Carbon impact of intra-regional transmission congestion

Carbon accounting guides policy and decision-making around renewable energy investments but remains myopic to real-world grid operations. Specifically, ongoing policy design often ignores transmission congestion within grid boundaries. This work evaluates the validity of this assumption by quantifying the impacts of intra-regional congestion on carbon emissions and accounting frameworks. This analysis is done for PJM and ERCOT using nodal locational marginal emissions data. We find that load that is 100% hourly matched through load shifting will often result in significant net operational emissions and sometimes even increase net emissions relative to annual matching. This work demonstrates that, in the absence of transmission expansion, grid-region boundaries are insufficient to ensure hourly matching is effective. Considering that intra-regional transmission congestion is shown to be vital for effective carbon accounting, we question assumptions that frequently ignore intra-regional congestion in studies and policy proposals. • Intra-regional transmission congestion significantly alters carbon outcomes • Hourly matching-based carbon accounting may overestimate emission reductions • Renewable and load siting decisions contribute to large emission disparities • Policy and accounting reforms must integrate congestion effects Policymakers and the private sector rely on carbon accounting frameworks to measure progress toward decarbonization and to evaluate the effectiveness of individual clean energy investments. These frameworks often assume that electricity produced anywhere within a grid region can seamlessly reach consumers in that same region. However, real power grids are constrained by limited transmission capacity, meaning that renewable electricity often cannot be delivered to where demand occurs. In this study, Sara E. Sofia and Yury Dvorkin quantify how these hidden bottlenecks—known as transmission congestion—affect carbon emissions across two major United States (US) power markets, PJM (one of the largest market interconnections in the world) and ERCOT (operates one of the largest renewable and battery fleets in the US). Using high-resolution “locational marginal emissions” (LMEs) data, it is shown that congestion can cause renewable power to be stranded in remote areas while fossil fuel plants continue supplying urban loads. As a result, even when electricity use is precisely matched with renewable generation on an hourly basis—a growing standard in carbon accounting—net emissions can remain substantial or increase. The findings reveal that nearly half of wind and solar generation in ERCOT and most in PJM cannot be fully delivered to major load centers without inducing additional emissions due to congestion. This disconnect has important implications for corporate carbon accounting, data center siting, and new policy mechanisms such as the US hydrogen production tax credit, which ties eligibility to hourly renewable matching. By highlighting the real-world consequences of ignoring transmission constraints, this work calls for a shift in how clean energy impacts are measured. Carbon accounting and investment frameworks must evolve from region-based to congestion-aware systems that reflect the actual deliverability of renewable power. Only then can clean electricity policies and investments achieve the emissions reductions they promise. Policies and carbon accounting frameworks often assume that clean electricity can be freely delivered within a grid region. Sara E. Sofia and Yury Dvorkin show that intra-regional transmission congestion breaks this assumption, causing “carbon-free” electricity to induce substantial emissions. Using locational marginal emissions data for PJM and ERCOT, accounting for congestion is shown to be essential for accurately measuring emissions and ensuring that clean energy investments and policies—such as the GHG Protocol or hydrogen tax credits—deliver real decarbonization benefits.