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Your body heat could power your wearable devices via Popular Mechanics
Why it matters: This could eliminate the need for batteries in smartwatches, pacemakers and other implants. Personal thermoelectric generators in development are small wearable devices that turn the heat your body naturally creates into electrical energy, providing renewable power for tech on and in our bodies.
“How to avoid a climate disaster” via GatesNotes
Why it matters: Bill Gates is applying his considerable resources to the climate problem. Eliminating emissions is the way forward, says Gates; how we get there is up for debate but that essential truth is not. His new book and continued passion are welcome contributions to the ongoing discussion. Like Bill, I believe we can and will rise to the occasion.
You should get to know “servitisation” via World Economic Forum
Why it matters: Servitisation is a clever way to reduce obstacles to energy efficiency. In this model, customers pay for a service without financing the equipment that provides it, for example, allowing customers to purchase solar energy instead of investing in the panels themselves. Considering the International Energy Agency says energy efficiency has the potential to deliver 35% of the cumulative carbon dioxide savings the Paris Agreement seeks through 2050, servitisation is well worth exploring (but might need a better name).
Options to take us “Beyond the Bag” via RetailLeader
Why it matters: “It is estimated that Americans use 100 billion plastic bags per year and that fewer than 10% are recycled.” In search of a solution, the Center for a Circular Economy held a challenge to find new concepts that serve the function of a single-use bag. The nine winners* offer some innovative solutions drawing on apps, the IoT and new recyclable materials based on cellulose fibers.
Assessing regulatory fairness through machine learning via ScienceDaily
Why it matters: If demographic information is applied to spot and analyze patterns of environmental violations, machine learning can improve both equity and efficiency of enforcement. In this case, EPA officials wanted to test a machine learning model they designed to improve enforcement of the Clean Water Act. A Stanford research team used an algorithm including demographics and spotted an unintended consequence of the EPA’s design: though it would improve efficiency sevenfold, it would miss the most severe violators — more likely to be in densely populated communities of color — shifting enforcement resources to rural, predominately white communities. A blind spot revealed is a blind spot that can be corrected.
*EDF doesn’t endorse specific business ventures.
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