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Modelling middle actors in the building energy retrofit system


Project Description

Background:

Transforming the UK’s existing buildings to a zero carbon building stock by 2050, in order to meet statutory targets, remains an enormous challenge and a major business opportunity. Despite new UK government commitments to “net zero” carbon, the Climate Change Committee’s review of how far UK housing is able to achieve that level of performance was pessimistic. Unless there are significant changes in the industrial systems which affect existing buildings, and in particular existing homes, the UKs targets will not be met.

However, technology, equipment and products do exist that can be used to meet the challenge of zero carbon building stock. Obstacles lie in consumer behaviour and, critically, in the behaviour of the construction trades and construction DIY practitioners. Consumer behaviour is well researched, but the behaviour of tradespeople as professionals has only recently started to be evaluated qualitatively. This qualitative understanding of how small building firms and DIY practitioners choose and use products and processes to achieve low carbon outcomes is revealing the importance of focussing on decision making in supply chains, local networks and interpersonal communications and trust. There is an urgent need to find ways which scale up these qualitative insights to quantitative models which advance system understanding and enable large scale action.

Agent-based modelling (ABM) comes from artificial intelligence and is rapidly becoming established as a standard tool for the social scientist. At the very core of ABM is the individual. Through ABM, individuals are ‘created’ and assigned unique behaviour and relationships. This ‘bottom-up’ representation allows new knowledge and behaviours to emerge from interactions between the agents (see Crooks and Heppenstall, 2012 for a detailed introduction). An attractive aspect of ABM is its ability to represent individuals and their relationships across different spatial scales, giving them the ability to learn, evolve, and make decisions adaptively in both space and time. ABM may allow small scale, qualitative insights within the energy arena to be combined with various forms of “big data” and qualitative sources in the form of behavioural rules. Specifically, spatially defined agent-based models with correctly parameterised actors (middle men and consumers) might help to assess which interventions are most effective in delivering the desired outcomes – warm, low cost, energy efficient homes.

PhD Research aim:

This studentship will develop and test the utility of spatial agent-based modelling to simulate the transformation of existing domestic residences into low carbon homes, through changing the technologies (in building fabric space heating and water heating) and connected behaviours of small construction firms and heating system installers.

Relevant areas of expertise or interest:

Methods
• Agent-based modelling
• Geographical Information Systems
• Network analysis
• Data analytics and data mining

Themes
• SME behaviours
• Construction industry supply chain
• Domestic energy retrofit

Funding Notes

This project is in competition for an EPSRC DTP 2020 Environment 3.5 years scholarship which has a value of: tuition fees (£4,500 for 2019/20), tax-free stipend (£15,009 for 2019/20), and a research training and support grant.

References

• Owen A, Heppenstall AJ. 2018. Making the case for simulation: Unlocking carbon reduction through simulation of individual ‘middle actor’ behaviour. Environment and Planning B: Urban Analytics and City Science.
• Committee on Climate Change. 2019. UK Housing: fit for the future? HMSO, London
• Crooks, A.T. and Heppenstall, A.J. (2012), Introduction to Agent-based Modelling, in Heppenstall, A.J., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 85-108.
• Janda, K. B.& Parag, Y. 2013. A middle-out approach for improving energy performance in buildings, Building Research & Information, 41:1, 39-50

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