Keywords
energy scenarios; dynamics; transformation paths; cross-impact balance analysis
Start Date
7-7-2022 9:00 AM
End Date
7-7-2022 9:30 AM
Abstract
Storylines currently in use for policy advice are based on a great number of simplifying assumptions. This includes assumptions of trends of the systems elements over the period under investigation; usually linear trends (e.g., for changes in GDP, population and fuel prices). This approach ignores the fact that the interrelationships between systems elements are often characterized by nonlinearities, due to e.g. boundary conditions, thresholds or decreasing marginal benefits or asynchronic behaviour of systems elements. For an assessment of energy futures and the specification of measures, novel approaches are necessary which can deal with non-linear trends. The most prominent examples are (long-run) business cycles as well as thresholds in socio-economic systems. In our paper, we show how cross-impact balance (CIB) analysis can be applied to map dynamic trends: By taking levels of stock variables, and descriptors with time sensitive changes in their states into consideration we assess which sets of descriptors’ states (being identified by using e.g. SzenarioWizard) are feasible at a select point in time. Stringing together possible sets of descriptors’ states we are able to identified and describe dynamic paths. With our version of CIB modeling, we are able to assess impacts of fluctuating parameters such as changes in policy attitudes and public perceptions. We are also able to deal with thresholds. Implementing non-linearities would allow to develop dynamic storylines which can be adapted to the framing of time paths in (quantitative) energy models. Energy models usually assume 5- or 10-year time steps. By considering these time steps within our dynamic storyline framework we are able to present and analyze sets of possible non-linear pathways to achieve e.g. a given CO2 reduction target in a similar way as being described, for instance, by Shepherd et al. (2018). By using a simple example of an energy system / storyline approach we show that the CIB approach is a very well-suited tool for the assessment of plausible future developments and thus, a perfect tool for the identification of efficient polices.
Implementing non-linearities in Storylines – a possible and required step to include dynamic system-related developments
Storylines currently in use for policy advice are based on a great number of simplifying assumptions. This includes assumptions of trends of the systems elements over the period under investigation; usually linear trends (e.g., for changes in GDP, population and fuel prices). This approach ignores the fact that the interrelationships between systems elements are often characterized by nonlinearities, due to e.g. boundary conditions, thresholds or decreasing marginal benefits or asynchronic behaviour of systems elements. For an assessment of energy futures and the specification of measures, novel approaches are necessary which can deal with non-linear trends. The most prominent examples are (long-run) business cycles as well as thresholds in socio-economic systems. In our paper, we show how cross-impact balance (CIB) analysis can be applied to map dynamic trends: By taking levels of stock variables, and descriptors with time sensitive changes in their states into consideration we assess which sets of descriptors’ states (being identified by using e.g. SzenarioWizard) are feasible at a select point in time. Stringing together possible sets of descriptors’ states we are able to identified and describe dynamic paths. With our version of CIB modeling, we are able to assess impacts of fluctuating parameters such as changes in policy attitudes and public perceptions. We are also able to deal with thresholds. Implementing non-linearities would allow to develop dynamic storylines which can be adapted to the framing of time paths in (quantitative) energy models. Energy models usually assume 5- or 10-year time steps. By considering these time steps within our dynamic storyline framework we are able to present and analyze sets of possible non-linear pathways to achieve e.g. a given CO2 reduction target in a similar way as being described, for instance, by Shepherd et al. (2018). By using a simple example of an energy system / storyline approach we show that the CIB approach is a very well-suited tool for the assessment of plausible future developments and thus, a perfect tool for the identification of efficient polices.
Stream and Session
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