How Artificial Intelligence Can Power Climate Change Strategy

Slowing down climate change is an urgent matter. If we fail, our world will face a more extensive crisis than we experienced because of the global COVID-19 pandemic.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningWhen () technology helps solve a problem, problem-solving can be done quicker, and the solution is often one that would have taken longer for humans to discover. Could power climate change strategy? Yes, and it’s already doing so.

Can Accelerate Our Response to Climate Change

There’s no time to waste: atmospheric CO2 levels are the highest ever (even with significant drops from the stay-at-home orders for COVID-19), average sea levels are rising (3 inches in the last 25 years alone), and 2019 was the hottest year on record for the world’s oceans. Artificial intelligence isn’t a silver bullet, but it can certainly help us reduce greenhouse gas (GHG) emissions in various ways. According to Capgemini Research Institute modeling, is estimated to assist organizations in industries from consumer products to retail to automotive and more fulfill up to 45% of the Paris Agreement targets by 2030. will likely reduce GHG emissions by 16%. Here are a few of the most promising ways that already is or can impact climate change strategy:

Improve Energy Efficiency

According to the Capgemini Research Institute, should improve power efficiency by 15% in the next three to five years. Machine learning supports efficiencies in power generation and distribution, from autonomous maintenance and leak monitoring to route optimization and fleet management. Google’s Deepmind can predict wind patterns up to 36 hours in advance to optimize wind farms. Electricity systems create vast amounts of data. So far, energy companies aren’t leveraging this data for learning to the extent that’s possible. Machine learning can comb through this data to understand and forecast energy generation and demand to help suppliers better use resources and fill in gaps with renewable resources while reducing waste. The uses of for energy efficiency might start at the industry level, but use cases go down to the household and individual levels.

Optimize Clean Energy Development

In the Amazon basin, developers of hydropower dams have typically developed one at a time with no long-term strategy. A group led by Cornell University that included computer scientists, researchers, and ecologists developed an computational model to find sites for dams (hundreds of hydropower dams are currently proposed for the basin) that can produce the lowest amounts of GHG emissions. The model revealed a more complicated and surprising set of considerations to lower GHG emissions than had ever been considered before.

Avoid Waste

Companies, governments, and leaders frequently deploy solutions to avoid waste. Whether is used to reduce energy waste from buildings (accounts for one-quarter of COemissions) or understand supply and demand, a huge way can power climate change strategy is to reduce waste in all forms (time, money, material, etc.)

Make Transportation More Efficient

Another quarter of global COemissions is from the transportation sector. is already the technology that powers autonomous vehicles, including shared cars and smart transportation systems in some cities. Further adoption will help curtail emissions in the future. Artificial intelligence optimizes routes for fleets, traffic signals, and more. All of these incremental changes add up to make a significant impact on climate change.

Tools to Help Understand Carbon Footprint

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

Monitor Environment

This year, there were severe weather events that caused massive destruction and loss. is used and will continue to be used to enhance weather prediction and response. Changes to complex systems such as cloud cover and ice sheet dynamics caused some recent weather changes. Grasses, trees, and other plant life store carbon, but deforestation and unsustainable agriculture release carbon into the air. As a result, this is a major contributor to climate change. Satellite imagery and help conservationists monitor where this is happening to create change. […] 

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