Forecasting waste volume and identifying barriers of Canadian photovoltaic waste management

The worldwide exponential upsurge of photovoltaic (PV) installations and the subsequent heights of PV waste is a swelling apprehension. The objectives of this study are to forecast the photovoltaic waste volume and to analyze the critical barriers for the photovoltaic waste management in Canada. In the first section, the solar waste volume forecasting was done using linear regression, 2nd order polynomial regression, and power regression models. In the second section, the barriers to photovoltaic solar waste management were identified through literature review and analyzed by formulating a framework from integrating Rough Analytical Hierarchy Process (RAHP), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods. The findings of the thesis show that for early loss the PV waste volume will be as high as 270,000 MT and as low as 180,000 MT and for regular loss the PV waste volume will be as high as 180,000 MT and as low as 160,000 MT for the year of 2045. The result of the barrier analysis shows that a complex causal relationship exists among the barriers with “Insufficient Generation of PV Waste”, and “Waste Collection Centre” are the top two crucial barriers with highest driving power and causal effect on others. This thesis is expected to contribute to the concerned government agencies to assess the upcoming volume of PV waste and then evaluate the relationship among the barriers to PV waste management for establishing a sustainable and resilient PV waste management plan for Canada.

Name thesis:degree_name

Master of Applied Science (MASc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering - Industrial Systems

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2022

Author dc:creator

• Romel, Monasib Ahmed

Advisor dc:contributor.advisor

• Kabir, Golam

Committee members dc:contributor.committeemember

• Ng, Kelvin Tsun Wai
• Khondoker, Mohammad

Language dc:language.iso

en

DOI

https://doi.org/10.82465/5122

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/15539