5 The Climate Cost Curve

This is a draft proposal in which to frame our thinking about the climatic impacts of global warming, without getting lost in trivial details, or questioning motives. It is an updated version of a draft posted on 26/10/2012.

The continual rise in greenhouse gases due to human emissions is predicted to cause a substantial rise in average global temperatures. This in turn is predicted to lead severe disruption of the global climate. Scientists project that the costs (both to humankind and other life forms) will be nothing short of globally catastrophic.

That is

CGW= f {K}                 (1)

The costs of global warming, CGW are a function of the change in the global average surface temperatures K. This is not a linear function, but of increasing costs per unit of temperature rise. That is

CGW= f {Kx} where x>1            (2)


The curve is largely unknown, with large variations in the estimate of the slope. Furthermore, the function may be discontinuous as, there may be tipping points, beyond which the costly impacts of warming become magnified many times. Being unknown, the cost curve is an expectation derived from computer models. The equation thus becomes

E(CGW)= f {Kx}                (3)

The cost curve can be considered as having a number of interrelated elements of magnitude M, time t and likelihood L. There are also the adaptation costs/benefits (which should lead to a planned credit) along with the costs involved in taking actions based on false expectations. Over a time period, costs are normally discounted by r. Then there are two subjective factors – The collective risk factor R, and, when considering a policy response, a weighting W should be given to the scientific evidence. That is

E(CGW)=f {M,1/t,L,A,│Pr-E()│,r,R,W}    (4)

Magnitude M is the both severity and extent of the impacts on humankind or the planet in general in a physical sense.

Time t is highly relevant to the severity of the problem. Rapid changes in conditions are far more costly than gradual changes. Also impacts in the near future are more costly than those in the more distant future due to the shorter time horizon to put in place measures to lessen those costs.

Likelihood L is also relevant to the issue. Discounting a possible cost that is not certain to happen by the expected likelihood of that occurrence enables due unlikely but catastrophic events to be considered alongside near certain events.

Adaptation A is for a project to adapt to the changed climate, to lessen or null the costs. It is the difference between the actual costs spent and the climate impacts saved. Upon completion, a project should have a net credit value.

│Pr-E()│ is the difference between the predicted outcome, based on the best analysis of current data at the local level, and the expected outcome, that forms the basis of adaptive responses. It can create a cost in two ways. If there is a failure to predict and adapt to changing conditions then there is a cost. If there is adaptation to an anticipated future condition that does not emerge, or is less severe than forecast, there is also a cost. │Pr-E()│= 0 when the outturn is exactly as forecast in every case. Given the uncertainty of future outcomes, there will always be costs incurred if the climate cost savings from adaptation is a unitary value. If there are a range of possible scenarios, then this value could be a credit.

Discount rate r is a device that recognizes that people prioritize according to time horizons. Discounting future costs or revenue enables us to evaluate the discount future alongside the near future.

Collective risk factor R, is the risk preference weighting. If policy-makers assume a collective risk-neutral position, then this weighting will be 1. Risk lovers – the gamblers and many self-made billionaires – have a weighting of less than one. Those who take out insurance are risk averse. Insurance gives a certain premium to compensate if a much greater probabilistic loss occurs. For instance, the probability of a £200,000 house being completely destroyed in a year is around 1 in 10,000. So the expected loss is just £20 in any year. Most people are risk averse when it comes to their most valuable asset, so would pay a premium of far greater than £20 compensate for this unlikely loss. With respect to potential catastrophes, we usually expect governments to take a risk-averse approach. That is to potentially spend more on certain costs (like flood defences) than the total expected losses from letting catastrophes from happening. For any problem on a vast scale, we need to articulate the risk preference weighting. The “precautionary principle”, used in arguing for tough and immediate mitigation policies, effectively creates a collective risk factor many times greater than 1. NB, as the costs of climate change will increase with time, a risk averse weighting is the equivalent of a negative discount rate r. That is, you could assume R = f {-r}.

Finally the Weighting (W) is concerned with the strength of the evidence. How much credence do you give to projections about the future? Here is where value judgements come into play. I believe that we should not completely ignore alarming projections about the future for which there is highly circumstantial evidence, but neither should we accept such evidence as the only possible future scenario. In fact, by its very nature the “evidence” will be highly circumstantial. Consider the costs of climate change graph again. The data we have (which needs to converted into evidence) is for a very short section, and for miniscule fluctuations in costs, compared to the predicted catastrophe.

This leads to a vast area of evidence quality as

  • Small errors or biases in temperature measurement will have huge impacts on future projections. (Impacts on historical climate sensitivity)
  • Small errors or biases in distinguishing between natural and human-caused extreme weather events or short-run climatic changes will have huge impacts on projected costs.

If we assume that some sort of climate catastrophe is going to happen, convincing an independent third-party could include

  • Science building a clear track record of short-run predictive successes, both on warming trends and damage impacts.
  • Learning from the errors and exaggerations.
  • Be very clear as to the quality and relevance of the evidence.
  • Corroborating evidence. Show the coherence of one part of the picture with another. For instance, trying to reconcile with estimates of polar ice cap rate of melt with the rate of sea level rise reveals some very interesting questions.
  • Corroboration of between different techniques and evaluation methods.
  • For a junior science, show the underlying methodology draws upon the best of the mature sciences and philosophies of science.

The prediction of catastrophe is highly emotive. There are comparisons here with the justice system in has Britain failed where there are highly emotive crimes, for instance the IRA bringing their bombing campaign to the British mainland in the early 1970s.

  • Clear separation of the understandable emotion, from the evidence gathering.
  • Developing, and continually improving, quality standards for evidence gathering
  • A dim view taken for tampering with, or suppression of evidence.
  • A dim view taken on influencing the jury.
  • Allowing the accused a strong defence. The lack of any credible defence argument, despite a strong defence team, will remove any “reasonable doubt” in the minds of the jury, where the accused is in denial of the overwhelming evidence of their guilt.
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