Abstract

This pilot study presents a surrogate modeling framework for estimating carbon emissions for 35 data centers in the New York City metropolitan area. Using publicly available facility data (square footage, operator type, location), we calculated the annual CO2e emissions based on standard industry assumptions. These calculated values, which represent modeled emissions rather than measured data, served as the target variable for surrogate model development. A Random Forest regression model was implemented. The model achieved strong performance in producing the calculated emissions with the test set with cross-validated performance (CV R2 = 0.960 ± 0.022 and CV MAE = 2431 ± 739 MT CO2e). Analysis indicated that data center size was the major predictor, accounting for 79.7% of the total feature importance, while location and operator type contributed 13.6% and 6.6%, respectively. As a localized, preliminary feasibility study, this case study demonstrates that surrogate modeling using only publicly available facility data can provide modeled carbon footprint estimates for infrastructure planning and grid decarbonization efforts. The reproducible methodology can be applied to other metropolitan regions, though generalizability requires further validation with larger datasets.

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