This repository contains a Python program to estimate the environmental impact of the agricultural steps of a product of the Open Food Facts database.
This tool gives an estimation of the environmental impact of the agricultural steps of a product by browsing its possible recipes according to its ingredient list and nutritional composition.
- Giving the exact environmental impact of a product
- Taking into account the origin of the ingredients
- Taking into account the packaging of the product
- Making a complete Life Cycle Assessment of the product
- Reverse engineering the recipe of the product
This program uses the PySCIPOpt package and the SCIP Optimization Suite V7.0. Installation instructions can be found here (PySCIPOpt) and here (SCIP).
See requirements.txt for the other required Python packages.
Impact estimation of a product can be done using impacts_estimation.estimate_impacts().
from impacts_estimation import estimate_impacts
from openfoodfacts import get_product
product = get_product(barcode='3175681790285')['product']
impact_categories = ['EF single score',
'Climate change']
impact_estimation_result = estimate_impacts(product=product,
impact_names=impact_categories)
for impact_category in impact_categories:
print(f"{impact_category}: "
f"{impact_estimation_result['impacts_geom_means'][impact_category]:.4} "
f"{impact_estimation_result['impacts_units'][impact_category]}")
# EF single score: 0.03832 mPt
# Climate change: 0.3819 kg CO2 eqIf safe_mode is set to True, it will change the parameters in case of error to ensure getting a result.
from impacts_estimation import estimate_impacts, RecipeCreationError
from openfoodfacts import get_product
product = get_product(barcode='3564707104920')['product']
try:
impact_estimation_result = estimate_impacts(product=product,
impact_names='EF single score',
safe_mode=False)
except RecipeCreationError:
print("No possible recipe with the given input data.")
# No possible recipe with the given input data.
impact_estimation_result = estimate_impacts(product=product,
impact_names='EF single score',
safe_mode=True)
print(impact_estimation_result['impacts_geom_means']['EF single score'])
# 0.16486248336651319
print(impact_estimation_result['warnings'])
# ['from impacts_estimation import estimate_impacts, RecipeCreationError
from openfoodfacts import get_product
product = get_product(barcode='3564707104920')['product']
try:
impact_estimation_result = estimate_impacts(product=product,
impact_names='EF single score',
safe_mode=False)
except RecipeCreationError:
print("No possible recipe with the given input data.")
# No possible recipe with the given input data.
impact_estimation_result = estimate_impacts(product=product,
impact_names='EF single score',
safe_mode=True)
print(impact_estimation_result['impacts_geom_means']['EF single score'])
# 0.050604882643004834
print(impact_estimation_result['warnings'])
# ['Parameter use_defined_prct has been set to False in order to get a result.']']The reporting module can be used to create HTML impact estimation reports.
from reporting import ProductImpactReport
reporter = ProductImpactReport(barcode='3292590830953')
reporter.to_html()The function impacts_estimation.estimate_impacts() accepts the following parameters:
| Parameter | Type | Default | Description |
|---|---|---|---|
| product | dict | Open Food Facts product to analyze | |
| impact_names | string or iterable | Names of the impacts to compute in French or in English. They must correspond to the impact categories used by Agribalyse. See list of available impact categories. | |
| quantity | float | 100 | Quantity of product in grams for which the impact must be calculated. If None, will use the 'product_quantity' attribute of the product. If 'product_quantity' is undefined, will use 100g by default. |
| ignore_unknown_ingredients | bool | True | Should ingredients absent of OFF taxonomy and without defined percentage be considered as parsing errors and ignored? |
| min_run_nb | int | 30 | Minimum number of run for the Monte-Carlo loop |
| max_run_nb | int | 1000 | Maximum number of run for the Monte-Carlo loop |
| forced_run_nb | int | None | Used to bypass natural Monte-Carlo stopping criteria and force the number of runs |
| confidence_interval_width | float | 0.05 | Width of the confidence interval that will determine the convergence detection. See documentation for more information. |
| confidence_level | float | 0.95 | Confidence level of the confidence interval. |
| use_nutritional_info | bool | True | Should nutritional information be used to estimate recipe? |
| maximum_evaporation | float | 0.8 | Upper bound of the evaporation coefficient [0-1[. I.e. maximum proportion of ingredients water that can evaporate. |
| total_mass_used | float | None | Total mass of ingredient used in grams, if known. |
| min_prct_dist_size | int | 30 | Minimum size of the reference ingredients percentage distribution that will be used to pick a proportion for an ingredient. If the distribution (adjusted to the possible value interval) has less data, uniform distribution will be used instead. See documentation for more information. |
| dual_gap_type | str | 'absolute' | 'absolute' or 'relative'. Determines the precision type of the variable optimization by the solver. |
| dual_gap_limit | float | 0.001 | Determines the precision of the variable optimization by the solver. Relative or absolute according to dual_gap_type. |
| solver_time_limit | float | 60 | Maximum time for the solver optimization (in seconds). Set to None or 0 to set no limit. |
| time_limit_dual_gap_limit | float | 0.01 | Accepted precision of the solver in case of time limit hit. Relative or absolute according to dual_gap_type |
| use_ingredients_impact_uncertainty | bool | True | Should ingredients impacts uncertainty data be used? |
| confidence_weighting | bool | True | Should the recipes be weighted by their confidence score (deviation of the recipes nutritional composition to the reference product's nutritional composition). See documentation for more information. |
| quantiles_points | iterable | (0.05, 0.25, 0.5, 0.75, 0.95) | List of impacts quantiles cutting points to return in the result. |
| distributions_as_result | bool | False | Should the recipes, the distributions of their impacts, the mean confidence interval and the confidence score be added to the result? |
| confidence_score_weighting_factor | float | 10 | Weighting factor used for the confidence score calculation. It corresponds to the weight of the nutritional distance against the absolute difference between the total mass and 100g/100g. See documentation for more information. |
| safe_mode | bool | True | If set to True, the constraints will be progressively relaxed in order to get a result. |
The result of impacts_estimation.estimate_impacts() is a dictionary containing the calculated impacts as well as
several additional data.
| Key | Description |
|---|---|
| impact_geom_means | Geometric means of the impacts of all sampled recipes in each impact category. The main result. |
| impact_geom_stdevs | Geometric standard deviations of the impacts of all sampled recipes in each impact category. |
| impacts_quantiles | Quantiles of the impacts of all sampled recipes in each impact category. Cutting points are defined by quantiles_points. |
| impacts_relative_interquartile | Relative interquartile of the impacts of all sampled recipes in each impact category. Useful to estimate the spread of the possible impact. |
| ingredients_impact_share | Average share of the impact carried by each ingredient for each impact category. |
| impacts_units | Units in which the impacts are expressed. |
| product_quantity | Quantity of product in grams for which the impact have been calculated. |
| warnings | List of possible text warnings. |
| ignored_unknown_ingredients | List of ingredients that have been ignored if ignore_unknown_ingredients have been set to True. |
| uncharacterized_ingredients | List of ingredients with no data about nutrition and/or environmental impact. |
| uncharacterized_ingredients_ratio | Ratio ingredients with no data about nutrition and/or environmental impact. |
| uncharacterized_ingredients_mass_proportion | Average mass proportion of ingredients with no data about nutrition and/or environmental impact. |
| number_of_runs | Number of runs before impact convergence. |
| number_of_ingredients | Number of ingredients of the product. |
| calculation_time | Impact calculation time. |
| impact_distributions | Distributions of the impacts of all sampled recipes in each impact category. |
| mean_confidence_interval_distribution | Distributions of the confidence interval of the mean of the impacts of all sampled recipes in each impact category. |
| confidence_score_distribution | Distributions of the confidence score of all sampled recipes. |
The ingredients environmental impact data come from Agribalyse. The impact categories are:
| French name | English name |
|---|---|
| Score unique EF | EF single score |
| Changement climatique | Climate change |
| Appauvrissement de la couche d'ozone | Ozone depletion |
| Rayonnements ionisants | Ionizing radiations |
| Formation photochimique d'ozone | Photochemical ozone formation |
| Particules | Particulate matter |
| Acidification terrestre et eaux douces | Terrestrial and freshwater acidification |
| Eutrophisation terreste | Terrestrial eutrophication |
| Eutrophisation eaux douces | Freshwater eutrophication |
| Eutrophisation marine | Marine eutrophication |
| Utilisation du sol | Land use |
| Écotoxicité pour écosystèmes aquatiques d'eau douce | Freshwater ecotoxicity |
| Épuisement des ressources eau | Water depletion |
| Épuisement des ressources énergétiques | Resource use, energy carriers |
| Épuisement des ressources minéraux | Resource use, minerals and metals |
The algorithm used by this program is based on a Monte-Carlo approach to estimate the impact of a product. Its principle is to pick random possible recipes of the product and compute their impact until the geometric mean of the impacts of all sampled recipes stabilizes within a given confidence interval. The sampling of possible recipes is made as accurate as possible by using a non-linear programming solver (SCIP), and nutritional information of the product.
off-product-environmental-impact.readthedocs.io
See the analysis directory for an analysis of the precision of this tool.
For contribution guidelines, please see CONTRIBUTING.md.
This project can be tested using pytest on the automated tests contained in the /tests/ directory.
The results given by this tool are estimates of the environmental impact of a food product. These estimates are subject to potential bias and uncertainties due to the stochastic nature of the algorithm and the uncertainty inherent to the background data. Thus, the accuracy of the result cannot be guaranteed.
Hardware compatibility issue have been observed for this software on a 2012 MacBook Pro (2.5 GHz Dual-Core Intel Core i5) running macOS Catalina.
ADEME - Agribalyse, 2020
Anses - Table de composition nutritionnelle des aliments Ciqual, 2020
Santé Canada - Fichier canadien sur les éléments nutritifs , 2015
