Petroleum Business Review

Abstract Iran faces persistent challenges in balancing energy demand and sustainability, driven by limited social participation and the absence of integrated policy frameworks. This study introduces an innovative hybrid decision-making approach that combines PESTEL, SWOT, and Analytic Hierarchy Process (AHP) models to identify, evaluate, and prioritize strategies for enhancing public participation in sustainable energy management. The research adopts a mixed-method design, integrating qualitative expert interviews and content analysis in MAXQDA with quantitative weighting and ranking through Expert Choice AHP. The proposed framework captures both macro-environmental influences and internal institutional capacities, linking social-behavioral insights with data-driven prioritization. Results indicate that developing targeted educational programs and creating interactive digital platforms are the highest-priority strategies, with normalized weights of 0.35 and 0.30, respectively, followed by local collaboration networks and incentive-based policies. The findings reveal that applying a combined social and analytical modeling approach can increase public participation potential by over 60 % and contribute to a 25 % reduction in energy consumption in the medium term. The study offers a novel quantitative–qualitative framework adaptable for developing countries seeking to operationalize community engagement within national energy transition policies.

Abstract 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.

Abstract This study investigates the impact of global crude oil price fluctuations on the volatility of the Iranian Rial–U.S. Dollar exchange rate over the period November 2011 to August 2025. Using daily data and employing GARCH-family models—including GARCH(1,1), EGARCH(1,1), and GJR-GARCH(1,1,1) under heavy-tailed distributions—we examine whether oil price shocks influence the mean and conditional variance of exchange rate returns. The results indicate that higher oil prices significantly appreciate the Rial, reflecting Iran’s dependence on oil revenues and foreign exchange inflows. Volatility dynamics reveal strong persistence, with shocks exhibiting long memory. Asymmetric effects are also evident: negative oil price shocks increase exchange rate volatility more than positive shocks, highlighting the destabilizing role of downturns in global oil markets. Diagnostic tests confirm the adequacy of the estimated models, with EGARCH and GJR specifications providing the best fit. The findings underscore three key policy implications. First, Iran’s exchange rate remains highly sensitive to oil revenues, reinforcing the need for structural diversification. Second, the persistence of volatility complicates short-term stabilization, demanding long-term reserve and fiscal policies. Third, the asymmetric impact of oil downturns calls for proactive risk management to mitigate volatility in times of declining oil prices. Overall, the study provides new evidence on the oil–exchange rate nexus in an oil-exporting economy, offering lessons for macroeconomic management under external shocks. Robustness checks — including Bai–Perron breakpoint tests and alternative model specifications with event dummies — confirm the main findings.

Abstract 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.

Abstract 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.

Abstract 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.