{"id":370264,"date":"2025-11-10T23:38:18","date_gmt":"2025-11-10T23:38:18","guid":{"rendered":"https:\/\/www.europesays.com\/us\/370264\/"},"modified":"2025-11-10T23:38:18","modified_gmt":"2025-11-10T23:38:18","slug":"environmental-impact-and-net-zero-pathways-for-sustainable-artificial-intelligence-servers-in-the-usa","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/370264\/","title":{"rendered":"Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA"},"content":{"rendered":"

The methodology framework of this study aims to achieve two goals: (1) draft the energy\u2013water\u2013climate impacts of AI servers in the United States from 2024 to 2030 to handle the massive concerns about AI developments, and (2) identify the best and worst practices of each influencing factor to scheme the net-zero pathways for realizing water and climate targets set for 2030. Compared with many previous climate pathway studies, which often extend predictions to 2050 for better integrating climate goals, this study focuses on the period from 2024 to 2030 due to the great uncertainties surrounding the future of AI applications and hardware development. For assessing these uncertainties, scenario-based projections are first constructed to obtain potential capacity-increasing patterns of AI servers. Technology dynamics, such as SUO and ALC adoption, are defined with best, base and worst scenarios, and a similar method is employed to capture the impact of grid decarbonization and spatial distribution. The utilized models and data required during the calculation process are illustrated in the following sections. More details on model assumptions and data generation are provided in sections 1\u20134 of Supplementary Information<\/a>.<\/p>\n

Data description and discussion<\/p>\n

This section provides a comprehensive overview of the data used in this study. Historical DGX (Nvidia\u2019s high-performance AI server line) parameters were sourced from official documentation, and future scenarios were projected on the basis of historical configurations and current industry forecasts. To attain the units of AI servers, we collected the most updated industrial report data for projecting the future manufacturing capacity of CoWoS technology, which is the bottleneck for top-tier AI server production. The data resources of the preceding process have been introduced and validated in section 1 of Supplementary Information<\/a>. AI server electricity usage was assessed using recent experimental data on maximum power44<\/a>,45<\/a>, idle power44<\/a>,46<\/a> and utilization rate46<\/a>,47<\/a>,48<\/a>,49<\/a>, derived from existing AI server systems. PUE and WUE values for AI data centres across different locations were calculated using operational data from previous studies30<\/a>,50<\/a> and industrial resources51<\/a>, combined with the collected average climate data for each state52<\/a>. The allocation ratios of AI servers to each state were determined on the basis of configurations of existing and planned AI data centres, which are collected from reports of major AI companies in the United States, as data resources detailed in section 2 of Supplementary Information<\/a>. In addition, projections for grid carbon and water factors were derived from the ReEDS model31<\/a>, using its default scenario data33<\/a>. All datasets employed in this study are publicly available, with most originating from well-established sources. A key uncertainty lies in estimating the number of manufactured AI server units, as official supply-chain reports remain largely opaque. To maintain transparency and ensure reproducibility, we rely on the best available industry reports rather than commercial sources such as International Data Cooperation data53<\/a>, which are not granted for open access and would limit future validation despite their potential to provide better estimates. The validations of applied data are further detailed in sections 1 and 4 of Supplementary Information<\/a>.<\/p>\n

AI server power capacity projections<\/p>\n

The energy consumption of AI servers is projected to be driven predominantly by top-tier models designed for large-scale generative AI computing6<\/a>,7<\/a>. This trend is attributed to their substantial power requirements and the increasing number of units being deployed. In this study, we estimate the power capacity of these high-performance AI servers by examining a critical manufacturing bottleneck: the CoWoS process54<\/a>. This process, which is controlled nearly exclusively by the Taiwan Semiconductor Manufacturing Company, serves as a key determinant of the manufacturing capacity for AI servers in recent years55<\/a>. Our analysis uses forecast data and projection assumptions of the CoWoS process to estimate total production capacity. Other factors are integral to translating this capacity into the power capacity of AI servers: the CoWoS size of AI chips, which determines how many chips can be produced by each wafer; the rated power of future AI servers, which reflects the power demand per unit; and the adoption patterns of AI servers, which dictate the mix of various server types over time. The values of these factors are derived mainly from the DGX systems produced by Nvidia, which is the dominant product for the top-tier AI server markets56<\/a>.<\/p>\n

Considering the influencing factors for the total AI server capacity shipments and existing uncertainties, we generate distinct scenarios as follows:<\/p>\n