|Photo by Department of Conservation on Flickr cc by 2.0|
This is post two in a series of three posts answering key questions about the Social Cost of Carbon (SCC). In the interests of getting you to the end of this article, I have included some bird breaks (hopefully these won't get too squawkward).
Where does the information come from?
Producing an estimate for an SCC is kind of like baking a cake. You need ingredients in set amounts in order to produce a good cake. The problem with the SCC is that the ingredients are far from well-defined and the amount of each ingredient that we add could be the difference between making pancakes and making a three-tiered wedding cake.
So what recipe are we following and what are the ingredients? There are a range of models that policy makers and economists employ to figure out climate damages and economic impacts.1 The estimation is necessarily complex because of how many earth and economic systems are being incorporated. At the top of the pecking order is a set of models called Integrated Assessment Models (IAMs), most notably the Dynamic Integrated model of Climate and the Economy (DICE). (At this point it would be fair to say that climate science and economics have a bit of an obsession with acronyms.)
Stick with me for a bit more explanation, and you get a bird.
These models usually have a set of equations that express the relationship between things like CO2 emissions and temperature, or temperature and economic impacts. For example, the latest DICE model (DICE-2016R)2 applies information about:
- population-weighted utility per capita of consumption
- the rate of return on capital investments
- an intergenerational discount rate
- the cost of mitigation
- estimates of globally averaged temperature changes as well as global mean surface area temperature and deep oceans mean temperature
- naturally occurring emissions as well as exogenous land-use emissions
- the change in radiative forcings due to anthropogenic emissions
- the carbon cycle in the atmosphere, upper ocean and biosphere and lower oceans
- estimates of future growth rates of GDP and the current rate of decline in CO2 emissions.
The challenge lies in integrating these variables to produce an SCC that approximates reality.3 Furthermore, some of these variables change over time. As technology develops, the cost of mitigation decreases, and as science improves, our estimates of the environmental impacts of emissions become more accurate. Even if our estimates are continuing to evolve, they need not be pigeonholed; there is an imperative to use the best available information to help inform climate policy.4
|Photo by Ben on Flickr cc by 2.0|
Bird break: Ngutu pare (Wrybill) are the only bird in the world to have a laterally curved bill – evolution doesn’t always do what we expect. They are quite small, weighing only 50g on average.
What intergenerational discount rates should we use?
This is the part that tends to ruffle a few feathers. The inputs into these models are based on the best information available at the time and a (mostly) clearly stated set of assumptions. One of the particularly contentious assumptions is the intergenerational discount rate.5 In classic economic terms, for individual decisions that reflect considerations of time, this allows us to place a present value on the costs and benefits of future climate change impacts.
In the case of climate change mitigation, however, individuals are not just deciding about their own future welfare, but are making judgments about the rights and opportunities of future generations. A high (low) social discount rate implies that future damages to future generations matter less (more). This discount rate can be decided on either a descriptive or prescriptive basis. Its magnitude depends entirely on who is making the value judgement and for whom they are making it.6
For example, Nordhaus employs a purely descriptive intergenerational discount rate. This means he takes the discount rate of the market today as it is. This captures the time preferences of those currently living – where we prefer to hold something now rather than leaving it in the future at the same value. The reason for this is because if we don’t use a resource now, it contains an element of uncertainty about future use, so we tend to place less value on resources to be consumed in the future. But the long-term consequences from current emissions impact future generations, whose “current” assets are our “future” assets. Although this is confusing, it forms the basis for the prescriptive approach. In contrast, a prescriptive approach asks what the intergenerational discount rate should be. Should we be allowed to value the environment of future generations less than we value our current environment? An answer of “no” implies an intergenerational discount rate of 0% and a very high SCC.5 An alternative is using an intergenerational discount rate that declines over time.7
The key theme among these challenges is uncertainty.8 Economic and mathematical modelling uses sensitivity analysis to deal with uncertainty. This gives us a range of possible values rather than just a single number. Not only is a range better in incorporating some of the issues highlighted above, but its width also gives us an idea of the overall amount of uncertainty there is.
|Photo by Vil Sandi on Flickr cc by 2.0|
Bird break: There are two kinds of Tīeke (Saddlebacks). South Island Tīeke are more endangered with only 650 left, compared to an estimated 7,000 for the North Island Tīeke. The wattles on either side of their beak get larger as they age.
Note: wherever possible I have referenced the source of information and added further reading. This topic is complex and detailed and I encourage those interested to look further into the fantastic analysis and research done by some of the world’s leading environmental scientists and economists. These sources can be found in the reference list below.
1. Stanton, E. A., Ackerman, F., & Kartha, S. (2009). Inside the integrated assessment models: Four issues in climate economics. Climate and Development, 1(2), 166-184. Chicago - here and Fleurbaey, M., Ferranna, M., Budolfson, M., Dennig, F., Mintz-Woo, K., Socolow, R., ... & Zuber, S. (2018). The Social Cost of Carbon: Valuing Inequality, Risk, and Population for Climate Policy. The Monist, 102(1), 84-109 - here.
2. Nordhaus (2016) - here
3. Stern, N., & Stern, N. H. (2007). The economics of climate change: the Stern review. Cambridge University press. (book)
4. Revesz, R. L., Howard, P. H., Arrow, K., Goulder, L. H., Kopp, R. E., Livermore, M. A., ... & Sterner, T. (2014). Global warming: Improve economic models of climate change. Nature News, 508(7495), 173 - here.
5. Nordhaus, W. D. (2017). Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences, 114(7), 1518-1523 - here.
6. Grantham Institute (2018) - here and Scarborough, H. (2011). Intergenerational equity and the social discount rate. Australian Journal of Agricultural and Resource Economics, 55(2), 145-158 - here.
7. Weitzman, M. L. (1998). Why the far-distant future should be discounted at its lowest possible rate. Journal of environmental economics and management, 36(3), 201-208 - here.
8. Anderson, B., Borgonovo, E., Galeotti, M., & Roson, R. (2014). Uncertainty in climate change modeling: can global sensitivity analysis be of help? Risk analysis, 34(2), 271-293 - here.
The first in this series of blogs can be found here.