The Whole Elephant: Reconciling the Motivations and Implications of Systems Models
Modeling is a means to produce facsimiles of both human-controlled and non-human-controlled systems in a way that can be manipulated. Charting the extent of the global impact of human activities requires researchers to identify the key elements of systems, resulting in a variety of models ranging from predictive economics to agricultural simulation.
In today’s human-shaped world, a perspective on the frameworks by which we understand our surroundings grants insight into the functional process of many natural and social sciences. A controversy arises, however, when one considers that models are inherently simplifications of broader systems. When this happens, we must address the conflict of complexity versus practical use.
In this session, we, with the help of current scholars, will attempt to bring clarity to the following questions:
How have spatial and behavioral models contributed to analysis of current environmental issues? Which issues see the most variance between models, and which show the most significant differences in how they are bounded?
How does use of endogenous vs. exogenous variables affect the outcome of modeling projects?
In what situations is modelling most valuable, and when is it best supplemented by other forms of analysis? Can we retain a useful level of complexity and interconnectedness while creating relevant scenarios?
Many models look to chart the extent to which human activities have impacted global systems. How do the values assigned to observed geological and ecological processes in differing models reflect anthropocenic intent?
- 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-