Under the hood of Environmental Insights Explorer’s latest transportation emissions release

By Christopher Bian, Sr. Software Engineer, Environmental Insights Explorer

In support of Google’s 2030 commitment to help more than 500 cities and local governments reduce an aggregate of 1 gigaton (that’s one billion tons) of carbon emissions per year by 2030 and beyond, we’re proud to announce the release of new 2019 transportation emissions data to Environmental Insights Explorer (EIE).

EIE is a free online platform developed by analyzing Google’s comprehensive global mapping data together with standard greenhouse gas (GHG) emission factors to help cities measure, plan, and track efforts in their climate action plans. Since first launching EIE in 2018, we’ve made significant investments to provide frequent data refreshes according to industry best practices and direct city feedback to help overcome common barriers where a lack of time, resources, and data have previously prevented cities from accelerating climate action commitments.

How we model transportation emissions

To help users better understand how EIE calculates transportation emissions, we use the following techniques and computations:

• Extract insights based on aggregated and anonymized data, including from the same datasets that power Google Maps. These datasets are most similar to Agent Based Activity (ABA) models used by the transportation planning domain.
• Distribute ABA trips pairwise to both origins and destinations, which enable travel direction (inbound, outbound, in-boundary) data for each administrative boundary.
• Compute aggregation counts by city, time period, mode, and travel direction, applying scaling factors for general population and average vehicle occupancies.
• Apply emissions factors, based on CURB¹, that contain fleet mix and fuel type distributions by country/region. This dataset is the same across multiple years.
• Sum overall carbon emissions by mode as metric tons of CO₂ equivalents.²

Evaluating EIE Transportation Data

In preparing for the new release of 2019 data, we worked with external experts to assess the accuracy and value that EIE data brings to cities. The Atmospheric Fund (TAF) in partnership with Dunsky Energy Consulting compared EIE data against its in-house transportation activity and GHG totals based on fuel sales methodologies for the Greater Toronto-Hamilton Area (GTHA) — which includes 26 municipalities in Ontario, Canada. They stated that “activity data in the transportation sector is notoriously difficult, time-consuming, and costly to obtain for Canadian municipalities. EIE estimates of VKT can offer robust, free, and timely estimates to support municipal GHG inventories and climate action plans. Cities can now benefit from digitized data available with the click of a button”.

In addition, TAF and Dunsky Energy Consulting compared EIE year-over-year changes and found that “Overall, EIE VKT and GHG emission estimates for automobiles are within an acceptable range to TAF estimates, once fine-tuned based on vehicle occupancy and emission factors. Moreover, the EIE estimates correlate well year-over-year to the GTHA and individual regions, which suggests reliability of the EIE data sets.”

These findings underscore our belief that EIE data are a significant addition to cities’ toolkits when evaluating policies to mitigate climate change.

What’s new in the 2019 release?

Better accounting for seasonal effects: EIE transportation measurements now better account for dynamic effects over the course of the year, such as seasonality. Privacy and anonymization techniques are applied before the final annual level of aggregation is published to EIE.

2019 population update: A city’s population is a dynamic figure that changes over time. In the 2019 data release, a 2019-based set of population figures is used, which can result in emissions differences depending on whether a city has grown or contracted. The population data may vary somewhat from other sources, depending on boundaries and other factors. The population dataset is based on the work of WorldPop, which develops open, high-resolution spatial datasets based on peer-reviewed research and methods.

Release notes: In order to better communicate ongoing improvements, EIE will include a running log of release notes that describe changes, features, and other noteworthy updates. This will be accessible from the clock icon on the upper right-hand corner of each city’s overview page. For additional information, see the EIE technology section of the Environmental Insights Explorer methodology page, particularly the “Transportation > Why are my numbers different?” section. The following resources may also be helpful in understanding and evaluating EIE data: the EIE Validation blog post, and ICLEI Technical Review.

Public transit: There are a wide variety of vehicle types and sizes on land and sea, and their usage and occupancy rates vary significantly. While EIE applies occupancy factors in its model, these do not account for regional differences which pose a challenge when modeling vehicular movement.

As a result, you may see that for some cities, public transit data (such as rail, ferries, subway, tram, buses) show larger changes when compared year-over-year. These changes are a result of improved inference models that better distinguish between modes of travel. Overall, these changes improve the accuracy and usability of emissions estimates in the long term.

Overall Google mapping improvements: EIE leverages Google Maps technologies and features, which are constantly improving. When Google Maps improves and users take advantage of these features, it results in better measurement of city activities and emissions. Examples include support for cyclists or mopeds, more efficient routing, and better inferences of mode of travel.

While this may sometimes result in shifts in certain slices of the data, these improvements more accurately measure the real world, and equip cities with more insights to inform climate action.

Looking forward

The EIE transportation emissions model was developed by drawing from concepts and techniques that have been in use across the broader transportation planning/modeling domain. The resulting approach is specifically designed for use with Google proprietary datasets that measure and infer transportation activities.

Modeling transportation flows is a complex endeavor, and we are constantly working to improve our ability to represent the world by combining world-class techniques, collaboration with local experts, computing power, and relevant, privacy-safe datasets. In doing so, we hope that by providing useful insights, we can effectively inform and accelerate climate action.

¹ Sourced from the Climate Action for Urban Sustainability tool (CURB).
² CO₂e accounts for the carbon emitted by multiple GHG, including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) based on their Global Warming Potential (GWP).