In recent years, global issues such as a dramatic increase in greenhouse gas emissions and environmental pollution have become apparent. The energy industry, as a primary source of carbon emissions, faces the formidable challenge of reducing emissions. Integrated energy systems, which encompass various forms of energy utilization, have emerged as a crucial platform for energy conservation and emission reduction. Therefore, this paper presents a low-carbon economic dispatch method for integrated energy systems based on multi-agent reinforcement learning and provides initial case study validation. Experimental results demonstrate that multi-agent reinforcement learning holds significant potential for application in the field of low-carbon economic dispatch for integrated energy systems.