main_notebook.ipynb

{
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#demograhpy\" data-toc-modified-id=\"demograhpy-1\">demograhpy</a></span></li><li><span><a href=\"#paybacks\" data-toc-modified-id=\"paybacks-2\">paybacks</a></span><ul class=\"toc-item\"><li><span><a href=\"#ECA\" data-toc-modified-id=\"ECA-2.1\">ECA</a></span></li><li><span><a href=\"#SERAFE\" data-toc-modified-id=\"SERAFE-2.2\">SERAFE</a></span></li></ul></li><li><span><a href=\"#tax-incomes\" data-toc-modified-id=\"tax-incomes-3\">tax incomes</a></span><ul class=\"toc-item\"><li><span><a href=\"#cars_fuels\" data-toc-modified-id=\"cars_fuels-3.1\">cars_fuels</a></span></li><li><span><a href=\"#circulation\" data-toc-modified-id=\"circulation-3.2\">circulation</a></span></li><li><span><a href=\"#computation-of-tax\" data-toc-modified-id=\"computation-of-tax-3.3\">computation of tax</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "# modules for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('notebook')\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "# module to save space\n",
    "import gc\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we start from *Switzerland*, and in particular canton **Vaud** as case study > all costs are in  **CHF/year**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, instead of start thinking from how to _get_ money, we think how to give back the tax first"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# demograhpy   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "basic demographic data, relevant for our case study (Switzerland)\n",
    "\n",
    "(these N are a taken just as a starting point -- relative to past statistics) -- sources:\n",
    "\n",
    "\n",
    "[population total](https://www.atlas.bfs.admin.ch/maps/13/fr/14459_72_71_70/23029.html)   \n",
    "[households N](http://www.scris.vd.ch/Data_Dir/ElementsDir/8235/2/F/Portraits-des-familles.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pop_vaud_2019 = 799145\n",
    "\n",
    "households_vaud_2013 = 323064"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# paybacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "we need to return that money immediately to citizen, to \n",
    "- build trust in the carbon reform \n",
    "- create a sustainable reform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "the first principle here is avoid at the beginning too large differences between residents, in particular among those ones living in countryside/city, where there is low/high public transport frequency \n",
    "\n",
    "Therefore, here the idea is to start payback on tax/public services that everybody needs (both in villages and cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## ECA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Given the increasing evidence of the relation between *pollution > increase frequency of sever weather events*, it makes sense that costs from natural disasters are payed more and more by who pollutes\n",
    "\n",
    "in canton **Vaud**: one idea would be to stop paying the basic fee for the [ECA](https://www.eca-vaud.ch)   \n",
    "(the public intervention service/insurance against natural disasters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "6461280"
      ]
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     "execution_count": 3,
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     "output_type": "execute_result"
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   ],
   "source": [
    "eca_basic_fee = 20\n",
    "\n",
    "eca_basic_tot = eca_basic_fee * households_vaud_2013\n",
    "eca_basic_tot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## SERAFE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "it might seems counter-intuitive, but paying the public news/radio-TV services might be also interesting, as this is a tax _every household_ has to pay, no matter where you live (city/countriside)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "117918360"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "serafe_fee = 365\n",
    "\n",
    "serafe_tot = serafe_fee * households_vaud_2013\n",
    "serafe_tot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tax incomes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the general idea is to tax fossil fuels usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "statistics on mobility: \n",
    "(mind that in EN sometimes you don't find everything - better in FR/DE)\n",
    "\n",
    "https://www.bfs.admin.ch/bfs/fr/home/statistiques/mobilite-transports.html \n",
    "\n",
    "\n",
    "[average daily road traffic](https://www.bfs.admin.ch/bfs/fr/home/statistiques/mobilite-transports/infrastructures-transport-vehicules/comptage-suisse-circulation-route.html)\n",
    "\n",
    "[costs-funding](https://www.bfs.admin.ch/bfs/en/home/statistics/mobility-transport/costs-funding.html)\n",
    "\n",
    "[N vehicles](https://www.bfs.admin.ch/bfs/fr/home/statistiques/mobilite-transports/infrastructures-transport-vehicules/vehicules/vehicules-routiers-parc-taux-motorisation.html)\n",
    "\n",
    "again, we consider only data for canton VAUD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## cars_fuels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "[petrol data](https://www.globalpetrolprices.com/Switzerland/gasoline_prices/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# gasoline_per_litre = 1.4 \n",
    "\n",
    "n_cars_vaud = 416941 \n",
    "n_electric = 2298 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "from this stats > [av. cost per vehicle](https://www.bfs.admin.ch/bfs/fr/home/statistiques/mobilite-transports/couts-financement/route-mobilite-douce.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12700.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## all are YEARLY averaged estimates -- by the OFSP\n",
    "\n",
    "cost_vehicle = 7620 # av. annual cost / vehi. \n",
    "cost_vehicle_km = 0.6 # av. annual cost / (vehi.*km) \n",
    "km_per_vehicle = cost_vehicle / cost_vehicle_km\n",
    "km_per_vehicle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "[cars efficiency in CH](https://www.eltis.org/discover/case-studies/energy-consumption-label-passenger-cars-switzerland)\n",
    "\n",
    "to play safe, we consider 6 l/100km – better to underestimate incomes, than over- :D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "litres_km = 0.06"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "(for the time being we don't need this fun, as the problem is linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def fuel_tax_tot(tax_per_litre):\n",
    "    \n",
    "    '''\n",
    "    enter tax in CHF -- for now \n",
    "    '''\n",
    "    return tax_per_litre * litres_km * km_per_vehicle * (n_cars_vaud - n_electric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## circulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "(for the time being we don't need this fun, as the problem is linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def circ_tax_tot(circ_tax):\n",
    "    \n",
    "    return n_cars_vaud * circ_tax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## computation of tax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for the time being, we can keep it to a simple linear problem:\n",
    "\n",
    "`Ax=b`, where \n",
    "`x`: unitary taxes (_increments_) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clearly, we need (N-1) _more_ constraints -- with N = number of tax income sources\n",
    "\n",
    "one way, would be to set the ratio among total incomes -- how much do we want each source to contribute?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4.16941000e+05 -3.15957966e+07]\n",
      " [ 4.16941000e+05  3.15957966e+08]]\n",
      "\n",
      "\n",
      "assuming a circ_to_fuel_ratio: 0.1\n",
      "(extra) circulation tax: 27.12 CHF/(y*veh)\n",
      "(extra) fuel tax: 0.358 CHF/l\n"
     ]
    }
   ],
   "source": [
    "circ_to_fuel_ratio = 0.1 # just a N for now\n",
    "\n",
    "total_paybacks = eca_basic_tot + serafe_tot\n",
    "b = np.array([0, total_paybacks])\n",
    "\n",
    "coeff_matrix = np.array(\n",
    "    [[n_cars_vaud, -circ_to_fuel_ratio * litres_km * km_per_vehicle * (n_cars_vaud - n_electric)],\n",
    "     [n_cars_vaud, litres_km * km_per_vehicle * (n_cars_vaud - n_electric)]])\n",
    "print(coeff_matrix)\n",
    "\n",
    "circ_tax, fuel_tax = np.linalg.solve(coeff_matrix, b)\n",
    "\n",
    "print('\\n\\nassuming a circ_to_fuel_ratio:', circ_to_fuel_ratio)\n",
    "print('(extra) circulation tax:', np.round(circ_tax, 3), 'CHF/(y*veh)')\n",
    "print('(extra) fuel tax:', np.round(fuel_tax, 3), 'CHF/l')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "graphical representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ratio_eq(fuel_tax):\n",
    "    return (b[0] - coeff_matrix[0, 1] * fuel_tax) / coeff_matrix[0,0]\n",
    "\n",
    "def main_eq(fuel_tax):\n",
    "    return (b[1] - coeff_matrix[1, 1] * fuel_tax) / coeff_matrix[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 267,
       "width": 395
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_fuel_tax = np.linspace(0, 0.4)\n",
    "\n",
    "plt.plot(x_fuel_tax, ratio_eq(x_fuel_tax), label='ratio-constraint')\n",
    "plt.plot(x_fuel_tax, main_eq(x_fuel_tax), label='main eq.')\n",
    "plt.legend()\n",
    "\n",
    "plt.xlabel('fuel tax (CHF/l)')\n",
    "plt.ylabel('circ. tax (CHF/(y*veh))');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "100px",
    "left": "826px",
    "top": "125.4000244140625px",
    "width": "207.8000030517578px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}