Socio-economic drivers - IFs: Difference between revisions
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In forecasting long-term change, it is useful to distinguish distal and proximate drivers. | In forecasting long-term change, it is useful to distinguish distal and proximate drivers. The former consist of driving variables that help account for long-term structural change. They may operate at some causal distance from the variable we are forecasting, but we know them to be deeply structurally related to that variable, often via multiple paths. On the other hand, proximate variables are those that create shorter-term variation, often in part as intermediate variables between the deeper or distal drivers and the target variable, but often also as levers that policy or other short-term factors may influence somewhat independently of the deeper drivers. Key formulations across the models of IFs generally involve a combination of distal and proximate drivers. | ||
The common approach across other IAMs represented in | The common approach across other IAMs represented in the IAMC Wiki is to use this section to discuss the representation of population and economic activity (exogenously or less often in IAMs endogenously) as drivers for the biophysical systems that that the IAMs typically model elaborately (e.g. energy). However, the IFs system contains a variety of models (see Model scope and methods/Model concept, solver and details) that also require some elaboration. These models are not only drivers, but driven subsystems of IFs, responding to each other and to change in biophysical systems, directly or via the economy. In keeping with the structure of the IAM Wiki, we will save discussion of the energy, agriculture, and environment models for other top-level topics and similarly elaborate the economic model in its own topic (labeled Macro-economy). But we will also provide topics in this documentation with summary information on the IFs models of education, health, governance, infrastructure, interstate politics, and socio-political variables. Because as of this writing the IAMC Wiki does not allow the addition of topics to its general organizational heading scheme, this latter set of sub-topics can be found clustered on that Wiki under the general heading of Non-climate sustainability dimension and its sub-heading of Other materials. | ||
Latest revision as of 11:49, 14 January 2025
Note: The documentation of IFs is 'under review' and is not yet 'published'!| Corresponding documentation | |
|---|---|
| Previous versions | |
| Model information | |
| Model link | |
| Institution | Frederick S. Pardee Center for International Futures, University of Denver (Pardee Center), Colorado, USA, https://pardee.du.edu/. |
| Solution concept | |
| Solution method | Dynamic recursive with annual time steps through 2100. |
| Anticipation | Myopic |
In forecasting long-term change, it is useful to distinguish distal and proximate drivers. The former consist of driving variables that help account for long-term structural change. They may operate at some causal distance from the variable we are forecasting, but we know them to be deeply structurally related to that variable, often via multiple paths. On the other hand, proximate variables are those that create shorter-term variation, often in part as intermediate variables between the deeper or distal drivers and the target variable, but often also as levers that policy or other short-term factors may influence somewhat independently of the deeper drivers. Key formulations across the models of IFs generally involve a combination of distal and proximate drivers.
The common approach across other IAMs represented in the IAMC Wiki is to use this section to discuss the representation of population and economic activity (exogenously or less often in IAMs endogenously) as drivers for the biophysical systems that that the IAMs typically model elaborately (e.g. energy). However, the IFs system contains a variety of models (see Model scope and methods/Model concept, solver and details) that also require some elaboration. These models are not only drivers, but driven subsystems of IFs, responding to each other and to change in biophysical systems, directly or via the economy. In keeping with the structure of the IAM Wiki, we will save discussion of the energy, agriculture, and environment models for other top-level topics and similarly elaborate the economic model in its own topic (labeled Macro-economy). But we will also provide topics in this documentation with summary information on the IFs models of education, health, governance, infrastructure, interstate politics, and socio-political variables. Because as of this writing the IAMC Wiki does not allow the addition of topics to its general organizational heading scheme, this latter set of sub-topics can be found clustered on that Wiki under the general heading of Non-climate sustainability dimension and its sub-heading of Other materials.