Beijing’s audacious promise to reach carbon neutrality by 2060 just encountered an unexpected reality check. Not from environmental activists or international critics, but from an AI system that methodically dissected the country’s energy policies and found them wanting.
Chinese researchers have completed the first large-scale deployment of ChatGPT to evaluate government policy effectiveness, analyzing 203 energy storage regulations from the past three years. The results paint a concerning picture: while most policies earn passing grades, significant gaps remain that could derail China’s climate ambitions.
The Digital Policy Audit
The experiment began with a simple premise. Traditional policy evaluation takes months, relies heavily on human interpretation, and often misses subtle but critical implementation flaws. What if artificial intelligence could do better?
A research team led by energy policy experts at Chinese universities fed 203 policy documents into a customized ChatGPT system. These weren’t minor regulations, they were the backbone of China’s energy storage strategy during the 14th Five-Year Plan period from 2021 to 2024.
The AI system evaluated each policy across multiple dimensions: internal consistency, implementation mechanisms, alignment with carbon targets, and practical enforceability. The AI was guided by the PMC-index model, a multi-criteria framework often used in public policy evaluation to quantify complex variables. Policies received scores on a 10-point scale, with anything below 6 considered problematic.
The results were sobering. Most policies clustered between 6 and 9 points, suggesting competent but not exceptional regulatory frameworks. More troubling, several policies fell below the 6-point threshold, indicating fundamental design flaws that could hamper China’s energy transition.
Why Energy Storage Matters Right Now
Energy storage sits at the heart of any serious climate strategy. Solar panels generate power during sunny afternoons. Wind turbines spin when breezes blow. But electricity demand peaks during evening hours when families return home and businesses ramp up operations.
Without massive battery systems and other storage technologies, renewable energy remains unreliable. China understands this equation better than most nations. The country leads global solar panel production and wind turbine installation, yet still burns more coal than any other nation.
The policy evaluation revealed a critical blind spot. Current regulations focus heavily on reducing storage system costs and extending equipment lifespans. That sounds practical, but it misses the broader ecosystem needed for commercial success: pricing mechanisms that reward storage operators, technical standards that ensure interoperability, and market structures that encourage private investment beyond government subsidies.
The AI Advantage in Policy Analysis
Traditional policy review resembles archaeological work. Analysts dig through dense regulatory text, compare implementation across different regions, and attempt to measure real-world impacts through scattered data points. The process takes months and often produces subjective conclusions that vary between reviewers.
ChatGPT approaches the task differently. The AI system processes natural language at massive scale, identifying patterns and inconsistencies that human reviewers might overlook. It can simultaneously evaluate policy coherence, implementation feasibility, and alignment with stated objectives.
The research team ran each policy through ChatGPT five times using identical prompts to ensure consistent results. While human evaluators averaged 8.36 out of 10 across the policies, ChatGPT’s assessments came in close at 7.46—suggesting AI can approximate expert judgment with remarkable speed.
Expert Validation
“ESS plays a pivotal role in advancing China’s energy transition, supporting carbon neutrality goals, and enhancing grid reliability,” the researchers concluded in their published findings. They emphasized that their “PMC-index framework significantly enhances the accuracy and efficiency of policy evaluation by leveraging AI capabilities.”
The study also highlighted a persistent challenge: “Current efforts to develop ESS mainly focus on cost reduction and extending system lifespans, falling short of addressing broader challenges” including market constraints and barriers to widespread commercial adoption.
The Broader Implications
This research represents more than academic curiosity about Chinese energy policy. It demonstrates how AI tools might revolutionize government decision-making worldwide.
Consider the timeline advantages. Traditional policy review cycles span years, often completing long after regulations have already shaped markets and investment decisions. AI-powered analysis could compress that timeline to weeks, enabling governments to identify and fix problems before they become entrenched.
The scalability factor is equally compelling. A single AI system could simultaneously evaluate energy policies, healthcare regulations, transportation rules, and education standards. Government efficiency gains of this magnitude could reshape public administration across developed and developing nations.
Yet the research also exposes important limitations. AI systems inherit biases from their training data and can produce inconsistent results based on prompt formulation. The Chinese team had to carefully engineer their queries and run multiple scoring rounds to ensure reliability. This suggests AI augments rather than replaces human expertise in complex policy evaluation.
What This Means for Climate Goals
The policy gaps identified by AI analysis carry real-world consequences. China’s energy storage sector remains heavily dependent on government subsidies rather than market forces. Without robust pricing mechanisms and technical standards, the transition from fossil fuels could stall despite massive public investment in renewable energy infrastructure.
International climate commitments depend on China’s success. The country produces roughly 30% of global carbon emissions, making its transition timeline critical for worldwide temperature targets. Policy weaknesses identified through AI analysis could help Beijing strengthen its regulatory framework before problems compound.
Other nations watching China’s climate progress might also benefit from similar AI-powered policy audits. The techniques demonstrated in this research could be adapted for different regulatory systems and policy priorities.
The bigger question is whether governments will embrace AI-assisted policy making or resist technological disruption of traditional governance methods. Early adopters could gain significant advantages in policy effectiveness and implementation speed.
What do you think about AI systems evaluating government policies? Could this approach help accelerate climate action globally?
As China chases carbon neutrality, its smartest advisor yet might not be a bureaucrat or economist, but a large language model with no politics—just pattern recognition.
