Using actual emissions for carbon accounting of residential buildings

Insights from analyzing a real world sample

The energy consumption of residential buildings has an enormous impact on the environment. For example, residential heating is the third largest CO2 emitting sector, making up around 10% of total CO2 emissions across European countries. Making our homes more energy efficient is a cornerstone in fighting climate change.

Banks have a key role to play in the energy transition of our homes. New and evolving regulations call for greater transparency and disclosure of ESG-related data, and a number of banks have adopted ambitious decarbonisation targets for their mortgage portfolios by 2030.

Data availability is a main challenge to meet carbon accounting and reporting standards in the Financial Industry

The Partnership for Carbon Accounting Financials’ Global GHG Accounting and Reporting Standard for the Financial Industry (PCAF standard)1 specifies how banks should do carbon accounting. It has separate methods for different asset classes. In this article we focus on the one for portfolios of residential mortgage loans.

In 2020 a number of banks in the Netherlands, the initiators of PCAF, performed a feasibility study where actual metered energy consumption data was used for GHG emission estimates2. After the study it was concluded that better access to individual and recent microdata is needed3.  

In this article we demonstrate how data from our platform can be used to continuously measure actual operational scope 2 emissions of properties. Thereby addressing the challenges many PCAF partners face regarding data availability. 

A bank’s financed emissions (scope 3) according to the PCAF standard is calculated by multiplying the scope 1 and scope 2 emissions of a financed property with an attribution factor given by the loan to value. The attribution factor is already known by banks and for buildings with electricity based heating, which is the most common in Sweden, the scope 1 emissions are not significant. Therefore we focus on scope 2 emissions in this article.

PCAF defines a data quality score that is assigned to the reported metrics. A lower score indicates that the underlying data used to calculate the metric is of higher quality.

Using the Hemma platform to access actual metered energy consumption

Many bankss are currently reporting data according to PCAF but with a poor data quality score. It is often between 3-4 due to lack of data. In these cases either the issued EPC label of the property is used (score 3) or a very rough estimate of it (score 4). In both cases the energy intensity corresponding to the EPC label is multiplied by the heated area of the property to get an estimated yearly energy consumption. This consumption is then multiplied by an average national grid factor to get scope 2 emissions. 

With Hemma banks are able to use the actual metered energy consumption of each property, multiply it with a regional grid factor and get PCAF emission metrics with data quality 1-2.

We demonstrate how this is done by analyzing a small recent sample of properties within our platform. The resulting metrics are then used to simulate actions that would transition the imaginary banks portfolio towards set decarbonization targets.

The sample:
Measurement period:
Number of properties:
Type of properties:
owner occupied, single family
Primary heating system of properties:
electricity based

Data sources:
Electricity consumption: Hemma (partner integrations)
Electricity bidding zone of property:
Hemma (geolocation), SVK geodata
Electricity grid factor:
Electricity Maps4 yearly average per electricity bidding zones

Calculating absolute emissions

Each property’s yearly electricity consumption is combined with data about which electricity bidding zone it is located within. Average annual emissions per kWh during the measurement period for each bidding zone is retrieved from Electricity Maps.

Total electricity consumption (GWh): 16
Total (scope 2) emissions (tCO2): 530

Bar chart showing emissions per property (electricity: scope , 2022)2)

Setting concrete decarbonisation targets

The Net-Zero Asset Owner Alliance’s Target-Setting Protocol (TSP)  specifies how asset owners should set decarbonisation targets in line with the Intergovernmental Panel on Climate Change’s (IPCC) 1.5°C low or no overshoot pathways.

An alliance member can set a target to achieve a relative reduction in financed emissions compared to a set baseline.

By using emission data from the previous steps we can calculate how many of the properties in this sample would need to transition given such a set target. If we e.g have a goal to decrease the total emissions of the sample by X% we can see what that translates to for the portfolio

But how can we achieve such a significant reduction in the properties’ emissions?

Although the latest TSP v3 doesn’t yet cover residential mortgage loans we can look at its existing section for directly held real estate assets for guidance. The TSP acknowledges that decarbonisation of this sector can be assisted by grid decarbonisation and renewable energy production but stresses that improving the buildings energy efficiency should be prioritized in decarbonization strategies. 

Identifying what energy efficiency measures can be considered is, among other things, dependent on the primary heating system used by the property. By looking at the emissions per primary heating system we can get an idea of which type of properties we should prioritize to look into.

Bar chat showing emissions per primary heating system in 2020

In this example, houses with air-to-air heat pumps as their primary heating system seems to represent the largest chunk of our emissions. To improve the energy efficiency of these houses we can look at the Kyoto Pyramid for guidance. Following the pyramids structure we should start by reducing the heat loss of the building. One cost effective way to reduce heat losses could be loft insulation.

Bar chart showing median energy intensity by loft insulation (air-to-air heat pump)

There are about 100 buildings in the sample with air-to-air heat pump and poor (0-10 cm) or unknown level of loft insulation. If we simulate that these properties are insulated and as a group achieve a median energy intensity in line with the ones with good loft insulation (30+ cm) the result is as follows:

Energy demand reduction (GWh): 0.22
Emission reduction (tCO2): 7.5
Aggregate emission reduction (%): 1.4

This is not enough at all given our ambitious targets though. To further improve the energy efficiency of these buildings we could look further up on the Kyoto pyramid and perhaps transition to a more efficient heating system like a geothermal- or air-to-water heat pump. By mapping out a roadmap like this for the portfolio based on actual emissions we can act on the set targets in a more accurate way.

Still, finding the right measure for each property is impossible without doing an individual analysis. Hemma helps banks to systematically and automatically do this on their building portfolio and deliver this data for individual analysis.


This example was done with a small sample of 1000 properties. It only included electricity based heating systems. For a representative sample, buildings with e.g. district heating and gas boiler based heating systems should have been included together with their respective grid emission factors. 

Nevertheless, it shows how banks can work systematically with real time data from the Hemma platform to accurately understand their current baseline emission level, set concrete and relevant goals on how to reduce it, and keep track of progress. By using Hemma’s platform, banks always get access to energy efficiency and emission related metrics based on the best available data quality score.

1 PCAF Standard,

2 CBS publishes CO2 emissions of Dutch banks mortgage portfolio

3 Updates from implementing GHG accounting for the financial sector in the Netherlands, 2021

4 Electricity Maps,

5 Net Zero Asset Owner Alliance, Target Setting Protocol Third Edition,

6 The Kyoto pyramid (Dokka and Rodsjo, 2005)