The challenge in clean palm oil production falls on the management of palm oil mill effluent which is a notable source of greenhouse gas emissions and water pollution. To address these critics against edible palm oil, an integrated palm oil-based complex (POBC) considering effluent elimination and refinery integration is suitable for environmental-food-energy-water (EFEW) nexus development. Optimal retrofit of palm oil mill into EFEW nexus-integrated POBC requires multi-objective considerations to balance the trade-offs between profitability, energy contribution, greenhouse gas, water and land footprints via fuzzy optimisation. With limited practical knowledge, potential flowsheet modifications should be investigated for flexible POBC design. In a cooperative game context, interconnecting processes act as multiple players cooperating to achieve the goal of the game, i.e., POBC performance, where each player has a distinctive impact on the outcome. In this work, such process performance was suggested to be distributed using cooperative game model, to target the EFEW-based anchor process, i.e., the process stage of greatest contribution in the weighted EFEW nexus, for desired flowsheet advancement. Considering these aspects, an integrated fuzzy and cooperative game optimisation framework was developed to identify the anchor process of an EFEW nexus-integrated POBC. EFEW objective-based process performance allocation in the fuzzy optimal POBC was weighted by the decision-maker to allocate the anchor process using developed models and Excel tools. Nut/kernel separation and cogeneration stage is the EFEW-based anchor process in the fuzzy optimal POBC with EFEW nexus score of 41% in this work. A comparative analysis between the proposed method with other approach was done. The favourability of EFEW contributions by POBC in terms of benefit-drawback ratio increased with the percentage of boiler efficiency increment within the targeted anchor process. Targeting anchor process aids planning for process maintenance and advancement to avoid resource wastage on sub-critical processes.

We propose a hybrid Trustworthy Human–Machine collective decision-making framework to manage Food–Energy–Water (FEW) resources. Decisions for managing such resources impact not only the environment but also influence the economic productivity of FEW sectors and the well-being of society. Therefore, while algorithms can be used to develop optimal solutions under various criteria, it is essential to explain such solutions to the community. More importantly, the community should accept such solutions to be able realistically to apply them. In our collaborative computational framework for decision support, machines and humans interact to converge on the best solutions accepted by the community. In this framework, trust among human actors during decision making is measured and managed using a novel trust management framework. Furthermore, such trust is used to encourage human actors, depending on their trust sensitivity, to choose among the solutions generated by algorithms that satisfy the community’s preferred trade-offs among various objectives. In this paper, we show different scenarios of decision making with continuous and discrete solutions. Then, we propose a game-theory approach where actors maximize their payoff regarding their share and trust weighted by their trust sensitivity. We run simulations for decision-making scenarios with actors having different distributions of trust sensitivities. Results showed that when actors have high trust sensitivity, a consensus is reached 52% faster than scenarios with low trust sensitivity. The utilization of ratings of ratings increased the solution trustworthiness by 50%. Also, the same level of solution trustworthiness is reached 2.7 times faster when ratings of ratings included.

Water, energy, food, land and climate form a tightly-connected nexus in which actions on one sector impact other sectors, creating feedbacks and unanticipated consequences. This is especially because at present, much scientific research and many policies are constrained to single discipline/sector silos that are often not interacting (e.g., water-related research/policy). However, experimenting with the interaction and determining how a change in one sector could impact another may require unreasonable time frames, be very difficult in practice and may be potentially dangerous, triggering any one of a number of unanticipated side-effects. Current modelling often neglects knowledge from practice. Therefore, a safe environment is required to test the potential cross-sectoral implications of policy decisions in one sector on other sectors. Serious games offer such an environment by creating realistic ‘simulations’, where long-term impacts of policies may be tested and rated. This paper describes how the ongoing (2016–2020) Horizon2020 project SIM4NEXUS will develop serious games investigating potential plausible cross-nexus implications and synergies due to policy interventions for 12 multi-scale case studies ranging from regional to global. What sets these games apart is that stakeholders and partners are involved in all aspects of the modelling definition and process, from case study conceptualisation, quantitative model development including the implementation and validation of each serious game. Learning from playing a serious game is justified by adopting a proof-of-concept for a specific regional case study in Sardinia (Italy). The value of multi-stakeholder involvement is demonstrated, and critical lessons learned for serious game development in general are presented.

Rapid population growth and increased urbanisation have contributed towards increased pressures on global energy, water and food (EWF) resource systems. Therefore, there is an imperative need for resilient and effective resource management that considers societal, environmental and economic components of sustainable development. In this regard, adopting a EWF Nexus approach that considers the interactions between sectors is fundamental. However, it requires the additional mobilisation of particular decision-making tools in order to completely address the question of resource utilisation. In fact, well-studied decision-making methodologies are necessary to sustain resources, address multi-scale challenges and ensure robust governance of EWF Nexus systems. This study highlights the current literature regarding the modelling approaches used for dynamic decision-making within the EWF Nexus focusing on mathematical optimisation, agent-based modelling and game theory. These algorithmic frameworks serve as strategic guidelines for policy makers and encourage effective decision-making related to maximising targets and minimising environmental burdens. Outcomes of this study demonstrate the complementary relationships that exist between the decision-making models highlighted, and how together with a EWF Nexus approach can be used for integrated resource management.