Infrastructure interdependencies : opportunities from complexity

Infrastructure networks, such as those for energy, transportation, and telecommunications, perform key functions for society. Although such systems have largely been developed and managed in isolation, infrastructure now functions as a system of systems, exhibiting complex interdependencies that can leave critical functions vulnerable to cascade failure. Consequently, research efforts and management strategies have focused on risks and negative aspects of complexity. This paper explores how interdependencies can be seen positively, representing opportunities to increase organizational resilience and sustainability. A typology is presented for classifying positive interdependencies, drawing on fundamental principles in ecology and validated using case studies. Understanding opportunities that arise from interdependency will enable better understanding and management of infrastructure complexity, which in turn will allow the use of such complexity to the advantage of society. Integrative thinking is necessary not only for mitigating risk but also for identifying innovations to make systems and organizations more sustainable and resilient

How much heat can we grow in our cities? Modelling UK urban biofuel production potential

Biofuel provides a globally significant opportunity to reduce fossil fuel dependence; however its sustainability can only be meaningfully explored for individual cases. It depends on multiple considerations including: life‐cycle GHG emissions, air quality impacts, food versus fuel trade‐offs, biodiversity impacts of land use change, and socio‐economic impacts of energy transitions. One solution that may address many of these issues is local production of biofuel on non‐agricultural land. Urban areas drive global change, for example they are responsible for 70% of global energy use, but are largely ignored in their resource production potential; however under‐used urban greenspaces could be utilised for biofuel production near the point of consumption. This could avoid food versus fuel land conflicts in agricultural land and long‐distance transport costs, provide ecosystem service benefits to urban dwellers, and increase the sustainability and resilience of cities and towns. Here, we use a GIS to identify urban greenspaces suitable for biofuel production, using exclusion criteria, in ten UK cities. We then model production potential of three different biofuels: Miscanthus grass, short rotation coppice willow and short rotation coppice poplar, within the greenspaces identified and extrapolate up to a UK‐scale. We demonstrate that approximately 10% of urban greenspace (3% of built‐up land) is potentially suitable for biofuel production. We estimate the potential of this to meet energy demand through heat generation, electricity, and combined heat and power (CHP) operations. Our findings show that, if fully utilised, urban biofuel production could meet nearly a fifth of demand for biomass in CHP systems in the UK’s climate‐compatible energy scenarios by 2030, with potentially similar implications for other comparable countries and regions

Using GIS-linked Bayesian Belief Networks as a tool for modelling urban biodiversity

The ability to predict spatial variation in biodiversity is a long-standing but elusive objective of landscape ecology. It depends on a detailed understanding of relationships between landscape and patch structure and taxonomic richness, and accurate spatial modelling. Complex heterogeneous environments such as cities pose particular challenges, as well as heightened relevance, given the increasing rate of urbanisation globally. Here we use a GIS-linked Bayesian Belief Network approach to test whether landscape and patch structural characteristics (including vegetation height, green-space patch size and their connectivity) drive measured taxonomic richness of numerous invertebrate, plant, and avian groups. We find that modelled richness is typically higher in larger and better-connected green-spaces with taller vegetation, indicative of more complex vegetation structure and consistent with the principle of ‘bigger, better, and more joined up’. Assessing the relative importance of these variables indicates that vegetation height is the most influential in determining richness for a majority of taxa. There is variation, however, between taxonomic groups in the relationships between richness and landscape structural characteristics, and the sensitivity of these relationships to particular predictors. Consequently, despite some broad commonalities, there will be trade-offs between different taxonomic groups when designing urban landscapes to maximise biodiversity. This research demonstrates the feasibility of using a GIS-coupled Bayesian Belief Network approach to model biodiversity at fine spatial scales in complex landscapes where current data and appropriate modelling approaches are lacking, and our findings have important implications for ecologists, conservationists and planners