Energy production and consumption is a big contributor of greenhouse gas emissions worldwide. We know we need to do something about it. But here’s the catch … calculating the emissions generated by your properties (i.e. private/rental homes or offices) isn't exactly straightforward. Collecting and analysing energy data is super complex and takes up a lot of your time (which you'd probably like to spend doing something else).

‍

The good news is that thanks to renewable resources and efforts for energy efficiency, emissions from energy consumption have decreased in the European Union (EU). From 2005 to 2021, greenhouse gas (GHG) emissions in EU buildings reduced by 31%. That’s a huge step in the right direction! We’ve said it before, and we’ll say it again (and again after that): Accuracy in carbon accounting is the key to decarbonisation (you can't manage what you don't measure, right?). Energy data is a big part of this equation. 

‍

In this article, we’ll cover the basics of energy data, explain the complexities of why it is so difficult to collect and emphasise its importance (that’s what comundo’s all about!).

‍

Understanding energy data

Energy data refers to energy production and consumption data – simple enough, right? In the context of buildings, energy data tells us how much energy a building has consumed to remain operational. 

‍

Energy consumption, whether electricity or heating, is typically metered by the providers for billing purposes. The energy data from these providers can vary based on factors such as building structure, energy source, and billing duration. To put it bluntly, there’s no standardisation when it comes to energy data (multiple sad faces). 

‍

Energy data is mainly used to calculate Scope 1 and Scope 2 emissions. However, depending on the ownership or use of assets (buildings), energy consumption may also count toward indirect emissions (more on that later). Energy data forms a key component of the overall emissions for residential and commercial structures. Building owners and tenants may be required to report their emissions, and accurate energy data is a must. 

‍

What does energy data include?

Energy data can include consumption data for electricity, natural gas and heating. It depends on where the building or property is located and its energy sources. For instance, in Denmark, electricity and district heating are the main energy sources in buildings. As these sources of energy are different, so are their units. Electricity consumption is typically calculated in kilowatt-hours (kWh), while gas consumption is measured in GJ, and sometimes cubic meters (m³). Once we've got this sorted out, we need to use emission factors to calculate emissions from those individual sources. 

‍

Energy data collection

You’d think that collecting energy data would be quite simple. You get a bill from the utility company, and you know exactly how much energy the building or a unit inside it consumes. For other utilities, too, like gas or water, it should be the same way.

‍

In buildings with multiple units, there are usually submeters for each unit. So, you have those units' total energy and individual consumption. This type of metering and billing makes it easier to calculate aggregate energy consumption as well as individual consumption for each unit. 

‍

This is typically the case for electricity data, but in many parts of the world, not for heating or water. So, it’s hard to know exactly how much energy a building's individual units consume via gas or water. For instance, building water consumption may be reported as aggregate data instead of individual readings for units inside the building.

Another complication (because life is never as simple as it should be) with energy data, specifically electricity consumption, is the energy source. A utility company may be using a combination of renewable and non-renewable sources. In many countries, utility providers are completely separate from energy producers, creating even more confusion.

‍

For example, in the US, companies that generate electricity typically don’t cater to consumers directly. Instead, private utilities buy this electricity from distributors and charge the residences or buildings. So, consumers, at best, know how much power they consume, not the sources used for generating that power. 

‍

Besides utility companies, government agencies also publish data on regional or national energy consumption. Although this data is great for climate accountability, it isn’t very helpful for calculating energy data for individual units inside buildings. Sigh.

‍

Complexities of energy data

As you can probably gather by now, collecting energy data and calculating emissions isn’t exactly straightforward. Here are four main reasons why:

‍

Scattered or incomplete consumption data

For large companies or investment firms with multiple real estate holdings, complexity arises from the various, often decentralised sources of data. 

‍

For instance, a company with offices in different countries or cities may have to deal with various providers who may not report data on the same timeline. Some may report monthly while others quarterly, which can complicate things. 

‍

Lack of granularity 

In some energy sectors, the data reported is aggregate, lacking the granularity needed to calculate an accurate carbon footprint. 

‍

For instance, Denmark uses a central district heating system for warming buildings, where the actual consumption of the entire building is reported. In most cases, there aren’t any individual meters for apartments or offices inside the buildings to calculate individual consumption. In the context of calculating energy emissions from electricity, the more granular the energy data, the more accurate the emissions data. 

‍

Various emission factors 

Clearly, the biggest challenge when converting energy data into emissions are the emission factors, which vary widely worldwide. Choosing the right emission factor can be difficult as several elements are involved, such as the energy source, its impact, the production process, location, and usage time. 

‍

In the EU, emission factors are readily available from utility companies, government agencies, and non-profits. Some of these utility companies provide incredibly detailed emission factors by hourly consumption (for instance, low emission during day use because of solar energy). The same can’t be said about other regions, where energy production and distribution vary greatly. 

‍

Lack of digital meters

Electricity utilities have largely shifted to smart meters, making data collection easy. Utility companies and consumers can track consumption and see reports remotely. As of 2021, it’s reported that 54% of EU households have smart electricity meters. 

‍

Unfortunately, the same isn’t the case for gas or water meters, which have yet to be digitised in many parts of the world, including many EU countries. Collecting data from non-digital meters is a laborious job.

Emission factors and calculating emissions using energy data

Collecting energy data is only half the job (sorry). The other half is converting it into accurate and usable emissions data. To calculate emissions from energy data, you need emission factors, of which there are many. 

‍

Now, with emission factors, there are two approaches: 

• Market-based
• Location-based 

‍

Market-based vs. location-based emission factors

Location-based emission factors are like looking at your neighbourhood's average emissions intensity. They're a simple calculation that considers the typical mix of fuels used to generate electricity in your region. This reflects the physical emissions released into the atmosphere where you're located. 

‍

They’re considered an accurate representation of the actual emissions, giving a clear picture of your environmental impact based on the grid you're connected to. However, they may not account for your green efforts to reduce your environmental impact through energy use. 

‍

Market-based emission factors focus on the emissions associated with the specific energy you purchase. This takes your clean energy choices into account. If you're buying renewable energy certificates (RECs) or have direct contracts with renewable energy sources, the market-based factor will reflect the lower emissions from those sources. 

‍

This approach credits companies actively choosing cleaner options and highlights their commitment to environmental responsibility. That said, it might not fully capture the reality of your energy consumption, as RECs don't guarantee you're directly using clean energy, just that it's added to the grid.

‍

Differentiating Scope 1, 2 and 3 emissions from energy

Another complexity of turning energy data into emissions data is identifying the scope of emissions. The classification is fairly straightforward for energy directly generated by the users (Scope 1) or purchased from a third party (Scope 2). 

‍

However, things get complicated when some of the energy consumed is indirect. For instance, in a building leased to tenants, the energy consumption by each tenant unit would qualify as Scope 3. However, the consumption in communal areas like stairways or laundry rooms may come under Scope 2. 

‍

These differentiations are important for recognising opportunities for decarbonisation. For companies trying to calculate accurate energy data and emissions from it, these subtle variations in the Scopes can produce different results. Accuracy is extremely critical at this point for reducing emissions from energy consumption and for successful reporting.

Applications and benefits of energy data

Collecting, processing and analysing energy data requires resources and technological solutions. But it’s a worthy investment given the benefits of the data. Just take a look:

‍

• ESG reporting and compliance: Companies, including those with real estate assets in their ownership or leased, are required to report ESG data. The CSRD, the new regulation for ESG reporting introduced in the EU, requires nearly 50,000 companies to report data. This will also include energy consumption data, not just Scope 2 but also Scope 3. Accurate energy data can help companies identify which emissions are indirect. The CSRD also mandates reporting of Scope 3 emissions. The more accurate the data, the lesser the chances of regulatory penalties (and obviously greater stakeholder satisfaction)

• Building certifications: Energy data is also helpful in obtaining energy certifications like DGNB or LEED, as these certifying bodies require accurate, detailed energy consumption data. The auditors who provide such certifications consider how a building utilises energy and water, and if the building owner has easy and frequent access to data for the entire building, they’ll typically receive a higher certification score

• Green financing: Detailed energy reports and climate targets can help companies apply for green financing, such as green loans for renewable energy systems. That, in turn, can help them further reduce emissions from their energy consumption. By switching to renewable sources like solar, building owners or operators can significantly reduce their active carbon footprint

• Identifying areas for improvement: Perhaps the most underrated benefit of energy data is the opportunity to optimise efficiency. Energy data can reveal areas or issues causing high emissions. Companies can further investigate why energy consumption is high in certain areas of the building or as a whole

• Increasing property value: With increasing interest in energy-efficient properties from investors, buyers, and tenants, detailed energy data showcasing a lower carbon footprint can help amplify the property’s value and appeal. Energy efficiency can be the unique selling point for companies that deal in real estate investments. Energy consumption reports and green certifications can help make the property more desirable

‍

Turning data into decarbonisation

Energy data isn’t just a piece of the decarbonisation puzzle; it’s a key driver. With the right data in hand, companies can leverage more than just compliance; they can transform their entire energy approach, reduce operational costs, and enhance their environmental reputation. This isn’t only about keeping regulators happy but about paving the way toward a more sustainable future—one kilowatt-hour at a time.

‍

Ultimately, precision in energy data means better decisions for both the environment and the bottom line. As digital and smart metering technology advances, the path to reliable, detailed energy data is only getting clearer. So, don’t just look at energy data as a line item; see it as a strategic asset that keeps your buildings efficient, attractive and greener.

‍

Frequently Asked Questions

What’s the difference between primary and secondary energy data?

Primary energy data refers to raw data collected directly from energy sources, like meter readings from a building’s utility. Secondary data is derived from primary data, such as the emissions calculated based on energy consumption, which may include factors like location-based emission averages.

How can smart meters improve energy data accuracy?

Smart meters provide real-time energy usage information, which increases the accuracy of energy data collection. They allow building owners and tenants to monitor consumption instantly, reducing errors and inconsistencies in reported data. This level of detail is invaluable for more accurate carbon footprint calculations.

Why is granularity in energy data so important for emissions tracking?

Granular data breaks down energy consumption to a more detailed level, like specific units within a building or time-of-day usage. This allows for precise emissions calculations and helps identify areas where energy efficiency improvements can be made, ultimately leading to more targeted decarbonisation strategies.

How does regional variation affect emission factors in energy data? 

Emission factors can vary widely by region due to differences in energy production methods, such as the mix of renewable and non-renewable sources. For example, a region heavily reliant on coal will have a higher emission factor than one using primarily solar or wind energy. This variation affects the overall carbon footprint calculation based on location.

‍