decarbonization with ml

Decarbonization with ML: Employing AI to fight climate change

The rise in CO2 emissions has raised various concerns the world over and decreasing the effects of Climate Change is becoming a major subject of discussions. Various techniques are being worked on, out of which one using achieving decarbonization with ML based systems.

The world has been seeing an unprecedented rise in carbon emissions for a while now. Each year around 51 billion tons of CO2 is put out into the atmosphere and the number is still rising. We have already started to feel the effects of this carbon rise in the form of Climate change and the devastations that it brings with it. Just within the last decades, countries around the world have seen huge natural calamities such as flash floods and heavy storms and on the opposite side an increase in the number of wildfires and heatwaves.

In 2021, countries controlling around 70% of the world GDP, came together to create amendments and discuss possible solutions for the climate change problem and made resolutions to keep the rise in average global temperatures below 1.5 degrees Celsius. Many big businesses and organizations have started making goals to bring their net emissions to zero within a few decades and to find alternative technologies that could replace the current ones. Many new startups have already started to heavily work on solving this and the newer technologies that they are bringing have been revolutionary.

In such a pursuit, Artificial Intelligence has been shown to show some promise in solving and mitigating the emissions problem. Various clever methods can be deployed using AI systems that can efficiently lead to a lesser release of CO2 in the atmosphere and in some cases stop them outright. The problems or inefficiencies in the current conventional systems can be easily mitigated by such AI models and a more optimized system can be developed. AI-based designs can also help in reducing the wastage of energy and ultimately cause lesser emissions. Many such methods have been discussed further in this blog.

How can ML help with Decarbonization?


Instead of finding and researching new technology, let us start with the already existing systems and work on how they can be improved further. Machine Learning models have always been used to solve for the optimum solutions and hence they can help us here too. There are a lot of traditional systems that when optimized can lead to a tremendous decrease in the CO2 output without innovating anything new.

Here are some places where optimization can work:


Household and commercial electricity consumption lead to almost around 30 to 40 percent of the total CO2 emissions for that country. Still, coal-powered plants have been the main source of electricity generation for many nations, developing and developed, which is leading to such a rise. Efficiently managing such plants and using better strategies for distribution can lead to a minor but substantial decrease in the annual CO2 output.

Using ML to understand the power demand cycles and managing and planning accordingly beforehand can decrease the need for excess production of electricity. Such systems can go through the past data from years before and forecast power needs for the future way in advance. They can also recommend to the customers how to manage their home electric usage to decrease the load on the plant.

ML systems can also be used to efficiently connect homes having their own solar rooftop and a battery pack with the grid, to buy and distribute electricity within the whole region, creating a peer-to-peer network for energy transition just like a blockchain for energy systems. Companies can pay for such homes depending on the electricity they send to the entire grid. Although this is already happening around the world, ML-based systems can help them become more optimized and can help make cities better and more self-sufficient.

Traffic Management

The next strongest emitter of carbon gases is the transport industry which leads to around 16 percent of the total CO2 emissions. Most of the emissions occur when the cars are running idly at a traffic jam or a signal. AI-based systems can truly optimize traffic by planning the signal patterns, studying the traffic flow, and producing optimized road trip suggestions for the cars to reach their destinations. A pilot test conducted in Pittsburg reduced average waiting times by 40 percent and reduced the average time of travel by one-fourth showing that such systems do help when implemented.

Designing better using AI

AI can also be deployed to run through thousands of iterations of combinations to find the best design for a particular machine. In 2016, researchers at Boeing designed a plane fuselage using only AI. They ran the model and came up with designs that were then tested to see how much weight did it reduced for the plane. The best-chosen design seemed to replicate natural structures such as trees and bones and helped engineers design a plane that was much lighter hence leading to a lower fuel consumption during flight.

Designs made by AI can also help us build better cities, that have important places such as offices, schools, and other buildings at a minimum distance from all the residential homes, reducing the traffic congestion and even the use of cars, leading to again a decrease in the emissions. Buildings can be designed such that they use the minimum possible energy during the day and also at night as they have better light and heat management embedded within the design.


AI can be used for various research purposes to find out new techniques and alternatives for energy and new methods for re-absorbing the CO2 already in the air. Various ML methods can be deployed by engineers to test out which technique is less problematic for the environment. ML can be put out to test the viability of futuristic solutions such as nuclear fusion reactors, etc., and to test out various parameters that can affect them. They can also find solutions to suck in the already present CO2 in the atmosphere using various chemical and mechanical methods.


Building and deploying such systems will surely take some time, but given the 10-year deadline for reducing the emissions, such methods should be put into work as soon as possible. AI can now help us erase our wrongdoings and create a world as it was before the industrial revolution. AI systems are truly a boon to humanity but only if they are used for up-gradation of our human life and also the life of the planet we live on.

Build a better world together with ANAI

ANAI allows businesses to automate and enhance their workflow using a no code based UI and enables them to quickly take up, discover possibilities and implement various ML solutions within a span of weeks as compared to months or years.

ANAI provides an all-in-one platform to solve and automate all the data science related tasks, from data engineering to building/training ML models.

ANAI has more than 100 plus data integration methods and around 300 plus unique ML models (more under progress) to give organizations the flexibility to work on any use cases within their industry.

With ANAI, business stakeholders can also get explanations for their model’s working using ANAI’s eXplainable AI-based solutions and can detect and eliminate biases within their data and the ML model making the AI more responsible and fairer for everyone.

For more information on how your organization can build and execute an effective Machine Learning solution, explore ANAI’s offerings and contact us here for more details or visit

AI and NetZero, AI and Decarbonization, AI and ClimateTech, How AI can help in Carbon Reduction

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ANAI - AI for AI

+1 646 699 8676

We offer a comprehensive ecosystem

that blends Data Engineering and Auto-

mated Machine Learning that enables the

‘Democratizing of AI and ML’.