“We have reached a tipping point on the need for climate action. The disruption to our climate and our planet is already worse than we thought, and it is moving faster than predicted. … We must act now to prevent further irreversible damage.”
As per the UN Intergovernmental Panel on Climate Change (IPCC), humans are the cause of the outlandish upsurge in global temperatures. A considerable degree of cutback in greenhouse gas pollution is indispensable to put a stop to additional climate change, enhancing the need of each country in making exceptional changes, as reported by the IPCC.
The havoc triggered by the hurricanes, floods, wildfires and storms induced at the hands of climate change has led to the rise of artificial intelligence (AI) and Machine Learning tools for predicting and restraining the brunt of it.
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In 2015, the Paris Agreement, a legally binding international treaty on climate change, was adopted by 196 parties, with the purpose of restricting global warming to below 2, preferably to around 1.3 degrees celsius as opposed to pre-industrial levels. To achieve this primary goal, the countries made a commitment to work towards curbing their climate pollution and for strengthening their commitment and to enjoy a climate neutral world in the future.
You can learn about the Paris Agreement in detail by watching the video below :
For combating this climate change, interestingly, systems powered by AI adopting algorithms for detecting data set patterns, making predictions and suggestions in virtual or real-time settings have become the latest rising trend among tech firms, investors and even the government.
Examples of AI in Climate Change
Everyone is aware of the dire need to minimize the greenhouse gas emissions. Yet the actual challenge lies in companies actually undertaking strategies to cut down these emissions in today’s prevailing grim era.
Many companies have often resorted to brushing off or prolonging the endeavor owing to the inconvenience of measuring and minimizing the entire carbon emission extent.
In this regard, AI emerges as the turning point. The technology’s capacity to offer intricate insights regarding varied aspects of a platform’s carbon footprint and swift cost-cutting gains provides a favorable path for quickening up sustainable transformation and minimizing expenses during critical times.
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Since AI’s size allows it to have access to large sets of data. This puts companies at an advantage for deriving perks from it.
Examples of companies leveraging AI for influencing climate change include :
IBM’s project that facilitates suggestions for farm cultivation via digital farm twins where the upcoming soil and weather conditions of real world crops are simulated.
Many other researchers have been making use of AI generated images to help them in visualizing climate change.
We even have non profit companies like WattTime that are working towards cutting down on the carbon footprint of households with the automation of active appliances and electric vehicles depending on the availability of renewable energy.
Some more examples that highlight AI’s use in climate change include the weather service company based in Japan, Weathernews Inc, which has developed an AI chatbot for a UNESCO-supported disaster prevention programme.
This chatbot system will use the AI technology through a messaging app for offering information to the users concerning disasters, to remain in contact with them both before, during and after a disaster. The system is supposed to be introduced in East Africa.
How AI tackles Climate Change?
In spite of the pandemic forcing a massive section of the world’s population to recline at home, the time still continues to run out with the atmospheric CO2 levels rising at an alarming rate, and the average sea levels also continuing to rise at about a rate of around one-eighth of an inch per year.
While AI is definitely not a quick fix to all of these problems, the technology can definitely play a role in cutting down on greenhouse gas emissions.
How AI tackles Climate Change
As per Capgemini Research Institute, it is expected that AI will help to reduce GHG emissions by 16% in the next three to five years. Below, we’ve listed some favorable ways through which AI can tackle climate change :
Measure and minimize emissions for institutions
3 primary steps are required to be undertaken for companies to control their climate influence, mainly -
- Accurate measuring of the baseline
Companies are often hindered at the first step itself, with only a handful of selected companies that publicly reveal their emissions footprint, actually measuring their impact extensively across specific categories. This results in quite a struggle for these companies in moving ahead with their agenda.
Effective execution of these 3 steps is made possible by AI powered solutions which allows companies to tackle these steps through the same tool, which in turn inadvertently results in better decision making on the issue.
AI powered solutions allow swift, dependable and rigorous baselining of the entire emission footprint, with improved accuracy, AI based forecasts and simulations.
One excellent example of how AI induced solutions play a role in minimizing emissions would be Google’s collaboration with electricityMap.
electricityMap is an AI powered platform, which displays the cleanliness of electricity from across the world, facilitating carbon footprint data for the past present and even the forecasted electricity, by organizing it by country.
By collaborating with this platform Google has coordinated computing tasks with times of low-carbon electricity supply in the grid and as a result has cut down on the CO2 emissions caused by the consumption of electricity.
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Process optimisation parameters that are based on AI help in detecting any fresh potential for saving both the expenses and the GHG emissions. For instance, for a global steel company, around 10% of the carbon dioxide emissions and 1% of the expenses can be spared if we make use of AI models for simplifying these industrial operations.
Enable innovative business models for helping the climate
AI based algorithms can help in identifying most suitable lands and soils in an effective manner, for instance, by learning from existing carbon sequestration action data and interpreting the massive agricultural, meteorological, and geological databases.
They can also help in performing vast carbon measurement in the soil at minimal cost.
Alongside this, the technology can be leveraged with satellite imagery, such as for ensuring that the farmers follow the sowing practice alterations that are promised to the people that have stakes in the process and who desire to sell the CO2 certificates in the future.
Improve resistance for societies to adapt to climate change
Improving the resistance rate of the society so that they can adapt to climate change is definitely a considerable challenge.
AI steps up as the savior here by aiding in managing the variety, diversity and the data volume and by developing beneficial insight that can enhance resilience.
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An example is that of Google’s “Hydronet” solution. This can play a hand in accurately detecting vital weak spots to prepare for extreme river floods. The solution was adopted for helping in improving flood forecasting in India and Bangladesh, and had even covered over 250 million people who were exposed to the risk.
All plans for promptly mitigating the crisis, contingency and to make use of help whenever required can be drafted through AI powered solutions.
Boost Energy Efficiency
Machine learning has been profusely backing power generation efficiency, from keeping track of leakage, fleet management to optimizing routes.
For instance, Deepmind AI, owned by Google, has the ability to predict wind patterns, up to 36 hours in advance, for optimizing wind farms.
Machine Learning is capable of swifting through this data to comprehend and forecast energy generation and and even help out suppliers in utilizing resources and compensating gaps through renewable resources and also minimize waste.
Advance Renewable Energy
Many AI programs have been set up for optimizing renewable energy, one evident example is IBM's program for the US Department of Energy’s SunShot Initiative.
This initiative combines self learning weather models, historical weather data sheets, live measurements via local weather stations, sensor networks as well as the cloud information that is gained through satellite imagery and sky cameras. This has led to an evident improvement in the solar forecasting accuracy.
As large data sets start getting available, the predictions are now no longer restricted to the weather and can also predict aspects like the extra power that is used up during festivals, or for large scale events.
AI plays an integral role when it comes to waste management. Intelligent garbage bins are one role they offer. With IoT sensors keeping track of the availability of trash receptacles across the city, this enables municipalities to adjust and improve the routes, time and the frequency of waste gathering.
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With the speed of AI powered machines being far ahead than that offered by human labour, this becomes an immediate and effective advantage for AI in case of automated sorting.
Transportation contributes to a certain extent towards global CO2 emissions. Electric cars, buses and other electric vehicles have become the latest trend among the public.
While it does shift the focus towards a range of problems ranging from how the prices of electric vehicles need to be minimized and how the charging duration of batteries needs to be reduced and a boost in capacity is needed, yet the technology helps in offering an efficient and environment friendly transportation.
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With platforms such as Virgin Atlantic airways emerging as the first biofuel commercial flight based on weight in 2018, we can observe that the focus has also shifted towards alternate fuels.
While grasses, trees and plant life store up carbon, phenomena like deforestation and unsustainable agriculture release it back into the air, adversely supplementing the climate change.
Many recent weather events have created havoc across the world lately which range from the flash floods in Indonesia, bushfires in Austria, the cyclone Amphan in Bangladesh and India to the green snow in Antarctica.
To avoid such incidents, AI is being used for improving weather predictions and responses. We’ve got AI technologies working on keeping track of what is triggering this climate change.
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While the risks posed by AI upon the climate cannot be denied, the technology has definitely accelerated the response towards the global climate. It will be interesting to observe how AI is leveraged and applied for tackling the challenges hindering companies in prompt and swift scaling down of emissions.