btc_v0.2.ipynb

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  {
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   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from itertools import islice\n",
    "from sortedcontainers import SortedDict "
   ]
  },
  {
   "cell_type": "code",
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       "2.134502923976608"
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     "execution_count": 17
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   "source": [
    "def annualize_factor_2021():\n",
    "    return 365 / (datetime.datetime.fromtimestamp(last_ts) - datetime.datetime(2021,1,1)).days\n",
    "\n",
    "annualize_factor_2021()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 18,
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   "source": [
    "# from https://stackoverflow.com/questions/7934547\n",
    "def closest(sorted_dict, key):\n",
    "    \"Return closest key in `sorted_dict` to given `key`.\"\n",
    "    keys = list(islice(sorted_dict.irange(minimum=key), 1))\n",
    "    keys.extend(islice(sorted_dict.irange(maximum=key, reverse=True), 1))\n",
    "    return min(keys, key=lambda k: abs(key - k))"
   ]
  },
  {
   "source": [
    "## 1. Daily electricity consumption of the Bitcoin network\n",
    "\n",
    "* Data sources\n",
    "    * `total_energy_df`: [Cambridge Bitcoin Electricity Consumption Index](https://cbeci.org/); contains daily estimates of annualized total electricity consumption everyday since 2014-07-01 (in TWh/year)"
   ],
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       "    Timestamp        Date and Time      MAX      MIN     GUESS\n",
       "0  1404172800  2014-07-01T00:00:00  11.6968  9.84480  10.72210\n",
       "1  1404259200  2014-07-02T00:00:00  11.8727  5.31064   8.33358\n",
       "2  1404345600  2014-07-03T00:00:00  11.9899  3.80303   7.56635\n",
       "3  1404432000  2014-07-04T00:00:00  12.6422  3.08748   7.47565\n",
       "4  1404518400  2014-07-05T00:00:00  12.7873  2.64228   7.29972"
      ],
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     },
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   ],
   "source": [
    "total_energy_df = pd.read_csv(\"./source/cbeci_export.csv\")\n",
    "total_energy_df.head()"
   ]
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   "execution_count": 20,
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       "(1404172800, 1624320000)"
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   "source": [
    "first_ts = total_energy_df.iloc[0]['Timestamp']\n",
    "last_ts = total_energy_df.iloc[-1]['Timestamp']\n",
    "\n",
    "first_ts, last_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_energy_df['datetime'] = pd.to_datetime(total_energy_df['Date and Time'])\n",
    "total_energy_df['lower_gwh_d'] = total_energy_df['MIN'] * 1000 / 365\n",
    "total_energy_df['upper_gwh_d'] = total_energy_df['MAX'] * 1000 / 365\n",
    "total_energy_df['estimated_gwh_d'] = total_energy_df['GUESS'] * 1000 / 365"
   ]
  },
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   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = total_energy_df[['datetime', 'lower_gwh_d', 'upper_gwh_d', 'estimated_gwh_d']]\n",
    "df = df.set_index(total_energy_df['datetime'])"
   ]
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   "execution_count": 23,
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      "text/plain": [
       "             datetime  lower_gwh_d  upper_gwh_d  estimated_gwh_d\n",
       "datetime                                                        \n",
       "2014-07-01 2014-07-01    26.972055    32.046027        29.375616\n",
       "2014-07-02 2014-07-02    14.549699    32.527945        22.831726\n",
       "2014-07-03 2014-07-03    10.419260    32.849041        20.729726\n",
       "2014-07-04 2014-07-04     8.458849    34.636164        20.481233\n",
       "2014-07-05 2014-07-05     7.239123    35.033699        19.999233"
      ],
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    "df.head()"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df[['estimated_gwh_d', 'upper_gwh_d', 'lower_gwh_d']].plot()"
   ]
  },
  {
   "source": [
    "## 2. Emission factor of Bitcoin mining based on geographic distribution of hashrate and energy mixes\n",
    "\n",
    "The unit of resulting emission factor is kgCO2e / kWh"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             Country  Emission Factor     Source  Year\n",
       "0             Russia            0.352  IFI CM EC  2019\n",
       "1                 US            0.285  IFI CM EC  2019\n",
       "2           Malaysia            0.470  IFI CM EC  2019\n",
       "3               Iran            0.470  IFI CM EC  2019\n",
       "4         Kazakhstan            0.653  IFI CM EC  2019\n",
       "5              Libya            0.538  IFI CM EC  2019\n",
       "6             Canada            0.231  IFI CM EC  2019\n",
       "7             Norway            0.061  IFI CM EC  2019\n",
       "8            Germany            0.366  IFI CM EC  2019\n",
       "9           Thailand            0.390  IFI CM EC  2019\n",
       "10         Venezuela            0.345  IFI CM EC  2019\n",
       "11             World            0.414  IFI CM EC  2019\n",
       "12    China_Xinjiang            0.980  Li et al.  2020\n",
       "13     China_Sichuan            0.200  Li et al.  2020\n",
       "14  China_Nei_Mongol            1.020  Li et al.  2020\n",
       "15      China_Yunnan            0.120  Li et al.  2020\n",
       "16      China_Guansu            0.600  Li et al.  2020\n",
       "17     China_Beijing            0.800  Li et al.  2020\n",
       "18    China_Zhejiang            0.800  Li et al.  2020\n",
       "19      China_Shanxi            1.100  Li et al.  2020\n",
       "20     China_Guizhou            0.700  Li et al.  2020\n",
       "21   China_Guangdong            0.720  Li et al.  2020\n",
       "22         China_RoC            0.841  Li et al.  2020"
      ],
      "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>Country</th>\n      <th>Emission Factor</th>\n      <th>Source</th>\n      <th>Year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Russia</td>\n      <td>0.352</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>US</td>\n      <td>0.285</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Malaysia</td>\n      <td>0.470</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Iran</td>\n      <td>0.470</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kazakhstan</td>\n      <td>0.653</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Libya</td>\n      <td>0.538</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Canada</td>\n      <td>0.231</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Norway</td>\n      <td>0.061</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Germany</td>\n      <td>0.366</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Thailand</td>\n      <td>0.390</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Venezuela</td>\n      <td>0.345</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>World</td>\n      <td>0.414</td>\n      <td>IFI CM EC</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>China_Xinjiang</td>\n      <td>0.980</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>China_Sichuan</td>\n      <td>0.200</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>China_Nei_Mongol</td>\n      <td>1.020</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>China_Yunnan</td>\n      <td>0.120</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>China_Guansu</td>\n      <td>0.600</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>China_Beijing</td>\n      <td>0.800</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>China_Zhejiang</td>\n      <td>0.800</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>China_Shanxi</td>\n      <td>1.100</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>China_Guizhou</td>\n      <td>0.700</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>China_Guangdong</td>\n      <td>0.720</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>China_RoC</td>\n      <td>0.841</td>\n      <td>Li et al.</td>\n      <td>2020</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "emission_factor_df = pd.read_csv(\"./source/emission_factor.csv\")\n",
    "emission_factor_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "def emission_factor_by_country(country: str):\n",
    "    return round(float(emission_factor_df[emission_factor_df['Country'] == country]['Emission Factor']), 5)\n",
    "\n",
    "emission_factor_by_country(\"China_Beijing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Year  Month           Country  Hashrate Distribution\n",
       "0  2019      9    China_Xinjiang                 0.3178\n",
       "1  2019      9     China_Sichuan                 0.1960\n",
       "2  2019      9  China_Nei_Mongol                 0.0813\n",
       "3  2019      9         China_RoC                 0.0766\n",
       "4  2019      9            Russia                 0.0593"
      ],
      "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>Year</th>\n      <th>Month</th>\n      <th>Country</th>\n      <th>Hashrate Distribution</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2019</td>\n      <td>9</td>\n      <td>China_Xinjiang</td>\n      <td>0.3178</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2019</td>\n      <td>9</td>\n      <td>China_Sichuan</td>\n      <td>0.1960</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2019</td>\n      <td>9</td>\n      <td>China_Nei_Mongol</td>\n      <td>0.0813</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2019</td>\n      <td>9</td>\n      <td>China_RoC</td>\n      <td>0.0766</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2019</td>\n      <td>9</td>\n      <td>Russia</td>\n      <td>0.0593</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "hashrate_dist_df = pd.read_csv(\"./source/btc_hashrate_distribution.csv\")\n",
    "hashrate_dist_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "hashrate_dist_df = hashrate_dist_df.replace(\"Rest\", \"World\")\n",
    "hashrate_dist_df['emission_factor'] = hashrate_dist_df['Country'].apply(lambda x: emission_factor_by_country(x))\n",
    "hashrate_dist_df['emission_factor_dist'] = hashrate_dist_df['emission_factor'] * hashrate_dist_df['Hashrate Distribution']\n",
    "emission_factors_ser = hashrate_dist_df.groupby(['Year', 'Month'])['emission_factor_dist'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "emf = {}\n",
    "for ym, v in emission_factors_ser.items():\n",
    "    \n",
    "    ts = int(datetime.datetime(ym[0], ym[1], 1).timestamp())\n",
    "    emf[ts] = v\n",
    "emission_factors = SortedDict(emf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "SortedDict({1567321200: 0.6421450999999999, 1569913200: 0.6395414999999998, 1572591600: 0.6305423000000001, 1575187200: 0.6369704999999999, 1577865600: 0.6387163999999999, 1580544000: 0.6348021000000001, 1583049600: 0.6246918000000002, 1585724400: 0.6183217999999999})"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "emission_factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6183217999999999"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "def btc_emission_factor_by_ts(timestamp):\n",
    "    \"\"\"\n",
    "    Returns the BTC emission factor closest to timestamp.\n",
    "    This value represents overall emission factor kgCO2/kWh of the Bitcoin network\n",
    "    considering the geographic distribution of hashrate and the energy mix and emission factor\n",
    "    of each mining region. \n",
    "    \"\"\"\n",
    "    # return emission_factors.get(timestamp, emission_factors[min(emission_factors.keys(), key=lambda k: abs(k-timestamp))]) \n",
    "    return emission_factors[closest(emission_factors, timestamp)]\n",
    "\n",
    "\n",
    "btc_emission_factor_by_ts(datetime.datetime.now().timestamp())"
   ]
  },
  {
   "source": [
    "## 3. Daily carbon emissions of the Bitcoin network"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "emission (tCO2e) = energy (gWh) \\* emission_factor (kgCO2e/kWh) * 1000"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                    em_l           em_u           em_e\n",
       "datetime                                              \n",
       "2021-06-18  57722.796377  386818.730015  160576.138621\n",
       "2021-06-19  55440.257217  371523.312119  154226.397243\n",
       "2021-06-20  53388.445260  357772.851706  148518.524715\n",
       "2021-06-21  51506.375343  345160.781019  143282.948399\n",
       "2021-06-22  48997.005255  328344.122091  136302.010576"
      ],
      "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>em_l</th>\n      <th>em_u</th>\n      <th>em_e</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-06-18</th>\n      <td>57722.796377</td>\n      <td>386818.730015</td>\n      <td>160576.138621</td>\n    </tr>\n    <tr>\n      <th>2021-06-19</th>\n      <td>55440.257217</td>\n      <td>371523.312119</td>\n      <td>154226.397243</td>\n    </tr>\n    <tr>\n      <th>2021-06-20</th>\n      <td>53388.445260</td>\n      <td>357772.851706</td>\n      <td>148518.524715</td>\n    </tr>\n    <tr>\n      <th>2021-06-21</th>\n      <td>51506.375343</td>\n      <td>345160.781019</td>\n      <td>143282.948399</td>\n    </tr>\n    <tr>\n      <th>2021-06-22</th>\n      <td>48997.005255</td>\n      <td>328344.122091</td>\n      <td>136302.010576</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "df['emission_factor'] = df.apply(lambda x: btc_emission_factor_by_ts(x['datetime'].timestamp()), axis=1)\n",
    "df['em_l'] = df['lower_gwh_d'] * df['emission_factor'] * 1000\n",
    "df['em_u'] = df['upper_gwh_d'] * df['emission_factor'] * 1000\n",
    "df['em_e'] = df['estimated_gwh_d'] * df['emission_factor'] * 1000\n",
    "\n",
    "df[['em_l', 'em_u', 'em_e']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_emissions = {}\n",
    "\n",
    "for dt, row in df[['datetime', 'em_l', 'em_u', 'em_e']].iterrows():\n",
    "    daily_emissions[int(dt.timestamp())] = (row[1], row[2], row[3])\n",
    "\n",
    "daily_emissions = SortedDict(daily_emissions)\n"
   ]
  },
  {
   "source": [
    "### Comparing with Digiconomist's value\n",
    "Digiconomist reports annualized total bitcoin footprints as 63.12 Mt CO2 as of 2021-06-29"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "172931.50684931508"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "63.12 * 1000000 / 365"
   ]
  },
  {
   "source": [
    "## 4. Daily supply of Bitcoin"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             timestamp       supply\n",
       "timestamp                          \n",
       "1623438230  1623438230  18732487.50\n",
       "1623740706  1623740706  18735306.25\n",
       "1624038001  1624038001  18738100.00\n",
       "1624417918  1624417918  18740893.75\n",
       "1624833061  1624833061  18743693.75"
      ],
      "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>timestamp</th>\n      <th>supply</th>\n    </tr>\n    <tr>\n      <th>timestamp</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1623438230</th>\n      <td>1623438230</td>\n      <td>18732487.50</td>\n    </tr>\n    <tr>\n      <th>1623740706</th>\n      <td>1623740706</td>\n      <td>18735306.25</td>\n    </tr>\n    <tr>\n      <th>1624038001</th>\n      <td>1624038001</td>\n      <td>18738100.00</td>\n    </tr>\n    <tr>\n      <th>1624417918</th>\n      <td>1624417918</td>\n      <td>18740893.75</td>\n    </tr>\n    <tr>\n      <th>1624833061</th>\n      <td>1624833061</td>\n      <td>18743693.75</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "source": [
    "# total BTC in circulation\n",
    "supply_df = pd.read_csv(\"./source/total-bitcoins.csv\")\n",
    "supply_df.index = supply_df['timestamp']\n",
    "supply_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "timestamp\n",
       "1231006505          50.00\n",
       "1231933666       22400.00\n",
       "1232270118       44750.00\n",
       "1232633711       67100.00\n",
       "1232891596       89450.00\n",
       "                 ...     \n",
       "1623438230    18732487.50\n",
       "1623740706    18735306.25\n",
       "1624038001    18738100.00\n",
       "1624417918    18740893.75\n",
       "1624833061    18743693.75\n",
       "Name: supply, Length: 1501, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "source": [
    "supply_ser = supply_df['supply'].squeeze()\n",
    "supply_ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "supplies = {}\n",
    "for timestamp, supply in supply_ser.items():\n",
    "    supplies[timestamp] = supply\n",
    "supplies = SortedDict(supplies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "18743693.75"
      ]
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "source": [
    "def supply_by_ts(timestamp):\n",
    "    \"\"\"\n",
    "    Returns the BTC supply for a given timestamp\n",
    "    \"\"\"\n",
    "    return supplies[closest(supplies, timestamp)]\n",
    "\n",
    "supply_by_ts(datetime.datetime.now().timestamp())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             datetime  lower_gwh_d  upper_gwh_d  estimated_gwh_d  \\\n",
       "datetime                                                           \n",
       "2014-07-01 2014-07-01    26.972055    32.046027        29.375616   \n",
       "2014-07-02 2014-07-02    14.549699    32.527945        22.831726   \n",
       "2014-07-03 2014-07-03    10.419260    32.849041        20.729726   \n",
       "2014-07-04 2014-07-04     8.458849    34.636164        20.481233   \n",
       "2014-07-05 2014-07-05     7.239123    35.033699        19.999233   \n",
       "...               ...          ...          ...              ...   \n",
       "2021-06-18 2021-06-18    93.353973   625.594521       259.696712   \n",
       "2021-06-19 2021-06-19    89.662466   600.857534       249.427397   \n",
       "2021-06-20 2021-06-20    86.344110   578.619178       240.196164   \n",
       "2021-06-21 2021-06-21    83.300274   558.221918       231.728767   \n",
       "2021-06-22 2021-06-22    79.241918   531.024658       220.438630   \n",
       "\n",
       "            emission_factor          em_l           em_u           em_e  \\\n",
       "datetime                                                                  \n",
       "2014-07-01         0.642145  17319.972823   20578.199468   18863.408155   \n",
       "2014-07-02         0.642145   9343.017682   20887.660627   14661.280993   \n",
       "2014-07-03         0.642145   6690.676931   21093.850779   13311.491993   \n",
       "2014-07-04         0.642145   5431.808639   22241.443242   13151.923334   \n",
       "2014-07-05         0.642145   4648.567547   22496.717910   12842.409396   \n",
       "...                     ...           ...            ...            ...   \n",
       "2021-06-18         0.618322  57722.796377  386818.730015  160576.138621   \n",
       "2021-06-19         0.618322  55440.257217  371523.312119  154226.397243   \n",
       "2021-06-20         0.618322  53388.445260  357772.851706  148518.524715   \n",
       "2021-06-21         0.618322  51506.375343  345160.781019  143282.948399   \n",
       "2021-06-22         0.618322  48997.005255  328344.122091  136302.010576   \n",
       "\n",
       "                supply    1btc_l     1btc_u    1btc_e  \n",
       "datetime                                               \n",
       "2014-07-01  10373400.0  1.335722   1.586997  1.454752  \n",
       "2014-07-02  10373400.0  0.720537   1.610863  1.130683  \n",
       "2014-07-03  10382840.0  0.515518   1.625286  1.025653  \n",
       "2014-07-04  10382840.0  0.418522   1.713708  1.013358  \n",
       "2014-07-05  10382840.0  0.358173   1.733377  0.989510  \n",
       "...                ...       ...        ...       ...  \n",
       "2021-06-18  14990480.0  3.080504  20.643434  8.569500  \n",
       "2021-06-19  14990480.0  2.958692  19.827160  8.230632  \n",
       "2021-06-20  14990480.0  2.849192  19.093337  7.926018  \n",
       "2021-06-21  14992715.0  2.748341  18.417520  7.645470  \n",
       "2021-06-22  14992715.0  2.614443  17.520195  7.272973  \n",
       "\n",
       "[2549 rows x 12 columns]"
      ],
      "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>datetime</th>\n      <th>lower_gwh_d</th>\n      <th>upper_gwh_d</th>\n      <th>estimated_gwh_d</th>\n      <th>emission_factor</th>\n      <th>em_l</th>\n      <th>em_u</th>\n      <th>em_e</th>\n      <th>supply</th>\n      <th>1btc_l</th>\n      <th>1btc_u</th>\n      <th>1btc_e</th>\n    </tr>\n    <tr>\n      <th>datetime</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      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-07-01</th>\n      <td>2014-07-01</td>\n      <td>26.972055</td>\n      <td>32.046027</td>\n      <td>29.375616</td>\n      <td>0.642145</td>\n      <td>17319.972823</td>\n      <td>20578.199468</td>\n      <td>18863.408155</td>\n      <td>10373400.0</td>\n      <td>1.335722</td>\n      <td>1.586997</td>\n      <td>1.454752</td>\n    </tr>\n    <tr>\n      <th>2014-07-02</th>\n      <td>2014-07-02</td>\n      <td>14.549699</td>\n      <td>32.527945</td>\n      <td>22.831726</td>\n      <td>0.642145</td>\n      <td>9343.017682</td>\n      <td>20887.660627</td>\n      <td>14661.280993</td>\n      <td>10373400.0</td>\n      <td>0.720537</td>\n      <td>1.610863</td>\n      <td>1.130683</td>\n    </tr>\n    <tr>\n      <th>2014-07-03</th>\n      <td>2014-07-03</td>\n      <td>10.419260</td>\n      <td>32.849041</td>\n      <td>20.729726</td>\n      <td>0.642145</td>\n      <td>6690.676931</td>\n      <td>21093.850779</td>\n      <td>13311.491993</td>\n      <td>10382840.0</td>\n      <td>0.515518</td>\n      <td>1.625286</td>\n      <td>1.025653</td>\n    </tr>\n    <tr>\n      <th>2014-07-04</th>\n      <td>2014-07-04</td>\n      <td>8.458849</td>\n      <td>34.636164</td>\n      <td>20.481233</td>\n      <td>0.642145</td>\n      <td>5431.808639</td>\n      <td>22241.443242</td>\n      <td>13151.923334</td>\n      <td>10382840.0</td>\n      <td>0.418522</td>\n      <td>1.713708</td>\n      <td>1.013358</td>\n    </tr>\n    <tr>\n      <th>2014-07-05</th>\n      <td>2014-07-05</td>\n      <td>7.239123</td>\n      <td>35.033699</td>\n      <td>19.999233</td>\n      <td>0.642145</td>\n      <td>4648.567547</td>\n      <td>22496.717910</td>\n      <td>12842.409396</td>\n      <td>10382840.0</td>\n      <td>0.358173</td>\n      <td>1.733377</td>\n      <td>0.989510</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    </tr>\n    <tr>\n      <th>2021-06-18</th>\n      <td>2021-06-18</td>\n      <td>93.353973</td>\n      <td>625.594521</td>\n      <td>259.696712</td>\n      <td>0.618322</td>\n      <td>57722.796377</td>\n      <td>386818.730015</td>\n      <td>160576.138621</td>\n      <td>14990480.0</td>\n      <td>3.080504</td>\n      <td>20.643434</td>\n      <td>8.569500</td>\n    </tr>\n    <tr>\n      <th>2021-06-19</th>\n      <td>2021-06-19</td>\n      <td>89.662466</td>\n      <td>600.857534</td>\n      <td>249.427397</td>\n      <td>0.618322</td>\n      <td>55440.257217</td>\n      <td>371523.312119</td>\n      <td>154226.397243</td>\n      <td>14990480.0</td>\n      <td>2.958692</td>\n      <td>19.827160</td>\n      <td>8.230632</td>\n    </tr>\n    <tr>\n      <th>2021-06-20</th>\n      <td>2021-06-20</td>\n      <td>86.344110</td>\n      <td>578.619178</td>\n      <td>240.196164</td>\n      <td>0.618322</td>\n      <td>53388.445260</td>\n      <td>357772.851706</td>\n      <td>148518.524715</td>\n      <td>14990480.0</td>\n      <td>2.849192</td>\n      <td>19.093337</td>\n      <td>7.926018</td>\n    </tr>\n    <tr>\n      <th>2021-06-21</th>\n      <td>2021-06-21</td>\n      <td>83.300274</td>\n      <td>558.221918</td>\n      <td>231.728767</td>\n      <td>0.618322</td>\n      <td>51506.375343</td>\n      <td>345160.781019</td>\n      <td>143282.948399</td>\n      <td>14992715.0</td>\n      <td>2.748341</td>\n      <td>18.417520</td>\n      <td>7.645470</td>\n    </tr>\n    <tr>\n      <th>2021-06-22</th>\n      <td>2021-06-22</td>\n      <td>79.241918</td>\n      <td>531.024658</td>\n      <td>220.438630</td>\n      <td>0.618322</td>\n      <td>48997.005255</td>\n      <td>328344.122091</td>\n      <td>136302.010576</td>\n      <td>14992715.0</td>\n      <td>2.614443</td>\n      <td>17.520195</td>\n      <td>7.272973</td>\n    </tr>\n  </tbody>\n</table>\n<p>2549 rows × 12 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "ACTIVE_SUPPLY = 0.8\n",
    "\n",
    "df['supply'] = df.apply(lambda x: supply_by_ts(x['datetime'].timestamp()), axis=1) * ACTIVE_SUPPLY\n",
    "df"
   ]
  },
  {
   "source": [],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 5. Daily carbon emissions of holding 1 BTC\n",
    "\n",
    "emission_1btc (kgCO2) = emission_total (tCO2) / supply * 1000"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              1btc_l     1btc_u     1btc_e\n",
       "datetime                                  \n",
       "2014-07-01  1.669652   1.983747   1.818440\n",
       "2014-07-02  0.900671   2.013579   1.413353\n",
       "2014-07-03  0.644398   2.031607   1.282067\n",
       "2014-07-04  0.523152   2.142135   1.266698\n",
       "2014-07-05  0.447716   2.166721   1.236888\n",
       "...              ...        ...        ...\n",
       "2021-06-18  3.850630  25.804292  10.711874\n",
       "2021-06-19  3.698364  24.783950  10.288289\n",
       "2021-06-20  3.561490  23.866671   9.907523\n",
       "2021-06-21  3.435427  23.021900   9.556838\n",
       "2021-06-22  3.268054  21.900244   9.091216\n",
       "\n",
       "[2549 rows x 3 columns]"
      ],
      "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>1btc_l</th>\n      <th>1btc_u</th>\n      <th>1btc_e</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-07-01</th>\n      <td>1.669652</td>\n      <td>1.983747</td>\n      <td>1.818440</td>\n    </tr>\n    <tr>\n      <th>2014-07-02</th>\n      <td>0.900671</td>\n      <td>2.013579</td>\n      <td>1.413353</td>\n    </tr>\n    <tr>\n      <th>2014-07-03</th>\n      <td>0.644398</td>\n      <td>2.031607</td>\n      <td>1.282067</td>\n    </tr>\n    <tr>\n      <th>2014-07-04</th>\n      <td>0.523152</td>\n      <td>2.142135</td>\n      <td>1.266698</td>\n    </tr>\n    <tr>\n      <th>2014-07-05</th>\n      <td>0.447716</td>\n      <td>2.166721</td>\n      <td>1.236888</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2021-06-18</th>\n      <td>3.850630</td>\n      <td>25.804292</td>\n      <td>10.711874</td>\n    </tr>\n    <tr>\n      <th>2021-06-19</th>\n      <td>3.698364</td>\n      <td>24.783950</td>\n      <td>10.288289</td>\n    </tr>\n    <tr>\n      <th>2021-06-20</th>\n      <td>3.561490</td>\n      <td>23.866671</td>\n      <td>9.907523</td>\n    </tr>\n    <tr>\n      <th>2021-06-21</th>\n      <td>3.435427</td>\n      <td>23.021900</td>\n      <td>9.556838</td>\n    </tr>\n    <tr>\n      <th>2021-06-22</th>\n      <td>3.268054</td>\n      <td>21.900244</td>\n      <td>9.091216</td>\n    </tr>\n  </tbody>\n</table>\n<p>2549 rows × 3 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "source": [
    "df['1btc_l'] = df['em_l'] * 1000 / df['supply']\n",
    "df['1btc_u'] = df['em_u'] * 1000 / df['supply']\n",
    "df['1btc_e'] = df['em_e'] * 1000 / df['supply']\n",
    "df[['1btc_l', '1btc_u', '1btc_e']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb067bf41f0>"
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     "metadata": {},
     "execution_count": 72
    },
    {
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209.736763 \nL 144.262677 209.93888 \nL 144.525471 209.723551 \nL 144.656868 209.582115 \nL 144.919662 209.862062 \nL 145.182457 209.697465 \nL 145.313854 209.87359 \nL 145.445251 209.836641 \nL 145.576648 209.981761 \nL 145.97084 209.536829 \nL 146.102237 209.577038 \nL 146.233634 209.44608 \nL 146.365031 209.495218 \nL 146.627826 209.365549 \nL 146.89062 209.348772 \nL 147.416209 209.120682 \nL 147.941797 209.260089 \nL 148.073195 209.198705 \nL 148.335989 209.344649 \nL 148.467386 209.293038 \nL 148.598783 209.056602 \nL 148.730181 209.069535 \nL 149.124372 208.527122 \nL 149.518564 208.50006 \nL 149.781358 208.218962 \nL 151.09533 207.789429 \nL 151.226727 207.493551 \nL 151.358124 207.482511 \nL 151.620918 207.677875 \nL 152.01511 207.602594 \nL 152.146507 207.846857 \nL 152.409301 207.616654 \nL 152.540699 207.586778 \nL 152.672096 207.727042 \nL 152.93489 207.316384 \nL 153.329082 207.47405 \nL 153.591876 207.356321 \nL 154.117465 207.529112 \nL 154.380259 207.245406 \nL 154.511656 206.909669 \nL 154.643053 206.940454 \nL 155.037245 206.727941 \nL 155.168642 206.805952 \nL 155.431436 207.219035 \nL 156.219819 206.905623 \nL 156.614011 206.796088 \nL 157.008203 206.749239 \nL 157.402394 206.560839 \nL 157.533791 206.636223 \nL 157.927983 206.266656 \nL 158.05938 206.46843 \nL 158.453571 206.388723 \nL 158.584969 206.562492 \nL 158.97916 208.366411 \nL 159.504749 210.369572 \nL 159.636146 210.425775 \nL 160.293132 207.099187 \nL 160.555926 206.160903 \nL 160.687323 206.309342 \nL 160.818721 206.144556 \nL 161.344309 206.301348 \nL 161.475706 206.197168 \nL 161.607104 205.947315 \nL 162.132692 206.040896 \nL 162.395487 205.869813 \nL 162.526884 206.146915 \nL 162.658281 205.890619 \nL 162.789678 205.908866 \nL 163.052473 205.57773 \nL 163.18387 205.767869 \nL 163.315267 205.577754 \nL 163.578061 205.689526 \nL 164.10365 205.071572 \nL 164.235047 205.280972 \nL 164.366444 204.957296 \nL 165.02343 204.889098 \nL 166.074608 198.772593 \nL 166.206005 198.789666 \nL 166.337402 198.595467 \nL 166.600196 197.973734 \nL 166.994388 197.5014 \nL 167.125785 197.60934 \nL 167.519976 196.609582 \nL 167.651374 196.72398 \nL 168.045565 195.684916 \nL 168.308359 196.232649 \nL 168.833948 197.367846 \nL 168.965345 197.420414 \nL 169.22814 197.242974 \nL 169.490934 196.979638 \nL 169.622331 196.860588 \nL 169.753728 196.994048 \nL 170.016523 196.513726 \nL 170.279317 197.107827 \nL 170.410714 197.236359 \nL 170.542111 196.930423 \nL 170.804906 197.389402 \nL 170.936303 197.06536 \nL 171.199097 195.368262 \nL 171.330495 194.983388 \nL 171.461892 195.141804 \nL 171.593289 194.749301 \nL 171.856083 194.872355 \nL 171.98748 195.785449 \nL 172.513069 194.156323 \nL 172.644466 194.118144 \nL 172.775863 194.505163 \nL 172.907261 193.646835 \nL 173.301452 197.24849 \nL 173.827041 199.907853 \nL 173.958438 199.498985 \nL 174.221232 199.761243 \nL 174.35263 199.788114 \nL 174.615424 199.591478 \nL 174.878218 200.117996 \nL 175.141013 199.753056 \nL 175.27241 199.930739 \nL 175.535204 199.213796 \nL 175.666601 199.31693 \nL 175.929396 198.983514 \nL 177.11197 191.130371 \nL 177.243367 191.011999 \nL 177.374765 190.633162 \nL 177.637559 191.422604 \nL 177.768956 192.913816 \nL 177.900353 192.235214 \nL 178.294545 196.576402 \nL 178.557339 195.915301 \nL 178.688736 194.629936 \nL 178.820133 194.575003 \nL 179.214325 190.174935 \nL 179.345722 190.197674 \nL 179.608516 188.069324 \nL 179.739914 188.138503 \nL 180.002708 187.519276 \nL 180.134105 187.256254 \nL 180.265502 187.507335 \nL 180.3969 187.376271 \nL 180.528297 187.414549 \nL 180.659694 186.687412 \nL 180.791091 186.734572 \nL 181.053885 186.626386 \nL 181.185283 185.730209 \nL 181.31668 186.405104 \nL 181.448077 186.439366 \nL 181.579474 187.62033 \nL 181.710871 186.770323 \nL 181.842268 187.095149 \nL 181.973666 186.399769 \nL 182.105063 186.470975 \nL 182.23646 186.384283 \nL 182.367857 186.891446 \nL 182.499254 186.056093 \nL 182.630651 186.027935 \nL 182.762049 185.182994 \nL 183.024843 186.402213 \nL 183.287637 184.268013 \nL 183.419035 184.563948 \nL 183.550432 185.962282 \nL 183.681829 186.170622 \nL 183.813226 185.525457 \nL 184.07602 183.664517 \nL 184.207418 184.323412 \nL 184.338815 184.180135 \nL 184.470212 183.388766 \nL 184.733006 184.534282 \nL 185.389992 181.096183 \nL 185.652786 178.917807 \nL 185.915581 179.357933 \nL 186.046978 179.266551 \nL 186.309772 177.980691 \nL 186.572567 181.114811 \nL 187.098155 180.600078 \nL 187.229553 180.738213 \nL 187.36095 180.184206 \nL 187.492347 178.534577 \nL 187.755141 179.531453 \nL 187.886538 178.879114 \nL 188.017936 179.322021 \nL 188.149333 179.211294 \nL 188.28073 179.287577 \nL 188.674922 183.79845 \nL 188.806319 185.129154 \nL 188.937716 185.17527 \nL 189.069113 185.553343 \nL 189.331907 182.164943 \nL 189.594702 178.195271 \nL 189.726099 176.637515 \nL 189.857496 176.775992 \nL 189.988893 176.615961 \nL 190.12029 177.250337 \nL 190.251688 176.627179 \nL 190.645879 177.854944 \nL 191.040071 175.714098 \nL 191.171468 175.543428 \nL 191.697057 172.813282 \nL 191.828454 172.400549 \nL 191.959851 172.35801 \nL 192.222645 170.25159 \nL 192.879631 166.745591 \nL 193.011028 166.866314 \nL 193.40522 164.619125 \nL 193.668014 166.865947 \nL 193.799411 166.323275 \nL 194.062206 166.877763 \nL 194.193603 168.424831 \nL 194.325 168.345995 \nL 194.719192 166.138438 \nL 194.981986 165.239603 \nL 195.507575 161.036072 \nL 195.638972 161.866805 \nL 195.770369 160.627985 \nL 195.901766 160.456518 \nL 196.033163 159.94272 \nL 196.16456 160.302523 \nL 196.295958 159.863847 \nL 196.558752 156.961565 \nL 196.690149 157.086293 \nL 197.609929 150.881019 \nL 197.872724 148.13184 \nL 198.135518 146.639641 \nL 198.266915 146.53091 \nL 198.52971 145.322675 \nL 198.661107 145.479701 \nL 198.792504 145.147613 \nL 198.923901 144.131556 \nL 199.055298 146.356961 \nL 199.580887 143.505204 \nL 199.712284 141.08368 \nL 199.843681 140.857476 \nL 199.975078 138.324709 \nL 200.237873 140.590021 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181.03912 \nL 208.2531 180.612626 \nL 208.384498 180.675179 \nL 208.515895 179.830714 \nL 208.647292 180.824332 \nL 209.041484 179.661513 \nL 209.172881 179.225239 \nL 209.304278 177.954997 \nL 209.435675 178.441911 \nL 209.567072 177.652318 \nL 209.698469 177.754814 \nL 209.829867 177.357399 \nL 209.961264 177.459861 \nL 210.224058 179.998864 \nL 210.355455 179.6884 \nL 210.486852 180.538832 \nL 210.749647 180.041776 \nL 210.881044 179.819225 \nL 211.143838 178.437075 \nL 211.275235 178.489082 \nL 211.406633 177.322107 \nL 211.800824 178.941995 \nL 212.063619 178.120433 \nL 212.195016 178.389318 \nL 212.45781 177.515162 \nL 212.589207 178.027387 \nL 212.720604 177.61966 \nL 212.852002 176.747878 \nL 212.983399 177.403043 \nL 213.114796 176.629873 \nL 213.37759 177.193869 \nL 213.640385 176.091243 \nL 213.771782 176.728753 \nL 213.903179 176.221911 \nL 214.29737 173.452274 \nL 214.428768 173.263502 \nL 214.560165 173.517089 \nL 214.822959 171.690564 \nL 215.085754 172.467933 \nL 215.217151 172.506105 \nL 215.479945 169.920926 \nL 215.611342 170.140824 \nL 215.742739 170.367972 \nL 215.874137 170.354967 \nL 216.005534 169.910797 \nL 216.531122 172.139784 \nL 216.793917 171.367556 \nL 217.5823 165.817381 \nL 217.713697 165.72217 \nL 217.845094 165.790981 \nL 217.976491 164.9897 \nL 218.107889 165.105214 \nL 218.239286 165.442668 \nL 218.50208 167.958326 \nL 218.633477 168.335026 \nL 219.159066 173.312359 \nL 219.290463 172.744265 \nL 219.42186 173.263908 \nL 220.210243 169.173585 \nL 220.341641 169.010072 \nL 220.604435 170.828736 \nL 220.867229 176.676639 \nL 220.998626 176.454992 \nL 221.261421 173.919289 \nL 221.392818 173.665395 \nL 221.524215 173.665611 \nL 221.787009 165.092642 \nL 222.181201 163.986971 \nL 222.443995 162.308337 \nL 222.575392 162.19712 \nL 222.70679 162.763925 \nL 222.838187 162.31403 \nL 222.969584 162.526912 \nL 223.100981 162.053412 \nL 223.232378 160.973413 \nL 223.363776 158.367974 \nL 223.62657 158.571637 \nL 223.889364 158.015005 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176.326193 \nL 233.218564 177.186573 \nL 233.349961 176.995525 \nL 233.481358 177.135642 \nL 233.612755 176.383786 \nL 233.744152 176.885622 \nL 233.875549 176.367696 \nL 234.138344 177.732481 \nL 234.269741 177.706383 \nL 234.401138 177.973617 \nL 234.532535 177.897289 \nL 234.663932 178.252819 \nL 234.79533 178.252819 \nL 235.058124 175.708386 \nL 235.189521 175.707192 \nL 235.320918 174.752085 \nL 235.452316 175.412523 \nL 235.583713 175.487626 \nL 236.240699 180.7595 \nL 236.372096 179.893813 \nL 236.63489 180.351299 \nL 237.029082 180.001177 \nL 237.160479 179.876128 \nL 237.423273 181.998522 \nL 237.55467 181.913081 \nL 237.817465 183.895985 \nL 238.211656 185.079769 \nL 238.474451 186.647914 \nL 238.605848 186.7536 \nL 239.131436 191.444069 \nL 239.525628 193.381368 \nL 239.657025 193.252336 \nL 239.788422 193.492894 \nL 240.051217 194.240128 \nL 240.182614 194.250719 \nL 240.576805 194.889632 \nL 240.8396 193.746132 \nL 240.970997 194.150423 \nL 241.102394 193.437473 \nL 241.233791 193.494697 \nL 241.365188 193.258577 \nL 241.496586 193.474103 \nL 241.627983 193.927761 \nL 241.75938 193.426749 \nL 241.890777 192.14314 \nL 242.022174 192.398724 \nL 242.284969 191.131187 \nL 242.547763 188.835909 \nL 242.67916 189.414038 \nL 242.941954 188.669667 \nL 243.204749 187.71542 \nL 243.467543 187.87913 \nL 243.59894 186.895597 \nL 243.861735 187.511834 \nL 244.255926 186.908987 \nL 244.387323 186.935174 \nL 244.650118 188.026916 \nL 244.781515 187.938045 \nL 245.175706 190.777447 \nL 245.438501 191.317097 \nL 245.569898 190.821338 \nL 245.832692 191.147029 \nL 245.964089 190.973944 \nL 246.095487 190.259936 \nL 246.226884 190.531974 \nL 246.358281 190.387881 \nL 246.621075 191.268551 \nL 246.752473 191.043093 \nL 247.015267 191.788206 \nL 247.146664 191.363182 \nL 247.409458 191.565798 \nL 247.540856 191.530802 \nL 247.672253 191.735406 \nL 247.935047 191.178878 \nL 248.592033 191.77313 \nL 248.72343 191.623403 \nL 248.986224 192.441631 \nL 249.117622 192.304278 \nL 249.511813 190.396384 \nL 249.774608 191.488109 \nL 250.037402 190.308612 \nL 250.300196 190.885776 \nL 250.562991 190.235026 \nL 250.694388 189.353107 \nL 250.825785 189.346236 \nL 250.957182 189.623043 \nL 251.088579 189.394205 \nL 251.351374 189.851652 \nL 251.745565 190.775139 \nL 251.876962 190.633294 \nL 252.008359 190.883212 \nL 252.139757 190.760191 \nL 252.271154 190.919387 \nL 252.665345 190.259269 \nL 253.322331 186.958773 \nL 253.453728 187.109369 \nL 253.585126 186.732994 \nL 253.716523 187.021753 \nL 253.84792 186.566734 \nL 253.979317 186.707893 \nL 254.110714 187.569689 \nL 254.242111 187.692356 \nL 254.373509 187.420302 \nL 254.504906 187.588781 \nL 254.636303 187.568802 \nL 254.899097 188.19695 \nL 255.030495 187.577593 \nL 255.293289 187.932526 \nL 255.818878 186.079682 \nL 255.950275 186.194293 \nL 256.475863 183.160711 \nL 256.607261 183.251338 \nL 256.870055 182.094121 \nL 257.001452 182.157054 \nL 257.132849 182.350695 \nL 257.264246 182.317309 \nL 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211.992162 \nL 160.818721 211.933172 \nL 161.475706 211.95228 \nL 161.738501 211.877949 \nL 162.395487 211.833385 \nL 162.526884 211.934032 \nL 162.921075 211.761254 \nL 163.315267 211.727318 \nL 163.709458 211.715394 \nL 164.10365 211.543494 \nL 164.235047 211.619538 \nL 164.497841 211.49707 \nL 165.154827 211.29946 \nL 165.549019 210.783283 \nL 165.680416 210.708921 \nL 165.94321 210.157189 \nL 166.206005 210.0178 \nL 168.176962 209.186336 \nL 168.571154 209.358959 \nL 168.833948 209.566767 \nL 169.22814 209.527181 \nL 169.622331 209.405874 \nL 169.753728 209.448183 \nL 170.016523 209.295823 \nL 170.279317 209.484264 \nL 170.410714 209.525048 \nL 170.542111 209.428007 \nL 170.804906 209.573588 \nL 170.936303 209.47081 \nL 171.199097 209.061672 \nL 171.330495 208.994732 \nL 171.461892 209.09875 \nL 171.593289 209.025468 \nL 171.856083 209.177301 \nL 171.98748 209.448927 \nL 172.513069 208.964311 \nL 172.644466 208.952965 \nL 172.775863 209.068076 \nL 172.907261 208.812751 \nL 173.301452 209.595097 \nL 173.827041 209.981023 \nL 173.958438 209.840872 \nL 174.221232 209.930749 \nL 175.403807 209.922855 \nL 175.535204 209.85404 \nL 175.797998 209.901114 \nL 176.060793 209.698731 \nL 176.323587 209.374882 \nL 176.454984 209.329116 \nL 176.849176 208.665018 \nL 176.980573 208.685786 \nL 177.243367 208.351611 \nL 177.374765 208.244 \nL 177.637559 208.468293 \nL 177.768956 208.892048 \nL 177.900353 208.6992 \nL 178.294545 209.932754 \nL 178.557339 209.744895 \nL 178.688736 209.379657 \nL 178.820133 209.364047 \nL 179.214325 208.202767 \nL 179.345722 208.292738 \nL 179.608516 207.889642 \nL 179.871311 207.972207 \nL 180.265502 208.017216 \nL 181.053885 207.787923 \nL 181.185283 207.554662 \nL 181.579474 208.046671 \nL 181.710871 207.825379 \nL 181.842268 207.909957 \nL 181.973666 207.728967 \nL 182.367857 207.856942 \nL 182.762049 207.412245 \nL 183.024843 207.729567 \nL 183.419035 207.251103 \nL 183.681829 207.669307 \nL 183.813226 207.501373 \nL 184.07602 207.01699 \nL 184.207418 207.188476 \nL 184.338815 207.151175 \nL 184.470212 206.945188 \nL 184.733006 207.243362 \nL 185.389992 206.348435 \nL 185.652786 205.781451 \nL 185.915581 205.89599 \nL 186.046978 205.872233 \nL 186.309772 205.537505 \nL 186.572567 206.353332 \nL 187.098155 206.219344 \nL 187.229553 206.311879 \nL 187.36095 206.212544 \nL 187.492347 205.832114 \nL 187.755141 206.19493 \nL 187.886538 206.083827 \nL 188.017936 206.253403 \nL 188.28073 206.244793 \nL 188.674922 207.372867 \nL 188.806319 207.705655 \nL 189.069113 207.811735 \nL 189.331907 206.964359 \nL 189.594702 205.971598 \nL 189.726099 205.58207 \nL 190.12029 205.735288 \nL 190.251688 205.579476 \nL 190.645879 205.886496 \nL 191.040071 205.351121 \nL 191.171468 205.342793 \nL 191.697057 204.816125 \nL 191.959851 204.779709 \nL 192.222645 204.266136 \nL 192.879631 203.411228 \nL 193.011028 203.440635 \nL 193.40522 202.892707 \nL 193.668014 203.440609 \nL 193.799411 203.3083 \nL 194.062206 203.443419 \nL 194.193603 203.820643 \nL 194.325 203.801412 \nL 194.719192 203.2632 \nL 194.981986 203.044054 \nL 195.24478 202.517794 \nL 195.376177 202.593878 \nL 195.507575 202.47283 \nL 195.638972 202.806138 \nL 195.770369 202.657815 \nL 196.16456 202.871363 \nL 196.295958 202.772738 \nL 196.690149 202.147525 \nL 197.609929 200.750968 \nL 197.872724 200.132227 \nL 198.135518 199.796406 \nL 198.266915 199.771932 \nL 198.52971 199.500013 \nL 198.792504 199.460615 \nL 198.923901 199.231936 \nL 199.055298 199.732841 \nL 199.318093 199.502699 \nL 199.44949 199.59449 \nL 199.580887 199.413964 \nL 199.712284 198.987549 \nL 199.843681 199.045954 \nL 199.975078 198.57574 \nL 200.500667 200.034573 \nL 200.763462 200.714503 \nL 200.894859 200.555543 \nL 201.157653 200.912868 \nL 201.551845 200.74706 \nL 201.814639 201.019439 \nL 201.946036 200.693105 \nL 202.20883 200.691 \nL 202.340228 200.469452 \nL 202.471625 200.664312 \nL 202.603022 200.381376 \nL 202.734419 200.573347 \nL 202.997214 200.177641 \nL 203.128611 199.6152 \nL 203.260008 199.867737 \nL 203.785597 198.746853 \nL 203.916994 198.693773 \nL 204.048391 199.078853 \nL 204.311185 198.590443 \nL 204.442582 198.559501 \nL 204.705377 199.044823 \nL 205.099568 198.718445 \nL 205.230965 198.685992 \nL 205.362363 199.062962 \nL 205.625157 198.721646 \nL 205.756554 198.969903 \nL 205.887951 198.73555 \nL 206.150746 199.029371 \nL 206.282143 198.622623 \nL 206.41354 198.904619 \nL 206.676334 198.556468 \nL 206.807732 199.230467 \nL 206.939129 199.038629 \nL 207.596115 200.057131 \nL 207.727512 199.336335 \nL 208.121703 199.527407 \nL 208.2531 199.331518 \nL 208.384498 199.360265 \nL 208.515895 198.972384 \nL 208.647292 199.428774 \nL 209.041484 198.894659 \nL 209.172881 198.694264 \nL 209.304278 198.110779 \nL 209.435675 198.334432 \nL 209.567072 197.971731 \nL 209.698469 198.018839 \nL 209.829867 197.836267 \nL 209.961264 197.883359 \nL 210.224058 199.049622 \nL 210.355455 198.906997 \nL 210.486852 199.297635 \nL 210.881044 198.967078 \nL 211.143838 198.675547 \nL 211.275235 198.862294 \nL 211.406633 198.51597 \nL 211.800824 199.762119 \nL 212.063619 199.411835 \nL 212.195016 199.526497 \nL 212.45781 199.153809 \nL 212.589207 199.372195 \nL 212.720604 199.198343 \nL 212.852002 198.82669 \nL 212.983399 199.105976 \nL 213.114796 198.776374 \nL 213.37759 199.016804 \nL 213.640385 198.546757 \nL 213.771782 198.818514 \nL 213.903179 198.602456 \nL 214.29737 197.421678 \nL 214.428768 197.341203 \nL 214.560165 197.449319 \nL 214.822959 196.670658 \nL 215.217151 197.018315 \nL 215.479945 195.916247 \nL 215.611342 196.009842 \nL 215.874137 196.101221 \nL 216.005534 195.911851 \nL 216.531122 196.862147 \nL 216.793917 196.53293 \nL 217.5823 194.166818 \nL 217.845094 194.155436 \nL 217.976491 193.813811 \nL 218.239286 194.006938 \nL 218.50208 195.027513 \nL 218.633477 195.138188 \nL 219.159066 197.18354 \nL 219.290463 196.951186 \nL 219.42186 197.234671 \nL 219.947449 196.318265 \nL 220.078846 195.692148 \nL 220.341641 195.603753 \nL 220.604435 196.453758 \nL 220.867229 197.594025 \nL 220.998626 197.499884 \nL 221.261421 196.42344 \nL 221.524215 196.238111 \nL 221.787009 194.074482 \nL 222.181201 193.798247 \nL 222.443995 193.205642 \nL 222.575392 193.159431 \nL 222.70679 193.395312 \nL 222.838187 193.207941 \nL 222.969584 193.296628 \nL 223.100981 193.197844 \nL 223.232378 192.855061 \nL 223.363776 191.883665 \nL 223.757967 192.165322 \nL 223.889364 192.121335 \nL 224.020761 192.313818 \nL 224.152159 192.230126 \nL 224.414953 192.805663 \nL 224.54635 192.522055 \nL 224.677747 192.938649 \nL 224.809144 192.972508 \nL 224.940542 192.79795 \nL 225.203336 193.395692 \nL 225.334733 193.606435 \nL 225.46613 193.606435 \nL 225.597527 193.362051 \nL 225.728925 193.389598 \nL 226.123116 194.006272 \nL 226.517308 192.07988 \nL 226.648705 192.357572 \nL 227.042896 191.464379 \nL 227.568485 194.115546 \nL 227.831279 194.384574 \nL 227.962677 194.386162 \nL 228.094074 193.855008 \nL 228.225471 193.919826 \nL 228.356868 193.484117 \nL 228.488265 192.598353 \nL 228.619662 192.50714 \nL 229.013854 193.658227 \nL 229.276648 193.643723 \nL 229.408046 193.9018 \nL 229.539443 193.765004 \nL 230.065031 192.439067 \nL 230.196429 191.789755 \nL 230.459223 192.291555 \nL 230.59062 192.072605 \nL 230.722017 192.138282 \nL 230.984812 191.642431 \nL 231.116209 192.491582 \nL 231.247606 192.525144 \nL 231.379003 192.438202 \nL 231.904592 194.633506 \nL 232.035989 194.664772 \nL 232.430181 194.292415 \nL 232.561578 193.952816 \nL 232.692975 194.558974 \nL 232.955769 193.873122 \nL 233.087166 194.00298 \nL 233.218564 194.472461 \nL 233.349961 194.368241 \nL 233.481358 194.444656 \nL 233.612755 194.034405 \nL 233.744152 194.308262 \nL 233.875549 194.025651 \nL 234.138344 194.770347 \nL 234.269741 194.756122 \nL 234.401138 194.901949 \nL 234.532535 194.860284 \nL 234.79533 195.054305 \nL 235.058124 193.66587 \nL 235.189521 193.69121 \nL 235.320918 193.19928 \nL 235.846507 194.929413 \nL 236.240699 196.586425 \nL 236.372096 196.118172 \nL 236.766287 196.268612 \nL 236.897684 196.144925 \nL 237.160479 196.108582 \nL 237.423273 197.256624 \nL 237.55467 197.210411 \nL 237.817465 198.282989 \nL 238.080259 198.640056 \nL 238.211656 198.677262 \nL 238.474451 199.188427 \nL 238.605848 199.074393 \nL 239.262834 201.061951 \nL 239.525628 201.612517 \nL 239.657025 201.486275 \nL 239.788422 201.594626 \nL 240.051217 202.012255 \nL 240.182614 202.018939 \nL 240.576805 202.421314 \nL 240.8396 201.648895 \nL 240.970997 201.863297 \nL 241.102394 201.358216 \nL 241.233791 201.349805 \nL 241.365188 201.153915 \nL 241.627983 201.575435 \nL 241.75938 201.387551 \nL 241.890777 200.695299 \nL 242.022174 200.977799 \nL 242.416366 199.90005 \nL 242.547763 199.460024 \nL 242.67916 199.909511 \nL 242.941954 199.690245 \nL 243.204749 199.401231 \nL 243.467543 199.543097 \nL 243.59894 198.996887 \nL 243.861735 199.398164 \nL 244.124529 198.963205 \nL 244.387323 198.963205 \nL 244.650118 199.4843 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df[['1btc_l', '1btc_u', '1btc_e']].plot()"
   ]
  },
  {
   "source": [
    "### CSV Exports\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['1btc_l', '1btc_u', '1btc_e']].to_csv('1btc_emission_daily.csv', float_format='%g')"
   ]
  },
  {
   "source": [
    "## 6. Aggregate analysis "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               1btc_l       1btc_u       1btc_e\n",
       "datetime                                       \n",
       "2014        47.534517   195.098864   115.305443\n",
       "2015        82.158189   179.884492   126.935841\n",
       "2016       107.408177   512.081035   279.826551\n",
       "2017       259.870470  2197.939820   643.705444\n",
       "2018       885.804871  4737.037660  1949.438735\n",
       "2019      1056.686287  4983.063003  2431.899189\n",
       "2020      1454.164101  4856.704239  2949.267945\n",
       "2021       790.976400  7136.595778  2316.346154"
      ],
      "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>1btc_l</th>\n      <th>1btc_u</th>\n      <th>1btc_e</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014</th>\n      <td>47.534517</td>\n      <td>195.098864</td>\n      <td>115.305443</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>82.158189</td>\n      <td>179.884492</td>\n      <td>126.935841</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>107.408177</td>\n      <td>512.081035</td>\n      <td>279.826551</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>259.870470</td>\n      <td>2197.939820</td>\n      <td>643.705444</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>885.804871</td>\n      <td>4737.037660</td>\n      <td>1949.438735</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>1056.686287</td>\n      <td>4983.063003</td>\n      <td>2431.899189</td>\n    </tr>\n    <tr>\n      <th>2020</th>\n      <td>1454.164101</td>\n      <td>4856.704239</td>\n      <td>2949.267945</td>\n    </tr>\n    <tr>\n      <th>2021</th>\n      <td>790.976400</td>\n      <td>7136.595778</td>\n      <td>2316.346154</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 76
    }
   ],
   "source": [
    "yearly_df = df.groupby(df.datetime.dt.year).agg({'1btc_l': 'sum', '1btc_u': 'sum', '1btc_e': 'sum'})\n",
    "yearly_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               1btc_l        1btc_u       1btc_e\n",
       "datetime                                        \n",
       "2021      1688.341439  15233.084555  4944.247639"
      ],
      "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>1btc_l</th>\n      <th>1btc_u</th>\n      <th>1btc_e</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021</th>\n      <td>1688.341439</td>\n      <td>15233.084555</td>\n      <td>4944.247639</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 77
    }
   ],
   "source": [
    "yearly_df[yearly_df.index == 2021][['1btc_l', '1btc_u', '1btc_e']] * annualize_factor_2021()"
   ]
  },
  {
   "source": [
    "In 2021, holding 1 BTC for a year would produce a carbon footprint of 4944 kg."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}