Decision support systems for waste-to-energy technologies: A systematic literature review of methods and future directions for sustainable implementation in Ghana.

Waste-to-energy (WtE) technologies play a crucial role in sustainable waste management and energy production. Decision-making in this context requires a comprehensive and structured approach to balance the environmental, socio-economic, and technical dimensions. This systematic literature review aims to provide an overview of the applications of Decision Support Systems (DSS) in the field of WtE, identify key trends, and outline future research directions. The study utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach in analyzing and synthesizing a diverse range of studies to identify key observations, trends, and a pathway for future research. The study has addressed its primary objectives through a rigorous analysis of existing scholarship, identification of essential decision-making criteria, assessment of DSS methodologies' efficacy, and identification of knowledge gaps and future research directions. The review reveals that DSS methodologies, particularly Multi-Criteria Decision Analysis (MCDA), Geographic Information Systems (GIS), and Life Cycle Assessment (LCA), have significantly contributed to enhancing decision-making processes in the WtE sector globally. These tools have demonstrated their effectiveness in handling the complex, multi-faceted nature of WtE technology selection and implementation, addressing issues ranging from site selection to technology ranking based on multiple criteria. The research identifies key criteria for WtE technology selection in Ghana, including technical feasibility, environmental impact, economic viability, social acceptability, and energy efficiency. The study also highlights significant gaps in the current application of DSS to WtE technologies, particularly in the context of developing nations like Ghana. Many existing DSS approaches fail to adequately account for local socio-economic factors, rely on static data, and do not fully incorporate diverse stakeholder perspectives. Adapting global DSS applications to the Ghanaian context requires careful consideration of local priorities, data availability, and the role of the informal waste sector. The review proposes a holistic approach to DSS development, emphasizing the need for context-specific, dynamic, and inclusive methodologies. Future research directions identified by this study include the development of Ghana-specific DSS models, integration of real-time data collection methodologies, creation of user-friendly interfaces for local decision-makers, and exploration of emerging technologies such as blockchain and IoT or Machine learning (ML) for enhancing DSS in WtE management.