{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jqrbl-5KPRjH"
   },
   "source": [
    "#Workshop2: Waste IO\n",
    "Data:WIOdata2.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4-6WYbWVPYX4"
   },
   "source": [
    "Preparation for libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iGeRZ458OLgm"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy.linalg as la"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OuLt8JIvPdbR"
   },
   "source": [
    "Data input & labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gak8CHAMPgzJ"
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "WIOdata = pd.read_excel('WIOdata2.xlsx','WIOdata2', header=0,index_col=0)\n",
    "S = pd.read_excel('WIOdata2.xlsx','S', header=0,index_col=0).to_numpy()\n",
    "\n",
    "# Labels\n",
    "l_col = tuple(WIOdata.columns)\n",
    "l_row = tuple(WIOdata.index)\n",
    "n_x, n_t, n_y, n_w, n_v, n_f = 81, 10, 8, 99, 8, 7 # Sector, treatment, final demand, waste, value added, emission items\n",
    "# n_y does not include Exports\n",
    "\n",
    "n_xw = n_x + n_w\n",
    "n_xt = n_x + n_t\n",
    "n_xy = n_x + n_y\n",
    "n_xty = n_x + n_y + n_t\n",
    "\n",
    "l_x = l_col[ : n_x] # List of products\n",
    "l_t = l_col[n_x : n_xt] # List of treatment\n",
    "l_y = l_col[n_xt : n_xty] # List of domestic final demand\n",
    "l_y_all = l_col[n_xt : n_xty + 1] # List of final demand with Exports\n",
    "l_w = l_row[n_x : n_xw] # List of waste\n",
    "l_xt = l_x + l_t # List of production and treatment\n",
    "l_xw = l_x + l_w # List of production and waste\n",
    "l_f = l_row[n_xw + n_w + n_v:] # Emissions\n",
    "\n",
    "n_y_all = len(l_y_all)\n",
    "\n",
    "# Converting Pd.DatFrame to Numpy arrays\n",
    "Data = WIOdata.to_numpy()\n",
    "\n",
    "# Gross output\n",
    "x_ = Data[l_row.index(\"Gross output\"),:n_x]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZirhedOZPwBk"
   },
   "source": [
    "Waste output and net waste flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "clsrjPAVRA_b"
   },
   "outputs": [],
   "source": [
    "# Gross output of waste\n",
    "w_out = Data[n_x:n_x+n_w,:].sum(axis=1)\n",
    "\n",
    "# Net waste flow\n",
    "W_net = Data[n_x : n_xw, :] - Data[n_xw : n_xw + n_w, : ]\n",
    "w_net = W_net.sum(axis=1)\n",
    "\n",
    "# Direct generation from final demand\n",
    "W_y = Data[n_x : n_xw , n_xt : n_xty]\n",
    "\n",
    "# Net waste flow converted to treatment demand\n",
    "SW_ = S.T @ W_net\n",
    "\n",
    "# Treatment acitivities\n",
    "t_ = SW_.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zdQ7VRAbRGQb"
   },
   "source": [
    "Production and treatment activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4iOAmrCuRHQT"
   },
   "outputs": [],
   "source": [
    "# Combined flow matrix of production and treatment\n",
    "XSW_ = np.vstack(( Data[ :n_x, : ], SW_))\n",
    "\n",
    "# Combined activities (production and treatment)\n",
    "xt_ =  np.concatenate((x_, t_))\n",
    "\n",
    "# Final demand for treatment\n",
    "Y_t = XSW_[ n_x: , n_xt : n_xty ]\n",
    "\n",
    "# Final demand for products and treatment\n",
    "Y_xt = np.vstack(( Data[ :n_x, n_xt: n_xty], Y_t ))\n",
    "\n",
    "#%% Waste and emission coefficients\n",
    "# Waste output coefficients\n",
    "G_out =  Data[ n_x : n_xw , : n_xt ] / xt_\n",
    "\n",
    "# Waste input coeffficients\n",
    "G_in  =  Data[ n_xw : n_xw + n_w , : n_xt ] / xt_\n",
    "\n",
    "# Net waste output coeffcieints\n",
    "G = G_out - G_in\n",
    "\n",
    "# Emission coefficients\n",
    "F = Data[n_xw + n_w + n_v:, :n_xt] / xt_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zYyyW7tBRMnF"
   },
   "source": [
    "Input coefficients matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "E2J-r7KpROx1"
   },
   "outputs": [],
   "source": [
    "# A matrix (input coefficients matrix)\n",
    "A_xt = XSW_[:, :n_xt] / xt_\n",
    "\n",
    "# Domestic ratio adjustment\n",
    "# Imports and exports\n",
    "im_ = Data[:n_x, l_col.index(\"Imports\")]\n",
    "ex_ = Data[:n_x, l_col.index(\"Exports\")]\n",
    "\n",
    "# Domestic ratio: d = 1 for treatment\n",
    "d_ = np.ones(n_xt)\n",
    "d_[:n_x] = 1 + im_ / (x_ - ex_ - im_)\n",
    "\n",
    "# Adjusted A matrix for imports\n",
    "A_xt_d = A_xt\n",
    "A_xt_d = np.diag(d_) @ A_xt\n",
    "\n",
    "# Final demand for domestic products and treatment\n",
    "# Adjusting final demand (except Exports) for imports\n",
    "Y_xt_d = np.zeros(( n_xt, n_y_all ))\n",
    "Y_xt_d[ :, :n_y ] = Y_xt[ :, :n_y ]\n",
    "Y_xt_d[ :, :n_y ] =  np.diag(d_) @ Y_xt[ :, :n_y ]\n",
    "# Adding Exports as the last column element\n",
    "Y_xt_d[:n_x,n_y] = ex_\n",
    "\n",
    "# The row sum of final demand for domestic products and treatment\n",
    "y_xt_d = Y_xt_d.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "igFAMFQKRTUN"
   },
   "source": [
    "Leontief inverse and calculation of activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FeaDaLv2RVCX"
   },
   "outputs": [],
   "source": [
    "# Leontief inverse\n",
    "L = la.inv( np.eye( n_xt ) - A_xt_d )\n",
    "XT_y = L @ Y_xt_d\n",
    "xt_y = XT_y.sum(axis=1)\n",
    "\n",
    "# Consistency check\n",
    "epsilon = 0.00001\n",
    "indices = np.where(np.abs(xt_ - xt_y) > epsilon)[0]\n",
    "print(\"Inconsistent indices for XT_y:\", indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rmRhEnAwRZN2"
   },
   "source": [
    "Waste footptint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "GYIVWPQbRbwf"
   },
   "outputs": [],
   "source": [
    "# Waste footprint by final demand categories\n",
    "WF_y = pd.DataFrame(data = G @ XT_y, index=l_w, columns=l_y_all)\n",
    "\n",
    "# Waste footprint by final demand for products and treatment\n",
    "WF_Y = pd.DataFrame(data = G @ L @ np.diag(y_xt_d), index=l_w, columns=l_xt)\n",
    "\n",
    "# Emissions induced by final demand categories\n",
    "E_y = pd.DataFrame(data = F @ XT_y, index=l_f, columns=l_y_all)\n",
    "\n",
    "# Emissions induced by final demand for products and treatment\n",
    "E_Y = pd.DataFrame(data = F @ L @ np.diag(y_xt_d), index=l_f, columns=l_xt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outputs to XLSX Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "WF_y.to_excel('WF_y_category.xlsx', engine='openpyxl')\n",
    "\n",
    "WF_Y.to_excel('WF_y_products.xlsx', engine='openpyxl')\n",
    "\n",
    "E_y.to_excel('E_y_category.xlsx',  engine='openpyxl')\n",
    "\n",
    "E_Y.to_excel('E_y_products.xlsx',  engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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