the case. Through the division of the dispute into discrete phases, bifurcation enhances procedural efficiency, allowing the parties to focus their efforts on pivotal matters while avoiding unnecessary expenditure of time and resources on ancillary issues. As a result, this mechanism often contributes to notable reductions in both the duration and overall cost of the arbitral process. In determining whether to bifurcate proceedings, arbitral tribunals assess key criteria: the preliminary objection must demonstrate substantive merit, be capable of adjudication independently of the merits, and possess the potential to resolve the dispute conclusively. The tribunal’s decision to implement this procedural tool ultimately depends on the specific facts and prevailing circumstances of the case.

The recent surge of artificial-intelligence (AI) activity has sparked concern that large-scale model training, cloud inference, and data-centre expansion could accelerate national energy demand. We marshal a 21-year annual panel for the United States (2004–2024) that couples multiple AI proxies—technology-stock valuations and a ChatGPT-era dummy—with four aggregate energy series (fossil fuels, nuclear, renewables, total primary energy). A five-stage empirical protocol implemented in Python combines Engle–Granger cointegration testing, higher-order ADF stationarity checks, linear and nonlinear dependence diagnostics (Pearson, Dynamic Time Warping, mutual information), multicollinearity screening (variance-inflation factors), and out-of-sample forecasting with linear regression, decision trees, random forests, and support-vector machines augmented by SHAP explainability. Across all tests we find no evidence that AI developments imprint on national energy use: AI variables cointegrate only with one another, their short-run correlations with energy vanish once trends are removed, their mutual-information scores remain near zero, and their inclusion never improves predictive accuracy beyond a parsimonious macro model driven by GDP, inflation, and population. SHAP rankings confirm that AI features carry negligible explanatory weight relative to conventional fundamentals. We conclude that, to date, AI’s macro-level energy footprint is statistically invisible—any electricity it consumes is either too small or offset by efficiency gains within the wider economy. Policymakers should therefore continue to anchor long-range energy scenarios to established economic drivers while monitoring localised data-centre hotspots that national aggregates obscure.

The United States, recognized as the leading global producer and consumer of oil, significantly influences the dynamics of the international oil market. Therefore, changes in governments and their policies in the energy sector have a significant impact on the oil market, especially for oil-exporting countries like Iran. Hence, it is imperative to conduct an analytical assessment of the implications arising from the transition of political power in the United States on the global oil market dynamics. This study examines the implications of Trump's energy policies on the global petroleum market, focusing on Iran. The research employs a qualitative, descriptive-analytical method using desk research to explore how these policies impact Iran's oil exports, market share, and geopolitical standing. Under the Trump administration, the U.S. pursued an "energy dominance" agenda characterized by increased fossil fuel production, deregulation, and a confrontational stance towards OPEC. This neo- mercantilist approach aimed to bolster U.S. energy security and economic leverage. Trump’s Key policies include expanding fossil fuel production, lifting ecological restrictions, and promoting unilateralism in the oil and gas trade. This will likely lead to a decrease in oil prices in global markets, which will, in turn, result in reduced oil revenues for Iran and put pressure on its share. Additionally, Trump's policy of regional confrontation with Iran could also impact these outcomes.

Iran’s latest risk service contract, known as the Iran Petroleum Contract (IPC), has been introduced to the international upstream market with the aim of attracting foreign investment in the country’s oil and gas sector. Several terms and conditions distinguish the IPC from the previous Iranian upstream oil and gas contract, the buyback contract. The objective of this investigation is to examine the legal implications of signing contracts in Iran, as well as the contractual terms and conditions of a signed contract in the Cheshmeh Khosh field. This study offers valuable insights into the potential hazards of the IPCs from the perspective of the National Iranian Oil Company (NIOC) by providing realistic information about the IPC and utilizing qualitative methods such as library and documentary research. Moreover, it is designed to assist the NIOC in effectively responding to and monitoring these risks, thereby promoting investment in the development of Iran’s oil and gas initiatives and protecting the national interest of Iran.

Financing upstream oil and gas projects in Iran—particularly for non-governmental companies—faces numerous obstacles, including the lack of effective guarantee mechanisms, absence of credible collaterals, limited access to sovereign guarantees, and the skepticism of financial institutions. This paper, adopting a descriptive-analytical approach and grounded in the study of existing legal and institutional frameworks, explores the legal capacities for designing and utilizing alternative instruments to traditional guarantees in the financing process of high-risk projects. Within this framework, and based on the “Production and Infrastructure Financing Act”, the executive bylaw on the establishment of non-governmental guarantee funds, and other upstream legal documents, the establishment of an institution titled the "Oil Guarantee Fund" is proposed as an innovative solution. This fund is designed to mitigate default risk, facilitate project credit assessment, and replace bank or property-based guarantees for companies active in the oil and gas value chain. Its legal structure is envisioned based on the model of non-governmental institutions and private joint-stock companies. The proposed model has been formulated through a legal analysis of the relevant documents and informed by the outcomes of expert panels comprising specialists in energy law, finance, and public policy. Findings of the study indicate that the establishment of such a fund, capable of issuing valid guarantees based on contractual commitments, provides a practical tool to strengthen institutional trust and facilitate investment in upstream oil and gas projects. The article also presents a conceptual and institutional framework for the fund and offers recommendations for its policy-level and legal implementation.

Iran's gas industry plays a strategic role in supplying and distributing natural gas energy to society. The project-based, operational, and service-oriented nature of this industry, combined with its large workforce, underscores the necessity to address the risks confronting its human resource management system. This study was conducted to identify and analyse these human resource management risks. This study employed a mixed-methods approach. In the qualitative phase, the researcher's lived experience was utilized to identify and categorize 35 human resource management risk factors in Iran's gas industry. For the quantitative phase, a risk management framework was applied to evaluate each identified factor based on two key dimensions: risk probability and risk impact. The assessment results were subsequently visualized through a heat map. The study results demonstrated that none of the indicators fell within the low-risk zone, with 15 indicators classified as medium-risk and 20 as high-risk, while two critical indicators - imposition of salary and benefit restrictions with a risk rating of 9 and dependence on political affiliations for filling key positions with a risk rating of 8 - were identified as the most substantial challenges facing the human resource management system in Iran's gas industry.