Reducing CO2 wth the help of Data Science

The data scientist’s role in managing carbon in operations

While predictable variations in carbon intensity create an opportunity to reduce carbon, that doesn’t mean that it’s always economical or even feasible to take advantage of it. Taking action requires an organization to go through a few steps, and data scientists are in a good position to drive organizational change in each of these steps:

1. Make the business case: identify actions and estimate costs and benefits
2. Measure carbon footprint at an actionable level
3. Optimize carbon in operations

Make the business case

Find the sources of flexibility in your operations in time and location that can have the greatest impact on carbon. A good first step is a rough inventory of your company’s carbon footprint to understand where the largest emissions sources are. The Greenhouse Gas Protocol establishes international standards for carbon accounting and reporting, using three categories of emissions in their corporate accounting standards:

• Scope 1: direct emissions from sources that are owned or controlled by the company, for example, emissions from combustion in owned or controlled process equipment;
• Scope 2: emissions from the generation of purchased electricity consumed by the company;
• Scope 3: indirect emissions that are a consequence of the activities of the company, but occur from sources not owned or controlled by the company; examples are extraction and production of purchased materials, transportation of purchased fuels, and use of sold products and services.

If emissions from electricity use (scope 2 emissions) make up a significant share of your or your supplier’s carbon footprint, then is there short-term, day-ahead flexibility in your operations across sites or across hours of the day that you haven’t fully exploited? Longer-term, for new site location, are there alternative sites that might otherwise have breakeven economics, but would have very different carbon footprints?

Carbon should be optimized along with other costs and business objectives. Would optimizing for carbon also lower other costs, or would you face headwinds in trading off carbon versus increases in other costs or customer service objectives? Look for opportunities with tailwinds before you try to tackle those with headwinds. Flexibility creates value in operations, and in the pursuit of carbon reduction you may find that you’re developing operational flexibility that can also reduce other costs. Can you shift loads from your operational peaks, moving loads from peak times to off-peak times, or shifting loads between sites that don’t experience peak loads at the same time? If you can shift some of your operational loads in time or across sites, you can reduce costs as well as carbon. For example, at Google, developing the day-ahead capability to shift our compute loads in time or space reduces our carbon footprint, but also gives us the ability to smooth out peak compute and power usage over the day in each location. This in turn enables us to build less datacenter and server capacity, reducing our costs. Also, utilities may offer demand response incentives to pay its customers to shift loads away from peak usage times, which typically require the greatest share of fossil fuels (and have a higher marginal cost to supply due to the gas or coal fuel input cost).

Measure carbon at an actionable level

Once there’s a business case with specific sources of operational flexibility in mind, the data scientist can play a role in building the required data sets necessary to optimally exercise that flexibility. As a start, external data on carbon intensity of the grid needs to be at a necessary granularity to support optimizations, which may mean hourly carbon intensity for each operating region. Third-party providers like Tomorrow and WattTime estimate real-time carbon intensity for regions around the world and offer APIs to provide forecasts at least 24 hours ahead with at least hourly updates.

With data on carbon intensity in each operating region, the data scientists can build models to estimate the how the carbon footprint scales as a function of specific company operations. For example, in our datacenter application, we needed to build models to estimate power consumption as a function of compute and storage resource consumption, and we needed to do so at a fairly granular level so that we could tie carbon footprints to specific product usage. These models can also be used to estimate the carbon footprint of products derived from the operations in order to help the company’s customers optimize their carbon footprints.

Optimize carbon in operations

With the opportunities identified and the necessary data at hand, data scientists can build decision support systems to optimize carbon along with other costs. Building the capability to shift operations in time and space not only lowers carbon, but likely also yields a high ROI in a traditional financial sense if it enables operations to run with less capacity or at a lower energy cost. A decision support system will need to allow for tradeoffs at a granular, hour by hour level. At Google, we use a carbon price in our day ahead load-shifting model so that we can optimize our operational flexibility with a single, unified objective function that considers all cost types: energy, carbon, capacity, and any other cost associated with enabling flexibility in time or space (like networking costs in the case of spatial flexibility of datacenter workloads). For the carbon cost, we use a $/ton price of carbon that is consistent with what economists estimate would be needed now to avoid scenarios of greater than 1.5–2°C warming. (The 2015 Paris Agreement in Article 2 sets forth a goal of “Holding the increase in the global average temperature to well below 2°C above preindustrial levels and pursuing efforts to limit the temperature increase to 1.5°C above preindustrial levels.”) From the 2018 paper The Economics of 1.5°C Climate Change [6], median estimates of 2020 carbon prices needed to avoid 1.5°C and 2°C warming are $105 and $35 per metric ton, respectively. Industry does not need to wait for governments to enact a carbon tax to take action. We can embed a carbon price in our models that optimize operations, and we can do so while still providing a high ROI on a load shifting capability that can reduce both carbon and other operating costs. This is not a zero sum game: we have been able to realize both benefits by including both carbon and other costs in an objective function. Most of the time the carbon and business cost objectives are not in conflict, but our framework allows for economic tradeoffs in those hours or days when the objectives are competing.