Absolute emissions or not!

16th January 2012


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  • Mitigation ,
  • Carbon Trading ,
  • Reporting ,
  • Management



Is it best to use absolute emissions or intensity ratios for your organisation's carbon reduction targets? Niall Enright finds out

When it comes to climate change, there really is only one measure that matters - the total quantity of greenhouse gas in the atmosphere. That is why policymakers tend to favour absolute measures of emissions.

The UK Climate Change Act 2008 targets set out a legal obligation to reduce absolute emissions of "carbon units" by 80% by 2050 compared with 1990 levels. This equates to approximately a 3% reduction every year for the next 40 years.

But are absolute figures the best way for organisations to manage and report emissions?

A question of efficiency

Despite their environmental credentials, it does not take long to spot some obvious weaknesses with applying absolute emissions targets to organisations or individuals. The first issue is that of economic efficiency. Some organisations can achieve emissions reductions much more cheaply than others, and so setting a uniform 3% annual reduction target across the economy, or in an organisation, would not be the most efficient way to achieve a specific overall cut.

Another flaw with absolute targets is the issue of boundaries. Whereas a national frontier does not change, organisational boundaries are rarely static: acquisitions or disposals can give the appearance of better or poorer performance in absolute terms without any indication of true underlying improvement, unless there is readjustment of baseline data to account for the changes. To compound the boundary issue, there are aspects of equity ownership or control that make absolute emissions reporting at an organisational level even more complex.

This explains why the idea of emissions intensity, in which an organisation's emissions are divided by some form of activity measure - typically, a production or turnover figure - has gained widespread acceptance. Examples of emissions intensity metrics are all around us: from the grammes of CO2 per km quoted for motor vehicles through to gCO2 per pack used in product labelling. What's more, many companies use these to describe their emissions targets. The common use of these intensity ratios reflects their simplicity and the ease with which they can be understood.

An underlying attractiveness of intensity ratios is that they permit comparisons to be made. Knowing the g/km for a single vehicle is of relatively little value, but knowing that of a range of vehicles can inform consumer choice, help manufacturers to benchmark their performance against competitors and permit policymakers to set performance targets for different sectors of the economy.

These types of intensity ratios often rely on a defined scenario to set the benchmark - for example, the UK vehicle emissions are quoted for the "typical journey cycle". More complex industry benchmarks of energy and emissions, such as the Solomon Energy Intensity Index in the refining sector, have hugely complicated scenarios to enable comparisons to be made.

Hidden risks

Intensity ratios can also be used for internal benchmarking, typically tracking changes in product intensity over several years, and many companies quote these rather than absolute targets as they feel it will overcome the negative effect growth would have on absolute emissions. However, there is a huge hidden risk - referred to as the "baseload effect" - in using intensity ratios as a measure of emissions performance.

A practical example helps to illustrate the problem. Several years ago I met a senior executive for a major Canadian automotive manufacturer who proudly boasted that over the previous five years his company had continuously reduced the emissions intensity of the manufacture of its trucks even as volumes increased. In fact these emissions figures formed part of the marketing of the company concerned.

I sketched out a diagram to explain why publishing performance in the form of an intensity ratio could entail some risk. What the truck manufacturer did to establish its intensity was to take the total emissions in a particular year and divide them by the production that year - so, x tonnes for y products.

However, the intensity ratio suggests that there is a linear relationship back to zero - in other words, if there is no production, there will be no emissions. The reality is quite different.

Automotive factories in Canada use quite a lot of energy (and so generate emissions) completely independently of production, such as the lighting and heating of the assembly halls, offices and so forth. In fact it is not unheard of for the fixed-energy load in an automotive plant to approach 50% of the overall demand - reflected in the diagram by a red line.

So what does that mean for our Canadian example? As production rose through its factories, the fixed load was divided over more vehicles and so the intensity improved irrespective of the underlying performance of the plant. In effect, emissions tracked along the red line following the red arrow to the right of the reference ratio. The risk lay in the opposite effect - if volume fell below that of the reference year, then the plant would track in the direction of the blue arrow, significantly above the reference intensity, as the fixed baseload is divided by an ever decreasing volume of trucks.

So, improving intensity ratios do not necessarily reflect performance improvement and should be used with caution. A particularly dramatic example of how intensity ratios can confuse can be seen in the published environmental performance of a major UK water company. In 2006, it reported a more than 18% reduction in emissions per megalitre compared with 1990.

We already know from the previous example that when the volume of production increases the baseload effect tends to improve the intensity, so some of the improvement could have been as a result of the baseload effect. However, there was another, bigger, effect at work here: energy conversion factors. In 1990, the conversion from a kWh of electricity to a kg of CO2 was 0.77 kgCO2/kWh, while it was 0.52kgCO2/kWh in 2006/07 - a reduction of 32%.

The truth is that the emissions intensity of the company had improved. But the implication that this reflects underlying improvement rather than the decarbonisation of electricity supply in the UK is questionable. Because most emissions data are a product of an energy conversion factor there is an additional challenge in their interpretation, given the regional and temporal variations in these factors.

If a product is manufactured in a country with a low electricity conversion factor, such as France, which has a lot of nuclear power, it will have a lower emission per unit. This does not mean to say that the French factories are more efficient than those in countries with higher conversion factors.

Double jeopardy

If the use of intensity is problematic at a company level, it becomes doubly so as a means to measure operational performance, where different equipment and processes are driven by different variables, not just production volume but weather, for example. The most common operational modelling technique employs regression analysis to derive a formula in the form y=mx+c, where y is the expected emission, m is the emissions intensity per unit activity (x), such as production or weather, for example, and c is the baseload.

The effectiveness of this form of modelling has been proven in thousands of monitoring and targeting (MT) programmes - a cost-saving management technique, designed to detect wasteful use of resources - where it is employed to set targets to drive operational improvement.

MT targets differ from intensity targets in that they are rarely used for benchmarking, such as for different buildings, but to provide a comparison with historical performance against which corrective action can be taken. MT targets tend to work best at a very low level, such as for individual plants or pieces of equipment, and so a single facility can have many different targets using a variety of different variables, which can be complex and difficult for outsiders to understand.

Luckily, the performance can be aggregated up from these individual models using techniques such as CUSUM (cumulative sum control chart, which measures a cumulative deviation from the mean or a target value and is often used, for example, to detect changes in the average output of a machine), which can make the MT technique effective in tracking and reporting organisation-level performance.

Pros and cons

In reality a hybrid approach, involving both absolute and intensity metrics, is emerging to manage and report emissions. As long as they achieve their absolute "caps", policymakers are willing to incorporate more flexible intensity ratios in the "trade" element of many emissions reduction schemes. In phase III of the EU emissions trading scheme, which starts in January 2013, emissions permits will be allocated across more than 50 product categories on the basis of the average emissions intensity of the best 10% of producers.

If a company is in the top decile, then it will receive nearly all the emissions allocations it needs. By contrast, a poor performer according to the benchmark will have to buy more allocations in the market or undertake its own emissions abatement activities to compensate. The total emissions allowances remain capped - but their distribution is based on intensity ratios.

In the UK, most organisations impacted by the climate change levy, and the associated climate change agreements, have opted for an intensity-like product-mix algorithm to relate their emissions reduction target to the production output(s) of their facilities.

Even in the Carbon Reduction Commitment Energy Efficiency scheme performance league table there is some concession towards growth in that from this year 15% of the score is based on the emissions intensity change per pound turnover of private sector participants.

In fact the global vision for equitable emissions allocation combines an absolute goal - contraction (of total emissions) - with an intensity goal - convergence (on per capita emissions).

So, absolute, intensity and operational metrics all have their pros and cons when it comes to managing emissions and it is not unusual to see these used together at both a national and corporate level.

Ultimately, the form of reporting with the highest environmental integrity must remain absolute emissions. But, like a balance sheet, this form of reporting rarely provides enough insight to inform day-to-day performance management. For that we need the "management accounts" of emissions - intensity or operational metrics that provide many advantages so long as we avoid potential pitfalls.


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