How to retire coal, smarter and faster

Using their model, the team grouped 198 active U.S. coal plants into clusters such as High Health Impacts Plants, Expensive Plants, and Plants in Anti-Coal Regions, each linked to specific vulnerabilities that can be targeted with policy or advocacy. For example, plants associated with high asthma rates and poor air quality could be prioritized through public-health campaigns and environmental enforcement, while those struggling financially might respond more effectively to economic incentives or market-based mechanisms.

One striking example is Belews Creek in North Carolina - a nearly 50-year-old, 2.49-gigawatt coal plant that the study categorizes as both highly vulnerable to retirement and part of Group 0: Fuel Blend Plants. The facility can burn up to 50% natural gas, yet remains one of the nation's top particulate polluters, ranking 26th out of 198 for fine particle emissions. Financially, it's among the most unprofitable plants in the country, carrying roughly $46 million in debt as of 2020.

Belews Creek is also located in a state seeing rapid growth in solar development and the implementation of coal debt securitization policies designed to help utilities transition away from uneconomic fossil assets. "Given the drivers for retirements in this group," the authors noted, "advocates can leverage state and utility clean energy targets." There were even preliminary discussions about replacing Belews Creek with a small modular nuclear reactor, but the plant's owner, Duke Energy, has since postponed its retirement - underscoring the financial and operational complexities that the UCSB framework aims to untangle.

"We can simplify nearly 200 plants into clear groups and pair each with evidence-based strategies," Wu said. "That's a powerful approach to a geographically diverse and politically fragmented challenge."

Their analysis found that about 28% of coal plants without retirement plans are already highly vulnerable to closure - potential "quick wins" for policymakers and advocates. But it also revealed that the least vulnerable plants are spread across several groups, underscoring the need for a diverse set of strategies to address the most persistent facilities.

The implications extend beyond coal. Because the model captures the multi-dimensional forces that shape real-world decisions - economics, politics, health and grid reliability - it could be adapted to other complex decarbonization challenges.

Wu, whose research focuses on sustainable energy transition planning, said the framework bridges mathematical and applied environmental science in a way that could transform how analysts and decision-makers approach energy policy.

"This work takes state-of-the-art mathematical tools and puts them into the practitioner's toolbox," she said. "It's flexible, transparent and reproducible - exactly what we need to make smarter, more strategic decisions about the energy transition."

Gathrid, now a co-founder of an AI and data start-up, Krv Analytics, based in Los Angeles, said the framework's open-source design makes it especially valuable.

"The methods we developed are meant to be used," he said. "Whether you're working on coal, renewables or industrial emissions, the idea is the same - use the data you have to see where progress can happen first, and why."

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