IPCC and uranium supply limits?

I have been reading the database (and associated literature) of the recent IPCC 1.5 degrees special report. I am disappointed that there clearly is no consistent reporting requirements for the IAM models and for example most scenarios do not report critical input assumptions such as capital costs. However, I had a more careful look on how modellers choose to treat uranium supplies. I have noted earlier how some models restrict nuclear power in the scenarios by claiming world runs out of uranium. These seemed very silly to me already then, but now, having read more, they seem totally absurd.

Below are some examples of the used supply curves. Notice how extracting more pushes up the cost of fossil fuels, but how in the case of uranium, the opposite is typically expected. Small increase in price increases the resource base dramatically. (This is harder to see, since axis are switched, but there it is…). Strangely many IAM modellers seem to treat uranium differently from other options. They force a hard upper limit on how much uranium can be extracted. For the German REMIND model this is set at 23Mt of uranium. In others, it might vary depending in the narrative and might be, for example, 20 Mt for the SSP1 (Shared socioeconomic pathway) variants and somewhat higher with other narratives. This resource limit is only applied for nuclear power and there are no material limitations on renewable energy for example. How come?

REMIND model supply curves for fossil fuels.

Uranium supply curve from GEA ,used in several models if I understood correctly. Based on Schneider and Sailor, but with arbitrary cut-offs which vary depending on the desired narrative.

There are some “real” sources that are used to estimate uranium supply curves. There is the IAEA Red book, Bunn et al. paper on the economics of reprocessing vs. once through cycle, and Schneider and Sailor paper on the supply curve. Remarkably, none of these gives support to the claims of uranium scarcity. Modellers have to some extent used the supply curve from the papers, but then added arbitrary ceilings for the supply. Bunn et al. conclude that scarcity will not force transition into breeders since resource base will rapidly expand whenever needed. They also discuss reasons for this extensively. Schneider and Sailor also warn that their supply curves should not be used over generational timescales (as done by the IAM modellers) since they ignore learning effects. They anticipate, based on historical data, that if mining volumes increase, prizes will drop rather than rise. This is precisely opposite to what is assumed in the scarcity based IAM models. IAM modellers simply ignore all this.

So are the assumed uranium limits relevant or not? Over short time scales, they are obviously not, but over the century they are. To illustrate this, I checked from all the scenarios in the 1.5 database how much uranium they would (roughly) need and compared this to the 23Mt limit assumed in the REMIND model. There are some models (GCAM in particular) where massive amounts of nuclear power are constructed and the uranium limit doesn’t seem to matter a lot (see the peak in the figure below). However, for most of the scenarios the required amount of uranium is suspiciously close to the upper boundaries set as input assumptions.

Estimate of how much uranium relative to the 23Mt limit assumed in the REMIND model IPCC scenarios need.  Some models with only few scenarios are not marked. Flag indicates “continent”. I did not feel like splitting the European models further.

What does this mean in terms of the energy consumption of humanity during the next century? What have the modellers actually assumed? So let me add rough estimate of the assumed uranium supply curve into the supply curves for fossil fuels. This requires some unit conversions and I also convert from nuclear electricity to primary energy by dividing it by 0.33 to get a comparable cost per GJ. As you can see in the figure, the underlying assumption, for example, REMIND modellers use as an input, is that uranium is the first resource to “run out”. Constraint is so strict that nuclear power can only cover a small fraction of the energy consumption (something around 50-100ZJ maybe?) over the century.

For some weird reason, humanity stops mining uranium even when the fuel cost is still massively lower than for fossil fuels. What is going on? Why would this make any sense? The used references certainly do not support this. (Incidentally, is this why uranium supply curve is given in different units …to make this comparison harder? I hope not.) Many people read these scenarios as outcomes of “science”. Computer magically optimizes something and then gives us a guideline on how to behave and what decisions to take. This is not at all the case. Scenario modelling has its uses, but mainly in illustrating sensitivities etc. If silly constraints are imposed from the outset, silly outcomes will appear. Garbage in, garbage out.

The lowest blue line is my estimate for uranium in light water reactors. It ends when roughly  23Mt of U are used. (I hope I made the estimate right. I encourage you to double check.) Wow!

Added 3.11.2018: I did a similar check on the database for the 5th assesment report. Figure below. (I wonder why REMIND clustered around 1 earlier, but now more like 0.5? What changed? The REMIND scenarios clustering around 1/2 seem to change their data reporting from every 5 years to every decade at 2060. Could that have something to do with this? Curious.)

Same as earlier, but for the AR5. Not quite all the models/scenarios are included, but most are. I also added a small indicator to distinguish Message v.4 runs from the rest since it seemed behavior was rather different.

Response to ‘A comparative analysis of electricity generation costs from renewable, fossil fuel and nuclear sources in G20 countries for the period 2015–2030’

A Greenpeace report commissioned from 100%RE academics from Lappeenranta University of Technology (of course) on electricity generation costs was recently published in Journal of Cleaner Production. Details of the computations were kindly made available as a supplementary spreadsheet. The results left something to be desired. I wrote a response which has now been published. “Response to ‘A comparative analysis of electricity generation costs from renewable, fossil fuel and nuclear sources in G20 countries for the period 2015–2030’” (doi:10.1016/j.jclepro.2018.07.159). Link should work for few months after which you either need access to the journal or download it from here. Take away lesson as usual: Be critical and do not automatically trust the results or conclusions.

Added 18.10.2018: The free share link does not seem to work from WordPress so if you don’t have access otherwise you can download it from here.