AI is powering energy transition but not without costs

Large language models (LLMs) are rapidly moving beyond chat interfaces into the core of global energy systems, where they are being deployed to improve forecasting, optimize infrastructure, and support policy decisions. A new study finds that these AI systems are beginning to reshape how energy is produced, managed, and governed, but warns that their growing influence also introduces new environmental, ethical, and governance challenges.

The study, titled “Large Language Models in Sustainable Energy Systems: A Systematic Review on Modeling, Optimization, Governance, and Alignment to Sustainable Development Goals,” published in Energies, sheds light on how LLMs are being integrated across energy systems. It is based on a structured review methodology combining thematic analysis, case-based insights, and a conceptual framework to assess both opportunities and risks associated with these technologies.

LLMs are no longer peripheral tools but emerging as decision-support systems embedded in energy planning, operations, and governance. Their growing role reflects a broader shift toward AI-driven sustainability strategies, where data-intensive systems are expected to accelerate the global transition toward cleaner and more efficient energy systems.

AI expands its role across energy modeling and infrastructure optimization

The study shows that LLMs are increasingly being used to enhance modeling and optimization processes within energy systems. Their ability to process vast amounts of structured and unstructured data allows them to support complex tasks such as energy demand forecasting, grid management, and infrastructure planning.

Predictive modeling is one of the most notable applications. LLMs can integrate data from multiple sources, including weather patterns, consumption trends, and infrastructure performance, to generate more accurate forecasts of energy demand and supply. This capability is particularly important for renewable energy systems, where variability and uncertainty present ongoing challenges.

In addition to forecasting, LLMs are being used to optimize system performance. The study highlights their role in improving energy efficiency through intelligent scheduling, load balancing, and real-time decision-making. By analyzing patterns across large datasets, these systems can identify inefficiencies and recommend adjustments that reduce energy waste and operational costs.

The research also points to the use of LLMs in building energy management. AI-driven models can simulate energy consumption patterns in buildings and suggest improvements in design, operation, and maintenance. This has implications for reducing emissions in urban environments, where buildings account for a significant share of energy use.

Emerging applications extend into advanced energy technologies such as hydrogen systems and energy storage. LLMs are being used to analyze complex chemical and engineering data, supporting innovation in these areas and accelerating the development of next-generation energy solutions.

The integration of LLMs into infrastructure systems marks a shift toward more adaptive and intelligent energy networks. These systems are capable of responding dynamically to changing conditions, improving resilience and reliability in the face of increasing demand and environmental pressures.

Policy, governance, and decision-making are being reconfigured

The study also highlights the growing role of LLMs in governance and policy processes. These systems are being used to interpret regulatory frameworks, analyze policy documents, and support decision-making at institutional and governmental levels.

One key application is regulatory analysis. LLMs can process complex legal and policy texts, extracting relevant information and identifying implications for energy systems. This capability allows policymakers and organizations to navigate regulatory environments more efficiently and make informed decisions.

The study also identifies the use of LLMs in scenario planning and strategic forecasting. By modeling different policy and market scenarios, these systems can help decision-makers evaluate potential outcomes and design more effective interventions.

This shift reflects a broader transformation in governance, where AI systems are increasingly involved in shaping policy processes. Rather than relying solely on human expertise, institutions are beginning to integrate computational systems into decision-making workflows.

However, the research warns that this transformation raises important questions about transparency and accountability. LLMs operate as complex and often opaque systems, making it difficult to understand how decisions are generated. This lack of transparency can undermine trust and complicate efforts to ensure responsible governance.

The concentration of AI capabilities within specific organizations and regions further amplifies these concerns. Access to advanced LLM technologies is uneven, potentially creating disparities in how different countries and institutions can leverage AI for energy development.

Governance frameworks must evolve alongside technological adoption. This includes establishing clear guidelines for AI use, ensuring transparency in decision-making processes, and addressing issues related to data privacy and security.

Sustainability gains come with environmental and ethical trade-offs

While LLMs offer significant potential for advancing sustainable energy systems, the study highlights a critical paradox. The same technologies that enable efficiency and optimization also require substantial computational resources, raising concerns about their environmental impact.

Training and deploying large language models consumes significant amounts of energy, contributing to carbon emissions and resource use. This creates a tension between the goals of sustainability and the realities of AI infrastructure.

The study identifies lifecycle emissions as a key area of concern. From data center operations to hardware production, the environmental footprint of AI systems must be considered alongside their benefits. Without careful management, the expansion of AI could offset some of the gains achieved through energy optimization.

In addition to environmental impacts, the research highlights ethical challenges related to AI deployment. Issues such as bias, fairness, and inclusivity are particularly relevant in the context of energy systems, where decisions can have significant social and economic consequences.

For example, AI-driven optimization processes may prioritize efficiency over equity, potentially disadvantaging certain communities or regions. Ensuring that AI systems support inclusive and equitable outcomes requires deliberate design and oversight.

The study also points to challenges related to transparency and explainability. As LLMs become more integrated into critical infrastructure, understanding how they operate becomes increasingly important. Stakeholders must be able to assess the reliability and fairness of AI-driven decisions.

These challenges underscore the need for responsible AI frameworks that address both technical and social dimensions. The study calls for interdisciplinary collaboration among engineers, policymakers, and social scientists to develop solutions that balance innovation with accountability.

Alignment with sustainable development goals remains uneven

The research identifies several areas where AI can contribute to sustainability, particularly in relation to affordable and clean energy, industry innovation, responsible consumption, and climate action. LLMs can support the transition to renewable energy by improving forecasting and integration of variable energy sources. They can also enhance industrial efficiency and promote sustainable practices through data-driven optimization.

However, the study finds that alignment with sustainability goals is not automatic. The impact of LLMs depends on how they are implemented and governed. Without clear strategies, AI adoption could lead to unintended consequences that undermine sustainability objectives. For instance, the energy consumption associated with AI systems could conflict with climate goals if not managed effectively. Similarly, unequal access to AI technologies could exacerbate global disparities in energy development.

The research calls for integrated approaches that consider the full lifecycle of AI systems and their broader social and environmental impacts. This includes designing technologies that are energy-efficient, promoting equitable access, and ensuring that AI applications align with long-term sustainability objectives.