Frost & Sullivan: Green Computing Enters a New Model of "Two-Way Interaction"

From the perspective of industry commercialization, the technology that has entered the stage of large-scale application in energy conservation and carbon reduction for computing power is cold plate liquid cooling, with a market share of over 80%. Currently, not only leading brands such as Inspur and Huawei have natively supported servers with this technology, but new intelligent computing centers are basically configured with it. PUE can be stabilized between 1.15 and 1.25, making it the only technology path that can be "delivered and used immediately" at present. Although immersion liquid cooling offers better performance, it faces practical challenges such as high initial investment and the need to rebuild the operation and maintenance system, so it still relies on zero-carbon parks and benchmark projects from finance and operators. The industry predicts that it will take about 3 to 5 years before large-scale implementation. Waste heat recovery technology is mature, but its commercial closed loop is severely limited by geographical conditions: in the north, projects like Ningxia ZhongweiNingxia ZhongweiFrost & Sullivan, Frost & Sullivan China, LeadLeo, LeadLeo Research Institute, LeadLeo, YUAN CAPITAL, SULLIVAN TELE-TREND CLOUD TECHNOLOGY, TradeGo, MagnaTEC, Automotive & Mobility, Environmental Protection & Energy Saving Technology, Logistics & Supply Chain, MATERNAL AND INFANT, Education & Training, Real Estate & Property, Catering & New Retailing, Advanced Materials, Healthcare & Life Sciences, Semiconductor & Chip, COMMERCIAL AVIATION, Technology, Media and Telecom, LANDSCAPING, Big Data & AI, Infrastructure Construction & Utilities, Culture & Entertainment, AGRICULTURE, FORESTRY ANIMAL HUSBANDRY AND FISHERY, Food & Beverage, Fintech, SHIPPING AND PORTS, Dual Carbon & New Energy, Mining & Metals, Public Sector, Cross-Border E-commerce Trade, Building Technology, Construction & Decoration, Beauty & Fashion, Smart Homes, Digital Infrastructure, Enterprise Services, Consumer Electronics, and other projects have already integrated waste heat into the green electricity direct supply solution; in the south, they can only connect with scattered scenarios such as greenhouses and pools, with a long recovery period, and no independent replicable business model has yet been established.CPOis the most significant differentiation: the exchange side will start production in the second half of 2026. Nvidia's Quantum 3400 CPO switch has been scheduled for mass production, reducing transmission losses by 60%; however, scale-up optical interconnection between chips will not be scaled up until the Feynman architecture is mass-produced in 2028, when the market is expected to see a new wave of applications.

From the perspective of industry development, it is necessary to establish a national unified green computing power evaluation system, but achieving "complete comparability" at this stage is not realistic. The limitations of a single PUE indicator are already very obvious: it does not reflect the energy structure—a coal-based data center with PUE 1.1 may have several times higher carbon emissions than a green electricity data center with PUE 1.3; it also does not measure computing power output efficiency—a cluster with PUE 1.15 but a GPU utilization rate of only 30% is essentially "false efficiency"; moreover, scope three emissions such as construction period, equipment manufacturing, and decommissioning are completely outside the coverage of PUE. Policy efforts are already shifting: the 2026 Government Work Report included "computing power and electricity coordination" in the new infrastructure project for the first time. The National Data Bureau and the National Energy Administration have clearly defined two constraints: "green electricity proportion of new computing power facilities ≥ 80%," and "PUE ≤ 1.20 in the west region ≤ 1.25 in the east region." The China Academy of Information and Communications Technology is also promoting two new indicators for AI business outputs—computing power energy efficiency and Token energy efficiency—these two new indicators mark the shift of the industry's evaluation focus from "data center efficiency" to "business output efficiency". On the other hand, implementing a unified standard faces three substantial obstacles: first, the criteria for green electricity traceability have not been unified, with differences in the recognition standards for green certificates, green electricity trading,green electricity direct supply, and self-generation and self-use photovoltaic paths; second, there is a lack of industry consensus on the accounting method for scope three carbon emissions; third, the differences in regional resource endowments are too large. Western hubs like Ningxia can meet the requirements by relying on wind and solar power plus green electricity direct supply, while the Yangtze River Delta needs cross-provincial green certificates and energy storage support to achieve 80%. Forcing a comparison using the same score will cause certain concerns in both eastern and western industrial markets. Therefore, the more likely evolution path in the future is that the state will formulate a comprehensive framework, and industry associations will introduce hierarchical leading values, similar to the gold and silver grading logic of LEED certification, to achieve better implementation results.

Over the next three to five years, Frost & Sullivan believes that changes in the green computing power industry will focus on four interconnected main lines. The first is that "green electricity 80%" has shifted from policy advocacy to rigid constraints, fundamentally restructuring the investment logic for data centers. Among the eight major computing power hubs, only Ningxia, Gansu, and Inner Mongolia have mature green electricity direct supply channels, while the green electricity proportion in most other nodes is less than 30%. The 2026 Government Work Report has explicitly included this proportion as a constraint. This means that 2026 to 2028 is a window period to seize green electricity direct supply channels, sign long-term power purchase agreements, and secure source-grid-load-storage resources. Projects that miss this window will find it difficult to pass energy assessment. The second is that the industrial layout will further refine from "east data, west computing" to a new map of "green electricity hinterland training and storage, coastal reasoning, and hub transit". Training clusters above ten thousand units will basically be locked in the west region. The third is that liquid cooling combined with zero-carbon parks will shift from demonstration to a required standard. The "15th Five-Year Plan" explicitly requires the construction of 100 national zero-carbon parks, and the green and low-carbon transformation of computing power facilities has been separately listed. In the future, new large-scale clusters will basically feature a combination of liquid cooling, green electricity direct supply, and waste heat utilization. The fourth is that computing power and electricity coordination will generate new trading products. Data centers, as adjustable loads, will participate in grid peak shaving, green electricity spot trading, and non-real-time tasks across regions. These activities will make "computing power carbon efficiency" and "green electricity traceability" tradable assets. The return on investment for projects certified by CCER will be higher than pure hardware investment. Frost & Sullivan believes that in the next three to five years, what will truly differentiate companies is not hardware parameters, but the ability to sign green electricity procurement agreements, operate carbon assets, and schedule computing power assets—while hardware can be obtained by everyone, green electricity channels, carbon certification, and credible computing service assets are scarce resources.

Frost & Sullivan believes that the path of using AI technology to optimize the energy consumption of data centers actually progresses faster than most market expectations. However, a key boundary must be clarified: AI optimization is the most cost-effective lever for existing infrastructure transformation, but it cannot save old air-cooled data centers with PUE above 1.3—those data centers need to replace liquid cooling hardware first, and AI can only add value on this basis. Technological evolution has gone through three stages: the first stage is pre-optimization based on load prediction to avoid temporary peak impacts; the second stage is global optimization, combining optimization of chillers, water pumps, cooling towers, and CDU to extract an additional 10% to 30% energy efficiency from the existing energy-saving framework; the third stage is computing power and electricity coordination, linking cooling strategies with the timing of green electricity output and computing power migration. When green electricity is abundant, pre-cooling is done in advance, and batch processing tasks are run more frequently; when green electricity is scarce, non-real-time loads are actively reduced. Frost & Sullivan believes that the product form with real premium potential in the future may be a complete package of "AI optimization plus green electricity timing matching plus cross-regional load migration," rather than simply selling AI energy-saving software. A high-end equipment manufacturing company in Wuxi has entrusted EasyEnergy to manage the energy and computing power in its park through intelligent operation management. EasyEnergy uses AI to coordinate servers, cooling, photovoltaics, and energy storage systems, predicting computing power loads, equipment temperatures, and green electricity output, and dynamically adjusting cooling strategies, server operating states, and task timing to achieve integrated optimization of computing power, cooling, and electricity. When green electricity is abundant, the system can pre-cool and increase batch processing and reasoning tasks; during peak power periods, frequency reduction of servers, task off-peak scheduling, and energy storage adjustment are used to reduce peak energy consumption. This scenario upgrades traditional single-cooling energy saving to global optimization of computing power, cooling capacity, and power resources, reflecting the acceleration of "using AI to save energy with AI" from technical verification to actual operational scenarios.