书面答复 · 2024-01-09 · 第 14 届国会
AI算力与能源可持续平衡
National Artificial Intelligence Strategy 2.0 vs Energy and Environmental Sustainability
议员质询政府如何在推动国家AI战略2.0所需的算力增长与能源可持续目标之间取得平衡。政府回应强调通过提升数据中心能效、推广液冷技术、制定绿色标准及支持绿色计算方法，确保AI基础设施环保可持续。核心争议在于如何兼顾AI发展需求与气候承诺的矛盾。
关键要点
- • 提升数据中心能效
- • 推广液冷节能技术
- • 发展绿色计算方法
推动绿色高效AI算力发展
关注算力与能源平衡
加强绿色数据中心建设
"The Government is committed to anchoring the necessary compute power and growing the DC sector in a sustainable manner."
参与人员（2）
完整译文（中文）
Hansard 英文原文译文 · 翻译日期：2026-05-02
47 林伟杰医生向通讯及资讯部长提问，关于国家人工智能（AI）战略2.0，(a) 政府如何计划在满足AI发展所需的计算能力增长与新加坡的能源可持续发展目标之间取得平衡；(b) 有哪些举措或策略确保支持AI增长所需的基础设施和计算能力保持环境可持续性。
张玉娟女士：我们国家人工智能战略（NAIS）2.0的重要推动力之一，是设于新加坡的数据中心（DC）内的人工智能计算能力。政府致力于以可持续的方式锚定必要的计算能力并发展数据中心行业，这与我们的国际气候承诺保持一致。
一项关键策略是提升数据中心的能源效率并促进高效冷却解决方案的部署。例如，AI计算基础设施通常依赖液冷技术，相较于空气冷却，液冷在处理高强度工作负载时更节能。其他措施包括：(a) 通过经济发展局的排放资源效率补助金支持数据中心运营商减少温室气体排放；(b) 制定可持续发展标准，如资讯通信媒体发展局（IMDA）的热带数据中心标准，使数据中心能够在更高温度下安全运行并减少冷却能耗；(c) 审查建筑与建设局（BCA）与IMDA联合推出的数据中心绿色标志认证计划，更新能源效率标准。
除了可持续的AI计算基础设施外，投资绿色计算方法的发展也非常重要。这些方法包括：(a) 编码和算法优化，(b) 软件与硬件的优化，(c) 制定低数据量、低能耗AI模型的标准。政府将继续深化与研究界和产业伙伴的国际及国内合作。
英文原文
SPRS Hansard 原始记录 · 抓取日期：2026-05-02
47 Dr Lim Wee Kiak asked the Minister for Communications and Information with regard to the National Artificial Intelligence (AI) Strategy 2.0 (a) how does the Government plan to balance the increased computing power necessary for AI developments with Singapore's energy sustainability goals; and (b) what initiatives or strategies are in place to ensure that the infrastructure and computing power needed for AI growth remain environmentally sustainable.
Mrs Josephine Teo : An important enabler of our National Artificial Intelligence Strategy (NAIS) 2.0 strategy is artificial intelligence (AI) compute power housed in data centres (DCs) in Singapore. The Government is committed to anchoring the necessary compute power and growing the DC sector in a sustainable manner, consistent with our international climate commitments.
One key strategy is to improve the energy efficiency of DCs and facilitate the deployment of efficient cooling solutions. For example, AI compute infrastructure typically relies on liquid cooling, which is more energy-efficient than air cooling for such intensive workloads. Other measures include: (a) supporting DC operators to reduce greenhouse gas emissions through the Economic Development Board's Resource Efficiency Grant for Emissions; (b) developing sustainability standards, such as the Infocomm Media Development Authority's (IMDA)'s Tropical DC Standards that enable DCs to operate safely at higher temperatures and use less energy for cooling; and (c) reviewing Building and Construction Authority (BCA)-IMDA's Green Mark for DCs certification scheme to update the energy-efficiency criteria.
Beyond sustainable AI compute infrastructure, it is important to invest in the development of green computing methods. These methods include (a) coding and algorithm optimisation, (b) software-hardware optimisation, and (c) developing standards for low-data, low-energy AI models. The Government will continue to deepen international and domestic partnerships with the research community and industry partners on this front.