Simulating the spatial distribution of population and emissions to 2100

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 ex...

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Bibliographic Details
Published in:Environmental & resource economics 2008-03, Vol.39 (3), p.199-221
Main Author: Asadoorian, Malcolm O
Format: Article
Language:English
Subjects:
Aerosols
Air pollution
Carbon
Cities
Climate change
Computable general equilibrium model
Economic conditions
Economic development
Economic forecasts
Economic models
Economic Policy
Economic theory
Economics
Economics and Finance
Emissions
Emissions distribution
Environmental changes
Environmental Economics
Environmental Law/Policy/Ecojustice
Environmental Management
Environmental policy
Geographical distribution
Global climate
Growth models
Outdoor air quality
Pollutants
Population density
Population distribution
Spatial distribution
Spatial econometric
Studies
Urban areas
Urbanization
VOCs
Volatile organic compounds
Volatility
Zipf's Law
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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. It is common for research concerning long-run projections of global environmental change to use population density as the primary means to spatially distribute emissions projections. However, researchers typically utilize year 1990 cross-sectional population data to distribute their emissions projections for both the short- and long-term, without projecting any changes in population density. Thus, a beta distribution for individual countries/regions is estimated to describe the geographical distribution of population using a one-degree-by-one-degree 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.
ISSN:0924-6460
1573-1502
DOI:10.1007/s10640-007-9105-8