As more and more developers incorporate net zero ambitions into their project plans, the ability to monitor and respond to carbon emissions throughout the project life cycle will become increasingly important.
Accurate, holistic carbon modelling over the life of a garden community project depends on the ability to capture data from a variety of sources, including during construction and operational phases, and to pool that data to then input into the model. Data trusts and digital twins offer accessible and secure ways of doing so.
Using a data trust mechanism, data can be pooled from a variety of relevant sources: construction supply chains, operational supply chains, occupiers and users. The data trust can offer appropriate protection for that data; allow for its use for the purposes of carbon modelling and permitted related purposes; and provide an appropriate vehicle for ensuring the widest possible range of data sources is captured.
The project owner or promoter can use the output from the data trust to agree carbon reduction agreements with the supply chain and occupiers, in line with net zero targets. The output will also help the owner to comply with its own legal requirements to reduce carbon emissions over time whether by regulation or agreement, such as planning obligation requirements.
Data trusts for carbon modelling
Under one potential model, the project owner and promoter holds a majority stake in the data trust. Utility companies, local businesses and the local authority all hold similar shareholder-equivalent roles, giving them a stake in the data trust along with a percentage of any income generated.
It is for the data trust, as a separate entity, to enter into data sharing agreements with relevant data providers and data users – for example, construction phase suppliers, utility companies’ smart meter data providers, local transport companies, business occupiers and suppliers to the scheme during its operational phase. This data would then be used by the data trust to calculate carbon emissions for the project as a whole and to develop a carbon model for the scheme, to which relevant entities would eb given secure access.
Real-world examples
Digital twin technology was used at Trent Basin, a planned low-energy community within Nottingham’s Waterside district, to create an interactive platform providing the community with real-time data about their energy use. The platform, designed by Integrated Environmental Solutions (IES), provides users with information on renewable energy generation and storage, alongside energy consumption data and general information about the homes. Residents can compare household data with the community average, and see how much energy the project is producing overall and selling back to the grid.
IES also developed a 3D masterplanning and visualisation model, virtual testing and building performance optimisation for Nanyang Technological University’s flagship ‘EcoCampus’ in Singapore. The project used digital twin technology to deliver high-level visualisation and analysis of proposed energy reduction technologies on site, ahead of more detailed simulation and calibrated monitoring of 21 campus buildings. The project was delivered in two phases, with data gathered from the models used by IES to propose potential energy and cost saving solutions.
The EU Horizon 2020-funded +CityxChange (Positive City Exchange) programme is a smart cities project, led by the Norwegian University of Science and Technology and the leaders of two ‘lighthouse cities’ – Trondheim Kommune and Limerick City and County Council. In Limerick, the council is leading the implementation and testing of 11 demonstration projects seeking to reduce existing energy demand, replace fossil fuel energy generation sources, understand system interdependencies and explore the possibility of an energy trading platform, potentially based on blockchain technology, that would allow the community to trade energy on a peer-to-peer basis.
Pinsent Masons has produced a comprehensive guide to data trusts for garden communities (24-page / 6MB PDF).
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