In March 2021, the U.S. Department of Energy stated that it would be funding $34.5 million for state-of-the-art research tools towards new scientific discoveries, including clean energy and climate solutions. These funding opportunities are intended to back researchers using data science and computation-driven techniques, such as artificial intelligence (AI) and machine learning (ML) to enhance energy efficiency, further clean energy technologies and forecast extreme weather and climate patterns.
Climate change is one of the most important issues of this generation. Melting polar ice, shoreline erosion, catastrophic droughts, and rising sea levels will potentially force migrations in the millions from coastal areas and low-lying border nations — all of which have highly concerning economic, political, and social implications.
In 2020, there were a record-breaking 22 weather/climate disasters in the U.S. that reached $98.9 billion in damages. Extreme weather events are escalating and becoming more severe from human-induced climate change, and a recent Intergovernmental Panel on Climate Change report reveals that this is only a portent of things to come in the near future.
Data science applications are being explored to address and potentially reverse some of the dangerously escalating impacts of climate change.
Why Data Science Is Ideally Suited for This Climate Change Crisis
Data science techniques, systems and processes play a vital role in harnessing overwhelming amounts of data and applying it towards the study of climate change and the advancement of developments in this domain.
A few examples that illustrate why data science is particularly useful in fighting climate change include:
- The processing and computing power of the cloud can run ML algorithms built to discover patterns and derive crucial insights from massive quantities of climate data, like sea temperatures or polar ice levels.
- Climate issues, like sustainability and climate adaptation, are data-intensive and involve complicated data sets specifically suited to Big Data.
- High-performance computing (HPC) is needed to reduce the quantity of environmental data, such as satellite images, to make it more manageable for analytics.
Applications and Use Cases for Data Science and Climate Change
New technologies are proving incredibly valuable in addressing issues related to climate change:
Sustainable Traffic Management
ML can address traffic congestion that aggravates air quality and squanders fuel via traffic classification and prediction. According to Xtelligent, who is funded by the U.S. Department of Transportation, 1970’s technology still controls 98 percent of signalized intersections in the country.
Sensor technologies can connect city intersections, manage traffic signal networks in real-time and inject more sustainability into traffic management issues.
Airlines are also looking to optimize air traffic flow to minimize miles and fuel usage using AI-powered flight prediction. AI and ML tools can help airlines plan, track and act on recommendations for flight rerouting to sidestep problems like poor weather or congested airspace. Alaska Airlines’ use of this solution allowed them to save 480,000 gallons of fuel and prevent 4,600 tons of carbon emissions.
Energy Efficient Buildings
According to the Alliance to Save Energy, buildings comprise 40 percent of all U.S. energy consumption and a comparable amount of greenhouse gas emissions. AI-driven autonomous intelligence, cloud, and IoT can enable buildings to adapt to external temperatures and conditions, and introduce more energy efficiency into heating and cooling. Intelligent systems can autonomously respond to changes in the building’s environment and instantly sustain energy efficiency levels without compromising human comfort.
Smart building projects are taking off due to the pandemic. Data collected in real-time via IoT. IoT sensors enables lights and heat to be turned off in vacant rooms, and further improve resource efficiencies.
A critical space for venture capital investment today is addressing disasters and resilience via new technologies. For example, One Concern announced a $45 million funding initiative to advance a disaster resilience platform that models and simulates environmental impact and response to natural disasters through digital twins.
Monitoring Extreme Weather
From flash floods to rampageous wildfires, the pursuit to track extreme weather conditions is accelerating thanks to ubiquitous AI and ML techniques. Tools are being developed to augment human forecasters and monitor paths of severe weather with greater accuracy.
One specific example is the partnership between Google and the National Oceanic and Atmospheric Administration, which aims to explore the ways cloud-based ML tools can increase accuracy in weather forecasting to track extreme weather like tornados or hurricanes.
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Government entities and businesses alike are prioritizing data science to contribute to solutions that address the pervasive risks stemming from climate change. Data scientists are powerful assets in the strategic applications of data science in the research, discovery and development of new solutions and processes to battle the climate crisis.
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