The study, led by Camilla Citterio with Nicolò Golinucci, Lorenzo Rinaldi, and Matteo Vincenzo Rocco, is the first integrated, cross-paradigm assessment of its kind. Rather than comparing studies within a single methodology, the team aligned emission factors from process-based life cycle assessment databases such as Ecoinvent, environmentally extended input–output tables including EXIOBASE, EMERGING, EORA, GLORIA, and GTAP, primary data from the International Energy Agency, and harmonized reference values from the IPCC, the National Renewable Energy Laboratory, Electricity Maps, the European Commission’s Joint Research Centre, eGRID, and EMBER. To make these sources commensurable, the researchers mapped every technology category onto a shared taxonomy and standardized all greenhouse gas figures to 100-year Global Warming Potential values from the IPCC’s Fifth Assessment Report, then computed national grid intensities for EU countries and the United States using actual generation mixes from the ENTSO-E Transparency Platform and eGRID.

A central contribution of the paper is terminological clarity. The authors distinguish between direct carbon intensity—the operational emissions from generating one kilowatt-hour, equivalent to Scope 1 for a power producer—and life-cycle carbon intensity, which extends the boundary to upstream processes like fuel extraction and plant construction and sometimes downstream impacts such as transmission losses. They also draw the crucial line between attributional factors, which allocate the average emissions of a grid across all consumed electricity, and consequential factors, which estimate which power plant responds to an additional unit of demand. Their analysis focuses on national-scale attributional factors, the common ground for corporate carbon footprints, since consequential modeling requires dedicated tools suited to short-term operational signals like smart charging.

The results reveal a striking asymmetry. For direct operational emissions of fossil fuel plants, the sources show moderate agreement: the spread between the lowest and highest median estimates is 36 percent for coal, 52 percent for oil, and 116 percent for natural gas. Once the boundary expands to the full life cycle, however, consensus collapses. Life-cycle spreads for fossil technologies range from 101 to 631 percent, and for renewables—where emissions are dominated by manufacturing and decommissioning rather than combustion—the choice of data source becomes decisive. The reported life-cycle intensity for hydroelectric power in Ecoinvent and the upper end of the IPCC range substantially exceed every other source, a difference attributed to the inclusion of diverse plant types and fugitive reservoir emissions. For nuclear power, the Electricity Maps factor is less than half the IPCC median and falls below the minimum of the NREL harmonization range.

One discrepancy stands out as a cautionary tale about accounting rules. The IEA reports direct carbon intensities for biomass-fired electricity that can exceed 2,000 grams of CO2 equivalent per kilowatt-hour, while other sources report near-zero direct emissions. The gap stems from a single convention: the IEA counts biogenic CO2 in its factors, contrary to IPCC guidelines that treat such emissions as carbon-neutral within the energy system’s carbon cycle. A differing interpretation of one rule therefore produces results that differ by orders of magnitude—potentially misleading for policy and reporting alike, and a stark illustration that even ‘direct’ emissions are not as unambiguous as they appear.

Beneath the scatter, the researchers uncovered a systematic pattern they call the paradigm effect. EXIOBASE Hybrid, an environmentally extended input–output dataset, consistently yields the highest or among the highest life-cycle intensities for nearly every technology analyzed. For natural gas, the IPCC and NREL medians align at 490 grams per kilowatt-hour and Electricity Maps clusters tightly around 529, while Ecoinvent’s median reaches 598—but EXIOBASE Hybrid’s median soars to 780. For coal, the entire EXIOBASE Hybrid distribution sits above the third quartile of the other major sources. For wind, where all medians fall within a narrow 11 to 23 grams per kilowatt-hour, EXIOBASE Hybrid’s outliers surpass 150. The authors are careful to stress that this is not an error: input–output models capture the full economic web of services and capital goods that process-based inventories systematically truncate, trading technological specificity for completeness.

These technology-level divergences propagate directly to national grids. Comparing computed direct carbon intensities against the IEA reference, the relative difference between minimum and maximum estimates ranged from 72 to 730 percent across countries in 2017, and from 38 to 612 percent in 2023. Monetary-based sources—where emissions per euro are converted to physical units using regional electricity prices—tended to yield lower estimates, and their life-cycle spreads ran roughly 150 percent wider than physical-unit databases on average. EXIOBASE Hybrid stood above the country median in 96 percent of cases and was the single highest estimate for 20 of 28 countries in 2017. The authors emphasize that monetary factors are inherently mediated by electricity prices, making their uncritical use in formal reporting highly problematic.

The stakes become tangible in the paper’s worked example. A 10 megawatt-hour electricity bill in Italy, costing 1,600 euros at 2023 basic prices, produces a maximum total greenhouse gas estimate three times the minimum—a difference of four tonnes of CO2 equivalent for the same consumption, in the same country, in the same year. The methodological choice, in other words, can dominate the result. The researchers also note that corporate practice amplifies the problem: a 2025 study cited in the paper found that 25.8 percent of 10,867 reporting companies used spend-based approaches, and three-quarters of those specifying a method relied on single-region input–output models, implicitly applying domestic emission intensities to imported goods and underestimating the often dirtier footprint of global supply chains.

From these findings the team distills a practical decision framework rather than a single verdict. There is no optimal data source, they conclude—only context-appropriate ones. For basic Scope 2 reporting, primary data from the IEA offers a widely accepted foundation, provided users remain vigilant about biogenic carbon conventions. When a conservative, upper-bound estimate capturing the broadest economic context is desired, input–output databases are defensible, and the authors argue they are the only approach enabling coherent allocation of Scope 3 emissions across an entire value chain. When comparability with product life cycle assessments or Environmental Product Declarations is the goal, process-based sources are the necessary choice. Above all, every reported factor must be accompanied by a clear citation of its source and version—the minimum requirement for reproducibility.

The paper arrives as the GHG Protocol moves toward updating its Scope 2 Guidance with greater emphasis on temporal and geographical granularity, and as the Protocol and ISO work toward a harmonized standard. The authors welcome these developments but caution that they only partially address the core problem of consistency across data sources. Without more reliable primary data, definitive guidance on imports and exports, robust residual-mix methodologies, and systematic documentation, significant discrepancies are likely to persist. Their prescription is a centralized, regularly updated reference dataset applied consistently over time, complemented by primary data where feasible. In a rapidly decarbonizing world—and amid polarized debates over sustainability regulation—the message is blunt: the carbon intensity of electricity is not a fixed fact of nature but a context-dependent figure, and credibility in climate policy depends on transparent sourcing and uncompromising communication of analytical boundaries.

Subject of Research: Methodological comparison of electricity carbon intensity emission factors across life cycle assessment, input–output, and agency data sources

Article Title: Carbon intensity of electricity: a systematic methodological and quantitative review

Article References: Citterio, C., Golinucci, N., Rinaldi, L., & Rocco, M. V. (2026). Carbon intensity of electricity: a systematic methodological and quantitative review. Journal of Industrial Ecology, 30(4), 2173-2187. https://doi.org/10.1007/s44498-026-00148-3

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00148-3

Keywords: carbon intensity, electricity, emission factors, life cycle assessment, input-output analysis, EXIOBASE, Ecoinvent, GHG Protocol, Scope 2 emissions, corporate sustainability reporting, grid emissions, industrial ecology