Sustainability and Material Flow Analysis of Wind Turbine Blade Recycling in China

Abstract

Many decommissioned wind turbines (WTs) present significant recycling management challenges. Improper disposal wastes resources and generates additional carbon emissions, which contradicts the Sustainable Development Goals (SDGs). This study constructs a sine cosine algorithm (SCA)–ITransformer–BiLSTM deep learning prediction model, integrated with dynamic material flow analysis (DMFA) and a multi-dimensional Energy–Economy–Environment–Society (3E1S) sustainability assessment framework. This hybrid approach systematically reveals the spatiotemporal evolution patterns and circular economy value of WTs in China by synthesizing multi-source heterogeneous data encompassing policy dynamics, technological advancements, and regional resource endowments. Results demonstrate that China will enter a sustained wave of WT retirements post-2030, with an annual decommissioned capacity exceeding 15 GW. By 2050, new installations and retirements will reach a dynamic equilibrium. North and Northwest China are emerging as core retirement zones, accounting for approximately 50% of the national total. Inner Mongolia and Xinjiang face maximum recycling pressures. The recycling of decommissioned WTs could yield approximately CNY 198.5 billion in direct economic benefits and reduce CO2 equivalent emissions by 4.78 to 8.14 billion tons. The 3E1S framework fills critical gaps in quantifying the comprehensive benefits of equipment retirement, offering a theoretically grounded and practically actionable paradigm for the global wind industry’s circular transition.