The United States is at a critical juncture in its effort to overhaul the national energy system to meet bold climate objectives. The Biden Administration has committed to reducing greenhouse gas (GHG) emissions by 50–52% from 2005 levels by 2030 and reaching net-zero emissions by 2050. Meeting these goals demands a swift rollout of renewable energy sources while keeping the electricity grid reliable, affordable, and equitable. A key driver in this transition is big data analytics, which plays a vital role in forecasting the performance of variable renewable sources and supporting carbon-conscious decision-making. This paper explores how advanced data-driven technologies—such as machine learning, IoT sensors, satellite imaging, and predictive modeling—can help accelerate the U.S. shift toward clean energy. By examining recent peer-reviewed studies (2018–2022), policy developments, and case studies, it introduces a structured big data framework for forecasting renewable energy output and modeling carbon intensity. Findings show that data analytics can increase forecasting accuracy by up to 40%, cut reserve costs by billions annually, and reduce over 100 million metric tons of CO₂ through smarter, carbon-aware decisions. Highlighted case studies—including CarbonCast, predictive wind turbine maintenance, and open-source datasets—demonstrate the real-world technical and economic value of analytics. Cost-benefit assessments reveal that improving forecast accuracy by just 1% could cut CO₂ emissions by roughly 20 million metric tons each year. However, regional readiness varies: while CAISO and PJM are well-equipped for scaling these tools, ERCOT and ISO-NE face more infrastructure-related hurdles. The paper concludes that big data analytics is a powerful and timely tool for decarbonization. When integrated into energy markets, grid operations, and policy design, analytics can fast-track emission reductions while advancing equity, as seen in programs like Justice40. Future research should prioritize probabilistic forecasting, marginal carbon modeling, and inclusive data governance.
Arman et al. (Fri,) studied this question.