Yunnan, as a major global production base for cut roses, faces the challenge of high carbon emissions in its industry, including greenhouse gas emissions and resource consumption in planting, harvesting, processing, transportation, and sales. This study quantifies the carbon footprint across the entire supply chain using the LCA method, investigating carbon emissions and their main sources during the planting and transportation stages. AI technology is introduced to optimize planting forecasts using machine learning algorithms, blockchain enables transparent supply chain tracking, and an IoT-supported intelligent collaborative governance framework promotes collaboration among multiple stakeholders (farmers, businesses, and government) to achieve carbon reduction targets. The transformation path includes: (1) AI-assisted precision agriculture to reduce fertilizer input; (2) promoting renewable energy substitution under the ESG framework to enhance social responsibility and governance efficiency; and (3) establishing a low-carbon certification system to enhance market competitiveness. This study emphasizes the synergistic effect of AI and intelligent governance, providing theoretical and practical guidance for the sustainable development of Yunnan’s cut rose industry.
