Balancing AI Innovation and Environmental Impact

Dan Nicholson

Artificial Intelligence (AI) stands at the nexus of innovation and environmental responsibility, presenting both opportunities and challenges in the face of the climate crisis. As AI technologies continue to evolve and proliferate, so too does their impact on our planet's delicate ecosystem. Understanding and mitigating this impact is paramount in shaping a sustainable future. Let’s take a look at how this swiftly growing tech is affecting our planet, and what we stand to gain and lose from its use in building our future world. 

AI’s Carbon Footprint

The environmental ramifications of AI are profound, chiefly driven by the energy-intensive processes involved in training and running AI models. Studies estimate that the carbon footprint of training a single large language model (LLM) can equate to around 300,000 kg of carbon dioxide emissions, akin to approximately 125 round-trip flights between New York and Beijing. 

Notably, AI's energy demands are predominantly met by fossil fuels, with natural gas expected to maintain and sustain this technology through this decade, according to Wells Fargo analysts. That could throw the climate pledges of utilities and tech giants alike into disarray. By 2040, the emissions from the Information and Communications Technology (ICT) industry are projected to reach 14% of global emissions, with the majority stemming from ICT infrastructure like data centers and communication networks. There’s a push underway to ensure that this increased energy demand is met with lower-carbon sources— consider the Microsoft initiative or Amazon’s $650 million acquisition of a Pennsylvania center that sits next to one of the biggest U.S. nuclear power plants. 

“It is important for us to recognize the CO2 emissions of some of these large AI systems especially,” says Jesse Dodge, a research scientist at the Allen Institute for AI in Seattle. He adds, however, that “the impact of AI systems in general is going to be from the applications they’re built for, not necessarily the cost of training.”

AI’s Dual Role in the Climate Crisis 

While AI offers potential avenues for climate mitigation, its applications across various sectors can either alleviate or exacerbate environmental challenges, says Dodge. On the one hand, it can help reduce the effects of the climate crisis, such as in smart grid design, developing low-emission infrastructure, and modeling climate change predictions. 

On the other hand, AI is itself a significant emitter of carbon. 

This is one reason experts increasingly recommend treating AI’s emissions as only one aspect of its climate footprint. David Rolnick, a computer scientist at McGill University, likens AI to a hammer: “The primary impact of a hammer is what is being hammered,” he says, “not what is in the hammer.” Just as the tool can smash things to bits or pound in nails to build a house, artificial intelligence can hurt or help the environment.

For example, in 2019, Microsoft announced a new partnership with ExxonMobil and stated that the company would use Microsoft’s cloud-computing platform, Azure. The oil giant claimed that by using the technology—which relies on AI for certain tasks such as performance analysis—it could optimize mining operations and, by 2025, increase production by 50,000 oil-equivalent barrels per day. Microsoft’s AI is directly used to add more fossil fuels, which will release greenhouse gasses when burned, to the market. 

Or, take automated ads. You know the ones, when an eerily specific ad directed toward your recent interest in golf or weddings pops up on your Instagram or Facebook news feed, advertising algorithms are the wizard behind the curtain. This practice boosts overall consumptive behavior in society, Rolnick says. AI, in a way, is funding consumerism, which is undoubtedly bad for the planet.

“There’s examples like this across every sector, like forestry, land management, farming,” says Emma Strubell, a computer scientist at Carnegie Mellon University.

Meanwhile, AI has been to directly combat climate change and environmental emergencies. Technologies like Climate TRACE utilize AI to monitor and reduce emissions, aiding climate change mitigation. One application called xView2, which combines machine-learning models and computer vision with satellite imagery to identify buildings damaged in natural disasters, has been used to help first responders in natural disasters. Its models can assess damaged infrastructure, thereby reducing danger and saving time for search and rescue providers who would otherwise have to make those assessments themselves. 

The nuances of AI's influence on the climate underscore the need for discernment in its deployment.

Forging a Path Forward: How AI Can Be Environmentally Sustainable

Addressing AI's environmental impact demands a multifaceted strategy encompassing technological innovation, ethical considerations, and policy intervention. Efforts to develop energy-efficient hardware and AI algorithms, coupled with responsible data practices, can mitigate AI's energy consumption and carbon emissions. Policy recommendations aimed at integrating tech and climate policy and fostering transparency are essential in shaping a sustainable AI ecosystem. Collaboration emerges as the linchpin, facilitating informed dialogue and concerted action towards a greener, AI-enabled future.

However, experts point out that one key challenge is transparency. The opacity surrounding the carbon cost of large-scale AI systems underscores the urgency of establishing standardized metrics. Furthermore, the proliferation of "red AI," characterized by its high computational costs and carbon emissions, underscores the imperative of prioritizing "green AI" initiatives (those carbon-neutral or used-for-good AI projects). 

Again, collaboration is key. Initiatives such as Climate TRACE, which utilizes AI to monitor and reduce emissions, highlight the potential for technology to drive environmental stewardship. Transparently quantifying energy consumption and carbon emissions is imperative, as is the adoption of sustainable AI infrastructure.


As we navigate the intricate interplay between AI and the environment, one thing remains clear: the imperative of balancing innovation with sustainability with this quickly growing technology. By embracing ethical design principles, promoting transparency, and fostering collaboration, we can chart a course toward a future where AI serves as a catalyst for positive environmental change. Efforts to quantify energy consumption and carbon emissions transparently are imperative, as is the adoption of sustainable AI infrastructure. Collaboration emerges as the linchpin, facilitating informed dialogue and concerted action towards a greener, AI-enabled future. As we strive for environmental stewardship in the age of AI, transparency and collaboration will be paramount in ensuring that technological advancement aligns harmoniously with ecological preservation.


Bernstein Private Wealth Management

Scientific American


Dan Nicholson is the author of “Rigging the Game: How to Achieve Financial Certainty, Navigate Risk and Make Money on Your Own Terms,” deemed a best-seller by USA Today and The Wall Street Journal. In addition to founding the award-winning accounting and financial consulting firm Nth Degree CPAs, Dan has created and run multiple small businesses, including Certainty U and the Certified Certainty Advisor program.

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