Skip to main content

The carbon mountain: Dealing with the EU allowance surplus

It’s not news that the EU emissions trading system (EU-ETS) is in trouble. A build-up of surplus emission allowances has caused dangerous instability in the carbon market and a plunge in prices since the economic slump in 2008 began (See Figure 1, courtesy of David Hone).

Figure 1, courtesy of David Hone

The discussion at the All Party Parliamentary Climate Change Group’s (APPCCG) meeting on the 28th of January centred on the causes and consequences of the EU-ETS allowance surplus. The majority of speakers at this session had a background in the discipline of economics, so inevitably the exchange of views was… frank.  The panel were in agreement that EU-ETS is in crisis; but can and should it be saved?

Emissions trading schemes, of which EU-ETS is a canonical example, are an attempt to allow market forces to correct the so-called ‘market failure’ that is carbon emission. From the point of view of a classical economist, the participants in carbon emitting industries do not naturally feel the negative effects their activities cause to the environment. Emissions trading forces carbon emitters to ‘purchase’ the right to pollute on a market. In effect, they pay to receive permits (or allowances) to emit a certain level of emissions. If they do not reach this level of emission, the excess can be sold back onto the market, allowing others to make use of it. The prices of permits are determined by market forces, so cannot be fixed by the EU. The quantity of permits is within the control of the EU, and this is where the problem lies.

James Cameron, Chairman Climate Change Capital
In the aftermath of the 2008 slump, a surplus of allowances began to build up, leading to a crash in the price of allowances. Many commentators blame EU economic forecasting for this problem, as the recession and consequent reduction in economic activity was not factored in to the EU-ETS control mechanism. Criticism has been forthcoming for the economic models used, and some go as far as to liken the mismanagement of EU-ETS to the ‘wine-lake and butter-mountain’ days of the 1980s, where the Common Agricultural policy was allowed to consume over 70% of the EU’s budget. Perhaps the models are too simple - James Cameron, a speaker at the APPCCG event, spoke of the ‘premium on simplicity’ that exists in creating policy. Maybe that approach has extended itself into the mathematical models used to predict the performance of EU-ETS, rendering them over-simplistic?

Personally, I see things a little differently. It’s clear that economic models are often far from perfect; however, I’m not sure that’s where the problem lies. In the implementation of policy, decision makers have to draw on the implications of many separate models; for instance, they must consider the GDP growth of EU member states, their adoption rate of new energy efficiency standards and the relative industrialisation of their economies. To my mind, the greatest source of error is in the gaps and interfaces between these economic models. Policy makers must make decisions on how to interpret the way economic predictions will interact with one another, and these interpretations are always subject to value judgements. What we need is a more joined-up approach.

Climate science has long used ‘macro-models’ to incorporate a variety of physical processes into their predictions, an approach that could be adopted by economists as well. While the first economic macro-models may not achieve even a fraction of the accuracy of climate models, that is not to say they cannot be improved through collaboration and quantitative criticism. Perhaps now is the time to make a start?

This blog is written by Neeraj Oak, Cabot Institute.



Neeraj Oak

Popular posts from this blog

Converting probabilities between time-intervals

This is the first in an irregular sequence of snippets about some of the slightly more technical aspects of uncertainty and risk assessment.  If you have a slightly more technical question, then please email me and I will try to answer it with a snippet. Suppose that an event has a probability of 0.015 (or 1.5%) of happening at least once in the next five years. Then the probability of the event happening at least once in the next year is 0.015 / 5 = 0.003 (or 0.3%), and the probability of it happening at least once in the next 20 years is 0.015 * 4 = 0.06 (or 6%). Here is the rule for scaling probabilities to different time intervals: if both probabilities (the original one and the new one) are no larger than 0.1 (or 10%), then simply multiply the original probability by the ratio of the new time-interval to the original time-interval, to find the new probability. This rule is an approximation which breaks down if either of the probabilities is greater than 0.1. For example

1-in-200 year events

You often read or hear references to the ‘1-in-200 year event’, or ‘200-year event’, or ‘event with a return period of 200 years’. Other popular horizons are 1-in-30 years and 1-in-10,000 years. This term applies to hazards which can occur over a range of magnitudes, like volcanic eruptions, earthquakes, tsunamis, space weather, and various hydro-meteorological hazards like floods, storms, hot or cold spells, and droughts. ‘1-in-200 years’ refers to a particular magnitude. In floods this might be represented as a contour on a map, showing an area that is inundated. If this contour is labelled as ‘1-in-200 years’ this means that the current rate of floods at least as large as this is 1/200 /yr, or 0.005 /yr. So if your house is inside the contour, there is currently a 0.005 (0.5%) chance of being flooded in the next year, and a 0.025 (2.5%) chance of being flooded in the next five years. The general definition is this: ‘1-in-200 year magnitude is x’ = ‘the current rate for eve

Coconuts and climate change

Before pursuing an MSc in Climate Change Science and Policy at the University of Bristol, I completed my undergraduate studies in Environmental Science at the University of Colombo, Sri Lanka. During my final year I carried out a research project that explored the impact of extreme weather events on coconut productivity across the three climatic zones of Sri Lanka. A few months ago, I managed to get a paper published and I thought it would be a good idea to share my findings on this platform. Climate change and crop productivity  There has been a growing concern about the impact of extreme weather events on crop production across the globe, Sri Lanka being no exception. Coconut is becoming a rare commodity in the country, due to several reasons including the changing climate. The price hike in coconuts over the last few years is a good indication of how climate change is affecting coconut productivity across the country. Most coconut trees are no longer bearing fruits and thos