AlgoRail: On the trail of algorithms

Summer has finally arrived, but the Corona pandemic has already put a damper on many people’s holiday plans. That’s why we want to go on a journey in our new blog series “AlgoRail” – all over Europe and always on the trail of algorithmic systems. Where are algorithmic systems already in use in other countries? What opportunities and risks does this entail and what can we learn from it for the German debate?

GERMANY: How algorithms influence climate change in Germany

But before we head off into the distance, we first take a look around in Germany. After all, algorithmic systems are already being used in some places in our country, too. But it is not always clear whether their desired benefit is actually achieved.

One example that has caught the attention of Nicolas Kayser-Bril, reporter at AlgorithmWatch, is the promise of various institutions (like the European Commission or the German Data Ethics Committee) that algorithmic systems will help with fighting the climate crisis. However, his search for AI projects that have a clear positive effect on the climate ultimately led him in a completely different direction.

I started by reading through the 500-page report of the German Advisory Council on Global Change. It did stress that any strategy to mitigate the climate crisis needed a rethink of the moral values on which the market system is based but was much less clear on the specifics. New technology, the authors wrote, could be a “low-carbon enabler” as well as a “power drainer”.

In the hope of finding more concrete examples of how AI could contribute to climate preservation, I met with two academics close to the authors of the report. After our one-hour conversation in their shiny offices in the center of Berlin, my hopes were in tatters. One of the very few examples they came up with was – half-jokingly – the smart washing machine. An AI-powered washing machine could be automatically programmed to run at night, they said. This would spread electricity consumption more evenly over the day, allowing for more efficient production systems.

I’m no scientist, but my experience of washing machines taught me that they already have a timer, and that the reason we don’t run them at night is that it’s very noisy and will anger neighbors.

Muddy waters

If academia couldn’t help, maybe civil society would. Germany is home to the largest Friday For Future group. I asked Annika Rittmann, one of their spokespersons, if she knew of an instance where AI led to a net decrease in greenhouse gases emissions.

“We base our work on scientific results,” she wrote back. “In this area, they are still very muddy, so that we can’t say much on the topic.”

Wind turbines

Another civil society organization, GermanWatch (no relation to AlgorithmWatch), published a report in 2019 on the chances and risks of AI for the Energiewende, a German concept that literally means “energy turnabout” and concretely means that German consumers pay one third more than the EU average for their electricity in exchange for promises of making energy generation sustainable.

I finally found concrete examples. BirdVision, for instance, is the subsidiary of Bürgerwindpark Hohenlohe, a company operating 9 wind farms in southern Germany. It operates a computer vision software to detect birds and automatically reduces the turbine speed if they come too close. Eight turbines are equipped with the system, and the company says that, although the product is still in development, there is plenty of interest from potential users once it is brought to market. This, GermanWatch wrote, could lead to more acceptance of wind energy and encourage the construction of more wind turbines.

Energy efficiency was the area where AI was most promising, according to the report. Smart appliances could be programmed to run only when electricity from renewable sources was abundant, for instance. On the distribution side, “smart grids” could better balance production and consumption, reducing demand at peak times.

 “AI is not an end”

By now, I knew that report writers could get carried away. I asked a dozen companies active in renewable energy how they used AI.

Next Kraftwerke, which distributes electricity, uses machine learning to forecast the production levels of solar farms and the consumption volumes of its clients. The information is used by the commercial department and by personnel in charge of grid stability. A software developer at the company said that other projects were less successful (they tried to model the energy market, for instance). AI is “not an end in itself,” he said, but “a tool in a wider toolbox”.

Other companies told me they didn’t use AI at all.

The efficiency problem

While it seems clear that AI can boost energy efficiency, whether or not energy efficiency does anything to mitigate the climate crisis remains an open question. That lower consumption for some translates into lower energy costs and higher consumption for others is the basic tenet of a market economy. This is why the German Advisory Council on Global Change warned that a change in rules was needed before discussing any technical solutions.

Mitigating the climate crisis requires reducing the amount of greenhouse gases in the atmosphere, either by capturing it, a very expensive and immature technology, or by preventing its release.

Fire detectors

In the last two years, over 2,000 hectares of German forest went up in flames. A low number compared to southern Europe (43,000 hectares burnt in Portugal alone in 2018), but very high for Germany.

Considering that each hectare of burnt land releases between 15 and 35 tons of CO2, German wildfires were responsible for over 40,000 tons of CO2 emissions in 2019. Less than one hundredth of one hundredth of a percent of all the greenhouse gases emitted in the country that year, but still a consequent amount. And one where the rebound effect does not apply: Preventing a single wildfire is an actual contribution to climate mitigation, not a potential one.

AI can contribute to the fight against wildfire in several ways, from predicting where they’ll flare up to detecting fire activity using satellite imagery.

Hopeful again that I could find an example of AI having a net positive effect on climate, I emailed the emergency services and forest management authorities of the German regions most affected by wildfires (Brandenburg, Saxony and Saxony-Anhalt), eager to know how they used or planned to use AI. Alas! They don’t. Not that they’re technology-shy. They do use sensors and software to detect fires, such as automated smoke detectors, satellite imagery, drones and infrared cameras, just nothing that involves Artificial Intelligence.

AI for oil

I was about to look for another area where AI might be used to mitigate the climate crisis when Greenpeace ruined my quest. On 19 May, they published Oil in the Cloud, a report on how AI helped energy companies extract more oil and gas, a net contribution to the climate catastrophe.

Even though it focused on the United States, the report revealed that Amazon, Microsoft and, to a lesser extent, Google encouraged the fossil fuel industry to use their services. AI could, and did, help them find more hydrocarbons, faster.

In one case, Microsoft noted that its technologies could support production growth by “as much as 50,000 oil-equivalent barrels per day.” Once burned in a combustion engine or power plant, that’s over 20,000 tons of CO2 released in the atmosphere, half the amount released by German wildfires in 2019, every day, for just one project.

Getting to the source

Any efficiency gain that AI offers to operators of renewable energy also applies to those who burn hydrocarbons. Because fossil fuel companies are more powerful and larger, it seems fair to say that currently AI contributes to more CO2 emissions, not less. As long as AI is not demonstrably used in projects that lead to net decreases in CO2 concentrations, such as carbon capture and storage, stating that it contributes to mitigating the climate crisis is, at best, wishful thinking.

I finally sought the source the European Commission relied on to come to the conclusion that “AI would change our lives by contributing to climate change mitigation and adaptation.” After all, they might have access to documents and experts that I missed.

A source with knowledge of the matter sent me some links. The first one was a listicle titled “8 ways AI can help save the planet,” written by a PwC consultant, containing zero references and published by the World Economic Forum, a business group famous for its yearly gathering in Davos. The second one was a blog post based entirely on a report by the same World Economic Forum. The third was a peer-reviewed article in Nature, which stated that AI had potential to mitigate the climate crisis, but very clearly mentioned that it also had potential to make it worse.

The lead researchers of the German Data Ethics Commission did not answer my request for their source.

I’ll let the last word to Karsten Smid, who’s in charge of climate-related issues at Greenpeace Germany. In an email to AlgorithmWatch, he wrote: “We would be happy if climate mitigation used less Artificial Intelligence, and more human reason.”

That’s it for this first stop, enjoy the view from AlgoRail and look forward to our next destination: The Netherlands!


This story was shortened and translated into German by Dr. Sarah Fischer. The unabridged story  was published on the AlgorithmWatch website.

The blog series AlgoRail is part of the Automating Society Report 2020 by Bertelsmann Stiftung and AlgorithmWatch, which will be published this fall and is coordinated by Dr. Sarah Fischer. In addition to journalistic stories like this one, the report gives an overview of various examples of algorithmic systems as well as current debates, policy responses and key players in 15 countries. A first issue of the report was published in January 2019.


This text is licensed under a Creative Commons Attribution 4.0 International License



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