Socio-economic drivers - IFs - Revision history

Chantal Wuerschinger at 10:49, 14 January 2025

2025-01-14T10:49:56Z

← Older revision		Revision as of 12:49, 14 January 2025
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	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.		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.

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Barry Hughes: showing?

2024-12-28T23:03:12Z

showing?

← Older revision		Revision as of 01:03, 29 December 2024
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	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.		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.

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Barry Hughes: Socio-economic drivers

2024-12-28T22:07:18Z

Socio-economic drivers

← Older revision		Revision as of 00:07, 29 December 2024
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	\|DocumentationCategory=Socio-economic drivers		\|DocumentationCategory=Socio-economic 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.		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 ~~this ADVANCE project-based~~ Wiki is to use this section to discuss the representation of population and economic activity (exogenously or endogenously) as drivers for the biophysical systems that ~~other Wiki sections then elaborate~~. However, the IFs system contains a variety of models (see Model scope and methods/Model concept, solver and details) that require some elaboration ~~here~~. These models are not only drivers, but driven subsystems of IFs, responding to each other and to change in biophysical systems. In keeping with the structure of ~~this~~ Wiki, we will save discussion of the energy ~~model~~ for ~~another~~ top-level ~~topic~~ and similarly elaborate the economic model in its own topic. But ~~in addition to population and an introductory survey of economy activity,~~ we will also provide ~~subtopics here~~ on the IFs models of education, health, governance, infrastructure, interstate politics, and socio-political variables.		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.

Steve Hedden at 18:14, 9 August 2018

2018-08-09T18:14:14Z

← Older revision		Revision as of 20:14, 9 August 2018
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	}}		}}
	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.		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 this ADVANCE project-based Wiki is to use this section to discuss the representation of population and economic activity (exogenously or endogenously) as drivers for the biophysical systems that other Wiki sections then elaborate. However, the IFs system contains a variety of models (see Model scope and methods/Model concept, solver and details) that require some elaboration here. These models are not only drivers, but driven subsystems of IFs, responding to each other and to change in biophysical systems. In keeping with the structure of this Wiki, we will save discussion of the energy model for another top-level topic and similarly elaborate the economic model in its own topic. But in addition to population and an introductory survey of economy activity, we will also provide subtopics here on the IFs models of education, health, governance, infrastructure, interstate politics, and socio-political variables.		The common approach across other IAMs represented in this ADVANCE project-based Wiki is to use this section to discuss the representation of population and economic activity (exogenously or endogenously) as drivers for the biophysical systems that other Wiki sections then elaborate. However, the IFs system contains a variety of models (see Model scope and methods/Model concept, solver and details) that require some elaboration here. These models are not only drivers, but driven subsystems of IFs, responding to each other and to change in biophysical systems. In keeping with the structure of this Wiki, we will save discussion of the energy model for another top-level topic and similarly elaborate the economic model in its own topic. But in addition to population and an introductory survey of economy activity, we will also provide subtopics here on the IFs models of education, health, governance, infrastructure, interstate politics, and socio-political variables.

Steve Hedden at 18:13, 9 August 2018

2018-08-09T18:13:57Z

← Older revision		Revision as of 20:13, 9 August 2018
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	{{ModelDocumentationTemplate		{{ModelDocumentationTemplate
			\|IsEmpty=No
	\|IsDocumentationOf=IFs		\|IsDocumentationOf=IFs
	\|DocumentationCategory=Socio-economic drivers		\|DocumentationCategory=Socio-economic 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 this ADVANCE project-based Wiki is to use this section to discuss the representation of population and economic activity (exogenously or endogenously) as drivers for the biophysical systems that other Wiki sections then elaborate. However, the IFs system contains a variety of models (see Model scope and methods/Model concept, solver and details) that require some elaboration here. These models are not only drivers, but driven subsystems of IFs, responding to each other and to change in biophysical systems. In keeping with the structure of this Wiki, we will save discussion of the energy model for another top-level topic and similarly elaborate the economic model in its own topic. But in addition to population and an introductory survey of economy activity, we will also provide subtopics here on the IFs models of education, health, governance, infrastructure, interstate politics, and socio-political variables.

Rineke Oostenrijk: Edited automatically from page IFs setup.

2018-02-28T09:11:21Z

Edited automatically from page IFs setup.

New page

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|IsDocumentationOf=IFs
|DocumentationCategory=Socio-economic drivers
}}