随着全球碳中和目标的逐步落地，数据中心作为信息基础设施的"能耗大户"正面临前所未有的转型压力。瀚思科技创新与发展协会2022年发布的《碳中和目标下的绿色计算技术路径》白皮书指出，数据中心能效优化正从单点改进进入全栈协同的新阶段。

传统数据中心的能效优化主要集中在物理层面——提升空调系统效率、改善散热设计、引入液冷技术等。这些措施在过去十年中将数据中心的PUE（能效使用效率）从平均的2.0左右降低到1.5左右。但要进一步突破，单靠物理层面的优化已经接近极限。

新一代绿色计算的核心理念是"全栈协同"——即从芯片设计、操作系统、虚拟化层、应用框架、AI模型一直到业务调度的全栈协同优化。在这个框架下，AI模型的能耗优化成为一个关键议题。研究表明，同等任务下，经过精心优化的AI模型能耗可以降低60%以上。

白皮书特别探讨了"AI for Green"与"Green for AI"的双向关系。一方面，AI可以帮助优化数据中心的能耗管理（AI for Green）——通过实时预测负载、动态调整算力分配、智能调度冷却系统等手段，实现整体能效的显著提升；另一方面，AI模型本身的训练和推理消耗大量能源，需要专门的"绿色AI"研究（Green for AI）——通过模型压缩、稀疏化、知识蒸馏等技术降低AI能耗。

在产业实践层面，全球头部科技公司已经开始大规模投入绿色计算技术研发。微软在欧洲建设的水下数据中心、Google的可再生能源全覆盖战略、阿里巴巴的液冷数据中心、华为的全栈节能方案，都代表了绿色计算的不同探索路径。

CTRIT专家委员会建议，绿色计算不应被简单视为环保议题，而应被理解为新一轮基础设施技术升级的核心驱动力。掌握绿色计算技术的企业，将在未来的全球科技竞争中占据先发优势。

As global carbon neutrality goals are gradually implemented, data centers, as major energy consumers in information infrastructure, are facing unprecedented pressure to transform. The Green Computing Technology Paths under Carbon Neutrality Goals white paper released by Hansi Association for Technology Innovation and Development in 2022 points out that data center energy efficiency optimization is moving from single-point improvement into a new stage of full-stack collaboration.

Energy efficiency optimization in traditional data centers has mainly focused on the physical layer: improving air-conditioning system efficiency, improving heat dissipation design, introducing liquid cooling technology, and similar measures. Over the past decade, these measures reduced the average PUE (power usage effectiveness) of data centers from around 2.0 to around 1.5. But to break through further, optimization at the physical layer alone is already approaching its limit.

The core idea of next-generation green computing is full-stack collaboration, meaning coordinated optimization across the full stack from chip design, operating systems, virtualization layers, application frameworks, and AI models to business scheduling. Under this framework, energy consumption optimization of AI models has become a key issue. Research shows that under the same task, carefully optimized AI models can reduce energy consumption by more than 60 percent.

The white paper pays special attention to the two-way relationship between AI for Green and Green for AI. On the one hand, AI can help optimize data center energy management through real-time load prediction, dynamic computing power allocation, intelligent cooling system scheduling, and other methods, thereby significantly improving overall energy efficiency. On the other hand, training and inference of AI models themselves consume large amounts of energy, requiring dedicated green AI research that reduces AI energy consumption through model compression, sparsification, knowledge distillation, and other technologies.

At the level of industrial practice, leading global technology companies have already begun investing heavily in green computing research and development. Microsoft underwater data center in Europe, Google renewable energy coverage strategy, Alibaba liquid-cooled data centers, and Huawei full-stack energy-saving solutions all represent different exploration paths for green computing.

The CTRIT Expert Committee recommends that green computing should not be viewed simply as an environmental issue, but should be understood as a core driving force for the next round of infrastructure technology upgrades. Enterprises that master green computing technologies will gain a first-mover advantage in future global technology competition.