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 CO 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 toCapgemini 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:
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.
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 ecologistsdeveloped 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.
