报告导读： 两会政府工作报告与发改委计划草案将算电协同、超大规模智算集群纳入新基 建工程，强化全国一体化算力调度、新型电力系统建设与节能降碳改造。两大电网"十五 五"投资加码，提供资金保障与工程载体。 3 月 5 日，全国两会期间国务院总理李强在十四届全国人大四次会议上作《政府工作报告》，同日国家发展改革委受国务院委托向大会提交《关于 2025 年 国民经济和社会发展计划执行情况与 2026 年国民经济和社会发展计划草案的报告》。两份报告强调 以 "人工智能 + "带动行业规模化应用，以全国一体化 算力能力与算电协同夯实新型基础设施，推进新型能源体系建设与节能降碳改造。 算电协同明确写入新基建工程并与全国一体化算力调度并列部署。 工作报告提出深化拓展"人工智能 + "，实施超大规模智算集群、算电协同等新基建工程， 加强全国一体化算力监测调度，支持公共云发展，同时提出打造" 5G+ 工业互联网"升级版、深化数据资源开发利用并建设高质量数据集、完善人工智能治 理。相比 2025 年工作报告， 2026 年报告强调把算电协同与智算集群明确写入新基建工程，并把算力监测调度作为全国一体化能力建设的硬任务，更突出 电力系 ...

面对新一轮科技革命和产业变革，人工智能已成为当代中国把握发展主动、塑造竞争优势的关键变量。 今年政府工作报告首次提出"打造智能经济新形态"，要"实施超大规模智算集群、算电协同等新基建工程"，将AI与 电力联动上升为国家战略。 以顶层设计擘画智能发展蓝图，以重大工程夯实数字经济底座。这步关乎未来的"先手棋"，中国究竟该如何落子？ 算电协同，AI时代的基础设施再升级 算电协同是通过数字化与智能调度，实现算力基础设施与电力系统深度整合、动态匹配，从而提升能源利用效率、 稳定算力供给，并推动绿色低碳升级的新型融合发展模式。 此次"算电协同"首次写入政府工作报告，且明确列为新基建工程，标志着这一概念从地方试点、部门政策正式升格 为国家战略部署，其本质，是一场AI时代的基础设施再升级。 过去20年，互联网消耗的主要是带宽、存储、服务器；数字化时代下，人工智能重塑算力产业成本，消耗的核心资 源变成两个字：电力。 中国信通院数据显示，2019~2024年，我国数据中心年度用电量从824亿千瓦时增至1660亿千瓦时。据相关研究， 在数据中心的运营成本中，电力及算力折旧成本合计占比可达70%左右。 到2030年，数据中心年度用电 ...

2026 年 3 月 8 日 电力设备新能源、环保 重点关注算电协同与 HALO 资产 环保 买入（维持） 作者 分析师：殷中枢 执业证书编号：S0930518040004 010-58452071 yinzs@ebscn.com 分析师：郝骞 执业证书编号：S0930520050001 021-52523827 haoqian@ebscn.com 分析师：陈无忌 执业证书编号：S0930522070001 021-52523693 chenwuji@ebscn.com ——电新环保行业周报 20260308 分析师：和霖 执业证书编号：S0930523070006 021-52523853 helin@ebscn.com 电力设备新能源 买入（维持） 行业与沪深 300 指数对比图 -20% 0% 20% 40% 60% 2025/3/6 2025/7/6 2025/11/5 2026/3/7 电力设备(申万) 环保(申万) 沪深300 资料来源：iFinD 要点 整体观点： 1、两会焦点关注：政府工作报告重点提及碳双控、氢能与绿色燃料、算电协同，市 场对于碳双控、氢能已有一定预期，算电协同正成为当前市场关 ...

Investment Rating - The industry investment rating is "Buy" with expectations for stock performance to exceed the market by more than 10% over the next 12 months [4]. Core Insights - The report highlights a historic investment opportunity in the "computing power and electricity collaboration" driven by government initiatives and the rapid development of renewable energy sources [4]. - The report emphasizes the importance of integrating artificial intelligence with electricity supply to enhance economic and social development, as outlined in the recent government work report and the draft of the 15th Five-Year Plan [4]. - The collaboration between computing power and electricity is expected to lead to significant infrastructure investments, particularly in green electricity and data centers, which are projected to see substantial growth in electricity consumption [4]. Summary by Sections Government Initiatives - The government has prioritized the development of a new intelligent economy, promoting the commercialization of artificial intelligence and the construction of large-scale computing power infrastructure [4]. - The draft of the 15th Five-Year Plan emphasizes the need for a robust data market and efficient supply of computing power, algorithms, and data resources [4]. Renewable Energy and Data Centers - The rapid growth of renewable energy in China, with over 430 million kilowatts of new wind and solar capacity added in 2025, is expected to provide competitive electricity prices and sufficient power supply [4]. - Data centers are projected to see an annual electricity consumption growth rate of approximately 20% from 2024 to 2030, with total electricity consumption reaching about 525.8 billion kilowatt-hours by 2030, accounting for 4.8% of total national electricity consumption [4]. Investment Recommendations - The report suggests focusing on companies providing power supply services for computing power, such as Southern Power Grid Technology and Fuling Power [4]. - It also recommends companies involved in computing power construction and services, including China Communication Services and State Grid Information & Communication [4]. - Additionally, it highlights data center power equipment providers like Sifang Co., Jinpan Technology, and Zhongheng Electric as potential investment opportunities [4].

Core Viewpoint - The integration of digital and green technologies, termed "dual transformation," is becoming a key pathway for upgrading urban industrial chains by enhancing efficiency and reducing carbon emissions [2][4]. Group 1: Dual Transformation in Urban Development - The "14th Five-Year Plan" has shifted urban development from scale expansion to quality enhancement, emphasizing a people-centered approach [3]. - The upcoming "15th Five-Year Plan" aims to accelerate new urbanization, focusing on quality improvements and sustainable development [3]. - The dual transformation is identified as a mainstream trend for urban industrial chain upgrades by 2025, driven by the deep integration of digital and green technologies [3][4]. Group 2: Impact on Traditional Industries - Dual transformation is crucial for transitioning traditional industries from carbon-intensive practices to low-carbon, innovative models, supporting the achievement of carbon neutrality goals [4][5]. - The report highlights that dual transformation enhances production efficiency and product quality by promoting precise allocation of resources and transitioning to circular, low-carbon production modes [4][5]. - Key sectors, such as automotive and steel, are increasingly adopting digital carbon management systems, with over 60% of automotive manufacturers and 40% of steel production capacity utilizing these technologies [5]. Group 3: Real Estate Sector Transformation - Real estate companies are shifting from mere developers to comprehensive operators of digital and green urban spaces, focusing on operational efficiency and shared industry value [6][7]. - The integration of sustainability and digital capabilities into corporate strategies is essential for enhancing cash flow resilience and asset valuation [6][7]. Group 4: Smart Cities and Computing Power - The development of smart cities and the enhancement of computing power efficiency are critical for achieving low-carbon upgrades in industrial chains [8][10]. - Smart cities leverage advanced technologies like IoT and AI to improve urban planning and management, while the demand for data processing drives the need for efficient computing infrastructure [9][10]. - Successful models, such as Shenzhen's smart city initiative, demonstrate the potential for low-carbon industrial clusters and energy-efficient data centers [10][11]. Group 5: Future Directions - The dual transformation should be central to future industrial development, emphasizing technological innovation and collaborative mechanisms [11][12]. - Continuous efforts are needed to optimize policies and enhance the synergistic effects of smart city construction and computing power efficiency, driving industries towards higher efficiency, sustainability, and intelligence [11][12].

Core Viewpoint - The article discusses the implementation opinions on promoting "Artificial Intelligence + Energy" for high-quality development, emphasizing the integration of AI technologies into various energy sectors to enhance efficiency, safety, and sustainability by 2027 and beyond [3][9][10]. Group 1: Overall Requirements - The initiative aims to deepen the integration of AI with the energy sector, focusing on enhancing innovation and application technology levels, and ensuring the safety and reliability of energy systems [10][11]. - By 2027, a preliminary integration system of energy and AI is expected to be established, with significant breakthroughs in core technologies and widespread applications [11][12]. Group 2: Accelerating Energy Application Scenarios - AI will be applied across various energy sectors, including power grids, new energy, and traditional energy sources, to optimize operations and enhance flexibility [4][17]. - Specific applications include virtual power plants, distributed energy storage, and intelligent microgrids, aimed at improving load control and dynamic response capabilities [4][19]. Group 3: Key Technology Supply - The focus is on addressing technical bottlenecks in the energy sector, such as data silos and high energy consumption in computing, by developing common key technologies [40]. - Emphasis is placed on building high-quality data sets, enhancing computational support, and improving model capabilities to meet the specific needs of the energy sector [40][41]. Group 4: Implementation Measures - The article outlines measures for effective implementation, including strengthening organizational frameworks, promoting collaborative innovation, and establishing standards for AI applications in the energy sector [42][43]. - Pilot demonstrations will be organized to showcase replicable and scalable AI applications in energy, encouraging cross-sector collaboration [44].