Simulating the Spatial Distribution of Population and Emissions to 2100

Joint Program Report
Simulating the Spatial Distribution of Population and Emissions to 2100
Asadoorian, M.O. (2005)
Joint Program Report Series, 18 pages

Report 123 [Download]

Abstract/Summary:

Urbanization and economic development have important implications for many environmental processes including global climate change. Although there is evidence that urbanization depends endogenously on economic variables, long-term forecasts of the spatial distribution of population are often made exogenously and independent of economic conditions. A beta distribution for individual countries/regions is estimated to describe the geographical distribution of population using a 1 ° by 1° latitude-longitude global population data set. Cross-sectional country/regional data are then used to estimate an empirical relationship between parameters of the beta distribution and macroeconomic variables as they vary among countries/regions. This conditional beta distribution allows the simulation of a changing distribution of population, including the growth of urban areas, driven by economic forecasts until the year 2100.

Citation:

Asadoorian, M.O. (2005): Simulating the Spatial Distribution of Population and Emissions to 2100. Joint Program Report Series Report 123, 18 pages (http://globalchange.mit.edu/publication/14438)
  • Joint Program Report
Simulating the Spatial Distribution of Population and Emissions to 2100

Asadoorian, M.O.

Report 

123
18 pages
2005

Abstract/Summary: 

Urbanization and economic development have important implications for many environmental processes including global climate change. Although there is evidence that urbanization depends endogenously on economic variables, long-term forecasts of the spatial distribution of population are often made exogenously and independent of economic conditions. A beta distribution for individual countries/regions is estimated to describe the geographical distribution of population using a 1 ° by 1° latitude-longitude global population data set. Cross-sectional country/regional data are then used to estimate an empirical relationship between parameters of the beta distribution and macroeconomic variables as they vary among countries/regions. This conditional beta distribution allows the simulation of a changing distribution of population, including the growth of urban areas, driven by economic forecasts until the year 2100.