AIoT-Driven Harmonic Lifecycle Assessment and Predictive Analytics for Green Music Production Supply Chain Systems

Traditional carbon footprint measurement approaches fail to address the unique requirements of green music production, where conventional lifecycle assessment methods cannot accommodate the dual imperatives of environmental impact reduction and acoustic performance preservation. Existing carbon accounting frameworks lack the granularity to capture real-time operational variations in creative environments, while standard IoT monitoring systems ignore the acoustic signatures that indicate both energy efficiency and production quality. This paper introduces harmonic music carbon assessment and prediction (HMCAP), a novel dual-phase methodology that harmonizes environmental impact assessment with acoustic performance requirements throughout green music production supply chains. The first phase divides the music production supply chain into five distinct stages and develops corresponding measurement models enhanced with acoustic-sensitive AIoT sensor networks, enabling quantitative carbon footprint evaluation across raw materials, manufacturing, distribution, usage, and end-of-life stages. The second phase constructs a long short-term memory networks - based prediction framework with parallel processing pathways for acoustic and energy features, creating a specialized carbon emission forecasting system for green music production environments. We validated our approach using two years of operational data from a leading music equipment manufacturer and showed a 99.4% prediction accuracy. Implementation resulted in 14.6% carbon intensity reduction across manufacturing operations and 22.3% decrease in recording session emissions, with 92% of artists reporting no perceptible quality difference—confirming HMCAP's capacity to balance sustainability with creative excellence in green music production.