Damage_function_sensitivity.ipynb

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    "colab": {
      "name": "plot 10 damage function sensitivity.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harrietgilmour/SIAM_FaIR/blob/main/Damage_function_sensitivity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "In the original BAU SIAM scenario, the large increases in emissions cause the GWP growth rate to rapidly reduce due to climate damages. Damages are shown through the Capital Decay Rate function (d = max(3,3+Sd(deltaT-1)). Currently, Sd (the decay sensitivity to climate change is very pessimistic at 0.75%/yr/^c above 1^c warming. I would like to reduce this to see how much this impacts the GWP overall. "
      ],
      "metadata": {
        "id": "iye84Y4W0dXZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BAU with original decay sensitivity (Ds) to temp change (0.75%/yr/^c)"
      ],
      "metadata": {
        "id": "frFL5QOkzMOE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SXYnXwFthxOZ"
      },
      "outputs": [],
      "source": [
        "  #import modules that are needed.\n",
        "\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from scipy.stats import norm\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating lists of all PARAMETERS\n",
        "#================================\n",
        "\n",
        "years = list(range(1982,2201)) #list of years from 1982-2100 (1980-81 need to be calculated separately from the loop)\n",
        "K_1980 = 450 #1980 total capital value (T$)\n",
        "A = 0.06 #Total factor productivity (%/yr) (6%/yr)\n",
        "sensitivity_GWP = 0.7 #emissions sensitivity to GWP (unitless) - 0.7 * capital^0.6\n",
        "emissions_GWP_ratio = 0.6 #ratio of emissions to GWP growth\n",
        "land_change_emissions = 1.5 #land use change emissions (GtC/yr)\n",
        "delta_u_1980 = 250 #1980 cumulative emissions (GtC)\n",
        "clim_sens = (1.1/600) #climate sensitivity (^c/GtC)\n",
        "base_decay = 0.03 #base decay rate of capital (%/yr) (3%/yr)\n",
        "Sd = 0.0075 #decay sensitivity to global mean surface temperature change (%/yr/^c) (0.75%/yr/^c)\n",
        "#emissions_scale = 3.793 #total global CO2 emissions in 1980 / emissions_1980"
      ],
      "metadata": {
        "id": "7Df4Qj2Kzs8_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1980 values (initial values for all variables in 1980)\n",
        "#==================================================================\n",
        "\n",
        "#already have initial capital (K_1980) and cumulative emissions (delta_u_1980) in parameters\n",
        "\n",
        "y_1980 = A*K_1980 #GWP in 1980 (T$/yr)\n",
        "u_1980 = (0.7*y_1980**0.6) #emissions rate in 1980 (GtC/yr)\n",
        "delta_t_1980 = clim_sens*delta_u_1980 #global mean surface temp change in 1980 (^C)\n",
        "d_1980 = max(0.03,0.03+Sd*(delta_t_1980-1)) #capital decay rate in 1980 (%/yr)"
      ],
      "metadata": {
        "id": "l5XJcF0u0BYW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1981 values (these need to be calculated as there are still specific equations for this year that can't go in the loop)\n",
        "#===================================================================================================================================\n",
        "\n",
        "K = (K_1980)+(y_1980)-base_decay*K_1980 #capital in 1981 (T$)\n",
        "y = A*K #GWP in 1981 (T$/yr)\n",
        "u = (0.7*y**0.6) #emissions rate in 1981 (GtC/yr)\n",
        "delta_u = delta_u_1980+u+land_change_emissions #cumulative emissions in 1981 (GtC)\n",
        "delta_t = clim_sens*delta_u #global mean surface temp change in 1981 (^C)\n",
        "d = max(0.03,0.03+Sd*(delta_t-1)) #capital decay rate in 1981 (%/yr)\n",
        "y_growth_rate = (y-y_1980)/y_1980 #GWP growth rate in 1981 (%/yr)"
      ],
      "metadata": {
        "id": "MiQT-vaV0DWn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 1980, 1981 and eventually 1982-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series=[] #empty list for cumulative emissions\n",
        "delta_u_full_series.append(delta_u_1980) #adding the 1980 value\n",
        "delta_u_full_series.append(delta_u) #adding the 1981 value\n",
        "print(delta_u_full_series)\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series=[]\n",
        "delta_t_full_series.append(delta_t_1980)\n",
        "delta_t_full_series.append(delta_t)\n",
        "print(delta_t_full_series)\n",
        "\n",
        "#total capital series:\n",
        "K_full_series=[]\n",
        "K_full_series.append(K_1980)\n",
        "K_full_series.append(K)\n",
        "print(K_full_series)\n",
        "\n",
        "#emissions series:\n",
        "u_full_series=[]\n",
        "u_full_series.append(u_1980)\n",
        "u_full_series.append(u)\n",
        "print(u_full_series)\n",
        "\n",
        "#capital decay rate series:\n",
        "d_full_series=[]\n",
        "d_full_series.append(d_1980)\n",
        "d_full_series.append(d)\n",
        "print(d_full_series)\n",
        "\n",
        "#GWP series:\n",
        "y_full_series=[]\n",
        "y_full_series.append(y_1980)\n",
        "y_full_series.append(y)\n",
        "print(y_full_series)\n",
        "\n",
        "#GWP growth rate series:\n",
        "y_growth_rate_series=[]\n",
        "y_growth_rate_series.append(0)\n",
        "y_growth_rate_series.append(y_growth_rate)\n",
        "print(y_growth_rate_series)"
      ],
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          "text": [
            "[250, 256.6477640585382]\n",
            "[0.45833333333333337, 0.4705209007739867]\n",
            "[450, 463.5]\n",
            "[5.057271839089452, 5.147764058538205]\n",
            "[0.03, 0.03]\n",
            "[27.0, 27.81]\n",
            "[0, 0.029999999999999954]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 1982-2100\n",
        "#============================================\n",
        "years_array=(np.array(years))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 1982 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array.size): #loop for the number of years between 1982-2100 (119)\n",
        "  K = K+y-d*K\n",
        "  y = A*K\n",
        "  u = (0.7*y**0.6)\n",
        "  delta_u = delta_u+u+land_change_emissions\n",
        "  delta_t = clim_sens*delta_u\n",
        "  d = max(0.03,0.03+Sd*(delta_t-1))\n",
        "  #y_growth_rate = (y-[y-1])/[y-1]\n",
        "  delta_u_full_series.append(delta_u) #adding the 1982-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series.append(delta_t) #adding the temp change from 1982-2100\n",
        "  K_full_series.append(K) #adding 1982-2100 total capital to the list\n",
        "  u_full_series.append(u) #adding 1982-2100 emissions to the list\n",
        "  d_full_series.append(d) #adding 1982-2100 capital decay rate to the list\n",
        "  y_full_series.append(y) #adding 1982-2100 GWP to the list"
      ],
      "metadata": {
        "id": "Ki5XgVVk0HeA"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array that includes all years from 1980-2100\n",
        "#========================================================\n",
        "years_full = list(range(1980,2201)) #creating a new years array so that 1980 and 1981 are included and x and y shapes match\n",
        "years_array_full=(np.array(years_full))"
      ],
      "metadata": {
        "id": "sm2rt8R80SI9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.DataFrame()\n",
        "\n",
        "df1['Years']=years_full\n",
        "df1['capital']=K_full_series\n",
        "df1['GWP']=y_full_series\n",
        "df1['emissions']=u_full_series\n",
        "df1['cumulative_emissions']=delta_u_full_series\n",
        "df1['temp_change']=delta_t_full_series\n",
        "df1['decay_rate']=d_full_series\n",
        "df1['GWP_growth_rate']=df1['GWP'].pct_change()"
      ],
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        "id": "lbIAgAM40KLJ"
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      "execution_count": null,
      "outputs": []
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        "df1"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Years      capital         GWP  emissions  cumulative_emissions  \\\n",
              "0     1980   450.000000   27.000000   5.057272            250.000000   \n",
              "1     1981   463.500000   27.810000   5.147764            256.647764   \n",
              "2     1982   477.405000   28.644300   5.239875            263.387640   \n",
              "3     1983   491.727150   29.503629   5.333635            270.221275   \n",
              "4     1984   506.478964   30.388738   5.429072            277.150347   \n",
              "..     ...          ...         ...        ...                   ...   \n",
              "216   2196  2511.142437  150.668546  14.187687           4573.703957   \n",
              "217   2197  2447.388474  146.843308  13.970455           4589.174412   \n",
              "218   2198  2384.732519  143.083951  13.754747           4604.429158   \n",
              "219   2199  2323.180425  139.390825  13.540620           4619.469779   \n",
              "220   2200  2262.736591  135.764195  13.328129           4634.297908   \n",
              "\n",
              "     temp_change  decay_rate  GWP_growth_rate  \n",
              "0       0.458333    0.030000              NaN  \n",
              "1       0.470521    0.030000         0.030000  \n",
              "2       0.482877    0.030000         0.030000  \n",
              "3       0.495406    0.030000         0.030000  \n",
              "4       0.508109    0.030000         0.030000  \n",
              "..           ...         ...              ...  \n",
              "216     8.385124    0.085388        -0.025173  \n",
              "217     8.413486    0.085601        -0.025388  \n",
              "218     8.441453    0.085811        -0.025601  \n",
              "219     8.469028    0.086018        -0.025811  \n",
              "220     8.496213    0.086222        -0.026018  \n",
              "\n",
              "[221 rows x 8 columns]"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Years</th>\n",
              "      <th>capital</th>\n",
              "      <th>GWP</th>\n",
              "      <th>emissions</th>\n",
              "      <th>cumulative_emissions</th>\n",
              "      <th>temp_change</th>\n",
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              "      <th>GWP_growth_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>5.057272</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1981</td>\n",
              "      <td>463.500000</td>\n",
              "      <td>27.810000</td>\n",
              "      <td>5.147764</td>\n",
              "      <td>256.647764</td>\n",
              "      <td>0.470521</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1982</td>\n",
              "      <td>477.405000</td>\n",
              "      <td>28.644300</td>\n",
              "      <td>5.239875</td>\n",
              "      <td>263.387640</td>\n",
              "      <td>0.482877</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1983</td>\n",
              "      <td>491.727150</td>\n",
              "      <td>29.503629</td>\n",
              "      <td>5.333635</td>\n",
              "      <td>270.221275</td>\n",
              "      <td>0.495406</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1984</td>\n",
              "      <td>506.478964</td>\n",
              "      <td>30.388738</td>\n",
              "      <td>5.429072</td>\n",
              "      <td>277.150347</td>\n",
              "      <td>0.508109</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>2196</td>\n",
              "      <td>2511.142437</td>\n",
              "      <td>150.668546</td>\n",
              "      <td>14.187687</td>\n",
              "      <td>4573.703957</td>\n",
              "      <td>8.385124</td>\n",
              "      <td>0.085388</td>\n",
              "      <td>-0.025173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>2197</td>\n",
              "      <td>2447.388474</td>\n",
              "      <td>146.843308</td>\n",
              "      <td>13.970455</td>\n",
              "      <td>4589.174412</td>\n",
              "      <td>8.413486</td>\n",
              "      <td>0.085601</td>\n",
              "      <td>-0.025388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>2198</td>\n",
              "      <td>2384.732519</td>\n",
              "      <td>143.083951</td>\n",
              "      <td>13.754747</td>\n",
              "      <td>4604.429158</td>\n",
              "      <td>8.441453</td>\n",
              "      <td>0.085811</td>\n",
              "      <td>-0.025601</td>\n",
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              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>2199</td>\n",
              "      <td>2323.180425</td>\n",
              "      <td>139.390825</td>\n",
              "      <td>13.540620</td>\n",
              "      <td>4619.469779</td>\n",
              "      <td>8.469028</td>\n",
              "      <td>0.086018</td>\n",
              "      <td>-0.025811</td>\n",
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              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>2200</td>\n",
              "      <td>2262.736591</td>\n",
              "      <td>135.764195</td>\n",
              "      <td>13.328129</td>\n",
              "      <td>4634.297908</td>\n",
              "      <td>8.496213</td>\n",
              "      <td>0.086222</td>\n",
              "      <td>-0.026018</td>\n",
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              "          const element = document.querySelector('#df-f6fed14b-45e8-41fe-a3bb-1408b410b40e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BAU with lower decay sensitivity (Ds).\n",
        "This time it will be 20% lower, so 0.006 (6%/yr/^C)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "dJ5LF0Ss0WOE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating lists of all PARAMETERS\n",
        "#================================\n",
        "#all other parameters will be the same\n",
        "\n",
        "Sd20 = 0.006 #NEW decay sensitivity to global mean surface temperature change (%/yr/^c) (0.75%/yr/^c)\n"
      ],
      "metadata": {
        "id": "zkjyV3_K0Tzf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1980 values (initial values for all variables in 1980)\n",
        "#==================================================================\n",
        "\n",
        "#already have initial capital (K_1980) and cumulative emissions (delta_u_1980) in parameters\n",
        "\n",
        "y_1980 = A*K_1980 #GWP in 1980 (T$/yr)\n",
        "u_1980 = (0.7*y_1980**0.6) #emissions rate in 1980 (GtC/yr)\n",
        "delta_t_1980 = clim_sens*delta_u_1980 #global mean surface temp change in 1980 (^C)\n",
        "d_1980_Sd20 = max(0.03,0.03+Sd20*(delta_t_1980-1)) #capital decay rate in 1980 (%/yr)"
      ],
      "metadata": {
        "id": "dtysB9W-2K8S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1981 values (these need to be calculated as there are still specific equations for this year that can't go in the loop)\n",
        "#===================================================================================================================================\n",
        "\n",
        "K_Sd20 = (K_1980)+(y_1980)-base_decay*K_1980 #capital in 1981 (T$)\n",
        "y_Sd20 = A*K_Sd20 #GWP in 1981 (T$/yr)\n",
        "u_Sd20 = (0.7*y_Sd20**0.6) #emissions rate in 1981 (GtC/yr)\n",
        "delta_u_Sd20 = delta_u_1980+u_Sd20+land_change_emissions #cumulative emissions in 1981 (GtC)\n",
        "delta_t_Sd20 = clim_sens*delta_u_Sd20 #global mean surface temp change in 1981 (^C)\n",
        "d_Sd20 = max(0.03,0.03+Sd20*(delta_t_Sd20-1)) #capital decay rate in 1981 (%/yr)\n",
        "y_growth_rate_Sd20 = (y_Sd20-y_1980)/y_1980 #GWP growth rate in 1981 (%/yr)"
      ],
      "metadata": {
        "id": "b7PsKDYS2Y9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 1980, 1981 and eventually 1982-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series_Sd20=[] #empty list for cumulative emissions\n",
        "delta_u_full_series_Sd20.append(delta_u_1980) #adding the 1980 value\n",
        "delta_u_full_series_Sd20.append(delta_u_Sd20) #adding the 1981 value\n",
        "print(delta_u_full_series_Sd20)\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series_Sd20=[]\n",
        "delta_t_full_series_Sd20.append(delta_t_1980)\n",
        "delta_t_full_series_Sd20.append(delta_t_Sd20)\n",
        "print(delta_t_full_series_Sd20)\n",
        "\n",
        "#total capital series:\n",
        "K_full_series_Sd20=[]\n",
        "K_full_series_Sd20.append(K_1980)\n",
        "K_full_series_Sd20.append(K_Sd20)\n",
        "print(K_full_series_Sd20)\n",
        "\n",
        "#emissions series:\n",
        "u_full_series_Sd20=[]\n",
        "u_full_series_Sd20.append(u_1980)\n",
        "u_full_series_Sd20.append(u_Sd20)\n",
        "print(u_full_series_Sd20)\n",
        "\n",
        "#capital decay rate series:\n",
        "d_full_series_Sd20=[]\n",
        "d_full_series_Sd20.append(d_1980_Sd20)\n",
        "d_full_series_Sd20.append(d_Sd20)\n",
        "print(d_full_series_Sd20)\n",
        "\n",
        "#GWP series:\n",
        "y_full_series_Sd20=[]\n",
        "y_full_series_Sd20.append(y_1980)\n",
        "y_full_series_Sd20.append(y_Sd20)\n",
        "print(y_full_series_Sd20)\n",
        "\n",
        "#GWP growth rate series:\n",
        "y_growth_rate_series_Sd20=[]\n",
        "y_growth_rate_series_Sd20.append(0)\n",
        "y_growth_rate_series_Sd20.append(y_growth_rate_Sd20)\n",
        "print(y_growth_rate_series_Sd20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jn-uvfDF2or_",
        "outputId": "6840bdb1-fd29-4d5d-c9dd-6ef4964f0b0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[250, 256.6477640585382]\n",
            "[0.45833333333333337, 0.4705209007739867]\n",
            "[450, 463.5]\n",
            "[5.057271839089452, 5.147764058538205]\n",
            "[0.03, 0.03]\n",
            "[27.0, 27.81]\n",
            "[0, 0.029999999999999954]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 1982-2100\n",
        "#============================================\n",
        "years_array=(np.array(years))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 1982 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array.size): #loop for the number of years between 1982-2100 (119)\n",
        "  K_Sd20 = K_Sd20+y_Sd20-d_Sd20*K_Sd20\n",
        "  y_Sd20 = A*K_Sd20\n",
        "  u_Sd20 = (0.7*y_Sd20**0.6)\n",
        "  delta_u_Sd20 = delta_u_Sd20+u_Sd20+land_change_emissions\n",
        "  delta_t_Sd20 = clim_sens*delta_u_Sd20\n",
        "  d_Sd20 = max(0.03,0.03+Sd20*(delta_t_Sd20-1))\n",
        "  #y_growth_rate = (y-[y-1])/[y-1]\n",
        "  delta_u_full_series_Sd20.append(delta_u_Sd20) #adding the 1982-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series_Sd20.append(delta_t_Sd20) #adding the temp change from 1982-2100\n",
        "  K_full_series_Sd20.append(K_Sd20) #adding 1982-2100 total capital to the list\n",
        "  u_full_series_Sd20.append(u_Sd20) #adding 1982-2100 emissions to the list\n",
        "  d_full_series_Sd20.append(d_Sd20) #adding 1982-2100 capital decay rate to the list\n",
        "  y_full_series_Sd20.append(y_Sd20) #adding 1982-2100 GWP to the list"
      ],
      "metadata": {
        "id": "S7JBWJ2v319N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2=pd.DataFrame() #new dataframe with reduces decay sensitivity\n",
        "\n",
        "df2['Years']=years_full\n",
        "df2['capital']=K_full_series_Sd20\n",
        "df2['GWP']=y_full_series_Sd20\n",
        "df2['emissions']=u_full_series_Sd20\n",
        "df2['cumulative_emissions']=delta_u_full_series_Sd20\n",
        "df2['temp_change']=delta_t_full_series_Sd20\n",
        "df2['decay_rate']=d_full_series_Sd20\n",
        "df2['GWP_growth_rate']=df2['GWP'].pct_change()"
      ],
      "metadata": {
        "id": "y5B-rDzy4Zvm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "yZBo0PUG4sFb",
        "outputId": "74c95b73-aeb2-494a-dd5d-0965e2abf892"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Years      capital         GWP  emissions  cumulative_emissions  \\\n",
              "0     1980   450.000000   27.000000   5.057272            250.000000   \n",
              "1     1981   463.500000   27.810000   5.147764            256.647764   \n",
              "2     1982   477.405000   28.644300   5.239875            263.387640   \n",
              "3     1983   491.727150   29.503629   5.333635            270.221275   \n",
              "4     1984   506.478964   30.388738   5.429072            277.150347   \n",
              "..     ...          ...         ...        ...                   ...   \n",
              "216   2196  4695.443602  281.726616  20.653553           5175.734739   \n",
              "217   2197  4597.153495  275.829210  20.393050           5197.627789   \n",
              "218   2198  4499.813801  269.988828  20.132862           5219.260651   \n",
              "219   2199  4403.464386  264.207863  19.873094           5240.633745   \n",
              "220   2200  4308.142719  258.488563  19.613850           5261.747594   \n",
              "\n",
              "     temp_change  decay_rate  GWP_growth_rate  \n",
              "0       0.458333    0.030000              NaN  \n",
              "1       0.470521    0.030000         0.030000  \n",
              "2       0.482877    0.030000         0.030000  \n",
              "3       0.495406    0.030000         0.030000  \n",
              "4       0.508109    0.030000         0.030000  \n",
              "..           ...         ...              ...  \n",
              "216     9.488847    0.080933        -0.020689  \n",
              "217     9.528984    0.081174        -0.020933  \n",
              "218     9.568645    0.081412        -0.021174  \n",
              "219     9.607829    0.081647        -0.021412  \n",
              "220     9.646537    0.081879        -0.021647  \n",
              "\n",
              "[221 rows x 8 columns]"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Years</th>\n",
              "      <th>capital</th>\n",
              "      <th>GWP</th>\n",
              "      <th>emissions</th>\n",
              "      <th>cumulative_emissions</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>5.057272</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1981</td>\n",
              "      <td>463.500000</td>\n",
              "      <td>27.810000</td>\n",
              "      <td>5.147764</td>\n",
              "      <td>256.647764</td>\n",
              "      <td>0.470521</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1982</td>\n",
              "      <td>477.405000</td>\n",
              "      <td>28.644300</td>\n",
              "      <td>5.239875</td>\n",
              "      <td>263.387640</td>\n",
              "      <td>0.482877</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1983</td>\n",
              "      <td>491.727150</td>\n",
              "      <td>29.503629</td>\n",
              "      <td>5.333635</td>\n",
              "      <td>270.221275</td>\n",
              "      <td>0.495406</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1984</td>\n",
              "      <td>506.478964</td>\n",
              "      <td>30.388738</td>\n",
              "      <td>5.429072</td>\n",
              "      <td>277.150347</td>\n",
              "      <td>0.508109</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>216</th>\n",
              "      <td>2196</td>\n",
              "      <td>4695.443602</td>\n",
              "      <td>281.726616</td>\n",
              "      <td>20.653553</td>\n",
              "      <td>5175.734739</td>\n",
              "      <td>9.488847</td>\n",
              "      <td>0.080933</td>\n",
              "      <td>-0.020689</td>\n",
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              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>2197</td>\n",
              "      <td>4597.153495</td>\n",
              "      <td>275.829210</td>\n",
              "      <td>20.393050</td>\n",
              "      <td>5197.627789</td>\n",
              "      <td>9.528984</td>\n",
              "      <td>0.081174</td>\n",
              "      <td>-0.020933</td>\n",
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              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>2198</td>\n",
              "      <td>4499.813801</td>\n",
              "      <td>269.988828</td>\n",
              "      <td>20.132862</td>\n",
              "      <td>5219.260651</td>\n",
              "      <td>9.568645</td>\n",
              "      <td>0.081412</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df1['Years'],df1['GWP_growth_rate'],label='0.75%/yr/^C')\n",
        "plt.plot(df2['Years'],df2['GWP_growth_rate'],label='0.6%/yr/^C')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('GWP Growth Rate')\n",
        "plt.legend(loc='best')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "rT38EdFT4ud2",
        "outputId": "b451fa40-f2a7-412a-9a62-96490345c30a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa25729cf50>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BAU with half the original decay sensitivity\n",
        "This time it will be 50% lower, so 0.375%/yr/^C)"
      ],
      "metadata": {
        "id": "KJi6Ac0U5ioR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating lists of all PARAMETERS\n",
        "#================================\n",
        "#all other parameters will be the same\n",
        "\n",
        "Sd50 = 0.00375 #NEW decay sensitivity to global mean surface temperature change (%/yr/^c) (0.75%/yr/^c)"
      ],
      "metadata": {
        "id": "yGEpxH8d42XZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1980 values (initial values for all variables in 1980)\n",
        "#==================================================================\n",
        "\n",
        "#already have initial capital (K_1980) and cumulative emissions (delta_u_1980) in parameters\n",
        "\n",
        "y_1980 = A*K_1980 #GWP in 1980 (T$/yr)\n",
        "u_1980 = (0.7*y_1980**0.6) #emissions rate in 1980 (GtC/yr)\n",
        "delta_t_1980 = clim_sens*delta_u_1980 #global mean surface temp change in 1980 (^C)\n",
        "d_1980_Sd50 = max(0.03,0.03+Sd50*(delta_t_1980-1)) #capital decay rate in 1980 (%/yr)"
      ],
      "metadata": {
        "id": "sYSlyQWT56cE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1981 values (these need to be calculated as there are still specific equations for this year that can't go in the loop)\n",
        "#===================================================================================================================================\n",
        "\n",
        "K_Sd50 = (K_1980)+(y_1980)-base_decay*K_1980 #capital in 1981 (T$)\n",
        "y_Sd50 = A*K_Sd50 #GWP in 1981 (T$/yr)\n",
        "u_Sd50 = (0.7*y_Sd50**0.6) #emissions rate in 1981 (GtC/yr)\n",
        "delta_u_Sd50 = delta_u_1980+u_Sd50+land_change_emissions #cumulative emissions in 1981 (GtC)\n",
        "delta_t_Sd50 = clim_sens*delta_u_Sd50 #global mean surface temp change in 1981 (^C)\n",
        "d_Sd50 = max(0.03,0.03+Sd50*(delta_t_Sd50-1)) #capital decay rate in 1981 (%/yr)\n",
        "y_growth_rate_Sd50 = (y_Sd50-y_1980)/y_1980 #GWP growth rate in 1981 (%/yr)"
      ],
      "metadata": {
        "id": "79PMZ5Xn5_iQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 1980, 1981 and eventually 1982-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series_Sd50=[] #empty list for cumulative emissions\n",
        "delta_u_full_series_Sd50.append(delta_u_1980) #adding the 1980 value\n",
        "delta_u_full_series_Sd50.append(delta_u_Sd50) #adding the 1981 value\n",
        "print(delta_u_full_series_Sd50)\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series_Sd50=[]\n",
        "delta_t_full_series_Sd50.append(delta_t_1980)\n",
        "delta_t_full_series_Sd50.append(delta_t_Sd50)\n",
        "print(delta_t_full_series_Sd50)\n",
        "\n",
        "#total capital series:\n",
        "K_full_series_Sd50=[]\n",
        "K_full_series_Sd50.append(K_1980)\n",
        "K_full_series_Sd50.append(K_Sd50)\n",
        "print(K_full_series_Sd50)\n",
        "\n",
        "#emissions series:\n",
        "u_full_series_Sd50=[]\n",
        "u_full_series_Sd50.append(u_1980)\n",
        "u_full_series_Sd50.append(u_Sd50)\n",
        "print(u_full_series_Sd50)\n",
        "\n",
        "#capital decay rate series:\n",
        "d_full_series_Sd50=[]\n",
        "d_full_series_Sd50.append(d_1980_Sd50)\n",
        "d_full_series_Sd50.append(d_Sd50)\n",
        "print(d_full_series_Sd50)\n",
        "\n",
        "#GWP series:\n",
        "y_full_series_Sd50=[]\n",
        "y_full_series_Sd50.append(y_1980)\n",
        "y_full_series_Sd50.append(y_Sd50)\n",
        "print(y_full_series_Sd50)\n",
        "\n",
        "#GWP growth rate series:\n",
        "y_growth_rate_series_Sd50=[]\n",
        "y_growth_rate_series_Sd50.append(0)\n",
        "y_growth_rate_series_Sd50.append(y_growth_rate_Sd50)\n",
        "print(y_growth_rate_series_Sd50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O13jeKh16JQd",
        "outputId": "e4f4a69d-2a67-4141-d874-feed18f3f0b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[250, 256.6477640585382]\n",
            "[0.45833333333333337, 0.4705209007739867]\n",
            "[450, 463.5]\n",
            "[5.057271839089452, 5.147764058538205]\n",
            "[0.03, 0.03]\n",
            "[27.0, 27.81]\n",
            "[0, 0.029999999999999954]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 1982-2100\n",
        "#============================================\n",
        "years_array=(np.array(years))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 1982 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array.size): #loop for the number of years between 1982-2100 (119)\n",
        "  K_Sd50 = K_Sd50+y_Sd50-d_Sd50*K_Sd50\n",
        "  y_Sd50 = A*K_Sd50\n",
        "  u_Sd50 = (0.7*y_Sd50**0.6)\n",
        "  delta_u_Sd50 = delta_u_Sd50+u_Sd50+land_change_emissions\n",
        "  delta_t_Sd50 = clim_sens*delta_u_Sd50\n",
        "  d_Sd50 = max(0.03,0.03+Sd50*(delta_t_Sd50-1))\n",
        "  #y_growth_rate = (y-[y-1])/[y-1]\n",
        "  delta_u_full_series_Sd50.append(delta_u_Sd50) #adding the 1982-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series_Sd50.append(delta_t_Sd50) #adding the temp change from 1982-2100\n",
        "  K_full_series_Sd50.append(K_Sd50) #adding 1982-2100 total capital to the list\n",
        "  u_full_series_Sd50.append(u_Sd50) #adding 1982-2100 emissions to the list\n",
        "  d_full_series_Sd50.append(d_Sd50) #adding 1982-2100 capital decay rate to the list\n",
        "  y_full_series_Sd50.append(y_Sd50) #adding 1982-2100 GWP to the list"
      ],
      "metadata": {
        "id": "4GChM6cx6dAQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3=pd.DataFrame() #new dataframe with reduces decay sensitivity\n",
        "\n",
        "df3['Years']=years_full\n",
        "df3['capital']=K_full_series_Sd50\n",
        "df3['GWP']=y_full_series_Sd50\n",
        "df3['emissions']=u_full_series_Sd50\n",
        "df3['cumulative_emissions']=delta_u_full_series_Sd50\n",
        "df3['temp_change']=delta_t_full_series_Sd50\n",
        "df3['decay_rate']=d_full_series_Sd50\n",
        "df3['GWP_growth_rate']=df3['GWP'].pct_change()"
      ],
      "metadata": {
        "id": "eLFhSJMQ6n67"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "ukt-r9YQ6yo0",
        "outputId": "8b8a52c0-7bc5-4a3b-fc0e-22b70a61d620"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Years       capital         GWP  emissions  cumulative_emissions  \\\n",
              "0     1980    450.000000   27.000000   5.057272            250.000000   \n",
              "1     1981    463.500000   27.810000   5.147764            256.647764   \n",
              "2     1982    477.405000   28.644300   5.239875            263.387640   \n",
              "3     1983    491.727150   29.503629   5.333635            270.221275   \n",
              "4     1984    506.478964   30.388738   5.429072            277.150347   \n",
              "..     ...           ...         ...        ...                   ...   \n",
              "216   2196  14406.861166  864.411670  40.469740           6582.352795   \n",
              "217   2197  14241.129311  854.467759  40.189763           6624.042558   \n",
              "218   2198  14073.222230  844.393334  39.904780           6665.447338   \n",
              "219   2199  13903.288769  834.197326  39.614969           6706.562306   \n",
              "220   2200  13731.477267  823.888636  39.320510           6747.382817   \n",
              "\n",
              "     temp_change  decay_rate  GWP_growth_rate  \n",
              "0       0.458333    0.030000              NaN  \n",
              "1       0.470521    0.030000         0.030000  \n",
              "2       0.482877    0.030000         0.030000  \n",
              "3       0.495406    0.030000         0.030000  \n",
              "4       0.508109    0.030000         0.030000  \n",
              "..           ...         ...              ...  \n",
              "216    12.067647    0.071504        -0.011215  \n",
              "217    12.144078    0.071790        -0.011504  \n",
              "218    12.219987    0.072075        -0.011790  \n",
              "219    12.295364    0.072358        -0.012075  \n",
              "220    12.370202    0.072638        -0.012358  \n",
              "\n",
              "[221 rows x 8 columns]"
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              "      <th></th>\n",
              "      <th>Years</th>\n",
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              "      <th>0</th>\n",
              "      <td>1980</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>5.057272</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1981</td>\n",
              "      <td>463.500000</td>\n",
              "      <td>27.810000</td>\n",
              "      <td>5.147764</td>\n",
              "      <td>256.647764</td>\n",
              "      <td>0.470521</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1982</td>\n",
              "      <td>477.405000</td>\n",
              "      <td>28.644300</td>\n",
              "      <td>5.239875</td>\n",
              "      <td>263.387640</td>\n",
              "      <td>0.482877</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1983</td>\n",
              "      <td>491.727150</td>\n",
              "      <td>29.503629</td>\n",
              "      <td>5.333635</td>\n",
              "      <td>270.221275</td>\n",
              "      <td>0.495406</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1984</td>\n",
              "      <td>506.478964</td>\n",
              "      <td>30.388738</td>\n",
              "      <td>5.429072</td>\n",
              "      <td>277.150347</td>\n",
              "      <td>0.508109</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.030000</td>\n",
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              "      <th>...</th>\n",
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              "      <th>216</th>\n",
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              "      <td>14406.861166</td>\n",
              "      <td>864.411670</td>\n",
              "      <td>40.469740</td>\n",
              "      <td>6582.352795</td>\n",
              "      <td>12.067647</td>\n",
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              "      <th>217</th>\n",
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              "      <td>14241.129311</td>\n",
              "      <td>854.467759</td>\n",
              "      <td>40.189763</td>\n",
              "      <td>6624.042558</td>\n",
              "      <td>12.144078</td>\n",
              "      <td>0.071790</td>\n",
              "      <td>-0.011504</td>\n",
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              "      <th>218</th>\n",
              "      <td>2198</td>\n",
              "      <td>14073.222230</td>\n",
              "      <td>844.393334</td>\n",
              "      <td>39.904780</td>\n",
              "      <td>6665.447338</td>\n",
              "      <td>12.219987</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df1['Years'][:120],df1['emissions'][:120],label='0.75%/yr/^C')\n",
        "plt.plot(df2['Years'][:120],df2['emissions'][:120],label='0.6%/yr/^C')\n",
        "plt.plot(df3['Years'][:120],df3['emissions'][:120],label='0.375%/yr/^C')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('emissions')\n",
        "plt.legend(loc='best')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "84ML5mPMumPP",
        "outputId": "00310844-45e3-4d62-a4eb-474d47df271f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa257280090>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3['emissions'][120]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "do7KlfM_zLm_",
        "outputId": "9378c598-2ff3-4ff3-d1b5-d1378e00ba91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33.05279819945958"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[8,6])\n",
        "plt.tick_params(axis='both', which='major', labelsize=18)\n",
        "\n",
        "plt.plot(df1['Years'],df1['GWP_growth_rate'],label='0.75 %/yr/K')\n",
        "plt.plot(df2['Years'],df2['GWP_growth_rate'],label='0.6 %/yr/K')\n",
        "plt.plot(df3['Years'],df3['GWP_growth_rate'],label='0.375 %/yr/K')\n",
        "plt.ylabel('GWP Growth Rate',fontsize=16)\n",
        "#plt.title('Impact of varying decay sensitivity on GWP growth rate')\n",
        "plt.axhline(y=0,color='black',linestyle='dashed',alpha=0.4)\n",
        "plt.axvline(x=2119,color='black',linestyle='dashed',alpha=0.4)\n",
        "plt.axvline(x=2132,color='black',linestyle='dashed',alpha=0.4)\n",
        "plt.axvline(x=2160,color='black',linestyle='dashed',alpha=0.4)\n",
        "\n",
        "plt.xlim(1980,2200)\n",
        "plt.legend(loc='best',fontsize=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "D2SWYFec6zrV",
        "outputId": "689a4933-8787-4c1d-ca00-3cb18d85d73e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa256c36c90>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decarb scenario with original Sd (decay sensitivity) of 0.75%/yr"
      ],
      "metadata": {
        "id": "FTF7JZxiqR0i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "ikXbJasKqLaF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating lists of all PARAMETERS\n",
        "#================================\n",
        "\n",
        "years = list(range(1982,2101)) #list of years from 1982-2100 (1980-81 need to be calculated separately from the loop)\n",
        "K_1980 = 450 #1980 total capital value (T$)\n",
        "A = 0.06 #Total factor productivity (%/yr) (6%/yr)\n",
        "sensitivity_GWP = 0.7 #emissions sensitivity to GWP (unitless) - 0.7 * capital^0.6\n",
        "emissions_GWP_ratio = 0.6 #ratio of emissions to GWP growth\n",
        "land_change_emissions = 1.5 #land use change emissions (GtC/yr)\n",
        "delta_u_1980 = 250 #1980 cumulative emissions (GtC)\n",
        "clim_sens = (1.1/600) #climate sensitivity (^c/GtC)\n",
        "base_decay = 0.03 #base decay rate of capital (%/yr) (3%/yr)\n",
        "Sd = 0.0075 #decay sensitivity to global mean surface temperature change (%/yr/^c) (0.75%/yr/^c)\n",
        "#emissions_scale = 3.793 #total global CO2 emissions in 1980 / emissions_1980"
      ],
      "metadata": {
        "id": "mS7Kelq5q4_2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1980 values (initial values for all variables in 1980)\n",
        "#==================================================================\n",
        "\n",
        "#already have initial capital (K_1980) and cumulative emissions (delta_u_1980) in parameters\n",
        "\n",
        "y_1980 = A*K_1980 #GWP in 1980 (T$/yr)\n",
        "u_1980 = (0.7*y_1980**0.6) #emissions rate in 1980 (GtC/yr)\n",
        "delta_t_1980 = clim_sens*delta_u_1980 #global mean surface temp change in 1980 (^C)\n",
        "d_1980 = max(0.03,0.03+Sd*(delta_t_1980-1)) #capital decay rate in 1980 (%/yr)"
      ],
      "metadata": {
        "id": "PntOAEKLq7Dl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1981 values (these need to be calculated as there are still specific equations for this year that can't go in the loop)\n",
        "#===================================================================================================================================\n",
        "\n",
        "K = (K_1980)+(y_1980)-base_decay*K_1980 #capital in 1981 (T$)\n",
        "y = A*K #GWP in 1981 (T$/yr)\n",
        "u = (0.7*y**0.6) #emissions rate in 1981 (GtC/yr)\n",
        "delta_u = delta_u_1980+u+land_change_emissions #cumulative emissions in 1981 (GtC)\n",
        "delta_t = clim_sens*delta_u #global mean surface temp change in 1981 (^C)\n",
        "d = max(0.03,0.03+Sd*(delta_t-1)) #capital decay rate in 1981 (%/yr)\n",
        "y_growth_rate = (y-y_1980)/y_1980 #GWP growth rate in 1981 (%/yr)"
      ],
      "metadata": {
        "id": "KoOC5uLdq88Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 1980, 1981 and eventually 1982-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series=[] #empty list for cumulative emissions\n",
        "delta_u_full_series.append(delta_u_1980) #adding the 1980 value\n",
        "delta_u_full_series.append(delta_u) #adding the 1981 value\n",
        "print(delta_u_full_series)\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series=[]\n",
        "delta_t_full_series.append(delta_t_1980)\n",
        "delta_t_full_series.append(delta_t)\n",
        "print(delta_t_full_series)\n",
        "\n",
        "#total capital series:\n",
        "K_full_series=[]\n",
        "K_full_series.append(K_1980)\n",
        "K_full_series.append(K)\n",
        "print(K_full_series)\n",
        "\n",
        "#emissions series:\n",
        "u_full_series=[]\n",
        "u_full_series.append(u_1980)\n",
        "u_full_series.append(u)\n",
        "print(u_full_series)\n",
        "\n",
        "#capital decay rate series:\n",
        "d_full_series=[]\n",
        "d_full_series.append(d_1980)\n",
        "d_full_series.append(d)\n",
        "print(d_full_series)\n",
        "\n",
        "#GWP series:\n",
        "y_full_series=[]\n",
        "y_full_series.append(y_1980)\n",
        "y_full_series.append(y)\n",
        "print(y_full_series)\n",
        "\n",
        "#GWP growth rate series:\n",
        "y_growth_rate_series=[]\n",
        "y_growth_rate_series.append(0)\n",
        "y_growth_rate_series.append(y_growth_rate)\n",
        "print(y_growth_rate_series)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5G8t75PrCP3",
        "outputId": "56e4ee0d-20c9-457f-e838-67d3538b3c10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[250, 256.6477640585382]\n",
            "[0.45833333333333337, 0.4705209007739867]\n",
            "[450, 463.5]\n",
            "[5.057271839089452, 5.147764058538205]\n",
            "[0.03, 0.03]\n",
            "[27.0, 27.81]\n",
            "[0, 0.029999999999999954]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 1982-2100\n",
        "#============================================\n",
        "years_array=(np.array(years))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 1982 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array.size): #loop for the number of years between 1982-2100 (119)\n",
        "  K = K+y-d*K\n",
        "  y = A*K\n",
        "  u = (0.7*y**0.6)\n",
        "  delta_u = delta_u+u+land_change_emissions\n",
        "  delta_t = clim_sens*delta_u\n",
        "  d = max(0.03,0.03+Sd*(delta_t-1))\n",
        "  #y_growth_rate = (y-[y-1])/[y-1]\n",
        "  delta_u_full_series.append(delta_u) #adding the 1982-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series.append(delta_t) #adding the temp change from 1982-2100\n",
        "  K_full_series.append(K) #adding 1982-2100 total capital to the list\n",
        "  u_full_series.append(u) #adding 1982-2100 emissions to the list\n",
        "  d_full_series.append(d) #adding 1982-2100 capital decay rate to the list\n",
        "  y_full_series.append(y) #adding 1982-2100 GWP to the list"
      ],
      "metadata": {
        "id": "TfbvnnscrC8V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array that includes all years from 1980-2100\n",
        "#========================================================\n",
        "years_full = list(range(1980,2101)) #creating a new years array so that 1980 and 1981 are included and x and y shapes match\n",
        "years_array_full=(np.array(years_full))"
      ],
      "metadata": {
        "id": "zXookv0UrFBr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.DataFrame()\n",
        "\n",
        "df1['Years']=years_full\n",
        "df1['capital']=K_full_series\n",
        "df1['GWP']=y_full_series\n",
        "df1['emissions']=u_full_series\n",
        "df1['cumulative_emissions']=delta_u_full_series\n",
        "df1['temp_change']=delta_t_full_series\n",
        "df1['decay_rate']=d_full_series\n",
        "df1['GWP_growth_rate']=df1['GWP'].pct_change()"
      ],
      "metadata": {
        "id": "3F18UE9VrGsY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#setting out the initial values for the HCE and LCE split in 2020\n",
        "#================================================================\n",
        "\n",
        "#Capital in 2020\n",
        "HCE_K_2020=df1['capital'][40]*0.8 #High Carbon Economy total capital in 2020 (T$)\n",
        "LCE_K_2020=df1['capital'][40]*0.2 #Low Carbon Economy total capital in 2020 (T$)"
      ],
      "metadata": {
        "id": "VlCGFoCHrIZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#parameters needed for GWP calculations:\n",
        "\n",
        "A=0.06 #aggregate energy productivity (%) (6%)\n",
        "h=0.95 #how productive the LCE is compared to the HCE (%) (95%)\n",
        "A_HCE=A/(0.8+0.2*h) #High Carbon Economy productivity (%)\n",
        "A_LCE=h*A_HCE #Low Carbon Economy productivity (%)\n",
        "\n",
        "#GWP\n",
        "HCE_GWP_2020=HCE_K_2020*A_HCE #High Carbon Economy GWP in 2020 (T$/yr)\n",
        "LCE_GWP_2020=LCE_K_2020*A_LCE #Low Carbon Economy GWP in 2020 (T$/yr)\n",
        "Total_GWP_2020=HCE_GWP_2020+LCE_GWP_2020 #Total GWP in 2020(T$/yr)\n",
        "\n",
        "#carbon intensity parameter\n",
        "HCE_C_intensity_2020=df1['emissions'][40]/HCE_GWP_2020 #carbon intensity of the High Carbon Economy in 2020 (effects the emissions rate)\n",
        "u_2020=HCE_C_intensity_2020*HCE_GWP_2020 #emissions in 2020 (GtC/yr), using the carbon intensity of HCE GWP in 2020\n",
        "\n",
        "#setting the remaining 2020 values needed\n",
        "delta_u_2020=df1['cumulative_emissions'][40] #cumulative emissions in 2020 (GtC)\n",
        "delta_t_2020=df1['temp_change'][40] #temperature change in 2020 (^c)\n",
        "d_2020=df1['decay_rate'][40] #high carbon infrastructure decay rate in 2020(%/yr) (3.08%/yr)"
      ],
      "metadata": {
        "id": "MmMEQkW1rL52"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2021 values for decarbonisation scenario:\n",
        "#=========================================\n",
        "\n",
        "HCE_K=HCE_K_2020-d_2020*HCE_K_2020 #High Carbon Economy capital in 2021 (T$)\n",
        "LCE_K=LCE_K_2020+Total_GWP_2020-d_2020*LCE_K_2020 #Low Carbon Economy capital in 2021 (T$)\n",
        "\n",
        "HCE_GWP=HCE_K*A_HCE #High Carbon Economy GWP in 2021 (T$/yr)\n",
        "LCE_GWP=LCE_K*A_LCE #Low Carbon Economy GWP in 2021 (T$/yr)\n",
        "u_decarb=HCE_C_intensity_2020*HCE_GWP #total economy emissions in 2021 (GtC/yr)\n",
        "delta_u_decarb=delta_u_2020+u_2020+land_change_emissions #total economy cumulative emissions in 2021 (GtC)\n",
        "delta_t_decarb=clim_sens*delta_u_decarb #total economy temp change in 2021 (^c)\n",
        "d_decarb=max(0.03,0.03+Sd*(delta_t_decarb-1)) #high carbon infrastructure decay rate (%/yr) in 2021\n",
        "Total_GWP=HCE_GWP+LCE_GWP #total GWP in 2021 (T$/yr)"
      ],
      "metadata": {
        "id": "3HNzabV6rN79"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 2020, 2021 and eventually 2022-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#capital series:\n",
        "HCE_K_full_series=[] #empty list for HCE capital \n",
        "HCE_K_full_series.append(HCE_K_2020) #adding the 2020 value to the list\n",
        "HCE_K_full_series.append(HCE_K) #adding the 2021 value to the list\n",
        "print(HCE_K_full_series)\n",
        "\n",
        "LCE_K_full_series=[] #empty list for LCE capital\n",
        "LCE_K_full_series.append(LCE_K_2020) #adding the 2020 value to the list\n",
        "LCE_K_full_series.append(LCE_K) #adding the 2021 value to the list\n",
        "print(LCE_K_full_series)\n",
        "\n",
        "\n",
        "#GWP series:\n",
        "HCE_GWP_full_series=[] #empty list for HCE GWP\n",
        "HCE_GWP_full_series.append(HCE_GWP_2020)\n",
        "HCE_GWP_full_series.append(HCE_GWP)\n",
        "print(HCE_GWP_full_series)\n",
        "\n",
        "LCE_GWP_full_series=[] #empty list for LCE GWP\n",
        "LCE_GWP_full_series.append(LCE_GWP_2020)\n",
        "LCE_GWP_full_series.append(LCE_GWP)\n",
        "print(LCE_GWP_full_series)\n",
        "\n",
        "Total_GWP_full_series=[] #empty list for total economy GWP \n",
        "Total_GWP_full_series.append(Total_GWP_2020)\n",
        "Total_GWP_full_series.append(Total_GWP)\n",
        "print(Total_GWP_full_series)\n",
        "\n",
        "\n",
        "#emissions series:\n",
        "u_full_series_decarb=[] #empty list for total economy emissions\n",
        "u_full_series_decarb.append(u_2020)\n",
        "u_full_series_decarb.append(u_decarb)\n",
        "print(u_full_series_decarb)\n",
        "\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series_decarb=[] #empty list for cumulative emissions\n",
        "delta_u_full_series_decarb.append(delta_u_2020) \n",
        "delta_u_full_series_decarb.append(delta_u_decarb)\n",
        "print(delta_u_full_series_decarb)\n",
        "\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series_decarb=[] #empty list for total economy temp change \n",
        "delta_t_full_series_decarb.append(delta_t_2020)\n",
        "delta_t_full_series_decarb.append(delta_t_decarb)\n",
        "print(delta_t_full_series_decarb)\n",
        "\n",
        "\n",
        "#high C infrastructure decay rate series:\n",
        "d_full_series_decarb=[] #empty list for high C infrastructure decay rate\n",
        "d_full_series_decarb.append(d_2020)\n",
        "d_full_series_decarb.append(d_decarb)\n",
        "print(d_full_series_decarb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBRkZhqXrPqc",
        "outputId": "b2243dbc-c08a-46c7-d5e8-5d2316929b09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1172.215703580962, 1136.0556313172]\n",
            "[293.0539258952405, 371.93008559787216]\n",
            "[71.04337597460376, 68.85185644346667]\n",
            "[16.872801793968392, 21.41415644351385]\n",
            "[87.91617776857215, 90.26601288698052]\n",
            "[10.269264008636307, 9.952481587522554]\n",
            "[607.1001129537939, 618.8693769624302]\n",
            "[1.1130168737486221, 1.1345938577644556]\n",
            "[0.030847626553114665, 0.031009453933233417]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 2022-2100\n",
        "#============================================\n",
        "years_decarb = list(range(2022,2101)) #YEARS\n",
        "years_array_decarb=(np.array(years_decarb))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 2022 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array_decarb.size): #loop for the number of years between 2022-2100\n",
        "  delta_u_decarb = delta_u_decarb+u_decarb+land_change_emissions #cumulative emissions equation (GtC)\n",
        "  HCE_K = HCE_K-d_decarb*HCE_K #HCE capital (T$)\n",
        "  LCE_K = LCE_K+Total_GWP-d_decarb*LCE_K #LCE capital (T$)\n",
        "  HCE_GWP = A_HCE*HCE_K #HCE GWP (T$/yr)\n",
        "  LCE_GWP = A_LCE*LCE_K #LCE GWP (T$/yr)\n",
        "  Total_GWP = HCE_GWP+LCE_GWP #Total economy GWP (T$/yr)\n",
        "  u_decarb = HCE_C_intensity_2020*HCE_GWP #emissions from HCE sector (GtC/yr) \n",
        "  delta_t_decarb = clim_sens*delta_u_decarb # temp change (^C)\n",
        "  d_decarb = max(0.03,0.03+Sd*(delta_t_decarb-1)) #high C capital decay rate (%/yr)\n",
        "\n",
        "  #appending values created in loop to their subsequent lists\n",
        "  delta_u_full_series_decarb.append(delta_u_decarb) #adding the 2022-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series_decarb.append(delta_t_decarb) #adding the temp change from 2022-2100\n",
        "  HCE_K_full_series.append(HCE_K) #adding 2022-2100 HCE capital to the list\n",
        "  LCE_K_full_series.append(LCE_K) #adding 2022-2100 LCE capital to the list\n",
        "  HCE_GWP_full_series.append(HCE_GWP)\n",
        "  LCE_GWP_full_series.append(LCE_GWP)\n",
        "  Total_GWP_full_series.append(Total_GWP)\n",
        "  u_full_series_decarb.append(u_decarb)\n",
        "  d_full_series_decarb.append(d_decarb)"
      ],
      "metadata": {
        "id": "XXXDnendrT52"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "years_full_decarb = list(range(2020,2101)) #creating a new years array so that 2020 and 1981 are included and x and y shapes match"
      ],
      "metadata": {
        "id": "UMZbfh_KrWlK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2= pd.DataFrame()\n",
        "\n",
        "df2['Years']=years_full_decarb\n",
        "df2['HCE capital']=HCE_K_full_series\n",
        "df2['HCE GWP']=HCE_GWP_full_series\n",
        "df2['HCE GWP growth rate']=df2['HCE GWP'].pct_change()\n",
        "df2['LCE capital']=LCE_K_full_series\n",
        "df2['LCE GWP']=LCE_GWP_full_series\n",
        "df2['LCE GWP growth rate']=df2['LCE GWP'].pct_change()\n",
        "df2['emissions']=u_full_series_decarb\n",
        "df2['cumulative emissions']=delta_u_full_series_decarb\n",
        "df2['temp change']=delta_t_full_series_decarb\n",
        "df2['decay rate']=d_full_series_decarb\n",
        "df2['capital']=df2['HCE capital']+df2['LCE capital']\n",
        "df2['GWP']=Total_GWP_full_series\n",
        "df2['total GWP growth rate']=df2['GWP'].pct_change()"
      ],
      "metadata": {
        "id": "_k4AfW9HrYPQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "DN86du3IraXn",
        "outputId": "2daa14e2-d306-4231-b665-9947681f6355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Years  HCE capital    HCE GWP  HCE GWP growth rate  LCE capital  \\\n",
              "0    2020  1172.215704  71.043376                  NaN   293.053926   \n",
              "1    2021  1136.055631  68.851856            -0.030848   371.930086   \n",
              "2    2022  1100.827167  66.716798            -0.031009   450.662750   \n",
              "3    2023  1066.517768  64.637440            -0.031167   529.281024   \n",
              "4    2024  1033.114268  62.612986            -0.031320   607.815058   \n",
              "..    ...          ...        ...                  ...          ...   \n",
              "76   2096    82.283409   4.986873            -0.036333  8656.508019   \n",
              "77   2097    79.291249   4.805530            -0.036364  8845.113987   \n",
              "78   2098    76.405475   4.630635            -0.036395  9037.269161   \n",
              "79   2099    73.622422   4.461965            -0.036425  9233.046797   \n",
              "80   2100    70.938544   4.299306            -0.036455  9432.521239   \n",
              "\n",
              "       LCE GWP  LCE GWP growth rate  emissions  cumulative emissions  \\\n",
              "0    16.872802                  NaN  10.269264            607.100113   \n",
              "1    21.414156             0.269152   9.952482            618.869377   \n",
              "2    25.947249             0.211687   9.643861            630.321859   \n",
              "3    30.473756             0.174450   9.343291            641.465719   \n",
              "4    34.995412             0.148379   9.050658            652.309010   \n",
              "..         ...                  ...        ...                   ...   \n",
              "76  498.405007             0.021853   0.720849           1008.296409   \n",
              "77  509.264139             0.021788   0.694636           1010.517257   \n",
              "78  520.327618             0.021724   0.669355           1012.711893   \n",
              "79  531.599664             0.021663   0.644974           1014.881248   \n",
              "80  543.084556             0.021604   0.621461           1017.026221   \n",
              "\n",
              "    temp change  decay rate      capital         GWP  total GWP growth rate  \n",
              "0      1.113017    0.030848  1465.269629   87.916178                    NaN  \n",
              "1      1.134594    0.031009  1507.985717   90.266013               0.026728  \n",
              "2      1.155590    0.031167  1551.489916   92.664047               0.026566  \n",
              "3      1.176020    0.031320  1595.798793   95.111196               0.026409  \n",
              "4      1.195900    0.031469  1640.929326   97.608398               0.026256  \n",
              "..          ...         ...          ...         ...                    ...  \n",
              "76     1.848543    0.036364  8738.791428  503.391880               0.021243  \n",
              "77     1.852615    0.036395  8924.405236  514.069669               0.021212  \n",
              "78     1.856638    0.036425  9113.674636  524.958253               0.021181  \n",
              "79     1.860616    0.036455  9306.669218  536.061629               0.021151  \n",
              "80     1.864548    0.036484  9503.459784  547.383862               0.021121  \n",
              "\n",
              "[81 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-26bb4a7c-a3ae-4a55-ac18-4bb2216879a4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Years</th>\n",
              "      <th>HCE capital</th>\n",
              "      <th>HCE GWP</th>\n",
              "      <th>HCE GWP growth rate</th>\n",
              "      <th>LCE capital</th>\n",
              "      <th>LCE GWP</th>\n",
              "      <th>LCE GWP growth rate</th>\n",
              "      <th>emissions</th>\n",
              "      <th>cumulative emissions</th>\n",
              "      <th>temp change</th>\n",
              "      <th>decay rate</th>\n",
              "      <th>capital</th>\n",
              "      <th>GWP</th>\n",
              "      <th>total GWP growth rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020</td>\n",
              "      <td>1172.215704</td>\n",
              "      <td>71.043376</td>\n",
              "      <td>NaN</td>\n",
              "      <td>293.053926</td>\n",
              "      <td>16.872802</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.269264</td>\n",
              "      <td>607.100113</td>\n",
              "      <td>1.113017</td>\n",
              "      <td>0.030848</td>\n",
              "      <td>1465.269629</td>\n",
              "      <td>87.916178</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021</td>\n",
              "      <td>1136.055631</td>\n",
              "      <td>68.851856</td>\n",
              "      <td>-0.030848</td>\n",
              "      <td>371.930086</td>\n",
              "      <td>21.414156</td>\n",
              "      <td>0.269152</td>\n",
              "      <td>9.952482</td>\n",
              "      <td>618.869377</td>\n",
              "      <td>1.134594</td>\n",
              "      <td>0.031009</td>\n",
              "      <td>1507.985717</td>\n",
              "      <td>90.266013</td>\n",
              "      <td>0.026728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022</td>\n",
              "      <td>1100.827167</td>\n",
              "      <td>66.716798</td>\n",
              "      <td>-0.031009</td>\n",
              "      <td>450.662750</td>\n",
              "      <td>25.947249</td>\n",
              "      <td>0.211687</td>\n",
              "      <td>9.643861</td>\n",
              "      <td>630.321859</td>\n",
              "      <td>1.155590</td>\n",
              "      <td>0.031167</td>\n",
              "      <td>1551.489916</td>\n",
              "      <td>92.664047</td>\n",
              "      <td>0.026566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023</td>\n",
              "      <td>1066.517768</td>\n",
              "      <td>64.637440</td>\n",
              "      <td>-0.031167</td>\n",
              "      <td>529.281024</td>\n",
              "      <td>30.473756</td>\n",
              "      <td>0.174450</td>\n",
              "      <td>9.343291</td>\n",
              "      <td>641.465719</td>\n",
              "      <td>1.176020</td>\n",
              "      <td>0.031320</td>\n",
              "      <td>1595.798793</td>\n",
              "      <td>95.111196</td>\n",
              "      <td>0.026409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024</td>\n",
              "      <td>1033.114268</td>\n",
              "      <td>62.612986</td>\n",
              "      <td>-0.031320</td>\n",
              "      <td>607.815058</td>\n",
              "      <td>34.995412</td>\n",
              "      <td>0.148379</td>\n",
              "      <td>9.050658</td>\n",
              "      <td>652.309010</td>\n",
              "      <td>1.195900</td>\n",
              "      <td>0.031469</td>\n",
              "      <td>1640.929326</td>\n",
              "      <td>97.608398</td>\n",
              "      <td>0.026256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>2096</td>\n",
              "      <td>82.283409</td>\n",
              "      <td>4.986873</td>\n",
              "      <td>-0.036333</td>\n",
              "      <td>8656.508019</td>\n",
              "      <td>498.405007</td>\n",
              "      <td>0.021853</td>\n",
              "      <td>0.720849</td>\n",
              "      <td>1008.296409</td>\n",
              "      <td>1.848543</td>\n",
              "      <td>0.036364</td>\n",
              "      <td>8738.791428</td>\n",
              "      <td>503.391880</td>\n",
              "      <td>0.021243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>2097</td>\n",
              "      <td>79.291249</td>\n",
              "      <td>4.805530</td>\n",
              "      <td>-0.036364</td>\n",
              "      <td>8845.113987</td>\n",
              "      <td>509.264139</td>\n",
              "      <td>0.021788</td>\n",
              "      <td>0.694636</td>\n",
              "      <td>1010.517257</td>\n",
              "      <td>1.852615</td>\n",
              "      <td>0.036395</td>\n",
              "      <td>8924.405236</td>\n",
              "      <td>514.069669</td>\n",
              "      <td>0.021212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2098</td>\n",
              "      <td>76.405475</td>\n",
              "      <td>4.630635</td>\n",
              "      <td>-0.036395</td>\n",
              "      <td>9037.269161</td>\n",
              "      <td>520.327618</td>\n",
              "      <td>0.021724</td>\n",
              "      <td>0.669355</td>\n",
              "      <td>1012.711893</td>\n",
              "      <td>1.856638</td>\n",
              "      <td>0.036425</td>\n",
              "      <td>9113.674636</td>\n",
              "      <td>524.958253</td>\n",
              "      <td>0.021181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2099</td>\n",
              "      <td>73.622422</td>\n",
              "      <td>4.461965</td>\n",
              "      <td>-0.036425</td>\n",
              "      <td>9233.046797</td>\n",
              "      <td>531.599664</td>\n",
              "      <td>0.021663</td>\n",
              "      <td>0.644974</td>\n",
              "      <td>1014.881248</td>\n",
              "      <td>1.860616</td>\n",
              "      <td>0.036455</td>\n",
              "      <td>9306.669218</td>\n",
              "      <td>536.061629</td>\n",
              "      <td>0.021151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2100</td>\n",
              "      <td>70.938544</td>\n",
              "      <td>4.299306</td>\n",
              "      <td>-0.036455</td>\n",
              "      <td>9432.521239</td>\n",
              "      <td>543.084556</td>\n",
              "      <td>0.021604</td>\n",
              "      <td>0.621461</td>\n",
              "      <td>1017.026221</td>\n",
              "      <td>1.864548</td>\n",
              "      <td>0.036484</td>\n",
              "      <td>9503.459784</td>\n",
              "      <td>547.383862</td>\n",
              "      <td>0.021121</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26bb4a7c-a3ae-4a55-ac18-4bb2216879a4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df2['Years'],df2['temp change'])\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Temperature Change')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "XuXbroGDrb-i",
        "outputId": "75dd6a35-34bc-44b1-cded-bdda97c0e33d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Temperature Change')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decarb scenario but with double the decay sensitivity rate (1.5%/yr/^c)"
      ],
      "metadata": {
        "id": "4xE0GsvRrvSn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Sd_x2 = 0.0075*2 #decay sensitivity to global mean surface temperature change (%/yr/^c) (1.5%/yr/^c)"
      ],
      "metadata": {
        "id": "SYusXMvErpIy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1980 values (initial values for all variables in 1980)\n",
        "#==================================================================\n",
        "\n",
        "#already have initial capital (K_1980) and cumulative emissions (delta_u_1980) in parameters\n",
        "\n",
        "y_1980_x2 = A*K_1980 #GWP in 1980 (T$/yr)\n",
        "u_1980_x2 = (0.7*y_1980_x2**0.6) #emissions rate in 1980 (GtC/yr)\n",
        "delta_t_1980_x2 = clim_sens*delta_u_1980 #global mean surface temp change in 1980 (^C)\n",
        "d_1980_x2 = max(0.03,0.03+Sd_x2*(delta_t_1980_x2-1)) #capital decay rate in 1980 (%/yr)"
      ],
      "metadata": {
        "id": "PmcBAtU1sCsq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1981 values (these need to be calculated as there are still specific equations for this year that can't go in the loop)\n",
        "#===================================================================================================================================\n",
        "\n",
        "K_x2 = (K_1980)+(y_1980_x2)-base_decay*K_1980 #capital in 1981 (T$)\n",
        "y_x2 = A*K_x2 #GWP in 1981 (T$/yr)\n",
        "u_x2 = (0.7*y_x2**0.6) #emissions rate in 1981 (GtC/yr)\n",
        "delta_u_x2 = delta_u_1980+u_x2+land_change_emissions #cumulative emissions in 1981 (GtC)\n",
        "delta_t_x2 = clim_sens*delta_u_x2 #global mean surface temp change in 1981 (^C)\n",
        "d_x2 = max(0.03,0.03+Sd_x2*(delta_t_x2-1)) #capital decay rate in 1981 (%/yr)\n",
        "y_growth_rate_x2 = (y_x2-y_1980_x2)/y_1980_x2 #GWP growth rate in 1981 (%/yr)"
      ],
      "metadata": {
        "id": "KMqUqtYNsWRp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 1980, 1981 and eventually 1982-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series_x2=[] #empty list for cumulative emissions\n",
        "delta_u_full_series_x2.append(delta_u_1980) #adding the 1980 value\n",
        "delta_u_full_series_x2.append(delta_u_x2) #adding the 1981 value\n",
        "print(delta_u_full_series_x2)\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series_x2=[]\n",
        "delta_t_full_series_x2.append(delta_t_1980_x2)\n",
        "delta_t_full_series_x2.append(delta_t_x2)\n",
        "print(delta_t_full_series_x2)\n",
        "\n",
        "#total capital series:\n",
        "K_full_series_x2=[]\n",
        "K_full_series_x2.append(K_1980)\n",
        "K_full_series_x2.append(K_x2)\n",
        "print(K_full_series_x2)\n",
        "\n",
        "#emissions series:\n",
        "u_full_series_x2=[]\n",
        "u_full_series_x2.append(u_1980_x2)\n",
        "u_full_series_x2.append(u_x2)\n",
        "print(u_full_series_x2)\n",
        "\n",
        "#capital decay rate series:\n",
        "d_full_series_x2=[]\n",
        "d_full_series_x2.append(d_1980_x2)\n",
        "d_full_series_x2.append(d_x2)\n",
        "print(d_full_series_x2)\n",
        "\n",
        "#GWP series:\n",
        "y_full_series_x2=[]\n",
        "y_full_series_x2.append(y_1980_x2)\n",
        "y_full_series_x2.append(y_x2)\n",
        "print(y_full_series_x2)\n",
        "\n",
        "#GWP growth rate series:\n",
        "y_growth_rate_series_x2=[]\n",
        "y_growth_rate_series_x2.append(0)\n",
        "y_growth_rate_series_x2.append(y_growth_rate_x2)\n",
        "print(y_growth_rate_series_x2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EPuTQxzspG7",
        "outputId": "ad0a2ac9-ab1f-4bef-cc85-e514a85df4af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[250, 256.6477640585382]\n",
            "[0.45833333333333337, 0.4705209007739867]\n",
            "[450, 463.5]\n",
            "[5.057271839089452, 5.147764058538205]\n",
            "[0.03, 0.03]\n",
            "[27.0, 27.81]\n",
            "[0, 0.029999999999999954]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 1982-2100\n",
        "#============================================\n",
        "years_array=(np.array(years))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 1982 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array.size): #loop for the number of years between 1982-2100 (119)\n",
        "  K_x2 = K_x2+y_x2-d_x2*K_x2\n",
        "  y_x2 = A*K_x2\n",
        "  u_x2 = (0.7*y_x2**0.6)\n",
        "  delta_u_x2 = delta_u_x2+u_x2+land_change_emissions\n",
        "  delta_t_x2 = clim_sens*delta_u_x2\n",
        "  d_x2 = max(0.03,0.03+Sd_x2*(delta_t_x2-1))\n",
        "  #y_growth_rate = (y-[y-1])/[y-1]\n",
        "  delta_u_full_series_x2.append(delta_u_x2) #adding the 1982-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series_x2.append(delta_t_x2) #adding the temp change from 1982-2100\n",
        "  K_full_series_x2.append(K_x2) #adding 1982-2100 total capital to the list\n",
        "  u_full_series_x2.append(u_x2) #adding 1982-2100 emissions to the list\n",
        "  d_full_series_x2.append(d_x2) #adding 1982-2100 capital decay rate to the list\n",
        "  y_full_series_x2.append(y_x2) #adding 1982-2100 GWP to the list"
      ],
      "metadata": {
        "id": "3jVybQe-tIbK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array that includes all years from 1980-2100\n",
        "#========================================================\n",
        "years_full = list(range(1980,2101)) #creating a new years array so that 1980 and 1981 are included and x and y shapes match\n",
        "years_array_full=(np.array(years_full))"
      ],
      "metadata": {
        "id": "SYlj4mpGt49H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4=pd.DataFrame()\n",
        "\n",
        "df4['Years']=years_full\n",
        "df4['capital']=K_full_series_x2\n",
        "df4['GWP']=y_full_series_x2\n",
        "df4['emissions']=u_full_series_x2\n",
        "df4['cumulative_emissions']=delta_u_full_series_x2\n",
        "df4['temp_change']=delta_t_full_series_x2\n",
        "df4['decay_rate']=d_full_series_x2\n",
        "df4['GWP_growth_rate']=df4['GWP'].pct_change()"
      ],
      "metadata": {
        "id": "xnMQ7yj6tciO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#setting out the initial values for the HCE and LCE split in 2020\n",
        "#================================================================\n",
        "\n",
        "#Capital in 2020\n",
        "HCE_K_2020_x2=df4['capital'][40]*0.8 #High Carbon Economy total capital in 2020 (T$)\n",
        "LCE_K_2020_x2=df4['capital'][40]*0.2 #Low Carbon Economy total capital in 2020 (T$)"
      ],
      "metadata": {
        "id": "WvNRpPuytxny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#parameters needed for GWP calculations:\n",
        "\n",
        "A=0.06 #aggregate energy productivity (%) (6%)\n",
        "h=0.95 #how productive the LCE is compared to the HCE (%) (95%)\n",
        "A_HCE=A/(0.8+0.2*h) #High Carbon Economy productivity (%)\n",
        "A_LCE=h*A_HCE #Low Carbon Economy productivity (%)\n",
        "\n",
        "#GWP\n",
        "HCE_GWP_2020_x2=HCE_K_2020_x2*A_HCE #High Carbon Economy GWP in 2020 (T$/yr)\n",
        "LCE_GWP_2020_x2=LCE_K_2020_x2*A_LCE #Low Carbon Economy GWP in 2020 (T$/yr)\n",
        "Total_GWP_2020_x2=HCE_GWP_2020_x2+LCE_GWP_2020_x2 #Total GWP in 2020(T$/yr)\n",
        "\n",
        "#carbon intensity parameter\n",
        "HCE_C_intensity_2020_x2=df4['emissions'][40]/HCE_GWP_2020_x2 #carbon intensity of the High Carbon Economy in 2020 (effects the emissions rate)\n",
        "u_2020_x2=HCE_C_intensity_2020_x2*HCE_GWP_2020_x2 #emissions in 2020 (GtC/yr), using the carbon intensity of HCE GWP in 2020\n",
        "\n",
        "#setting the remaining 2020 values needed\n",
        "delta_u_2020_x2=df4['cumulative_emissions'][40] #cumulative emissions in 2020 (GtC)\n",
        "delta_t_2020_x2=df4['temp_change'][40] #temperature change in 2020 (^c)\n",
        "d_2020_x2=df4['decay_rate'][40] #high carbon infrastructure decay rate in 2020(%/yr) (3.08%/yr)"
      ],
      "metadata": {
        "id": "t8XQTW5_Jq9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2021 values for decarbonisation scenario:\n",
        "#=========================================\n",
        "\n",
        "HCE_K_x2=HCE_K_2020_x2-d_2020_x2*HCE_K_2020_x2 #High Carbon Economy capital in 2021 (T$)\n",
        "LCE_K_x2=LCE_K_2020_x2+Total_GWP_2020_x2-d_2020_x2*LCE_K_2020_x2 #Low Carbon Economy capital in 2021 (T$)\n",
        "\n",
        "HCE_GWP_x2=HCE_K_x2*A_HCE #High Carbon Economy GWP in 2021 (T$/yr)\n",
        "LCE_GWP_x2=LCE_K_x2*A_LCE #Low Carbon Economy GWP in 2021 (T$/yr)\n",
        "u_decarb_x2=HCE_C_intensity_2020_x2*HCE_GWP_x2 #total economy emissions in 2021 (GtC/yr)\n",
        "delta_u_decarb_x2=delta_u_2020_x2+u_2020_x2+land_change_emissions #total economy cumulative emissions in 2021 (GtC)\n",
        "delta_t_decarb_x2=clim_sens*delta_u_decarb_x2 #total economy temp change in 2021 (^c)\n",
        "d_decarb_x2=max(0.03,0.03+Sd_x2*(delta_t_decarb_x2-1)) #high carbon infrastructure decay rate (%/yr) in 2021\n",
        "Total_GWP_x2=HCE_GWP_x2+LCE_GWP_x2 #total GWP in 2021 (T$/yr)"
      ],
      "metadata": {
        "id": "7w9dJ3IEKG28"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 2020, 2021 and eventually 2022-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#capital series:\n",
        "HCE_K_full_series_x2=[] #empty list for HCE capital \n",
        "HCE_K_full_series_x2.append(HCE_K_2020_x2) #adding the 2020 value to the list\n",
        "HCE_K_full_series_x2.append(HCE_K_x2) #adding the 2021 value to the list\n",
        "print(HCE_K_full_series_x2)\n",
        "\n",
        "LCE_K_full_series_x2=[] #empty list for LCE capital\n",
        "LCE_K_full_series_x2.append(LCE_K_2020_x2) #adding the 2020 value to the list\n",
        "LCE_K_full_series_x2.append(LCE_K_x2) #adding the 2021 value to the list\n",
        "print(LCE_K_full_series_x2)\n",
        "\n",
        "\n",
        "#GWP series:\n",
        "HCE_GWP_full_series_x2=[] #empty list for HCE GWP\n",
        "HCE_GWP_full_series_x2.append(HCE_GWP_2020_x2)\n",
        "HCE_GWP_full_series_x2.append(HCE_GWP_x2)\n",
        "print(HCE_GWP_full_series_x2)\n",
        "\n",
        "LCE_GWP_full_series_x2=[] #empty list for LCE GWP\n",
        "LCE_GWP_full_series_x2.append(LCE_GWP_2020_x2)\n",
        "LCE_GWP_full_series_x2.append(LCE_GWP_x2)\n",
        "print(LCE_GWP_full_series_x2)\n",
        "\n",
        "Total_GWP_full_series_x2=[] #empty list for total economy GWP \n",
        "Total_GWP_full_series_x2.append(Total_GWP_2020_x2)\n",
        "Total_GWP_full_series_x2.append(Total_GWP_x2)\n",
        "print(Total_GWP_full_series_x2)\n",
        "\n",
        "\n",
        "#emissions series:\n",
        "u_full_series_decarb_x2=[] #empty list for total economy emissions\n",
        "u_full_series_decarb_x2.append(u_2020_x2)\n",
        "u_full_series_decarb_x2.append(u_decarb_x2)\n",
        "print(u_full_series_decarb_x2)\n",
        "\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series_decarb_x2=[] #empty list for cumulative emissions\n",
        "delta_u_full_series_decarb_x2.append(delta_u_2020_x2) \n",
        "delta_u_full_series_decarb_x2.append(delta_u_decarb_x2)\n",
        "print(delta_u_full_series_decarb_x2)\n",
        "\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series_decarb_x2=[] #empty list for total economy temp change \n",
        "delta_t_full_series_decarb_x2.append(delta_t_2020_x2)\n",
        "delta_t_full_series_decarb_x2.append(delta_t_decarb_x2)\n",
        "print(delta_t_full_series_decarb_x2)\n",
        "\n",
        "\n",
        "#high C infrastructure decay rate series:\n",
        "d_full_series_decarb_x2=[] #empty list for high C infrastructure decay rate\n",
        "d_full_series_decarb_x2.append(d_2020_x2)\n",
        "d_full_series_decarb_x2.append(d_decarb_x2)\n",
        "print(d_full_series_decarb_x2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "utNk56S0KdRB",
        "outputId": "672dcd45-956e-4d2e-9eeb-50f95861f6b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1170.1012312584905, 1133.0153383628435]\n",
            "[292.52530781462264, 371.0114269350977]\n",
            "[70.91522613687822, 68.66759626441475]\n",
            "[16.842366207508576, 21.361263975051077]\n",
            "[87.7575923443868, 90.02886023946583]\n",
            "[10.25814562074944, 9.933017777417547]\n",
            "[607.0764385763189, 618.8345841970684]\n",
            "[1.1129734707232515, 1.1345300710279587]\n",
            "[0.03169460206084877, 0.03201795106541938]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 2022-2100\n",
        "#============================================\n",
        "years_decarb = list(range(2022,2101)) #YEARS\n",
        "years_array_decarb=(np.array(years_decarb))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 2022 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array_decarb.size): #loop for the number of years between 2022-2100\n",
        "  delta_u_decarb_x2 = delta_u_decarb_x2+u_decarb_x2+land_change_emissions #cumulative emissions equation (GtC)\n",
        "  HCE_K_x2 = HCE_K_x2-d_decarb_x2*HCE_K_x2 #HCE capital (T$)\n",
        "  LCE_K_x2 = LCE_K_x2+Total_GWP_x2-d_decarb_x2*LCE_K_x2 #LCE capital (T$)\n",
        "  HCE_GWP_x2 = A_HCE*HCE_K_x2 #HCE GWP (T$/yr)\n",
        "  LCE_GWP_x2 = A_LCE*LCE_K_x2 #LCE GWP (T$/yr)\n",
        "  Total_GWP_x2 = HCE_GWP_x2+LCE_GWP_x2 #Total economy GWP (T$/yr)\n",
        "  u_decarb_x2 = HCE_C_intensity_2020_x2*HCE_GWP_x2 #emissions from HCE sector (GtC/yr) \n",
        "  delta_t_decarb_x2 = clim_sens*delta_u_decarb_x2 # temp change (^C)\n",
        "  d_decarb_x2 = max(0.03,0.03+Sd_x2*(delta_t_decarb_x2-1)) #high C capital decay rate (%/yr)\n",
        "\n",
        "  #appending values created in loop to their subsequent lists\n",
        "  delta_u_full_series_decarb_x2.append(delta_u_decarb_x2) #adding the 2022-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series_decarb_x2.append(delta_t_decarb_x2) #adding the temp change from 2022-2100\n",
        "  HCE_K_full_series_x2.append(HCE_K_x2) #adding 2022-2100 HCE capital to the list\n",
        "  LCE_K_full_series_x2.append(LCE_K_x2) #adding 2022-2100 LCE capital to the list\n",
        "  HCE_GWP_full_series_x2.append(HCE_GWP_x2)\n",
        "  LCE_GWP_full_series_x2.append(LCE_GWP_x2)\n",
        "  Total_GWP_full_series_x2.append(Total_GWP_x2)\n",
        "  u_full_series_decarb_x2.append(u_decarb_x2)\n",
        "  d_full_series_decarb_x2.append(d_decarb_x2)"
      ],
      "metadata": {
        "id": "YdTr9nTZK-Pr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df5= pd.DataFrame()\n",
        "\n",
        "df5['Years']=years_full_decarb\n",
        "df5['HCE capital']=HCE_K_full_series_x2\n",
        "df5['HCE GWP']=HCE_GWP_full_series_x2\n",
        "df5['HCE GWP growth rate']=df5['HCE GWP'].pct_change()\n",
        "df5['LCE capital']=LCE_K_full_series_x2\n",
        "df5['LCE GWP']=LCE_GWP_full_series_x2\n",
        "df5['LCE GWP growth rate']=df5['LCE GWP'].pct_change()\n",
        "df5['emissions']=u_full_series_decarb_x2\n",
        "df5['cumulative emissions']=delta_u_full_series_decarb_x2\n",
        "df5['temp change']=delta_t_full_series_decarb_x2\n",
        "df5['decay rate']=d_full_series_decarb_x2\n",
        "df5['capital']=df2['HCE capital']+df5['LCE capital']\n",
        "df5['GWP']=Total_GWP_full_series_x2\n",
        "df5['total GWP growth rate']=df5['GWP'].pct_change()"
      ],
      "metadata": {
        "id": "7_XswrJ4LfwX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "-bfWFzcgLxWy",
        "outputId": "def2a758-8b18-4753-b76d-dfd55e7df1f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Years  HCE capital    HCE GWP  HCE GWP growth rate  LCE capital  \\\n",
              "0    2020  1170.101231  70.915226                  NaN   292.525308   \n",
              "1    2021  1133.015338  68.667596            -0.031695   371.011427   \n",
              "2    2022  1096.738509  66.469001            -0.032018   449.161261   \n",
              "3    2023  1061.278365  64.319901            -0.032332   526.968619   \n",
              "4    2024  1026.640340  62.220627            -0.032638   604.429924   \n",
              "..    ...          ...        ...                  ...          ...   \n",
              "76   2096    59.276663   3.592525            -0.042173  6353.100072   \n",
              "77   2097    56.773433   3.440814            -0.042230  6454.188284   \n",
              "78   2098    54.372761   3.295319            -0.042285  6556.317628   \n",
              "79   2099    52.070614   3.155795            -0.042340  6659.502910   \n",
              "80   2100    49.863110   3.022007            -0.042394  6763.758765   \n",
              "\n",
              "       LCE GWP  LCE GWP growth rate  emissions  cumulative emissions  \\\n",
              "0    16.842366                  NaN  10.258146            607.076439   \n",
              "1    21.361264             0.268305   9.933018            618.834584   \n",
              "2    25.860800             0.210640   9.614983            630.267602   \n",
              "3    30.340617             0.173228   9.304108            641.382585   \n",
              "4    34.800511             0.146994   9.000440            652.186693   \n",
              "..         ...                  ...        ...                   ...   \n",
              "76  365.784550             0.016002   0.519672            990.167206   \n",
              "77  371.604780             0.015912   0.497726            992.186878   \n",
              "78  377.484954             0.015824   0.476680            994.184604   \n",
              "79  383.425925             0.015738   0.456497            996.161284   \n",
              "80  389.428535             0.015655   0.437144            998.117781   \n",
              "\n",
              "    temp change  decay rate      capital         GWP  total GWP growth rate  \n",
              "0      1.112973    0.031695  1464.741011   87.757592                    NaN  \n",
              "1      1.134530    0.032018  1507.067058   90.028860               0.025881  \n",
              "2      1.155491    0.032332  1549.988428   92.329800               0.025558  \n",
              "3      1.175868    0.032638  1593.486387   94.660518               0.025243  \n",
              "4      1.195676    0.032935  1637.544192   97.021137               0.024938  \n",
              "..          ...         ...          ...         ...                    ...  \n",
              "76     1.815307    0.042230  6435.383481  369.377075               0.015402  \n",
              "77     1.819009    0.042285  6533.479533  375.045594               0.015346  \n",
              "78     1.822672    0.042340  6632.723103  380.780273               0.015291  \n",
              "79     1.826296    0.042394  6733.125332  386.581720               0.015236  \n",
              "80     1.829883    0.042448  6834.697309  392.450542               0.015181  \n",
              "\n",
              "[81 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Years</th>\n",
              "      <th>HCE capital</th>\n",
              "      <th>HCE GWP</th>\n",
              "      <th>HCE GWP growth rate</th>\n",
              "      <th>LCE capital</th>\n",
              "      <th>LCE GWP</th>\n",
              "      <th>LCE GWP growth rate</th>\n",
              "      <th>emissions</th>\n",
              "      <th>cumulative emissions</th>\n",
              "      <th>temp change</th>\n",
              "      <th>decay rate</th>\n",
              "      <th>capital</th>\n",
              "      <th>GWP</th>\n",
              "      <th>total GWP growth rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020</td>\n",
              "      <td>1170.101231</td>\n",
              "      <td>70.915226</td>\n",
              "      <td>NaN</td>\n",
              "      <td>292.525308</td>\n",
              "      <td>16.842366</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.258146</td>\n",
              "      <td>607.076439</td>\n",
              "      <td>1.112973</td>\n",
              "      <td>0.031695</td>\n",
              "      <td>1464.741011</td>\n",
              "      <td>87.757592</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021</td>\n",
              "      <td>1133.015338</td>\n",
              "      <td>68.667596</td>\n",
              "      <td>-0.031695</td>\n",
              "      <td>371.011427</td>\n",
              "      <td>21.361264</td>\n",
              "      <td>0.268305</td>\n",
              "      <td>9.933018</td>\n",
              "      <td>618.834584</td>\n",
              "      <td>1.134530</td>\n",
              "      <td>0.032018</td>\n",
              "      <td>1507.067058</td>\n",
              "      <td>90.028860</td>\n",
              "      <td>0.025881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022</td>\n",
              "      <td>1096.738509</td>\n",
              "      <td>66.469001</td>\n",
              "      <td>-0.032018</td>\n",
              "      <td>449.161261</td>\n",
              "      <td>25.860800</td>\n",
              "      <td>0.210640</td>\n",
              "      <td>9.614983</td>\n",
              "      <td>630.267602</td>\n",
              "      <td>1.155491</td>\n",
              "      <td>0.032332</td>\n",
              "      <td>1549.988428</td>\n",
              "      <td>92.329800</td>\n",
              "      <td>0.025558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023</td>\n",
              "      <td>1061.278365</td>\n",
              "      <td>64.319901</td>\n",
              "      <td>-0.032332</td>\n",
              "      <td>526.968619</td>\n",
              "      <td>30.340617</td>\n",
              "      <td>0.173228</td>\n",
              "      <td>9.304108</td>\n",
              "      <td>641.382585</td>\n",
              "      <td>1.175868</td>\n",
              "      <td>0.032638</td>\n",
              "      <td>1593.486387</td>\n",
              "      <td>94.660518</td>\n",
              "      <td>0.025243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024</td>\n",
              "      <td>1026.640340</td>\n",
              "      <td>62.220627</td>\n",
              "      <td>-0.032638</td>\n",
              "      <td>604.429924</td>\n",
              "      <td>34.800511</td>\n",
              "      <td>0.146994</td>\n",
              "      <td>9.000440</td>\n",
              "      <td>652.186693</td>\n",
              "      <td>1.195676</td>\n",
              "      <td>0.032935</td>\n",
              "      <td>1637.544192</td>\n",
              "      <td>97.021137</td>\n",
              "      <td>0.024938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>2096</td>\n",
              "      <td>59.276663</td>\n",
              "      <td>3.592525</td>\n",
              "      <td>-0.042173</td>\n",
              "      <td>6353.100072</td>\n",
              "      <td>365.784550</td>\n",
              "      <td>0.016002</td>\n",
              "      <td>0.519672</td>\n",
              "      <td>990.167206</td>\n",
              "      <td>1.815307</td>\n",
              "      <td>0.042230</td>\n",
              "      <td>6435.383481</td>\n",
              "      <td>369.377075</td>\n",
              "      <td>0.015402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>2097</td>\n",
              "      <td>56.773433</td>\n",
              "      <td>3.440814</td>\n",
              "      <td>-0.042230</td>\n",
              "      <td>6454.188284</td>\n",
              "      <td>371.604780</td>\n",
              "      <td>0.015912</td>\n",
              "      <td>0.497726</td>\n",
              "      <td>992.186878</td>\n",
              "      <td>1.819009</td>\n",
              "      <td>0.042285</td>\n",
              "      <td>6533.479533</td>\n",
              "      <td>375.045594</td>\n",
              "      <td>0.015346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2098</td>\n",
              "      <td>54.372761</td>\n",
              "      <td>3.295319</td>\n",
              "      <td>-0.042285</td>\n",
              "      <td>6556.317628</td>\n",
              "      <td>377.484954</td>\n",
              "      <td>0.015824</td>\n",
              "      <td>0.476680</td>\n",
              "      <td>994.184604</td>\n",
              "      <td>1.822672</td>\n",
              "      <td>0.042340</td>\n",
              "      <td>6632.723103</td>\n",
              "      <td>380.780273</td>\n",
              "      <td>0.015291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2099</td>\n",
              "      <td>52.070614</td>\n",
              "      <td>3.155795</td>\n",
              "      <td>-0.042340</td>\n",
              "      <td>6659.502910</td>\n",
              "      <td>383.425925</td>\n",
              "      <td>0.015738</td>\n",
              "      <td>0.456497</td>\n",
              "      <td>996.161284</td>\n",
              "      <td>1.826296</td>\n",
              "      <td>0.042394</td>\n",
              "      <td>6733.125332</td>\n",
              "      <td>386.581720</td>\n",
              "      <td>0.015236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2100</td>\n",
              "      <td>49.863110</td>\n",
              "      <td>3.022007</td>\n",
              "      <td>-0.042394</td>\n",
              "      <td>6763.758765</td>\n",
              "      <td>389.428535</td>\n",
              "      <td>0.015655</td>\n",
              "      <td>0.437144</td>\n",
              "      <td>998.117781</td>\n",
              "      <td>1.829883</td>\n",
              "      <td>0.042448</td>\n",
              "      <td>6834.697309</td>\n",
              "      <td>392.450542</td>\n",
              "      <td>0.015181</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 14 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88450415-bbc6-4326-a107-9d3a36bafb3d')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df5['Years'],df5['temp change'],label='double decay sensitivity')\n",
        "plt.plot(df2['Years'],df2['temp change'],label='original')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Temperature Change')\n",
        "plt.legend(loc='best')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_K0jiF6mL1UP",
        "outputId": "bf0a5042-5434-461b-8c7f-98e91f09b5cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa2542e1a50>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decarb scenario with 4x decay sensitivity (3.0%/yr/^c)"
      ],
      "metadata": {
        "id": "evH4NALgNKbN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Sd_x4 = 0.0075*4 #decay sensitivity to global mean surface temperature change (%/yr/^c) (1.5%/yr/^c)"
      ],
      "metadata": {
        "id": "HoTe-sTyNXQS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1980 values (initial values for all variables in 1980)\n",
        "#==================================================================\n",
        "\n",
        "#already have initial capital (K_1980) and cumulative emissions (delta_u_1980) in parameters\n",
        "\n",
        "y_1980_x4 = A*K_1980 #GWP in 1980 (T$/yr)\n",
        "u_1980_x4 = (0.7*y_1980_x4**0.6) #emissions rate in 1980 (GtC/yr)\n",
        "delta_t_1980_x4 = clim_sens*delta_u_1980 #global mean surface temp change in 1980 (^C)\n",
        "d_1980_x4 = max(0.03,0.03+Sd_x4*(delta_t_1980_x4-1)) #capital decay rate in 1980 (%/yr)"
      ],
      "metadata": {
        "id": "L4OJ2v6rNZ6t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating 1981 values (these need to be calculated as there are still specific equations for this year that can't go in the loop)\n",
        "#===================================================================================================================================\n",
        "\n",
        "K_x4 = (K_1980)+(y_1980_x4)-base_decay*K_1980 #capital in 1981 (T$)\n",
        "y_x4 = A*K_x4 #GWP in 1981 (T$/yr)\n",
        "u_x4 = (0.7*y_x4**0.6) #emissions rate in 1981 (GtC/yr)\n",
        "delta_u_x4 = delta_u_1980+u_x4+land_change_emissions #cumulative emissions in 1981 (GtC)\n",
        "delta_t_x4 = clim_sens*delta_u_x4 #global mean surface temp change in 1981 (^C)\n",
        "d_x4 = max(0.03,0.03+Sd_x4*(delta_t_x4-1)) #capital decay rate in 1981 (%/yr)\n",
        "y_growth_rate_x4 = (y_x4-y_1980_x4)/y_1980_x4 #GWP growth rate in 1981 (%/yr)"
      ],
      "metadata": {
        "id": "J2HMQy63NdMO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 1980, 1981 and eventually 1982-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series_x4=[] #empty list for cumulative emissions\n",
        "delta_u_full_series_x4.append(delta_u_1980) #adding the 1980 value\n",
        "delta_u_full_series_x4.append(delta_u_x4) #adding the 1981 value\n",
        "print(delta_u_full_series_x4)\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series_x4=[]\n",
        "delta_t_full_series_x4.append(delta_t_1980_x4)\n",
        "delta_t_full_series_x4.append(delta_t_x4)\n",
        "print(delta_t_full_series_x4)\n",
        "\n",
        "#total capital series:\n",
        "K_full_series_x4=[]\n",
        "K_full_series_x4.append(K_1980)\n",
        "K_full_series_x4.append(K_x4)\n",
        "print(K_full_series_x4)\n",
        "\n",
        "#emissions series:\n",
        "u_full_series_x4=[]\n",
        "u_full_series_x4.append(u_1980_x4)\n",
        "u_full_series_x4.append(u_x4)\n",
        "print(u_full_series_x4)\n",
        "\n",
        "#capital decay rate series:\n",
        "d_full_series_x4=[]\n",
        "d_full_series_x4.append(d_1980_x4)\n",
        "d_full_series_x4.append(d_x4)\n",
        "print(d_full_series_x4)\n",
        "\n",
        "#GWP series:\n",
        "y_full_series_x4=[]\n",
        "y_full_series_x4.append(y_1980_x4)\n",
        "y_full_series_x4.append(y_x4)\n",
        "print(y_full_series_x4)\n",
        "\n",
        "#GWP growth rate series:\n",
        "y_growth_rate_series_x4=[]\n",
        "y_growth_rate_series_x4.append(0)\n",
        "y_growth_rate_series_x4.append(y_growth_rate_x4)\n",
        "print(y_growth_rate_series_x4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs7tWy23NgGq",
        "outputId": "12126d8c-a612-4f60-f230-2e49b1104192"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[250, 256.6477640585382]\n",
            "[0.45833333333333337, 0.4705209007739867]\n",
            "[450, 463.5]\n",
            "[5.057271839089452, 5.147764058538205]\n",
            "[0.03, 0.03]\n",
            "[27.0, 27.81]\n",
            "[0, 0.029999999999999954]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 1982-2100\n",
        "#============================================\n",
        "years_array=(np.array(years))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 1982 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array.size): #loop for the number of years between 1982-2100 (119)\n",
        "  K_x4 = K_x4+y_x4-d_x4*K_x4\n",
        "  y_x4 = A*K_x4\n",
        "  u_x4 = (0.7*y_x4**0.6)\n",
        "  delta_u_x4 = delta_u_x4+u_x4+land_change_emissions\n",
        "  delta_t_x4 = clim_sens*delta_u_x4\n",
        "  d_x4 = max(0.03,0.03+Sd_x4*(delta_t_x4-1))\n",
        "  #y_growth_rate = (y-[y-1])/[y-1]\n",
        "  delta_u_full_series_x4.append(delta_u_x4) #adding the 1982-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series_x4.append(delta_t_x4) #adding the temp change from 1982-2100\n",
        "  K_full_series_x4.append(K_x4) #adding 1982-2100 total capital to the list\n",
        "  u_full_series_x4.append(u_x4) #adding 1982-2100 emissions to the list\n",
        "  d_full_series_x4.append(d_x4) #adding 1982-2100 capital decay rate to the list\n",
        "  y_full_series_x4.append(y_x4) #adding 1982-2100 GWP to the list"
      ],
      "metadata": {
        "id": "5U3WAJK3NjNW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array that includes all years from 1980-2100\n",
        "#========================================================\n",
        "years_full = list(range(1980,2101)) #creating a new years array so that 1980 and 1981 are included and x and y shapes match\n",
        "years_array_full=(np.array(years_full))"
      ],
      "metadata": {
        "id": "LqtsRU7wNnuI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df6=pd.DataFrame()\n",
        "\n",
        "df6['Years']=years_full\n",
        "df6['capital']=K_full_series_x4\n",
        "df6['GWP']=y_full_series_x4\n",
        "df6['emissions']=u_full_series_x4\n",
        "df6['cumulative_emissions']=delta_u_full_series_x4\n",
        "df6['temp_change']=delta_t_full_series_x4\n",
        "df6['decay_rate']=d_full_series_x4\n",
        "df6['GWP_growth_rate']=df6['GWP'].pct_change()"
      ],
      "metadata": {
        "id": "4HHQ2g34NrLd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#setting out the initial values for the HCE and LCE split in 2020\n",
        "#================================================================\n",
        "\n",
        "#Capital in 2020\n",
        "HCE_K_2020_x4=df6['capital'][40]*0.8 #High Carbon Economy total capital in 2020 (T$)\n",
        "LCE_K_2020_x4=df6['capital'][40]*0.2 #Low Carbon Economy total capital in 2020 (T$)"
      ],
      "metadata": {
        "id": "_oVW9Nc4NuTm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#GWP\n",
        "HCE_GWP_2020_x4=HCE_K_2020_x4*A_HCE #High Carbon Economy GWP in 2020 (T$/yr)\n",
        "LCE_GWP_2020_x4=LCE_K_2020_x4*A_LCE #Low Carbon Economy GWP in 2020 (T$/yr)\n",
        "Total_GWP_2020_x4=HCE_GWP_2020_x4+LCE_GWP_2020_x4 #Total GWP in 2020(T$/yr)\n",
        "\n",
        "#carbon intensity parameter\n",
        "HCE_C_intensity_2020_x4=df6['emissions'][40]/HCE_GWP_2020_x4 #carbon intensity of the High Carbon Economy in 2020 (effects the emissions rate)\n",
        "u_2020_x4=HCE_C_intensity_2020_x4*HCE_GWP_2020_x4 #emissions in 2020 (GtC/yr), using the carbon intensity of HCE GWP in 2020\n",
        "\n",
        "#setting the remaining 2020 values needed\n",
        "delta_u_2020_x4=df6['cumulative_emissions'][40] #cumulative emissions in 2020 (GtC)\n",
        "delta_t_2020_x4=df6['temp_change'][40] #temperature change in 2020 (^c)\n",
        "d_2020_x4=df6['decay_rate'][40] #high carbon infrastructure decay rate in 2020(%/yr) (3.08%/yr)"
      ],
      "metadata": {
        "id": "mOBp8s4CNykb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2021 values for decarbonisation scenario:\n",
        "#=========================================\n",
        "\n",
        "HCE_K_x4=HCE_K_2020_x4-d_2020_x4*HCE_K_2020_x4 #High Carbon Economy capital in 2021 (T$)\n",
        "LCE_K_x4=LCE_K_2020_x4+Total_GWP_2020_x4-d_2020_x4*LCE_K_2020_x4 #Low Carbon Economy capital in 2021 (T$)\n",
        "\n",
        "HCE_GWP_x4=HCE_K_x4*A_HCE #High Carbon Economy GWP in 2021 (T$/yr)\n",
        "LCE_GWP_x4=LCE_K_x4*A_LCE #Low Carbon Economy GWP in 2021 (T$/yr)\n",
        "u_decarb_x4=HCE_C_intensity_2020_x4*HCE_GWP_x4 #total economy emissions in 2021 (GtC/yr)\n",
        "delta_u_decarb_x4=delta_u_2020_x4+u_2020_x4+land_change_emissions #total economy cumulative emissions in 2021 (GtC)\n",
        "delta_t_decarb_x4=clim_sens*delta_u_decarb_x4 #total economy temp change in 2021 (^c)\n",
        "d_decarb_x4=max(0.03,0.03+Sd_x4*(delta_t_decarb_x4-1)) #high carbon infrastructure decay rate (%/yr) in 2021\n",
        "Total_GWP_x4=HCE_GWP_x4+LCE_GWP_x4 #total GWP in 2021 (T$/yr)"
      ],
      "metadata": {
        "id": "SKh7GMe6N3Ip"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the list that the 2020, 2021 and eventually 2022-2100 will be appended to:\n",
        "#===================================================================================\n",
        "\n",
        "#capital series:\n",
        "HCE_K_full_series_x4=[] #empty list for HCE capital \n",
        "HCE_K_full_series_x4.append(HCE_K_2020_x4) #adding the 2020 value to the list\n",
        "HCE_K_full_series_x4.append(HCE_K_x4) #adding the 2021 value to the list\n",
        "print(HCE_K_full_series_x4)\n",
        "\n",
        "LCE_K_full_series_x4=[] #empty list for LCE capital\n",
        "LCE_K_full_series_x4.append(LCE_K_2020_x4) #adding the 2020 value to the list\n",
        "LCE_K_full_series_x4.append(LCE_K_x4) #adding the 2021 value to the list\n",
        "print(LCE_K_full_series_x4)\n",
        "\n",
        "\n",
        "#GWP series:\n",
        "HCE_GWP_full_series_x4=[] #empty list for HCE GWP\n",
        "HCE_GWP_full_series_x4.append(HCE_GWP_2020_x4)\n",
        "HCE_GWP_full_series_x4.append(HCE_GWP_x4)\n",
        "print(HCE_GWP_full_series_x4)\n",
        "\n",
        "LCE_GWP_full_series_x4=[] #empty list for LCE GWP\n",
        "LCE_GWP_full_series_x4.append(LCE_GWP_2020_x4)\n",
        "LCE_GWP_full_series_x4.append(LCE_GWP_x4)\n",
        "print(LCE_GWP_full_series_x4)\n",
        "\n",
        "Total_GWP_full_series_x4=[] #empty list for total economy GWP \n",
        "Total_GWP_full_series_x4.append(Total_GWP_2020_x4)\n",
        "Total_GWP_full_series_x4.append(Total_GWP_x4)\n",
        "print(Total_GWP_full_series_x4)\n",
        "\n",
        "\n",
        "#emissions series:\n",
        "u_full_series_decarb_x4=[] #empty list for total economy emissions\n",
        "u_full_series_decarb_x4.append(u_2020_x4)\n",
        "u_full_series_decarb_x4.append(u_decarb_x4)\n",
        "print(u_full_series_decarb_x4)\n",
        "\n",
        "\n",
        "#cumulative emissions series:\n",
        "delta_u_full_series_decarb_x4=[] #empty list for cumulative emissions\n",
        "delta_u_full_series_decarb_x4.append(delta_u_2020_x4) \n",
        "delta_u_full_series_decarb_x4.append(delta_u_decarb_x4)\n",
        "print(delta_u_full_series_decarb_x4)\n",
        "\n",
        "\n",
        "#temperature change series:\n",
        "delta_t_full_series_decarb_x4=[] #empty list for total economy temp change \n",
        "delta_t_full_series_decarb_x4.append(delta_t_2020_x4)\n",
        "delta_t_full_series_decarb_x4.append(delta_t_decarb_x4)\n",
        "print(delta_t_full_series_decarb_x4)\n",
        "\n",
        "\n",
        "#high C infrastructure decay rate series:\n",
        "d_full_series_decarb_x4=[] #empty list for high C infrastructure decay rate\n",
        "d_full_series_decarb_x4.append(d_2020_x4)\n",
        "d_full_series_decarb_x4.append(d_decarb_x4)\n",
        "print(d_full_series_decarb_x4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8liQtYVLN7Yd",
        "outputId": "4da1c3c0-7c12-407b-8433-6690319d51b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1165.882554226751, 1126.9576971033764]\n",
            "[291.47063855668773, 369.18061584285044]\n",
            "[70.65954874101521, 68.30046649111372]\n",
            "[16.78164282599111, 21.255853639436843]\n",
            "[87.44119156700633, 89.55632013055057]\n",
            "[10.235938792800992, 9.89419557553702]\n",
            "[607.0291320550981, 618.7650708478991]\n",
            "[1.1128867421010133, 1.1344026298878152]\n",
            "[0.0333866022630304, 0.034032078896634455]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an array of years between 2022-2100\n",
        "#============================================\n",
        "years_decarb = list(range(2022,2101)) #YEARS\n",
        "years_array_decarb=(np.array(years_decarb))\n",
        "\n",
        "\n",
        "#go through loop for all variables to get from 2022 to 2100\n",
        "#==========================================================\n",
        "for x in range(0,years_array_decarb.size): #loop for the number of years between 2022-2100\n",
        "  delta_u_decarb_x4 = delta_u_decarb_x4+u_decarb_x4+land_change_emissions #cumulative emissions equation (GtC)\n",
        "  HCE_K_x4 = HCE_K_x4-d_decarb_x4*HCE_K_x4 #HCE capital (T$)\n",
        "  LCE_K_x4 = LCE_K_x4+Total_GWP_x4-d_decarb_x4*LCE_K_x4 #LCE capital (T$)\n",
        "  HCE_GWP_x4 = A_HCE*HCE_K_x4 #HCE GWP (T$/yr)\n",
        "  LCE_GWP_x4 = A_LCE*LCE_K_x4 #LCE GWP (T$/yr)\n",
        "  Total_GWP_x4 = HCE_GWP_x4+LCE_GWP_x4 #Total economy GWP (T$/yr)\n",
        "  u_decarb_x4 = HCE_C_intensity_2020_x4*HCE_GWP_x4 #emissions from HCE sector (GtC/yr) \n",
        "  delta_t_decarb_x4 = clim_sens*delta_u_decarb_x4 # temp change (^C)\n",
        "  d_decarb_x4 = max(0.03,0.03+Sd_x4*(delta_t_decarb_x4-1)) #high C capital decay rate (%/yr)\n",
        "\n",
        "  #appending values created in loop to their subsequent lists\n",
        "  delta_u_full_series_decarb_x4.append(delta_u_decarb_x4) #adding the 2022-2100 cumulative emissions values to the list in previous cell\n",
        "  delta_t_full_series_decarb_x4.append(delta_t_decarb_x4) #adding the temp change from 2022-2100\n",
        "  HCE_K_full_series_x4.append(HCE_K_x4) #adding 2022-2100 HCE capital to the list\n",
        "  LCE_K_full_series_x4.append(LCE_K_x4) #adding 2022-2100 LCE capital to the list\n",
        "  HCE_GWP_full_series_x4.append(HCE_GWP_x4)\n",
        "  LCE_GWP_full_series_x4.append(LCE_GWP_x4)\n",
        "  Total_GWP_full_series_x4.append(Total_GWP_x4)\n",
        "  u_full_series_decarb_x4.append(u_decarb_x4)\n",
        "  d_full_series_decarb_x4.append(d_decarb_x4)"
      ],
      "metadata": {
        "id": "31qVecZjODN_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df7= pd.DataFrame()\n",
        "\n",
        "df7['Years']=years_full_decarb\n",
        "df7['HCE capital']=HCE_K_full_series_x4\n",
        "df7['HCE GWP']=HCE_GWP_full_series_x4\n",
        "df7['HCE GWP growth rate']=df7['HCE GWP'].pct_change()\n",
        "df7['LCE capital']=LCE_K_full_series_x4\n",
        "df7['LCE GWP']=LCE_GWP_full_series_x4\n",
        "df7['LCE GWP growth rate']=df7['LCE GWP'].pct_change()\n",
        "df7['emissions']=u_full_series_decarb_x4\n",
        "df7['cumulative emissions']=delta_u_full_series_decarb_x4\n",
        "df7['temp change']=delta_t_full_series_decarb_x4\n",
        "df7['decay rate']=d_full_series_decarb_x4\n",
        "df7['capital']=df2['HCE capital']+df7['LCE capital']\n",
        "df7['GWP']=Total_GWP_full_series_x4\n",
        "df7['total GWP growth rate']=df7['GWP'].pct_change()"
      ],
      "metadata": {
        "id": "2v9iZOiLOI5j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df2['Years'],df2['temp change'],label='original')\n",
        "plt.plot(df5['Years'],df5['temp change'],label='double decay sensitivity')\n",
        "plt.plot(df7['Years'],df7['temp change'],label='quadruple decay sensitivity')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Temperature Change')\n",
        "plt.title('Projected Temperature change under DECARB scenario with varying decay rate sensitivity of HCE')\n",
        "plt.legend(loc='best')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "tAutVCsDON9r",
        "outputId": "9b25ba5c-c9a0-4eb4-bedd-592c88c1d600"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa254228b50>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3T2BEL9XPjHZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}