The rapid growth of artificial intelligence (AI) is contributing to a significant expansion in data center infrastructure. As organizations increasingly adopt AI-enabled tools and platforms, the digital infrastructure required to support these technologies is scaling alongside broader growth in cloud computing and digital services.
While AI presents opportunities for efficiency and innovation, it also introduces environmental considerations that organizations are beginning to evaluate more closely. Energy demand, water use, increased generation of electronic waste, and infrastructure requirements associated with data centers are becoming more visible, prompting organizations to consider how these impacts align with broader sustainability goals.
For companies integrating AI into their operations, understanding and preparing for these impacts is becoming an important part of long-term sustainability planning.
Environmental Impacts of AI Data Centers
AI workloads require substantial processing power and continuous operation. As a result, data centers supporting AI applications can be energy- and resource-intensive.
Energy consumption is one of the most significant factors. Large-scale data centers require consistent electricity to power servers and maintain operations. The process of training large language models (LLMs) is particularly resource-intensive, requiring thousands of Graphics Processing Units (GPUs), AI Chips (TPUs), and Central Processing Units (CPUs) running in parallel for weeks or even months, and LLMs also require frequent retraining to remain relevant. This can contribute to indirect greenhouse gas emissions depending on the energy mix of the grid and the sourcing strategies of data center providers. While many major providers are investing in renewable energy and pursuing low-carbon operations, the rapid increase in demand associated with AI and digital services continues to place pressure on energy systems.
Water use is another important consideration. Some data centers rely on water-based cooling systems to manage heat generated by high-performance computing, which can increase demand on local water resources. However, water use varies significantly depending on cooling technologies, facility design, and geographic location. Some operators are adopting alternative or more water-efficient approaches.
In addition, the physical infrastructure required to support AI, including servers, storage systems, and facility construction, introduces material and resource considerations across the full lifecycle, from raw material extraction to end-of-life management. According to a study by Nature Computational Science, generative AI could create up to 5 million tons of total e-waste by 2030. Although some components are recyclable, high-performance hardware found in data centers and server farms also contains hazardous materials, including lead, chromium, and mercury.
At the same time, ongoing improvements in hardware and system design are helping to increase efficiency per unit of computation. While these gains can reduce the energy intensity of individual processes, overall demand growth continues to drive total resource consumption upward.
Why This Matters for Firms With ESG Committments
As sustainability expectations continue to evolve, the environmental impacts of digital infrastructure are becoming more relevant to corporate decision-making.
For many organizations, AI adoption may contribute to Scope 2 emissions through electricity use and Scope 3 emissions through reliance on third-party providers such as cloud services. This can affect progress toward emissions reduction targets and broader ESG commitments.
Stakeholders, including investors, customers, and regulators, are also placing greater emphasis on transparency around environmental performance. This includes increased scrutiny of energy use, emissions, and resource consumption across operations and supply chains.
In the United States, there is currently no unified regulatory framework specifically addressing the environmental impacts of AI data centers. However, existing environmental regulations related to air quality, energy use, and water management still apply. At the same time, emerging state-level initiatives and ongoing policy discussions indicate growing attention to data center siting, energy demand, and resource use. Globally, regulatory developments, particularly in the European Union, are also increasing expectations around energy efficiency, emissions reporting, and sustainability disclosures.
Taken together, these trends suggest that organizations may face increasing pressure to understand, measure, and report on the environmental impacts associated with AI and digital infrastructure.
Steps You Can Take Now
While requirements continue to evolve, there are practical steps organizations can take to better manage and prepare for these impacts.
Improving visibility is a critical first step. Organizations should assess where AI workloads are hosted, whether on-premise or through third-party providers, and track associated energy use, emissions, and resource consumption.
Energy sourcing is another key consideration. Engaging with cloud and data center providers on their renewable energy use, decarbonization strategies, and efficiency practices can help organizations better align digital operations with sustainability goals.
Cooling methods and geographic factors may also influence environmental impact. Data center location, local climate conditions, grid carbon intensity, and water availability can all affect the overall footprint of digital infrastructure.
Organizations can also incorporate sustainability criteria into vendor selection and procurement processes. This may include evaluating providers based on transparency, emissions reporting, energy sourcing, and environmental commitments.
Finally, integrating AI-related impacts into existing sustainability and ESG reporting processes can support more consistent tracking, improve data quality, and enable more informed decision-making over time.
Key Takeaways for Sustainability-focused Firms
As AI adoption continues to expand, its environmental implications are becoming an increasingly important consideration for organizations.
Companies that begin assessing these impacts early may be better positioned to respond to evolving stakeholder expectations and align digital growth with sustainability objectives. This includes improving visibility into energy use and emissions, engaging with providers, and incorporating environmental considerations into decision-making processes.
While regulatory frameworks specific to AI data centers are still developing, the overall direction is clear. Greater transparency, accountability, and resource efficiency are likely to play an increasing role in how digital infrastructure is evaluated.
Conclusion
AI is expected to play a central role in future business operations, driving innovation and efficiency across industries. At the same time, the infrastructure supporting these technologies carries environmental implications that organizations should consider as part of broader sustainability efforts.
By taking a proactive approach, focusing on visibility, responsible sourcing, and integration with existing sustainability strategies, organizations can better prepare for evolving expectations while continuing to benefit from AI-driven advancements.
Organizations evaluating the environmental impacts of AI data centers or broader digital infrastructure can benefit from early assessment and planning. Tetra Tech supports companies in understanding and managing environmental data, improving transparency, and preparing for evolving reporting and compliance expectations. Contact our team at [email protected] to learn more about how we can support your efforts.
