Can AI Be the Solution to the Much-Debated Climate Change Issue

How ironic is it that the species considered as the most intelligent in the entire universe is on the verge of destroying its only home. Last month several teenagers, among other people took to the streets to protest about the lack of action by political leaders on the issue of climate change. Whether people may choose to believe it or not, climate change is happening for real and is a more complex problem than it seems.

We can understand some part of the problem from the fact that a mere 16-year-old Swedish campaigner Greta Thunberg had to come to the UN and enlighten the world leaders about the seriousness of the issue.

According to recent research by NASA, the leading cause of rapid change in the climate is the emission of greenhouse gases that lead to the phenomenon of global warming. As we try to look behind its roots, we find that there is a 95 percent probability that human actions in the past 50 years have warmed the planet.

This and more recent cases have shaken the world and made us realize the importance of stern measures that must be taken to combat the challenge. The climate change issue is an existential crisis, and the sooner everyone realizes it, the better it would be.

But, it’s not just strict policies that can cause an impact and save our only home. We are far more advanced than we were decades back. Thanks to all the technology that we’ve developed, we can now look beyond our planet to conduct research and study the solar system.

This technology can also be utilized to tackle the issues of climate change. One such cutting edge technology that has penetrated every sector of the economy and every other walk of life is Artificial Intelligence.

A research paper that proves this theory came into the limelight this year. Titled Tackling Climate Change with Machine Learning, the paper is written by some of the pioneers of artificial intelligence and machine learning. It highlights the utility of machine learning and AI for battling the climate change crisis.

Understanding the impact of AI-powered niches like neural networks, GANs, image processing, computer vision, etc. have caused around the world, it’s a great idea to use them and find solutions for situations like global warming.

Why Not AI for Climate Change?

Until now, we have limitedly used AI’s potential to drive business growth, aid doctors in the healthcare sector or reform the travel experience. However, since the climate change issue has escalated beyond the point that we can solve it through the political wand, we need AI’s intervention to take charge. Because if we can use technology for almost everything, why can’t we use it to fight one of the scariest battles the world is witnessing?

One of the authors of the paper, David Rolnick, who is a postdoctoral fellow at the University of Pennsylvania, highlighted the fact that the research paper was a ‘call to arms’. The motive of the article was to bring researchers together and give a thought to the climate change problems that machine learning can contribute to.

Although physics-based models have been used in climate change predictions, they are bottom-up approaches and forecast only based on physical boundary conditions. General circular models or GCMs were developed by the numerical representation of atmospheric physical conditions.

Similarly, Earth System Models (ECMs) consider features like biochemical cycling and atmospheric chemistry. They are advanced GCM models used for current climatic studies. However, these models suffer from significant forecast uncertainties, which is why researchers began exploring neural networks and other advanced algorithms for the job.

But, those who see the disciplines of AI as a silver bullet might also be heading in the wrong direction. Artificial intelligence and machine learning need not necessarily provide an ultimate solution to the problem. But they help us see beyond the issue by offering valuable insights. Once we have the ideas at hand, both the government and the private sector can deploy cutting-edge technology and policies to drive the battle against climate change.

AI-Powered Devices

Artificial intelligence through ML techniques and powered devices help in the scenarios where we are uncertain and want to start a chain reaction that will approach the climate change issue in two ways. In one way, it will reduce any further deterioration of the environment.

While in the other way, it will provide futuristic solutions to improve the conditions and maybe even restore the ecosystem to a certain extent. A learning-based AI could do much more than crunching CO2 emission numbers. It could record those numbers, study causes and solutions, and ultimately recommend the best fitting solution.

Smart Thermostats

With climate change happening all across the world, more and more heating and cooling systems are being put to use. They demand a ton of energy and account for half of a residential’s energy requirements. However, with smart AI-based thermostats in the picture, this issue could be approached in a better manner.

Smart thermostats automatically adjust the indoor temperature setting by analyzing external temperatures and humidity settings. Nest’s learning thermostat is an example of an AI-powered device that works on a Wi-Fi connection. The smart thermostat also uses fans whenever necessary and automatically adjust the temperature of your home depending on whether you’re at home or not.

It also tracks user behavior and learns from manual adjustments so that it’s better able to adjust temperatures. Research indicates that using smart thermostats for the home can lower energy consumption by up to 15 percent annually.

Irrigation Systems

A lot of American households have large landscapes around them. In other instance, agriculture is extensively practiced in various parts of the world. All of this leads to a massive amount of water consumption. While agriculture is fundamental to life, green landscapes have their own perks.

Statistics suggest that American households use about 320 gallons of water per day, out of which 30 percent is used for maintaining these landscapes. Meanwhile, agriculture accounts for 70 percent of the world’s freshwater withdrawals.

Using a smart AI-backed irrigation system can save a significant amount of water and help conserve a part of the environment. Like other devices, they work on a wireless internet connection and stay up to date with the local weather of the area.

These devices understand when to not irrigate the lands during a downpour or after a storm. Based on the manual irrigation pattern of a user, these devices learn and suggest a schedule for optimal water usage. It can be used to maintain the desired level of moisture in the soil and help an individual conserve water to a great extent.

Pest Control

Pesticides not only harm the soil but also deplete it of certain essential nutrients. Moreover, the crops harnessed from such soil lacks nutrients. Artificial intelligence can be put to use in such a scenario to identify and fight specific insects without harming the others. The technology that is being commercially used in some parts of the world, uses image recognition to identify and treat harmful pests without harming the natural environment around it.

Smart Pest Controls can significantly lower the costs for a farmer and prevent the collapse of a useful colony of insects that are fundamental to the food chain and environment. Similarly, using this device can enable farmers or other concerned technicians to click a picture of a pest and upload it to find out the best treatment methods against it.

IoT Energy Meter

Energy conservation is a must for the conversation of the environment. The IoT energy meters can help track energy consumption with its smart sensors. They just need to be clamped into electrical circuits to start tracking the amount of energy being consumed.

It is easy and convenient to install in households and sends data securely over the Wi-Fi. They charge directly from circuit panels and do not require a battery. One such system is developed by Verdigris technologies.

Leading Organizations Developing AI-based Systems

Health of Forests


Forests are the most important parts of our planet, which is why their conservation is our utmost priority. SilviaTerra, an AI-powered tool developed by Microsoft, uses satellite images to predict the sizes, species, and the health of forests. This is helping to save countless hours of manual fieldwork, which now researchers can put in improving the quality of green cover on the planet.

Reducing the Carbon Footprint of Data Centres

No matter how powerful we consider the company Google to be, it could seldom do anything to reduce its in-pb2-rzvffvbaf-guvf-erny-gvzr-qngnivm-fubjf-ubj-zhpu/" data-wpel-link="external" target="_blank" rel="nofollow noopener noreferrer">pneoba sbbgcevag. The point is that Google’s widespread services across the world call for a large number of data centers. These data centers take up a lot of energy for their cooling process, leaving a massive amount of carbon footprint in the environment.

To address this issue, Google recently collaborated with a company called DeepMind to build an AI-powered system. This new neural network-based tool could teach itself to use a bare minimum amount of energy to cool down Google’s data centers.

As a result, the tech titan was able to cut down their emissions by 40 percent. Moreover, there was also a 15 percent reduction in total power saving. Due to the general approach of the algorithm, the two companies also plan to work together on building more energy-saving applications in the future.

Self-configuring Pollution Forecasts

Unless we measure the impact of climate change, it is difficult to understand whether we’re impacting anything. A plethora of cities fail to measure their emissions, and as a result, fail to take measures to reduce them. Having said this, another tech giant IBM laid the foundation of the Green Horizon Project.

The Green Horizon Project is an artificial intelligence-based system that creates self-configuring weather and pollution forecasts. It was created with the vision to help cities become more efficient in terms of energy in today’s generation. The project found ground truth through its implementation in China, where it helped the city of Beijing reduce their average smog levels by 35 percent.

Images of Weather Events

Researchers at Cornell University created an artificial intelligence-based system to produce images before and after an extreme weather event takes place. Even though it might not sound like a problem-solving technology on its own, it is very helpful when it comes to studying the impact of climate change.

Researchers can use these images to predict the impact of certain climate changes, therefore, helping prioritize efforts towards adequate measures. The research used a niche of AI known as General Adversarial Networks (GANs) for the job. GANs are basically network-based algorithms that help in generating statistics or new pieces of information.

Energy Consumption of Commercial Buildings

Among the world’s top carbon emission contributors are commercial and industrial buildings. But with AI-based remedies, energy savings mode can be utilized. Verdigris Technologies, which developed numerous award-winning AI systems, joined forces with global electrification leader ABB. This combined Verdigris’ machine learning solution with ABB’s connected low-voltage switching fabric products.

In its entirety, the goal here was to foresee unexpected surges in power consumption for commercial buildings. It is ABB’s first energy forecasting and intelligent alerts app for building a sustainable environment that aims at reducing the energy consumption of commercial buildings between 10-20 percent.

Earth Science AI

Earth’s climate change is not just impacting the atmosphere. It is also deciding the fate of several businesses, societies, and countries. The London-based tech firm Cervest has designed an AI platform called Earth Science AI to predict the impact of climate change. The platform analyzes billions of data points to forecast how changes in the climate will impact the future of countries and individual landscapes, anywhere across the world.

The three years of research combines AI, machine learning, and statistics with modeling techniques from Earth sciences such as meteorology, hydrology, and atmosphere science. The beta version of the platform is expected to launch next year.

AI Technologies with the Potential to Fight Climate Change

Machine Learning

Machine Learning is one of the most popular technologies under the umbrella of artificial intelligence solutions that can be used to combat climate change. It can help draw conclusions based on a plethora of data one throws at it.

Having said this, predictive analysis is one of the fundamental elements that can help in learning the existing data and forecasting results based on it. Machine learning can help generate statistical models such as regression analysis for climate change forecasting.


General Adversarial networks are gaining popularity these days. Thanks to their statistics and new information generative capabilities, they can now be used to look at what houses appear before and after a weather event takes place.

GANs can be used to generate images that depict accurate, vivid, and personalized outcomes of climate change. A better version called Cyclic GANs collects the training data to extract the mapping function in order to generate realistic images.

Neural Networks

Neural networks are one of the best technologies that have emerged out of machine learning. They take the training data as input, whether or not they are labeled, only to learn from them and process a series of output known as trends. For example, neural networks can take the temperature data of 30 years and predict the rise and fall for the next ten years.

Deep Neural Networks or DNNs can be used for a time series analysis along with detection and rigorous classification. In spite of their great potential, DNNs have been seldom used for climate change issues. The accuracy of the best global model is found to be 97 percent using LeNet for the convolution neural networks.

Computer Vision

Computer vision is the discipline of AI which helps computers gain a high-level understanding from digital images and videos. It automates the task performed by human vision. Computer vision can be used to analyze images obtained from satellites and send signals about any alarming situations to researchers. For example, they can look at satellite images of forests and investigate whether there is a possibility of a fire within a short time.

BIM and Parametric Estimation Algorithms

Building Information Modelling or BIMs have existed for years but are seldom used to design energy-efficient architectural marvels. Parametric design and building information modeling go hand in hand when designing buildings.

Parametric design is basically a process based on the ML algorithm that allows specific variables to be manipulated to alter the outcomes of an equation. The three-dimensional model-based technology, when powered with parametric estimation algorithms, can help build cost and energy-saving architectures.

The Two Approaches to AI

But solving any problem takes time. The insights and knowledge that we now have about climate change took more than 40 years of rigorous study. In such a long time, the human race has at least been possible to study the climate and deduce that climate change exists.

If humans put another forty or fifty years of research, who knows, we might come up with nature-friendly solutions that require less energy and resources. But the question is, with the ongoing crisis, do we have this much time left? The answer is obvious, and that’s why we need machines to do this job much faster and more accurately.

As we proceed to harness the potential of artificial intelligence for climate change, we find two different approaches that can be utilized. While one is the rules-based approach to artificial intelligence, the other is based on learning. The rules-based approach is more focused on quantitative results. For example, it can help scientists compile specific data analytics, crunch numbers, etc.

The motto of this approach is to solve simple problems with if-then statements in their code. For climate change, it can help scientists find answers to complex issues within a short span of time.

The learning-based approach is a more qualitative approach to solving a problem. It is unlike the rules-based approach that has no memory capabilities and solves problems defined by humans. Instead, the learning-based method diagnoses a problem by interacting with it.

For example, if you ask the rules-based AI for a garment, it will help you find one based on the size and preference you’ve mentioned. On the other hand, when you assign the same task to a learning-based AI, it would instead study your past purchasing patterns to find out the correct size, color, or nature of the garment you are probable to like.

For climate change, both the approaches of Artificial intelligence can serve as saviors. One can tell you about the number of carbon footprints you’ve left in a day, week, month, or a lifetime, while the other can study these to find its sources and suggest ways in which you can reduce them.

AI-Powered Initiatives for the Future

In the practical scenario, there are presently only a handful of ways where machine learning and artificial intelligence can be applied.

Let’s take a look at a few of them.

Predictions of Electricity Consumption

Electricity is one of the primary renewable resources of society. But, since we’re going to need even more of it with growing time, AI algorithms must be able to predict our requirements. Although some of the algorithms already exist that can forecast the electricity demand, we need improved versions of them that can help operators schedule the resources. These must be able to predict the electricity demand in real-time by taking the local weather, household behavior, etc. into account.

Finding More Sustainable Materials

There is a need to find materials that can store, harvest, and utilize energy more efficiently. When done manually, the process of finding such materials is laborious and time-consuming. However, with machine learning to the rescue, it can be accelerated and new materials with desirable designs, structure, properties, etc. can be discovered or created. For example, we could find a material that can absorb excess carbon dioxide emissions or the one that can store solar energy more efficiently.

Optimizing Transportation and Freight

Transportation and freight require a significant chunk of energy, in terms of fuel, etc. With the ever-changing destinations across the world, transportation must be able to cater to them. With machine learning, we could develop a model that studies the freight trends and find ways to bundle shipments. This can help in minimizing the overall trips and demonstrating resilience when transportation is disrupted.

Promote Electric-Vehicle Adoption

We find a lot of electric vehicles existing today than they were decades ago. But, their adoption is still a matter of concern. One of the issues that hamper adoption is battery usage. AI-based algorithms can help in increasing adoption by improving the battery management so that the vehicles can last for a longer duration with one successful charging. They can also predict overall charging behavior so that grid operators can make decisions and manage load more appropriately.

More Environment-Friendly Buildings

With intelligent control systems in the picture, we can help make the energy consumption by buildings even more efficient. For example, a smart system could analyze weather conditions, occupancy, etc. to adjust heating, cooling, ventilation etc. needed in an enclosed space. AI could also communicate with the power grid and control the supply of power depending upon the requirements. One such example of this use case is the AI platform developed by Verdigris technologies.

Better Estimates of Energy Consumption

Very few people today have an idea about their carbon footprint. When it comes to buildings, this number is close to none. Artificial intelligence-based algorithms can help estimate the energy consumption of buildings from satellite imagery, etc. and determine it for an entire town or city.

With this information, it would be more convenient to develop effective mitigation strategies and reduce greenhouse emissions on a city’s level. Moreover, with the assistance of computer vision, this method can also be utilized to maximize the efficiency of energy consumption and find out the precise footprint of a building.

Enhance Tracking of Deforestation

Deforestation results in nearly 24 percent of greenhouse emissions today. It is one of the reasons that our planet is getting warmer and warmer with each passing year. But, tackling and preventing it can be a real hassle when it is done manually. Satellite images and computer vision algorithms can together work and find out any illegal encroachment at the earliest.

The loss of tree cover can also be identified by fitting sensors in the ground that can warn the authorities of any chainsaw sounds, etc. Moreover, we can also extend this idea to drones that can monitor extensive forests or green areas and send any warning signs before something like a fire breaks out.

Wildfire Detection

chooch wildfire alert

One of the best things about artificial intelligence is that it can help refine images and reconstruct meaning out of it. AI-backed image processing helps in finding meaningful information in images that might not be visible to the naked eye.

This approach can be extensively applied to the detection of wildfires in forest areas. Chooch AI, a San Francisco-based technology company, is currently working on the development of an artificial intelligence-based system that could decrease the time between fire upsurge and the moment it is first spotted.

The platform uses hyper-detailed imagery obtained from satellites for evidence of wildfires. These images, when refined using AI, could lead to earlier fire detection and conservation of lives and green cover.

Encourage the Idea of Precision Agriculture

No matter where you go around the globe, all you can find in terms of agriculture is one crop dominating and growing on a swath of land. Also known as a monoculture, this practice has been helping farmers exercise more control over their fields with tractors and other necessary tools.

However, monoculture is also responsible for depleting the soil of its essential nutrients and leaving it less fertile. To combat this problem, farmers use nitrogen-based fertilizers that only add to the existing carbon emissions. AI-based models can, on the other hand, help farmers identify and manage a mix of crops that regenerate the soil and reduce the need for any fertilizers.


The purpose of artificial intelligence remains to assist the human race in combating the climate change crisis. We cannot leave it on machines to come up with an all-encompassing solution to a particular problem. But, with their intervention in the existing problem, we can hope for a positive future outlook.

The only idea is to start right away and not wait for the next Amazon fire to break out or the next city to suffer from water scarcity. The sooner we realize that we might be nearing the doomsday, the faster it is for us to build models and deploy artificial intelligence disciplines to fight the battle. Maybe, we could even use AI to predict the doomsday? Only if this is what it takes to drive a response from us.

The post Can AI Be the Solution to the Much-Debated Climate Change Issue appeared first on Dumb Little Man.

Leave a Reply

Your email address will not be published. Required fields are marked *

The owner of this website is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon properties including, but not limited to,,,,, or
Home Privacy Policy Terms Of Use Anti Spam Policy Contact Us Affiliate Disclosure Amazon Affiliate Disclaimer DMCA Earnings Disclaimer