New and improved data accuracy is changing the understanding of Scope 2 emissions and enabling radical carbon reductions
PPN 06/21 carbon reduction plans are quickly becoming universal. Under this framework, showing carbon reductions is critical. Indeed, by 2028, NHS precurement applications will be judged on progress towards net zero. Therefore, many businesses should not consider their absolute footprint, but rather demonstrating impressive reductions.
Until now, scope 2 has represented a problematic feature of most carbon footprints with respect to carbon reductions. However, new datasets have enabled a radical new approach which will unlock the major carbon reductions potential.
What is Scope 2?
Scope 2 refers to a specific category of greenhouse gas emissions that are associated with the consumption of purchased electricity, heat, or steam by an organisation. It is part of the internationally recognized greenhouse gas accounting framework known as the Greenhouse Gas Protocol.
In the context of carbon footprint, Scope 2 emissions are indirect emissions that result from the generation of electricity or heat by a third party, which is then used by the organisation. These emissions occur outside of the organisation's operational boundaries but are considered relevant because they are a consequence of the organisation's activities.
Understanding and Addressing Scope 2 Emissions
For many companies scope 2 represents the most immediate and problematic aspect of their carbon emissions. As, for many non-industrialised companies, scope 1 is not so significant, scope 2 represents the most direct and controllable source of emissions.
Furthermore, scope 2 emissions are very difficult to reduce. Although one simple recourse is to purchase green energy certificates (REGOs), for many companies this is something of a cop-out as real emissions (i.e. the emissions of the electricity that you drew from the grid) are none-the-less present, real, and unaffected. This leaves companies with little recourse as reducing electricity consumption is very tricky for a modern company and the installation of renewable energy production capacity can be prohibitively expensive or impractical.
Typical calculation approach and limitations
Therefore, a keen understanding of scope 2 emissions is paramount in a typical company's efforts to reduce carbon emissions. However, traditional calculation methodologies are found lacking. Typically, companies simply multiply their yearly kWh consumption with the average emissions factor over the year in the country in question.
This approach fails to account for the dramatic differences in both geographic location within the nation and the time at which the electricity is consumed.
First, let’s focus on geography. Consider the two images below.
These images were captured as I write this blog. The carbon intensity of the grid varies dramatically from 371 grams of CO2e per kWh in South Wales, double the national average, to a staggering 1 gram of CO2e per kWh in North East England, facilitated by the connection with the incredible green energy production capacity of Norway. This huge range of carbon emissions factors is critical in understanding the true emissions of a company. Can energy-consuming processing be moved to different regions of the country where energy is greener. Such decisions cannot be grappled with without the critical information these types of granular emissions factors facilitate.
Although the above images cannot capture it, these carbon intensities change drastically throughout the day. The grid responds to demand and if there are spikes, typically in morning and evening, traditional power stations increase production to match demand. However, this generally increases the carbon intensity of the grid.
Consider the below image.
This reflects the national average emissions over a 72-hour period. The emissions peak in the morning when people wake up and again in the evening. However, in the middle of the night the grid mix reduces dramatically. Again this is important information in terms of decision making that cannot be captured with typical methods of calculation.
You may notice the above plot shows an actual emissions and a forecast emissions. This data was generated by carbonintensity.org.uk who go further than just reporting the grid emissions but rather work to forecast it.
carbonintensity.org.uk and Advanced Infrastructure Methodology
For Carbon Intensity, the demand and generation by fuel type (gas, coal, wind, nuclear, solar, etc.) for each region were forecast several days ahead at a 30-minute temporal resolution using an ensemble of state-of-the-art supervised Machine Learning (ML) regression models. An advanced model ensembling technique was employed to blend the ML models and generate a new optimised meta-model. The forecasts were updated every 30 minutes using a nowcasting technique to adjust the forecasts for a short period ahead.
Advanced Infrastructure is a private company that has gone even further to break down the emissions of the grid. They use similar techniques to break down the grid on the postcode level and in 5 minute increments. This provides a staggering degree of accuracy.
Using this data
First and foremost, the basic implications of this data are use energy at night and move operations to the North East and Scotland. In particular, buying battery capacity to charge in the night and discharge during the day could half electricity emissions.
For those with more operational capacity, using the remarkably accurate emissions forecasting capacity could allow high level short term decisions to be made to reduce emissions. For instance charging of electric vehicles could be timed to accord with low emissions periods or could be triggered by low emissions periods.
In conclusion, the traditional methods of calculating carbon emissions are deeply flawed due to their inability to account for the fluctuations in the carbon intensity of electricity throughout the day and across different regions. This results in an over-simplified picture of a company's actual emissions, hindering their efforts to effectively reduce their carbon footprint. With the advent of Carbon Intensity’s and Advanced Infrastructure’s methodologies and machine learning algorithms, we are now able to access more granular and accurate data about the carbon intensity of electricity.
Utilising this data can help companies make strategic decisions such as shifting energy-consuming processes to regions with greener energy and choosing off-peak hours to consume electricity. Furthermore, the predictive models developed by organisations like Carbon Intensity provide opportunities for companies to plan ahead and optimise their operations based on forecasted emissions. This approach, although complex, can potentially lead to significant reductions in a company's scope 2 emissions.
However, to truly leverage these advanced methodologies, there needs to be a paradigm shift in how companies approach their carbon emissions. This shift necessitates not only an investment in technology and data analysis, but also a commitment to transparency and a willingness to make operational changes based on the insights derived from the data.
The path to a sustainable future is not easy, but with the right tools and commitment, companies can significantly contribute to reducing the global carbon footprint. This advanced, data-driven approach to managing scope 2 emissions is a critical step towards that goal.