Difference between revisions of "Socio-economic drivers - GCAM"

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<math display="block">\text{Equation 1: } GDP_{r,t+1} = POP_{r,t+1}( 1+GRO_{r,t})^{tStep}( \frac{GDP_{r,t}}{POP_{r,t}} ) P^{ \alpha }_{r,t+1}</math>Where r=region, t=the period, tStep=number of years in the time step, GDPr,t=population in region r in period t, POPr,t=population in region r in period t and GROr,t=annual average per capita GDP growth rate in region r in period t.
 
<math display="block">\text{Equation 1: } GDP_{r,t+1} = POP_{r,t+1}( 1+GRO_{r,t})^{tStep}( \frac{GDP_{r,t}}{POP_{r,t}} ) P^{ \alpha }_{r,t+1}</math>Where r=region, t=the period, tStep=number of years in the time step, GDPr,t=population in region r in period t, POPr,t=population in region r in period t and GROr,t=annual average per capita GDP growth rate in region r in period t.
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== References ==
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Edmonds, J. and J. Reilly. 1983. “A Long-Term, Global, Energy-Economic Model of Carbon Dioxide Release From Fossil Fuel Use,” Energy Economics, 5(2):74-88.
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Moss, R. H., J. A. Edmonds, K. A. Hibbard, M. R. Manning, S. K. Rose, D. P. van Vuuren, T. R. Carter, S. Emori, M. Kainuma, T. Kram, G. A. Meehl, J. F. B. Mitchell, N. Nakicenovic, K. Riahi, S. J. Smith, R. J. Stouffer, A. M. Thomson, J. P. Weyant and T. J. Wilbanks (2010). “The next generation of scenarios for climate change research and assessment.” Nature 463(7282): 747-756.
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van Vuuren, D. P., K. Riahi, K. Calvin, R. Dellink, J. Emmerling, S. Fujimori, S. Kc, E. Kriegler and B. O’Neill (2017). “The Shared Socio-economic Pathways: Trajectories for human development and global environmental change.” Global Environmental Change 42: 148-152.

Latest revision as of 22:48, 28 July 2020

Alert-warning.png Note: The documentation of GCAM is 'in preparation' and is not yet 'published'!

Model Documentation - GCAM

Corresponding documentation
Previous versions
No previous version available
Model information
Model link http://www.globalchange.umd.edu/gcam/
Institution Joint Global Change Research Institute (JGCRI), USA, http://www.globalchange.umd.edu.
Solution concept General equilibrium (closed economy)GCAM solves all energy, water, and land markets simultaneously
Solution method Recursive dynamic solution method
Anticipation GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences.

The GCAM Macro-Economic System

The socioeconomic components of GCAM set the scale of economic activity and associated demands for model simulations. Assumptions about population and per capita GDP growth for each of the 32 geo-political regions together determine the Gross Domestic Product (GDP). GDP and population both can drive the demands for a range of different demands within GCAM. Population and economic activity are used in GCAM through a one-way transfer of information to other GCAM components. For example, neither the price nor quantity of energy nor the quantity of energy services provided to the economy affect the calculation of the principle model output of the GCAM macro-economic system, GDP.

Inputs and Outputs

GCAM’s inputs include information on population and the rate of per capita income growth for each of GCAM’s energy-economic regions. GCAM requires globally consistent data sets for each of its historical model periods, currently 1990, 2005, 2010 and 2015, to initialize the model. Each scenario requires assumptions about population and per capita GDP growth for future time periods.

  • Population: The number of people living in each GCAM region in the benchmark and projection years.
  • GDP Per Capita Growth: The annual average rate of growth for per capita GDP over each time step in the projection. Time steps are 5 years by default.

The macro-economic module takes both of these to produce overall GDP in each GCAM energy-economic region.

Macro-Economic Modeling Approach

Regional GDP is calculated using a simple one-equation model:

Where r=region, t=the period, tStep=number of years in the time step, GDPr,t=population in region r in period t, POPr,t=population in region r in period t and GROr,t=annual average per capita GDP growth rate in region r in period t.

References

Edmonds, J. and J. Reilly. 1983. “A Long-Term, Global, Energy-Economic Model of Carbon Dioxide Release From Fossil Fuel Use,” Energy Economics, 5(2):74-88.

Moss, R. H., J. A. Edmonds, K. A. Hibbard, M. R. Manning, S. K. Rose, D. P. van Vuuren, T. R. Carter, S. Emori, M. Kainuma, T. Kram, G. A. Meehl, J. F. B. Mitchell, N. Nakicenovic, K. Riahi, S. J. Smith, R. J. Stouffer, A. M. Thomson, J. P. Weyant and T. J. Wilbanks (2010). “The next generation of scenarios for climate change research and assessment.” Nature 463(7282): 747-756.

van Vuuren, D. P., K. Riahi, K. Calvin, R. Dellink, J. Emmerling, S. Fujimori, S. Kc, E. Kriegler and B. O’Neill (2017). “The Shared Socio-economic Pathways: Trajectories for human development and global environmental change.” Global Environmental Change 42: 148-152.