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JGCRI
A query related to level 1 transportation chunk in GCAM v7.0
While I was exploring transportation UCD chunk in GCAM 7.0, I have found that the costs are similar for all the scenarios, namely, CORE, SSP1, SSP3 and SSP5, as shown in picture attached. Should not it be incorrect to have same cost values for all the scenarios?
Specifically, I am talking about the chunk line number 93 to 96
of 'zenergy_L154.transportation_UCD.R'.
Hello @RamSatyamYadav . The transport data in GCAM by SSP is initialized from the UCD database. As an example, see an input file used for SSP1 here -https://github.com/JGCRI/gcam-core/blob/master/input/gcamdata/inst/extdata/energy/UCD_trn_data_SSP1.csv.
The database at the moment does not differentiate costs by SSP. Likely it is accounting for only intensities etc under alternative socio-economic scenarios of the SSPs. If this is updated in the future, we will include it.
Alternatively, you can edit the input files to add different costs by SSPs. Let us know what you think.
Hello and Thank you for your reply. I am concerned, why all variables including intensities & all have same value for different scenarios in the picture share in my previous comment..
Even though I got your point that SSPs should not differentiate costs.
Currently, I am exploring the operations involved in transportation chunks. Let me understand it better for some days and I will ask more questions on this in future.
Thankyou
I wouldn't recommend changing any values in 2020 or earlier years as these could impact calibration, but users should feel free to revise the existing set of assumptions. The transportation module is comprised of thousands of assumptions that each have their own uncertainties, and most variables could reasonably be expected to vary by SSP. When we developed the transportation module, most of our efforts were just focused on putting together a single set of coherent assumptions of all of these inter-related variables to 2100, in all of the different regions, for all of the different technologies. For differentiating the SSPs, we focused on a few targeted variables such as speeds of public transport modes, and energy intensities of selected vehicles types. But all of the assumptions are available for modification, and if you just revise the CSV data tables and re-build the XMLs, your changes will be reflected in the model output.
