Technological Portfolio Optimisation for de-risking within the energy, water and food nexus

Food insecurity is one of the major issues that have loomed as a result of rapid population growth. In recent studies addressing modern-day resource issues, the energy, water and food (EWF) nexus approach was introduced as a holistic method to aid decision-makers in pre-empting unintended, and possibly damaging consequences resulting from an imbalance made in any part of the nexus system. Existing studies have focused on centralised approaches to study the interlinkages amongst EWF systems by focusing mostly on two elements of the EWF nexus. However, the current and future transitions should address the entire EWF system, holistically and sustainably. Besides, the existing indicator frameworks do not expressly capture the key interactions between the EWF resources to address the main challenges and risk factors associated with the EWF systems. Consequently, the aim of this study is to tackle this knowledge gap by integrating the analytical hierarchy process (AHP) tool along with EWF risk-associated optimisation as means to assess the changes in EWF resource availability, and therefore identify the tradeoffs of the large-scale implementation of decentralised energy and water technologies. The proposed framework utilises a nexus approach to optimise the energy and water technological portfolios in an efficient way; thus, supporting the decision-making for resource management in high-risk environments, such as the State of Qatar. The methodology consists of designing a composite risk indicator using the AHP method to determine the weight of different risk criteria for energy and water technologies. The obtained values are then used as an input for multi-objective optimisation that aims at minimising the tradeoffs between two objectives (i.e. risk and GWP). Results of the AHP indicate that wind energy is 11.8% risker than solar energy, where the key issue is that the availability of these energy sources is far from constant but seasonal. In addition, treated wastewater was the most environmentally friendly, having the lowest impact on the ecosystem and resources, however, treated wastewater must be properly treated as it has the highest risk level (12.5%) and can lead to health issues. Finally, the multi-objective optimization model generates relatively 70 optimal solutions due to the mutual conflict between the two objectives.