Add processing for conventional CDR in Chapter 7

iiasa · Jun 2, 2024 · 7b45fc3 · 7b45fc3
1 parent 66d452c
commit 7b45fc3
Showing 1 changed file with 107 additions and 27 deletions.
diff --git a/data/chapter-7-compilation.ipynb b/data/chapter-7-compilation.ipynb
@@ -55,92 +55,172 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ae8810c4-2d4d-432c-afdf-f1763d8c5641",
+   "id": "e95a8933-448f-4c15-ab22-82a9a79a8fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_novel = pyam.IamDataFrame(\n",
+    "    data,\n",
+    "    **iamc_args,\n",
+    "    value=data.columns,\n",
+    "    unit=\"t CO2\",\n",
+    "    region=\"World\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb8a5635-38ca-4074-879e-a27fdd95792a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_novel.rename(\n",
+    "    variable={\n",
+    "        \"BECCS\": \"Bioenergy with CCS\",\n",
+    "        \"Bio-oil Storage\": \"Bio-Oil Storage\",\n",
+    "        \"DACCS\": \"Direct Air Capture with CCS\",\n",
+    "        \"ERW\": \"Enhanced Weathering\",\n",
+    "        \"Macroalgae\": \"Biomass Sinking\",\n",
+    "        \"Mineralisation\": \"Mineral Products\",\n",
+    "    },\n",
+    "    inplace=True,\n",
+    ")\n",
+    "df_novel.rename(\n",
+    "    variable=dict([(i, f\"Carbon Removal|Novel|{i}\") for i in df_novel.variable]),\n",
+    "    inplace=True,\n",
+    ")\n",
+    "df_novel.aggregate(f\"Carbon Removal|Novel\", append=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10c3d999-5b68-4039-80a1-995bcbd15855",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_novel.rename(unit={\"t CO2\": \"t CO2/yr\"}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cafced12-86c6-4eeb-a40f-c784cf64f837",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_novel.convert_unit(\"t CO2/yr\", \"Mt CO2/yr\", inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7013bed3-333c-43a0-8eda-588cbaa28bab",
    "metadata": {},
    "outputs": [],
    "source": [
+    "data = (\n",
+    "    pd.read_excel(\"source/7_Deployment/Data_Chapter7_Figure2.xlsx\")\n",
+    "    .rename(columns={\"Unnamed: 0\": \"year\"})\n",
+    "    .set_index(\"year\")\n",
+    ")\n",
     "data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e95a8933-448f-4c15-ab22-82a9a79a8fb5",
+   "id": "d47ec491-6152-4846-a8f1-81f58d72843e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pyam.IamDataFrame(\n",
+    "df_conventional = pyam.IamDataFrame(\n",
     "    data,\n",
     "    **iamc_args,\n",
     "    value=data.columns,\n",
-    "    unit=\"t CO2\",\n",
+    "    unit=\"Mt CO2/yr\",\n",
     "    region=\"World\",\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f553c9ee-b625-4dbc-b8d0-24839f1ef677",
+   "id": "bff25dd8-42d4-4efc-b14f-4a1fa934978b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df"
+    "df_conventional.rename(variable={\"Mean of models\": \"Mean of Models\"}, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "689b261a-9002-4f7a-8f12-beee22796910",
+   "id": "e633d9f3-cac8-4349-9481-65f75f05faae",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.variable"
+    "df_conventional.variable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fb8a5635-38ca-4074-879e-a27fdd95792a",
+   "id": "d1205de1-ebdf-4ee5-930f-d779016de7f9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.rename(\n",
-    "    variable={\n",
-    "        \"BECCS\": \"Bioenergy with CCS\",\n",
-    "        \"Bio-oil Storage\": \"Bio-Oil Storage\",\n",
-    "        \"DACCS\": \"Direct Air Capture with CCS\",\n",
-    "        \"ERW\": \"Enhanced Weathering\",\n",
-    "        \"Macroalgae\": \"Biomass Sinking\",\n",
-    "        \"Mineralisation\": \"Mineral Products\",\n",
-    "    },\n",
+    "df_conventional.rename(\n",
+    "    variable= dict(\n",
+    "        [(i, f\"Carbon Flux|Conventional CDR [{i}]\") for i in [\"BLUE\", \"H&C2023\", \"OSCAR\", \"Mean of Models\"]]\n",
+    "    ),\n",
     "    inplace=True,\n",
-    ")\n",
-    "df.rename(\n",
-    "    variable=dict([(i, f\"Carbon Removal|Novel|{i}\") for i in df.variable]),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "888d4fd5-3452-4533-9e19-b10d70fa0194",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nghgi_mapping = {\n",
+    "    \"NGHGIs (lower-bound)\": \"Minimum\",\n",
+    "    \"NGHGIs (mean)\": \"Median\",\n",
+    "    \"NGHGIs (upper-bound)\": \"Maximum\",\n",
+    "}\n",
+    "\n",
+    "df_conventional.rename(\n",
+    "    variable= dict(\n",
+    "        [\n",
+    "            (key, f\"Carbon Flux|Conventional CDR [Model-Adjusted Inventories][{value}]\")\n",
+    "             for key, value in nghgi_mapping.items()\n",
+    "        ]\n",
+    "    ),\n",
     "    inplace=True,\n",
-    ")\n",
-    "df.aggregate(f\"Carbon Removal|Novel\", append=True)"
+    ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "10c3d999-5b68-4039-80a1-995bcbd15855",
+   "id": "5b6f1931-c26b-4b93-977d-b561af7878ca",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.rename(unit={\"t CO2\": \"t CO2/yr\"}, inplace=True)"
+    "df_conventional.convert_unit(\"Mt CO2/yr\", \"Gt CO2/yr\", inplace=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cafced12-86c6-4eeb-a40f-c784cf64f837",
+   "id": "c1ea1cf0-dd4c-485b-b3bf-f2b2377f0aad",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.convert_unit(\"t CO2/yr\", \"Mt CO2/yr\", inplace=True)"
+    "df = pyam.concat([df_novel, df_conventional])"
    ]
   },
   {