The methodology behind our latest global data expansion

This week we’re excited to announce a major, global geographic expansion of our flagship Marginal Operating Emissions Rate (MOER) carbon data signal — growing from 40 to 210 total countries and territories. You can read the full press release here. With this expansion to ~170 new geographies, WattTime now offers actionable marginal emissions data — available with 5-minute granularity and a combination of historical, real-time, and rolling 3-day forecast perspectives — for 99% of the world's electricity consumption.

MOER coverage map

Accurate, location-specific, timely, and granular MOER signals are central to a trio of solutions — carbon-aware load shifting, emissionality-based renewables siting and procurement, and supply chain decarbonization — that can save more than 9 gigatons of emissions every year. That’s equal to nearly 20% of total global carbon emissions. These solutions also accelerate the reduction of harmful air pollution that disproportionately affects the developing countries that often are last to gain access to technological solutions that improve their lives.

Adopting those three solutions at scale (and unlocking those 9+ gigatons of emissions reductions) depends on a truly global MOER signal. Which is why this week’s announcement represents an enormous step-change for what’s possible.

In this article, we’ll take a closer look at the foundational methodology driving our MOER models thus far, as well as the new methodologies that allowed our team to expand to substantially global data coverage.

The science behind WattTime’s current MOERs

When electricity demand rises or falls, or new wind or solar capacity gets built, which generators respond varies. Namely, a certain generator (or generators) ramp up or down — or turn on or off — in response to changes in load or new renewables added to the grid mix. These power plants on the edge of the dispatch stack order are what’s known as marginal generators, and their associated emissions are what’s described by our MOER signal. Sometimes polluting, fossil-fueled peaker plants might set the marginal emissions rate; at other times, surplus renewables being curtailed might be on the margin.

Scientists agree that such a marginal signal is the best way to guide (and measure the impact of) interventions such as cleaner EV charging to reduce emissions or the avoided emissions of building a new wind farm on a coal-heavy grid.

Our current-best MOER — with foundations in academic literature and iterated on by WattTime for over ten years — is based on causal, empirical modeling. In the grid regions of North America and Europe, we use robust, detailed, generator-level data inputs to refine and train our algorithms. For example, we incorporate generation and emissions data from individual power plants via the US EPA’s Continuous Emissions Monitoring System (CEMS). We get demand, interchange, and generation by fuel type data from the US Energy Information Administration (EIA) and European Network of Transmission System Operators for Electricity (ENTSO-E). We also integrate myriad other data sources into our overall modeling. For regions where these data are available, we feed it all into a binned regression model. The result is today considered WattTime's highest-quality methodology for the MOER signal type.

In regions where we have access to partial real-time and forecasted information about the status of the grid (such as demand or energy prices), but are lacking specific ground truth time series data such as emissions or generation by fuel type required to train a binned regression model, we use a proxy regression model where we use machine learning to identify a data-rich grid with similar characteristics to use as a proxy for the grid with partial data. The power plants for the region of interest are characterized by Climate TRACE and assembled into a supply curve that resembles economic dispatch for the proxy region, which is then used to estimate the MOER based on the real-time demand in the region. MOER data from this “Proxy Regression” model is available in countries such as Brazil, India, Chile, and Turkey. (Longtime WattTime partner Microsoft helped fund development of this method.)

But after deploying these higher-quality MOER signals for the grids of all countries with the necessary data, we still had not covered about 170 countries of the world. Yet many of these are the same countries where emissions are not yet falling, and it’s not good enough to just ignore them. Our latest MOER expansion fixes that in big ways, thanks to a novel modeling approach from our team.

Inside the methodology powering our global MOER expansion

With the goal of making MOER data truly global, we’ve employed a synthetic demand model for countries where we lack historical and real-time grid information. It extends and improves upon the 2021 work of Mattsson et al.

In lieu of actual real-time demand data (like those used in our proxy regression models), the Mattsson framework uses atmospheric estimations derived from ERA5 climate and weather data, along with demographic information, to model synthetic power demand with a mean absolute percentage error of only 8% when averaged by month and hour.

To model the MOER, we expanded upon Mattsson’s academic model to account for more geographic and economic diversity. This enabled us to produce estimates of real-time and forecasted demand. To productionize this, we needed to build a sophisticated weather data modeling pipeline, ingesting gigabytes of global weather data four times a day, thus producing timely synthetic demand estimates. Currently, we’re using these synthetic demand estimates as inputs to our existing proxy regression models in order to produce MOERs using a model we are calling the “Synthetic Demand Proxy Regression” model, or “Synthetic Proxy” for short.

While the MOER signals resulting from this method are inherently less accurate than those that we can derive using the binned regression model, they’re nevertheless useful for both carbon-aware load shifting and renewables siting — which unlocks significant emissions-reduction potential. Of course, data transparency will only increase in regions across the globe. As it does, we will vigilantly be upgrading our MOER signals according to higher-quality binned regression methodologies.

Putting global MOER data into action 

The MOER signals generated via our synthetic proxy regression models unlock potential to enable much more strategic emissions reductions decisions and automations, and in regions that have never before enjoyed access to such actionable data. Historically, granular and accessible emissions data have been far more accessible for countries in the Global North. This release is a major step in closing this gap, and opens up opportunities to drive emissions reductions and new solution possibilities across the Global South.

All WattTime data can be accessed through our API. Basic access is available for free to all users. Partners on our Pro data plan get full access to historical and forecast data, including premium support. Our partners with a global data license will automatically gain access to these new grid regions. Contact our team to learn more.

WattTime expands marginal emissions dataset globally to cover nearly 100% of world's electricity consumption

The nonprofit is working to eliminate the world's emissions data divide — and the inequitable approach to the energy transition — with the first-ever electricity marginal emissions dataset covering nearly every country and region, which can be leveraged to save the world more than 9 gigatons of carbon emissions annually.

OAKLAND, Calif., Oct. 23, 2024 /PRNewswire-PRWeb/ -- Environmental tech nonprofit WattTime has announced the completion of the first-ever hourly electricity marginal emissions dataset for nearly every country worldwide. Today's data release expands the availability of hourly marginal emissions data to 210 countries and territories, covering nearly 100% of global electricity consumption on the world's power grids. Previously, only around 40 countries had such data. WattTime developed this dataset to enable more strategic climate action and emissions reductions decisions, particularly in regions that never had access to such granular and actionable data before.

These data allow users to estimate emissions based on when and where electricity is used, as well as the avoided emissions that can be achieved by investing in renewable energy projects in specific locations. Marginal emissions data empower corporate leaders, policymakers, and consumers to make decisions that reduce or avoid the most emissions.

Historically, a lack of accurate and actionable marginal emissions data has hindered climate action in much of the Global South and other developing countries. This data divide has slowed meaningful climate progress and clean energy deployments in the regions where it is needed most.

"Climate progress is needed worldwide, but all too often, the most cutting-edge, data-driven solutions are only made available in a select few wealthy countries," said Gavin McCormick, founder and executive director of WattTime. "But we will never beat climate change if emissions data experts keep ignoring the rest of the world — because frankly, that's where most emissions are. We're beyond excited to be leaping forward in our mission to give anyone, anywhere the tools needed to slash the emissions. Because we're all in this together."

"At Meta, we believe that using more accurate emissions data drives more informed and impactful climate action. The emissions from a megawatt-hour of electricity can vary widely by time and location, both within and across grids. WattTime's vastly expanded dataset will help all grid participants more accurately assess their carbon footprints and make more targeted climate investments to accelerate grid decarbonization globally," said Brent Morgan, Principal, Energy Strategy at Meta.

"Amazon is committed to making the global power grid carbon-free and more reliable for everyone. We recognize that carbon emissions from electricity generation vary by time and location, making accurate measurement complex. With the right data, we can now better understand the emissions impact of our energy consumption and clean energy purchases. The expanded data from WattTime offers crucial insights to target energy projects where they can have the greatest impact, helping to decarbonize the grid and make it more reliable for all," said Jake Oster, Amazon Web Services (AWS) Director of Energy, Environment and Sustainability Policy.

Marginal emissions data have many uses, but three use cases in particular have dramatic potential to reduce global emissions. Using data from the U.S. Department of Energy, the United Nations, and its own work, WattTime estimates that full global adoption of these three techniques alone could save the world over 9 gigatons of carbon emissions annually:

Renewables siting

Also referred to as "emissionality," this approach uses granular marginal emissions data to help renewable energy buyers target the dirtiest hours and locations on the grid. This approach allows buyers to maximize the climate benefit of their investments by displacing more carbon-intensive power.

WattTime has worked with partners like Apple, Boston University, ClearloopGeneral Motors (GM) Meta, Nucor, Salesforce, and The Nature Conservancy to enable emissionality-based procurement.

"You can't fix what you can't measure. At Salesforce, we use marginal emissions data to guide our procurement of renewables in locations around the world that can maximize emissions impact," said Megan Lorenzen, Director, Climate & Energy, Salesforce. "The expansion of this dataset will accelerate that work and help close the global data divide — a critical step in reaching our collective climate goals."

"In addition to speeding global emissions reductions, building more renewables in lower-income countries can provide concrete benefits for promoting peace in fragile regions. We at Energy Peace Partners know firsthand the power of expanding clean energy access among vulnerable populations. This dataset — if used well — will help corporate buyers better optimize their procurement and send stronger demand signals for clean energy projects that deliver decarbonization and social benefits together," said Doug Miller, director of market development at Energy Peace Partners.

Now that an emissionality-based approach is possible on a global scale, cloud computing company and WattTime partner PagerDuty has provided WattTime with a grant to help raise awareness of the solution among decision-makers in Global South countries.

Load shifting

Marginal emissions data can be used to power automated emissions reduction (AER) technology and other features that allow for the scheduling of flexible energy demand to reduce electricity-related emissions. Internet-connected devices, like smart thermostats and EV chargers, can use the data to forecast when energy consumption will be cleanest and shift power use to align with those times.

WattTime has worked with companies like Amazon, AppleBMWMicrosoft, and Toyota to deploy load-shifting solutions driven by marginal emissions data.

Supply chain decarbonization

WattTime's marginal emissions data can also be used to better understand the electricity-related emissions of a company's suppliers, allowing them to make better supplier decisions that contribute to decreases in Scope 3 emissions. WattTime is advancing this use case rapidly in its work with Climate TRACE — a global nonprofit coalition that provides open access to source-level emissions data for every sector and country in the world.

The full dataset is now available to WattTime partners through licensing agreements. A free and simplified version of the dataset suited for many emissions reduction use cases can be accessed by anyone via the WattTime API.

To learn more about opportunities to support or partner with WattTime, contact the team here.

About WattTime

WattTime is an environmental tech nonprofit that empowers all people, companies, policymakers, and countries to slash emissions and choose cleaner energy. Founded by UC Berkeley researchers, we develop data-driven tools and policies that increase environmental and social good. During the energy transition from a fossil-fueled past to a zero-carbon future, WattTime 'bends the curve' of emissions reductions to realize deeper, faster benefits for people and planet. Learn more at www.WattTime.org.

Media Contact

Nikki Arnone, Inflection Point Agency for WattTime, 1 (719) 357-8344, nikki@inflectionpointagency.comwww.watttime.org

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