How AI can help us solve Climate Change

Photo by Li-An Lim on Unsplash

As we all know, climate change is an issue that it’s only getting worse as days keep passing. It is always a discussion that is brought upon the main table, however, the solutions are often complicated for an issue with an immense amount of damage. I got my hands on Bill Gates's new book, “How to avoid a Climate Disaster”, and although I have not finished it yet, he mentions the main things we must do for the world to get to zero greenhouse gasses. Everyone will have to adjust their way of living so we can achieve that number. As we all know the temperature has increased drastically in the past 30 years. This is caused because greenhouse gases trap heat and prevent it from leaving the Earth’s atmosphere, they are responsible for the temperature spike. Human activities are responsible for almost all of the increase in greenhouse gasses in the atmosphere over the last 150 years. The largest source of greenhouse gas emissions from human activities in the United States is from burning fossil fuels for electricity, heat, and transportation.

Average Temperature by County ( °C)

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. AI Can Accelerate Our Response to Climate Change. 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

Optimize Clean Energy Development

Avoid Waste

Make Transportation More Efficient

Tools to Help Understand Carbon Footprint

Monitor Environment

Create New Low-Carbon Materials

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.

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