|Title||Exploring energy and economic futures using agent-based modeling and scenario discovery|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||P Wang, MD Gerst, and ME Borsuk|
|Journal||Understanding complex systems|
|Pagination||251 - 269|
By illuminating a range of possible futures, scenario analysis has proven valuable as a means for organizing and communicating the many uncertainties associated with predicting the development of the linked energy, economic, and climate systems. Thus far, scenarios have mostly been defined according to a sequential, piecewise process in which experts create plausible storylines that are then used as inputs to formal models. However, as the storylines are drafted separately from model construction, it is often difficult for models to engage completely with scenario themes. As a solution, methods of 'scenario discovery' have been proposed which apply statistical techniques to sets of model simulations to identify regions of the stochastic parameter space that result in distinctively different levels of policy performance. In our previous work, we described a novel multiattribute scenario discovery method and demonstrated application to ENGAGE, an agent-based model (ABM) of economic growth, energy technology, and carbon emissions. In the current contribution, we further demonstrate the utility of this approach by using ENGAGE to generate socioeconomic scenarios relevant to a given emissions target. We find that population growth, improvement in labor productivity, and efficiency of learning-by-doing regarding carbon-free energy technology are the key factors driving the success rate in achieving the selected target. This implies that these features should form essential elements of the storylines underlying socioeconomic scenarios if they are to provide a meaningful exploration of policy efficacy. Such results are consistent with those of more conceptual approaches. However, by being derived from the results of a quantitative model, our formulation is intrinsically consistent with practicable modeling assumptions and specifications. © Springer Science+Business Media New York 2013.
|Short Title||Understanding complex systems|