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Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

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How Artificial Intelligence Can Power Climate Change Strategy

2 July 2021

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. When artificial intelligence (AI) 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 artificial intelligence power climate change strategy? Yes, and it’s already doing so.

AI 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, AI 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. AI will likely reduce GHG emissions by 16%. Here are a few of the most promising ways that artificial intelligence already is or can impact climate change strategy:

Improve Energy Efficiency

According to the Capgemini Research Institute, artificial intelligence 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 AI 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 AI 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 AI 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 AI 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 AI solutions to avoid waste. Whether AI is used to reduce energy waste from buildings (accounts for one-quarter of CO2 emissions) or understand supply and demand, a huge way AI can power climate change strategy is to reduce waste in all forms (time, money, material, etc.)

Make Transportation More Efficient

Another quarter of global CO2 emissions is from the transportation sector. AI 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, AI 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. AI 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 AI help conservationists monitor where this is happening to create change.

Create New Low-Carbon Materials

The production of steel and cement accounts for 9% of global GHG emissions. If AI could develop new materials with similar properties but with a smaller carbon footprint, it could help slow climate change. Artificial intelligence supports scientists by making the process of mixing various chemical compounds into combinations never tested before much faster and easier.

Doesn’t AI Have a Carbon Footprint? 

The appeal of AI to mitigate climate change was questioned after a report released by the University of Massachusetts at Amherst estimated the power required to train a neural network was approximately five times the average U.S. car’s lifetime emissions (including its manufacturing). Yes, artificial intelligence does have a carbon footprint, and it’s quite severe when the model is developed.

Researchers are making progress in reducing the power required to train AI models. The adoption of AI server farms powered by renewable resources, development of AI once-for-all neural networks, and more are ways researchers reduce AI’s carbon footprint. In the meantime, when considering AI’s carbon footprint, the tremendous value of AI, and the real-world outcomes it can have on reducing carbon emissions need to be considered as well. Some of those downstream offsets can counterbalance the emissions created when the model is trained.

Where to go from here

If you would like to know more about measuring HR effectiveness, check out my articles on:

Or browse the Artificial Intelligence & Machine Learning to find the metrics that matter most to you.


Data Strategy Book | Bernard Marr

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