{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "77b94cf9",
   "metadata": {},
   "source": [
    "# TP5 — Optimisation numérique dans DICE\n",
    "\n",
    "**Séance du 16 septembre 2026 — M2 Actuariat, Sorbonne Université**\n",
    "\n",
    "Ce notebook traite de la tarification du carbone et du contrôle optimal. Nous passons des scénarios — ce qui pourrait arriver — au problème du planificateur social — ce qui devrait arriver — puis calculons numériquement le coût social du carbone (CSC).\n",
    "\n",
    "**Fichier de modèle :** le notebook charge `toolbox/DICE.py`, fourni avec la séance.\n",
    "\n",
    "**Programme**\n",
    "1. Charger le modèle et examiner les états et les contrôles.\n",
    "2. Définir l’objectif du planificateur et l’approximation à horizon fini.\n",
    "3. Résoudre le modèle avec une fenêtre glissante et lire le CSC.\n",
    "4. Étudier la sensibilité aux dommages et exporter les résultats.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d86b13",
   "metadata": {},
   "source": [
    "## 1) Préparation : installer les dépendances et charger DICE\n",
    "\n",
    "Exécutez la cellule de préparation, puis les cellules suivantes. Les paquets pip manquants sont installés ; `DICE.py` est fourni dans `toolbox/`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "athena-dependency-setup",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:08.296795Z",
     "iopub.status.busy": "2026-09-16T08:20:08.296471Z",
     "iopub.status.idle": "2026-09-16T08:20:08.304626Z",
     "shell.execute_reply": "2026-09-16T08:20:08.303549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environnement Python et module DICE prêts.\n"
     ]
    }
   ],
   "source": [
    "# Préparation commune des TP Python\n",
    "# Le notebook utilise uniquement les ressources fournies dans le dossier codes/.\n",
    "from pathlib import Path\n",
    "import importlib.util\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "REQUIRED_PACKAGES = {'numpy': 'numpy', 'pandas': 'pandas', 'matplotlib': 'matplotlib', 'scipy': 'scipy', 'numba': 'numba', 'tqdm': 'tqdm'}\n",
    "missing = [package for module, package in REQUIRED_PACKAGES.items()\n",
    "           if importlib.util.find_spec(module) is None]\n",
    "if missing:\n",
    "    print(\"Installation des paquets manquants :\", \", \".join(missing))\n",
    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", *missing])\n",
    "\n",
    "WORKDIR = Path.cwd().resolve()\n",
    "CODES_DIR = WORKDIR.parent if WORKDIR.name == \"correction\" else WORKDIR\n",
    "TOOLBOX_DIR = CODES_DIR / \"toolbox\"\n",
    "DATA_DIR = CODES_DIR / \"data\"\n",
    "if not CODES_DIR.is_dir():\n",
    "    raise FileNotFoundError(\"Exécutez le notebook depuis le dossier codes/.\" )\n",
    "if TOOLBOX_DIR.is_dir():\n",
    "    sys.path.insert(0, str(TOOLBOX_DIR))\n",
    "\n",
    "DICE_FILE = TOOLBOX_DIR / \"DICE.py\"\n",
    "if not DICE_FILE.is_file():\n",
    "    raise FileNotFoundError(\n",
    "        f\"Module du cours introuvable : {DICE_FILE}. \"\n",
    "        \"Téléchargez le dossier codes complet, avec toolbox/.\"\n",
    "    )\n",
    "\n",
    "print(\"Environnement prêt :\", CODES_DIR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46864897",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:08.306309Z",
     "iopub.status.busy": "2026-09-16T08:20:08.306147Z",
     "iopub.status.idle": "2026-09-16T08:20:09.600692Z",
     "shell.execute_reply": "2026-09-16T08:20:09.599938Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paramètres DICE chargés ; nombre de périodes : 18\n"
     ]
    }
   ],
   "source": [
    "import importlib.util\n",
    "\n",
    "if importlib.util.find_spec(\"DICE\") is None:\n",
    "    raise ModuleNotFoundError(\n",
    "        \"DICE.py est introuvable. Réexécutez la cellule de préparation.\"\n",
    "    )\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from DICE import (\n",
    "    Params,\n",
    "    init_states,\n",
    "    update_path,\n",
    "    mat_to_df,\n",
    "    obj_fun,\n",
    "    run_optimal_policy,\n",
    ")\n",
    "\n",
    "p = Params()\n",
    "print(\"Paramètres DICE chargés ; nombre de périodes :\", p.nT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90998489",
   "metadata": {},
   "source": [
    "## 2) Structure du modèle\n",
    "\n",
    "- **États :** capital $K_t$, productivité $A_t$, population $L_t$, stocks de carbone $M_t^{AT},M_t^{UP},M_t^{LO}$ et températures $T_t^{AT},T_t^{LO}$.\n",
    "- **Contrôles :** taux d’épargne $s_t$ et taux de réduction des émissions $\\mu_t$ ; ce dernier détermine implicitement le prix du carbone.\n",
    "- **Unités :** $C_t$ est en milliers de milliards de dollars et $L_t$ en millions d’habitants, donc $c_t=1000C_t/L_t$ est en milliers de dollars par habitant.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a7b46f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:09.602042Z",
     "iopub.status.busy": "2026-09-16T08:20:09.601849Z",
     "iopub.status.idle": "2026-09-16T08:20:10.009020Z",
     "shell.execute_reply": "2026-09-16T08:20:10.008142Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns: ['time', 'A', 's', 'Y', 'Q', 'C', 'K', 'L', 'sigma', 'theta1', 'mu', 'E', 'F', 'T_AT', 'T_LO', 'E_land', 'F_EX', 'M_AT', 'M_UP', 'M_LO', 'Tax']\n",
      "Longueur de l’horizon (périodes) : 18  — années: 2100.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Paramètres instantanés et construire un chemin initial\n",
    "p = Params()\n",
    "sim = init_states(p)\n",
    "\n",
    "# Contrôles de référence pour l'initialisation (pas optimaux): petite réduction, économie constante\n",
    "sim[:, p.i_mu] = 0.03\n",
    "sim[:, p.i_s]  = 0.20\n",
    "\n",
    "# Mise à jour du chemin pour toutes les variables endogènes compte tenu des contrôles\n",
    "timevec = range(1, p.nT)\n",
    "sim = update_path(sim, timevec, p)\n",
    "\n",
    "# Peek at available columns\n",
    "print(\"Columns:\", p.col)\n",
    "print(\"Longueur de l’horizon (périodes) :\", p.nT, \" — années:\", sim[-1, p.i_time])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c6f853",
   "metadata": {},
   "source": [
    "## 3) Objectif du planificateur\n",
    "\n",
    "Nous maximisons le bien-être social actualisé\n",
    "$$\n",
    "W=\\sum_{i=0}^{I^\\star}\\beta_\\Delta^iL_{t+i\\Delta}u\\!\\left(1000\\frac{C_{t+i\\Delta}}{L_{t+i\\Delta}}\\right),\\qquad \\beta_\\Delta=(1+\\rho)^{-\\Delta},\n",
    "$$\n",
    "avec une utilité CRRA $u(c)=(c^{1-\\gamma}-1)/(1-\\gamma)$. Le module fournit la fonction `obj_fun`.\n",
    "\n",
    "L’horizon infini est remplacé par un horizon fini $I^\\star$ dont la contribution résiduelle est inférieure à une tolérance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "714a7150",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:10.010393Z",
     "iopub.status.busy": "2026-09-16T08:20:10.010265Z",
     "iopub.status.idle": "2026-09-16T08:20:10.013329Z",
     "shell.execute_reply": "2026-09-16T08:20:10.012542Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective value (negative welfare) on constant s=0.20 path: -173569.0673395565\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Examiner l'objectif sur un chemin de contrôle constant (contrôle de la sanité)\n",
    "# Ici, nous 'flattons' un contrôle (par exemple, l'épargne seulement) et évaluons le bien-être.\n",
    "x_const = np.full(p.nT-1, 0.20)  # constant saving\n",
    "W_val = obj_fun(x_const, sim, timevec, p, [p.i_s])\n",
    "print(\"Objective value (negative welfare) on constant s=0.20 path:\", W_val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b23158dc",
   "metadata": {},
   "source": [
    "## 4) Fenêtre de planification à horizon fini $I^\\star$\n",
    "\n",
    "La fenêtre est choisie de façon que le poids d’actualisation du dernier terme soit inférieur à `p.toly`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca736d64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:10.014587Z",
     "iopub.status.busy": "2026-09-16T08:20:10.014450Z",
     "iopub.status.idle": "2026-09-16T08:20:10.017390Z",
     "shell.execute_reply": "2026-09-16T08:20:10.016683Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fenêtre de planification retenue (périodes) : 94  (each period = Δ années = 5 )\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculer une fenêtre de planification analogue à celle de DICE.run_optimal_policy\n",
    "disc, Tplanner = 1.0, 1\n",
    "while disc > p.toly:\n",
    "    Tplanner += 1\n",
    "    disc *= (1.0/(1.0 + p.rho))**p.Delta\n",
    "\n",
    "print(\"Fenêtre de planification retenue (périodes) :\", Tplanner, \" (each period = Δ années =\", p.Delta, \")\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "281626cc",
   "metadata": {},
   "source": [
    "## 5) Premier problème : optimiser seulement l’épargne\n",
    "\n",
    "Nous optimisons $s_t$ dans ses bornes et maintenons $\\mu_t$ constant. Cette solution constitue la référence sans transition.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ad344e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:10.018648Z",
     "iopub.status.busy": "2026-09-16T08:20:10.018533Z",
     "iopub.status.idle": "2026-09-16T08:20:18.841070Z",
     "shell.execute_reply": "2026-09-16T08:20:18.839684Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Résolution : trajectoire optimale de l’épargne...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:   0%|          | 0/17 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:   6%|▌         | 1/17 [00:00<00:11,  1.45it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  12%|█▏        | 2/17 [00:01<00:09,  1.54it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  18%|█▊        | 3/17 [00:01<00:08,  1.63it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  24%|██▎       | 4/17 [00:02<00:07,  1.65it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  29%|██▉       | 5/17 [00:03<00:07,  1.70it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  35%|███▌      | 6/17 [00:03<00:05,  1.91it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  41%|████      | 7/17 [00:04<00:05,  1.70it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  47%|████▋     | 8/17 [00:04<00:05,  1.70it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  53%|█████▎    | 9/17 [00:05<00:04,  1.75it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  59%|█████▉    | 10/17 [00:05<00:03,  1.77it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  65%|██████▍   | 11/17 [00:06<00:03,  1.74it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  71%|███████   | 12/17 [00:06<00:02,  1.93it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  76%|███████▋  | 13/17 [00:07<00:01,  2.17it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  82%|████████▏ | 14/17 [00:07<00:01,  2.30it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  88%|████████▊ | 15/17 [00:07<00:00,  2.44it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  94%|█████████▍| 16/17 [00:08<00:00,  2.33it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing: 100%|██████████| 17/17 [00:08<00:00,  2.26it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing: 100%|██████████| 17/17 [00:08<00:00,  1.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done. Preview of s_t path (first 5): [0.2        0.26397465 0.25815643 0.25382203 0.25077345]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Résolution : trajectoire optimale de l’épargne...\")\n",
    "bounds_s   = (p.s_lower, p.s_upper)\n",
    "control_id = [p.i_s]\n",
    "path_opt_s = run_optimal_policy(sim.copy(), timevec, p, bounds_s, control_id)\n",
    "\n",
    "print(\"Done. Preview of s_t path (first 5):\", path_opt_s[:5, p.i_s])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa566dc8",
   "metadata": {},
   "source": [
    "## 6) Optimiser l’épargne et la réduction des émissions\n",
    "\n",
    "Nous optimisons maintenant $(s_t,\\mu_t)$. Le prix implicite du carbone est stocké dans la colonne `Tax`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94c9a760",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:18.843703Z",
     "iopub.status.busy": "2026-09-16T08:20:18.843423Z",
     "iopub.status.idle": "2026-09-16T08:20:45.664144Z",
     "shell.execute_reply": "2026-09-16T08:20:45.663070Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Résolution : trajectoire optimale jointe de (s_t, μ_t)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:   0%|          | 0/17 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:   6%|▌         | 1/17 [00:06<01:46,  6.66s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  12%|█▏        | 2/17 [00:07<00:49,  3.33s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  18%|█▊        | 3/17 [00:08<00:32,  2.29s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  24%|██▎       | 4/17 [00:09<00:20,  1.59s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  29%|██▉       | 5/17 [00:10<00:15,  1.33s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  35%|███▌      | 6/17 [00:11<00:15,  1.38s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  41%|████      | 7/17 [00:14<00:18,  1.86s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  47%|████▋     | 8/17 [00:15<00:13,  1.54s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  53%|█████▎    | 9/17 [00:16<00:10,  1.37s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  59%|█████▉    | 10/17 [00:17<00:08,  1.20s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  65%|██████▍   | 11/17 [00:18<00:08,  1.37s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  71%|███████   | 12/17 [00:20<00:06,  1.35s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  76%|███████▋  | 13/17 [00:21<00:04,  1.23s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  82%|████████▏ | 14/17 [00:21<00:03,  1.06s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  88%|████████▊ | 15/17 [00:25<00:03,  1.72s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  94%|█████████▍| 16/17 [00:26<00:01,  1.53s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing: 100%|██████████| 17/17 [00:26<00:00,  1.28s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing: 100%|██████████| 17/17 [00:26<00:00,  1.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done. Preview of μ_t path (first 5): [0.03       0.16101647 0.18519147 0.21005311 0.23478373]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Résolution : trajectoire optimale jointe de (s_t, μ_t)...\")\n",
    "bounds_smu   = [(p.s_lower, p.s_upper), (0.0, 1.0)]\n",
    "control_id   = [p.i_s, p.i_mu]\n",
    "path_opt_smu = run_optimal_policy(sim.copy(), timevec, p, bounds_smu, control_id)\n",
    "\n",
    "print(\"Done. Preview of μ_t path (first 5):\", path_opt_smu[:5, p.i_mu])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91840d6f",
   "metadata": {},
   "source": [
    "## 7) Visualiser les contrôles et le prix du carbone\n",
    "\n",
    "Tracez séparément l’épargne optimale $s_t$, le taux de réduction $\\mu_t$ et le prix implicite du carbone `Tax`, en USD/tCO₂.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1c5aef9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:45.665950Z",
     "iopub.status.busy": "2026-09-16T08:20:45.665707Z",
     "iopub.status.idle": "2026-09-16T08:20:45.833455Z",
     "shell.execute_reply": "2026-09-16T08:20:45.832744Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taux d'épargne du lot\n",
    "plt.figure(figsize=(6,3.8))\n",
    "plt.plot(path_opt_smu[:, p.i_time], path_opt_smu[:, p.i_s], label=\"Optimal transition (s_t)\")\n",
    "plt.plot(path_opt_s[:,  p.i_time], path_opt_s[:,  p.i_s],  linestyle=\"--\", label=\"No transition (s_t)\")\n",
    "plt.xlabel(\"Année\"); plt.ylabel(\"Saving rate s_t\"); plt.title(\"Optimal saving rate\")\n",
    "plt.legend(); plt.grid(True); plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c921bb0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:45.834804Z",
     "iopub.status.busy": "2026-09-16T08:20:45.834678Z",
     "iopub.status.idle": "2026-09-16T08:20:45.928453Z",
     "shell.execute_reply": "2026-09-16T08:20:45.927605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taux de réduction des parcelles\n",
    "plt.figure(figsize=(6,3.8))\n",
    "plt.plot(path_opt_smu[:, p.i_time], path_opt_smu[:, p.i_mu], label=\"Optimal transition (μ_t)\")\n",
    "plt.xlabel(\"Année\"); plt.ylabel(\"Abatement rate μ_t\"); plt.title(\"Optimal abatement rate\")\n",
    "plt.legend(); plt.grid(True); plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9245f84b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:45.929890Z",
     "iopub.status.busy": "2026-09-16T08:20:45.929694Z",
     "iopub.status.idle": "2026-09-16T08:20:46.032098Z",
     "shell.execute_reply": "2026-09-16T08:20:46.031479Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taxe sur le carbone implicite (USD par tCO2)\n",
    "plt.figure(figsize=(6,3.8))\n",
    "plt.plot(path_opt_smu[:, p.i_time], path_opt_smu[:, p.i_Tax], label=\"Implied carbon tax (USD/tCO₂)\")\n",
    "plt.xlabel(\"Année\"); plt.ylabel(\"Carbon tax (USD/tCO₂)\"); plt.title(\"Optimal carbon price / SCC (model-implied)\")\n",
    "plt.legend(); plt.grid(True); plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efad3ca",
   "metadata": {},
   "source": [
    "## 8) Lire le coût social du carbone\n",
    "\n",
    "- La colonne `Tax` contient le prix optimal du carbone, en USD/tCO₂.\n",
    "- Le CSC est le prix d’ombre des émissions dans le problème du planificateur. Il peut aussi être estimé par une petite perturbation des émissions puis conversion de la perte marginale de bien-être en dollars.\n",
    "\n",
    "Le squelette ci-dessous illustre cette seconde méthode ; il reste commenté pour réduire le temps d’exécution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5170e76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:46.033452Z",
     "iopub.status.busy": "2026-09-16T08:20:46.033329Z",
     "iopub.status.idle": "2026-09-16T08:20:46.035931Z",
     "shell.execute_reply": "2026-09-16T08:20:46.035226Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# -- Optional: perturbation-based SCC at a chosen date (slow; left as a template) --\n",
    "# t_idx = 10         # pick a period index\n",
    "# eps = 1e-3 # ajouter 0,001 GtC (ajuster les unités au besoin) aux émissions à t idx\n",
    "# \n",
    "# base = path_opt_smu.copy()\n",
    "# pert = path_opt_smu.copy()\n",
    "# \n",
    "# # Injecter une petite augmentation des émissions à t idx en réduisant la réduction d'un cran\n",
    "# # (Ici comme une illustration; dans une expérience complète vous modifieriez E t directement ou ajouteriez au cycle du carbone)\n",
    "# pert[t_idx, p.i_mu] = max(0.0, pert[t_idx, p.i_mu] - 1e-5)\n",
    "# \n",
    "# base_W = obj_fun(np.hstack([base[1:, p.i_s], base[1:, p.i_mu]]), base, range(1, p.nT), p, [p.i_s, p.i_mu])\n",
    "# pert_W = obj_fun(np.hstack([pert[1:, p.i_s], pert[1:, p.i_mu]]), pert, range(1, p.nT), p, [p.i_s, p.i_mu])\n",
    "# dW = pert_W - base_W  # (negative welfare) difference\n",
    "# print(\"ΔW de ε-perturbation:\", dW)\n",
    "# # Convertir en USD/tCO2 en utilisant l'utilitaire marginal à t idx si nécessaire.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac5e356",
   "metadata": {},
   "source": [
    "## 9) Sensibilité à des dommages plus élevés\n",
    "\n",
    "Modifiez la fonction de dommages avec `p.user_damage_fn`, puis relancez l’optimisation pour observer la réaction de $\\mu_t$ et du prix du carbone.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8ac967e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:20:46.037262Z",
     "iopub.status.busy": "2026-09-16T08:20:46.037139Z",
     "iopub.status.idle": "2026-09-16T08:21:11.927830Z",
     "shell.execute_reply": "2026-09-16T08:21:11.927157Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:   0%|          | 0/17 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:   6%|▌         | 1/17 [00:06<01:48,  6.79s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  12%|█▏        | 2/17 [00:07<00:52,  3.48s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  18%|█▊        | 3/17 [00:08<00:32,  2.30s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  24%|██▎       | 4/17 [00:09<00:20,  1.61s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  29%|██▉       | 5/17 [00:09<00:14,  1.23s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  35%|███▌      | 6/17 [00:11<00:14,  1.32s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  41%|████      | 7/17 [00:14<00:17,  1.76s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  47%|████▋     | 8/17 [00:14<00:12,  1.43s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  53%|█████▎    | 9/17 [00:15<00:10,  1.28s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  59%|█████▉    | 10/17 [00:16<00:08,  1.16s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  65%|██████▍   | 11/17 [00:17<00:06,  1.10s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  71%|███████   | 12/17 [00:19<00:06,  1.22s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  76%|███████▋  | 13/17 [00:20<00:04,  1.14s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  82%|████████▏ | 14/17 [00:21<00:03,  1.07s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  88%|████████▊ | 15/17 [00:23<00:03,  1.61s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing:  94%|█████████▍| 16/17 [00:24<00:01,  1.37s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing: 100%|██████████| 17/17 [00:25<00:00,  1.25s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Optimizing: 100%|██████████| 17/17 [00:25<00:00,  1.51s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def higher_damage(T, p_local: Params):\n",
    "    # Slightly steeper quadratic damages as an illustration\n",
    "    return 0.0030 * (np.asarray(T) ** 2)\n",
    "\n",
    "p_sens = Params()\n",
    "sim_sens = init_states(p_sens)\n",
    "sim_sens[:, p_sens.i_mu] = 0.03\n",
    "sim_sens[:, p_sens.i_s]  = 0.20\n",
    "sim_sens = update_path(sim_sens, range(1, p_sens.nT), p_sens)\n",
    "\n",
    "p_sens.user_damage_fn = higher_damage\n",
    "path_opt_smu_highDam = run_optimal_policy(sim_sens.copy(), range(1, p_sens.nT), p_sens, [(p_sens.s_lower,p_sens.s_upper),(0.0,1.0)], [p_sens.i_s, p_sens.i_mu])\n",
    "\n",
    "# Comparaison par parcelle pour μ t\n",
    "plt.figure(figsize=(6,3.8))\n",
    "plt.plot(path_opt_smu[:, p.i_time], path_opt_smu[:, p.i_mu], linestyle=\"--\", label=\"Baseline damages\")\n",
    "plt.plot(path_opt_smu_highDam[:, p.i_time], path_opt_smu_highDam[:, p.i_mu], label=\"Higher damages\")\n",
    "plt.xlabel(\"Année\"); plt.ylabel(\"Abatement rate μ_t\"); plt.title(\"Damage sensitivity: optimal abatement\")\n",
    "plt.legend(); plt.grid(True); plt.tight_layout()\n",
    "\n",
    "# Comparaison par parcelle pour la taxe implicite sur le carbone\n",
    "plt.figure(figsize=(6,3.8))\n",
    "plt.plot(path_opt_smu[:, p.i_time], path_opt_smu[:, p.i_Tax], linestyle=\"--\", label=\"Baseline damages\")\n",
    "plt.plot(path_opt_smu_highDam[:, p.i_time], path_opt_smu_highDam[:, p.i_Tax], label=\"Higher damages\")\n",
    "plt.xlabel(\"Année\"); plt.ylabel(\"Carbon tax (USD/tCO₂)\"); plt.title(\"Damage sensitivity: optimal carbon price\")\n",
    "plt.legend(); plt.grid(True); plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef164da",
   "metadata": {},
   "source": [
    "## 10) Exporter les résultats\n",
    "\n",
    "Convertissez les tableaux en `DataFrame` pour poursuivre l’analyse ou produire d’autres graphiques.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98fa4a4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T08:21:11.929675Z",
     "iopub.status.busy": "2026-09-16T08:21:11.929536Z",
     "iopub.status.idle": "2026-09-16T08:21:11.944216Z",
     "shell.execute_reply": "2026-09-16T08:21:11.943420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>s</th>\n",
       "      <th>mu</th>\n",
       "      <th>tax</th>\n",
       "      <th>E</th>\n",
       "      <th>T_AT</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.264623</td>\n",
       "      <td>0.161016</td>\n",
       "      <td>7.872059</td>\n",
       "      <td>170.725556</td>\n",
       "      <td>0.989258</td>\n",
       "      <td>428.874227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025.0</td>\n",
       "      <td>0.258865</td>\n",
       "      <td>0.185191</td>\n",
       "      <td>9.600504</td>\n",
       "      <td>181.811014</td>\n",
       "      <td>1.135512</td>\n",
       "      <td>517.220239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2030.0</td>\n",
       "      <td>0.254679</td>\n",
       "      <td>0.210053</td>\n",
       "      <td>11.450691</td>\n",
       "      <td>189.775618</td>\n",
       "      <td>1.287820</td>\n",
       "      <td>609.125900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2035.0</td>\n",
       "      <td>0.251709</td>\n",
       "      <td>0.234784</td>\n",
       "      <td>13.340697</td>\n",
       "      <td>195.430577</td>\n",
       "      <td>1.445025</td>\n",
       "      <td>705.631734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year         s        mu        tax           E      T_AT           C\n",
       "0  2015.0  0.200000  0.030000        NaN         NaN  0.850000         NaN\n",
       "1  2020.0  0.264623  0.161016   7.872059  170.725556  0.989258  428.874227\n",
       "2  2025.0  0.258865  0.185191   9.600504  181.811014  1.135512  517.220239\n",
       "3  2030.0  0.254679  0.210053  11.450691  189.775618  1.287820  609.125900\n",
       "4  2035.0  0.251709  0.234784  13.340697  195.430577  1.445025  705.631734"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_baseline = pd.DataFrame({\n",
    "    \"year\": path_opt_smu[:, p.i_time],\n",
    "    \"s\":    path_opt_smu[:, p.i_s],\n",
    "    \"mu\":   path_opt_smu[:, p.i_mu],\n",
    "    \"tax\":  path_opt_smu[:, p.i_Tax],\n",
    "    \"E\":    path_opt_smu[:, p.i_E],\n",
    "    \"T_AT\": path_opt_smu[:, p.i_T_AT],\n",
    "    \"C\":    path_opt_smu[:, p.i_C],\n",
    "})\n",
    "df_baseline.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dfc29bf",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "- Le prix optimal `Tax` constitue la recommandation normative du modèle.\n",
    "- La fenêtre finie met en œuvre la troncation temporelle.\n",
    "- La résolution par fenêtre glissante produit les trajectoires complètes des états et des contrôles.\n",
    "- Les hypothèses de dommages modifient directement $\\mu_t$ et le prix implicite du carbone.\n"
   ]
  }
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