Carbon Management Planning and the EAUC Annual Conference

Co-authored by Russ Avery & Will Jenkins.

In the last few months, Carbon Credentials has supported a number of universities with their carbon management efforts. This support has ranged from discrete to large scale projects involving data management, energy audits, and stakeholder engagement. In light of our work in this area, and thanks to the encouragement of our clients and partners in the Higher Education sector, we decided to support The 2014 EAUC (Environmental Association for Universities and Colleges) Annual Conference as its Headline Sponsor.

We showed the video below during our slot of Thursday morning’s plenary presentation.

This three-day event, kindly hosted by Nottingham Trent University, brought together delegates from every area of campus life, including those responsible for environmental management, carbon reduction and sustainability. My colleagues and I were very impressed with the quality and maturity of the conversations, and it was wonderful to hear some of our clients both presenting and telling their peers about the progress they have made on their sustainability journeys, specifically over the last 12 months. Throughout the conference, something that became clear to us was this: the Higher Education sector is making significant strides forward, particularly when it comes to Carbon Management Planning.

Here at Carbon Credentials we take a data-driven approach to carbon and energy management. We find that an information-rich carbon programme helps with opportunity identification, staff and student engagement and the robust measurement and verification of carbon emissions reductions.

Having put a lot of thought into how intensity metrics can be used to contextualise carbon performance our team was keen to present recent data analytics during the conference which opened up some good debate.

Using intensity metrics to benchmark carbon performance

Universities are not static, and over the last five years we have seen increasing revenue, student numbers and a higher demand for energy intensive equipment. Consequently, there is a need to evaluate carbon performance in the context of university operations in order to accurately describe the progress that has been made. Similarly, benchmarking performance against peers requires an assessment across multiple factors to build up a more complete story. No two universities are the same, despite the apparent similarities found within the sector.

The annual publication of the Higher Education Statistics Agency’s (HESA) Estates Management Record provides an opportunity to take a big data approach to performance assessment. By delving into this data set we have been able to develop a more complete understanding of carbon performance and the factors that affect it, allowing us to identify how specific universities should be tracking their progress.

The examples below give a high-level snapshot of this approach.

Scope 1 & 2 Carbon Emissions: by FTE staff and students and type of student

Universities often use the number of Full Time Equivalent (FTE) staff and students to normalise carbon emissions, improving their understanding of how and why performance tracks over time. When we looked at this metric at a sector scale it did not tell the whole story. While the regression model described 82% of the variance, further analysis revealed that the type of student (i.e. research or teaching) significantly influences scope 1 and 2 carbon emissions.

It is clear those universities with a lower ratio of research to teaching students have much lower carbon emissions than universities with a similar number of FTE staff and students. In the graphic below, this can be seen by the smaller dots residing below the regression line.

Scope 1 & 2 Carbon Emissions: by income and floor area

Using total income and gross internal area to normalise carbon emissions is also commonplace across the sector. By combining these metrics we are able to unpick and explain performance in a more comprehensive manner. This is demonstrated in the graphic below, where a comparison of London School of Economics & Political Science and the Open University highlights the efficiencies that can be achieved through distance learning. Evidently this business model will be key to delivering low carbon education.

The validity of using carbon emissions by income as a benchmarking metric between institutions is undermined when you look at the efficiencies achieved by research funded institutions like University College London, the University of Oxford and the University of Cambridge. While there is a link between carbon emissions and income, as evidenced by the regression model explaining 93% of variance, it should be used with caution due to the numerous other factors that affect carbon performance.

Key learnings

Through our work with reviewing and building Energy Strategies and Carbon Management Plans for Higher Education institutions across the UK, we have learnt a great deal, but it can really be boiled down to three key observations:

  1. Data quality and management will help you to understand performance, identify new opportunities, engage stakeholders and confidently report on success
  2. A bespoke approach that carefully considers the unique nature of your organisation is required
  3. Collaboration and strengthening existing systems will support effective implementation
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