EcoWidgy - Consumer Disclosure Contracts

EcoWidgy is a browser extension that lets consumers understand the environmental and social impact of the purchases they are going to make.

Team Name: EcoWidgy

Participants: Eleftherios Avramidis, Görkem Cetinkaya, Alex Han, Dmonik Jung, Joshua Overbye, Marc Shakory (Mentors: Sam Tuke, Oliver Bley)

Node: Berlin, Germany (ESMT Berlin - NetImpact)

Discord channel:

Contributing Prompt:

Pitch Presentation

Contribution summary

Ecowidgy is an app that aims at showing the environmental effect of specific products to the consumers. It has been originally conceived to operate as a browser plugin that would enable a widget on online shops. The concept can be extended to other interfaces such as a Q-code scanner or a search engine. The idea was developed by a Berlin-based team during the first weekend of the 2019 Collabathon for the Open Climate Platform. The goal of the app is to inform the consumers about the environmental and possibly also the social impact of the products they are buying and therefore to form consumer awareness. Additionally, the increased consumer awareness may be used to press companies to increase the transparency of the production, including aspects such as their life-cycle assessment, their supply chains and their material flows.

The contribution includes:


EcoWidgy is a browser extension that lets consumers understand the environmental and social impact of the purchases they are going to make.


It is a fact that consumers don't know about the social and environmental impact of their daily consumption. Nevertheless, a huge amount of Greenhouse gas is emitted in order to cover their shopping needs. A big part of the shopping activity takes place through e-commerce, leading to predictions that the e-commerce sales will reach 3 Trillion USD by 2022. The top 5 categories of e-commerce are clothing and apparel, health and beauty, books, office supplies and household goods. Therefore, the online shopping activities of the consumers seem to be a major point, where action for awareness can have a major impact. An obvious advantage is that efforts for awareness can be easily implemented through software tool.

Background: Identifying consumer informational needs

The importance of considering the information needs of the consumers, as a means of forming awareness, has been highlighted in several existing theoretical works. A short review of existing articles is given below:

  • Must be wary about how conscious consumerism can lead to complacency - some critics warn that it’s more impactful to vote for politicians than to vote “with your dollar”

    • Some say “shopping to sustainability” is fundamentally flawed - need to reduce consumption on the whole

  • Are there still benefits to conscious consumerism?

    • Material benefit may be inconclusive: conscious consumers still tend to be well-educated, meaning they have good incomes and buy more of the non-environmentally friendly stuff (plane tickets, cars, etc.)

    • Ideological benefit: can potentially help people think about resources (but the real change is with govts and corporations)

Harvard Business Review: The Elusive Green Consumer

  • How to narrow the “intention-action” gap - people who want to be environmentally-friendly don’t follow through with their actions

    • Use social influence:

      • “Telling online shoppers that other people were buying eco-friendly products led to a 65% increase in making at least one sustainable purchase”

      • One approach is to use health competition between social groups (not sure how this can be implemented for this product)

    • Shape good habits

      • Feedback on performance can be one way to drive habits

    • Leverage the domino effect

    • Decide whether to talk to the heart or the brain

      • Emotional appeal

      • Rational appeal (Unilever’s palm oil campaign)

        • Self efficacy: one key to marketing sustainable products is by communicating what effect its use will have on the environment. Communicate clear outcomes: “for every upcycled bracelet bought from this company, one pound of trash will be removed from the ocean”

        • Make future consequences salient, frame info in dollars, scale up energy costs ten-fold

    • Favor experiences over ownership

  • Statistics:

    • 68% of millennials bought a product with a social or environmental benefit in the past 12 months.

    • 87% of consumers will have a more positive image of a company that supports social or environmental issues.

    • 88% will be more loyal to a company that supports social or environmental issues.

    • 87% would buy a product with a social and environmental benefit if given the opportunity.

    • 92% will be more likely to trust a company that supports social or environmental issues.

  • Measurable outcomes:

    • Use of recyclable packaging

    • Use of wind or solar power

    • Recyclable vs disposable options

    • Eco-friendly educational materials

    • Waste reduction policies

    • Energy conservation policies

This study presents statistics on what parts of the world, what generations, etc. care about more environmentally-conscious companies

Concept and User Experience

Intended functionality

Here we describe the intended functionality of the software. An extension (or add-on) can be installed by the user on their browser. When the user is browsing the page of a product in an online shop, this extension can produce a widget (or pop-up) which displays one or more aspects of the environmental impact related to buying this product.

The following image indicates a mock-up widget that is displayed in the case of a user that is browsing the Amazon shop for buying a soap bar. The has identified the type of the product and displayed the CO2 emissions per 100g of soap. Then the user can see a link for the sources and the original database, as well as useful information about the production of the soap.

Basic mock-up widget that displays the estimated CO2 emissions for a bar of soap

Extended features

The following images indicate the functionality of an extended app, where additional features are considered. These features are subject to availability of data.

User journey

The following diagram shows the improvements at the user journey through the adoption of this application.

Possible extensions


  • Show how many people rank that company as eco-friendly

  • Show how many dollars will be saved over 10-years, as opposed to a less energy-efficient option

  • Rank the company on the measurable outcomes above

    • Use of recyclable packaging

    • Use of sustainable energy sources

    • Quality of waste reduction or energy conservation policies

    • Transparency of production and supply chains


Prototype software

The GitHub repository contains the implementation of a back-end that can present the environmental impact of product categories, based on public Life Cycle Assessment (LCA) databases.

Calculation of the environmental impact

The current version is based the LCAcommons database provided by the US Authorities. This database contains the byproducts of the manufacturing process for several product categories (e.g. soap products, dairy, telephony devices etc.). Among the byproducts one can see fluid, soil and air outputs, and most importantly the CO2 emissions, which are considered crucial for their impact to the heating of the planet. For many product categories, the database provides the mass of emissions (in kg) that were emitted proportionally to one dollar of product (based on the Producer Price Index of 2013).

In order to calculate the emissions of a product, we multiple the quantity or the mass (kg) of the product with the producer price of the product ($/kg) and with the emissions of the manufacturing per dollar of products (kg/$). For lack of official data on the producer price of the products, we provide rough estimates based on averaged consumer prices.

Back-end functionality

The back-end includes a database which contains the information about the environmental impact of specific product categories. It also provides a search function for particular products given their name and can perform the calculation of the emissions given a specific amount of the product.

Basic diagram of the functionality of the back-end based on LCA

A basic web interface is provided, whereas there is the possibility of providing a web-service API for supporting the function of browser-based widgets.

Database structure

The back-end operates on a digested version of the LCA database, which contains only the data that are directly relevant to the application. The installation process (shown below) includes a script that crawls particular categories of products from the entire LCAcommons repository, downloads the data in JSON format and then converts the entries into the internal database model.

As shown in the UML diagram that describes the database scheme, it is based on 5 tables:

The ProductType table describes the generic type of products, such as soap, plastic bag, paper bag, Every product has a price based on the official US Producer Price Index (2013)

The Effect table stores a measured value for the sustainability effect of the lifecycle of a product. E.g. a sustainability effect of one piece of soap would be the CO_2 emissions having a mass of 0.16 grams. This would be stored here as: 'value': 0.16, 'product_type'->'soap', 'effect_type'->'O_2 emissions' One product may have several effects measured, each of them belongs to a different type

The EffectType refers to one particular type of sustainability effect for the lifecycle of a product. The most obvious is O2 emissions, but the existence of this model allows for tracking other type of effects, such as methane emissions, water/soil polution, etc. Every type has an effect to one resource, e.g. O2 emissions affect the air.

Every type is expressed by one Property, e.g. O2 emissions are expressed by their mass, measured in kg

Diagram of the simplified database scheme


  1. Download and install miniconda for the latest Python 3. This installation has been tested on a linux installation. If you have a x86_64 system, these commands would be:

cd ~/Downloads
# if installation is successful, delete the installer file

2. Create and enable a Python virtual environment through miniconda

conda create --name ecowidgy
conda activate ecowidgy

3. Install django

pip install django

4. Download the ecowidgy-backend code

cd ecowidgy_backend-master/

5. Open a terminal in the root folder of the project and run the following commands:

python migrate
python createsuperuser
python fetch_data
python runserver 10000

6. Point your browser to http://localhost:10000

Development framework

The back-end is implemented in Python Django.


The current implementation has been provided in order to achieve a minimum viable product given the goals, the restricted time-frame of the hackathon and the available resources. The following aspects need to be noted:

Calculation of carbon footprint

After long research, one can conclude that the exact carbon footprint per single product is an unsolvable problem. It appears that supply chains are severely intransparent, and even the retail companies are unable to trace the exact path of their products from the original material through the whole manufacturing process. Disclosing this or any relevant information to the consumers remains yet another additional challenge. For this reason, detailed supply chain data are difficult to find and process.

The LCA databases seem a good approximation to the problem. Nevertheless, there are also several issues here:

  • Detailed LCA databases are provided through a highly costly license fee, which was not payable as part of this development. Nevertheless, our knowledge about LCA is limited and no review of the proprietary licenses was done.

  • The publicly available LCA database only provides data on a level of product category (e.g. soap products). Therefore, it is at the moment not possible to compare different variations of the same product, or even different products of the same category (shampoo vs. soap).

  • LCAcommons also provides emissions related to the produce price of the product at the manufacturing stage. Resolving the real producer price of the product given its name remain as challenge and we only provide here a rough calculation.