Market Overview - On January 14, the general steel sector experienced a decline of 0.2% compared to the previous trading day, with Lingang Co., Ltd. leading the drop [1] - The Shanghai Composite Index closed at 4126.09, down 0.31%, while the Shenzhen Component Index closed at 14248.6, up 0.56% [1] Individual Stock Performance - Hangang Co., Ltd. saw a significant increase in its stock price, closing at 65.6 with a rise of 8.12%, and a trading volume of 2.91 million shares, amounting to 2.772 billion yuan [1] - Other notable performers included Jiugang Hongxing, which rose by 3.74% to close at 1.94, and Xinxing Ductile Iron Pipes, which increased by 2.83% to close at 4.36 [1] - Conversely, Lingang Co., Ltd. closed at 2.11, down 2.76%, with a trading volume of 402,500 shares and a transaction value of 86.302 million yuan [2] Capital Flow Analysis - The general steel sector saw a net inflow of 278 million yuan from main funds, while retail investors experienced a net outflow of 227 million yuan [2] - The main funds showed significant net inflows in stocks like Hangang Co., Ltd. (495.1 million yuan) and Xinxing Ductile Iron Pipes (49.14 million yuan) [3] - Retail investors had notable outflows in stocks such as Hangang Co., Ltd. (-263 million yuan) and Xinxing Ductile Iron Pipes (-45.12 million yuan) [3]

作为钢铁生产核心工序，高炉占生产总成本的70%左右，其长期稳定运行直接关系企业盈利状况。入炉 原料的成分波动、炉内气流的分布以及温度的微小变化，都可能引发连锁反应。炉内每减少10摄氏度的 温度波动，每吨铁水就能少消耗1千克焦炭，成本可降低3元。 不仅是高炉，宝钢股份母公司中国宝武集团正规划建设钢铁大模型能力图谱，将预测大模型、视觉大模 型、科学计算大模型等AI能力，延伸到钢铁生产中的原料、炼铁、炼钢、轧钢、新材料研发等流程， 覆盖连铸质量根因分析、热轧板型预测、钢材表面质检等上百个应用场景。 如今，智能场景在宝钢股份各生产环节落地：热轧产品表面缺陷识别模型半年内准确率提升至96%，并 快速复制至多基地产线；热轧自然宽展预测模型完成在线部署，参与生产实时控制；冷轧"AI主操"上 线，显著提高机组生产稳定性…… "此前，我们常面临炉内状态看不清、操作反馈跟不上、连锁反应控不住、经验传承传不下等难题。"宝 钢股份炼铁厂大数据应用首席工程师王士彬介绍。 面对难题，宝钢股份与华为合作，按照"数实融合、同题共答"的攻关路径，以自身业务需求为导向选定 应用场景、输出行业知识，用AI、大数据、云计算等领域的前沿技术构建解决方 ...

Core Viewpoint - Smart manufacturing is the core engine for high-quality development in the manufacturing industry, marking a critical leap from digitalization and networking to intelligence in China's manufacturing sector [1][5]. Group 1: Overview of Leading Smart Factories - The Ministry of Industry and Information Technology and five other departments announced the first batch of 15 leading smart factories, covering key industries such as equipment manufacturing, raw materials, and electronic information [1][4]. - These leading factories represent the highest level of smart manufacturing in China and showcase the breadth and depth of the country's intelligent manufacturing development [4][5]. Group 2: Classification and Development of Smart Factories - A four-tier system for smart factory cultivation has been established: foundational, advanced, excellent, and leading levels, with 35,000 foundational, over 7,300 advanced, 500 excellent, and 15 leading smart factories built to date [7]. - The leading level aims for global leadership and deep integration of AI, while the excellent level requires at least 20% AI application scenarios [7]. Group 3: Key Elements for Leading Smart Factories - Six key elements define a leading smart factory: industry leadership, AI technology application, innovative smart manufacturing models, performance leadership, replication leadership, and nurturing plans [10][12]. - Longfei Optical Fiber exemplifies these elements by integrating AI throughout its production process, achieving a global record in fiber drawing speed and precision [12][13]. Group 4: Case Studies of Leading Smart Factories - Weichai Power has implemented a digital twin technology that reduces R&D cycles by 20% and enhances production flexibility through AI-driven systems [20][21]. - Baosteel is innovating with AI-driven predictive manufacturing, aiming to establish 1,200 AI scenarios and 25 benchmark production lines by 2027, while also investing in talent development [25][27]. Group 5: Future Manufacturing Models - The exploration of future manufacturing models focuses on high customization, complex product production, and efficient supply chain organization, aiming to enhance China's position in the global supply chain [17][29]. - The emergence of these leading smart factories not only represents individual breakthroughs but also reflects a systemic transformation in China's manufacturing industry, providing replicable and scalable models for industry upgrades [29].

（编辑 丛可心） 证券日报网讯 1月12日，宝钢股份在互动平台回答投资者提问时表示，作为宝信软件的股东，宝钢股份 将充分尊重其作为上市公司的独立性，秉持市场化原则推进双方业务合作，并严格在资本市场相关法律 法规的框架内，支持其可持续发展。 ...

Core Viewpoint - Smart manufacturing is identified as the core engine for high-quality development in the manufacturing industry, marking a significant transition from digitalization and networking to intelligent manufacturing in China [1][15]. Group 1: Overview of Smart Factories - The Ministry of Industry and Information Technology, along with five other departments, announced the first batch of 15 leading smart factories, covering key industries such as equipment manufacturing, raw materials, and electronic information [1][3]. - These leading factories represent the highest level of intelligent manufacturing in China and showcase a broad and deep development of smart manufacturing across various regions [1][3]. - The four-tier system for smart factory cultivation includes foundational, advanced, excellent, and leading levels, with specific requirements for each tier [3]. Group 2: Key Elements of Leading Smart Factories - To qualify as a leading smart factory, six key elements are required: industry leadership, AI technology application, innovative smart manufacturing models, performance leadership, replication leadership, and nurturing plans [3][5]. - Longfei Optical Fiber exemplifies an industry leader by integrating AI throughout its production process, achieving a pulling speed of 3,500 meters per minute with no human intervention [5][6]. - Weichai Power demonstrates performance leadership by reducing maintenance costs by 30% through its engine health management system, showcasing the effectiveness of its smart manufacturing practices [10]. Group 3: Future Manufacturing Models - The exploration of leading smart factories aims to create a flexible and efficient supply network capable of high customization and rapid iteration in production [8][16]. - Baosteel is innovating with AI-driven predictive manufacturing, moving away from traditional order-based production to proactively managing supply chains and production resources [11][13]. - The establishment of at least 1,200 AI scenarios and 25 benchmark production lines by Baosteel by 2027 reflects a commitment to integrating AI into manufacturing processes [13]. Group 4: Systemic Transformation in Manufacturing - The emergence of the first batch of leading smart factories signifies a systemic transformation in China's manufacturing sector, with a focus on technological breakthroughs, efficiency improvements, and green transitions [16]. - The experiences and models developed by these leading factories are expected to be replicable and promote the digital transformation of more manufacturing enterprises [16].

新浪财经ESG评级中心提供包括资讯、报告、培训、咨询等在内的14项ESG服务，助力上市公司传播ESG理念，提升ESG可持续发展表现。点 击查看【 ESG评级中心服务手册】 在全球可持续发展浪潮席卷而来的当下，ESG（环境、社会、公司治理）已成为衡量企业高质量发展的核心标尺，更是连接企业价值与社会价值的关键纽 带。随着国内ESG生态体系的加速完善，政策监管持续收紧、资本市场对ESG表现的关注度不断飙升，企业的可持续发展能力愈发成为其核心竞争力的重 要组成部分。 在此行业背景下，新浪财经重磅发布"2025中国企业ESG百强"榜单。该榜单依托新浪财经专业的ESG评级体系，以5000余家A股上市公司及在港上市内地 企业为评价对象，创新性搭建18套行业ESG评价模型，纳入150余项ESG指标，通过量化模型综合演算，对企业ESG表现进行全面、客观的综合评价，最 终筛选出中国ESG实践的标杆企业。榜单不仅为行业树立了发展典范，更为投资者提供了极具参考价值的决策依据。 宝钢股份在环境、社会、公司治理领域开展了大量工作，积累了丰富的创新实践与扎实的落地成果。凭借在ESG各领域的卓越表现，宝钢股份成功入选本 次新浪财经"2025 ...

Group 1 - Morgan Stanley's preference order for the materials sector in 2026 is copper/gold > aluminum > lithium > coal > steel [1] - The MSCI China Materials Index is expected to outperform the MSCI China Index this year due to supply disruptions or tight supply and further M&A activities [1] - Zijin Mining (02899) remains Morgan Stanley's top pick for the year, with continued optimism for Luoyang Molybdenum (03993), China Aluminum (02600), and China Hongqiao (01378) [1] Group 2 - Jiangxi Copper (00358) has been upgraded to neutral based on a positive outlook for copper [1] - Chinese policies are still the main driver of commodity prices, but the execution and intensity of anti-involution policies are expected to be milder than anticipated starting from Q4 2025 [1] - Steel profit margins are expected to remain low without significant production cuts, leading to a downgrade of Baoshan Iron & Steel (600019.SH) to neutral and Ansteel (00347) to underweight [1]

Group 1 - Morgan Stanley's report indicates a preference order for the materials sector in 2026: Copper/Gold > Aluminum > Lithium > Coal > Steel [1] - The MSCI China Materials Index is expected to outperform the MSCI China Index this year due to supply disruptions or tight supply and further M&A activities [1] - Zijin Mining (601899)(02899) remains Morgan Stanley's top pick for the year, with continued optimism for Luoyang Molybdenum (603993)(03993), China Aluminum (601600)(02600), and China Hongqiao (01378) [1] Group 2 - Based on a positive outlook for copper, Jiangxi Copper (600362)(00358) rating is upgraded to Neutral [1] - Chinese policies are seen as the main driver of commodity prices, but the execution and intensity of anti-involution policies post-Q4 2025 are expected to be milder than anticipated [1] - The effort to reduce excess capacity in the steel sector is a long-term endeavor, and without significant production cuts, steel profit margins are expected to remain low [1] Group 3 - Baosteel (600019)(600019.SH) rating is downgraded to Neutral, while Ansteel (000898)(00347) is downgraded to Underweight [1]

据中国宝武官微消息，1月9日，宝钢股份湛江钢铁炼钢厂5号特厚板坯连铸机全面建成投产。作为全国 最大断面2700mm、中国宝武首台可稳定生产460mm特厚板坯的连铸机，其成功贯通不仅是湛江钢铁在 高端冶金装备领域的一次重大突破，更标志着我国在大断面、大厚度、高性能板坯的自主化、稳定化生 产技术上迈出了关键一步，为服务国家重大战略需求再添"大国重器"。项目达产后，具备年产350万吨 高质量特厚板坯的能力，专用于稳定生产顶级风电钢、桥梁钢等高强结构钢，为海上风电、重型机械等 国家重大工程与高端制造产业链提供关键材料保障，成为公司响应战略市场需求的强有力支点。 ...

Core Viewpoint - The collaboration between the labor model innovation studios of Lei Yutian and Jin Guoping aims to develop a visual intelligent identification system for electric furnaces, marking a significant step towards addressing common industry challenges and promoting high-quality development in enterprises [1][2]. Group 1: Project Overview - The project focuses on the development of a "visual intelligent identification system" for electric furnaces, which has completed preliminary verification and detailed design, now entering the equipment manufacturing phase [1][5]. - This initiative is part of the national trade union's "Labor Model Craftsman Assist Enterprises" campaign, highlighting cross-regional collaboration between skilled workers from Shanghai and Suzhou [1][2]. Group 2: Industry Context - Electric furnace steelmaking is recognized as a crucial pathway for low-carbon steel production, gaining momentum under the national "dual carbon" strategy [2]. - The electric furnace process has historically faced challenges due to its "black box" nature, with extreme conditions making it difficult for operators to monitor and control effectively [2]. Group 3: Technical Challenges - The project team must overcome three major technical challenges: the extreme environment of high temperatures and dust, the difficulty in accurately defining observation ranges, and stringent installation conditions that must not disrupt normal production [3]. Group 4: Innovative Solutions - The project has introduced a "flexible design" concept, allowing the new observational equipment to adjust its range and angle, akin to the flexibility of human vision [4]. - This shift in design philosophy represents a significant breakthrough, moving from "adapting equipment to the environment" to "adapting equipment to changes" [5]. Group 5: Expected Impact - The system is expected to provide six core functions, including real-time monitoring, intelligent flame recognition, online tracking of molten steel levels, and evaluation of refractory material status, thus supporting the full-process intelligence of electric furnace steelmaking [6]. - Successful implementation of this technology could lead to substantial energy cost savings, estimated in the hundreds of thousands of yuan annually per electric furnace, and contribute to the green and intelligent transformation of the steel industry [6].