The role of AI in climate tech

Artificial intelligence is emerging as an unexpected category in climate tech, and a catalyst for mitigation and adaptation.

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AI has often been likened to “fire” or “electricity” to illustrate the scope of its potential impacts– opportunities and risks alike. But this analogy resonates even more as it becomes a critical tool for addressing the enormous challenges (and novel solutions needed) for humanity to thrive on a warming planet.

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To date, “climate tech” has often referred to categories in renewables, like solar and wind, or connected infrastructure across energy, buildings, and transportation. But increasingly it’s all tech on deck, and AI offers a wide (and growing) range of techniques, software, and hardware with perception, analysis, and predictive capabilities for both mitigation and adaptation.

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• To predict and optimize energy loads based on demand, not only in energy grids, but in data centers, cloud workloads, virtual environments, electrified fleets, and through digital twins of smart home and city applications. 
• To monitor and mitigate risks to agricultural land and yields, by pulling in a wide range of weather, soil, pests and other datasets to analyze, verify, and improve output while maintaining stable growing conditions. 
• To develop new bio-based formulas to displace petrochemical-based materials used in numerous industries. By mining massive genomic and molecular data sets and recognizing patterns, machine and deep learning help “recommend” alternative recipes for compounds and materials that do not require fossil fuel extraction. 
• To analyze alternative material footprints to help designers and product teams across industries meet specific product and performance niches, while avoiding over extraction of ecosystems. 
• To preserve biodiversity and advance conservation efforts through improved intelligence and accountability. Whether via computer vision-enabled drones or satellite imagery, these proverbial ‘eyes in the sky’ are helping on-the-ground human stewards gain longitudinal data and insights into animal behavior, landscape impacts, risks of poachers, and other human influences. 
• To automate circularity and product reuse, machine learning is helping companies across the entire circular value chain. For example, by analyzing excess inventory to resell to the highest value channel based on need, software platforms are helping companies seamlessly triage customer returns, reduce waste, and win revenues.
• To reduce waste and improve recycling, computer vision-enabled robots sorting garbage, or connected waste bins to weight, measure, and analyze materials for more efficient triage, inventory, and waste management prediction.
• To support behavioral changes via personalized nudges, transparency, visualization, and contextual content to build awareness around more conscious consumer decisions, whether deciding what to eat, what to buy, where to recycle, or optimizing which route to take. 

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These are just a few of hundreds of examples of AI used to address the many challenges facing businesses, communities, and policy-makers to develop a more resilient economy in concert with the planet. This trend is only poised for growth, considering the widespread movement of tech talent into climate jobs, intersection with other AI trends such as cybersecurity or predictive maintenance, the increasing investment in ESG (environmental, social and corporate governance) programs, and the continued rise in both collection and varieties of climate-related data, and their implications for what we measure. 

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This article first appeared in a collective interview, read that and more here.