Identify climate change problems where existing gaps can be filled by machine learning.

https://arxiv.org/pdf/1906.05433v2.pdf

Highlights

Industry

  • ML demonstrates considerable potential for reducing industrial GHG emissions under the following circumstances.

    • when there is enough accessible, high-quality data around specific processes or transport routes.
    • when firms have an incentive to share their proprietary data and/or algorithms with researchers and other firms.
    • When aspects of production or shipping can be readily fine-tuned or adjusted, and there are clear objective functions.
    • When firm’s incentives align with reducing emissions(for example, through efficiency gains, regulatory compliance, or high GHG prices).

  • Optimizing supply chains

ML may help reduce emissions in supply chains by intelligently predicting supply and demand, identifying lower-carbon products, and optimizing shipping routes.

  • Reducing overproduction
    By improving demand forecasting.

  • Recommender systems
    Helping identify climate-friendly options for consumers and purchasing firms.

  • Reducing food waste
    Optimizing delivery routes and improving demand forecasting at the point of sale.