GintautasLuksas/waste_to_wardrobe_index

Waste-to-Wardrobe Index

Waste-to-Wardrobe Index is a Streamlit portfolio app that estimates how much CO2e could be avoided if a selected share of textile waste were redirected into second-hand resale.

The project was designed as a data science portfolio piece for circular fashion. It combines public textile waste data, editable population assumptions, and a transparent avoided-emissions factor inspired by Vinted's public impact reporting.

What the App Shows

• —Textile waste per person by country
• —A user-selected resale or reuse scenario
• —Estimated textile volume redirected from waste to reuse
• —Estimated avoided CO2e emissions
• —A country ranking, choropleth map, and downloadable scenario table
• —Editable assumptions so reviewers can test sensitivity instead of treating the model as fixed truth

Data Sources

1. European Textile Waste

Source: European Environment Agency (EEA)

Local file:

data/raw/EEA_europe_waste_per_capita_2020.xlsx

Column used:

Total value

This column is treated as textile waste per capita in kilograms per person. The app renames it to:

Textile Waste (kg/person)

Rows such as EU-27, EU27, EU28, Europe, OECD, and other aggregate regions are removed so the analysis only compares individual countries.

EEA reuse note: unless otherwise indicated, EEA website content can be reused for commercial or non-commercial purposes free of charge when the source is acknowledged. Copyright holder: European Commission, European Environment Agency.

2. United States Textile Waste

Source: US Environmental Protection Agency (EPA), textiles in municipal solid waste.

The app includes the United States as a manual row:

United States = 40.22 kg/person

This lets the app compare European EEA data with a large non-European benchmark market.

3. Population Assumptions

The app includes default population estimates in millions for each country. These are used to scale per-person textile waste into national opportunity size.

Population values are editable in the Streamlit sidebar. That is intentional: this is a scenario model, so population assumptions should be visible and easy to change.

4. Vinted Impact Reporting

Source: Vinted and Ipsos, Environmental Impact of Second-Hand Fashion in Europe / Vinted Impact Report 2023.

Vinted's public reporting is used as methodological inspiration for the avoided-emissions-per-resale assumption. The app default is:

1.25 kg CO2e avoided per reused item

This value is editable in the sidebar. The app does not claim to reproduce Vinted's internal model or measured company impact. It uses a simplified, transparent factor to create a portfolio-ready "what if" scenario.

5. ISO Country Codes

The app uses an internal ISO3 mapping dictionary so the Plotly choropleth can render countries consistently without needing to download a country-code file at runtime.

Data Cleaning and Conversion

The app performs these transformations:

1. 1.Loads the EEA Excel workbook from data/raw.
2. 2.Renames the first column to Country.
3. 3.Renames Total value to Textile Waste (kg/person).
4. 4.Removes aggregate geography rows.
5. 5.Normalizes a few country names, such as Türkiye to Turkey.
6. 6.Adds a manual United States row.
7. 7.Adds ISO3 country codes for mapping.
8. 8.Combines selected countries with editable population assumptions.

Methodology

The app calculates a scenario, not a measured emissions inventory.

Core formula:

reused_kg_per_person = textile_waste_kg_per_person * reuse_share
reused_item_equivalents = reused_kg_per_person * population / 1 kg_per_item
co2e_avoided_kt = reused_item_equivalents * avoided_co2e_kg_per_item / 1,000,000

Default assumptions:

1 reused item equivalent = 1 kg of textile waste redirected
1 reused item equivalent = 1.25 kg CO2e avoided

Example interpretation:

If a country has 20 kg/person of textile waste and the user chooses a 25% reuse scenario, the model treats 5 kg/person as redirected into second-hand reuse. That amount is converted into reused item equivalents and multiplied by the avoided CO2e factor.

App Features

• —Country multiselect
• —Scenario slider for textile waste redirected to resale
• —Editable avoided CO2e factor
• —Editable population values
• —Total avoided-emissions metric
• —Total reused-textile metric
• —Top-opportunity country metric
• —Median textile-waste metric
• —Plotly horizontal ranking chart
• —Plotly choropleth map
• —Downloadable CSV output
• —Method tab explaining sources and formulas

How to Run Locally

Clone the repository:

git clone https://github.com/GintautasLuksas/waste_to_wardrobe_index.git
cd waste_to_wardrobe_index

Create and activate a virtual environment:

python -m venv .venv
.venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Run the app:

streamlit run streamlit_app.py

Requirements

Main libraries:

• —Streamlit
• —pandas
• —numpy
• —Plotly
• —openpyxl

Limitations

• —The model is intentionally simplified for a portfolio use case.
• —The EEA and EPA data are not from the same reporting system or year.
• —The avoided CO2e factor is a transparent assumption, not a universal emissions constant.
• —The model assumes 1 kg of redirected textile waste equals 1 reused item equivalent.
• —Results should be read as directional opportunity sizing, not regulatory-grade lifecycle assessment.

Future Improvements

• —Add year-by-year textile waste trends where public data is available.
• —Add uncertainty ranges for the avoided CO2e factor.
• —Separate apparel, footwear, and household textiles if source data allows.
• —Add Vinted market-presence overlays by country.
• —Add a second scenario comparing resale, recycling, landfill, and incineration pathways.

Acknowledgements

• —European Environment Agency
• —US Environmental Protection Agency
• —Vinted and Ipsos public impact reporting
• —Streamlit
• —Plotly
• —pandas