Decision-makers require analytical tools to formulate effective policies, and good quality data to understand and track changes in energy use. CREDS work has contributed to a number of tools and to understanding data needs.
We have developed a unique approach to modelling future energy demand, using linked sectoral models and an energy system model , see also The potential for reducing demand.
We have developed a Place Based Carbon Calculator for England combining spatially disaggregated transport and housing data  and launched it as a freely available tool, in particular for local authorities.
We have developed techniques for predicting spatially disaggregated car ownership, showing that neighbourhoods with high predicted car ownership will have higher energy use per capita and those with relatively high electric vehicle uptake will have lower energy use .
We have continued to use and develop the UK Transport Energy Air Pollution Model UK-TEAM , in particular to address questions at higher levels of spatial disaggregation and to soft-link to energy systems models .
Our research shows that the correlation between EPCs and energy use is weak, both in homes [6, 7] and in non-residential buildings. Better energy ratings for buildings are needed . EPC accuracy and usefulness could be considerably enhanced using measured energy data to obtain in-use heat transfer coefficients through the Smart Meter Enhanced Thermal Energy Rating (SMETER) programme .
With the 3DStock and SimStock models  it will be possible for the first time to provide a geo-located, 3 dimensional representations of every building. This includes mixed use (residential and non-residential) buildings, which have historically not been well-understood.
We have developed a novel high-resolution methodology for assessment of the impact of electrification of heat and transport impact on transformer headroom, using socio-economic indicators to inform the likely penetration of different low carbon technologies and linking to relevant spatial datasets .
We have also developed a model which simulates anthropogenic heat emissions across London. This has the potential to overcome limitations in temporal disaggregation of energy demand .
We have further developed our Multi-Regional Input Output modelling , which enables analysis of resource efficiency  and provides key insights to a range of government departments and the Climate Change Committee.
CREDS research has also progressed work on UK energy systems models, UKTM and ESTIMO, in particular to understand pathways to heat decarbonisation, by adding new vectors and developing the concept of evolvability. The models have contrasting strengths: ESTIMO has sufficient temporal granularity to model system operability reliably , whereas UKTM models long-run technology evolution  and can be used to integrate energy service rich sectoral models .
We have developed an original method for analysing energy and transport poverty in the UK, using data on heating needs, energy efficiency, access to services and social vulnerability.
Data availability remains a challenge. Data infrastructure (monitoring, reporting and transparent processes) is the foundation of any policy design for net-zero, and is not sufficiently in place. There is a lack of harmonised data on energy and transport use across the four countries of the UK. The absence of accessible high-quality data is a significant barrier to assessments of mitigation potential in many sectors. Data on energy use in industry is particularly inadequate and we have developed a data strategy to improve this .
Infra-red visualisation is now practical and cost effective on-site during construction to identify building defects. Modern heating technologies can provide a range of data to help improve heating system performance. Along with analysis and visualisation of data from monitored buildings during construction and in occupation, these can help reduce the performance gap, and therefore assist UK buildings transition to net-zero. Much of the data is already being collected or can now be collected at minimal cost .
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