（文/观察者网 张志峰） 当特朗普再次抛出"中国新能源是产业威胁"的片面论调，美国商务部长动辄以"产能过剩""不公平竞争"为由炒作贸易壁垒，一场关于全球能源转型的认知 博弈已然升温。 当地时间1月21日，远景科技集团董事长张雷在瑞士达沃斯2026世界经济论坛的重磅反击，为中国新能源正名。 "中国新能源，是一种文明级的输出。"张雷直言，本质上是为全球构建新型能源基础设施的先进生产力工具——正如工业革命时期的蒸汽机，为世界工业 体系奠定基础一样。 "中国选择投资的是一种会不断升值的资产，而美国却仍然抓着正在贬值的资产不放。"美国前副总统、诺贝尔和平奖得主阿尔·戈尔（Al Gore）高度认同 张雷的观点。 面对这场争议，更多国家和地区选择用脚投票。 在尼日利亚、肯尼亚、赞比亚等国家，太阳能安装量激增，在巴基斯坦的一些地区，新娘的嫁妆里常见的是三块太阳能板和一个逆变器。戈尔在现场指 出，2025年，中国出口的绿色技术总价值，已经比美国出口的所有化石燃料价值高出50%。 不惟发展中国家，就在几天前，加拿大总理时隔9年再次访华，中加两国关系迎来"破冰式"修复，并一次性把关税、联委会、能源、农业、人文等关键议 题重新拉回到 ...

Core Insights - The first batch of national-level zero-carbon parks has completed the review process, with the final list expected to be announced soon [1] - The establishment of zero-carbon parks is a key strategic measure to achieve China's dual carbon goals, with a target of building around 100 national-level zero-carbon parks during the 14th Five-Year Plan period [2][3] - The construction of zero-carbon parks faces challenges such as high costs and the need for technological integration, despite the push for investment and construction [1][3] Policy and Regulatory Framework - The National Development and Reform Commission (NDRC) and other departments have issued guidelines on the basic conditions, indicators, and carbon emission accounting methods for zero-carbon park construction [2] - The core indicator for evaluating zero-carbon parks is "unit energy consumption carbon emissions," which aims to guide parks to achieve near-zero carbon emissions while ensuring enterprise development [3][4] Technical and Operational Standards - The "National Level Zero-Carbon Park Construction Indicator System (Trial)" specifies that parks with an annual comprehensive energy consumption of 20-100 million tons of standard coal must have unit energy consumption carbon emissions of ≤0.2 tons/ton of standard coal, while those with consumption ≥100 million tons must meet ≤0.3 tons/ton [4][5] - Additional guiding indicators include a clean energy consumption ratio of ≥90%, industrial solid waste utilization rate of ≥80%, and industrial water reuse rate of ≥80% [5][7] Regional and Industry Challenges - The construction of zero-carbon parks varies significantly across regions, with eastern regions facing high industrial demand for green electricity and western regions having abundant renewable resources but weaker industrial foundations [3] - The current average unit energy consumption carbon emissions across parks is approximately 2.1 tons/ton of standard coal, indicating a need for a 90% reduction to meet zero-carbon standards [6] Investment and Market Dynamics - Companies are motivated to engage in zero-carbon park construction due to policy incentives and market access requirements, particularly in export-oriented industries [10] - The construction of zero-carbon parks requires a stable and high proportion of green electricity supply, which poses challenges in balancing renewable energy variability with continuous industrial production needs [10][11] Collaborative Efforts and Innovations - Successful zero-carbon park projects involve collaboration among various stakeholders, including park management committees, energy investment companies, and production enterprises [12] - Innovative solutions, such as AI-powered energy management systems, are being developed to enhance energy efficiency and stability in zero-carbon parks [11][12]

Core Viewpoint - The article emphasizes the strategic importance of developing zero-carbon industrial parks in China as a key initiative for achieving green transformation and carbon neutrality goals during the "14th Five-Year Plan" period, with a target of establishing approximately 100 national-level zero-carbon parks [1][3]. Group 1: National and Local Initiatives - The 20th National Congress of the Communist Party of China calls for accelerating the formation of green production and lifestyle, with a focus on ecological environment management and industrial layout optimization [3]. - Various regions are actively constructing zero-carbon parks, leveraging local resources and clean energy advantages, such as Inner Mongolia and Qinghai focusing on low-cost energy supply, while coastal areas like Jiangsu and Guangdong emphasize digitalization and smart manufacturing [3][5]. - The establishment of zero-carbon parks is seen as a critical step in transitioning to a low-carbon economy, with local governments implementing supportive policies to attract businesses and investments [5][7]. Group 2: Economic and Competitive Implications - The shift towards zero-carbon parks is driven by the need for industries to adapt to global green trade barriers and the increasing competitiveness of low-carbon products in international markets [8]. - Zero-carbon parks are expected to create high-quality green jobs and attract low-carbon industries, enhancing overall economic competitiveness [7][8]. - The integration of renewable energy technologies and energy management systems in these parks is projected to significantly improve energy efficiency and reduce carbon emissions [8]. Group 3: Challenges and Solutions - Despite the enthusiasm for zero-carbon park development, challenges such as the lack of strong implementation bodies and the need for a coordinated approach among stakeholders are highlighted [10]. - Key difficulties include standardization issues, cost-benefit analysis, and resource supply constraints, which need to be addressed to ensure effective implementation [10][11]. - The article suggests that innovative financing mechanisms and the establishment of specialized funds could support the development of integrated energy solutions within zero-carbon parks [14]. Group 4: Future Outlook - The article envisions that the proliferation of zero-carbon parks across China will contribute to high-quality economic development and provide a model for global low-carbon transitions [15].

Core Viewpoint - The article highlights the development of a green ammonia project in Inner Mongolia by Envision Group, emphasizing its potential to revolutionize energy production and reduce carbon emissions through the use of renewable energy sources [1][6]. Group 1: Company Overview - Envision Energy, founded by Zhang Lei, has a valuation of 1.1 billion, focusing on wind turbine manufacturing, energy storage, and hydrogen energy [1]. - Envision Power, led by Wan Sui, is valued at 730 million and specializes in power battery manufacturing [1]. - Envision Intelligent, also founded by Zhang Lei, has a valuation of 265 million and operates in the smart IoT sector [1]. Group 2: Project Details - The green hydrogen and ammonia project in Chifeng aims to produce 152,000 tons of green ammonia annually, which could reduce over 9 million tons of carbon emissions, equivalent to the annual emissions of 15 million fuel vehicles [7]. - The project is expected to achieve a scale of 20 GW, generating an annual electricity output of 60 billion kWh, comparable to Portugal's total electricity consumption [8][9]. Group 3: Technological Innovations - Envision has developed an AI power system to optimize energy management, allowing for efficient adjustments based on weather conditions and energy availability [18]. - The project utilizes self-developed wind turbines and energy storage systems to produce green hydrogen through electrolysis [5][17]. Group 4: Market Strategy - Envision has secured long-term green ammonia purchase agreements with major companies like Marubeni Corporation and DHL, targeting applications in shipping fuel, hydrogen derivatives, and fertilizers [19]. - The company is also investing in key equipment manufacturing to accelerate the commercialization of green ammonia [19]. Group 5: Future Aspirations - Zhang Lei envisions that significant reductions in energy costs could enable ambitious projects like large-scale seawater desalination and low-cost interstellar transportation [22].

Carbon Neutrality Policies - China, the EU, and Brazil have jointly established the "Carbon Emission Trading Market Open Alliance" to enhance international cooperation on carbon pricing mechanisms and emission trading systems, aiming for a transparent global compliance carbon market network [1] - The National Energy Administration of China has issued guidelines to promote the integrated development of renewable energy, emphasizing multi-dimensional collaboration and non-electric utilization by 2030 [2] - China has signed 55 climate change cooperation documents with 43 developing countries, focusing on capacity building and technology transfer in climate governance [3] Local Developments - Wuhan has signed 14 green low-carbon technology projects, including a CO2 capture and electrochemical conversion technology, showcasing its commitment to a comprehensive green transition [4][5] Corporate Practices - Envision Group has launched the world's largest AI-driven power system, enhancing the economic viability and stability of renewable energy systems, successfully applied in a zero-carbon hydrogen industry park [6] - Green computing is emerging as a new investment focus, with a significant portion of data center emissions stemming from electricity consumption, prompting a shift towards energy efficiency and green electricity usage [7]

Carbon Neutrality Policy - The establishment of the Carbon Market Open Alliance by China, the EU, and Brazil aims to create a framework for international cooperation on carbon pricing mechanisms and emission trading systems, enhancing the voice of developing countries in the global carbon pricing system [2] - The National Energy Administration of China has issued guidelines to promote the integrated development of renewable energy, emphasizing multi-dimensional collaboration and non-electric utilization, with a goal for integrated development to become a key approach by 2030 [3] - China has signed 55 climate change cooperation documents with 43 developing countries, focusing on capacity building, technology transfer, and infrastructure support in climate governance [4] Local Developments - Wuhan has signed 14 green low-carbon technology projects, including advancements in CO2 capture and electrochemical conversion technology, showcasing a comprehensive green transformation approach [5][6] Corporate Practices - Envision Group has launched the world's largest AI-driven power system, enhancing the economic viability and stability of renewable energy systems, successfully applied in the global largest green hydrogen and ammonia industrial base [7] - Green computing is emerging as a new investment focus, with a significant portion of carbon emissions from data centers stemming from electricity consumption, highlighting the importance of energy efficiency and green electricity usage [8]

Core Concept - The concept of "Physical AI" is introduced as a new paradigm that combines AI with physical laws and knowledge graphs, aiming to eliminate the "hallucinations" of traditional language models and enable reliable AI applications in the physical world [2][3]. Group 1: Development of Physical AI - The future development of Physical AI is seen as a significant direction, particularly in the context of energy systems [2]. - The integration of data intelligence with physical laws like energy conservation and aerodynamics is expected to enhance AI's reliability in real-world applications [2]. Group 2: Energy Model and AI Applications - The energy model is considered crucial for reconstructing energy systems, providing a rich application scenario for Physical AI [4]. - AI's ability to process vast amounts of data in milliseconds can help optimize decision-making in complex energy systems, addressing human anxieties related to energy management [4][5]. Group 3: Competitive Landscape - The U.S. is viewed as lacking the necessary industrial scenarios and complex energy systems to support the development of Physical AI and energy models, giving China a potential advantage in this field [3][5]. - Companies that only specialize in single areas like wind or solar energy may struggle to develop comprehensive energy models due to a lack of holistic understanding and data [5]. Group 4: Addressing Industry Challenges - The energy sector is currently facing issues of overcapacity and price wars, particularly in solar and wind energy, which have led to significant financial losses for many companies [7]. - Physical AI and energy models are proposed as solutions to end the cycle of homogeneous competition and shift the focus from material assets to intelligent assets [8]. Group 5: Future Outlook - The development of energy models is expected to evolve into a robust system capable of generating significant value within 1-3 years [11]. - The future energy system is envisioned as an ecosystem of intelligent agents rather than just a collection of devices, aimed at better integrating renewable energy sources and providing energy at lower costs [11].

Core Insights - The core argument presented by Zhang Lei, Chairman of Envision Technology Group, is that artificial intelligence (AI) is evolving from a mere tool to a主体, fundamentally transforming the energy sector into an "intelligent agent" ecosystem rather than just a collection of physical assets [1][2][4] Group 1: AI's Role in Energy Systems - AI is seen as a revolutionary force that can handle the increasing complexity and market uncertainties associated with high proportions of renewable energy in the grid [2][3] - The concept of "Physical AI" is introduced, which integrates AI with physical laws and knowledge graphs, enhancing its reliability in real-world applications [2][3] Group 2: Technological Advancements - Envision has made significant breakthroughs in large models, particularly with the "Tianji" meteorological model, which improves medium to long-term weather forecasting accuracy, crucial for the reliable operation of renewable energy [3] - The "Tianshu" energy model, capable of real-time control through advanced algorithms, is successfully applied to optimize energy trading and asset investment decisions [3] Group 3: Future Competitiveness - The future competitiveness of energy companies will shift from traditional metrics like installed capacity to the scale of "AI assets" [3][4] - The industry is urged to focus on the intelligence of their models and the scale of their AI capabilities, marking a significant transition from physical to intelligent assets [3]