Solving the Greenhouse Gas Problem through Sustainable Meat Consumption (Watson Analytics)


This is my team’s official entry to the 2017 Watson Analytics Global Competition. Beyond our hope to win the competition is the hope that our recommendations will be put to use by policy makers in the different countries. We believe that this is something that can make a difference. Team members are Ruoxuan Gong and Liyi Li.


People are rarely aware of meat consumption’s contribution to greenhouse gas emissions. The purpose of this study is to utilize IBM Watson Analytics to identify relationships among meat consumption, greenhouse gas emission, and potential thermal depolymerization by-products from meat production funnels. Thorough data collection, data preprocessing, and data analysis, using both descriptive and predictive analytics, were conducted. As a result, three solutions: policies to optimize meat consumption, transformation of solid waste to sustainable by-products, and social media methods to increase people’s awareness have been proposed in this project.

The dashboard and research-based data-driven golden information can be used by environmental policy makers, business owners, and the public to exponentially make meat consumption more sustainable in the long run. Network effects can be expected from the improvement of public awareness.


  1. Data collection from OECD, FAO, and other sources.
  2. Data processing to relate meat production & consumption data with greenhouse gas emission data
  3. Variable Selection
  4. Data Analysis
    • Chart creation
    • Dashboarding
    • Simulation of thermal depolymerization by-product conversion
    • Retrospective Analysis
    • Social Media Awareness Analysis
  5. Conclusion and Recommendations

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