diff --git a/Glossaire.docx b/Glossaire.docx
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index e69de29..0000000
diff --git a/Interpretation_methode.docx b/Interpretation_methode.docx
deleted file mode 100644
index d3052ca..0000000
Binary files a/Interpretation_methode.docx and /dev/null differ
diff --git a/Methodologie.docx b/Methodologie.docx
new file mode 100644
index 0000000..9a7a6a7
Binary files /dev/null and b/Methodologie.docx differ
diff --git a/README.md b/README.md
index 462a67d..2f3bae4 100644
--- a/README.md
+++ b/README.md
@@ -1,82 +1,87 @@
# Bilan CO2 EPFL
+
## Description
-Calcul le bilan CO2 sur la base de données provenant des differents pôles de l'université.
+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
-- Déchets
-- Eau
- Alimentation
- Numerique
- Achats (papier)
Ce qui ne sont pas pris en compte pour l'instant sont:
-- Equipement
-- Consommable
-- Achats
-- Construction
-
-Questions:
-- Est-ce cohérent de prendre en compte les déchets alors que les achats ne sont pas pris en compte ? Situation où on a un impact positif car on recycle de l'alu.
-En attendant on peut dire qu'on a eu au minimum l'impact de l'achat de l'aluminium ?
+- 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 du bilan:
+## Perimètre et hypothèses:
+Perimètre spatial:
- Energie: Tout les sites sauf Fribourg/Geneve
+- Mobilité: Tout les sites
+- Alimentation: Site d'ecublens
+- Numerique: Site d'ecublens
+
## Structure
./data
- Brutes: Contient quelques données:
- reporting_pendulaire.xslx
- reporting_voyages.xlsx
- Resultats: Contient les données générées:
- All_data.xlsx : Les valeurs physiques associées aux facteurs d'émissions et impact CO2
- bilan.xlsx : Le bilan CO2 aggrégés par années et par categories.
- emission_factor.xlsx est un excel regroupant tout les facteurs d'émissions utilisés (ou non). C'est la base
- join_factor_xxxx.xlsx sont les "modes" de jointure entre les données et les facteurs d'émissions. 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
- input_path.yaml regroupe les chemins vers les bases de données utilisées pour créer le bilan
- footprint.py contient la classe footprint qui permet de générer le bilan
## Data Owners (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
-
## Utilisation
+
+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
Generer un nouveau bilan:
- Installer les dépendances
- pip install -r requirements.txt
- Adapter si besoin join_factor_xxxx.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
Update le projet:
- Clone le repository:
"git clone https://c4science.ch/source/bilan_CO2.git"
### Updates à faire:
- Bug si join_factor a une date sans campus
-
Convertir objectif 2030 en MWH, calcul de 2006
Objectif voyages professionnel -50% de 2019, d'ici à 2030
A partir de 2014, le facteur elecrtricité change
\ No newline at end of file
diff --git a/Tableau/Bilan_hugo_2.0.twb b/Tableau/Bilan_hugo_2.0.twb
index a183851..ebddc40 100644
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+
+
+
+
+
+
-
-
+
+
<_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
-
+
-
-
+
+
-
+
-
Bilan CO2 EPFL
"Restauration"
"Alimentation"
"Boissons"
"Coffee"
"Menu veg"
"Menu viande"
"Sandwich veg"
"Sandwich viande"
"Thé/café"
"Europcar - véhicule de location"
"Mobility diesel"
"Mobilité pendulaire"
"Mobility essence"
"moto/scooter"
"Total Domestique"
"Total International"
"Véhicules de service diesel"
"Véhicules de service essence"
"Véhicules privés"
"voiture"
"transports publics"
"Avion etudiant-e-s"
"Avion staff"
"Voyages professionnels"
"Numérique (fabrication)"
"CAD"
"Mazout"
"Gaz"
"Electricité"
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
Thème:
]]>
Æ
Année:
]]>
Æ
Facteur d'émission:
<[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]>]]>
Æ
CO2 (kg):
]]>
<
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
> %
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[avg:Factor (kg CO2eq):qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914207850501:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
+
+
+
+ 0.20854924375
+ "voiture"
+ 2022
+ 3600529.8833804769
+ "kg CO2/km"
+ "kg CO2/km"
+ 9
+
+
+
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[avg:Factor (kg CO2eq):qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914207850501:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
%null%
"Avion staff"
2022
11033560.96340045
%null%
"Facteurs multiples"
29
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[avg:Factor (kg CO2eq):qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914207850501:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
%null%
"Avion etudiant-e-s"
2022
1591157.0
%null%
"Facteurs multiples"
4
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[avg:Factor (kg CO2eq):qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914207850501:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
+
+
+
+ %null%
+ "Restauration"
+ 2022
+ 5896637.5147970105
+ %null%
+ "Facteurs multiples"
+ 15
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[avg:Factor (kg CO2eq):qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914207850501:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
+
+
+
+ %null%
+ "Numérique (fabrication)"
+ 2022
+ 2900000.0
+ %null%
+ "Facteurs multiples"
+ 7
+
+
+
Objectif 2030 interpolation for electricity
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
Æ
Year:
]]>
Æ
Data description:
]]>
Æ
CO2:
]]>
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
Electricité Objectifs 2024 & 2030
"[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596495876:qk]"
"[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596172291:qk]"
"[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2 (copy)_999517661598375941:qk]"
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[Multiple Values]
([federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok] / [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names])
Bilan CO2 EPFL
+
+
"Alimentation"
"Numérique"
"Mobilité pendulaire"
"Voyages professionnels"
"Numérique (fabrication)"
"CAD"
"Mazout"
"Gaz"
"Electricité"
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
Theme:
]]>
Æ
Year:
]]>
Æ
CO2 (kg):
]]>
Æ
% CO2
:
%]]>
+ Æ
+ ]]>
]]>
%
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk]
"Voyages professionnels"
2022
13009287.025318189
38
2
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
"Alimentation"
2022
6061710.5909273485
16
16
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
"Mobilité pendulaire"
2022
4031120.8557980745
10
10
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk]
"Gaz"
2022
5805217.8358292943
17
1
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
"Numérique (fabrication)"
2022
2900000.0
7
7
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
"Electricité"
2022
9018625.0
23
23
"Alimentation"
"Numérique"
"Mobilité pendulaire"
"Voyages professionnels"
"CAD"
"Mazout"
"Gaz"
"Electricité"
2016
2030
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
Æ
Year:
]]>
Æ
Data description:
]]>
Æ
CO2:
]]>
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
+
+
+
+
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
Year:
]]>
Æ
Value:
]]>
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Value:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
-
+
+
+
-
-
+
+
-
-
-
+
+
+
-
+
-
-
-
+
+
+
-
-
+
+
+
+
+
+
+
+
+
+
+
+ Open source code:
+ https://c4science.ch/source/bilan_CO2/
+ Æ
+
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
+ Open source code:
+ https://c4science.ch/source/bilan_CO2/
+ Æ
+
-
-
-
+
+
+
-
-
+
+
-
-
-
+
+
+
-
+
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Objectif:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
+
-
+
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Calculation_1052997914207850501:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Calculation_1052997914208116742:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[io:Set 1:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data type:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:qk]
+
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[io:Set 2:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data type:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[io:Set 2:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data type:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:qk]
[Parameters].[Data description Parameter]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[io:Set 3:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[io:Set 3:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[avg:Factor (kg CO2eq):qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914207850501:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
+
+
+
+ 125.0
+ "Electricité"
+ 2022
+ 9018625.0
+ "kg CO2/MWH"
+ "kg CO2/MWH"
+ 23
+
+
+
+
-
+
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- Thème:
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- Æ
- Année:
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-
-
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-
-
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-
-
- %null%
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-
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- %null%
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-
-
-
-
-
-
-
-
-
-
- Objectif 2030 interpolation for electricity
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-
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-
-
-
-
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-
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- Æ
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-
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- Electricité Objectifs 2024 & 2030
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-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[Multiple Values]
- ([federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok] / [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names])
-
-
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-
- Bilan CO2 EPFL
-
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-
-
-
- "Alimentation"
- "Numérique"
- "Mobilité pendulaire"
- "Voyages professionnels"
- "Numérique (fabrication)"
- "CAD"
- "Mazout"
- "Gaz"
- "Electricité"
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
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- Theme:
- ]]>
- Æ
- Year:
- ]]>
- Æ
- CO2 (kg):
- ]]>
- Æ
- % CO2
- :
- %]]>
-
-
-
-
- ]]>
- %
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk]
-
-
-
- "Voyages professionnels"
- 2022
- 13009287.025318189
- 38
- 2
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
-
-
-
- "Alimentation"
- 2022
- 6061710.5909273485
- 16
- 16
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
-
-
-
- "Mobilité pendulaire"
- 2022
- 4031120.8557980745
- 10
- 10
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk]
-
-
-
- "Gaz"
- 2022
- 5805217.8358292943
- 17
- 1
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
-
-
-
- "Numérique (fabrication)"
- 2022
- 2900000.0
- 7
- 7
-
-
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:4]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
-
-
-
- "Electricité"
- 2022
- 9018625.0
- 23
- 23
-
-
-
-
-
-
-
-
-
-
-
-
-
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-
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-
-
-
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-
-
-
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-
-
-
-
-
-
-
-
-
- "Alimentation"
- "Numérique"
- "Mobilité pendulaire"
- "Voyages professionnels"
- "CAD"
- "Mazout"
- "Gaz"
- "Electricité"
-
-
-
- 2016
- 2030
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:qk]
-
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:qk]
-
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
-
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- Æ
- Year:
- ]]>
- Æ
- Data description:
- ]]>
- Æ
- CO2:
- ]]>
-
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
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- Year:
- ]]>
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- ]]>
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- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Value:qk]
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diff --git a/data/Brute/pendulaire/42.05.01-reporting-pendulaire-2021-20220412.xlsx b/data/Brute/Backup/pendulaire/42.05.01-reporting-pendulaire-2021-20220412.xlsx
similarity index 100%
rename from data/Brute/pendulaire/42.05.01-reporting-pendulaire-2021-20220412.xlsx
rename to data/Brute/Backup/pendulaire/42.05.01-reporting-pendulaire-2021-20220412.xlsx
diff --git a/data/Brute/pendulaire/Thumbs.db b/data/Brute/Backup/pendulaire/Thumbs.db
similarity index 100%
rename from data/Brute/pendulaire/Thumbs.db
rename to data/Brute/Backup/pendulaire/Thumbs.db
diff --git a/data/Brute/voyages/42.06.01-Reporting-voyages-2023.xlsx b/data/Brute/Backup/voyages/42.06.01-Reporting-voyages-2023.xlsx
similarity index 100%
rename from data/Brute/voyages/42.06.01-Reporting-voyages-2023.xlsx
rename to data/Brute/Backup/voyages/42.06.01-Reporting-voyages-2023.xlsx
diff --git a/data/Brute/~$bilan_strategie.xlsx b/data/Brute/~$bilan_strategie.xlsx
new file mode 100644
index 0000000..2c6bc29
Binary files /dev/null and b/data/Brute/~$bilan_strategie.xlsx differ
diff --git a/data/Resultats/All_data.xlsx b/data/Resultats/All_data.xlsx
index d327917..b7dbd96 100644
Binary files a/data/Resultats/All_data.xlsx and b/data/Resultats/All_data.xlsx differ
diff --git a/data/Resultats/Objectives_data.xlsx b/data/Resultats/Objectives_data.xlsx
index 957fceb..68bb549 100644
Binary files a/data/Resultats/Objectives_data.xlsx and b/data/Resultats/Objectives_data.xlsx differ
diff --git a/data/Resultats/extrapolated_data.xlsx b/data/Resultats/extrapolated_data.xlsx
index d79155b..d290591 100644
Binary files a/data/Resultats/extrapolated_data.xlsx and b/data/Resultats/extrapolated_data.xlsx differ
diff --git a/scripts/__pycache__/footprint.cpython-310.pyc b/scripts/__pycache__/footprint.cpython-310.pyc
index 6328c29..57886f6 100644
Binary files a/scripts/__pycache__/footprint.cpython-310.pyc and b/scripts/__pycache__/footprint.cpython-310.pyc differ
diff --git a/scripts/footprint.py b/scripts/footprint.py
index 867765a..2216e54 100644
--- a/scripts/footprint.py
+++ b/scripts/footprint.py
@@ -1,437 +1,516 @@
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 = {}
#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 read_factor_join_path(self, factor_join_path):
self.factor_join = pd.read_excel(factor_join_path)
+ def set_category_name(self, Language="FR"):
+ if Language == "FR":
+ self.category_name["Electricité"] = "Electricité"
+ self.category_name["Mazout"] = "Mazout"
+ self.category_name["Gaz"] = "Gaz"
+ self.category_name["CAD"] = "CAD"
+ self.category_name["Avion etudiant-e-s"] = "Avion etudiant-e-s"
+ self.category_name["Avion collaborateur-trice-s"] = "Avion collaborateur-trice-s"
+ self.category_name["Train domestique"] = "Train domestique"
+ self.category_name["Train international"] = "Train international"
+ self.category_name["Véhicules de service essence"] = "Véhicules de service essence"
+ self.category_name["Véhicules de service diesel"] = "Véhicules de service diesel"
+ self.category_name["Véhicules de service électrique"] = "Véhicules de service électrique"
+ self.category_name["Véhicules de service hybride"] = "Véhicules de service hybride"
+ self.category_name["Alimentation"] = "Alimentation"
+ self.category_name["Déchets"] = "Déchets"
+ self.category_name["Numérique (fabrication)" ] = "Numérique (fabrication)"
+
+ self.Theme_name["Electricité"] = "Electricité"
+ self.Theme_name["Mazout"] = "Mazout"
+ self.Theme_name["Gaz"] = "Gaz"
+ self.Theme_name["CAD"] = "CAD"
+ self.Theme_name["Voyages professionnels"] = "Voyages professionnels"
+ self.Theme_name["Mobilité pendulaire"] = "Mobilité pendulaire"
+ self.Theme_name["Dechets"] = "Dechets"
+ self.Theme_name["Alimentation"] = "Alimentation"
+ self.Theme_name["Numérique"] = "Numérique (fabrication)"
+ else:
+ self.category_name["Electricité"] = "Electricity"
+ self.category_name["Mazout"] = "Mazout"
+ self.category_name["Gaz"] = "Gaz"
+ self.category_name["CAD"] = "CAD"
+ self.category_name["Avion etudiant-e-s"] = "Avion etudiant-e-s"
+ self.category_name["Avion collaborateur-trice-s"] = "Avion collaborateur-trice-s"
+ self.category_name["Train domestique"] = "Train domestique"
+ self.category_name["Train international"] = "Train international"
+ self.category_name["Véhicules de service essence"] = "Véhicules de service essence"
+ self.category_name["Véhicules de service diesel"] = "Véhicules de service diesel"
+ self.category_name["Véhicules de service électrique"] = "Véhicules de service électrique"
+ self.category_name["Véhicules de service hybride"] = "Véhicules de service hybride"
+ self.category_name["Alimentation"] = "Alimentation"
+ self.category_name["Déchets"] = "Déchets"
+ self.category_name["Numérique (fabrication)" ] = "Numérique (fabrication)"
+
+ self.Theme_name["Electricité"] = "Electricité"
+ self.Theme_name["Mazout"] = "Mazout"
+ self.Theme_name["Gaz"] = "Gaz"
+ self.Theme_name["CAD"] = "CAD"
+ self.Theme_name["Voyages professionnels"] = "Voyages professionnels"
+ self.Theme_name["Mobilité pendulaire"] = "Mobilité pendulaire"
+ self.Theme_name["Dechets"] = "Dechets"
+ self.Theme_name["Alimentation"] = "Alimentation"
+ self.Theme_name["Numérique"] = "Numérique (fabrication)"
+
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.loc[df["Category"]=="Electricite","Category"]="Electricité"
+ df.loc[df["Category"]=="Electricite","Category"] = self.category_name["Electricité"]
df["Theme"] = df["Category"] #We understand that the theme is Energy, however it's more relevant to separate when we visualize it
-
- mask_mazout = df["Category"]=="Mazout"
- mask_electricity = df["Category"]=="Electricité"
- mask_CAD = df["Category"]=="CAD"
- mask_gaz = df["Category"]=="Gaz"
df["Scope"] = 2
df["Unit"] = df["unit"]
df.drop("unit", axis=1, inplace=True)
+
+ mask_mazout = df["Category"]=="Mazout"
+ mask_gaz = df["Category"]=="Gaz"
df.loc[mask_gaz|mask_mazout, "Scope"] = 1
self.data = pd.concat([self.data, df], ignore_index=True)
def run_dechets_emission(self, dechets_path):
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.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_students_plane = "Avion etudiant-e-s"
- self.Category_staff_plane = "Avion staff"
- df_staff["Category"] = self.Category_staff_plane
- df_stud["Category"] = self.Category_students_plane
+ df_staff["Category"] = self.category_name["Avion etudiant-e-s"]
+ df_stud["Category"] = self.category_name["Avion collaborateur-trice-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
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"
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'electricité 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["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
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
self.data = pd.concat([self.data, db], ignore_index=True)
def run_numeric(self):
df = pd.DataFrame(data= {"CO2": [4829,2900], "Year":[2019, 2022]})
df["CO2"] = df["CO2"] * 1000 #Convert to kg
df["Campus"] = "EPFL"
self.numeric_name = "Numérique (fabrication)"
df["Category"] = self.numeric_name
df["Theme"] = "Numérique (fabrication)"
df["Scope"] = 3
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
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
+ df["Campus"] = "EPFL"
+ self.achats_name = "Achats (estimation)"
+ df["Category"] = self.achats_name
+ df["Theme"] = "Achats (estimation)"
+ df["Scope"] = 3
+ 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"]
df["CO2"] = df["CO2"]*1000 #Convert to kg
df["Scope"] = [2,1,1,3,3,3,3]
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"]
self.data = pd.concat([self.data, df], ignore_index=True)
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.Category_staff_plane, self.Category_students_plane, self.numeric_name])
+ Check_join_data_category(self.data, join_factor, ignore_column = [self.category_name["Avion etudiant-e-s"], self.category_name["Avion collaborateur-trice-s"], self.numeric_name])
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
-
+ #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")
#Mask inutile mais permet d'implementer une fonctionnalité future
mask = data_L2["CO2"].isna()
data_L2.loc[mask, "CO2"] = data_L2.loc[mask, "Value"]*data_L2.loc[mask, self.CO2_col]
+ #Complete les données maskées par des données brutes de CO2
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)
-
bilan = data_L2.groupby(["Category", "Year"])["CO2"].sum().unstack().T
self.data_L2 = data_L2
self.bilan = pd.concat([self.bilan,bilan], axis=1, sort=True)
def objectives(self):
- # Objectifs de la confédération d'ici à 2030
- # Objectifs de l'EPFL d'ici à 2024 (energie)
data_objectives = self.data_L3A.copy()
#Set year reference which set the start of interpolation
year_reference = 2022
- #15% d'ici à 2024 par rapport au CO2
- #Mask Theme = "Electricité" or "Mazout" or "CAD" or "Gaz" and year=2019
- df_2024 = objectives_2024(data_objectives, year_reference)
+ #Objectif 2024
+ self.df_objectifs_2024 = objectives_2024(data_objectives, year_reference)
+ #Objectif 2030
+ df = pd.DataFrame()
+ df["Theme"] = data_objectives["Theme"].unique()
+ df["Year"] = 2030
+ df["Campus"] = "EPFL"
#Definition des objectifs 2030:
mask = (data_objectives["Theme"] == "Electricité")|(data_objectives["Theme"] == "Mazout")|(data_objectives["Theme"]=="CAD")|(data_objectives["Theme"] == "Gaz")
Energy_2006 = data_objectives.loc[(data_objectives["Year"]==2006)&(mask)].sum()
Objective_2030_energy = Energy_2006["CO2"]*0.5
- mask_voyages_prof = (data_objectives["Theme"]=="Voyages professionnels")
+ gaz_2030 = data_objectives.loc[(data_objectives["Year"]==2006)&(data_objectives["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"]=="Electricité", "CO2"] = elec_2030.squeeze()
+ df.loc[df["Theme"]=="Electricité", "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 Electricité"
+ df.loc[df["Theme"]=="CAD", "CO2"] = 0
+ df.loc[df["Theme"]=="CAD", "Objectif"] = "Remplacement par Electricité"
+ mask_voyages_prof = (data_objectives["Theme"]=="Voyages professionnels")
trip_2019 = data_objectives.loc[(data_objectives["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_objectives.loc[(data_objectives["Year"]==2019)&(data_objectives["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_objectives.loc[(data_objectives["Year"]==2019)&(data_objectives["Theme"]=="Alimentation")]["CO2"].sum()
Objective_2030_alimentation = Alimentation_2019*0.6
-
- df = pd.DataFrame()
- df["Theme"] = data_objectives["Theme"].unique()
- df["Year"] = 2030
- df["Campus"] = "EPFL"
- gaz_2030 = data_objectives.loc[(data_objectives["Year"]==2006)&(data_objectives["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"]=="Electricité", "CO2"] = elec_2030.squeeze()
- df.loc[df["Theme"]=="Voyages professionnels", "CO2"] = Objective_2030_trip
- df.loc[df["Theme"]=="Mobilité pendulaire", "CO2"] = Objective_2030_pendulaire
df.loc[df["Theme"]=="Alimentation", "CO2"] = Objective_2030_alimentation
- df.loc[df["Theme"]=="Mazout", "CO2"] = 0
- df.loc[df["Theme"]=="CAD", "CO2"] = 0
- df.loc[df["Theme"]=="Gaz", "CO2"] = gaz_2030.squeeze()
- df.loc[df["Theme"]=="Numérique (fabrication)", "CO2"] = 4829e3#data_objectives.loc[(data_objectives["Theme"]=="Numérique")].iloc[-1]["CO2"]
+ 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"]=="Dechets", "CO2"] = data_objectives.loc[(data_objectives["Year"]==2019)&(data_objectives["Theme"]=="Dechets")]["CO2"].sum()
- df["Data description"] = "Objectifs fédéraux 2030"
+
+ df["Data description"] = "Objectifs 2030 (confédération+EPFL)"
df["Category"] = df["Theme"]
+ self.data_objectives = df
+ def interpolation_objectifs(self):
Elect_emission_factor = self.data_L2[self.data_L2["Theme"]=="Electricité"][self.CO2_col].mean()
+ df = self.data_objectives.copy()
df.loc[df["Category"]=="Electricité", "Value"] = df.loc[df["Category"]=="Electricité"]["CO2"].iloc[0]/Elect_emission_factor
+ data_objectives = self.data_objectives
years = np.arange(data_objectives["Year"].max(),2030)
for Theme in df["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)"
+
mask = (data_objectives["Year"]==data_objectives["Year"].max())&(data_objectives["Theme"]==Theme)
- df_temp["Data description"] = "Objectifs fédéraux 2030"
- if Theme=="Voyages professionnels":
- print(1)
+
if mask.sum():
df_temp["CO2"] = np.interp(year, [data_objectives["Year"].max(), 2030], [data_objectives.loc[mask, "CO2"].sum(), df.loc[(df["Theme"]==Theme)&(df["Year"]==2030), "CO2"].sum()])
- if year == data_objectives["Year"].max():
- pass #The data should not be added twice
- else:
+
+ if year != data_objectives["Year"].max():
df = df.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_objectives["Year"].max(), 2030], [data_objectives.loc[(data_objectives["Year"]==data_objectives["Year"].max()-1)&(data_objectives["Theme"]==Theme), "CO2"].iloc[0], df.loc[(df["Theme"]==Theme)&(df["Year"]==2030), "CO2"].sum()])
df = df.append(df_temp, ignore_index=True)
-
- data_objectives = pd.concat([data_objectives, df, df_2024], ignore_index=True)
+ data_objectives = pd.concat([data_objectives, df, self.df_objectifs_2024], ignore_index=True)
self.data_L3B = data_objectives.copy()
-
-
def extrapolate(self):
data = self.data_L2.copy()
years = data["Year"].unique()
years.sort()
years = years[:-1]
EPT_year = self.EPT_year
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():
+ 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 == "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]
-
+ serie["CO2"] = (pendular_EPT_a*pendular_CO2_n/pendular_EPT_n)[0]
else:
- if Theme=="Voyages professionnels":
- print(1)
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()
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 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/main.py b/scripts/main.py
index b1d0b1b..c3115a1 100644
--- a/scripts/main.py
+++ b/scripts/main.py
@@ -1,34 +1,36 @@
import yaml
from footprint import *
import plotly.express as px
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"])
fp.read_emission_factor(directories["emission_factor"])
-
+fp.set_category_name()
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["strategy_bilan"])
fp.assign_factor(directories["factor_join"])
fp.calculate_emission()
fp.extrapolate()
fp.objectives()
+fp.interpolation_objectifs()
#fp.simulations()
fp.data_L2.to_excel("data/Resultats/All_data.xlsx")
fp.data_L3A.to_excel("data/Resultats/extrapolated_data.xlsx")
fp.data_L3B.to_excel("data/Resultats/Objectives_data.xlsx")
bilan = fp.data_L3A.groupby(["Theme", "Year", "Category"]).sum().reset_index().drop(["Scope"], axis=1)
fig = px.bar(bilan, x="Year", y="CO2", color="Theme", barmode="stack")
fig.show()