Green tech involves REE extraction – destroying the environment

Most people view technology as the future, a force of good that will generally improve quality of life around the world. In the business sector, Silicon Valley and tech startups exhibit massive growth potential; in manufacturing, new machinery and automation are boosting efficiency; and in the environmental realm, green technology presents the best prospects for decarbonization.

But as much as technology is hailed as the panacea of the future, most of these innovations have a dirty underside: production of these new technologies requires companies to dig up what are referred to as rare earth elements (REEs).

REEs describe the 15 lanthanides on the periodic table (La-Lu), plus Scandium (Sc) and Yttrium (Y). Contrary to what the name suggests, REEs are abundant in the earth’s crust. The catch is that they come in low concentrations in minerals, and even when found, they are hard to separate from other elements, which is what makes them “rare.” Perhaps these elements also get their name from being rarely discussed, even though everything from the iPhone to the Tesla electric engine to LED lights use REEs. Demand for these elements is projected to spike in coming years as governments, organizations, and individuals increasingly invest in clean energy. An electric car requires six times the mineral inputs of a conventional car, and a wind plant requires nine times more minerals than a gas-fired plant. With current estimates, demand for REEs could increase six-fold by 2040. Lithium and cobalt demand could increase ten to twenty times by 2050 because of electric cars. Demand for dysprosium and neodymium is estimated to increase seven to twenty-six times over the next 25 years as a result of electric vehicles and wind turbines. But REEs also have grim prospects: the way companies extract REEs largely damages communities and contaminates surrounding areas.

There are two primary methods for REE mining, both of which release toxic chemicals into the environment. The first involves removing topsoil and creating a leaching pond where chemicals are added to the extracted earth to separate metals. This form of chemical erosion is common since the chemicals dissolve the rare earth, allowing it to be concentrated and then refined. However, leaching ponds, full of toxic chemicals, may leak into groundwater when not properly secured and can sometimes affect entire waterways.

The second method involves drilling holes into the ground using polyvinyl chloride (PVC) pipes and rubber hoses to pump chemicals into the earth, which also creates a leaching pond with similar problems. Additionally, PVC pipes are sometimes left in areas that are never cleaned up.

Both methods produce mountains of toxic waste, with high risk of environmental and health hazards. For every ton of rare earth produced, the mining process yields 13kg of dust, 9,600-12,000 cubic meters of waste gas, 75 cubic meters of wastewater, and one ton of radioactive residue. This stems from the fact that rare earth element ores have metals that, when mixed with leaching pond chemicals, contaminate air, water, and soil. Most worrying is that rare earth ores are often laced with radioactive thorium and uranium, which result in especially detrimental health effects. Overall, for every ton of rare earth, 2,000 tons of toxic waste are produced.

While personalized medicine is a good potential application of AI, there are dangers. Current business models for AI-based health applications tend to focus on building a single system—for example, a deterioration predictor—that can be sold to many buyers. However, these systems often do not generalize beyond their training data. Even differences in how clinical tests are ordered can throw off predictors, and, over time, a system’s accuracy will often degrade as practices change. Clinicians and administrators are not well-equipped to monitor and manage these issues, and insufficient thought given to the human factors of AI integration has led to oscillation between mistrust of the system (ignoring it) and over-reliance on the system (trusting it even when it is wrong), a central concern of the 2016 AI100 report.

These concerns are troubling in general in the high-risk setting that is healthcare, and even more so because marginalized populations—those that already face discrimination from the health system from both structural factors (like lack of access) and scientific factors (like guidelines that were developed from trials on other populations)—may lose even more. Today and in the near future, AI systems built on machine learning are used to determine post-operative personalized pain management plans for some patients and in others to predict the likelihood that an individual will develop breast cancer. AI algorithms are playing a role in decisions concerning distributing organs, vaccines, and other elements of healthcare. Biases in these approaches can have literal life-and-death stakes.

In 2019, the story broke that Optum, a health-services algorithm used to determine which patients may benefit from extra medical care, exhibited fundamental racial biases. The system designers ensured that race was precluded from consideration, but they also asked the algorithm to consider the future cost of a patient to the healthcare system.17 While intended to capture a sense of medical severity, this feature in fact served as a proxy for race: controlling for medical needs, care for Black patients averages $1,800 less per year.

New technologies are being developed every day to treat serious medical issues. A new algorithm trained to identify melanomas was shown to be more accurate than doctors in a recent study, but the potential for the algorithm to be biased against Black patients is significant as the algorithm was trained using majority light-skinned groups.18 The stakes are especially high for melanoma diagnoses, where the five-year survival rate is 17 percentage points less for Black Americans than white. While technology has the potential to generate quicker diagnoses and thus close this survival gap, a machine-learning algorithm is only as good as its data set. An improperly trained algorithm could do more harm than good for patients at risk, missing cancers altogether or generating false positives. As new algorithms saturate the market with promises of medical miracles, losing sight of the biases ingrained in their outcomes could contribute to a loss of human biodiversity, as individuals who are left out of initial data sets are denied adequate care. While the exact long-term effects of algorithms in healthcare are unknown, their potential for bias replication means any advancement they produce for the population in aggregate—from diagnosis to resource distribution—may come at the expense of the most vulnerable.