AI-Driven Assessment of Waste Management Project Feasibility in OECD Countries : A Project Management Perspective
Lotchi, Kodjovi (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024110727582
https://urn.fi/URN:NBN:fi:amk-2024110727582
Tiivistelmä
The efficient handling of waste is a significant challenge for nations striving for sustainable growth, environmental protection, and the conservation of natural resources. However, most face challenges due to varying economic conditions, technological capabilities, and environmental policies. It is crucial for policymakers to have a comprehensive understanding of factors that influence waste management projects. This study investigates the indicators that impact waste management methods across countries that are members of the Organisation for Economic Co-operation and Development (OECD). It also sought to construct and validate an artificial intelligence (AI) driven Waste Management Project Feasibility Index (WaPFI). The index is designed to evaluate and rank OECD countries' waste management capabilities and project feasibility.
The research employs the Principal Component Analysis (PCA) technique and correlation and regression analysis to examine a comprehensive dataset that integrates various criteria, including economic capabilities, waste management methods, technological advancements, and environmental policies.
The result of the study shed light on factors that impact waste generation, recycling, and disposal. Furthermore, the WaPFI index provides a quantitative foundation for ranking OECD countries based on their feasibility of waste management. Denmark, Germany, and Norway ranked as top performers, demonstrating strong economic capabilities, technological strength, stringent environmental policies, and high rates of waste recovery. The research findings have led to the proposal of policy implications and recommendations that enable policymakers, and other stakeholders to identify and benchmark waste management best practices, support sustainable development, and contribute to achieving the Sustainable Development Goals (SDGs).
The research employs the Principal Component Analysis (PCA) technique and correlation and regression analysis to examine a comprehensive dataset that integrates various criteria, including economic capabilities, waste management methods, technological advancements, and environmental policies.
The result of the study shed light on factors that impact waste generation, recycling, and disposal. Furthermore, the WaPFI index provides a quantitative foundation for ranking OECD countries based on their feasibility of waste management. Denmark, Germany, and Norway ranked as top performers, demonstrating strong economic capabilities, technological strength, stringent environmental policies, and high rates of waste recovery. The research findings have led to the proposal of policy implications and recommendations that enable policymakers, and other stakeholders to identify and benchmark waste management best practices, support sustainable development, and contribute to achieving the Sustainable Development Goals (SDGs).