Among the more captivating uses of systems modeling is its ability to predict outcomes of events. From Malthus’ Essay on the Principle of Population to modern models of sea-level rise, predictive models have long captivated public imagination with their forecasts of worst-case scenarios. But models, however useful, are necessarily simplified representations of systems.
In the 20th century, we began to turn predictive models upon ourselves. By anticipating the growth of human populations and its effects, we recognize that we are an integral part of the world around us. Modern models have become increasingly sophisticated: using the immense analytical powers of the computing age, scientists are able to make guesses at the future of large, complex systems. Equipped with this power, we are able to consider not only how global systems affect us, but how we affect global systems.
When scientists forecast the future, they try to generate the range of possible outcomes. As a result of this variety, predictions emerge at one end of the results spectrum which border on the apocalyptic. Among such results, the outcomes predicted by climate change models in recent years have become increasingly dire. There is necessarily a degree of uncertainty in the worst-case outcomes of climate scientists, but we are left to determine what use can be made of them.
In this session, we, with the help of current scholars, will attempt to bring clarity to the following questions:
- How do we react to dire predictions when they contain a degree of uncertainty?
- Variables in a model may either be taken as constants or may vary within the model (they may be endogenous or exogenous). How do climate scientists walk the line between over-complex and over-simplified representations of systems?
- Researchers carefully consider which aspects of systems are within or beyond the scope of any given model. Why do climate models differ in their interpretations of certain variables?
- Joshua Howe (Reed College). Assistant Professor of History and Environmental Studies, and author of Behind the Curve: Science and the Politics of Global Warming (2014).
- Jessica Kleiss (Lewis & Clark College). Assistant Professor of Geological Science and Oceanographer.
- McKenzie Southworth (Lewis & Clark College ’14). Alumna in Environmental Studies and Philosophy, with a thesis on the relationship between climate change, extreme weather and public discourse.
- Castree, Noel. 2009. “Modeling and Simulation,” in A Companion to Environmental Geography. Chichester, UK: Wiley-Blackwell.
This chapter is an extremely useful overview of modeling as a technology, and the practical applications that it has. This can serve as an effective baseline for the other works cited here.
- Desanker, Paul V., and Christopher O. Justice. 2001. “Africa and Global Climate Change: Critical Issues and Suggestions for Further Research and Integrated Assessment Modeling.” Climate Research 17 (2): 93–103. doi:10.3354/cr017093. http://www.int-res.com/articles/cr/17/c017p093.pdf
This article suggests how climate models can be used to demonstrate possible impacts of climate change to African Nations, and identifies how relevant issues can be addressed in the socio-economic environments specific to the nations represented.
- Liu, Jenny H., and Jeff Renfro. 2013. Carbon Tax and Shift: How to Make It Work for Oregon’s Economy. Northwest Economic Research Center (NERC), College of Urban and Public Affairs, Portland State University. http://www.pdx.edu/nerc/sites/www.pdx.edu.nerc/files/carbontax2013.pdf
This report by the Northwest Economic Research Center examines the effect of implementing a carbon tax in Oregon, under different scenarios of revenue repatriation. Revenue would be used to reduce income taxes and corporate burden, with the proportions allocated to each determining the direct, indirect and induced economic impacts of the tax (which largely implies job markets and select industries).
- Mote, Philip, Levi Brekke, Philip B Duffy, and Ed Maurer. 2011. “Guidelines for Constructing Climate Scenarios.” Eos, Transactions American Geophysical Union 92 (31): 257–64. http://occri.net/wp-content/uploads/2011/08/EOSScenarios.pdf
This article, from the International Panel on Climate Change, provides guidance on how to select, treat, and combine vast amounts of climate model output into useful climate scenarios. It delves into how scientists can best get a grasp on uncertainty, as well as key considerations for combining select model outputs to create relevant scenarios.
- Samson, J., D. Berteaux, B. J. McGill, and M. M. Humphries. 2011. “Geographic Disparities and Moral Hazards in the Predicted Impacts of Climate Change on Human Populations.” Global Ecology and Biogeography 20 (4): 532–44. http://fuqar.uqar.ca/files/biodiversite-nordique/Samsonetal2011GEB.pdf
This study uses spatial models, principally focused on population density and geographic climate change predictions, to look for future climate change vulnerabilities. Samson et al.’s article would be interesting to compare to other climate change modelling because it seems to be approaching the issue from a neo-Malthusian, ecological perspective.
- Sohngen, Brent, Robert Mendelsohn, and Roger Sedjo. 2001. “A Global Model of Climate Change Impacts on Timber Markets.” Journal of Agricultural and Resource Economics 26 (2): 326–43. https://environment.yale.edu/files/biblio/YaleFES-00000277.pdf
This article details an analysis of the effect of climate change on global timber stocks, and the subsequent impact on regional welfare. Though the study was limited to two climate change scenarios, Sohngen et al.’s models incorporate a combination of ecological and economic factors in a way that are interesting to compare to other global studies on climate change. The authors note that forestry, as a capital-intensive sector, requires dynamic models to explore fully.
- Von Lampe, Martin, Dirk Willenbockel, Helal Ahammad, Elodie Blanc, Yongxia Cai, Katherine Calvin, Shinichiro Fujimori, et al. 2014. “Why Do Global Long-Term Scenarios for Agriculture Differ? An Overview of the AgMIP Global Economic Model Intercomparison.” Agricultural Economics 45 (1): 3–20. http://onlinelibrary.wiley.com/doi/10.1111/agec.12086/pdf
This article examines the perplexing problem that recent studies of global food security have reached different and even contradictory conclusions using modelling. The authors attempt to weed out the relevant input factors in each of 10 models to determine key drivers. This article is relevant because the authors identify the lack of both variable and model parameter standardization, as well as the need for more interdisciplinary modelling, as the sources of the disparity between results. We will be attempting a similarly-structured inter-