top of page


How AI is Helping Achieve Net Zero

Originally Published in Cognitive Times Vol. 7 No. 2 // 2023

Read on Cognitive Times Website:

Is Net Zero truly achievable? Let's find out.

The world has been talking about achieving net zero for years. There are forums discussing this at length, looking through data, and working with experts, and the jury is still out on whether it is truly achievable.
The definition of net zero is simple. It is the point at which our total global emissions are less than or equal to the emission removed from the environment. The Intergovernmental Panel on Climate Change (IPCC) states that in order to limit the temperature increase to 1.5°C / 34.7°F below pre-industrial levels, emissions would have to peak before 2030, and global net zero emissions have to be attained by 2050.

At the Time Machine Interactive event in Austin, Texas, panelists Rob Budny, Annette Anderson, Rachit Gupta, and Jae Choi reassured us – net zero is indeed possible. During the session, titled “Jumpstarting Net Zero,” they explored case studies and examples from organizations like bp, Wind Energy, and Ford Motor Company to show how renewable and decarbonization endeavors are bringing us closer to balancing the equation for the safety and sustainability of our planet.

Many different technologies have been labeled game changers to the net zero charge, but none as much as artificial intelligence. AI alone is now expected to reduce energy use from data centers around the world by controlling systems and keeping them cool. AI is also being deployed to improve the efficiency of renewable energy systems like wind, water, and solar. It uses turbine data to make power generation more predictable and conducts real- time risk assessment to maximize capabilities.

The panelists spoke of AI assisting in carbon capture, utilization, and optimizing battery and storage technology so that companies can adopt solutions that would minimize global climate change.
If countries like Bhutan and Suriname can already be negative in emissions, leading global economies can work more rigorously to achieve net zero pledges, and AI can be a primary partner.

As it currently stands, humans need to slash annual CO2 emissions by 5% per year from 2023 to 2030 to have a shot at achieving the 1.5°C / 34.7°F mark. The panelists and many forums around the world believe the best way to do that is by proliferating climate policy, employing clean, renewable energy, adopting technologies like AI, and continuing research and development.

The Boston Consulting Group states that AI can help reduce 2.6 to 5.3 gigatons of greenhouse gas emissions, which is as much as 10% of the total by 2030. AI and machine learning, along with deep learning, can accelerate the development of Carbon Capture, Utilization, and Storage (CCUS) in a cost-effective process. These approaches use statistical tools and algorithms that help classify, predict, and optimize data so power plants, industries, and data centers can become more “green, efficient, and profitable.

AI is even self-assessing its own carbon footprint as concerns for its computing power come to the forefront. Projects like CodeCarbon calculate emissions created by AI and give developers suggestions and insights on geographic infrastructure that can be accessed for “clean” running. Collaborations between Google and AI platform “electricityMap” use the same methodology to calculate optimal times for computing processes by searching for clean energy sources across the grid.

The panelists also talked about the various challenges faced with achieving net zero. Such as how solar installed capacity has gone up by a factor of four and energy storage by a factor of eight. They emphasized that it needs to be doubled three more times for it to be effective. They were, however, very hopeful, considering the success both these industries have seen. In fact, renewable energy is no longer seen as “alternative” energy but is now viewed as a mainstream power source.

Speaking on renewable energy, the Time Machine Interactive panel stated that the biggest challenge to their field was intermittency since wind, solar, and water are not “on” all the time. And the challenge is growing larger as the penetration for these energy sources increases, affecting merchant pricing. However, AI has also stepped in to predict weather patterns and optimize energy storage: helping to drive return on investments. The panelists noted they were sure that AI will play a vital role with these systems in the coming years.

It is becoming obvious that energy storage has been a real challenge to decarbonization. And it is stated that even though almost 90% of the executives in the private sector believe sustainability is important, most large corporations have their strategies geared towards short-term profits and growth targets. There are also delays in green investments, ranging up to two years due to government regulations.
But hope is on the horizon. Prospects for greenhouse gas (GHG) mitigation in the United States has improved since 2021, with the passage of the Infrastructure Investment and Jobs Act and the House of Representatives’ passage of the Build Back Better (BBB) Bill. Stalled in the Senate, BBB would earmark $555 billion for measures aimed at reducing GHG emissions 50-52 percent below 2005 levels by 2035. While the bill focuses on many sectors of the economy, it would reduce emissions the most in the transportation and electricity sectors.

Meanwhile, the European Green Deal led to the enactment of a European Union law that seeks climate neutrality by 2050 and sets the EU’s Paris Agreement target for 2030 to at least 55 percent below 1990 GHG emissions levels. The EU also introduced a “Fit for 55” package of 16 legislative proposals aligned with that target and a Sustainable Finance Framework to re-orient capital flows toward sustainable investment. The EU is also working to phase out dependence on Russian fossil fuel imports.

In parallel, governments around the world are working on legislation and sustainability efforts to help their respective countries reach their emission reduction goals. Building on these legislations, perhaps we can expect that countries will come closer to their net zero targets and utilize AI advancements to optimize their processes, so they can get a bottom line that benefits them as well as their consumers.

From an economic standpoint, the viability of AI-powered energy solutions can be due to a variety of reasons, including improving operational efficiency, rising interest in energy efficiency, expanding decentralized power generation, and growing interest in battery storage systems. With AI systems, we can now predict energy spikes, discharge energy where needed and eventually help consumers control energy costs. Moreover, using accumulated data on consumer habits can allow AI algorithms to predict energy usage in advance. AI can also predict and make energy storage management decisions by considering forecast demand, renewable energy generation, prices and network congestion, and other variables for better battery storage solutions. AI can also be used to tackle the complex problems of designing solar plants, which results in cost savings and improved efficiencies–and eventually, better investment returns.

While additional AI use cases for net zero are on the horizon, by the end of the Time Machine Interactive panel, it was clear that the panelists believe “Jumpstarting Net Zero” is dependent on AI’s ability to provide efficient decarbonization and optimal operations in renewables. Net zero can be an achievable target as long as energy providers and consumers are committed to a greener tomorrow!

bottom of page