This is the next in a series of posts by recipients of the 2018 Career Services Summer Funding Grant. We’ve asked funding recipients to reflect on their summer experiences and talk about the industries in which they’ve been spending their summer. You can read the entire series here.
This entry is by Ayrton Kessel, SEAS ’20
This summer I conducted research on the links between climate change and the economy with Professor Irina Marinov. Currently, there is limited literature exploring said links. However, the implications of anthropogenic climate change and its effects on the global economy are becoming more important for policy implementation.
There are models within this field that are frequently used, called Integrated Assessment Models (also known as IAM’s). This summer, I collected multiple IAM’s and examined each in detail. The Dynamic Integrated Climate – Economy Model (DICE) was selected as the best IAM to begin our research with. DICE was developed by Dr. William Nordhaus, an economics professor at Yale University. The DICE model optimizes a social welfare function, which is the discounted sum of the population-weighted utility of per capita consumption. The climate model is represented by two temperature boxes, with the temperature split between that of the atmosphere and that of the lower ocean. Radiative forcing is a function of atmospheric carbon concentration, emissions, and exogenous forcing. In turn, the radiative forcing is added to the atmospheric temperature. The carbon model is three boxed, split between the atmosphere, upper ocean, and lower ocean. Emissions are sent into just the atmosphere. There are certain parameters that influence the flow rate within both the climate and carbon model.
The damage function is the link between the climate/carbon models and the economic model within DICE. The damage function is composed of atmospheric temperature, and influences the net economic output. The gross economic output is a Cobb – Douglas equation, and the discount rate is a Ramsey equation.
There are several outputs of DICE. For example, there are industrial emissions, population, net economic output, etc. over the duration of the simulation that can be examined as an output. The temperature and carbon changes are also included in the yield.
The most important output of DICE is the social cost of carbon (SCC). The SCC assesses the total discounted damage to social welfare caused by an emission of CO2 occurring in a particular year. It is expressed as the dollar value of the total damages from emitting one ton of CO2 into the atmosphere.
After finding a version of DICE available in MATLAB, I began to conduct parameter analysis. In total, there are around thirty parameters that can be varied. And each variable has a different effect on different outputs. Within DICE, I found that climate sensitivity effects the social cost of carbon the most. Climate sensitivity is the equilibrium temperature change in response to changes of the radiative forcing. This is finding is important because climate sensitivity is uncertain and has a normal probability distribution, according to the Intergovernmental Panel on Climate Change (IPCC). This can drastically affect policy planning.
Finally, I began to replicate research papers that used DICE in some format. For example, I replicated a Nordhaus paper from 2017 that examined the social cost of carbon in depth. Sea ice loss was a scenario that I examined from another paper. Permafrost feedback was the most recent scenario I replicated.
In the future, I will continue my research into the school year. The next step is to conceive our own scenarios or introduce probability. This research was a worthwhile experience, especially since IAMs are an emerging field in climate science. I would like to thank VPUL and everybody involved for helping me financially this summer. Without the grant, this would not have been possible!
