A carbon intensity signal expresses the amount of greenhouse gases associated with producing one unit of electricity at a given time and location. It is typically expressed in grams of CO₂-equivalent per kilowatt-hour (g CO₂e/kWh).
At its core, carbon intensity reflects the composition of the electricity generation mix and the operating conditions of the power system. Because generation sources and system conditions vary, carbon intensity is not a fixed value but a metric that can change over time and across geographic areas.
By linking electricity production to its associated emissions, a carbon intensity signal provides a quantitative basis for interpreting electricity-related carbon impacts under varying system conditions.
At its core, a carbon intensity signal reflects the emissions associated with the electricity generated on a power system at a given time and location. It is derived by combining generation data by source with emission factors for each generation technology.
In its most common form, average carbon intensity is calculated as:
Carbon intensity = Σ (Electricity generated by source × Emission factor of that source) / Total electricity generated
Where:
This calculation produces an average emissions intensity for the electricity system over the specified time interval and geographic boundary.
Computing a carbon intensity signal requires three core inputs:
Generation mix data
Real-time or aggregated data on electricity production by technology, typically published by transmission system operators (TSOs), market operators, or energy regulators.
Technology-specific emission factors
Standardized emission factors for each generation technology, generally derived from national greenhouse gas inventories or established lifecycle assessment methodologies.
System boundary definition
A clear definition of the geographic scope (national grid, regional zone, or local distribution network) and time resolution (annual, monthly, hourly, or sub-hourly).
In systems with high shares of variable renewables and cross-border exchanges, additional methodological choices may be required to account for imports, exports, and storage. These decisions influence whether the signal reflects strictly domestic generation or a broader system footprint.
The value of a carbon intensity signal depends not only on how it is calculated, but also on the temporal and geographic resolution at which it is defined.
Historically, carbon intensity has often been reported using annual national averages. This approach aligns with traditional electricity systems dominated by centralized generation connected to high-voltage transmission networks, where power flows were relatively predictable and geographically aggregated.
However, electricity systems are evolving. Renewable generation, particularly solar and wind, is expanding rapidly, and an increasing share is connected directly to distribution networks rather than only to high-voltage transmission grids. According to the European Court of Auditors, approximately 70 % of renewable capacity is expected to be connected at distribution level by 2030, rising to around 80 % by 2040 (i). This structural shift changes how electricity is produced and where variability emerges within the system.
National experience reflects this trend. In France, for example, the energy regulator (CRE) reports that low-voltage photovoltaic capacity connected to the distribution network more than doubled from 5.5 GW in 2021 to 11.5 GW in 2024 (ii), contributing to operational challenges linked to intermittency and local balancing requirements.
As distributed and variable generation grows, carbon intensity can differ not only between countries but also between regions — and even within the same day. A national annual average may smooth out significant intra-day or local variations in how electricity is actually produced.
For this reason, carbon intensity signals can be computed at different levels of granularity:
Higher temporal resolution captures fluctuations driven by weather-dependent generation and demand patterns. More localized signals reflect structural differences between grid areas, including congestion, embedded renewables, and cross-border exchanges.
The appropriate level of resolution depends on the intended use of the signal. Aggregated annual values may be sufficient for high-level reporting, while more granular signals may better reflect operational realities in systems where generation is increasingly decentralized and time-dependent.
Carbon intensity signals are derived from operational power system data and therefore depend on the quality, scope, and timing of available information.
Data latency.
So-called “real-time” carbon intensity typically relies on generation data published with a short delay. Depending on the system operator and reporting infrastructure, this delay can range from a few minutes to longer intervals. As a result, real-time signals are near-real-time approximations rather than instantaneous physical measurements.
Storage and system balancing effects.
As electricity systems integrate more batteries and other storage technologies, generation and consumption can be shifted across time periods. This affects how emissions are attributed within a given interval. The methodological treatment of storage can influence calculated carbon intensity values.
Emission factor assumptions.
Carbon intensity calculations rely on emission factors assigned to each generation technology. These factors may be based on national greenhouse gas inventories, lifecycle assessment databases, or standardized methodologies. Differences in assumptions, for example, whether lifecycle emissions are included or only direct combustion emissions, can produce variations in reported intensity.
In applied contexts, these methodological considerations must be addressed transparently.
To mitigate data latency, some providers complement near-real-time signals with short-term forecasts, enabling users to anticipate expected carbon intensity over the coming hours rather than relying solely on past measurements. For example, Nodera provides a 72-hour forward signal designed to support operational planning under evolving grid conditions.
Regarding storage, while its current contribution to overall electricity volumes remains limited in many systems, its growing role is likely to require increasingly explicit methodological treatment over time.
Finally, the choice of emission factors is central to signal credibility. Nodera’s carbon intensity calculations are based on emission factor references provided by ADEME, ensuring consistency with recognized national inventory methodologies.
For organizations with significant electricity consumption, carbon intensity signals provide context for interpreting the emissions associated with their energy use.
Traditional reporting frameworks often rely on aggregated annual emission factors. While appropriate for high-level disclosure, these averages do not reflect how the emissions associated with electricity can vary across time and grid areas. Carbon intensity signals make this variability visible.
For large electricity consumers, such as data centers, industrial facilities, or extensive IT infrastructures, this visibility has practical implications. When electricity consumption is concentrated in periods of higher carbon intensity, associated emissions increase; when consumption aligns with lower-intensity periods, emissions decrease, even if total energy use remains constant.
Carbon intensity signals therefore do not change the accounting principles of Scope 2, but they refine the understanding of how electricity-related emissions arise under evolving system conditions. In systems with growing shares of variable and distributed generation, this distinction becomes increasingly relevant.
More granular carbon intensity data inform how organizations interpret their electricity-related emissions, assess trade-offs, and evaluate the potential impact of operational or procurement decisions. As electricity systems become more decentralized and time-dependent, understanding the carbon characteristics of electricity consumption becomes part of understanding how the electricity system operates.
A carbon intensity signal quantifies the greenhouse gas emissions associated with producing electricity at a specific time and location, translating power system conditions into a measurable indicator expressed in g CO₂e per kWh.
(i)Making the EUelectricity grid fit fornet-zero emissions, European Court of Auditors
(ii) Development of a smart electricity grid, CRE, December 2025
