Measuring Global Economic Damages from Global Carbon Emissions though an Excel-Based Integrated Assessment Model

“Measuring Global Economic Damages from Global Carbon Concentrations through an Excel-based Integrated Assessment Model”
Jill Freedman^1,2, Timothy Canty¹, Robert Brammer^3
1Dept. of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland

²Smith School of Business, University of Maryland, College Park, Maryland

³Brammer Technology, Andover, Massachusetts

Objective:

We have developed an integrated assessment model simple enough to be built on an Excel platform and understood by advanced undergraduates, yet sufficiently complete to provide credible estimates of temperature growth and GDP impacts. Excel is familiar to many undergraduates and provides transparency for all data and formulas. Students can interact with the model and see the results of various climate and economic scenarios.

An integrated assessment modal (IAM) allows for estimation of the effects of a changing climate on the global economy under various scenarios. The model extends the work of Tsigaris and Wood (TW)[1] and also combines the work of Nobel Laureates Robert M. Solow[2] and William D. Nordhaus[3]. A damages function is added to the Solow Model to measure the impact of carbon concentrations on temperature anomalies, global productivity, and gross domestic product (GDP) output. Comparisons of our model’s results with those from Nordhaus’s more complex and sophisticated Dynamic Integrated Climate-Economy Model (DICE) and other IAMs show similar conclusions.

Bringing in external outputs:

The user of the model can select among various time series for population, carbon concentrations based on the Representative Concentration Pathways adopted by the IPCC for the fifth assessment report (AR5), and technology growth, as well as several scalar parameters to see the results of alternative scenarios on temperature anomaly and global GDP for the years 2020 through 2100. The scalar parameters include those in the Solow Model that estimate GDP from population and economic capital. The time-series data comes from the United Nations, the Federal Reserve Board’s Economic Database, the Intergovernmental Panel on Climate Change, CO₂ Earth[4], and the DICE model[5].

After determining each parameter value, the model analyzes the sensitivity of the GDP outputs to changes in the scalar parameters. The model can also incorporate different damage functions to determine the range of possible economic impacts. The exponent in the damage function defined by Weitzman[6] turned out to have the highest sensitivity of all of our model parameters, emphasizing the importance of understanding this function. Another significant parameter in our model is the coefficient, , which measures the influence carbon has on temperature. As a baseline, the model uses the value reported by Matthews et al.[7], of 0.0018 degrees Celsius per gigatonne of carbon, with a 95% confidence range of 0.001 to 0.0025. We tested the impact the parameter would have ranging from 0.001 to 0.0025 degrees Celsius per billion tons of carbon, with a mode of 0.0018, to determine the sensitivity of temperature increases to variations in this parameter.

Having determined these various sensitivities, the model uses Monte Carlo simulations to create probability distributions for GDP between 2020 and 2100. Monte Carlo simulations provide probability estimates for the impacts different concentration pathways will have on temperature anomalies, world GDP, and total productivity. These probability distributions are essential inputs for a variety of risk management applications, including risk assessments for potential climate change mitigation strategies, including possible carbon taxes.

Results:

Our model provides flexibility for a large number of scenarios by varying several parameters, population forecasts, and concentration pathways. For example, the GDP and temperature anomaly estimates for the scenario of “business as usual (BAU)” carbon concentrations are very different from the estimates for the scenario including concentrations for a “2-degree pathway.” The BAU case assumes no changes in mitigation, leaving concentrations to continue rising. The 2 deg path involves significant efforts across the world to reduce carbon concentration levels so that the average temperature does not increase more than 2 degrees Celsius above pre-industrial levels. Under the UN medium population variant for both scenarios, GDP has a wide probability distribution range that increases as time approaches 2100. After 5,000 Monte Carlo runs, the median value of GDP for BAU in 2050 is $230.23 trillion. This value increases to $245.63 trillion by 2100, with a 25% probability of GDP being below $128 trillion. Under the 2-degree pathway scenario, the median value starts at $238.74 trillion in 2050 and increases to a median of $646.9 trillion, with a 25% probability of GDP being below $598.3 trillion in 2100. Following the same process, the temperature anomaly has a median of 2.73 degrees in 2050, that rises to 5.79 degrees in 2100, with a 25% probability of temperature rising above 6.49 degrees. The 2-degree pathway scenario estimates a median of 2.28 degrees in 2050, increasing to 2.68 degrees in 2100. With only a 5% probability of rising over 3.43 degrees in 2100, the 2-degree pathway shows that holding down concentrations makes it likely temperature will not rise above 3 degrees in this century. These vast differences depend on mitigation efforts taken across the world and show the type of conclusions our model can draw.

[1] Tsigaris, Panagiotis. & Wood Joel. (2016). A simple climate-Solow model for introducing the economics of climate change to undergraduate students. International Review of Economics Education, pp. 65-81.

[2] Solow, Robert. M. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, Vol. 70, pp. 65-94.

[3] Nordhaus, William. D. (2017). Evolution of Assessments of the Economics of Global Warming: Changes in the DICE Model, 1992-2017. National Bureau of Economic Research.

[4] https://www.co2.earth/global-co2-emissions

[5] https://sites.google.com/site/williamdnordhaus/dice-rice

[6] Weitzman, Martin. L. (2010). GHG Targets as Insurance Against Catastrophic Climate Damages. The National Bureau of Economic Research.

[7] Matthews, Damon. H., Gillet, Nathan. P., Stott, Peter. A., & Zickfield, Kirsten. The proportionality of global warming to cumulative carbon emissions. Nature, Vol 459.