Whether artificial intelligence (AI) can help defeat climate change is a complicated question. The answer is both yes, and no. There is a lot of excitement and hope around the potential of AI, and what AI may be able to achieve when it comes to climate change. However, to make these benefits a reality, society must engage with AI in the right way. AI is not magic fairy dust that can simply be sprinkled on the challenge of climate change and expect it to fix itself. People still have to take personal responsibility, but AI can be one of the tools in the toolkit to help businesses make their fight against climate change easier.
AI, another tool in the toolbox
While the relationship between AI and climate change is complicated, there are four ways in which AI could potentially impact climate change. These concepts have also been discussed by the Brookings Institution, a Washington, DC-based think tank. These particularly poignant ways include energy supply, energy demand, climate modelling, and climate policy.
- Energy supply – AI is already helping to improve the supply of energy. For example, machine learning systems are being used to map underground deposits of oil and gas, helping companies to better understand their size and value. In the non-hydrocarbon space, AI is also being used for solar forecasting, allowing solar generation companies to participate more efficiently in the electricity markets.
- Energy demand – Today, AI is enhancing efficiency in energy consumption by lowering demand and emissions. These capabilities are set to increase significantly over time. AI can help align energy consumption with real-time changes in energy markets, resulting in significant reductions in demand. The self-healing of power grids is a possibility, too. Today, according to Brookings, potential uses for AI to better manage energy demand are “barely tapped.”
- Climate modelling – AI could be used to help create models that can drive policy-making aimed at reducing consumption. AI should also be able to significantly improve today’s climate change models – for example, by improving the accuracy of predictions for local climate change impacts.
- Climate policy – The reality – despite what others may think – is that the world is already seeing the effects of climate change. AI can help governments and other organisations better shape climate policy to reduce harm to people and the environment. For example, smart adaptation strategies can reduce losses, and can aid preparation for dealing with extreme climate events.
AI is not a magic wand
These areas of development for AI are very exciting, but AI is not a magic wand. It is easy for businesses to be sucked into the hype around the possibilities of AI to help manage climate change. Indeed, there are already snake oil salespeople promising the moon with little to back up their claims. People also tend to put a lot of focus on the promise of algorithms – there is much exciting work here, but businesses must learn to think critically about them to avoid unintended consequences.
Amongst all of this, the data involved in AI climate change solutions is often ignored, which is a major mistake. It is important to ensure that all data associated with the AI applications being used is managed correctly so that it can be shared effectively. As an example, sharing data between government agencies and academics, or between companies that are partnering together – businesses cannot afford to share information that is incorrect or based off skewed data.
It is equally important to manage data around climate change in a way that builds trust. This is crucial for both the data being fed into an AI algorithm, as well as the output it produces. Managing data poorly can result in inaccurate results from the AI’s algorithms. Businesses need to know key information about the data within their organisation; such as who has access to the data, what was it used for previously, how it was obtained etc.
If a business inputs data that is ungoverned and potentially unreliable, then it cannot trust the results gained from an AI algorithm. Put simply, it is a case of if garbage data is put in, then a garbage output can be expected, resulting in potentially poor decision making. Alternatively, businesses need to be careful of where the information they produce from these AI algorithms is being stored. For example, information could be used for negative purposes – such as analysing energy use patterns from consumers to determine when a house is unoccupied, and ripe to be burgled.
Today, society is at a critical juncture for both climate change and AI. It is important to build trust within society for both how climate change is addressed and how AI solutions function. Strong data management practices have a foundational role to play in building that trust, and in supporting AI-based climate change solutions that deliver on their promises.
Stijn Christiaens, CTO & Co-founder, Collibra