dist_climate_bb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replication code for\n",
    "# Carbon Taxes vs. Green Subsidies: Generational Conflicts and Distributional Consequences\n",
    "### Gregor Boehl and Flora Budianto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, install the necessary packages. Do this either here (uncomment the cell below) or with your prefered package manager."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install econpizza\n",
    "#!pip install grgrlib\n",
    "#!pip mpl_axes_aligner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The documentation for the EP package, which is used for the simulations in the paper, can be found [here](https://econpizza.readthedocs.io/en/stable/index.html).\n",
    "\n",
    "Do some imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import econpizza as ep\n",
    "from grgrlib import * \n",
    "from mpl_axes_aligner import align\n",
    "\n",
    "# plotting setup\n",
    "plt.rc('text', usetex=True)\n",
    "\n",
    "# if you run this in a jupyter notebook:\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The YAML file which contains the HANK model with energy sector is hosted as part of the example models for EP: https://github.com/gboehl/econpizza/blob/master/econpizza/examples/hank_climate.yml\n",
    "\n",
    "All the models from the paper, and some others, are included: https://github.com/gboehl/econpizza/tree/master/econpizza/examples\n",
    "\n",
    "That means, if you are on econpizza version 0.6.8 and above, the model for this exercise is included in the examples. You can simply do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_yaml = ep.examples.hank_climate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are on econpizza version 0.6.7 and below, you have to take the rocky road:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import tempfile\n",
    "import os\n",
    "\n",
    "url_yaml = \"https://raw.githubusercontent.com/gboehl/econpizza/refs/heads/master/econpizza/examples/hank_climate.yml\"\n",
    "url_funcs = \"https://raw.githubusercontent.com/gboehl/econpizza/refs/heads/master/econpizza/examples/hank_climate_functions.py\"\n",
    "\n",
    "# download yaml\n",
    "dl_yaml = requests.get(url_yaml)\n",
    "dl_funcs = requests.get(url_funcs)\n",
    "\n",
    "# write content to temporary directory\n",
    "tmpdir = tempfile.TemporaryDirectory()\n",
    "tmpdirname = tmpdir.name\n",
    "\n",
    "path_yaml = os.path.join(tmpdirname, 'hank_climate.yaml')\n",
    "path_funcs = os.path.join(tmpdirname, 'hank_climate_functions.py')\n",
    "\n",
    "with open(path_yaml, 'wb') as f:\n",
    "    f.write(dl_yaml.content)\n",
    "\n",
    "with open(path_funcs, 'wb') as f:\n",
    "    f.write(dl_funcs.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Either way, continue here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse the model\n",
    "mod_dict = ep.parse(path_yaml)\n",
    "\n",
    "# production values, meaning a large grid\n",
    "mod_dict['globals']['a_n'] = 200\n",
    "mod_dict['globals']['a_max'] = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating grid variables:\n",
      "    ...adding exogenous Rouwenhorst-grid for 'skills' with objects 'skills_grid', 'skills_transition' and 'skills_stationary'.\n",
      "    ...expecting the object 'a_grid' for the endogenous grid of 'a'.\n",
      "(load:) Parsing done.\n",
      "    Iteration   1 | max. error 6.48e+00 | lapsed 3.1879\n",
      "    Iteration   2 | max. error 2.01e+00 | lapsed 4.9018\n",
      "    Iteration   3 | max. error 5.03e-01 | lapsed 6.6663\n",
      "    Iteration   4 | max. error 6.19e-02 | lapsed 8.3698\n",
      "    Iteration   5 | max. error 1.32e-03 | lapsed 10.0492\n",
      "    Iteration   6 | max. error 6.30e-07 | lapsed 11.6833\n",
      "(solve_stst:) Steady state found (13.594s). The solution converged.\n"
     ]
    }
   ],
   "source": [
    "# load model and solve for steady state\n",
    "mod = ep.load(mod_dict, raise_errors=True)\n",
    "res_stst = mod.solve_stst(tol=1e-8, tol_forwards=1e-12, maxit=25, relaxation=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rearrange the model dict to express the model in paramters only\n",
    "mod_dict['steady_state']['fixed_values']['BR'] = mod.stst['BR']\n",
    "mod_dict['steady_state']['fixed_values']['AGR'] = mod.pars['AGR']\n",
    "mod_dict['steady_state']['fixed_values']['zRND'] = mod.pars['zRND']\n",
    "mod_dict['steady_state']['fixed_values']['z'] = mod.stst['z']\n",
    "mod_dict['steady_state']['init_guesses']['g2y'] = mod.pars['g2y']\n",
    "mod_dict['steady_state']['fixed_values']['g'] = mod.stst['g']\n",
    "del mod_dict['steady_state']['fixed_values']['g2y']\n",
    "del mod_dict['steady_state']['fixed_values']['BR2E']\n",
    "del mod_dict['steady_state']['fixed_values']['y']\n",
    "del mod_dict['steady_state']['fixed_values']['Am']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split in two different models for the two policies\n",
    "mod_dict_qbr = ep.deepcopy(mod_dict)\n",
    "mod_dict_qgr = ep.deepcopy(mod_dict)\n",
    "\n",
    "mod_dict_qbr['steady_state']['init_guesses']['tau_qBR'] = mod_dict_qbr['steady_state']['fixed_values']['tau_qBR']\n",
    "mod_dict_qgr['steady_state']['init_guesses']['tau_qGR'] = mod_dict_qgr['steady_state']['fixed_values']['tau_qGR']\n",
    "del mod_dict_qbr['steady_state']['fixed_values']['tau_qBR']\n",
    "del mod_dict_qgr['steady_state']['fixed_values']['tau_qGR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# require a reduction of carbon output by 50%\n",
    "reduction = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(anneal_stst:) 1 value(s) in queue. Trying BR=0.4549353112165853...\n",
      "    Iteration   1 | max. error 3.13e+00 | lapsed 3.2857\n",
      "    Iteration   2 | max. error 1.72e+00 | lapsed 5.3835\n",
      "    Iteration   3 | max. error 4.78e-01 | lapsed 7.2691\n",
      "    Iteration   4 | max. error 8.75e-02 | lapsed 9.0018\n",
      "    Iteration   5 | max. error 5.66e-03 | lapsed 10.7155\n",
      "    Iteration   6 | max. error 3.17e-05 | lapsed 12.4661\n",
      "(solve_stst:) Steady state found (14.3s). The solution converged.\n",
      "(anneal_stst:) Success! Values are:\n",
      "\n",
      "        Am: 2.0340570856652405\n",
      "        BR2E: 0.2921624442750476\n",
      "        C: 0.6060572216148036\n",
      "        div: 0.20604559766257702\n",
      "        divRND: 0.01709640674747383\n",
      "        e: 1.5571313840348515\n",
      "        GR: 1.1021960739350425\n",
      "        i: 0.17494451602277752\n",
      "        k: 6.997780640911102\n",
      "        kBR: 0.20696613739173134\n",
      "        kGR: 0.8727188300886733\n",
      "        ky: 5.918095674504696\n",
      "        n: 0.8762517649545152\n",
      "        pricing_f: 7.96624608359834\n",
      "        pricing_s: 7.96624608359834\n",
      "        qBR: 0.031076098238621678\n",
      "        qe: 0.05408692919525587\n",
      "        qGR: 0.05408692919525587\n",
      "        R: 1.008442125255524\n",
      "        rk: 0.02846201195388835\n",
      "        Rr: 1.0034620119538875\n",
      "        Rstar: 1.008442125255524\n",
      "        s: 0.029643721380325484\n",
      "        tau_qBR: 2.0969149308293114\n",
      "        tax: 0.24146174043581722\n",
      "        Top10A: 0.541970229278149\n",
      "        Top10C: 0.31764353231838444\n",
      "        VE: 0.7312073127283027\n",
      "        w: 0.6728011377445029\n",
      "        xi: 1.372335853986115\n",
      "        y: 1.0106454589920455\n",
      "        g2y: 0.19789333462148784\n"
     ]
    }
   ],
   "source": [
    "# calculate steady state for tax model\n",
    "mod_qbr, _ = ep.tools.anneal_stst(mod_dict_qbr, ('steady_state', 'fixed_values', 'BR'), mod.stst['BR']*(1-reduction), tol=1e-8, tol_forwards=1e-11, maxit=15, max_sequence=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(anneal_stst:) 1 value(s) in queue. Trying BR=0.4549353112165853...\n",
      "    Iteration   1 | max. error 3.13e+00 | lapsed 3.3051\n",
      "    Iteration   2 | max. error 3.53e+00 | lapsed 5.0861\n",
      "    Iteration   3 | max. error 4.11e-01 | lapsed 6.7744\n",
      "    Iteration   4 | max. error 5.39e-02 | lapsed 8.4670\n",
      "    Iteration   5 | max. error 3.47e-04 | lapsed 10.2122\n",
      "    Iteration   6 | max. error 3.02e-07 | lapsed 11.9130\n",
      "(solve_stst:) Steady state found (13.668s). The solution converged.\n",
      "(anneal_stst:) Success! Values are:\n",
      "\n",
      "        Am: 4.379454401212591\n",
      "        BR2E: 0.14175823369266047\n",
      "        C: 0.6586228176550092\n",
      "        div: 0.2281337379057634\n",
      "        divRND: 0.01627528595767377\n",
      "        e: 3.209233773354869\n",
      "        GR: 2.7542984621382836\n",
      "        i: 0.22693619733580814\n",
      "        k: 9.077447893432327\n",
      "        kBR: 0.20696613739173134\n",
      "        kGR: 1.8625116818903242\n",
      "        ky: 7.007970074150273\n",
      "        n: 0.9048893349948621\n",
      "        pricing_f: 7.745111426293026\n",
      "        pricing_s: 7.745111426293026\n",
      "        qBR: 0.029845749161151344\n",
      "        qe: 0.029845749161151344\n",
      "        qGR: 0.04436299345900585\n",
      "        R: 1.0073096792559988\n",
      "        rk: 0.02733515845118911\n",
      "        Rr: 1.002335158451189\n",
      "        Rstar: 1.007309679255999\n",
      "        s: 0.06382481939771785\n",
      "        tau_qGR: 0.7209123560505133\n",
      "        tax: 0.2750122010046032\n",
      "        Top10A: 0.562201124267367\n",
      "        Top10C: 0.31846227325083276\n",
      "        VE: 0.7303861920153429\n",
      "        w: 0.7409457460310538\n",
      "        xi: 1.3723358538384494\n",
      "        y: 1.1493838343885352\n",
      "        g2y: 0.17400627537657928\n"
     ]
    }
   ],
   "source": [
    "# calculate steady state for subsidy model\n",
    "mod_qgr, _ = ep.tools.anneal_stst(mod_dict_qgr, ('steady_state', 'fixed_values', 'BR'), mod.stst['BR']*(1-reduction), tol=1e-8, tol_forwards=1e-11, maxit=15, max_sequence=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Level of cabon tax:  0.7404671840150431\n",
      "Level of green subsidy:  -0.3272377079619153\n"
     ]
    }
   ],
   "source": [
    "print('Level of cabon tax: ', jnp.log(mod_qbr.stst['tau_qBR']))\n",
    "print('Level of green subsidy: ', jnp.log(mod_qgr.stst['tau_qGR']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup to simulate transitions\n",
    "init_stst = mod['stst'].copy()\n",
    "init_dist = mod['steady_state']['distributions']\n",
    "\n",
    "horizon = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(get_derivatives:) Derivatives calculation done (8.419s).\n",
      "(get_jacobian:) Jacobian accumulation and decomposition done (22.117s).\n",
      "    Iteration  1 | fev.  12 | error 1.18e+00 | inner 4.42e-06 | dampening 1.000\n",
      "    Iteration  2 | fev.  24 | error 3.22e-01 | inner 4.58e-06 | dampening 1.000\n",
      "    Iteration  3 | fev.  36 | error 8.54e-02 | inner 7.19e-06 | dampening 1.000\n",
      "    Iteration  4 | fev.  41 | error 3.15e-03 | inner 8.49e-06 | dampening 1.000\n",
      "    Iteration  5 | fev.  45 | error 4.72e-06 | inner 4.39e-07 | dampening 1.000\n",
      "    Iteration  6 | fev.  49 | error 2.42e-07 | inner 2.25e-08 | dampening 1.000\n",
      "    Iteration  7 | fev.  79 | error 1.25e-08 | inner 7.88e-08 | dampening 0.912\n",
      "    Iteration  8 | fev.  84 | error 7.57e-08 | inner 5.44e-09 | dampening 0.697\n",
      "    Iteration  9 | fev.  89 | error 2.72e-09 | inner 1.96e-10 | dampening 0.697 | lapsed 41.5497s\n",
      "(find_path:) Stacking done (72.305s). The solution converged.\n"
     ]
    }
   ],
   "source": [
    "# simulate tax model transition\n",
    "xst_qbr, flags = mod_qbr.find_path(init_state=init_stst.values(), init_dist=init_dist, horizon=horizon, tol=1e-8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(get_derivatives:) Derivatives calculation done (3.899s).\n",
      "(get_jacobian:) Jacobian accumulation and decomposition done (21.662s).\n",
      "    Iteration  1 | fev.  18 | error 2.63e+00 | inner 9.08e-06 | dampening 1.000\n",
      "    Iteration  2 | fev.  48 | error 1.99e+00 | inner 7.83e-05 | dampening 1.000\n",
      "    Iteration  3 | fev.  78 | error 1.58e+00 | inner 2.50e-02 | dampening 1.000\n",
      "    Iteration  4 | fev. 108 | error 1.31e+00 | inner 2.55e-02 | dampening 1.000\n",
      "    Iteration  5 | fev. 138 | error 9.94e-01 | inner 3.13e-05 | dampening 1.000\n",
      "    Iteration  6 | fev. 168 | error 7.73e-01 | inner 2.38e+00 | dampening 1.000\n",
      "    Iteration  7 | fev. 198 | error 1.16e+00 | inner 1.14e+01 | dampening 0.880\n",
      "    Iteration  8 | fev. 228 | error 3.60e+00 | inner 4.98e+01 | dampening 0.281\n",
      "    Iteration  9 | fev. 258 | error 2.11e+00 | inner 7.88e+00 | dampening 0.640\n",
      "    Iteration 10 | fev. 288 | error 1.82e+00 | inner 4.92e-01 | dampening 0.346\n",
      "    Iteration 11 | fev. 318 | error 1.37e+00 | inner 3.53e-01 | dampening 0.367\n",
      "    Iteration 12 | fev. 348 | error 1.04e+00 | inner 1.22e+00 | dampening 0.394\n",
      "    Iteration 13 | fev. 378 | error 7.57e-01 | inner 4.42e+00 | dampening 0.434\n",
      "    Iteration 14 | fev. 408 | error 5.99e-01 | inner 6.72e+00 | dampening 0.545\n",
      "    Iteration 15 | fev. 438 | error 1.51e+00 | inner 3.91e-03 | dampening 0.298\n",
      "    Iteration 16 | fev. 468 | error 1.22e+00 | inner 3.21e-03 | dampening 0.310\n",
      "    Iteration 17 | fev. 498 | error 9.75e-01 | inner 2.63e-03 | dampening 0.321\n",
      "    Iteration 18 | fev. 528 | error 7.83e-01 | inner 2.15e-03 | dampening 0.330\n",
      "    Iteration 19 | fev. 558 | error 6.27e-01 | inner 1.77e-03 | dampening 0.338\n",
      "    Iteration 20 | fev. 588 | error 5.03e-01 | inner 1.45e-03 | dampening 0.345\n",
      "    Iteration 21 | fev. 618 | error 4.03e-01 | inner 1.19e-03 | dampening 0.350\n",
      "    Iteration 22 | fev. 648 | error 3.23e-01 | inner 9.74e-04 | dampening 0.355\n",
      "    Iteration 23 | fev. 678 | error 2.59e-01 | inner 7.99e-04 | dampening 0.359\n",
      "    Iteration 24 | fev. 708 | error 2.07e-01 | inner 6.55e-04 | dampening 0.362\n",
      "    Iteration 25 | fev. 738 | error 1.66e-01 | inner 5.37e-04 | dampening 0.365\n",
      "    Iteration 26 | fev. 768 | error 1.33e-01 | inner 4.40e-04 | dampening 0.367\n",
      "    Iteration 27 | fev. 798 | error 1.06e-01 | inner 3.61e-04 | dampening 0.369\n",
      "    Iteration 28 | fev. 828 | error 8.50e-02 | inner 2.96e-04 | dampening 0.371\n",
      "    Iteration 29 | fev. 858 | error 6.80e-02 | inner 2.42e-04 | dampening 0.372\n",
      "    Iteration 30 | fev. 888 | error 5.44e-02 | inner 1.98e-04 | dampening 0.374\n",
      "    Iteration 31 | fev. 918 | error 4.36e-02 | inner 1.63e-04 | dampening 0.375\n",
      "    Iteration 32 | fev. 948 | error 3.49e-02 | inner 1.33e-04 | dampening 0.376\n",
      "    Iteration 33 | fev. 978 | error 2.79e-02 | inner 1.09e-04 | dampening 0.377\n",
      "    Iteration 34 | fev. 1008 | error 2.23e-02 | inner 8.91e-05 | dampening 0.377\n",
      "    Iteration 35 | fev. 1038 | error 1.79e-02 | inner 7.29e-05 | dampening 0.378\n",
      "    Iteration 36 | fev. 1068 | error 1.43e-02 | inner 5.97e-05 | dampening 0.379\n",
      "    Iteration 37 | fev. 1098 | error 1.14e-02 | inner 4.88e-05 | dampening 0.379\n",
      "    Iteration 38 | fev. 1128 | error 9.14e-03 | inner 3.99e-05 | dampening 0.380\n",
      "    Iteration 39 | fev. 1158 | error 7.32e-03 | inner 3.26e-05 | dampening 0.381\n",
      "    Iteration 40 | fev. 1188 | error 5.85e-03 | inner 2.67e-05 | dampening 0.381\n",
      "    Iteration 41 | fev. 1218 | error 4.68e-03 | inner 2.18e-05 | dampening 0.382\n",
      "    Iteration 42 | fev. 1248 | error 3.75e-03 | inner 1.78e-05 | dampening 0.382\n",
      "    Iteration 43 | fev. 1278 | error 3.00e-03 | inner 1.46e-05 | dampening 0.383\n",
      "    Iteration 44 | fev. 1308 | error 2.40e-03 | inner 1.19e-05 | dampening 0.383\n",
      "    Iteration 45 | fev. 1338 | error 1.92e-03 | inner 9.72e-06 | dampening 0.384\n",
      "    Iteration 46 | fev. 1365 | error 1.54e-03 | inner 9.84e-06 | dampening 0.385\n",
      "    Iteration 47 | fev. 1390 | error 1.23e-03 | inner 9.31e-06 | dampening 0.385\n",
      "    Iteration 48 | fev. 1412 | error 9.83e-04 | inner 9.51e-06 | dampening 0.386\n",
      "    Iteration 49 | fev. 1431 | error 7.86e-04 | inner 9.78e-06 | dampening 0.388\n",
      "    Iteration 50 | fev. 1449 | error 6.30e-04 | inner 8.80e-06 | dampening 0.389\n",
      "    Iteration 51 | fev. 1467 | error 5.03e-04 | inner 7.62e-06 | dampening 0.392\n",
      "    Iteration 52 | fev. 1484 | error 4.02e-04 | inner 9.96e-06 | dampening 0.394\n",
      "    Iteration 53 | fev. 1501 | error 3.23e-04 | inner 9.69e-06 | dampening 0.396\n",
      "    Iteration 54 | fev. 1518 | error 2.59e-04 | inner 9.43e-06 | dampening 0.398\n",
      "    Iteration 55 | fev. 1535 | error 2.09e-04 | inner 9.14e-06 | dampening 0.400\n",
      "    Iteration 56 | fev. 1552 | error 1.68e-04 | inner 8.81e-06 | dampening 0.402\n",
      "    Iteration 57 | fev. 1569 | error 1.35e-04 | inner 8.42e-06 | dampening 0.403\n",
      "    Iteration 58 | fev. 1585 | error 1.09e-04 | inner 9.40e-06 | dampening 0.405\n",
      "    Iteration 59 | fev. 1604 | error 8.63e-05 | inner 7.72e-06 | dampening 0.411\n",
      "    Iteration 60 | fev. 1623 | error 6.99e-05 | inner 6.74e-06 | dampening 0.411\n",
      "    Iteration 61 | fev. 1643 | error 5.66e-05 | inner 5.08e-06 | dampening 0.412\n",
      "    Iteration 62 | fev. 1667 | error 4.48e-05 | inner 4.06e-06 | dampening 0.417\n",
      "    Iteration 63 | fev. 1695 | error 3.56e-05 | inner 3.42e-06 | dampening 0.421\n",
      "    Iteration 64 | fev. 1725 | error 2.83e-05 | inner 3.49e-06 | dampening 0.424\n",
      "    Iteration 65 | fev. 1755 | error 2.25e-05 | inner 4.32e-06 | dampening 0.428\n",
      "    Iteration 66 | fev. 1785 | error 1.77e-05 | inner 6.34e-06 | dampening 0.434\n",
      "    Iteration 67 | fev. 1815 | error 1.37e-05 | inner 1.36e-05 | dampening 0.445\n",
      "    Iteration 68 | fev. 1845 | error 9.14e-06 | inner 6.43e-05 | dampening 0.446\n",
      "    Iteration 69 | fev. 1859 | error 1.19e-05 | inner 9.42e-07 | dampening 0.384\n",
      "    Iteration 70 | fev. 1875 | error 9.34e-06 | inner 7.18e-07 | dampening 0.390\n",
      "    Iteration 71 | fev. 1893 | error 7.36e-06 | inner 6.06e-07 | dampening 0.396\n",
      "    Iteration 72 | fev. 1913 | error 5.79e-06 | inner 5.69e-07 | dampening 0.402\n",
      "    Iteration 73 | fev. 1937 | error 4.54e-06 | inner 4.51e-07 | dampening 0.408\n",
      "    Iteration 74 | fev. 1967 | error 3.55e-06 | inner 3.25e-07 | dampening 0.414\n",
      "    Iteration 75 | fev. 1997 | error 2.79e-06 | inner 5.11e-07 | dampening 0.418\n",
      "    Iteration 76 | fev. 2027 | error 2.14e-06 | inner 1.42e-06 | dampening 0.427\n",
      "    Iteration 77 | fev. 2057 | error 1.44e-06 | inner 2.47e-05 | dampening 0.441\n",
      "    Iteration 78 | fev. 2065 | error 3.80e-06 | inner 3.12e-07 | dampening 0.348\n",
      "    Iteration 79 | fev. 2073 | error 3.00e-06 | inner 2.89e-07 | dampening 0.351\n",
      "    Iteration 80 | fev. 2082 | error 2.37e-06 | inner 1.67e-07 | dampening 0.355\n",
      "    Iteration 81 | fev. 2091 | error 1.91e-06 | inner 1.59e-07 | dampening 0.358\n",
      "    Iteration 82 | fev. 2100 | error 1.55e-06 | inner 1.53e-07 | dampening 0.361\n",
      "    Iteration 83 | fev. 2110 | error 1.26e-06 | inner 9.88e-08 | dampening 0.364\n",
      "    Iteration 84 | fev. 2121 | error 9.96e-07 | inner 7.29e-08 | dampening 0.369\n",
      "    Iteration 85 | fev. 2132 | error 8.07e-07 | inner 7.53e-08 | dampening 0.373\n",
      "    Iteration 86 | fev. 2144 | error 6.56e-07 | inner 5.59e-08 | dampening 0.377\n",
      "    Iteration 87 | fev. 2157 | error 5.17e-07 | inner 5.17e-08 | dampening 0.383\n",
      "    Iteration 88 | fev. 2171 | error 4.21e-07 | inner 4.09e-08 | dampening 0.387\n",
      "    Iteration 89 | fev. 2188 | error 3.31e-07 | inner 2.82e-08 | dampening 0.394\n",
      "    Iteration 90 | fev. 2206 | error 2.69e-07 | inner 2.25e-08 | dampening 0.396\n",
      "    Iteration 91 | fev. 2227 | error 2.12e-07 | inner 1.88e-08 | dampening 0.403\n",
      "    Iteration 92 | fev. 2248 | error 1.73e-07 | inner 1.65e-08 | dampening 0.404\n",
      "    Iteration 93 | fev. 2270 | error 1.41e-07 | inner 1.19e-08 | dampening 0.404\n",
      "    Iteration 94 | fev. 2296 | error 1.11e-07 | inner 9.98e-09 | dampening 0.410\n",
      "    Iteration 95 | fev. 2326 | error 8.68e-08 | inner 9.86e-09 | dampening 0.415\n",
      "    Iteration 96 | fev. 2356 | error 6.77e-08 | inner 1.85e-08 | dampening 0.421\n",
      "    Iteration 97 | fev. 2386 | error 5.08e-08 | inner 9.71e-08 | dampening 0.435\n",
      "    Iteration 98 | fev. 2416 | error 2.16e-08 | inner 1.55e-07 | dampening 0.434\n",
      "    Iteration 99 | fev. 2446 | error 1.28e-08 | inner 6.62e-08 | dampening 0.428\n",
      "    Iteration 100 | fev. 2476 | error 6.76e-09 | inner 4.87e-07 | dampening 0.441 | lapsed 981.6199s\n",
      "(find_path:) Stacking done (1007.196s). The solution converged.\n"
     ]
    }
   ],
   "source": [
    "# simulate subsidy model transition\n",
    "xst_qgr, flags = mod_qgr.find_path(init_state=init_stst.values(), init_dist=init_dist, horizon=horizon, tol=1e-8, relaxation=.2, maxit=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting setup\n",
    "xstst_ini = jnp.ones_like(xst_qbr)*ep.parser.d2jnp(init_stst)\n",
    "xstst_qbr = jnp.ones_like(xst_qbr)*ep.parser.d2jnp(mod_qbr.stst)\n",
    "xstst_qgr = jnp.ones_like(xst_qgr)*ep.parser.d2jnp(mod_qgr.stst)\n",
    "\n",
    "# variable choice\n",
    "choice = {\n",
    "          'qe': 'energy prices',\n",
    "          'qBR': 'brown energy prices',\n",
    "          'qGR': 'green energy prices',\n",
    "          'e': 'total energy',\n",
    "            #\n",
    "          'y': 'output',\n",
    "          'C': 'consumption',\n",
    "          'pi': 'inflation',\n",
    "          'Rr': 'real interest rate',\n",
    "            #\n",
    "          'BR': 'brown energy',\n",
    "          'GR': 'green energy',\n",
    "          's': r'R\\&D investment',\n",
    "          'Am': r'green R\\&D',\n",
    "            #\n",
    "          'k': 'total capital',\n",
    "          'ky': 'capital: final good',\n",
    "          'kBR': 'capital: brown energy',\n",
    "          'kGR': 'capital: green energy',\n",
    "            #\n",
    "          'n': 'labor',\n",
    "          'w': 'wages',\n",
    "          'rk': 'rental rate of capital',\n",
    "          'tax': 'lump-sum taxes',\n",
    "          }\n",
    "\n",
    "# more setup\n",
    "labels = None, 'carbon tax', 'green subsidy', None, None\n",
    "inds = [mod.var_names.index(c) for c in list(choice.keys())]\n",
    "plot_horizon = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define function for variable transformations\n",
    "def transform(x):\n",
    "    x = x[:plot_horizon,inds]\n",
    "    xss = xstst_ini[:plot_horizon,inds]\n",
    "    x /= xss\n",
    "    x = x.at[:,6].multiply(xss[:,6])\n",
    "    x = x.at[:,6].power(4)\n",
    "    x = x.at[:,7].multiply(xss[:,7])\n",
    "    x = x.at[:,7].power(4)\n",
    "    x = x.at[:,-2].set((1+x[:,-2]*xss[:,-2])**4)\n",
    "    x -= 1\n",
    "    x *= 100\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x900 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, axs = figurator(5,4, figsize=(10,9))\n",
    "pplot((transform(xstst_ini), transform(xst_qbr), transform(xst_qgr), transform(xstst_qbr), transform(xstst_qgr)),\n",
    "      xscale=jnp.arange(plot_horizon)/4,\n",
    "      styles=('-', '-', '-', '--', '--'), colors=('gray', 'C0', 'C1', 'C0', 'C1'), ax=axs, labels=list(choice.values()), legend=labels)\n",
    "axs[2].legend(framealpha=0)\n",
    "[ax.set_xlabel('years') for ax in axs[-4:]]\n",
    "[ax.set_xticks((0,30)) for ax in axs]\n",
    "figs[0].tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get values to calculate welfare\n",
    "a_grid = mod['context']['a_grid']\n",
    "e_stat = mod['context']['skills_grid']\n",
    "het_qgr = mod_qgr.get_distributions(xst_qgr, init_dist=init_dist)\n",
    "het_qbr = mod_qbr.get_distributions(xst_qbr, init_dist=init_dist)\n",
    "values_stst_ini = mod['steady_state']['value_functions'][0]\n",
    "\n",
    "tolifetime = lambda V: ((1-mod.stst['beta'])*(1-mod.pars['sigma_c'])*V)**(1/(1-mod.pars['sigma_c']))\n",
    "interpolate_numpy = jnp.vectorize(jnp.interp, signature='(nq),(n),(n)->(nq)')\n",
    "xs = jnp.linspace(0,1,4)\n",
    "\n",
    "# for climate tax\n",
    "values_qgr = het_qgr['VPrime']\n",
    "dists_qgr = het_qgr['dist']\n",
    "binned_weight_qgr = dists_qgr.sum(0).cumsum(0)\n",
    "binned_values_qgr = (dists_qgr*tolifetime(values_qgr)).sum(0).cumsum(0)\n",
    "bins_aggr_qgr = interpolate_numpy(xs[1:], binned_weight_qgr.T, binned_values_qgr.T)\n",
    "welf_aggr_qgr = (dists_qgr*values_qgr).sum((0,1))\n",
    "min_values_qgr = jnp.min(values_qgr, (0,1))\n",
    "\n",
    "# for green subsidy\n",
    "values_qbr = het_qbr['VPrime']\n",
    "dists_qbr = het_qbr['dist']\n",
    "binned_weight_qbr = dists_qbr.sum(0).cumsum(0)\n",
    "binned_values_qbr = (dists_qbr*tolifetime(values_qbr)).sum(0).cumsum(0)\n",
    "bins_aggr_qbr = interpolate_numpy(xs[1:], binned_weight_qbr.T, binned_values_qbr.T)\n",
    "welf_aggr_qbr = (dists_qbr*values_qbr).sum((0,1))\n",
    "min_values_qbr = jnp.min(values_qbr, (0,1))\n",
    "\n",
    "ref_util = jnp.sum(values_stst_ini*init_dist)\n",
    "ref_mima = jnp.min(values_stst_ini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get values to calculate CEVs\n",
    "a_grid = mod['context']['a_grid']\n",
    "e_stat = mod['context']['skills_stationary']\n",
    "dist = init_dist[0]\n",
    "bins = jnp.linspace(0,1,10)\n",
    "\n",
    "# calculate consumption equivalent variation per CDF for now\n",
    "cev = 100*((values_qgr[...,0]/values_stst_ini)**(1/(1-mod.pars['sigma_c'])) - 1)\n",
    "cev_a = jnp.sum(cev*e_stat[:,None], 0)\n",
    "dist_a = jnp.sum(dist, 0)\n",
    "cev_in_bins_qgr = jnp.diff(jnp.interp(bins[1:], dist_a.cumsum(), (dist_a*cev_a).cumsum()), prepend=0)*10\n",
    "\n",
    "cev = 100*((values_qbr[...,0]/values_stst_ini)**(1/(1-mod.pars['sigma_c'])) - 1)\n",
    "cev_a = jnp.sum(cev*e_stat[:,None], 0)\n",
    "dist_a = jnp.sum(dist, 0)\n",
    "cev_in_bins_qbr = jnp.diff(jnp.interp(bins[1:], dist_a.cumsum(), (dist_a*cev_a).cumsum()), prepend=0)*10\n",
    "\n",
    "# calculate consumption equivalent variation per CDF for then\n",
    "cev = 100*((values_qgr[...,30*4]/values_stst_ini)**(1/(1-mod.pars['sigma_c'])) - 1)\n",
    "cev_a = jnp.sum(cev*e_stat[:,None], 0)\n",
    "dist_a = jnp.sum(dist, 0)\n",
    "dist_a_then = jnp.sum(dists_qgr[...,30*4], 0)\n",
    "cev_in_bins_qgr_then = jnp.diff(jnp.interp(bins[1:], dist_a_then.cumsum(), (dist_a_then*cev_a).cumsum()), prepend=0)*10\n",
    "\n",
    "cev = 100*((values_qbr[...,30*4]/values_stst_ini)**(1/(1-mod.pars['sigma_c'])) - 1)\n",
    "cev_a = jnp.sum(cev*e_stat[:,None], 0)\n",
    "dist_a = jnp.sum(dist, 0)\n",
    "dist_a_then = jnp.sum(dists_qbr[...,30*4], 0)\n",
    "cev_in_bins_qbr_then = jnp.diff(jnp.interp(bins[1:], dist_a_then.cumsum(), (dist_a*cev_a).cumsum()), prepend=0)*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot difference in utilities\n",
    "fig, axs = plt.subplots(2)\n",
    "axs[0].bar(bins[:-1], cev_in_bins_qbr, width=0.1, alpha=0.3, align='edge', label='carbon tax')\n",
    "axs[0].bar(bins[:-1], cev_in_bins_qgr, width=0.1, alpha=0.3, align='edge', label='green subsidy')\n",
    "axs[0].set_ylabel('CEV \\\\%')\n",
    "axs[1].bar(bins[:-1], cev_in_bins_qbr_then, width=0.1, alpha=0.3, align='edge', label='carbon tax')\n",
    "axs[1].bar(bins[:-1], cev_in_bins_qgr_then, width=0.1, alpha=0.3, align='edge', label='green subsidy')\n",
    "axs[1].set_xlabel('CDF')\n",
    "axs[1].set_ylabel('CEV \\\\%')\n",
    "axs[0].legend(framealpha=0)\n",
    "axs[0].set_title('Current generation')\n",
    "axs[1].set_title('Next generation')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting\n",
    "fig, axs = plt.subplots(2, sharex=True)\n",
    "axs[0].set_title('Utilitarian welfare')\n",
    "axs[0].plot(jnp.arange(plot_horizon)/4, welf_aggr_qbr[:plot_horizon], label='carbon tax')\n",
    "axs[0].plot(jnp.arange(plot_horizon)/4, welf_aggr_qgr[:plot_horizon], label='green subsidy')\n",
    "axs[0].plot(jnp.arange(plot_horizon)/4, jnp.ones(plot_horizon)*ref_util, label='no policy', c='k', ls='--', alpha=.6)\n",
    "axs[1].set_title('Max-min welfare')\n",
    "axs[1].plot(jnp.arange(plot_horizon)/4, min_values_qbr[:plot_horizon], label='carbon tax')\n",
    "axs[1].plot(jnp.arange(plot_horizon)/4, min_values_qgr[:plot_horizon], label='green subsidy')\n",
    "axs[1].plot(jnp.arange(plot_horizon)/4, jnp.ones(plot_horizon)*ref_mima, label='no policy', c='k', ls='--', alpha=.6)\n",
    "axs[1].set_xlabel('years')\n",
    "axs[0].legend(framealpha=.3)\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}