orm_2_Installations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The European Transaction Log (EUTL) is the backbone of the European Union Emissions Trading System (EUETS). It implements the transfer of emission allowances between parties active in the EUETS. The EUTL provides data on regulated installations, their emissions as well as transfers of allowances.  \n",
    "\n",
    "Assuming that the database has been successfully populated with the EUTL data (see 1_create_database.ipynb), this notebook illustrates how to analyze compliance and transactions of a single installation.\n",
    "\n",
    "As a starting point, we assume that the installation identifier is known. The most easy way to find the identifier is going to www.EUETS.INFO/installations and use the search field.\n",
    "\n",
    "Specifically, we consider the Niederaussem Power Station with installation identifier DE_1649. To analyze another installation, simply change the assignment determining the installation identifier (but the text most likely no longer fits;).\n",
    "\n",
    "The analysis takes place in three steps:\n",
    "1. Use the installation identifier to get the installation object.\n",
    "2. The installation object provides methods to obtain compliance and surrendering details as well as associated accounts\n",
    "3. Account objects provide methods to assess related transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_inst = \"PL_1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Packages and options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyeutl.orm import DataAccessLayer    # data access layer to access the database\n",
    "from pyeutl.orm import Installation       # EUETS Installation object\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Database connection settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "connectionSettings = dict(\n",
    "    user=\"postgres\", \n",
    "    host=\"localhost\", \n",
    "    db=\"eutl2024\", \n",
    "    passw=\"password\",\n",
    "    port=5432\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connect to database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connect to database and get a session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dal = DataAccessLayer(**connectionSettings)\n",
    "session = dal.session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the installation id, use the session to query the installation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PL_1: PGE GiEK S.A. Oddział Elektrownia Bełchatów\n",
      "Wola Grzymalina 3\n",
      "97-406 Bełchatów\n",
      "Poland\n",
      "Activity: Combustion of fuels\n"
     ]
    }
   ],
   "source": [
    "inst = session.get(Installation, id_inst)\n",
    "print(inst.id + \": \" + inst.name)\n",
    "print(inst.address)\n",
    "print(\"Activity:\", inst.activity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze compliance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compliance is a property of the installation. For convenience, .get_compliance() provides the compliance as pandas DataFrame. \n",
    "\n",
    "Due to linking with the Swiss Emissions Trading System, compliance might be reported in the Swiss system. Since is the case only for aircraft operators, we simply drop the respective column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>allocatedTotal</th>\n",
       "      <th>verified</th>\n",
       "      <th>surrendered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>30.794300</td>\n",
       "      <td>31.970055</td>\n",
       "      <td>31.970055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>30.794300</td>\n",
       "      <td>30.083123</td>\n",
       "      <td>30.083123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>30.794300</td>\n",
       "      <td>28.322323</td>\n",
       "      <td>28.322323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>26.937155</td>\n",
       "      <td>30.862792</td>\n",
       "      <td>30.862792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>26.937155</td>\n",
       "      <td>29.473072</td>\n",
       "      <td>29.473072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>26.937155</td>\n",
       "      <td>29.659590</td>\n",
       "      <td>29.659590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>26.937155</td>\n",
       "      <td>32.823146</td>\n",
       "      <td>32.823146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>31.749901</td>\n",
       "      <td>35.193844</td>\n",
       "      <td>35.193844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>16.816401</td>\n",
       "      <td>37.178165</td>\n",
       "      <td>37.178165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>15.541610</td>\n",
       "      <td>36.886458</td>\n",
       "      <td>36.886458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>13.501970</td>\n",
       "      <td>37.051920</td>\n",
       "      <td>37.051920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>10.282843</td>\n",
       "      <td>34.941622</td>\n",
       "      <td>34.941622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>7.788822</td>\n",
       "      <td>37.646220</td>\n",
       "      <td>37.646220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>6.211022</td>\n",
       "      <td>38.348069</td>\n",
       "      <td>38.348069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>4.627419</td>\n",
       "      <td>32.741604</td>\n",
       "      <td>32.741604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.051048</td>\n",
       "      <td>30.088805</td>\n",
       "      <td>30.088805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.036694</td>\n",
       "      <td>33.161232</td>\n",
       "      <td>33.161232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.035752</td>\n",
       "      <td>35.064004</td>\n",
       "      <td>35.064004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>0.034809</td>\n",
       "      <td>26.248237</td>\n",
       "      <td>26.248237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>0.033867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>0.032923</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      allocatedTotal   verified  surrendered\n",
       "year                                        \n",
       "2005       30.794300  31.970055    31.970055\n",
       "2006       30.794300  30.083123    30.083123\n",
       "2007       30.794300  28.322323    28.322323\n",
       "2008       26.937155  30.862792    30.862792\n",
       "2009       26.937155  29.473072    29.473072\n",
       "2010       26.937155  29.659590    29.659590\n",
       "2011       26.937155  32.823146    32.823146\n",
       "2012       31.749901  35.193844    35.193844\n",
       "2013       16.816401  37.178165    37.178165\n",
       "2014       15.541610  36.886458    36.886458\n",
       "2015       13.501970  37.051920    37.051920\n",
       "2016       10.282843  34.941622    34.941622\n",
       "2017        7.788822  37.646220    37.646220\n",
       "2018        6.211022  38.348069    38.348069\n",
       "2019        4.627419  32.741604    32.741604\n",
       "2020        0.051048  30.088805    30.088805\n",
       "2021        0.036694  33.161232    33.161232\n",
       "2022        0.035752  35.064004    35.064004\n",
       "2023        0.034809  26.248237    26.248237\n",
       "2024        0.033867        NaN          NaN\n",
       "2025        0.032923        NaN          NaN\n",
       "2026        0.000000        NaN          NaN\n",
       "2027        0.000000        NaN          NaN\n",
       "2028        0.000000        NaN          NaN\n",
       "2029        0.000000        NaN          NaN\n",
       "2030        0.000000        NaN          NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst.compliance\n",
    "df_compliance = inst.get_compliance().drop(\"reportedInSystem_id\", axis=1)\n",
    "df_compliance[[\"year\", \"allocatedTotal\", \"verified\", \"surrendered\"]].set_index(\"year\").div(1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a first impression, plot compliance, i.e., allocated and surrendered allowances and verified emissions over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (\n",
    "    df_compliance\n",
    "    [[\"year\", \"allocatedTotal\", \"verified\", \"surrendered\"]]\n",
    "    .query(\"year < 2024\")\n",
    "    .set_index(\"year\").div(1000000)\n",
    "    .plot(kind=\"bar\", figsize=(10,4), ylabel=\"Million tCO$_2$\", grid=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Surrendering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The installation was in need to acquire additional allowances to be compliant with the EUETS. This offers the question, which allowances have been surrendered. For the first two periods (2005-2007, 2008-2012) the EUTL reports the type of allowances surrendered (e.g., EUA, CER, ERU) as well as the country in which the surrendered certificate has been created. \n",
    "\n",
    "Technically, surrendering unit details are also accessible through the installation object (property: surrendering) and the method get_surrendering() provides a Pandas dataframe:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For periods prior to 2013 we observe the country of origin for surrendered units: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>reportedInSystem_id</th>\n",
       "      <th>year</th>\n",
       "      <th>amount</th>\n",
       "      <th>installation_id</th>\n",
       "      <th>unitType_id</th>\n",
       "      <th>originatingRegistry_id</th>\n",
       "      <th>project_id</th>\n",
       "      <th>unitType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>euets</td>\n",
       "      <td>2021</td>\n",
       "      <td>16562269</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>euets</td>\n",
       "      <td>2021</td>\n",
       "      <td>16598963</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>euets</td>\n",
       "      <td>2022</td>\n",
       "      <td>19891556</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>euets</td>\n",
       "      <td>2022</td>\n",
       "      <td>15172448</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>euets</td>\n",
       "      <td>2023</td>\n",
       "      <td>7330277</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>euets</td>\n",
       "      <td>2023</td>\n",
       "      <td>18917960</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>euets</td>\n",
       "      <td>2013</td>\n",
       "      <td>37178165</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>euets</td>\n",
       "      <td>2014</td>\n",
       "      <td>36886458</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>euets</td>\n",
       "      <td>2015</td>\n",
       "      <td>37051920</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>euets</td>\n",
       "      <td>2016</td>\n",
       "      <td>34941622</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>euets</td>\n",
       "      <td>2017</td>\n",
       "      <td>37646220</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>euets</td>\n",
       "      <td>2018</td>\n",
       "      <td>38348069</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>euets</td>\n",
       "      <td>2019</td>\n",
       "      <td>32741604</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>euets</td>\n",
       "      <td>2020</td>\n",
       "      <td>30088805</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2013</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU General Allowances (EUA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>euets</td>\n",
       "      <td>2008</td>\n",
       "      <td>5000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>BR</td>\n",
       "      <td>116.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>euets</td>\n",
       "      <td>2008</td>\n",
       "      <td>25000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>euets</td>\n",
       "      <td>2008</td>\n",
       "      <td>100000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>euets</td>\n",
       "      <td>2008</td>\n",
       "      <td>22000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>306.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>euets</td>\n",
       "      <td>2008</td>\n",
       "      <td>183000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>IN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>euets</td>\n",
       "      <td>2008</td>\n",
       "      <td>5000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>IN</td>\n",
       "      <td>115.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>euets</td>\n",
       "      <td>2008</td>\n",
       "      <td>25000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>IN</td>\n",
       "      <td>325.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>euets</td>\n",
       "      <td>2008</td>\n",
       "      <td>30497792</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA_2008</td>\n",
       "      <td>PL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAU - Assigned Amount Unit - Allowance issued ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>37000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>AR</td>\n",
       "      <td>426.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>135974</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>BR</td>\n",
       "      <td>116.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>16943</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>BR</td>\n",
       "      <td>467.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>55093</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1083.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>11577</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>21903</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>452339</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>6000</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>16352</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>17648</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>64492</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>euets</td>\n",
       "      <td>2009</td>\n",
       "      <td>50099</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>CER</td>\n",
       "      <td>CN</td>\n",
       "      <td>1619.0</td>\n",
       "      <td>CER - Certified Emission Reduction Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>euets</td>\n",
       "      <td>2005</td>\n",
       "      <td>31970055</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>PL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allowance issued for the 2005-2007 period and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>euets</td>\n",
       "      <td>2006</td>\n",
       "      <td>30083123</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>PL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allowance issued for the 2005-2007 period and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>euets</td>\n",
       "      <td>2007</td>\n",
       "      <td>28322323</td>\n",
       "      <td>PL_1</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>PL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allowance issued for the 2005-2007 period and ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reportedInSystem_id  year    amount installation_id unitType_id  \\\n",
       "0                euets  2021  16562269            PL_1    EUA_2013   \n",
       "1                euets  2021  16598963            PL_1    EUA_2013   \n",
       "2                euets  2022  19891556            PL_1    EUA_2013   \n",
       "3                euets  2022  15172448            PL_1    EUA_2013   \n",
       "4                euets  2023   7330277            PL_1    EUA_2013   \n",
       "5                euets  2023  18917960            PL_1    EUA_2013   \n",
       "6                euets  2013  37178165            PL_1    EUA_2013   \n",
       "7                euets  2014  36886458            PL_1    EUA_2013   \n",
       "8                euets  2015  37051920            PL_1    EUA_2013   \n",
       "9                euets  2016  34941622            PL_1    EUA_2013   \n",
       "10               euets  2017  37646220            PL_1    EUA_2013   \n",
       "11               euets  2018  38348069            PL_1    EUA_2013   \n",
       "12               euets  2019  32741604            PL_1    EUA_2013   \n",
       "13               euets  2020  30088805            PL_1    EUA_2013   \n",
       "14               euets  2008      5000            PL_1         CER   \n",
       "15               euets  2008     25000            PL_1         CER   \n",
       "16               euets  2008    100000            PL_1         CER   \n",
       "17               euets  2008     22000            PL_1         CER   \n",
       "18               euets  2008    183000            PL_1         CER   \n",
       "19               euets  2008      5000            PL_1         CER   \n",
       "20               euets  2008     25000            PL_1         CER   \n",
       "21               euets  2008  30497792            PL_1    EUA_2008   \n",
       "22               euets  2009     37000            PL_1         CER   \n",
       "23               euets  2009    135974            PL_1         CER   \n",
       "24               euets  2009     16943            PL_1         CER   \n",
       "25               euets  2009     55093            PL_1         CER   \n",
       "26               euets  2009     11577            PL_1         CER   \n",
       "27               euets  2009     21903            PL_1         CER   \n",
       "28               euets  2009    452339            PL_1         CER   \n",
       "29               euets  2009      6000            PL_1         CER   \n",
       "30               euets  2009     16352            PL_1         CER   \n",
       "31               euets  2009     17648            PL_1         CER   \n",
       "32               euets  2009     64492            PL_1         CER   \n",
       "33               euets  2009     50099            PL_1         CER   \n",
       "34               euets  2005  31970055            PL_1     EUA2005   \n",
       "35               euets  2006  30083123            PL_1     EUA2005   \n",
       "36               euets  2007  28322323            PL_1     EUA2005   \n",
       "\n",
       "   originatingRegistry_id  project_id  \\\n",
       "0                    None         NaN   \n",
       "1                    None         NaN   \n",
       "2                    None         NaN   \n",
       "3                    None         NaN   \n",
       "4                    None         NaN   \n",
       "5                    None         NaN   \n",
       "6                    None         NaN   \n",
       "7                    None         NaN   \n",
       "8                    None         NaN   \n",
       "9                    None         NaN   \n",
       "10                   None         NaN   \n",
       "11                   None         NaN   \n",
       "12                   None         NaN   \n",
       "13                   None         NaN   \n",
       "14                     BR       116.0   \n",
       "15                     CN      1105.0   \n",
       "16                     CN      1238.0   \n",
       "17                     CN       306.0   \n",
       "18                     IN         1.0   \n",
       "19                     IN       115.0   \n",
       "20                     IN       325.0   \n",
       "21                     PL         NaN   \n",
       "22                     AR       426.0   \n",
       "23                     BR       116.0   \n",
       "24                     BR       467.0   \n",
       "25                     CN      1083.0   \n",
       "26                     CN        11.0   \n",
       "27                     CN      1194.0   \n",
       "28                     CN      1238.0   \n",
       "29                     CN      1320.0   \n",
       "30                     CN      1439.0   \n",
       "31                     CN      1485.0   \n",
       "32                     CN      1609.0   \n",
       "33                     CN      1619.0   \n",
       "34                     PL         NaN   \n",
       "35                     PL         NaN   \n",
       "36                     PL         NaN   \n",
       "\n",
       "                                             unitType  \n",
       "0                         EU General Allowances (EUA)  \n",
       "1                         EU General Allowances (EUA)  \n",
       "2                         EU General Allowances (EUA)  \n",
       "3                         EU General Allowances (EUA)  \n",
       "4                         EU General Allowances (EUA)  \n",
       "5                         EU General Allowances (EUA)  \n",
       "6                         EU General Allowances (EUA)  \n",
       "7                         EU General Allowances (EUA)  \n",
       "8                         EU General Allowances (EUA)  \n",
       "9                         EU General Allowances (EUA)  \n",
       "10                        EU General Allowances (EUA)  \n",
       "11                        EU General Allowances (EUA)  \n",
       "12                        EU General Allowances (EUA)  \n",
       "13                        EU General Allowances (EUA)  \n",
       "14            CER - Certified Emission Reduction Unit  \n",
       "15            CER - Certified Emission Reduction Unit  \n",
       "16            CER - Certified Emission Reduction Unit  \n",
       "17            CER - Certified Emission Reduction Unit  \n",
       "18            CER - Certified Emission Reduction Unit  \n",
       "19            CER - Certified Emission Reduction Unit  \n",
       "20            CER - Certified Emission Reduction Unit  \n",
       "21  AAU - Assigned Amount Unit - Allowance issued ...  \n",
       "22            CER - Certified Emission Reduction Unit  \n",
       "23            CER - Certified Emission Reduction Unit  \n",
       "24            CER - Certified Emission Reduction Unit  \n",
       "25            CER - Certified Emission Reduction Unit  \n",
       "26            CER - Certified Emission Reduction Unit  \n",
       "27            CER - Certified Emission Reduction Unit  \n",
       "28            CER - Certified Emission Reduction Unit  \n",
       "29            CER - Certified Emission Reduction Unit  \n",
       "30            CER - Certified Emission Reduction Unit  \n",
       "31            CER - Certified Emission Reduction Unit  \n",
       "32            CER - Certified Emission Reduction Unit  \n",
       "33            CER - Certified Emission Reduction Unit  \n",
       "34  Allowance issued for the 2005-2007 period and ...  \n",
       "35  Allowance issued for the 2005-2007 period and ...  \n",
       "36  Allowance issued for the 2005-2007 period and ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst.get_surrendering()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "originatingRegistry_id\n",
       "AR           0.037000\n",
       "BR           0.081459\n",
       "CN           0.126278\n",
       "IN           0.071000\n",
       "PL         120.873293\n",
       "Unknown    332.119600\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_p = (\n",
    "    inst\n",
    "    .get_surrendering()\n",
    "    [[\"year\", \"originatingRegistry_id\", \"amount\"]]\n",
    "    .assign(originatingRegistry_id=lambda x: x.originatingRegistry_id.fillna(\"Unknown\"))\n",
    "    .pivot_table(\"amount\", \"year\", \"originatingRegistry_id\")\n",
    "    .div(1000000)\n",
    ")\n",
    "\n",
    "ax = df_p.plot(kind=\"bar\", stacked=True,  figsize=(10,4),)\n",
    "ax.legend(bbox_to_anchor=(0.9, -0.2), loc=0, ncol=8)\n",
    "ax.set_ylabel(\"Million Allowances\")\n",
    "df_p.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 2009, the surrendering data seem to have missing values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our installation shows some variety of allowances used for surrendering. We now turn to the question, how the installation acquired these allowances. To find an answer to this this questions, we need to examine the transaction behavior of related <i>Accounts</i>.\n",
    "\n",
    "The EUETS implements emissions trading. Industrial plants regulated are called <i>Installations</i>. Each installation has to be related to an accounts. These accounts might (and are likely to) change over time but at each instance in time an installation is represented by an account. Accounts are the backbone of the trading system, i.e., allowances are transferred between accounts not between installations. Accounts related to installations are called operator holding accounts. As transfers of allowances take place at the account level, we need to find out which accounts represent our installation.\n",
    "\n",
    "Technically, related accounts are again implemented as property on the installation object (property: accounts). Accounts related to installations are called operator holding accounts (oha):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Account(92917, 'PGE GiEK S.A. Oddział Elektrownia Bełchatów', 'PL', '100-7')>,\n",
       " <Account(20128, 'PGE Górnictwo i Energetyka Konwencjonalna S.A.', 'PL', '120-0')>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohas = inst.accounts\n",
    "ohas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two different accounts. 120-0 denotes former operator holding accounts whereas 100-7 denotes the operating holding account. This account structure is common for installations that have been regulated under the EUETS already before 2013. Due to the change in the EUTL, all installation had to convert their account from 120-0 to 100-7 accounts. Consequently, our 2005 analysis has to focus in the 120-0 account with the meaningful name \"Operator Account\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transactions are provided as property on the respective account (property: transactions). To separate transaction by whether the selected account is acquiring or transferring allowances, separate transactions exist (acquiringTransactions, transferringTransactions). get_transactions() provides again a Pandas dataframe with transaction by the respective account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1146 entries, 2007-02-21 12:30:24 to 2021-04-15 13:57:04\n",
      "Data columns (total 21 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   transferringYear                 0 non-null      object \n",
      " 1   amount                           1146 non-null   int64  \n",
      " 2   transactionID                    1146 non-null   object \n",
      " 3   transactionTypeMain_id           1146 non-null   int64  \n",
      " 4   transactionTypeSupplementary_id  1146 non-null   int64  \n",
      " 5   tradingSystem_id                 1146 non-null   object \n",
      " 6   transferringAccount_id           1146 non-null   int64  \n",
      " 7   acquiringAccount_id              1146 non-null   int64  \n",
      " 8   id                               1146 non-null   int64  \n",
      " 9   unitType_id                      1146 non-null   object \n",
      " 10  acquiringYear                    0 non-null      object \n",
      " 11  project_id                       917 non-null    float64\n",
      " 12  unitType                         1146 non-null   object \n",
      " 13  transactionTypeMain              1146 non-null   object \n",
      " 14  transactionTypeSupplementary     386 non-null    object \n",
      " 15  acquiringAccountName             1146 non-null   object \n",
      " 16  acquiringAccountType             1146 non-null   object \n",
      " 17  transferringAccountName          1146 non-null   object \n",
      " 18  transferringAccountType          1146 non-null   object \n",
      " 19  direction                        1146 non-null   int64  \n",
      " 20  amount_directed                  1146 non-null   int64  \n",
      "dtypes: float64(1), int64(8), object(12)\n",
      "memory usage: 197.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_trans = pd.concat([o.get_transactions() for o in ohas]).sort_index()\n",
    "df_trans.info()\n",
    "#df_trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall we observe over 1144 entries in the transaction data. However, that does not mean that the transaction was involved in 1144 transactions. If a transaction involves different unit types or units from different origins, this is reported as a row in the transaction data. To see the number of transactions carried out, we count the number of transactionIDs as provided by the EUTL: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of transactions: 586\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of transactions: %d\" % len(df_trans[\"transactionID\"].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So only 584 transactions have been carried out. Before continuing to analyze the first year of transaction activities, we show how to calculate allowance holdings over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Allowances holdings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe contains a column \"direction\" which equals 1 if the account is \n",
    "acquiring and -1 if the account is transferring allowances. The column \"amount_directed\" \n",
    "contains the amount of each transaction multiplied by the direction. \n",
    "We can use it plot transfers over time and also the cumulative allowance holding. \n",
    "\n",
    "Since allowances have not been transferrable from the 2005 to 2007 period, \n",
    "we exclude all transactions with the unit type \"EUA2005\" that indicates units \n",
    "used in the trial period. Moreover, we exclude the banking transaction. These\n",
    "transactions took place between 2012 and 2013 and should be a transformation of\n",
    "unit types. However, it seems that only the handing of allowances to the EU\n",
    "commission is in the data but the corresponding transfer back seems to be missing.\n",
    "Therefore, we exclude transactions with supplementary type 34. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14190.603854166666, 18732.581296296295)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ = (\n",
    "    df_trans\n",
    "    .query(\"unitType_id != 'EUA2005'\")\n",
    "    .query(f\"transactionTypeSupplementary_id != 34\")\n",
    "    .assign(\n",
    "        amount_directed=lambda df: df[[\"amount_directed\"]]/1000000,\n",
    "        holding=lambda df: df[[\"amount_directed\"]].cumsum()\n",
    "    )\n",
    ")\n",
    "df_\n",
    "ax = df_[[\"holding\", \"amount_directed\"]].plot( figsize=(15,5), ylabel=\"Million Allowances\")\n",
    "lims = (df_.index.min(), df_.index.max())\n",
    "ax.hlines(0, *lims, colors=\"k\", ls=\":\")\n",
    "ax.set_xlim(*lims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets finally take a closer look at transactions in the first, trial period of the EUETS\n",
    "from 2005 to 2007. We filter on these transactions using the unit type \"EUA2005\".\n",
    "We do not aim to track the origin of allowances and, thus, aggregate data to transactions that took place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>transactionID</th>\n",
       "      <th>transactionTypeMain</th>\n",
       "      <th>transactionTypeSupplementary</th>\n",
       "      <th>transferringAccountName</th>\n",
       "      <th>unitType_id</th>\n",
       "      <th>transferringAccountType</th>\n",
       "      <th>acquiringAccountName</th>\n",
       "      <th>acquiringAccountType</th>\n",
       "      <th>amount_directed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-02-21 12:30:24</th>\n",
       "      <td>PL3180</td>\n",
       "      <td>Internal - Internal transfer of unit/supplemen...</td>\n",
       "      <td>Allowance allocation</td>\n",
       "      <td>Krajowy rachunek wydawania uprawnień</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>Party Holding Account</td>\n",
       "      <td>PGE Górnictwo i Energetyka Konwencjonalna S.A.</td>\n",
       "      <td>Former Operator Holding Account</td>\n",
       "      <td>30794300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-21 12:40:08</th>\n",
       "      <td>PL3194</td>\n",
       "      <td>Internal - Internal transfer of unit/supplemen...</td>\n",
       "      <td>Allowance allocation</td>\n",
       "      <td>Krajowy rachunek wydawania uprawnień</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>Party Holding Account</td>\n",
       "      <td>PGE Górnictwo i Energetyka Konwencjonalna S.A.</td>\n",
       "      <td>Former Operator Holding Account</td>\n",
       "      <td>30794300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-21 12:41:37</th>\n",
       "      <td>PL3202</td>\n",
       "      <td>Internal - Internal transfer of unit/supplemen...</td>\n",
       "      <td>Allowance surrender</td>\n",
       "      <td>PGE Górnictwo i Energetyka Konwencjonalna S.A.</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>Former Operator Holding Account</td>\n",
       "      <td>Krajowy rachunek umorzenia</td>\n",
       "      <td>Party Holding Account</td>\n",
       "      <td>-31970055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-26 09:13:05</th>\n",
       "      <td>PL3219</td>\n",
       "      <td>Internal - Internal transfer of unit/supplemen...</td>\n",
       "      <td>Allowance allocation</td>\n",
       "      <td>Krajowy rachunek wydawania uprawnień</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>Party Holding Account</td>\n",
       "      <td>PGE Górnictwo i Energetyka Konwencjonalna S.A.</td>\n",
       "      <td>Former Operator Holding Account</td>\n",
       "      <td>30794300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-20 11:27:12</th>\n",
       "      <td>PL4689</td>\n",
       "      <td>Internal - Internal transfer of unit/supplemen...</td>\n",
       "      <td>Allowance surrender</td>\n",
       "      <td>PGE Górnictwo i Energetyka Konwencjonalna S.A.</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>Former Operator Holding Account</td>\n",
       "      <td>Krajowy rachunek umorzenia</td>\n",
       "      <td>Party Holding Account</td>\n",
       "      <td>-30083123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-11 11:33:01</th>\n",
       "      <td>PL6642</td>\n",
       "      <td>Internal - Internal transfer of unit/supplemen...</td>\n",
       "      <td>Allowance surrender</td>\n",
       "      <td>PGE Górnictwo i Energetyka Konwencjonalna S.A.</td>\n",
       "      <td>EUA2005</td>\n",
       "      <td>Former Operator Holding Account</td>\n",
       "      <td>Krajowy rachunek umorzenia</td>\n",
       "      <td>Party Holding Account</td>\n",
       "      <td>-28322323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    transactionID  \\\n",
       "date                                \n",
       "2007-02-21 12:30:24        PL3180   \n",
       "2007-02-21 12:40:08        PL3194   \n",
       "2007-02-21 12:41:37        PL3202   \n",
       "2007-02-26 09:13:05        PL3219   \n",
       "2007-04-20 11:27:12        PL4689   \n",
       "2008-04-11 11:33:01        PL6642   \n",
       "\n",
       "                                                   transactionTypeMain  \\\n",
       "date                                                                     \n",
       "2007-02-21 12:30:24  Internal - Internal transfer of unit/supplemen...   \n",
       "2007-02-21 12:40:08  Internal - Internal transfer of unit/supplemen...   \n",
       "2007-02-21 12:41:37  Internal - Internal transfer of unit/supplemen...   \n",
       "2007-02-26 09:13:05  Internal - Internal transfer of unit/supplemen...   \n",
       "2007-04-20 11:27:12  Internal - Internal transfer of unit/supplemen...   \n",
       "2008-04-11 11:33:01  Internal - Internal transfer of unit/supplemen...   \n",
       "\n",
       "                    transactionTypeSupplementary  \\\n",
       "date                                               \n",
       "2007-02-21 12:30:24         Allowance allocation   \n",
       "2007-02-21 12:40:08         Allowance allocation   \n",
       "2007-02-21 12:41:37          Allowance surrender   \n",
       "2007-02-26 09:13:05         Allowance allocation   \n",
       "2007-04-20 11:27:12          Allowance surrender   \n",
       "2008-04-11 11:33:01          Allowance surrender   \n",
       "\n",
       "                                            transferringAccountName  \\\n",
       "date                                                                  \n",
       "2007-02-21 12:30:24            Krajowy rachunek wydawania uprawnień   \n",
       "2007-02-21 12:40:08            Krajowy rachunek wydawania uprawnień   \n",
       "2007-02-21 12:41:37  PGE Górnictwo i Energetyka Konwencjonalna S.A.   \n",
       "2007-02-26 09:13:05            Krajowy rachunek wydawania uprawnień   \n",
       "2007-04-20 11:27:12  PGE Górnictwo i Energetyka Konwencjonalna S.A.   \n",
       "2008-04-11 11:33:01  PGE Górnictwo i Energetyka Konwencjonalna S.A.   \n",
       "\n",
       "                    unitType_id          transferringAccountType  \\\n",
       "date                                                               \n",
       "2007-02-21 12:30:24     EUA2005            Party Holding Account   \n",
       "2007-02-21 12:40:08     EUA2005            Party Holding Account   \n",
       "2007-02-21 12:41:37     EUA2005  Former Operator Holding Account   \n",
       "2007-02-26 09:13:05     EUA2005            Party Holding Account   \n",
       "2007-04-20 11:27:12     EUA2005  Former Operator Holding Account   \n",
       "2008-04-11 11:33:01     EUA2005  Former Operator Holding Account   \n",
       "\n",
       "                                               acquiringAccountName  \\\n",
       "date                                                                  \n",
       "2007-02-21 12:30:24  PGE Górnictwo i Energetyka Konwencjonalna S.A.   \n",
       "2007-02-21 12:40:08  PGE Górnictwo i Energetyka Konwencjonalna S.A.   \n",
       "2007-02-21 12:41:37                      Krajowy rachunek umorzenia   \n",
       "2007-02-26 09:13:05  PGE Górnictwo i Energetyka Konwencjonalna S.A.   \n",
       "2007-04-20 11:27:12                      Krajowy rachunek umorzenia   \n",
       "2008-04-11 11:33:01                      Krajowy rachunek umorzenia   \n",
       "\n",
       "                                acquiringAccountType  amount_directed  \n",
       "date                                                                   \n",
       "2007-02-21 12:30:24  Former Operator Holding Account         30794300  \n",
       "2007-02-21 12:40:08  Former Operator Holding Account         30794300  \n",
       "2007-02-21 12:41:37            Party Holding Account        -31970055  \n",
       "2007-02-26 09:13:05  Former Operator Holding Account         30794300  \n",
       "2007-04-20 11:27:12            Party Holding Account        -30083123  \n",
       "2008-04-11 11:33:01            Party Holding Account        -28322323  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(   df_trans\n",
    "    .reset_index()\n",
    "    .query(\"unitType_id == 'EUA2005'\")\n",
    "    .groupby(\n",
    "        [\"date\", \"transactionID\", \"transactionTypeMain\", \n",
    "         \"transactionTypeSupplementary\", \"transferringAccountName\", \n",
    "         \"unitType_id\",\n",
    "         \"transferringAccountType\", \"acquiringAccountName\", \"acquiringAccountType\"], \n",
    "         as_index=False)\n",
    "    .amount_directed\n",
    "    .sum()\n",
    "    .set_index(\"date\")\n",
    "    .sort_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe a typical problem related to the 2005-2007 trial period. The first four \n",
    "transactions are surrendering and allocation transfers that relate to earlier years\n",
    "but are booked in 2007 and should have been booked earlier. E.g., the first transaction\n",
    "is the 2005 allocation of allowances and should have taken place in the year 2005."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how the EUTL database provided by EUETS.INFO can be used to analyze compliance and transaction behavior of a single installation. The analysis takes place in three steps:\n",
    "1. Use the installation identifier to get the installation object.\n",
    "2. The installation object provides methods to obtain compliance and surrendering details as well as associated accounts\n",
    "3. Account objects provide methods to assess related transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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