How is Cost Estimated?
There are three places in the application that provide indicative cost estimates for decarbonisation initiatives:
Data Explorer
Projects
Scenarios
Data Explorer: Low Carbon Technology Costs
The dataset ‘Low Carbon Technology Costs’ provides indicative material and labour costs for a range of low carbon technologies across all domestic properties and some non-domestic properties. This dataset is accessible from the Data Explorer under the category ‘Renewable Potential’.
Costs are specific to each property and are based on factors such as the recommended heat pump size for the property (see dataset Air Source Heat Pump Potential), the floor area, wall area and maximum potential PV array size (see dataset Rooftop PV Potential) and other inputs.
Projects: Cost Assessments
Labour and material cost assessments are available for any project created in the application. Our default costs templates are based on the sizes of the assets selected in your project. See ‘Sources’ (below) for the assumptions behind asset costs. If users have their own cost (or carbon) assumptions, these can be used in place of the default templates through a simple interface or upload (for more details, see our article: Creating custom cost and carbon assumption templates).
Scenarios: Cost Assessments
If a Scenario results in the deployment of an asset on a property, the cost of the material and labour required to install on that property is taken from ‘Low Carbon Technology Costs’. This dataset provides indicative material and labour costs for a range of low carbon technologies across all domestic properties and some non-domestic properties.
These costs are specific to each property which a Scenario applies changes to and are based on factors such as the recommended heat pump size for the property (see dataset Air Source Heat Pump Potential), the floor area, wall area and maximum potential PV array size (see dataset Rooftop PV Potential) and other inputs.
Scenario costs account for inflation at a rate of 1% a year.
Assumptions & Limitations
Material and labour costs are collected from various sources (see ‘Sources’ below) and standardised to £/kW. These are regularly updated to reflect the market. For some technologies, a model - based on these standardised figures - is used to enable the calculation of costs.
Rooftop PV
Using real data points, a regression analysis was conducted to determine the most accurate model for estimating PV material costs. A piecewise regression model with two breakpoints has been identified as the optimal approach, achieving a coefficient of determination (R²) of 0.91. This high R² value indicates that the model accurately fits the data. The piecewise regression model segments the data based on system size, using optimal breakpoints to better fit the linear segments to the data. This method allows the model to account for different rates of cost decrease as system size increases, effectively incorporating economies of scale into the cost estimates.
Labour cost data for roof mounted PV systems is available for systems ranging from 1-100 kW. For systems between 100-500 kW, it is assumed that labour costs maintain the same rate as the 1-100 kW range. This assumption is made because the fundamental installation processes and required skills remain consistent across different system sizes. Additionally, efficiencies gained during the installation of larger systems can offset potential increases in complexity.
Assumptions
Due to limited data, labour cost estimates for PV systems above 100 kW are assumed to maintain the same rate as for systems ranging from 1-100 kW. This assumption is based on the consistency of installation processes and skills required for PV system installation.
Labour cost estimates account for manpower only and do not include costs for electrical upgrades, permits, or structural modifications, which may be necessary for larger PV system installations.
Electric Vehicle Charging Systems
Material costs for EV charging systems ranging from 3 kW to 350 kW are collected from 10+ different suppliers or brands for each system size, and an average cost calculated. While labour cost data is available for systems up to 22 kW, data for higher power ratings (50 kW, 150 kW, and 350 kW) is not regularly published. Therefore, a linear regression model based on the available data for 3-22 kW systems is used to predict labour costs for higher power ratings. This model provides a straightforward and reasonable approximation based on observed trends.
Assumptions
Due to limited data, labour cost estimates for EV chargers above 22 kW are based on a linear extrapolation of costs from 3-22 kW systems. This method assumes that the trend observed in smaller systems can reasonably predict the labour costs for larger systems.
Labour cost estimates take into account manpower only. It is important to note that these estimates do not include costs for electrical upgrades, permits, and groundworks, which are often associated with larger EV charger system installations.
Heat Pumps
The cost of heat pumps up to 16kW are provided. These costs have been extracted from a market survey of multiple providers and are used to provide ‘typical’ costs for material and labour. The sources section (below) lists the main sources.
Sources
Cost assumptions are updated regularly from a number of sources which include the following:
The Committee on Climate Change, Carbon Budgets (here)
Department for Energy Security and Net Zero, Deployment Data (here)
Department for Energy Security and Net Zero, What does it cost to retrofit homes? (here)
Department for Energy Security and Net Zero, Solar photovoltaic (PV) cost data (here)
Department for Energy Security and Net Zero, Cost of installing heating measures in domestic properties (here)
The Heat Pump Warehouse
Spon's Architects' and Builders' Price Book 2023
DESNZ Solar PV Cost Data (here)
BEIS Electricity Generation Costs (here)
Greenmatch Solar Panels for Commercial Use (here)
GeoGreenPower Commercial Solar Panels (here)
AquaSwitch Business Solar Panel Cost (here)
The EcoExperts (here)
Check a Trade (here)