AI Research Summary
Before Human ReviewBased on abstract · Full text not reviewed
This is an abstract-based summary, and the full text was not reviewed. Using China’s carbon-neutral energy transition as a case, the paper proposes an AI–SD integrated framework and iteratively combines SD’s structural constraints with AI’s nonlinear calibration. Simulations showed good historical fit and long-term scenario extrapolation, and demonstrated the possibility of balancing emission-reduction effects and social costs.
Key Points from the Abstract
- The hybrid AI–SD was flexible for historical fit and long-term extrapolation.
- Cumulative emission reductions were high in multi-policy synergy scenarios.
- Technology investment and energy costs were associated with sensitivity, and carbon pricing was scenario-dependent.
Relevance to AI Law and Policy
Useful as a reference for research combining energy transition governance, long-term scenario analysis, and AI-mechanistic model integration.
Limitations to Consider
- A proof-of-concept in the case of China.
- Depends on the defined parameter space and scenario assumptions.
- The scope of generalization cannot be determined from the abstract alone.
Please compare with the original text before citing or using it in your assessment.
Abstract Preview
Amidst growing uncertainty regarding global sustainable development goals and the increasing complexity of social system…Read more in the abstract and original text Research Topic
Original text and source
Intelligent Governance of Complex Social Systems: A Hybrid AI–System Dynamics Model for the Simulation and Projection of Sustainable Energy Transition
The Korean title on this page was translated by AI.- Database
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