International Journal of Metaverse & Virtual Transformation

Abstract In recent years, Iran’s gas and electricity imbalance has transcended simple supply-demand technicalities, emerging as a critical strategic challenge threatening national energy security, infrastructure sustainability, and broader economic development. Given the country’s profound reliance on natural gas for power generation—and the increasing integration of smart grids and digital energy management—analyzing future imbalances is vital. This research utilizes the LEAP model to conduct a scenario-based analysis of Iran’s energy deficit through the 2050 horizon, specifically evaluating its impact on the resilience of smart energy infrastructure. The study examines two distinct scenarios: the continuation of current trends and a comprehensive demand optimization strategy, both framed within a stress test regarding supply stabilization post-2025. Findings reveal that under the “status quo” scenario, the widening gap between supply and demand will severely erode energy export capacity, diminish critical foreign exchange earnings, and heighten the risk of failures within smart energy networks. Conversely, while the demand optimization scenario successfully reduces energy intensity and mitigates part of the deficit, it is not a panacea. Ultimately, the results demonstrate that demand management alone cannot resolve the imbalance. Instead, it must be coupled with expanded generation, transmission, and storage capacities. This study concludes that a synergistic approach—combining aggressive infrastructure investment, persistent improvements in energy efficiency, and intelligent demand-side management—is essential. By implementing such a comprehensive strategy, Iran can effectively reduce its chronic energy imbalance while significantly enhancing the overall resilience and long-term stability of its smart energy infrastructure by 2050.

Abstract This study aimed to model the role of business risks, capital structure, liquidity, and credit risk in ESG sustainability, emphasizing smart control solutions. Given the exploratory nature of the topic, a qualitative approach and thematic analysis were employed. The research population consisted of experts in risk management, finance, corporate governance, and smart technologies, selected through purposive sampling until theoretical saturation was achieved. Ultimately, 16 experts participated in semi-structured interviews. Following full implementation, data were coded and analyzed using Braun and Clarke’s six-stage thematic analysis framework. The findings revealed that business risk impacts an organization’s ability to achieve environmental, social, and governance goals through market fluctuations, changing customer behavior, intense competition, and environmental uncertainty. Capital structure risk undermines sustainable decision-making and responsible investment via high leverage, capital costs, short-term debt dependence, and funding volatility. Liquidity risk, particularly regarding cash flow shortages, delayed receivables, and income-cost mismatches, threatens ESG policy implementation, while credit risk—characterized by defaults, poor creditworthiness, and delinquent receivables—affects stakeholder relationships and ESG reputation. Thematic analysis demonstrated that these risks possess systemic, synergistic links; management weaknesses in one area inevitably impact others. Conversely, intelligent control solutions based on artificial intelligence, machine learning, real-time data analysis, management dashboards, predictive algorithms, and early warning systems offer significant potential for improving risk monitoring and forecasting. Accordingly, the proposed conceptual model emphasizes the necessity of integrating financial risk management and ESG within a robust, data-driven, and intelligent governance framework to ensure long-term organizational sustainability.

Abstract The COVID-19 pandemic has highlighted the necessity of redesigning public health policies and integrating smart technologies to enhance health systems’ capacity for infectious disease surveillance, prevention, and control. This study aims to assess the status of public health policies within the Ministry of Health, Treatment, and Medical Education regarding the application of smart technologies for COVID-19 management and future health crises. The research is applied in purpose and employs a descriptive-survey methodology. The statistical population included experts in health policy, digital health, health information technology, artificial intelligence in healthcare, and health system management, selected through purposive sampling. Data were collected using a researcher-made questionnaire, and validity and reliability were confirmed through expert assessment, Cronbach’s alpha, composite reliability, and average variance extracted indices. Data analysis was conducted using partial least squares structural equation modeling (PLS-SEM). The findings validated a multidimensional framework comprising four dimensions: facilitating participation in disease control, managerial and organizational policy orientation, individual-oriented public health policies, and health technology policies. The results indicate that effective smart health governance requires the integration of technological, organizational, and social capabilities rather than merely developing digital infrastructure. Within this framework, artificial intelligence, digital twins, and immersive health environments are identified as enabling technologies that support predictive analytics, real-time decision-making, simulation-based policy development, and stakeholder collaboration. Therefore, strengthening digital health governance, ensuring secure data management, improving digital health literacy, and promoting participatory approaches are essential for developing resilient and adaptive health systems capable of managing future global health challenges.

Abstract The digital economy has become one of the most important drivers of economic growth, innovation, and technological transformation in recent decades, yet its environmental impacts continue to be the subject of much attention and debate in the economic literature. This study investigates the impact of the digital economy on environmental quality and analyzes the moderating role of institutional quality in selected Middle East and North Africa countries from 2005 to 2023. In this regard, the Panel Smooth Transition Regression (PSTR) model was used to identify nonlinear relationships and threshold effects. The study's dependent variable is the environmental quality index, and the model includes the digital economy development index, institutional quality, gross domestic product, government effectiveness, corruption control, and regulatory quality as explanatory variables. The research findings show that institutional quality plays a decisive role in the direction and intensity of the impact of the digital economy on environmental quality; in such a way that improving the quality of institutions moderates the impact of the digital economy on environmental quality and can affect the path of its impact in different directions (improving or weakening environmental quality). Accordingly, strengthening institutional reforms and improving the quality of governance should be the focus of policymakers' attention simultaneously with the development of the digital economy in the countries under study