Starting today, we need to accelerate the transition to net zero rapidly. This transition requires mitigation and adaptation measures that reduce GHG emissions and build resilience towards climate-related disasters. Artificial Intelligence (AI) and machine learning can play a meaningful role in enhancing the current understanding of climate change and contribute to combating the climate crisis more effectively.
Alan Turing is credited with the origin of the concept when he speculated in 1950 about “thinking machines” that could reason at the level of a human being. A few years later, John McCarthy coined the term “artificial intelligence” to denote machines that could think autonomously.
AI systems and definitions are now evolving. A publication by the World Economic Forum refers to AI as “computer systems that can sense their environment, think, learn, and act in response to what they sense and their programmed objectives.” Technology Giant IBM states that “artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
AI capabilities are already being deployed in a diverse array of domains from commerce and healthcare to transportation and cybersecurity, and now climate change.
Research shows that the application of AI levers could reduce worldwide GHG emissions by 4% in 2030, an amount equivalent to 2.4 Gt CO2e – equivalent to the annual emissions in 2030 of Australia, Canada and Japan combined.
Another study estimates that by 2030, AI-enabled use cases have the potential to help organizations fulfil 11–45% of the ‘Economic Emission Intensity’ targets of the Paris Agreement, depending on the scale of AI adoption across sectors.
Globally, AI is helping to fight hazards like wildfires, flood forecasting and other efforts to make our planet more liveable for years to come. The energy sector reduces emissions by using AI technology to forecast the supply and demand of power in the grid, improve the scheduling of renewables, and reduce the use of fossil fuel through predictive maintenance. In the transport sector, AI can enable more accurate traffic predictions, optimize freight transportation, and create better modelling of demand and shared mobility options. AI applied in agriculture can help improve crop yields, reduce the need for chemicals and minimise food waste through forecasting demand and identifying spoiled produce.
While AI has great potential to tackle climate change, it also comes with many risks that are inextricably connected with the opportunities. For one, the use of AI itself has a massive carbon footprint. Power-intensive GPUs to AI systems contribute to increased CO2 emissions. This is why it is important to assess the carbon footprint of AI in order to ensure that efforts to harness the potential of this technology far outweigh its environmental cost.
Secondly, AI may also be used to facilitate activities associated with high GHG emissions. For example, AI and other advanced analytics techniques have been used extensively in oil and gas exploration and extraction.
And finally, since the emergence of AI-based climate solutions is relatively recent, several capacity gaps will need to be addressed, such as the lack of AI-for-climate knowledge, lack of cross-disciplinary and cross-functional teams for AI, and the relatively few standards and best practices tools available for reference and learning.
Artificial intelligence has significant opportunities to accelerate strategies for climate change mitigation and adaptation, across areas such as energy, power , transport and disaster response. But much more needs to be done to identify and learn from positive, climate-focused AI solutions from across sectors, domains, and regions of the world. The rapid implementation of large-scale AI literacy and upskilling programs for policymakers, leaders in climate-relevant industries, and civil society is a critical step ahead. It is also necessary to develop the right tools and standards for deploying and evaluating AI-for-climate efforts (for example standards for the responsible collection and use of data, participatory design and stakeholder engagement, data science and impact assessment).