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

Jianling Li (), Juan He and Zihan Xu
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Jianling Li: School of Political and Economic Management, Guizhou Minzu University, Guiyang 550025, China
Juan He: School of Political and Economic Management, Guizhou Minzu University, Guiyang 550025, China
Zihan Xu: School of Economics and Management, Xinjiang University, Urumqi 830046, China

Sustainability, 2025, vol. 17, issue 10, 1-30

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 CO 2 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.

Keywords: decommissioned wind turbines; spatiotemporal evolution pattern; multi-dimensional sustainability assessment; dynamic material flow analysis; deep learning; optimization algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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