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Leverage AI to Spur Climate Action

Jason Cassidy
By Jason Cassidy Founder & CEO, Shinydocs
The manufacturing sector produces one fifth of global carbon emissions and consumes 54% of the world’s energy. Advances in AI can use data to streamline operations, identify inefficiencies and transition a production process away from fossil fuels quickly and economically.
The manufacturing sector produces one fifth of global carbon emissions and consumes 54% of the world’s energy. Advances in AI can use data to streamline operations, identify inefficiencies and transition a production process away from fossil fuels quickly and economically.

In December 2023, when the 28th UN Framework Convention on Climate Change (UNFCCC) came to a close in Dubai, UAE, we heard ambitious promises to strengthen climate finance, transition into a just and orderly energy decarbonization away from fossil fuels, and an emphasis on “focusing on people, lives and livelihoods” in the quest to keep global warming below a devastating 1.5°C increase.

These promises are echoes of agreements long past. Yet what is no longer the elephant in the room is a commonly known and immensely troubling reality: we face an unparalleled political stasis when it comes to taking significant climate action in the areas that matter most.

The manufacturing sector stands out like a sore thumb, producing one fifth of global carbon emissions and consuming 54% of the world’s energy, all while producing 16% of the world’s gross domestic product (GDP).

Despite the fact that humans are at the brink of tipping a climatic threshold of catastrophic warming, where “scientists have found climate disasters will become so extreme that people will not be able to adapt,” we have yet to act.  

Why the Stagnation?

We’re dealing with a global collective action problem. Despite collaboration and cooperation being the clearest and most optimal path for all parties, self-interested actions dominate because there is no global police or sovereign authority coming to lay down the law if someone breaks the rules or acts selfishly.

In other words, it’s sadly still cheaper and more efficient for the manufacturing and production sectors to continue with business as usual rather than decarbonizing. Some players are taking real action, while others are engaging in greenwashing to win the public’s heart.

The real question is therefore not what the scientific data tells us—we have known this since the very first meeting of the Intergovernmental Panel on Climate Change (IPCC) in 1988—but how we can use this data to leverage self-interested actors in the industrial sector.

One answer lies in how the emerging advances of artificial intelligence (AI) can use data to streamline operations, identify inefficiencies and transition production processes away from fossil fuels in a fast and economically beneficial way.

AI is a tool—albeit an incredibly powerful one—that learns progressively by adjusting its own algorithms based on feedback and stimuli, thereby mimicking what humans call ”learning.” AI is not a panacea for super-wicked problems like climate change, as recent issues with ChatGPT’s lack of understanding and conceptualization have illustrated. As this situation makes clear, AI requires intelligent human guidance to select clean and unbiased data—as much as possible—to feed into its algorithms in order to identify and produce patterns and results.

When it comes to climate change, AI might finally be able to answer—clearly, decisively and in myriad ways—how companies, states and individuals can maximize their own self-interest through climate-friendly strategies and solutions. This no doubt extends to the manufacturing sector as well.

As the World Economic Forum recently noted, effective decarbonization efforts are prevented “due to the complexity of global supply chains and the lack of transparency in data.” With supply chain emissions over “11 times higher than operational emissions,” AI could produce data indicating the pathway to and production of decarbonized supply chains that would save producers money through efficient production and shipping processes.

Within the U.S., it’s reported that one third of carbon emissions originate from the industrial sector, and much of this stems from inefficiencies and waste produced on production floors, recalls for production defects and the extra labor power required to deal with these mistakes. To solve this problem, the industrial world is turning to AI and automation, leveraging these techs to embed human-level AI that can extract actionable insights directly on the production floor, augmenting a human’s ability to tell good from bad production—and at the same time, saving money and energy in the process.


If good data goes into AI in the manufacturing sector, mass efficiencies can emerge that can also aid decarbonization efforts.

The future of AI and climate change governance thus lies in policymakers and all sectors selecting clean and accurate data—with as little bias as possible—to input into AI systems that can then measure, predict and chart courses out of climate inaction that maximize the self-interested benefits for individual actors, while aligning these efficiencies with the common social and environmental good—to reduce and eliminate the emission of carbon dioxide and other greenhouse gases.

A recent article in the academic journal “AI and Society” stressed that relying on artificial intelligence for climate solutions is a “gambit” because “it involves a sacrifice” (i.e. the ethical risks of handing control from a human to AI, and a potentially increased carbon footprint thanks to the power needed to make AI operational on a large scale). However, if the gain of using AI to manufacturers decarbonize while becoming more efficient is worth exploring—going green to make more green—then the time is now. Industrial leaders must remember that the scientific data about climate change is plentiful and “unequivocal,” but that it’s impossible to sort through the entirety of it all. The economic and social winners going forth will be the manufacturers that identify, distill, and input clear directives and data into their AI systems to yield specific pathways and answers to complex problems that have, thus far, been intractable to human minds.

AI can, and should, be able to help us finally square the circle of the catastrophic global collective action problem of climate change and economic production. It’s up to industry leaders to understand that AI is no panacea. Our future will depend on how we train our AI systems to ingest only high-quality data. The results will be mirrored in the economic and environmental world that looms on our horizon.

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