@article { , title = {An innovative approach to multi-method integrated assessment modelling of global climate change}, abstract = {© 2020, University of Surrey. All rights reserved. Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in creating Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs are created as steady state optimisation models. They typically employ a nested structure of neoclassical production functions to represent the energy-economy system, holding aggregate views on variables, and hence are unable to capture a finer level of details of the underlying system components. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent-Based Modelling, often used in the field of Social Simulation. But simulating huge numbers of individual entities can quickly become an issue, as it requires large amounts of computational resources. The goal of this paper is to introduce a conceptual framework for developing hybrid IAMs. This novel modelling approach allows us to reuse existing rigid, but well-established IAMs, and adds more flexibility by replacing aggregate stocks with a community of vibrant interacting entities. We provide a proof-of-concept of the application of this conceptual framework in form of an illustrative example. Our test case takes the settings of the US. It is solely created for the purpose of demonstrating our hybrid modelling approach; we do not claim that it has predictive powers.}, doi = {10.18564/jasss.4209}, issn = {1460-7425}, issue = {1}, journal = {Journal of Artificial Societies and Social Simulation}, note = {"Authors and others are requested not to make electronic copies of JASSS articles for hosting on other servers. Not only is this a waste of effort and time (because the original versions of JASSS articles are freely available to everyone without subscription), but having more than one copy can lead to confusion over which version is the current or definitive version. "--http://jasss.soc.surrey.ac.uk/admin/copyright.html.}, publicationstatus = {Published}, publisher = {SimSoc Consortium}, url = {https://nottingham-repository.worktribe.com/output/4034618}, volume = {23}, keyword = {Computer Science (miscellaneous), General Social Sciences}, year = {2020}, author = {Siebers, Peer Olaf and Lim, Zhi En and Figueredo, Grazziela P. and Hey, James} }