她表示，大陆拥有广阔市场和丰富的应用空间，具备完整的产业链和坚定的政策支持。台湾拥有全球顶 尖的半导体制造技术和深厚的硬件研发基础，尤其是在高精密制造和商业服务领域有着精细化管理的宝 贵经验，同时也面临能源供给等挑战。两岸优势互补的格局并非是零和博弈，而是共创双赢。 中新社北京2月3日电 (记者 朱贺 陈建新)国共两党智库论坛3日在北京举行。中国信息通信研究院人工智 能研究所国际发展部主任许珊作专题发言时建议，推动两岸算电协同发展，共建绿色算力中心技术标 准，深化中华文化语料库共享共建。 "'人工智能+'浪潮正在深刻重构千行百业。"在许珊看来，人工智能发展离不开全球合作，而两岸合作 是其中不可或缺的一环。 许珊提出三方面建议：一是推动两岸算电协同发展，形成"台湾研发、推理+大陆训练、存储"的绿色低 碳合作模式，优化两岸算力成本结构，缓解岛内电力负荷。二是共建绿色算力中心技术标准，发挥台湾 在服务器硬件设计上的优势，结合大陆在液冷机房、绿色数据中心建设上的规模化经验，共同制定能源 使用效率优化标准。三是深化中华文化语料库共享共建，两岸学术界可与产业界合作，整合繁简体中文 优质语料资源，共同开发面向全球华人的通用 ...

Core Viewpoint - The implementation opinion aims to promote the integration of artificial intelligence (AI) and the energy sector, establishing a framework for high-quality development by 2027 and achieving world-leading levels by 2030 [1][10][12]. Group 1: Implementation Goals - By 2027, the initial framework for the integration of energy and AI will be established, focusing on the deep application of over five professional large models in various energy sectors such as power grids, generation, coal, and oil and gas [1][12]. - The plan includes identifying over ten replicable and competitive demonstration projects and exploring a hundred typical application scenarios [1][4][12]. - By 2030, the goal is to achieve systematic breakthroughs in AI-specific technologies and applications within the energy sector, enhancing safety, green transformation, and efficiency [5][13]. Group 2: Key Tasks - The implementation opinion outlines several key tasks, including empowering various energy scenarios with AI, focusing on coal, electricity, oil, and gas [6][7]. - It emphasizes the need for a comprehensive approach to AI applications across eight major scenarios, including smart grid, new energy, and nuclear power [7][8]. - A total of 37 key tasks have been identified, with specific applications in oil and gas, coal, electricity, and renewable energy [7][8]. Group 3: Technical Support - The opinion highlights the importance of strengthening the foundational technologies for AI applications in the energy sector, focusing on data, computing power, and algorithms [8][32]. - It calls for the establishment of high-quality data sets and a collaborative development mechanism for computing power and electricity [32][33]. - The need for enhancing model capabilities and addressing issues related to data security and algorithm transparency is also emphasized [32][33]. Group 4: Implementation Measures - The document stresses the importance of organizational implementation, encouraging local energy authorities and enterprises to establish mechanisms for promoting AI in the energy sector [34][35]. - It advocates for collaborative innovation among enterprises, research institutions, and universities to build a robust ecosystem for AI and energy integration [34][35]. - The need for pilot demonstrations and the selection of replicable scenarios for AI applications in the energy sector is also highlighted [35][36].