Contributors: Stan Hill, Cloud Architect & Sustainability Lead
Climate change is the defining crisis of our time, and it is happening even more quickly than we feared. The current warming trend is highly significant, and it is extremely likely (greater than 95% probability) to be the result of human activity since the mid-20th century. We have known about these negative effects of our behaviour on the climate for several decades now, yet there has been limited action taken to prevent it.
To find out more about Greentonic, Datatonic’s sustainability initiative, as well as possible climate change solutions using Climate Technology, we spoke with Stan Hill, Datatonic’s Sustainability Lead.
What is Greentonic?
Stan Hill: Greentonic is Datatonic’s initiative for Sustainability and Climate Tech innovation. Using our expertise in Data Engineering, Analytics and AI + ML, our mission is to enable the vital transition to a greener future and accelerate technology solutions for combatting Climate Change.
Climate change is having significant effects on the earth: sea levels have risen over 20cm in the last century; there has been a global temperature rise of around 1.2 degrees Celsius since the late 19th Century, as well as ocean warming, shrinking ice sheets, glacial retreat, and extreme weather events becoming more frequent. These are just a few of the visible effects of climate change.
Climate change also has considerable knock-on effects on a human level, such as increased global inequality. As less developed countries struggle to cope with warmer temperatures, crops cannot grow, creating food and water insecurity and loss of jobs. Elsewhere, rising sea levels are causing homelessness as buildings near water are destroyed. With so much evidence of its effects, it is easy to see that climate change is the biggest problem facing our generation.
Why is there a need for Greentonic?
Stan Hill: Climate Change is the biggest threat facing humankind at present. It’s undeniable that human influence is warming our atmosphere at a rate that is unprecedented in at least the last 2000 years, and over the past decade, the frequency of extreme weather events has increased significantly.
According to a May 2022 report by the World Meteorological Organization, there’s a 50:50 chance of average global temperatures reaching 1.5 degrees Celsius above pre-industrial levels in the next five years (failing the goal set by the Paris Agreement), and that likelihood is increasing all the time. We’re certainly at a tipping point in human history, where our actions will determine the fabric of human society for the coming decades and centuries.
Of 400 IPCC emissions models that land us below two degrees Celsius of warming, 344 require negative emissions – meaning more CO2 is taken out of the atmosphere than we emit. However, currently, we are emitting more than ever before.
It’s clear that large-scale changes, driven by technological innovation, are required to both minimise the carbon footprint of our society, as well as actively seek to remove carbon from the atmosphere.
Climate Tech innovation relies on the use of the Cloud, Data Analytics and Machine Learning – which is exactly what we specialise in at Datatonic! One of Datatonic’s core business values is Purposeful Impact. The mission of Greentonic is to facilitate Purposeful Impact in the fight against Climate Change by utilising our deep expertise in Data and AI + ML to accelerate Climate Tech solutions and enable the transition to a Greener future.
“Climate Tech innovation will be at the heart of driving solutions against Climate Change.” – Stan Hill, Sustainability Lead, Datatonic
Climate technology, and Machine Learning in particular, are already being used to help monitor and combat climate change. In our second Computer Vision blog, we discussed exploiting existing hardware such as CCTV and satellites to implement powerful Computer Vision models. But, this also applies to climate technology.
One example involves satellites; satellites continuously monitor the ocean’s surface, giving scientists useful insight into how our oceans are changing as the earth gets warmer. NASA’s Surface Water and Ocean Topography (SWOT) satellite mission, scheduled to launch in November, aims to observe the ocean’s surface in unprecedented detail compared with current satellites.
However, the satellite cannot observe the entire ocean at once. It can only “see” a portion of the ocean beneath it, and the SWOT satellite will take 21 days to complete a full cycle around the earth. To solve this, Machine Learning algorithms will use data from the SWOT satellite to fill in the missing data between each SWOT revolution.
Even without developing complex Machine Learning models, simply aggregating the right types of data can help to reduce our level of emissions. For example, if we can compile accurate data about upcoming cloud cover, we can know exactly how much solar-generated electricity can be provided on a given day.
As a result, we would not need to generate unnecessary electricity from other sources, namely fossil fuels such as gas, as we would be less likely to underestimate the amount of energy we can generate sustainably. This is just one way that data can help to reduce carbon emissions.
Another example of changing the way we use data is in the electric vehicle sector. By looking at factors such as vehicle use, sales patterns, and customers’ favourite types of cars, data can be shared to ensure that charging stations, battery replacement services, and other resources are provided to maximise the adoption of electric cars. Accelerating the shift from traditional petrol and diesel cars to electric vehicles will greatly reduce the amount of CO2 emitted over the next few years.
What are interesting use cases of AI + ML that you’ve seen that relate to sustainability or climate technolgy?
Stan Hill: One awesome use case that I read about earlier this year is Kaluza’s ‘vehicle-to-grid (V2G)’ programme that enables energy to flow bidirectionally between electric cars and the grid. It allows energy to be imported (i.e., charge the car) when renewable energy is abundant, but also allows energy to be exported back to the grid from the vehicle based on the real-time needs of the energy grid. V2G uses AI and real-time market data to do this, and it enables V2G owners to charge their cars on the cheapest, greenest energy, while also being paid for the energy that gets sold back to the grid!
Another great use case is Unilever’s partnership with Google Cloud, which uses satellite imagery on Google Earth Engine with AI to gain a complete picture of the environmental impact of their supply chains (e.g., views of forests, water cycles, and biodiversity), which enables them to detect deforestation and remove it from their supply chains.
In our mission to become more sustainable, and use our expertise to combat climate change, Datatonic has worked on a number of sustainability-related projects as part of our Greentonic initiative.
Stan Hill: In a project with Transport for London, Datatonic developed a Deep Learning model capable of accurately predicting traffic conditions 40 minutes into the future, based on over 120 billion data points! This model allows proactive identification of upcoming congestion, facilitating a better-coordinated flow of rush-hour traffic and ultimately lowering emissions from vehicles, as they will be in traffic jams less.
There are a whole host of use cases that use AI + ML for Sustainability innovation – from Carbon Capture, Energy, Transport, and Agriculture. I’d encourage everyone to do some research. I’d recommend Bill Gates’ “How to Avoid a Climate Disaster”, which goes into a lot of detail regarding Climate Tech innovation!
Where do you see climate technology heading in the next few years?
Stan Hill: The Climate Technology market is going to explode in the coming years. The amount of investment that is being pumped in is growing exponentially. To put it into context, around US$222bn was invested in climate tech between 2013 and H1 2021, with 210% growth year on year… and rightly so. Climate Change is the biggest issue facing society, so it makes sense for the business world to reflect this.
But, while it’s great to see such growth in Climate Tech investment, this has to be translated into innovative solutions and ultimately start to contribute to lowering emissions and hitting our sustainability goals. I’m optimistic that this will happen over the coming years and decades.
One sector where we expect to see an explosion of innovation is the energy sector. It simply has to, because the current energy infrastructure across the world is still far too dependent on fossil fuels. This will involve the use of Data Analytics and Machine Learning to facilitate the wider-scale rollout of solar and wind power, both on industrial and local scales, as well as innovation in Hydrogen power.
British Prime Minister, Boris Johnson, has said previously that 100% of the country’s electricity could come from renewables by 2035. Yet, this is going to require a huge transformation of the energy sector over the next few years. As we approach the deadline for this target, it seems more and more likely that technological innovation will be the driving force for this change.
Stan Hill: So many other industries will also need to accelerate their technological innovation. One example is the Construction Industry. As the global population grows, more cities will emerge and expand to facilitate this. Building construction is a massive carbon emitter, with cement production alone being responsible for 6% of carbon emissions globally.
Another example is Agriculture. Food production methods, farming practices, land management, and food supply chains are areas where innovation will be crucial for reducing Greenhouse Gas (GHG) emissions, and also where the use of Analytics and Machine Learning will play a massive role.
Already, Artificial Intelligence is being used in areas such as plant health detection and monitoring, planting, harvesting, and advanced analysis of weather conditions to plan ahead, reducing food waste and helping businesses to cope with the increased frequency of extreme weather events.
The examples discussed in this blog show that practically all businesses and industries can modify the way they use and share their data, and benefit from AI and ML models, to become more sustainable. As climate change becomes an even more pressing issue, innovative technological solutions will become crucial to building a sustainable future and meeting climate change targets.
To find out how Data, AI, and Machine Learning can benefit your business and help you to become more sustainable, get in touch here.
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