Data science plays an increasing role in the fight against climate change. It can be used to help with everything from predicting supply and demand for clean energy systems to identifying which buildings to target for energy efficiency upgrades. As we use data science to develop innovative solutions to climate change, we should be careful not to replicate, or worsen, existing inequities—or create new ones.
The effects of climate change, from extreme weather to debilitating drought, will hit harder in poor countries and communities least equipped to deal with the consequences. Unfortunately, many of the solutions we are developing are also inequitable. Poor households that still cook with coal and kerosene can’t afford solar roofs and energy-efficient appliances. Low-income parents juggling work and childcare have little time to spend at charging stations to charge electric vehicles and may not have a garage or driveway to charge them at home.
As we use data science to help fight climate change, we should look for solutions that are both greener and more equitable. For example, many communities don’t have access to reliable public transportation. According to Tierra Bills, assistant professor of civil and environmental engineering at UCLA, data on transportation needs is often unavailable or inaccurate, especially for groups such as the elderly or disabled who face access issues. New geolocation data sources and methods for collecting transit preferences directly from users can offer a more comprehensive picture of people’s transportation needs. Data science could help us optimize route planning and create on-demand bus routes and carpool matching services for underserved areas.
Another area where data science can support more equitable solutions is access to clean, reliable energy. A staggering 760 million people worldwide lack electricity, and many more experience frequent disruptions. According to energy expert Xin Ma, managing director of the Asia Platform at TotalEnergies Ventures, advances in data science can help us expand and democratize access to energy. For example, microgrids and minigrids can bring energy to remote and underserved communities by enabling small-scale, localized electricity generation that is not dependent on the traditional power grid. Data science is used to forecast energy supply based on weather patterns and to monitor and optimize the condition of solar panels and batteries. In recent years, microgrids and minigrids have brought power to more than 11 million people worldwide.