Carbon matching is primarily an accounting methodology, not a prescriptive procurement strategy. However, by focusing on the emissions impact of clean energy purchases, it can shape and inform procurement decisions over time. Under a carbon matching approach, companies are incentivized to prioritize procurement choices that deliver the greatest emissions reductions — potentially leading to strategic shifts in sourcing, timing, and geography.

Under the current accounting standards, companies are encouraged to reduce their Scope 2 Greenhouse Gas (GHG) emissions by procuring clean electricity. Procurement options leverage energy attribute certificates (e.g. RECs) which represent 1 MWh of 0 emission clean electricity. This is distinct from carbon credits which represent 1 mt CO2e. EFP believes it is important for energy attribute certificates to remain separate from the voluntary carbon market.

The current GHG Protocol guidance allows companies to reduce their market-based emissions by matching 1 MWh of electricity load with 1 MWh of clean energy purchased within the same market and the same year. However, advances in data availability reveal that this approach does not always reflect real-world variations in carbon intensity. In the current system: Time: A clean MWh entering the grid at 2 p.m. is accounted for the same way as one entering at 8 p.m. Location: A MWh within the same market boundary is valued equally regardless of its emission impact. Deliverability: All electricity within a grid or market boundary is assumed to be deliverable (able to be delivered to the purchaser) with no loss. This is a fundamentally flawed assumption that is not mitigated by getting more granular with time or location. Carbon matching addresses these issues by recognizing that deliverability is a flawed assumption and prioritizing, instead, the emissions impact of renewable energy projects.

Carbon matching relies on MERs to capture the effects of various actions. MERs measure the change in system-wide emissions resulting from an increase or decrease in demand or load at a specific location. By comparison, the average grid emission rate (AER), which is often calculated as total system emissions divided by total generation, does not accurately reflect what is happening at the margin and may overstate or understate the benefit of the clean generation. The amount of carbon displaced by clean energy is directly related to the emission rate of the marginal generators, measured as the MER at the renewable energy’s interconnection node. By leveraging MERs we can more precisely measure grid impacts resulting from changes in consumption and generation at a specific location and time.

A carbon matching approach can be effectively implemented with the data available today. As more granular data becomes accessible over time, this data can be leveraged with the same underlying calculation, leading to increasingly accurate results. To implement carbon matching effectively, a company needs data on the time and location of both its energy consumption and the generation of the renewable energy it procures. Key data elements include: Hourly consumption data: Knowing when the company is using electricity is crucial, as the emissions intensity of the grid fluctuates throughout the day. Hourly generation data: This measures when and where the renewable energy (e.g., wind or solar) is being produced. Grid marginal emissions factors: You’ll need MERs from the grid to understand what the carbon intensity is at various times and locations. Quite often, MERs are publicly available by grid operators. For example, PJM has added them to their data mining tool (Marginal Emission Rates Added to Data Miner Tool | PJM Inside Lines). With these inputs, companies can more precisely match their energy use to renewable energy production that offsets emissions at the times when it matters most. For companies that lack hourly data, carbon matching can still provide valuable benefits! With either monthly or annual data, companies can still perform these calculations and gain a clear understanding of the emissions impact of their energy usage. We are also seeing a growing number of third-party providers and platforms emerge to help companies access more granular emissions data.

Carbon matching uses historical marginal emissions rates based on real, calculated emissions impact. This method looks historically at the displaced energy source at each node on the grid to determine the marginal emissions rate. To calculate carbon emissions at the end of each year, an organization would be looking back at the past year of published MER data.

EFP believes that additionality is an important principle and looks to RE100's Impactful Procurement Criteria as the best source to define this complex term.

In the current GHG Protocol approach, annualized MWh metrics fail to recognize or value the benefits of energy storage. Under carbon matching, battery storage can be clearly valued and accounted for because storage can shift generation to higher emission periods. The MER will accurately measure the emissions impact at the time and location the battery is discharged, incentivizing batteries to discharge when it can have the highest emissions impact.

Some ways to get involved include: Send your feedback to the GHG Protocol Secretariat, the Independent Standards Board (ISB), or other contacts in the GHG Protocol’s governance Join the Emissions First Partnership as an ally, signatory, or steering committee member through this form Participate in the upcoming GHG Protocol public consultation period (October 2025) Stay up to date on the GHG Protocol review process here

Carbon matching is primarily an accounting methodology, not a prescriptive procurement strategy. However, by focusing on the emissions impact of clean energy purchases, it can shape and inform procurement decisions over time. Under a carbon matching approach, companies are incentivized to prioritize procurement choices that deliver the greatest emissions reductions — potentially leading to strategic shifts in sourcing, timing, and geography.

Under the current accounting standards, companies are encouraged to reduce their Scope 2 Greenhouse Gas (GHG) emissions by procuring clean electricity. Procurement options leverage energy attribute certificates (e.g. RECs) which represent 1 MWh of 0 emission clean electricity. This is distinct from carbon credits which represent 1 mt CO2e. EFP believes it is important for energy attribute certificates to remain separate from the voluntary carbon market.

The current GHG Protocol guidance allows companies to reduce their market-based emissions by matching 1 MWh of electricity load with 1 MWh of clean energy purchased within the same market and the same year. However, advances in data availability reveal that this approach does not always reflect real-world variations in carbon intensity. In the current system: Time: A clean MWh entering the grid at 2 p.m. is accounted for the same way as one entering at 8 p.m. Location: A MWh within the same market boundary is valued equally regardless of its emission impact. Deliverability: All electricity within a grid or market boundary is assumed to be deliverable (able to be delivered to the purchaser) with no loss. This is a fundamentally flawed assumption that is not mitigated by getting more granular with time or location. Carbon matching addresses these issues by recognizing that deliverability is a flawed assumption and prioritizing, instead, the emissions impact of renewable energy projects.

Carbon matching relies on MERs to capture the effects of various actions. MERs measure the change in system-wide emissions resulting from an increase or decrease in demand or load at a specific location. By comparison, the average grid emission rate (AER), which is often calculated as total system emissions divided by total generation, does not accurately reflect what is happening at the margin and may overstate or understate the benefit of the clean generation. The amount of carbon displaced by clean energy is directly related to the emission rate of the marginal generators, measured as the MER at the renewable energy’s interconnection node. By leveraging MERs we can more precisely measure grid impacts resulting from changes in consumption and generation at a specific location and time.

A carbon matching approach can be effectively implemented with the data available today. As more granular data becomes accessible over time, this data can be leveraged with the same underlying calculation, leading to increasingly accurate results. To implement carbon matching effectively, a company needs data on the time and location of both its energy consumption and the generation of the renewable energy it procures. Key data elements include: Hourly consumption data: Knowing when the company is using electricity is crucial, as the emissions intensity of the grid fluctuates throughout the day. Hourly generation data: This measures when and where the renewable energy (e.g., wind or solar) is being produced. Grid marginal emissions factors: You’ll need MERs from the grid to understand what the carbon intensity is at various times and locations. Quite often, MERs are publicly available by grid operators. For example, PJM has added them to their data mining tool (Marginal Emission Rates Added to Data Miner Tool | PJM Inside Lines). With these inputs, companies can more precisely match their energy use to renewable energy production that offsets emissions at the times when it matters most. For companies that lack hourly data, carbon matching can still provide valuable benefits! With either monthly or annual data, companies can still perform these calculations and gain a clear understanding of the emissions impact of their energy usage. We are also seeing a growing number of third-party providers and platforms emerge to help companies access more granular emissions data.

Carbon matching uses historical marginal emissions rates based on real, calculated emissions impact. This method looks historically at the displaced energy source at each node on the grid to determine the marginal emissions rate. To calculate carbon emissions at the end of each year, an organization would be looking back at the past year of published MER data.

EFP believes that additionality is an important principle and looks to RE100's Impactful Procurement Criteria as the best source to define this complex term.

In the current GHG Protocol approach, annualized MWh metrics fail to recognize or value the benefits of energy storage. Under carbon matching, battery storage can be clearly valued and accounted for because storage can shift generation to higher emission periods. The MER will accurately measure the emissions impact at the time and location the battery is discharged, incentivizing batteries to discharge when it can have the highest emissions impact.

Some ways to get involved include: Send your feedback to the GHG Protocol Secretariat, the Independent Standards Board (ISB), or other contacts in the GHG Protocol’s governance Join the Emissions First Partnership as an ally, signatory, or steering committee member through this form Participate in the upcoming GHG Protocol public consultation period (October 2025) Stay up to date on the GHG Protocol review process here