A suite of ‘new’ technology, produced by pilot projects, is becoming commonplace within the Climate Strategy. A new class of ‘new technology’ is advancing from pilot projects to mainstream Climate Strategy as the window of opportunity to limit warming below 1.5°C draws closer. Transforming an AI platform into an operational nerve centre for a city, corporation, or government is no longer the stuff of experiments, and they are racing to make more happen to monitor emissions, optimize renewable energy and predict climate disasters. As AI-powered climate tech draws billions of dollars in both public and private funding, its role in transforming our way of analyzing, reducing and coping with an increasingly dynamic planet is rapidly becoming a reality.
Internally, they are simply digital ecosystems with the ability to consume huge amounts of scattered information from satellite data, IoT sensor networks, data from the supply chain, atmospheric models, economic data and more, and interpret it into actionable information. Today’s AI systems forecast, optimize, and automate on the fly, instead of pasting visual representations of historical climate data. They help level electricity grids to match the intermittent generation of wind and solar power; identify methane emissions from extensive oil and gas networks; predict crop production in response to fluctuating rainfall, and lastly, achieve a level of granularity in pricing physical climatic risk, innovative even for financial institutions. The change represents a step away from a passive approach to a more active one.
Real-world applications are already rapidly scaling and beginning to delight users. AI flood-prediction systems in Southeast Asia are activating preventative evacuations hours early, without relying solely on storm data, by aggregating data from the tide level, urban drainage, drainage channels and historical weather trends. In Europe, through smart grid machine learning, utility companies are able to move renewables in real-time through the national grid — making a huge difference for curtailment and stemming the use of fossil-fuel peaker plants. At the other end, and much more obviously, multinational retailers are deploying AI supply chain platforms to account for Scope 3 emissions at the level of individual shipping routes and even warehouse operations, allowing them to put in place decarbonization programmes uniquely targeted towards them, a mathematical impossibility five years ago.
As concerns over financial disclosures escalate, pressure continues to grow from businesses to become “net zero” carbon emitters, and the economic impact of the climate crisis grows ever more dire, analysts estimate the AI climate tech market will reach well over $130 billion by 2028.
But the swift rise of AI Climate platforms comes with its challenges. While quite a bit of energy is spent on training large foundational models, critics are rightly pointing out that runaway energy consumption may lead to an adverse effect on emissions if it is not powered by clean energy. Then there is an emerging worry of data monopolies — platforms that exclude smaller municipalities and developing countries from critical data and, more importantly, critical climate insights.
A new challenge for regulators is the phenomenon of “AI washing”, when some vendors claim to have an ability that they don’t. The rise of ‘AI washing’ occurs when vendors’ claims and/or their use of uncertain metrics render them eligible for green financing. Experts point to the need for deployment to be accompanied by transparency, interoperable data standards and having the data validated by a third party. If they are not present, the technology could serve as a selective influence of the powers that are well supplied and not as a public good.
But policymakers are now taking steps. The recently released EU AI Climate Accountability Framework mandates that environmental AI systems report on training data origins and carbon footprints, as well as on benchmarks of performance with peer-reviewed target accuracy. There are also efforts underway to build open-source applications customized for value chains in low-resource areas. Meanwhile, the UN-backed Open Climate AI Consortium has started a multi-year project that focuses on application localization, specifically in the areas of drought forecasting, agriculture resilience, and early-warning systems. There are also changes in how funds are collected: multilateral development banks are increasingly linking the sale of green bonds to impact-backed, air quality-backed targets, in addition to targets self-reported by companies.
We need tools to tackle the climate crisis that are just as big, fast, and complex as the issue itself. AI-driven platforms are likely a key vehicle to help connect ambition to action, as they transform disjointed data into jointly coordinated and evidence-based action.
Technology cannot take the place of political will, or equitable financing or grassroots mobilization. These systems will become ever more complex and will present the following challenges: working for the most vulnerable first; being transparent; and staying grounded in peer-reviewed climate science. When properly understood and manageable, AI could not only be a help to navigate the climate era as such, but it could also be a tool that helps redesign it, by emphasizing ethical deployment and global cooperation.
Maher Asaad Baker
ماهر أسعد بكر
https://maher.solav.me
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This post was previously published on medium.com.
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