PEFAP
PEFAP : Estimating the environmental footprint of food products from packaging data - Published in JOSS (2022)
https://framagit.org/GustaveCoste/off-product-environmental-impact
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Environmental impact estimation of Open Food Facts products
Basic Info
- Host: framagit.org
- Owner: GustaveCoste
- License: mit
- Default Branch: master
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Created about 5 years ago
https://framagit.org/GustaveCoste/off-product-environmental-impact/blob/master/
# PEFAP: Product Environmental Footprint According to the Packaging data
[](https://doi.org/10.21105/joss.03329)
This repository contains a Python program to estimate the environmental impact of the agricultural steps of a product of the Open Food Facts database or another data source such as an ordered list of ingredients and a nutritional table.
## Disclaimer
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.
## What this tool does
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.
## What this tool does **NOT** do
* 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
## Installation
This program uses the [PySCIPOpt](https://github.com/SCIP-Interfaces/PySCIPOpt) package and
the [SCIP Optimization Suite](https://www.scipopt.org/) V7.0. Installation instructions can be
found [here (PySCIPOpt)](https://github.com/SCIP-Interfaces/PySCIPOpt/blob/master/INSTALL.md)
and [here (SCIP)](https://www.scipopt.org/doc/html/INSTALL.php).
See [requirements.txt](requirements.txt) for the other required Python packages.
## Need of PEFAP
Product environmental footprint is an eternal compromise: The assessment has to be specific, to best represent production and processing choices in the value chain. Unfortunately, this need for specific data quickly becomes an obstacle and makes the work too big to do on a large scale. In contrast, generic data offer a less expensive result, but these default values only allow for inter-category comparisons, so differentiating products within a category is impossible. This balance between specificity and simplicity needs to be overcome.
The proportions of ingredients in food products are a determining factor for the environmental footprint and reveal intra-category discrepancies. The answer would be to adapt the results to industrial recipes, directly by the food industry. However, access to information and the costs of this approach make this difficult. A cost-effective solution is therefore necessary, for estimating the impact from free-access data, which is available on the package.
Based on the partial list of ingredients (an ordered list, but with often unknown proportions) and nutritional data available on packaging, the algorithm explores the range of possible recipes through a Monte Carlo approach. In each iteration, the masses of ingredients are randomly chosen according to the possible proportions of ingredients and ensuring the best possible preservation of nutrient contents (the nutrients of the product being considered as the sum of the nutrients of all its ingredients). PEFAP retrieves for each ingredient, the environmental impacts and the nutrient data. It finds the most likely footprint by the convergence of the result over Monte Carlo runs.
This offers a compromise between a "generic" assessment, which is not very accurate but already available, and a "specific" one, which is effective but expensive and impossible to perform by a third party. To provide "semi-specific" product footprint, PEFAP needs:
- The list of ingredients and the nutritional values from the product packaging. This can be provided as Python objects or by indicating the barcode of a food product, the open Food Facts database is then used to obtain this data. This is the only information that the user needs to fill in specifically for a given product.
- Environmental footprint and nutrient composition databases for ingredients. The [_Agribalyse_](https://ecolab.ademe.fr/agribalyse) and [_Ciqual_](https://ciqual.anses.fr/) databases are used because of their scope and availability.
## Usage
Impact estimation of a product can be done using `impacts_estimation.estimate_impacts()`.
```python
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 eq
```
If `safe_mode` is set to `True`, it will change the parameters in case of error to ensure getting a result.
```python
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.
```python
from reporting import ProductImpactReport
reporter = ProductImpactReport(barcode='3292590830953')
reporter.to_html()
```
### Parameters
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](#available-environmental-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 runs for the Monte-Carlo loop |
| max_run_nb | int | 1000 | Maximum number of runs 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](https://off-product-environmental-impact.readthedocs.io/en/latest/estimating-product-impact.html) 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 ingredients before cooking (including water) 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](https://off-product-environmental-impact.readthedocs.io/en/latest/getting-possible-recipe-from-product-information.html#choosing-the-ingredient-proportion) 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](https://off-product-environmental-impact.readthedocs.io/en/latest/calculating-recipe-confidence-score.html) 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](https://off-product-environmental-impact.readthedocs.io/en/latest/calculating-recipe-confidence-score.html#weighting-factor) for more information. |
| safe_mode | bool | True | If set to True, the constraints will be progressively relaxed in order to get a result. | |
### 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. |
### Available environmental impact categories
The ingredients environmental impact data come from [_Agribalyse_](https://ecolab.ademe.fr/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 |
| cotoxicit pour cosystmes aquatiques d'eau douce | Freshwater ecotoxicity |
| puisement des ressources eau | Water depletion |
| puisement des ressources nergtiques | Resource use, energy carriers |
| puisement des ressources minraux | Resource use, minerals and metals |
### Algorithm
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](http://scip.zib.de/)), and nutritional information of the product.
## Documentation
[off-product-environmental-impact.readthedocs.io](https://off-product-environmental-impact.readthedocs.io/en/latest/)
## Contributing
For contribution guidelines, please see [CONTRIBUTING.md](CONTRIBUTING.md).
## Testing
This project can be tested using [pytest](https://docs.pytest.org) on the automated tests contained in the `/tests/` directory.
## Hardware compatibility issues
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.
## References
[_ADEME - Agribalyse_](https://ecolab.ademe.fr/agribalyse), 2020
[_Anses - Table de composition nutritionnelle des aliments Ciqual_](https://ciqual.anses.fr/), 2020
[_Sant Canada - Fichier canadien sur les lments
nutritifs_](https://www.canada.ca/fr/sante-canada/services/aliments-nutrition/saine-alimentation/donnees-nutritionnelles/fichier-canadien-elements-nutritifs-propos-nous.html)
, 2015
[_The SCIP Optimization Suite_](https://scipopt.org/)
JOSS Publication
PEFAP : Estimating the environmental footprint of food products from packaging data
Published
May 25, 2022
Volume 7, Issue 73, Page 3329
Authors
Gustave Coste
INRAE, Univ Montpellier, LBE, 102 avenue des Etangs, 11100, Narbonne, France, ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France, ELSA, Research group for environmental life cycle sustainability assessment, Montpellier, France
INRAE, Univ Montpellier, LBE, 102 avenue des Etangs, 11100, Narbonne, France, ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France, ELSA, Research group for environmental life cycle sustainability assessment, Montpellier, France
Arnaud Hélias
ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France, ELSA, Research group for environmental life cycle sustainability assessment, Montpellier, France
ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France, ELSA, Research group for environmental life cycle sustainability assessment, Montpellier, France
Editor
Vissarion Fisikopoulos
Tags
Open Food Facts Food Environment Life Cycle Assessment EcolabellingCommitters
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