Open Access

ARTICLE

Evaluating Scope-2 Emission Factor Calculation Methods Based on Historical Energy Consumption

Aditya Mairal^*, Todd Rossi, Michael Muller

Center for Advanced Energy Systems, Department of Mechanical Engineering, Rutgers University, New Brunswick, NJ, USA

* Corresponding Author: Aditya Mairal. Email:

(This article belongs to the Special Issue: Science, Engineering, and Policy Innovations Driving the Global Energy Transition)

Energy Engineering 2026, 123(4), 25 https://doi.org/10.32604/ee.2026.075576

Received 04 November 2025; Accepted 03 February 2026; Issue published 27 March 2026

Abstract

An integral part of the effort to reduce greenhouse gas emissions is carbon footprint accounting. EPA categorizes facility carbon footprints in three scopes. Scope-2 emissions include electricity, heat or steam purchased from a utility provider. This paper evaluates the existing calculation methods for scope-2 CO₂ emissions for purchased electricity. The electricity grid in US is complex and is divided spatially into states, eGRID regions, balancing authorities (BAs), and utilities. Up to hourly temporal granularity can be obtained from available datasets. A matrix is developed that categorizes different datasets based on the complexity to calculate the carbon emission factors. Spatial and temporal variations are evaluated. There are significant spatial overlap between regions in different categories and emission factors within a region show sub-regional variation. An area analysis is done using zip-code polygons to determine whether a state or balancing authority is smaller for all the overlapping cases. Temporal variations in emission factors are significant depending on the balancing authority considered. A single method to calculate scope-2 emission factors may not be accurate and efficient in every case and a nuanced assessment of emission factors is warranted. An implementation pathway for a “smart carbon calculator”—one that gives accurate carbon footprint that is the spatially and temporally most granular is suggested.

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