AI Infrastructure and Regional Electricity Demand: Evidence from U.S. Interconnection Queu

U.S. interconnection queue data used to study the impact of data center buildout on regional electricity demand. The replication package for Lamb (2026) includes analysis-ready panels, Python and Stata scripts, and final outputs. The main empirical result is a 34.8 index-point gap in ERCOT minimum hourly demand against a synthetic counterfactual.

Use Cases

• Replicate econometric panel regression analysis based on the described four-layer identification strategy.
• Benchmark electricity demand forecasting models based on the described ARIMA, Prophet, and XGBoost pipeline.
• Study regional energy impacts of data center expansion based on evidence from U.S. interconnection queues.
• Validate synthetic control methods for energy policy analysis based on the constructed ERCOT counterfactual.

Strengths

• Includes replication materials for a 2026 academic paper with a specific 34.8 index-point empirical result.
• Provides a multi-method analysis pipeline combining panel regression, synthetic control, difference-in-differences, and narrative validation.
• Code is released under the MIT License and derived data under CC BY 4.0, facilitating reuse.

Limitations

• Raw third-party data is not redistributed; must be acquired separately as documented in the README.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

Harvard Dataverse, author Lamb, Alan.

Collection Method

Likely compiled from U.S. interconnection queue data and other third-party sources for econometric analysis.

Time Range

null

Freshness

Last updated 2026-04-28 15:02:54.

Geography

United States, with a focus on the ERCOT region.