Google Environmental Insights Explorer

Posted on: 6/05/2023 - Updated on: 6/05/2023

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Without significant and immediate action, the environmental, social, and economic impacts of climate change will continue to escalate, according to the Intergovernmental Panel on Climate Change (IPCC).

Google’s Environmental Insights Explorer (EIE) is founded on the idea that data and technologies can help accelerate the world’s transition to a low-carbon future. EIE aims to simplify the process of setting an emissions baseline and identifying tangible reduction opportunities, which sets the foundation for effective action.

EIE uses unique Google data sources and modeling capabilities to produce estimates of activity, emissions, and reduction opportunities. By surfacing environmental information in a robust platform free of charge, we aim to serve decision makers and researchers working on these issues and solutions for cities globally.

The insights are a modeled estimate based on actual measurements of activity and infrastructure, which is the same underlying information that is made available in Google Maps. We use advanced machine learning techniques to understand how people are moving around the world, and then apply scaling, efficiency, and emissions factors. In generating these estimates, EIE has worked with experts to make methodology choices, while acknowledging that cities may make different methodology choices that generate different results. For more information on where the differences may originate, see the sections below.

The information in EIE is in use by cities around the world for climate action planning and endorsed by leading organizations. Taking action on climate change can’t wait; EIE provides insights to help accelerate action.

Core Insights:

  1. Building Emissions: Estimated emissions from heating, cooling, and powering residential and non-residential buildings, based on Google Maps data.
  2. Transportation Emissions: Estimated emissions of all trips that start or end within city boundaries based on aggregated, anonymized Location History data.
  3. Rooftop Solar Potential: Estimated solar energy production potential of buildings based on total sunshine exposure, weather patterns, and roof dimensions.
  4. Tree Canopy: Estimated tree canopy coverage across city regions, based on aerial imagery and machine learning algorithms.

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