diff --git a/Metadata.json b/Metadata.json
new file mode 100644
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--- /dev/null
+++ b/Metadata.json
@@ -0,0 +1,87 @@
+{
+ "data_source": "Bilan CO2",
+ "description": "Centralise les données à impact climatique, les associes à des facteurs d'émissions, interpole et extrapole les données manquantes pour créer le bilan carbone",
+ "fields": [
+ {
+ "name": "Year",
+ "description": "Année de la donnée",
+ "data_type": "integer"
+ },
+ {
+ "name": "Campus",
+ "description": "Site/campus de la donnée",
+ "data_type": "string"
+ },
+ {
+ "name": "Value",
+ "description": "Valeur physique de la donnée, celle-ci peut avoir plusieurs unités specifiée dans le champ 'Unit'",
+ "data_type": "float"
+ },
+ {
+ "name": "Unit",
+ "description": "Unité de 'Value'",
+ "data_type": "string"
+ },
+ {
+ "name": "Theme",
+ "description": "Thematique dans laquelle s'inscrit la donnée",
+ "data_type": "string"
+ },
+ {
+ "name": "Category",
+ "description": "Catégorie plus précise de la Thématique",
+ "data_type": "string"
+ },
+ {
+ "name": "Path",
+ "description": "Chemin vers la donnée brute",
+ "data_type": "string"
+ },
+ {
+ "name": "Scope",
+ "description": "scope selon le GHG Protocol",
+ "data_type": "integer"
+ },
+ {
+ "name": "CO2",
+ "description": "Quantité de CO2 émise en kg",
+ "data_type": "float"
+ },
+ {
+ "name": "Data description",
+ "description": "Description de la donnée (donnée calculée/estimée/Objectif EPFL)",
+ "data_type": "string"
+ },
+ {
+ "name": "factor name",
+ "description": "Nom attribué au facteur d'émission",
+ "data_type": "string"
+ },
+ {
+ "name": "Factor (kg CO2eq)",
+ "description": "Facteur d'émission utilisé pour calculer l'impact CO2",
+ "data_type": "float"
+ },
+ {
+ "name": "Source",
+ "description": "Source du facteur d'émission utilisé",
+ "data_type": "string"
+ }
+ ],
+ "data_quality": "Quelques données doivent être vérifiées: ALimentation 2019, les émissions de 2006, les objectifs de 2030 doivent être adaptés",
+ "last_updated": "2023-07-19",
+ "next_update": "?",
+ "data_owner": "Hugo Cruz",
+ "contact_email": "hugo.cruz@epfl.ch",
+ "access": "Open source",
+ "related_data": [
+ "Population EPFL",
+ "Déchets EPFL",
+ "Inventaire EPFL"
+ ],
+ "data_dictionary": {
+ "Campus": "Divers: Sous-entend les quelques autres sites de l'EPFL trop petit pour être repertoriés/EPFL: Couvre l'entiereté de l'EPFL",
+ "Data description": "Valeur calculée: Valeur calculée à partir d'une donnée mesurée. 'Estimée': Valeur interpolée ou extrapolée sur la base du dernier point mesuré ou sur l'objectif. "
+ }
+ }
+
\ No newline at end of file
diff --git a/README.md b/README.md
index 15ba332..15e252f 100644
--- a/README.md
+++ b/README.md
@@ -1,88 +1,84 @@
# Bilan CO2 EPFL
+Titre du projet: Bilan CO2 EPFL
+Localisation sur le serveur VPT: M:\40-DURABILITE\40.04-Donnees\40.04.01-Donnees-Durabilite\Projets transversaux\Bilan_CO2
+Visualisation en ligne ou sur Tableau: https://tableau.epfl.ch/#/workbooks/692/views
# Description
Calcul le bilan CO2 sur la base de données provenant des differentes actions à impact de l'université.
Les domaines pris en compte sont:
- Mobilité (pendulaire, professionnelle)
- Energie
- Alimentation
- Numerique
- Achats (papier)
Ce qui ne sont pas pris en compte pour l'instant sont:
- Equipement (donnée indisponible)
- Consommable (donnée indisponible)
- Achats (donnée indisponible)
- Construction (donnée indisponible)
- Déchets (donnée sans valeur si les achats ne sont pas considérés)
- Eau (donnée à faible impact CO2 mais à fort impact environnemental (stress hydrique))
# Perimètre et hypothèses:
Perimètre spatial:
- Energie: Tout les sites sauf Genève
- Mobilité: Tout les sites
- Alimentation: Campus principal
- Numerique: Campus principal
# Structure
./data
- /Brutes: Contient quelques données d'ordre public:
- Bilan_2006.xlsx: Bilan CO2 de 2006
- df_EPT.xlsx: EPT de l'EPFL
- /Resultats: Contient les données générées:
- L1_data.xlsx : Les valeurs physiques ayant un potentiel impact CO2
- L2_data.xlsx: Les valeurs physiques associées aux facteurs d'émissions et impact CO2
- L3A_data.xlsx: Valeurs physiques et impacts associés avec extrapolation des années où la donnée est manquante
- L3A_data.xlsx: Valeurs physiques et impacts associés avec extrapolation des années où la donnée est manquante + Interpolation avec les objectifs fixés par la stratégie EPFL
- emission_factor.xlsx est un excel regroupant tout les facteurs d'émissions utilisés (ou non). C'est un échantillon de plusieurs bases de données comme Ecoinvent, KBOB etc
- join_factor.xlsx joint les données et les facteurs d'émissions. C'est la mapping table. Selon ce fichier, différents facteurs sont choisis, les facteurs peuvent varier en fonction de la catégorie, du campus et de l'année.
- /scripts contient les scripts servant pour lire les données et les aggreger
- main.py est central, il utilise la classe footprint pour générer et exporter le bilan voulu
- footprint.py contient la classe footprint qui permet de générer le bilan
- input_path.yaml regroupe les chemins vers les bases de données utilisées pour créer le bilan
- functions.py contient différentes fonctions utiles
# Data Steward (à l'EPFL)
- Mobilité:
- Pendulaire:
- Luca Fontana & Alexia & Hugo Cruz
- Voyages:
- Luca Fontana & Alexia & Hugo Cruz
- Energie:
- David Gremaud
- Déchets:
- Stephen Poplineau
- Numerique:
- Manuel Cubero-Castan
- Alimentation:
- Aurore Nembrini & Hugo Cruz
+
# Utilisation
- Clone the repository to your local machine using the command:
`git clone https://c4science.ch/source/bilan_CO2.git`
- Use Python 3 and install the requirements with:
`pip install -r requirements.txt`
- Run script/main.py by selecting the correct year of reference and local_run = True
Pour des raisons de confidentialité, la donnée brute n'est pas disponible pour le public.
Pour des demandes particulières, contacter hugo.cruz@epfl.ch
Visualisation du bilan:
-https://tableau.epfl.ch/#/workbooks/595/views
+https://www.epfl.ch/about/sustainability/fr/strategie/empreinte-carbone/
+
-Generer un nouveau bilan:
-- Installer les dépendances
- - pip install -r requirements.txt
-- Adapter si besoin join_factor.xlsx
- - Choisir les facteurs d'émissions à utiliser
- - Choisir les campus à prendre en compte
- - Choisir les années à prendre en compte
-- Adapter input_path.yaml si besoin
-- Lancer le script main.py
# Updates à venir:
- Bug si join_factor a une date sans campus
-A partir de 2014, le facteur elecrtricité change
\ No newline at end of file
+- A partir de 2014, le facteur elecrtricité change
\ No newline at end of file
diff --git a/Ressources/Bilan CO2 graph.html b/Ressources/Bilan CO2 graph.html
deleted file mode 100644
index bd2a674..0000000
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\ No newline at end of file
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ "Restauration"
+ "Alimentation"
+ "Boissons"
+ "Coffee"
+ "Menu veg"
+ "Menu viande"
+ "Sandwich veg"
+ "Sandwich viande"
+ "Thé/café"
+ "moto/scooter"
+ "Mobilité pendulaire"
+ "moto"
+ "transports publics"
+ "Total Domestique"
+ "Europcar - véhicule de location"
+ "Véhicules privés"
+ "voiture"
+ "Avion staff"
+ "Total International"
+ "Avion etudiant-e-s"
+ "Voyages professionnels"
+ "Avion collaborateur-trice-s"
+ "Numérique (fabrication)"
+ "CAD"
+ "Electricité"
+ "Electricite"
+ "Mobility diesel"
+ "Mobility essence"
+ "Véhicules de service diesel"
+ "Véhicules de service essence"
+ "Gaz"
+ "Mazout"
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Category:
+ ]]>
+ Æ
+ Value:
+ <[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Unit:nk]>]]>
+ Æ
+ CO2e
+ t
+]]>
+ Scope:
+ ]]>
+ Æ
+ Facteur d'émission:
+ <[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]>]]>
+
+
+
+
+ <
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
+ > %
+ Æ
+ ]]>
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Value:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+
+
+
+
+
+ Final energy consumed
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
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+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ "Restauration"
+ "Alimentation"
+ "Boissons"
+ "Coffee"
+ "Menu veg"
+ "Menu viande"
+ "Sandwich veg"
+ "Sandwich viande"
+ "Thé/café"
+ "moto/scooter"
+ "Mobilité pendulaire"
+ "moto"
+ "transports publics"
+ "Total Domestique"
+ "Europcar - véhicule de location"
+ "Véhicules privés"
+ "voiture"
+ "Avion staff"
+ "Total International"
+ "Avion etudiant-e-s"
+ "Voyages professionnels"
+ "Avion collaborateur-trice-s"
+ "Numérique (fabrication)"
+ "CAD"
+ "Electricité"
+ "Electricite"
+ "Mobility diesel"
+ "Mobility essence"
+ "Véhicules de service diesel"
+ "Véhicules de service essence"
+ "Gaz"
+ "Mazout"
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Category:
+ ]]>
+ Æ
+ Value:
+ <[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Unit:nk]>]]>
+ Æ
+ CO2e
+ t
+]]>
+ Scope:
+ ]]>
+ Æ
+ Emission factor:
+ <[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]>]]>
+
+
+
+
+ <
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
+ > %
+ Æ
+ ]]>
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Value:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ]]>
+
+tonnes de CO2-eq émises par l'EPFL pour l'année
+ ]]>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ gweg
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ]]>
+
+tons CO2-eq is emitted by EPFL for the year
+ ]]>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Bilan CO2 EPFL
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ "Restauration"
+ "Alimentation"
+ "Boissons"
+ "Coffee"
+ "Menu veg"
+ "Menu viande"
+ "Sandwich veg"
+ "Sandwich viande"
+ "Thé/café"
+ "moto/scooter"
+ "Mobilité pendulaire"
+ "moto"
+ "transports publics"
+ "Total Domestique"
+ "Europcar - véhicule de location"
+ "Mobility diesel"
+ "Mobility essence"
+ "Véhicules de service diesel"
+ "Véhicules de service essence"
+ "Véhicules privés"
+ "voiture"
+ "Avion staff"
+ "Total International"
+ "Avion etudiant-e-s"
+ "Voyages professionnels"
+ "Avion collaborateur-trice-s"
+ "Numérique (fabrication)"
+ "CAD"
+ "Mazout"
+ "Gaz"
+ "Electricité"
+ "Electricite"
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Theme:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Category:
+ ]]>
+ Æ
+ Thème:
+ ]]>
+ Æ
+ Année:
+ ]]>
+ Æ
+ Facteur d'émission:
+ <[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]>]]>
+ Æ
+ CO2 (kg):
+ ]]>
+ Æ
+ ]]>
+ Æ
+ <[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Unit:nk]>]]>
+
+
+
+
+
<
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ >
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+
+
+
+
+
+ Carbon footprint EPFL by category
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ "Restauration"
+ "Alimentation"
+ "Boissons"
+ "Coffee"
+ "Menu veg"
+ "Menu viande"
+ "Sandwich veg"
+ "Sandwich viande"
+ "Thé/café"
+ "moto/scooter"
+ "Mobilité pendulaire"
+ "moto"
+ "transports publics"
+ "Total Domestique"
+ "Europcar - véhicule de location"
+ "Véhicules privés"
+ "voiture"
+ "Avion staff"
+ "Total International"
+ "Avion etudiant-e-s"
+ "Voyages professionnels"
+ "Avion collaborateur-trice-s"
+ "Numérique (fabrication)"
+ "CAD"
+ "Electricité"
+ "Electricite"
+ "Mobility diesel"
+ "Mobility essence"
+ "Véhicules de service diesel"
+ "Véhicules de service essence"
+ "Gaz"
+ "Mazout"
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Category:
+ ]]>
+ Æ
+ <[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Unit:nk]>]]>
+ Æ
+ CO2 (kg):
+ ]]>
+ Æ
+ ]]>
+ Æ
+ Facteur d'émission:
+ <[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]>]]>
+
+
+
+
+ <
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
+ > %
+ Æ
+ ]]>
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+
+
+
+
+
+ Emissions CO2eq par EPT
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.09xbt491r8ci33145k2s61psbvj3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ([federated.09xbt491r8ci33145k2s61psbvj3].[usr:Calculation_41376881237925888:qk] + [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk])
+ [federated.09xbt491r8ci33145k2s61psbvj3].[none:Year:qk]
+
+
+
+
+
+
+
+ Bilan CO2 par scope
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 3
+ 2
+ 1
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Le scope
+ ]]>
+ Æ
+ représente
+ ]]>
+ tonnes CO2-eq pour l'année
+ ]]>
+ Æ
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 3
+ 2
+ 1
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Scope
+ ]]>
+ Æ
+ represents
+ ]]>
+ tons CO2-eq for the year
+ Æ
+ ]]>
+ Æ
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Electricité Objectifs 2024 & 2030
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
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+
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+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+ "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596495876:qk]"
+ "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596172291:qk]"
+ "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2 (copy)_999517661598375941:qk]"
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names]
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[Multiple Values]
+ ([federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok] / [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names])
+
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+
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+
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+
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+
+
+
+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Category:
+ ]]>
+ Æ
+ Dataset name:
+ ]]>
+ Æ
+ Unit formated:
+ ]]>
+ Æ
+ Scope:
+ ]]>
+ Æ
+ Theme:
+ ]]>
+ Æ
+ Facteur d'emission (kg CO2eq):
+ ]]>
+ Æ
+ ]]>
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
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+
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+
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+
+
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+
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+
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+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ "Achats (estimation)"
+ "Numérique (fabrication)"
+ "Alimentation"
+ "CAD"
+ "Electricite"
+ "Gaz"
+ "Mazout"
+ "Mobilité pendulaire"
+ "Voyages professionnels"
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Path:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
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+
+
+
+
+
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+
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+
+
+
+
+
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+
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Path:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+
+
+
+
+
+
+
+ Bilan CO2 par catégorie
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
+
+
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+
+
+
+
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+
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+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+ "Achats (estimation)"
+ "Alimentation"
+ "Numérique"
+ "Numérique (fabrication)"
+ "Mobilité pendulaire"
+ "Voyages professionnels"
+ "Electricité"
+ "Electricite"
+ "CAD"
+ "Gaz"
+ "Mazout"
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ La catégorie
+ ]]>
+ représente une émission de
+ t CO2-eq]]>
+ en
+ .]]>
+
Cela correspond à
+ ]]>
+ % du bilan carbone de l'année.
+
+
+
+
+
+ ]]>
+ %
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Gaz"
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+ "Valeur calculée"
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+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Voyages professionnels"
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+ 38720.068732327331
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+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Mobilité pendulaire"
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+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Numérique (fabrication)"
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+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Alimentation"
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+
+
+
+
+
+
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
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+
+
+ "Electricite"
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+ 38668.288153343608
+
+
+
+
+
+
+
+
+
+
+
+ Carbon footprint EPFL
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ "Achats (estimation)"
+ "Alimentation"
+ "Numérique (fabrication)"
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+ "Voyages professionnels"
+ "Electricite"
+ "CAD"
+ "Gaz"
+ "Mazout"
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+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ The category
+ ]]>
+ represents an emission of
+ t CO2-eq]]>
+ in
+ ]]>
+ .
+
It corresponds to
+ ]]>
+ % of the yearly carbon footprint.
+
+
+
+
+
+ ]]>
+ %
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+ Bilan CO2 EPFL
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+ Scope 1
+ :
+ émissions directes
+ Æ
+ Scope 2
+ :
+ émissions indirectes induites par l'université
+ Æ
+ Scope 3
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+ Scope 1
+ :
+ émissions directes
+ Æ
+ Scope 2
+ :
+ émissions indirectes induites par l'université
+ Æ
+ Scope 3
+ :
+ émissions indirectes par des tiers
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+
+
+ Open source code:
+ https://c4science.ch/source/bilan_CO2/
+ Æ
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+
+ Open source code:
+ https://c4science.ch/source/bilan_CO2/
+ Æ
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+ [Parameters].[Data description Parameter]
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diff --git a/Tableau/Bilan_CO2_EPFL_sandbox.twb b/Tableau/Bilan_CO2_EPFL_sandbox.twb
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+tonnes de CO2-eq émise par l'EPFL pour l'année
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+ 3
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+ Æ
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+ Æ
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+
+
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+ Æ
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+ 2
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+ 1
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+
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+
+
+
+
+
+
+
+
+
+ 3
+ 2
+ 1
+
+
+
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+
+
+
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+
+
+ Scope
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+ Æ
+ represents
+ ]]>
+ tons CO2-eq for the year
+ Æ
+ ]]>
+ Æ
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+
+
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+ Electricité Objectifs 2024 & 2030
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+ "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2 (copy)_999517661598375941:qk]"
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+ Category:
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+ Æ
+ Dataset name:
+ ]]>
+ Æ
+ Unit formated:
+ ]]>
+ Æ
+ Scope:
+ ]]>
+ Æ
+ Theme:
+ ]]>
+ Æ
+ Facteur d'emission (kg CO2eq):
+ ]]>
+ Æ
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+ "Achats (estimation)"
+ "Numérique (fabrication)"
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+
+
+
+
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+ Bilan CO2 Energy
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+ "Achats (estimation)"
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+ "Voyages professionnels"
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ La catégorie
+ ]]>
+ représente une émission de
+ t CO2-eq]]>
+ en
+ .]]>
+
Cela correspond à
+ ]]>
+ % du bilan carbone de l'année.
+
+
+
+
+
+ ]]>
+ %
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+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+
+ "Gaz"
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
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+
+
+ "Numérique (fabrication)"
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Alimentation"
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
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+
+ "Electricite"
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+
+
+
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+
+ Carbon footprint EPFL
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+
+ "Achats (estimation)"
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+ "Numérique (fabrication)"
+ "Mobilité pendulaire"
+ "Voyages professionnels"
+ "Electricite"
+ "CAD"
+ "Gaz"
+ "Mazout"
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+
+
+
+
+
+
+
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+
+
+
+
+
+
+ The category
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+ represents an emission of
+ t CO2-eq]]>
+ in
+ ]]>
+ .
+
It corresponds to
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+ % of the yearly carbon footprint.
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+
+
+
+
+ ]]>
+ %
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+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:3]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Voyages professionnels"
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+
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+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:3]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Mobilité pendulaire"
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+ "Valeur calculée"
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+ 12
+ 2
+ 33853.551672596855
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+
+
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+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:3]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
+
+
+
+ "Numérique (fabrication)"
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+ 1
+ 33853.551672596855
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+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Year:
+ ]]>
+ Æ
+ Value:
+ MWh]]>
+ Æ
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+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Value:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Open source code:
+ https://c4science.ch/source/bilan_CO2/
+ Æ
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Open source code:
+ https://c4science.ch/source/bilan_CO2/
+ Æ
+
+
+
+
+
+
+
+
+
+
+
+
+
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+ [Parameters].[Data description Parameter]
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diff --git a/Tableau/Bilan_hugo_2.0.twb b/Tableau/Bilan_hugo_2.0.twb
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- "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596495876:qk]"
- "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596172291:qk]"
- "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2 (copy)_999517661598375941:qk]"
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[Multiple Values]
- ([federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok] / [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names])
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
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- Category:
- ]]>
- Æ
- Dataset name:
- ]]>
- Æ
- Unit formated:
- ]]>
- Æ
- Scope:
- ]]>
- Æ
- Theme:
- ]]>
- Æ
- Facteur d'emission (kg CO2eq):
- ]]>
- Æ
- ]]>
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
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- "Achats (estimation)"
- "Numérique (fabrication)"
- "Alimentation"
- "CAD"
- "Electricite"
- "Gaz"
- "Mazout"
- "Mobilité pendulaire"
- "Voyages professionnels"
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Path:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Path:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
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- Bilan CO2 EPFL
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-
-
-
-
- "Achats (estimation)"
- "Alimentation"
- "Numérique"
- "Numérique (fabrication)"
- "Mobilité pendulaire"
- "Voyages professionnels"
- "CAD"
- "Mazout"
- "Gaz"
- "Electricité"
- "Electricite"
-
-
-
-
-
-
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-
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
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-
-
- La catégorie
- ]]>
- est responsable d'une émission de
- t CO2e]]>
- en
- .]]>
-
Cela correspond à
- ]]>
- % du bilan carbone de l'année.
-
-
-
-
-
- ]]>
- %
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Gaz"
- 2022
- 3600.4421622251289
- "Valeur calculée"
- %null%
- 9
- 9
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
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-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Voyages professionnels"
- 2022
- 13009.28702531819
- "Valeur calculée"
- %null%
- 34
- 34
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Mobilité pendulaire"
- 2022
- 4031.1208557980744
- "Valeur calculée"
- %null%
- 10
- 10
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Numérique (fabrication)"
- 2022
- 2900.0
- "Valeur calculée"
- %null%
- 7
- 7
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Alimentation"
- 2022
- 6061.7105909273487
- "Valeur estimée"
- %null%
- 16
- 16
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Electricite"
- 2022
- 9018.625
- %null%
- 23
- 23
- 38668.288153343608
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
-
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- Year:
- ]]>
- Æ
- Value:
- MWh]]>
- Æ
-
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Value:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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- Open source code:
- https://c4science.ch/source/bilan_CO2/
- Æ
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- Open source code:
- https://c4science.ch/source/bilan_CO2/
- Æ
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Calculation_1052997914208116742:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
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- [Parameters].[Data description Parameter]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[io:Set 2:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data type:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:qk]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Calculation_1370501696121757696:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Calculation_1052997914208116742:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Dataset name:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Factor (kg CO2eq):qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Source:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:factor name:nk]
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-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
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- Electricité Objectifs 2024 & 2030
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- "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596495876:qk]"
- "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596172291:qk]"
- "[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2 (copy)_999517661598375941:qk]"
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[Multiple Values]
- ([federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok] / [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names])
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
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- Category:
- ]]>
- Æ
- Dataset name:
- ]]>
- Æ
- Unit formated:
- ]]>
- Æ
- Scope:
- ]]>
- Æ
- Theme:
- ]]>
- Æ
- Facteur d'emission (kg CO2eq):
- ]]>
- Æ
- ]]>
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
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- "Achats (estimation)"
- "Numérique (fabrication)"
- "Alimentation"
- "CAD"
- "Electricite"
- "Gaz"
- "Mazout"
- "Mobilité pendulaire"
- "Voyages professionnels"
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Path:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Path:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
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- Bilan CO2 EPFL
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-
- "Achats (estimation)"
- "Alimentation"
- "Numérique"
- "Numérique (fabrication)"
- "Mobilité pendulaire"
- "Voyages professionnels"
- "CAD"
- "Mazout"
- "Gaz"
- "Electricité"
- "Electricite"
-
-
-
-
-
-
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-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
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-
- La catégorie
- ]]>
- est responsable d'une émission de
- t CO2e]]>
- en
- .]]>
-
Cela correspond à
- ]]>
- % du bilan carbone de l'année.
-
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-
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-
- ]]>
- %
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-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Gaz"
- 2022
- 3600.4421622251289
- "Valeur calculée"
- %null%
- 9
- 9
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Voyages professionnels"
- 2022
- 13009.28702531819
- "Valeur calculée"
- %null%
- 34
- 34
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Mobilité pendulaire"
- 2022
- 4031.1208557980744
- "Valeur calculée"
- %null%
- 10
- 10
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Numérique (fabrication)"
- 2022
- 2900.0
- "Valeur calculée"
- %null%
- 7
- 7
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Alimentation"
- 2022
- 6061.7105909273487
- "Valeur estimée"
- %null%
- 16
- 16
- 38720.068732327331
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Objectif:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
-
-
-
- "Electricite"
- 2022
- 9018.625
- %null%
- 23
- 23
- 38668.288153343608
-
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
-
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- Year:
- ]]>
- Æ
- Value:
- MWh]]>
- Æ
-
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Value:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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- https://www.epfl.ch/about/sustainability/fr/strategie/empreinte-carbone/
- Æ
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- Open source code:
- https://c4science.ch/source/bilan_CO2/
- Æ
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- Open source code:
- https://c4science.ch/source/bilan_CO2/
- Æ
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diff --git a/data/Brute/Bilan_2006.xlsx b/data/Brute/Bilan_2006.xlsx
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--- a/scripts/footprint.py
+++ b/scripts/footprint.py
@@ -1,512 +1,538 @@
import pandas as pd
import numpy as np
from functions import *
class footprint():
def __init__(self, CO2_col, EPT_infile):
self.general_attributes = {
"author": "Hugo Cruz",
"author_mail": "hugo.cruz@epfl.ch",
}
self.CO2_col = CO2_col
self.bilan = pd.DataFrame()
self.data = pd.DataFrame()
self.category_name = {}
self.Theme_name = {}
self.ignore_column = []
#open EPT file, this EPT is not up to date, the evolution is not yet taken into account
EPT = pd.read_excel(EPT_infile)
self.EPT_year = EPT[EPT["Category"]== 'Population GRI'].groupby("Year").sum()["EPT"]
def read_emission_factor(self, emission_factor_path):
if "google" in emission_factor_path:
factor = pd.read_csv(emission_factor_path, encoding= 'cp1252', delimiter=';', decimal=",")
else:
factor = pd.read_excel(emission_factor_path, index_col=0)
CO2_col = self.CO2_col
#Standardize unit: MWh, m3, km
factor_df = factor.copy()
mask_kWh = factor_df["Unit"]=="kWh"
factor_df[CO2_col] = factor_df[CO2_col].where(~mask_kWh,factor_df[CO2_col]*1000)
factor_df["Unit"] = factor_df["Unit"].replace("kWh", "MWh")
mask_MJ = factor_df["Unit"]=="MJ"
factor_df[CO2_col] = factor_df[CO2_col].where(~mask_MJ,factor_df[CO2_col]*3600)
factor_df["Unit"] = factor_df["Unit"].replace("MJ", "MWh")
mask_m = factor_df["Unit"]=="m"
factor_df[CO2_col] = factor_df[CO2_col].where(~mask_m,factor_df[CO2_col]/1000)
factor_df["Unit"] = factor_df["Unit"].replace("m", "km")
self.emission_factor = factor_df
def run_energy_emission(self, elec_path):
df = pd.read_excel(elec_path)
df = df.rename(columns={"year": "Year", "campus": "Campus", "category": "Category", "value": "Value"})
df["Theme"] = df["Category"] #We understand that the theme is Energy, however it's more relevant to separate when we visualize it
df["Scope"] = 2
df["Unit"] = df["unit"]
df["Path"] = elec_path
df.drop("unit", axis=1, inplace=True)
mask_mazout = df["Category"]=="Mazout"
mask_gaz = df["Category"]=="Gaz"
df.loc[df["Category"]=="Electricite","Category"] = "Electricite"
df.loc[mask_gaz|mask_mazout, "Scope"] = 1
#ADD FRIBOURG ELECTRICITE BECAUSE NOT IN GRI
Fribourg_electricite = (36003.81+ 206631.15)/1000 #MWh
#Add to df Fribourg_eletricite
df = df.append({"Category": "Electricite", "Year": 2022, "Campus": "Fribourg", "Value": Fribourg_electricite, "Theme": "Electricite", "Scope": 2, "Unit": "MWh", "Path": "Bilan CO2 code"}, ignore_index=True)
self.data = pd.concat([self.data, df], ignore_index=True)
def run_dechets_emission(self, dechets_path):
#Dechets is not yet taken into account
df = pd.read_excel(dechets_path)
df = df.rename(columns={"waste_type":"Category", "kg":"Value", "year": "Year"})
df["Scope"] = 3
df["Unit"] = "kg"
df["Campus"] = "Vaud"
df["Theme"] = "Dechets"
df["ID_category"] = df["Category"]
#Strategy ojective 2025
mask = (df["disposal_method"]=="Recycling")&(df["Year"]==2022)
recycled = df[(df["disposal_method"]=="Recycling")&(df["Year"]==2022)]["Value"].sum()
incineration = df[(df["disposal_method"]=="incineration")&(df["Year"]==2022)]["Value"].sum()
total = recycled+incineration
recycled_2025 = total*0.8
incineration_2025 = total*0.2
df["Path"] = dechets_path
df.drop("disposal_method", axis=1, inplace=True)
print("In 2025, the amount of recycled waste should be {} kg based on 80% recycled in 2022".format(recycled_2025))
self.data = pd.concat([self.data, df], ignore_index=True)
def run_plane_emission(self,path):
staff_emission = pd.read_excel(path, sheet_name="Avions (staff)", skiprows=1)
staff_CO2 = staff_emission.set_index("Data*").loc["CO2 footprint (tons) / Year Données: Atmosfair"]
staff_CO2.index = staff_CO2.index.astype(int)
staff_CO2 = staff_CO2.astype(float)
Students_emission = pd.read_excel(path, sheet_name="Avions (étudiant-e-s)")
Students_group = Students_emission.groupby("Year").sum()
Students_CO2 = Students_group["CO2 RFI2,7"]
CO2_staff = staff_CO2*1000 #Convert t to kg
CO2_students = Students_CO2*1000 #Convert t to kg
df_stud = pd.DataFrame(CO2_students).reset_index()
df_staff = pd.DataFrame(CO2_staff).reset_index()
self.category_name["Avion etudiant-e-s"] = "Avion etudiant-e-s"
self.category_name["Avion collaborateur-trice-s"] = "Avion collaborateur-trice-s"
df_staff["Category"] = self.category_name["Avion collaborateur-trice-s"]
df_stud["Category"] = self.category_name["Avion etudiant-e-s"]
df_staff.columns = ["Year", "CO2", "Category"]
df_stud.columns = ["Year", "CO2", "Category"]
df = pd.concat([df_stud, df_staff], ignore_index=True)
df["Campus"] = "EPFL"
df["Theme"] = "Voyages professionnels"
df["Unit"] = "kg CO2e"
df["Scope"] = 3
df["Path"] = path
self.ignore_column = np.append(self.ignore_column, df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
def run_train_emission(self, input_file):
#Open and format the data
df_train = pd.read_excel(input_file,skiprows=1, sheet_name="Train")
df_train = df_train.set_index("Km").stack().reset_index()
df_train.columns=["Category", "Year", "Value"]
#Select only interesting data
df = df_train[(df_train["Category"]=="Total Domestique")|(df_train["Category"]=="Total International")]
df["Year"] = df["Year"].astype(int)
df["Value"] = df["Value"].astype(float)
df["Unit"] = "km"
df["Theme"] = "Voyages professionnels"
df["Scope"] = 3
df["Campus"] = "EPFL"
df["Path"] = input_file
self.data = pd.concat([self.data, df], ignore_index=True)
def run_cars_emission(self, input_file):
df_cars = pd.read_excel(input_file, skiprows=1, sheet_name="Voiture")
V_service = df_cars.iloc[0:5]
Mobility = df_cars.iloc[9:12]
Mobility.columns = df_cars.iloc[8]
col_int = list(Mobility.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
#Set Mobility.columns[1:] to col_int
Mobility.columns = ["Litres / kW pour électriques"] + col_int
db_Mobility = Mobility.set_index("Litres / kW pour électriques").stack().reset_index()
db_Mobility.columns=["Category", "Year", "Value"]
db_Mobility["Value"] = db_Mobility["Value"].astype(str).str.replace("-", "NaN").str.split(".").str[0].astype(float)
db_Mobility["Year"] = db_Mobility["Year"].astype(int)
db_Mobility["Unit"] = "L"
db_service = V_service.set_index("Kilomètres parcourus").stack().reset_index()
db_service.columns=["Category", "Year", "Value"]
db_service["Year"] = db_service["Year"].astype(int)
db_service["Value"] = db_service["Value"].astype(float)
db_service["Unit"] = "km"
V_service_L = df_cars.iloc[15:17]
V_service_L.columns = df_cars.iloc[14]
col_int = list(V_service_L.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
#Set Mobility.columns[1:] to col_int
V_service_L.columns = ["Litres"] + col_int
db_service_L = V_service_L.set_index("Litres").stack().reset_index()
db_service_L.columns=["Category", "Year", "Value"]
db_service_L["Year"] = db_service_L["Year"].astype(int)
db_service_L["Value"] = db_service_L["Value"].astype(float)
db_service_L["Unit"] = "L"
#Merge db_service db_Mobility and db_service_L into new dataframe
#Avoid a double count by selecting only the volume of L used in service vehicles:
db_service = db_service[~db_service["Category"].isin(["Véhicules de service essence", "Véhicules de service diesel", "Mobility carsharing"])]
db_Mobility = db_Mobility[~db_Mobility["Category"].isin(["Mobility électriques"])] #L'Electricite est achetée par l'EPFL et redistribuée, elle est donc deja comptabilisée
df = pd.concat([db_service, db_Mobility, db_service_L], ignore_index=True)
df["Campus"] = "EPFL"
df["Theme"] = "Voyages professionnels"
df["Scope"] = 3
df.loc[df["Category"]=="Véhicules de service essence", "Scope"] = 1
df.loc[df["Category"]=="Véhicules de service diesel", "Scope"] = 1
df.loc[df["Category"]=="Mobility diesel", "Scope"] = 1
df.loc[df["Category"]=="Mobility essence", "Scope"] = 1
df["Path"] = input_file
self.data = pd.concat([self.data, df], ignore_index=True)
def run_mobilite_EPFL(self, pendulair_final):
df = pd.read_excel(pendulair_final, skiprows=2, index_col="Unnamed: 0")
#Select columns to keep in dataframe
col_dist=[]
for col in df.columns:
if "km" in col:
col_dist.append(col)
df_dist = df.iloc[:7][col_dist].T.replace("-", np.nan)
#if an index contain "jour" drop it
for idx in df_dist.index:
if "jour" in idx:
df_dist = df_dist.drop(idx)
if "Hiver" in idx:
#Replace "hiver" in index by ""
df_dist.loc[idx] = df_dist.loc[idx.replace("Hiver", "Ete")]+df_dist.loc[idx]
#Rename idx by idx.replace("Hiver", "")
df_dist = df_dist.rename(index={idx:idx.replace("Hiver", "")})
#Drop idx.replace("Hiver", "Ete")
df_dist = df_dist.drop(idx.replace("Hiver", "Ete"))
df_dist.index = df_dist.index.str.extract("(\d+)").squeeze()
#if index==np.nan drop it
df_dist = df_dist.loc[~df_dist.index.isna()]
df_dist = df_dist.reset_index().rename(columns={0:"Year"})
#Columns of df_dist are transport mode, convert df_dist to database
db = df_dist.set_index("Year").stack().reset_index()
db.rename(columns={"level_1":"Category", 0:"Value"}, inplace=True)
#Convert index to int
db["Year"] = db["Year"].astype(int)
db["Theme"] = "Mobilité pendulaire"
db["Scope"] = 3
db["Unit"] = "km"
db["Campus"] = "EPFL"
#Filter Category=="autres"
db = db[db["Category"]!="autres"]
#Remove from db db["Year"] below 2018. Before 2018, the data collection has changed and is not comparable (3 order of magnitude lower)
self.raw_pendular = db.copy()
db.loc[db["Year"]<2018, "Value"] = np.nan
db["Path"] = pendulair_final
self.data = pd.concat([self.data, db], ignore_index=True)
- def run_numeric(self):
- df = pd.DataFrame(data= {"CO2": [4829,2429.05], "Year":[2019, 2022]})
- df["CO2"] = df["CO2"] * 1000 #Convert to kg
+ def run_numeric(self, numeric_infile):
+ df_manual_input = pd.read_excel(numeric_infile)
+ df_numeric = df_manual_input[~df_manual_input["Numérique"].isna()]
+ df = pd.DataFrame()
+ df["CO2"] = df_numeric["Numérique"] * 1000 #Convert to kg
+ df["Year"] = df_numeric["Year"]
df["Campus"] = "EPFL"
self.numeric_name = "Numérique (fabrication)"
df["Category"] = self.numeric_name
df["Theme"] = "Numérique (fabrication)"
df["Scope"] = 3
- df["Path"] = "Bilan CO2 code"
+ df["Path"] = numeric_infile
self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
def run_alimentation(self, alimentation_infile):
df = pd.read_excel(alimentation_infile) #Read file of computed CO2 by quantis
df["CO2"] = df["CO2"]*1000 #Convert to kg
df["Campus"] = "EPFL"
df["Theme"] = "Alimentation"
df["Scope"] = 3
df["Path"] = alimentation_infile
+
+ #Filter all values before 2020 because the methodology has changed and extrapolation of 2019 seems incorrect:
+ df = df[df["Year"]>=2020]
#append self.ignore_column with df["Category"].unique()
self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
- def run_achats(self):
- df = pd.DataFrame(data= {"CO2": [42000], "Year":[2019]})
- df["CO2"] = df["CO2"] * 1000 #Convert to kg
+ def run_achats(self, achat_infile):
+ df_manual_input = pd.read_excel(achat_infile)
+ df_achats = df_manual_input[~df_manual_input["Achats"].isna()]
+ df = pd.DataFrame()
+ df["CO2"] = df_achats["Achats"]*1000 #Convert to kg
+ df["Year"] = df_achats["Year"]
df["Campus"] = "EPFL"
self.achats_name = "Achats (estimation)"
df["Category"] = self.achats_name
df["Theme"] = "Achats (estimation)"
df["Scope"] = 3
- df["Path"]= "Bilan CO2 code"
+ df["Path"]= achat_infile
self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
def add_2006_emission(self, path_2006):
- df = pd.read_excel(path_2006).iloc[0].reset_index().iloc[1:-1]
- df.columns = ["Theme", "CO2"]
+ self.Scope1_categories = ["Mazout", "Gaz", "CAD"]
+ self.Scope2_categories = ["Electricite"]
+ df = pd.read_excel(path_2006)
+ df = df[df["Year"]==2006]
+ #Set columns of df as new variable and the variables of df as values
+ df = df.set_index("Year").stack().reset_index()
+ #Set names of columns as ["Year", "Theme", "CO2"]
+ df.columns = ["Year", "Theme", "CO2"]
df["CO2"] = df["CO2"]*1000 #Convert to kg
- df["Scope"] = [2,1,1,3,3,3]
+ #Assign scope based on Scope1_categories and Scope2_categories
+ df.loc[df["Theme"].isin(self.Scope1_categories), "Scope"] = 1
+ df.loc[df["Theme"].isin(self.Scope2_categories), "Scope"] = 2
+ df.loc[~df["Theme"].isin(self.Scope1_categories+self.Scope2_categories), "Scope"] = 3
+
df["Campus"] = "EPFL"
df["Year"]=2006
df["Category"] = df["Theme"]
#Drop Theme=="Alimentation" car il est compté dans la database Quantis
df = df[df["Theme"]!="Alimentation"]
df["Path"] = path_2006
self.ignore_column = np.append(self.ignore_column, df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
def export_database(self, path, Level="L1"):
if Level=="L1":
self.data.to_excel(path, index=False)
if Level=="L2":
self.data_L2.to_excel(path, index=False)
if Level=="L3A":
self.data_L3A.to_excel(path, index=False)
if Level=="L3B":
self.data_L3B.to_excel(path, index=False)
def import_database(self, path):
self.data = pd.read_excel(path)
def assign_factor(self, join_factor_path):
#Assigne un facteur d'emission à chacune des lignes de la database self.data
#Join_factor_path: path to the file containing the factor to join to the database
#Aucun calcul n'est fait, juste une assignation de facteur. Les calculs sont fait dans la fonction calculate_emission
join_factor = pd.read_excel(join_factor_path)
Check_join_data_category(self.data, join_factor, ignore_column=self.ignore_column, ignore_year=2006)
self.data["Year"] = self.data["Year"].astype(int)
self.data["Value"] = self.data["Value"].astype(float)
self.data["Data description"] = "Valeur calculée"
df = self.data
df["factor name"] = np.nan
# First assign factor to Year
mask_year = ~join_factor["Year"].isna()
df_year = join_factor.loc[mask_year]
for year, campus, category, factor in df_year[["Year", "Campus", "Category", "factor name"]].values:
mask = (df["Year"]==year) & (df["Campus"]==campus) & (df["Category"]==category) & df["factor name"].isna()
df.loc[mask, "factor name"] = factor
# If no Year specified, assign factor to Campus
mask_campus = join_factor["Year"].isna() & ~join_factor["Campus"].isna()
df_campus = join_factor.loc[mask_campus].set_index(["Campus", "Category"])
for campus, category in df_campus.index.unique():
if (mask := (df["Campus"]==campus) & (df["Category"]==category) & df["factor name"].isna()).any():
factor = df_campus.loc[(campus, category)]["factor name"]
df.loc[mask, "factor name"] = factor
# If no year and no campus, assign factor to Category
mask_category = join_factor["Year"].isna() & join_factor["Campus"].isna()
df_category = join_factor.loc[mask_category].set_index("Category")["factor name"]
for category in df["Category"].unique():
if (mask := (df["Category"]==category) & df["factor name"].isna()).any():
factor = df_category.get(category)
#If factor have multiple value, assign the first one
if isinstance(factor, pd.Series):
factor = factor.iloc[0]
df.loc[mask, "factor name"] = factor
self.data = df.copy()
#Print missing factor, les facteurs non trouvés
print_missing_factor(self.data, self.emission_factor)
def calculate_emission(self):
#Calcule les emissions liées à chaque ligne de la database self.data
#Mask les valeurs n'ayant pas de facteur d'emission (ex: avions dont la donnée de base n'est pas utilisée mais on importe directement le CO2)
mask_na_factor = self.data["factor name"].isna()|~self.data["CO2"].isna()
data_L2 = self.data[~mask_na_factor].join(self.emission_factor.set_index("Data name mapping"), on="factor name", rsuffix="_CO2", how="left")
#Complete les données maskées par des données brutes de CO2
data_L2["CO2"] = data_L2["Value"]*data_L2[self.CO2_col]
mask_brute_CO2 = ~self.data["CO2"].isna()
data_L2 = pd.concat([data_L2, self.data[mask_brute_CO2]], ignore_index=True)
#Drop unecessary columns
data_L2.drop(columns =["Utilisation", "Comments", "Lien source"], inplace=True)
self.data_L2 = data_L2.copy()
+ ## Add new column called "Equivalence string" and "Equivalence value"
+ # Create mask of "Category"=="Electricite" and fill with "Equivalence string"="km en train Suisse"
+ Train_suisse = 1.6 #kg CO2/aller-retour zurich en train
+
+ mask_electricity = self.data_L2["Category"]=="Electricite"
+
+
def extrapolate(self):
#Si une valeur est manquante pour une année, l'algorithme cherche une valeur dans le future et l'adapte selon la croissance
#Si c'est une valeur dans le future n'est pas disponible, alors elle va chercher dans le passé et adpate selon croissance
data = self.data_L2.copy()
years = data["Year"].unique()
years.sort()
years = years[:-1]
EPT_year = self.EPT_year
## Student plane interpolation
Category = self.category_name["Avion etudiant-e-s"]
Stud_years = [2016,2017, 2018]
data = extrapolate_student_plane(data, EPT_year, Stud_years, Category)
for year in years:
for Theme in data["Theme"].unique():
mask = (data["Year"]==year)&(data["Theme"]==Theme)
#For each Theme where there is no data in this year, take the data from the nearest year
if data.loc[mask, "CO2"].isna().all():
#Find the nearest year where data["Theme"] is not empty
nearest_year = year
while (data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "CO2"].isna().all()):
nearest_year += 1
if nearest_year>2100:
nearest_year = year
while (data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "CO2"].isna().all()):
nearest_year -= 1
nearest_mask = (data["Year"]==nearest_year)&(data["Theme"]==Theme)
data_masked = data.loc[nearest_mask]
for category in data_masked["Category"].unique():
serie = pd.Series()
serie["Year"] = year
serie["Theme"] = Theme
serie["Category"] = category
if Theme == "Electricite":
print(1)
if Theme == "Mobilité pendulaire":
#Produit en croix pour estimer la valeur de la mobilité pendulaire en fonction de l'évolution de la population
data_pendular = data.loc[(data["Year"]==nearest_year)&(data["Theme"]=="Mobilité pendulaire")]
pendular_year = self.raw_pendular.loc[(self.raw_pendular["Year"] == year)&(self.raw_pendular["Theme"]=="Mobilité pendulaire")&(self.raw_pendular["Category"]==category)]
pendular_year["Part_modale"] = pendular_year["Value"]/pendular_year["Value"].sum()
pendular_nearest = self.raw_pendular.loc[(self.raw_pendular["Year"] == nearest_year)&(self.raw_pendular["Theme"]=="Mobilité pendulaire")&(self.raw_pendular["Category"]==category)]
pendular_nearest["Part_modale"] = pendular_nearest["Value"]/pendular_nearest["Value"].sum()
mask = data_pendular["Category"]==category
pendular_EPT_a = pendular_year[pendular_year["Category"]==category]["Part_modale"].values*EPT_year[year]
pendular_CO2_n = data_pendular[mask]["CO2"].values
pendular_EPT_n = pendular_nearest[pendular_nearest["Category"]==category]["Part_modale"].values*EPT_year[nearest_year]
#Produit en croix:
serie["CO2"] = (pendular_EPT_a*pendular_CO2_n/pendular_EPT_n)[0]
else:
ratio_evolution = ((EPT_year.loc[year]-EPT_year.loc[nearest_year])/(EPT_year.loc[nearest_year]))
serie["CO2"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme)&(data["Category"]==category), "CO2"].sum()* (1+ratio_evolution)
serie["Value"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme)&(data["Category"]==category), "Value"].sum()*(1+ratio_evolution)
serie["Data description"] = "Estimé"
serie["Scope"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "Scope"].median()
serie["Campus"] = "EPFL"
serie["Category"] = Theme
data = data.append(serie, ignore_index=True)
self.data_L3A = data
def objectives(self, year_reference=2022):
data_L3A = self.data_L3A.copy()
#Objectif 2024
self.df_objectifs_2024 = objectives_2024(data_L3A, year_reference)
self.year_reference = year_reference
#Objectif 2030
df = pd.DataFrame()
df["Theme"] = data_L3A["Theme"].unique()
df["Year"] = 2030
df["Campus"] = "EPFL"
df["Scope"] = np.nan
#Definition des objectifs 2030:
mask = (data_L3A["Theme"] == "Electricite")|(data_L3A["Theme"] == "Mazout")|(data_L3A["Theme"]=="CAD")|(data_L3A["Theme"] == "Gaz")
Energy_2006 = data_L3A.loc[(data_L3A["Year"]==2006)&(mask)].sum()
Objective_2030_energy = Energy_2006["CO2"]*0.5
gaz_2030 = data_L3A.loc[(data_L3A["Year"]==2006)&(data_L3A["Theme"] == "Gaz")]["CO2"]*0.5 #Assume que le gaz en 2030 est la moitié de 2006
elec_2030 = Objective_2030_energy-gaz_2030
df.loc[df["Theme"]=="Electricite", "CO2"] = elec_2030.squeeze()
df.loc[df["Theme"]=="Electricite", "Objectif"] = "-50% energie par rapport à 2006"
df.loc[df["Theme"]=="Gaz", "CO2"] = gaz_2030.squeeze()
df.loc[df["Theme"]=="Gaz", "Objectif"] = "-50% energie par rapport à 2006"
df.loc[df["Theme"]=="Mazout", "CO2"] = 0
df.loc[df["Theme"]=="Mazout", "Objectif"] = "Remplacement par Electricite"
df.loc[df["Theme"]=="CAD", "CO2"] = 0
df.loc[df["Theme"]=="CAD", "Objectif"] = "Remplacement par Electricite"
mask_voyages_prof = (data_L3A["Theme"]=="Voyages professionnels")
trip_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(mask_voyages_prof)]["CO2"].sum()
Objective_2030_trip = trip_2019*0.7
df.loc[df["Theme"]=="Voyages professionnels", "CO2"] = Objective_2030_trip
df.loc[df["Theme"]=="Voyages professionnels", "Objectif"] = "-30% par rapport à 2019"
Pendulaire_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Mobilité pendulaire")]["CO2"].sum()
Objective_2030_pendulaire = Pendulaire_2019*0.7
df.loc[df["Theme"]=="Mobilité pendulaire", "CO2"] = Objective_2030_pendulaire
df.loc[df["Theme"]=="Mobilité pendulaire", "Objectif"] = "-30% par rapport à 2019"
Alimentation_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Alimentation")]["CO2"].sum()
Objective_2030_alimentation = Alimentation_2019*0.6
df.loc[df["Theme"]=="Alimentation", "CO2"] = Objective_2030_alimentation
df.loc[df["Theme"]=="Alimentation", "Objectif"] = "-40% par rapport à 2019"
df.loc[df["Theme"]=="Numérique (fabrication)", "CO2"] = 4829e3
df.loc[df["Theme"]=="Numérique (fabrication)", "Objectif"] = "Malgré la croissance, maintenir l'impact à 2019"
df.loc[df["Theme"]==self.achats_name, "CO2"] = 42000e3
df.loc[df["Theme"]==self.achats_name, "Objectif"] = "Malgré la croissance, maintenir l'impact à 2019"
df.loc[df["Theme"]=="Dechets", "CO2"] = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Dechets")]["CO2"].sum()
df["Data description"] = "Objectifs 2030 (confédération+EPFL)"
df["Category"] = df["Theme"]
for Theme in df["Theme"].unique():
df.loc[df["Theme"]==Theme, "Scope"] = data_L3A[data_L3A["Theme"] == Theme]["Scope"].iloc[0]
self.df_2030 = df
def interpolation_objectifs(self):
data_L3A = self.data_L3A.copy()
df_2030 = self.df_2030
years = np.arange(data_L3A["Year"].max(),2030)
for Theme in df_2030["Theme"].unique():
for year in years:
df_temp = pd.Series()
df_temp["Theme"] = Theme
df_temp["Year"] = year
df_temp["Campus"] = "EPFL"
df_temp["Category"] = df_temp["Theme"]
df_temp["Data description"] = "Objectifs 2030 (confédération+EPFL)"
df_temp["Scope"] = data_L3A[data_L3A["Theme"] == Theme]["Scope"].iloc[0]
mask = (data_L3A["Year"]==data_L3A["Year"].max())&(data_L3A["Theme"]==Theme)
if mask.sum():
df_temp["CO2"] = np.interp(year, [data_L3A["Year"].max(), 2030], [data_L3A.loc[mask, "CO2"].sum(), df_2030.loc[(df_2030["Theme"]==Theme)&(df_2030["Year"]==2030), "CO2"].sum()])
if year != data_L3A["Year"].max():
df_2030 = df_2030.append(df_temp, ignore_index=True)
else:
#If this data is not available for a specific theme, we take the value of the last year
df_temp["CO2"] = np.interp(year, [data_L3A["Year"].max(), 2030], [data_L3A.loc[(data_L3A["Year"]==data_L3A["Year"].max()-1)&(data_L3A["Theme"]==Theme), "CO2"].iloc[0], df_2030.loc[(df_2030["Theme"]==Theme)&(df_2030["Year"]==2030), "CO2"].sum()])
df_2030 = df_2030.append(df_temp, ignore_index=True)
data_L3B = pd.concat([data_L3A, df_2030, self.df_objectifs_2024], ignore_index=True)
Elect_emission_factor = data_L3B[(data_L3B["Theme"]=="Electricite")&(data_L3B["Year"]==self.year_reference)][self.CO2_col].mean()
nan_mask = (data_L3B["Value"].isna())&(data_L3B["Category"] == "Electricite")
data_L3B.loc[nan_mask, "Value"] = data_L3B[nan_mask]["CO2"].iloc[0]/Elect_emission_factor
self.data_L3B = data_L3B.copy()
def simulation():
#Ajoute les reformes et leurs impacts sur le bilan CO2 et les predictions: Exemple: politique voyage
#Dans le cas de la simple croissance, des simulations peuvent être faites.
print(1)
\ No newline at end of file
diff --git a/scripts/input_path.yaml b/scripts/input_path.yaml
index 3b507d8..4c679eb 100644
--- a/scripts/input_path.yaml
+++ b/scripts/input_path.yaml
@@ -1,24 +1,24 @@
#L0 Data
-reporting_pendulaire: "Z:/42-DUR-MOB-E-E/42.05-Mobilite/42.05.01-Aff-en-cours/05-Reporting-inventaires/Rapport-environnemental-2022/42.05.01-reporting-pendulaire-2021-20220412.xlsx"
-reporting_voyages: "Z:/42-DUR-MOB-E-E/42.06-Voyages/42.06.01-Aff-en-cours/05-Reporting/42.06.01-Reporting-voyages-2023.xlsx"
-plane_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Avions/data/Resultats/atmosfair/atmosfair_evolution.xlsx"
-dechets_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Dechets/data/Resultats/waste.xlsx"
-papier_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Achats/papier_epfl/data/Resultats/papier_db.xlsx"
-energy_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Energy/data/Resultats/energy_db.xlsx"
-eau_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Energy/data/Resultats/eau_db.xlsx"
-alimentation_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Alimentation/data/Resultats/Format_Alimentation_CO2.xlsx"
+reporting_pendulaire: "M:/42-DUR-MOB-E-E/42.05-Mobilite/42.05.01-Aff-en-cours/05-Reporting-inventaires/Rapport-environnemental-2022/42.05.01-reporting-pendulaire-2021-20220412.xlsx"
+reporting_voyages: "M:/42-DUR-MOB-E-E/42.06-Voyages/42.06.01-Aff-en-cours/05-Reporting/42.06.01-Reporting-voyages-2023.xlsx"
+plane_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Avions/data/Resultats/atmosfair/atmosfair_evolution.xlsx"
+dechets_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Dechets/data/Resultats/waste.xlsx"
+papier_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Achats/papier_epfl/data/Resultats/papier_db.xlsx"
+energy_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Energy/data/Resultats/energy_db.xlsx"
+eau_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Energy/data/Resultats/eau_db.xlsx"
+alimentation_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Alimentation/data/Resultats/Format_Alimentation_CO2.xlsx"
#L0 Data Local Files:
-bilan_2006: "data/Brute/Bilan_2006.xlsx"
+Manual_input: "data/Brute/manual_input.xlsx"
EPT_infile: "data/Brute/df_EPT.xlsx"
factor_join: "data/join_factor.xlsx"
emission_factor: "data/emission_factor.xlsx"
#Backup files:
-reporting_pendulaire_backup: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Backup/pendulaire/42.05.01-reporting-pendulaire-2021-20220412.xlsx"
-reporting_voyages_backup: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Backup/voyages/42.06.01-Reporting-voyages-2023.xlsx"
+reporting_pendulaire_backup: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Backup/pendulaire/42.05.01-reporting-pendulaire-2021-20220412.xlsx"
+reporting_voyages_backup: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Backup/voyages/42.06.01-Reporting-voyages-2023.xlsx"
#Output
L1_data: "data/Resultats/L1_data.xlsx"
L2_data: "data/Resultats/L2_data.xlsx"
L3A_data: "data/Resultats/L3A_data.xlsx"
L3B_data: "data/Resultats/L3B_data.xlsx"
\ No newline at end of file
diff --git a/scripts/main.py b/scripts/main.py
index 59ac5d7..196e02f 100644
--- a/scripts/main.py
+++ b/scripts/main.py
@@ -1,38 +1,37 @@
import yaml
from footprint import *
-import plotly.express as px
year_reference = 2022
local_run = True
with open("scripts/input_path.yaml", "r", encoding='utf8') as f:
directories = yaml.load(f, Loader=yaml.FullLoader)
fp = footprint(CO2_col='Factor [kg CO2eq]', EPT_infile=directories["EPT_infile"])
if ~local_run:
fp.run_energy_emission(directories["energy_infile"])
fp.run_cars_emission(directories["reporting_voyages"])
fp.run_train_emission(directories["reporting_voyages"])
fp.run_plane_emission(directories["reporting_voyages"])
fp.run_mobilite_EPFL(directories["reporting_pendulaire"])
#fp.run_dechets_emission(directories["dechets_infile"])
fp.run_alimentation(directories["alimentation_infile"])
- fp.run_achats()
- fp.run_numeric()
- fp.add_2006_emission(directories["bilan_2006"])
+ fp.run_achats(directories["Manual_input"])
+ fp.run_numeric(directories["Manual_input"])
+ fp.add_2006_emission(directories["Manual_input"])
fp.export_database(directories["L1_data"], Level="L1")
else:
fp.import_database(directories["L1_data"], Level="L1")
fp.read_emission_factor(directories["emission_factor"])
fp.assign_factor(directories["factor_join"])
fp.calculate_emission()
fp.export_database(directories["L2_data"], Level="L2")
fp.extrapolate()
fp.export_database(directories["L3A_data"], Level="L3A")
fp.objectives(year_reference=year_reference)
fp.interpolation_objectifs()
fp.export_database(directories["L3B_data"], Level="L3B")
#fp.simulations()
\ No newline at end of file