A probabilistic approach to exploring low-dimensional global dynamics


Grigg, N.J., Boschetti, F., Brede, M. and Finnigan, J.J. (2011) A probabilistic approach to exploring low-dimensional global dynamics. [in special issue: Earth System Science 2010: Global Change, Climate and People] Procedia Environmental Sciences, 6, 122-135. (doi:10.1016/j.proenv.2011.05.013).

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Description/Abstract

We demonstrate an approach to low-dimensional modeling of world population, carbon dioxide (CO2) emissions and gross domestic product (GDP) interactions in a way that explicitly characterizes the variability in the data informing model assumptions and the uncertainty in functional relationships. Our model choice was informed by the following considerations and choices. First, even a low-dimensional conceptualization of the interactions between these three global variables requires a model to illuminate the consequences of chains of cause and effect and feedback loops. Such interactions warrant analysis as they offer insights into influences on aggregate global dynamics. Second, rates are constrained to be consistent with world datasets where feasible thereby embedding a data driven philosophy into the dynamic model. Third, a probabilistic approach offers an effective way to deal with uncertain specification of functional relationships and the variability inherent in data informing such relationships. We use the model to highlight key features that result from the relative rates of change in the system and the nature of the feedback loops. Such an aggregated analysis offers a useful lens through which to study and interpret more detailed and realistic integrated models of human-biosphere dynamics.

Item Type: Article
ISSNs: 1878-0296 (print)
Keywords: low-dimensional modeling, carbon, poplulation, gdp, model uncertainty
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > HC Economic History and Conditions
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
ePrint ID: 272896
Date Deposited: 29 Sep 2011 16:41
Last Modified: 27 Mar 2014 20:18
Further Information:Google Scholar
ISI Citation Count:0
URI: http://eprints.soton.ac.uk/id/eprint/272896

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