search

ONEcampaign / oda_data_package

A python package to access DAC ODA data
MIT License
3 stars 0 forks source link

Create a "climate mitigation and adaptation total" indicator #39

Open jm-rivera opened 1 year ago

jm-rivera commented 1 year ago 
          The simplest way to get a "climate mitigation and adaptation total" indicator would be to add "mitigation total" and "adaptation total" indicators, and subtract the overlap indicator.

Issue: I need to confirm the assumption above that a project cannot be principal mitigation and still be either 1 or 2 for adaptation, as the wording from the OECD is a bit confusing - it could be interpreted as principal for climate change, not specifically adaptation or mitigation. Link: "Aid focused on climate change overall comprises activities classified as “principal” or “significant” by either the climate change mitigation or adaptation marker; projects marked with both Rio markers are subtracted from the total to avoid double counting."

_Originally posted by @Mattie-P in https://github.com/ONEcampaign/oda_data_package/issues/37#issuecomment-1401898742_

jm-rivera commented 1 year ago

@Mattie-P let's discuss on this specific indicator here

Mattie-P commented 1 year ago

Apologies, missed this and will move the comments here.

To test, we could make 3 test indicators for 'overlap' with the filters:

adaptation (2), mitigation (1) adaptation (1), mitigation (2) adaptation (2), mitigation (2)

And if they are all 0 across the years, we can stick with this assumption and delete the indicators? If not, the overlap and total indicators may be a bit more complicated

Mattie-P commented 1 year ago

Latest data available here. However, you were right, looks like these are commitments, not disbursements. Although, these figures are slightly inflated on the graph Sara wants duplicating, so need to figure out why that is the case too.

Mattie-P commented 1 year ago

Apologies, missed this and will move the comments here.

To test, we could make 3 test indicators for 'overlap' with the filters:

adaptation (2), mitigation (1) adaptation (1), mitigation (2) adaptation (2), mitigation (2)

And if they are all 0 across the years, we can stick with this assumption and delete the indicators? If not, the overlap and total indicators may be a bit more complicated

This is not neccessary. We can see from the donor perspective data linked in the previous comment that an activity can be principal for both mitigation and adaptation. Hence, it makes sense to follow your suggestion for the "overlap" indicator to filter as follows: "climate_mitigation": ["1", "2"], "climate_adaptation": ["1", "2"]