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Data-Driven Sustainability: Enhancing Consumer Product LCA for Reduced Carbon Footprint and Social Impact

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This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.

You can find our upcoming AI Innovation Challenges at https://omdena.com/projects.

The problem

This project aims to address the critical issue of high carbon footprint and social impact associated with consumer products. Life Cycle Assessment (LCA) is a methodology used to evaluate the environmental and social impacts of a product throughout its entire life cycle, from raw material extraction to disposal. However, the lack of comprehensive and relevant data sources for conducting LCAs poses a significant challenge in accurately assessing the environmental and social footprints of consumer products.

The first problem that will be addressed is the limited availability of relevant data sources for conducting LCAs. Many existing LCA solutions rely on outdated or generalized data, which may not reflect the true environmental and social impacts of specific consumer products. Without access to accurate and up-to-date data, companies and policymakers struggle to make informed decisions about sustainable product design, manufacturing, and consumption. The second will be the high carbon footprint and social impact of consumer products. The production and consumption of consumer goods contribute significantly to greenhouse gas emissions, deforestation, water pollution, and other environmental issues. Additionally, the supply chains of consumer products often involve labor practices that can be harmful to workers and local communities, leading to social inequalities and human rights violations.

Impact of the problem:

The impact of the above-stated problems is multi-faceted and has wide-ranging consequences for the environment, society, and the economy.

  • Environmental Impact: The lack of comprehensive LCA data and inaccurate assessments can lead to an underestimation of the environmental impacts of consumer products. This, in turn, hinders efforts to identify and implement effective strategies to reduce carbon emissions, conserve natural resources, and address climate change. Without accurate LCA data, companies and policymakers may overlook opportunities for sustainable innovation and may inadvertently contribute to further environmental degradation.
  • Social Impact: Consumer product supply chains often involve complex networks of suppliers and subcontractors, making it challenging to monitor labor practices and social conditions. Inadequate data on social impacts can result in human rights violations, poor working conditions, and unequal distribution of benefits and risks among stakeholders. This can lead to reputational risks for companies, decreased consumer trust, and potential legal liabilities.
  • Economic Impact: Unsustainable consumer products can also have economic repercussions. As environmental regulations tighten and consumer preferences shift towards eco-friendly options, companies with high carbon footprints and social impacts may face increased costs, market competition, and difficulties in accessing certain markets. On the other hand, businesses that can demonstrate sustainable practices and transparent LCA data may gain a competitive advantage and attract environmentally conscious consumers.

Solution:

The project’s goal to add new relevant data sources to the core AI-assisted LCA solution of the startup aims to address the identified problems and their impact. Ultimately, the project’s mission to enhance the LCA solution and reduce the total carbon footprint and social impact of consumer products can pave the way for a more sustainable and equitable future. By addressing the identified problems and leveraging data-driven insights, the project seeks to drive positive change and foster a culture of responsible production and consumption in the consumer goods industry.

The project goals

The main project goal is to enhance Data Sources. The primary goal of the project is to add new, relevant, and comprehensive data sources that can be utilized for conducting Life Cycle Assessments (LCAs) of various consumer products. These data sources will encompass environmental and social impact indicators to provide a holistic view of the product’s life cycle.

**More details will be shared with the selected candidate.

Why join? The uniqueness of Omdena Top Talent Projects

Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.

Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.

If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.

Find more information on how an Omdena Top Talent Program works

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Eligibility to join an Omdena Top Talent project

Finished at least one AI Innovation Challenge

Received a recommendation from the Omdena Core Team Member/ Project Owner (PO) is a plus



Skill requirements

Good English

Machine Learning Engineer

Experience working with NLP is a plus.



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