Dynamic economic-entropy regulation of community-scale green hydrogen supply chains with carbon-microgrid coupling

Deep decarbonization through green hydrogen deployment faces economic uncertainty and market coordination challenges. This study develops a dynamic economic entropy regulation framework that transforms uncertainty from a passive diagnostic attribute into an actively controllable system variable. By embedding entropy minimization within a deep reinforcement learning-based closed-loop optimization architecture, proactive uncertainty regulation is achieved across the green hydrogen supply chain. A ternary coupling model integrating carbon trading, green hydrogen systems, and community-scale smart microgrids quantifies carbon price transmission effects. Analysis of 10-year empirical data across five community archetypes demonstrates 55.4% reduction in system-level economic entropy, over 90% renewable energy utilization, and enhanced investment performance under realistic carbon price regimes. Life cycle assessment shows 81.7% lower global warming potential compared with gray hydrogen production.