Drivers: Difference between revisions

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|ExternalModel=ENV-Growth model; MAGNET model;
|ExternalModel=ENV-Growth model; MAGNET model;
|KeyReference=IPCC, 2000; OECD, 2012; MA, 2005;
|KeyReference=IPCC, 2000; OECD, 2012; MA, 2005;
|Description=In order for the IMAGE model to explore future scenarios, exogenous assumptions need to be made for a range of factors that shape the direction and rate of change in key model variables and results. Together with the endogenous functional relationships and endogenous model parameters that jointly typify the behaviour of the model, these exogenous assumptions drive the outcome of any particular model calculation. In other words, these assumptions are the drivers that determine the model results subject to the assumed external conditions.
|Description=To explore future scenarios, exogenous assumptions need to be made for a range of factors that shape the direction and rate of change in key model variables and results. Together with the endogenous functional relationships and model parameters that typify model behaviour, these exogenous assumptions drive the outcome of model calculations. These assumptions are the drivers that determine the model results, subject to the assumed external conditions.
In IMAGE, six groups of assumptions are distinguished that make up the scenario drivers. These six groups are the basis for all scenarios and are embedded in a scenario narrative or storyline. This includes cases where current trends and dynamics are assumed to continue into the future, commonly referred to as reference or ‘business-as-usual’ scenarios. But scenario drivers can also be used to describe a set of contrasting future futures to explore the relevant range of uncertain yet plausible developments.


For IMAGE, six groups of assumptions are distinguished that make up the scenario drivers:
As a rule, scenario drivers are not numerical model inputs but, in qualitative or semi-quantitative terms, govern a detailed set of exogenous assumptions in terms of model input to the various components of the model framework. Numerical model drivers for a specific scenario are established on the basis of the six generic scenario drivers. The model drivers for the various IMAGE models and modules (called components on the site) are explained in the component pages (see [[Framework overview]]) .  
# demographics;
The scenario drivers and underlying narrative, together with the quantitative model drivers, form a scenario that is inextricably linked with the results from an IMAGE scenario run.
# economics;
# culture and lifestyle;
# policy and governance;
# natural resource availability;
# technology dynamics.
These drivers are the basis of any scenario and are generally rooted in a scenario narrative or storyline. This includes cases where current trends and dynamics are assumed to continue into the future; commonly referred to as reference or ‘business-as-usual’ cases. As a rule, scenario drivers do not constitute numerical model inputs, but they do govern in qualitative or semi-quantitative terms the more elaborate set of exogenous assumptions in terms of model input files for the various parts of the model framework. In other words, numerical model drivers for any specific scenario will need to be established on the basis of the six more generic scenario drivers. The model drivers for the various modules are explained in more detail in the model components (these can be accessed via [[Framework_overview]]) . The scenario drivers and underlying narrative, together with the concrete quantitative model drivers form a scenario, inextricably linked with the results from the IMAGE scenario run.
|ComponentCode=D
|ComponentCode=D
|FrameworkElementType=driver component
|FrameworkElementType=driver component
}}
}}
[[Page has default form::DriverComponentForm| ]]
[[Page has default form::DriverComponentForm| ]]

Revision as of 11:48, 9 May 2014

Scenario development and model drivers for IMAGE 3.0
Flowchart Drivers. Model drives are inferred from scenario storylines taking into account external data sources, such as time series, cross-sector data, and literature sources.