Carbon Emission Reduction Path Analysis and Decision Support Based on Knowledge Graphs

As global climate change becomes an increasingly severe issue, carbon emission reduction has become one of the core tasks for countries to achieve carbon neutrality. This study constructs a carbon emission reduction path analysis model, optimizing the implementation sequence of reduction measures by quantifying the potential, cost, and difficulty of emission reduction technologies. By integrating multi-source data through knowledge graph technology, it supports path optimization, combining energy substitution, efficiency improvement, and carbon capture methods. Based on graph reasoning and graph convolutional network reinforcement learning, the model enables dynamic adjustment of carbon reduction paths. Experimental results show that the proposed method can effectively optimize carbon reduction paths in various industries, significantly improving reduction effectiveness while lowering implementation costs.