Introduction
Climate change is happening, and it is likely accelerating.
This is not jumbo mumbo. We seem to be living in a world where every summer is warmer than the last.
In this data report, I have collected some of the most important data points around climate change, and visualized the status quo through the lens of a data nerd. We will also debunk some misconceptions about climate change, why there is hope and how AI, among other technologies can help us tackle climate challenges. Or make it worse. 🤔
💡 To see the full code, click Sign in and create a personal Datalore account. Then click on three dots near the Recalculate all button in the top right-hand corner and select Edit copy. To try Datalore free for your team, visit https://jb.gg/try-datalore/
Unfolding Climate Story
A warming world
Last year, 2023, was the warmest year on record since 1850, making it an unusual year. In fact, this is the first time that the annual average temperature has exceeded the pre-industrial baseline period by more than 1.5 °C as some datasets suggest. If you are not familiar with this jargon, the pre-industrial period is reference period from 1880 to 1900.
You might argue this might be an anomaly, and this is caused by El Niño during this year. But from what we can see on this graph, it looks like a very solid upward trend in average temperature in the last four decades.
Increasing atmospheric CO2 level
Under the Paris Agreement, many countries have set an aspirational goal of limiting long-term global warming to no more than 1.5 °C (2.7 °F). That target is based on the state of the climate averaged over many years, so a single year exceeding 1.5 degrees is not automatically considered as breaching this target. However, this is a stark warning sign of how close the overall climate system has come to exceeding this Paris Agreement goal.
As humans continue to pump more and more carbon dioxide into the atmosphere, it is likely that climate warming will regularly exceed 1.5 °C in the next decade.
As you can see on this graph, the CO2 level in the atmosphere in the past million years has never been above this line. But the past 70 years have witnessed a sharp increase in the atmospheric CO2 level.
The rise in CO2 emissions in the past century mostly come from fossil fuels and industry, or in other words, human activities. The graph below shows the CO2 emissions measured in tonnes of carbon dioxide-equivalents over a 100-year timescale.
There has been an exponential increase in the total emission level across the world in the last 70 years.
The Melting Ice and Rising Seas
Extreme weather set aside, there’s an equally big problem: melting ice at the poles. This can be captured by satellite data and ground observations.
Since the beginning of the 21st century, the ice sheets in the Antarctic and Greenland have both started to decrease in mass.
Together, the Antarctic and Greenland Ice Sheets contain more than 99% of freshwater ice on Earth. If they both completely melted, they would raise sea level by an estimated 67.4 meters (223 feet). (https://nsidc.org/ice-sheets-today)
Environmental impact of AI
How can AI help to tackle climate change?
2023 and 2024 so far have been an unprecedented time with respect to progress in artificial intelligence, with GPT4, Gemini and many open-source language models becoming available to the public.
Although AI cannot draft new climate policies and enforce them (yet), or rather, politicians won’t let it, you might be asking, what can AI actually do to help us tackle many climate challenges?
AI, at the most basic level, can help us understand better what’s going on, and stop denying the problems! The main uses of AI I’ve seen in my research fall into these categories: monitoring, predicting, and optimizing.
You can read more about these use cases on the blog post on Jetbrains’ Datalore website here.
How AI can make it worse
However, some skeptics think we shouldn’t be too romantic about AI saving the planet. They believe claims that artificial intelligence will help solve the climate crisis are misguided.
Models like GPT4 and Gemini require a huge amount of energy to train and to run. In the most simple form, the carbon footprint of an AI model is equal to the energy needed to train the model, plus the number of queries and how much energy each query would require. All of this will be multiplied by the energy efficiency of the hardware.
Footprint = (electrical energy train + queries × electrical energy inference) × CO2efficieny of data center/KWh
Most companies spend much more energy on serving an AI model (performing inference) than on training it. In fact, it’s estimated that 90% of the energy is spent on serving.
To put that into perspective, the cost of training GPT-4 is the equivalent of driving a gasoline car for nearly 18 million miles or the equivalent of powering more than 1,300 homes for one year.
As 90% of the energy is spent on serving these models, we can expect that the total energy consumption of these models is at least 10 folds of the figures presented above.
In the US, AI needs so much power that old coal plants are sticking around. That’s quite terrifying
Trends in the past decades
To see if there is any upward trends in fossile fuels in the more recent years. We’ll turn to the energy consumption over the last 10 years.
Coal consumption in rich countries has actually plummetted in rich countries like US, UK. It has also leveled off in upper-middle-income countries like China. These countries are also the largest consumers of coal.
The same trend can be seen for oil and gas.
Given that fossil fuels - coal, oil and gas, accounting for over 75 percent of global greenhouse gas emissions, it seems that this is a promising trend.
Also, technologies are making it cheaper and more attractive to produce renewable energy, such as wind and solar power. Electric cars have become the norm for in many countries in Europe as more people prefer environment-friendly solutions.
It is not to say that all is well and the developing of AI models hasn’t caused any visible harm to the environment. This can likely only be observed more clearly in the next few years.
Conclusions
Data has made it so crystal clear how climate change is heading, if we don’t do anything.
Artificial intelligence is promising, but it also seems to be a double-edged sword if we are not careful. Right now, there is not enough concrete evidence to say the benefits out-weight the costs.
We have also seen that there is no shortage of solutions and human ingenuity. Many people, start-ups and organizations are working to solve different aspects of climate risks.
This makes me feel so positive, because if we want to make a change to the world, we first need to believe that change is possible and no problem is too big to solve.
With more younger people moving into influential positions, they prioritize climate change and work on new solutions. And hopefully, in a few years’ time, AI will help us make bigger steps toward a better climate future, and the benefits actually out-weight the costs.
You can read the full blog post on Jetbrains’ Datalore website here.