Tag: Carbon Accounting

Carbon Accounting with the Greenhouse Gas Protocols: Successes and Emerging Challenges

David Luke Oates
David Luke Oates is a carbon accounting subject matter expert.
David Luke Oates

By David Luke Oates, SVP of Power Markets Research, REsurety

The Greenhouse Gas Protocol is a foundational component of modern climate standards. It is incorporated into the Task Force on Climate-Related Financial Disclosures’ (TCFD) guidelines for voluntary climate disclosures1, as well as the Science-Based Targets Initiative’s (SBTi’s) recommendations for aligning corporate targets with climate goals.2 It has also largely been paralleled in the U.S. Security and Exchange Commission’s recent proposed rule on climate disclosures.3

The GHG Protocol has achieved considerable success in providing a common framework for voluntary disclosures. But it is now a fairly outdated standard, and its flaws are becoming more impactful and problematic. The GHG Protocol Corporate Standard was originally released in the early 2000s, with updated Scope 2 guidance released in 2015. The nearly seven years since that release have featured dramatic increases in corporate clean energy purchases and interest in accurate corporate climate disclosures.4 There is now growing interest in updating the GHG Protocol and addressing some of its shortcomings.

At REsurety, we spend much of our time helping buyers and sellers of clean electricity to manage their financial risks and achieve their decarbonization goals. We are particularly interested in ensuring that Scope 2 accounting is as effective as possible. Today, the GHG Protocol Scope 2 Guidance has two major flaws: 1) it does not ensure that all actual carbon emissions are accounted for across entities and 2) it often doesn’t create the right incentives for entities interested in decarbonization. 

On the first item, the GHG Protocol’s Market-Based method for Scope 2 accounting allows reporting entities to apply REC purchases to cover their consumption at an emissions rate of 0 tons/MWh. It also allows entities to account for their grid consumption by applying a simple-average emissions rate. This average emissions rate reflects the same clean energy claimed through REC retirements, effectively double-counting the impact of clean energy and contributing to under-reporting of emissions.5 While this double-counting may have been of little concern a decade ago, the volume of today’s clean energy purchases make it a more serious problem.

On the second item, by relying on average emissions rates with low temporal and spatial granularity, current Scope 2 guidance risks send the wrong signals to entities interested in decarbonization. Consider an entity purchasing solar energy that mostly displaces coal generation, in a grid that also includes considerable baseload nuclear. Since the average emissions rate of this grid is much lower than the emissions rate of the displaced coal, the reduction in the entity’s carbon footprint would not reflect the solar energy’s full carbon impact. In general, the activities achieving the greatest amount of decarbonization are not fully rewarded under the current GHG Protocol, creating a misalignment of incentives. We think there is an opportunity to fix both of these problems.

Governments and corporate entities have recently made ambitious climate mitigation commitments. Truly delivering on these commitments will require a modernized set of carbon accounting rules to align incentives and avoid double-counting. We believe that a revised Scope 2 carbon accounting framework based on granular marginal emissions data can help address some of the shortcomings we mentioned above. We look forward to sharing more details on potential solutions to these challenges in the months to come.

In the interim, we love talking with anyone who shares our goals of more accurate carbon impact measurement and the tools to maximize that impact – so please contact us at [email protected] if you have any questions or want to connect and discuss.

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Footnotes:

[1] See p. 21, Implementing the Recommendations of the Task Force on Climate-related Financial Disclosures, October 2021

[2] See p.3, SBTi Criteria and Recommendations, Version 5.0, October 2021

[3] See §I.D.2. (p. 40), The Enhancement and Standardization of Climate-Related Disclosures for Investors, SEC Proposed Rule, File No. S7-10-22

[4] U.S. corporate clean energy purchases grew from 1.2 GW/year in 2014 to over 11 GW/year in 2021. See Clean Energy Buyers Association Deal Tracker

[5] While this double-counting could theoretically be corrected by applying the residual mix emissions rate to all parties’ grid consumption, this approach is not feasible in many jurisdictions. Calculating the residual mix emissions rate depends on visibility into all private contracts for RECs between counterparties, something that individual reporting entities aren’t able to provide. In jurisdictions (such as the U.S.) where residual mix emissions rates are not available, current GHG Protocol guidance is to apply the average emissions rate to grid purchases. See GHG Protocol Scope 2 Guidance §6.11.4


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High-resolution carbon emissions data now available for PJM Mid-Atlantic power grid

Adam Reeve

New data empowers wind, solar and energy storage projects and load centers to accurately calculate their carbon impact

BOSTON, March 17, 2022 – REsurety announced today the expansion of its breakthrough Locational Marginal Emissions (LME) carbon data tool to the PJM power grid in the U.S. Mid-Atlantic region. Previously available only in Texas’s ERCOT grid, the expansion of the data set into PJM results in a dramatic increase in the number of locations for which the high-resolution emissions data is available, with now nearly 15,000 distinct locations served. It’s also the first time that REsurety will be releasing data at the five-minute level, which is particularly valuable for understanding the impact of storage on the PJM grid. The company intends to scale LMEs to the rest of the United States and internationally.

Adam Reeve, senior vice president of software, REsurety

The Locational Marginal Emissions data set measures marginal carbon emissions rates at each node on the grid, enabling insight to the impact of each specific clean energy project site or load location. This capability allows project developers, investors, and corporations to accurately understand the carbon impact of their activities.

“This data is critical for efficiently decarbonizing the grid, as we can now see the impact of specific projects and activities on system-wide emissions,” said Adam Reeve, Senior Vice President of Software, REsurety. “By understanding the carbon emissions impact of specific technologies at specific locations, we can ensure that clean energy strategies are more precisely targeted to where they can have the biggest impact.”

Reeve continued, “We are especially excited about what this means for the value proposition of energy storage. While many people intuitively understand that storage is a necessary technology for decarbonization, historically the industry has lacked the tools to measure its impact accurately.

“But with this level of nodal granularity, we can measure the impact of specific storage projects on the grid during both charging and discharging. We can see, for example, how some projects charge when the marginal generator is clean, and then discharge when the marginal generator is dirty, avoiding a significant amount of carbon emissions in the process.”

“We can also see how other storage projects, unfortunately, can actually increase system emissions. It’s not a one-size-fits-all technology. Where you site energy storage and how you schedule its dispatch can mean the difference between significant increases or decreases in carbon emissions. This data empowers investors and storage operators to measure and maximize their carbon reduction impact.”

Recognizing the value of marginal carbon emissions data, PJM started publishing marginal emissions rates at load node locations starting in January, 2021. REsurety’s data set builds on that initial step in a number ways, including by extending the data set to cover generator nodes and correcting anomalous data points (or outliers) with values consistent with the actual topology of the transmission grid. REsurety also leverages its own models to extend the nodal data back several years, enabling analysis of longer-term market trends.

The resulting LME data set takes into account real-time grid congestion, actual emissions rates by each generator unit, and the physical power flows throughout the system. The data set is available via an API and being integrated into REsurety’s other software tools.

“LMEs can be used to measure the carbon impact of any sustainability strategy – whether it is focusing on local procurement, 24/7 matching, or maximizing your carbon emissions impact,” said Reeve. “We’re excited for this high-resolution emissions data to enable better measurement and decision-making across the board.”

REsurety’s Locational Marginal Emissions data is currently available in ERCOT and PJM, and will be available for other markets later this year. To learn how your company can better measure and maximize the carbon impact of your clean energy initiatives, contact us at [email protected].

About REsurety
REsurety is the leading analytics company empowering the clean energy economy. Operating at the intersection of weather, power markets, and financial modeling, we enable the industry’s decision-makers to thrive through best-in-class value and risk intelligence, and the tools to act on it. For more information, visit www.resurety.com or follow REsurety on LinkedIn.