How Artificial Intelligence Can Strengthen Climate Change Strategy

How Artificial Intelligence Can Power Climate Change Strategy

Slowing down climate change is an urgent matter. If we fail, our world will face a bigger crisis than we have experienced because of the global COVID-19 pandemic. When artificial intelligence (AI) technology helps solve a problem, problem solving can be done faster, and the solution is often one that would have taken humans longer to discover. Could Artificial Intelligence Strengthen Climate Change Strategy? Yes, and it already does.

AI can accelerate our response to climate change

There is no time to waste: atmospheric CO2 are the highest levels ever (even with significant declines in stay-at-home orders for COVID-19), mean sea levels are rising (3 inches in the past 25 years alone), and 2019 was the warmest year on record for the world’s oceans. Artificial intelligence is not a panacea, but it can certainly help us reduce greenhouse gas (GHG) emissions in several ways. According to models from the Capgemini Research Institute, AI is estimated to help organizations across industries, from consumer products to retail to automotive and more, achieve up to 45% of the Paris Agreement goals by 2030. AI will likely cut greenhouse gas emissions by 16%. Here are some of the most promising ways artificial intelligence is already or could have an impact on climate change strategy:

Improve energy efficiency

According to the Capgemini Research Institute, artificial intelligence should improve energy efficiency by 15% over the next three to five years. Machine learning supports efficiency in energy generation and distribution, from autonomous maintenance and leak monitoring to route optimization and fleet management. Google’s Deepmind AI can predict wind patterns up to 36 hours in advance to optimize wind farms. Electricity systems create enormous amounts of data. Until now, energy companies have not used this data as far as possible to learn. Machine learning can sift through this data to understand and predict energy generation and demand to help suppliers make better use of resources and fill gaps with renewable resources while reducing waste. Using AI for energy efficiency may start at the industry level, but the use cases extend to the household and individual levels.

Optimize the development of clean energy

In the Amazon basin, hydropower dam developers have typically developed one by one without a long-term strategy. A group led by Cornell University, made up of computer scientists, researchers and ecologists, has developed an AI computer model to find locations for dams (hundreds of hydropower dams are currently proposed for the basin) that can produce the lowest amounts of greenhouse gas emissions. The AI ​​model revealed a more complicated and surprising set of considerations for reducing greenhouse gas emissions than had ever been considered before.

Prevent waste

Companies, governments and leaders regularly deploy AI solutions to prevent waste. Whether AI is used to reduce energy waste in buildings (accounting for a quarter of the CO2 emissions) or to understand supply and demand is a huge way that AI can drive climate change strategy, reducing waste in all forms (time, money, material, etc.)

Make transportation more efficient

Another quarter of the global CO2 the emissions come from the transport sector. AI is already the technology that powers autonomous vehicles, including shared cars and smart transportation systems in some cities. Further adoption will help reduce emissions in the future. Artificial intelligence optimizes routes for fleets, traffic signals and more. All these incremental changes combined have a significant impact on climate change.

Tools to help understand the carbon footprint

They say that “knowledge is power,” and when it comes to climate change mitigation, AI can help develop tools to help individuals and companies understand their environmental footprint and what actions they can take to reduce it.

Monitor the environment

This year there were severe weather events that caused massive destruction and loss. AI is being used and will continue to be used to improve weather forecasting and response. Changes in complex systems such as cloud cover and ice sheet dynamics caused some recent weather changes. Grasses, trees and other plants store carbon, but deforestation and unsustainable agriculture release carbon into the air. This makes an important contribution to climate change. Satellite imagery and AI help conservationists track where this is happening to create change.

Create new low-carbon materials

Steel and cement production is responsible for 9% of global greenhouse gas emissions. If AI could develop new materials with similar properties, but with a smaller carbon footprint, it could slow climate change. Artificial intelligence supports scientists by making the process of mixing different chemical compounds into never-before-tested combinations much faster and easier.

Does AI not have a carbon footprint?

The appeal of AI to mitigate climate change was questioned after a report from the University of Massachusetts at Amherst estimated that the power required to train a neural network is about five times the average emissions of a U.S. car during the lifespan (including production). Yes, artificial intelligence has a carbon footprint, and it’s pretty serious when the model is developed.

Researchers are making progress in reducing the power needed to train AI models. The adoption of AI server farms powered by renewable resources, the development of AI neural networks once and for all, and more are ways researchers are reducing AI’s carbon footprint. In the meantime, when considering the carbon footprint of AI, the tremendous value of AI and the real results it can have in reducing its carbon emissions must also be taken into account. Some of those downstream offsets can offset the emissions that occur when the model is trained.

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