Robust Data-driven Macro-socioeconomic-energy Model, 7see-GB
Data CreatorRoberts, Simon
PublisherUniversity of Edinburgh. School of Informatics. Institute for Adaptive and Neural Computation
Relation (Is Referenced By)http://dx.doi.org/10.1016/j.spc.2016.01.003
MetadataShow full item record
CitationRoberts, Simon; Axon, Colin; Foran, Barney; Goddard, Nigel; Warr, Benjamin. (2015). Robust Data-driven Macro-socioeconomic-energy Model, 7see-GB, [dataset]. University of Edinburgh. School of Informatics. Institute for Adaptive and Neural Computation. http://dx.doi.org/10.7488/ds/231.
DescriptionIn a resource-constrained world with growing population and demand for energy, goods, and services with commensurate environmental impacts, we need to understand how these trends relate to various aspects of economic activity. 7see-GB is a computational model that links energy demand through to final economic consumption, and is used to explore decadal scenarios for the UK macroeconomy. This dataset includes two published models (*.vpm) from the source model 7see-GB, version 5-10 (22Apr15). They show how results were created for the paper “A Robust Data-driven Macro-socioeconomic-energy Model”. The source model was developed in Vensim® (5.8b) and these published models can be viewed with the Vensim Reader, as provided with this dataset. There are instructions on how to navigate the published models and inspect variables shown in the paper. The .exe and .dmg files are free "Model Reader" executables for Windows/OSX which allow a user to run the model without buying the Vensim simulator.
Published model “fbc1” in which investment feedback to the three larger industries is disconnected. It includes three model runs for various levels of feedback for each of the larger industries (9.653Mb)
Published model “fbc3” in which investment feedback to the three larger industries is reconnected. It includes three model runs for various levels damping of investment feedback. (9.676Mb)
Instructions on installing the Vensim Model Reader, opening the published models, navigating around the models and some examples of variables to inspect. (336.3Kb)