Since the pandemic, the amount of carbon dioxide released by the United States has decreased. One factor in the emission of carbon dioxide is the release of greenhouse gases. Between 2019 to 2020, the admission of greenhouse gasses decreased by 9%. While greenhouse gases decrease over the years, they are still the leading producer of gas emissions globally. Even with decreasing emmision of greehouse gases, the United States is the leading country for carbon dioxide with almost three times the global average. As the world begins to start operating as normal after the pandemic, it is important to keep the emissions levels of greenhouse gasses low to help decrease the change of effecting climate change. While there is no ways to decrease the levels of greenhouse gas admissions to zero, there are ways to reduce the number of greenhouse gasses produced. One way to help reduce greenhouse gas emissions is determining what food production process has the largest effect on the number of greenhouse gases released. After collecting this data, the country can alter the product of food groups and the costs associated with the foods. Changes within the production of greenhouse gases is imporant to reduce the effects of climate change in the future.
set 1: https://www.kaggle.com/datasets/selfvivek/environment-impact-of-food-production?resource=download
set 2: https://www.kaggle.com/datasets/abdulwahabkabani/healthy-diet-breakdown
This data can be used to determine which food groups have the largest negative effect on the enviroment and could be a large effect in CO2 emissions. This data set can also be used to compare again other data connected to most consumed foods within the United States. The Healthy Diet data set show what a healthy diet consumption looks like and can be applied to determine the effects of food consumptions to the envioment
import pandas as pnd
df_food_production = pnd.read_csv('Food_Production.csv')
df_food_production
| Food product | Land use change | Animal Feed | Farm | Processing | Transport | Packging | Retail | Total_emissions | Eutrophying emissions per 1000kcal (gPO₄eq per 1000kcal) | ... | Freshwater withdrawals per 100g protein (liters per 100g protein) | Freshwater withdrawals per kilogram (liters per kilogram) | Greenhouse gas emissions per 1000kcal (kgCO₂eq per 1000kcal) | Greenhouse gas emissions per 100g protein (kgCO₂eq per 100g protein) | Land use per 1000kcal (m² per 1000kcal) | Land use per kilogram (m² per kilogram) | Land use per 100g protein (m² per 100g protein) | Scarcity-weighted water use per kilogram (liters per kilogram) | Scarcity-weighted water use per 100g protein (liters per 100g protein) | Scarcity-weighted water use per 1000kcal (liters per 1000 kilocalories) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Wheat & Rye (Bread) | 0.1 | 0.0 | 0.8 | 0.2 | 0.1 | 0.1 | 0.1 | 1.4 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | Maize (Meal) | 0.3 | 0.0 | 0.5 | 0.1 | 0.1 | 0.1 | 0.0 | 1.1 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | Barley (Beer) | 0.0 | 0.0 | 0.2 | 0.1 | 0.0 | 0.5 | 0.3 | 1.1 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | Oatmeal | 0.0 | 0.0 | 1.4 | 0.0 | 0.1 | 0.1 | 0.0 | 1.6 | 4.281357 | ... | 371.076923 | 482.4 | 0.945482 | 1.907692 | 2.897446 | 7.6 | 5.846154 | 18786.2 | 14450.92308 | 7162.104461 |
| 4 | Rice | 0.0 | 0.0 | 3.6 | 0.1 | 0.1 | 0.1 | 0.1 | 4.0 | 9.514379 | ... | 3166.760563 | 2248.4 | 1.207271 | 6.267606 | 0.759631 | 2.8 | 3.943662 | 49576.3 | 69825.77465 | 13449.891480 |
5 rows × 23 columns
df_daily_food_servings = pnd.read_csv('recommended_food_serving_df.csv')
df_daily_food_servings
| Unnamed: 0 | food_group | servings_per_day | servings_per_week | serving_size | original_units | serving_example | total_daily_consumption(original_units) | units_to_grams | total_daily_consumption(grams) | total_yearly_consumption(kg) | servings_per_year | serving_weight (grams) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Vegetables | 5.0 | NaN | 0.5 | cups | 1/2 cup cut-up vegetables | 2.500000 | 120 | 300.0 | 109.500 | 1825.0 | 60.000000 |
| 1 | 1 | Fruits | 4.0 | NaN | 0.5 | cups | 1/2 cup cut-up fruit | 2.000000 | 120 | 240.0 | 87.600 | 1460.0 | 60.000000 |
| 2 | 2 | Grains | 6.0 | NaN | 0.5 | cups | 1/2 cup cooked rice, pasta, or cereal | 3.000000 | 240 | 720.0 | 262.800 | 2190.0 | 120.000000 |
| 3 | 3 | Dairy | 3.0 | NaN | 1.0 | cups | 1 cup milk or yogurt | 3.000000 | 240 | 720.0 | 262.800 | 1095.0 | 240.000000 |
| 4 | 4 | Poultry, meat and eggs | NaN | 9.0 | 3.0 | oz | 3 oz cooked meat or poultry | 3.857143 | 28 | 108.0 | 39.420 | 468.0 | 84.230769 |
| 5 | 5 | Fish and other seafood | NaN | 3.0 | 3.0 | oz | 3 oz cooked fish or seafood | 1.285714 | 28 | 36.0 | 13.140 | 156.0 | 84.230769 |
| 6 | 6 | Nuts, seeds, beans and legumes | NaN | 5.0 | 0.5 | oz | 1/2 cup cooked beans or peas | 0.357143 | 28 | 10.0 | 3.650 | 260.0 | 14.038462 |
| 7 | 7 | Fats and oils | 3.0 | NaN | 1.0 | Tbsp | 1 Tbsp vegetable oil (canola, corn, olive, soy... | 3.000000 | 15 | 45.0 | 16.425 | 1095.0 | 15.000000 |
We will use Regression to determine which food group has the largest negative impact on the environment because of the amount of CO2 produced from producing and selling the product and the amount of each food consumed over the year. Doing this will allow us to determine if there should be an adjustment in how much of a good group is available to citizens.