Economic activity - GCAM: Difference between revisions
mNo edit summary |
mNo edit summary |
||
Line 3: | Line 3: | ||
|DocumentationCategory=Economic activity | |DocumentationCategory=Economic activity | ||
}} | }} | ||
== Inputs and Outputs == | |||
GCAM’s '''inputs''' include information on 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 per capita GDP growth for future time periods. | |||
* 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. [http://jgcri.github.io/gcam-doc/macro-econ.html <nowiki>[1]</nowiki>]. | |||
== Economic Choice == | |||
Most of the economic activities represented in GCAM present us with a choice among several ways to produce the end result of the activity. Examples of these choices include choosing between different fuels or feed stocks, between different technologies, and between transportation modes. In some cases the choice is between different uses of a limited resource, such as when we allocate land area to different uses. In each of these cases we must allocate the total activity to the available alternatives. | Most of the economic activities represented in GCAM present us with a choice among several ways to produce the end result of the activity. Examples of these choices include choosing between different fuels or feed stocks, between different technologies, and between transportation modes. In some cases the choice is between different uses of a limited resource, such as when we allocate land area to different uses. In each of these cases we must allocate the total activity to the available alternatives. | ||
Choice in GCAM is based on a single numerical value that orders the alternatives by preference. Generically, we call this the ''choice indicator'', p. In practice the choice indicator is either cost or profit rate, though other indicators are possible in principle. In cases where multiple factors influence a choice, such as passenger transportation (where faster modes are more desirable), the additional factors are converted into a cost penalty and added to the basic cost to produce a single indicator that incorporates all of the relevant factors [http://jgcri.github.io/gcam-doc/choice.html <nowiki>[ | Choice in GCAM is based on a single numerical value that orders the alternatives by preference. Generically, we call this the ''choice indicator'', p. In practice the choice indicator is either cost or profit rate, though other indicators are possible in principle. In cases where multiple factors influence a choice, such as passenger transportation (where faster modes are more desirable), the additional factors are converted into a cost penalty and added to the basic cost to produce a single indicator that incorporates all of the relevant factors [http://jgcri.github.io/gcam-doc/choice.html <nowiki>[2]</nowiki>]. |
Revision as of 16:08, 19 August 2020
Corresponding documentation | |
---|---|
Previous versions | |
No previous version available | |
Model information | |
Model link | |
Institution | Pacific Northwest National Laboratory, Joint Global Change Research Institute (PNNL, JGCRI), USA, https://www.pnnl.gov/projects/jgcri. |
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. |
Inputs and Outputs
GCAM’s inputs include information on 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 per capita GDP growth for future time periods.
- 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. [1].
Economic Choice
Most of the economic activities represented in GCAM present us with a choice among several ways to produce the end result of the activity. Examples of these choices include choosing between different fuels or feed stocks, between different technologies, and between transportation modes. In some cases the choice is between different uses of a limited resource, such as when we allocate land area to different uses. In each of these cases we must allocate the total activity to the available alternatives.
Choice in GCAM is based on a single numerical value that orders the alternatives by preference. Generically, we call this the choice indicator, p. In practice the choice indicator is either cost or profit rate, though other indicators are possible in principle. In cases where multiple factors influence a choice, such as passenger transportation (where faster modes are more desirable), the additional factors are converted into a cost penalty and added to the basic cost to produce a single indicator that incorporates all of the relevant factors [2].