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China is directing much of its new large-scale computing capacity toward designated hubs outside its biggest eastern cities because those cities face tighter land and energy constraints, while some western regions have more room and access to renewable generation. The policy is not a blanket move of all AI work: it pairs remote capacity with eastern demand through a planned national network, while keeping latency-sensitive services closer to users when needed.
Why China is steering data centers away from major cities
China’s “East Data, West Computing” initiative is a national infrastructure plan, not simply a search for cheaper rural land. In a 2022 explanation, the National Development and Reform Commission (NDRC) said eastern regions were facing increasingly tight land and energy resources, while western regions had more resources, particularly renewable energy. The strategy is intended to coordinate computing capacity with demand across regions, improve efficiency through larger clusters, and support regional development.
The plan organizes large and extra-large data centers around eight national computing hubs, with clusters within those hubs. Concentrating capacity is meant to make it easier to connect facilities, coordinate power and computing, and use resources more efficiently than if large projects were built wherever land happened to be available.
Energy matters because data centers need dependable electricity around the clock. A National Energy Administration-sponsored report, citing industry estimates, says electricity represents about 60% to 70% of data-center operating costs. The same report attributes to the China Academy of Information and Communications Technology (CAICT) estimates of 82.4 terawatt-hours of data-center electricity consumption in China in 2019 and 166 TWh in 2024. Those figures are reported through a secondary source, rather than directly here from CAICT’s underlying publication.
Which AI work can move west—and which may stay near users
Distance is a trade-off, not an absolute barrier. Moving computation away from users can add network delay and data-transfer requirements, so workload location depends on how quickly a response is needed and how much data must travel.
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| Workload or service | Location fit described in policy | Why |
|---|---|---|
| Model training, machine learning, video rendering, offline analysis, and backup | Good candidates for western hubs when their transfer and timing requirements allow | These jobs can generally be scheduled or run without an immediate response to an individual user. The National Data Administration’s August 2024 opinion names them among workloads to shift westward. |
| AI inference and services such as industrial internet, finance, disaster warning, remote medical services, and video calls | May need capacity in eastern hubs or closer to users and businesses | The NDRC’s 2022 explanation identifies these as examples with demanding network requirements. It does not say every instance must remain in the east. |
| Jobs that can be scheduled across regions | Potentially served through east-to-east, west-to-west, or east-to-west computing | The 2024 opinion calls for coordination across all three patterns, rather than sending every task across the country. |
In practice, “AI data center” does not mean a facility dedicated to one kind of AI work. A hub can support a mix of training, inference, and other computing; the useful question is whether a particular job can tolerate the network delay and transfer costs associated with its location.
What the national hub policy requires
The National Data Administration’s August 2024 implementation opinion makes the siting direction more explicit: in principle, no new large or extra-large data centers should be built outside national hub nodes. It calls for new computing capacity to cluster in those nodes and for eastern, central, and western capacity to be coordinated.
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The policy still allows efficient, low-carbon regional data centers outside the national hubs where renewable energy, location, and industrial conditions are favorable. It also promotes pooled computing resources, cross-region scheduling, high-throughput data links, and lower transmission costs. The goal is a connected system, not a rule that every workload—or every data center—must move west.
The 2024 opinion set an end-of-2025 target for more than 60% of newly added computing capacity to be located in national hubs, and for green electricity to exceed 80% at newly built hub data centers. These are policy targets; the cited material does not establish whether they were achieved.
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Why western sites can offer an energy advantage
Some western locations are close to large wind and solar resources, and some can use outside air for cooling for much of the year. Those conditions can improve a facility’s energy profile, but they do not make every western data center renewable-powered or equally efficient. China’s Green and Low-Carbon Development Action Plan calls for planners to account for both energy and water demand, improve efficiency, increase renewable-energy use, coordinate facilities with wind and solar bases, and explore direct green-power supply.
The plan also points to facility-level measures such as liquid, evaporative, heat-pipe, and natural cooling; efficient power systems; and changes to energy storage. These are engineering options whose suitability depends on the facility and local conditions—not evidence that any one cooling method is used everywhere.
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Zhongwei illustrates the opportunity—and the limits of a local example
A National Energy Administration-sponsored 2026 report describes Zhongwei in Ningxia as one example. It says a 500-megawatt solar farm supplies the area and a 1.5-gigawatt wind project was under construction at the time of reporting. The renewable-power company operating the projects said they could meet about 2.29 billion kilowatt-hours of annual demand from the existing and planned Zhongwei cluster. A China Mobile Ningxia operations official said natural cooling could be used there for eight to ten months a year. Those project and cooling details apply to the reported Zhongwei example; they should not be generalized to all western sites.
The same report says Zhongwei had ten large data-center parks, with six of China’s ten largest internet data-center operators present. Municipal officials said local facilities supported training and inference for 13 large AI models and served more than 4,900 companies. Zhongwei’s data administration attributed to the city a fifth-place ranking among 302 cities on a 2025 computing-power index and a capacity figure of 283,100 petaflops. These are local figures reported by officials, not independently audited measures of national capacity.
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Fiber and data-transfer capacity
A remote hub is useful only if networks can move data to and from it at adequate speed and cost. The 2024 policy calls for high-throughput links and cross-region scheduling, but a June 25, 2026 Digital China report, crediting Xinwen Lianbo, said east-west fiber links were struggling to keep pace with computing expansion. It described new backbone routes along high-speed rail lines and reported that more than 30 data centers were under construction within a three-kilometer radius in Horinger New Area near Hohhot, Inner Mongolia.
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That report also described a China Unicom facility in Zhongwei designed for AI servers, with extra overhead routing space; it said those systems require more than 200 times the optical fibers of traditional servers. Treat that as a reported facility example and specification, not a universal measure for AI servers. The underlying constraint is broader: adding remote computing capacity also requires enough network capacity to connect it to demand.
Reliable power, water, and efficient utilization
Wind and solar availability does not by itself guarantee steady power at every hour. The low-carbon plan calls for matching facilities with stable supply and flexible power, while the energy report emphasizes the need for reliable, affordable electricity at continuously operating centers. Planners must also account for water, so a favorable renewable-energy profile is not a complete site assessment.
Finally, building more capacity does not automatically make the system more efficient. The policy’s emphasis on clusters, scheduling, and pooled resources reflects a need to connect supply with actual workloads and avoid redundant construction. Without adequate network links and coordination, distant capacity may be harder or more expensive to use for the jobs that need it.
What the strategy means for AI users
The location shift is chiefly an infrastructure and planning choice; it does not mean that users’ AI services will necessarily become slower or that all AI processing will take place far from cities. Work that benefits from immediate responses can be placed closer to users, while less time-sensitive computation is a better candidate for remote hubs. The national plan aims to make those locations work together, but actual performance depends on workload placement, network capacity, power supply, and facility efficiency.
A Zhongwei data-administration official, Meng Fanzhen, described planned work to build a platform that dynamically coordinates computing capacity and power supply, using computing demand to optimize electricity use. That is a stated plan, not evidence that such a service is already operating at scale.
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