Artificial intelligence (AI) and machine learning have the potential to play a crucial role in addressing the impacts of climate change. Scientists use AI to analyze data sets, create predictive models, and evaluate the potential effects of different actions. Energy companies use it to improve their grids and maximize the output of renewable energy sources.
Vehicle fleet owners use AI to reduce fossil fuel emissions through predictive maintenance and more efficient scheduling. AI can also be used to make everything from agriculture to office buildings more sustainable.
However, it is important to remember that AI is just a tool, and it has its limitations and potential pitfalls when misused. Our success in using AI to tackle climate-related challenges will depend on being mindful of these limitations as we plan our use cases and approaches for applying AI.
One example of how AI is being used to combat climate change is through remote sensing. The European Union, the U.S., and other countries are deploying advanced satellites that provide unprecedented insight into the causes and impacts of climate change.
AI capabilities are becoming increasingly significant in helping to translate this data into a real-time understanding of the dynamics at play in creating current and future climate conditions.
Another example is regulatory enforcement. Many agencies tasked with enforcing climate-related regulations often find themselves overstretched and under-resourced.
AI can be an indispensable tool for them to help spot early warning signs of potential environmental hazards caused by companies or utilities. Companies are also turning to AI tools to drive their regulatory compliance.
AI also has a role in citizen science, which is when scientific projects engage volunteers to conduct scientific research and monitoring activities. Some of these climate-related projects range from tracking earlier bloom times for plants in the spring to altered arrival times and locations of migratory birds and the shifting habitats of frogs and pollinators.
AI tools can drive collaboration and translate citizen-collected data into helpful insights.
However, when using data and AI, it’s important to proceed with caution. Unintended consequences or outcomes can occur due to data gaps and privacy issues. Data can be skewed by explicit or implicit biases and can contain personally identifiable information or be used to point towards it. Using an AI model trained on bad data can also affect its outputs.
To avoid these issues, it is very essential to work with experts from fields such as anthropology, law, and sociology to ensure the right questions are asked and to pre-empt privacy concerns. Additionally, when it comes to data, more is not always better. It’s critical to focus on the quality and relevance of the data rather than the quantity.