How the Fourth Industrial Revolution will help predict and deal with climate change

The development of artificial intelligence (AI) is accelerating with an increasing impact on society. Although industrialization can be identified as the root cause of many environmental problems, the current “Fourth Industrial Revolution”, with rapid innovations and new connected technologies, provides opportunities for societal shifts that can help address Earth’s environmental challenges and change how we manage our environment.

What is the Fourth Industrial Revolution?


Figure 1 [1]

The “Fourth Industrial Revolution” was the official theme of the 2016 World Economic Forum’s (WEF) annual conference. They defined this as the “fusion of technologies that is blurring the lines between physical, digital and biological spheres”. This revolution distinguishes itself from the previous ones by its velocity, scope and impact. We have seen immense progress in AI in the past few years, mostly because of exponential increase in computing power and a huge increase in data volume. Therefore, developments in AI are now truly revolutionary to society.

How can AI contribute to environmental challenges?

There is no short answer to this question, because AI shows promise in many areas. You may think that AI is just about making robots, but data is literally everywhere. There’s an overwhelming amount of climate data from satellites, environmental sensors and climate-model simulations and energy data like energy use data, GIS data, weather data, social media data, and electric vehicle data.

In a collaboration between the WEF and PwC, a report was published, where they identified AI applications in the following challenge areas: climate change, biodiversity & conversation, health oceans, water security, clean air, and weather & disaster resilience. Many large companies like Google and Microsoft are developing environmentally friendly AI systems. As you can see in figure 2, we have many developments to come from AI that will change how we deal with environmental problems.

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Figure 2: timeline AI applications [2]

An overview of all opportunities goes way beyond the scope of this post, so I will just give two examples of fields where AI has a large potential impact: climate forecasts and energy grids. These are some of the first upcoming developments, so I will explain how these are changing or will change the current system.

Climate models and forecasts

A news article published on the Nature website talks about how machine learning could improve climate forecasts. Since AI systems improve when they get more information, climate science can benefit a lot from combining with AI, since one run of a climate model can produce 1 petabyte of data. Conventionally, climate models are based on rules entered into the model by climate scientists. Deep learning models, a specific type of AI that is based on neural networks in the human brain, “learn” the rules by analysing large amounts of data. Deep learning was first used in 2016 when researchers used it to predict tropical cyclones, atmospheric rivers and weather fronts.

According to Claire Monteleoni, climate is now a data problem. The collaboration between climate scientists and data scientists has caused the emergence of a new discipline: climate informatics. For example, the IPCC reports are based on the ensemble of many conventional climate models that have high variance in predictions. On average, combining these models results in better predictions, but it has always been a challenge to determine the most accurate way to do this. AI now aims to solve this problem by helping climate scientists rank climate models.

In practice, these developments (will) lead to better forecasting of extreme weather events like hurricanes and a better understanding of the effects of climate change, and it will be much cheaper and faster as well, since new machine learning methods are much better at analysing large amounts of data compared to current climate models which need a huge amount of computational power. The advantage lies not just in better predictions, but in the potential of revolutionary change in the system, because everything will be connected, so that risks can be monitored in real-time and dangers can be detected automatically.

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Figure 3: squared error between prediction and (simulated) observations. Best expert is the best out of the 20 models (in hindsight). Learn-a is the model developed by Monteleoni that learns the best way to combine climate models [3]

Smart energy grids

As described in the previous blogpost, the transition to intermittent renewable energy sources poses threats to the electricity grid. Not only batteries, but also AI has the potential to contribute a lot to the stability of the grid, for example by using data to forecast energy demand and supply. Machine learning techniques are becoming better and better at forecasting. Balancing demand and supply in the grid can be done by demand-side management, which affects patterns and magnitude of consumption. Figure 4 shows how the demand curve can be altered.

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Figure 4: types of demand-side management [4]

Demand-side management is just one example. The smart grid is considered a technological paradigm shift. Energy data from smart grids allows for automatic and real-time monitoring of consumption, early blackout warnings, automatic billing, detection of energy losses, fast disturbance detection, and intelligent and real-time energy planning and pricing. All this will come with connected home devices and smart meters as part of the 4th Industrial Revolution. Then the power grid can be controlled to deliver exactly the amount of energy needed so no energy is wasted, and renewable energy can be integrated.


Of course, there are also downsides to these technological developments. It is possible that AI developments could accelerate environmental degradation. Furthermore, methods like deep learning usually work like a “black box”: it is not intuitively clear what happens exactly inside the system, so climate scientists are still a bit reluctant to trust the results. For example, if AI is used to create an early-warning system for floods, and it’s too much of a mystery how the model gets these predictions, then the risk of false alarms may be perceived as too high.


I’m aware that this may all sound quite optimistic, but these are real developments that I’m pretty excited about. While these solutions will probably not solve the entire climate problem, at least it will make it easier to predict events and it will help limit impact. It is necessary to figure out how to deal with the risks related to AI, but we have plenty of positive consequences to look out for.

8 thoughts on “How the Fourth Industrial Revolution will help predict and deal with climate change

  1. By coincidence, I just found out that Claire Monteleoni (one of the founders of climate informatics) is coming to CWI next week for a guest lecture (I’m doing an internship there) so if anyone’s interested: (I think it will be quite technical but interesting!)


  2. Hi Samantha! It’s interesting to learn about the many different opportunities that AI has in this field. You mention: “Fourth Industrial Revolution”, with rapid innovations and new connected technologies, provides opportunities for societal shifts that can help address Earth’s environmental challenges and change how we manage our environment”. I was wondering if you could elaborate or provide examples on the last part, about managing our environment. As many of the projects you mentioned (and I could think of) were focussed on processing and modelling data. How do you see that this can directly help us manage our environment?


  3. Hi Tamara! There are many examples of projects that can do this. Often these are also based on modelling data, but then these results provide valuable information for the government/policy makers etc. So the advantage is mostly that it can help us analyze and monitor the environment better, and in some cases optimal management can even be done automatically. Here are some examples:
    – Oceans:
    – early identification of water pollution, for example from a leaking underwater pipe, by robotic fish
    – drones that analyze health of whales
    – Google Fishing Watch – monitors illegal fishing
    – Biodiversity:
    – eBird model – predicts patterns of bird migration so their habitat can be protected
    – Blue River Technology – using AI to make decisions about biodiversity of a habitat, e.g. presence of invasive weeds, leading to significant savings in pesticide use and optimal fertiliser use
    – Water:
    – drones that automatically deliver water quality reports
    – Flo Technologies – system for household water management and control
    – system that predicts water pipe’s likelihood of failure or detects leakages to prevent water loss
    You can find more in this report:


  4. Hi Samantha, super interesting blogpost! Under your risk section you mention that “AI developments could accelerate environmental degradation.” Could you give an example of that? I feel it would rather lead to over-precaution, instead of accelerated degradation. Additionally, do there exists ways to overcome the risk of accelerated degradation?


  5. Hi Suzanne! I was thinking of additional energy use for example due to increased need for computing power or increased use of these new technologies. For example, increased car use with autonomous cars, as @julianauc mentioned in his blogpost. On the other hand, AI has allowed Google to decrease energy use in its data centre by 40%, and AI can also contribute to integrating renewable energy, so I think this will be very valuable. Another risk is related to the safety of AI developments, for example the possibility of developing autonomous weapons, which would have catastrophic consequences.

    Liked by 1 person

  6. Hey Samantha, great post! I agree with your last comment that AI and other innovative technologies can (and currently do) lead to a significant increase in energy consumption. This article highlights that data centers are currently consuming more energy in a year than the total consumption of the UK. Hence, this is a major issue that should be addressed, otherwise, climate indeed would become a “data problem” but rather because it is the cause of the problem.

    I would like to ask you about the part where you mention that: “Deep learning models, a specific type of AI that is based on neural networks in the human brain, “learn” the rules by analyzing large amounts of data.”. I was wondering how the deep learning models will be able to incorporate future changes into their predictions (of for example extreme weather events). Climate scientists can include the fact that extreme weather events are predicted to increase in severeness. As AI models predict the future solely based on historical data, how do they include such properties? And will this not cause underestimation in predictions?


  7. Hey Katja! What you mentioned about data centres sounds pretty shocking! Which article are you referring to? If this is true then it is indeed a major issue that should be addressed, since the negative effect would be much larger.

    I don’t really know how these models would work exactly since that is usually quite unclear as I said in the article. However, I think that these future changes could be predicted because the models are trained on large amounts of historical data, so that it learns what would cause these extreme weather events. So yes, if for example extreme weather events increase in severeness and you don’t account for this increase but just predict them to stay the same, that would lead to underestimation. However, since this increase in severeness would be caused by many factors that can be measured, the model can be trained on these data.


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