Using the social cost of carbon to value earth observing systems

Abstract

The goal of this study is to show how to quantify the benefits of accelerated learning about key parameters of the climatic system and use this knowledge to improve decision-making on climate policy. The US social cost of carbon (SCC) methodology is used in innovative ways to value new Earth observing systems (EOSs). The study departs from the strict US SCC methodology, and from previous work, in that net benefits are used instead of only damages to calculate the value of information of the enhanced systems. In other respects the US SCC methodology is followed closely. We compute the surfeit expected net benefits of learning the actionable information earlier, with the enhanced system, versus learning later with existing systems. The enhanced systems are designed to give reliable information about climate sensitivity on accelerated timescales relative to existing systems; therefore, the decision context stipulates that a global reduced emissions path would be deployed upon receiving suitable information on the rate of temperature rise with a suitable level of confidence. By placing the enhanced observing system in a decision context, the SCC enables valuing this system as a real option.

Policy relevance

Uncertainty in key parameters of the climatic system is often cited as a barrier for near-term reductions of carbon emissions. It is a truism among risk managers that uncertainty costs money, and its reduction has economic value. Advancing policy making under uncertainty requires valuing the reduction in uncertainty. Using CLARREO, a new proposed EOS,as an example, this article applies value of information/real option theory to value the reduction of uncertainty in the decadal rate of temperature rise. The US interagency social cost of carbon directive provides the decision context for the valuations. It is shown that the real option value of the uncertainty reduction, relative to existing observing systems, is a very large multiple of the new system's cost.

Keywords:

• CLARREO
• climate observing system
• DICE
• real option value
• social cost of carbon
• value of information

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. An abatement cost in DICE 2009 has the same analytical representation as in DICE 2007 (Nordhaus, 2008) or in DICE 2013 (Nordhaus & Sztorc, 2013). The abatement cost is expressed in units of output and has the form of a convex function of the share of abated flow emissions. Exogenous technological changes (which reduce abatement cost) are captured in the scaling coefficient.

2. The "DICE optimal emissions path optimizes social welfare for climate sensitivity equal to 3 °C, for other values it is not optimal.

3. The underlying model is written in VBA and is relatively easy to use with the excel version of DICE 2009. Authors will send source code upon request.

4. The units in eq (1) are (°C yr^-1)².

5. Switching to the 2.5 °C stabilization path does not mean that temperature in 2100 is stabilized at 2.5 °C. When the policy is adopted upon the trigger being met, the emissions then are reduced to the level required in that year on the new emissions pathway. There is no make-up for the past ‘over accumulated' emissions (the difference in total emissions between BAU and the new emissions pathway). There would be a ‘cliff' effect with a radical drop in emissions at time of switch to the new pathway. Since emissions are not made up for to reflect the full reduction path of the original policy, then the policy path would no longer attain the original goal associated with it (e.g. you wouldn't hit the 2.5 °C goal, or what DICE thinks is optimal). This aspect of the decision problem and calculation is beyond the scope of this article.

6. This is based on (Nordhaus, 2008), where Stern industrial emissions per decade are given out to 2105. Industrial emissions for Stern are zero beyond 2095. Total Stern emissions are determined by adding emissions due to land-use changes, which are the same for all scenarios.