Machine Learning + CleanTech + Climate Risks, Part II — CleanTech Talk Podcast
In the second half of this CleanTech Talk interview, Michael Barnard sits down with Paul Werbos, Ph.D. Co-Director of the Center for Intelligent Optimization and Networks, to talk about all things climate change. You can listen to the full conversation in the embedded player below. Below that embedded SoundCloud player is a brief summary of the topics covered, but tune into the podcast to follow the full discussion.
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Paul has worn a number of impressive professional hats. As Co-Director of the Center for Intelligent Optimization and Networks, he was credited for the original development of black propagation and adaptive dynamic programming in the 1960s and 1970s. As a Brookings Fellow in the office of Senator Specter, he was responsible for climate, energy and space policy. And finally, he was formerly Program Director at the US National Science Foundation. Paul also wrote the forward for Michael Barnard’s new machine learning report published on CleanTechnica.
Mike and Paul continued the second half of this podcast by talking a bit more about politics before transitioning into agriculture and land use. Paul explained that agriculture is almost as important as other big greenhouse gas emitters like electricity and transportation. It also might be one of the easier sectors to engage with, he said. Mike and Paul explore the technology and policy that could be used to make significant progress within the industry that combats climate change.
Both Mike and Paul are interested in low-tillage agriculture and biochar, which is burned organic material that has the ability to store carbon. Paul agrees with Mike that there is great room for improvement on what the world is doing to encourage better agricultural practices through systems thinking. They both talk in more detail about low-tillage agriculture, pricing on carbon, and soil carbon capture. Paul believes that there needs to be both better information flow to bring together a range of solutions as well as more moderate, rational incentives to fund some of those solutions.
Mike then moved the conversation toward geoengineering, which Paul believes needs more attention. Mike sees a fundamental problem in pretending like the world could geoengineer its way out of climate change without causing other problems, whereas Paul thinks there is a balance to be found. Mike and Paul wrap up the second half of this podcast with a talk on Paul’s work in the world of machine learning and how critical it is in solving some of these pressing future challenges.
To hear more on these topics, listen to the show!
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