Prompts

ALL Data Sets and Prompt Details at: https://github.com/YaleOpenLab/hack4climate

Presentations are to be 3 minutes with a 1 minute Q&A.

 
 

Data prompt 1a

Explore and Visualize National Climate Data

Explore data on historic emissions, cumulative emissions, projected emissions, GDP, climate action pledges, and vulnerability to climate change. Understand the differences between countries’ carbon emissions and their efforts to address global climate change (e.g., are high emitting countries also taking the most ambitious action?).

Create data visualizations that help track progress towards climate goals, capture the urgency of climate action, or illustrate the need for improved burden sharing.


data prompt 1b

Understanding Cities’ Climate Action

Visualize the data:

  • Spatially/geographically - similar to this map of city, company, and university climate action in the United States.

  • By sector - e.g., renewable energy, energy efficiency, buildings, emission reductions, forests, agriculture, private finance, carbon price, transport, etc.

  • Analyze any patterns or trends in the data:

    • Conduct a text analysis using Rapidminer, TextStat, Wmatrix, or a different tool of your choice, and visualize common themes and words mentioned in climate actions.

    • By key contextual information - e.g., city population, land area, or emissions.

    • Ways to identify ‘peer cities’ - what cities seem similar to each other?

    • Based on the current database of subnational commitments, can we predict what other cities are likely to take action/pledge commitments?

Slides: https://drive.google.com/open?id=1iDYCv40OOxaydlSvrffV9h-CxenrtFq_


data prompt 2

IoT Data Oracle

Create a Data Validation Oracle, for reaching a consensus on the ‘true’ or statistically wighted amount of energy generated by a distributed energy resource, such as rooftop solar. This could include a combination of IoT, environmental, remote sensing and benchmark data to create the most robust mechanism to evaluate data streams, create device reputation scores, detect anomalies and ultimately output a consensus result. Possible approach to use machine learning and other advanced anomaly detection algorithms.

Slides: https://drive.google.com/open?id=1qeeuj3z1Sei-mHnuMqeC5E1Pw5PwvLGP


 
 

Blockchain Prompt 1

Intuitive Frontend for interacting with B-CAT Smart contracts

Design and build an intuitive frontend interface that would compliment the commit-me platform to allow states and non-state actor pledges.

 

blockchain prompt 2

Smart contracts for innovative fund and reward schemes for B-CAT pledges

Commit-me right now presents functionality for a person to donate funds towards existing climate action pledge at Oxford University. These funds in the form of cryptocurrencies (ETH) / stablecoins (DAI) can be destined towards the costs of executing the projects associated with the pledges.

Slides: https://drive.google.com/open?id=1JYiISPSvLshccGLnRV0R8_l_2q0l1T0v