2026年宏观经济展望 G2格局再平衡 明明 中信证券首席经济学家 2025年11月10日 请务必阅读末页的免责条款和声明 2026年我国GDP增速或为4.9%左右 目录 CONTENTS 历年GDP增速及预测（%） 7.0 6.8 6.9 6.8 6.1 2.3 8.6 3.1 5.4 5.0 5.0 4.9 0 2 4 6 8 10 12 14 16 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025E 2026E 实际GDP 名义GDP 2 1. 中国经济波动复苏 2. G2格局再平衡 3. 美国经济面临矛盾 4. 宏观政策更加积极 5. 全球大类资产展望 6. 风险因素 2026年分季度GDP增速预测（%） 4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 一季度 二季度 三季度 四季度 实际GDP 名义GDP 资料来源：Wind，中信证券研究部预测 资料来源：Wind，中信证券研究部预测 经济增长动能：产出强于需求、外需强于内需的宏观格局仍在延续 42 44 46 48 50 52 54 56 58 PMI:生产 PMI:新订单 ...

"十五五"规划建议提出，2035年人均国内生产总值达到中等发达国家水平，这不仅是简单数字的跃迁， 还意味着人民生活幸福水平的提升，更是我国实现民族伟大复兴的重要标志。图为游客近日在云南省大 理市喜洲古镇乘坐观光小火车。新华社 开栏的话 站在"十四五"收官与"十五五"启航的历史交汇点，深入学习贯彻党的二十届四中全会精神，全面解 读"十五五"规划建议的战略部署，是把握发展大势、增强发展主动、凝聚奋进力量的关键举措。从"到 2035年人均国内生产总值达到中等发达国家水平"的远景目标，到"发展新质生产力"的战略导向；从治 理方式由"管得住"向"管得好"的转型升级，到"投资于物"与"投资于人"的紧密结合；从推动形成内需主 导、消费拉动的经济增长模式，到应对人口结构变化带来的新挑战，再到如期实现碳达峰目标的绿色转 型路径——每一项部署都紧扣高质量发展这一首要任务，每一步探索都关乎中国式现代化的扎实推进。 为此，本报推出"学习贯彻四中全会精神·'十五五'规划建议七问七答"系列报道，聚焦重点议题，回应公 众关切，与广大读者一道，共同探寻全面建设社会主义现代化国家的实践路径，为凝聚社会共识、汇聚 发展合力贡献智慧与力量。 中国 ...

从以扩大投资规模为导向的加法式增长，转向投资于人和投资于物相结合的乘法式发展。 投资于人与投资于物的关系，正成为理解"十五五"期间经济增长逻辑的重要线索。这一转型体现为，从以扩大投资规模为导向的加法式增长，转向投资于人 和投资于物相结合的乘法式发展，根本目标是推动全要素生产率的持续提升。实现乘法效应可以聚焦三条路径：以新质生产力为抓手，推动先进物质资本与 高素质人力资本的高阶匹配；将人力资本投资重心转向质量提升，以"质量红利"延长人口红利；建立同步投入机制，确保物质资本与人力资本在时序和结构 上协同共振。 投资于人和投资于物的四象限结构 投资于人的理念早已在实践中生根。从2023年中央财经委员会提出要将投资于物与投资于人紧密结合，2025年政府工作报告明确其宏观政策地位，再到"十 五五"规划建议将两者并列写入，投资政策逻辑正在发生转变。这不仅是对财政和产业政策的优化完善，更是对增长方式的系统重塑，迈向由人力资本与物 质资本协同驱动的新发展范式。 从经济学原理来看，经典的柯布-道格拉斯生产函数Y=AKαL1α为理解两类投资的关系提供了理论基础。在工业经济时代，A代表全要素生产率（TFP），K 代表资本，L代表劳 ...

专题：2025分析师大会：资本市场"奥斯卡"！机构称A股迎全球资本涌入的大牛市 炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 11月28日，2025分析师大会举行，摩根士丹利中国首席经济学家邢自强主题演讲《中国新篇章： 科技 与再平衡》。 邢自强表示，依托丰富的人才储备，中国处于AI创新前沿，中国大语言模型的性价比较高。AI既能增 益、也能替代劳动力，缓解人工智能对劳动力市场造成的扰动，还需更多政策支持。 邢自强认为，人形机器人5万亿美元全球市场大幕拉开，预测到2050年人形机器人累计应用规模将达到 10亿台，其中约30%来自中国。 附演讲PPT 美降温超预期,但持久性缓和仍难以实现 中美缓和超预期 然而在竞争性对抗格局下，中美缓和仍较为懿弱 | | 货量进口 如果大豆 | 2025 Jan | | | | --- | --- | --- | --- | --- | | 一点 在的按照片 | - 10 2 2 10 10 2 2 4 10 10 2 2 10 | | | | | | 全身实质性描述明新芬太原资讯 | Sels | NULL | | | BOTACUL 201202 ...

Core Insights - The article discusses the impact of knowledge spillover on total factor productivity through digital transformation and agglomeration networks among companies [1][3][4] Group 1: Knowledge Spillover Metrics - Specialized knowledge spillover (SK_Spillover) is measured by the knowledge spillover effect from companies within the same industry located within a 50 km radius, indicating stronger effects with larger values [1][3] - Diversified knowledge spillover (DK_Spillover) is calculated similarly but includes companies from different industries within the same 50 km range [1][4] Group 2: Data and Sample Size - The study includes over 10,000 samples from 1,343 companies, with nearly 3,000 non-zero effective values for specialized knowledge spillover and over 10,000 for diversified knowledge spillover [1] - The data encompasses original data, calculation codes, and final results, allowing for verification of accuracy [1] Group 3: Mechanism Examination - The mechanism examination shows that digital transformation enhances the effectiveness of knowledge production and transfer among companies, facilitating rapid exchange of social information and knowledge [3][4] - The results indicate that both digital transformation and agglomeration networks significantly promote diversified knowledge spillover, with robust effects even after addressing endogeneity issues [4] Group 4: Yearly Knowledge Spillover Data - The data table presents yearly values for specialized and diversified knowledge spillover for two companies from 2014 to 2024, showing trends in knowledge spillover over the years [5]

Core Insights - The core argument is that in China's new development stage, the main constraint on economic growth has shifted from the supply side to the demand side, with resident consumption becoming the decisive driving force for growth [1][2]. Demand-Side Constraints - Demand-side constraints, particularly in resident consumption, have become the primary limitation on China's economic growth, influenced by factors such as declining manufacturing advantages, the transition to high-quality development, and demographic changes like population decline and aging [2][3]. - The transition from investment-driven to consumption-driven growth is essential as China faces challenges from a decreasing population and slower income growth, which significantly suppresses resident consumption [2][5]. Economic Growth Dynamics - The relationship between resident consumption rates and economic growth is critical; higher consumption rates correlate with lower probabilities of significant economic slowdown, highlighting the importance of maintaining consumption at levels consistent with development stages to avoid the middle-income trap [3][4]. Barriers to Consumption Growth - Several barriers must be overcome to enhance resident consumption, including the long-term trend of slowing GDP and disposable income growth, which is exacerbated by demographic shifts and the transition to a higher economic development stage [5][6]. - The existing income distribution gap, characterized by a high Gini coefficient, limits overall consumption demand as lower-income households tend to have a higher marginal propensity to consume [6][7]. - Rapid aging and the phenomenon of "getting old before getting rich" further complicate consumption dynamics, as older populations typically have lower consumption rates and face multiple financial burdens [7][8]. Policy Recommendations - To foster necessary changes in consumption dynamics, a shift in mindset and policy is required, focusing on long-term human capital development and job creation to support household income and consumption [8][9]. - Improving income distribution through effective tax and transfer systems is crucial, as current redistributive measures in China are significantly lower than those in many OECD countries, indicating substantial potential for improvement [9][10]. - Expanding the provision of public goods and services is essential, as increased government spending on social services can enhance overall living standards and indirectly support consumption growth [10][11].

Core Insights - The report analyzes corporate resilience from 2006 to 2024 using a production function framework, highlighting the relationship between resource allocation and productivity metrics during disruptions [1][2] - Resilience is defined as the ability of a company to withstand shocks and recover quickly, measured through changes in total factor productivity (TFP) [1][2] Summary by Sections Methodology - The analysis employs the Cobb-Douglas production function to estimate TFP, deriving resilience from regression residuals [1] - Resilience consists of two dynamic components: minimizing losses during disruptions (resistance) and striving for recovery afterward [1] Data Scope - The study includes over 61,000 samples from 5,495 companies, providing original data, calculation codes, and final results for verification [2] - The reference paper discusses the differentiated roles of patient capital in enhancing the resilience of new enterprises [2] Company Performance - The resilience data for Vanke Co., Ltd. (万科A) shows fluctuations in TFP from 2006 to 2024, with notable values such as 0.1532 in 2024 and -0.1133 in 2009 [2]

Core Insights - The integration of AI in manufacturing, particularly in Xiaomi's automotive factory, has significantly enhanced efficiency and precision, with AI quality inspection outperforming human capabilities by over five times [2] - The future of humanoid robots in Xiaomi's factories is set to revolutionize operations, moving from traditional manufacturing to intelligent collaboration across the supply chain [3] - The AI industry in China is experiencing rapid growth, with projections indicating a market size exceeding 900 billion yuan in 2024, reflecting a 24% year-on-year increase [4] Group 1: AI in Manufacturing - AI quality inspection systems in Xiaomi's factory can detect defects in components with a 99.9% accuracy rate, reducing inspection time from 20 seconds to just 2 seconds per part [2] - The entire production line's quality inspection process has been streamlined from 45 minutes to 28 minutes, showcasing a significant leap in productivity [2] - The use of AI transforms traditional manufacturing by enhancing overall productivity through real-time data analysis, leading to improved equipment utilization and process turnaround rates [2] Group 2: Humanoid Robots and Industry Transformation - Xiaomi plans to deploy humanoid robots in its factories within the next five years, indicating a shift towards more advanced automation [3] - These robots are not merely tools but represent new nodes in the industrial chain, facilitating collaboration among various sectors [3] - The transition from large-scale production to intelligent collaboration is reshaping the manufacturing landscape, with Xiaomi partnering with leading firms across the supply chain [3] Group 3: Market Growth and Future Prospects - The AI industry in China is projected to reach a scale of over 900 billion yuan in 2024, with the global industrial AI market expected to grow from $43.6 billion to $153.9 billion by 2030 [4] - The demand for humanoid robots in household settings is anticipated to surge, as industrial applications pave the way for domestic use [4] - The economic logic behind AI adoption is driving a complete industrial ecosystem, from traditional applications to new consumer markets, creating a closed-loop system [4] Group 4: AI as a Universal Technology - AI is positioned as a universal technology, akin to electricity, fundamentally altering the underlying logic of various industries [5] - Traditional industry pain points such as redundancy and information asymmetry are being addressed through AI and supply chain collaboration [5] - The projected market size for AI in China is expected to reach $313.86 billion by 2025 and aim for $1.59 trillion by 2030, highlighting significant opportunities for both traditional and emerging companies [5]

[ 初步研究发现，全要素生产率增长中约有22%可以归因于新质生产力，其中企业内部创新成长贡献约 9%，资源配置效率改善贡献约13%。 ] 创新政策是影响新质生产力形成的关键制度变量。此处创新政策的影响，不但包括了对企业个体的影 响，也包括了对企业内部、企业间、产业间、地区间等不同维度的配置效率改善，即创新引致的配置效 率提升。根据政策介入的时点和作用机制不同，可将创新政策分为事前政策和事后政策。 事前政策主要通过研发补贴、税收抵扣、财政扶持、政府引导基金等方式降低创新活动的进入门槛，使 企业更容易开展研发活动。在追赶型发展阶段，这类政策在推动创新扩散、弥补企业投入能力不足方面 具有非常好的作用，中国过去几十年的成功受这类政策的影响很大。然而，对于不确定程度更高的情 形，事前政策在资源配置机制上存在结构性约束，政府难以精准识别未来具有突破潜力的创新方向，容 易出现"选错对象"的问题。此外，在实践中，事前政策往往更容易流向规模大、资源多的企业，而这些 企业本身创新能力较强，财政支持对其边际激励作用有限。更为重要的是，事前政策可能诱导企业形 成"策略性创新"行为，即为获取补贴而开展形式性创新，弱化了创新质量和创新动 ...

Group 1 - The core argument emphasizes that the growth of new quality productivity relies on both "innovation" and "innovation-induced allocation" improvements, neglecting either aspect is inappropriate [1][3] - The competitiveness of a country's industry is primarily determined by the cost of production factors and technological levels, with China's past growth driven by relatively low production factor costs [1][2] - As China's economy expands and resource constraints increase, the comparative advantage based on low costs is declining, necessitating a shift from factor-driven growth to innovation-driven growth [1][2] Group 2 - The traditional growth model relies on large-scale input of capital, labor, and land, but recent declines in capital and human capital returns necessitate a transition towards efficiency and technological progress [2][3] - New quality productivity development is strategically significant for enhancing total factor productivity, with micro-level improvements through R&D, digital transformation, and organizational management [2][3] - Approximately 22% of total factor productivity growth can be attributed to new quality productivity, with internal innovation contributing about 9% and resource allocation efficiency contributing about 13% [3] Group 3 - The formation of new quality productivity requires supportive institutional frameworks and market demand guidance, focusing on the diffusion effects of innovation on resource allocation and industrial upgrading [4][5] - Innovation policies are critical institutional variables influencing the formation of new quality productivity, affecting both individual enterprises and broader market efficiencies [4][5] - Innovation policies can be categorized into pre-emptive policies, which lower barriers to innovation, and post-innovation policies, which protect intellectual property and ensure returns on innovation [5][6] Group 4 - Pre-emptive policies have historically played a significant role in promoting innovation diffusion but may lead to structural constraints and misallocation of resources [5][6] - Post-innovation policies are essential for protecting the returns on successful innovations, motivating enterprises to undertake long-term R&D despite uncertainties [6] - The transition from imitation-based to original innovation necessitates a shift in the innovation policy framework from pre-emptive support to post-innovation incentives, allowing market forces to guide innovation directions [6]