From c40f7c957a5e690f50987dedfb8f5d22bd3374c4 Mon Sep 17 00:00:00 2001 From: bsteubing Date: Mon, 14 Feb 2022 15:12:10 +0100 Subject: [PATCH] Adding the notebooks --- data/matching/Matching file.xlsx | Bin 1431649 -> 1432519 bytes data/matching/hiot_to_miot_dict.pickle | Bin 13566 -> 13566 bytes data/matching/miot_to_hiot_dict.pickle | Bin 14428 -> 14428 bytes data/matching/miot_to_new_miot.pickle | Bin 8026 -> 8026 bytes .../A - Preparation of ecoinvent data.ipynb | 277 +- notebooks/B - Matching.ipynb | 685 +- notebooks/C - Analysis.ipynb | 238 +- .../D - Comparison of economic coverage.ipynb | 5955 +++-------------- requirements.txt | 11 + .../2019-12-02_WW/exiobase/hiot_results.xlsx | Bin 476990 -> 477013 bytes .../matching/geo_EI_to_EX.pickle | Bin 2336 -> 2336 bytes .../matching/geo_EX_to_EI.pickle | Bin 3368 -> 3368 bytes .../matching/process_EI_to_EX.pickle | Bin 980188 -> 980188 bytes .../matching/process_EX_to_EI.pickle | Bin 133047 -> 133047 bytes .../matching/product_EI_to_EX.pickle | Bin 123693 -> 123693 bytes .../matching/product_EX_to_EI.pickle | Bin 90635 -> 90635 bytes .../matching/units_EI_to_EX.pickle | Bin 53 -> 53 bytes .../matching/units_EX_to_EI.pickle | Bin 47 -> 47 bytes 18 files changed, 1286 insertions(+), 5880 deletions(-) create mode 100644 requirements.txt diff --git a/data/matching/Matching file.xlsx b/data/matching/Matching file.xlsx index 792b4255a4ce46ad9c9776de7351caaa5f7f41b4..6d2ac654e47ad3dd6378e245a3eaec47fa00acb2 100644 GIT binary patch delta 1281537 zcmb5UWmuF^*Z2zvA}!J)h$!7%0|q4_0@B^xIfULc2uL?WNh3qUNJ{t6Jv7o%B9h9v z$LBfkd;aIcxvukNU%y#vueI}DyJoFQyAPpV#fl3EH`8og-@(8*!^6NJ!N9-bxF<$|x3eeU0@oK#dJqr!G-el=T}2kETyhD^b*5 zBFRqh_I)MUt9LumbHrGtW#d_M2=Pd-i7=J##WFF}7nt#~~E4=BL+&4n%LH z*$tinq8m7LnLHX9IV`}fknh&I?Z9OsCPwI^%u;nr3sgdZVBgP>)VhEy3Mw%zUW(WT zPMn^P%50iGR?RVFb7EGZ^9(ZU)P!w%e6Orw8e&`;cNlHLD8BN%(;B4YV(Dw&)PZOg z%0i8>x)UswTc>i1;uCj%Rpv54D&jWDd@vFz 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zECsc>@UNcPTXXGCSifz+RM%&<`M8y?@n>XoatZa)2wx0|viz>cse=X8=a=x|U_U=#@M3|Ik99Tf^H$q1DI(z*b~ zBnn|#Q%8JXB}9XQ!h`zXqaGa!5-?dhrydImYR2pT zQU(Yi{_jR0%d=o+*jgs^A8hELcdZ97CWLhkOaQ~g1yPs-lMn`W$=0M8Awr$T5z#^~}IUopQ0SF(mGY6)Fk<5o+&Vz+vI*uVK^I#kOJgg0 uNY^wNMEE~n4Kx%~91IlHyLbNAh#7(yErSJNXh$JA%V2T@q6zT(>i+{bQgsRd diff --git a/data/matching/hiot_to_miot_dict.pickle b/data/matching/hiot_to_miot_dict.pickle index 7edd07064f10133ae93e7a01189f74b23f63e787..5bf16413c5c6f1895c32160414f034243ae8b536 100644 GIT binary patch delta 11 ScmeyD`7e{HfpsI(9}@s0Vg)$> delta 11 ScmeyD`7e{Hfn_7p9}@s0T?IG* diff --git a/data/matching/miot_to_hiot_dict.pickle b/data/matching/miot_to_hiot_dict.pickle index 96cebe438f44eee2364785745b1a038466663d74..d929979512601c4fe1098980d14cf08fb023401d 100644 GIT binary patch delta 11 ScmcapaHoK&fpsHOlm!4E_ylDD delta 11 ScmcapaHoK&fn_68lm!4E^8{o7 diff --git a/data/matching/miot_to_new_miot.pickle b/data/matching/miot_to_new_miot.pickle index ff251bcec53e9330ec347bb6d8f8ee1dfa3dea3b..d1665185cd10c344e09e434c9f3ea86d281e0f3e 100644 GIT binary patch delta 11 Scmca*cgv2cfpsHOgggKoF9YHL delta 11 Scmca*cgv2cfn_68gggKoDg)sF diff --git a/notebooks/A - Preparation of ecoinvent data.ipynb b/notebooks/A - Preparation of ecoinvent data.ipynb index 165c9c0..9fcec49 100644 --- a/notebooks/A - Preparation of ecoinvent data.ipynb +++ b/notebooks/A - Preparation of ecoinvent data.ipynb @@ -10,16 +10,21 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using environment variable BRIGHTWAY2_DIR for data directory:\n", + "C:\\Users\\steub\\OneDrive - Universiteit Leiden\\Brightway3\n" + ] + } + ], "source": [ "import brightway2 as bw\n", "import pandas as pd\n", - "import operator\n", - "from itertools import islice\n", - "import multiprocessing\n", - "import numpy as np\n", "\n", "DATA_PATH = \"../data/\"" ] @@ -109,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -146,7 +151,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -155,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -167,17 +171,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{('ecoinvent 3.4 cutoff', 'f17ad296e98b487f99c56ec5232f3028'): 1.0},\n", - " {('ecoinvent 3.4 cutoff', '8908fda13881d7c66ffacde9eec9792e'): -1.0}]" + "[{('ecoinvent 3.4 cutoff', 'a1716ba88fa010623183e091d41f6c7e'): -1.0},\n", + " {('ecoinvent 3.4 cutoff', '061b18e96ecc15ee17944576986f7ee3'): 1.0}]" ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -206,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -219,7 +223,7 @@ " 'num_cfs': 61}" ] }, - "execution_count": 30, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -230,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -274,14 +278,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "170.7026059627533\n" + "157.3614466190338\n" ] } ], @@ -310,22 +314,22 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[7.48451650e-01],\n", - " [1.99555345e+00],\n", - " [2.80866979e+00],\n", + "array([[5.45996822e-03],\n", + " [1.91703397e+00],\n", + " [1.99954821e-02],\n", " ...,\n", - " [9.56070389e+04],\n", - " [1.00171445e-01],\n", - " [2.36150720e+00]])" + " [5.12467108e+00],\n", + " [1.28617232e+00],\n", + " [6.21756477e+00]])" ] }, - "execution_count": 36, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -336,17 +340,14 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(14889, 2)\n", - "key object\n", - "(IPCC 2013 as in EXIOBASE, climate change, GWP 100a) float64\n", - "dtype: object\n" + "(14889, 2)\n" ] }, { @@ -377,28 +378,28 @@ " \n", " \n", " 0\n", - " ('ecoinvent 3.4 cutoff', 'f17ad296e98b487f99c5...\n", - " 0.748452\n", + " ('ecoinvent 3.4 cutoff', 'a1716ba88fa010623183...\n", + " 0.005460\n", " \n", " \n", " 1\n", - " ('ecoinvent 3.4 cutoff', '8908fda13881d7c66ffa...\n", - " 1.995553\n", + " ('ecoinvent 3.4 cutoff', '061b18e96ecc15ee1794...\n", + " 1.917034\n", " \n", " \n", " 2\n", - " ('ecoinvent 3.4 cutoff', '33053e7d9e459bc2453d...\n", - " 2.808670\n", + " ('ecoinvent 3.4 cutoff', 'ac1dc8332fc3e3ef9b54...\n", + " 0.019995\n", " \n", " \n", " 3\n", - " ('ecoinvent 3.4 cutoff', 'd9b9b19e7cae0a447de5...\n", - " 0.018378\n", + " ('ecoinvent 3.4 cutoff', '7f09ea1ef9f039565af1...\n", + " 0.621921\n", " \n", " \n", " 4\n", - " ('ecoinvent 3.4 cutoff', 'c260d00631b97a7e3a5e...\n", - " 692.789888\n", + " ('ecoinvent 3.4 cutoff', '7ccfd8682007bdd32edd...\n", + " 0.000000\n", " \n", " \n", "\n", @@ -406,21 +407,21 @@ ], "text/plain": [ " key \\\n", - "0 ('ecoinvent 3.4 cutoff', 'f17ad296e98b487f99c5... \n", - "1 ('ecoinvent 3.4 cutoff', '8908fda13881d7c66ffa... \n", - "2 ('ecoinvent 3.4 cutoff', '33053e7d9e459bc2453d... \n", - "3 ('ecoinvent 3.4 cutoff', 'd9b9b19e7cae0a447de5... \n", - "4 ('ecoinvent 3.4 cutoff', 'c260d00631b97a7e3a5e... \n", + "0 ('ecoinvent 3.4 cutoff', 'a1716ba88fa010623183... \n", + "1 ('ecoinvent 3.4 cutoff', '061b18e96ecc15ee1794... \n", + "2 ('ecoinvent 3.4 cutoff', 'ac1dc8332fc3e3ef9b54... \n", + "3 ('ecoinvent 3.4 cutoff', '7f09ea1ef9f039565af1... \n", + "4 ('ecoinvent 3.4 cutoff', '7ccfd8682007bdd32edd... \n", "\n", " (IPCC 2013 as in EXIOBASE, climate change, GWP 100a) \n", - "0 0.748452 \n", - "1 1.995553 \n", - "2 2.808670 \n", - "3 0.018378 \n", - "4 692.789888 " + "0 0.005460 \n", + "1 1.917034 \n", + "2 0.019995 \n", + "3 0.621921 \n", + "4 0.000000 " ] }, - "execution_count": 75, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -443,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -587,7 +588,7 @@ "[2 rows x 35 columns]" ] }, - "execution_count": 86, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -608,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -745,7 +746,7 @@ "[2 rows x 36 columns]" ] }, - "execution_count": 88, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -756,6 +757,162 @@ "AOr.head(2)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### rename the IPCC column" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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        2 rows × 36 columns

        \n", + "
        " + ], + "text/plain": [ + " key # \\\n", + "0 ('ecoinvent 3.4 cutoff', '0008dcb8309bd0ca4820... 3712 \n", + "1 ('ecoinvent 3.4 cutoff', '001145735c7cee63ae10... 402 \n", + "\n", + " id \\\n", + "0 400461dd-45f4-4bcc-8eae-afa68e57664a \n", + "1 0766970d-6c54-497e-b458-2626f56d9e0c \n", + "\n", + " filename \\\n", + "0 400461dd-45f4-4bcc-8eae-afa68e57664a_2966d161-... \n", + "1 0766970d-6c54-497e-b458-2626f56d9e0c_45fbbc41-... \n", + "\n", + " activityName geography startDate \\\n", + "0 market group for heavy fuel oil RER 2015-01-01 \n", + "1 treatment of municipal solid waste, incineration AT 2006-01-01 \n", + "\n", + " endDate specialActivityType technologyLevel ... amount \\\n", + "0 2017-12-31 market group Current ... 1 \n", + "1 2017-12-31 ordinary transforming activity Current ... 1 \n", + "\n", + " unitName productionVolumeAmount absolute PV productionVolumeComment price \\\n", + "0 kg 1.019820e+11 True NaN 0.0000 \n", + "1 MJ 4.823674e+09 True NaN 0.0106 \n", + "\n", + " By-product classification mft functional unit \\\n", + "0 allocatable product non-mft 1 \n", + "1 allocatable product non-mft 1 \n", + "\n", + " (IPCC 2013, climate change, GWP 100a) \n", + "0 0.470319 \n", + "1 0.000000 \n", + "\n", + "[2 rows x 36 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AOr.rename(columns={('IPCC 2013 as in EXIOBASE', 'climate change', 'GWP 100a'): ('IPCC 2013', 'climate change', 'GWP 100a')}, inplace=True)\n", + "AOr.head(2)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -765,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ diff --git a/notebooks/B - Matching.ipynb b/notebooks/B - Matching.ipynb index 3bbb321..8b3e425 100644 --- a/notebooks/B - Matching.ipynb +++ b/notebooks/B - Matching.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Comparing ecoinvent and EXIOBASE\n", + "# Matching (of ecoinvent and EXIOBASE datasets)\n", "Part 1) Matching dict preparation\n", "\n", "Part 2) Matching" @@ -90,15 +90,13 @@ "\n", "# EXIOBASE\n", "hiot_results = f\"{DATA_PATH}exiobase/hiot_results_PTM_v2.xlsx\"\n", - "#hiot_results_sheet = \"v_3_3_18_2019-11-22\"\n", "hiot_results_sheet = scenario\n", "\n", "# Matching\n", - "product_matching = f\"{DATA_PATH}matching/match_v4.xlsx\"\n", - "product_matching_sheet = 'concordance'\n", - "process_matching = f\"{DATA_PATH}matching/match_v2_electricity.xlsx\"\n", - "region_matching = f\"{DATA_PATH}matching/matching_ecoinvent_Exiobase_geographies.xlsx\"\n", - "region_matching_sheet = \"matched_191117\"" + "matching_file = f\"{DATA_PATH}matching/Matching file.xlsx\"\n", + "product_matching_sheet = 'product matching'\n", + "process_matching_sheet = 'process matching electricity'\n", + "region_matching_sheet = \"regional matching\"" ] }, { @@ -148,6 +146,7 @@ " \n", " \n", " \n", + " Unnamed: 0\n", " key\n", " #\n", " id\n", @@ -157,8 +156,8 @@ " startDate\n", " endDate\n", " specialActivityType\n", - " technologyLevel\n", " ...\n", + " amount\n", " unitName\n", " productionVolumeAmount\n", " absolute PV\n", @@ -167,13 +166,13 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", " \n", " \n", " \n", " \n", " 0\n", + " 0\n", " ('ecoinvent 3.4 cutoff', '0008dcb8309bd0ca4820...\n", " 3712\n", " 400461dd-45f4-4bcc-8eae-afa68e57664a\n", @@ -183,8 +182,8 @@ " 2015-01-01\n", " 2017-12-31\n", " market group\n", - " Current\n", " ...\n", + " 1\n", " kg\n", " 1.019820e+11\n", " True\n", @@ -193,11 +192,11 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 0.47094\n", " 0.470319\n", " \n", " \n", " 1\n", + " 1\n", " ('ecoinvent 3.4 cutoff', '001145735c7cee63ae10...\n", " 402\n", " 0766970d-6c54-497e-b458-2626f56d9e0c\n", @@ -207,8 +206,8 @@ " 2006-01-01\n", " 2017-12-31\n", " ordinary transforming activity\n", - " Current\n", " ...\n", + " 1\n", " MJ\n", " 4.823674e+09\n", " True\n", @@ -217,7 +216,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 0.00000\n", " 0.000000\n", " \n", " \n", @@ -226,9 +224,9 @@ "" ], "text/plain": [ - " key # \\\n", - "0 ('ecoinvent 3.4 cutoff', '0008dcb8309bd0ca4820... 3712 \n", - "1 ('ecoinvent 3.4 cutoff', '001145735c7cee63ae10... 402 \n", + " Unnamed: 0 key # \\\n", + "0 0 ('ecoinvent 3.4 cutoff', '0008dcb8309bd0ca4820... 3712 \n", + "1 1 ('ecoinvent 3.4 cutoff', '001145735c7cee63ae10... 402 \n", "\n", " id \\\n", "0 400461dd-45f4-4bcc-8eae-afa68e57664a \n", @@ -242,9 +240,9 @@ "0 market group for heavy fuel oil RER 2015-01-01 \n", "1 treatment of municipal solid waste, incineration AT 2006-01-01 \n", "\n", - " endDate specialActivityType technologyLevel ... unitName \\\n", - "0 2017-12-31 market group Current ... kg \n", - "1 2017-12-31 ordinary transforming activity Current ... MJ \n", + " endDate specialActivityType ... amount unitName \\\n", + "0 2017-12-31 market group ... 1 kg \n", + "1 2017-12-31 ordinary transforming activity ... 1 MJ \n", "\n", " productionVolumeAmount absolute PV productionVolumeComment price \\\n", "0 1.019820e+11 True NaN 0.0000 \n", @@ -254,10 +252,6 @@ "0 allocatable product non-mft 1 \n", "1 allocatable product non-mft 1 \n", "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs \\\n", - "0 0.47094 \n", - "1 0.00000 \n", - "\n", " ('IPCC 2013', 'climate change', 'GWP 100a') \n", "0 0.470319 \n", "1 0.000000 \n", @@ -272,7 +266,6 @@ ], "source": [ "AO = pd.read_excel(activity_overview)\n", - "#ao.set_index(0, inplace=True)\n", "print(AO.shape)\n", "AO.head(2)" ] @@ -382,7 +375,6 @@ } ], "source": [ - "#EX = pd.read_excel(f\"{PATH}exiobase/results.xlsx\")\n", "EX = pd.read_excel(hiot_results, sheet_name=hiot_results_sheet)\n", "EX = EX[EX.columns.drop(list(EX.filter(regex='Unnamed')))]\n", "print(EX.shape)\n", @@ -770,7 +762,7 @@ } ], "source": [ - "pm = pd.read_excel(product_matching, sheet_name=product_matching_sheet, skiprows=[0, 2])\n", + "pm = pd.read_excel(matching_file, sheet_name=product_matching_sheet, skiprows=[0, 2])\n", "pm.drop(labels=[\"Unnamed: 0\", \"Unnamed: 2\", 102, \"Comments\", \"Unnamed: 204\"], axis=1, inplace=True)\n", "pm.set_index(\"Unnamed: 1\", inplace=True)\n", "pm.index.names = ['product']\n", @@ -1070,7 +1062,7 @@ } ], "source": [ - "ppm = pd.read_excel(process_matching, skiprows=[0, 2])\n", + "ppm = pd.read_excel(matching_file, sheet_name=process_matching_sheet, skiprows=[0, 2])\n", "ppm.drop(labels=[\"Unnamed: 0\", \n", " \"Unnamed: 2\", \n", " \"Unnamed: 3\", \n", @@ -1155,7 +1147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(261, 11)\n" + "(261, 4)\n" ] }, { @@ -1182,14 +1174,7 @@ " #\n", " # Activities\n", " ecoinvent 3.4\n", - " geography name\n", - " geography shortcut\n", - " ecoinvent long name\n", - " Continent\n", " Exiobase 3\n", - " Alternative\n", - " Matching type\n", - " Comment\n", " \n", " \n", " \n", @@ -1198,45 +1183,23 @@ " 1\n", " 5\n", " AE\n", - " Asia\n", - " United Arab Emirates\n", - " United Arab Emirates\n", - " Asia\n", " WM\n", - " NaN\n", - " EXIOBASE broader\n", - " NaN\n", " \n", " \n", " 1\n", " 2\n", " 8\n", " AL\n", - " Albania\n", - " Albania\n", - " Albania\n", - " Europe\n", " WE\n", - " NaN\n", - " EXIOBASE broader\n", - " NaN\n", " \n", " \n", "\n", "" ], "text/plain": [ - " # # Activities ecoinvent 3.4 geography name geography shortcut \\\n", - "0 1 5 AE Asia United Arab Emirates \n", - "1 2 8 AL Albania Albania \n", - "\n", - " ecoinvent long name Continent Exiobase 3 Alternative Matching type \\\n", - "0 United Arab Emirates Asia WM NaN EXIOBASE broader \n", - "1 Albania Europe WE NaN EXIOBASE broader \n", - "\n", - " Comment \n", - "0 NaN \n", - "1 NaN " + " # # Activities ecoinvent 3.4 Exiobase 3\n", + "0 1 5 AE WM\n", + "1 2 8 AL WE" ] }, "execution_count": 19, @@ -1245,7 +1208,7 @@ } ], "source": [ - "GEO = pd.read_excel(region_matching, sheet_name=region_matching_sheet, skiprows=[0])\n", + "GEO = pd.read_excel(matching_file, sheet_name=region_matching_sheet, skiprows=[0])\n", "print(GEO.shape)\n", "GEO.head(2)" ] @@ -1421,13 +1384,10 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", "pd.set_option('display.max_columns', 500)\n", "pd.options.display.float_format = '{:.3g}'.format\n", - "import numpy as np\n", - "import pickle\n", + "\n", "from itertools import islice\n", - "import random\n", "\n", "import helper_functions as hf\n", "\n", @@ -1861,7 +1821,7 @@ } ], "source": [ - "ISIC = pd.read_excel(f\"{DATA_PATH}matching/ISIC_nb_activities_units_geographies_in_ecoinvent.xlsx\")\n", + "ISIC = pd.read_excel(f\"{DATA_PATH}matching/ISIC.xlsx\")\n", "#ao.set_index(0, inplace=True)\n", "ISIC = ISIC.astype(str)\n", "ISIC['#activities'] = ISIC['#activities'].astype(int)\n", @@ -1904,6 +1864,7 @@ " \n", " \n", " \n", + " Unnamed: 0_x\n", " key\n", " #\n", " id\n", @@ -1939,9 +1900,8 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", - " Unnamed: 0\n", + " Unnamed: 0_y\n", " #activities\n", " Code (ecoinvent)\n", " Description (ecoinvent)\n", @@ -1966,6 +1926,7 @@ " \n", " \n", " 0\n", + " 0\n", " ('ecoinvent 3.4 cutoff', '0008dcb8309bd0ca4820...\n", " 3712\n", " 400461dd-45f4-4bcc-8eae-afa68e57664a\n", @@ -2001,7 +1962,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 0.471\n", " 0.47\n", " 64\n", " 158\n", @@ -2026,6 +1986,7 @@ " \n", " \n", " 1\n", + " 242\n", " ('ecoinvent 3.4 cutoff', '048ac7e1860972d562e9...\n", " 7691\n", " 831198c8-1ddd-49ff-be80-9dce40d83a7e\n", @@ -2061,7 +2022,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 0.94\n", " 0.939\n", " 64\n", " 158\n", @@ -2089,9 +2049,9 @@ "" ], "text/plain": [ - " key # \\\n", - "0 ('ecoinvent 3.4 cutoff', '0008dcb8309bd0ca4820... 3712 \n", - "1 ('ecoinvent 3.4 cutoff', '048ac7e1860972d562e9... 7691 \n", + " Unnamed: 0_x key # \\\n", + "0 0 ('ecoinvent 3.4 cutoff', '0008dcb8309bd0ca4820... 3712 \n", + "1 242 ('ecoinvent 3.4 cutoff', '048ac7e1860972d562e9... 7691 \n", "\n", " id \\\n", "0 400461dd-45f4-4bcc-8eae-afa68e57664a \n", @@ -2145,13 +2105,9 @@ "0 allocatable product non-mft 1 \n", "1 allocatable product non-mft 1 \n", "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs \\\n", - "0 0.471 \n", - "1 0.94 \n", - "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0 #activities \\\n", - "0 0.47 64 158 \n", - "1 0.939 64 158 \n", + " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0_y #activities \\\n", + "0 0.47 64 158 \n", + "1 0.939 64 158 \n", "\n", " Code (ecoinvent) Description (ecoinvent) ISIC level \\\n", "0 1920 Manufacture of refined petroleum products 4 \n", @@ -2362,9 +2318,6 @@ } ], "source": [ - "#EX = pd.read_excel(f\"{PATH}exiobase/hiot_results.xlsx\")\n", - "#EX = pd.read_excel(f\"{PATH}exiobase/hiot_results_PTM_v2.xlsx\", sheet_name=\"v_3_3_18_2019-11-22\")\n", - "#EX.drop([0], axis=0, inplace=True)\n", "EX['CarbonFootprint'].replace(0, np.nan, inplace=True) # replace 0 values with nan (otherwise mean, etc. are disturbed)\n", "print(EX.shape)\n", "EX.dropna(inplace=True)\n", @@ -2372,22 +2325,6 @@ "EX.head(2)" ] }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# #EX = pd.read_excel(f\"{PATH}exiobase/hiot_results.xlsx\")\n", - "# EX = pd.read_excel(f\"{PATH}exiobase/hiot_results_PTM_v2_units_converted.xlsx\", sheet_name=\"v_3_3_18\")\n", - "# #EX.drop([0], axis=0, inplace=True)\n", - "# #EX['CarbonFootprint'].replace(0, np.nan, inplace=True) # replace 0 values with nan (otherwise mean, etc. are disturbed)\n", - "# print(EX.shape)\n", - "# EX.dropna(inplace=True)\n", - "# print(EX.shape)\n", - "# EX.head(2)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -2397,14 +2334,14 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(200, 11)\n" + "(200, 3)\n" ] }, { @@ -2429,75 +2366,47 @@ " \n", " \n", " old_name\n", - " old_code\n", - " old_synonym\n", " new_name\n", - " new_code\n", - " new_synonym\n", - " old_idx\n", - " new_idx\n", " old_or_new\n", - " ISIC 1\n", - " our_name\n", " \n", " \n", " \n", " \n", " 0\n", " Paddy rice\n", - " p01.a\n", - " C_PARI\n", " Cultivation of paddy rice\n", - " c01.a\n", - " C_PARI\n", - " 1\n", - " 1\n", " old\n", - " A\n", - " Paddy rice\n", " \n", " \n", " 1\n", " Wheat\n", - " p01.b\n", - " C_WHEA\n", " Cultivation of wheat\n", - " c01.b\n", - " C_WHEA\n", - " 2\n", - " 2\n", " old\n", - " A\n", - " Wheat\n", " \n", " \n", "\n", "" ], "text/plain": [ - " old_name old_code old_synonym new_name new_code \\\n", - "0 Paddy rice p01.a C_PARI Cultivation of paddy rice c01.a \n", - "1 Wheat p01.b C_WHEA Cultivation of wheat c01.b \n", - "\n", - " new_synonym old_idx new_idx old_or_new ISIC 1 our_name \n", - "0 C_PARI 1 1 old A Paddy rice \n", - "1 C_WHEA 2 2 old A Wheat " + " old_name new_name old_or_new\n", + "0 Paddy rice Cultivation of paddy rice old\n", + "1 Wheat Cultivation of wheat old" ] }, - "execution_count": 49, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pm = pd.read_excel(f\"{DATA_PATH}matching/product_match.xlsx\", sheet_name=\"product_aggregation\")\n", + "pm = pd.read_excel(matching_file, sheet_name=\"miot_hiot\")\n", "print(pm.shape)\n", "pm.head(2)" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -2541,7 +2450,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -2555,7 +2464,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": { "scrolled": true }, @@ -2648,7 +2557,7 @@ "2 0.266 kg CO2-eq/kg Cereal grains nec " ] }, - "execution_count": 52, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -2663,7 +2572,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -2680,7 +2589,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -2718,7 +2627,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -2732,7 +2641,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2741,7 +2650,7 @@ "[('WM', 'AE'), ('WM', 'BH'), ('WM', 'IAI Area, Gulf Cooperation Council')]" ] }, - "execution_count": 56, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -2752,7 +2661,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2808,7 +2717,7 @@ "1 heat and power co-generation unit, 1MW electri... Construction work" ] }, - "execution_count": 57, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -2822,7 +2731,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -2882,7 +2791,7 @@ "1 Electricity by biomass and waste " ] }, - "execution_count": 58, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -2896,7 +2805,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -2952,7 +2861,7 @@ "1 WM BH" ] }, - "execution_count": 59, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -2966,7 +2875,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -3028,7 +2937,7 @@ "2 MJ kWh" ] }, - "execution_count": 60, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -3049,7 +2958,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 60, "metadata": { "scrolled": true }, @@ -3145,7 +3054,7 @@ "1 0.266 kg CO2-eq/kg Cereal grains nec AU " ] }, - "execution_count": 61, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -3158,7 +3067,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -3255,7 +3164,7 @@ "1 0.266 kg CO2-eq/kg Cereal grains nec AU kg " ] }, - "execution_count": 62, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -3268,7 +3177,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -3368,7 +3277,7 @@ "1 kg CO2-eq/kg Wheat AU kg wheat grain, feed " ] }, - "execution_count": 63, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -3381,7 +3290,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -3485,7 +3394,7 @@ "1 ('ecoinvent 3.4 cutoff', '6f335a441b837359e503... " ] }, - "execution_count": 64, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -3498,7 +3407,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -3507,7 +3416,7 @@ "array(['MJ'], dtype=object)" ] }, - "execution_count": 65, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -3516,18 +3425,6 @@ "EX_geo_unit_process[\"PhysicalUnit\"].unique()" ] }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "# checks... \n", - "#E = EX_geo_product_process_unit\n", - "#print(E.shape)\n", - "#E[(E[\"key\"].notnull() & E[\"unitName\"].notnull() )]" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -3537,7 +3434,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -3582,6 +3479,7 @@ " geography\n", " unitName\n", " name\n", + " Unnamed: 0_x\n", " key\n", " #_y\n", " id\n", @@ -3614,9 +3512,8 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", - " Unnamed: 0\n", + " Unnamed: 0_y\n", " #activities\n", " Code (ecoinvent)\n", " Description (ecoinvent)\n", @@ -3655,6 +3552,7 @@ " AU\n", " kg\n", " wheat grain\n", + " 12141\n", " ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d...\n", " 3759\n", " 40d2fd8e-c1c6-421a-a7e7-0723c4599f56\n", @@ -3687,7 +3585,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 1.64\n", " 0.656\n", " 0\n", " 205\n", @@ -3726,6 +3623,7 @@ " Canada without Quebec\n", " kg\n", " wheat grain\n", + " 13998\n", " ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97...\n", " 14158\n", " f34462a8-dcd0-4706-ac4b-aa748b36a0f4\n", @@ -3758,7 +3656,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 0.503\n", " 0.501\n", " 0\n", " 205\n", @@ -3798,9 +3695,9 @@ "0 kg CO2-eq/kg Wheat AU kg wheat grain \n", "1 kg CO2-eq/kg Wheat Canada without Quebec kg wheat grain \n", "\n", - " key #_y \\\n", - "0 ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d... 3759 \n", - "1 ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97... 14158 \n", + " Unnamed: 0_x key #_y \\\n", + "0 12141 ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d... 3759 \n", + "1 13998 ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97... 14158 \n", "\n", " id \\\n", "0 40d2fd8e-c1c6-421a-a7e7-0723c4599f56 \n", @@ -3842,13 +3739,9 @@ "0 0.131 allocatable product non-mft 1 \n", "1 0.131 allocatable product non-mft 1 \n", "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs \\\n", - "0 1.64 \n", - "1 0.503 \n", - "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0 #activities \\\n", - "0 0.656 0 205 \n", - "1 0.501 0 205 \n", + " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0_y #activities \\\n", + "0 0.656 0 205 \n", + "1 0.501 0 205 \n", "\n", " Code (ecoinvent) Description (ecoinvent) \\\n", "0 0111 Growing of cereals (except rice), leguminous c... \n", @@ -3891,7 +3784,7 @@ "1 0111:Growing of cereals (except rice), legumin... nan " ] }, - "execution_count": 67, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -3907,7 +3800,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -3955,7 +3848,7 @@ "317 9.36 " ] }, - "execution_count": 68, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -3967,7 +3860,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -4012,6 +3905,7 @@ " geography\n", " unitName\n", " key\n", + " Unnamed: 0_x\n", " #_y\n", " id\n", " filename\n", @@ -4044,9 +3938,8 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", - " Unnamed: 0\n", + " Unnamed: 0_y\n", " #activities\n", " Code (ecoinvent)\n", " Description (ecoinvent)\n", @@ -4085,6 +3978,7 @@ " AU\n", " kWh\n", " ('ecoinvent 3.4 cutoff', 'c4d3dcc31c97a01ef7b1...\n", + " 11458\n", " 14092\n", " f2513c58-268c-4b43-a617-f227ca3ac5a1\n", " f2513c58-268c-4b43-a617-f227ca3ac5a1_66c93e71-...\n", @@ -4118,7 +4012,6 @@ " non-mft\n", " 1\n", " 1.11\n", - " 1.11\n", " 138\n", " 2817\n", " 3510\n", @@ -4156,6 +4049,7 @@ " AU\n", " kWh\n", " ('ecoinvent 3.4 cutoff', '3a54ec6f33bd5af8782a...\n", + " 3320\n", " 11601\n", " c6d76656-4cdb-4efd-82a2-95ee3a5f67ab\n", " c6d76656-4cdb-4efd-82a2-95ee3a5f67ab_66c93e71-...\n", @@ -4189,7 +4083,6 @@ " non-mft\n", " 1\n", " 1.24\n", - " 1.24\n", " 138\n", " 2817\n", " 3510\n", @@ -4228,9 +4121,9 @@ "0 0.361 kg CO2-eq/MJ Electricity by coal AU kWh \n", "1 0.361 kg CO2-eq/MJ Electricity by coal AU kWh \n", "\n", - " key #_y \\\n", - "0 ('ecoinvent 3.4 cutoff', 'c4d3dcc31c97a01ef7b1... 14092 \n", - "1 ('ecoinvent 3.4 cutoff', '3a54ec6f33bd5af8782a... 11601 \n", + " key Unnamed: 0_x #_y \\\n", + "0 ('ecoinvent 3.4 cutoff', 'c4d3dcc31c97a01ef7b1... 11458 14092 \n", + "1 ('ecoinvent 3.4 cutoff', '3a54ec6f33bd5af8782a... 3320 11601 \n", "\n", " id \\\n", "0 f2513c58-268c-4b43-a617-f227ca3ac5a1 \n", @@ -4284,25 +4177,21 @@ "0 Calculated value 0.0977 allocatable product non-mft \n", "1 Calculated value 0.0977 allocatable product non-mft \n", "\n", - " functional unit ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs \\\n", - "0 1 1.11 \n", - "1 1 1.24 \n", + " functional unit ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0_y \\\n", + "0 1 1.11 138 \n", + "1 1 1.24 138 \n", "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0 #activities \\\n", - "0 1.11 138 2817 \n", - "1 1.24 138 2817 \n", + " #activities Code (ecoinvent) \\\n", + "0 2817 3510 \n", + "1 2817 3510 \n", "\n", - " Code (ecoinvent) Description (ecoinvent) \\\n", - "0 3510 Electric power generation, transmission and di... \n", - "1 3510 Electric power generation, transmission and di... \n", + " Description (ecoinvent) ISIC level Code 1 Code 2 \\\n", + "0 Electric power generation, transmission and di... 4 D 35.0 \n", + "1 Electric power generation, transmission and di... 4 D 35.0 \n", "\n", - " ISIC level Code 1 Code 2 Code 3 Code 4 Code 5 \\\n", - "0 4 D 35.0 351.0 3510.0 nan \n", - "1 4 D 35.0 351.0 3510.0 nan \n", - "\n", - " Description 1 \\\n", - "0 Electricity, gas, steam and air conditioning s... \n", - "1 Electricity, gas, steam and air conditioning s... \n", + " Code 3 Code 4 Code 5 Description 1 \\\n", + "0 351.0 3510.0 nan Electricity, gas, steam and air conditioning s... \n", + "1 351.0 3510.0 nan Electricity, gas, steam and air conditioning s... \n", "\n", " Description 2 \\\n", "0 Electricity, gas, steam and air conditioning s... \n", @@ -4333,7 +4222,7 @@ "1 3510:Electric power generation, transmission a... nan " ] }, - "execution_count": 69, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -4349,7 +4238,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 68, "metadata": { "scrolled": true }, @@ -4397,6 +4286,7 @@ " geography\n", " unitName\n", " name\n", + " Unnamed: 0_x\n", " key\n", " #_y\n", " id\n", @@ -4429,9 +4319,8 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", - " Unnamed: 0\n", + " Unnamed: 0_y\n", " #activities\n", " Code (ecoinvent)\n", " Description (ecoinvent)\n", @@ -4471,6 +4360,7 @@ " AU\n", " kg\n", " wheat grain\n", + " 12141\n", " ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d...\n", " 3759\n", " 40d2fd8e-c1c6-421a-a7e7-0723c4599f56\n", @@ -4503,7 +4393,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 1.64\n", " 0.656\n", " 0\n", " 205\n", @@ -4543,6 +4432,7 @@ " Canada without Quebec\n", " kg\n", " wheat grain\n", + " 13998\n", " ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97...\n", " 14158\n", " f34462a8-dcd0-4706-ac4b-aa748b36a0f4\n", @@ -4575,7 +4465,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 0.503\n", " 0.501\n", " 0\n", " 205\n", @@ -4615,13 +4504,17 @@ "0 0.307 kg CO2-eq/kg Wheat AU \n", "1 0.327 kg CO2-eq/kg Wheat Canada without Quebec \n", "\n", - " unitName name key \\\n", - "0 kg wheat grain ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d... \n", - "1 kg wheat grain ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97... \n", + " unitName name Unnamed: 0_x \\\n", + "0 kg wheat grain 12141 \n", + "1 kg wheat grain 13998 \n", + "\n", + " key #_y \\\n", + "0 ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d... 3759 \n", + "1 ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97... 14158 \n", "\n", - " #_y id \\\n", - "0 3759 40d2fd8e-c1c6-421a-a7e7-0723c4599f56 \n", - "1 14158 f34462a8-dcd0-4706-ac4b-aa748b36a0f4 \n", + " id \\\n", + "0 40d2fd8e-c1c6-421a-a7e7-0723c4599f56 \n", + "1 f34462a8-dcd0-4706-ac4b-aa748b36a0f4 \n", "\n", " filename activityName \\\n", "0 40d2fd8e-c1c6-421a-a7e7-0723c4599f56_0201ee86-... wheat production \n", @@ -4659,13 +4552,9 @@ "0 0.131 allocatable product non-mft 1 \n", "1 0.131 allocatable product non-mft 1 \n", "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs \\\n", - "0 1.64 \n", - "1 0.503 \n", - "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0 #activities \\\n", - "0 0.656 0 205 \n", - "1 0.501 0 205 \n", + " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0_y #activities \\\n", + "0 0.656 0 205 \n", + "1 0.501 0 205 \n", "\n", " Code (ecoinvent) Description (ecoinvent) \\\n", "0 0111 Growing of cereals (except rice), leguminous c... \n", @@ -4708,7 +4597,7 @@ "1 0111:Growing of cereals (except rice), legumin... nan " ] }, - "execution_count": 70, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -4722,7 +4611,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -4731,7 +4620,7 @@ "RangeIndex(start=0, stop=4567, step=1)" ] }, - "execution_count": 71, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -4742,7 +4631,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -4751,7 +4640,7 @@ "155036.32570588624" ] }, - "execution_count": 72, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -4765,7 +4654,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -4822,6 +4711,7 @@ " geography\n", " unitName\n", " name\n", + " Unnamed: 0_x\n", " key\n", " #_y\n", " id\n", @@ -4854,9 +4744,8 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", - " Unnamed: 0\n", + " Unnamed: 0_y\n", " #activities\n", " Code (ecoinvent)\n", " Description (ecoinvent)\n", @@ -4896,6 +4785,7 @@ " AU\n", " kg\n", " wheat grain\n", + " 12141\n", " ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d...\n", " 3759\n", " 40d2fd8e-c1c6-421a-a7e7-0723c4599f56\n", @@ -4928,7 +4818,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 1.64\n", " 0.656\n", " 0\n", " 205\n", @@ -4968,6 +4857,7 @@ " Canada without Quebec\n", " kg\n", " wheat grain\n", + " 13998\n", " ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97...\n", " 14158\n", " f34462a8-dcd0-4706-ac4b-aa748b36a0f4\n", @@ -5000,7 +4890,6 @@ " allocatable product\n", " non-mft\n", " 1\n", - " 0.503\n", " 0.501\n", " 0\n", " 205\n", @@ -5040,13 +4929,17 @@ "0 0.307 kg CO2-eq/kg Wheat AU \n", "1 0.327 kg CO2-eq/kg Wheat Canada without Quebec \n", "\n", - " unitName name key \\\n", - "0 kg wheat grain ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d... \n", - "1 kg wheat grain ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97... \n", + " unitName name Unnamed: 0_x \\\n", + "0 kg wheat grain 12141 \n", + "1 kg wheat grain 13998 \n", + "\n", + " key #_y \\\n", + "0 ('ecoinvent 3.4 cutoff', 'd0bd9c43e8becfcc1a3d... 3759 \n", + "1 ('ecoinvent 3.4 cutoff', 'f053a42fddfcd4278d97... 14158 \n", "\n", - " #_y id \\\n", - "0 3759 40d2fd8e-c1c6-421a-a7e7-0723c4599f56 \n", - "1 14158 f34462a8-dcd0-4706-ac4b-aa748b36a0f4 \n", + " id \\\n", + "0 40d2fd8e-c1c6-421a-a7e7-0723c4599f56 \n", + "1 f34462a8-dcd0-4706-ac4b-aa748b36a0f4 \n", "\n", " filename activityName \\\n", "0 40d2fd8e-c1c6-421a-a7e7-0723c4599f56_0201ee86-... wheat production \n", @@ -5084,13 +4977,9 @@ "0 0.131 allocatable product non-mft 1 \n", "1 0.131 allocatable product non-mft 1 \n", "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs \\\n", - "0 1.64 \n", - "1 0.503 \n", - "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0 #activities \\\n", - "0 0.656 0 205 \n", - "1 0.501 0 205 \n", + " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0_y #activities \\\n", + "0 0.656 0 205 \n", + "1 0.501 0 205 \n", "\n", " Code (ecoinvent) Description (ecoinvent) \\\n", "0 0111 Growing of cereals (except rice), leguminous c... \n", @@ -5133,7 +5022,7 @@ "1 0111:Growing of cereals (except rice), legumin... nan " ] }, - "execution_count": 73, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -5141,7 +5030,6 @@ "source": [ "# convert MJ to kWh \n", "index_kWh = M.loc[M.unitName == \"kWh\"].index\n", - "#print(M.loc[317])\n", "print(\"Converted\", len(index_kWh), \"values from MJ to kWh.\")\n", "M[\"CarbonFootprint\"].loc[index_kWh] = M[\"CarbonFootprint\"].loc[index_kWh] * 3.6\n", "M.head(2)" @@ -5149,7 +5037,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 72, "metadata": { "scrolled": true }, @@ -5160,7 +5048,7 @@ "155482.2792929991" ] }, - "execution_count": 74, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -5173,7 +5061,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -5195,7 +5083,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 74, "metadata": { "scrolled": true }, @@ -5245,7 +5133,7 @@ "317 9.36 " ] }, - "execution_count": 76, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -5255,124 +5143,6 @@ "M[(M['ProductTypeName'] == 'Cattle') & (M['CountryCode'] == 'CA')][['GHGs-eq - tonnes', 'CarbonFootprint', \"('IPCC 2013', 'climate change', 'GWP 100a')\"]]" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add continents" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "#DF_geo = pd.read_excel(f\"{DATA_PATH}matching/matching_ecoinvent_Exiobase_geographies.xlsx\", sheet_name='matched_191117', skiprows=1)\n", - "#DF_geo.rename(columns={'Exiobase 3': 'CountryCode'}, inplace=True)\n", - "#DF_geo = DF_geo[['Continent', 'CountryCode']].drop_duplicates(keep=False)\n", - "#DF_geo.set_index('CountryCode', inplace=True)\n", - "#print(DF_geo.shape)\n", - "#DF_geo.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(261, 1)\n" - ] - }, - { - "data": { - "text/html": [ - "
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        ALEurope
        \n", - "
        " - ], - "text/plain": [ - " Continent\n", - "geography \n", - "AE Asia\n", - "AL Europe" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# we use ecoinvent geographies\n", - "#DF_geo = pd.read_excel(f\"{DATA_PATH}matching/matching_ecoinvent_Exiobase_geographies.xlsx\", sheet_name='matched_191117', skiprows=1)\n", - "DF_geo = GEO\n", - "DF_geo.rename(columns={'ecoinvent 3.4': 'geography'}, inplace=True)\n", - "DF_geo = DF_geo[['Continent', 'geography']] #.drop_duplicates(keep=False)\n", - "DF_geo.set_index('geography', inplace=True)\n", - "print(DF_geo.shape)\n", - "DF_geo.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4567, 70)" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = M.join(DF_geo, on=['geography'], how='left')\n", - "M.shape" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -5382,7 +5152,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -5391,7 +5161,7 @@ "str" ] }, - "execution_count": 80, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -5402,7 +5172,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -5441,6 +5211,7 @@ " geography\n", " unitName\n", " name\n", + " Unnamed: 0_x\n", " key\n", " #_y\n", " id\n", @@ -5473,9 +5244,8 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", - " Unnamed: 0\n", + " Unnamed: 0_y\n", " #activities\n", " Code (ecoinvent)\n", " Description (ecoinvent)\n", @@ -5495,7 +5265,6 @@ " ISIC 3\n", " ISIC 4\n", " ISIC 5\n", - " Continent\n", " \n", " \n", " \n", @@ -5516,6 +5285,7 @@ " GLO\n", " kg\n", " coconut husk\n", + " 10901\n", " ('ecoinvent 3.4 cutoff', 'bb51d5d91410516e9aaf...\n", " 14830\n", " ff02f2dc-6b11-4037-8e1e-542147379d21\n", @@ -5549,7 +5319,6 @@ " non-mft\n", " 1\n", " 0\n", - " 0\n", " 185\n", " 163\n", " nan\n", @@ -5570,7 +5339,6 @@ " nan\n", " nan\n", " nan\n", - " Global\n", " \n", " \n", " 327\n", @@ -5589,6 +5357,7 @@ " GLO\n", " kg\n", " manure, liquid, cattle\n", + " 12424\n", " ('ecoinvent 3.4 cutoff', 'd5d3559ca53fb23c22bb...\n", " 9940\n", " aa9becc0-421b-49f1-89ee-6a3b1a6777de\n", @@ -5622,7 +5391,6 @@ " non-mft\n", " 1\n", " 0\n", - " 0\n", " 185\n", " 163\n", " nan\n", @@ -5643,7 +5411,6 @@ " nan\n", " nan\n", " nan\n", - " Global\n", " \n", " \n", " 328\n", @@ -5662,6 +5429,7 @@ " GLO\n", " kg\n", " manure, liquid, swine\n", + " 7619\n", " ('ecoinvent 3.4 cutoff', '840b95f5c81c831cd94c...\n", " 10918\n", " bac8f06d-d7be-4331-b541-8bacd978985a\n", @@ -5695,7 +5463,6 @@ " non-mft\n", " 1\n", " 0\n", - " 0\n", " 185\n", " 163\n", " nan\n", @@ -5716,7 +5483,6 @@ " nan\n", " nan\n", " nan\n", - " Global\n", " \n", " \n", " 329\n", @@ -5735,6 +5501,7 @@ " GLO\n", " kg\n", " manure, solid, cattle\n", + " 2128\n", " ('ecoinvent 3.4 cutoff', '24e07826db670824d951...\n", " 10815\n", " b8dc5812-becf-4c34-bd73-798c498b7ce5\n", @@ -5768,7 +5535,6 @@ " non-mft\n", " 1\n", " 0\n", - " 0\n", " 185\n", " 163\n", " nan\n", @@ -5789,7 +5555,6 @@ " nan\n", " nan\n", " nan\n", - " Global\n", " \n", " \n", " 639\n", @@ -5808,6 +5573,7 @@ " GLO\n", " kg\n", " waste paper, unsorted\n", + " 7856\n", " ('ecoinvent 3.4 cutoff', '880f21ffa12b9851ed66...\n", " 11222\n", " bfec9189-4f24-417d-bf5a-1820a1315882\n", @@ -5841,7 +5607,6 @@ " non-mft\n", " 1\n", " 0\n", - " 0\n", " 185\n", " 163\n", " nan\n", @@ -5862,7 +5627,6 @@ " nan\n", " nan\n", " nan\n", - " Global\n", " \n", " \n", "\n", @@ -5904,12 +5668,12 @@ "329 Manure (conventional treatment) GLO kg \n", "639 Secondary paper for treatment, Re-processing o... GLO kg \n", "\n", - " name \\\n", - "250 coconut husk \n", - "327 manure, liquid, cattle \n", - "328 manure, liquid, swine \n", - "329 manure, solid, cattle \n", - "639 waste paper, unsorted \n", + " name Unnamed: 0_x \\\n", + "250 coconut husk 10901 \n", + "327 manure, liquid, cattle 12424 \n", + "328 manure, liquid, swine 7619 \n", + "329 manure, solid, cattle 2128 \n", + "639 waste paper, unsorted 7856 \n", "\n", " key #_y \\\n", "250 ('ecoinvent 3.4 cutoff', 'bb51d5d91410516e9aaf... 14830 \n", @@ -5995,19 +5759,12 @@ "329 Recyclable non-mft 1 \n", "639 Recyclable non-mft 1 \n", "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs \\\n", - "250 0 \n", - "327 0 \n", - "328 0 \n", - "329 0 \n", - "639 0 \n", - "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0 #activities \\\n", - "250 0 185 163 \n", - "327 0 185 163 \n", - "328 0 185 163 \n", - "329 0 185 163 \n", - "639 0 185 163 \n", + " ('IPCC 2013', 'climate change', 'GWP 100a') Unnamed: 0_y #activities \\\n", + "250 0 185 163 \n", + "327 0 185 163 \n", + "328 0 185 163 \n", + "329 0 185 163 \n", + "639 0 185 163 \n", "\n", " Code (ecoinvent) Description (ecoinvent) ISIC level Code 1 Code 2 Code 3 \\\n", "250 nan not available now 17 nan nan nan \n", @@ -6023,15 +5780,15 @@ "329 nan nan nan nan nan nan \n", "639 nan nan nan nan nan nan \n", "\n", - " Description 5 ISIC 1 ISIC 2 ISIC 3 ISIC 4 ISIC 5 Continent \n", - "250 nan nan nan nan nan nan Global \n", - "327 nan nan nan nan nan nan Global \n", - "328 nan nan nan nan nan nan Global \n", - "329 nan nan nan nan nan nan Global \n", - "639 nan nan nan nan nan nan Global " + " Description 5 ISIC 1 ISIC 2 ISIC 3 ISIC 4 ISIC 5 \n", + "250 nan nan nan nan nan nan \n", + "327 nan nan nan nan nan nan \n", + "328 nan nan nan nan nan nan \n", + "329 nan nan nan nan nan nan \n", + "639 nan nan nan nan nan nan " ] }, - "execution_count": 81, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -6042,7 +5799,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -6081,6 +5838,7 @@ " geography\n", " unitName\n", " name\n", + " Unnamed: 0_x\n", " key\n", " #_y\n", " id\n", @@ -6113,9 +5871,8 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", - " Unnamed: 0\n", + " Unnamed: 0_y\n", " #activities\n", " Code (ecoinvent)\n", " Description (ecoinvent)\n", @@ -6135,7 +5892,6 @@ " ISIC 3\n", " ISIC 4\n", " ISIC 5\n", - " Continent\n", " \n", " \n", " \n", @@ -6145,11 +5901,11 @@ ], "text/plain": [ "Empty DataFrame\n", - "Columns: [index, #_x, CountryCode, ProductTypeName_of_hiot, product code 1, product code 2, OriginalPhysicalUnit, GHGs-eq - tonnes, PhysicalUnit, CarbonFootprint, unit, ProductTypeName, geography, unitName, name, key, #_y, id, filename, activityName, startDate, endDate, specialActivityType, technologyLevel, inheritance status, parentActivityId, tags, synonyms, ISIC v4, ISIC code, ISIC name, specialty production, constrained market, accessRestrictedTo, dataEntryBy, dataGenerator, isActiveAuthor, group, CPC, amount, productionVolumeAmount, absolute PV, productionVolumeComment, price, By-product classification, mft, functional unit, ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs, ('IPCC 2013', 'climate change', 'GWP 100a'), Unnamed: 0, #activities, Code (ecoinvent), Description (ecoinvent), ISIC level, Code 1, Code 2, Code 3, Code 4, Code 5, Description 1, Description 2, Description 3, Description 4, Description 5, ISIC 1, ISIC 2, ISIC 3, ISIC 4, ISIC 5, Continent]\n", + "Columns: [index, #_x, CountryCode, ProductTypeName_of_hiot, product code 1, product code 2, OriginalPhysicalUnit, GHGs-eq - tonnes, PhysicalUnit, CarbonFootprint, unit, ProductTypeName, geography, unitName, name, Unnamed: 0_x, key, #_y, id, filename, activityName, startDate, endDate, specialActivityType, technologyLevel, inheritance status, parentActivityId, tags, synonyms, ISIC v4, ISIC code, ISIC name, specialty production, constrained market, accessRestrictedTo, dataEntryBy, dataGenerator, isActiveAuthor, group, CPC, amount, productionVolumeAmount, absolute PV, productionVolumeComment, price, By-product classification, mft, functional unit, ('IPCC 2013', 'climate change', 'GWP 100a'), Unnamed: 0_y, #activities, Code (ecoinvent), Description (ecoinvent), ISIC level, Code 1, Code 2, Code 3, Code 4, Code 5, Description 1, Description 2, Description 3, Description 4, Description 5, ISIC 1, ISIC 2, ISIC 3, ISIC 4, ISIC 5]\n", "Index: []" ] }, - "execution_count": 88, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -6167,13 +5923,11 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "M[\"diff\"] = M[\"CarbonFootprint\"] - M[\"('IPCC 2013', 'climate change', 'GWP 100a')\"]\n", - "# difference of ecoinvent result from EXIOBASE (1 = perfect match; 0 = no ecoinvent score; 2 = twice the impact)\n", - "#M[\"diff%\"] = 1 - (M[\"CarbonFootprint\"] - M[\"('IPCC 2013', 'climate change', 'GWP 100a')\"])/M[\"CarbonFootprint\"]\n", "M[\"dev\"] = M[\"('IPCC 2013', 'climate change', 'GWP 100a')\"]/M[\"CarbonFootprint\"] - 1.0" ] }, @@ -6186,7 +5940,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -6195,26 +5949,24 @@ "Index(['index', '#_x', 'CountryCode', 'ProductTypeName_of_hiot',\n", " 'product code 1', 'product code 2', 'OriginalPhysicalUnit',\n", " 'GHGs-eq - tonnes', 'PhysicalUnit', 'CarbonFootprint', 'unit',\n", - " 'ProductTypeName', 'geography', 'unitName', 'name', 'key', '#_y', 'id',\n", - " 'filename', 'activityName', 'startDate', 'endDate',\n", + " 'ProductTypeName', 'geography', 'unitName', 'name', 'Unnamed: 0_x',\n", + " 'key', '#_y', 'id', 'filename', 'activityName', 'startDate', 'endDate',\n", " 'specialActivityType', 'technologyLevel', 'inheritance status',\n", " 'parentActivityId', 'tags', 'synonyms', 'ISIC v4', 'ISIC code',\n", " 'ISIC name', 'specialty production', 'constrained market',\n", " 'accessRestrictedTo', 'dataEntryBy', 'dataGenerator', 'isActiveAuthor',\n", " 'group', 'CPC', 'amount', 'productionVolumeAmount', 'absolute PV',\n", " 'productionVolumeComment', 'price', 'By-product classification', 'mft',\n", - " 'functional unit',\n", - " '('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs',\n", - " '('IPCC 2013', 'climate change', 'GWP 100a')', 'Unnamed: 0',\n", - " '#activities', 'Code (ecoinvent)', 'Description (ecoinvent)',\n", - " 'ISIC level', 'Code 1', 'Code 2', 'Code 3', 'Code 4', 'Code 5',\n", - " 'Description 1', 'Description 2', 'Description 3', 'Description 4',\n", - " 'Description 5', 'ISIC 1', 'ISIC 2', 'ISIC 3', 'ISIC 4', 'ISIC 5',\n", - " 'Continent', 'diff', 'dev'],\n", + " 'functional unit', '('IPCC 2013', 'climate change', 'GWP 100a')',\n", + " 'Unnamed: 0_y', '#activities', 'Code (ecoinvent)',\n", + " 'Description (ecoinvent)', 'ISIC level', 'Code 1', 'Code 2', 'Code 3',\n", + " 'Code 4', 'Code 5', 'Description 1', 'Description 2', 'Description 3',\n", + " 'Description 4', 'Description 5', 'ISIC 1', 'ISIC 2', 'ISIC 3',\n", + " 'ISIC 4', 'ISIC 5', 'diff', 'dev'],\n", " dtype='object')" ] }, - "execution_count": 90, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -6225,7 +5977,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -6246,7 +5998,7 @@ "\"ProductTypeName\",\n", "\"geography\",\n", "\"CountryCode\",\n", - "\"Continent\",\n", + "#\"Continent\",\n", "\"('IPCC 2013', 'climate change', 'GWP 100a')\",\n", "\"CarbonFootprint\",\n", "\"diff\",\n", @@ -6255,7 +6007,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 84, "metadata": { "scrolled": true }, @@ -6269,41 +6021,6 @@ " truncate_sheet=False,\n", " startrow=1)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -6322,7 +6039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.12" }, "varInspector": { "cols": { diff --git a/notebooks/C - Analysis.ipynb b/notebooks/C - Analysis.ipynb index 85f3d0f..398292f 100644 --- a/notebooks/C - Analysis.ipynb +++ b/notebooks/C - Analysis.ipynb @@ -17,19 +17,15 @@ "pd.set_option('display.max_columns', 500)\n", "pd.options.display.float_format = '{:.3g}'.format\n", "import numpy as np\n", - "import pickle\n", - "from itertools import islice, cycle\n", - "import random\n", + "from itertools import cycle\n", "\n", "import helper_functions as hf\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "from matplotlib import rc, rcParams\n", - "#rc('font', weight='normal')\n", "%matplotlib inline \n", "\n", - "\n", "import seaborn as sns\n", "#sns.set(font_scale=1.0)\n", "\n", @@ -37,9 +33,8 @@ "import math\n", "from adjustText import adjust_text\n", "\n", - "\n", - "# plt.rcParams[\"font.weight\"] = \"normal\"\n", "plt.style.use(['ggplot']) # seaborn-whitegrid\n", + "# plt.rcParams[\"font.weight\"] = \"normal\"\n", "#plt.rcParams[\"font.scale\"] = 1.0" ] }, @@ -97,7 +92,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(4567, 22)\n" + "(4567, 21)\n" ] }, { @@ -138,7 +133,6 @@ " ProductTypeName\n", " geography\n", " CountryCode\n", - " Continent\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", " CarbonFootprint\n", " diff\n", @@ -165,7 +159,6 @@ " Wheat\n", " AU\n", " AU\n", - " Australia\n", " 0.656\n", " 0.307\n", " -0.349\n", @@ -190,7 +183,6 @@ " Wheat\n", " Canada without Quebec\n", " CA\n", - " North America\n", " 0.501\n", " 0.327\n", " -0.173\n", @@ -233,13 +225,13 @@ "0 wheat production wheat grain Wheat AU \n", "1 wheat production wheat grain Wheat Canada without Quebec \n", "\n", - " CountryCode Continent ('IPCC 2013', 'climate change', 'GWP 100a') \\\n", - "0 AU Australia 0.656 \n", - "1 CA North America 0.501 \n", + " CountryCode ('IPCC 2013', 'climate change', 'GWP 100a') CarbonFootprint \\\n", + "0 AU 0.656 0.307 \n", + "1 CA 0.501 0.327 \n", "\n", - " CarbonFootprint diff dev \n", - "0 0.307 -0.349 1.14 \n", - "1 0.327 -0.173 0.53 " + " diff dev \n", + "0 -0.349 1.14 \n", + "1 -0.173 0.53 " ] }, "execution_count": 4, @@ -296,6 +288,7 @@ " \n", " \n", " \n", + " Unnamed: 0\n", " key\n", " #\n", " id\n", @@ -331,13 +324,13 @@ " By-product classification\n", " mft\n", " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs\n", " ('IPCC 2013', 'climate change', 'GWP 100a')\n", " \n", " \n", " \n", " \n", " 1\n", + " 1\n", " ('ecoinvent 3.4 cutoff', '001145735c7cee63ae10...\n", " 402\n", " 0766970d-6c54-497e-b458-2626f56d9e0c\n", @@ -374,10 +367,10 @@ " non-mft\n", " 1\n", " 0\n", - " 0\n", " \n", " \n", " 2\n", + " 2\n", " ('ecoinvent 3.4 cutoff', '00127979f70f2a8748ac...\n", " 14707\n", " fcbe495e-7474-40c0-a735-f4a55937edac\n", @@ -414,16 +407,15 @@ " non-mft\n", " 1\n", " 0.113\n", - " 0.113\n", " \n", " \n", "\n", "" ], "text/plain": [ - " key # \\\n", - "1 ('ecoinvent 3.4 cutoff', '001145735c7cee63ae10... 402 \n", - "2 ('ecoinvent 3.4 cutoff', '00127979f70f2a8748ac... 14707 \n", + " Unnamed: 0 key # \\\n", + "1 1 ('ecoinvent 3.4 cutoff', '001145735c7cee63ae10... 402 \n", + "2 2 ('ecoinvent 3.4 cutoff', '00127979f70f2a8748ac... 14707 \n", "\n", " id \\\n", "1 0766970d-6c54-497e-b458-2626f56d9e0c \n", @@ -485,10 +477,6 @@ "1 allocatable product non-mft 1 \n", "2 allocatable product non-mft 1 \n", "\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')_all_CFs \\\n", - "1 0 \n", - "2 0.113 \n", - "\n", " ('IPCC 2013', 'climate change', 'GWP 100a') \n", "1 0 \n", "2 0.113 " @@ -756,8 +744,7 @@ "# set limits to number of matched products\n", "ax.set_xlim(0,len(M))\n", "\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/relative_deviation.png\", bbox_inches='tight')\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Figure4.svg\", bbox_inches='tight')\n", + "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 2 - Relative Deviation.jpg\", bbox_inches='tight', dpi=300)\n", "\n", "print(\" 0 = perfect match.\\n 1 = EI twice as high. \\n-0.5 = EI has half the impacts. \\\n", " \\n -1 = EI has zero impacts. \\n <-1 = opposite sign\")" @@ -776,10 +763,10 @@ "text": [ "50.4 % (2301, 'EI higher.')\n", "18.7 % (852, 'less than 20% difference.')\n", - "68.6 % (3132, 'less than 100% difference.')\n", + "68.6 % (3131, 'less than 100% difference.')\n", "27.7 % (1265, 'EI has more than 2x the impact.')\n", - "3.2 % (144, 'EI has zero impacts.')\n", - "0.6 % (26, 'datasets with opposite sign.')\n" + "3.2 % (147, 'EI has zero impacts.')\n", + "0.5 % (24, 'datasets with opposite sign.')\n" ] } ], @@ -805,9 +792,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "EI lower. 49.0 %\n", + "EI lower. 49.1 %\n", "EI higher. 50.4 %\n", - "Opposite sign. 0.6 %\n", + "Opposite sign. 0.5 %\n", "EI > factor 2 lower. 24.0 %\n", "EI = zero impact 3.2 %\n", "less than factor 2 difference. 44.5 %\n", @@ -841,7 +828,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Deviation minimum: -9.30310338402766 Deviation maximum: 83658811.53515759\n" + "Deviation minimum: -9.30310338402767 Deviation maximum: 83658811.53515755\n" ] } ], @@ -865,7 +852,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(4532, 22)\n", + "(4532, 21)\n", "A:Agriculture, forestry and fishing\n", "B:Mining and quarrying\n", "C:Manufacturing\n", @@ -875,17 +862,6 @@ "N:Administrative and support service activities\n" ] }, - { - "ename": "NameError", - "evalue": "name 'e' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13796/498566665.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmini\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmini\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'--'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmini\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 31\u001b[0m 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\n", @@ -927,10 +903,8 @@ "ax.set_xscale('log')\n", "ax.plot([mini,maxi], [mini,maxi], color='black', linestyle='--')\n", "ax.set_ylim(mini,maxi)\n", - "ax.set_xlim(e-9,maxi)\n", "plt.show()\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/correlation_all_ISIC1_loglog.png\", bbox_inches='tight')\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Figure3.svg\", bbox_inches='tight')" + "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 1 - Comparison of CFs.jpg\", bbox_inches='tight', dpi=300)" ] }, { @@ -941,7 +915,7 @@ { "data": { "text/plain": [ - "0.7059159969056591" + "0.705923489594283" ] }, "execution_count": 14, @@ -1086,11 +1060,11 @@ " \n", " \n", " Electricity by hydro\n", - " -0.0347\n", + " -0.0345\n", " \n", " \n", " Electricity nec\n", - " -0.0639\n", + " -0.0474\n", " \n", " \n", " Electricity by nuclear\n", @@ -1098,7 +1072,7 @@ " \n", " \n", " Electricity by biomass and waste\n", - " -0.165\n", + " -0.163\n", " \n", " \n", " Electricity by solar photovoltaic\n", @@ -1119,10 +1093,10 @@ "Electricity by wind 0.384\n", "Electricity by gas 0.224\n", "Electricity by petroleum and other oil derivatives 0.182\n", - "Electricity by hydro -0.0347\n", - "Electricity nec -0.0639\n", + "Electricity by hydro -0.0345\n", + "Electricity nec -0.0474\n", "Electricity by nuclear -0.0758\n", - "Electricity by biomass and waste -0.165\n", + "Electricity by biomass and waste -0.163\n", "Electricity by solar photovoltaic -0.196\n", "Electricity by Geothermal NaN" ] @@ -1166,7 +1140,7 @@ { "data": { "text/plain": [ - "(1717, 22)" + "(1717, 21)" ] }, "execution_count": 18, @@ -1239,7 +1213,6 @@ "source": [ "# Plot\n", "fig, ax = plt.subplots(figsize=(8,6))\n", - "#ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n", "\n", "mini, maxi = 0, 2.5\n", "\n", @@ -1251,8 +1224,7 @@ "ax.set_ylabel('ecoinvent (kg CO2-eq.)')\n", "ax.plot([mini,maxi], [mini,maxi], color='black', linestyle='--')\n", "plt.show()\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/correlation_electricity.png\", bbox_inches='tight')\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Figure7.svg\", bbox_inches='tight')" + "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 5 - Correlation Electricity.jpg\", bbox_inches='tight', dpi=300)" ] }, { @@ -1464,7 +1436,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1484,8 +1456,6 @@ "df_dev = pd.DataFrame({col:vals['dev'] for col,vals in grouped}) # cols: PTN, vals: dev\n", "# sort alphabetically\n", "df_dev.sort_index(axis=1, inplace=True, ascending=False)\n", - "#print(df_dev.shape)\n", - "#print(df_dev.describe())\n", "\n", "# figure\n", "plt.style.use(['seaborn-whitegrid'])\n", @@ -1496,11 +1466,8 @@ "ax.axvline(x=0, linewidth=2, color='r')\n", "ax.axvline(x=-1, linewidth=1, color='grey')\n", "ax.set_xlim(-10, 10)\n", - "#print(df_dev.shape)\n", - "#fig.savefig(f\"{SCENARIO_PATH}figures/Dev_by_ISIC1_manual.png\", bbox_inches='tight')\n", - "#fig.savefig(f\"{SCENARIO_PATH}figures/Figure5.svg\", bbox_inches='tight')\n", - "#fig.savefig(f\"{SCENARIO_PATH}figures/Figure5.pdf\", bbox_inches='tight')\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 3.tif\", bbox_inches='tight', dpi=300)" + "\n", + "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 3 - Relative Deviation by Sector.jpg\", bbox_inches='tight', dpi=300)" ] }, { @@ -1579,7 +1546,7 @@ "fig, ax = plt.subplots(figsize=figsize)\n", "ax = sns.heatmap(grouped.unstack(), annot=False, ax=ax, cmap=\"coolwarm\", center=0, vmin=-2, vmax=2)\n", "ytl = ax.set_yticklabels(add_sample_size(ax.get_yticklabels(), 'ISIC 1')) # add sample sizes to yticklabels\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Dev_by_ISIC1_region_manual.svg\", bbox_inches='tight')" + "#fig.savefig(f\"{SCENARIO_PATH}figures/Dev_by_ISIC1_region_manual.svg\", bbox_inches='tight')" ] }, { @@ -2435,13 +2402,6 @@ "unstacked.head()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 32, @@ -2938,19 +2898,9 @@ } ], "source": [ - "# sort by ISIC 1 and PTN and add both as index\n", - "#unstacked = unstacked.sort_values(by = ['ISIC 1', 'ProductTypeName'])\n", - "\n", - "# Filter out treatment\n", - "\n", - "# Filter out where no ISIC 1 information is available\n", - "#unstacked['ISIC 1'].unique()\n", - "#unstacked = unstacked.drop(unstacked[unstacked['ISIC 1'] == \"\"].index)\n", - "\n", "# Drop ISIC from index and reset to PTN\n", "unstacked = unstacked.drop('ISIC 1', axis=1)\n", "unstacked = unstacked.reset_index().set_index(['ProductTypeName'])\n", - "#unstacked = unstacked.reset_index().set_index(['ISIC 1', 'ProductTypeName'])\n", "print(unstacked.shape)\n", "unstacked.head()" ] @@ -2979,8 +2929,6 @@ } ], "source": [ - "#grouped = M['dev'].groupby([M['ProductTypeName'], M[\"CountryCode\"]]).median() #.agg(\"mean\")\n", - "#unstacked = grouped.unstack()\n", "print(unstacked.shape)\n", "\n", "sns.set(font_scale=1.0)\n", @@ -3016,10 +2964,7 @@ "ax.set_ylabel('Product')\n", "ax.set_xlabel('Region')\n", "\n", - "#fig.savefig(f\"{SCENARIO_PATH}figures/Dev_by_PTN_region_manual.png\", bbox_inches='tight') \n", - "#fig.savefig(f\"{SCENARIO_PATH}figures/Figure6.svg\", bbox_inches='tight') \n", - "#fig.savefig(f\"{SCENARIO_PATH}figures/Figure6.pdf\", bbox_inches='tight') \n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 4.jpg\", bbox_inches='tight', dpi=300) " + "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 4 - Median Relative Deviation by sector and region.jpg\", bbox_inches='tight', dpi=300) " ] }, { @@ -3586,9 +3531,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\steub\\miniconda3\\envs\\ab\\lib\\site-packages\\numpy\\core\\_methods.py:263: RuntimeWarning: Degrees of freedom <= 0 for slice\n", - " keepdims=keepdims, where=where)\n", - "C:\\Users\\steub\\miniconda3\\envs\\ab\\lib\\site-packages\\numpy\\core\\_methods.py:254: RuntimeWarning: invalid value encountered in double_scalars\n", + "C:\\Users\\steub\\miniconda3\\envs\\ab38\\lib\\site-packages\\numpy\\core\\_methods.py:262: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", + "C:\\Users\\steub\\miniconda3\\envs\\ab38\\lib\\site-packages\\numpy\\core\\_methods.py:254: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ] }, @@ -5429,12 +5374,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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        " ] @@ -5444,98 +5389,49 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(10,8))\n", - "plt.style.use(['seaborn-whitegrid']) # 'default'\n", - "\n", - "# exclude sectors with too small ecoinvent sample size\n", + "# Data\n", "samplesize_ecoinvent_cutoff = 30\n", "display_cutoff = 30\n", + "\n", "DF = EXI[EXI['count_y'] >= samplesize_ecoinvent_cutoff]\n", + "x = DF['EX_relative_std']\n", + "y = DF['EI_relative_std']\n", + "c=DF['improve_diff'], # color of circle\n", + "s=DF['count_y'] * 2, # size of circle\n", "\n", "mini = round(DF['improve_diff'].min())\n", "maxi = round(DF['improve_diff'].max())\n", "\n", - "divnorm = mpl.colors.TwoSlopeNorm(vmin=mini, vcenter=0, vmax=maxi)\n", + "# Plot\n", + "fig, ax = plt.subplots(figsize=(10,8))\n", + "plt.style.use(['seaborn-whitegrid']) # 'default'\n", "\n", - "ax = DF.plot.scatter(ax=ax, x='EX_relative_std', y='EI_relative_std', \n", - " s=DF['count_y'] * 2, # size of circle\n", - " c=DF['improve_diff'], # color of circle\n", - " #norm=divnorm,\n", - " colormap=\"coolwarm\", \n", - " )\n", + "colormap = plt.cm.coolwarm #or any other colormap\n", + "divnorm = mpl.colors.TwoSlopeNorm(vmin=mini, vcenter=0, vmax=maxi)\n", "\n", - "ax.set_xlabel('CV EXIOBASE')\n", - "ax.set_ylabel('CV ecoinvent')\n", + "sc = plt.scatter(x, y, \n", + " c=c, \n", + " s=s, \n", + " marker='o',\n", + " cmap=colormap, \n", + " norm=divnorm,)\n", "\n", - "# diagonal line\n", - "ax.plot([0.5, maxi], [0.5, maxi], color='lightgrey', linestyle='--')\n", + "plt.colorbar(sc)\n", "\n", - "# text annotation\n", "texts = []\n", "for k, v in DF.iterrows():\n", " if v['count_y'] > display_cutoff:\n", " texts.append(plt.text(v['EX_relative_std'], v['EI_relative_std'], limit_string(k, max_length=50), \n", " fontsize=5+0.2*math.sqrt(v['count_y']), \n", " ))\n", - "\n", - "adjust_text(texts, arrowprops=dict(arrowstyle=\"-\", color='grey', lw=0.5)) \n", " \n", - "fig.savefig(f\"{SCENARIO_PATH}figures/figure_relative_std.png\", bbox_inches='tight')\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Figure8.svg\", bbox_inches='tight')\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Figure8.pdf\", bbox_inches='tight')\n", - "#fig.savefig(f\"{SCENARIO_PATH}figures/Figure 6.tif\", bbox_inches='tight', dpi=300) # 18 MB\n", - "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 6.jpg\", bbox_inches='tight', dpi=300)" + "adjust_text(texts, arrowprops=dict(arrowstyle=\"-\", color='grey', lw=0.5)) \n", + "\n", + "fig.savefig(f\"{SCENARIO_PATH}figures/Figure 6 - Coefficient of Variation.jpg\", bbox_inches='tight', dpi=300)\n", + "\n", + "# run again to get gridlines" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -5560,7 +5456,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.12" }, "varInspector": { "cols": { diff --git a/notebooks/D - Comparison of economic coverage.ipynb b/notebooks/D - Comparison of economic coverage.ipynb index ebdad61..94b2863 100644 --- a/notebooks/D - Comparison of economic coverage.ipynb +++ b/notebooks/D - Comparison of economic coverage.ipynb @@ -34,8 +34,7 @@ "metadata": {}, "outputs": [], "source": [ - "scenario = '2019-12-02_WW' # must be the same as the HIOT Excel sheetname\n", - "#scenario = 'sensitivity_no_service_sectors'" + "scenario = '2019-12-02_WW' # must be the same as the HIOT Excel sheetname" ] }, { @@ -92,14 +91,13 @@ "\n", "# EXIOBASE\n", "hiot_results = f\"{DATA_PATH}exiobase/hiot_results_PTM_v2.xlsx\"\n", - "hiot_results_sheet = \"v_3_3_18_2019-11-22\"\n", + "hiot_results_sheet = scenario\n", "\n", "# Matching\n", - "product_matching = f\"{DATA_PATH}matching/match_v4.xlsx\"\n", - "product_matching_sheet = 'concordance'\n", - "process_matching = f\"{DATA_PATH}matching/match_v2_electricity.xlsx\"\n", - "region_matching = f\"{DATA_PATH}matching/matching_ecoinvent_Exiobase_geographies.xlsx\"\n", - "region_matching_sheet = \"matched_191117\"" + "matching_file = f\"{DATA_PATH}matching/Matching file.xlsx\"\n", + "product_matching_sheet = 'product matching'\n", + "process_matching_sheet = 'process matching electricity'\n", + "region_matching_sheet = \"regional matching\"" ] }, { @@ -245,7 +243,7 @@ } ], "source": [ - "ISIC = pd.read_excel(f\"{DATA_PATH}matching/isic_codes_with_ecoinvent_level5.xlsx\")\n", + "ISIC = pd.read_excel(f\"{DATA_PATH}matching/ISIC.xlsx\", sheet_name='ISIC4')\n", "print(ISIC.shape)\n", "ISIC.head(2)" ] @@ -509,15 +507,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(14889, 57)\n", - "(10805, 57)\n" + "(14889, 58)\n", + "(10805, 58)\n" ] }, { @@ -542,6 +540,7 @@ " \n", " \n", " Unnamed: 0\n", + " Unnamed: 0_x\n", " key\n", " #\n", " id\n", @@ -550,7 +549,6 @@ " geography\n", " startDate\n", " endDate\n", - " specialActivityType\n", " ...\n", " Description 1\n", " Description 2\n", @@ -566,107 +564,107 @@ " \n", " \n", " \n", - " 0\n", - " 0\n", - " ('ecoinvent 3.4 cutoff', '60274947cf82e6633d8c...\n", - " 2607\n", - " 2d83c1a0-0c9b-4589-bb9b-6a81e815f5e2\n", - " 2d83c1a0-0c9b-4589-bb9b-6a81e815f5e2_4c2b1cc3-...\n", - " [sulfonyl]urea-compound production\n", + " 1\n", + " 1\n", + " 242\n", + " ('ecoinvent 3.4 cutoff', '048ac7e1860972d562e9...\n", + " 7691\n", + " 831198c8-1ddd-49ff-be80-9dce40d83a7e\n", + " 831198c8-1ddd-49ff-be80-9dce40d83a7e_4cf8fef9-...\n", + " molecular sieve separation of naphtha\n", " RER\n", - " 2000-01-01\n", + " 1998-01-01\n", " 2017-12-31\n", - " ordinary transforming activity\n", " ...\n", " Manufacturing\n", - " Manufacture of chemicals and chemical products\n", - " Manufacture of other chemical products\n", - " Manufacture of pesticides and other agrochemic...\n", + " Manufacture of coke and refined petroleum prod...\n", + " Manufacture of refined petroleum products\n", + " Manufacture of refined petroleum products\n", " NaN\n", " C:Manufacturing\n", - " 20:Manufacture of chemicals and chemical products\n", - " 202:Manufacture of other chemical products\n", - " 2021:Manufacture of pesticides and other agroc...\n", + " 19:Manufacture of coke and refined petroleum p...\n", + " 192:Manufacture of refined petroleum products\n", + " 1920:Manufacture of refined petroleum products\n", " NaN\n", " \n", " \n", - " 1\n", - " 1\n", - " ('ecoinvent 3.4 cutoff', '7dd298333a92144698cf...\n", - " 5307\n", - " 59913008-58ba-4f1a-8bf2-86925bbd6005\n", - " 59913008-58ba-4f1a-8bf2-86925bbd6005_4c2b1cc3-...\n", - " [sulfonyl]urea-compound production\n", - " RoW\n", - " 2000-01-01\n", + " 2\n", + " 2\n", + " 251\n", + " ('ecoinvent 3.4 cutoff', '04b39baa4b5d28eb8c39...\n", + " 13213\n", + " e31199c9-a24f-4cd8-b770-d6d21802ae88\n", + " e31199c9-a24f-4cd8-b770-d6d21802ae88_b658a402-...\n", + " propylene production\n", + " RER\n", + " 1999-01-01\n", " 2017-12-31\n", - " ordinary transforming activity\n", " ...\n", " Manufacturing\n", - " Manufacture of chemicals and chemical products\n", - " Manufacture of other chemical products\n", - " Manufacture of pesticides and other agrochemic...\n", + " Manufacture of coke and refined petroleum prod...\n", + " Manufacture of refined petroleum products\n", + " Manufacture of refined petroleum products\n", " NaN\n", " C:Manufacturing\n", - " 20:Manufacture of chemicals and chemical products\n", - " 202:Manufacture of other chemical products\n", - " 2021:Manufacture of pesticides and other agroc...\n", + " 19:Manufacture of coke and refined petroleum p...\n", + " 192:Manufacture of refined petroleum products\n", + " 1920:Manufacture of refined petroleum products\n", " NaN\n", " \n", " \n", "\n", - "

        2 rows × 57 columns

        \n", + "

        2 rows × 58 columns

        \n", "" ], "text/plain": [ - " Unnamed: 0 key # \\\n", - "0 0 ('ecoinvent 3.4 cutoff', '60274947cf82e6633d8c... 2607 \n", - "1 1 ('ecoinvent 3.4 cutoff', '7dd298333a92144698cf... 5307 \n", + " Unnamed: 0 Unnamed: 0_x \\\n", + "1 1 242 \n", + "2 2 251 \n", + "\n", + " key # \\\n", + "1 ('ecoinvent 3.4 cutoff', '048ac7e1860972d562e9... 7691 \n", + "2 ('ecoinvent 3.4 cutoff', '04b39baa4b5d28eb8c39... 13213 \n", "\n", " id \\\n", - "0 2d83c1a0-0c9b-4589-bb9b-6a81e815f5e2 \n", - "1 59913008-58ba-4f1a-8bf2-86925bbd6005 \n", + "1 831198c8-1ddd-49ff-be80-9dce40d83a7e \n", + "2 e31199c9-a24f-4cd8-b770-d6d21802ae88 \n", "\n", " filename \\\n", - "0 2d83c1a0-0c9b-4589-bb9b-6a81e815f5e2_4c2b1cc3-... \n", - "1 59913008-58ba-4f1a-8bf2-86925bbd6005_4c2b1cc3-... \n", + "1 831198c8-1ddd-49ff-be80-9dce40d83a7e_4cf8fef9-... \n", + "2 e31199c9-a24f-4cd8-b770-d6d21802ae88_b658a402-... \n", "\n", - " activityName geography startDate endDate \\\n", - "0 [sulfonyl]urea-compound production RER 2000-01-01 2017-12-31 \n", - "1 [sulfonyl]urea-compound production RoW 2000-01-01 2017-12-31 \n", + " activityName geography startDate endDate \\\n", + "1 molecular sieve separation of naphtha RER 1998-01-01 2017-12-31 \n", + "2 propylene production RER 1999-01-01 2017-12-31 \n", "\n", - " specialActivityType ... Description 1 \\\n", - "0 ordinary transforming activity ... Manufacturing \n", - "1 ordinary transforming activity ... Manufacturing \n", + " ... Description 1 Description 2 \\\n", + "1 ... Manufacturing Manufacture of coke and refined petroleum prod... \n", + "2 ... Manufacturing Manufacture of coke and refined petroleum prod... \n", "\n", - " Description 2 \\\n", - "0 Manufacture of chemicals and chemical products \n", - "1 Manufacture of chemicals and chemical products \n", + " Description 3 \\\n", + "1 Manufacture of refined petroleum products \n", + "2 Manufacture of refined petroleum products \n", "\n", - " Description 3 \\\n", - "0 Manufacture of other chemical products \n", - "1 Manufacture of other chemical products \n", + " Description 4 Description 5 ISIC 1 \\\n", + "1 Manufacture of refined petroleum products NaN C:Manufacturing \n", + "2 Manufacture of refined petroleum products NaN C:Manufacturing \n", "\n", - " Description 4 Description 5 \\\n", - "0 Manufacture of pesticides and other agrochemic... NaN \n", - "1 Manufacture of pesticides and other agrochemic... NaN \n", + " ISIC 2 \\\n", + "1 19:Manufacture of coke and refined petroleum p... \n", + "2 19:Manufacture of coke and refined petroleum p... \n", "\n", - " ISIC 1 ISIC 2 \\\n", - "0 C:Manufacturing 20:Manufacture of chemicals and chemical products \n", - "1 C:Manufacturing 20:Manufacture of chemicals and chemical products \n", + " ISIC 3 \\\n", + "1 192:Manufacture of refined petroleum products \n", + "2 192:Manufacture of refined petroleum products \n", "\n", - " ISIC 3 \\\n", - "0 202:Manufacture of other chemical products \n", - "1 202:Manufacture of other chemical products \n", + " ISIC 4 ISIC 5 \n", + "1 1920:Manufacture of refined petroleum products NaN \n", + "2 1920:Manufacture of refined petroleum products NaN \n", "\n", - " ISIC 4 ISIC 5 \n", - "0 2021:Manufacture of pesticides and other agroc... NaN \n", - "1 2021:Manufacture of pesticides and other agroc... NaN \n", - "\n", - "[2 rows x 57 columns]" + "[2 rows x 58 columns]" ] }, - "execution_count": 32, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -682,16 +680,50 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(7986, 57)" + "Index(['Unnamed: 0', 'Unnamed: 0_x', 'key', '#', 'id', 'filename',\n", + " 'activityName', 'geography', 'startDate', 'endDate',\n", + " 'specialActivityType', 'technologyLevel', 'inheritance status',\n", + " 'parentActivityId', 'tags', 'synonyms', 'ISIC v4', 'ISIC code',\n", + " 'ISIC name', 'specialty production', 'constrained market',\n", + " 'accessRestrictedTo', 'dataEntryBy', 'dataGenerator', 'isActiveAuthor',\n", + " 'group', 'name', 'CPC', 'amount', 'unitName', 'productionVolumeAmount',\n", + " 'absolute PV', 'productionVolumeComment', 'price',\n", + " 'By-product classification', 'mft', 'functional unit',\n", + " '('IPCC 2013', 'climate change', 'GWP 100a')', 'Unnamed: 0_y',\n", + " '#activities', 'Code (ecoinvent)', 'Description (ecoinvent)',\n", + " 'ISIC level', 'Code 1', 'Code 2', 'Code 3', 'Code 4', 'Code 5',\n", + " 'Description 1', 'Description 2', 'Description 3', 'Description 4',\n", + " 'Description 5', 'ISIC 1', 'ISIC 2', 'ISIC 3', 'ISIC 4', 'ISIC 5'],\n", + " dtype='object')" ] }, - "execution_count": 34, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AO.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(7986, 58)" + ] + }, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -703,7 +735,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -723,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -743,7 +775,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -809,7 +841,7 @@ " 'indium rich leaching residues, from zinc production//primary zinc production from concentrate'}" ] }, - "execution_count": 50, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -821,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -841,522 +873,7 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        Unnamed: 0key#idfilenameactivityNamegeographystartDateendDatespecialActivityType...Description 1Description 2Description 3Description 4Description 5ISIC 1ISIC 2ISIC 3ISIC 4ISIC 5
        11('ecoinvent 3.4 cutoff', '7dd298333a92144698cf...530759913008-58ba-4f1a-8bf2-86925bbd600559913008-58ba-4f1a-8bf2-86925bbd6005_4c2b1cc3-...[sulfonyl]urea-compound productionRoW2000-01-012017-12-31ordinary transforming activity...ManufacturingManufacture of chemicals and chemical productsManufacture of other chemical productsManufacture of pesticides and other agrochemic...NaNC:Manufacturing20:Manufacture of chemicals and chemical products202:Manufacture of other chemical products2021:Manufacture of pesticides and other agroc...NaN
        33('ecoinvent 3.4 cutoff', '35e16ca3f2007a0186f0...77548419276e-31ac-4859-b9ce-05d4f05199c68419276e-31ac-4859-b9ce-05d4f05199c6_1be4f7e4-...[thio]carbamate-compound productionRoW2000-01-012017-12-31ordinary transforming activity...ManufacturingManufacture of chemicals and chemical productsManufacture of other chemical productsManufacture of pesticides and other agrochemic...NaNC:Manufacturing20:Manufacture of chemicals and chemical products202:Manufacture of other chemical products2021:Manufacture of pesticides and other agroc...NaN
        55('ecoinvent 3.4 cutoff', '555a1f49dfd1dd13e5a2...578161ee25d4-accb-4d44-b381-9b78e0f057d561ee25d4-accb-4d44-b381-9b78e0f057d5_2487d46f-...acetamide-anillide-compound production, unspec...RoW2000-01-012017-12-31ordinary transforming activity...ManufacturingManufacture of chemicals and chemical productsManufacture of other chemical productsManufacture of pesticides and other agrochemic...NaNC:Manufacturing20:Manufacture of chemicals and chemical products202:Manufacture of other chemical products2021:Manufacture of pesticides and other agroc...NaN
        77('ecoinvent 3.4 cutoff', '49aa7ecf75011a9cdbac...12231d1b0e57c-90c3-49c1-b6e3-19e5e162a569d1b0e57c-90c3-49c1-b6e3-19e5e162a569_906ace6c-...aclonifen productionRoW2000-01-012017-12-31ordinary transforming activity...ManufacturingManufacture of chemicals and chemical productsManufacture of other chemical productsManufacture of pesticides and other agrochemic...NaNC:Manufacturing20:Manufacture of chemicals and chemical products202:Manufacture of other chemical products2021:Manufacture of pesticides and other agroc...NaN
        99('ecoinvent 3.4 cutoff', '14406fd5b1ec4fbe5112...899299d608c8-6d0e-4ee6-96aa-783e2a30690f99d608c8-6d0e-4ee6-96aa-783e2a30690f_bc06c7e3-...atrazine productionRoW2000-01-012017-12-31ordinary transforming activity...ManufacturingManufacture of chemicals and chemical productsManufacture of other chemical productsManufacture of pesticides and other agrochemic...NaNC:Manufacturing20:Manufacture of chemicals and chemical products202:Manufacture of other chemical products2021:Manufacture of pesticides and other agroc...NaN
        ..................................................................
        1486114861('ecoinvent 3.4 cutoff', '06c94c3c4da6e53b4873...14264f52990d1-7b30-4be6-8af7-719934c01f70f52990d1-7b30-4be6-8af7-719934c01f70_a90c974c-...tube insulation production, elastomereRoW1997-01-012017-12-31ordinary transforming activity...ManufacturingManufacture of rubber and plastics productsManufacture of rubber productsManufacture of other rubber productsNaNC:Manufacturing22:Manufacture of rubber and plastics products221:Manufacture of rubber products2219:Manufacture of other rubber productsNaN
        1486314863('ecoinvent 3.4 cutoff', '861c0a3ea73ad63e000d...228827f369f3-985d-4146-a3e3-07a008c7a38c27f369f3-985d-4146-a3e3-07a008c7a38c_dd0af955-...uranium hexafluoride productionRoW1982-01-012017-12-31ordinary transforming activity...ManufacturingManufacture of basic metalsManufacture of basic precious and other non-fe...Manufacture of basic precious and other non-fe...Smelting and refining of uraniumC:Manufacturing24:Manufacture of basic metals242:Manufacture of basic precious and other no...2420:Manufacture of basic precious and other n...2420a: Smelting and refining of uranium
        1487814878('ecoinvent 3.4 cutoff', 'b2511a5336e78b9c1829...91999d5f632b-2d29-45ba-9eaa-136ab4ac75b39d5f632b-2d29-45ba-9eaa-136ab4ac75b3_8578a99c-...power sawing, without catalytic converterRoW2011-01-012017-12-31ordinary transforming activity...Agriculture, forestry and fishingForestry and loggingSilviculture and other forestry activitiesSilviculture and other forestry activitiesNaNA:Agriculture, forestry and fishing02:Forestry and logging021:Silviculture and other forestry activities0210:Silviculture and other forestry activitiesNaN
        1488414884('ecoinvent 3.4 cutoff', 'e657c56e8e73a80ce50a...82578cc16b95-47c4-487c-9277-b534d40f8bdb8cc16b95-47c4-487c-9277-b534d40f8bdb_5d4272ad-...transport, passenger trainRoW2000-01-012017-12-31ordinary transforming activity...Transportation and storageLand transport and transport via pipelinesTransport via railwaysPassenger rail transport, interurbanNaNH:Transportation and storage49:Land transport and transport via pipelines491:Transport via railways4911:Passenger rail transport, interurbanNaN
        1488814888('ecoinvent 3.4 cutoff', 'a1336aabee4520d84b2a...853991de71ca-0823-4aed-911b-1d4fd425114891de71ca-0823-4aed-911b-1d4fd4251148_5d4272ad-...transport, passenger train, high-speedRoW2000-01-012017-12-31ordinary transforming activity...Transportation and storageLand transport and transport via pipelinesTransport via railwaysPassenger rail transport, interurbanNaNH:Transportation and storage49:Land transport and transport via pipelines491:Transport via railways4911:Passenger rail transport, interurbanNaN
        \n", - "

        2819 rows × 57 columns

        \n", - "
        " - ], - "text/plain": [ - " Unnamed: 0 key # \\\n", - "1 1 ('ecoinvent 3.4 cutoff', '7dd298333a92144698cf... 5307 \n", - "3 3 ('ecoinvent 3.4 cutoff', '35e16ca3f2007a0186f0... 7754 \n", - "5 5 ('ecoinvent 3.4 cutoff', '555a1f49dfd1dd13e5a2... 5781 \n", - "7 7 ('ecoinvent 3.4 cutoff', '49aa7ecf75011a9cdbac... 12231 \n", - "9 9 ('ecoinvent 3.4 cutoff', '14406fd5b1ec4fbe5112... 8992 \n", - "... ... ... ... \n", - "14861 14861 ('ecoinvent 3.4 cutoff', '06c94c3c4da6e53b4873... 14264 \n", - "14863 14863 ('ecoinvent 3.4 cutoff', '861c0a3ea73ad63e000d... 2288 \n", - "14878 14878 ('ecoinvent 3.4 cutoff', 'b2511a5336e78b9c1829... 9199 \n", - "14884 14884 ('ecoinvent 3.4 cutoff', 'e657c56e8e73a80ce50a... 8257 \n", - "14888 14888 ('ecoinvent 3.4 cutoff', 'a1336aabee4520d84b2a... 8539 \n", - "\n", - " id \\\n", - "1 59913008-58ba-4f1a-8bf2-86925bbd6005 \n", - "3 8419276e-31ac-4859-b9ce-05d4f05199c6 \n", - "5 61ee25d4-accb-4d44-b381-9b78e0f057d5 \n", - "7 d1b0e57c-90c3-49c1-b6e3-19e5e162a569 \n", - "9 99d608c8-6d0e-4ee6-96aa-783e2a30690f \n", - "... ... \n", - "14861 f52990d1-7b30-4be6-8af7-719934c01f70 \n", - "14863 27f369f3-985d-4146-a3e3-07a008c7a38c \n", - "14878 9d5f632b-2d29-45ba-9eaa-136ab4ac75b3 \n", - "14884 8cc16b95-47c4-487c-9277-b534d40f8bdb \n", - "14888 91de71ca-0823-4aed-911b-1d4fd4251148 \n", - "\n", - " filename \\\n", - "1 59913008-58ba-4f1a-8bf2-86925bbd6005_4c2b1cc3-... \n", - "3 8419276e-31ac-4859-b9ce-05d4f05199c6_1be4f7e4-... \n", - "5 61ee25d4-accb-4d44-b381-9b78e0f057d5_2487d46f-... \n", - "7 d1b0e57c-90c3-49c1-b6e3-19e5e162a569_906ace6c-... \n", - "9 99d608c8-6d0e-4ee6-96aa-783e2a30690f_bc06c7e3-... \n", - "... ... \n", - "14861 f52990d1-7b30-4be6-8af7-719934c01f70_a90c974c-... \n", - "14863 27f369f3-985d-4146-a3e3-07a008c7a38c_dd0af955-... \n", - "14878 9d5f632b-2d29-45ba-9eaa-136ab4ac75b3_8578a99c-... \n", - "14884 8cc16b95-47c4-487c-9277-b534d40f8bdb_5d4272ad-... \n", - "14888 91de71ca-0823-4aed-911b-1d4fd4251148_5d4272ad-... \n", - "\n", - " activityName geography \\\n", - "1 [sulfonyl]urea-compound production RoW \n", - "3 [thio]carbamate-compound production RoW \n", - "5 acetamide-anillide-compound production, unspec... RoW \n", - "7 aclonifen production RoW \n", - "9 atrazine production RoW \n", - "... ... ... \n", - "14861 tube insulation production, elastomere RoW \n", - "14863 uranium hexafluoride production RoW \n", - "14878 power sawing, without catalytic converter RoW \n", - "14884 transport, passenger train RoW \n", - "14888 transport, passenger train, high-speed RoW \n", - "\n", - " startDate endDate specialActivityType ... \\\n", - "1 2000-01-01 2017-12-31 ordinary transforming activity ... \n", - "3 2000-01-01 2017-12-31 ordinary transforming activity ... \n", - "5 2000-01-01 2017-12-31 ordinary transforming activity ... \n", - "7 2000-01-01 2017-12-31 ordinary transforming activity ... \n", - "9 2000-01-01 2017-12-31 ordinary transforming activity ... \n", - "... ... ... ... ... \n", - "14861 1997-01-01 2017-12-31 ordinary transforming activity ... \n", - "14863 1982-01-01 2017-12-31 ordinary transforming activity ... \n", - "14878 2011-01-01 2017-12-31 ordinary transforming activity ... \n", - "14884 2000-01-01 2017-12-31 ordinary transforming activity ... \n", - "14888 2000-01-01 2017-12-31 ordinary transforming activity ... \n", - "\n", - " Description 1 \\\n", - "1 Manufacturing \n", - "3 Manufacturing \n", - "5 Manufacturing \n", - "7 Manufacturing \n", - "9 Manufacturing \n", - "... ... \n", - "14861 Manufacturing \n", - "14863 Manufacturing \n", - "14878 Agriculture, forestry and fishing \n", - "14884 Transportation and storage \n", - "14888 Transportation and storage \n", - "\n", - " Description 2 \\\n", - "1 Manufacture of chemicals and chemical products \n", - "3 Manufacture of chemicals and chemical products \n", - "5 Manufacture of chemicals and chemical products \n", - "7 Manufacture of chemicals and chemical products \n", - "9 Manufacture of chemicals and chemical products \n", - "... ... \n", - "14861 Manufacture of rubber and plastics products \n", - "14863 Manufacture of basic metals \n", - "14878 Forestry and logging \n", - "14884 Land transport and transport via pipelines \n", - "14888 Land transport and transport via pipelines \n", - "\n", - " Description 3 \\\n", - "1 Manufacture of other chemical products \n", - "3 Manufacture of other chemical products \n", - "5 Manufacture of other chemical products \n", - "7 Manufacture of other chemical products \n", - "9 Manufacture of other chemical products \n", - "... ... \n", - "14861 Manufacture of rubber products \n", - "14863 Manufacture of basic precious and other non-fe... \n", - "14878 Silviculture and other forestry activities \n", - "14884 Transport via railways \n", - "14888 Transport via railways \n", - "\n", - " Description 4 \\\n", - "1 Manufacture of pesticides and other agrochemic... \n", - "3 Manufacture of pesticides and other agrochemic... \n", - "5 Manufacture of pesticides and other agrochemic... \n", - "7 Manufacture of pesticides and other agrochemic... \n", - "9 Manufacture of pesticides and other agrochemic... \n", - "... ... \n", - "14861 Manufacture of other rubber products \n", - "14863 Manufacture of basic precious and other non-fe... \n", - "14878 Silviculture and other forestry activities \n", - "14884 Passenger rail transport, interurban \n", - "14888 Passenger rail transport, interurban \n", - "\n", - " Description 5 ISIC 1 \\\n", - "1 NaN C:Manufacturing \n", - "3 NaN C:Manufacturing \n", - "5 NaN C:Manufacturing \n", - "7 NaN C:Manufacturing \n", - "9 NaN C:Manufacturing \n", - "... ... ... \n", - "14861 NaN C:Manufacturing \n", - "14863 Smelting and refining of uranium C:Manufacturing \n", - "14878 NaN A:Agriculture, forestry and fishing \n", - "14884 NaN H:Transportation and storage \n", - "14888 NaN H:Transportation and storage \n", - "\n", - " ISIC 2 \\\n", - "1 20:Manufacture of chemicals and chemical products \n", - "3 20:Manufacture of chemicals and chemical products \n", - "5 20:Manufacture of chemicals and chemical products \n", - "7 20:Manufacture of chemicals and chemical products \n", - "9 20:Manufacture of chemicals and chemical products \n", - "... ... \n", - "14861 22:Manufacture of rubber and plastics products \n", - "14863 24:Manufacture of basic metals \n", - "14878 02:Forestry and logging \n", - "14884 49:Land transport and transport via pipelines \n", - "14888 49:Land transport and transport via pipelines \n", - "\n", - " ISIC 3 \\\n", - "1 202:Manufacture of other chemical products \n", - "3 202:Manufacture of other chemical products \n", - "5 202:Manufacture of other chemical products \n", - "7 202:Manufacture of other chemical products \n", - "9 202:Manufacture of other chemical products \n", - "... ... \n", - "14861 221:Manufacture of rubber products \n", - "14863 242:Manufacture of basic precious and other no... \n", - "14878 021:Silviculture and other forestry activities \n", - "14884 491:Transport via railways \n", - "14888 491:Transport via railways \n", - "\n", - " ISIC 4 \\\n", - "1 2021:Manufacture of pesticides and other agroc... \n", - "3 2021:Manufacture of pesticides and other agroc... \n", - "5 2021:Manufacture of pesticides and other agroc... \n", - "7 2021:Manufacture of pesticides and other agroc... \n", - "9 2021:Manufacture of pesticides and other agroc... \n", - "... ... \n", - "14861 2219:Manufacture of other rubber products \n", - "14863 2420:Manufacture of basic precious and other n... \n", - "14878 0210:Silviculture and other forestry activities \n", - "14884 4911:Passenger rail transport, interurban \n", - "14888 4911:Passenger rail transport, interurban \n", - "\n", - " ISIC 5 \n", - "1 NaN \n", - "3 NaN \n", - "5 NaN \n", - "7 NaN \n", - "9 NaN \n", - "... ... \n", - "14861 NaN \n", - "14863 2420a: Smelting and refining of uranium \n", - "14878 NaN \n", - "14884 NaN \n", - "14888 NaN \n", - "\n", - "[2819 rows x 57 columns]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diff = pd.concat([AO.groupby, AOnRoW]).drop_duplicates(keep=False)\n", - "diff" - ] - }, - { - "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1379,7 +896,7 @@ "Name: ecoinvent, w/o markets, dtype: int64" ] }, - "execution_count": 11, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1393,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1413,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1574,33 +1091,54 @@ "" ], "text/plain": [ - " Code ... ecoinvent, w/o markets\n", - "0 A ... 808\n", - "1 B ... 284\n", - "2 C ... 3907\n", - "3 D ... 3162\n", - "4 E ... 1504\n", - "5 F ... 525\n", - "6 G ... 18\n", - "7 H ... 248\n", - "8 I ... 0\n", - "9 J ... 13\n", - "10 K ... 0\n", - "11 L ... 200\n", - "12 M ... 4\n", - "13 N ... 85\n", - "14 O ... 0\n", - "15 P ... 0\n", - "16 Q ... 0\n", - "17 R ... 0\n", - "18 S ... 2\n", - "19 T ... 0\n", - "20 U ... 0\n", - "\n", - "[21 rows x 3 columns]" + " Code Description \\\n", + "0 A Agriculture, forestry and fishing \n", + "1 B Mining and quarrying \n", + "2 C Manufacturing \n", + "3 D Electricity, gas, steam and air conditioning s... \n", + "4 E Water supply; sewerage, waste management and r... \n", + "5 F Construction \n", + "6 G Wholesale and retail trade; repair of motor ve... \n", + "7 H Transportation and storage \n", + "8 I Accommodation and food service activities \n", + "9 J Information and communication \n", + "10 K Financial and insurance activities \n", + "11 L Real estate activities \n", + "12 M Professional, scientific and technical activities \n", + "13 N Administrative and support service activities \n", + "14 O Public administration and defence; compulsory ... \n", + "15 P Education \n", + "16 Q Human health and social work activities \n", + "17 R Arts, entertainment and recreation \n", + "18 S Other service activities \n", + "19 T Activities of households as employers; undiffe... \n", + "20 U Activities of extraterritorial organizations a... \n", + "\n", + " ecoinvent, w/o markets \n", + "0 808 \n", + "1 284 \n", + "2 3907 \n", + "3 3162 \n", + "4 1504 \n", + "5 525 \n", + "6 18 \n", + "7 248 \n", + "8 0 \n", + "9 13 \n", + "10 0 \n", + "11 200 \n", + "12 4 \n", + "13 85 \n", + "14 0 \n", + "15 0 \n", + "16 0 \n", + "17 0 \n", + "18 2 \n", + "19 0 \n", + "20 0 " ] }, - "execution_count": 13, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1634,14 +1172,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(4164, 57)\n" + "(4164, 58)\n" ] }, { @@ -1669,49 +1207,13 @@ " activityName\n", " Description 1\n", " Unnamed: 0\n", + " Unnamed: 0_x\n", " key\n", " #\n", " id\n", " filename\n", " geography\n", - " startDate\n", - " endDate\n", - " specialActivityType\n", - " technologyLevel\n", - " inheritance status\n", - " parentActivityId\n", - " tags\n", - " synonyms\n", - " ISIC v4\n", - " ISIC code\n", - " ISIC name\n", - " specialty production\n", - " constrained market\n", - " accessRestrictedTo\n", - " dataEntryBy\n", - " dataGenerator\n", - " isActiveAuthor\n", - " group\n", - " CPC\n", - " amount\n", - " unitName\n", - " productionVolumeAmount\n", - " absolute PV\n", - " productionVolumeComment\n", - " price\n", - " By-product classification\n", - " mft\n", - " functional unit\n", - " ('IPCC 2013', 'climate change', 'GWP 100a')\n", - " Unnamed: 0.1\n", - " #activities\n", - " Code (ecoinvent)\n", - " Description (ecoinvent)\n", - " ISIC level\n", - " Code 1\n", - " Code 2\n", - " Code 3\n", - " Code 4\n", + " ...\n", " Code 5\n", " Description 2\n", " Description 3\n", @@ -1737,3298 +1239,46 @@ " 2\n", " 2\n", " 2\n", + " ...\n", + " 0\n", " 2\n", " 2\n", " 2\n", + " 0\n", " 2\n", " 2\n", " 2\n", " 2\n", + " 0\n", + " \n", + " \n", + " 1\n", + " 1,1-dimethylcyclopentane\n", + " molecular sieve separation of naphtha\n", + " Manufacturing\n", " 2\n", " 2\n", " 2\n", " 2\n", - " 0\n", - " 2\n", " 2\n", " 2\n", " 2\n", + " ...\n", + " 0\n", " 2\n", " 2\n", " 2\n", + " 0\n", " 2\n", " 2\n", " 2\n", " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 1\n", - " 1,1-dimethylcyclopentane\n", - " molecular sieve separation of naphtha\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 1\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 2\n", - " 1-butanol\n", - " hydroformylation of propylene\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 3\n", - " 1-pentanol\n", - " hydroformylation of butene\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 4\n", - " 1-propanol\n", - " 1-propanol production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 5\n", - " 2,3-dimethylbutan\n", - " molecular sieve separation of naphtha\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 1\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 6\n", - " 2,4-di-tert-butylphenol\n", - " 2,4-di-tert-butylphenol production\n", - " Manufacturing\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 7\n", - " 2,4-dichlorophenol\n", - " 2,4-dichlorophenol production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 8\n", - " 2,4-dichlorotoluene\n", - " 2,4-dichlorotoluene production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 9\n", - " 2,4-dinitrotoluene\n", - " 2,4-dinitrotoluene production\n", - " Manufacturing\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 10\n", - " 2,5-dimethylhexane-2,5-dihydroperoxide\n", - " 2,5-dimethylhexane-2,5-dihydroperoxide production\n", - " Manufacturing\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 11\n", - " 2,6-di-tert-butylphenol\n", - " 2,6-di-tert-butylphenol production\n", - " Manufacturing\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 12\n", - " 2-butanol\n", - " 2-butanol production by hydration of butene\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 13\n", - " 2-cyclopentone\n", - " decarboxylative cyclization of adipic acid\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 14\n", - " 2-methyl-1-butanol\n", - " hydroformylation of butene\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 15\n", - " 2-methyl-2-butanol\n", - " 2-methyl-2-butanol production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 1\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 16\n", - " 2-methylpentane\n", - " molecular sieve separation of naphtha\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 1\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 17\n", - " 2-nitroaniline\n", - " 2-nitroaniline production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 18\n", - " 2-pyridinol\n", - " 2-pyridinol production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 19\n", - " 3-methyl-1-butanol\n", - " hydroformylation of butene\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 20\n", - " 3-methyl-1-butyl acetate\n", - " 3-methyl-1-butyl acetate production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 21\n", - " 4-methyl-2-pentanone\n", - " 4-methyl-2-pentanone production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 1\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 22\n", - " 4-tert-butylbenzaldehyde\n", - " 4-tert-butylbenzaldehyde production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 23\n", - " 4-tert-butyltoluene\n", - " 4-tert-butyltoluene production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 24\n", - " DTPA, diethylenetriaminepentaacetic acid\n", - " DTPA production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 25\n", - " Distiller's Dried Grains with Solubles\n", - " ethanol production from maize\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " \n", - " \n", - " 26\n", - " Distiller's Dried Grains with Solubles\n", - " ethanol production from potatoes\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " \n", - " \n", - " 27\n", - " Distiller's Dried Grains with Solubles\n", - " ethanol production from rye\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 0\n", - " 2\n", - " \n", - " \n", - " 28\n", - " EDTA, ethylenediaminetetraacetic acid\n", - " EDTA production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 29\n", - " EUR-flat pallet\n", - " EUR-flat pallet production\n", - " Manufacturing\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 4134\n", - " zinc concentrate\n", - " treatment of waste paint, municipal incinerati...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4135\n", - " zinc concentrate\n", - " treatment of waste paperboard, municipal incin...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4136\n", - " zinc concentrate\n", - " treatment of waste plastic, consumer electroni...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4137\n", - " zinc concentrate\n", - " treatment of waste plastic, industrial electro...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4138\n", - " zinc concentrate\n", - " treatment of waste plastic, mixture, municipal...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4139\n", - " zinc concentrate\n", - " treatment of waste polyethylene terephtalate, ...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4140\n", - " zinc concentrate\n", - " treatment of waste polyethylene, municipal inc...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4141\n", - " zinc concentrate\n", - " treatment of waste polypropylene, municipal in...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4142\n", - " zinc concentrate\n", - " treatment of waste polystyrene, municipal inci...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4143\n", - " zinc concentrate\n", - " treatment of waste polyurethane, municipal inc...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4144\n", - " zinc concentrate\n", - " treatment of waste polyvinylchloride, municipa...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4145\n", - " zinc concentrate\n", - " treatment of waste polyvinylfluoride, municipa...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4146\n", - " zinc concentrate\n", - " treatment of waste rubber, unspecified, munici...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4147\n", - " zinc concentrate\n", - " treatment of waste sealing sheet, polyethylene...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4148\n", - " zinc concentrate\n", - " treatment of waste sealing sheet, polyvinylchl...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4149\n", - " zinc concentrate\n", - " treatment of waste textile, soiled, municipal ...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4150\n", - " zinc concentrate\n", - " treatment of waste vapour barrier, flame-retar...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4151\n", - " zinc concentrate\n", - " treatment of waste wire plastic, municipal inc...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4152\n", - " zinc concentrate\n", - " treatment of waste wood pole, chrome preserved...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4153\n", - " zinc concentrate\n", - " treatment of waste wood, untreated, municipal ...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4154\n", - " zinc concentrate\n", - " treatment of zinc in car shredder residue, mun...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4155\n", - " zinc concentrate\n", - " zinc-lead mine operation\n", - " Mining and quarrying\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 4156\n", - " zinc in car shredder residue\n", - " treatment of zinc in car shredder residue, mun...\n", - " Water supply; sewerage, waste management and r...\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 1\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " \n", - " \n", - " 4157\n", - " zinc in car shredder residue\n", - " treatment of zinc in car shredder residue, mun...\n", - " Water supply; sewerage, waste management and r...\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", " 0\n", " \n", " \n", - " 4158\n", - " zinc monosulfate\n", - " zinc monosulfate production\n", + " 2\n", + " 1-butanol\n", + " hydroformylation of propylene\n", " Manufacturing\n", " 2\n", " 2\n", @@ -5037,22 +1287,23 @@ " 2\n", " 2\n", " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " ...\n", " 0\n", " 2\n", " 2\n", " 2\n", - " 2\n", - " 2\n", " 0\n", " 2\n", " 2\n", " 2\n", " 2\n", + " 0\n", + " \n", + " \n", + " 3\n", + " 1-pentanol\n", + " hydroformylation of butene\n", + " Manufacturing\n", " 2\n", " 2\n", " 2\n", @@ -5060,13 +1311,23 @@ " 2\n", " 2\n", " 2\n", + " ...\n", + " 0\n", " 2\n", " 2\n", " 2\n", + " 0\n", " 2\n", " 2\n", " 2\n", " 2\n", + " 0\n", + " \n", + " \n", + " 4\n", + " 1-propanol\n", + " 1-propanol production\n", + " Manufacturing\n", " 2\n", " 2\n", " 2\n", @@ -5074,6 +1335,7 @@ " 2\n", " 2\n", " 2\n", + " ...\n", " 0\n", " 2\n", " 2\n", @@ -5086,6 +1348,30 @@ " 0\n", " \n", " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", " 4159\n", " zinc oxide\n", " zinc oxide production\n", @@ -5097,43 +1383,7 @@ " 2\n", " 2\n", " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " ...\n", " 0\n", " 2\n", " 2\n", @@ -5157,43 +1407,7 @@ " 2\n", " 2\n", " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " ...\n", " 0\n", " 2\n", " 2\n", @@ -5217,43 +1431,7 @@ " 2\n", " 2\n", " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " ...\n", " 0\n", " 2\n", " 2\n", @@ -5277,43 +1455,7 @@ " 2\n", " 2\n", " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " ...\n", " 0\n", " 2\n", " 2\n", @@ -5325,48 +1467,11 @@ " 2\n", " 0\n", " \n", - " \n", - " 4163\n", - " zucchini\n", - " zucchini production\n", - " Agriculture, forestry and fishing\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", + " \n", + " 4163\n", + " zucchini\n", + " zucchini production\n", + " Agriculture, forestry and fishing\n", " 1\n", " 1\n", " 1\n", @@ -5374,6 +1479,7 @@ " 1\n", " 1\n", " 1\n", + " ...\n", " 0\n", " 1\n", " 1\n", @@ -5387,77 +1493,66 @@ " \n", " \n", "\n", - "

        4164 rows × 57 columns

        \n", + "

        4164 rows × 58 columns

        \n", "" ], "text/plain": [ - " name ... ISIC 5\n", - "0 1,1-difluoroethane, HFC-152a ... 0\n", - "1 1,1-dimethylcyclopentane ... 0\n", - "2 1-butanol ... 0\n", - "3 1-pentanol ... 0\n", - "4 1-propanol ... 0\n", - "5 2,3-dimethylbutan ... 0\n", - "6 2,4-di-tert-butylphenol ... 0\n", - "7 2,4-dichlorophenol ... 0\n", - "8 2,4-dichlorotoluene ... 0\n", - "9 2,4-dinitrotoluene ... 0\n", - "10 2,5-dimethylhexane-2,5-dihydroperoxide ... 0\n", - "11 2,6-di-tert-butylphenol ... 0\n", - "12 2-butanol ... 0\n", - "13 2-cyclopentone ... 0\n", - "14 2-methyl-1-butanol ... 0\n", - "15 2-methyl-2-butanol ... 0\n", - "16 2-methylpentane ... 0\n", - "17 2-nitroaniline ... 0\n", - "18 2-pyridinol ... 0\n", - "19 3-methyl-1-butanol ... 0\n", - "20 3-methyl-1-butyl acetate ... 0\n", - "21 4-methyl-2-pentanone ... 0\n", - "22 4-tert-butylbenzaldehyde ... 0\n", - "23 4-tert-butyltoluene ... 0\n", - "24 DTPA, diethylenetriaminepentaacetic acid ... 0\n", - "25 Distiller's Dried Grains with Solubles ... 2\n", - "26 Distiller's Dried Grains with Solubles ... 2\n", - "27 Distiller's Dried Grains with Solubles ... 2\n", - "28 EDTA, ethylenediaminetetraacetic acid ... 0\n", - "29 EUR-flat pallet ... 0\n", - "... ... ... ...\n", - "4134 zinc concentrate ... 0\n", - "4135 zinc concentrate ... 0\n", - "4136 zinc concentrate ... 0\n", - "4137 zinc concentrate ... 0\n", - "4138 zinc concentrate ... 0\n", - "4139 zinc concentrate ... 0\n", - "4140 zinc concentrate ... 0\n", - "4141 zinc concentrate ... 0\n", - "4142 zinc concentrate ... 0\n", - "4143 zinc concentrate ... 0\n", - "4144 zinc concentrate ... 0\n", - "4145 zinc concentrate ... 0\n", - "4146 zinc concentrate ... 0\n", - "4147 zinc concentrate ... 0\n", - "4148 zinc concentrate ... 0\n", - "4149 zinc concentrate ... 0\n", - "4150 zinc concentrate ... 0\n", - "4151 zinc concentrate ... 0\n", - "4152 zinc concentrate ... 0\n", - "4153 zinc concentrate ... 0\n", - "4154 zinc concentrate ... 0\n", - "4155 zinc concentrate ... 0\n", - "4156 zinc in car shredder residue ... 0\n", - "4157 zinc in car shredder residue ... 0\n", - "4158 zinc monosulfate ... 0\n", - "4159 zinc oxide ... 0\n", - "4160 zinc sulfide ... 0\n", - "4161 zircon, 50% zirconium ... 0\n", - "4162 zirconium oxide ... 0\n", - "4163 zucchini ... 0\n", - "\n", - "[4164 rows x 57 columns]" + " name activityName \\\n", + "0 1,1-difluoroethane, HFC-152a 1,1-difluoroethane production, HFC-152a \n", + "1 1,1-dimethylcyclopentane molecular sieve separation of naphtha \n", + "2 1-butanol hydroformylation of propylene \n", + "3 1-pentanol hydroformylation of butene \n", + "4 1-propanol 1-propanol production \n", + "... ... ... \n", + "4159 zinc oxide zinc oxide production \n", + "4160 zinc sulfide zinc sulfide production \n", + "4161 zircon, 50% zirconium heavy mineral sand quarry operation \n", + "4162 zirconium oxide zirconium oxide production \n", + "4163 zucchini zucchini production \n", + "\n", + " Description 1 Unnamed: 0 Unnamed: 0_x key # id \\\n", + "0 Manufacturing 2 2 2 2 2 \n", + "1 Manufacturing 2 2 2 2 2 \n", + "2 Manufacturing 2 2 2 2 2 \n", + "3 Manufacturing 2 2 2 2 2 \n", + "4 Manufacturing 2 2 2 2 2 \n", + "... ... ... ... ... .. .. \n", + "4159 Manufacturing 2 2 2 2 2 \n", + "4160 Manufacturing 2 2 2 2 2 \n", + "4161 Mining and quarrying 2 2 2 2 2 \n", + "4162 Manufacturing 2 2 2 2 2 \n", + "4163 Agriculture, forestry and fishing 1 1 1 1 1 \n", + "\n", + " filename geography ... Code 5 Description 2 Description 3 \\\n", + "0 2 2 ... 0 2 2 \n", + "1 2 2 ... 0 2 2 \n", + "2 2 2 ... 0 2 2 \n", + "3 2 2 ... 0 2 2 \n", + "4 2 2 ... 0 2 2 \n", + "... ... ... ... ... ... ... \n", + "4159 2 2 ... 0 2 2 \n", + "4160 2 2 ... 0 2 2 \n", + "4161 2 2 ... 0 2 2 \n", + "4162 2 2 ... 0 2 2 \n", + "4163 1 1 ... 0 1 1 \n", + "\n", + " Description 4 Description 5 ISIC 1 ISIC 2 ISIC 3 ISIC 4 ISIC 5 \n", + "0 2 0 2 2 2 2 0 \n", + "1 2 0 2 2 2 2 0 \n", + "2 2 0 2 2 2 2 0 \n", + "3 2 0 2 2 2 2 0 \n", + "4 2 0 2 2 2 2 0 \n", + "... ... ... ... ... ... ... ... \n", + "4159 2 0 2 2 2 2 0 \n", + "4160 2 0 2 2 2 2 0 \n", + "4161 2 0 2 2 2 2 0 \n", + "4162 2 0 2 2 2 2 0 \n", + "4163 1 0 1 1 1 1 0 \n", + "\n", + "[4164 rows x 58 columns]" ] }, - "execution_count": 14, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -5473,7 +1568,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -5503,7 +1598,7 @@ "Name: ecoinvent, w/o markets and geographies, dtype: int64" ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -5524,7 +1619,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -5707,33 +1802,54 @@ "" ], "text/plain": [ - " Code ... ecoinvent, w/o markets and geographies\n", - "0 A ... 398\n", - "1 B ... 135\n", - "2 C ... 2059\n", - "3 D ... 343\n", - "4 E ... 741\n", - "5 F ... 279\n", - "6 G ... 10\n", - "7 H ... 151\n", - "8 I ... 0\n", - "9 J ... 6\n", - "10 K ... 0\n", - "11 L ... 13\n", - "12 M ... 2\n", - "13 N ... 26\n", - "14 O ... 0\n", - "15 P ... 0\n", - "16 Q ... 0\n", - "17 R ... 0\n", - "18 S ... 1\n", - "19 T ... 0\n", - "20 U ... 0\n", - "\n", - "[21 rows x 4 columns]" + " Code Description \\\n", + "0 A Agriculture, forestry and fishing \n", + "1 B Mining and quarrying \n", + "2 C Manufacturing \n", + "3 D Electricity, gas, steam and air conditioning s... \n", + "4 E Water supply; sewerage, waste management and r... \n", + "5 F Construction \n", + "6 G Wholesale and retail trade; repair of motor ve... \n", + "7 H Transportation and storage \n", + "8 I Accommodation and food service activities \n", + "9 J Information and communication \n", + "10 K Financial and insurance activities \n", + "11 L Real estate activities \n", + "12 M Professional, scientific and technical activities \n", + "13 N Administrative and support service activities \n", + "14 O Public administration and defence; compulsory ... \n", + "15 P Education \n", + "16 Q Human health and social work activities \n", + "17 R Arts, entertainment and recreation \n", + "18 S Other service activities \n", + "19 T Activities of households as employers; undiffe... \n", + "20 U Activities of extraterritorial organizations a... \n", + "\n", + " ecoinvent, w/o markets ecoinvent, w/o markets and geographies \n", + "0 808 398 \n", + "1 284 135 \n", + "2 3907 2059 \n", + "3 3162 343 \n", + "4 1504 741 \n", + "5 525 279 \n", + "6 18 10 \n", + "7 248 151 \n", + "8 0 0 \n", + "9 13 6 \n", + "10 0 0 \n", + "11 200 13 \n", + "12 4 2 \n", + "13 85 26 \n", + "14 0 0 \n", + "15 0 0 \n", + "16 0 0 \n", + "17 0 0 \n", + "18 2 1 \n", + "19 0 0 \n", + "20 0 0 " ] }, - "execution_count": 16, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -5760,7 +1876,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -5837,28 +1953,35 @@ "" ], "text/plain": [ - " # Code ProductTypeName_of_hiot product code 1 product code 2\n", - "0 1 A Cultivation of paddy rice c01.a P_PARI\n", - "1 2 A Cultivation of wheat c01.b P_WHEA\n", - "2 3 A Cultivation of cereal grains nec c01.c P_OCER\n", - "3 4 A Cultivation of vegetables, fruit, nuts c01.d P_FVEG\n", - "4 5 A Cultivation of oil seeds c01.e P_OILS" + " # Code ProductTypeName_of_hiot product code 1 \\\n", + "0 1 A Cultivation of paddy rice c01.a \n", + "1 2 A Cultivation of wheat c01.b \n", + "2 3 A Cultivation of cereal grains nec c01.c \n", + "3 4 A Cultivation of vegetables, fruit, nuts c01.d \n", + "4 5 A Cultivation of oil seeds c01.e \n", + "\n", + " product code 2 \n", + "0 P_PARI \n", + "1 P_WHEA \n", + "2 P_OCER \n", + "3 P_FVEG \n", + "4 P_OILS " ] }, - "execution_count": 17, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "HIOT_ISIC = pd.read_excel(f\"{DATA_PATH}matching/ISIC_level1_to_EXIOBASE.xlsx\")\n", + "HIOT_ISIC = pd.read_excel(f\"{DATA_PATH}matching/ISIC.xlsx\", sheet_name='exiobase')\n", "HIOT_ISIC.rename(columns={\"ISIC Code 1\": \"Code\"}, inplace=True)\n", "HIOT_ISIC.head()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -5889,7 +2012,7 @@ "Name: #, dtype: int64" ] }, - "execution_count": 18, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -5908,7 +2031,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -5926,420 +2049,95 @@ " .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", - 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        #CountryCodeProductTypeName_of_hiotproduct code 1product code 2OriginalPhysicalUnitGHGs-eq - tonnesPhysicalUnitCarbonFootprintunit
        01AUCultivation of paddy ricec01.aP_PARItonnes1.88kg1.88kg CO2-eq/kg
        12AUCultivation of wheatc01.bP_WHEAtonnes0.361kg0.361kg CO2-eq/kg
        23AUCultivation of cereal grains necc01.cP_OCERtonnes0.313kg0.313kg CO2-eq/kg
        34AUCultivation of vegetables, fruit, nutsc01.dP_FVEGtonnes1.98kg1.98kg CO2-eq/kg
        45AUCultivation of oil seedsc01.eP_OILStonnes0.43kg0.43kg CO2-eq/kg
        56AUCultivation of sugar cane, sugar beetc01.fP_SUGBtonnes0.124kg0.124kg CO2-eq/kg
        67AUCultivation of plant-based fibersc01.gP_FIBRtonnes1.58kg1.58kg CO2-eq/kg
        78AUCultivation of crops necc01.hP_OTCRtonnes71.9kg71.9kg CO2-eq/kg
        89AUCattle farmingc01.iP_CATLtonnes66.4kg66.4kg CO2-eq/kg
        910AUPigs farmingc01.jP_PIGStonnes4.92kg4.92kg CO2-eq/kg
        1011AUPoultry farmingc01.kP_PLTRtonnes5.54kg5.54kg CO2-eq/kg
        1112AUMeat animals necc01.lP_OMEAtonnes6.3kg6.3kg CO2-eq/kg
        1213AUAnimal products necc01.mP_OANPtonnes12.7kg12.7kg CO2-eq/kg
        1314AURaw milkc01.nP_MILKtonnes10.7kg10.7kg CO2-eq/kg
        1415AUWool, silk-worm cocoonsc01.oP_WOOLtonnes27.8kg27.8kg CO2-eq/kg
        1516AUManure treatment (conventional), storage and l...c01.w.1P_MANCtonnes0.2kg0.2kg CO2-eq/kg
        1617AUManure treatment (biogas), storage and land ap...c01.w.2P_MANBtonnes0kg0kg CO2-eq/kg
        1718AUForestry, logging and related service activiti...c02P_FOREtonnes0.0639kg0.0639kg CO2-eq/kg
        1819AUFishing, operating of fish hatcheries and fish...c05P_FISHtonnes9.52kg9.52kg CO2-eq/kg
        1920AUMining of coal and lignite; extraction of peat...c10P_COALtonnes0.229kg0.229kg CO2-eq/kg
        2021AUExtraction of crude petroleum and services rel...c11.aP_COILtonnes0.41kg0.41kg CO2-eq/kg
        2122AUExtraction of natural gas and services related...c11.bP_GASEtonnes0.358kg0.358kg CO2-eq/kg
        2223AUExtraction, liquefaction, and regasification o...c11.cP_OGPLtonnes0kg0kg CO2-eq/kg
        2324AUMining of uranium and thorium ores (12)c12P_ORANtonnes0.568kg0.568kg CO2-eq/kg
        2425AUMining of iron oresc13.1P_IRONtonnes0.00969kg0.00969kg CO2-eq/kg
        \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6356,331 +2154,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6751,73 +2224,49 @@ "" ], "text/plain": [ - " # CountryCode ... CarbonFootprint unit\n", - "0 1 AU ... 1.88 kg CO2-eq/kg\n", - "1 2 AU ... 0.361 kg CO2-eq/kg\n", - "2 3 AU ... 0.313 kg CO2-eq/kg\n", - "3 4 AU ... 1.98 kg CO2-eq/kg\n", - "4 5 AU ... 0.43 kg CO2-eq/kg\n", - "5 6 AU ... 0.124 kg CO2-eq/kg\n", - "6 7 AU ... 1.58 kg CO2-eq/kg\n", - "7 8 AU ... 71.9 kg CO2-eq/kg\n", - "8 9 AU ... 66.4 kg CO2-eq/kg\n", - "9 10 AU ... 4.92 kg CO2-eq/kg\n", - "10 11 AU ... 5.54 kg CO2-eq/kg\n", - "11 12 AU ... 6.3 kg CO2-eq/kg\n", - "12 13 AU ... 12.7 kg CO2-eq/kg\n", - "13 14 AU ... 10.7 kg CO2-eq/kg\n", - "14 15 AU ... 27.8 kg CO2-eq/kg\n", - "15 16 AU ... 0.2 kg CO2-eq/kg\n", - "16 17 AU ... 0 kg CO2-eq/kg\n", - "17 18 AU ... 0.0639 kg CO2-eq/kg\n", - "18 19 AU ... 9.52 kg CO2-eq/kg\n", - "19 20 AU ... 0.229 kg CO2-eq/kg\n", - "20 21 AU ... 0.41 kg CO2-eq/kg\n", - "21 22 AU ... 0.358 kg CO2-eq/kg\n", - "22 23 AU ... 0 kg CO2-eq/kg\n", - "23 24 AU ... 0.568 kg CO2-eq/kg\n", - "24 25 AU ... 0.00969 kg CO2-eq/kg\n", - "25 26 AU ... 1.44 kg CO2-eq/kg\n", - "26 27 AU ... 6.88 kg CO2-eq/kg\n", - "27 28 AU ... 0.0186 kg CO2-eq/kg\n", - "28 29 AU ... 91.2 kg CO2-eq/kg\n", - "29 30 AU ... 0.403 kg CO2-eq/kg\n", - "... ... ... ... ... ...\n", - "8170 8171 EU-28 ... 152 kg CO2-eq/Meuro\n", - "8171 8172 EU-28 ... 122 kg CO2-eq/Meuro\n", - "8172 8173 EU-28 ... 178 kg CO2-eq/Meuro\n", - "8173 8174 EU-28 ... 130 kg CO2-eq/Meuro\n", - "8174 8175 EU-28 ... 171 kg CO2-eq/Meuro\n", - "8175 8176 EU-28 ... 0.128 kg CO2-eq/kg\n", - "8176 8177 EU-28 ... -0.242 kg CO2-eq/kg\n", - "8177 8178 EU-28 ... 1.94 kg CO2-eq/kg\n", - "8178 8179 EU-28 ... 0.445 kg CO2-eq/kg\n", - "8179 8180 EU-28 ... -0.442 kg CO2-eq/kg\n", - "8180 8181 EU-28 ... -0.0215 kg CO2-eq/kg\n", - "8181 8182 EU-28 ... 2.49 kg CO2-eq/kg\n", - "8182 8183 EU-28 ... -0.229 kg CO2-eq/kg\n", - "8183 8184 EU-28 ... -0.0556 kg CO2-eq/kg\n", - "8184 8185 EU-28 ... -0.974 kg CO2-eq/kg\n", - "8185 8186 EU-28 ... 0.451 kg CO2-eq/kg\n", - "8186 8187 EU-28 ... 0.864 kg CO2-eq/kg\n", - "8187 8188 EU-28 ... 21.2 kg CO2-eq/kg\n", - "8188 8189 EU-28 ... 0.284 kg CO2-eq/kg\n", - "8189 8190 EU-28 ... 3 kg CO2-eq/kg\n", - "8190 8191 EU-28 ... 2.02 kg CO2-eq/kg\n", - "8191 8192 EU-28 ... 0.167 kg CO2-eq/kg\n", - "8192 8193 EU-28 ... 0.0108 kg CO2-eq/kg\n", - "8193 8194 EU-28 ... 1.34 kg CO2-eq/kg\n", - "8194 8195 EU-28 ... 0.202 kg CO2-eq/kg\n", - "8195 8196 EU-28 ... 294 kg CO2-eq/Meuro\n", - "8196 8197 EU-28 ... 206 kg CO2-eq/Meuro\n", - "8197 8198 EU-28 ... 158 kg CO2-eq/Meuro\n", - "8198 8199 EU-28 ... 108 kg CO2-eq/Meuro\n", - "8199 8200 EU-28 ... 0 kg CO2-eq/Meuro\n", + " # CountryCode ProductTypeName_of_hiot \\\n", + "0 1 AU Cultivation of paddy rice \n", + "1 2 AU Cultivation of wheat \n", + "2 3 AU Cultivation of cereal grains nec \n", + "3 4 AU Cultivation of vegetables, fruit, nuts \n", + "4 5 AU Cultivation of oil seeds \n", + "... ... ... ... \n", + "8195 8196 EU-28 Activities of membership organisation n.e.c. (91) \n", + "8196 8197 EU-28 Recreational, cultural and sporting activities... \n", + "8197 8198 EU-28 Other service activities (93) \n", + "8198 8199 EU-28 Private households with employed persons (95) \n", + "8199 8200 EU-28 Extra-territorial organizations and bodies \n", + "\n", + " product code 1 product code 2 OriginalPhysicalUnit GHGs-eq - tonnes \\\n", + "0 c01.a P_PARI tonnes 1.6 \n", + "1 c01.b P_WHEA tonnes 0.307 \n", + "2 c01.c P_OCER tonnes 0.266 \n", + "3 c01.d P_FVEG tonnes 0.724 \n", + "4 c01.e P_OILS tonnes 0.395 \n", + "... ... ... ... ... \n", + "8195 c91 P_ORGA Meuro 294 \n", + "8196 c92 P_RECR Meuro 206 \n", + "8197 c93 P_OSER Meuro 158 \n", + "8198 c95 P_PRHH Meuro 108 \n", + "8199 c99 P_EXTO Meuro 0 \n", + "\n", + " PhysicalUnit CarbonFootprint unit \n", + "0 kg 1.6 kg CO2-eq/kg \n", + "1 kg 0.307 kg CO2-eq/kg \n", + "2 kg 0.266 kg CO2-eq/kg \n", + "3 kg 0.724 kg CO2-eq/kg \n", + "4 kg 0.395 kg CO2-eq/kg \n", + "... ... ... ... \n", + "8195 Meuro 294 kg CO2-eq/Meuro \n", + "8196 Meuro 206 kg CO2-eq/Meuro \n", + "8197 Meuro 158 kg CO2-eq/Meuro \n", + "8198 Meuro 108 kg CO2-eq/Meuro \n", + "8199 Meuro 0 kg CO2-eq/Meuro \n", "\n", "[8200 rows x 10 columns]" ] }, - "execution_count": 19, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -6837,7 +2286,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -6866,7 +2315,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -6919,9 +2368,9 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6933,9 +2382,9 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6947,9 +2396,9 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6961,9 +2410,9 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6975,9 +2424,9 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6986,17 +2435,29 @@ "" ], "text/plain": [ - " # CountryCode ... unit Code\n", - "0 1 AU ... kg CO2-eq/kg A\n", - "1 493 BR ... kg CO2-eq/kg A\n", - "2 657 BG ... kg CO2-eq/kg A\n", - "3 985 CN ... kg CO2-eq/kg A\n", - "4 1969 FR ... kg CO2-eq/kg A\n", - "\n", - "[5 rows x 11 columns]" + " # CountryCode ProductTypeName_of_hiot product code 1 product code 2 \\\n", + "0 1 AU Cultivation of paddy rice c01.a P_PARI \n", + "1 493 BR Cultivation of paddy rice c01.a P_PARI \n", + "2 657 BG Cultivation of paddy rice c01.a P_PARI \n", + "3 985 CN Cultivation of paddy rice c01.a P_PARI \n", + "4 1969 FR Cultivation of paddy rice c01.a P_PARI \n", + "\n", + " OriginalPhysicalUnit GHGs-eq - tonnes PhysicalUnit CarbonFootprint \\\n", + "0 tonnes 1.6 kg 1.6 \n", + "1 tonnes 0.697 kg 0.697 \n", + "2 tonnes 0.43 kg 0.43 \n", + "3 tonnes 2.02 kg 2.02 \n", + "4 tonnes 0.31 kg 0.31 \n", + "\n", + " unit Code \n", + "0 kg CO2-eq/kg A \n", + "1 kg CO2-eq/kg A \n", + "2 kg CO2-eq/kg A \n", + "3 kg CO2-eq/kg A \n", + "4 kg CO2-eq/kg A " ] }, - "execution_count": 21, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -7009,7 +2470,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -7039,7 +2500,7 @@ "Name: #, dtype: int64" ] }, - "execution_count": 22, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -7058,7 +2519,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -7089,7 +2550,7 @@ "Name: #, dtype: int64" ] }, - "execution_count": 23, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -7100,7 +2561,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -7327,33 +2788,77 @@ "" ], "text/plain": [ - " Code ... EXIOBASE\n", - "0 A ... 761\n", - "1 B ... 437\n", - "2 C ... 2.18e+03\n", - "3 D ... 572\n", - "4 E ... 1.35e+03\n", - "5 F ... 48\n", - "6 G ... 192\n", - "7 H ... 336\n", - "8 I ... 48\n", - "9 J ... 48\n", - "10 K ... 144\n", - "11 L ... 96\n", - "12 M ... 96\n", - "13 N ... 48\n", - "14 O ... 48\n", - "15 P ... 48\n", - "16 Q ... 48\n", - "17 R ... 48\n", - "18 S ... 96\n", - "19 T ... 46\n", - "20 U ... 0\n", - "\n", - "[21 rows x 6 columns]" + " Code Description \\\n", + "0 A Agriculture, forestry and fishing \n", + "1 B Mining and quarrying \n", + "2 C Manufacturing \n", + "3 D Electricity, gas, steam and air conditioning s... \n", + "4 E Water supply; sewerage, waste management and r... \n", + "5 F Construction \n", + "6 G Wholesale and retail trade; repair of motor ve... \n", + "7 H Transportation and storage \n", + "8 I Accommodation and food service activities \n", + "9 J Information and communication \n", + "10 K Financial and insurance activities \n", + "11 L Real estate activities \n", + "12 M Professional, scientific and technical activities \n", + "13 N Administrative and support service activities \n", + "14 O Public administration and defence; compulsory ... \n", + "15 P Education \n", + "16 Q Human health and social work activities \n", + "17 R Arts, entertainment and recreation \n", + "18 S Other service activities \n", + "19 T Activities of households as employers; undiffe... \n", + "20 U Activities of extraterritorial organizations a... \n", + "\n", + " ecoinvent, w/o markets ecoinvent, w/o markets and geographies \\\n", + "0 808 398 \n", + "1 284 135 \n", + "2 3907 2059 \n", + "3 3162 343 \n", + "4 1504 741 \n", + "5 525 279 \n", + "6 18 10 \n", + "7 248 151 \n", + "8 0 0 \n", + "9 13 6 \n", + "10 0 0 \n", + "11 200 13 \n", + "12 4 2 \n", + "13 85 26 \n", + "14 0 0 \n", + "15 0 0 \n", + "16 0 0 \n", + "17 0 0 \n", + "18 2 1 \n", + "19 0 0 \n", + "20 0 0 \n", + "\n", + " EXIOBASE, non-regionalized EXIOBASE \n", + "0 19 761 \n", + "1 15 437 \n", + "2 48 2.18e+03 \n", + "3 17 572 \n", + "4 35 1.35e+03 \n", + "5 1 48 \n", + "6 4 192 \n", + "7 7 336 \n", + "8 1 48 \n", + "9 1 48 \n", + "10 3 144 \n", + "11 2 96 \n", + "12 2 96 \n", + "13 1 48 \n", + "14 1 48 \n", + "15 1 48 \n", + "16 1 48 \n", + "17 1 48 \n", + "18 2 96 \n", + "19 1 46 \n", + "20 1 0 " ] }, - "execution_count": 24, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -7382,7 +2887,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -7646,40 +3151,84 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
        #CountryCodeProductTypeName_of_hiotproduct code 1product code 2OriginalPhysicalUnitGHGs-eq - tonnesPhysicalUnitCarbonFootprintunit
        252601AUMining of copper ores and concentratesc13.20.11P_COPOCultivation of paddy ricec01.aP_PARItonnes1.441.6kg1.441.6kg CO2-eq/kg
        262712AUMining of nickel ores and concentratesc13.20.12P_NIKOCultivation of wheatc01.bP_WHEAtonnes6.880.307kg6.880.307kg CO2-eq/kg
        272823AUMining of aluminium ores and concentratesc13.20.13P_ALUOCultivation of cereal grains necc01.cP_OCERtonnes0.01860.266kg0.01860.266kg CO2-eq/kg
        282934AUMining of precious metal ores and concentratesc13.20.14P_PREOCultivation of vegetables, fruit, nutsc01.dP_FVEGtonnes91.20.724kg91.20.724kg CO2-eq/kg
        293045AUMining of lead, zinc and tin ores and concentr...c13.20.15P_LZTOCultivation of oil seedsc01.eP_OILStonnes0.4030.395kg0.4030.395kg CO2-eq/kg
        ...
        81708171EU-28Research and development (73)c73P_RESDMeuro152Meuro152kg CO2-eq/Meuro
        81718172EU-28Other business activities (74)c74P_OBUSMeuro122Meuro122kg CO2-eq/Meuro
        81728173EU-28Public administration and defence; compulsory ...c75P_PADFMeuro178Meuro178kg CO2-eq/Meuro
        81738174EU-28Education (80)c80P_EDUCMeuro130Meuro130kg CO2-eq/Meuro
        81748175EU-28Health and social work (85)c85P_HEALMeuro171Meuro171kg CO2-eq/Meuro
        81758176EU-28Incineration of waste: Foodc90.1.aP_INCFtonnes0.128kg0.128kg CO2-eq/kg
        81768177EU-28Incineration of waste: Paperc90.1.bP_INCPtonnes-0.242kg-0.242kg CO2-eq/kg
        81778178EU-28Incineration of waste: Plasticc90.1.cP_INCLtonnes1.94kg1.94kg CO2-eq/kg
        81788179EU-28Incineration of waste: Metals and Inert materialsc90.1.dP_INCMtonnes0.445kg0.445kg CO2-eq/kg
        81798180EU-28Incineration of waste: Textilesc90.1.eP_INCTtonnes-0.442kg-0.442kg CO2-eq/kg
        81808181EU-28Incineration of waste: Woodc90.1.fP_INCWtonnes-0.0215kg-0.0215kg CO2-eq/kg
        81818182EU-28Incineration of waste: Oil/Hazardous wastec90.1.gP_INCOtonnes2.49kg2.49kg CO2-eq/kg
        81828183EU-28Biogasification of food waste, incl. land appl...c90.2.aP_BIOFtonnes-0.229kg-0.229kg CO2-eq/kg
        81838184EU-28Biogasification of paper, incl. land applicationc90.2.bP_BIOPtonnes-0.0556kg-0.0556kg CO2-eq/kg
        81848185EU-28Biogasification of sewage slugde, incl. land a...c90.2.cP_BIOStonnes-0.974kg-0.974kg CO2-eq/kg
        81858186EU-28Composting of food waste, incl. land applicationc90.3.aP_COMFtonnes0.451kg0.451kg CO2-eq/kg
        81868187EU-28Composting of paper and wood, incl. land appli...c90.3.bP_COMWtonnes0.864kg0.864kg CO2-eq/kg
        81878188EU-28Waste water treatment, foodc90.4.aP_WASFtonnes21.2kg21.2kg CO2-eq/kg
        81888189EU-28Waste water treatment, otherc90.4.bP_WASOtonnes0.284kg0.284kg CO2-eq/kg
        81898190EU-28Landfill of waste: Foodc90.5.aP_LANFtonnes3kg3kg CO2-eq/kg
        81908191EU-28Landfill of waste: Paperc90.5.bP_LANPtonnes2.02kg2.02kg CO2-eq/kg
        81918192EU-28Landfill of waste: Plasticc90.5.cP_LANLtonnes0.167kg0.167kg CO2-eq/kg
        81928193EU-28Landfill of waste: Inert/metal/hazardousc90.5.dP_LANItonnes0.0108kg0.0108kg CO2-eq/kg
        81938194EU-28Landfill of waste: Textilesc90.5.eP_LANTtonnes1.34kg1.34kg CO2-eq/kg
        81948195EU-28Landfill of waste: Woodc90.5.fP_LANWtonnes0.202kg0.202kg CO2-eq/kg
        81958196EU-28c01.aP_PARItonnes1.881.6kg1.881.6kg CO2-eq/kgA
        c01.aP_PARItonnes0.820.697kg0.820.697kg CO2-eq/kgA
        c01.aP_PARItonnes0.5060.43kg0.5060.43kg CO2-eq/kgA
        c01.aP_PARItonnes2.382.02kg2.382.02kg CO2-eq/kgA
        c01.aP_PARItonnes0.3650.31kg0.3650.31kg CO2-eq/kgA
        100nanNaN
        \n", "" ], "text/plain": [ - " Code ... Ratio\n", - "0 A ... 1.06\n", - "1 B ... 0.65\n", - "2 C ... 1.79\n", - "3 D ... 5.53\n", - "4 E ... 1.12\n", - "5 F ... 10.9\n", - "6 G ... 0.0938\n", - "7 H ... 0.738\n", - "8 I ... 0\n", - "9 J ... 0.271\n", - "10 K ... 0\n", - "11 L ... 2.08\n", - "12 M ... 0.0417\n", - "13 N ... 1.77\n", - "14 O ... 0\n", - "15 P ... 0\n", - "16 Q ... 0\n", - "17 R ... 0\n", - "18 S ... 0.0208\n", - "19 T ... 0\n", - "20 U ... nan\n", - "\n", - "[21 rows x 8 columns]" + " Code Description \\\n", + "0 A Agriculture, forestry and fishing \n", + "1 B Mining and quarrying \n", + "2 C Manufacturing \n", + "3 D Electricity, gas, steam and air conditioning s... \n", + "4 E Water supply; sewerage, waste management and r... \n", + "5 F Construction \n", + "6 G Wholesale and retail trade; repair of motor ve... \n", + "7 H Transportation and storage \n", + "8 I Accommodation and food service activities \n", + "9 J Information and communication \n", + "10 K Financial and insurance activities \n", + "11 L Real estate activities \n", + "12 M Professional, scientific and technical activities \n", + "13 N Administrative and support service activities \n", + "14 O Public administration and defence; compulsory ... \n", + "15 P Education \n", + "16 Q Human health and social work activities \n", + "17 R Arts, entertainment and recreation \n", + "18 S Other service activities \n", + "19 T Activities of households as employers; undiffe... \n", + "20 U Activities of extraterritorial organizations a... \n", + "\n", + " ecoinvent, w/o markets ecoinvent, w/o markets and geographies \\\n", + "0 808 398 \n", + "1 284 135 \n", + "2 3907 2059 \n", + "3 3162 343 \n", + "4 1504 741 \n", + "5 525 279 \n", + "6 18 10 \n", + "7 248 151 \n", + "8 0 0 \n", + "9 13 6 \n", + "10 0 0 \n", + "11 200 13 \n", + "12 4 2 \n", + "13 85 26 \n", + "14 0 0 \n", + "15 0 0 \n", + "16 0 0 \n", + "17 0 0 \n", + "18 2 1 \n", + "19 0 0 \n", + "20 0 0 \n", + "\n", + " EXIOBASE, non-regionalized EXIOBASE Ratio, non-regionalized Ratio \n", + "0 19 761 20.9 1.06 \n", + "1 15 437 9 0.65 \n", + "2 48 2.18e+03 42.9 1.79 \n", + "3 17 572 20.2 5.53 \n", + "4 35 1.35e+03 21.2 1.12 \n", + "5 1 48 279 10.9 \n", + "6 4 192 2.5 0.0938 \n", + "7 7 336 21.6 0.738 \n", + "8 1 48 0 0 \n", + "9 1 48 6 0.271 \n", + "10 3 144 0 0 \n", + "11 2 96 6.5 2.08 \n", + "12 2 96 1 0.0417 \n", + "13 1 48 26 1.77 \n", + "14 1 48 0 0 \n", + "15 1 48 0 0 \n", + "16 1 48 0 0 \n", + "17 1 48 0 0 \n", + "18 2 96 0.5 0.0208 \n", + "19 1 46 0 0 \n", + "20 1 0 0 NaN " ] }, - "execution_count": 25, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -7699,7 +3248,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -7717,7 +3266,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -7981,40 +3530,84 @@ " 0\n", " 0\n", " 0\n", - " nan\n", + " NaN\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Code Description ... EXIOBASE Ratio\n", - "0 A Agriculture, forestry and fishing ... 761 1.06\n", - "1 B Mining and quarrying ... 437 0.65\n", - "2 C Manufacturing ... 2.18e+03 1.79\n", - "3 D Electricity, gas, steam and air conditioning s... ... 572 5.53\n", - "4 E Water supply; sewerage, waste management and r... ... 1.35e+03 1.12\n", - "5 F Construction ... 48 10.9\n", - "6 G Wholesale and retail trade; repair of motor ve... ... 192 0.0938\n", - "7 H Transportation and storage ... 336 0.738\n", - "8 I Accommodation and food service activities ... 48 0\n", - "9 J Information and communication ... 48 0.271\n", - "10 K Financial and insurance activities ... 144 0\n", - "11 L Real estate activities ... 96 2.08\n", - "12 M Professional, scientific and technical activities ... 96 0.0417\n", - "13 N Administrative and support service activities ... 48 1.77\n", - "14 O Public administration and defence; compulsory ... ... 48 0\n", - "15 P Education ... 48 0\n", - "16 Q Human health and social work activities ... 48 0\n", - "17 R Arts, entertainment and recreation ... 48 0\n", - "18 S Other service activities ... 96 0.0208\n", - "19 T Activities of households as employers; undiffe... ... 46 0\n", - "20 U Activities of extraterritorial organizations a... ... 0 nan\n", - "\n", - "[21 rows x 8 columns]" + " Code Description \\\n", + "0 A Agriculture, forestry and fishing \n", + "1 B Mining and quarrying \n", + "2 C Manufacturing \n", + "3 D Electricity, gas, steam and air conditioning s... \n", + "4 E Water supply; sewerage, waste management and r... \n", + "5 F Construction \n", + "6 G Wholesale and retail trade; repair of motor ve... \n", + "7 H Transportation and storage \n", + "8 I Accommodation and food service activities \n", + "9 J Information and communication \n", + "10 K Financial and insurance activities \n", + "11 L Real estate activities \n", + "12 M Professional, scientific and technical activities \n", + "13 N Administrative and support service activities \n", + "14 O Public administration and defence; compulsory ... \n", + "15 P Education \n", + "16 Q Human health and social work activities \n", + "17 R Arts, entertainment and recreation \n", + "18 S Other service activities \n", + "19 T Activities of households as employers; undiffe... \n", + "20 U Activities of extraterritorial organizations a... \n", + "\n", + " ecoinvent, w/o markets and geographies EXIOBASE, non-regionalized \\\n", + "0 398 19 \n", + "1 135 15 \n", + "2 2059 48 \n", + "3 343 17 \n", + "4 741 35 \n", + "5 279 1 \n", + "6 10 4 \n", + "7 151 7 \n", + "8 0 1 \n", + "9 6 1 \n", + "10 0 3 \n", + "11 13 2 \n", + "12 2 2 \n", + "13 26 1 \n", + "14 0 1 \n", + "15 0 1 \n", + "16 0 1 \n", + "17 0 1 \n", + "18 1 2 \n", + "19 0 1 \n", + "20 0 1 \n", + "\n", + " Ratio, non-regionalized ecoinvent, w/o markets EXIOBASE Ratio \n", + "0 20.9 808 761 1.06 \n", + "1 9 284 437 0.65 \n", + "2 42.9 3907 2.18e+03 1.79 \n", + "3 20.2 3162 572 5.53 \n", + "4 21.2 1504 1.35e+03 1.12 \n", + "5 279 525 48 10.9 \n", + "6 2.5 18 192 0.0938 \n", + "7 21.6 248 336 0.738 \n", + "8 0 0 48 0 \n", + "9 6 13 48 0.271 \n", + "10 0 0 144 0 \n", + "11 6.5 200 96 2.08 \n", + "12 1 4 96 0.0417 \n", + "13 26 85 48 1.77 \n", + "14 0 0 48 0 \n", + "15 0 0 48 0 \n", + "16 0 0 48 0 \n", + "17 0 0 48 0 \n", + "18 0.5 2 96 0.0208 \n", + "19 0 0 46 0 \n", + "20 0 0 0 NaN " ] }, - "execution_count": 27, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -8033,7 +3626,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -8297,7 +3890,7 @@ " 0\n", " 0\n", " 0\n", - " nan\n", + " NaN\n", " \n", " \n", " 21\n", @@ -8315,34 +3908,80 @@ "" ], "text/plain": [ - " Code Description ... EXIOBASE Ratio\n", - "0 A Agriculture, forestry and fishing ... 761 1.06\n", - "1 B Mining and quarrying ... 437 0.65\n", - "2 C Manufacturing ... 2.18e+03 1.79\n", - "3 D Electricity, gas, steam and air conditioning s... ... 572 5.53\n", - "4 E Water supply; sewerage, waste management and r... ... 1.35e+03 1.12\n", - "5 F Construction ... 48 10.9\n", - "6 G Wholesale and retail trade; repair of motor ve... ... 192 0.0938\n", - "7 H Transportation and storage ... 336 0.738\n", - "8 I Accommodation and food service activities ... 48 0\n", - "9 J Information and communication ... 48 0.271\n", - "10 K Financial and insurance activities ... 144 0\n", - "11 L Real estate activities ... 96 2.08\n", - "12 M Professional, scientific and technical activities ... 96 0.0417\n", - "13 N Administrative and support service activities ... 48 1.77\n", - "14 O Public administration and defence; compulsory ... ... 48 0\n", - "15 P Education ... 48 0\n", - "16 Q Human health and social work activities ... 48 0\n", - "17 R Arts, entertainment and recreation ... 48 0\n", - "18 S Other service activities ... 96 0.0208\n", - "19 T Activities of households as employers; undiffe... ... 46 0\n", - "20 U Activities of extraterritorial organizations a... ... 0 nan\n", - "21 NaN NaN ... 6.69e+03 26.1\n", - "\n", - "[22 rows x 8 columns]" + " Code Description \\\n", + "0 A Agriculture, forestry and fishing \n", + "1 B Mining and quarrying \n", + "2 C Manufacturing \n", + "3 D Electricity, gas, steam and air conditioning s... \n", + "4 E Water supply; sewerage, waste management and r... \n", + "5 F Construction \n", + "6 G Wholesale and retail trade; repair of motor ve... \n", + "7 H Transportation and storage \n", + "8 I Accommodation and food service activities \n", + "9 J Information and communication \n", + "10 K Financial and insurance activities \n", + "11 L Real estate activities \n", + "12 M Professional, scientific and technical activities \n", + "13 N Administrative and support service activities \n", + "14 O Public administration and defence; compulsory ... \n", + "15 P Education \n", + "16 Q Human health and social work activities \n", + "17 R Arts, entertainment and recreation \n", + "18 S Other service activities \n", + "19 T Activities of households as employers; undiffe... \n", + "20 U Activities of extraterritorial organizations a... \n", + "21 NaN NaN \n", + "\n", + " ecoinvent, w/o markets and geographies EXIOBASE, non-regionalized \\\n", + "0 398 19 \n", + "1 135 15 \n", + "2 2.06e+03 48 \n", + "3 343 17 \n", + "4 741 35 \n", + "5 279 1 \n", + "6 10 4 \n", + "7 151 7 \n", + "8 0 1 \n", + "9 6 1 \n", + "10 0 3 \n", + "11 13 2 \n", + "12 2 2 \n", + "13 26 1 \n", + "14 0 1 \n", + "15 0 1 \n", + "16 0 1 \n", + "17 0 1 \n", + "18 1 2 \n", + "19 0 1 \n", + "20 0 1 \n", + "21 4.16e+03 164 \n", + "\n", + " Ratio, non-regionalized ecoinvent, w/o markets EXIOBASE Ratio \n", + "0 20.9 808 761 1.06 \n", + "1 9 284 437 0.65 \n", + "2 42.9 3.91e+03 2.18e+03 1.79 \n", + "3 20.2 3.16e+03 572 5.53 \n", + "4 21.2 1.5e+03 1.35e+03 1.12 \n", + "5 279 525 48 10.9 \n", + "6 2.5 18 192 0.0938 \n", + "7 21.6 248 336 0.738 \n", + "8 0 0 48 0 \n", + "9 6 13 48 0.271 \n", + "10 0 0 144 0 \n", + "11 6.5 200 96 2.08 \n", + "12 1 4 96 0.0417 \n", + "13 26 85 48 1.77 \n", + "14 0 0 48 0 \n", + "15 0 0 48 0 \n", + "16 0 0 48 0 \n", + "17 0 0 48 0 \n", + "18 0.5 2 96 0.0208 \n", + "19 0 0 46 0 \n", + "20 0 0 0 NaN \n", + "21 457 1.08e+04 6.69e+03 26.1 " ] }, - "execution_count": 28, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -8352,25 +3991,11 @@ "DF_FINAL.to_clipboard()\n", "DF_FINAL" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", 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