Metrics Matter

When evaluating the impacts of a decision or producing a business case, few would argue that consideration of people, planet, and profit, or the “triple bottom line” is a bad idea. However, measuring the impacts on the planet or its environment is far from straightforward, and it gets especially complicated when trying to compare different options against one another. If we look at this specifically from a waste management perspective, what do we need to consider as an environmental impact? CO2e emissions? Recycling rate? Landfill diversion? Air quality? Water pollution? The list goes on.

At present, it could be argued that, of the environmental considerations, CO2e emissions should be of the greatest concern to local authorities and their service providers, given that many of these have declared climate emergencies in recent years and have set net-zero ambitions. However, with pEPR payments tied to services being operated “effectively” and “efficiently” to achieve the maximum payments, the recycling rate will undoubtedly also be a key concern. The difficulty then lies in how we measure both recycling rate and carbon impacts.

If we take the recycling rate, different systems have been shown to deliver similar results (for example, two-stream systems compared to fully co-mingled systems) using current calculation methods; however, these are likely to grossly underestimate the amount of processing loss (akin to a manufacturing loss) and contamination extracted during reprocessing, simply due to where we currently draw the line when measuring recycling rates. It is therefore very difficult to absolutely compare collection methods and their true successes. A further difficulty is that once material is aggregated at a reprocessor (likely having already been aggregated at sorting facilities), it becomes virtually impossible to determine the differences in rejects and contamination between sources of material (in terms of collection method).

If we then look at carbon emissions, few carbon metrics account for the differences in the processing that the recycled material may have been through. Additionally, where some attempt has been made to distinguish this (for example, WRAP’s Carbon WARM2), it has necessitated the use of many underlying assumptions to create a workable set of factors. But this leaves unanswered questions such as, how does an ultramodern MRF that has AI sorting, fewer operatives, and generates some of its own renewable energy from solar panels compare to an older facility that has less sorting technology, higher numbers of people and no on-site renewables? The answer, in terms of CO2e emissions, is we don’t know. A further complication is the travelling distances to the MRF or reprocessor, which tend not to be separately calculated and are instead captured as a broad assumption in the recycling carbon factors.

These uncertainties matter when we are comparing authorities against one another where it can have real consequences, not only in terms of the funding they receive but also in how effective decisions can be made. Our methods of calculating recycling rates and CO2 emissions are largely driven by the availability of data and represent our current “best estimate”. The increase in the use of AI should help with waste data tracking, identification of contaminants, waste classification, and material sorting accuracy. However, as data improves this is likely to result in a fall in recycling rates as the accuracy of recording contamination and losses improves. Additionally, the increase in technological development such as in renewable energy, electric vehicles, and AI, will lead to a reduction, one hopes, in carbon emissions, and carbon factors for recycling will need to be adapted to take account of the widening gap in emissions from different processes, if we are going to compare and benchmark these against one another.

But do these uncertainties in what the true performance is really matter when surely improvement is the target regardless? The answer is, potentially yes in two key areas:

• The first is in enabling authorities to track their own progress towards their net zero ambitions. Authorities can track reductions in things such as energy use, fuel use, total waste generation, and recycling rates, but the carbon metrics aren’t nuanced enough to account for all variables where improvements could be made.

• The second is perhaps not an issue in the short-term but could conceivably be in the longer term depending on how pEPR payments are calculated and how the definitions of “efficient” and “effective” evolve over time. Hear-in lies the crux of the challenge: how to create a fair system that rewards those who are most efficient and effective, yet accounts for where authorities are in their contracting and purchasing cycles and doesn’t just reward those who are fortunate in timing to be at the forefront of the technological developments.

If you’re trying to understand how the developing policy landscape might affect your services, then get in touch!