据Wind统计，A股成交额突破3万亿元此前总共发生了6次，分别是2024年10月8日，2025年8月25日、8 月27日、8月28日和9月18日，2026年1月9日。 截至1月12日下午收盘，沪指涨超1%，录得"17连阳"，深证成指涨1.75%，创业板指涨1.82%。全市成交 额3.64万亿元，刷新此前在2024年10月8日创下的34549.33亿元的成交额历史纪录，再创新高；超4100只 个股上涨。 板块方面，商业航天、AI应用两大方向领涨市场，脑机接口概念走高，电商、短剧游戏等板块领涨； 算力硬件、保险、油气等方向跌幅居前。 "当前，A股的火热历史罕见，连回调都没有！"一位广州的老股民李先生惊呼，1月12日上午开盘仅一 个小时，两市成交量接近1.7万亿，半日成交额破2.31万亿元，资金跑步进场，热点全面开花。截至上午 收盘，沪指报4151.14，涨0.75%；深成指涨1.31%，创业板指走出"V"字反弹，涨1.17%。 中航证券首席经济学家董忠云认为，从2026年第一周来观察，春季躁动或已迎来主升浪，行情主要来自 基本面、政策面和基本面改善的支撑。他指出，2025年12月国内通胀数据持续改善，其中，CPI ...

（来源：江苏省可再生能源行业协会） L THE = 77 培育一批能源行业人工智能技术应用 研发创新平台 形成昌有江苏特色的 充分发挥我省现有规模化能源应用市场和 丰富实践案例优势,支持建设一批综合性、集 成式的人工智能与能源融合应用场景，促进能 源资源公平高效配置。 "十五五"期间 推动 > 个左右垂直领域专业大模型 在能源行业深度应用 培育塑造 100 个以上有竞争 力、可推广、易复制的实践案例和 典型应用场景 能源领域人工智能创新发展模式 02 主要任务 人工智能+电网 电网智能规划建设 电力设备智能运检 电网智能安全管控 电网智能调度运行 电网装备制造 电力用户智慧服务 电力市场智慧交易 人工智能+新能源 气象预报与新能源功率精准预测 偏远场站智能运维与密集区风机安全运维 新刊士休取△宗측ビ県由絹江ホ旦知 字字字本 以 人 2 月 1 人 1 1 1 1 2 2 1 2 1 2 1 2 2 2 1 2 1 2 新能源装备制造 人工智能+能源新业态 绿色燃料智慧生产 新型储能智能运行 虚拟电厂智能运营 运维 人工智能+火电 火电装备制造 火电智能运维 智慧燃煤管控 电厂智能认知决策 人工智能+煤炭 煤矿 ...

Core Viewpoint - The 2026 National Energy Work Conference emphasizes the importance of energy security, carbon reduction, technological innovation, and systemic reforms in the energy sector to support China's modernization and achieve the 2030 carbon peak goal [1][3][4]. Summary by Sections 2025 Energy Work Achievements - The energy sector made significant progress in 2025, effectively supporting economic and social development, with the "14th Five-Year" energy planning goals nearing completion [3]. - Energy security was notably strong, with coal production and supply stability, successful oil and gas resource strategies, and a stable electricity system [5]. - The share of non-fossil energy consumption exceeded the 20% target, and investments in green energy surged [5]. - Technological innovation in the energy sector improved, with advancements in artificial intelligence integration and new business models emerging [5]. 2026 Key Tasks - The energy work for 2026 will focus on high-quality planning and implementation of the "15th Five-Year" energy plan, ensuring scientific and proactive energy planning [6]. - Enhancing energy security will be a priority, with efforts to solidify coal supply, improve electricity provision, and optimize energy infrastructure [6]. - The transition to green and low-carbon energy will be accelerated, aiming for over 20 million kilowatts of new wind and solar power installations [6]. - The integration of artificial intelligence in energy systems will be promoted, alongside advancements in hydrogen and nuclear energy sectors [6]. Energy Reform and International Cooperation - The conference highlighted the need for deepening energy reforms and legal frameworks to adapt to a new energy system [7]. - Strengthening international cooperation in clean energy and global energy governance will be pursued [7]. - The importance of maintaining strong leadership and political direction in energy work was emphasized, alongside the need for effective implementation of central directives [7].

Core Viewpoint - The "Implementation Opinions" aim to establish a management system for the integration of "Artificial Intelligence+" in the energy sector, providing a top-level design and action guide to promote high-quality development in the industry [1] Group 1: Current Challenges and Development Goals - The current state of AI application in the energy sector is characterized by fragmented development, leading to resource redundancy and systemic barriers, which hinder long-term AI development [2] - The "Implementation Opinions" set two key development goals for 2027 and 2030, focusing on foundational work and establishing benchmarks in the first phase, and achieving world-leading AI technology in the energy sector by 2030 [2] Group 2: Implementation Pathways - The "Implementation Opinions" outline a systematic approach to enhance the quality and efficiency of AI in the energy sector, emphasizing breakthroughs in key technologies, widespread application of industry-level models, and deep empowerment of high-value scenarios [3] - Key technology support areas include solidifying data foundations, enhancing computational power, and improving model capabilities to provide a reliable basis for AI technology validation and continuous iteration [3] Group 3: Specialized AI Model Development - The transition from general AI models to specialized models is crucial, with a focus on developing over five specialized models tailored to the characteristics of energy sectors such as electricity, coal, and oil and gas [4] - The "Implementation Opinions" emphasize the need for deep applications in high-value scenarios, including power grids and new energy sources, to enhance AI's role in energy supply-demand balance and safety monitoring [4] Group 4: Innovation Ecosystem - The "Implementation Opinions" focus on building an innovation ecosystem by promoting pilot demonstrations, establishing standards, and fostering collaborative mechanisms to stimulate sustainable development in the "Artificial Intelligence+" energy sector [5] - A comprehensive standard system covering AI technology development, application, and evaluation will be established to ensure orderly industry development and facilitate the sharing of data and computational resources [6] - Collaborative innovation will be strengthened through the establishment of innovation platforms and alliances, promoting a virtuous cycle of integration between industry, academia, and research [6]

Core Viewpoint - The article emphasizes the urgent need for the integration of artificial intelligence (AI) in the energy sector, highlighting its role as a key technological engine for building a new energy system and driving industry innovation [3][4]. Group 1: Necessity for Breakthrough - AI is recognized as a strategic force leading a new wave of technological revolution and industrial transformation, significantly impacting energy production and consumption [3]. - Current AI applications in the energy sector are fragmented, leading to resource redundancy and systemic barriers, which hinder long-term development [3][4]. Group 2: Development Goals - The "Implementation Opinions" set two key development targets for 2027 and 2030, focusing on foundational work and establishing benchmarks in the initial phase, followed by comprehensive empowerment and ecosystem construction in the later phase [4]. - The 2027 goal aims to establish industry-level professional large models and typical scenario exploration, while the 2030 goal seeks to achieve world-leading levels in AI technology within the energy sector [4]. Group 3: Implementation Pathways - The article outlines a systematic approach to enhance the quality and efficiency of AI in the energy sector, focusing on key technological breakthroughs, widespread application of industry-level large models, and deep empowerment of high-value scenarios [5][6]. - Key technical directions include solidifying data foundations, enhancing computational power, and improving model capabilities to address common challenges in the energy sector [7]. Group 4: Deep Application of AI - The "Implementation Opinions" propose focusing on high-value application scenarios in areas such as power grids, new energy, and traditional energy sources, aiming to enhance AI's role in energy supply-demand balance and safety monitoring [9]. - The goal is to create an intelligent closed-loop system for perception, analysis, decision-making, and execution, driving energy security and green transformation [9]. Group 5: Innovation Ecosystem - The article stresses the importance of building an open and collaborative industrial ecosystem to support systemic changes in the energy sector [10]. - It highlights the need for pilot demonstrations to stimulate innovation, establish standard norms for orderly development, and strengthen collaborative innovation mechanisms [11][13][14].

Group 1: Macroeconomic Insights - The "Big and Beautiful Act" in the U.S. may exacerbate long-term debt risks, with national debt projected to rise to $36 trillion, potentially leading to a debt-to-GDP ratio of 134%-149% by 2035[3] - The probability of a U.S. economic recession is increasing, with defensive sectors outperforming cyclical sectors, showing a year-to-date increase of 10.7% in defensive sector valuations compared to cyclical sectors[4] - Emerging markets are outperforming developed markets, driven by a weaker dollar, which reduces the cost of holding emerging market assets and alleviates debt pressures[4] Group 2: Market Trends and Fund Manager Behavior - Global fund managers have increased their allocation to European stocks, with net overweight rising from 22% to 35%, the highest level since October 2017[5] - U.S. trade policy uncertainty is identified as a major risk for U.S. equities, with a close correlation between the Bloomberg U.S. Trade Policy Uncertainty Index and the S&P 500 Index[5] - The implied volatility of USD/HKD risk reversal options has dropped to historically low levels, indicating a dominant bearish sentiment towards the HKD[6] Group 3: Valuation and Currency Movements - The forward P/E ratio premium of the "Seven Giants" in U.S. stocks has decreased to a historical low of 46%, with a forward P/E of 31 compared to 21 for the S&P 500 excluding these giants[8] - The Japanese yen has depreciated significantly, becoming the weakest among major Asian currencies, as the Bank of Japan shifts from being a net buyer to a net seller of Japanese government bonds[8] - Following the U.S.-China tariff suspension agreement, the offshore RMB exchange rate broke the 7.17 mark, reaching a new high for the year, driven by weakened dollar credibility[9]