Meet the Data Science for the Public Good team for this project

  • Undergraduate Interns: Saratou Bako Bagassa and Ziad Bushnaq​
  • Graduate Mentors: Suyog Gautam and Xiaofan Zhu​
  • Faculty Advisors: Michael Cary and Le Wang

Project description 

The rapid growth of cloud computing and large language models has driven unprecedented investment in data centers. Virginia and Texas — two states where data center development has been concentrated—offer tax incentives based on the expectation that these facilities will support local economic growth. However, although data center jobs tend to be well paid, these facilities employ relatively few workers over the long term. Evidence of their broader effects on local economies also remains limited.

This project examines the economic and environmental effects of data centers. Our findings show no clear overall effect on the economic outcomes examined. ​However, data centers create a measurable thermal footprint, increasing land surface temperatures in nearby areas by approximately 0.35°C. These results suggest that policymakers should weigh data centers’ limited long-term employment benefits against their environmental costs when designing tax incentives and siting regulations.

"Data center development is a complex policy issue, and our findings provide important evidence for understanding its local economic and environmental effects. While we do not find clear overall economic benefits in the outcomes we examined, we do find a measurable increase in land surface temperatures near data centers. These findings can help policymakers more carefully evaluate the costs and benefits of tax incentives and consider environmental impacts when making decisions about future development, thanks to our team's research.” - Le Wang, professor and David M. Kohl Chair
Students presenting a research poster
Two students stand in front of a screen presenting
Two students presenting

Intern insights

Sara: Over the past weeks, I have been working on the impact of data centers on communities, with a primary focus on the economic aspects. I was very excited to work on that project because I am a double major in computer science and economics, and it was therefore the perfect fit for me.

I applied economic concepts to investigate the impact of data centers, drawing on my computer science skills. During my time at DSPG, one of the activities I enjoyed most was our weekly progress reports, as they were an opportunity to learn more about projects I was not working on. I even learned more about Random Forest regression because one of the cohort members was using it for their project, and after further reading, I realized how much the concepts I have learned in my data structure classes can be applied to solve real-world problems.

After graduation, I plan to attend graduate school, and this experience has genuinely reinforced that decision. It gave me enough exposure to research to realize that it is something I truly enjoy and something I can use to make a positive impact on the world. This experience ended up being so much more than I had hoped for; it exceeded my expectations in every way. I learned many new skills, gained deeper insight into what graduate school and research actually look like in practice, had inspiring discussions with professionals across interdisciplinary fields, and expanded my network in ways I did not anticipate coming in. 

Ziad: This summer I have been working on looking into the impact of data centers on communities for the Virginia Land Use-Value Assessment program, with a focus on the heat effect that each facility releases. I ranked this project as my top choice, as I live in Northern Virginia, where large amounts of these data centers are being built, and it is such a current and important issue.

These facilities are currently receiving tax incentives in the state of Virginia to entice development, but our heat and economic findings together may show policymakers that these incentives might need to be reconsidered. We used difference-in-difference models to compare land surface temperature in areas immediately surrounding the facilities to further control rings.

It was a good experience working on this kind of causal analysis project where I was able to use the VT ARC high-performance computing clusters to handle large satellite data files. I gained valuable experience with difference-in-difference models and spatial analysis, which I hope to continue to use in the future. 

Graduate mentor insights

Suyog: I came into Virginia Tech's Data Science for the Public Good program as a graduate mentor with a geography background, so maps, satellite imagery, and spatial analysis were familiar ground. The economics side was not. Sitting in on conversations about regression discontinuity and difference-in-differences, methods used to figure out whether a policy caused a change rather than just happening around the same time, stretched me in ways I did not expect. The other graduate mentors and our faculty leads were patient with my questions and took the time to walk me through the reasoning, and I learned a great deal from them over the course of the summer. 

I also got the chance to work with a group of really talented interns from a range of academic backgrounds, and they brought plenty of new ideas to the table that I would not have thought of on my own. The collaboration between the economic and geospatial sides of our team proved genuinely meaningful. Overall, these ten weeks were inspiring, productive, and a lot of fun. If I take one lesson forward, it is that the most useful public-interest research happens where methods from different disciplines are forced to talk to each other.

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