Yadav Dinesh, Kumar Sanjeev, Paramasivam Prabhu, Kanti Praveen Kumar, Gupta Rupesh, Yusuf Mohamed
Mechanical Engineering Department Delhi Skill and Entrepreneurship University Delhi 110077 India.
Department of Research and Innovation Saveetha School of Engineering, SIMATS Chennai Tamil Nadu 602105 India.
Glob Chall. 2025 Feb 24;9(4):2400300. doi: 10.1002/gch2.202400300. eCollection 2025 Apr.
The rapid expansion of photovoltaic (PV) technology has raised concerns about sustainable PV waste management, particularly in India, where inadequate infrastructure and technical limitations hinder effective recycling. Addressing these challenges is crucial for minimizing environmental risks and promoting a circular economy in the renewable energy sector. This study presents a smart multi-criteria decision-making (MCDM) approach that integrates Principal Component Analysis (PCA) and the Analytic Hierarchy Process (AHP) to assess technological challenges in PV waste management. PCA is applied to prioritize key challenges, while AHP evaluated their interrelationships through criteria weights. Despite the effectiveness of PCA and AHP, their combined application in PV waste recovery remains underexplored, particularly in the Indian context. Eight key challenges are identified, with hazardous recycling methods (83.2%) and low recycling potential (83.4%) ranking highest in PCA. AHP results highlighted the lack of advanced recycling technology (0.2298) and hazardous recycling methods (0.2084) as the most critical barriers. A multi-criteria utility function is developed to illustrate these interdependencies. This research bridges critical knowledge gaps by offering data-driven insights into PV waste recovery in India, contributing to sustainable waste management strategies and the development of an efficient recycling framework.
光伏(PV)技术的迅速扩张引发了对可持续光伏废物管理的担忧,尤其是在印度,基础设施不足和技术限制阻碍了有效的回收利用。应对这些挑战对于将环境风险降至最低并促进可再生能源领域的循环经济至关重要。本研究提出了一种智能多标准决策(MCDM)方法,该方法整合了主成分分析(PCA)和层次分析法(AHP),以评估光伏废物管理中的技术挑战。PCA用于对关键挑战进行优先级排序,而AHP则通过标准权重评估它们之间的相互关系。尽管PCA和AHP有效,但它们在光伏废物回收中的联合应用仍未得到充分探索,尤其是在印度背景下。确定了八个关键挑战,其中危险回收方法(83.2%)和低回收潜力(83.4%)在PCA中排名最高。AHP结果突出显示,缺乏先进回收技术(0.2298)和危险回收方法(0.2084)是最关键的障碍。开发了一个多标准效用函数来说明这些相互依存关系。本研究通过提供关于印度光伏废物回收的数据驱动见解,弥补了关键的知识空白,为可持续废物管理战略和高效回收框架的发展做出了贡献。