Energy conversion - GCAM: Difference between revisions
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Broadly, the energy transformation sectors in GCAM consist of all supplysectors between the energy resources and the final demands, where the latter are identified by the final-energy keywords “buildings”, “industry”, or “transportation”. Energy transformation sectors consume energy goods which are supplied either by resources or other energy transformation sectors, and they produce energy goods which are consumed either by other energy transformation sectors or by final demand sectors. This category is also considered to include a number of “pass-through” supplysectors whose purpose is explicit tracking of cost mark-ups and efficiency losses in the inter-sectoral transportation of energy goods. The main energy transformation sectors highlighted in | Broadly, the energy transformation sectors in GCAM consist of all supplysectors between the energy resources and the final demands, where the latter are identified by the final-energy keywords “buildings”, “industry”, or “transportation”. Energy transformation sectors consume energy goods which are supplied either by resources or other energy transformation sectors, and they produce energy goods which are consumed either by other energy transformation sectors or by final demand sectors. This category is also considered to include a number of “pass-through” supplysectors whose purpose is explicit tracking of cost mark-ups and efficiency losses in the inter-sectoral transportation of energy goods. The main energy transformation sectors highlighted in GCAM's documentation are electricity, refining, gas processing, hydrogen production, and district services. | ||
In energy transformation sectors, the output unit and input unit are EJ (per year), the price unit is 1975$ per GJ of output, and the subsector nest is used for competition between different fuels (or feedstocks). The competition between subsectors takes place according to a calibrated logit sharing function, detailed in [https://jgcri.github.io/gcam-doc/choice.html choice function]. Within the subsectors, there may be multiple competing technologies, where technologies typically represent either different efficiency levels, and/or the application of carbon dioxide capture and storage (CCS). The parameters relevant for technologies in GCAM are identified and explained in [https://jgcri.github.io/gcam-doc/en_technologies.html energy technologies] [http://jgcri.github.io/gcam-doc/energy.html#energy-transformation <nowiki>[1]</nowiki>]. | In energy transformation sectors, the output unit and input unit are EJ (per year), the price unit is 1975$ per GJ of output, and the subsector nest is used for competition between different fuels (or feedstocks). The competition between subsectors takes place according to a calibrated logit sharing function, detailed in [https://jgcri.github.io/gcam-doc/choice.html choice function]. Within the subsectors, there may be multiple competing technologies, where technologies typically represent either different efficiency levels, and/or the application of carbon dioxide capture and storage (CCS). The parameters relevant for technologies in GCAM are identified and explained in [https://jgcri.github.io/gcam-doc/en_technologies.html energy technologies] [http://jgcri.github.io/gcam-doc/energy.html#energy-transformation <nowiki>[1]</nowiki>]. | ||
Revision as of 17:42, 24 August 2020
Note: The documentation of GCAM is 'under review' and is not yet 'published'!| Corresponding documentation | |
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| 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. |
Broadly, the energy transformation sectors in GCAM consist of all supplysectors between the energy resources and the final demands, where the latter are identified by the final-energy keywords “buildings”, “industry”, or “transportation”. Energy transformation sectors consume energy goods which are supplied either by resources or other energy transformation sectors, and they produce energy goods which are consumed either by other energy transformation sectors or by final demand sectors. This category is also considered to include a number of “pass-through” supplysectors whose purpose is explicit tracking of cost mark-ups and efficiency losses in the inter-sectoral transportation of energy goods. The main energy transformation sectors highlighted in GCAM's documentation are electricity, refining, gas processing, hydrogen production, and district services.
In energy transformation sectors, the output unit and input unit are EJ (per year), the price unit is 1975$ per GJ of output, and the subsector nest is used for competition between different fuels (or feedstocks). The competition between subsectors takes place according to a calibrated logit sharing function, detailed in choice function. Within the subsectors, there may be multiple competing technologies, where technologies typically represent either different efficiency levels, and/or the application of carbon dioxide capture and storage (CCS). The parameters relevant for technologies in GCAM are identified and explained in energy technologies [1].