Indus Journal of Critical Inquiry (IJCI)

Analyze the Impact of Energy Consumption and Carbon Footprint on Large Scale AI Models and Data Centers

Faisal Aziz — 2026-05-08

The rapid expansion of artificial intelligence technologies and large scale data processing has significantly increased global computational demand. Large scale artificial intelligence models such as deep learning systems, generative artificial intelligence models, and natural language processing architectures require massive computational resources for training and deployment. These processes rely heavily on high performance computing infrastructure located in large data centers that consume substantial amounts of electrical energy. As the scale and complexity of artificial intelligence systems continue to grow, concerns regarding their environmental sustainability have also increased. Energy consumption associated with artificial intelligence training and inference processes contributes directly to carbon emissions when electricity is generated from fossil fuel sources. Consequently, the carbon footprint of artificial intelligence systems and data centers has become an important issue in sustainability research, environmental policy, and digital infrastructure management. This study analyzes the impact of energy consumption and carbon footprint on the sustainability of large-scale artificial intelligence models and data center operations. The research develops a conceptual framework that examines the relationships between artificial intelligence computational intensity, data center energy consumption, carbon footprint, and sustainable artificial intelligence infrastructure practices. Data were collected from information technology engineers, cloud infrastructure managers, and artificial intelligence researchers involved in high performance computing environments. Structural Equation Modeling using Smart Partial Least Squares was employed to analyze the relationships among constructs. The results demonstrate that increasing computational intensity of artificial intelligence models significantly contributes to higher energy consumption within data centers. Furthermore, energy consumption strongly influences carbon footprint levels associated with artificial intelligence infrastructure. The findings also indicate that the adoption of sustainable computing practices such as energy efficient hardware, renewable energy integration, and optimized algorithms can significantly reduce environmental impacts. This study contributes to the growing field of sustainable computing by providing empirical insights into the environmental implications of large-scale artificial intelligence systems. The results highlight the importance of integrating sustainability principles into artificial intelligence development and data center management strategies in order to mitigate environmental impacts while maintaining technological innovation.

Developing AI Based Cultural Heritage Preservation Models Using Digital Twin Technologies

Surayya Jamroz — 2026-05-08

Cultural heritage represents the collective memory, identity, and historical continuity of societies. However, cultural heritage assets including monuments, historical buildings, archaeological sites, and artifacts are increasingly threatened by environmental degradation, climate change, natural disasters, urbanization, and human conflict. Traditional heritage preservation methods often rely on manual documentation and reactive conservation practices which limit their ability to monitor and protect heritage sites in real time. Recent technological advancements in artificial intelligence and digital twin technologies present a transformative opportunity to enhance cultural heritage preservation through intelligent monitoring, predictive maintenance, and virtual replication of physical heritage environments. This study develops an artificial intelligence based cultural heritage preservation model using digital twin technologies to support proactive heritage conservation strategies. The proposed research integrates artificial intelligence algorithms with digital twin frameworks to create dynamic virtual representations of heritage assets that continuously synchronize with real world data through sensors, imaging technologies, and geographic information systems. The conceptual model evaluates the relationship between digital twin adoption, artificial intelligence analytics capability, real time monitoring, predictive conservation capability, and sustainable heritage preservation outcomes. The study applies Smart PLS based structural equation modeling to analyze the relationships among these constructs using survey data collected from heritage conservation experts, digital archivists, and technology specialists. Results demonstrate that digital twin infrastructure and artificial intelligence analytics significantly influence real time monitoring and predictive preservation capabilities which subsequently enhance long term sustainability of cultural heritage management. The findings highlight the strategic importance of integrating intelligent technologies in heritage conservation policies and digital heritage ecosystems. The research contributes to interdisciplinary scholarship by bridging heritage science, digital humanities, and artificial intelligence driven smart infrastructure management. The proposed framework offers practical guidance for governments, cultural institutions, and conservation organizations seeking to implement intelligent digital heritage preservation systems.

Analyzing the Effect of Ethical and Security Risks of Generative Artificial Intelligence on Critical Infrastructure Systems

Muhammad Janbaz Adil — 2026-05-08

Generative artificial intelligence has emerged as one of the most transformative technological innovations of the digital era. Systems based on large language models, generative adversarial networks, and multimodal artificial intelligence are capable of producing realistic text, images, code, and automated decision support. While these technologies provide significant benefits for automation, productivity, and innovation, they also introduce new ethical and security risks that may threaten critical infrastructure systems. Critical infrastructure sectors such as energy grids, transportation networks, healthcare systems, financial services, and communication platforms rely heavily on digital technologies and secure information systems for stable operation. The misuse or manipulation of generative artificial intelligence within these infrastructures could lead to misinformation attacks, automated cyber threats, privacy violations, and operational disruptions. This study analyzes the impact of ethical and security risks associated with generative artificial intelligence on the resilience of critical infrastructure systems. The research develops a conceptual model that examines how generative artificial intelligence capability, ethical risk perception, and cybersecurity vulnerability influence organizational governance mechanisms and infrastructure resilience. Data were collected from cybersecurity experts, artificial intelligence engineers, infrastructure managers, and policy analysts working in sectors such as energy, finance, and telecommunications. Structural Equation Modeling using Smart Partial Least Squares was employed to test the relationships between constructs. The results reveal that increased generative artificial intelligence capability significantly intensifies ethical risks and cybersecurity vulnerabilities within critical infrastructure systems. The findings further demonstrate that strong governance frameworks and responsible artificial intelligence management strategies play an essential role in mitigating these risks and strengthening infrastructure resilience. The study contributes to emerging research on artificial intelligence governance and digital infrastructure security by providing empirical evidence regarding the complex relationship between generative artificial intelligence technologies and critical infrastructure protection. The results highlight the importance of ethical regulation, robust cybersecurity frameworks, and responsible artificial intelligence deployment strategies to safeguard critical infrastructure in the era of advanced generative technologies.

Evaluating ESG Strategies for Corporate Sustainability in the Context of Climate Risk and Regulatory Pressure

Muhammad Nawaz Khan — 2026-05-08

Environmental social and governance strategies have become an essential component of corporate sustainability and responsible business practices in the modern global economy. Increasing climate risks and expanding regulatory requirements have forced organizations to adopt sustainability frameworks that address environmental impact, social responsibility, and governance accountability. Investors, policymakers, and stakeholders increasingly evaluate corporate performance not only through financial metrics but also through environmental sustainability and ethical governance indicators. As climate change intensifies and governments introduce stricter environmental regulations, organizations face significant pressure to integrate environmental social and governance strategies into their operational and strategic decision-making processes. This study evaluates the effectiveness of environmental social and governance strategies in enhancing corporate sustainability within the context of climate risk exposure and regulatory pressure. The research proposes a conceptual model that examines the relationships between climate risk awareness, regulatory pressure, environmental social and governance strategy implementation, and corporate sustainability performance. The study applies quantitative research methods and uses Structural Equation Modeling with Smart Partial Least Squares to analyze data collected from corporate managers, sustainability officers, and environmental compliance specialists working in various industries. The results demonstrate that climate risk awareness and regulatory pressure significantly influence the adoption of environmental social and governance strategies by organizations. Furthermore, the findings reveal that effective implementation of environmental social and governance practices positively contributes to long term corporate sustainability performance. The research highlights the strategic importance of integrating environmental and governance policies within corporate management frameworks in order to address environmental challenges and regulatory expectations. This study contributes to sustainability management literature by providing empirical evidence regarding the role of environmental social and governance strategies in strengthening corporate resilience and sustainable development. The findings provide valuable insights for policymakers, corporate leaders, and sustainability practitioners seeking to enhance organizational sustainability in an era characterized by increasing climate risks and evolving environmental regulations

Analyzing the Potential of Quantum Computing to Disrupt Global Cybersecurity Systems

Muhammad Rahman — 2026-05-08

Quantum computing is emerging as a transformative technological paradigm capable of solving complex computational problems that remain infeasible for classical computing systems. While the technology offers revolutionary opportunities for scientific discovery, optimization, and artificial intelligence, it also poses significant risks to existing global cybersecurity infrastructures. Contemporary cryptographic systems including RSA, Elliptic Curve Cryptography, and other public key encryption mechanisms rely heavily on the computational limitations of classical computers. Quantum algorithms such as Shor’s algorithm have demonstrated the theoretical ability to break widely used encryption standards by efficiently solving integer factorization and discrete logarithm problems. As quantum hardware continues to progress, cybersecurity experts and governments worldwide are increasingly concerned about the disruptive implications of quantum computing for digital security frameworks. This study analyzes the potential of quantum computing to disrupt global cybersecurity systems by examining the relationships between quantum computing advancement, cryptographic vulnerability, cybersecurity preparedness, and adoption of post quantum cryptographic solutions. The research proposes a conceptual model that explains how technological progress in quantum computing may increase risks to conventional encryption while simultaneously accelerating the development of quantum resistant security mechanisms. Data were collected from cybersecurity professionals, IT infrastructure managers, and academic researchers in the field of cryptography and quantum technologies. Structural Equation Modeling using Smart PLS was employed to examine the relationships between constructs. The findings indicate that advancements in quantum computing significantly increase perceived cryptographic vulnerability within global cybersecurity infrastructures. The results also demonstrate that cybersecurity preparedness and the adoption of post quantum cryptographic frameworks play a critical role in mitigating the disruptive impact of quantum computing technologies. The study contributes to cybersecurity research by providing empirical insights into how emerging quantum technologies may reshape digital security strategies and policy development. The findings highlight the urgent need for organizations and governments to transition toward quantum resistant encryption frameworks in order to ensure the long-term resilience of global cybersecurity systems.