China is increasingly leveraging artificial intelligence (AI) to transform its energy system, impacting how power is generated, distributed, and consumed. This initiative is central to the nation's push for cleaner energy and is evident in day-to-day operations rather than just policy discussions.

A prime example of this integration is a renewable-powered factory in Chifeng, northern China. This facility produces hydrogen and ammonia exclusively using electricity from nearby wind and solar farms. Operating as a closed system, independent of the wider grid, it faces the inherent challenge of renewable energy's intermittent nature. To counteract this, the factory relies on an AI-driven control system developed by its owner, Envision. This software dynamically adjusts production in real time based on fluctuations in wind and sunlight, rather than adhering to fixed schedules. Zhang Jian, Envision’s chief engineer for hydrogen energy, likens the system to a conductor, orchestrating electricity supply and industrial demand. This enables high efficiency despite renewable energy volatility and is crucial for China's broader plans for hydrogen and ammonia as fuels to decarbonize heavy industries like steelmaking and shipping.

This factory exemplifies a wider national strategy: using AI to manage the growing complexity introduced by the expansion of renewable power on China's grid. Researchers, such as Zheng Saina of Southeast University, highlight AI's potential in areas from emissions tracking to forecasting electricity supply and demand, all contributing to China’s climate goals. This strategic thinking was formalized in September with Beijing’s introduction of an “AI+ energy” strategy. The plan calls for deeper integration between AI and the energy sector, including the development of large AI models for grid operations, power generation, and industrial use. By 2027, the government aims to launch dozens of pilot projects and test AI across over 100 use cases, with a goal of achieving world-leading AI integration in energy within three years.

Cory Combs of Trivium China notes that China's focus is on highly specialized AI tools tailored for specific tasks, such as managing wind farms, nuclear plants, or grid balancing, rather than general-purpose AI. This approach contrasts with the United States, where investment has largely concentrated on building advanced large-language models, as observed by Hu Guangzhou of the China Europe International Business School.

One area where AI is poised for immediate impact is demand forecasting. Fang Lurui of Xi’an Jiaotong-Liverpool University emphasizes that accurate forecasts of renewable output and electricity use are vital for power grids to constantly match supply and demand, thereby preventing outages. Such forecasting allows operators to plan effectively, enabling energy storage in batteries when appropriate and reducing reliance on coal-fired backup plants.

Several cities are already experimenting with advanced AI applications. Shanghai, for instance, has launched a citywide virtual power plant that connects various operators, including data centers, building systems, and electric vehicle chargers, into a single coordinated network. During a trial last August, this system successfully reduced peak demand by more than 160 megawatts, equivalent to the output of a small coal plant. Combs underscores the importance of such robust, predictive systems in managing modern power generation, which is increasingly scattered and intermittent.

Beyond grid management, China is also exploring AI's application in its national carbon market, which encompasses over 3,000 companies in emission-intensive sectors like power, steel, cement, and aluminium, collectively responsible for over 60% of the country’s carbon emissions. Chen Zhibin of adelphi suggests AI could assist regulators in verifying emissions data, refining the allocation of free allowances, and providing companies with clearer insights into their production costs.

However, the rapid expansion of AI also brings growing risks. Studies project that by 2030, China’s AI data centers could consume more than 1,000 terawatt-hours of electricity annually, roughly equivalent to Japan’s current annual usage. Furthermore, the lifecycle emissions from the AI sector are predicted to peak well after China’s 2030 emissions target. Xiong Qiyang, a doctoral researcher at Renmin University of China, warns that this rapid AI growth could complicate national climate goals if the transition to cleaner energy sources does not accelerate sufficiently, given that coal still dominates China’s power mix.

In response to these concerns, regulators have begun implementing tighter rules. A 2024 action plan mandates that data centers improve energy efficiency and increase their use of renewable power by 10% annually. Other initiatives promote building new AI facilities in western regions where wind and solar resources are abundant. Operators on the east coast are also exploring innovative solutions, such as an underwater data center near Shanghai that will use seawater for cooling, significantly cutting energy and water consumption. Developed by Hailanyun, this facility plans to draw most of its power from an offshore wind farm and could be replicated if successful.

Despite the rising energy demands of AI, Xiong Qiyang maintains that its overall impact on emissions could still be positive if deployed strategically. When used to optimize heavy industry, power systems, and carbon markets, AI could remain an indispensable tool in China’s efforts to reduce emissions, even as it presents new challenges that policymakers must proactively manage.