Population - IMAGE: Difference between revisions

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==Population==
The future state of the world depends on the population because total demand for goods and services equals the number of people times demand per capita.
The future state of the world depends on the population because total demand for goods and services equals the number of people times demand per capita.


Most population projections used as input to the IMAGE model have been adopted from published sources, such as data from the United Nations [[CiteRef::IMG_UN_2013]] and projections by the International Institute for Applied Systems Analysis (IIASA) [[CiteRef::IMG_Lutz_2010]]. Behind these numerical projections are economic, technical, educational and policy assumptions that determine the estimated future population as the net outcome of fertility and mortality, adjusted for migration flows. This has provided internally consistent, overall population scenarios on the basis of underlying demographic trends.
Most population projections used as input to the IMAGE model have been adopted from published sources, such as data from the United Nations [[CiteRef::IMG_UN_2013]] and projections by the International Institute for Applied Systems Analysis (IIASA) [[CiteRef::IMG_Lutz_2010]]. Behind these numerical projections are economic, technical, educational and policy assumptions that determine the estimated future population as the net outcome of fertility and mortality, adjusted for migration flows. This has provided internally consistent, overall population scenarios on the basis of underlying demographic trends.


In addition to total number of people, the population is broken down into gender, income classes, urban and rural, and educational level. These attributes are relevant for issues such as consumption preferences and patterns, and access to goods and services. Using a downscaling procedure [[CiteRef::IMG_VanVuuren_2007b]], national and regional population can be projected at grid level to account for trends in urbanisation and migration within countries and regions.
In addition to total number of people, the population is broken down into gender, income classes, urban and rural, and educational level. These attributes are relevant for issues such as consumption preferences and patterns, and access to goods and services. Using a downscaling procedure [[CiteRef::IMG_vanVuuren_2007b]], national and regional population can be projected at grid level to account for trends in urbanisation and migration within countries and regions.


Population data are used in energy and agricultural economics modelling, and in other IMAGE components, such as water stress, nutrients, flood risks and human health.
Population data are used in energy and agricultural economics modelling, and in other IMAGE components, such as water stress, nutrients, flood risks and human health.


==GDP==
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At the most aggregated level, economic activity is described in terms of gross domestic product (GDP) per capita. Models outside the IMAGE 3.0 framework, such as the OECD ENV-Growth model, project long-term GDP growth based on developments in key production factors (e.g., capital, labour, natural resources), and the sector composition of the economy. The various components of GDP on the production side (in particular value added (VA) per sector) and expenditures (in particular private consumption) are estimated with more detailed models that take account of inter-sector linkages, own and cross-price responses, and other factors [[CiteRef::IMG_Chateau_2013]].
 
In IMAGE 3.0, economic variables are used as model drivers for the energy demand model, and non-agricultural water demand contributing to water stress. To meet the requirements of the household energy demand model, average income is broken down into urban and rural population, and each population into quintiles of income levels. The latter is derived from the assumed uneven income distribution using the GINI factor, a measure of income disparity in a population. The macro indicator GDP per capita is also used directly in IMAGE components, such as human health, flood risk, and nutrients (for calculating urban wastewater). The agriculture model MAGNET is an economy-wide computable general equilibrium (CGE) model that reproduces exogenous GDP growth projections made in less complex economic growth models.

Latest revision as of 16:15, 23 June 2020

Model Documentation - IMAGE

Corresponding documentation
Previous versions
Model information
Model link
Institution PBL Netherlands Environmental Assessment Agency (PBL), Netherlands, https://www.pbl.nl/en.
Solution concept Partial equilibrium (price elastic demand)
Solution method Simulation
Anticipation Simulation modelling framework, without foresight. However, a simplified version of the energy/climate part of the model (called FAIR) can be run prior to running the framework to obtain data for climate policy simulations.

The future state of the world depends on the population because total demand for goods and services equals the number of people times demand per capita.

Most population projections used as input to the IMAGE model have been adopted from published sources, such as data from the United Nations IMG_UN_2013 and projections by the International Institute for Applied Systems Analysis (IIASA) IMG_Lutz_2010. Behind these numerical projections are economic, technical, educational and policy assumptions that determine the estimated future population as the net outcome of fertility and mortality, adjusted for migration flows. This has provided internally consistent, overall population scenarios on the basis of underlying demographic trends.

In addition to total number of people, the population is broken down into gender, income classes, urban and rural, and educational level. These attributes are relevant for issues such as consumption preferences and patterns, and access to goods and services. Using a downscaling procedure IMG_vanVuuren_2007b, national and regional population can be projected at grid level to account for trends in urbanisation and migration within countries and regions.

Population data are used in energy and agricultural economics modelling, and in other IMAGE components, such as water stress, nutrients, flood risks and human health.

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