Decarbonisation of existing infrastructure systems requires a dynamic roll-out of technology at an unprecedented scale. The potential disruption in supply of critical materials could endanger such a transition to low-carbon infrastructure and, by extension, compromise energy security more broadly because low carbon technologies are reliant on these materials in a way that fossil-fuelled energy infrastructure is not. Criticality is currently defined as the combination of the potential for supply disruption and the exposure of a system of interest to that disruption. We build on this definition and develop a dynamic approach to quantifying criticality, which monitors the change in criticality during the transition towards a low-carbon infrastructure goal. This allows us to assess the relative risk of different technology pathways to reach a particular goal and reduce the probability of being ‘locked in’ to currently attractive but potentially future-critical technologies. To demonstrate, we apply our method to criticality of the proposed UK electricity system transition, with a focus on neodymium. We anticipate that the supply disruption potential of neodymium will decrease by almost 30% by 2050; however, our results show the criticality of low carbon electricity production increases ninefold over this period, as a result of increasing exposure to neodymium-reliant technologies

The transition to low carbon infrastructure systems required to meet climate change mitigation targets will involve an unprecedented roll-out of technologies reliant upon materials not previously widespread in infrastructure. Many of these materials (including lithium and rare earth metals) are at risk of supply disruption. To ensure the future sustainability and resilience of infrastructure, circular economy policies must be crafted to manage these critical materials effectively. These policies can only be effective if supported by an understanding of the material demands of infrastructure transition and what reuse and recycling options are possible given the future availability of end-of-life stocks. This Article presents a novel, enhanced stocks and flows model for the dynamic assessment of material demands resulting from infrastructure transitions. By including a hierarchical, nested description of infrastructure technologies, their components, and the materials they contain, this model can be used to quantify the effectiveness of recovery at both a technology remanufacturing and reuse level and a material recycling level. The model’s potential is demonstrated on a case study on the roll-out of electric vehicles in the UK forecast by UK Department of Energy and Climate Change scenarios. The results suggest policy action should be taken to ensure Li-ion battery recycling infrastructure is in place by 2025 and NdFeB motor magnets should be designed for reuse. This could result in a reduction in primary demand for lithium of 40% and neodymium of 70%.

This paper presents an integrated modelling approach for value assessments, focusing on resource recovery from waste. The method tracks and forecasts a range of values across environmental, social, economic and technical domains by attaching these to material-flows, thus building upon and integrating unidimensional models such as material flow analysis (MFA) and lifecycle assessment (LCA). We argue that the usual classification of metrics into these separate domains is useful for interpreting the outputs of multidimensional assessments, but unnecessary for modelling. We thus suggest that multidimensional assessments can be better performed by integrating the calculation methods of unidimensional models rather than their outputs. To achieve this, we propose a new metric typology that forms the foundation of a multidimensional model. This enables dynamic simulations to be performed with material-flows (or values in any domain) driven by changes in value in other domains. We then apply the model in an illustrative case highlighting links between the UK coal-based electricity-production and concrete/cement industries, investigating potential impacts that may follow the increased use of low-carbon fuels (biomass and solid recovered fuels; SRF) in the former. We explore synergies and trade-offs in value across domains and regions, e.g. how changes in carbon emissions in one part of the system may affect mortality elsewhere. This highlights the advantages of recognising complex system dynamics and making high-level inferences of their effects, even when rigorous analysis is not possible. We also indicate how changes in social, environmental and economic 'values' can be understood as being driven by changes in the technical value of resources. Our work thus emphasises the advantages of building fully integrated models to inform conventional sustainability assessments, rather than applying hybrid approaches that integrate outputs from parallel models. The approach we present demonstrates that this is feasible and lays the foundations for such an integrated model.

Generating entanglement by simply cooling a system into a stationary state which is highly entangled has many advantages. Schemes based on this idea are robust against parameter fluctuations, tolerate relatively large spontaneous decay rates, and achieve high fidelities independent of their initial state. A possible implementation of this idea in atom-cavity systems has recently been proposed by Kastoryano et al., [Kastoryano et al., Phys. Rev. Lett. 106, 090502 (2011).]. Here we propose an improved entanglement cooling scheme for two atoms inside an optical cavity which achieves higher fidelities for comparable single-atom cooperativity parameters $C$. For example, we predict fidelities above $90\%$ even for $C$ as low as 20 without having to detect photons. DOI: http://dx.doi.org/10.1103/PhysRevA.84.022316 ©2011 American Physical Society