Update global warming potential factors (#1905)

* Add global warming potential factors data

* Improve metadata

dag/emissions.yml

@@ -115,6 +115,16 @@ steps:
     - data://meadow/emissions/2023-10-10/net_zero_tracker
   data://grapher/emissions/2023-10-10/net_zero_tracker:
     - data://garden/emissions/2023-10-10/net_zero_tracker
+  #
+  # Global Warming Potential factors (IPCC, 2021).
+  #
+  data://meadow/emissions/2023-11-06/global_warming_potential_factors:
+    - snapshot://emissions/2023-11-06/global_warming_potential_factors.csv
+  data://garden/emissions/2023-11-06/global_warming_potential_factors:
+    - data://meadow/emissions/2023-11-06/global_warming_potential_factors
+  data://grapher/emissions/2023-11-06/global_warming_potential_factors:
+    - data://garden/emissions/2023-11-06/global_warming_potential_factors
+
   ######################################################################################################################
   # Older versions to be archived once they are not used by any other steps.
   #

etl/steps/data/garden/emissions/2023-11-06/global_warming_potential_factors.meta.yml

@@ -0,0 +1,33 @@
+# NOTE: To learn more about the fields, hover over their names.
+definitions:
+  common:
+    presentation:
+      topic_tags:
+        - CO2 & Greenhouse Gas Emissions
+        - Climate Change
+
+
+# Learn more about the available fields:
+# http://docs.owid.io/projects/etl/architecture/metadata/reference/dataset/
+dataset:
+  title: Global warming potential factors
+  update_period_days: 365
+
+# Learn more about the available fields:
+# http://docs.owid.io/projects/etl/architecture/metadata/reference/tables/
+tables:
+  global_warming_potential_factors:
+    # Learn more about the available fields:
+    # http://docs.owid.io/projects/etl/architecture/metadata/reference/indicator/
+    variables:
+      gwp_100:
+        title: Global Warming Potentials with a 100-year time horizon (GWP-100)
+        unit: ""
+        short_unit: ""
+        description_short: "[Global warming potential](#dod:gwp) measures the relative warming impact of one unit mass of a greenhouse gas relative to carbon dioxide."
+        description_key:
+        - GWP values are used to convert greenhouse gases into a carbon dioxide equivalent metric by multiplying emissions in mass terms by their respective GWP factors.
+        - For example, a GWP value of 25 for a certain gas means that one tonne of that ags has 25 times the warming impact of one tonne of carbon dioxide.
+        processing_level: minor
+        display:
+          numDecimalPlaces: 1

etl/steps/data/garden/emissions/2023-11-06/global_warming_potential_factors.py

@@ -0,0 +1,35 @@
+"""Load a meadow dataset and create a garden dataset."""
+
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Load meadow dataset and read its main table.
+    ds_meadow = paths.load_dataset("global_warming_potential_factors")
+    tb = ds_meadow["global_warming_potential_factors"].reset_index()
+
+    #
+    # Process data.
+    #
+    # Combine the name and the formula of each greenhouse gas.
+    tb = tb.astype({"name": str, "formula": str})
+    tb["greenhouse_gas"] = tb["name"] + " (" + tb["formula"] + ")"
+    tb = tb.drop(columns=["name", "formula"])
+
+    # Set an appropriate index and sort conveniently.
+    tb = tb.set_index(["greenhouse_gas"], verify_integrity=True).sort_index()
+
+    #
+    # Save outputs.
+    #
+    # Create a new garden dataset with the same metadata as the meadow dataset.
+    ds_garden = create_dataset(
+        dest_dir, tables=[tb], check_variables_metadata=True, default_metadata=ds_meadow.metadata
+    )
+    ds_garden.save()

etl/steps/data/grapher/emissions/2023-11-06/global_warming_potential_factors.py

@@ -0,0 +1,35 @@
+"""Load a garden dataset and create a grapher dataset."""
+
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Load garden dataset and read its main table.
+    ds_garden = paths.load_dataset("global_warming_potential_factors")
+    tb = ds_garden["global_warming_potential_factors"].reset_index()
+
+    #
+    # Process data.
+    #
+    # Grapher requires a column named "country", and another named "year".
+    # We assign the year of publication of the document where the data was extracted from.
+    tb = tb.rename(columns={"greenhouse_gas": "country"})
+    tb["year"] = int(tb["gwp_100"].metadata.origins[0].date_published.split("-")[0])
+
+    # Set an appropriate index and sort conveniently.
+    tb = tb.set_index(["country", "year"], verify_integrity=True).sort_index()
+
+    #
+    # Save outputs.
+    #
+    # Create a new grapher dataset with the same metadata as the garden dataset.
+    ds_grapher = create_dataset(
+        dest_dir, tables=[tb], check_variables_metadata=True, default_metadata=ds_garden.metadata
+    )
+    ds_grapher.save()

etl/steps/data/meadow/emissions/2023-11-06/global_warming_potential_factors.py

@@ -0,0 +1,32 @@
+"""Load a snapshot and create a meadow dataset."""
+
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Retrieve snapshot.
+    snap = paths.load_snapshot("global_warming_potential_factors.csv")
+
+    # Load data from snapshot.
+    tb = snap.read()
+
+    #
+    # Process data.
+    #
+    # Ensure all columns are snake-case, set an appropriate index, and sort conveniently.
+    tb = tb.underscore().set_index(["name"], verify_integrity=True).sort_index()
+
+    #
+    # Save outputs.
+    #
+    # Create a new meadow dataset with the same metadata as the snapshot.
+    ds_meadow = create_dataset(dest_dir, tables=[tb], check_variables_metadata=True, default_metadata=snap.metadata)
+
+    # Save changes in the new meadow dataset.
+    ds_meadow.save()

snapshots/emissions/2023-11-06/global_warming_potential_factors.csv.dvc

@@ -0,0 +1,37 @@
+# Learn more at:
+# http://docs.owid.io/projects/etl/architecture/metadata/reference/origin/
+meta:
+  origin:
+    # Data product / Snapshot
+    title: IPCC 6th Assessment Report, Working Group I, Chapter 7 Supplementary Material
+    description: |-
+      Global warming potential for a 100-year time horizon (GWP-100) for a selection of greenhouse gases.
+
+    date_published: 2021-08-09
+
+    # Citation
+    producer: IPCC
+    citation_full: |-
+      Working Group I (WGI) Contribution to the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), Chapter 7: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity.
+
+      Data extracted from the Supplementary Materials, Table 7.SM.7: Greenhouse gas lifetimes, radiative efficiencies, global warming potentials (GWPs), global temperature potentials (GTPs) and cumulative global temperature potentials (CGTPs).
+
+      Forster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923-1054, [doi: 10.1017/9781009157896.009](https://www.cambridge.org/core/books/climate-change-2021-the-physical-science-basis/earths-energy-budget-climate-feedbacks-and-climate-sensitivity/AE57C97E588FF3060C7C7E47DD4F3C6E).
+    attribution_short: IPCC
+
+    # Files
+    url_main: https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7/
+    url_download: 
+      https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07_SM.pdf
+    date_accessed: 2023-11-06
+
+    # License
+    license:
+      name: CC BY 4.0
+      url: https://www.ipcc.ch/report/ar6/wg1/resources/data-access/
+
+wdir: ../../../data/snapshots/emissions/2023-11-06
+outs:
+- md5: 71d94db83502c78221b63670daab2cce
+  size: 173
+  path: global_warming_potential_factors.csv

snapshots/emissions/2023-11-06/global_warming_potential_factors.py

@@ -0,0 +1,37 @@
+"""Script to create a snapshot of dataset."""
+
+from pathlib import Path
+
+import click
+
+from etl.snapshot import Snapshot
+
+# Version for current snapshot dataset.
+SNAPSHOT_VERSION = Path(__file__).parent.name
+
+
+@click.command()
+@click.option("--upload/--skip-upload", default=True, type=bool, help="Upload dataset to Snapshot")
+def main(upload: bool) -> None:
+    # Create a new snapshot.
+    snap = Snapshot(f"emissions/{SNAPSHOT_VERSION}/global_warming_potential_factors.csv")
+
+    # Manually extract data on global warming potential (GWP-100) from Table 7.SM.7.
+    tb = snap.read_from_records(
+        [
+            ("Carbon dioxide", "CO₂", 1),
+            ("Methane", "CH₄", 27.9),
+            ("Nitrous oxide", "N₂O", 273),
+            ("PFC-14", "CF₄", 7380),
+            ("HFC-152a", "CH₃CHF₂", 164),
+            ("Sulphur hexafluoride", "SF₆", 24300),
+        ],
+        columns=["Name", "Formula", "GWP-100"],
+    )
+
+    # Download data from source, add file to DVC and upload to S3.
+    snap.create_snapshot(upload=upload, data=tb)
+
+
+if __name__ == "__main__":
+    main()