TRG发布美国24州AI数据中心就绪度排名弗吉尼亚州居首

Aerial view of data centers in Northern Virginia

Aerial view of a dense cluster of data centers, including facilities under construction, in Loudoun County’s Data Center Alley in Northern Virginia.Getty Images航拍视角下，弗吉尼亚州北部劳登县Data Center Alley内一片密集的数据中心集群，其中包括在建设施。Getty Images

Power supply and water availability could help states outside established data center hubs compete for AI infrastructure investment, according to a new survey of US states from TRG DataCenters. The report, published in August 2026, compares 24 states using measures of infrastructure scale and environmental exposure and points to a more geographically diverse market as AI demand expands.TRG DataCenters针对美国各州的一项最新调查显示，电力供应和水资源可用性可能帮助成熟数据中心枢纽以外的州争夺AI基础设施投资。该报告于2026年8月发布，以基础设施规模和环境暴露度等指标对24个州进行比较，并指出随着AI需求扩大，市场的地域分布将更加多元。

Virginia topped the August ranking of states positioned for AI data center growth, but the findings also suggest smaller markets can compete by offering a favorable combination of electricity supply, lower-carbon generation, and water availability.弗吉尼亚州在8月的AI数据中心增长有利州排名中位居榜首，但研究结果也表明，较小市场可以凭借电力供应、低碳发电和水资源可用性的有利组合参与竞争。

“When planning infrastructure for the long term, that balance between scale and resource efficiency matters as much as raw megawatts,” Chris Hinkle, CEO of TRG DataCenters, told Data Center Knowledge.“长期规划基础设施时，规模与资源效率之间的平衡与单纯的兆瓦容量同样重要，”TRG DataCenters首席执行官Chris Hinkle告诉Data Center Knowledge。

TRG’s analysis considered mapped large-scale AI data center sites, power capacity, electricity use, estimated annual carbon emissions, and the proportion of mapped facilities in areas of high water stress. Capacity figures reflect the AI infrastructure included in TRG’s dataset and do not represent each state’s total data center market.TRG的分析考量了已标注的大型AI数据中心场址、电力容量、电力用量、年度预估碳排放量，以及已标注设施中位于高水资源压力地区的比例。容量数字反映TRG数据集中包含的AI基础设施，并不代表每个州的数据中心市场总量。

Virginia’s Lead Comes with Water Constraints弗吉尼亚州领先地位背后的水资源约束

Virginia received a readiness score of 100, followed by Ohio at 88.2, Tennessee at 83.7, Georgia at 81.8, and Texas at 80.8. TRG’s dataset mapped seven large AI sites in Virginia totaling 1,085 MW of capacity, while Ohio and Tennessee showed higher mapped capacities – 1,410 MW in Ohio and 1,373 MW in Tennessee. Virginia’s advantage reflects performance across several measures, including lower estimated grid carbon intensity serving the mapped facilities.弗吉尼亚州的就绪度得分为100，俄亥俄州、田纳西州、佐治亚州和得克萨斯州分别为88.2、83.7、81.8和80.8。TRG的数据集在弗吉尼亚州标注了7个大型AI场址，总容量为1,085 MW；而俄亥俄州和田纳西州的已标注容量更高——俄亥俄州为1,410 MW，田纳西州为1,373 MW。弗吉尼亚州的优势体现在多项指标上，包括为已标注设施供电的电网预估碳强度更低。

Hinkle said Ohio’s mapped AI capacity was around 30% greater, but the grid serving Virginia’s facilities had roughly one-third lower carbon intensity than Ohio’s. “As a result, the AI facilities in our dataset are associated with around 2.3 million tons of CO2 emissions annually, compared with roughly 4.5 million tons in Ohio and 4.3 million in Tennessee,” he said.Hinkle表示，俄亥俄州的已标注AI容量高出约30%，但为弗吉尼亚州设施供电的电网碳强度比俄亥俄州低约三分之一。他说：“因此，我们数据集中的AI设施相关年度CO2排放量约为230万吨，而俄亥俄州约为450万吨，田纳西州约为430万吨。”

Water – or lack thereof – is a complicating factor. About one-third of Virginia’s mapped data centers are located in areas of high water stress, compared with none in Ohio and just 7% in Tennessee. “One factor Virginia will need to continue managing as the market expands is site selection around water availability,” Hinkle said. Those differences make the ranking a starting point for evaluating markets rather than evidence that development conditions are uniform within a state.水资源——或缺水问题——是一个复杂因素。弗吉尼亚州约三分之一的已标注数据中心位于高水资源压力地区，而俄亥俄州没有此类设施，田纳西州仅为7%。Hinkle说：“随着市场扩张，弗吉尼亚州需要继续应对的一个因素是围绕水资源可用性开展场址选择。”这些差异使该排名成为评估各市场的起点，而不能证明一个州内部的开发条件完全一致。

Smaller Markets Offer Competing Advantages较小市场具备参与竞争的优势

South Carolina placed sixth with a score of 80.5, just behind Texas, despite having only one large AI site and 142 MW of capacity in the study. “What surprised us most was South Carolina,” Hinkle said. “It has just one large AI data center site and around 142 MW of capacity, yet it finished almost level with Texas in our overall readiness ranking, despite Texas having 13 large sites.” He attributed that performance partly to the state’s nuclear-heavy electricity supply and the absence of mapped facilities in areas of high water stress.南卡罗来纳州以80.5分位列第六，仅落后于得克萨斯州，尽管该研究中它只有一个大型AI站点和142 MW容量。“最让我们惊讶的是南卡罗来纳州，”欣克尔说。“它只有一个大型AI数据中心站点和约142 MW容量，却在我们的综合就绪度排名中与得克萨斯州几乎持平，而得克萨斯州有13个大型站点。”他将这一表现部分归因于该州以核电为主的电力供应，以及高水资源压力地区没有已绘测设施。

Hinkle also pointed to New York, Iowa, and Mississippi. New York combined relatively limited large-scale AI capacity with one of the lowest-carbon grids in the dataset, while Mississippi already supported more than 500 MW of AI capacity. “States that can combine reliable power with a relatively low-carbon electricity mix and favorable water conditions could become increasingly attractive as demand expands,” he said.欣克尔还提到了纽约州、艾奥瓦州和密西西比州。纽约州的大规模AI容量相对有限，却拥有数据集中碳强度最低的电网之一，而密西西比州已支撑超过500 MW的AI容量。“能够将可靠电力与相对低碳的电力结构及有利的水资源条件结合起来的州，随着需求扩张，可能会变得越来越有吸引力，”他说。

Texas Faces a Siting Challenge得克萨斯州面临选址挑战

Texas had the largest number of AI sites in the study – 13, with a combined capacity of 1,301 MW. However, 58.8% of those facilities were located in areas of high water stress. Hinkle said he expects those conditions to influence cooling choices and project locations. “That is likely to encourage more sophisticated siting and infrastructure strategies rather than simply slowing development,” he said. “We could see greater use of approaches such as closed-loop or air-based cooling, recycled water, and locations where local water conditions are more favorable.”在该研究中，得克萨斯州的AI站点数量最多——13个，总容量为1,301 MW。然而，这些设施中有58.8%位于高水资源压力地区。欣克尔表示，他预计这些条件将影响制冷选择和项目选址。“这可能会推动更精细的选址和基础设施策略，而不是简单地拖慢开发，”他说。“我们可能会看到更多采用闭环冷却或风冷、再生水等方法，并选址于当地水资源条件更有利的位置。”

TRG notes that the water-stress measure describes facility locations and does not establish actual water consumption or the impact of individual cooling systems. Developers still need to assess those conditions at the project level.TRG指出，水资源压力指标描述的是设施所在位置，并不能确定实际用水量或单个制冷系统的影响。开发商仍需在项目层面评估这些条件。

From Readiness to Deliverable Infrastructure从就绪度到可交付基础设施

Statewide advantages must also translate into infrastructure that developers can use. A relatively small share of statewide electricity use does not, by itself, establish that a particular site has power available for expansion.全州范围的优势还必须转化为开发商可利用的基础设施。全州用电总量中相对较小的占比本身，并不能证明某个特定场地具备可供扩张的电力。

“Grid connections, transmission capacity, fiber connectivity, build-ready sites, and long-term power availability can all influence where major projects ultimately land,” Hinkle said. For emerging markets, those requirements determine whether attractive resource conditions can support projects at scale. For established hubs, the requirements shape where additional development can proceed.“电网接入、输电容量、光纤连接、建设就绪场地以及长期电力供应能力，都会影响重大项目最终落户于何处，”欣克尔说。对于新兴市场，这些要求决定着具吸引力的资源条件能否支撑规模化项目。对于成熟枢纽，这些要求决定着可在何处推进进一步开发。

“The result could be a more geographically diverse AI infrastructure market as developers match projects with the locations best equipped to support them.”“其结果可能是一个地理分布更多元化的AI基础设施市场，因为开发商会将项目匹配到最有条件支撑它们的地点。”

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Nathan Eddy covers data center trends and technologies across multiple industries. A graduate of Northwestern University’s Medill School of Journalism, he is also a documentary filmmaker specializing in architecture and urban planning. He currently lives in Berlin, Germany.内森·埃迪报道多个行业的数据中心趋势与技术。他毕业于西北大学麦迪尔新闻学院，同时也是专门从事建筑与城市规划题材的纪录片制片人。他目前居住在德国柏林。