Emmision COf a cat and a dog with python

Hi,

I've found a small article that talks about pet emissions, so I've decided to display the CO2 emissions if they don't exist.

Code :
https://github.com/victordalet/Kaggle_analysis/tree/feat/dog_co2

Sources:

• https://www.lekaba.fr/article/l-empreinte-carbone-des-chiens-et-des-chats-un-amour-qui-pese-lourd-sur-le-climat

• https://www.umweltbundesamt.de/en/image/global-f-gas-emissions-up-to-2050-total

• https://www.rover.com/fr/blog/combien-y-a-t-il-de-chats-dans-le-monde/

---

I - Get data

First, I obtain data estimating the world's CO2 consumption, the average emission of a dog and a cat, and the number of these pets.

import plotly.express as px


class Main:
    def __init__(self):
        self.estimation = {
            "2005": 750,
            "2010": 900,
            "2020": 1300,
            "2030": 1800,
            "2040": 2700,
            "2050": 4000,
        }

        self.estimation_no_cat = {
            "2005": 750,
            "2010": 900,
            "2020": 1300,
            "2030": 1800,
            "2040": 2700,
            "2050": 4000,
        }

        self.estimation_no_dog = {
            "2005": 750,
            "2010": 900,
            "2020": 1300,
            "2030": 1800,
            "2040": 2700,
            "2050": 4000,
        }

        self.estimation_no_cat_and_dog = {
            "2005": 750,
            "2010": 900,
            "2020": 1300,
            "2030": 1800,
            "2040": 2700,
            "2050": 4000,
        }

        self.cat_emission = 240
        self.dog_emission = 358
        self.nb_cats = 600000000
        self.nb_dogs = 900000000

II - Transformation

The total emission is in millions of tons, so I've created a method for converting the animal data, which are in kg.

    @staticmethod
    def transform_to_million_of_tonnes(value):
        return value / (1000000 * 1000)

III - Calcule

To modify the estimate without cat or dog, go through the first estimate and replace the values of the other dictionaries with the values found in the first step.

    def calculate(self):
        for year, value in self.estimation.items():
            self.estimation_no_cat[year] = value - self.transform_to_million_of_tonnes(
                self.cat_emission * self.nb_cats
            )
            self.estimation_no_dog[year] = value - self.transform_to_million_of_tonnes(
                self.dog_emission * self.nb_dogs
            )
            self.estimation_no_cat_and_dog[year] = (
                value
                - self.transform_to_million_of_tonnes(self.cat_emission * self.nb_cats)
                - self.transform_to_million_of_tonnes(self.dog_emission * self.nb_dogs)
            )

IV - Display result

To display a graph with all the data, I use the plotly library.

Code for pip installation :

pip install plotly

Code to display the three estimates :

    def display(self):
        fig = px.line(
            x=list(self.estimation.keys()),
            y=[
                list(self.estimation.values()),
                list(self.estimation_no_cat.values()),
                list(self.estimation_no_dog.values()),
                list(self.estimation_no_cat_and_dog.values()),
            ],
            labels={
                "x": "Year",
                "y": "CO2 Emission (in million of tonnes)",
                "color": "Legend",
            },
            title="CO2 Emission with and without cats and dogs",
            color_discrete_map={
                "CO2 Emission": "blue",
                "CO2 Emission without cats": "green",
                "CO2 Emission without dogs": "red",
                "CO2 Emission without cats and dogs": "orange",
            },
        )
        fig.show()

So now we have the chart with our results.

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