Walzberg Julien, Cooperman Aubryn, Watts Liam, Eberle Annika L, Carpenter Alberta, Heath Garvin A
National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA.
Joint Institute for Strategic Energy Analysis, 15013 Denver West Parkway Golden, CO 80401, USA.
iScience. 2022 Jul 9;25(8):104734. doi: 10.1016/j.isci.2022.104734. eCollection 2022 Aug 19.
The growing number of end-of-life (EOL) wind blades could further strain US landfills or be a valuable composite materials source, depending on stakeholders' behaviors. Technical solutions based on circular economy (CE) principles have been proposed but are not guaranteed to solve the issue of EOL management. Transitioning to CE implies changing how business models, supply chains, and behaviors deal with products and waste. A spatially resolved agent-based modeling combined with a machine-learning metamodel shows that including behavioral factors is crucial to designing effective policies. Logistical barriers and transportation costs significantly affect the results: lowering blade shredding costs by a third before transportation makes EOL blades a source of valuable materials, decreasing the 2050 cumulative landfill rate below 50%. In another scenario, parameter settings simulating policy interventions aiming at boosting early adoption incites new social norms favorable to recycling, lowering the cumulative landfill rate below 10%.
随着寿命终了（EOL）的风力叶片数量不断增加，这可能会给美国的垃圾填埋场带来更大压力，或者成为一种有价值的复合材料来源，这取决于利益相关者的行为。基于循环经济（CE）原则的技术解决方案已经提出，但并不能保证解决寿命终了管理问题。向循环经济转型意味着改变商业模式、供应链以及处理产品和废物的行为方式。一种结合机器学习元模型的空间解析基于主体的建模表明，纳入行为因素对于设计有效政策至关重要。物流障碍和运输成本会显著影响结果：在运输前将叶片粉碎成本降低三分之一会使寿命终了的叶片成为有价值材料的来源，将2050年的累计填埋率降至50%以下。在另一种情况下，模拟旨在促进早期采用的政策干预的参数设置会激发有利于回收利用的新社会规范，将累计填埋率降至10%以下。
