A new generation of data scientists could be our best weapon against climate change

Data science is emerging as a powerful weapon in the fight against climate change. For example, an international team of researchers recently identified the top sites in the world releasing methane. They used machine learning algorithms to analyze thousands of satellite images and pinpoint “ultra-emitter” sites responsible for about 10% of the oil and gas industry’s global methane emissions. Many are pipeline leaks that are impossible to detect from the ground, but relatively simple and cost-effective to fix once they have been found.

This new research on methane emissions is just the beginning. Using data science to get a global picture of pollution and identify easy targets for reducing it represents a leap forward in our efforts to address climate change.

Data scientists will help us implement precision agriculture that reduces water use and soil depletion, optimize supply chains to minimize overproduction and waste, and determine when is the most energy-efficient time of day for families to run their home appliances. Efforts to fix climate change touch many areas of our lives, and data scientists can contribute to almost all of them.

Data scientists will be critical to building a clean energy economy and ensuring a consistent power supply from renewables like solar and wind. Machine learning models can forecast clean power supply based on weather and precipitation and predict demand based on past energy use patterns. We can incorporate sensors and smart grid technology into homes and businesses to provide even more precise data on energy needs.

The world’s biggest companies are starting to see the potential of using data science to tackle climate change. Data scientists at Microsoft created a Sustainability Calculator for customers to estimate the emissions produced by their use of Microsoft software and cloud computing products and identify ways to reduce their carbon footprint.

IBM data scientists developed an Environmental Intelligence Suite for companies to track emissions and analyze the business risks created by climate change. The A.I.-driven software can help organizations predict and respond to severe weather events and transform their businesses to be better suited to a changing climate.

In transportation, data scientists will help create public transit routes that respond to user demand in real time and extend the range of electric vehicles with better techniques for battery management. Data scientists at GM are using data on vehicle travel patterns collected from the OnStar system in its vehicles to decide where to locate new electric vehicle charging stations. An app will help drivers track their charging needs and battery usage and find optimal charging locations.

Buildings, which account for a third of our energy use and energy-related greenhouse gas emissions, are another area where we could see significant gains. Data scientists are helping architects design green buildings that use sensors and continual data monitoring to identify water leaks as soon as they happen and modulate heating and cooling based on when people are using the building.

For existing buildings, energy use could be reduced up to 90% through changes like energy efficiency upgrades and smart heating and cooling systems. However, the best fixes vary from building to building depending on the structure’s age, materials, use, and other characteristics. Data scientists can help us identify which buildings to target for retrofitting to get the biggest bang for our buck.

I recently co-chaired a datathon where nearly 4,000participants from more than 95 countries took up this challenge. The datathon was hosted by Women in Data Science (WiDS)–an initiative to inspire and educate data scientists worldwide and to support women in the field–in collaboration with Climate Change A.I., Lawrence Berkeley National Laboratory, the EPA, and MIT Critical Data.

Using data on building characteristics from the Department of Energy, these data scientists developed models to predict a building’s energy consumption. Then they took on a tougher challenge, using other datasets such as satellite images to predict building characteristics when that data isn’t available. If a city doesn’t track how old its buildings are, these models could estimate building ages using aerial photos and then target the oldest structures for efficiency upgrades.

The WiDS datathon showcases one of the many compelling ways data scientists can help us fight climate change. What’s especially exciting is that anyone can participate. You don’t need access to expensive lab equipment or deep expertise in climate models or green building design to contribute. Many of our datathon participants had never worked on environmental problems before. Anyone who understands the basic tools of data science, like coding, statistics, and modeling, can help in the fight against climate change.

Data science is interdisciplinary and democratizing–this is what makes it so transformative. Some datathon participants were inspired to seek out and create similar building datasets in their own regions to tackle energy consumption problems locally. A few energy companies expressed interest in partnering with us to crowd-source solutions to some of their hardest modeling problems. To make these scientific solutions a reality, data scientists need to collaborate with other scientists and engineers with domain expertise, as well as policymakers and social scientists.

There are important challenges to be addressed. We need to make data more open and available for people to use, while also protecting privacy. We must ensure the models we develop don’t replicate existing biases and inequalities or create solutions that only work for the wealthy. We need tools and techniques that are simple enough to be scalable without requiring local governments and small businesses to hire huge squads of data scientists.

Data science is more than just a tool to build better apps or optimize our social media feeds. There are so many ways data scientists can contribute to solving our most important challenges, from climate change to healthcare, education, and political polarization. My hope is that more data scientists can find opportunities to use their skills and expertise for social good.

Weiwei Pan is a research associate at the Institute of Applied Computational Sciences at Harvard University, where she works on machine learning research in the Data to Actionable Knowledge (DtAK) lab. She is also co-chair of the 2022 Women in Data Science (WiDS) Datathon. Microsoft, IBM, and GM are all sponsors of the WiDS initiative.

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