How to Retire Coal Plants Smarter and Faster, UCSB Study Shows
- MM24 News Desk
- 7 hours ago
- 2 min read
New UCSB study offers data-driven strategies for shuttering America’s remaining coal plants.Credit:UCSB
Even as coal power steadily declines in the United States, over 100 coal plants continue to operate with no retirement plans, threatening national climate targets. A new study led by researchers at UC Santa Barbara (UCSB) offers a data-driven roadmap to accelerate the coal phaseout and meet net-zero emissions goals.
Published in Nature Energy, the study addresses a pressing question: if market forces have already led to the closure of many coal facilities, why do so many plants remain active? Currently, about 105 gigawatts of coal capacity—spread across 114 plants—are still scheduled to operate through 2035, even though a complete phaseout is considered essential for achieving climate objectives.
“Coal is a complex challenge—there’s no single solution,” said Sidney Gathrid ’22, the study’s lead author. “Our goal was to develop tools that capture that complexity, allowing policymakers and advocates to target the problem in multiple ways.”
Working with Assistant Professor Grace C. Wu, senior author on the paper, and collaborators including Jeremy Wayland, Stuart Wayland ‘22, and Ranjit Deshmukh, the UCSB team developed a novel framework using graph theory and topological data analysis. This approach classifies the entire U.S. coal fleet into eight distinct groups based on 68 technical, economic, environmental, and socio-political factors, and introduces a “contextual retirement vulnerability” score to measure each plant’s likelihood of early retirement.
The framework also identifies retirement archetypes—patterns explaining why certain plants close, ranging from regulatory pressures and public health concerns to economic challenges and political factors. These insights provide policymakers with a clear set of levers to accelerate retirements.
Using this model, the team analyzed 198 active U.S. coal plants, grouping them into categories such as High Health Impact Plants, Expensive Plants, and Plants in Anti-Coal Regions, each with specific vulnerabilities. For instance, facilities linked to poor air quality could be prioritized through public health campaigns and environmental enforcement, while financially struggling plants might respond better to market-based incentives.
One notable example is Belews Creek in North Carolina—a nearly 50-year-old, 2.49-gigawatt coal plant classified as highly vulnerable to retirement. Despite its ability to burn up to 50% natural gas, it remains a top particulate polluter and is financially unprofitable, carrying roughly $46 million in debt. With rapid solar growth in the state and emerging coal debt securitization policies, Belews Creek illustrates how targeted strategies could facilitate its closure.
“By simplifying nearly 200 plants into clear groups and pairing each with evidence-based strategies, we offer a practical roadmap for a geographically diverse and politically fragmented challenge,” said Wu.
The study found that 28% of plants without retirement plans are already highly vulnerable, representing “quick wins” for advocates. At the same time, the least vulnerable facilities are scattered across multiple groups, highlighting the need for tailored, multi-faceted approaches.
Beyond coal, this framework can guide other decarbonization efforts, integrating economics, politics, health, and grid reliability. Its open-source, flexible design makes it a powerful tool for analysts, policymakers, and practitioners tackling complex energy transitions.
“The methods we developed are meant to be used,” said Gathrid. “Whether for coal, renewables, or industrial emissions, the principle is the same: use data to identify where progress can happen first—and why.”
Comments