Models of Sustainable Living Without Urban Concentration

Scotland is one of several nations in which rural life is arguably central to its culture and identity. At the same time, urban densification is being promoted as one way to transition to lower-impact lifestyles. On the face of it, concentrating more people in smaller space entails less infrastructure per capita for transport, water, power, information and heating; and frees up land for food and biofuel production, carbon sequestration, habitat provision and recreation. Cheaper and easier access to services and businesses constitute a significant ‘pull’ factor towards urban areas from rural, though this ignores negative externalities with respect to health (with particular emphasis on pollution, communicable disease and access to green space). Spatial interaction models are the classic way to model these pull factors, drawing on analogies with gravitation. Social complexity – the webs of inter-relationships among family and friends, and people and businesses and services – means that there is more to the story of urbanization than can be told with spatial interaction models. They are, however, perfect for exploration with agent-based models, which can also be used to explore scenarios of ‘life-as-it-could-be’ that would be critical to developing alternatives to urban densification more conducive to sustainably preserving Scottish lifestyles. The project has strong links with the Scottish Government’s Strategic Research Programme 2022-27, particularly the ‘Rural Futures’ theme, where there is concern about disproportionate impacts on rural economies following EU exit and the Covid-19 pandemic, but also a recognition of the potential for a ‘green recovery’ in these areas. That said, we do not anticipate limiting the research to case studies in Scotland. The central interest is in the role of spatial concentration/dispersal in creating economies supporting Net Zero lifestyles.

 The aim of the proposed Ph. D. project is to develop and evaluate alternative scenarios for spatially dispersed Net Zero lifestyles  that entail viable rural communities and economies; with the viability being demonstrated through spatial agent-based social simulation. The following research questions could be investigated in pursuit of this aim, though the student will be encouraged to develop these questions themself:

 1.      How does simulating social complexity affect findings from spatial interaction modelling? Does it exacerbate or ameliorate the pull of urban areas? Can we use agent-based models to ‘grow’ the emergence of urban areas with greater pull?

2.      What are the trade-offs between the sustainability gains from densification and the losses from addressing health issues? Taking a whole-systems approach (i.e. bearing in mind food transportation, travel to see friends and family and for recreation, impact of health), how do energy consumption and greenhouse gas emissions per capita change with spatial density of human occupation?

3.      What are the options for sustainable living that do not entail densification? How could extra environmental costs for transport, water, power, information and heating in less dense scenarios be compensated for? Are there alternative technologies that take advantage of opportunities not open to those living in compact spaces? Are there options that entail delivering in a different way some of the same benefits from land freed by densification?

 The main methods taken will be through a combination of literature review and computational modelling, with an emphasis on agent-based modelling. The student will be encouraged to develop and take ownership of the project aim and research questions, including modelling approach, with the proviso that the approach taken does justice to social complexity and takes a whole systems perspective.

 The main activities in the first 12-18 months of the project will be reviewing relevant interdisciplinary literature, training on modelling methodologies (especially agent-based modelling) and underpinning technology and infrastructure, and developing and refining a prototype agent-based model to address the first of the above research questions.